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Teacher satisfaction in public, private, and charter schools a multi-level analysis
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Sentovich, Christina
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teacher attitudes
job satisfaction
SASS
HLM
NCES
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ABSTRACT: The 1999-2000 restricted-use School and Staffing Survey (SASS) dataset was used to construct hierarchical linear models to determine to what degree administrative support, resources, collegiality, parental support, school atmosphere, credentialing requirements, professional development, classroom and school autonomy, and compensation can predict teacher satisfaction in public, private, and charter schools after controlling for teacher background and school characteristics. Variables were selected in part because it is possible for them to be manipulated by policy. The study also reports on efforts to refine and validate subscales of items chosen based on theory and literature from the SASS to represent teacher satisfaction and predictors of satisfaction. SASS collected a nationally representative complex random sample of public, private, and charter schools with teachers randomly selected from schools. The conceptual framework of this study identifies level of opportunity and amount of power to access and use resources as the most significant aspects of a position as related workplace conditions. Though teaching is often characterized by isolation from adults, results of this study show that relationships with others are important. Key relationships focus on principals of schools for administrative support and leadership, teachers and school staff for cooperative environment and collegiality, parents for parental support, and students in terms of respect and behavior. Teachers also report higher levels of satisfaction when they have adequate resources like time and materials, when they have autonomy in their own classrooms, and when they are satisfied with their class sizes and salary. Principals of schools appear to be in the best position to directly influence teacher job satisfaction, but they need support from their community and school districts.
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Thesis (Ph.D.)--University of South Florida, 2004.
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by Christina Sentovich.
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Teacher Satisfaction in Public, Private, and Charter Schools: A Multi-Level Analysis by Christina Sentovich A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Measurement and Research College of Education University of South Florida Co-Major Professor: John Ferron, Ph.D. Co-Major Professor: Lou Carey, Ph.D. Cynthia Parshall, Ph.D. Richard Austin, Ph.D. Date of Approval: 6-3-04 Keywords: NCES, SASS, HLM, Job Satisfaction, Teacher Attitudes Copyright 2004 Christina Sentovich

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Dedication This manuscript is lovingly dedicated to my family: my husband Mark, my five children: Toni, Lacey, Cheree, Mark, Jr., and Christian, and to my mother, Anita. I know that there is nothing better for men than to be happy and do good while they live. That everyone may eat and drink, and find satisfaction in all his toil this is the gift of God. Ecclesiastes 3: 12,13

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Acknowledgements I am very grateful to the following individuals who helped me in many ways: Dr. John Ferron, the best and kindest mentor possible, Dr. Lou Carey who encouraged me at every turn, Dr. Cynthia Parshall who taught me about computer-based testing, and Dr. Richard Austin who has been helpful through both my masters and doctoral programs. Also I am grateful to Dr. Mahdabi Chatterji who first inspired my interest in measurement and survey research, to the AERA Grants Program which sponsored this work in part, to Dr. Neal Berger and the Institute for Instructional Research and Practice who have been supportive and provided an awesome opportunity to work in computer-based testing, and to my dear friend and colleague, Dr. Jane Adamson. This research was supported by a grant from the American Educational Research Association which receives funds for its AERA Grants Program from the National Center for Education Statistics and the Office of Educational Research and Improvement (U.S. Department of Education) and the National Science Foundation under Grant #REC-9980573. Opinions reflect those of the author and do not necessarily reflect those of the granting agencies.

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TABLE OF CONTENTS LIST OF TABLES.............................................................................................................iv LIST OF FIGURES...........................................................................................................ix ABSTRACT........................................................................................................................x CHAPTER I INTRODUCTION.........................................................................................1 Statement of the Problem................................................................................................1 Purpose of the Study.......................................................................................................3 Research Questions.........................................................................................................4 Rationale for the Study...................................................................................................5 Limitations......................................................................................................................6 Definitions.......................................................................................................................7 CHAPTER II REVIEW OF THE LITERATURE..............................................................9 Theories of Job Satisfaction............................................................................................9 Need Fulfillment Theory.............................................................................................9 Two Factor Theory...................................................................................................10 Valence-Satisfaction Theory.....................................................................................12 Discrepancy Theory..................................................................................................13 Equity-Inequity Theory.............................................................................................14 Kanters Structural Theory of Organizational Behavior...........................................14 A Conceptual Framework for Teacher Job Satisfaction...............................................15 Definitions of Job Satisfaction......................................................................................17 Measurement of Job Satisfaction..................................................................................18 Factors Related to Job Satisfaction...............................................................................19 Administrative Support and Leadership...................................................................20 Resources..................................................................................................................20 Cooperative Environment and Collegiality..............................................................21 Parental Support........................................................................................................22 Student Behavior and School Atmosphere...............................................................23 Credentialing Requirements......................................................................................23 Professional Development........................................................................................24 Autonomy in the Classroom.....................................................................................25 Autonomy in the School...........................................................................................25 Compensation...........................................................................................................26 Summary.......................................................................................................................27 CHAPTER III METHODS...............................................................................................30 Purpose of the Study.....................................................................................................30 i

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Research Questions.......................................................................................................30 Data Analysis................................................................................................................32 Participants....................................................................................................................32 Procedures.....................................................................................................................34 Variables.......................................................................................................................37 Data Analysis Phase I................................................................................................42 Data Analysis Phase II...............................................................................................43 Assumptions of the Hierarchical Linear Model............................................................50 Models and Equations...................................................................................................52 Null Model................................................................................................................53 Background Models Add Teacher Background and School Characteristics.........53 Background Model plus X ijk *...................................................................................54 Overall Model...........................................................................................................55 Pilot Study.....................................................................................................................56 Summary.......................................................................................................................61 CHAPTER IV LITERATURE REVIEW.........................................................................63 Exploratory and Confirmatory Factor Analyses Results..............................................63 Administrative Support and Leadership...................................................................66 Student Behavior and School Atmosphere...............................................................69 Parental Support........................................................................................................74 Professional Development........................................................................................77 Autonomy in the School...........................................................................................80 Autonomy in the Classroom.....................................................................................83 Satisfaction................................................................................................................86 Compensation and Credentials.................................................................................95 Collegiality................................................................................................................96 Resources..................................................................................................................97 Distribution of Subscale Scores....................................................................................97 Weights.........................................................................................................................99 Hierarchical Linear Model Results.............................................................................100 Null Model..............................................................................................................100 Background Model..................................................................................................101 Research Question 1 Administrative Support and Leadership............................104 Research Question 2 Resources............................................................................109 Research Question 3 Cooperative Environment and Collegiality.......................113 Research Question 4 Parental Support.................................................................117 Research Question 5 Student Behavior and School Atmosphere........................121 Research Question 6 Credentialing Requirements..............................................125 Research Question 7 Professional Development.................................................129 Research Question 8Autonomy in the Classroom................................................133 Research Question 9 Autonomy in the School....................................................137 Research Question 10 Compensation...................................................................141 Research Question 11 All Predictor Variables....................................................145 Summary of HLM Results..........................................................................................149 ii

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Potential Range of Impact of Overall Model Coefficients on Satisfaction Scores.149 Summary of R 2 Values in All Models....................................................................154 CHAPTER V DISCUSSION..........................................................................................156 Purpose........................................................................................................................156 Framework..................................................................................................................157 Models.........................................................................................................................157 Limitations..................................................................................................................161 Implications.................................................................................................................162 Future Research..........................................................................................................163 Summary.....................................................................................................................166 REFERENCES...............................................................................................................167 Appendix A.................................................................................................................177 Appendix B.................................................................................................................186 ABOUT THE AUTHOR................................................................................................196 iii

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LIST OF TABLES Table 1. Number of Selected Teachers, In-Scope Cases, Completions, and Response Rates.........................................................................................................34 Table 2. Number of Selected Teachers, In-Scope Cases, Completions, and Response Rates.........................................................................................................34 Table 3. Summary of Unweighted Item Response Rates for the Teacher Survey............36 Table 4. Items Included in Proposed Teacher Satisfaction Subscale................................57 Table 5. Items Included in the Administrative Support and Leadership Subscale...........58 Table 6. Administrative Support and Leadership Estimates for the Unconditional Model and Model with One Predictor Variable........................................................59 Table 7. First 20 Eigenvalues from the Exploratory Factor Analyses..............................64 Table 8. Names of Factors in the Twelve Factor Exploratory Factor Analyses...............65 Table 9. Administrative Support and Leadership Exploratory Factor Analysis Loadings....................................................................................................................67 Table 10. Administrative Support and Leadership Alphas and Proposed One-Factor Confirmatory Model.................................................................................................67 Table 11. Student Behavior and School Atmosphere (Aggression, Major Student Problems, and Tardiness..............................................................................70 Table 12. Behavior Alphas and Proposed One-Factor Confirmatory Model...................72 Table 13. Aggression Alphas and Proposed One-Factor Confirmatory Model................73 Table 14. Tardiness Alphas...............................................................................................73 Table 15. Aggression and Tardy Alphas and Adjusted Two-Factor Confirmatory Model........................................................................................................................74 Table 16. Parental Support Exploratory Factor Analysis Loadings.................................75 iv

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Table 17. Parental Support Alphas...................................................................................76 Table 18. Parental Support Alphas and Adjusted One-Factor Confirmatory Model........76 Table 19. Professional Development Exploratory Factor Analysis Loadings..................77 Table 20. Professional Development Alphas and Proposed One-Factor Confirmatory Model.................................................................................................79 Table 21. Professional Development Alphas and Adjusted One-Factor Confirmatory Model........................................................................................................................79 Table 22. Autonomy in the School Exploratory Factor Analysis Loadings.....................81 Table 23. Autonomy in the School Alphas and Proposed One-Factor Confirmatory Model........................................................................................................................82 Table 24. Autonomy in the School Alphas and Adjusted One-Factor Confirmatory .......... Model........................................................................................................................82 Table 25. Autonomy in the Classroom Exploratory Factor Analysis Loadings...............84 Table 26. Autonomy in the Classroom Alphas and Proposed One-Factor Confirmatory Model........................................................................................................................85 Table 27. Autonomy in the Classroom Alphas and Adjusted One-Factor Confirmatory Model........................................................................................................................86 Table 28. Teacher Job Satisfaction Exploratory Factor Analysis.....................................88 Table 29. Teacher Job Satisfaction Alphas and Proposed One-Factor Confirmatory Model........................................................................................................................88 Table 30. Correlations Among Potential Satisfaction Indicators Public/Private/Charter...............................................................................................90 Table 31. Administrative Support and Teacher Job Satisfaction Split Satisfaction into Two Factors Satis1 and Satis2................................................................................91 Table 32. R 2 Values for One-Predictor Regression Models and Model with Ten Variables from Public School Data...........................................................................................93 Table 33. R 2 Values for Regression Models Using Ten Predictor Variables Plus v

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Control Variables......................................................................................................94 Table 34. Compensation and Credentials Exploratory Factor Analysis Loadings.........96 Table 35. Collegiality Exploratory Factor Analysis Loadings.......................................96 Table 36. Collegiality Alphas and Proposed One-Factor Confirmatory Model.............97 Table 37. Collegiality Alphas.........................................................................................97 Table 38. Descriptive Statistics for Public School Data...................................................98 Table 39. Descriptive Statistics for Private School Data..................................................98 Table 40. Descriptive Statistics for Charter School Data.................................................99 Table 41. Fixed and Random Effects for Administrative Support and Leadership in Public Schools.....................................................................................................105 Table 42. Fixed and Random Effects for Administrative Support and Leadership in Private Schools...................................................................................................106 Table 43. Fixed and Random Effects for Administrative Support and Leadership in Charter Schools...................................................................................................107 Table 44. Fixed and Random Effects for Resources in Public Schools..........................110 Table 45. Fixed and Random Effects for Resources in Private Schools........................111 Table 46. Fixed and Random Effects for Resources in Charter Schools........................112 Table 47. Fixed and Random Effects for Cooperative Environment and Collegiality in Public Schools....................................................................................................114 Table 48. Fixed and Random Effects for Cooperative Environment and Collegiality in Private Schools..................................................................................................115 Table 49. Fixed and Random Effects for Cooperative Environment and Collegiality in Charter Schools.......................................................................................................116 Table 50. Fixed and Random Effects for Parental Support in Public Schools...............118 Table 51. Fixed and Random Effects for Parental Support in Private Schools..............119 Table 52. Fixed and Random Effects for Parental Support in Charter Schools.............120 vi

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Table 53. Fixed and Random Effects for Student Behavior and School Atmosphere in Public Schools....................................................................................................122 Table 54. Fixed and Random Effects for Student Behavior and School Atmosphere in Private Schools..................................................................................................123 Table 55. Fixed and Random Effects for Student Behavior and School Atmosphere in Charter Schools..................................................................................................124 Table 56. Fixed and Random Effects for Credentialing Requirements in Public Schools.........................................................................................................126 Table 57. Fixed and Random Effects for Credentialing Requirements in Private Schools........................................................................................................127 Table 58. Fixed and Random Effects for Credentialing Requirements in Charter Schools.......................................................................................................128 Table 59. Fixed and Random Effects for Professional Development in Public Schools.........................................................................................................130 Table 60. Fixed and Random Effects for Professional Development in Private Schools........................................................................................................131 Table 61. Fixed and Random Effects for Professional Development in Charter Schools.......................................................................................................132 Table 62. Fixed and Random Effects forAutonomy in the Classroom in Public Schools.........................................................................................................134 Table 63. Fixed and Random Effects forAutonomy in the Classroom in Private Schools........................................................................................................135 Table 64. Fixed and Random Effects forAutonomy in the Classroom in Charter Schools.......................................................................................................136 Table 65. Fixed and Random Effects forAutonomy in the School in Public Schools.........................................................................................................138 Table 66. Fixed and Random Effects forAutonomy in the School in Private Schools........................................................................................................139 Table 67. Fixed and Random Effects forAutonomy in the School in Charter Schools.......................................................................................................140 vii

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Table 68. Fixed and Random Effects for Compensation in Public Schools...................142 Table 69. Fixed and Random Effects for Compensation in Private Schools..................143 Table 70. Fixed and Random Effects for Compensation in Public Schools...................144 Table 71. Fixed and Random Effects for Overal Model in Public Schools....................146 Table 72. Fixed and Random Effects for Overal Model in Private Schools...................147 Table 73. Fixed and Random Effects for Overal Model in Charter Schools..................148 Table 74. Potential Range of Impact of Predictor Varible Coefficients from the Overall Model on Teacher Job Satisfaction in Public Schools............................150 Table 75. Potential Range of Impact of Predictor Varible Coefficients from the Overall Model on Teacher Job Satisfaction in Private Schools...........................151 Table 76. Potential Range of Impact of Predictor Varible Coefficients from the Overall Model on Teacher Job Satisfaction in Charter Schools..........................152 Table 77. Summary of R 2 Values for All Models in Public, Private, and Charter Schools..........................................................................................................155 viii

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LIST OF FIGURES Figure 1. Predictor Variables and their Relationship to Opportunity, Capacity, and Job Satisfaction................................................................................................................38 Figure 2. Administrative Support and Leadership Proposed One-Factor Confirmatory Model........................................................................................................................69 Figure 3. Behavior (Aggression and Tardy) Adjusted Two-Factor Confirmatory Model74 Figure 4. Parental Support Adjusted One-Factor Confirmatory Model...........................76 Figure 5. Professional Development Adjusted One-Factor Confirmatory Model............80 Figure 6. Autonomy in the School Adjusted One-Factor Confirmatory Model...............83 Figure 7. Autonomy in the Classroom Adjusted One-Factor Confirmatory Model.........86 Figure 8. Teacher Job Satisfaction Proposed One-Factor Confirmatory Model..............89 Figure 9. Administrative Support and Leadership and with Satisfaction split into two factors Adjusted Three-Factor Confirmatory Model................................................92 ix

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TEACHER SATISFACTION IN PUBLIC, PRIVATE, AND CHARTER SCHOOLS: A MULTI-LEVEL ANALYSIS Christina Sentovich ABSTRACT The 1999-2000 restricted-use School and Staffing Survey (SASS) dataset was used to construct hierarchical linear models to determine to what degree administrative support, resources, collegiality, parental support, school atmosphere, credentialing requirements, professional development, classroom and school autonomy, and compensation can predict teacher satisfaction in public, private, and charter schools after controlling for teacher background and school characteristics. Variables were selected in part because it is possible for them to be manipulated by policy. The study also reports on efforts to refine and validate subscales of items chosen based on theory and literature from the SASS to represent teacher satisfaction and predictors of satisfaction. SASS collected a nationally representative complex random sample of public, private, and charter schools with teachers randomly selected from schools. The conceptual framework of this study identifies level of opportunity and amount of power to access and use resources as the most significant aspects of a position as related workplace conditions. Though teaching is often characterized by isolation from adults, results of this study show that relationships with others are important. Key relationships focus on principals of schools for administrative support and leadership, x

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teachers and school staff for cooperative environment and collegiality, parents for parental support, and students in terms of respect and behavior. Teachers also report higher levels of satisfaction when they have adequate resources like time and materials, when they have autonomy in their own classrooms, and when they are satisfied with their class sizes and salary. Principals of schools appear to be in the best position to directly influence teacher job satisfaction, but they need support from their community and school districts. xi

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Teacher Satisfaction in Public, Private, and Charter Schools: A Multi-level Analysis CHAPTER I INTRODUCTION Statement of the Problem Teacher job satisfaction is an important policy issue because of its relationship to perceived efficacy and classroom effectiveness. Successfully reaching students is a teachers major source of intrinsic reward and job satisfaction (Ingersoll, Alsalam, Quinn, & Bobbitt, 1997; Yee, 1990). As a result, the definition of teacher job satisfaction is strongly tied to teacher efficacy, a teachers belief that he or she makes a difference with students (Lee, Dedrick, & Smith, 1991). Teachers who have a strong sense of efficacy perceive that their actions and effort personally influence student achievement, and teachers who are satisfied with their work tend to have a stronger sense of efficacy and to have programs that generate student success (Bruening & Hoover, 1991; Taylor & Tashakkori, 1994). Higher levels of teacher satisfaction have been linked to higher levels of student achievement and lower levels of teacher satisfaction with decreased levels of student achievement (Black, 2001; Connolly, 2000; Darling-Hammond & Sclan, 1996; Lumsden, 1998). In light of the current teacher shortage, teacher satisfaction has become an increasingly important policy issue because of its relationship to recruiting and retaining 1

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teachers (Ingersoll et al., 1997). In the 11-year period between the 1998-1999 and 2008-2009 school years, an unprecedented two million new public school teachers, and an additional 500,000 private school teachers must be hired to meet the projected need to replace teachers who retire or leave the profession and to fill new positions in growing districts (Hussar, 1999). The increased competition from occupational fields that offer better working conditions, pay, and professional opportunities for able college students is further eroding the supply of quality new teachers (Devaney & Sykes, 1988). This is especially problematic in finding an adequate supply of science and mathematics teachers. Furthermore, there are contradictory policies affecting the hiring of new teachers. One policy moves toward professionalization of the teaching field by upgrading the knowledge of teachers and raising the standards in training and education required of teachers while another attempts to overcome the teacher shortage by making exceptions to the standards in order to fill the classroom with someone, whether licensed or not. The latter type teachers are most often hired to teach in central cities with high concentrations of minority students and in poor rural areas. Disparities in salaries and working conditions in these areas as compared to more affluent areas contribute to teacher shortages in the central cities and poor rural areas. As the nations schools seek to produce students able to contribute to society in a post-industrial, knowledge-based economy, the latter policy may be counterproductive, given that numerous studies support the position that fully prepared and certified teachers are more effective than those who lack one or more of the often required elements of licensing such as content knowledge, clinical experience, and knowledge of how to teach and how students learn (Darling-Hammond & Sclan, 1996). 2

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Job satisfaction as related to working conditions and level of professionalism is a key factor in successfully recruiting and retaining teachers (Cooley & Yovanoff, 1996; Eberhard, Reinhardt-Mondragon, & Stottlemyer, 2000; Fresko & et al., 1997; Gonzalez, 1995; Hoover & Aakhus, 1998; Karge & Freiberg, 1992; Stansbury & Zimmerman, 2000). Career satisfaction for teachers hinges on the ability to pursue the personal values and beliefs that led them into teaching to be of service and to make valued contributions to young students (McLaughlin & Yee, 1988, p39). Purpose of the Study The purpose of the study is to identify through theory and literature the major workplace factors that contribute to job satisfaction among teachers and to build three separate sets of models: one set each for public schools, private schools, and charter schools. Each set of models establishes a baseline estimating the mean teacher satisfaction score across the schools used in the model and then controls for school characteristics and teacher background characteristics by including these variables in a background model. While holding the background variables constant, variables that form the major constructs that are possible to manipulate by policy decisions are then added to form additional models variables such as administrative support and leadership, resources, cooperative environment and collegiality, parental support, student behavior and school atmosphere, credentialing requirements, professional development opportunities, autonomy and authority in the classroom and the school, and compensation. Finally an overall model that includes all the variables at the same time is developed for each sector. Each model that contains one or more main variables is compared to the corresponding background model and coefficients are interpreted. 3

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Research Questions After controlling for teacher background and school characteristics: 1. To what degree can administrative support and leadership predict teacher satisfaction in public, private, and charter schools? 2. To what degree can resources predict teacher satisfaction in public, private, and charter schools? 3. To what degree can cooperative environment and collegiality predict teacher satisfaction in public, private, and charter schools? 4. To what degree can parental support predict teacher satisfaction in public, private, and charter schools? 5. To what degree can student behavior and school atmosphere predict teacher satisfaction in public, private, and charter schools? 6. To what degree can credentialing requirements predict teacher satisfaction in public, private, and charter schools 7. To what degree can professional development opportunities predict teacher satisfaction in public, private, and charter schools? 8. To what degree can the level of autonomy in the classroom predict teacher satisfaction in public, private, and charter schools? 9. To what degree can the level of autonomy in the school predict teacher satisfaction in public, private, and charter schools 10. To what degree can compensation predict teacher satisfaction in public, private, and charter schools 4

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11. To what degree can factors representing opportunity and capacity (administrative support and leadership, resources, cooperative environment and collegiality, parental support, student behavior and school atmosphere, credentialing requirements, professional development opportunities, autonomy and authority in the classroom and the school, and compensation) predict teacher satisfaction in public, private, and charter schools? Rationale for the Study This study uses data from the 1999-2000 School and Staffing Surveys (SASS) collected by the National Center for Education Statistics (NCES). These surveys are designed to provide data appropriate for analyzing policy issues related to school and teacher characteristics and teachers attitudes toward their profession. By focusing on workplace conditions, this report expands on the work of two previous studies that were based on the 1993-1994 School and Staffing Survey (SASS) data: 1) Teacher Professionalization and Teacher Commitment: A Multilevel Analysis and 2) Job Satisfaction Among Americas Teachers: Effects of Workplace Conditions, Background Characteristics and Teacher Compensation (Ingersoll et al., 1997; Perie & Baker, 1997). The NCES Research Agenda for the 1999-2000 data calls for more exploratory studies and for studies about charter schools. This study uses the new data to provide updated information about teacher satisfaction in public and private schools. Additionally charter schools are included for the first time. An important feature of this study essential in policy research is that the variables of interest can potentially be manipulated by state, district, and school policies. The 5

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variables in this study include teachers perceptions of administrative support and leadership, resources, cooperative environment among teachers and administration, parental support, student behavior and school atmosphere, compensation, teacher authority in the school and the classroom, professional development opportunities, and credentialing requirements. All of these have the potential to be influenced by various levels of policy. Teacher background characteristics are more difficult to manipulate by policy; nevertheless, they provide important information about how various subgroups of the population of teachers vary. The degree that a single factor contributes to predicting teacher satisfaction indicates the association between satisfaction and that factor while holding the other factors in a model constant. For example, if the analysis shows that higher levels of satisfaction are associated with higher levels of administrative support and leadership after controlling for teacher background characteristics and for school size, location, and level then it means that teacher satisfaction and administrative support and leadership are related regardless of such background characteristics and regardless of school size, location, and level. These variables are included primarily as control variables. Limitations Job satisfaction is a construct that is impossible to measure directly. As a result, researchers must rely on direct measures of subjective types of indicators such as employees attitudes and perceptions that are theoretically supportable. It is not uncommon to see the terms job satisfaction and job attitudes used interchangeably in the literature. In combination, theoretically relevant indicators give a reasonably accurate measure of such a construct (Hair, Anderson, Tatham, & Black, 1995) 6

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This study employs a secondary analysis of previously collected data. As a result, the research is limited to the variables about which information was collected (Kiecolt & Nathan, 1985). The study, however, is conducted in line with U.S. Department of Educations intent as SASS was designed to collect data that permit researchers to analyze policy issues such as school and teacher characteristics, teacher supply and demand, teacher workplace conditions, and school programs and policies (NCES, 2002). Data collected by the SASS was self-report data. The level of honesty of the respondents limits the findings. Nevertheless, self-report data on a variable such as job satisfaction that is internal to a respondent are considered more reliable than third party observations (Bacharach, Bauer, & Conley, 1986; Starnaman & Miller, 1992). Definitions Job Satisfaction how people feel about their jobs and different aspects of their jobs (Spector, 1997, p. 2); or an overall feeling about ones job or career in terms of specific facets of the job or career (Perie & Baker, 1997, p. 2). Opportunity access to advancement and chances to grow in competencies and skills, to contribute the main organizational goals, and to be challenged by ones work. Level of opportunity for teachers includes gaining competence in ones job through professional development, collegial and mentoring relationships, credentialing processes, feedback on performance and general support of efforts to try new ways of doing things and to acquire new skills (Kanter, 1977; McLaughlin & Yee, 1988). Capacity power or autonomy; a workers access to and authority to mobilize resources and to influence the goals and direction of their institution (McLaughlin & Yee, 1988). 7

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Public School an institution that provides educational services for at least one of any grades 1-12, has one or more teachers, is located in one or more buildings, receives primary financial support from public funds, has at least one administrator, and is operated by an education agency. Public schools are the subset of all public schools in the United States except public charter schools (Gruber, et. al., 2002; Tompkins, 1995). Private Schools a school not in the public system that provides educational instruction for any of grades 1-12 where instruction is not given in a private home (Gruber, et.al., 2002; Tompkins, 1995). Charter Schools public schools that have been freed from many state and local regulations, enabling teachers, parents, and students to become stakeholders in a common vision of goals and curriculum (Blackman et al., 1997). A public charter school is a public school that, in accordance with an enabling state statute, has been granted a charter exempting it from selected state or local rules and regulations. (Gruber, et. al., 2002) A charter school may be a new school, recently created, or in the past it might have been a public or private school. (Gruber, et. al., 2002). 8

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CHAPTER II REVIEW OF THE LITERATURE This section will first discuss several general theories of job satisfaction followed by the description of a conceptual framework for teacher satisfaction in particular. A discussion of several measures of job satisfaction will then be discussed. Finally an overview of some of the job satisfaction literature will identify the main factors that are related to teacher job satisfaction and tie these factors to theory. Theories of Job Satisfaction As in any area of research, theories are defined and tested, refined and challenged. Beginning in the 1950s, the predominant theory of job satisfaction tended to combine the work of Maslow and Herzberg into a need-based structure. Much research was generated by these theories and even today it is not uncommon to see studies based on them. Needs based theory; however, has been de-emphasized as researchers have shifted attention from underlying needs to cognitive processes (Spector, 1997). The more dominant theory today focuses on attitudes. Need Fulfillment Theory In the 1950s, Abraham Maslow proposed a hierarchical structure of five needs that individuals are motivated to fulfill (Cheung, 1999; Chung, 1977; Steers & Porter, 1991) in their lives. It is hierarchical in the sense that a lower level of need must be satisfied before the individual will seek to fulfill the next higher level of need. These five 9

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need levels are 1) physiological needs including food, water and shelter which are essential to survival; 2) safety needs or feeling safe from physical and/or emotional danger; 3) social needs or feeling a sense of belonging, affection, acceptance, and friendship; 4) esteem needs that include self-confidence derived from achievement and the recognition, status, and prestige that accompany achievement; and 5) self actualization, realizing ones dreams and maximum potential. Though the theorys emphasis is broader than need fulfillment in terms of job satisfaction, many of these needs are fulfilled in the context of the world of work. For instance, a basic salary that pays at least for the essentials of life may fulfill the first level. In a school setting, the second level may be an issue since some school environments are threatening to teachers while others feel safe. The top three levels of the hierarchy are likewise related to job satisfaction. Maslows theory has incurred some criticism because of its emphasis on a hierarchical structure that indicates that one level of needs must be satisfied before the next will be attempted. Some people believe that the theory has been interpreted too literally and many researchers find this hierarchical view to be simplistic and rigid in light of the complexity of human behavior that they believe would naturally exhibit an overlap of these characteristics or a shift from a higher level to a lower level at various times in a life cycle (Stueart & Moran, 1993). Two Factor Theory One of the most often researched and cited theories of job factors related to satisfaction is Frederick Herzbergs Two Factor Hygiene and Motivation Theory. This theory also arose in the 1950s and builds on Maslows theory. The first part of the theory 10

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is called the hygiene theory and is concerned with the environmental or extrinsic aspects of a job. The extrinsic factors include the company along with its policies and administration, the type of supervision an employee receives on the job, the workplace conditions, relations with other workers, salary, benefits, status and prestige, and job security. This group of factors does not actually motivate an employee, but if they are lacking, an employee will experience dissatisfaction with the job. Yee expounds on this idea for teachers in particular noting that these factors become more salient when a teacher fails to experience the rewards of doing a job well. If the teachers feel they are not reaching their students or are not respected and appreciated, they will look outward and evaluate the extrinsic factors in relation to the whole of their lives. If job security is important as it may be to a people who have children to support, they may stay even if they are miserable. If they value summers off for travel or personal freedom or if they chose teaching because its schedule fits with their childrens schedules, they may stay, though they may become less and less involved in their work (Yee, 1990). On the other hand, for the teachers who are experiencing the intrinsic rewards of a job well done, these extrinsic factors are still necessary to keep them from becoming dissatisfied with their work. The second set of factors is the actual motivators or satisfiers, the intrinsic generators of satisfaction in an employee. The motivators include achievement, recognition for achievement, interest in the work itself, responsibility, and growth and advancement. All these factors are related to continuous learning and for a teacher, to success in teaching. 11

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For satisfaction to occur, both the hygiene and the motivational factors must be present. Satisfiers emanate from what the employee does while dissatisfiers stem from the environment where the employee works. Two prominent themes emerge from two-factor theory (Chung, 1977). The first is that when employees fail to be satisfied with the dissatisfiers, the dissatisfiers contribute to job dissatisfaction, but satisfaction with dissatisfiers does not cause job satisfaction or increased performance on the job. The second is that the satisfiers produce a tendency toward increasing both job satisfaction and performance. Numerous research studies, however, fail to support delineation between the two factors (House & Wigdor, 1967; Locke, 1975). Herzbergs theory generated huge amounts of research and new thinking about job satisfaction. Some believe it lost credibility in the academic literature as a result of the previously cited studies (Thomas, 2000). Many other needs fulfillment theories are adaptations of Herzbergs theory. In general, research studies and the spin off theories from Herzberg support the idea that both extrinsic and intrinsic factors contribute to job satisfaction when they are present and to job dissatisfaction when they are not. Valence-Satisfaction Theory Instead of focusing on need fulfillment in the present, Vroom (1964) conceived of job satisfaction as an event that occurs in the future as a result of anticipating satisfaction of needs or valued outcomes (the valence). He proposes that workers are attracted to a particular incentive because it is perceived to have the potential to satisfy needs. Vroom measures job satisfaction by summing the total amount of valued outcomes available to an employee. Employee performance depends on the total amount of valued outcomes 12

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available in relation to the anticipation that expended effort will yield valued outcomes (Chung, 1977). Dissatisfaction is then a necessary prerequisite that motivates an employee to perform in the expectation that valued outcomes will be realized. It is necessary however, that an employee have reinforcement experience that allows this connection to be perceived. Discrepancy Theory Discrepancy theory examines the difference between what a worker receives in a job and what he or she expects to receive. At least two definitions of job satisfaction have arisen from this theory. The first proposed by Porter and Lawler (1968) focuses on equity in the comparison and defines job satisfaction as the degree that rewards received on the job meet or exceed the workers perception of what the equitable reward level should be. Porter modified Maslows theory by eliminating the physiological needs at the lowest level and adding autonomy between self-esteem and self-actualization (Lee, 2002). Lawler and Porter examined the relationship between intrinsic and extrinsic rewards. They concluded that rather than satisfaction causing performance, performance causes satisfaction. They began to focus on which employees and what types of needs are satisfied in an organization, rather than on maximizing satisfaction generally (Lawler & Porter, 1967). Locke (1969), in the second definition of discrepancy, shifts the emphasis from equity to aspiration (Chung, 1977). He defines job satisfaction as the extent that received rewards differ from what the worker would like to receive, or in other words, what the worker values. Locke made a distinction between what a worker needs and what he or she values. Job satisfaction depends on the magnitude of the gap between actual rewards 13

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and the perception of either equitable or desired rewards. As the gap narrows, satisfaction is expected to increase. In either case, even if workers receive the same rewards, discrepancy theory helps explain why some may be satisfied while others are not Equity-Inequity Theory Adams (1963, 1965) builds on discrepancy theory; he accepts the idea that satisfaction is determined by size of the gap between expectation and reality, but introduces the concept that it is also determined by comparing his or her own input-output ratio to other workers input-output ratios. Workers compare their own expenditure and contribution of effort, knowledge, experience, and skills for instance with those of others in their workplace or field and evaluate whether what they are receiving is equitable in comparison. Satisfaction results if they perceive equity in the comparison. Employees who perceive that they are over rewarded in comparison to others may attempt to increase performance to justify the reward while those who perceive they are under rewarded may decrease performance. Kanters Structural Theory of Organizational Behavior Rosabeth Moss Kanter (1977), in her book Men and Women of the Corporation, presents a structural theory of organizational behavior that relates individual effectiveness in a job to the way the position itself is structured and located in a workplace system to the abilities of the individual holding the position. Behaviorally, she identifies the level of opportunity and the amount of power available to the person holding the position as the most significant aspects of the position held and related workplace conditions in an organization. Power is defined as having access to resources along with the capacity to activate their use and the needed tools to efficiently get the job done. Opportunity means 14

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both access to advancement and chances to grow in competencies and skills, to contribute to the main organizational goals, and to be challenged by the work. Workers who hold positions that offer opportunity are considered motivated to perform, tend to empower those they supervise, and are more committed to the goals of the organization and to the organization itself. Workers without opportunity tend instead to withdraw and fail to value their skills or aspire to accomplishments. Powerless supervisors tend to become petty and tyrannical, open neither to change nor innovation, and they deny their subordinates opportunities to grow in confidence or new competencies. (Kanter, 1977, 1983; McLaughlin & Yee, 1988; Stein & Kanter, 1980). Overviews of several theories of job satisfaction have now been presented. Each one contributes a bit more understanding of the construct and has generated research on the topic. Kanters structural theory of organizational behavior, though developed in a business context of a large corporate environment, has been noted by researchers as having particular merit for thinking specifically about job satisfaction among teachers. The current study employs an adaptation of Kanters theory to provide its conceptual framework. A Conceptual Framework for Teacher Job Satisfaction McLaughlin and Yee (1988) further developed Kanters structural theory of organizational behavior specifically in the context of teaching and job satisfaction. Interpreting the results of their two year study that explored what makes a satisfying teaching career, they concluded that level of opportunity and level of capacity (power) vary significantly across institutional settings and play a primary role in defining an 15

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individuals career as a teacher and the satisfaction derived from it (McLaughlin & Yee, 1988, p. 26). In general the concept of career can take on either an institutional view or an individually based view. The institutional or traditional view expects that an employee will begin in an entry-level position and be promoted into increasingly more responsible and higher paying positions over the course of the career. In contrast, an individually based view may or may not involve movement through such a hierarchy. Instead it depends on advancement and satisfaction as defined internally by the worker. Those who choose teaching as a career are often planted firmly in this second conception (McLaughlin & Yee, 1988). Although some teachers do move into various administrative positions, most do not, and of those who do not, most would not view a change to administration as a desirable promotion because they like to work with students and they want to teach. Teaching is their chosen career. McLaughlin and Yee customize the meanings of level of opportunity and of power for working conditions in the teaching profession. Level of opportunity includes gaining competence in ones job through professional development, collegial and mentoring relationships, credentialing processes, feedback on performance, general support of efforts to try new ways of doing things, and to acquire new skills. Power is instead called capacity, which refers to a workers access to and authority to mobilize resources and to influence the goals and direction of their institution. Power and autonomy are synonyms for capacity. 16

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McLaughlin and Yee (1988) summarize their research results concerning the workplace conditions of teachers who experience or fail to experience opportunity and capacity in their work environments: Teachers with rich opportunities to grow and learn are enthusiastic about their work and are motivated to find ways to do even better. Teachers with low levels of opportunity become burned out, trading on old skills and routines. They wind up feeling stuck in dead-end jobs, going nowhere in terms of their career. Teachers with a sense of capacity tend to pursue effectiveness in the classroom, express commitment to the organization and career, and report a high level of professional satisfaction. Lacking a sense of power, teachers who care often end up acting in ways that are educationally counterproductive by coping lowering their aspirations, disengaging from the setting, and framing their goals only in terms of getting through the day (McLaughlin & Yee, 1988). Changes in teacher education, more time for professional development, and decentralization of the decision-making process in the schools are supported goals to increase the qualifications, opportunities, and capacities of teachers (Darling-Hammond, 1992; Engvall, 1997) and thereby increase the effectiveness and satisfaction of teachers. Definitions of Job Satisfaction Several definitions of job satisfaction have already been discussed as related to the various theories of job satisfaction. These included definitions centering on need fulfillment or gratification, anticipated need satisfaction, perceived satisfaction of valued outcomes, and perceived equity. As previously mentioned, the emphasis on needs based definitions has faded and has been replaced with definitions focusing on cognitive functioning centering on attitudes. Job satisfaction is simply how people feel about their jobs and different aspects of their jobs (Spector, 1997). Perie and Baker (1997) concur defining job satisfaction as an overall feeling about ones job or career in terms of specific facets of the job or career. 17

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Measurement of Job Satisfaction Usually, job satisfaction is measured using either Likert-type survey items or interview questions. Interviews, because of their greater cost and time intensity are more likely used in the development stages of a survey when researchers are interested in identifying the major areas of job satisfaction for a segment of employees (Spector, 1997). Job satisfaction surveys can take a global approach, a facet approach, or a combination of the two in measuring job satisfaction. Surveys using the facet approach attempt to measure many separate aspects of job satisfaction in order to identify particular areas of satisfaction or dissatisfaction. Most often these have included some subset of the following: appreciation, communication, coworkers, fringe benefits, job conditions, nature of the work itself, organization itself, organizations policies and procedures, pay, personal growth, promotion opportunities, recognition, security, and supervision (Spector, 1997). Factor analyses have reduced these to four major categories of concern: rewards, other people, nature of the work, and organizational context (Locke, 1976). A sampling of facet scales that have commonly been used in research include the Job Satisfaction Survey (JSS) (Spector, 1985), the Minnesota Satisfaction Questionnaire (MSQ) (Weiss, Dawis, England, & Lofquist, 1967), the Job Diagnostic Survey (JDS) (Hackman & Oldham, 1975), and the Job Descriptive Index (JDI) (Smith, Kendall, & Hulin, 1969), the most often used of all in research studies of job satisfaction. These surveys measure from 5 to 20 facets each and contain from 2 to 20 items to measure each facet. Inter-correlations tend to be very high between some facets when many separate 18

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facets are measured such as in the 20 measured by the MSQ. Response options for single items vary from three to seven point scales, depending on the particular survey. Some surveys use a summation across the facets to generate an overall satisfaction score; others are critical of this method and employ a separate scale to measure global satisfaction. For instance, the Job in General Scale (JIG) (Ironson, Smith, Brannick, Gibson, & Paul, 1989) is modeled after the JDI using the same three response option format with 18 items, but it measures overall job satisfaction rather than a particular facet. The authors of the JIG argue that summing facet scores to get a measure of overall job satisfaction makes the unlikely assumption that each facet is contributing equally to the global score. Another often-used global satisfaction measure is the Michigan Organizational Assessment Questionnaire Subscale (Cammann, Fichman, Jenkins, & Klesh, 1979). This is a three-item subscale using seven response options ranging from strongly disagree to strongly agree. The reported alpha is .77. The three items in the subscale include: 1) All in all I am satisfied with my job; 2) In general, I dont like my job; and 3) In general, I like working here (Spector, 1997). Factors Related to Job Satisfaction The literature identifies many factors that are related to teacher job satisfaction. These factors include administrative support and leadership, resources, cooperative environment and collegiality, parental support, student behavior and school atmosphere, credentialing requirements, professional development opportunities, autonomy and authority in the classroom and the school, and compensation. This section identifies these 19

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factors citing relevant studies that tie them to job satisfaction and explains their place within the conceptual framework of opportunity and capacity. Administrative Support and Leadership Among the most often cited working conditions related to teacher job satisfaction is administrative support and leadership (Eberhard et al., 2000; Karge & Freiberg, 1992; Krueger, 2000; Lumsden, 1998; Perie & Baker, 1997; Wiggs, 1998; Wright, 1991). The quality and type of school leadership can set the tone of the school and correlates highly with a teachers perception of the school culture itself (Darling-Hammond & Sclan, 1996). Aspects of administrative support and leadership include clearly defined expectations and vision, behavior toward staff that is supportive and encouraging in areas like school rules and instructional practices, structured and predictable work environments, recognition and rewards for a job well done, and fair distribution of teaching assignments (Eberhard et al., 2000; Karge & Freiberg, 1992; Pearson, 1998; Taylor & Tashakkori, 1994). Opportunity for teachers is increased when principals provide frequent feedback, convey high expectations, and ensure opportunities for teacher learning. They are empowered when principals involve teachers in decision-making and provide necessary support and materials, helping ensure the conditions that allow them to be effective (Blase & Kirby, 1992; Rosenholtz, 1989). Resources Having adequate resources like materials, textbooks, copy machines, time, and freedom from too much paperwork can influence teacher satisfaction levels (Black, 2001; Eberhard et al., 2000; Krueger, 2000; Pearson, 1998). Without these tools, teachers may feel unable to excel in their work and their sense of efficacy may decline. Teachers in 20

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urban, inner-city schools, the very places where teacher shortages are most acute, tend to experience the greatest shortage of needed materials (McLaughlin & Yee, 1988). A lack of resources can be very frustrating for teachers, severely limiting their capacity to teach effectively. On the other hand, McLaughlin and Yee point out that once teachers do have the basic resources they need to teach, it is other nonmaterial facets of the school setting that actually define a teachers sense of satisfaction and career. This lends support to Herzbergs contention that some factors tend to lead to dissatisfaction when they are absent, but fail to create satisfaction when they are present. It is not uncommon to find enthusiastic, creative, and satisfied teachers in schools with limited but minimally adequate resources while some teachers in resource-rich schools may be disgruntled, cynical, and dissatisfied with teaching and their perception of their effectiveness as teachers. In contrast, increased availability of resources opens new opportunities for teachers to expand their competencies and skills as they use new materials and learn from the accompanying documentation and allows teachers to be challenged in new ways as they use new resources. Cooperative Environment and Collegiality Cooperative environment and collegiality among staff members contribute to satisfaction with teaching (Brunetti, 2001; Cockburn, 2000; Connolly, 2000; Cooley & Yovanoff, 1996; Krueger, 2000; Stansbury & Zimmerman, 2000). Teachers who experience community and collaboration in their workplaces and who sense that their colleagues recognize their efforts and communicate with one another tend to be more satisfied than those who do not (Lee et al., 1991). A collegial environment increases teachers opportunity to learn from each other, gaining both encouragement and new 21

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ideas that increase their effectiveness in the classroom. Teaching is often characterized by isolation from other adults. A sense of professional community that involves camaraderie and contact with others who know you exist and would miss you if you were gone meets basic human needs to be significant to others. Some teachers report that informal gatherings such as getting advice around the lunch table, spending time with colleagues going over materials, and words of encouragement from a more experienced colleague were the most helpful aspects of their learning and sticking through the difficult early days (and years) of teaching (Yee, 1990). Collegial interactions with colleagues in an effort directed toward improving performance can be a valued reward in itself (McLaughlin & Yee, 1988). Furthermore, when collegial relations exist, the school becomes less segmented, and teachers are not so isolated. Problems can be acknowledged and discussed rather than hidden and denied (McLaughlin & Yee, 1988). Teachers are empowered by having access to colleagues expertise and support in problem solving. Parental Support Teachers tend to be more satisfied when they receive support for their work from parents. Supportive parents increase the teachers capacity to accomplish their job successfully by encouraging and supervising homework and attendance, and providing general support for the teachers rules and efforts, enabling the teacher to be more respected and effective in the classroom (Lumsden, 1998; Wiggs, 1998). Some parents also spend time helping in classrooms and may increase opportunity to teachers by allowing them the freedom to try new ways of doing things with an additional adult in the classroom. 22

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Student Behavior and School Atmosphere Student behavior and school atmosphere including school safety issues, willingness of students to learn, and the degree to which tardiness, class cutting, and misbehavior interfere with teaching are related to satisfaction (Lumsden, 1998; Perie & Baker, 1997). Many of these same aspects relate back to administrative support and leadership. Both district and school level policies can affect student absences and determine what behaviors are tolerated, for instance, and either empower or fail to empower teachers. In addition, student behavior may be influenced by aspects of opportunity that allow for a high level of training and continued professional development that would equip teachers with the most effective skills in classroom management and in understanding the diverse needs of their students. Although such training cannot guarantee the improved behavior of students, it is the least that teacher training should provide for teachers who are thrust into threatening environments that they are often ill equipped to manage. As one teacher aptly observed: You can only learn to be a teacher if you have a supportive nurturing environment. If you are a soldier in a war zone, you dont plant a garden. If you do, the garden doesnt do very well (McLaughlin & Yee, 1988, p. 29). Credentialing Requirements Credentialing requirements such as certification, degree requirements, and testing increase opportunity and capacity in becoming effective, satisfied teachers (Darling-Hammond, 1995; Prelip, 2001). Though hotly debated, there are numerous study results supporting the position that fully prepared and certified teachers are more effective than those who lack one or more of the often required elements of licensing such as content 23

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knowledge, clinical experience, and knowledge of how to teach and of how students learn (Darling-Hammond & Sclan, 1996). Acquiring credentials lends credibility to the concept of teacher as expert and helps justify giving teachers autonomy in their classroom and influence in the school. Obtaining credentials often provides teachers with the opportunity to study the content, methods, student growth, development, and diversity, classroom management, and assessment skills that they need to be effective teachers. These skills are increasingly important as the nation moves from an industrial to a knowledge-based workforce. Developing expertise in these areas and possessing the books and developed materials from class work expands teacher capacity by increasing the internal and external resources that teachers can access as they are teaching. Internships are generally a part of the credentialing process, providing further opportunity to actually teach under the supervision of fully credentialed teacher. Professional Development Professional development provides opportunities for teachers to grow personally and professionally and increases their capacity for effectiveness. Activities such as graduate studies, participation in teachers unions and organizations, participation in workshops or conferences, getting grants to do research, observing other teachers in action or being observed themselves, seeking national board certification, etc. (Hoover & Aakhus, 1998; Karge & Freiberg, 1992) reward teachers by equipping them to accomplish what matters most to them personal satisfaction from a job well done, from making a real difference in student learning (McLaughlin & Yee, 1988, p. 38). In addition such experiences increase the opportunity to interact with colleagues, to get a 24

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fresh vision for teaching, to learn or develop a new method of teaching or a new way to assess student learning or another way to manage a classroom or how to introduce technology into the current curriculum. Professional development holds the possibility to expand opportunity and capacity in a myriad of ways. Autonomy in the Classroom Increasing the professional autonomy of teachers holds the potential to increase dramatically levels of opportunity and capacity (Bogler, 1999; Brunetti, 2001; Connolly, 2000; Herbst, 1989; Hill, 1995; Lumsden, 1998; Pearson, 1998; Vanourek, et al., & Hudson Inst. Indianapolis IN., 1997). Professional autonomy in the classroom means that teachers are in charge of the classroom, the curriculum, and the day-to-day pedagogical tasks. Opportunity is increased as teachers are permitted, encouraged, and expected to try new ideas in teaching and to design appropriate methods to reach their diverse student populations, thus being increasingly challenged by their work. The teacher has power in the classroom to use resources according their best judgment. Autonomy in the School Teachers capacity in the school is enhanced when they are allowed to help determine and contribute to the goals and direction of their institution (Kanter, 1977, 1983; Stein & Kanter, 1980; McLaughlin & Yee, 1988). Such influence arises from opportunities to provide input into the hiring and evaluation of new teachers, to set discipline policy, to help determine the content of professional development opportunities, to vote on how the school budget will be spent, to select and establish curriculum, to help set performance standards, and to participate in other such activities. 25

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Given a task to accomplish, professionals make the decision how best to proceed and are empowered to act on their decisions. Teachers want greater input so they can be more competent in their work, not just to make teaching easier... The degree to which employees are empowered with discretion and influence in the workplace is a condition that influences a teachers involvement, satisfaction, and sense of efficacy (Yee, 1990). In 1990, nearly 50% of teachers reported that they were not satisfied with the control they had over their professional lives; this was double the number who made the same report in 1987. During the 1990-1991 school year, just over 60% of all teachers indicated that they had little influence in helping to determine school policies such as which curriculum to use, how to group students, which discipline procedures to use, or which topics would be offered for in-service training (Choy et al., 1993). Further, 25% of new teachers reported that they were required to follow rules that conflicted with their best professional judgment (Sclan, 1993). Those teachers least likely to report having influence on school policies in any category were teaching in central city schools or schools with higher minority enrollments (Darling-Hammond & Sclan, 1996). Though nearly all teachers care about having a sufficient level of professional autonomy, those possessing the highest levels of academic achievement tend to care the most and are more likely to indicate a planned exit from teaching when it is lacking (Darling-Hammond & Sclan, 1996). Compensation High levels of compensation such as salary and benefits are especially important in attracting able recruits in a competitive market where many career paths offer better financial rewards. It empowers teachers to remain in teaching by providing the 26

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opportunity to earn a living wage (Collins & ERIC Clearinghouse on Rural Education and Small Schools Charleston WV., 1999; Connolly, 2000; Eberhard et al., 2000; Wright, 1991). Compensation includes not only higher beginning salaries but also higher career-long and career ending salaries (Devaney & Sykes, 1988; Perie & Baker, 1997). It is curious that for teachers, level of compensation does not generally correlate highly with teacher satisfaction (Perie & Baker, 1997). Logically it must be important to some teachers because it has motivated them to form and join unions in an effort to command reasonable salaries, benefits, and job security. As previously discussed, many teachers are drawn to teaching to contribute to society and to be of service to others and gain satisfaction from working with students and helping them learn (Yee, 1990). Teachers may also view other rewards as more important than compensation. For instance, many teachers report a preference to teach in private schools as opposed to public schools because autonomy and professional growth opportunities are often better, even though compensation is generally lower (Darling-Hammond & Sclan, 1996). This is likely a good example of a variable that contributes to dissatisfaction when not present but is overshadowed in importance by intrinsic satisfiers when it comes to predicting satisfaction. Summary Teacher job satisfaction is a measure of how teachers feel about their jobs and various facets of their jobs or career. This definition has evolved over several decades that began with needs-based definitions but in recent years has been replaced with a more cognitive functioning definition that focuses on attitudes. These attitudes about work are most often measured by Likert type items on a survey. 27

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The framework for teacher satisfaction that is adopted for this study emphasizes that the levels of opportunity and capacity afforded to teachers greatly influence job satisfaction among teachers. Opportunity is access to advancement and chances to grow in competencies and skills, to contribute the main organizational goals, and to be challenged by ones work. Capacity is power or autonomy, a teachers access to and authority to mobilize resources and to influence the goals and direction of their institution. Variables that comprise opportunity for a teacher include administrative support and leadership, cooperative environment and collegiality, student behavior and school atmosphere, credentials, professional development, authority in the classroom, authority in school, and compensation. Teacher capacity includes all these same variables plus resources and parental support. It is also possible that attitudes about job satisfaction may vary depending on various characteristics of teachers or schools. In policy research an effort is often made to differentiate between those variables that can be manipulated and those that cannot. Teacher characteristics that may be related to job satisfaction include gender, race/ethnicity, and years of teaching experience. In general, these teacher characteristics cannot or should not be manipulated. Among schools teacher attitudes may vary depending on the school level, school sector, community type, student body minority composition, and school size. This report expands on the work of two previous studies that were based on the 1993-1994 School and Staffing Survey (SASS) data: 1) Teacher Professionalization and Teacher Commitment: A Multilevel Analysis and 2) Job Satisfaction Among Americas Teachers: Effects of Workplace Conditions, Background Characteristics and Teacher 28

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Compensation (Ingersoll et al., 1997; Perie & Baker, 1997). Perie and Bakers teacher satisfaction study presented results of an OLS regression that first controlled for various teacher and school characteristics and then added variables representing workplace conditions including administrative support, student behavior, social environment, and teacher control over work, and compensation. Each of these five factors was added individually to the background model and R 2 values reported. An overall model included all variables and accounted for 22% of the variability in teacher satisfaction responses. The paper by Ingersoll, et. al., on teacher commitment has elements related to teacher satisfaction but also presents an improved methodology for analyzing data with a nested structure, such as teachers within schools. This study builds on these by using the latest data from the 1999-2000 School and Staffing surveys which includes data from public, private, and for the first time charter schools. It is important to examine this topic with the new data using a methodology that takes into account the nested structure of the data as the level of teacher satisfaction and the relationship among key variables may have changed over the six-year period between the 1993-1994 and 1999-2000 surveys. 29

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CHAPTER III METHODS Purpose of the Study The purpose of the study is to identify through theory and literature the major workplace factors that contribute to job satisfaction among teachers and to build three separate sets of models: one set each for public schools, private schools, and charter schools. Each set of models establishes a baseline estimating the mean teacher satisfaction score across the schools used in the model and then controls for school characteristics and teacher background characteristics by including these variables in a background model. While holding the background variables constant, variables that form the major constructs that are possible to manipulate by policy decisions are then added to form additional models variables such as administrative support and leadership, resources, cooperative environment and collegiality, parental support, student behavior and school atmosphere, credentialing requirements, professional development opportunities, autonomy and authority in the classroom and the school, and compensation. Finally an overall model that includes all the variables at the same time is developed for each sector. Each model that contains one or more main variables is compared to the corresponding background model and coefficients are interpreted. Research Questions After controlling for teacher background and school characteristics: 30

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1. To what degree can administrative support and leadership predict teacher satisfaction in public, private, and charter schools? 2. To what degree can resources predict teacher satisfaction in public, private, and charter schools? 3. To what degree can cooperative environment and collegiality predict teacher satisfaction in public, private, and charter schools? 4. To what degree can parental support predict teacher satisfaction in public, private, and charter schools? 5. To what degree can student behavior and school atmosphere predict teacher satisfaction in public, private, and charter schools? 6. To what degree can credentialing requirements predict teacher satisfaction in public, private, and charter schools? 7. To what degree can professional development opportunities predict teacher satisfaction in public, private, and charter schools? 8. To what degree can the level of autonomy in the classroom predict teacher satisfaction in public, private, and charter schools? 9. To what degree can the level of autonomy in the school predict teacher satisfaction in public, private, and charter schools? 10. To what degree can compensation predict teacher satisfaction in public, private, and charter schools? 11. To what degree can factors representing opportunity and capacity (administrative support and leadership, resources, cooperative environment and collegiality, parental support, student behavior and 31

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school atmosphere, credentialing requirements, professional development opportunities, autonomy and authority in the classroom and the school, and compensation) predict teacher satisfaction in public, private, and charter schools? Data Analysis The data analysis occurs in two parts. Phase I works toward validating the questions and subscales for teacher satisfaction and related constructs using exploratory and confirmatory factor analysis. Phase II uses hierarchical linear modeling to construct the three sets of models, one each for public, private, and charter schools. A complete description of these analyses follows in a later part of this chapter. Participants Restricted-use data from the 1999-2000 Schools and Staffing Surveys (SASS) of teachers is used for this study. The teacher survey is one of several SASS surveys; others include district, school, and library media center surveys and a follow-up teacher survey. Together the SASS measures five main policy issues: teacher shortage and demand, characteristics of elementary and secondary teachers, teacher workplace conditions, characteristics of school principals, and school programs and policies (NCES, 2002). The public schools sampling frame was the 1997-1998 Common Core of Data (CCD) school file. CCD is the Department of Educations primary database that includes all elementary and secondary public schools and public charter schools in the United States. SASS selects a nationally representative complex random sample of public schools stratified by state, sector, and school level and for the first time has included all charter schools. Schools run by the Department of Defense, schools offering only 32

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kindergarten or below or only adult education were not included in the SASS sample (NCES, 2002). The sampling frame for private schools was the 1997-1998 Private School Universe Survey (PSS) list, which was updated with association lists. A supplement to this frame was obtained based on private school canvassing in specific geographical areas. The private school sample was stratified by affiliation (NCES, 2002). NCES defines a public charter school for purposes of the School and Staffing Survey as follows: A public charter school is a public school that, in accordance with an enabling state statute, has been granted a charter exempting it from selected state or local rules and regulations. (Gruber, et. al., 2002). A charter school may be a new school, recently created, or in the past it might have been a public or private school. All public charter schools that were open during the 1998-99 school year and which remained open in the 1999-2000 school year were surveyed. The Office of Educational Research and Improvement (OERI) provided the information upon which charter school sampling frame was based. (Gruber, et. al., 2002). Within each selected school, a subset of teachers, the number of whom was dependent on school size, was randomly selected to answer the survey questions. The teacher file from National Center for Education Statistics also contains some school level data incorporated from the school survey data such as school size, location, and percent minority. In Tables 1 and 2 respectively, summaries of the number of teachers and schools selected to participate and their weighted response rates are presented (Gruber, Wiley, Broughman, Strizik, & Burian-Fitzgerald, 2002). 33

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Table 1 Number of Selected Teachers, In-Scope Cases, Completions, and Response Rates Sector # Surveys Sent to Teachers # In Scope Teacher Cases* # Complete Teacher Interviews** Unweighted Teacher Response Rate*** Public 56,354 51811 42086 81.2% Private 10,760 9472 7098 74.9. % Charter 4,438 3617 2847 78.7% Table 2 Number of Selected Schools, In-Scope Cases, Completions, and Response Rates Sector # Surveys Sent to Schools # In Scope School Cases* # Complete School Interviews** Unweighted School Response Rate*** Public 9,893 9527 8432 88.5% Private 3,558 3233 2611 80.8% Charter 1,122 1010 870 86.1% *To be considered in-scope the selected school must still have been operational and the teacher still employed by the selected school at the time the survey data was collected **The number of complete interviews is the unweighted number of in-scope cases that responded to enough items to be considered a valid respondent ***Unweighted response rates are defined as the number of complete interviews divided by the number of in-scope sample cases. Procedures Data collection was conducted by the U.S. Census Bureau, which began by sending advance letters to the selected schools in September 1999. Questionnaires were mailed to the schools in October with a postcard reminder as a follow-up several weeks later. Non-responding teachers were followed up using Computer-Assisted Telephone Interviewing (CATI) (NCES, 2002). Data editing was performed by the U.S. Census Bureau, which coded each questionnaire depending on whether it was completed, not completed, or from an out-of-scope respondent such as a closed school or a teacher that no longer worked at the school. 34

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Next each survey passed through several edits including one for consistency until finally a decision on eligibility was made, and if so, whether there was enough response data to consider the case as an interview. Requirements for an interview included answering a certain number of critical items and a percentage of the remaining items had to have non-missing values. If coded as an interview, any missing data were imputed using the following methods: 1) data from other items, 2) data from a related SASS component, 3) data from the sampling frame, and 4) hot deck method (data from a sample case that had similar characteristics) (NCES, 2002). In Table 3 the unweighted response rates for items on the teacher survey are summarized. Most items had response rates of 90 percent or more while a few had response rates of 75-89 percent and fewer still had response rates of less than 75%. The forthcoming 1999-2000 School and Staffing Survey: Data File Users Manual will address these issues of missing data more completely and at the item level. A preliminary report lists item numbers that had less than a 75% response rate. None of the items from the teacher survey that are used in this study were among the listed items. The data files contain a variable called the imputation flag that indicates both the source and method used for imputation. For example, on the Public School Questionnaire, f_s0111=7 indicates that a donor (similar school) was used to impute variable s0111 (Gruber et al., 2002), Appendix B. A small amount of missing data (fewer than 2% of schools or teachers) from one variable, percent minority enrollment, was imputed by the researcher using mean substitution. An alternative method of listwise deletion was also tried for comparison purposes. There were no changes in significance of main variables in the overall model or in the individual models. In a few cases the R 2 values changed by one percentage point in 35

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the overall model as compared to the background model. There were very small changes in the standard deviation in each sector; for example in public schools the standard deviation changed from 31.68 to 31.75. Based on these observations, the mean substitution was judged to be the preferable method since no cases were lost and the weights continued to be properly assigned across the entire sample. Table 3 Summary of unweighted item response rates for the teacher survey Sector Range of item response rates Percent of items with a response rate of 90 percent or more Percent of items with a response rate of 75-89 percent Percent of items with a response rate of less than 75 percent Public 48-100 89 7 4 Private 10-100 83 11 6 Charter 48-100 82 10 8 The 1999-2000 restricted use data was obtained by the researcher through the National Center for Education Statistics. After downloading the Restricted-Use Data Procedures Manual from the NCES website, the researcher reviewed the manual and followed instructions to enlist the support of her advisor. Together they submitted a letter to NCES on letterhead stationery requesting the data, signed the license document and affidavits of non-disclosure, created a security plan, and submitted all documents to NCES for review. The University of South Florida received approval for a license to use the data, and the 1999-2000 restricted-use data with electronic codebook first became available and was mailed to approved researchers in January, 2003. About six months before that, interim data was made available to the researcher in permanent SAS dataset format. The public use data was not available during the writing and research for this study. 36

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Variables Phase 1 of the data analysis of this study includes factor analytic measurement work designed to test and refine the proposed variables. Before any analysis was done, items were selected from the survey based on theory, literature, and previous studies to represent each of the constructs of interest. After the factor analytic work was completed some of the operational definitions changed based on the results. Appendix A contains a summary of the variables as proposed before any factor analytic work was done, and Appendix B contains a summary of the variables as defined and used after the factor analytic work was completed. Each appendix includes a list of all study variables as originally conceived (Appendix A) or as actually used in the study (Appendix B) with a short description of the related conceptual framework for each variable along with item content, item number from the SASS teacher survey, the range of response options and the orientation of the question wording. Four school-level variables were proposed and used as control variables. Factor analytic work was not conducted on the control variables since none of them were conceived as subscales of combined items. The control variables included school level (elementary, secondary, and combined), community type (large or mid-size central city, urban fringe of large or mid-size city, and small town/rural), school size, and percent minority. In addition three teacher background characteristics were planned and used as control variables. These included gender, race/ethnicity (American Indian/Alaskan Native, non-Hispanic, Asian or Pacific Islander, non-Hispanic, Black, non-Hispanic, White, non-Hispanic, or Hispanic, regardless of race), and total years of teaching experience. Age was also considered, but in previous studies age and total years of 37

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teaching experience were so highly correlated as to be problematic including both in the same regression equation. Figure 1 contains an illustration of the ten predictor variables in the study, not including the control variables. The predictors of job satisfaction as related to opportunity and capacity included administrative support and leadership, resources, cooperative environment and collegiality, parental support, student behavior and school atmosphere, credentialing requirements, professional development opportunities, autonomy and authority in the classroom and the school, and compensation. Figure 1. Predictor Variables and their Relationship to Job Satisfaction Items that showed promise for being included in subscales measuring each construct are discussed next. Results of the exploratory and confirmatory factor analyses 38

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in many cases resulted in additions or deletions of items to these originally proposed subscales. Four items from the survey were originally considered for inclusion in a subscale measuring the outcome variable, teacher job satisfaction. Among these proposed items teachers rated their general satisfaction with teaching in their school and whether they felt it is sometimes a waste of time to be a teacher on a four-point scale from strongly agree to strongly disagree. In a third item teachers indicate if they would become a teacher now if they could start college all over again and used a five point rating scale from certainly would to certainly would not. On a fourth item teachers tell how long they plan to remain in teaching using a five-point scale ranging from as long as I am able to definitely plan to leave. The items that were considered for the administrative support and leadership subscale consisted of six items concerning a teachers perception of whether the school principal communicates expectations, is supportive and encouraging, enforces school rules and backs teachers up, talks with teachers about instructional practices, has communicated a vision for the school, and recognizes teachers for a job well done. Response options for each item use a four-point scale ranging from strongly agree to strongly disagree. The proposed two-item subscales for resources use the same response options. Teachers are asked to indicate if necessary materials are available as needed and if routine duties and paperwork interfere with teaching. The proposed cooperative environment and collegiality subscale consisted of five items, which also use the four-point scale ranging from strongly agree to strongly 39

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disagree. Questions concern whether rules are consistent and are enforced by all teachers for all students, colleagues share the raters beliefs and values about the central mission of the school, there is cooperative effort among staff, the rater plans cooperatively with media services, and the rater coordinates course content with other teachers. Two items composed the proposed parental support subscale. One uses the strongly agree to strongly disagree response options asking if a teacher receives a great deal of support from parents. In the other item, teachers are asked to what extent lack of parental involvement is a problem with responses ranging from serious problem to not a problem on a four-point scale. The originally proposed student behavior and school atmosphere subscale included two items formatted using the four-point strongly agree to strongly disagree response options. Teachers are asked about whether student tardiness, class cutting, and misbehavior interfere with teaching. The same question about several different potential problems is posed to teachers in an additional set of 13 items: To what extent is each of the following a problem in this school? The problems include tardiness, absenteeism, class cutting, physical conflict, theft, vandalism, alcohol use, drug abuse, weapons, disrespect for teachers, students dropping out, student apathy, and lack of preparation for learning. Each of these items is rated on a four-point scale from serious problem to not a problem. Questions about credentials was proposed to be the sum of 1=yes or 0=no to five questions about whether teachers are certified in their main teaching area, their minor teaching area, have any additional certifications, and hold bachelors and/or masters degrees. 40

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Professional development was originally proposed to be represented with one item that asks teachers to think about all the professional development they have participated in over the past 12 months and to rate it on a five-point scale as not useful at all to very useful. In addition it was proposed that all items on the survey related to professional development be included in the exploratory factor analysis and considered for the confirmatory factor analysis depending on the results. There are extensive numbers of questions on the SASS related to professional development. Teachers were asked to rate how much influence they have over school policy in seven different areas in the proposed autonomy in the school subscale: setting performance standards, establishing curriculum, determining the content of in-service professional development, evaluating teachers, hiring new teachers, setting discipline policy, and deciding how the school budget will be spent. The response options range from no influence to great influence on a fivepoint scale. Teachers to rate how much control they think they have in their classroom over planning and teaching in six areas in the proposed autonomy in the classroom subscale: selecting instructional materials, selecting what to teach, selecting teaching techniques, evaluating students, disciplining students, and assigning homework. Teachers rate their level of control on a five-point scale ranging from no control to complete control. Compensation was proposed to be measured on a four-point scale of the teachers stated satisfaction level with his or her salary with response options ranging from strongly agree to strongly disagree. Using the log of the actual base salary figure was considered, but without a cost of living adjustment the relationship between salary and satisfaction level would not be consistently meaningful. 41

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Data Analysis Phase I The constructs representing opportunity and capacity (administrative support and leadership, resources, cooperative environment and collegiality, parental support, student behavior and school atmosphere, credentialing requirements, professional development opportunities, autonomy and authority in the classroom and the school, and compensation) have been defined based on the literature review, previous studies using data from the 1993-1994 SASS, and the researchers hypotheses about which items best relate to the constructs of interest. Phase I of the analysis attempts to validate the questions and subscales that have been chosen to represent the various constructs through the following steps: 1. Randomly split the merged dataset from all schools (public, private, and charter) into two groups one for use in exploratory work and the other for confirmatory work. These are identified as Dataset 1 and Dataset 2, respectively. 2. Exploratory factor analysis on items from Dataset 1 gives information about whether items selected to represent a particular construct actually load together. 3. Univariate frequency distributions of each variable were examined for the shape and scale of each variable and to identify any outlying observations. 4. Bivariate plots of each continuous variable with the outcome variable were inspected for any nonlinear relationships. 42

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5. Confirmatory factor analysis used on the constructs identified and refined in Dataset 1 allowed the subscales to be cleaned up so they function more optimally. 6. Confirmatory factor analysis on Dataset 2 was expected to independently support findings in Dataset 1 and provide confidence in the validity of the subscales and items. 7. Cronbachs alpha is used to report the internal consistency reliability of subscales. In addition, separate alpha values are reported for items of subscales in each sector (public, private, and charter). Alpha can be interpreted as the lower bound of the proportion of variance in the responses explained by common factors underlying the item responses. The operational definitions of the main variables in the study were refined after the completion of exploratory and confirmatory factor analysis and after considering the reliability of potential subscales. Data Analysis Phase II The analysis of the data was accomplished by the use of hierarchical linear modeling (HLM),using SAS Proc Mixed with restricted maximum likelihood estimation. HLM is a multi-level multiple regression technique useful in analyzing nested data. The SASS survey data is reported by teachers nested in schools. It is likely that since several teachers from each school answer the survey questions that the responses from teachers within the same school cannot be considered independent. At least two options exist for overcoming this lack of independence. One is to take the scores of all teachers within a school to form a school mean and then regressing the school means of the independent 43

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variables on the dependent variable, teacher satisfaction. Using this type of analysis, multiple regression, leads to loss of information about the variability among teachers within the various schools. In contrast, results from hierarchical linear models includes information about both the teacher level variability and the school level variability by indicating which percentage of the accounted for variation in the dependent variable comes from within the teachers in the same school and which percentage comes from the variation between schools. In addition, an intra-class correlation is computed that estimates how much dependence there is in answers given by teachers in the same school. There are a couple possible ways to proceed in using HLM. One way is to include sector as a variable in a single set of models for teacher satisfaction (Raudenbush & Bryk, 2002). A second way is to operate on a more theoretical basis, noting that since previous studies have shown that some factors related to teacher satisfaction vary significantly depending on school sector (public or private), a separate set of models could be developed for each sector (Ingersoll et al., 1997; Perie & Baker, 1997). Additionally this is the first opportunity to examine SASS data from charter schools. This study takes the second route. Three separate sets of models are constructed: models for public schools, private schools, and charter schools. The initial model for each set of models is the unconditional model, also called the null model. This model estimates the mean teacher satisfaction score across the schools used in the model, indicates if there is statistically significant variability in these means, and specifies the amount of variability at the school level and the teacher level before any predictor variables are entered. 44

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Teacher level variables are entered into the model in an attempt to account for the identified teacher-level variability, and school level variables are entered to account for the school-level variability (Singer, 1998). It is quite common in educational studies such as this for the within school variance of the outcome variable to be much larger than the between school variance, indicating that the individuals within schools differ more from each other than schools differ from other schools (Kreft & De Leeuw, 1998). Next, three models hereafter referred to as the background models are established, one model for each of the three sectors. The background models include and control for school characteristics and teacher background characteristics, the variables that are most unlikely to be manipulated by policy. The emphasis in this study is to predict teacher satisfaction based on teacher level responses to survey items that tap teacher attitudes about workplace conditions related to opportunity and capacity. Only a few school level variables are used in the analysis school geographic location, school level, school size, and percent minority. They are included in the models not because of intent to study school effects but because they are likely related to teacher satisfaction and yet are difficult to manipulate by school policy. They are entered as control variables. Once the three background models are established, several additional models for each of the three sectors is added. Each of these additional models begins with one of the background models and then adds only one set of variables that forms one of the major constructs that are possible to manipulate by policy decisions administrative support and leadership, resources, cooperative environment, parental support, student behavior and school atmosphere, credentialing requirements, professional development opportunities, autonomy and authority in the classroom and the school, and 45

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compensation. Each of these models can then be compared to the corresponding background model to see how much variance is accounted for at each of the teacher and school levels. Finally an overall model for each sector is developed. The overall model includes all the factors related to teacher satisfaction. All coefficients are fixed except for the intercept. The within and between variances of the previously discussed null model can serve as the baseline for estimation of two different R 2 values in two level models. The first level or within R 2 is based on the error variance at the teacher-level and corresponds to the concept known in traditional regression analysis. The R 2 value for level-2 is a different concept based on school-level variance component. The level-2 variance component in the null model places an effective ceiling on the amount of variation in school means that are ever explainable by a school-level factor (Singer, 1998). The first level R 2 value is of main interest in this study. A reduction in the amount of error variance at either level can be quantified as a percentage reduction. The within (or level-1) reduction (R 2 value) is calculated by subtracting the within variance for a new model from the within variance in the null model and dividing the difference by the within variance in the null model. Similarly the between (or level-2) reduction is calculated by subtracting the between variance for a new model from the between variance in the null model and dividing the difference by the between variance in the null model. This same type of percentage reduction for within or between variance can be calculated to compare the background model to a model with a major predictor variable or variables added by subtracting the within or between variance for a new model from the corresponding 46

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47 within or between variance in the background model and dividing by the within or between variance for the background model. In order to use the R2 concepts, the models must not contain any random slope coefficients. Only the intercept is allowed to be random if one wishes to compare R2 values across models because the R2 cannot be uniquely defined in models with random slopes. Not using random slopes makes the assumption that all schools have a common slope. To test whether this is plausible, each model was run three times, once with all applicable variables having a fixed slope as proposed, a second time allowing the slopes to vary by making them random, and a third time allowing the slopes to be random and in addition estimating the covariances among the slopes. Results from across the three models were compared for differences in the statistical significance of the coefficients. No differences were observed so the more st raightforward interpretation of coefficients and R2 values were used. Kreft and de Leeuw (1998) emphasize that theory and the purpose of a study must determine model choices: “If the effects of schools are the subject of the study, then the random slope model is most appropriate. If schools are not the subject of study, a fixed slope may be a better choice”, (p. 68). When raw scores are used in a model, the intercept 0j is defined as the expected outcome for a teacher from school j with a value of zero on Xijk (the kth predictor variable for the ith person in the jth school). Using raw scores is recommended when the researcher is mostly interested in a model that ‘explains’ as much variation in the response variable as possible and when the researcher is more interested in the effects on individual performance than in school effects (Kreft & De Leeuw, 1998), p. 115-116. Both conditions apply in this study. If an Xijk value of 0 is not meaningful, it is often helpful to

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use grand mean centering of the variable to aid interpretation of the intercept. For instance, the scales of most of the independent variables used in this study are based on responses to survey items that range from 1 to 4 or 5. In most instances, several of these items are grouped together to form subscales. Further, the proposed administrative support and leadership subscale is composed of six items (see Table 5). Responses to each item can range from 1 to 4. When the items are grouped as a subscale then the range of possible scores on the subscale is from 6 to 24. When raw variables are used, then the intercept tells the value of the outcome variable when all the independent variable values are set to zero. Since the true range of the administrative support and leadership scale is from 6 to 24, zero is not a possible value, and therefore interpreting the intercept based on setting its value to zero is not meaningful. Centering refers to subtracting a value from an independent variable to make the zero point on a subscale meaningful. For example, the grand mean for administrative support and leadership in the charter school data is 18.57 (see the pilot study later in this chapter). Subtracting 18.57 from each of the possible subscale values of 6-24 creates a new range of .57 to 5.43. Zero actually exists on this subscale range and is meaningful, as it indicates the average value for administrative support and leadership. Other first level variables such as years of experience, classroom size, and compensation are not part of a subscale but may also be grand mean centered for consistency in interpretation. Categorical variables such as gender, ethnicity, school level, and geographic location are not grand mean centered. Percent minority at the second level can also be grand mean centered. Though the values of some parameters will change, grand mean centered and raw score models are equivalent linear models because one can be transposed into the other 48

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49 by simply adding or subtracting constants to the differing parameters (Kreft & De Leeuw, 1998). In grand mean centering, the Xijk is replaced by (Xijk – X..), where X.. represents the grand mean of all the teacher level (level-1) observations for the variable Xijk. The intercept 0j becomes the expected outcome for a teacher whose value of Xijk is equal to the grand mean. Grand mean centering affects the values of three types of parameters in a model: the intercept, the variance of the intercept, and the second level coefficients of second level grand mean centered variables (Kreft & De Leeuw, 1998). When grand mean centering is used, the intercept can be interpreted as an adjusted mean from school j and the variance of the intercept is the variance among schools with adjusted means. The first and second level equations below are typical of the equations that are planned for this study (see the section on equations later in this chapter): Yij = 0j + 1* (Male) + 2*(Experience) + 3*(American Indian/Alaskan Native) + 4*(Asian/Pacific Islander) + 5*(Black) + 6*(Hispanic) + 7*(Administrative Support) + rij 0j = 00 + 01*(%Minority) + 02*(Secondary) + 03*(Combined) + 04*(Urban) + 05*(Rural) + 06*(School Size) + u0j In the equations above, grand mean centering would involve subtracting the grand mean over all teacher responses to administrative support and leadership from an individual’s response on the administrative support and leadership subscale, leaving the categorical variables such as gender, ethni city, school level, and geographical location untouched, and subtracting the grand mean respectively from each of the other continuous variables – years of experience, percent minority, and school size.

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50 Using grand mean centered scores instead of raw scores aids interpretation of the intercept, while the grand mean centered model and the raw score model remain equivalent linear models. Though equivalent models may return unequal parameter estimates, such models result in the ‘same fit, the same predicted values, and the same residuals’ and the parameter estimates can be translated into each other by adding or subtracting a constant to the changed parameter (Kreft & De Leeuw, 1998, p. 109). Assumptions of the Hierarchical Linear Model The validity of inferences based on the results of hierarchical linear models depend on how tenable are the assumptions of the structural and random parts of the model. The following are the assumptions of the hierarchical linear models used in this study: 1. Conditional on the level-1 variables, the within school errors (rij’s) are normally distributed and independent with mean 0 in each school and with equal variances across schools. 2. Whatever teacher level predictors of teacher job satisfaction are excluded from the model and thereby relegated to the level-1 error term (rij) are independent of the level-1 variables that are included in the model (covariance equals 0). 3. In a random intercept only model, each school has a school-level residual, u0j. The distribution of these school-level residuals is normally distributed with mean 0 and variance 00. 4. The effects of any excluded school-level predictors from the model for the intercept are independent of other level-2 variables (covariance equals 0).

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5. The level-1 error, r ij is independent of the level-2 residual, u 0j (covariance equals 0). 6. Whatever teacher-level predictors are excluded from the level-1 model and as a result relegated to the error term, r ij are independent of the level-2 predictors in the model (covariance equals 0). In addition whatever school-level predictors are excluded from the model and as a result relegated to the level-2 random effect, u 0j are not correlated with teacher level predictors (covariance equals 0). The even numbered assumptions (2, 4, and 6) are concerned with the relationship among the variables that compose the structural part of the model (the level-1 and level-2 predictor variables) and the factors that are relegated to the error terms, r ij and u 0j Adequacy of model specification is a concern and the tenability of the assumptions determines if the estimates of the level-2 coefficients are biased (Raudenbush & Bryk, 2002), p 255. Snijders and Bosker (1999) point out that The main dangers of model misspecification are the general misrepresentation of the relations in the data (e.g., if the effect of X on Y is curvilinear increasing for X below average and decreasing again for X above average, but only the linear effect of X is tested, then one might obtain a non-significant effect and conclude mistakenly that X has no effect on Y) p. 120. During phase I of the data analysis, bivariate plots of continuous variables with the outcome variable were inspected for any nonlinear relationships. The odd numbered assumptions (1, 3, and 5) are concerned with the random part of the model (the r ij and u 0j ). The consistency of the estimates of the standard errors of level-2 coefficients, the accuracy of the level-1 random coefficients, the level-1 estimated 51

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52 variances for level-1 and level-2, and the accuracy of hypothesis tests and confidence intervals (Raudenbush & Bryk, 2002) p. 255. Gross misspecification of the random part of the model leads to inaccurate standard errors and therefore hypothesis tests that miss the mark (Snijders & Bosker, 1999). As previously discussed, using a random only intercept model is making the assumption that all schools have a common slope, which may or may not be a tenable assumption for the data in this study. To test if this is plausible, each model was run three times, once with all applicable variables having a fixed slope as proposed, a second time allowing the slopes to vary by making them random, and a third time allowing the slopes to vary by making them random and additionally estimating the covariances among the slopes. Models and Equations Presented below are the equations for the models that were run to answer the research questions. Equations for the null model, the background model, the models that add one (or in one case two) major predictor variables at a time, and the overall model are written out in each case for the level 1 model, the level 2 model and the combined model. Each specified model was run three times, one time each for public, private and charter schools respectively. Continuous variables were grand mean centered as previously discussed. The definitions of the symbols used in the equations are listed first: i = teacher j = school Yij is the satisfaction score for teacher i in school j 0j is the intercept for school j

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53 rij is the residual, the random error, associated with the ith teacher in the jth school. It is the within school variation across teachers, a random effect that exists by default. 00 is the fixed intercept, the mean school-level teacher satisfaction score in the population, a fixed effect (Note: this is not the same as the average teacherlevel satisfaction score, Singer, p. 330) u0j are a series of random deviations from 00; the between school variability, the school effect, a random effect rij ~ N(0, 2) u0j ~ N(0, 00) 2 is the variability within schools, the variance of the rij ’s 00 is the variability among school means. Null Model Level 1 Null Model Yij = 0j + rij Level 2 Null Model 0j = 00 + u0j Combined Null Model Yij = 00 + u0j + rij Background Models – Add Teacher Background and School Characteristics The teacher background variables are added at the first level as control variables since policy generally is unable to have broad control over them. The school

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54 characteristics are all added to the level 2 model and are all necessarily fixed. Though these school level variables are at times manipulated to one degree or another by various policies, they are not the subject of study in this investigation but they may be related to teacher satisfaction. Level 1 Background Model Yij = 0j + 1* (Male) + 2*(Experience) + 3*(American Indian/Alaskan Native) + 4*(Asian/Pacific Islander) + 5*(Black) + 6*(Hispanic) + rij Level 2 Background Model 0j = 00 + 01*(%Minority) + 02*(Secondary) + 03*(Combined) + 04*(Urban) + 05*(Rural) + 06*(School Size) + u0j Combined Background Model Yij = 00 + 1* (Male) + 2*(Experience) + 3*(American Indian/Alaskan Native) + 4*(Asian/Pacific Islander) + 5*(Black) + 6*(Hispanic) + 01*(%Minority) + 02*(Secondary) + 03*(Combined) + 04*(Urban) + 05*(Rural) + 06*(School Size) + u0j + rij Background Model plus Xijk* Level 1 Model Yij = 0j + 1* (Male) + 2*(Experience) + 3*(American Indian/Alaskan Native) + 4*(Asian/Pacific Islander) + 5*(Black) + 6*(Hispanic) + 7*(Xijk) + rij Level 2 Model 0j = 00 + 01*(%Minority) + 02*(Secondary) + 03*(Combined) + 04*(Urban) + 05*(Rural) + 06*(School Size) + u0j

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55 Combined Model Yij = 00 + 1* (Male) + 2*(Experience) + 3*(American Indian/Alaskan Native) + 4*(Asian/Pacific Islander) + 5*(Black) + 6*(Hispanic) + 7*(Xijk) + 01*(%Minority) + 02*(Secondary) + 03*(Combined) + 04*(Urban) + 05*(Rural) + 06*(School Size) + u0j + rij *Xijk = the kth major predictor variable for the ith teacher in the jth school as follows: Xij7 = Administrative Support and Leadership Xij8 = Resources (Final Models include Resources 1 and Resources 2) Xij9 = Collegiality Xij10 = Parental Support Xij11 = Student Behavior and School Atmosphere (Final models include separate variables called Aggression, Tardiness, and Classroom Size as measures of Student Behavior and School Atmosphere) Xij12 = Credentials Xij13 = Professional Development Xij14 = School Autonomy Xij15 = Classroom Autonomy Xij16 = Compensation Overall Model Level 1 Overall Model Yij = 0j + 1* (Male) + 2*(Experience) + 3*(American Indian/Alaskan Native) + 4*(Asian/Pacific Islander) + 5*(Black) + 6*(Hispanic) + 7*(Administrative Support) + 8*(Resources) + 9*(Collegiality) + 10*(Parental Support) 11*(Student Behavior and School Atmosphere) + 12*(Classroom size) + 13*(Credentials) + 14*(Professional Development) + 15*(School Autonomy) + 16*(Classroom Autonomy) + 17*(Compensation) + rij

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56 Level 2 Overall Model 0j = 00 + 01*(%Minority) + 02*(Secondary) + 03*(Combined) + 04*(Urban)+ 05*(Rural) + 06*(School Size) + u0j Combined Overall Model Yij = 00 + 1* (Male) + 2*(Experience) + 3*(American Indian/Alaskan Native) + 4*(Asian/Pacific Islander) + 5*(Black) + 6*(Hispanic) + 01*(%Minority) + 02*(Secondary) + 03*(Combined) + 04*(Urban) + 05*(Rural) + 06*(School Size) + 7*(Administrative Support) + 8*(Resources) + 9*(Collegiality) + 10*(Parental Support) 11*(Student Behavior and School Atmosphere) + 12*(Classroom size) + 13*(Credentials) + 14*(Professional Development) + 15*(School Autonomy) + 16*(Classroom Autonomy) + 17*(Compensation) + u0j + rij Pilot Study A pilot study using the SASS data from the charter school teacher survey was conducted to test the workability of a small part of the proposed plan. An unconditional model for the charter school data was run plus a model with one predictor, administrative support and leadership. SAS Proc Mixed was used to estimate the models, using REML. Both models converged quickly with no problems or warnings identified in the log or output. The outcome variable, teacher satisfaction, was defined using the four items in Table 4. All items having a positive orientation were recoded so that strong agreement with a statement takes on the highest value making the higher scores on the summed subscale represent higher level of teacher satisfaction. Values of the subscale range from

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4 to 17 with a mean value of 14 and standard deviation of 2.67, making for a negatively skewed distribution with a skewness value of -.86. Cronbachs alpha for this subscale of four items is .66. Table 4. Items Included in Proposed Teacher Satisfaction Subscale Item Content Item # Response Options Orientation I sometimes feel it is a waste of time to try to do my best as a teacher 0318 1 Strongly Agree 4 Strongly Disagree Reflection not required I am generally satisfied with being a teacher at this school 0320 1 Strongly Agree 4 Strongly Disagree Reflection required If you could go back to your college days and start over again, would you become a teacher or not? 0339 1 Certainly would 5 Certainly would not Reflection required How long do you plan to remain in teaching? 0340 1 As long as I am able 2 Until retirement 3 Probably continue unless something better comes along 4 Definitely plan to leave 5 Undecided Reflection required undecided is out of order. Recode as 3. One predictor variable was then added to the unconditional model. The items from the survey that compose the administrative support and leadership subscale are listed in the Table 5. Values of the subscale range from 6 to 24 with a mean value of 18.57 and standard deviation of 4.28. The distribution is negatively skewed with skewness value of -.86. The Cronbachs alpha for this subscale is .87. 57

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Table 5 Items Included in Administrative Support and Leadership Subscale Item Content Item # Response Options Orientation The principal lets staff members know what is expected of them. 0299 1 Strongly Agree 4 Strongly Disagree Reflection required The school administrators behavior toward the staff is supportive and encouraging. 0300 1 Strongly Agree 4 Strongly Disagree Reflection required My principal enforces school rules and backs me up when I need it. 0306 1 Strongly Agree 4 Strongly Disagree Reflection required The principal talks with me frequently about my instructional practices. 0307 1 Strongly Agree 4 Strongly Disagree Reflection required The principal knows what kind of school he/she wants and has communicated it to the staff. 0310 1 Strongly Agree 4 Strongly Disagree Reflection required In this school, staff members are recognized for a job well done. 0312 1 Strongly Agree 4 Strongly Disagree Reflection required The results for the unconditional model and the model with one predictor are displayed in Table 6. 58

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Table 6 Administrative Support and Leadership Estimates for the Unconditional Model and a Model with one Predictor Variable Unconditional Model Model with One Predictor Fixed Effect Coefficient S.E. Coefficient S.E. Intercept 14.00 .06 9.62 .21 Coefficient of Admin. Support and Leadership N/A .24 .01 Random Effect Vcomp S.E. Vcomp S.E. Level two variance .47 .12 .31 .09 Level one variance 6.67 .20 5.83 .17 *Vcomp is used to abbreviate Variance Component The unconditional model covariance parameter estimates from SAS indicate a variance of .47 between schools (standard error is .12, significant at .0001) with variance 6.67 within schools (standard error is .20, significant at .0001). No weights were used in running these pilot study models. The intraclass correlation coefficient in this case is .47/(.47 + 6.67) = .07. This is consistent with results from other educational research where values between .05 and .20 are often seen. This indicates that there is similarity in the results of different teachers in the same schools, although as is generally always the case, within-school differences among teachers are far larger than between school differences (Snijders & Bosker, 1999). The solution for fixed effect for the intercept is 14.00 with standard error .06 with 818 degrees of freedom and is significant at the .0001 level. This analysis used 2847 teachers in 819 schools. The addition of one variable to the unconditional model (administrative support and leadership) reduces the between school variance to .31 and the within school variance 59

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to 5.83 with standard errors of .09 and .17 respectively. The between variance is significant at the .0005 level and the within variance is significant at .0001 level. The coefficient for administrative support as a fixed effect is .24 with standard error .01 with 2027 degrees of freedom and is also significant at the .0001 level. This coefficient (as well as all the level-1 and level-2 regression coefficients) can be interpreted as an unstandardized regression coefficient in the usual way: while holding any other predictor variables constant, a one unit increase in the value of X ijk (administrative support and leadership) is associated with an average increase in Y ij (teacher satisfaction) of .24 units (Snijders & Bosker, 1999) p. 48. The solution for fixed effect for the intercept is 9.62 on a scale ranging from 6 to 24 with standard error .21 with 818 degrees of freedom and is significant at the .0001 level. This intercept value is not meaningful unless one knows the scale(s) for the variables. One insight gained from doing the pilot study is that using grand mean centering in the actual study aids interpretation of the intercept. The R 2 value for the teacher level can be calculated as (6.67 5.83)/6.67 = .13 and interpreted that 13% of the variability in teacher satisfaction scores is accounted for by teachers perceptions of administrative support and leadership. Recall that the data analysis occurred in two parts. Phase I works toward validating the questions and subscales for teacher satisfaction and related constructs using exploratory and confirmatory factor analysis. These results are based on the proposed constructs as defined before the measurement work including the factor analyses was completed. 60

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Summary The analysis presented in this paper uses hierarchical linear modeling to describe the strengths of the association between teacher satisfaction and those workplace conditions related to opportunity and capacity after accounting for several relevant teacher and school characteristics. Aspects of opportunity and capacity such as administrative support and leadership, resources, cooperative environment and collegiality, parental support, student behavior and school atmosphere, credentialing requirements, professional development opportunities, autonomy and authority in the classroom and the school, and compensation are investigated individually for their predictive power and strength of relationship with teacher satisfaction while holding background characteristics of teachers and schools constant. Finally, three separate equations including all these factors at the same time, one each for public, private, and charter schools is developed for use in predicting a teachers satisfaction level. The emphasis of this study is on teacher level effects combined with a plan to use the R 2 values as an indication of accounted for variance. The nature of this study requires that all models be explicitly defined a priori as opposed to using an exploratory approach designed to select a few key variables for each model based on the outcomes of exploration. In order to include all planned variables without introducing additional complexity and to use R 2 values a random intercept only model is used with all other coefficients fixed and no cross-level interactions. To allow for meaningful interpretation of the intercept and second level coefficients, all independent variables except for the categorical ones are grand mean centered. 61

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It was expected that a significant amount of the variability in teacher satisfaction would be accounted for by the independent variables in the study and that insights would be gained into the relationship between teacher satisfaction and variables representing opportunity and capacity in the workplace after controlling for teacher background variables. Such information is valuable to policy makers as they seek to improve retention rates, efficacy levels, and commitment of teachers by manipulating key workplace conditions that have been shown to relate to teacher satisfaction. The results also contribute to further exploration of Kanters structural theory of organizational behavior, examining opportunity and capacity in a school setting as proposed by McLaughlin and Yee. 62

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CHAPTER IV RESULTS Exploratory and Confirmatory Factor Analyses Results Several different exploratory factor analysis were run on each of the public, private, and charter school data sets rotating six though fourteen factors in an effort to identify one that gave the best representation of the proposed variables in the study: administrative support and leadership, resources, cooperative environment and collegiality, parental support, student behavior and school atmosphere, credentialing requirements, professional development opportunities, autonomy and authority in the classroom and the school, compensation, and the dependent variable teacher job satisfaction. Finally a twelve-factor oblique (correlated factors) solution was selected based on interpretability. The first 20 eigenvalues for each sector are presented in Table 7 and the factor names are presented in Table 8. 63

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Table 7 First 20 Eigenvalues from the Exploratory Factor Analysis Factor Public Private Charter 1 11.11 9.65 11.23 2 4.48 4.35 5.53 3 2.93 2.74 3.03 4 2.18 2.63 2.46 5 1.47 1.81 1.88 6 1.38 1.49 1.49 7 1.00 .93 1.10 8 .89 .90 .96 9 .72 .66 .89 10 .71 .62 .82 11 .61 .60 .62 12 .48 .51 .59 13 .45 .51 .48 14 .33 .43 .39 15 .28 .36 .37 16 .25 .29 .30 17 .24 .27 .28 18 .19 .21 .27 19 .17 .19 .22 20 .13 .17 .21 Note: The average eigen values for public, private, and charter schools were .37, .37, and .42 respectively. 64

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Table 8 Names of Factors in the Twelve Factor Exploratory Factor Analyses for Public, Private, and Charter Schools Factor # Public Schools Private Schools Charter Schools 1 Administrative Support Administrative Support /Collegiality Administrative Support 2 Major Student Problems Parental Support/Student Aggression Major Student Problems 3 Parental Support Autonomy in the School Autonomy in the School 4 Professional Development Professional Development Professional Development 5 Autonomy in the School Classroom Autonomy Student Aggression 6 Classroom Autonomy Major Student Problems Parental Support 7 Student Aggression Tardy Classroom Autonomy 8 Satisfaction Satisfaction Tardy 9 Tardy Satisfaction 10 Collegiality Compensation/Credentials Compensation/Credentials 11 Compensation/Credentials Curriculum 12 Curriculum Collegiality The Factor #s 1-12 appear in parentheses in subsequent tables that present factor loadings. Nine of the proposed factors plus teacher job satisfaction are present in the factor analysis. Student behavior split into three separate factors (major student problems, student aggression, and tardiness) while in another case two proposed factors loaded together on a single factor (compensation and credentials). In the private school sector two other sets of proposed factors loaded together administrative support with collegiality and parental support with student aggression. The two items originally proposed to represent additional resources did not load together. Each of the next sections looks at one of the ten predictor variables or the dependent variable, teacher satisfaction. Results for the exploratory factor analysis, the one-factor confirmatory factor analyses using the items originally proposed to represent the factor, and then one or more additional confirmatory models referred to as adjusted models are considered variable by variable. Alpha values for each of the proposed and 65

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adjusted subscales are presented in tables with the confirmatory results. The adjusted models take advantage of information from the exploratory and originally proposed confirmatory analysis to add additional items, or delete some of the originally proposed items. Though not rigidly adhered to, the following guidelines were employed in making decisions about including items in subscales. In general, items with loadings of .40 or above in the exploratory factor analysis were considered for inclusion of a subscale. In the confirmatory models, RMSEA values .05 or less and CFA values of .90 or greater (Hatcher, 1994, p. 291) were desirable. Alpha values of .7 and above are desirable (Nunnally, 1978). Hatcher (1994) indicates this should be used only as a rule of thumb and points out that the social science literature sometimes reports studies that use variables with alphas less than .70 and in come cases less than .60. Administrative Support and Leadership Administrative support and leadership was the first factor extracted (Factor 1) in each of the three datasets. In Table 9 the six items selected to represent this factor are displayed indicating that all items had loadings between .51 and .84 regardless of school sector. In charter schools the general satisfaction with teaching in the school loaded with the administrative support items while in the private sector, items from the collegiality subscale loaded with administrative support items. 66

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Table 9 Administrative Support and Leadership Exploratory Factor Analysis Loadings Factor Item # Label Public Private Charter Adm. Supp. T0299 Agree princ com expec .83 (1) .80 (1) .84 (1) T0300 Agree admin supportive .77 (1) .78 (1) .80 (1) T0306 Agree princ enforces discipline .70 (1) .74 (1) .76 (1) T0307 Agree princ dis practices .51 (1) .55 (1) .61 (1) T0310 Agree princ sch kind .81 (1) .82 (1) .82 (1) T0312 Agree staff recognized .58 (1) .65 (1) .64 (1) The numbers in parentheses identify the factor # from the exploratory factor analysis presented in Table 8. Alpha values for the proposed Administrative Support and Leadership subscale are presented in the Table 10 along with the one-factor confirmatory results. The proposed model showed solid alpha values ranging from .86 to .87. The CFI was in the desired range above .95, but the RMSEA was higher than .05 in every sector. The chi-square values varied greatly depending on the sample size, the larger the sample size, the greater the chi-square. The model was found to be acceptable as proposed and no adjustments were made. Table 10 Administrative Support and Leadership Alphas and Proposed One-Factor Confirmatory Model T0299, T0300, T0306, T0307, T0310, T0312 Public Private Charter n 21043 3549 1423 Alpha (raw/std) .87/.87 .86/.86 .87/.87 Chi Square 1378.71 197.85 53.48 df 9 9 9 RMSEA .09 .08 .06 CFI .97 .98 .99 The standardized path coefficients and R 2 values for the one-factor confirmatory factor analysis of the Administrative Support and Leadership subscale are presented in Figure 2. The standardized path coefficients and R 2 values are in each case presented in 67

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groups of three. For instance the path from administrative support to Item T0299 (principal communicates expectations) displays .78/.75/.79 which are the standardized path coefficients for public/private/charter schools with .78 corresponding to public schools, .75 corresponding to private schools, and .79 corresponding to charter schools. The same structure appears for the R 2 values of .61/.57/.62 for the principal communicates expectations item and throughout this model and all others in this paper. All the path coefficients are significant in all the models presented. Theoretically, this is expected, but large sample sizes can also contribute to the consistent significance of every path. The path coefficients, also referred to as factor loadings in the confirmatory models, are all standardized. The first path coefficient for public schools and item T0299 (principal communicates expectations) has a value of .78. This is interpreted to mean that as the value of Administrative Support and Leadership increases by 1 standard deviation, the value of T0299 (principal communicates expectations) is expected to increase by .78 standard deviations. Each R 2 value is the square of the path coefficient; for instance (.78) 2 = .61 (rounding may cause this value to be inexact). The R 2 value of .61 indicates that 61% of the variability in T0299 (principal communicates expectations) is explained by this conceptualization of Administrative Support and Leadership. 68

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Figure 2. Administrative Support and Leadership Proposed One-Factor Confirmatory Model T0300 R2 = .57/.62/.59 T0312 R2 = .47/.47/.49 T0299 R2 = .61/.57/.62 e300 e306 e307 e310 e312 .78/.75/.79 .75/.78/.77 ..73/.73/.73 .58/.56/.62 .80/.77/.77 .69/.69/.70 T0306 R2 = .53/.53/.54 T0307 R2 = .34/.31/.39 T0310 R2 = .64/.60/.60 e299 Admin Support Student Behavior and School Atmosphere The exploratory factor analysis results for all items from the SASS thought to be related to student behavior are presented in Table 11. This set of items was larger than the set proposed by the researcher to represent behavior because in the exploratory factor analysis the researchers wanted to gain as much information about a possible behavior factor as was available in the SASS. The Behavior factor in the exploratory factor analysis split into three separate factors (Major Student Problems, Aggression, and Tardiness). The second factor extracted (Factor 2) in public and charter schools and sixth factor extracted (Factor 6) in private schools concern significant student problems such as drug and alcohol use, pregnancy, dropouts, and class cutting. In the private sector drug and alcohol abuse combined with theft to form a factor. Factor 7 in public schools and 69

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factor 5 in charter schools are related to aggressive problem behavior of students such as theft, vandalism, physical conflicts, disrespect for teacher, and weapons. Table 11 Student Behavior and School Atmosphere (Aggression, Major Student Problems, and Tardiness) Exploratory Factor Analysis Loadings Factor Item# Label Public Private Charter Aggression T0326 Problem theft .74 (7) .35 (2) .38 (6) .73 (5) T0327 Problem vandalism .69 (7) .34 (2) .71 (5) T0325 Problem phys conflicts .65 (7) .51 (2) .68 (5) T0332 Problem disrespect for tchrs .38 (7) .31 (2) .49 (12) .58 (5) T0323 Problem tchr absenteeism .24 (7) .27 (2) .40 (5) T0302 Agree misbehavior interferes .29 (7) .16 (2) .36 (12) .37 (5) T0331 Problem weapons .44 (7) .39 (2) .47 (2) .38 (5) .44 (2) Major Student Problems T0329 Problem alcohol use .89 (2) .91 (6) .90 (2) T0330 Problem drug abuse .88 (2) .89 (6) .90 (2) T0326 Problem theft .38 (6) .35 (2) T0328 Problem student pregnancy .71 (2) .38 (2) .78 (2) T0333 Problem drop outs .65 (2) .47 (2) .71 (2) T0324 Problem class cutting .43 (2) .34 (7) .57 (2) T0331 *Problem weapons .39 (2) .44 (7) .47 (2) .44 (2) .38 (5) Tardiness T0317 Agree tardiness interferes .68 (9) .56 (7) .73 (8) T0321 Problem student tardiness .76 (9) .75 (7) .80 (8) T0322 Problem student absenteeism .65 (9) .64 (7) .63 (8) The numbers in parentheses identify the factor # from the exploratory factor analysis presented in Table 8. Item T0302 dealing with misbehaviors that interfere with teaching had loadings of less than .4 in all sectors. Teacher absenteeism also loaded on this factor in charter schools, but the item is not included for further consideration of this subscale since it is not a measure of student aggression and since it did not load on in the public and private 70

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sectors. In the private sector, theft was associated with both substance abuse and aggressive behaviors while in public and charter schools theft had a high loading on the aggressive behaviors factor but not on substance abuse. In the private sector many of the aggressive behavior items loaded in conjunction with the parent support items possibly indicating that private school teachers associate these types of problems with home life while public and charter teachers tend to view them differently. Tardiness formed the third behavior factor (Factor 7, 8, or 9 depending on the sector). The proposed behavior model included eleven items as listed in the title for Table 12. While very good alpha values (.83-.87) were obtained with these items, they contained items from the three separate factors identified in the exploratory factor analysis. The fit indices on the confirmatory factor analysis were extremely poor supporting the findings in the exploratory factor analysis that the behavior factor is not unidimensional as it is proposed. In addition, the modification indices showed that the errors of several subgroups of the behavior items show high correlations, which exacerbate the poor fit. The original proposed model included the variables concerning student apathy and unprepared students. In the exploratory factor analysis the unprepared students item T0327 loaded instead on the parent subscale while the student apathy item T0334 did not load on any factor consistently and showed split loadings on more than one factor. This item was deleted from use in the study. Deleting these two items from the proposed behavior model did not improve the fit indices for the confirmatory factor analysis. These findings stimulated much more exploratory work on the three behavior factors identified in the exploratory factor analysis (Major Student Problems, Aggression, 71

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and Tardiness). One goal that was never realized was to find a single unidimensional factor that could best represent student behavior as a predictor of teacher job satisfaction. The modification indices from a three-factor model indicated that better fit could be obtained by allowing several items in Major Student Problems to load on both Aggression and Tardiness. Further consideration of the theory related to this subscale resulted in the conclusion that the behaviors in a classroom resulting from Major Student Problems such as drug and alcohol abuse are to some large extent measured in the aggression and tardy subscales. Perhaps this is why they want to load on both the other factors. The Major Student Problems factor was dropped from the analysis and further work conducted on the Aggression and Tardiness subscales. Alphas for the adjusted Aggression subscale ranged from .65 to .77, RMSEAs from .05 to .08 and that all CFIs were .99, as shown in Table 13. Alphas for the 3 item tardiness subscale shown in Table 14 ranged from .74 to .81. Table 12 Behavior Alphas and Proposed One-Factor Confirmatory Model T0326, T0327, T0325, T0332, T0302, T0329, T0330, T0317, T0321, T0334, T0337 Public Private Charter n 21043 3549 1423 Alpha (raw/std) .87/.87 .83/.83 .87/.87 Chi Square 39523 6996.71 3165.77 df 45 45 45 RMSEA .20 .21 .22 CFI .64 .57 .60 72

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Table 13 Aggression Alphas and Adjusted One-Factor Confirmatory Model T0327, T0325 T0332, T0331 Public Private Charter n 21043 3549 1423 Alpha (raw/std) .77/.77 .65/.68 .73/.74 Chi Square 187.52 22.9 18.2 df 2 2 2 RMSEA .07 .05 .08 CFI .99 .99 .99 Table 14 Tardiness Alphas T0317, T0321, T0322 Public Private Charter n 21043 3549 1423 Alpha (raw/std) .80/.80 .74/.74 .81/.81 A two factor confirmatory model of Aggression and Tardiness as presented in Table 15 showed reasonable fit (RMSEA = .04 -.07; CFI = .96-.98). The errors for disrespect for teachers and misbehavior in the classroom were highly correlated. The misbehavior in the classroom item was dropped since it did not have loadings of .4 or above in any sector and because it was contributing to the misfit. Errors for theft and vandalism items also were also highly correlated, causing significant misfit. The theft item was dropped to improve fit. Possibly the two items were interpreted as asking nearly the same thing given that vandalism and theft may be considered synonyms. The path coefficients and R 2 values for the adjusted model are presented in Figure 3. 73

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Table 15 Aggression and Tardy Alphas and Adjusted Two-Factor Confirmatory Model T0327, T0325 T0332, T0331, T0317, T0321, T0322 Public Private Charter n 21043 3549 1423 Chi Square 493.59 208.03 73.63 df 13 13 13 RMSEA .04 .07 .06 CFI .99 .96 .98 Figure 3. Behavior (Aggression and Tardy) Adjusted Two-Factor Confirmatory Model T0327 R2 = .47/.36/.41 T0325 R2 = .49/.40/.48 e327 e325 e332 Aggression Tardy T0317 R2 = .40/.33/.46 T0331 R2 = .39/.19/.26 .69/.57/.61 e331 e317 e321 e322 .69/.60/.65 .70/.63/.69 .70/.68/.73 .63/.44/.51 .63/.57/.68 .84/.80/.86 .81/.74/.78 T0321 R2 = .71/.64/.74 T0322 R2 = .66/.55/.61 T0332 R2 = .49/.46/. 53 Parental Support The Parental Support factor was the third factor extracted in the public data, second in the private data and sixth in the charter data. The originally proposed Parent factor included only items T0335 and T0303 that deal with parental involvement and parental support. The exploratory factor analysis results appear in Table 16 and also 74

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loaded items concerning poverty, student health, and preparedness of students on the parent factor. In addition, student apathy loaded on the parent factor in the public school data, but this loading did not occur in private or charter schools. Among private school teachers aggressive problem behaviors such as physical conflicts and weapons also loaded on the parent factor, but these items were retained in the Aggression factor previously discussed and are not used in the Parent factor. Though factor loadings were fairly high for the parental involvement item (.63 .74) the loadings on parent support were much lower (.30 .50) with the public and private loadings both under .40. Table 16 Parental Support Exploratory Factor Analysis Loadings Factor Item# Label Public Private Charter Parent T0334 Problem student apathy .40 (3) .22 (2) .32 (2) .50 (12) .32 (6) .29 (5) T0335 Problem parental involvement .74 (3) .63 (2) .72 (6) T0337 Problem unprepared students .70 (3) .53 (2) .65 (6) T0303 Agree parent support .34 (3) .30 (2) .50 (6) T0336 Problem poverty .82 (3) .72 (2) .82 (6) T0338 Problem student health .63 (3) .67 (2) .63 (6) The numbers in parentheses identify the factor # from the exploratory factor analysis presented in Table 8. Since the proposed Parental Support subscale included only two items, the fit could not be tested using a one factor confirmatory model. Alpha values for the two items ranged from .59 to .71 and are presented in Table 17. The adjusted model added the additional items to Parent Support as identified in the exploratory factor analysis. The alphas on this revised subscale were much improved (.78-.86) as shown in Table 18. The RMSEA for the adjusted model ranged from .12 to .18, much higher than desired, while the CFIs were much better ranging from .96 to .98. One pair of items from the subscale, T0336 and T0338 (poverty and student health) showed fairly high correlations among the 75

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errors. These highly correlated errors contributed substantially to the poor RMSEA indices. The path coefficients and R 2 values for the adjusted model are presented in Figure 4. Table 17 Parental Support Alphas T0303, T0335, Public Private Charter n 21043 3549 1423 Alpha (raw/std) .62/.62 .59/.59 .70/.71 Table 18 Parental Support Alphas and Adjusted One-Factor Confirmatory Model T0335, T0337, T0336, T0338 Public Private Charter n 21043 3549 1423 Alpha (raw/std) .82/.83 .78/.79 .86/.86 Chi Square 1329.89 141.15 44.2 df 2 2 2 RMSEA .18 .14 .12 CFI .96 .96 .98 Figure 4. Parental Support Adjusted One-Factor Confirmatory Model Parental Support T0335 R2 = .58/.52/.59 e335 e337 e336 e338 .76/.72/.77 .78./.73/.79 .74/.67/.77 .66/.65/.72 T0337 R2 = .61/.54/.63 T0336 R2 = .54/.45/.60 T0338 R2 = .44/.43/. 51 76

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Professional Development Professional Development was the fourth factor extracted (Factor 4) in all sectors. This factor was originally conceived as nine facets of professional development including opportunities to participate and rewards received, actual professional development in content, standards, methods, computers, testing, and classroom management, and a teachers overall perception of the usefulness of these experiences. Loadings above .4 were found in all sectors and on all facets except computers and rewards as reported in Table 19. Classroom management also had a loading below .4 for the public sector (.24). Table 19 Professional Development Exploratory Factor Analysis Loadings Subscale Item # Label Public Private Charter Opportunities .61 (3) .59 (4) .61 (4) T0150 Univ courses cert T0152 Univ courses other T0153 Research T0154 Formal collaboration T0155 Mentoring T0156 Teacher network T0157 Workshops attended T0158 Workshops presenter Content .58 (3) .60 (4) .62 (4) T0159 Prof dev in-depth study T0160 Prof dev in-depth study hrs T0161 Prof dev in-depth study impact Standards .62 (3) .64 (4) .67 (4) T0162 Prof dev standards T0163 Prof dev standards hours T0164 Prof dev standards impact Methods .54 (3) .64 (4) .65 (4) T0165 Prof dev methods of tchng T0166 Prof dev methods hours T0167 Prof dev methods impact Computers .33 (3) .25 (4) .34 (4) T0168 Prof dev ed tech T0169 Prof dev ed tech hours 77

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T0170 Prof dev ed tech impact Testing .45 (3) .50 (4) .57 (4) T0171 Prof dev stu assessment T0172 Prof dev assessment hours T0273 Prof dev assessment impact Mgt .24 (3) .46 (4) .48 (4) T0174 Prof dev discipline T0175 Prof dev discipline hours T0176 Prof dev discipline impact SuppReward .34 (3) .38 (4) .36 (4) T0179 Release time T0180 Scheduled time T0181 Stipend T0182 Reimbursement for tuition T0183 Reimbursement fees T0184 Reimbursement -expenses T0185 Rewards cert credits T0186 Rewards pay increase T0187 Rewards recognition T0178 T0178 How useful all professional development .54 (3) .53 (4) .53 (4) The numbers in parentheses identify the factor # from the exploratory factor analysis presented in Table 8. The standardized alpha values in the proposed model are presented in Table 20 and ranged from .76 to .79. In the adjusted model presented in Table 21 and Figure 5 the computers and support/reward subscales are dropped and the alphas decrease a very small amount. The RMSEA values are all above .05 in the proposed model, higher than desired, but the CFIs are all in the acceptable range above .90. In the adjusted models the RMSEAs are a little higher in public and charter schools showing slightly worse fit, but the CFI values increase slightly indicating a little better fit. The chi square values drop in the adjusted model but the ratio of chi square/df is lower in the proposed model. Overall there seems little difference in the two models, but the adjusted model is less complex. 78

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Table 20 Professional Development Alphas and Proposed One-Factor Confirmatory Model T178, Opportunities, Content, Standards, Methods, Testing, Classroom Management, Computers, SuppReward Public Private Charter n 21043 3549 1423 Alpha (raw/std) .72/.76 .74/.77 .76/.79 Chi Square 2666.93 433.33 224.79 df 27 27 27 RMSEA .07 .07 .07 CFI .92 .93 .93 Table 21 Professional Development Alphas and Adjusted One-Factor Confirmatory Model T178, Opportunities, Content, Standards, Methods, Testing, Classroom Management Public Private Charter n 21043 3549 1423 Alpha (raw/std) .71/.75 .73/.76 .75/.78 Chi Square 1708.59 276.79 153.79 df 14 14 14 RMSEA .08 .07 .08 CFI .94 .94 .94 79

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Figure 5. Professional Development Adjusted One-Factor Confirmatory Model Professional Development Opportunities R2 = .32/.37/.38 Testing R2 = .30/.27/.33 T0178 R2 = .34/.27/.32 eO pp eCon eStd eMeth eTes t .58/.52/.57 .56/.61/.62 ..61/.60/.64 .65/.66/.66 .55/.61/.63 .54/.52/.57 Content R2 = .37/.36/.40 Standards R2 = .42/.43/.44 Methods R2 = .30/.38/.39 e178 Classroom Mgt R2 = .11/.17/.17 eM gt .33/.42/.41 Autonomy in the School Autonomy in the School is presented in Table 22 as the fifth extracted factor (Factor 5) for the public school sector and the third factor extracted for the private and charter schools. The items in this subscale loaded together as predicted with all factor loadings above .4 with the single exception of influence on the choice of curriculum in charter schools. 80

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Table 22 Autonomy in the School Exploratory Factor Analysis Loadings Factor Item # Label Public Private Charter Autonomy School T0286 Influence performance standards .47 (5) .48 (3) .49 (3) T0287 Influence curriculum .45 (5) .41 (12) .51 (3) .39 (3) .39 (11) T0288 Influence prof dev content .48 (5) .62 (3) .68 (3) T0289 Influence tchr evaluation .62 (5) .75 (3) .76 (3) T0290 Influence tchr hiring .69 (5) .70 (3) .79 (3) T0291 Influence discipline .65 (5) .52 (3) .66 (3) T0292 Influence sch budget .60 (5) .62 (3) .70 (3) The numbers in parentheses identify the factor # from the exploratory factor analysis presented in Table 3. The curriculum item in the autonomy in the school subscale loaded on two separate factors in public and charter schools in the exploratory factor analysis. This suggested that the item should not be included in the subscale. Comparing the Tables 23 and 24 shows that deleting the item caused small decreases in the alpha values for each sector; alphas in the proposed model ranged from .80 to .86 while alphas in the adjusted model ranged from .78 to .85. However, the chi-square values decreased, as did the degrees of freedom, and the ratios of chi square/df was lower in the adjusted model. RMSEA values in the proposed model were all .12 or .13, but in the adjusted decreased to between .08 and .12. CFIs ranged from .88 to .91 in the proposed model but ranged from .92 to .96 in the adjusted model. The R 2 values and the path coefficients for the adjusted one-factor confirmatory model are presented in Figure 6. 81

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Table 23 Autonomy in the School Alphas and Proposed One-Factor Confirmatory Model T0286, T0287, T9288, T0289, T0290, T0291, T0292 Public Private Charter n 21043 3549 1423 Alpha (raw/std) .80/.80 .83/.83 .86/.86 Chi Square 4485.41 913.40 377.10 df 14 14 14 RMSEA .12 .13 .13 CFI .88 .89 .91 Table 24 Autonomy in the School Alphas and Adjusted One-Factor Confirmatory Model T0286, T0288, T0289, T0290, T0291, T0292 Public Private Charter n 21043 3549 1423 Alpha (raw/std) .78/.78 .81/.81 .85/.85 Chi Square 1236.00 467.67 143.7 df 9 9 9 RMSEA .08 .12 .10 CFI .96 .92 .96 82

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Figure 6. Autonomy in the School Adjusted One-Factor Confirmatory Model School Autonomy T0288 R2 = .36/.47/.54 T0292 R2 = .33/.39/.50 T0286 R2 = .31/.34/.37 e288 e289 e290 e291 e292 .56/.58/.61 .60/.68/.73 ..60/.72/.70 .60/.65/.72 .71/.62/.74 .57/.62/.71 T0289 R2 = .36/.52/.48 T0290 R2 = .36/.42/.52 T0291 R2 = .50/.38/.55 e286 Autonomy in the Classroom The sixth extracted factor (Factor 6) for public schools, the fifth for private schools, and the seventh for charter schools is autonomy in the classroom. Four of the six proposed items for this subscale had loadings over .5 for all three sectors as displayed in Table 25. Item T0293 concerning control over selecting curriculum materials had loadings less than .4 for public and charter schools while that same item plus item T0294 concerning control over content selection loaded on two separate factors a factor describing autonomy in the classroom and a factor about curriculum in general (the twelfth extracted factor in public schools and the eleventh in charter schools.) 83

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Table 25 Autonomy in the Classroom Exploratory Factor Analysis Loadings Item # Label Public Private Charter Autonomy Class T0295 Control selecting technique .71 (6) .68 (5) .74 (7) T0296 Control eval students .75 (6) .70 (5) .79 (7) T0297 Control discipline .51 (6) .57 (5) .54 (7) T0298 Control homework .65 (6) .59 (5) .67 (7) T0293 Control selecting materials .33 (6) .42 (12) .42 (5) .30 (7) .48 (11) T0294 Control selecting content .47 .41 (12) .56 (5) .45 (7) .51 (11) The numbers in parentheses identify the factor # from the exploratory factor analysis presented in Table 8 The originally proposed subscale included all six of the items identified in the exploratory factor analysis. Results of the one-factor confirmatory analysis are displayed in Table 26 and showed poor fit when all six of the items were used with all RMSEA values greater than .05 and all CFIs under .90. The standardized alpha values in the proposed model ranged from .78 to .80. T0293 and T0294 (control over selecting materials and content) are problematic in this subscale, but they are important in defining the construct. The errors for the two items are highly correlated and contributing significantly to the poor fit. At least two options exist for modifying this subscale. The items could be deleted to improve fit or the correlation in the errors could be modeled to improve fit. When the two items were deleted from the model alpha values dropped ranging from .73 to .77 but there was a substantial improvement in all the fit indices for the confirmatory factor analysis. All CFIs were .99 and above and RMSEAs were .03 for public and charter schools and .07 for private schools. The problem with deleting the items is that it affects the substantive meaning of the subscale by eliminating selection of materials and content from the operational definition of autonomy in the classroom. This 84

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seems unacceptable, so the second option of modeling the correlation of the error was selected. This seems like the best choice to preserve the meaning of autonomy and to model the additional complexity of the factor created by including in the subscale two similar but important items as selecting content and materials. Results for the one-factor confirmatory model that models the correlation in the errors of these two items are included in Table 27. Standardized alphas range from .73 to .77, RMSEAs from .06 to .09, and CFIs from .96 to .98. The path coefficients and R 2 values for the adjusted model are presented in Figure 7. Table 26 Autonomy in the Classroom Alphas and Proposed One-Factor Confirmatory Model T0293, T0294, T0295, T0296, T0297, T0298 Public Private Charter n 21043 3549 1423 Alpha (raw/std) .77/.79 .76/.78 .79/.80 Chi Square 4016 953.05 291.62 df 9 9 9 RMSEA .15 .17 .15 CFI .88 .83 .89 85

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Table 27 Autonomy in the Classroom Alphas and Adjusted One-Factor Confirmatory Model T0293, T0294, T0295, T0296, T0297, T0298 Errors for T0293 and T0294 allowed to correlate Public Private Charter n 21043 3549 1423 Alpha (raw/std) .77/.79 .76/.78 .79/.80 Chi Square 669.18 221.54 81.28 df 8 8 8 RMSEA .06 .09 .08 CFI .98 .96 .97 Figure 7. Autonomy in the Classroom Adjusted One-Factor Confirmatory Model Classroom Autonomy T0294 R2 = .30/.30/.33 T0298 R2 = .38/.32/.41 T0293 R2 = .19/.20/.22 e294 e295 e296 e297 e298 .43/.44/.47 .55/.55/.58 ..76/.69/.78 .74/.72/.75 .50/.57/.50 .62/.57/.64 T0295 R2 = .57/.48/.61 T0296 R2 = .56/.52/.56 T0297 R2 = .26/.32/.25 e293 41/.48/.40 Satisfaction 86 Satisfaction was the eighth factor extracted (Factor 8) in the public and private sectors and the ninth extracted factor (Factor 9) in the charter school sector. Four items on the School and Staffing survey were proposed as relevant to teacher job satisfaction. A previous study about teacher job satisfaction used the 1994 SASS data (Perie and Baker, 1997) employing an IRT scale using three of these items as the measure of satisfaction:

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T0339 would become a teacher again, T0340 remain in teaching, and T0318 waste of time. A second study (Ingersoll, et. al., 1997) used item T0339 (would become a teacher again) to operationalize teacher commitment. The fourth item T0320 (agree generally satisfied) appears to be new with the 1999-2000 data and is the item that is most consistent with the definition of teacher satisfaction used in this study. Investigation of the best operational definition of job satisfaction began with the originally proposed subscale consisting of all four of the items. The exploratory factor analysis results are presented in Table 28. All four items loaded on the satisfaction factor in the public sector, but only the two items dealing with becoming a teacher again and how long a teacher plans to remain in teaching showed strong loadings that ranged from .56 to .70 across all three sectors. In private and charter schools, item T0320 (agree generally satisfied) tended to load on the administrative support factor, which is not entirely surprising given the emphasis in the literature of the importance of a supportive administration to satisfied teachers. A fifth item concerning satisfaction with salary tended to load with the satisfaction indicators in the private sector. This item is the designated measure of compensation, one of the predictor variables, and were not used as part of a general satisfaction measure. 87

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Table 28 Teacher Job Satisfaction Exploratory Factor Analysis Loadings Factor Item # Label Public Private Charter Satisfaction T0339 Would be a tchr .70 (8) .64 (8) .70 (9) T0340 Remain in tchng .58 (8) .56 (8) .68 (9) T0318 Agree waste of time .41 (8) .28 (8) .36 (9) T0320 Agree generally satisfied .40 (8) .30 (1) .28 (8) .38 (1) .29 (9) .45 (1) T0301 Agree satisfied w/salary .39 (8) .43 (9) .30 (9) The numbers in parentheses identify the factor # from the exploratory factor analysis presented in Table 8. The alpha for this subscale of four items ranged from .66 to .67 and that the one-factor model showed poor fit with RMSEA values well over .05. These results are displayed in Table 29. The public school model showed acceptable CFI of .94 but the other sectors had CFIs under .9. Table 29 Teacher Job Satisfaction Alphas and Proposed One-Factor Confirmatory Model T0318, T0320, T0339, T0340 Public Private Charter n 21043 3549 1423 Alpha (raw/std) .66/.67 .65/.66 .66/.67 Chi Square 809.48 287.74 125.27 df 2 2 2 RMSEA .14 .20 .21 CFI .94 .86 .86 The factor loadings for the one-factor confirmatory model are presented in Table 28 and are highest for T0339, the item that asked if a teacher would become a teacher again if he or she could start over and which was used by Ingersoll, et. al., to define commitment. The R 2 value indicates that one-half of the variability in T0339 (would become a teacher again) is explained by this four-indicator definition of job satisfaction, but only one-third or less of the variability in any of the other indicators is explained. 88

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This is somewhat unbalanced especially in the private and charter sectors compared to factor loadings that are seen on the other subscales in this study when alpha values are strong and fit is good. The exploratory and confirmatory factor analyses do not provide strong support for using this subscale as it exists; questions arise as to whether the four items are interchangeable and whether the scale is unidimensional. The originally proposed model for teacher job satisfaction is presented in Figure 8. Figure 8. Teacher Job Satisfaction Proposed One-Factor Confirmatory Model Job Satisfaction T0318 R2 = .26/.18/.20 T0320 R2 = .31/.24/.19 T0339 R2 = .48/.45/.48 T0340 R2 = .33/.44/.50 .57/.66/.71 .55/.49/.43 .69/.67/.69 e318 e320 e339 e340 .51/.43/. 44 Further investigation of the subscale seemed desirable, but one-factor confirmatory models of subsets of the four items were not possible to run because the models would not meet the over-identification requirement. Models with three indicators would all show perfect fit and models with two items would be under-identified. As previously mentioned, item T0320 (agree generally satisfied) tended to load on the satisfaction and the administrative support and leadership subscales in private and charter schools. A look at a two factor model of Administrative Support and Leadership with the 89

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Satisfaction variable allowed for further exploration of the this relationship and opened the way to observe the fit of Satisfaction when it was specified with fewer indicators. Correlations among the indicators are presented in Table 30 and show that T0318 (waste of time) and T0320 (generally satisfied) with correlations depending on sector that range from .38 to .40 are more highly correlated with each other than with the other two variables and that T0340 (remain in teaching) and T0339 (would become a teacher again) show a similar relationship (r = .45-.52). Table 30 Correlations Among Potential Satisfaction Indicators Public/Private/Charter T0318 T0320 T0339 T0340 T0318 waste of time T0320 generally satisfied .38/.40/.40 T0339 would become a teacher again .33/.24/.26 .35/.29/.26 T0340 remain in teaching .24/.23/.28 .29/.29/.26 .45/.49/.52 Considerable improvement in fit was observed when satisfaction was split into two factors with T0340 (remain in teaching) and T0339 (would be a teacher again) in one factor and T0318 (waste of time) and T0320 (generally satisfied) in another. Results for the three-factor model with Administrative Support and Leadership and Satisfaction split into two separate factors are presented in Table 31 and Figure 9. Both the RMSEA and the CFI indices are in the acceptable range for good fit with only the public school RMSEA value being over .05. The model fit for private and charter schools is better than in public schools for this model, a reversal compared to the previous model. 90

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Table 31 Administrative Support and Teacher Job Satisfaction Split Satisfaction into two factors Satis1 and Satis2 Public Private Charter n 21043 3549 1423 Chi Square 2540 369 142 df 32 32 32 RMSEA .06 .05 .05 CFI .97 .97 .98 The factor with indicators T0318 (waste of time) and T0320 (generally satisfied) is now called Satis1 and the factor with T0339 (would be a teacher again) and T0340 (remain in teaching) is Satis2. T0320, the general satisfaction item is the dominant item in Satis1. Satis1 accounted for 57-63 percent of the variability in T0320 (generally satisfied) compared with only 23-26 percent in T0318 (waste of time). Again there is an imbalance in the factor loadings leading to questions about the interchangeability of these two items if used in a subscale together. The R 2 value is itself a measure of the reliability of an item in a subscale (Hatcher, 1994). The reliability of the item as an R 2 value ranged from .19 to .31 when it was part of the four-item subscale but ranged from .57 to .63 in the two-item subscale paired with T0318 (waste of time). Satis2 shows a better balance in the factor loadings. The dominance of T0339 (would be a teacher again) is now evident only in the public sector; factor loadings in the private and charter sectors are very similar. The correlation between Satis1 with Administrative Support is higher than the correlation between Satis1 and Satis2. If Satis1 and Satis2 were a single construct then they would be expected to correlate more highly with each other than either would with Administrative Support. 91

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Figure 9. Administrative Support and Leadership and with Satisfaction split into two factors Adjusted Three-Factor Confirmatory Model Satis1 T0318 R2 = .26/.23/.26 T0320 R2 = .57/.70/.63 T0339 R2 = .57/.50/.51 T0340 R2 = .35/.49/.53 e318 e320 e339 e340 Satis2 Admin Support T0300 R2 = .58/.62/.60 T0312 R2 = .48/.49/.51 T0299 R2 = .50/.55/.60 .28/.25/.23 .68/.66/.74 .66/.51/.50 e299 e300 e306 e307 e310 e312 .51/.48/.51 .76/.84/.79 .75/.70/.72 .59/.70/.73 .71/.74/.77 .76/.79/.78 .73/.73/.73 .58/.55/.62 .79/.79/.77 .70/.70/.72 T0306 R2 = .54/.53/.53 T0307 R2 = .34/.30/.39 T0310 R2 = .62/.62/. 59 Satis1 = Sum (of T0318 T0320) Satis2= Sum (T0339 T0340) A series of regressions followed in an attempt to gain a better understanding of the relationship of key variables in the study with the outcome variable. According to measurement theory, if a subscale consists of n interchangeable items, then less measurement error is expected with n items than with fewer than n items. In other words, as the number of items in a subscale increases, reliability is normally expected to increase. Higher correlations of an outcome variable with the predictor variables would 92

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be expected when a quality four-item subscale represents the outcome variable than would be expected when a two-item subscale or one single item represents the construct. To test whether this would hold in this data, several one-predictor regression models were run using the public school data. Four different definitions of the outcome variable were examined. The R 2 values for each of these regression models in the public data are presented in the Table 32. In general, the predictors explained the largest amount of variability when the outcome variable was T0320 (generally satisfied) by itself. Models that included T0320 (generally satisfied) in combination with other indicators (Satis and Satis1) fared much better than those that contained T0339 (would become a teacher again) and T0340 (remain in teaching) as in Satis2. Table 32 R 2 Values for One Predictor Regression Models and Model with Ten Variables Public School Data IV T0320 Satis Satis1 Satis2 AdmSupp .25 .14 .20 .04 Resources .09 .07 .09 .03 Colleg1 .17 .11 .15 .04 Parent2 .10 .09 .12 .03 Cred 0 .001 0 0 Prof .02 .04 .03 .04 School1 .10 .09 .10 .04 Class1 .06 .05 .07 .02 Beh .11 .10 .15 .04 T0301 (satis salary) .04 .06 .03 .06 Ten predictor variables .34 .28 .32 .14 Satis = Sum (of T0318 T0320 T0339 T0340) Satis1 = Sum (of T0318 T0320) Satis2= Sum (T0339 T0340) Once these relationships were observed in the public data, further exploration of operational definitions that include T0320 (generally satisfied) were investigated in all three sectors. Results confirmed that the models that deleted T0339 (would become a 93

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teacher again) and T0340 (remain in teaching) consistently showed much higher R 2 values. The results shown in Table 33 used the ten major predictors included in the models shown in Table 32 plus the background school and teacher control variables. The control variables are used in the hierarchical linear models and include some teacher variables such as gender and race and some school variables like school level, size, urbanicity, and percent of minority students enrolled. Table 33 R 2 Values for Regression Models Using Ten Predictor Variables plus Control Variables Outcome Variable Public Private Charter Satis .28 .25 .28 Satis1 .34 .34 .40 Satis2 .15 .10 .12 T0320 .35 .38 .44 Satis = Sum (of T0318 T0320 T0339 T0340) alphas (raw/std) for public = .66/.67, private=.65/.66, charter = .66/.67 Satis1 = Sum (of T0318 T0320) alphas (raw/std) for public = .55/.55, private= .57/.57, charter = .57/.56 Satis2 = Sum (of T0339 T0340) alphas (raw/std) for public = .60/.62, private= .66/.66, charter = .68/.69 From the conception of this study, Item T0320 (generally satisfied) was believed to be the best and most obvious indicator of teacher job satisfaction. The other three items had been used successfully in a previous study, but the focus there was on job satisfaction with a teaching career rather than on satisfaction with a current teaching position in a particular school. The four indicators were originally proposed to be used together because in general one expects to gain better reliability by using a subscale rather than by using a single item for the outcome variable. In addition it is possible to easily assess the reliability of a subscale. While it is not possible to assess the reliability of Item T0320 (generally satisfied) with a particular statistic such as Cronbachs alpha for use as the outcome variable, the fact that it is most highly correlated with the predictor variables is 94

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very significant. This pattern of correlation is inconsistent with expectations and supports the hypothesis that the single item indicator is in this case more reliable than the four-item subscale and that the four indicators in the originally proposed subscale are likely not interchangeable items. In addition, item T0320 (generally satisfied) asks exactly the type of question that has been successful in one often-used global satisfaction measure, the Michigan Organizational Assessment Questionnaire Subscale (Cammann, Fichman, Jenkins, & Klesh, 1979). This is a three-item subscale using seven response options ranging from strongly disagree to strongly agree. The reported alpha is .77. The three items in the subscale include: 1) All in all I am satisfied with my job; 2) In general, I dont like my job; and 3) In general, I like working here (Spector, 1997). Along these same lines, item T0320 asks teachers to rate on a four point scale from strongly agree to strongly disagree with the statement: I am generally satisfied with being a teacher in this school. While it would be preferable to have more similar items and even more response options, this appears to be an excellent item to measure job satisfaction in a school and the best item available on the SASS. Compensation and Credentials Three items related to compensation and credentials loaded marginally on a single factor. Results shown in Table 34 indicate that the standardized alpha values for public, private, and charter schools were quite low (.42/.44/.53), and the raw alpha values were all equal to 0. No changes are planned to the proposed measures of compensation and credentials based on the exploratory results. 95

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Table 34 Compensation and Credentials Exploratory Factor Analysis Loadings Factor Item # Label Public Private Charter Compensation T0347 T0347 Sch yr amt tchr pay .59 (11) .52 (10) .58 (10) Graduate .51 (11) .44 (10) .46 (10) Cert Cert .20 (11) .34 (10) .46 (10) The numbers in parentheses identify the factor # from the exploratory factor analysis presented in Table 8 Collegiality Collegiality items composed the tenth extracted factor (Factor 10) in public schools and the twelfth extracted factor (Factor 12) in charter schools. Results of the exploratory factor analysis are presented in Table 35. In private schools the collegiality items loaded with the administrative support factor. Three items T0309 (colleagues share beliefs), T0308 (staff cooperation), and T0311 (teachers enforce rules) had loadings above .4 across all sectors with the single exception of a .36 loading on T0309 (colleagues share beliefs) for private schools. Table 35 Collegiality Exploratory Factor Analysis Loadings Factor Item # Label Public Private Charter Collegiality T0309 Agree colleagues share beliefs .65 (10) .36 (1) .50 (12) T0308 Agree staff cooperation .52 (10) .47 (1) .46 (12) T0311 Agree tchrs enf rules .43 (10) .43 (1) .40 (12) T0316 Agree coordinate content .37 (10) -.30 (1) .22 (12) T0319 Agree plan w/librarian .23 (10) 0 (1) .06 (12) The numbers in parentheses identify the factor # from the exploratory factor analysis presented in Table 8. The alpha values for the proposed and adjusted collegiality subscale are presented in Tables 36 and 37. Alphas were improved from the low .60s to low .70s by dropping items T0319 (coordinate content) and T0316 (plan with the librarian). The fit of the three-item subscale could not be tested because a three-item model will always show perfect fit since it is just-identified. The fit indices on the proposed model which are also 96

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presented in Table 36 looked promising with RMSEA values over .05 but between .06 and .08 while all CFIs were well over .90. Table 36 Collegiality Alphas and Proposed One-Factor Confirmatory Model T0308, T0309, T0311, T0319, T0316 Public Private Charter n 21043 3549 1423 Alpha (raw/std) .61/.63 .58/.62 .61/.63 Chi Square 351.37 125.09 34.7 df 5 5 5 RMSEA .06 .08 .06 CFI .98 .96 .97 Table 37 Collegiality Alphas T0308, T0309, T0311 n 21043 3549 1423 Alpha (raw/std) .72/.73 .73/.74 .74/.74 Resources The proposed resources factor included T0304 and T0305. Neither of these items loaded on any of the 12 factors. Distribution of Subscale Scores Descriptive statistics for all variables in the study at the conclusion of the factor analytic work are presented in Tables 38, 39, and 40. No weights were applied in the calculation of any of these statistics. 97

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Table 38 Descriptive Statistics for Public School Variables Uncentered Grand Mean Centered Min Max Mean Med Min Max Mean Med SD Skew Kurtosis Satisfaction 1 4 3.38 4 0.75 -1.18 1.12 Total Experience 1 62 14.72 13 -13.72 47.28 0 -1.72 10.04 0.37 -0.98 Minority Enrollment 0 100 30.64 17.39 -30.64 69.36 0 -13.25 31.68 0.96 -0.39 School Size 1 12 7.18 7 -6.18 4.82 0 -0.18 2.6 -0.04 -0.36 Adm. Support 6 24 17.9 18 -11.9 6.1 0 0.1 4.15 -0.67 -0.1 Resources 1 1 4 3.07 3 -2.07 0.93 0 -0.07 0.92 -0.73 -0.34 Resources 2 1 4 2.11 2 -1.11 1.89 0 -0.11 0.92 0.5 -0.55 Collegiality 3 12 8.72 9 -5.72 3.28 0 0.28 2.06 -0.35 -0.29 Parent Support 4 16 9.2 9 -5.2 6.8 0 -0.2 2.94 0.11 -0.72 Tardy 3 12 7.9 8 -4.9 4.1 0 0.1 2.37 -0.26 -0.67 Aggression 4 16 12.12 12 -8.12 3.88 0 -0.12 2.44 -0.54 0.01 Satis Class Size 1 4 2.96 3 -1.96 1.04 0 0.04 1.02 -0.64 -0.75 Credentials 0 5 2.69 3 -2.69 2.31 0 0.31 0.83 0.34 -0.02 Professional Dev 1 64 27.15 27 -26.15 36.85 0 -0.15 12.75 0.18 -0.57 School Autonomy 6 30 14.59 14 -8.59 15.41 0 -0.59 4.84 0.4 -0.21 Classroom Autonomy 6 30 24.7 25 -18.7 5.3 0 0.3 3.9 -0.89 1.05 Satisfaction Salary 1 4 2.07 2 -1.07 1.93 0 -0.07 1 0.37 -1.09 Table 39 Descriptive Statistics for Private School Variables Uncentered Grand Mean Centered Min Max Mean Med Min Max Mean Med SD Skew Kurtosis Satisfaction 1 4 3.57 4 0.68 -1.63 2.56 Total Experience 1 67 12.31 10 -11.31 54.69 0 -2.31 10.21 0.95 0.37 Minority Enrollment 0 100 18.81 7.93 -18.81 81.19 0 -10.88 25.52 1.95 2.98 School Size 1 12 4.91 5 -3.91 7.09 0 0.09 2.22 0.24 -0.18 Adm. Support 6 24 19.3 20 -13.3 4.7 0 0.7 3.83 -1.04 0.71 Resources 1 1 4 3.45 4 -2.45 0.55 0 0.55 0.78 -1.4 1.43 Resources 2 1 4 2.7 3 -1.7 1.3 0 0.3 0.95 -0.06 -1.03 Collegiality 3 12 9.97 10 -6.97 2.03 0 0.03 1.9 -0.92 0.44 Parent Support 4 16 12.95 13 -8.95 3.05 0 0.05 2.52 -0.98 0.83 Tardy 3 12 9.85 10 -6.85 2.15 0 0.15 1.93 -0.91 0.51 Aggression 4 16 14.42 15 -10.42 1.58 0 0.58 1.68 -1.7 4.56 Satis Class Size 1 4 3.38 4 -2.38 0.62 0 0.62 0.87 -1.32 0.84 Credentials 0 5 2.04 2 -2.04 2.96 0 -0.04 1 0.21 -0.04 Professional Dev 1 64 21.93 20 -20.93 42.07 0 -1.93 12.97 0.47 -0.46 School Autonomy 6 30 16.38 16 -10.38 13.62 0 -0.38 5.21 0.21 -0.4 Classroom Autonomy 6 30 25.78 26 -19.78 4.22 0 0.22 3.54 -1.1 1.85 Satisfaction Salary 1 4 2.24 2 -1.24 1.76 0 -0.24 1.06 0.21 -1.24 98

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Table 40 Descriptive Statistics for Charter School Variables Uncentered Grand Mean Centered Min Max Mean Med Min Max Mean Med SD Skew Kurtosis Satisfaction 1 4 3.33 4 0.82 -1.16 0.8 Total Experience 1 49 7.01 4 -6.01 41.99 0 -3.01 7.86 1.98 3.89 Minority Enrollment 0 100 48.33 35.96 -48.33 51.67 0 -6.19 35.96 0.24 -1.51 School Size 1 12 4.99 5 -3.99 7.01 0 0.01 2.26 0.43 0.21 Adm. Support 6 24 18.57 20 -12.57 5.43 0 1.43 4.28 -0.84 0.1 Resources 1 1 4 3.04 3 -2.04 0.96 0 -0.04 0.95 -0.67 -0.52 Resources 2 1 4 2.43 2 -1.43 1.57 0 -0.43 0.97 0.15 -0.94 Collegiality 3 12 9.42 10 -6.42 2.58 0 0.58 2.06 -0.69 0.02 Parent Support 4 16 10.35 10 -6.35 5.65 0 -0.35 3.32 -0.13 -0.88 Tardy 3 12 7.98 8 -4.98 4.02 0 0.02 2.52 -0.25 -0.83 Aggression 4 16 12.96 13 -8.96 3.04 0 0.04 2.41 -0.82 0.28 Satisfaction Class Size 1 4 3.23 4 -2.23 0.77 0 0.77 0.97 -1.07 0.04 Credentials 0 5 2.22 2 -2.22 2.78 0 -0.22 0.95 0.17 0.16 Professional Dev 1 64 28 28 -27 36 0 0 14.13 0.08 -0.77 School Autonomy 6 30 17.07 17 -11.07 12.93 0 -0.07 6.06 0.18 -0.74 Classroom Autonomy 6 30 24.92 26 -18.92 5.08 0 1.08 4.3 -1.12 1.47 Satisfaction Salary 1 4 2.3 2 -1.3 1.7 0 -0.3 1.04 0.1 -1.23 Weights The data in this study is taken from a survey where the schools and teachers within schools were selected with known but unequal probabilities. Weights are assigned by NCES to each teacher for use in statistical analysis designed to produce unbiased population estimates. The weights are inversely proportional to the probability of selection. Weights depend on both the sampling plan and the conceptual orientation of the study, so using the teacher level weights seems appropriate for both the sampling plan as designed by NCES and for this study, which focuses on teacher level inferences. The weights (tfnlwgt) assigned by NCES were used in all the hierarchical linear models. The raw weights were normalized by multiplying the weight variable by n (the number of observations in the dataset) and dividing the results by the sum of the weights so that the 99

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weights summed to the sample size and the level 1 variance estimates were scaled accurately. Hierarchical Linear Model Results Null Model The purpose of the null model is to estimate the mean teacher satisfaction score across the schools used in the model, indicate if there is statistically significant variability in these means, and specify the amount of variability at the school level and the teacher level before any predictor variables are entered. The fixed effect for the intercept is the overall grand mean of all school means on teacher satisfaction. The coefficients for the null model are presented in Tables 41-43 as 3.40/3.57/3.33 for public, private, and charter schools respectively. Results for the null model are repeated in each set of tables for public, private, and charter schools through Table 73 for comparison purposes. Random effects for the null model intercept are .10/.09/.09 in public, private, and charter schools respectively; random effects for the null model residual are .45/36/.56. Hypothesis tests on the random effects in the null model indicate that both variance components are significantly different from 0 in all sectors. The p-values are not reported in Tables 41-43, but both are <.0001 in each sector and can be calculated by dividing each variance component by its standard error. This suggests that schools do differ in their average teacher satisfaction scores and that they do differ among teachers within schools. The intraclass correlation is a statistic that calculates the portion of total variance that is between schools. The intraclass correlation coefficient for the sample as calculated 100

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from the null model in public schools is .10/(.10 + .45) = .18. In private schools it is .20 and in charter schools .14. Background Model The background model is designed to control for background characteristics of teachers including gender, total years of experience, and race. It also controls for some school characteristics including school level, urbanicity, percent minority enrollment, and school size. Some of these variables are coded as dummy variables. These include gender (male or female) where female is the reference group, race (Indian, Asian, Black, Hispanic, or White) where white is the reference group, school level (elementary, secondary, or combined) where elementary is the reference group, and urbanicity (urban, rural, or suburban) where suburban is the reference group. The fixed effects for the intercept are presented in Tables 41-43 as 3.45/3.55/3.28 for public, private, and charter schools respectively. The same results for the background model are repeated in each set of tables for public, private, and charter schools through Table 73 for comparison purposes. The fixed effect for the intercept estimates the school mean for teacher satisfaction when the other predictors are equal to zero. Recall that the continuous variables including total years of experience, percent of minority enrollment and school size are grand mean centered to aid the interpretation of the intercept. For public schools the interpretation for the fixed effect for the intercept would be that the school mean for teacher satisfaction is 3.45 for a white female public elementary teacher with average number of years experience teaching in a suburban school with an average number of minority students enrolled in an average sized school. The intercept for black public female teachers with other characteristics the same as just described for the white 101

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teacher would be adjusted by adding the value of the coefficient for public school black teachers (.122) so the adjusted intercept value would be 3.447 + .122 = 3.569 (see Table 41). The other variables in the background model total years of experience, percent of minority enrollment, and school size are continuous variables rather than dummy variables. The coefficients for these variables are partial slopes that describe the relationship between teacher satisfaction and the variable itself. For instance, total years of teaching experience is statistically significant in all sectors in the background model. For private schools the coefficient is .007 and is interpreted to mean that holding other variables constant, as years of experience increase by 1, satisfaction scores increase by .007. Extending this to more years, 10 for instance, as years of experience increase by 10, satisfaction scores are expected to increase by .07. This means that holding other variables constant, teachers with more experience tend to rate their satisfaction level higher than those with less experience. Percent minority enrollment is also statistically significant in all sectors. In this case however, the coefficient is negative rather than positive in each sector. In public schools the coefficient is -.003. This means that as percent of minority enrollment increases by 1 that the teacher satisfaction score can be expected to decrease by .003 and correspondingly that as minority enrollment increases by 10% above the average minority enrollment that satisfaction scores would be expected to decrease by .03. The two random effects in the background model and all subsequent models are variances and are referred to as variance components (*Vcomp) one representing the 102

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variation in intercepts among schools and the other the variation within schools (residual variance). A comparison of the variances in the background model can be made to the variances in the null model to see how much of the variance has been explained. An examination of the parameters comparing the two models shows almost no change indicating that the teacher background variables combined with the school characteristic variables are not helping to explain why some teachers are more (or less) satisfied than others. For instance the variance of the intercept for charter schools as shown in Table 43 changes from .087 to .086 and the variance for the residual from .5588 to .5503, both very small changes accounting for only two percent of the variability in teacher satisfaction at each level of the model. The methods chapter of this document discussed the fact that in order to use the R 2 concepts, the models must not contain any random slope coefficients. Only the intercept is allowed to be random if one wishes to compare R 2 values across models because the R 2 cannot be uniquely defined in models with random slopes. Not using random slopes makes the assumption that all schools have a common slope. To test if this is plausible, each model was run without random slopes and then with random slopes in two different structures one allowing covariances among the variance components to be estimated and one not allowing the covariances to be estimated. Most of these models were successfully estimated but not all of them. Some would not run in the public data set due to lack of memory in the computer used; a few did not run because the G matrix was not positive definite. Fixed coefficients for administrative support and leadership, resources, cooperative environment and collegiality, parental support, student behavior 103

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and school atmosphere, credentialing requirements, professional development opportunities, autonomy and authority in the classroom and the school, and satisfaction with salary were examined across the models that had the same fixed coefficients but different random components. Fixed coefficients maintained similar values across models and statistical significance did not change in any case. The decision was made to exclude the random slopes in favor of using the more straightforward interpretation of coefficients and R 2 values. In the next section the basic research questions for the study will be restated and results for each one presented. Results designed to answer each question will be accompanied by a table that will present fixed and random effects in separate blocks for public, private, and charter schools. Each table will repeat the data for the null model and the background models and will add data from the model that is specifically designed to answer a particular research questions. Data for the null and background models are repeated in each table to facilitate comparisons of the new model to the baseline null and background models. Because the research questions are asking to what degree the studied variables can predict teacher satisfaction after controlling for background variables, the background model is the most important baseline in this study. Research Question 1 Administrative Support and Leadership After controlling for teacher background and school characteristics, to what degree can administrative support and leadership predict teacher satisfaction in public, private, and charter schools? The fixed effects coefficients for administrative support and leadership for public, private, and charter schools that are presented in Tables 41 43 and are the estimated 104

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average slopes for each sector and describe the relationship between teacher job satisfaction and administrative support and leadership. Table 41 Fixed and Random Effects for Administrative Support and Leadership in Public Schools Null Background Adm. Support Fixed Effects Coef f SE Coef f SE Coef f SE Intercept 3.4011 0.0053 3.4467 0.0091 3.4006 0.0077 Gender -0.0131 0.0086 -0.0357 0.0074 Total Experience 0.0019 0.0004 0.0037 0.0003 Indian 0.0204 0.0378 0.0170 0.0327 Asian -0.0130 0.0289 -0.0153 0.0250 Black 0.1224 0.0153 0.0307 0.0132 Hispanic 0.0733 0.0171 0.0369 0.0148 Minority Enrolled -0.0033 0.0002 -0.0025 0.0002 Secondary -0.0730 0.0121 0.0085 0.0103 Combined -0.0597 0.0263 0.0143 0.0225 Urban -0.0310 0.0139 -0.0247 0.0117 Rural -0.0341 0.0130 -0.0198 0.0110 School Size -0.0040 0.0027 0.0024 0.0023 Adm. Support 0.0944 0.0008 Random Effects *Vcomp SE *Vcomp SE *Vcomp SE Intercept 0.0973 0.0029 0.0884 0.0027 0.0595 0.0019 Residual 0.4482 0.0033 0.4466 0.0033 0.3374 0.0025 Intercept Residual Intercept Residual R 2 Compared to Null Model 0.09 0.00 0.39 0.25 R 2 Compared to Background Model 0.33 0.24 *Vcomp is used to abbreviate Variance Component 105

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Table 42 Fixed and Random Effects for Administrative Support and Leadership in Private Schools Null Background Adm. Support Fixed Effects Coef f SE Coef f SE Coef f SE Intercept 3.5660 0.0100 3.5479 0.0188 3.5503 0.0155 Gender 0.0142 0.0191 0.0025 0.0164 Total Experience 0.0069 0.0008 0.0066 0.0007 Indian 0.1837 0.1042 0.1407 0.0891 Asian -0.1477 0.0629 -0.1597 0.0538 Black 0.0389 0.0479 -0.0422 0.0405 Hispanic 0.0627 0.0387 -0.0082 0.0330 Minority Enrolled -0.0018 0.0004 -0.0011 0.0003 Secondary -0.0134 0.0289 0.0569 0.0240 Combined 0.0450 0.0233 0.0386 0.0192 Urban -0.0074 0.0215 -0.0228 0.0177 Rural 0.0160 0.0335 -0.0014 0.0277 School Size 0.0009 0.0050 0.0129 0.0042 Adm. Support 0.0940 0.0018 Random Effects *Vcomp SE *Vcomp SE *Vcomp SE Intercept 0.0854 0.0063 0.0840 0.0062 0.0504 0.0042 Residual 0.3630 0.0071 0.3577 0.0070 0.2668 0.0052 Intercept Residual Intercept Residual R 2 Compared to Null Model 0.02 0.01 0.41 0.27 R 2 Compared to Background Model 0.40 0.25 *Vcomp is used to abbreviate Variance Component 106

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Table 43 Fixed and Random Effects for Administrative Support and Leadership in Charter Schools Null Background Adm. Support Fixed Effects Coef f SE Coef f SE Coef f SE Intercept 3.3329 0.0188 3.2786 0.0359 3.2843 0.0284 Gender -0.0018 0.0349 -0.0034 0.0293 Total Experience 0.0076 0.0019 0.0071 0.0016 Indian 0.1349 0.1420 0.2612 0.1188 Asian -0.0594 0.0971 -0.1230 0.0815 Black 0.1695 0.0529 0.0775 0.0438 Hispanic 0.0911 0.0579 0.0317 0.0483 Minority Enrolled -0.0022 0.0006 -0.0019 0.0005 Secondary 0.0463 0.0491 0.0604 0.0388 Combined 0.0095 0.0504 0.0180 0.0396 Urban -0.0051 0.0444 0.0254 0.0350 Rural 0.1918 0.0674 0.1274 0.0537 School Size 0.0200 0.0087 0.0164 0.0067 Adm. Support 0.1051 0.0030 Random Effects *Vcomp SE *Vcomp SE *Vcomp SE Intercept 0.0872 0.0133 0.0859 0.0130 0.0396 0.0078 Residual 0.5588 0.0168 0.5503 0.0165 0.3997 0.0119 Intercept Residual Intercept Residual R 2 Compared to Null Model 0.02 0.02 0.55 0.28 R 2 Compared to Background Model 0.54 0.27 *Vcomp is used to abbreviate Variance Component The estimated coefficients were .09/.09/.11 (see tables 41, 42, and 43) in public, private, and charter schools respectively. In charter schools the estimated coefficient of .11 indicates that while holding background variables constant, as the administrative support and leadership score increases by one point, teacher satisfaction is expected to increase by .11 points. Dividing .09 by the standard error of .0030 leads to a p-value of <.0001. The null hypothesis that there is no relationship between administrative support and leadership with teacher satisfaction in charter schools is rejected. The scale for administrative support and leadership ranged from 6-24 with a charter school mean of 107

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18.57 and standard deviation of 4.28 and was rescaled to have a mean of 0 (see Table 40). The mean of 18.57 was subtracted from each teachers original administrative support and leadership score to rescale it to mean of 0. The adjusted range of scores is .57 to 5.43, so a coefficient of .11 as estimated for charter schools has the potential to affect a teacher satisfaction score from .38 to .60, on the four point satisfaction scale (.57 .11 = -1.38; 5.43 .11 = .60). The random effects are also presented in Tables 41-43. The variance of the intercepts among schools changes from .09/.08/.09 in the background model to .06/.05/.04 when administrative support and leadership is added. Administrative support and leadership is explaining a large portion of the existing school-to-school variation. An R 2 value for between schools can be calculated to summarize the amount of explainable variance between schools that is accounted for by administrative support and leadership in each sector. The R 2 values in public, private, and charter schools respectively when compared to the background model are .33/.40/.54. In public schools this is calculated as (.0884-.0595)/.0884 = .33. Administrative support and leadership accounts for 33 percent of the variability in teacher satisfaction that is between public schools after controlling for background characteristics. Similar calculations for private and charter schools show that administrative support and leadership accounts for 40 percent of the variability in teacher satisfaction that is between private schools and 54 percent between charter schools after controlling for background characteristics. These R 2 values are summarized in Tables 41-43 in the R 2 Compared to Background Model row for the intercept in each of the respective blocks of the table for each sector. 108

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The portion of the explained within school variance of teacher satisfaction can also be calculated. The total residual or within school variance in the background model was .45/.36/.55 for public, private, and charter schools respectively. The residual variances decreased to .34/.27/.40 when administrative support and leadership was added to the background model. The R 2 values are .24/.25/.27 for public, private, and charter schools. The R 2 value for within schools for public schools is calculated as (.4466-.3374)/.4466 = .24 where .4466 is the total variance within schools in the background model, and .3374 is the variance component for public schools in the current model which adds administrative support and leadership to the background model. Perceptions of administrative support and leadership accounts for 24 percent of the variability in teacher satisfaction that is within public schools after controlling for background characteristics. In like manner, administrative support and leadership accounts for 25 percent of the variability in teacher satisfaction that is within private schools and 27 percent in charter schools after controlling for background characteristics. These R 2 values are summarized in Tables 41-43 in the R 2 Compared to Background Model row for the residual. Research Question 2 Resources After controlling for teacher background and school characteristics, to what degree can resources predict teacher satisfaction in public, private, and charter schools? The fixed effects coefficients for resources for public, private, and charter schools are presented in Tables 44-46. 109

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Table 44 Fixed and Random Effects for Resources in Public Schools Null Background Resources Fixed Effects Coef f SE Coef f SE Coef f SE Intercept 3.4011 0.0053 3.4467 0.0091 3.4599 0.0085 Gender -0.0131 0.0086 -0.0526 0.0082 Total Experience 0.0019 0.0004 0.0017 0.0003 Indian 0.0204 0.0378 0.0172 0.0361 Asian -0.0130 0.0289 -0.0104 0.0276 Black 0.1224 0.0153 0.0796 0.0146 Hispanic 0.0733 0.0171 0.0548 0.0163 Minority Enrollment -0.0033 0.0002 -0.0023 0.0002 Secondary -0.0730 0.0121 -0.0699 0.0113 Combined -0.0597 0.0263 -0.0558 0.0249 Urban -0.0310 0.0139 -0.0065 0.0130 Rural -0.0341 0.0130 -0.0443 0.0122 School Size -0.0040 0.0027 0.0045 0.0025 Resources 1 0.2068 0.0039 Resources 2 0.1080 0.0038 Random Effects *Vcomp SE *Vcomp SE *Vcomp SE Intercept 0.0973 0.0029 0.0884 0.0027 0.0732 0.0023 Residual 0.4482 0.0033 0.4466 0.0033 0.4104 0.0030 Intercept Residual Intercept Residual R 2 Compared to Null Model 0.09 0.00 0.25 0.08 R 2 Compared to Background Model 0.17 0.08 *Vcomp is used to abbreviate Variance Component 110

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Table 45 Fixed and Random Effects for Resources in Private Schools Null Background Resources Fixed Effects Coef f SE Coef f SE Coef f SE Intercept 3.5660 0.0100 3.5479 0.0188 3.5588 0.0170 Gender 0.0142 0.0191 -0.0076 0.0179 Total Experience 0.0069 0.0008 0.0054 0.0007 Indian 0.1837 0.1042 0.1500 0.0973 Asian -0.1477 0.0629 -0.1157 0.0587 Black 0.0389 0.0479 0.0507 0.0442 Hispanic 0.0627 0.0387 0.0360 Minority Enrollment -0.0018 0.0004 -0.0010 0.0004 Secondary -0.0134 0.0289 0.0027 0.0262 Combined 0.0450 0.0233 0.0225 0.0210 Urban -0.0074 0.0215 -0.0035 0.0193 Rural 0.0160 0.0335 0.0072 0.0303 School Size 0.0009 0.0050 0.0003 0.0045 Resources 1 0.2617 0.0099 Resources 2 0.1182 0.0078 Random Effects *Vcomp SE *Vcomp SE *Vcomp SE Intercept 0.0854 0.0063 0.0840 0.0062 0.0605 0.0050 Residual 0.3630 0.0071 0.3577 0.0070 0.3177 0.0062 Intercept Residual Intercept Residual R 2 Compared to Null Model 0.02 0.01 0.29 0.12 R 2 Compared to Background Model 0.28 0.11 0.0312 *Vcomp is used to abbreviate Variance Component 111

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Table 46 Fixed and Random Effects for Resources in Charter Schools Null Background Resources Fixed Effects Coef f SE Coef f SE Coef f SE Intercept 3.3329 0.0188 3.2786 0.0359 3.3025 0.0316 Gender -0.0018 0.0349 0.0024 0.0325 Total Experience 0.0076 0.0019 0.0051 0.0018 Indian 0.1349 0.1420 0.0464 0.1317 Asian -0.0594 0.0971 -0.0885 0.0904 Black 0.1695 0.0529 0.0807 0.0487 Hispanic 0.0911 0.0579 0.0182 0.0536 Minority Enrollment -0.0022 0.0006 -0.0012 0.0005 Secondary 0.0463 0.0491 0.0430 0.0431 Combined 0.0095 0.0504 0.0364 0.0440 Urban -0.0051 0.0444 -0.0138 0.0388 Rural 0.1918 0.0674 0.1148 0.0599 School Size 0.0200 0.0087 0.0203 0.0075 Resources 1 0.2726 0.0153 Resources 2 0.1447 0.0145 Random Effects *Vcomp SE *Vcomp SE *Vcomp SE Intercept 0.0872 0.0133 0.0859 0.0130 0.0493 0.0098 Residual 0.5588 0.0168 0.5503 0.0165 0.4908 0.0146 Intercept Residual Intercept Residual R2 Compared to Null Model 0.02 0.02 0.44 0.12 R2 Compared to Background Model 0.43 0.11 *Vcomp is used to abbreviate Variance Component Two types of resources were entered in the model: one dealing with having necessary materials available and the other with the level of interference from paperwork and routine duties. A separate fixed coefficient is estimated for each type of resource. The estimated fixed coefficients were .21/.26/.27 for having necessary materials and .11/.12/.15 for level of interference (see Tables 44-46). This suggests that greater resources are associated with greater levels of teacher satisfaction when holding other variables constant. Small standard errors led to p-values of <.0001 in each case. The null 112

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hypothesis that there is no relationship between resources of either type and teacher satisfaction is rejected. The random effects for resources along with the R 2 values for both between and within schools are also presented in Tables 44-46 by sector. The R 2 values for between schools in the three sectors are .17/.28/.43 when compared to the background model. This means that depending on the sector, the amount of explained variation among schools ranges from 17-43 percent. Within schools, the R 2 values for public, private, and charter schools respectively are .08/.11/.11 when compared to the background models. Depending on the sector, resources accounts for 8-11 percent of the variability in teacher satisfaction that is within schools after controlling for background characteristics. Research Question 3 Cooperative Environment and Collegiality After controlling for teacher background and school characteristics, to what degree can cooperative environment and collegiality predict teacher satisfaction in public, private, and charter schools? The fixed effect coefficients for cooperative environment and collegiality for public, private, and charter schools are presented in Tables 47-49. 113

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Table 47 Fixed and Random Effects for Cooperative Environment and Collegiality in Public Schools Null Background Collegiality Fixed Effects Coef f SE Coef f SE Coef f SE Intercept 3.4011 0.0053 3.4467 0.0091 3.3616 0.0081 Gender -0.0131 0.0086 -0.0014 0.0079 Total Experience 0.0019 0.0004 0.0005 0.0003 Indian 0.0204 0.0378 0.0245 0.0346 Asian -0.0130 0.0289 -0.0221 0.0265 Black 0.1224 0.0153 0.0696 0.0140 Hispanic 0.0733 0.0171 0.0555 0.0156 Minority Enrollment -0.0033 0.0002 -0.0023 0.0002 Secondary -0.0730 0.0121 0.0503 0.0108 Combined -0.0597 0.0263 0.0160 0.0237 Urban -0.0310 0.0139 -0.0282 0.0123 Rural -0.0341 0.0130 -0.0129 0.0116 School Size -0.0040 0.0027 0.0121 0.0024 Collegiality 0.1537 0.0017 Random Effects *Vcomp SE *Vcomp SE *Vcomp SE Intercept 0.0973 0.0029 0.0884 0.0027 0.0644 0.0021 Residual 0.4482 0.0033 0.4466 0.0033 0.3783 0.0028 Intercept Residual Intercept Residual R 2 Compared to Null Model 0.09 0.00 0.34 0.16 R 2 Compared to Background Model 0.27 0.15 *Vcomp is used to abbreviate Variance Component 114

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Table 48 Fixed and Random Effects for Cooperative Environment and Collegiality in Private Schools Null Background Collegiality Fixed Effects Coef f SE Coef f SE Coef f SE Intercept 3.5660 0.0100 3.5479 0.0188 3.5150 0.0162 Gender 0.0142 0.0191 0.0537 0.0171 Total Experience 0.0069 0.0008 0.0055 0.0007 Indian 0.1837 0.1042 0.1534 0.0932 Asian -0.1477 0.0629 -0.1318 0.0563 Black 0.0389 0.0479 -0.0253 0.0423 Hispanic 0.0627 0.0387 0.0324 0.0345 Minority Enrollment -0.0018 0.0004 -0.0011 0.0004 Secondary -0.0134 0.0289 0.0912 0.0252 Combined 0.0450 0.0233 0.0545 0.0201 Urban -0.0074 0.0215 -0.0023 0.0185 Rural 0.0160 0.0335 0.0072 0.0289 School Size 0.0009 0.0050 0.0181 0.0043 Collegiality 0.1643 0.0039 Random Effects *Vcomp SE *Vcomp SE *Vcomp SE Intercept 0.0854 0.0063 0.0840 0.0062 0.0546 0.0046 Residual 0.3630 0.0071 0.3577 0.0070 0.2924 0.0057 Intercept Residual Intercept Residual R 2 Compared to Null Model 0.02 0.01 0.36 0.19 R 2 Compared to Background Model 0.35 0.18 *Vcomp is used to abbreviate Variance Component 115

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Table 49 Fixed and Random Effects for Cooperative Environment and Collegiality in Charter Schools Null Background Collegiality Fixed Effects Coef f SE Coef f SE Coef f SE Intercept 3.3329 0.0188 3.2786 0.0359 3.2559 0.0306 Gender -0.0018 0.0349 0.0406 0.0313 Total Experience 0.0076 0.0019 0.0051 0.0017 Indian 0.1349 0.1420 0.1440 0.1267 Asian -0.0594 0.0971 -0.0405 0.0869 Black 0.1695 0.0529 0.0898 0.0468 Hispanic 0.0911 0.0579 0.0167 0.0516 Minority Enrollment -0.0022 0.0006 -0.0011 0.0005 Secondary 0.0463 0.0491 0.1117 0.0419 Combined 0.0095 0.0504 0.0231 0.0427 Urban -0.0051 0.0444 0.0136 0.0377 Rural 0.1918 0.0674 0.1675 0.0578 School Size 0.0200 0.0087 0.0265 0.0073 Collegiality 0.1821 0.0068 Random Effects *Vcomp SE *Vcomp SE *Vcomp SE Intercept 0.0872 0.0133 0.0859 0.0130 0.0493 0.0093 Residual 0.5588 0.0168 0.5503 0.0165 0.4512 0.0135 Intercept Residual Intercept Residual R2 Compared to Null Model 0.02 0.02 0.43 0.19 R2 Compared to Background Model 0.43 0.18 *Vcomp is used to abbreviate Variance Component The coefficients for cooperative environment and collegiality are .15/.16/.18 for public, private, and charter schools respectively (see Tables 47-49). This suggests that increased levels of cooperative environment and collegiality are associated with greater levels of teacher satisfaction when holding other variables constant. The standard errors are small enough that p-values are all less than .0001 in each sector. The null hypothesis that there is no relationship between cooperative environment and collegiality and teacher satisfaction is rejected. 116

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The random effects for cooperative environment and collegiality along with the R 2 values for both between and within schools are also presented in Tables 47-49 by sector. The R 2 values for between schools in the three sectors are .27/.35/.43 when compared to the background model. This means that depending on the sector, the amount of explained variation among schools ranges from 27 43 percent. Within schools, the R 2 values for public, private, and charter schools respectively are .15/.18/.18 when compared to the background models. Depending on the sector, cooperative environment and collegiality accounts for 15 18 percent of the variability in teacher satisfaction that is within schools after controlling for background characteristics. Research Question 4 Parental Support After controlling for teacher background and school characteristics, to what degree can parental support predict teacher satisfaction in public, private, and charter schools? The fixed effect coefficients for parental support for public, private, and charter schools are presented in Tables 50-52. 117

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Table 50 Fixed and Random Effects for Parental Support in Public Schools Null Background Parental Support Fixed Effects Coef f SE Coef f SE Coef f SE Intercept 3.4011 0.0053 3.4467 0.0091 3.3605 0.0087 Gender -0.0131 0.0086 -0.0122 0.0082 Total Experience 0.0019 0.0004 0.0016 0.0003 Indian 0.0204 0.0378 -0.0156 0.0361 Asian -0.0130 0.0289 -0.0804 0.0276 Black 0.1224 0.0153 0.0437 0.0146 Hispanic 0.0733 0.0171 0.0092 0.0163 Minority Enrollment -0.0033 0.0002 -0.0004 0.0002 Secondary -0.0730 0.0121 0.0502 0.0115 Combined -0.0597 0.0263 0.0333 0.0250 Urban -0.0310 0.0139 -0.0125 0.0130 Rural -0.0341 0.0130 0.0182 0.0122 School Size -0.0040 0.0027 0.0004 0.0025 Parental Support 0.0843 0.0013 Random Effects *Vcomp SE *Vcomp SE *Vcomp SE Intercept 0.0973 0.0029 0.0884 0.0027 0.0753 0.0024 Residual 0.4482 0.0033 0.4466 0.0033 0.4088 0.0030 Intercept Residual Intercept Residual R 2 Compared to Null Model 0.09 0.00 0.23 0.09 R 2 Compared to Background Model 0.15 0.08 *Vcomp is used to abbreviate Variance Component 118

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Table 51 Fixed and Random Effects for Parental Support in Private Schools Null Background Parental Support Fixed Effects Coef f SE Coef f SE Coef f SE Intercept 3.5660 0.0100 3.5479 0.0188 3.4922 0.0177 Gender 0.0142 0.0191 0.0487 0.0181 Total Experience 0.0069 0.0008 0.0053 0.0007 Indian 0.1837 0.1042 0.1429 0.0986 Asian -0.1477 0.0629 -0.1639 0.0595 Black 0.0389 0.0479 -0.0055 0.0453 Hispanic 0.0627 0.0387 0.0567 0.0366 Minority Enrollment -0.0018 0.0004 0.0002 0.0004 Secondary -0.0134 0.0289 0.1403 0.0277 Combined 0.0450 0.0233 0.1132 0.0221 Urban -0.0074 0.0215 -0.0010 0.0202 Rural 0.0160 0.0335 0.0000 0.0315 School Size 0.0009 0.0050 -0.0132 0.0047 Parental Support 0.0945 0.0032 Random Effects *Vcomp SE *Vcomp SE *Vcomp SE Intercept 0.0854 0.0063 0.0840 0.0062 0.0729 0.0054 Residual 0.3630 0.0071 0.3577 0.0070 0.3209 0.0062 Intercept Residual Intercept Residual R 2 Compared to Null Model 0.02 0.01 0.15 0.12 R 2 Compared to Background Model 0.13 0.10 *Vcomp is used to abbreviate Variance Component 119

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Table 52 Fixed and Random Effects for Parental Support in Charter Schools Null Background Parental Support Fixed Effects Coef f SE Coef f SE Coef f SE Intercept 3.3329 0.01880.0349Asian Secondary 3.2786 0.0359 3.2190 0.0329 Gender -0.0018 0.0579 0.0331 Total Experience 0.0076 0.0019 0.0057 0.0018 Indian 0.1349 0.1420 -0.0371 0.1342 -0.0594 0.0971 -0.1005 0.0918 Black 0.1695 0.0529 0.0487 0.0499 Hispanic 0.0911 0.0579 0.0250 0.0546 Minority Enrollment -0.0022 0.0006 0.0008 0.0006 0.0463 0.0491 0.2064 0.0456 Combined 0.0095 0.0504 0.1108 0.0461 Urban -0.0051 0.0444 -0.0122 0.0404 Rural 0.1918 0.0674 0.2181 0.0618 School Size 0.0200 0.0087 0.0132 0.0078 Parental Support 0.0916 0.0049 Random Effects *Vcomp SE *Vcomp SE *Vcomp SE Intercept 0.0872 0.0133 0.0859 0.0130 0.0607 0.0107 Residual 0.5588 0.0168 0.5503 0.0165 0.4998 0.0149 Intercept Residual Intercept Residual R 2 Compared to Null Model 0.02 0.02 0.30 0.11 R 2 Compared to Background Model 0.29 0.09 *Vcomp is used to abbreviate Variance Component The coefficients for parental support are .08/.09/.09 for public, private, and charter schools respectively (see Tables 50-52). This suggests that increased levels of parental support are associated with greater levels of teacher satisfaction when holding other variables constant. The standard errors are small enough that p-values are all less than .0001 in each sector. The null hypothesis that there is no relationship between parental support and teacher satisfaction is rejected. The random effects for parental support along with the R 2 values for both between and within schools are also presented in Tables 50-52 by sector. The R 2 values for between schools in the three sectors are .15/.13/.29 when compared to the background 120

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model. This means that depending on the sector, the amount of explained variation among schools ranges from 15 29 percent. Within schools, the R 2 values for public, private, and charter schools respectively are .08/.10/.09 when compared to the background models. Depending on the sector, parental support accounts for 8 10 percent of the variability in teacher satisfaction that is within schools after controlling for background characteristics. Research Question 5 Student Behavior and School Atmosphere After controlling for teacher background and school characteristics, to what degree can student behavior and school atmosphere predict teacher satisfaction in public, private, and charter schools? The fixed effects coefficients for student behavior and school atmosphere for public, private, and charter schools are presented in Tables 53-55. 121

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Table 53 Fixed and Random Effects for Student Behavior and School Atmosphere in Public Schools Null Background School Atmosphere Fixed Effects Coef f SE Coef f SE Coef f SE Intercept 3.4011 0.0053 3.4467 0.0091 3.0556 0.0126 Gender -0.0131 0.0086 -0.0210 0.0081 Total Experience 0.0019 0.0004 0.0010 0.0003 Indian 0.0204 0.0378 0.0153 0.0358 Asian -0.0130 0.0289 -0.0425 0.0274 Black 0.1224 0.0153 0.0801 0.0145 Hispanic 0.0733 0.0171 0.0275 0.0162 Minority Enrollment -0.0033 0.0002 -0.0011 0.0002 Secondary -0.0730 0.0121 0.0228 0.0115 Combined -0.0597 0.0263 -0.0163 0.0247 Urban -0.0310 0.0139 0.0046 0.0129 Rural -0.0341 0.0130 -0.0111 0.0121 School Size -0.0040 0.0027 0.0210 0.0025 Tardy 0.0285 0.0019 Aggression 0.0741 0.0018 Satis Class Size 0.1099 0.0033 Random Effects *Vcomp SE *Vcomp SE *Vcomp SE Intercept 0.0973 0.0029 0.0884 0.0027 0.0719 0.0023 Residual 0.4482 0.0033 0.4466 0.0033 0.4034 0.0030 Intercept Residual Intercept Residual R 2 Compared to Null Model 0.09 0.00 0.26 0.10 R 2 Compared to Background Model 0.19 0.10 *Vcomp is used to abbreviate Variance Component 122

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Table 54 Fixed and Random Effects for Student Behavior and School Atmosphere in Private Schools Null Background School Atmosphere Fixed Effects Coef f SE Coef f SE Coef f SE Intercept 3.5660 0.0100 3.5479 0.0188 2.9666 0.0348 Gender 0.0142 0.0191 0.0152 0.0181 Total Experience 0.0069 0.0008 0.0046 0.0984-0.14770.03890.0004-0.01340.04500.0052 0.0008 Indian 0.1837 0.1042 0.1947 Asian 0.0629 -0.0603 0.0594 Black 0.0479 0.0228 0.0451 Hispanic 0.0627 0.0387 0.0566 0.0365 Minority Enrollment -0.0018 0.0004 -0.0003 Secondary 0.0289 0.0533 0.0274 Combined 0.0233 0.0367 0.0222 Urban -0.0074 0.0215 -0.0008 0.0201 Rural 0.0160 0.0335 -0.0041 0.0313 School Size 0.0009 0.0050 0.0095 0.0048 Tardy 0.0312 0.0044 Aggression 0.0767 Satis Class Size 0.1685 0.0092 Random Effects *Vcomp SE *Vcomp0.3200 Intercept0.02Rpared to Background Model SE *Vcomp SE Intercept 0.0854 0.0063 0.0840 0.0062 0.0712 0.0055 Residual 0.3630 0.0071 0.3577 0.0070 0.0063 Residual Intercept Residual R 2 Compared to Null Model 0.01 0.17 0.12 2 Com 0.15 0.11 *Vcomp is used to abbreviate Variance Component 123

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Table 55 Fixed and Random Effects for Student Behavior and School Atmosphere in Charter Schools Null Background School Atmosphere Fixed Effects Coef f SE Coef f SE Coef f SE Intercept 3.3329 0.0188 3.2786 0.0359 2.8093 0.0565 Gender -0.0018 0.0349 -0.0073 0.03260.00190.00180.14200.09710.04870.05360.00060.00050.04910.04370.04380.04440.06740.00870.0912 0.1385 Total Experience 0.0076 0.0057 Indian 0.1349 0.0442 0.1320 Asian -0.0594 -0.0551 0.0906 Black 0.1695 0.0529 0.1057 Hispanic 0.0911 0.0579 0.0462 Minority Enrollment -0.0022 0.0001 Secondary 0.0463 0.1366 Combined 0.0095 0.0504 0.0152 Urban -0.0051 0.0138 0.0386 Rural 0.1918 0.1845 0.0593 School Size 0.0200 0.0214 0.0075 Tardy 0.0280 0.0068 Aggression 0.0071 Satis Class Size 0.0146 Random Effects *Vcomp SE *Vcomp SE0.0130Residual InterceptR2 Compared to Null Model 0.020.020.47 *Vcomp SE Intercept 0.0872 0.0133 0.0859 0.0465 0.0094 0.5588 0.0168 0.5503 0.0165 0.4950 0.0147 Residual Intercept Residual 0.11 R 2 Compared to Background Model 0.46 0.10 *Vcomp is used to abbreviate Variance Component Three types of student behavior and school atmosphere were entered in the model: tardiness, aggression, and class size. A separate fixed coefficient is estimated for each of these variables. The estimated coefficients were .03/.03/.03 for tardiness and .07/.08/.09 for aggression, and .11/.17/.14 for satisfaction with class size (see Tables 53-55). The scales on the tardiness and aggression variables were set so that higher levels of tardiness and aggression take on smaller values. For example, one item from the tardiness subscale was the amount of student tardiness and class cutting in this school interferes with my 124

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teaching. Answer choices ranged from 1=Strongly Agree to 4=Strongly Disagree. As a result the variables are more correctly described as non-tardiness and non-aggression. This suggests that increased levels of non-tardiness, non-aggression, and satisfaction with class size are related to greater levels of teacher satisfaction when holding other variables constant. Small standard errors led to p-values of <.0001 in each case. The null hypothesis that there is no relationship between student behavior and school atmosphere represented by any of the three variables and teacher satisfaction is rejected. The random effects for student behavior and school atmosphere along with the R 2 values for both between and within schools are also presented in Tables 53-55 by sector. The R 2 values for between schools in the three sectors are .19/.15/.46 when compared to the background model. This means that depending on the sector, the amount of explained variation among schools ranges from 15-46 percent. Within schools, the R2 values for public, private, and charter schools respectively are .10/.11/.10 when compared to the background models. Depending on the sector, student behavior and school atmosphere accounts for 10-11 percent of the variability in teacher satisfaction that is within schools after controlling for background characteristics. Research Question 6 Credentialing Requirements After controlling for teacher background and school characteristics, to what degree can credentialing requirements predict teacher satisfaction in public, private, and charter schools? The fixed effect coefficients for credentialing requirements for public, private, and charter schools are presented in Tables 56-58. 125

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Table 56 Fixed and Random Effects for Credentialing Requirements in Public Schools Null Background Credentials Fixed Effects Coef f SE Coef f SE Coef f SE Intercept 3.4011 0.0053 3.4467 0.00910.00910.00860.00040.03780.02890.01530.01710.00020.00020.01210.01210.02630.02630.0139Rural 0.0130 3.4466 Gender -0.0131 0.0086 -0.0151 Total Experience 0.0019 0.0026 0.0004 Indian 0.0204 0.0233 0.0378 Asian -0.0130 0.0289 -0.0147 Black 0.1224 0.0153 0.1185 Hispanic 0.0733 0.0171 0.0693 Minority Enrollment -0.0033 -0.0033 Secondary -0.0730 -0.0700 Combined -0.0597 -0.0547 Urban -0.0310 -0.0298 0.0139 -0.0341 -0.0369 0.0130 School Size -0.0040 0.0027 -0.0036 0.0027 Credentials -0.0331 0.0046 Random Effects *Vcomp SE *Vcomp SE *Vcomp SE Intercept 0.0973 0.0029 0.0884 0.0027InterceptRmpared to Null Model 0.0884 0.0027 Residual 0.4482 0.0033 0.4466 0.0033 0.4460 0.0033 Residual Intercept Residual 2 Co 0.09 0.00 0.09 0.00 R 2 Compared to Background Model 0.00 0.00 *Vcomp is used to abbreviate Variance Component 126

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Table 57 Fixed and Random Effects for Credentialing Requirements in Private Schools Null Background Credentials Fixed Effects Coef f SE Coef f SE Coef f SE Intercept 3.5660 0.0100 3.5479 0.01880.01910.00080.00080.10420.10400.04790.03870.00040.0289Credentials 3.5486 0.0187 Gender 0.0142 0.0191 0.0123 Total Experience 0.0069 0.0081 Indian 0.1837 0.1763 Asian -0.1477 0.0629 -0.1564 0.0628 Black 0.0389 0.0180 0.0480 Hispanic 0.0627 0.0514 0.0387 Minority Enrollment -0.0018 -0.0019 0.0004 Secondary -0.0134 -0.0055 0.0289 Combined 0.0450 0.0233 0.0404 0.0233 Urban -0.0074 0.0215 -0.0046 0.0214 Rural 0.0160 0.0335 0.0107 0.0334 School Size 0.0009 0.0050 0.0035 0.0050 -0.0464 0.0085 Random Effects *Vcomp SE *Vcomp SE *Vcomp SE Intercept 0.0854 0.0063 0.0840 0.0062 0.0836 0.0062 Residual 0.3630 0.0071 0.3577 0.0070 0.3563 0.0070 Intercept Residual Intercept Residual R 2 Compared to Null Model 0.02 0.01 0.02 0.02 R 2 Compared to Background Model 0.00 0.00 *Vcomp is used to abbreviate Variance Component 127

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Table 58 Fixed and Random Effects for Credentialing Requirements in Charter Schools Null Background Credentials Coef f SE Coef f SE Coef f SE Intercept 3.3329 0.0188 3.2786 0.0359 3.2788 -0.0019 Secondary 0.0360 Gender -0.0018 0.0349 0.0349 Total Experience 0.0076 0.0019 0.0076 0.0020 Indian 0.1349 0.1420 0.1345 0.1421 Asian -0.0594 0.0971 -0.0594 0.0972 Black 0.1695 0.0529 0.1690 0.0534 Hispanic 0.0579 0.0907 0.0581 Minority Enrollment -0.0022 0.0006 -0.0022 0.0006 0.0463 0.0491 0.0464 0.0491 Combined 0.0095 0.0504 0.0093 0.0504 Urban -0.0051 0.0444 -0.0052 0.0445 Rural 0.1918 0.0674 0.1917 0.0675 School Size 0.0200 0.0087 0.0201 0.0087 Credentials -0.0014 0.0172 Random Effects *Vcomp SE *Vcomp SE *Vcomp SE Intercept 0.0872 0.0133 0.0859 0.0130 0.0859 0.0130 Residual 0.5588 0.0168 0.5503 0.0165 0.5504 0.0165 Intercept Residual Intercept Residual R 2 Compared to Null Model 0.02 0.02 0.02 0.02 R 2 Compared to Background Model 0.00 0.00 Fixed Effects 0.0911 *Vcomp is used to abbreviate Variance Component The coefficients for credentialing requirements are -.03/-.05/-.00 for public, private, and charter schools respectively (see Tables 56-58). The standard errors are small enough that p-values are all less than .0001 except for charter schools where p=.9343. The null hypothesis that there is no relationship between credentialing requirements and teacher satisfaction is rejected in public and private schools but not in charter schools; however, it is unexpected that the relationship is negative rather than positive. In public and private schools as credentialing requirements increase, job satisfactions tends to decrease holding other variables constant. 128

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The random effects for credentialing requirements along with the R 2 values for both between and within schools are also presented in Tables 56-58 by sector. The R 2 values for between schools in the three sectors are all .00 when compared to the background model. This means that none of the variation among schools is explained by credentialing requirements. Within schools, the R 2 values for public, private, and charter schools respectively are all .00 when compared to the background models. Credentialing requirements does not account for any of the variability in teacher satisfaction that is within schools after controlling for background characteristics. Research Question 7 Professional Development After controlling for teacher background and school characteristics, to what degree can professional development opportunities predict teacher satisfaction in public, private, and charter schools? The fixed effect coefficients for professional development for public, private, and charter schools are presented in Tables 59-61. 129

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Table 59 Fixed and Random Effects for Professional Development in Public Schools Null Background Prof. Dev. Fixed Effects Coef f SE Coef f SE Coef f SE Intercept 3.4011 0.0053 3.4467 0.0091 3.4240 0.0091 Gender -0.0131 0.0086 0.0093 0.0085 Total Experience 0.0019 0.0004 0.0016 0.0004 Indian 0.0204 0.0378 0.0151 0.0374 Asian -0.0130 0.0289 -0.0309 0.0286 Black 0.1224 0.0153 0.0863 0.0152 Hispanic 0.0733 0.0171 0.0626 0.0169 Minority Enrollment -0.0033 0.0002 -0.0034 0.0002 Secondary -0.0730 0.0121 -0.0432 0.0120 Combined -0.0597 0.0263 -0.0392 0.0260 Urban -0.0310 0.0139 -0.0377 0.0138 Rural -0.0341 0.0130 -0.0287 0.0129 School Size -0.0040 0.0027 -0.0034 0.0027 Prof. Dev. 0.0088 0.0003 Random Effects *Vcomp SE *Vcomp SE *Vcomp SE Intercept 0.0973 0.0029 0.0884 0.0027 0.0870 0.0027 Residual 0.4482 0.0033 0.4466 0.0033 0.4367 0.0032 Intercept Residual Intercept Residual R 2 Compared to Null Model 0.09 0.00 0.11 0.03 R 2 Compared to Background Model 0.02 0.02 *Vcomp is used to abbreviate Variance Component 130

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Table 60 Fixed and Random Effects for Professional Development in Private Schools Null Background Prof. Dev. Fixed Effects Coef f SE Coef f SE Coef f SE Intercept 3.5660 0.0100 3.5479 0.0188 3.5443 0.0186 Gender 0.0142 0.0191 0.0252 0.0191 Total Experience 0.0069 0.0008 0.0062 0.0008 Indian 0.1837 0.1042 0.1525 0.1038 Asian -0.1477 0.0629 -0.1563 0.0626 Black 0.0389 0.0479 0.0146 0.0478 Hispanic 0.0627 0.0387 0.0536 0.0385 Minority Enrollment -0.0018 0.0004 -0.0020 0.0004 Secondary -0.0134 0.0289 -0.0068 0.0288 Combined 0.0450 0.0233 0.0515 0.0232 Urban -0.0074 0.0215 -0.0112 0.0213 Rural 0.0160 0.0335 0.0214 0.0333 School Size 0.0009 0.0050 0.0000 0.0050 Prof. Dev. 0.0053 0.0006 Random Effects *Vcomp SE *Vcomp SE0.01 *Vcomp SE Intercept 0.0854 0.0063 0.0840 0.0062 0.0825 0.0062 Residual 0.3630 0.0071 0.3577 0.0070 0.3544 0.0069 Intercept Residual Intercept Residual R 2 Compared to Null Model 0.02 0.03 0.02 R 2 Compared to Background Model 0.02 0.01 *Vcomp is used to abbreviate Variance Component 131

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Table 61 Fixed and Random Effects for Professional Development in Charter Schools Null Background Prof. Dev. Fixed Effects Coef f SE Coef f SE Coef f SE Intercept 3.3329 0.0188 3.2786 0.0359 3.2684 0.0357 Gender -0.0018 0.0349 0.0102 0.0344 Total Experience 0.0076 0.0019 0.0058 0.0019 Indian 0.1349 0.1420 0.1137 0.1399 Asian -0.0594 0.0971 -0.0352 0.0957 Black 0.1695 0.0529 0.1339 0.0524 Hispanic 0.0911 0.0579 0.0751 0.0572 Minority Enrollment -0.0022 0.0006 -0.0024 0.0006 Secondary 0.0463 0.0491 0.0920 0.0489 Combined 0.0095 0.0504 0.0260 0.0500 Urban -0.0051 0.0444 -0.0140 0.0441 Rural 0.1918 0.0674 0.1848 0.0669 School Size 0.0200 0.0087 0.0148 0.0086 Prof. Dev. 0.0101 0.0011 Random Effects *Vcomp SE *Vcomp SE *Vcomp SE Intercept 0.0872 0.0133 0.0859 0.0130 0.0872 0.0128 Residual 0.5588 0.0168 0.5503 0.0165 0.5320 0.0160 Intercept Residual Intercept Residual R 2 Compared to Null Model 0.02 0.02 0.00 0.05 R 2 Compared to Background Model -0.02 0.03 *Vcomp is used to abbreviate Variance Component The coefficients for professional development are .01/.01/.01 for public, private, and charter schools respectively. This suggests that as professional development activities and opportunities increase, the level of teacher satisfaction also tends to increase. The standard errors are small enough that p-values are all less than .0001 in each sector. The null hypothesis that there is no relationship between professional development and teacher satisfaction is rejected. The random effects for professional development along with the R 2 values for both between and within schools are also presented in Tables 59-61 by sector. The R 2 132

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values for between schools in the three sectors are .02/.02/-.02 when compared to the background model. Although sampling error can lead to small negative estimates of R 2 values like this one for charter schools, negative values are not conceptually plausible and are interpreted like R 2 estimates of zero. Within schools, the R 2 values for public, private, and charter schools respectively are .02/.01/.03 when compared to the background models. Depending on the sector, professional development accounts for 1-3 percent of the variability in teacher satisfaction that is within schools after controlling for background characteristics. Research Question 8Autonomy in the Classroom After controlling for teacher background and school characteristics, to what degree can the level of autonomy in the classroom predict teacher satisfaction in public, private, and charter schools? The fixed effect coefficients for autonomy in the classroom for public, private, and charter schools are presented in Tables 62-64. 133

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Table 62 Fixed and Random Effects for Autonomy in the Classroom in Public Schools Null Background Classroom Autonomy Fixed Effects Coef f SE Coef f SE Coef f SE Intercept 3.4011 0.0053 3.4467 0.0091 3.4992 0.0088 Gender -0.0131 0.0086 -0.0095 0.0083 Total Experience 0.0019 0.0004 0.0022 0.0003 Indian 0.0204 0.0378 0.0202 0.0365 Asian -0.0130 0.0289 -0.0383 0.0279 Black 0.1224 0.0153 0.1209 0.0148 Hispanic 0.0733 0.0171 0.0608 0.0165 Minority Enrollment -0.0033 0.0002 -0.0024 0.0002 Secondary -0.0730 0.0121 -0.1421 0.0117 Combined -0.0597 0.0263 -0.1004 0.0254 Urban -0.0310 0.0139 -0.0252 0.0134 Rural -0.0341 0.0130 -0.0583 0.0125 School Size -0.0040 0.0027 0.0009 0.0026 Classroom Autonomy 0.0485 0.0009 Random Effects *Vcomp SE *Vcomp SE *Vcomp SE Intercept 0.0973 0.0029 0.0884 0.0027 0.0810 0.0025 Residual 0.4482 0.0033 0.4466 0.0033 0.4176 0.0031 Intercept Residual Intercept Residual R 2 Compared to Null Model 0.09 0.00 0.17 0.07 R 2 Compared to Background Model 0.08 0.06 *Vcomp is used to abbreviate Variance Component 134

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Table 63 Fixed and Random Effects for Autonomy in the Classroom in Private Schools Null Background Classroom Autonomy Fixed Effects Coef f SE Coef f SE Coef f SE Intercept 3.5660 0.0100 3.5479 0.0188 3.5628 0.0183 Gender 0.0142 0.0191 0.0087 0.0187 Total Experience 0.0069 0.0008 0.0055 0.0008 Indian 0.1837 0.1042 0.1812 0.1017 Asian -0.1477 0.0629 -0.1454 0.0614 Black 0.0389 0.0479 0.0565 0.0468 Hispanic 0.0627 0.0387 0.0573 0.0378 Minority Enrollment -0.0018 0.0004 -0.0017 0.0004 Secondary -0.0134 0.0289 -0.0569 0.0283 Combined 0.0450 0.0233 0.0421 0.0228 Urban -0.0074 0.0215 -0.0049 0.0209 Rural 0.0160 0.0335 0.0177 0.0327 School Size 0.0009 0.0050 0.0013 0.0049 Classroom Autonomy 0.0409 0.0022 Random Effects *Vcomp SE *Vcomp SE *Vcomp SE Intercept 0.0854 0.0063 0.0840 0.0062 0.0801 0.0059 Residual 0.3630 0.0071 0.3577 0.0070 0.3405 0.0066 Intercept Residual Intercept Residual R 2 Compared to Null Model 0.02 0.01 0.06 0.06 R 2 Compared to Background Model 0.05 0.05 *Vcomp is used to abbreviate Variance Component 135

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Table 64 Fixed and Random Effects for Autonomy in the Classroom in Charter Schools Null Background Classroom Autonomy Fixed Effects Coef f SE Coef f SE Coef f SE Intercept 3.3329 0.0188 3.2786 0.0359 3.2914 0.0345 Gender -0.0018 0.0349 -0.0142 0.0333 Total Experience 0.0076 0.0019 0.0064 0.0018 Indian 0.1349 0.1420 0.1240 0.1355 Asian -0.0594 0.0971 -0.0477 0.0927 Black 0.1695-0.0045 0.0529 0.1945 0.0506 Hispanic 0.0911 0.0579 0.0930 0.0553 Minority Enrollment -0.0022 0.0006 -0.0019 0.0006 Secondary 0.0463 0.0491 0.0473 Combined 0.0095 0.0504 -0.0291 0.0485 Urban -0.0051 0.0444 0.0165 0.0428 Rural 0.1918 0.0674 0.1988 0.0648 School Size 0.0200 0.0087 0.0360 0.0084 Classroom Autonomy 0.0580 0.0035 Random Effects *Vcomp SE *Vcomp SE *Vcomp SE Intercept 0.0872 0.0133 0.0859 0.0130 0.0818 0.0123 Residual 0.5588 0.0168 0.5503 0.0165 0.4989 0.0150 Intercept Residual Intercept Residual R 2 Compared to Null Model 0.02 0.02 0.06 0.11 R 2 Compared to Background Model 0.05 0.09 *Vcomp is used to abbreviate Variance Component The coefficients for autonomy in the classroom are .05/.04/.06 for public, private, and charter schools respectively (see Tables 62-64). This suggests that greater levels of autonomy in the classroom are related to greater levels of teacher satisfaction, holding other variables constant. The standard errors are small enough that p-values are all less than .0001 in each sector. The null hypothesis that there is no relationship between autonomy in the classroom and teacher satisfaction is rejected. The random effects for autonomy in the classroom along with the R 2 values for both between and within schools are also presented in Tables 62-64 by sector. The R 2 values for between schools in the three sectors are .08/.05/.05 when compared to the 136

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background model. This means that depending on the sector, the amount of explained variation among schools ranges from 5-8 percent. Within schools, the R 2 values for public, private, and charter schools respectively are .06/.05/.09 when compared to the background models. Depending on the sector, autonomy in the classroom accounts for 5-9 percent of the variability in teacher satisfaction that is within schools after controlling for background characteristics. Research Question 9 Autonomy in the School After controlling for teacher background and school characteristics, to what degree can the level of autonomy in the school predict teacher satisfaction in public, private, and charter schools? The fixed effect coefficients for autonomy in the school for public, private, and charter schools are presented in Tables 65-67. 137

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Table 6 5 Fixed and Random Effects for Autonomy in the School in Public Schools Null Background School Autonomy Fixed Effects Coef f SE Coef f SE Coef f SE Intercept 3.4011 0.0053 3.4467 0.0091 3.4380 0.0086 Gender -0.0131 0.0086 -0.0159 0.0082 Total Experience 0.0019 0.0004 0.0025 0.0003 Indian 0.0204 0.0378 0.0005 0.0361 Asian -0.0130 0.0289 -0.0623 0.0276 Black 0.1224 0.0153 0.0794 0.0146 Hispanic 0.0733 0.0171 0.0430 0.0163 Minority Enrollment -0.0033 0.0002 -0.0024 0.0002 Secondary -0.0730 0.0121 -0.0490 0.0114 Combined -0.0597 0.0263 -0.0362 0.0249 Urban -0.0310 0.0139 -0.0366 0.0131 Rural -0.0341 0.0130 -0.0256 0.0122 School Size -0.0040 0.0027 0.0020 0.0025 School Autonomy 0.0464 0.0007 Random Effects *Vcomp SE *Vcomp SE *Vcomp SE Intercept 0.0973 0.0029 0.0884 0.0027 0.0759 0.0024 Residual 0.4482 0.0033 0.4466 0.0033 0.4092 0.0030 Intercept Residual Intercept Residual R 2 Compared to Null Model 0.09 0.00 0.22 0.09 R 2 Compared to Background Model 0.14 0.08 *Vcomp is used to abbreviate Variance Component 138

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Table 66 Fixed and Random Effects for Autonomy in the School in Private Schools Null Background School Autonomy Fixed Effects Coef f SE Coef f SE Coef f SE Intercept 3.5660 0.0100 3.5479 0.0188 3.5809 0.0179 Gender 0.0142 0.0191 -0.0083 0.0184 Total Experience 0.0069 0.0008 0.0064 0.0008 Indian 0.1837 0.1042 0.1020 0.1000 Asian -0.1477 0.0629 -0.1731 0.0603 Black 0.0389 0.0479 0.0557 0.0459 Hispanic 0.0627 0.0387 0.0424 0.0335 0.0371 Minority Enrollment -0.0018 0.0004 -0.0015 0.0004 Secondary -0.0134 0.0289 -0.0224 0.0276 Combined 0.0450 0.0233 0.0298 0.0223 Urban -0.0074 0.0215 -0.0228 0.0205 Rural 0.0160 -0.0086 0.0319 School Size 0.0009 0.0050 0.0083 0.0048 School Autonomy 0.0379 0.0015 Random Effects *Vcomp SE *Vcomp SE *Vcomp SE Intercept 0.0854 0.0063 0.0840 0.0062 0.0752 0.0056 Residual 0.3630 0.0071 0.3577 0.0070 0.3296 0.0064 Intercept Residual Intercept Residual R 2 Compared to Null Model 0.02 0.01 0.12 0.09 R 2 Compared to Background Model 0.11 0.08 *Vcomp is used to abbreviate Variance Component 139

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Table 67 Fixed and Random Effects for Autonomy in the School in Charter Schools Null Background School Autonomy Fixed Effects Coef f SE Coef f SE Coef f SE Intercept 3.3329 0.0188 3.2786 0.0359 3.2872 0.0317 Gender -0.0018 0.0349 -0.0102 0.0323 Total Experience 0.0076 0.0019 0.0064 0.0018 Indian 0.1349 0.1420 0.1946 0.1312 Asian -0.0594 0.0971 -0.0827 0.0900 Black 0.1695 0.0529 0.1727 0.0484 Hispanic 0.0911 0.0579 0.0985 0.0533 Minority Enrollment -0.0013 0.0549 Rural 0.1452 0.0208 -0.0022 0.0006 0.0005 Secondary 0.0463 0.0491 0.0433 Combined 0.0095 0.0504 0.0392 0.0442 Urban -0.0051 0.0444 -0.0188 0.0391 0.1918 0.0674 0.0599 School Size 0.0200 0.0087 0.0075 School Autonomy 0.0529 0.0024 Random Effects *Vcomp SE *Vcomp SE *Vcomp SE Intercept 0.0872 0.0133 0.0859 0.0130 0.0527 0.0101 Residual 0.5588 0.0168 0.5503 0.0165 0.4837 0.0145 Intercept Residual Intercept Residual Rmpared to Null Model 2 Co 0.02 0.02 0.40 0.13 R 2 Compared to Background Model 0.39 0.12 *Vcomp is used to abbreviate Variance Component The coefficients for autonomy in the school are .05/.04/.05 for public, private, and charter schools respectively (see Tables 65-67). This suggests that greater levels of autonomy in the school are related to greater levels of teacher satisfaction, holding other variables constant. The standard errors are small enough that p-values are all less than .0001 in each sector. The null hypothesis that there is no relationship between autonomy in the school and teacher satisfaction is rejected. The random effects for autonomy in the school along with the R 2 values for both between and within schools are also presented in Tables 65-67 by sector. The R 2 values for between schools in the three sectors are .14/.11/.39 when compared to the 140

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background model. This means that depending on the sector, the amount of explained variation among schools ranges from 11-39 percent. Within schools, the R 2 values for public, private, and charter schools respectively are .08/.08/.12 when compared to the background models. Depending on the sector, autonomy in the school accounts for 8-12 percent of the variability in teacher satisfaction that is within schools after controlling for background characteristics. Research Question 10 Compensation After controlling for teacher background and school characteristics, to what degree can a teachers satisfaction with salary predict teacher satisfaction in public, private, and charter schools? The fixed effect coefficients for satisfaction with salary for public, private, and charter schools are presented in Tables 68-70. 141

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Table 68 Fixed and Random Effects for Compensation in Public Schools Null Background Satis. Salary Fixed Effects Coef f SE Coef f SE Coef f SE Intercept 3.40110.0090Total Experience 0.01500.0852 0.0168-0.0033-0.0030 -0.0730-0.05970.0258-0.0178 0.0136-0.0129 0.0127-0.0040-0.0026 0.00260.1362 0.0053 3.4467 0.0091 3.4332 Gender -0.0131 0.0086 -0.0081 0.0084 0.0019 0.0004 0.0021 0.0003 Indian 0.0204 0.0378 0.0299 0.0372 Asian -0.0130 0.0289 -0.0072 0.0285 Black 0.1224 0.0153 0.1507 Hispanic 0.0733 0.0171 Minority Enrollment 0.0002 0.0002 Secondary 0.0121 -0.0857 0.0118 Combined 0.0263 -0.0740 Urban -0.0310 0.0139 Rural -0.0341 0.0130 School Size 0.0027 Satis. Salary 0.0037 Random Effects *Vcomp SE *Vcomp SE0.00290.00330.4330 *Vcomp SE Intercept 0.0973 0.0884 0.0027 0.0843 0.0026 Residual 0.4482 0.4466 0.0033 0.0032 Intercept Residual Intercept Residual R 2 Compared to Null Model 0.09 0.00 0.13 0.03 R 2 Compared to Background Model 0.05 0.03 *Vcomp is used to abbreviate Variance Component 142

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Table 69 Fixed and Random Effects for Compensation in Private Schools Null Background Satis. Salary Fixed Effects SE Coef f SE Intercept 3.5660 0.0100 3.5479 0.0188 3.6008 0.0177Gender -0.0323 0.01830.00690.0053 0.18370.06290.04550.03870.03690.00040.00040.02720.02330.02150.03140.00470.0075 0.0142 0.0191 Total Experience 0.0008 0.0008 Indian 0.1042 0.1462 0.0995 Asian -0.1477 -0.1586 0.0600 Black 0.0389 0.0479 0.0474 Hispanic 0.0627 0.0299 Minority Enrollment -0.0018 -0.0014 Secondary -0.0134 0.0289 -0.0554 Combined 0.0450 -0.0340 0.0221 Urban -0.0074 -0.0103 0.0201 Rural 0.0160 0.0335 0.0096 School Size 0.0009 0.0050 0.0127 Satis. Salary 0.2006 Random Effects *Vcomp SE *Vcomp*Vcomp SE0.08400.0695 0.00550.35770.3291 InterceptRmpared to Null Model SE Intercept 0.0854 0.0063 0.0062 Residual 0.3630 0.0071 0.0070 0.0064 Residual Intercept Residual 2 Co 0.02 0.01 0.19 0.09 R 2 Compared to Background Model 0.17 0.08 Coef f SECoef f *Vcomp is used to abbreviate Variance Component 143

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Table 70 Fixed and Random Effects for Compensation in Charter Schools Null Background Satis. Salary Fixed Effects Coef f SE Coef f SE Coef f SE Intercept 3.3329 0.0188 3.2786 0.0359 3.2845 0.0344 Gender -0.0018 0.0349 0.0162 0.0341-0.05940.00060.04710.05040.04810.04440.04250.0087 Total Experience 0.0076 0.0019 0.0072 0.0018 Indian 0.1349 0.1420 0.0748 0.1383 Asian 0.0971 -0.0573 0.0947 Black 0.1695 0.0529 0.1937 0.0514 Hispanic 0.0911 0.0579 0.1005 0.0564 Minority Enrollment -0.0022 -0.0025 0.0006 Secondary 0.0463 0.0491 0.0042 Combined 0.0095 -0.0096 Urban -0.0051 -0.0014 Rural 0.1918 0.0674 0.1677 0.0647 School Size 0.0200 0.0206 0.0083 Satis. Salary 0.1765 0.0142 Random Effects *Vcomp SE *Vcomp0.08590.17 0.16 SE *Vcomp SE Intercept 0.0872 0.0133 0.0130 0.0724 0.0117 Residual 0.5588 0.0168 0.5503 0.0165 0.5278 0.0158 Intercept Residual Intercept Residual R 2 Compared to Null Model 0.02 0.02 0.06 R 2 Compared to Background Model 0.04 *Vcomp is used to abbreviate Variance Component The coefficients for satisfaction with salary are .14/.20/.18 for public, private, and charter schools respectively (see Tables 68-70). This suggests that greater levels of satisfaction with salary are related to greater levels of teacher satisfaction, holding other variables constant. The standard errors are small enough that p-values are all less than .0001 in each sector. The null hypothesis that there is no relationship between satisfaction with salary and teacher satisfaction is rejected. The random effects for satisfaction with salary along with the R 2 values for both between and within schools are also presented in Tables 68-70 by sector. The R 2 values for between schools in the three sectors are .05/.17/.16 when compared to the 144

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background model. This means that depending on the sector, the amount of explained variation among schools ranges from 5-17 percent. Within schools, the R 2 values for public, private, and charter schools respectively are .03/.08/.04 when compared to the background models. Depending on the sector, satisfaction with salary accounts for 3-8 percent of the variability in teacher satisfaction that is within schools after controlling for background characteristics. Research Question 11 All Predictor Variables After controlling for teacher background and school characteristics, to what degree can factors representing opportunity and capacity (administrative support and leadership, resources, cooperative environment and collegiality, parental support, student behavior and school atmosphere, credentialing requirements, professional development opportunities, autonomy and authority in the classroom and the school, and compensation) predict teacher satisfaction in public, private, and charter schools? The fixed effect coefficients for the overall models for public, private, and charter schools are presented in Tables 71-73. 145

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Table 71 Fixed and Random Effects for Overall Model in Public Schools Null Background Overall Coef f SE Coef f SE Coef f SE 0.0053 3.4467 0.0091 3.3733 0.0074 Gender -0.0131 0.0086 -0.0244 0.0071 Total Experience 0.0019 0.0004 0.0026 0.0003 Indian 0.0204 0.0378 0.0130 Asian 0.0289 -0.0557 0.0237 Black 0.1224 0.0153 0.0080 0.0126 Hispanic 0.0733 0.0171 0.0070 0.0140 Minority Enrollment 0.0002 -0.0008 0.0002 Secondary 0.0121 0.0386 0.0099 Combined -0.0597 0.0263 0.0237 0.0212 Urban -0.0310 0.0139 -0.0031 0.0108 Rural -0.0341 0.0130 -0.0055 0.0102 School Size -0.0040 0.0027 0.0196 0.0021 Adm. Support 0.0589 0.0010 Resources 1 0.0514 Resources 2 0.0343 0.0034 Collegiality 0.0477 0.0019 Parent 0.0143 0.0014 Tardy 0.0017 Aggression 0.0250 0.0017 Class Size 0.0516 0.0030 Credentials -0.0081 0.0038 Prof. Dev. 0.0032 0.0002 School Autonomy 0.0032 0.0007 Class Autonomy 0.0172 0.0008 Satis. Salary 0.0487 0.0032 Random Effects *Vcomp SE *Vcomp SE *Vcomp SE Intercept 0.0973 0.0029 0.0884 0.0027 0.0476 0.0016 Residual 0.4482 0.0033 0.4466 0.0033 0.3050 0.0022 Intercept Residual Intercept Residual R 2 Compared to Null Model 0.09 0.00 0.51 0.32 R 2 Compared to Background Model 0.46 0.32 Fixed Effects Intercept 3.4011 0.0310 -0.0130-0.0033-0.07300.0036 0.0001 *Vcomp is used to abbreviate Variance Component 146

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Table 72 Fixed and Random Effects for Overall Model in Private Schools Null Background Overall Fixed Effects Coef f SE Coef f SE Coef f SE Intercept 3.5660 0.0100 3.5479 0.0188 3.5583 0.0145 Gender 0.0142 0.0191 0.0051 0.0155 Total Experience 0.0069 0.0008 0.0039 0.0007 Indian 0.1837 0.1042 0.1017 0.0831 Asian -0.1477 0.0629 -0.1263 0.0503 Black 0.03890.0003 -0.0074 0.0479 -0.0416 0.0377 Hispanic 0.0627 0.0387 -0.0212 0.0307 Minority Enrollment -0.0018 0.0004 0.0000 Secondary -0.0134 0.0289 0.0778 0.0227 Combined 0.0450 0.0233 0.0053 0.0181 Urban 0.0215 -0.0138 0.0161 Rural 0.0160 0.0335 -0.0074 0.0253 School Size 0.0009 0.0050 0.0197 0.0039 Admsupp 0.0534 0.0023 Resources 1 0.0903 0.0091 Resources 2 0.0420 0.0069 Collegiality 0.0493 0.0045 Parent 0.0208 0.0037 Tardy -0.0008 0.0038 Aggression 0.0136 0.0051 Class Size 0.0776 0.0080 Credentials -0.0066 0.0069 Prof. Dev. 0.0025 0.0005 School Autonomy 0.0018 0.0014 Class Autonomy 0.0115 0.0019 Satis. Salary 0.0682 0.0068 Random Effects *Vcomp SE *Vcomp SE *Vcomp SE Intercept 0.0854 0.0063 0.0840 0.0062 0.0380 0.0034 Residual 0.3630 0.0071 0.3577 0.0070 0.2345 0.0045 Intercept Residual Intercept Residual R 2 Compared to Null Model 0.02 0.01 0.56 0.35 R 2 Compared to Background Model 0.55 0.34 *Vcomp is used to abbreviate Variance Component 147

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Table 73 Fixed and Random Effects for Compensation in Charter Schools Null Background Overall Fixed Effects Coef f SE Coef f SE Coef f SE Intercept 3.3329 0.0188 3.2786 0.0359 3.2736 0.0258 Gender -0.0018 0.0349 0.0265 0.0276 Total Experience 0.0076 0.0019 0.0038 0.0015 Indian 0.1349 0.1420 0.1142 0.1105 Asian -0.0594 0.0971 -0.0942 0.0757 Black 0.1695 0.0529 0.0546 0.0412 Hispanic 0.0911 0.0579 -0.0028 0.0449 Minority Enrollment -0.0022 0.0006 -0.0002 0.0005 Secondary 0.0463 0.0491 0.0984 0.0362 Combined 0.0095 0.0504 0.0315 0.0359 Urban -0.0051 0.0444 0.0201 0.0314 Rural 0.1918 0.0674 0.1200 0.0487 School Size 0.0200 0.0087 0.0249 0.0062 Admsupp 0.0596 0.0037 Resources 1 0.0794 0.0139 Resources 2 0.0585 0.0125 Collegiality 0.0513 0.0075 Parent 0.0201 0.0052 Tardy 0.0003 0.0059 Aggression 0.0225 0.0068 Class Size 0.0500 0.0125 Credentials 0.0100 0.0134 Prof. Dev. 0.0017 0.0009 School Autonomy 0.0056 0.0025 Class Autonomy 0.0198 0.0031 Satis. Salary 0.0577 0.0117 Random Effects *Vcomp SE *Vcomp SE *Vcomp SE Intercept 0.0872 0.0133 0.0859 0.0130 0.0248 0.0062 Residual 0.5588 0.0168 0.5503 0.0165 0.3501 0.0104 Intercept Residual Intercept Residual R 2 Compared to Null Model 0.02 0.02 0.72 0.37 R 2 Compared to Background Model 0.71 0.36 *Vcomp is used to abbreviate Variance Component The coefficients for administrative support and leadership, resources, cooperative environment and collegiality, parental support, student behavior and school atmosphere, 148

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credentialing requirements, professional development opportunities, autonomy and authority in the classroom and the school, and satisfaction with salary are partial slopes that describe the relationship of each of these variables with teacher satisfaction after controlling for other variables in the model (see Tables 71-73). Further results and interpretation of these coefficients are presented in Tables 74-76. The random effects for the overall models along with the R 2 values for both between and within schools are also presented in Tables 71-73 by sector. The variance of the intercepts among schools changes from .09/.08/.09 in the background model for public, private, and charter schools to .05/.04/.03 when all variables are added to the model. The R 2 values for between schools in the three sectors are .46/.55/.71 when compared to the background model. This means that depending on the sector, the amount of explained variation among schools ranges from 46-71 percent. The p-value for the intercept variance in each sector is <.0001. The within school variance changes from .45/.36/.55 in the background models for public, private, and charter schools to .31/.23/.35 in the models that add all predictors. Within schools, the R 2 values for public, private, and charter schools respectively are .32/.34/.36 when compared to the background models. Depending on the sector, the overall models accounts for 32-36 percent of the variability in teacher satisfaction that is within schools after controlling for background characteristics. Summary of HLM Results Potential Range of Impact of Overall Model Coefficients on Satisfaction Scores Each of the predictor variables in the Overall Model for public, private, and charter schools respectively are listed in Tables 74-76. The tables are designed to display 149

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the potential impact that fixed coefficients from each sector can have on the outcome variable, the teacher satisfaction score. Important summary information can be gleaned from the tables. An attempt has been made to arrange the ten major predictor variables beginning with administrative support and leadership within each table in order of the largest range of impact of the coefficients to the smallest ranges of impact. The three components of School Atmosphere and the two components of Resources are kept together in each table. Table 74 Potential Range of Impact of Predictor Variable Coefficients from the Overall Model on Teacher Job Satisfaction Scores in Public Schools Coef f SE Min Max Range of Impact Fixed Intercept 3.3733 0.0074 Gender -0.0244 0.0071 0.00 1.00 0.00 -0.02 Total Experience 0.0026 0.0003 -13.72 47.28 -0.04 0.12 Indian 0.0130 0.0310 0.00 1.00 0.00 0.01 Asian -0.0557 0.0237 0.00 1.00 0.00 -0.06 Black 0.0080 0.0126 0.00 1.00 0.00 0.01 Hispanic 0.0070 0.0140 0.00 1.00 0.00 0.01 Percent Minority -0.0008 0.0002 -30.64 69.36 0.02 -0.05 Secondary 0.0386 0.0099 0.00 1.00 0.00 0.04 Combined 0.0237 0.0212 0.00 1.00 0.00 0.02 Urban -0.0031 0.0108 0.00 1.00 0.00 0.00 Rural -0.0055 0.0102 0.00 1.00 0.00 -0.01 School Size 0.0196 0.0021 -6.18 4.82 -0.12 0.09 Adm. Support 0.0589 0.0010 -11.90 6.10 -0.70 0.36 Collegiality 0.0477 0.0019 -5.72 3.28 -0.27 0.16 Classroom Autonomy 0.0172 0.0008 -18.70 5.30 -0.32 0.09 School Atmosphere Aggression 0.0250 0.0017 -8.12 3.88 -0.20 0.10 Class Size 0.0516 0.0030 -1.96 1.04 -0.10 0.05 Tardy 0.0001 0.0017 -4.90 4.10 0.00 0.00 Resources1 0.0514 0.0036 -2.07 0.93 -0.11 0.05 Resources2 0.0343 0.0034 -1.11 1.89 -0.04 0.06 Prof. Development 0.0032 0.0002 -26.15 36.85 -0.08 0.12 Parental Support 0.0143 0.0014 -5.20 6.80 -0.07 0.10 Satis Salary 0.0487 0.0032 -1.07 1.93 -0.05 0.09 School Autonomy 0.0032 0.0007 -8.59 15.41 -0.03 0.05 Credentials -0.0081 0.0038 -2.69 2.31 0.02 -0.02 150

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Table 75 Potential Range of Impact of Predictor Variable Coefficients on Teacher Job Satisfaction Scores in Private Schools Coef f SE Min Max Range of Impact Fixed Intercept 3.5583 0.0145 Gender 0.0051 0.0155 0.00 1.00 0.00 0.01 Total Experience 0.0039 0.0007 -11.31 54.69 -0.04 0.21 Indian 0.1017 0.0831 0.00 1.00 0.00 0.10 Asian -0.1263 0.0503 0.00 1.00 0.00 -0.130.0903-2.38-0.19 Black -0.0416 0.0377 0.00 1.00 0.00 -0.04 Hispanic -0.0212 0.0307 0.00 1.00 0.00 -0.02 Percent Minority 0.0000 0.0003 -18.81 81.19 0.00 0.00 Secondary 0.0778 0.0227 0.00 1.00 0.00 0.08 Combined 0.0053 0.0181 0.00 1.00 0.00 0.01 Urban -0.0138 0.0161 0.00 1.00 0.00 -0.01 Rural -0.0074 0.0253 0.00 1.00 0.00 -0.01 School Size 0.0197 0.0039 -3.91 7.09 -0.08 0.14 Adm. Support 0.0534 0.0023 -13.30 4.70 -0.71 0.25 Collegiality 0.0493 0.0045 -6.97 2.03 -0.34 0.10 Class Autonomy 0.0115 0.0019 -19.78 4.22 -0.23 0.05 Resources1 0.0091 -2.45 0.55 -0.22 0.05 Resources2 0.0420 0.0069 -1.70 1.30 -0.07 0.05 School Atmosphere Class Size 0.0776 0.0080 0.62 -0.18 0.05 Aggression 0.0136 0.0051 -10.42 1.58 -0.14 0.02 Tardy -0.0008 0.0038 -6.85 2.15 0.01 0.00 Parental Support 0.0208 0.0037 -8.95 3.05 0.06 Satis Salary 0.0682 0.0068 -1.24 1.76 -0.08 0.12 Prof. Development 0.0025 0.0005 -20.93 42.07 -0.05 0.11 School Autonomy 0.0018 0.0014 -10.38 13.62 -0.02 0.02 Credentials -0.0066 0.0069 -2.04 2.96 0.01 -0.02 151

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Table 76 Potential Range of Impact of Predictor Variable Coefficients on Teacher Job Satisfaction Scores in Charter Schools Coef f SE Min Max Range of Impact Fixed Intercept 3.2736 0.0258 Gender 0.0265 0.0276 0.00 1.00 0.00 0.03 Total Experience 0.0038 0.0015 -6.01 41.99 -0.02 0.16 Indian 0.1142 0.1105 0.00 1.00 0.00 0.11 Asian -0.0942 0.0757 0.00 1.00 0.00 -0.09 Black 0.0546 0.0412 0.00 1.00 0.00 0.05 Hispanic -0.0028 0.0449 0.00 1.00 0.00 0.00 Percent Minority -0.0002 0.0005 -48.33 51.67 0.01 -0.01 Secondary 0.0984 0.0362 0.00 1.00 0.00 0.10 Combined 0.00-18.92-2.232.78 0.0315 0.0359 0.00 1.00 0.00 0.03 Urban 0.0201 0.0314 0.00 1.00 0.02 Rural 0.1200 0.0487 0.00 1.00 0.00 0.12 School Size 0.0249 0.0062 -3.99 7.01 -0.10 0.17 Adm. Support 0.0596 0.0037 -12.57 5.43 -0.75 0.32 Class Autonomy 0.0198 0.0031 5.08 -0.37 0.10 Collegiality 0.0513 0.0075 -6.42 2.58 -0.33 0.13 School Atmosphere Aggression 0.0225 0.0068 -8.96 3.04 -0.20 0.07 Class Size 0.0500 0.0125 0.77 -0.11 0.04 Tardy 0.0003 0.0059 -4.98 4.02 0.00 0.00 Resources1 0.0794 0.0139 -2.04 0.96 -0.16 0.08 Resources2 0.0585 0.0125 -1.43 1.57 -0.08 0.09 Parental Support 0.0201 0.0052 -6.35 5.65 -0.13 0.11 Satis Salary 0.0577 0.0117 -1.30 1.70 -0.08 0.10 School Autonomy 0.0056 0.0025 -11.07 12.93 -0.06 0.07 Prof. Development 0.0017 0.0009 -27.00 36.00 -0.05 0.06 Credentials 0.0100 0.0134 -2.22 -0.02 0.03 The coefficients reported Tables 74-76 can be examined to help determine if the data is showing a relationship between job satisfaction and a particular variable. A confidence interval can be created around each coefficient by multiplying the SE (standard error) by 2 and then adding and subtracting the resulting value from the coefficient. If 0 is not a value in the interval of +/2 SE then this is good support for the claim that there is a relationship between the variable and teacher job satisfaction. In Table 74, the public school table, nine of the ten major predictor variables meet the 152

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criteria. Only Tardy, which is part of the School Atmosphere variable, fails to meet the criteria. In private schools, Tardy, School Autonomy, and Credentials do not meet the criteria. In charter schools Tardy, Professional Development, and Credentials do not meet the criteria. Many of the teacher background and school characteristics also do not meet the criteria. Tables 74-76 also contain information needed to summarize the potential range of impact each coefficient could have on a job satisfaction rating. As a reminder of the interpretation of the coefficients, in charter schools the estimated coefficient of .06 for administrative support and leadership indicates that while holding background variables constant, as the administrative support and leadership score increases by one point, teacher satisfaction is expected to increase by .06 points. It follows that if administrative support and leadership increases by 10 points the increase in the satisfaction score is expected to be .6 on a scale of 1 to 4. When the non-dummy variables were grand mean centered, the mean of each variable was subtracted from each teachers raw score to rescale each variable to have mean of 0. Raw scores on the administrative support and leadership variable ranged from 6 to 24. In charter schools for instance, the mean on administrative support and leaderships raw scale was 18.57, so this value was subtracted from each raw score to grand mean center the variable in charter schools. Compared to a person who scores 0 on administrative support and leadership (the new mean) in charter schools, a low person scores 12.57 points below the mean and a high person scores 5.43 points above the mean of 0.The adjusted range of scores is .57 to 5.43; this range of scores is reported in 153

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Table 76 in the columns labeled Min and Max. The coefficient of .06 as estimated for charter schools has the potential to affect a teacher satisfaction score from .75 to .32, on the four point satisfaction scale (.57 .0596 = -.75; 5.43 .0596 = .32). The equations presented in an earlier part of the paper indicate this multiplication process to determine what to add or subtract to the Fixed Intercept in predicting the outcome score for a particular teacher. This summary information indicates that seven of the ten major predictor variables: Administrative Support and Leadership, Cooperative Environment and Collegiality, Autonomy in the Classroom, Student Behavior and School Atmosphere, Resources, Parental Support, and Satisfaction with Salary are showing a relationship to teacher job satisfaction based on the created confidence intervals across all three sectors and allows for an assessment of how strong each relationship may be. Administrative Support and Leadership is the single strongest predictor variable in all three sectors. There is variability across the three sectors among the other variables concerning which ones are having the greatest potential range of impact on teacher satisfaction scores. Summary of R 2 Values in All Models The R 2 values from each of the models reported in Tables 41-73 for both between and within schools in each sector are summarized in Table 77. 154

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Table 7 7 -0.02 Summary of R 2 Values for All Models in Public, Private, and Charter Schools Intercept (Between Schools) Residual (Within Schools) Public Private Charter Public Private Charter Overall Model 0.46 0.55 0.71 0.32 0.34 0.36 Adm. Support Model 0.33 0.40 0.54 0.24 0.25 0.27 Collegiality Model 0.27 0.35 0.43 0.15 0.18 0.18 School Atmosphere Model 0.19 0.15 0.46 0.10 0.11 0.10 Resources Model 0.17 0.28 0.43 0.08 0.11 0.11 Parent Support Model 0.15 0.13 0.29 0.08 0.10 0.09 School Autonomy Model 0.14 0.11 0.39 0.08 0.08 0.12 Class Autonomy Model 0.08 0.05 0.05 0.06 0.05 0.09 Satis Salary Model 0.05 0.17 0.16 0.03 0.08 0.04 Prof. Dev. Model 0.02 0.02 0.02 0.01 0.03 Credentials Model 0.00 0.00 0.00 0.00 0.00 0.00 As expected, the Overall Model, which contained the background variables plus all the predictor variables, accounted for the most variability both between and within schools of any single model. An attempt has been made to arrange these models in order of the amount of variability each one accounts for after controlling for background variables; however, this varies somewhat from sector to sector so it is not possible to achieve an absolute ordering. The other models, which added background variables plus a single construct such as Administrative Support and Leadership, are listed after the Overall Model. Of these Administrative Support and Leadership is accounting for the most variability followed by the other variables which include Collegiality, School Atmosphere, Resources, Parental Support, School Autonomy, Classroom Autonomy, Satisfaction with Salary, Professional Development, and finally the last one, Credentialing Requirements, is not accounting for any variability at all. 155

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CHAPTER V DISCUSSION Purpose The purpose of the study is to identify through theory and literature the major workplace factors that contribute to job satisfaction among teachers and to build three separate sets of models: one set each for public schools, private schools, and charter schools. Each set of models establishes a baseline estimating the mean teacher satisfaction score across the schools used in the model and then controls for school characteristics and teacher background characteristics by including these variables in a background model. While holding the background variables constant, variables that form the major constructs that are possible to manipulate by policy decisions are then added to form additional models variables such as administrative support and leadership, resources, cooperative environment and collegiality, parental support, student behavior and school atmosphere, credentialing requirements, professional development opportunities, autonomy and authority in the classroom and the school, and compensation. Finally an overall model that includes all the variables at the same time is developed for each sector. Each model that contains one or more main variables is compared to the corresponding background model and coefficients are interpreted. 156

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Framework Rosabeth Moss Kanter, in her book Men and Women of the Corporation (1977) presents a structural theory of organizational behavior that identifies the level of opportunity and the amount of power available to the person holding the position as the most significant aspects of the position held and related workplace conditions in an organization. Power is defined as having access to resources along with the capacity to activate their use and the needed tools to efficiently get the job done. Opportunity means both access to advancement and chances to grow in competencies and skills, to contribute to the main organizational goals, and to be challenged by the work. The current study employed an adaptation of Kanters theory to provide its conceptual framework. McLaughlin and Yee (1988) provide this framework as they further developed Kanters theory specifically in the context of teaching and job satisfaction. They customized the meanings of level of opportunity and of power for working conditions in the teaching profession. Level of opportunity includes gaining competence in ones job through professional development, collegial and mentoring relationships, credentialing processes, feedback on performance, general support of efforts to try new ways of doing things, and to acquire new skills. Power is instead called capacity, which refers to a workers access to and authority to mobilize resources and to influence the goals and direction of their institution. Power and autonomy are synonyms for capacity. Models The design of the study looked first at teacher background variables and school level variables as control variables. The teacher background variables included gender, race, and number of years of teaching experience. School variables included school level 157

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(elementary, secondary, or combined), geographic location (urban, suburban, or rural), school size, and percent minority enrollment. In order to discern the relationship between teacher satisfaction and the main study variables it was necessary to hold the control variables constant. The heart of the analysis focused on the effects of teacher job satisfaction, after controlling for the above teacher-to-teacher and school-to-school differences in teacher satisfaction levels. The main study variables were structured to look at each variable in two different ways: 1) to examine how much variability could be explained by a single construct such as administrative support and leadership when it was the only variable added to a model that controlled for several teacher and school characteristics and 2) to estimate the effect of each of the ten major study variables when included together in a single model to predict teacher job satisfaction and to examine how much variability was explained in the overall model after controlling for teacher and school characteristics. Table 77 summarized information designed to examine how much variability could be explained by each of the major constructs in the study and by the overall model. The first ten models, which added background variables plus a single major construct, each accounted for some of the variability both between and within schools with the exception of Credentialing Requirements in all sectors and Professional Development between charter schools. Administrative Support and Leadership accounted for the most variability in each sector followed by the other variables which include Collegiality, School Atmosphere, Resources, Parental Support, School Autonomy, Classroom Autonomy, and Satisfaction with Salary. 158

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As expected, the overall model, which contained the background variables plus all the predictor variables, accounted for the most variability both between and within schools of any single model, 46-71 percent of the variability among schools after controlling for background characteristics and 32-36 percent of the variability in teacher satisfaction that is within schools. When all variables were present in the model, control variables plus all ten major facets of opportunity and capacity, seven in particular stood out in all three sectors for their relationship to satisfaction: perceived level of administrative support and leadership, perceived levels of cooperative environment and collegiality, perceived levels of parental support, two aspects of student behavior and school atmosphere including lower levels of student aggression and satisfaction with class size, the reported amounts of teacher classroom autonomy; reported levels of adequate resources such as textbooks, supplies, and copy machines, and freedom from paperwork that interferes with teaching, and satisfaction with salary. Teachers and schools with higher levels of each of these characteristics (lower levels of aggression) had higher levels of teacher satisfaction, after controlling for the other factors. In addition, professional development was related to job satisfaction in both public and private schools but not clearly in charter schools and school autonomy was related to job satisfaction in both public and charter schools but not clearly in private schools. Credentialing requirements did not account for any variability in any of the three sectors when it was the only variable added to a model that controlled for teacher and school characteristics, nor was its fixed coefficient statistically significant in the overall 159

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model in any sector. This study theorizes that one of the most satisfying aspects of teaching is reaching students effectively. Though there are numerous study results supporting the position that fully prepared and certified teachers are more effective than those who lack one or more of the often required elements of licensing such as content knowledge, clinical experience, and knowledge of how to teach and of how students learn (Darling-Hammond & Sclan, 1996), this is hotly debated, especially the idea of professionalizing the field by requiring specific educational experiences through a school of education versus alternative certification routes. However, either route does lead to certification and generally requires a college degree and demonstration of content knowledge through testing and/or coursework. The 1997 study of teacher commitment that used the SASS data also found that credentialing requirements were not statistically significant in predicting a teachers commitment to teaching (Ingersoll, Alsalam, Qunii, & Bobbitt, 1997). Possibly the measures used here do not effectively measure credentialing requirements, or perhaps teachers who have higher educational levels and more certifications are more critical of their situations because they have developed higher or different expectations of what job satisfaction should be or believe they should be better compensated. This finding requires more investigation. Teacher job satisfaction is only one of many important outcomes for schools and individual teachers. Factors that are related to job satisfaction are not necessarily related to other teacher and school outcomes that are just as important. Similarly, a lack of relationship between other variables that were expected to predict job satisfaction, such as credentialing requirements, does not mean that such variables are not important, or that they are inconsequential for teachers or schools. 160

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Limitations The results of this study must be interpreted with certain cautions in mind. Since this is an analysis of secondary data, research is limited to variables about which information was collected by the National Center for Education Statistics. However, studying teacher satisfaction and related constructs using such a large nationally representative sample of teachers from public, private, and charter schools creates a worthwhile opportunity in itself. Nevertheless, only a portion of the variance in average reported teacher satisfaction is accounted for by the variables examined in each model. Although it is not obvious from the literature review and theory that variables of importance are missing from the models, it is always possible that variables exist that havent been identified. If such variables do exist and are not included in the models this can lead to specification error or biased coefficients and potentially misleading statements. Measurement error can also lead to bias in predictors. Many of the main variables that are studied are constructs that cannot be measured directly. As a result, SASS uses self-report data. On a variable like job satisfaction that is internal to the respondent, self-report data are considered more reliable than third-party observations (Bacharach, Bauer, & Conley, 1986; Starnaman & Miller, 1992), and in combination, theoretically relevant indicators give a reasonably accurate measure of such a construct. Also, the dependent variable in the analysis is measured with a single item. It would be preferable to measure job satisfaction with several related items that can be tested for internal consistency reliability, but even so, overall results of the study would not be expected to change. One must keep in mind that relationships estimated between teacher satisfaction and the other 161

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variables do not imply causality, but instead indicate associations. The hierarchical linear models cannot themselves determine whether the main predictor variables are causes of greater levels of job satisfaction for teachers. Implications The variables selected for inclusion in this study were selected for a number of reasons, but among the main reasons was that each was possible to one degree or another to be manipulated by policy makers. Some of these variables such as administrative support and leadership and cooperative environment involve entirely reasonable expectations that are facilitated by excellent management strategies by school principals. Others such as class size and salary require the involvement of money and resources that are usually allocated by entities outside the schools themselves. A few variables, such as parental support, can be influenced by educators and policy makers but ultimately the control is in the hands of individuals outside the educational or political domain. The administrative leaders of a school appear to be in the strongest position to influence job satisfaction among teachers. Administrative support and leadership has the single strongest relationship to teacher job satisfaction. A principal who is providing successful support and leadership is in a position to influence the level of cooperative environment and collegiality within a school, and this is another strong predictor of teacher job satisfaction as identified in this study. Additionally, principals have a certain amount of power over the budget for resources, for determining how much autonomy teachers have in the school and in the classroom, and can influence student behavior and school atmosphere, and possibly have some influence on the level of parental support. They cannot, however, accomplish all these things without community and district 162

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support. Some aspects such as class size and teacher salary are likely out of the realm of the principals control. With this in mind, it is recommended that policy makers should be proactive in selecting, training, and equipping principals to provide the desired administrative support and leadership that has the potential to influence so many other areas related to job satisfaction. It is further recommended that policy makers should allocate the necessary resources to ensure this occurs and continue to focus on appropriate teacher salaries and classroom sizes. Though principals may be in the best position to influence teacher satisfaction, the fact remains that there is greater variability in satisfaction levels within schools than between schools. This raises the question of how it is possible for administrative support and leadership to be the single best predictor of job satisfaction both between and within schools. Perhaps administrators tend to treat teachers in their school differentially for various reasons, or the style match or mismatch between particular teachers and administrators may cause teachers to perceive the leadership in their school differently from other teachers in the same school. In addition, some researchers theorize that within each individual resides a latent satisfaction trait that determines to some large degree whether a person will be satisfied or dissatisfied, regardless of important variables in the workplace, thus creating a certain amount of inescapable within school variability. Future Research Much of the study of teacher job satisfaction has been correlational research which is an excellent method for identifying and testing relationships, but is weak in establishing causality. True experiments, on the other hand, have the potential to establish causality. With this in mind consideration of experimental research designs would be 163

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valuable. Since administrative support and leadership and cooperative environment and collegiality are the strongest indicators of teacher job satisfaction, they may be among the best variables to study. Perhaps a state or school district could begin by identifying schools that currently need improvement in these areas. Schools could then be randomly assigned to one or more treatments designed to improve the schools level of cooperative environment and collegiality, for instance. The Department of Education is currently interested in funding various types of experimental research and designs have been submitted that use random assignment in such a way that a control groups that receives no treatment at all is not necessary. Given the strong relationship between job satisfaction and administrative support and leadership, one type of future research could concentrate on reconciling these results with the research base on satisfaction in educational leadership, and the need to derive from such a research synthesis a practical list of what principals/others should do to help positively influence teacher job satisfaction. Considerably more work in studying teacher satisfaction could also be accomplished using the SASS. For instance more direct comparisons across sectors could be accomplished if the three data sets were merged and sector used as a dummy variable. The current study allows for a good look at explained variation in each of the three sectors but produces different fixed coefficients in each sector that are best not directly compared (though they could be to some extent if confidence intervals were used). Since the mean teacher satisfaction score tends to be fairly high, it would be interesting to compare the most dissatisfied teachers to more satisfied teachers looking for similarities and differences in their answers to other questions on the survey to see if 164

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any other relationships could be revealed. Perie and Baker pursued such a strategy in using previous data in their 1997 study. The SASS provides a wealth of information about new teachers that is not collected for all teachers. Studying this subset of new teachers with the additional variables could prove profitable especially since the attrition rate of new teachers tends to be high and there is interest in keeping these new teachers in the workforce if they show talent and promise as teachers. Researchers from the various states could look at how their state compares to the nation as a whole and whether the hot issues relevant to the state are meaningful to teacher satisfaction. Data for the next SASS is already being collected and processed and will be useful for such studies. For instance, class size has been a big issue in Florida in recent years as the legislature has mandated reduced class size which has created budget concerns and increased teacher and classroom shortages. This along with NCLB has led to new alternative certification avenues that make it easier for more and different types of teachers to enter the workforce without necessarily pursuing degrees in education. The number of examinees taking teacher certification exams has doubled in recent years as a result of this legislation. It would be interesting to know if Florida teachers are responding differently to some of the survey questions than are teachers from other states where class size reduction and alternative certification are not such important issues. Another possibility would be to look at Florida teachers (or some other subset of teacher) over time, comparing responses from the current survey to those from the upcoming survey. 165

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Summary Teacher job satisfaction is an important policy issue because of its relationship to perceived efficacy and classroom effectiveness. Though teaching is often characterized by isolation from other adults, it is clear from the results of this study that relationships with other people are of primary importance to their satisfaction levels. Key relationships focus on the principals of schools in terms of administrative support and leadership, other teachers and school staff in terms of cooperative environment and collegiality, parents in terms of parental support, and students in terms of respect and behavior. In addition teachers report higher levels of satisfaction when they have adequate resources such as time and materials, when they have autonomy in their own classrooms, and when they are satisfied with their class sizes and salary. All of these variables were selected for study at least partly because it is possible for them to be manipulated by policy. Principals of schools appear to be in the best position to directly influence teacher job satisfaction, but they need support from their community and school districts. 166

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REFERENCES Adams, S. J. (1963). Toward an understanding of inequity. Journal of Abnormal and Social Psychology, 67 422-426. Adams, S. J. (1965). Inequity in Social Exchange. In L. Berkowitz (ed), Experimental Social Psychology, 2 (pp. 267-299). New York: Academic Press. Bacharach, S. B., Bauer, S. C., & Conley, S. (1986). Organizational analysis of stress: The case of elementary and secondary schools., Work and Occupations (Vol. 13, pp. 7-32). Black, S. (2001). Morale Matters: When Teachers Feel Good about Their Work, Research Shows, Student Achievement Rises. American School Board Journal, 188 (1), 40-43. Blackman, J., Curry, B., Jackson, S., Lavely, C., Mann, K., & Pammer, M. (1997). Charter Schools: Issues and Challenges Tampa, FL: The Institute for At-Risk Infants, Children & Youth and Their Families, College of Education, University of South Florida. Blase, J., & Kirby, P. C. (1992). Bringing out the best in teachers Newbury Park, CA: Corwin. Bogler, R. (1999, April 19-23). Reassessing the Behavior of Principals as a Multiple-Factor in Teachers' Job Satisfaction. Paper presented at the Annual Meeting of the American Educational Research Association, Montreal, Quebec, Canada. Bruening, T. H., & Hoover, T. S. (1991). Personal life factors as related to effectiveness and satisfaction of secondary agricultural teachers. Journal of Agricultural Education, 32 (4), 37-43. 167

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Brunetti, G. J. (2001). Why Do They Teach? A Study of Job Satisfaction among Long-Term High School Teachers. Teacher Education Quarterly, 28 (3), 49-74. Cammann, C., Fichman, M., Jenkins, D., & Klesh, J. (1979). The Michigan Organizational Assessment Questionnaire. University of Michigan, Ann Arbor. Cheung, J. (1999). Theories of Motivation and Its Practical Application in Public Libraries Available: www.slis.ualberta.ca/cap99/jcheung/emotive.htm [2002, August]. Choy, S. P., Bobbitt, S. A., Henke, R. R., Medrich, E. A., Horn, L. J., & Lieberman, J. (1993). America's Teachers: Profile of a Profession Washington, D.C.: NCES, U.S. Department of Education. Chung, K. H. (1977). Motivational Theories and Practices Columbus: Grid, Inc. Cockburn, A. D. (2000). Elementary Teachers' Needs: Issues of Retention and Recruitment. Teaching and Teacher Education, 16 (2), 223-238. Collins, T., & ERIC Clearinghouse on Rural Education and Small Schools Charleston WV. (1999). Attracting and Retaining Teachers in Rural Areas. ERIC Digest West Virginia, U.S. Connolly, R. A. (2000). Why Do Good Teachers Leave the Profession? What Can Be Done To Retain Them? Momentum, 31 (3), 55-57. Cooley, E., & Yovanoff, P. (1996). Supporting Professionals-at-Risk: Evaluating Interventions to Reduce Burnout and Improve Retention of Special Educators. Exceptional Children, 62 (4), 336-355. Darling-Hammond, L. (1992). Reframing the School Reform Agenda. The School Administrator, 49 (10), 22-27. 168

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Darling-Hammond, L. (1995). Changing Conceptions of Teaching and Teacher Development. Teacher Education Quarterly, 22 (4), 9-26. Darling-Hammond, L., & Sclan, E. M. (1996). Who Teaches and Why. In J. Sikula (Ed.), Handbook of Research on Teacher Education (pp. 67-101). New York, NY: Simon Schuster Macmillan. Devaney, K., & Sykes, G. (1988). Making the Case for Professionalism. In A. Liberman (Ed.), Building a Professional Culture in Schools (pp. 3-21). New York: Teachers College Press. Eberhard, J., Reinhardt-Mondragon, P., & Stottlemyer, B. (2000). Strategies for New Teacher Retention: Creating a Climate of Authentic Professional Development for Teachers with Three or Less Years of Experience Texas, U.S.: Texas A and M Univ. Corpus Christi. South Texas Research and Development Center. Engvall, R. P. (1997). The Professionalization of Teaching Is it truly much ado about nothing? Lanham, MD: University Press of America, Inc. Fresko, B., & et al. (1997). Predicting Teacher Commitment. Teaching and Teacher Education, 13 (4), 429-438. Gonzalez, P. (1995). Strategies for Teacher Retention Alexandria, VA: National Association of State Directors of Special Education. Gruber, K. J., Wiley, S. D., Broughman, S. P., Strizik, G. A., & Burian-Fitzgerald, M. (2002). School and Staffing Survey, 1999-2000: Overview of the Data for Public, Private, Public Charter, and Bureau of Indian Affairs Elementary and Secondary Schools .: NCES; ESSI/AIR. 169

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Hackman, J. R., & Oldham, G. R. (1975). Development of the Job Diagnostic Survey. Journal of Applied Psychology, 60 159-170. Hair, J. F. J., Anderson, R. E., Tatham, R. L., & Black, W. C. (1995). Multivariate data analysis Englewood Cliffs, NJ: Prentice-Hall, Inc. Hatcher, L. (1994). SAS system for factor analysis and structural equation modeling Cary, NC: SAS Institute, Inc. Herbst, J. (1989). And Sadly Teach: Teacher Education and Professionalization in American Culture Madison, WI: The University of Wisconson Press. Hill, L. T. (1995). Helping Teachers Love Their Work. Child Care Information Exchange (104), 30. Hoover, J. H., & Aakhus, B. P. (1998, March 25-28). Staying, Leaving, and Job Satisfaction in a Rural/Remote State: A Matter of Roots. Paper presented at the American Council On Rural Special Education, Charleston, SC. House, R. J., & Wigdor, L. A. (1967). Herzberg's Dual-Factor Theory of Job Satisfaction and Motivation: A Review of the Evidence and a Criticism. Personnel Psychology, 20 (4), 369-389. Hussar, W. J. (1999). Predicting the Need for Newly Hired Teachers in the United States to 2008-9. Education Statistics Quarterly, 1 (4), 45-50. Ingersoll, R. M. (1997). Teacher Turnover, Teacher Shortages, and the Organization of Schools. A CTP Working Paper. Paper presented at the Annual Meeting of the American Sociological Association, Toronto, Ontario, Canada. Ingersoll, R. M., Alsalam, N., Quinn, P., & Bobbitt, S. (1997). Teacher Professionalization and Teacher Commitment: A Multilevel Analysis. (Statistical 170

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Analysis Report NCES 97-069). District of Columbia, U.S.: National Center for Education Statistics. Ironson, G. H., Smith, P. C., Brannick, M. T., Gibson, W. M., & Paul, K. B. (1989). Constituion of a Job in General Scale: A comparison of global, composite, and specific measures. Journal of Applied Psychology, 74 193-200. Kanter, R. M. (1983). The change masters: Innovations for productivity in the American corporation. New York: Simon and Shuster. Kanter, R. M. (1977). Men and Women of the Corporation. New York: Basic Books. Karge, B. D., & Freiberg, M. R. (1992, April 20-24). Beginning Special Education Teachers: At Risk for Attrition. Paper presented at the Annual Meeting of the American Educational Research Association, San Francisco, CA. Kiecolt, K. J., & Nathan, L. E. (1985). Secondary Analysis of Survey Data Beverly Hills, CA: Sage Publications, Inc. Kreft, I., & De Leeuw, J. (1998). Introducing Multilevel Modeling London: SAGE Publications. Krueger, P. J. (2000). Beginning Music Teachers: Will They Leave the Profession? Update: Applications of research in Music Education, 19 (1), 22-26. Lawler, E. E., & Porter, L. W. (1967).The effect of performance on job satisfaction. Industrial Relations, 7 20-28. Lee, A. M. (2002). Job Satisfaction of 6th 12th Grade Teachers in Florida's Charter Schools. University of South Florida, Tampa. 171

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Lee, V. E., Dedrick, R. F., & Smith, J. B. (1991). The effect of the social organization of schools on teachers' efficacy and satisfaction. Sociology of Education, 64 (3), 190-208. Locke, E. A. (1969). What is job satisfaction? OB and Human Performance, 4 309-336. Locke, E. A. (1975). Personnel Attitudes and Motivation. Annual Review of Psychology, 26 457-480. Locke, E. A. (1976). The Nature and Causes of Job Satisfaction. In M. D. Dunnette (Ed.), Handbook of industrial and organizational psychology (pp. 1297-1349). Chicago: Rand McNally. Lumsden, L. (1998). Teacher Morale. ERIC Digest, Number 120 Eugene, OR: Eric Clearinghouse on Educational Management. McLaughlin, M. W., & Yee, S. M.-L. (1988). School as a Place to Have a Career. In A. Liberman (Ed.), Building a Professional Culture in Schools New York, NY: Teachers College Press. NCES. (2002). Schools and Staffing Survey, 1999-2000: Overview of the Data for Public, Private, Public Charter, and Bureau of Indian Affairs Elementary and Secondary Schools Washington, D.C.: National Center for Education Statistics. Nunnally, J. (1978). Psychometric theory New York: McGraw-Hill. Pearson, L. C. (1998). The Prediction of Teacher Autonomy. Educational Research Quarterly, 22 (1), 33-46. Perie, M., & Baker, D. P. (1997). Job Satisfaction among America's Teachers: Effects of Workplace Conditions, Background Characteristics, and Teacher 172

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Compensation. Statistical Analysis Report Washington, DC: American Institutes for Research. Porter, L. W. & Lawler, E. E. (1968). Managerial Attitudes and Performance Homewood, IL: Irwin. Prelip, M. L. (2001). Job Satisfaction in Health Education and the Value of Added Credentialing. American Journal of Health Education, 32 (1), 26-30. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage Publications. Rosenholtz, S. J. (1989). Teacher's workplace. The social organization of schools. New York: Longman. Sclan, E. M. (1993). The effect of perceived workplace conditions on beginning teachers' work commitment, career choice commitment, and planned retention. Unpublished Dissertation. Singer, J. D. (1998). Using SAS PROC MIXED to Fit Multilevel Models, Hierarchical Models, and Individual Growth Models. Journal of Educational and Behavioral Statistics, 24 (4), 323-355. Smith, P. C., Kendall, L. M., & Hulin, C. L. (1969). Measurement of Satisfaction in Work and Retirement Chicago: Rand McNally. Snijders, T. A. B., & Bosker, R. J. (1999). Multilevel Analysis An introduction to basic and advanced multilevel modeling Thousand Oaks, CA: SAGE Publications Inc. Spector, P. E. (1985). Measurement of human service staff satisfaction: Development of the Job Satisfaction Survey. American Journal of Community Psychology, 13 693-713. 173

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Spector, P. E. (1997). Job Satisfaction: Application, Assessment, Causes, and Consequences Thousand Oaks, CA: SAGE Publications, Inc. Stansbury, K., & Zimmerman, J. (2000). Lifelines to the Classroom: Designing Support for Beginning Teachers. Knowledge Brief San Francisco, CA: WestEd. Starnaman, S. M., & Miller, K. I. (1992). A test of a causal model of communication and burnout in the teaching profession. Communication Education, 41 40-53. Steers, R. M., & Porter, L. W. (Eds.). (1991). Motivation and Work Behavior (Fifth ed.). New York: McGraw-Hill, Inc. Stein, B. A., & Kanter, R. M. (1980). Building the parallel organization: Creating mechanisms for permanent quality of work life. The Journal of Applied Behavioral Science, 16 (3), 371-387. Stueart, R. D., & Moran, B. B. (1993). Library and Information Center Management Englewood, CO: Libraries Unlim ited. Taylor, D. L., & Tashakkori, A. (1994). Predicting teachers' sense of efficacy and job satisfaction using school climate and participatory decision making. Paper presented at the Southwest Educational Research Association, San Antonio, TX. Thomas, K. W. (2000). Intrinsic Motivation at Work San Francisco: Berrett-Koehler Publishers, Inc. Tompkins, P. L. (1995). Burnout and Attrition Among U.S. Teachers. Unpublished Dissertation, Mississippi State. 174

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Vanourek, G., et al., & Hudson Inst. Indianapolis IN. (1997). Charter Schools As Seen by Those Who Know Them Best: Students, Teachers, and Parents. Charter Schools in Action Project, Final Report--Part I Indiana, U.S. Vroom, V. H. (1964). Work and Motivation New York: Wiley. Weiss, D. J., Dawis, R. V., England, G. W., & Lofquist, L. H. (1967). Manual for the Minnesota Satisfaction Questionnaire. Minneapolis: University of Minnesota. Wiggs, L. H. (1998). Job Satisfaction of Missouri Business Teachers. NABTE Review Spring 1998--Article 3. Business Education Forum, 52 (4), 15-20. Wright, M. D. (1991). Retaining Teachers in Technology Education: Probable Causes, Possible Solutions. Journal of Technology Education, 3 (1), 55-69. Yee, S. M.-L. (1990). Careers in the Classroom When Teaching is More Than a Job New York and London: Teachers College Press. 175

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Appendices 176

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Appendix A Listing of Variables with Related Framework, Content, Item Number or Codes, Range of Response Options and Orientation of Item Variable Conceptual Framework Teacher Job Satisfaction Definition of Job Satisfaction: how people feel about their jobs and different aspects of their jobs (Spector, 1997, p. 2); or an overall feeling about ones job or career in terms of specific facets of the job or career (Perie & Baker, 1997, p. 2). Opportunity access to advancement and chances to grow in competencies and skills, to contribute the main organizational goals, and to be challenged by ones work Capacity power or autonomy; a workers access to and authority to mobilize resources and to influence the goals and direction of their institution. Item Content Item Number or Code Response Options Orientation I sometimes feel it is a waste of time to try to do my best as a teacher 0318 1 Strongly Agree 4 Strongly Disagree Reflection not required I am generally satisfied with being a teacher at this school 0320 1 Strongly Agree 4 Strongly Disagree Reflection required If you could go back to your college days and start over again, would you become a teacher or not? 0339 1 Certainly would 5 Certainly would not Reflection required How long do you plan to remain in teaching? 0340 1 As long as I am able 2 Until retirement 3 Probably continue unless something better comes along 4 Definitely plan to leave 5 Undecided Reflection Required Undecided is out of order. Recode as 3. 177

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Variable Conceptual Framework School Characteristics School Characteristics are added to the study as control variables. Sub variable Item Content Item Code Response Options Orientation School level SCHLEVEL 1 Elementary 2 Secondary 3 Combined Community type URBANIC 1 large or mi d-size central city 2 urban fringe of large or mid-size city 3 small town/rural School size SCHSIZE Number of students in the school (categorical) 1 1-49 2 50-99 3 100-149 4 150 199 5 200-349 6 350-499 7 500-749 8 750-999 9 1,000 1,199 10 1, 200 1,499 11 1,500 1,999 12 2,000 or more Percent minority MINENR Created Variable percent miniority students at this school. MINENR = round (((NMINST_C/ENRK 12UG)*100), .01) 178

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Variable Conceptual Framework Teacher Background Characteristics Teacher background characteristics are added to the models as control variables. Sub variable Item Content Item Number or Code Response Options Orientation Gender Are you male or female? 0356 1 -Male 2 -Female 1 American Indian or Alaska Native, non-Hispanic 2 Asian or Pacific Islander, non-Hispanic 3 Black non-Hispanic 4 White non-Hispanic 5 Hispanic, regardless of race Years of teaching experience TOTEXPER Teachers total number of years teaching full and part-time and in private and public schools TOTEXPER = T0065 + T0066 + T0068 + T0069 Race/ethnicity RACETH_ 179

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Variable Conceptual Framework Administrative support and leadership Opportunity increases when principals provide frequent feedback, convey high expectations, and ensure opportunities for teach learning Capacity teachers are empowered when principals involve teachers in decision-making and provide necessary support and materials, helping ensure the conditions that allow them to be effective Item Content Item Number Response Options Orientation The principal lets staff members know what is expected of them. 0299 1 Strongly Agree 4 Strongly Disagree Reflection required The school administrators behavior toward the staff is supportive and encouraging. 0300 1 Strongly Agree Reflection required 4 Strongly Disagree My principal enforces school rules and backs me up when I need it. 0306 1 Strongly Agree 4 Strongly Disagree Reflection required The principal talk s with me frequently about my instructional practices. 0307 1 Strongly Agree 4 Strongly Disagree Reflection required The principal knows what kind of school he/she wants and has communicated it to the staff. 0310 1 Strongly Agree 4 Strongly Disagree Reflection required In this school, staff members are recognized for a job well done. 0312 1 Strongly Agree 4 Strongly Disagree Reflection required Variable Conceptual Framework Opportunity Increased availability of resources opens new opportunities for teachers to expand their competencies and skills as they use new materials and learn from the accompanying documentation and allows teachers to be challenged in new ways as they use new resources. Capacity Lack of resources can limit capacity to teach effectively and excel in their work. Resources Item Content Item Number Response Options Orientation 0304 1 Strongly Agree 4 Strongly Disagree Reflection required Routine duties and paperwork interfere with my job of teaching 0305 1 Strongly Agree 4 Strongly Disagree Reflection not required Necessary materials such as textbooks, supplies, copy machines are available as needed by the staff 180

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Variable Conceptual Framework Cooperative environment and collegiality Opportunity teachers learn from each other and get encouragement and new ideas Capacity Teachers are empowered by access to colleagues expertise and support in solving problems; influence in school may increase as isolation decreases and school becomes less segmented Item Content Item Number Response Options Orientation Rules for student behavior are consistently enforced by teachers in this school, even for students who are not in their classes. 0308 1 Strongly Agree 4 Strongly Disagree Reflection required Most of my colleagues share my beliefs and values about what the central mission of the school should be. 0309 1 Strongly Agree 4 Strongly Disagree Reflection required There is a great deal of cooperative effort among the staff members. 0311 1 Strongly Agree 4 Strongly Disagree Reflection required I plan with the library media specialist/librarian for the integration of library media services into my teaching. 0319 1 Strongly Agree 4 Strongly Disagree Reflection required I make a conscious effort to coordinate the content of my courses with that of other teachers. 0316 1 Strongly Agree 4 Strongly Disagree Reflection required Variable Conceptual Framework Parental support Capacity parents can in fluence students to do homework, attend school, and respect teachers rules and efforts, enabling the teacher to be more respected and effective in the classroom Opportunity when parents spend time in a classroom opportunities to try new ways of doing things may increase Item Content Item Number Response Options Orientation I receive a great deal of support from parents for the work I do. 0303 1 Strongly Agree 4 Strongly Disagree Reflection required To what extent is lack of parental involvement a problem in this school? 0335 1 Serious Problem 4 Not a Problem Reflection not required 181

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Variable Conceptual Framework Student behavior and school atmosphere Opportunity Student behavior may be influenced by aspects of opportunity that allow for a high level of training and continued professional development that would equip teachers with the most effective skills in classroom management and in understanding the diverse needs of their students. Capacity Teachers are em powered when student behavior and school atmosphere allow for effective teaching and encourage attendance. Item Content Item Number Response Options Orientation The am ount of student tardiness and class cutting in this school interferes with my teaching 0317 1 Strongly Agree 4 Strongly Disagree Reflection not required 0302 1 Strongly Agree 4 Strongly Disagree Reflection not required To what extent is each of the following a problem in this school? Student tardiness 0321 1 Serious Problem 4 Not a Problem Reflection not required Student absenteeism 0322 1 Serious Problem 4 Not a Problem Reflection not required Students cutting class 0324 Reflection not required 1 Serious Problem 4 Not a Problem Physical conflicts among students 0325 1 Serious Problem 4 Not a Problem Reflection not required Robbery or theft 0326 1 Serious Problem 4 Not a Problem Reflection not required Vandalism of school property 0327 1 Serious Problem 4 Not a Problem Reflection not required Student use of alcohol 0329 1 Serious Problem 4 Not a Problem Reflection not required Student drug abuse 0330 1 Serious Problem 4 Not a Problem Reflection not required 0331 1 Serious Problem 4 Not a Problem Reflection not required Student disrespect for teachers 0332 1 Serious Problem 4 Not a Problem Reflection not required Students dropping out 0333 1 Serious Problem 4 Not a Problem Reflection not required Student apathy 0334 1 Serious Problem 4 Not a Problem Reflection not required The level of student misbehavior in this school interferes with my teaching. Student possession of weapons 182

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Students come to school unprepared to learn 0337 1 Serious Problem 4 Not a Problem Reflection not required Variable Conceptual Framework Credentials Opportunity Learn content, methods, student growth, development, diversity, classroom management, and assessment skills needed by effective teachers Capacity Expanded by increasing available internal and external resources gained during the credentialing process. Item Content Item Number Response Options Orientation Do you have a teaching certificate in this state in your MAIN teaching assignment field? 0103 1 Yes 2 No Recode to No = 0 Do you have a teaching certificate in this state in your OTHER teaching assignment field at this school 0111 1 Yes 2 No Recode to No = 0 Do you currently hold ANY ADDITIONAL regular or standard state certificate or advanced professional certificates in this state or any other state? 0113 1 Yes 2 No Recode to No = 0 Do you have a bachelors degree? 0070 1 Yes 2 No Recode to No = 0 Do you have a masters degree? 0080 1 Yes 2 No Recode to No = 0 Variable Conceptual Framework Professional Development Opportunity increased personal and professional growth, interaction with colleagues, fresh visions, new learning reward teachers by equipping them to accomplish what matters most to them success in the classroom Capacity increased internal and external resources and empowers teachers to be mo re effective Item Content Item Number Response Options Orientation Thinking about ALL the professional development you have participated in over the past 12 months, how useful was it? 0178 1 Not useful at all 5 Very useful Reflection not required 183

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Variable Conceptual Framework Autonomy in the school Opportunity increased chances to contribute to the goals of the organization Capacity increased influence to help determine the goals and directions of a teachers institution Item Content Item Number Response Options Orientation How much influence do you think teachers have over school policy? Setting performance standards 0286 1 No influence 5 Great influence Reflection not required Establishing curriculum 0287 1 No influence 5 Great influence Reflection not required Determining the content of in-service professional development 0288 1 No influence 5 Great influence Reflection not required Evaluating teachers 0289 1 No influence 5 Great influence Reflection not required 0290 1 No influence 5 Great influence Reflection not required Setting discipline policy 0291 1 No influence 5 Great influence Reflection not required Deciding how the school budget will be spent 0292 Reflection not required 1 No influence 5 Great influence Hiring new teachers 184

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Variable Conceptual Framework Authority in the classroom Opportunity increased as teachers are permitted, encouraged, and expected to try new ideas in teaching and to design appropriate methods to reach their diverse student populations Capacity teachers are empowered to act on their decisions in the classroom Item Content Item Number Response Options Orientation How much control do you think you have in your classroom over planning and teaching? Selecting textbooks and other instructional material 0293 1 No control 5 Complete control Reflection not required Selecting content, topics, and skills to be taught 0294 1 No control 5 Complete control Reflection not required Selecting teaching techniques 0295 1 No control 5 Complete control Reflection not required Evaluating and grading students 0296 1 No control 5 Complete control Reflection not required Disciplining students 0297 1 No control 5 Complete control Reflection not required Determining the amount of homework to be assigned 0298 1 No control 5 Complete control Reflection not required Variable Conceptual Framework Compensation Capacity empowers teachers to remain in teaching by providing resources needed to earn a living wage Item Content Item Number Response Options Orientation I am satisfied with my teaching salary. 0301 1 Strongly Agree 4 Strongly Disagree Reflection required 185

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Appendix B Revised Listing of Variables with Related Framework, Content, Item Number or Codes, Range of Response Options and Orientation of Item revised after Confirmatory Factor Analyses Variable Conceptual Framework Teacher Job Satisfaction Definition of Job Satisfaction: how people feel about their jobs and different aspects of their jobs (Spector, 1997, p. 2); or an overall feeling about ones job or career in terms of specific facets of the job or career (Perie & Baker, 1997, p. 2). Opportunity access to advancement and chances to grow in competencies and skills, to contribute the main organizational goals, and to be challenged by ones work Capacity power or autonomy; a workers access to and authority to mobilize resources and to influence the goals and direction of their institution. Item Number or Code Response Options Orientation I am generally satisfied with being a teacher at this school 4 Strongly Disagree Reflection required Conceptual Framework School Characteristics School Characteristics are added to the study as control variables. Sub variable Item Code Response Options Orientation School level 1 Elementary 2 Secondary 3 Combined Community type 1 large or mid-size central city 2 urban fringe of large or mid-size city 3 small town/rural Item Content 0320 1 Strongly Agree Variable Item Content SCHLEVEL URBANIC 186

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School size Number of students in the school (categorical) 1 1-49 2 50-99 3 100-149 4 150 199 5 200-349 6 350-499 8 750-999 9 1,000 1,199 10 1, 200 1,499 11 1,500 1,999 12 2,000 or more MINENR Created Variable percent miniority students at this school. MINENR = round (((NMINST_C/ENRK 12UG)*100), .01) Conceptual Framework Teacher Background Characteristics Teacher background characteristics are added to the models as control variables. Sub variable Item Number or Code Response Options Orientation Gender Are you male or female? 1 -Male 2 -Female Race/ethnicity RACETH_ 2 Asian or Pacific Islander, non-Hispanic 3 Black non-Hispanic 4 White non-Hispanic 5 Hispanic, regardless of race TOTEXPER Teachers total number of years teaching full and part-time and in private and public schools TOTEXPER = T0065 + T0066 + T0068 + T0069 SCHSIZE 7 500-749 Percent minority Variable Item Content 0356 1 American Indian or Alaska Native, non-Hispanic Years of teaching experience 187

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Variable Conceptual Framework Administrative support and leadership Opportunity increases when principals provide frequent feedback, convey high expectations, and ensure opportunities for teach learning Capacity teachers are empowered when principals involve teachers in decision-making and provide necessary support and materials, helping ensure the conditions that allow them to be effective Item Content Item Number Response Options Orientation The principal lets staff members know what is expected of them. 0299 1 Strongly Agree 4 Strongly Disagree Reflection required The school administrators behavior toward the staff is supportive and encouraging. 0300 1 Strongly Agree 4 Strongly Disagree Reflection required My principal enforces school rules and backs me up when I need it. 0306 1 Strongly Agree 4 Strongly Disagree Reflection required The principal talks with me frequently about my instructional practices. 0307 1 Strongly Agree 4 Strongly Disagree Reflection required The principal knows what kind of school he/she wants and has communicated it to the staff. 0310 1 Strongly Agree 4 Strongly Disagree Reflection required In this school, staff members are recognized for a job well done. 0312 1 Strongly Agree 4 Strongly Disagree Reflection required Variable Conceptual Framework Resources 1 Resources 2 Opportunity Increased availability of resources opens new opportunities for teachers to expand their competencies and skills as they use new materials and learn from the accompanying documentation and allows teachers to be challenged in new ways as they use new resources. Capacity Lack of resources can limit capacity to teach effectively and excel in their work. Item Content Resources 1 Item Number Response Options Orientation Necessary materials such as textbooks, supplies, copy machines are available as needed by the staff 0304 1 Strongly Agree 4 Strongly Disagree Reflection required Item Content Resources 2 Item Number Response Options Orientation Routine duties and paperwork interfere with my job of teaching 0305 1 Strongly Agree 4 Strongly Disagree Reflection not required 188

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Variable Conceptual Framework Cooperative environment and collegiality Opportunity teachers learn from each other and get encouragement and new ideas Capacity Teachers are empowered by access to colleagues expertise and support in solving problems; influence in school may increase as isolation decreases and school becomes less segmented Item Content Item Number Response Options Orientation Rules for student behavior are consistently enforced by teachers in this school, even for students who are not in their classes. 0308 1 Strongly Agree 4 Strongly Disagree Reflection required Most of my colleagues share my beliefs and values about what the central mission of the school should be. 0309 1 Strongly Agree 4 Strongly Disagree Reflection required There is a great deal of cooperative effort among the staff members. 0311 1 Strongly Agree 4 Strongly Disagree Reflection required Variable Conceptual Framework Parental support Capacity parents can influence students to do homework, attend school, and respect teachers rules and efforts, enabling the teacher to be more respected and effective in the classroom Opportunity when parents spend time in a classroom opportunities to try new ways of doing things may increase Item Content Item Number Response Options Orientation To what extent is lack of parental involvement a problem in this school? 0335 1 Serious Problem 4 Not a Problem Reflection not required To what extent is poverty a problem in this school? 0336 1 Serious Problem 4 Not a Problem Reflection not required To what extent is students coming to school unprepared to learn a problem in this school? 0337 1 Serious Problem 4 Not a Problem Reflection not required To what extent is student apathy a problem in this school? 0338 1 Serious Problem 4 Not a Problem Reflection not required 189

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Variable Conceptual Framework Student behavior and school atmosphere Tardy Aggression Class Size Opportunity Student behavior may be influenced by aspects of opportunity that allow for a high level of training and continued professional development that would equip teachers with the most effective skills in classroom management and in understanding the diverse needs of their students. Capacity Teachers are empowered when student behavior and school atmosphere allow for effective teaching and encourage attendance. Item Content Tardy Item Number Response Options Orientation The amount of student tardiness and class cutting in this school interferes with my teaching 0317 1 Strongly Agree 4 Strongly Disagree Reflection not required To what extent is each of the following a problem in this school? Student tardiness 0321 1 Serious Problem 4 Not a Problem Reflection not required Student absenteeism 0322 1 Serious Problem 4 Not a Problem Reflection not required Item Content Aggression Item Number Response Options Orientation To what extent is each of the following a problem in this school? Physical conflicts among students 0325 1 Serious Problem 4 Not a Problem Reflection not required Vandalism of school property 0327 1 Serious Problem 4 Not a Problem Reflection not required Student possession of weapons 0331 1 Serious Problem 4 Not a Problem Reflection not required Student disrespect for teachers 0332 1 Serious Problem 4 Not a Problem Reflection not required Item Content Class Size Item Number Response Options Orientation I am satisfied with my class size(s). 0315 1 Strongly Agree 4 Strongly Disagree Reflection not required 190

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Variable Conceptual Framework Credentials Opportunity Learn content, methods, student growth, development, diversity, classroom management, and assessment skills needed by effective teachers Capacity Expanded by increasing available internal and external resources gained during the credentialing process. Item Content Item Number Response Options Orientation Do you have a teaching certificate in this state in your MAIN teaching assignment field? 0103 1 Yes 2 No Recode to No = 0 Do you have a teaching certificate in this state in your OTHER teaching assignment field at this school 0111 1 Yes 2 No Recode to No = 0 Do you currently hold ANY ADDITIONAL regular or standard state certificate or advanced professional certificates in this state or any other state? 0113 1 Yes 2 No Recode to No = 0 Do you have a bachelors degree? 0070 1 Yes 2 No Recode to No = 0 Do you have a masters degree? 0080 1 Yes 2 No Recode to No = 0 Variable Conceptual Framework Professional Development Opportunity increased personal and professional growth, interaction with colleagues, fresh visions, new learning reward teachers by equipping them to accomplish what matters most to them success in the classroom Capacity increased internal and external resources and empowers teachers to be more effective Item Content Item Number Response Options Orientation In the past 12 months, have you participated in the following activities RELATED TO TEACHING? University courses taken for recertification or advanced certification in your MAIN teaching assignment field or other teaching field? 0150 1 Yes 2 No Recode to No = 0 University courses in your MAIN teaching assignment field. 0151 1 Yes 2 No Recode to No = 0 Observational visits to other schools 0152 1 Yes Recode to No = 0 191

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Item Content Item Number Response Options Orientation 2 No Individual or collaborative research on a topic of interest to you professionally 0153 1 Yes 2 No Recode to No = 0 Regularly-scheduled collaboration with other teachers on issues of instruction 0154 1 Yes 2 No Recode to No = 0 Mentoring and/or peer observation and coaching, as part of a formal arrangement that is recognized or supported by the school 0155 1 Yes 2 No Recode to No = 0 Participating in a network of teachers (e.g., one organized by an outside agency or over the Internet) 0156 1 Yes 2 No Recode to No = 0 Attending workshops, conferences or training 0157 1 Yes 2 No Recode to No = 0 Workshops, conferences or training in which you were the presenter 0158 1 Yes 2 No Recode to No = 0 In the past 12 months, have you participated in any professional development activities that focused on in-depth study of the content in your MAIN teaching assignment field? 0159 1 Yes 2 No Recode to No = 0 In the past 12 months, how many hours did you spend on the activities? 0160 1 8 hours or less 4 33 hours or more Reflection not required Overall, how useful were these activities to you? 0161 1 Not useful at all 5 Very useful Reflection not required In the past 12 months, have you participated in any professional development activities that focused on content and performance standards in your MAIN teaching assignment field? 0162 1 Yes 2 No Recode to No = 0 In the past 12 months, how many hours did you spend on the activities? 0163 1 8 hours or less 4 33 hours or more Reflection not required Overall, how useful were these activities to you? 0164 1 Not useful at all 5 Very useful Reflection not required In the past 12 months, have you participated in any professional development activities that focused on methods of teaching? 0165 1 Yes 2 No Recode to No = 0 In the past 12 months, how many hours did you spend on the activities? 0166 1 8 hours or less 4 33 hours or more Reflection not required Overall, how useful were these activities to you? 0167 1 Not useful at all Reflection not required 192

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Item Content Item Number Response Options Orientation 5 Very useful In the past 12 months, have you participated in any professional development activities that focused on methods of teaching? 0171 1 Yes 2 No Recode to No = 0 In the past 12 months, how many hours did you spend on the activities? 0172 1 8 hours or less 4 33 hours or more Reflection not required Overall, how useful were these activities to you? 0173 1 Not useful at all 5 Very useful Reflection not required In the past 12 months, have you participated in any professional development activities that focused on student discipline and management in the classroom? 0174 1 Yes 2 No Recode to No = 0 In the past 12 months, how many hours did you spend on the activities? 0175 1 8 hours or less 4 33 hours or more Reflection not required Overall, how useful were these activities to you? 0176 1 Not useful at all 5 Very useful Reflection not required Thinking about ALL the professional development you have participated in over the past 12 months, how useful was it? 0178 1 Not useful at all 5 Very useful Reflection not required 193

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Variable Conceptual Framework Authority in the school Opportunity increased chances to contribute to the goals of the organization Capacity increased influence to help determine the goals and directions of a teachers institution Item Content Item Number Response Options Orientation How much influence do you think teachers have over school policy? Setting performance st andards 0286 1 No influence 5 Great influence Reflection not required Determining the content of in-service professional development 0288 1 No influence 5 Great influence Reflection not required Evaluating teachers 0289 1 No influence 5 Great influence Reflection not required Hiring new teachers 0290 1 No influence 5 Great influence Reflection not required Setting discipline policy 0291 1 No influence 5 Great influence Reflection not required Deciding how the school budget will be spent 0292 1 No influence 5 Great influence Reflection not required 194

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Variable Conceptual Framework Authority in the classroom Opportunity increased as teachers are permitted, encouraged, and expected to try new ideas in teaching and to design appropriate methods to reach their diverse student populations Capacity teachers are empowered to act on their decisions in the classroom Item Content Item Number Response Options Orientation How much control do you think you have in your classroom over planning and teaching? Selecting textbooks and other instructional material 0293 1 No control 5 Complete control Reflection not required Selecting content, topics, and skills to be taught 0294 1 No control 5 Complete control Reflection not required Selecting teaching techniques 0295 1 No control 5 Complete control Reflection not required Evaluating and grading students 0296 1 No control 5 Complete control Reflection not required Disciplining students 0297 1 No control 5 Complete control Reflection not required Determining the amount of homework to be assigned 0298 1 No control 5 Complete control Reflection not required Variable Conceptual Framework Compensation Capacity empowers teachers to remain in teaching by providing resources needed to earn a living wage Item Content Item Number Response Options Orientation I am satisfied with my teaching salary. 0301 1 Strongly Agree 4 Strongly Disagree Reflection required 195

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ABOUT THE AUTHOR Christina Sentovich lives in Seffner, Florida with her husband and five children. She works for the Institute for Instructional Research and Practice at the University of South Florida as a psychometrician and coordinator of computer-based testing. She graduated from Abilene Christian University with a degree in Psychology and received her masters degree in Math Education from the University of South Florida. Previously she taught math in middle and high schools and at Hillsborough Community College in Tampa, and for several years she home schooled her three daughters. She became interested in measurement and research while studying for her masters degree. Receiving a fellowship from the University of South Florida for her first year of study got her started in a doctoral program and receiving a dissertation grant from the American Educational Research Association helped her complete the program. She has presented many papers at various conferences including the Florida Educational Research Association and the American Educational Research Association and has co-authored two journal articles. 196


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Teacher satisfaction in public, private, and charter schools
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2004.
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ABSTRACT: The 1999-2000 restricted-use School and Staffing Survey (SASS) dataset was used to construct hierarchical linear models to determine to what degree administrative support, resources, collegiality, parental support, school atmosphere, credentialing requirements, professional development, classroom and school autonomy, and compensation can predict teacher satisfaction in public, private, and charter schools after controlling for teacher background and school characteristics. Variables were selected in part because it is possible for them to be manipulated by policy. The study also reports on efforts to refine and validate subscales of items chosen based on theory and literature from the SASS to represent teacher satisfaction and predictors of satisfaction. SASS collected a nationally representative complex random sample of public, private, and charter schools with teachers randomly selected from schools. The conceptual framework of this study identifies level of opportunity and amount of power to access and use resources as the most significant aspects of a position as related workplace conditions. Though teaching is often characterized by isolation from adults, results of this study show that relationships with others are important. Key relationships focus on principals of schools for administrative support and leadership, teachers and school staff for cooperative environment and collegiality, parents for parental support, and students in terms of respect and behavior. Teachers also report higher levels of satisfaction when they have adequate resources like time and materials, when they have autonomy in their own classrooms, and when they are satisfied with their class sizes and salary. Principals of schools appear to be in the best position to directly influence teacher job satisfaction, but they need support from their community and school districts.
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