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Factors influencing special educators' perceptives regarding the reintegration of special education students

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Title:
Factors influencing special educators' perceptives regarding the reintegration of special education students
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Book
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English
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Tanner, Brandi L
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University of South Florida
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Tampa, Fla
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Curriculum-based measurement
Reintegration
Special education teachers
Reading
Decision-making
Elementary students
Dissertations, Academic -- School Psychology -- Specialist -- USF   ( lcsh )
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bibliography   ( marcgt )
theses   ( marcgt )
non-fiction   ( marcgt )

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Abstract:
ABSTRACT: The purpose of this investigation was to obtain information about special educators' perceptions of the reintegration of special education students into general education classrooms and factors affecting their reintegration decisions. Reintegration is the process of determining when it is appropriate to fade and eventually remove special education services for a student. Special educators often are in a good position to both initiate and facilitate this process. The current study surveyed special educators. Participants read vignettes depicting special education students with mild disabilities and answered a set of survey questions regarding their perceptions about reintegrating the students into the general education classroom. Specifically, two research questions were investigated: (1) Does performance data on achievement affect special educators' willingness to reintegrate students for reading instruction beyond their willingness based only on anecdotal information and, (2) What type of student performance data were most influential in special educators' ratings of willingness to reintegrate special education students?This study found that data on academic performance does affect special educators' willingness to reintegrate students with disabilities into general education classrooms. Multiple regression analyses were conducted to investigate what factors were most influential in the decision-making process. Results of these analyses varied as a function of disability category of the student depicted in the vignette. In two of the four vignettes, pre-data willingness, or anecdotal information accounted for the largest percentage of unique variance indicating that non-academic data is very influential in reintegration decision-making. Implications of the findings for practice, including promotion of the use of CBM data in reintegration decision-making are discussed.
Thesis:
Thesis (Ed.S.)--University of South Florida, 2006.
Bibliography:
Includes bibliographical references.
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Mode of access: World Wide Web.
Statement of Responsibility:
by Brandi L. Tanner.
General Note:
Title from PDF of title page.
General Note:
Document formatted into pages; contains 116 pages.

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aleph - 001927401
oclc - 192003015
usfldc doi - E14-SFE0001926
usfldc handle - e14.1926
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SFS0026244:00001


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ABSTRACT: The purpose of this investigation was to obtain information about special educators' perceptions of the reintegration of special education students into general education classrooms and factors affecting their reintegration decisions. Reintegration is the process of determining when it is appropriate to fade and eventually remove special education services for a student. Special educators often are in a good position to both initiate and facilitate this process. The current study surveyed special educators. Participants read vignettes depicting special education students with mild disabilities and answered a set of survey questions regarding their perceptions about reintegrating the students into the general education classroom. Specifically, two research questions were investigated: (1) Does performance data on achievement affect special educators' willingness to reintegrate students for reading instruction beyond their willingness based only on anecdotal information and, (2) What type of student performance data were most influential in special educators' ratings of willingness to reintegrate special education students?This study found that data on academic performance does affect special educators' willingness to reintegrate students with disabilities into general education classrooms. Multiple regression analyses were conducted to investigate what factors were most influential in the decision-making process. Results of these analyses varied as a function of disability category of the student depicted in the vignette. In two of the four vignettes, pre-data willingness, or anecdotal information accounted for the largest percentage of unique variance indicating that non-academic data is very influential in reintegration decision-making. Implications of the findings for practice, including promotion of the use of CBM data in reintegration decision-making are discussed.
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Factors Influencing Special Educators’ Perspectives Regarding the Reintegration of Special Education Students by Brandi L. Tanner A thesis submitted in partial fulfillment of the requirements for the degree of Education Specialist Department of Psychological and Social Foundations College of Education University of South Florida Major Professor: Kelly A. Powell-Smith, Ph.D. Michael J. Curtis, Ph.D. Constance V. Hines, Ph.D. Date of Approval: November 15, 2006 Keywords: (curriculum-based measurement, reintegration, special education teachers, reading, decision-making, elementary students) Copyright 2007, Brandi L. Tanner

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i i Table of Contents List of Tables iii List of Figures v Abstract vi Chapter I – Introduction 1 Current Special Education Practices 2 Models Addressing Reintegration 4 When Should Reintegration Be Considered? 5 Legal Basis for Reintegration 6 Purpose of the Study 6 Definitions 8 Chapter II – Review of the Related Literature 11 Curriculum Based Measurement (CBM) 11 Case-by-Case Decision Making and Evaluation of Reintegration Success 14 General Education Teachers’ Willingness to Attempt Reintegration 21 Parents’ Willingness to Attempt Reintegration 25 Special Education Teachers’ Perceptions of Reintegration 29 Chapter III – Method 31 Participants 31 Measures 33 Special Education Teacher Survey on Reintegration (SETS-R) 33 Vignettes 34 Bias Control Measures 37 Procedures 38 Recruitment 38 Survey Administration 39 Data Analysis 40 Chapter IV – Results 45 Descriptive Statistics 46 ANOVA Model 46 Results of ANOVA 48 Analysis for SLD and EH categories 48 Analysis for OHI and EMR categories 51

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ii ii Status by time interaction for OHI and EMR Categories 51 Disability by Time Interaction Effect for OHI and EMR 54 Summary of Analysis I and II Results 55 Overview of Research Question Two Analyses 57 Results of Multiple Regressions 57 Regression Analysis for SLD 58 Regression Analysis for OHI 59 Regression Analysis for EMR 61 Regression analysis for EH 63 Summary of Multiple Regression Analyses Results 65 Chapter V – Discussion 68 Comparison with Previous Research 70 Studies Examining PC or UC Status 70 Attitudinal Studies of Reintegration 71 Additional Findings 71 Disability Category 72 Grade Equivalent Scores Versus Percentile Ranks 74 Data from Published Norm-Referenced Tests Versus CBM Data 75 Significance and Contribution 77 Implications for Practice 79 Limitations and Delimitations 80 Suggestions for Future Research 80 Conclusions 81 References 83 Appendices 88 Appendix A: Informed Consent 89 Appendix B: Standardized Survey Administration Procedures 91 Appendix C: Special Education Teacher Survey on Reintegration – 1 92 Appendix D: Special Education Teacher Survey on Reintegration – 2 Form A 97 Appendix E: Special Education Teacher Survey on Reintegration – 2 Form B 107

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iii iii List of Tables Table 1 Participants’ Level of Education 32 Table 2 Participants’ Years of Teaching Experience 32 Table 3 Means and SDs of Ratings of Willingness to Reintegrate by Type of Data Student Status, and Disability Category 47 Table 4 Analysis of Variance of Ratings of Willingness to Reintegrate by Status and Time for SLD and EH Categories 49 Table 5 Analysis of Variance of Ratings of Willingness to Reintegrate by Status and Time for EMR and OHI 52 Table 6 Summary of Significance Results Between Analyses I and II 56 Table 7 Means, Standard Deviations, and Intercorrelations among Predictor Variables for SLD 58 Table 8 Summary of Regression Analysis for SLD Vignette 59 Table 9 Means, Standard Deviations, and Intercorrelations among Predictor Variables for OHI 60 Table 10 Summary of Regression Analysis for OHI Vignette 61 Table 11 Means, Standard Deviations, and Intercorrelations among Predictor Variables for EMR 62 Table 12 Summary of Regression Analysis for EMR Vignette 63

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iv iv Table 13 Means, Standard Deviations, and Intercorrelations among Predictor Variables for EH 64 Table 14 Summary of Regression Analysis for EH Vignette 65 Table 15 Squared Semi-partial Proportions Correlations and R2 66

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v v List of Figures Figure 1 Status by Disability by Time Interaction SLD and EH Categories 50 Figure 2 Status by Time Interaction for OHI and EMR 53 Figure 3 Disability by Time Interaction for OHI and EMR 55

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vi vi Special Education Teachers’ Perceptions of Reintegrating Special Education Students into General Education Classrooms Brandi L. Tanner ABSTRACT The purpose of this investigation was to obtain information about special educators’ perceptions of the reintegration of special education students into general education classrooms and factors affecting their reintegration decisions. Reintegration is the process of determining when it is appropriate to fade and eventually remove special education services for a student. Special educators often are in a good position to both initiate and facilitate this process. The current study surveyed special educators. Participants read vignettes depicting special education students with mild disabilities and answered a set of survey questions regarding their perceptions about reintegrating the students into the general education classroom. Specifically, two research questions were investigated: (1) Does performance data on achievement affect special educators’ willingness to reintegrate students for reading instruction beyond their willingness based only on anecdotal information and, (2) What type of student performance data were most influential in special educators’ ratings of willingness to reintegrate special education students?

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vii vii This study found that data on academic performance does affect special educators’ willingness to reintegrate students with disabilities into general education classrooms. Multiple regression analyses were conducted to investigate what factors were most influential in the decision-making process. Results of these analyses varied as a function of disability category of the student depicted in the vignette. In two of the four vignettes, pre-data willingness, or anecdotal information accounted for the largest percentage of unique variance indicating that non-academic data is very influential in reintegration decision-making. Implications of the findings for practice, including promotion of the use of CBM data in reintegration decision-making are discussed.

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1 1 Chapter I Introduction Since the passage of the Education for All Handicapped Children Act (1975), the number of children served in special education has been steadily increasing. This escalation of numbers has lead to the sentiment that too many children are served in special education (Ball, 1997; Fuchs, Fuchs, and Fernstrom, 1992). One explanation is that children with mild disabilities are infrequently moved to less restrictive environments (Rodden-Nord, Shinn, & Good, 1992). Fuchs, Roberts, Fuchs, and Bowers (1996) describe special education as being a “terminal assignment,” while Powell-Smith and Stewart (1998) refer to students who receive special education services as being “stuck” in a service delivery model that has few procedures for eventual exit. Research demonstrating this problem has found that approximately 40% of students receiving special education services could be candidates for reintegration based on local norms (i.e., they read as well or better than general education peers) (Shinn, Rodden-Nord, & Knuston, 1993). Yet, data suggest that as few as 2% to 6% of students exit special education each year (Lytle & Penn, 1986; Shinn, 1986).

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2 2 Current Special Education Practices Special education services may not be needed throughout a student’s educational career. A continuum of service delivery models exists, ranging from education in a residential facility to full-time placement in general education classes. Federal law mandates that students are to be served in the Least Restrictive Environment (LRE). Ideally, as students’ skills improve they should be able to increase time in the general education classroom and eventually exit special education. Unfortunately, reintegration into general education may not be a topic addressed at the time of special education eligibility determination and initial placement. For example, the majority of parents in a study by Green and Shinn (1994) did not recall discussing special education exit criteria at the point of eligibility determination Thus, consideration of reintegration may be an afterthought rather than an integral part of the Individual Education Program (IEP) process and service delivery (Powell-Smith & Ball, 2002). Fuchs, et al. (1996) hypothesized that the Individuals with Disabilities Education Act (IDEA) is partly to blame for this problem because reintegration is not indicated as being part of special educators’ post-placement responsibilities. Specifics regarding exactly what must be included when developing an Individualized Educational Program (IEP), including conducting annual reviews of progress, and triennial reevaluations are not clearly delineated in the law. The law may inadvertently discourage reintegration efforts by creating only two options – continuing in the current special education setting or placement in the mainstream and removal of supports.

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3 3 The Least Restrictive Environment (LRE) clause of the Individuals with Disabilities Education Act (IDEA) has been in place since the original special education legislation was passed in 1975. The LRE clause states that, to the maximum extent appropriate, children with disabilities should be educated with children without disabilities. Also, removal of children with disabilities from the general education environment should only occur when the nature or severity of the disability is such that education in regular classes cannot be achieved satisfactorily. Increasingly, emphasis has been placed on demonstrable outcomes as a result of special education (Green & Shinn, 1994). Conceptually, the IEP should serve as a tool to demonstrate satisfactory progress of a student in special education. Accurate data regarding the mastery of goals and objectives are required as a standard part of the IEP. However, parents are generally unaware of, or do not understand, how decisions are made regarding the progress of their children. The most recent reauthorization of IDEA is the Individuals with Disabilities Education Improvement Act (IDEIA) of 2004 which requires inclusion of special education students in regular classrooms to the greatest extent appropriate. This legislation also incorporated increased attention to outcomes and accountability for special education students relative to the general education curriculum. Data collection and interpretation practices of special educators may not be accurate to represent demonstrable outcomes relative to expectations of the general education curriculum. Fuchs and Fuchs (1984) reported that special educators relied on unsystematic observation to evaluate student performance on objectives and failed to recognize when objectives were not met. Without accurate data, special educators may

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4 4 fail to satisfy the requirements of IDEIA in a substantive way and make inappropriate decisions about LRE and reintegration. Reintegration is a process that involves determining when it is appropriate to fade and eventually remove special education services for a student (Powell-Smith & Ball, 2002). Reintegration is not necessarily a permanent removal of special education services for a student and is considered a trial process. Judgments about reintegration are best viewed along a continuum and may be considered for single or multiple domains. Models Addressing Reintegration Previously, reintegration decisions were based largely on philosophical positions and federal policy development (Shinn, Powell-Smith, Good, & Baker, 1997). However, increasingly, discussions of reintegration are based on student performance data (Fuchs & Fernstrom, 1992; Shinn, et al., 1993). Models for reintegration began to develop in the early 1990s. Fuchs and Fernstrom (1992) used the term “Responsible Reintegration” to represent a case-by-case approach for considering special education students for reintegration into general education. In a series of studies, the researchers employed a method called “Transenvironmental Programming” as a process for reintegrating students with disabilities into general education classrooms. The four steps of this process include (a) environmental assessment, (b) intervention and preparation, (c) promoting transfer across settings, and (d) evaluation in the mainstream. Powell-Smith and Stewart (1998) described in depth the Responsible Reintegration of Academically Competent Students (RReACS) model employed in a

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5 5 series of studies by Shinn and colleagues (Shinn, Rodden-Nord, & Knutson, 1993; Shinn, Powell-Smith, & Good, 1996; Shinn, Powell-Smith, Good, & Baker, 1997; Shinn, et al. (1993). The RReACS model was also based on case-by-case philosophy and used Curriculum-Based Measurement (CBM) data for decision-making. The RReACS model, as articulated by Powell-Smith and Stewart (1998) is composed of the following six steps: 1. Identifying potential candidates to be considered for reintegration 2. Comparing the academic performance of reintegration candidates to a comparison group using CBM 3. Reintegration decision making by an educational team 4. Planning for successful reintegration 5. Actual reintegration into general education 6. Evaluating the effects of reintegration A similar five-step process based on the problem-solving model has also been presented (Powell-Smith & Ball, 2002). The guiding principles of this model include “hypothesis testing, formative evaluation/ongoing progress monitoring, consideration of the classroom and school ecology, case-by-case decision making, and consideration of legal parameters (p. 538).” The steps of the model are as follows: 1. Student considered for reintegration 2. Plan for Reintegration 3. Monitor Reintegration Success 4. Determine Reintegration Success

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6 6 5. Special education exit in goal or domain area When Should Reintegration be Considered? Typically, candidates for reintegration have been identified by the special educators’ judgment that the student is ready for a trial placement in the general education classroom (Rodden-Nord, et al., 1992; Ball, 1997). The fact that widespread use of systematic models for making decisions regarding reintegration were lacking may have lead to this practice. Thus without decision-making models, the special educators relied on their judgment. In previous research special educators were very conservative in their judgments, generally rating students as unready for reintegration (Rodden-Nord, et.al., 1992). Parents have indicated that their attitudes towards reintegration were influenced more by special educators’ recommendations than by academic performance data (Green & Shinn, 1994; Ball, 1997). From the reintegration models discussed previously, students can be classified as potential candidates (PC) or unlikely candidates (UC) for reintegration based on the CBM data (Rodden-Nord (1990); Rodden-Nord, et. al (1992); and Shinn et. al (1993, 1996, & 1997). Special education students with CBM scores within the range of the low reading group peers would be considered potential candidates for reintegration. Special education students with CBM scores below the range of the low reading group peers would be considered unlikely candidates for reintegration. Legal Basis for Reintegration The U.S. Supreme Court has determined the performance standard against which a student’s appropriateness for general education is judged. Several court cases have

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7 7 examined this issue and standards for determining least restrictive environment have been adopted. For example, in Hendrick Hudson District Board of Education v. Rowley (1982), special education students were defined as benefiting from general education if their academic achievement was judged to be satisfactory according to the grading and achievement system within the general education setting. Powell-Smith and Ball (2002) contend that federal law (IDEA) does not guarantee every child with a disability an ideal educational opportunity. The purpose of special education is to provide a Free Appropriate Public Education (FAPE), not to maximize a child’s potential. This perspective does not differentiate between sufficient and maximum. People may believe that the achievement commensurate with low achieving peers is too low. Purpose of the Study The purpose of this study was to determine if performance data impact the willingness of special educators to reintegrate students into general education settings. Specifically, two research questions were investigated: (1) Does performance data on achievement affect special educators’ willingness to reintegrate students for reading instruction beyond their willingness based only on anecdotal information and, (2) What type of student performance data were most influential in special educators’ ratings of willingness to reintegrate special education students? The rationale for this study is based on a study by Rodden-Nord, et al. (1992) which investigated these phenomena among general education teachers. She found that the use of data regarding the students’ performance compared to peers in the classroom accounted for the majority of the variance in general education teachers’ willingness to

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8 8 reintegrate special education students into general education settings. Specifically, the teachers were most influenced by information on whether the student performed within or outside the range of low reading group peers as indicated by Curriculum Based Measurement (CBM) data. This study of the influence of data on special educators’ decision-making is important given documentation of parents’ reliance on special educators’ recommendations, and the lack of systematic data collection by special educators (Green & Shinn, 1994; Ball, 1997). This study is also significant because the majority of previous studies have focused primarily on general education teachers’ attitudes and parents’ attitudes. The research available has not focused on special education teachers’ attitudes and perceptions about reintegration. In many instances where theoretical frameworks or reintegration models were not employed, nomination by special educators has often been the impetus to reintegration efforts (Fuchs & Fuchs, 1984). Therefore, research on what factors influence special educators’ decisions to reintegrate special education students into general education settings contributes important information to the existing body of knowledge. Definitions Reintegration: Powell-Smith and Ball (2002) define reintegration as a process that involves determining when it is appropriate to fade and eventually remove special education services for a student. Reintegration is considered a trial process rather than necessarily permanent removal of special education services. Judgments about reintegration are best viewed along a continuum and may be considered for single or

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9 9 multiple domains. This definition of reintegration is presented in contrast to similar terms which may carry other meanings such as “mainstreaming” or “inclusion.” Mainstreaming: The term mainstreaming has been used to refer to the practice of placing special education students into environments with non-disabled peers for social/emotional benefits or for the purposes of receiving instruction in a less restrictive setting. Inclusion: Inclusion, particularly “full inclusion,” has referred to instructing students with disabilities with non-disabled peers, regardless of disability category. Essentially, special education students included in general education classes in a full inclusion model are not necessarily expected to meet the grading and achievement standards of general education. In contrast, special education students in a responsible reintegration model are expected to meet such standards. Satisfactory Achievement: For the purposes of this study, the definition of satisfactory achievement was taken from the RReACS model: “the lowest level of skills and rate of progress considered acceptable for general education students (Powell-Smith & Stewart, 1998, p. 258).” To define satisfactory achievement in relation to the local environment, tests representing the expectations for performance in the curriculum, local school populations used, and a criterion must be established. Curriculum Based Measurement (CBM) has been used as a procedure to determine satisfactory achievement in specific general education environments. Consistent with much of the literature, (Shinn et al., 1993, 1996, 1997; Powell-Smith & Stewart, 1998) potential candidates (PC) for reintegration were identified by comparing

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10 10 special education students’ scores on CBM oral reading measures with local classroom norms derived from low reading group peers. Special education students who read as well or better than at least one of the low reading group peers were defined as a potential candidate for reintegration.

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11 11 Chapter II Review of the Related Literature Research on the reintegration of special education students into general education classes has increased since the early 1980s. This review of the literature will evaluate and discuss the major studies that have been conducted during this period. Discussion will begin with an explanation of Curriculum-Based Measurement (CBM) as it relates to decision making about special education placement. Next, the concept of case-by-case decision making will be discussed along with the outcomes of previous reintegration trials. A review of research on attitudes towards reintegration will follow including studies examining attitudes of general education teachers, parents, and special educators in this regard. The literature review will conclude with a discussion of special educators’ current roles in the reintegration process and their perceptions of reintegration. Curriculum-Based Measurement (CBM) Curriculum-Based Measurement (CBM) is a technology for assessing student achievement that was designed to be reliable and valid, simple and efficient, easily understood, and inexpensive (Witt, Elliott, Daly, Gresham, & Kramer, 1998; Deno, 1985). It is a systematic set of procedures that produces a database which can be used for making a variety of educational decisions, including those regarding special education.

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12 12 Martson (1989) has summarized validity and reliability data on CBM measures. Multiple studies were conducted by the University of Minnesota Institute for Research on Learning Disabilities in the development of CBM technology to determine validity and reliability of CBM measures. Test-retest reliability involves administering the same set of test materials to the same student at two different times. Test-retest intervals ranging from 1 to 10 weeks yielded correlations of .82 to .97. Parallel form reliability for CBM reading measures have yielded correlations ranging from .84 to .96, again with most correlations being above .90. Interrater agreement coefficients were found to be .99 again confirming the reliability of CBM reading measures. Studies examining the validity of Curric ulum Based Measures as a function of a student’s oral reading from the basal reader and different published measures of global reading proficiency (e.g. Stanford Achievement Test and Woodcock Reading Mastery test) produced correlation coefficients of .63 to .90, with most coefficients being above .80. In comparison to four different basal reading series’ criterion-referenced mastery tests, correlations ranged from .57 to .86 with four of eight coefficients being above .80. In this comparison, the degree to which the curriculum-based measures were correlated with the basal mastery tests was directly proportional to those measures’ correlations with global measures of reading proficiency. Deno (1985) explained several advantages of CBM in comparison to informal observation and published norm-referenced tests. Compared to informal observation, CBM is administered using standardized procedures which increase reliability and validity over informal observations. CBM can lead to improved communication about

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13 13 student performance because scores obtained (e.g., number of words read correctly) are easily understood by teachers and parents and data can be presented graphically. These graphic displays can be used to evaluate program success. Sensitivity to change is another advantage of CBM. Because the probes are short and can be repeated frequently (e.g. twice weekly, weekly, monthly) the database for decision making is improved. Repeated measures allow the examiner to review the student’s performance at any stage in the decision-making process. Time series analysis, the examination of the functional relationship between the data and the instructional intervention, is possible by analyzing the slopes or the trend of the data. Instructional changes and interventions can be implemented immediately when decline or insufficient progress is noted. This type of sensitivity and ability to quickly determine instructional effectiveness is not possible with conventional published norm-referenced achievement tests. When using published norm-referenced tests, the overlap between what is taught and what is tested is questionable, limiting the utility of such data for decision making (Powell-Smith & Stewart, 1998). The tasks presented in CBM probes are natural and authentic academic behaviors and can be used at any grade level. Deno (1986) contends that “student performance in the school curriculum provides the most relevant data for making instructional programming decisions (p. 366).” CBM scores can be used to compare a student’s score to those of others in his or her class or grade level. By developing local school norms, expected outcomes can be linked easily to curriculum measures. In addition to use for special education decision

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14 14 making, CBM has other potential uses. Shinn, et al. (1996) concluded that CBM measures provide important information about the quality of instruction as it relates to all students. CBM data could be considered when identifying instructional variables which may explain differences in achievement in other groups. Overall, CBM yields data not available from informal observation or from published norm-referenced instruments alone. Data obtained from CBM procedures is considered reliable and valid measures and can be used for placement decisions and for formative evaluation. Analysis of CBM data relative to trend lines can used to identify the need for instructional changes and thereby increase rates of student performance. Case-by-Case Decision-Making and Evaluation of Reintegration Success CBM has been identified as an appropriate procedure for gathering data to make decisions about special education placement. Studies have investigated details of how CBM data should be used in reintegration decision-making, and in determining the success of reintegration trials. For example, Fuchs, Fuchs, and Fernstrom (1992) conducted a study to evaluate the effectiveness of a program to prepare students with disabilities to move from a resource room into a general education classroom for math instruction. A method called Transitional Planning (TP) was used to prepare students to reintegrate. Eleven special educators in seven elementary schools and one middle school were recruited for the study. The special education teachers began the student participant selection by nominating students who they believed at some point in the next school year might be ready for reintegration into a mainstream math class based on their judgment of

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15 15 the student’s math performance, classroom behavior, motivation, and understanding of the implicit school norms. Forty-four special education students were identified for reintegration and divided equally into an experimental group and a control group. The experimental group participated in the CBM and TP procedures in preparation for reintegration. Preparation for reintegration for students in the control group was executed by the special education teachers in the “typical” or “usual” manner as opposed to the experimental procedures. The average six-week grades for experimental students was 7.12 (SD = 12.97) points higher than that of control students. Four doctoral students in special education were assigned as project staff. Staff members were trained to provide assistance to the special and general education teachers and to collect teacher and student data. Staff members provided a median of 32.25 hours of support per reintegration candidate per staff member. Special education students in both the experimental and control group participated in a semi-structured interview designed to rate their perceived math ability and progress. Special education students in the experimental group were more positive about leaving special education, more confident in their skills, and believed that they had achieved greater progress in math than did the students in the control group. Fuchs and Fernstrom (1992) qualify their findings as “encouraging, rather than proof of effectiveness of the reintegration procedures” (p. 277) and disclose some limitations of the study. First, due to general education teachers’ concerns, only 60% of the students identified as candidates for reintegration were randomly assigned. Special and general education teachers volunteered to participate and were provided with a small

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16 16 cash stipend. Special education teachers chose candidates through personal judgment rather than by objective data-based procedures. Also, it is questionable as to whether the results would have been the same if the hours of onsite supervision provided by the project staff had not been available. Despite these limitations, the authors deem their procedures “Responsible Reintegration” because students were considered on a case-by-case basis. By using such an approach, the reintegration process is tailored to the needs of each individual child and short-term and long-term effects can be monitored. Results of use of the Transitional Planning (TP) program were also presented by Fuchs, Roberts, Fuchs, and Bowers (1996). This was a two-year longitudinal study designed to investigate both the short-term and long-term effects of this case-by-case reintegration approach. Twenty-seven special education teachers identified 47 students with learning disabilities who might be ready for reintegration into a mainstream math class. During year one, the students were divided into four groups (11 control, 13 TP + CBM, 12 TP only, and 11 CBM only) and received intervention accordingly. No intervention was implemented in year two of the study, only the collection of placement data. Of the 38 students reintegrated in year one, only 22 were receiving math instruction in the mainstream one year later. Students in the TP + CBM group had the highest rates of remaining in general education at the end of follow-up (66.7%). General education placement of control students was significantly lower than the three experimental groups combined.

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17 17 Several points were presented in the discussion. The process of identifying students for reintegration did not rely on a formalized procedure such as a cut off score. Instead, informal understanding of the school norms by the special education teacher served as the basis for inclusion. Also, the unit of analysis for this study was the student rather than the teacher. Though data were not collected systematically on the general education teachers, it was noted in the discussion that the general education teachers rarely modified instruction, and when they did so, the modifications were group oriented and minor. This lack of instructional modification may have accounted for the results of achievement testing also conducted. Math achievement was assessed through the Math Operations Test-Revised (MOT-R), a test encompassing the state’s entire operations curriculum. Although students in the CBM groups made steady progress in special education, they did not maintain the same rate of growth in general education. This study did appear to indicate a promising model for reintegration preparation. However, data on the long-term success of these students was not so promising as less than half remained in general education at one year follow-up. General education followup was hypothesized as a cause for these results. Shinn, Powell-Smith, and Good (1996) also subscribed to the “Responsible Reintegration” model presented by Fuchs and Fernstrom (1992). The purpose of their study was to determine the degree to which reintegration was beneficial for elementaryaged special education students who were rein tegrated into general education classrooms

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18 18 for reading instruction. These authors advocated the use of CBM in the identification process as a central concept. Special education students chosen for the study were in a pull-out program receiving reading instruction for less than one-half of the school day and recommended for reintegration by the IEP team. Thirty students were tested using CBM oral reading and CBM maze procedures. These students’ scores were then compared to three to six low achieving peers’ scores from the special education students’ general education classroom. These low achieving peers were tested at the same time as the special education student. After reports of the data were created, IEP teams decided to reintegrate 23 of the students. Eighteen of the students had been reintegrated for at least ten weeks by the end of the school year resulting in the final subject pool. A pool of 10 judges with expertise in single-subject research design and using CBM to make decisions about academic progress were selected to evaluate graphs to determine the success of reintegration on a case-by-case basis. CBM oral reading and maze data were presented for each student in comparison with his or her low achieving peers. Graphs were provided for Pre-reintegration, at week four, week eight, and a final graph at week 10 or 12 (end of school year). A 70% consensus standard for judgment agreement was used throughout the study. At the beginning of the reintegration trial, judges agreed that fourteen of the eighteen students were suitable candidates for reintegration, two students were not suitable for reintegration, and in two cases judgments were mixed. After four weeks of reintegration, judges agreed that the overall reintegration effects for 12 of 18 were not

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19 19 positive. Progress of the reintegrated students in the general education classroom appeared to improve from week four to week eight as ten students were judged to be benefiting from reintegration. However, there were more disagreements between judges. At the end of the trial, nine of the reintegrated students were rated as having a successful reintegration experience. Six of the judgments were mixed, while three were rated as unsuccessful. Overall, Shinn, Powell-Smith and Good (1996) advocated for the use of CBM in deciding to attempt a reintegration trial, and in evaluating the success of reintegration. The authors concluded group research is less helpful in understanding individual effects than a case-by-case approach. Also, reintegration trials should be conducted over an extended evaluation period (10 to 12 weeks) to evaluate the success of reintegration. In their discussion, the authors drew attention to the evaluation of the progress of the low reading group as it may be an indicator of overall quality of instruction. Though most of the reintegrated students read fewer words correctly per minute than their comparison peers at the Pre-reintegration phase, by week four most of the special education students’ performance graphs had greater slopes than low achieving peers indicating greater rates of skill acquisition. Throughout each period, the judges were in consensus that due to rates of progress displayed in the graphs, instructional changes were warranted for most of the students, both reintegrated students and general education peers. Shinn, Powell-Smith, Good, and Baker (1997) also reported on the effects of reintegration with an emphasis on the achievement of special education students in

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20 20 comparison to general education peers. IEP teams decided to reintegrate 23 of 30 students based on CBM data presented in a narrative and graphic form presented to the decision-making team. Reading improvement was evaluated continuously for the special education students in relation to the general education low reading peers in their own classroom who received reading instruction at the same time. Relative to student achievement, it was hypothesized that if reintegration were successful, the reading performance of the reintegrated students relative to their lowreading peers would maintain or improve. Special education students maintained their position relative to low achieving peers. However, neither reintegrated students nor their low-reading peers increased significantly on the CBM oral reading probes initially. Evidence presented in this study indicated that the reintegrated students were performing more like the general education peers rather than falling further behind. Parents, general education teachers, and special education teachers’ judgments of the success of the students’ reading programs were also evaluated. The groups did not differ systematically in their judgments of the success of the general education reading program in meeting the needs of the reintegrated students. All groups judged the reintegration program as neutral to somewhat positive. Student comfort with reintegration improved significantly from initial placement to week four then leveled at week eight. With respect to the most appropriate placement, there was dissention among the groups. General education teachers reported that general education was the preferred placement for all of the reintegrated students. In contrast, special education teachers and

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21 21 parents reported special education placement would be preferred for about 10% of the reintegrated students. Though all three groups were more likely to recommend general education placement over special education, the difference was more pronounced for general educators. Several important points are highlighted by the authors in their discussion. Foremost, the claim of potential widespread failure of reintegrated students was not supported by this study. Secondly, the use of systematic, data-based identification and continuous monitoring were again supported. Thirdly, the perception that general education teachers were reluctant to work with students with mild disabilities in their classrooms was not supported. All three of the studies presented in this section reported positive outcomes for reintegrated students and advocated for the use of a case-by-case approach in making reintegration decisions. In addition, an extended reintegration trial period was advocated to allow for stabilization of data. In addition to using CBM for special education decision making, the point was also made that CBM data can and should be used to monitor the quality of instruction for all students. General Education Teac hers’ Willingness to Attempt Reintegration CBM has been used in investigating other aspects of reintegration as well. Rodden-Nord, Shinn, and Good (1992) examined general education teachers’ attitudes towards reintegrating students with disabilities for reading instruction. The purposes of the study were to ascertain general education teachers’ willingness to reintegrate students

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22 22 with disabilities, and to determine if data about student performance affected their willingness to reintegrate the student. Rodden-Nord and colleagues (1992) differentiate their study from previous studies of reintegration by highlighting the methodological factor of a more real life condition. Previous studies employed methods that presented hypothetical children without actual student performance data. In this study, the general education teachers made judgments about students that were already in their classrooms part time so they were familiar with the students. Twenty-six first through fifth-grade general education teachers in five buildings were the subjects for the study. General education teachers were assigned to experimental groups based on the presence of a potential candidate (PC) or an unlikely candidate (UC) to consider for reintegration into their class. Students with learning disabilities were tested using CBM measures and their scores were compared to local classroom norms derived from low achieving peers. Thirteen students with learning disabilities were determined to be potential candidates (PC) for reintegration by comparing their scores on CBM measures with low reading group peers. Thirteen other students with learning disabilities were then determined to be unlikely candidates (UC) using the same criteria. General education teachers’ attitudes were assessed through two instruments: the Teacher Attitudes Toward Reintegration Pre-Data Questionnaire (TATR 1) and the Teacher Attitudes Toward Reintegration Post-Data Questionnaire (TATR 2) The first item on each questionnaire asked teachers to rate on a 7-point Likert-type scale “How

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23 23 willing would you be to place this student in the lowest reading group in your classroom for reading instruction?” On the TATR 1, the only information provided to the teacher was the name of the student from their classroom. The teachers answered the items as they pertained to the specific LD student in their classroom. The general education teacher did not have access to the special education teacher’s rating, testing data, or the PC or UC status of the student at this time. Special education teachers were given a one-item questionnaire regarding their opinion about each of the PC and UC students’ readiness for reintegration. Special education teachers were asked to rate the LD student’s readiness on a 7-point Likert-type scale on a continuum from 1=“totally unready for reintegration” to 7=“totally ready for reintegration.” Special education teachers were not informed of the PC or UC status of the students at the time of the rating. Special education teachers generally rated students as unready for reintegration (3 on the 7-point scale). One explanation offered was that they may also lack the relevant data for making decisions about the readiness of special education students for reintegration. Ratings by the special education teachers were later given to the general education teachers to consider on the TATR 2. Approximately four weeks later, the teachers were provided with the student’s standardized test data from the Woodcock-Johnson-Revised Broad Reading Cluster, CBM data, the number of students from the teacher’s low reading group who did as well or better on the CBM probes than the special education student, and the special education teacher’s rating of readiness for reintegration. At this time, general education teachers

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24 24 completed the TATR 2, again providing a judgment about if they would be willing to reintegrate the student into their general education classroom for reading instruction. Prior to being provided with academic information, general education teachers in both the PC and the UC groups were either not very willing, or neutral in their willingness to reintegrate students with LD into their classrooms. After being provided with the academic information, mean willingness ratings increased for teachers in the PC group, while decreasing for teachers in the UC group. An analysis of covariance (ANCOVA) was conducted to assess whether achievement information affected general education teachers’ ratings of willingness to reintegrate special education students. The main effect of the PC or UC status was significant, indicating that the willingness ratings of the PC and UC groups were significantly different after academic performance data were presented, F(1,23) = 84.55, p < .05. A step-wise multiple regression analysis was used to examine the magnitude of changes in teacher willingness as a function of the type of data presented to the teachers. Eleven independent variables were analyzed. Eighty-nine percent of the variability in teachers’ post-data willingness to reintegrate was accounted for by the variables considered. PC or UC status based upon CBM data explained the most variance at 58% percent. An important finding was that general education teachers’ willingness to reintegrate special education students changed markedly in response to the provision of information on a student’s academic skills. When provided with the information that the

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25 25 special education student read as proficiently as one or more students in the low reading group, they became significantly more willing to reintegrate. Another finding was that social behavior contributed to a much smaller percentage of the variance than classroom performance data. This finding is in contrast to previous speculation that students’ social behavior may be a large barrier to reintegration. Two limitations were expressed by the authors for consideration when interpreting the results. First, the study did not examine teacher’s actual behavior; it only examined their reported attitudes. Secondly, the author recommended that the study should be replicated with different populations of teachers and students because the results obtained could be unique to the sample. Overall, this study suggested that using CBM performance data is a promising and relevant practice for assisting in decision making about reintegration. Providing general education teachers with these data did result in substantial changes in their willingness to reintegrate students with learning disabilities into their class for reading instruction which is the first step in successful reintegration. Parents’ Willingness to Attempt Reintegration Attitudes of school professionals have a direct influence on the attitudes of parents. Green and Shinn (1995) studied parents’ attitudes towards reintegration through qualitative and quantitative methods. The purpose of the study was two-fold. First, they sought to discover what were parents’ attitudes about special education services, and what factors formed the basis for these attitudes. Further, in light of their level of satisfaction with special education services, they sought to determine what parents’

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26 26 attitudes were about potential reintegration into general education classrooms and the least restrictive environment for their child. Twenty-one parents and guardians of thirdthrough fifth-grade students in special education participated. An interview schedule including demographic information, issues related to current special education placement and progress, and initial parent reactions to special education placement, and expectations for outcomes was developed and administered. All 21 parents appeared to be satisfied with the special education services their child was receiving as they all endorsed the two most positive responses on the Likert scale. When asked how much it would help to place their child in the general education classroom for reading instruction right now, 52% of parents endorsed the most negative response. Explanations for parent satisfaction showed a strong pattern of subjective factors including self-esteem and characteristics of the special education teacher as opposed to skill acquisition of their child. Most parents did not rely on objective performance data when assessing their child’s progress, but indicated that they would be interested in receiving such information. Instead, perceptions were based on listening to their child read at home and a more positive attitude about reading from their child. No parent reported information from commercially available tests as the basis for their opinions. The basis for parent attitudes about reintegration appeared in part to be due to a vague understanding of what the criteria were for making changes in placement with respect to least restrictive environment or special education exit. Seventy-one percent of

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27 27 the parents said that at the time of initial placement, they had not discussed the ultimate goals or exit criteria for special education. When asked to consider the criterion of reading better than at least one child in the general education classroom as measured by CBM, parents did not respond positively. They indicated that learning such information would not influence their decision to return their own child to the general education classroom. With regards to professional opinions, parents chose the special education teacher to be more influential than the general education teacher or the school psychologist in decisions about special education services. The authors summarized that parents reported liking what special educators do or what they think they do, but parental satisfaction with the services provided to their children may not be related to their children’s academic performance. Recognizing that parents of special education students did not seem to be knowledgeable about their child’s academic st atus or expected outcomes of special education, Ball (1997) also investigated factors that influence parents’ willingness to reintegrate students with disabilities. Sixty-one parents of special education students were randomly assigned to one of two groups: parents who received CBM data only or parents who received CBM data and teacher recommendations. Four groups were created by adding in the status factor of PC or UC student. The study was conducted in three phases. In the first phase, Identification, CBM reading procedures were used to identify students as PC or UC status. Special education teachers, general education teachers, and parents completed the Pre-Data Opinion Surveys during phase two, the Initial Willingness Phase. In the final phase, the

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28 28 Experimental Phase, the data were presented to the parents. Parents were given the CBM graphic report alone or the CBM graphic report with the general and special education teachers’ recommendations. Parents then completed the Post-Data Opinion Surveys. Four main conclusions were drawn. First, parents’ willingness to reintegrate their child into the general education setting for reading instruction was most influenced by the special education teacher’s recommendations. Second, the impact of the general education teachers’ recommendations remained unclear. Parents in the study tended to rate all three sources of data (special education teacher recommendation, general education teacher recommendation, and data) as equivalent in their level of influence to reintegration decisions. Finally, special education teachers did not appear to use reading achievement data consistently to make reintegration recommendations. Both the Green and Shinn (1995) and the Ball (1997) studies drew similar conclusions. First, most parents did not have a clear understanding of the special education exit process or the goals of special education. Instead of using objective data, parents seemed to be relying on subjective information such as their child’s attitude. Parents were generally reluctant to remove their child from special education because they liked the one-on-one attention they believed their child received and personal characteristics of the special education teacher. Special education teacher recommendations were the most influential factor in a parent’s attitude toward reintegrating their child into general education. Both studies question special education teachers’ use of objective data.

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29 29 Special Education Teachers’ Perceptions of Reintegration Limited research was found examining the attitudes of special educators towards reintegration. Generally negative attitudes towards reintegration have been noted among special educators. In several of the studies mentioned previously, special education teachers have reported reluctance to reintegrate students with disabilities into the general education classroom (Rodden-Nord, et al., 1992; Green & Shinn, 1995; Ball, 1997). In the study by Shinn et al. (1997) general education teachers were more likely to choose general education as the appropriate placement for special education students than special education teachers. In 1985, Knoff reported on a survey of four hundred general and special educators in New York and Massachusetts. Special educators in both states expressed strong awareness of state and federal laws. They also felt more strongly than their general education counterparts that the presence of exceptional students would not harm the general education classroom. Despite these claims, both general and special educators perceived the special education classroom to be preferable for students with mild disabilities. More recently, Cook, Semmel, and Gerber (1999) predicted that special educators would be unsupportive of inclusion reforms. This study involved the survey of 49 school principals and 64 special education teachers. A general lack of support for inclusion was indicated by special education teachers. Two-thirds of this group disagreed with the statement that inclusion would increase the achievement levels of students with mild disabilities.

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30 30 Cook and colleagues (1999) hypothesized that the lack of support for inclusion may be based on negative experiences. Some authors speculate that part of the problem has been due to special educators’ lack of use of systematic data collection in decision making (Green & Shinn, 1994; Ball, 1997). Regardless of the reason, special education teacher nomination has been a primary mechanism used to identify students for reintegration (Fuchs & Fuchs, 1984; Fuchs, et at., 1996; Ball, 1997; Shinn, et al., 1997). Thus, as key personnel, the attitudes of special educators are very influential on reintegration initiation and success. The purpose of the current study was to investigate the effect of academic performance data on special education teachers’ willingness to reintegrate students with mild disabilities into general education classrooms. The rationale of this study is similar to that of the Rodden-Nord, Shinn, and Good study (1992) which examined the same question with general education teachers. Similar methods were used in this study. Research on factors that influence special education teachers’ decisions to reintegrate special education students into general education settings should contribute important information to the existing body of knowledge and promote data-based decision making.

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31 31 Chapter III Method In this chapter, descriptions of the participants, procedures, and study materials for this quasi-experimental study are presented. Subject recruitment and selection is explained first, followed by a description of vignettes and surveys utilized in the study. Next, administration procedures are specified. Lastly, procedures for analyzing data are described for each of the research questions. Participants Special educators in three southeastern school districts served as the participants for this study. These school districts represent a heterogeneous mixture of race, ethnicity, socioeconomic status, and rural, suburban, or urban status. For inclusion in the study, the special educators had to have taught students (a) in any of grades one through five; (b) with mild disabilities (i.e., learning disabilities, mild mental retardation, or emotional behavioral disorder); (c) who were receiving reading instruction in the special education classroom and; (d) who were served less than 50% of the day in special education. A minimum of 50% of the special educators’ work duties were assigned to teaching special education students. Fifty-six special educators served as participants in the study. Education levels of the participants are presented in Table 1. As is shown, 30% of the participants have

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32 32 earned advanced degrees. The mean number of complete years of teaching experience is presented in Table 2. Special education teaching experience ranged from 0 to 30 years with a mean of 9.7 years. The distribution of years of special education teaching experience was positively skewed with 39.3% of the sample reporting five years or less experience in special education teaching. More specifically, five participants indicated less than two years of experience. In addition to special education teaching experience, 18 of the special educators reported experience teaching general education. Of these 18, the range of general education teaching experience was 0.5 to 15 years with a mean of 1.9 years. Table 1 Participants’ Level of Education Level of Education n Percentage of Sample Bachelor’s Degree 39 69.6% Master’s 14 25.0% Specialist 2 3.6% Doctorate 1 1.8% Note N =56 Table 2 Participants’ Years of Teaching Experience 0-5 6-10 11-15 16-20 21+ n Mean SD SE 22 12 11 3 8 56 9.74 8.24 GE 12 5 2 0 0 18 1.92 3.36 Note N =56

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33 33 All of the participants taught multiple subjects and multiple grade levels ranging from pre-kindergarten through eight. The mean percentage of work duties devoted to teaching was 75.14%. The special educators taught an average of 15 students. On average, each taught 3 reading groups with approximately 8 students in each group. Nineteen special educators (34%) indicated that they participated in collaborative teaching. Of those who participated in collaborative teaching, the mean number of hours per week was 7.37. Over half (n=33) of the special educators reported that within the past two years, they had a student reintegrated into the general education curriculum in subjects in which they previously received special education services. Of those who had participated in reintegration, a mean of 6.85 students per teacher were reintegrated within the previous two school years. Across the entire sample of 56 special educators, an average of 4.04 students per teacher were reintegrated within the previous two school years. Measures Special Education Te achers’ Survey on Reintegration (SETS-R). The surveys used in this study (see Appendices C and D) were based on the Teacher Attitudes Toward Reintegration (TATR) Questionnaires developed by Rodden-Nord (1990). These instruments were previously used to study general education teacher’s willingness to reintegrate students with disabilities into their general education classrooms. On the TATR, teachers provided responses in reference to actual students that they taught. The SETS R added a hypothetical vignette as a prompt for reference. Adaptations in wording were made to the TATR surveys to modify them for use with special educators.

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34 34 Item clarity on the TATR Questionnaires was assessed by asking seven teachers who did not participate in the Rodden-Nord (1990, 1992) study to rate the clarity of each item on a five-point Likert-type scale where 1= “very unclear” and 5= “very clear.” The mean item clarity rating was 4.75, with a standard deviation of .25. The teachers who participated in the study were randomly selected to examine the stability of the responses and completed a second TATR1 two weeks after the completion of the first administration. The stability coefficients ranged from .78 to .96 with a mean of .88 which was considered acceptable. The surveys used in the current study (SETS-R1 and SETS-R2) were checked for technical adequacy using similar procedures. First, content validity was examined by a panel of four content area experts who had previously conducted research in the area of reintegration. Comments from the content area experts were favorable, thus the surveys were not modified. The surveys were then piloted with seven special education teachers who were not part of the sample. With this pilot administration, the special educators completed a follow-up item clarity questionnaire that asked them to rate the clarity of the items using a Likert-type scale where 1= “very unclear” and 5= “very clear.” On the SETS-R1, item clarity means ranged from 3.83 (SD = 0.75) to 4.83 (SD = 0.41). On the items receiving lower ratings, the special educators indicated that they needed more information on academic performance. This was to be expected because the survey was purposely designed to not present this information in the SETS-R1 and to introduce it in the SETS-R2. On the SETS-R2, mean item clarity ratings ranged from 4.50 (SD = 0.58)

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35 35 to 5.0 (SD = 0). No item clarity scores fell below 2.5, thus no items were modified or removed. Vignettes. Four vignettes were prepared by the researcher for use in this study and are included as a part of the surveys in Appendices C and D. Information presented in all vignettes included the student’s grade, disability, and number of years in special education. The narratives also incorporated information on the student’s classroom behavior, motivation, social skills, and academic performance. The subjects of the vignettes included a student with a learning disability, a student with mild mental retardation, a student with an emotional handicap, and a student with attention deficit hyperactivity disorder (ADHD) receiving special education services under the federal disability category of other health impaired (OHI). The Special Education Teacher’s Survey on Reintegration 1 (SETS-R1) (see Appendix C) is an adaptation of the Teacher Attitudes Toward Reintegration Pre-Data Questionnaire (TATR1) and examines special educators’ attitudes toward the reintegration of special education students based only on a narrative vignette describing the child’s academic and behavioral characteristics and other pertinent information. The original TATR1 presents two questions. These questions were (1) what do you think is the most appropriate reading placement for the student, (2) and how willing would you be to attempt to reintegrate the student into the general education classroom for reading instruction? The SETS-R1 adds a third open-ended question: “What is the single most important factor affecting your willingness to attempt to reintegrate this student into the general education classroom for reading instruction?” A set of demographic questions

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36 36 related to the teachers’ experience, education, and current assignments are presented at the end of the four vignettes. The Special Education Teacher’s Survey on Reintegration 2 (SETS-R2) (see Appendix D) drew from the Teacher Attitudes Toward Reintegration Post-Data Questionnaire (TATR2). The same narrative vignette as in the SETS-R1 was provided, and supplemented with performance data. This information included scores from a published norm-referenced test of reading achievement (standard score, grade-equivalent, and percentile rank), and graphic CBM data for the student in comparison to low reading group peers. Seven questions followed the vignette and data. Based on the CBM data presented in the SETS-R2, the depicted student in the vignette could be classified by the researcher as a Potential Candidate (PC) or Unlikely Candidate (UC) for reintegration using the guidelines provided by Rodden-Nord (1990), Rodden-Nord, et. al (1992), and Shinn et. al (1993, 1996, & 1997). CBM scores within the range of the low reading group peers indicated a PC student. CBM scores below that of the low reading group peers indicated a UC student. Following the vignette with performance data, the first four questions asked the teachers to indicate how each component of the data presented (grade level equivalent, percentile rank, correct words per minute, and comparison to low performing peers) affected their willingness to reintegrate the depicted student. Participants used a 7-point Likert-type rating scale to indicate the degree of influence of each piece of academic performance data had on their reintegration decision. The reponse scale ranged from

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37 37 1 = “Greatly Decreased Willingness” to 7= “Greatly Increased Willingness.” The three questions from the SETS-R1 followed. These questions were (1) what do you think is the most appropriate reading placement for the student, (2) how willing would you be to attempt to reintegrate the student into the general education classroom for reading instruction, and (3) what is the single most important factor affecting your willingness to attempt to reintegrate this student into the general education classroom for reading instruction? After the four vignettes, two additional sets of questions were presented. Fifteen questions asked about the importance of student factors affecting reintegration decisions. Four additional questions asked about the effect of special education team influence in reintegration decisions. These two sets of questions were in the form of a 7-point Likerttype scale and were adapted from the TATR2 for use with special education teachers instead of general education teachers. Though these questions were included in this survey administration, data from these questions were not used in this study. Bias control measures. Vignettes were presented in a systematic rotation order to counterbalance and reduce any bias which could be introduced by the order of the vignette presentation. Four different sequences were used, each beginning with a different vignette. The vignettes were presented to the participants in the same systematically rotated order as the SETS-R1 (i.e., both sets of surveys the participant received were in the same systematically rotated order). Color-coded paper was used to facilitate the distribution of the counter-balanced surveys.

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38 38 Alternate forms of the SETS-R2 were used to control for effects based on the disability category of the depicted student. In one-half of the surveys (Form A), the student with a Specific Learning Disability and the Emotionally Handicapped student were depicted as potential candidate for reintegration, while the student with Attention Deficit Hyperactivity Disorder and the Educable Mentally Handicapped students were depicted as an unlikely candidate for reintegration. Reintegration status was reversed on the other half of the surveys (Form B). Procedures Recruitment. Prior to the beginning of the study, school district permission and approval by the Institutional Review Board of the governing institution were obtained. Data were collected from December 2004 to March 2006 with the bulk of the survey administration sessions being held in the spring of 2005. Initially, the researcher contacted the special education leadership personnel in one district and obtained permission to attend a pre-scheduled meeting of approximately 75 special educators in elementary schools. At the end of the meeting, those who wished to participate remained to complete the surveys. Of the approximately 50 people who met inclusion criteria, 11 completed the surveys. To increase the sample size, special education leadership personnel in two other local counties were contacted. In these counties, the district leaders directed the researcher to two training sessions for special educators and to schools with higher numbers of special educators who would be available to participate. In the training sessions, special educators completed the surveys during the lunch break or at the end of

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39 39 the session. Of the approximately 30 potential participants, about half completed the surveys at the training sessions. Survey administration sessions in the schools were typically included as a part of a pre-scheduled meeting. In these sessions, there was nearly 100% participation. In this self-selected sample, approximately half participated in the study. Nearly all of the nonparticipants were approached during situations which would have required them to complete the survey on their own time (end of the day or on a lunch break). A total of 16 administration sessions were held. Survey administration. Both the SETS-R1 and the SETS-R2 were administered in single survey administration sessions. The researcher or her trained designee followed the administration protocol at each site (see Appendix B). At each meeting, inclusion criteria for the study were explained. Those who met the criteria and agreed to the terms of the informed consent were administered the surveys. In an effort to increase participation rates, individuals choosing to participate in the survey were entered in a drawing for a gift certificate to a local shopping mall. A single administration session was used instead of a mailed survey. This method was chosen to more closely monitor independence of responses, and to reduce attrition caused by people completing part one of the study, but not part two. The researcher began by obtaining informed consent and explaining to the educators that they were not required to provide any identifying information on any of the materials. Research identification numbers were used to track all study materials in lieu of participant names.

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40 40 Administration procedures were explained to the participants. While completing the SETS-R1, some educators asked for more information such as reading level to be able to respond to the questions. When questions arose, participants were directed to use the information provided, thus no answers were provided by the researcher. Participants completed the questionnaires independently without consulting any other participant. All materials were collected at the end of the administration. The researcher first presented the participants with the SETS-R1 which consisted of four narrative vignettes (without performance data) and three related questions for each vignette. The counterbalanced vignettes were presented in a systematic rotation to reduce any bias which could be introduced by the order of the vignette presentation. Participants read the narratives and completed the questionnaires based on the narrative information given in the vignette. As participants completed the SETS-R1, this survey was collected and the SETSR2 was distributed. The vignettes were presented to the participants in the same systematically rotated order as the SETS-R1 (i.e., both sets of surveys the participant received were in the same systematically rotated order). The researcher then collected all materials and thanked the participants for their time. Data Analysis Data analyses and statistical computations were conducted using the Statistical Analysis System Version 9.1.3. Multiple statistical methods were used to answer the two research questions. All analyses assumed a significance level of = .05. The analyses conducted for each question are described below.

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41 41 Research Question 1: Does performance data on achievement affect special educators’ willingness to reintegrate students for reading instruction beyond their willingness based only on anecdotal information? To address this question, two separate repeated measures Analyses of Variance (ANOVA) procedures were employed to obtain a balanced ANOVA design. Analysis I examined data for the disability categories SLD and EH. Analysis II examined data for the disability categories EMR and OHI. Based on the design and the number of vignettes in each form, each person responded to only 8 of the 16 status by disability combinations. The ANOVA model used for each analysis was a one-between two-within subjects design. Participants’ ratings of the willingness to reintegrate the student was the dependent variable. The between-subjects independent variable was reintegration status (potential candidate [PC] versus [UC] unlikely candidate). The within-subjects variables were (a) type of disability and (b) time (pre-data versus post-data). This analysis allowed the researcher to examine if the special educators’ willingness to reintegrate students presented in a narrative vignette changed after they were presented with additional information. Specifically, did their rating change from the anecdotal information (pre-data),with the presentation of numerical data on students’ academic performance (post-data) and as a function of student status (PC and UC) and disability category. After reading a narrative vignette (i.e., information on student’s grade, disability, number of years in special education, classroom behavior, motivation, social skills, and academic performance), special educators were asked to rate their willingness to

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42 42 reintegrate the student on a Likert-type scale (1=Very Unwilling to 7=Very Willing). The participants were then presented with the same vignette, supplemented with numerical performance data indicating the student’s level of academic achievement. The teachers were then asked to indicate their willingness to reintegrate that student using the same 7-point Likert-type scale post-data. The academic performance data presented the student as a potential candidate (PC) for reintegration or an unlikely candidate (UC) for reintegration. As was noted previously, design of the SETS-R1 and SETS-R2 instruments allowed for each participant to respond to only 8 of the 16 possible status (PC vs. UC) by disability conditions. Thus for data analysis purposes, it was necessary to conduct two separate ANOVA analyses. The degree to which Type I error rates are actually controlled to the specified alpha depends on how adequately the data meet the assumptions of independence, normality, homogeneity of variance, and sphericity (Stevens, 1999). Sample sizes were large enough to expect robustness to violations of the normality assumption. Sphericity is generally addressed by adjusting the df with the Greenhouse-Geisser (G-G) adjustment (Stevens, 1999). Because there were only two levels of each of the within-subjects variable, the sphericity assumption was not applicable, and consequently there was no need to use the G-G adjustment. Research Question 2: What types of student performance data were most influential in special educators’ ratings of willingness to reintegrate special education students? This second question was examined using multiple regression analysis. All

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43 43 possible subsets (APS) multiple regression procedure was employed to obtain the optimal combination of variables that predicted special educators’ willingness to reintegrate students into the general education setting. Five predictor variables were included in the regression analysis. These variables were the pre-data willingness rating from the SETSR1 and ratings of how influential each of four different types of academic achievement data (grade level equivalent, percentile rank, correct words per minute, and comparison to low performing peers), were in increasing or decreasing the special educators’ willingness to reintegrate the student into the general education classroom. These academic achievement data were presented in the SETS-R2 questions (questions 1 to 4). The dependent variable for this analysis was the participants’ post-data willingness ratings to reintegrate. Separate analyses were conducted for each disability category. The data were first screened for conformity to the assumptions of multiple regression. Conformity to the linearity and homoscedasticity assumptions was checked by visual inspection of a plot of residuals. In addition to these assumptions, the residuals were also checked for outliers which may have had a significant influence on the regression equation. Cook’s d values were examined. No values were equal to one or greater, indicating that there were no significant outliers in the data sets. Data were also examined for evidence of multicollinearity among predictor variables through examination of variance inflation factors for the predictors. None of these values exceeded ten, indicating that the correlation between variables was no reason for concern.

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44 44 Model selection was conducted by examining all possible regressions and the corresponding Mallow’s C(p) values The model containing all five variables emerged as being the best model and no other models provided substantial justification for alternate selection.

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45 45 Chapter 4 Results This chapter presents the results of the data analyses conducted to answer the two research questions of the study. Analysis of Variance (ANOVA) procedures were used to determine if presentation of a student as a potential candidate (PC) or unlikely candidate (UC) for reintegration into the general education setting significantly influenced special educators’ willingness to reintegrate the student. As mentioned in Chapter 2, students with CBM scores within the range of low achieving peers were considered to be potential candidates for reintegration, and those with CBM scores below the low achieving peers were considered to be unlikely candidates for reintegration. The special educators’ were asked to rate their willingness to reintegrate the student when only presented anecdotal information (pre-data), and again after specific academic performance data for the student were provided (post-data). Multiple regression analysis was used to determine what factor or linear combination of factors, from a given set of student performance data, best predicted special educators’ willingness to reintegrate students into general education. The specific research questions addressed were: 1. Does performance data on achievement affect special educators’ willingness to reintegrate students for reading instruction beyond their willingness based only on anecdotal information? 2. What type of student performance data were most influential in special educators’ ratings of willingness to reintegrate special education students?

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46 46 Descriptive Statistics Means and standard deviations of pre-data willingness and post-data willingness ratings by status and disability category were computed. These data are reported in Table 3. A cursory examination of Table 3 reveals values of cell means that range from 3.82 (post-data, UC status, EMR category) to 6.54 (post-data, PC status, OHI category). Willingness ratings for EMR and EH categories appear to be lower across all conditions in comparison to the SLD and OHI categories. To determine if the special educators’ ratings changed based on the students’ reintegration status (PC vs. UC), these data were subjected to a onebetween two-within subjects analysis of variance (ANOVA). ANOVA Model To suggest that differences would be found in the population, chance must be ruled out as a plausible explanation for the observed differences in sample means. To assess the tenability of a chance explanation, data were subjected to a one between twowithin subjects analysis of variance (ANOVA) procedure. The alpha level was set to .05 for each effect. As was noted in Chapter 3, two separate analyses were conducted each including data for two disability categories. In the model, the between-subjects factor was reintegration status with two levels, potential candidate (PC) and unlikely candidate (UC). Time was a within-subjects factor with two levels (pre-data and post-data), as was disability category with two levels in each analysis, namely SLD and EMR for Analysis I and EH and OHI for Analysis II. The data were screened to check for violations of

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47 47 Table 3 Means and SDs of Ratings of Willingness to Rein tegrate by Type of Data, Student Status, and Disability Category Pre-Data Post-Data Disability PC UC PC UC SLD Mean 6.18 6.39 6.07 5.07 SD 1.02 0.69 0.94 1.54 EH Mean 4.61 5.29 5.46 5.32 SD 1.45 1.49 1.77 1.52 EMR Mean 4.50 4.68 5.07 3.82 SD 1.43 1.54 1.27 1.59 OHI Mean 6.50 5.96 6.53 5.89 SD 0.64 1.35 0.63 1.59 Overall Mean 5.45 5.58 5.79 5.03 SD 1.48 1.45 1.34 1.64 Note: n =28 for each cell

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48 48 ANOVA assumptions. Based on the analysis of the assumptions, it was deemed reasonable to conduct the analyses. Results of the two ANOVA analyses are reported first individually. Next, significance data from these analyses are discussed holistically to answer the research question. Results of ANOVA Analysis for SLD and EH categories. Results of the ANOVA for Analysis I are presented in Table 4. The three-way interaction between status, disability, and time was significant, F (1, 168) = 7.97, p =.007. The two-way interaction between status and time was also significant, F (1, 168) = 12.00, p = .001. The main effect for disability was significant, F (1, 168) = 85.51, p = <.0001, as was the main effect for status, F (1,55) = 6.01, p = .007. The three-way interaction was interpreted prior to the two-way interactions or the main effects and is depicted in the graph shown in Figure 1 in which the status by time interaction is graphed for each disability category. For both the SLD and EH disability categories, there was a disordinal interaction. Tukey’s post hoc procedure was selected by the researcher as a follow-up test in order to make all possible pairwise comparisons between group mean willingness ratings (Stevens, 1999). Differences between pairs of mean willingness ratings were compared to Tukey’s Honest Significant Difference (HSD) value ( a =.05) which was 0.522, to determine statistical significance. Data were first examined to determine if there was a statistically significant difference between group mean willingness ratings at the two different time points (predata and post-data).

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49 49 Table 4 Analysis of Variance of Ratings of Willingness to Reintegrate by Status and Time for SLD and EH Disability Categories. Source df Type III SS Mean Square F Value Between 55 176.390 20.663 Status (A) 1 17.7118 17.718 6.01* Error (S/A) 54 159.277 2.946 Within 168 291.750 187.300 Disability (B) 1 162.862 162.862 85.51** Disability*Status(A/B) 1 0.040 0.040 0.02 Error (SA/B) 54 102.818 1.905 Time (C) 1 0.361 0.361 0.53 Status*Time (AC) 1 8.254 8.254 12.00** Error (SC/A) 54 37.134 0.688 Disability*Time (BC) 1 0.2817 0.282 0.29 Status*Disability*Time (ABC) 1 6.112 6.112 7.97* Error Disability*Time (SBC/A) 54 41.420 0.767 Total 223 468.141 207.963 *p<. 05 ** p<. 01

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50 50 4.61 5.46 5.29 5.32 4 4.5 5 5.5 6 6.5 7 Pre-dataPost-dataWillingness Ratings PC UC 6.18 6.07 6.39 5.07 4 4.5 5 5.5 6 6.5 7 Pre-dataPost-DataWillingness Ratings PC UC Specific Learning Disability Emotionally Handicapped Figure1. Status by disability by time interaction for SLD and EH categories. For the Specific Learning Disability category, there was a statistically significant difference in mean willingness ratings at post-data, but not at pre-data.. For the Emotionally Handicapped category, a statistically significant difference was found in the mean willingness ratings at pre-data, but not at post-data. Next, data were analyzed to determine if there was a change in mean willingness ratings from the pre-data condition (anecdotal information only) to the post-data condition (presentation of academic performance data). The change in mean willingness ratings across time was statistically significant ( p <.05) for the Specific Learning Disability UC condition and the Emotionally Handicapped PC condition. Thus, for the student with a Specific Learning Disability, willingness ratings decreased significantly when the student was presented with UC status, or with reading ability not within the range of low reading group peers in the general education classroom. For the student

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51 51 with an Emotional Handicap, willingness rating increased significantly when the student was presented with PC status, or reading ability within the range of low reading group peers in the general education classroom. For the other two conditions, mean ratings held near constant from pre-data to post-data. When examined holistically, these statistically significant data indicate that the Specific Learning Disability category had generally high willingness ratings at pre-data which significantly decreased when the student was presented as an unlikely candidate (UC) for reintegration. Conversely, for the Emotionally Handicapped category the special educators’ willingness to reintegrate significantly increased from preto post-data when the student was presented as a potential candidate for reintegration (PC). Analysis for OHI and EMR categories. Results for Analysis II are presented in Table 5. The three-way interaction in this analysis was not statistically significant, and consequently not interpreted. The two-way interaction between disability and time was significant, F (1, 168) = 22.22, p <.001, as well as the two-way interaction between status and time, F (1, 168) = 11.93, p = .0014. The main effect for disability was significant, F (1, 168) = 15.43. p =.0002. Status by time interaction for OHI and EMR In reference to the original research question, the intent was to determine if special educators’ willingness to reintegrate a student changed from pre-data to post-data, following presentation of achievement data designed to depict the student’s reintegration status as a potential candidate (PC) or unlikely candidate (UC) for reintegration into the general education setting. The two-

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52 52 way interaction effect of interest to answer this question is that between status (PC or UC) and time (pre-data to post-data), as is shown in Figure 2. Table 5 Analysis of Variance of Ratings of Willingness to Reintegrate by Status and Time for EMR and OHI Disability Categories. Source df Type III SS Mean Square F Value Between 55 164.499 3.265 Status (A) 1 0.219 0.219 0.07 Error (S/A) 54 164.491 3.046 465.249 Within 168 300.750 77.489 Disability (B) 1 32.254 32.254 15.43** Disability*Status(B/A) 1 6.111 6.111 2.92 Error (SB/A) 54 112.883 2.090 Time (C) 1 1.004 1.004 0.79 Status*Time (AC) 1 14.504 14.504 11.39* Error (SC/A) 54 68.741 1.273 Disability*Time (BC) 1 18.862 18.862 22.22** Status*Disability*Time (ABC) 1 0.540 0.540 0.64 Error Disability*Time (SBC/A) 54 45.848 0.849 Total 223 465.249 155.828 *p<.05, ** p<.01

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53 53 5.50 5.80 5.32 4.86 4 4.5 5 5.5 6 6.5 7 Pre-dataPost-dataWillingness Ratings PC UC Figure 2. Status by time interaction for OHI and EMR. Tukey’s post hoc procedure was used to obtain the critical value for the difference between pairs of means at the .05 level of significance (Stevens, 1999). Differences between pairs of mean willingness ratings were compared to Tukey’s Honest Significant Difference (HSD) value ( a =.05) which was 0.566 to determine statistical significance. Data were first examined to determine if there was a statistically significant difference between group mean willingness ratings at the two different time points (predata and post-data). A statistically significant difference ( p <.05) in mean willingness ratings was found at post-data, but not at pre-data. Thus, when given anecdotal information only, there was not a significant difference in willingness ratings between PC and UC conditions. After the addition of the academic performance data, the difference in mean willingness ratings for the UC condition was significantly lower (p< .05) than that for the PC condition

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54 54 Next, data were analyzed to determine if there was a change in mean willingness ratings from the pre-data (anecdotal information only) to the post-data (presentation of academic performance data) for each condition. Mean willingness ratings for the PC condition increased slightly while the mean willingness ratings for the UC condition decreased slightly. However, these changes in means were not statistically significant ( p <.05). Disability by Time Interaction Effect for OHI and EMR Categories. The two-way interaction effect between disability and time was also significant and is shown in Figure 3. In this ordinal interaction, the willingness ratings for the EMR category are greater than 1.5 points lower than the OHI category at both pre-data and post-data. Tukey’s post hoc procedure was used to obtain the critical value for the difference between pairs of means at the .05 level of significance (Stevens, 1999). Differences between pairs of means were compared to Tukey’s Honest Significant Difference (HSD) value ( a =.05) which was 0.566 to determine statistical significance. When holding the status condition constant only a slight change in mean willingness ratings occurred from pre-data to post-data for the OHI and EMR disability categories. The difference in mean willingness ratings between the two disability categories was statistically significant at both pre-data and post-data. Thus, when status was not considered, there was a statistically significant difference in means by disability category at both pre-data and post-data with EMR ratings being consistently lower than OHI.

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55 55 Figure 3. Disability by time interaction for OHI and EMR Summary of Analysis I and II results. The research question sought to determine if there was a change in special educators’ ratings of willingness to reintegrate from predata to post-data as a function of PC or UC status. A summary of the significant results for Analyses I and II is presented in Table 6 to more clearly examine this question. For SLD and EH, the three-way interaction of status, disability, and time was statistically significant. In contrast, this three-way interaction was not statistically significant for the EMR and OHI student. This finding indicates that the willingness values change from pre-data to post-data for SLD and EH students when both the factors of PC or UC status and disability category were included. The same was not true for EMR and OHI students. Two-way interactions were considered next. The interaction between status and time was significant in both analyses. 6.23 6.21 4.59 4.45 4 4.5 5 5.5 6 6.5 7 Pre-dataPost-dataWillingness Ratings OHI EMR

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56 56 Table 6 Summary of Significance Results Between Analyses I and II Analysis I (SLD and EH) Analysis II (EMR and OHI) Status (A) .0070 ns Disability (B) <.0001 .0002 Disability*Status(B/A) ns ns Time (C) ns ns Status*Time (AC) .001 .014 Disability*Time (BC) ns <.0001 Status*Disability*Time (ABC) .0067 ns When comparing group means at the pre-data and post-data time points, the difference in mean willingness ratings was statistically significant at post-data for the Specific Learning Disability, Other Health Impaired, and Educable Mentally Retarded categories. The difference in mean willingness ratings was statistically significant at predata for the Emotionally Handicapped category. When considering the change across time, two conditions demonstrated statistically significant changes (Specific Learning Disability UC and Emotionally Handicapped UC). Overall it appears that the presentation of academic performance data designed to depict the student as a potential candidate (PC) or unlikely candidate (UC) for reintegration had an effect on special educators’ willingness to reintegrate. However the nature of this effect is reflected differently between disability categories.

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57 57 Overview of Research Question Two Analyses Research question two addressed what types of student performance data were most influential in special educators’ ratings of willingness to reintegrate special education students. Given that academic achievement data had a significant impact on the post-data willingness to reintegrate, multiple regression analyses were conducted to determine which factors had the greatest influence on post-data willingness ratings. Variables in the regression model included ratings of how influential each of four types of academic performance data (grade equivalent, percentile rank, words correct per minute, and peer comparison,) on the educators’ willingness to reintegrate the student into the general education classroom. The academic performance data were such as to essentially present the student as a potential candidate or unlikely candidate for reintegretaion (i.e.,as PC or UC status). In addition to these numerical ratings, the respondents’ pre-data willingness rating was also included as a variable to assess the contribution of the anecdotal information. The data were screened to check for violations of multiple regression assumptions (see Chapter 3). Based on the analysis of the assumptions, it appeared reasonable to conduct the multiple regression analyses. Results of Multiple Regressions Results of individual regression analyses are presented first. Means, standard deviations, and intercorrelations among variables in the analyses are reported. These variables are post-data willingness ratings pre-data willingness ratings, and ratings of the influence that each of four academic performance data (grade equivalent (GE), percentile rank, words correct per minute (WCPM), and peer comparison) had on the

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58 58 special educators’ willingness to reintegrate. For the multiple regressions, beta weights, t-tests, and squared semi-partial correlations were examined and interpreted for each analysis. Finally, a summary of results from all four analyses is presented. Regression Analysis for SLD. Means, standard deviations, and bivariate correlations among predictor and outcome variables for the SLD category are reported in Table 7. The bivariate correlations indicate that all predictor variables, except pre-data willingness were significantly correlated ( p<. 01) with the outcome variable, post-data willingness to reintegrate. Ratings of the influence of the academic performance data (grade equivalent, percentile rank, words correct per minute, and peer comparison) on willingness to reintegrate were also highly correlated ( p<. 01) with each other. Correlations between pre-data ratings and the influence ratings of academic performance data were only statistically significant ( p<. 05) for the variable words correct per minute. Table 7 Means, Standard Deviations, and Intercorrelations among Predictor Variables for SLD Variable M SD 1 2 3 4 5 1. Post-Data 5.57 1.36 2. Pre-Data 6.38 0.87 0.24 3. GE Rating 4.31 1.41 0.59** 0.10 4. PR Rating 3.91 1.23 0.60** 0.16 0.70** 5. WCPM Rating 4.63 1.40 0.54** 0.27* 0.55** 0.57** 6. Peer Rating 4.46 1.50 0.65** 0.23 0.59** 0.67** .86** Note N =56 *p<. 05 ** p<. 01

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59 59 Results of the multiple regression analysis for the SLD student profile are presented in Table 8. The multiple regression equation accounted for approximately 52% of the variance in the special educators’ ratings of their willingness to reintegrate the SLD student F (5,53) = 10.21, p <.0001, adjusted R2 =0.47. Only the variable influence ratings of peer comparison was found to have a significant beta weight, .50 ( p <.05). As is shown by the squared semi-partial correlation, this variable accounted for approximately 10% of the unique variance in special educators’ willingness to reintegrate the SLD student. The unique contribution of all other variables was less than 10%. Table 8 Summary of Regression Analysis for SLD Vignette Variable Regression Coefficient Standard Error t -value Standardized Regression Coefficient ( ) Squared Semipartial Correlation Intercept 1.123319 1.06067 1.16 --Pre-Data 0.19213 0.16496 1.16 0.12196 .027 GE Rating 0.27389 0.14309 1.91 0.27966 .070 PR Rating 0.15427 0.17650 0.87 0.13753 .016 WCPM Rating -0.15218 0.19830 -0.77 -0.15460 .012 Peer Rating 0.45624 0.20127 2.27* 0.49529 .097 R2 = 0.515, F (5,53) = 10.21, p <.0001, adjusted R2 =0.47 *p<. 05 ** p<. 01

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60 60 Regression Analysis for OHI. Means, standard deviations, and bivariate correlations among predictor and outcome variables for the OHI category are reported in Table 9. Examination of the bivariate correlations shows that all predictor variables were significantly correlated ( p<. 01) with the outcome variable, post-data willingness to reintegrate. Influence ratings of the academic performance data (grade equivalent, percentile rank, words correct per minute, and peer comparison) were also highly correlated ( p<. 01) with each other, with the exception of the correlation between peer comparison and grade equivalent ratings. Correlations between pre-data willingness and any of the academic performance data were not significant. Table 9 Means, Standard Deviations and Intercorrelations among Predictor Variables for OHI M SD 1 2 3 4 5 1. Post-Data 6.21 1.00 2. Pre-Data 6.23 1.08 0.54** 3. GE Rating 6.00 1.20 0.58** 0.25 4. PR Rating 5.27 1.24 0.43** 0.09 0.62** 5. WCPM Rating 4.92 1.29 0.43** 0.24 0.45** 0.43** 6. Peer Rating 4.87 1.48 0.42** 0.17 0.41** 0.23 0.77** Note N =56 *p<. 05 ** p<. 01 Results of the multiple regression analysis for the OHI profile are presented in Table 10. The multiple regression equation accounted for approximately 55% of the variance in the special educators’ ratings of their willingness to reintegrate the OHI

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61 61 student, F (5,53) = 11.80, p <.0001, adjusted R2 =0.505. In this analysis, the beta weight for pre-data willingness was statistically significant ( p <.0001). The beta weight for the influence of grade equivalent data was also statistically significant ( p <.05). As is shown by the squared semi-partials, the variables pre-data willingness and influence ratings of grade equivalent data accounted for approximately 26% and 10% respectively of the unique variance in special educators’ willingness to reintegrate the OHI student. Beta weights for the influence of percentile rank, words correct per minute, and peer comparison data were not statistically significant. Table 10 Summary of Regression Analysis for OHI Vignette Variable Regression Coefficient Standard Error t -value Standardized Regression Coefficient ( ) Squared Semipartial Correlation Intercept 0.87593 0.71401 1.23 --Pre-Data 0.39344 0.09518 4.13** 0.42270 .263 GE Rating 0.26426 0.11610 2.28* 0.30545 .097 PR Rating 0.14403 0.1088 1.32 0.17454 .035 WCPM Rating -0.04479 0.13335 -0.34 -0.05640 .002 Peer Rating 0.15469 0.10971 1.41 0.22407 .040 R2 = 0.551, F (5,53) = 11.80, p <.0001, adjusted R2 =0.505 *p<. 05 ** p<. 01

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62 62 Regression analysis for EMR. Means, standard deviations, and bivariate correlations among predictor and outcome variables for the EMR category are reported in Table 11. The bivariate correlations show that all predictor variables were highly correlated ( p<. 01) with the outcome variable, post-data willingness ratings. The influence ratings on academic performance data (grade equivalent, percentile rank, words correct per minute, and peer comparison) were also highly correlated ( p<. 01) with each other. Correlations between pre-data willingness and any of the academic performance data were not significant. Table 11 Means, Standard Deviations and Intercorrelations among Predictor Variables for EMR M SD 1 2 3 4 5 1. Post-Data 4.45 1.56 2. Pre-Data 4.59 1.47 0.48** 3. GE Rating 3.71 1.44 0.52** 0.22 4. PR Rating 3.52 1.32 0.53** 0.16 0.84** 5. WCPM Rating 3.98 1.66 0.59** 0.18 0.73** 0.74** 6. Peer Rating 4.04 1.66 0.51** 0.14 0.54** 0.63** 0.84** Note N =56 *p<. 05 ** p<. 01 Results of the multiple regression analysis for the EMR student profile are presented in Table 12. The multiple regression equation accounted for approximately 51% of the variance in the special educators’ ratings of their willingness to reintegrate the EMR student, F (5,53) = 10.44, p <.0001, adjusted R2 =0.461. In this analysis, the beta weight for pre-data willingness rating was statistically significant ( p <.0001). As is

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63 63 shown by the squared semi-partial correlation, this variable accounted for approximately 22% of the unique variance in special educators’ willingness to reintegrate students into the general education classroom. No other beta weights were statistically significant. Table 12 Summary of Regression Analysis for EMR Vignette Variable Regression Coefficient Standard Error t -value Standardized Regression Coefficient ( ) Squared Semipartial Correlation Intercept 0.31204 0.62436 0.50 --Pre-Data 0.40675 0.10754 3.78** 0.38443 .222 GE Rating 0.03229 0.21960 0.15 0.02972 .000 PR Rating 0.20228 0.23387 0.86 0.17130 .015 WCPM Rating 0.28248 0.21621 1.31 0.300033 .033 Peer Rating 0.07713 0.18001 0.43 0.08218 .004 R2 = 0.510, F (5,53) = 10.44, p <.0001, adjusted R2 =0.461 *p<. 05 ** p<. 01 Regression analysis for EH. Means, standard deviations, and bivariate correlations among predictor and outcome variables for the EH category are reported in Table 13. The bivariate correlations show that all predictor variables were highly correlated ( p<. 01) with the outcome variable, post-data willingness to reintegrate. The influence ratings of the academic performance data (grade equivalent, percentile rank, words correct per minute, and peer comparison) were also highly correlated ( p<. 01) with

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64 64 each other. Correlations between pre-data willingness ratings and the influence ratings of the academic performance data were statistically significant ( p <.05), though weaker than the correlations between the academic performance data. The significant correlations between pre-data willingness and the influence ratings of the academic performance data variables is unique to the EH category. Table 13 Means, Standard Deviations and Intercorrelations among Predictor Variables for EH M SD 1 2 3 4 5 1. Post-Data 5.39 1.64 2. Pre-Data 4.95 1.49 0.45** 3. GE Rating 5.91 1.21 0.73** 0.48** 4. PR Rating 5.41 1.28 0.58** 0.28* 0.66** 5. WCPM Rating 5.35 1.27 0.50** 0.28* 0.59** 0.78** 6. Peer Rating 5.15 1.54 0.55** 0.28* 0.53** 0.68** 0.84** Note N =56 *p<. 05 ** p<. 01 Results of the multiple regression analysis for the EH student profile are presented in Table 14. The multiple regression equation accounted for approximately 70% of the variance in the special educators’ ratings of their willingness to reintegrate the EH student, F (5,53) = 22.79, p <.0001, adjusted R2 =0.669. In this analysis, the beta weight for the influence ratings of grade equivalent data was statistically significant ( p <.0001). As is shown by the squared semi-partial correlation, this variable accounted for approximately 36% of the unique variance in special educators’ willingness to reintegrate students. No other beta weights were statistically significant.

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65 65 Table 14 Summary of Regression Analysis for EH Vignette Variable Regression Coefficient Standard Error t -value Standardized Regression Coefficient ( ) Squared Semipartial Correlation (%) Intercept -1.19439 0.67069 -1.78 --Pre-Data 0.16827 0.09431 1.78 0.15940 .061 GE Rating 0.78921 0.15014 5.26** 0.60356 .361 PR Rating 0.21562 0.16924 1.27 0.17486 .032 WCPM Rating -0.18304 0.20903 -0.88 -0.14780 .015 Peer Rating 0.19147 0.14756 1.30 0.18661 .033 R2 = 0.699, F (5,53) = 22.79, p <.0001, adjusted R2 =0.669 *p<. 05 ** p<. 01 Summary of Multiple Regression Analyses Results Results of the multiple regression analyses for each of the four disability categories were next examined holistically. Patterns emerged in the correlation matrices for each analysis. The outcome variable, post-data willingness to reintegrate, was almost always highly correlated with all of the predictor variables. The influence ratings for the academic performance data variables were generally highly correlated with each other. With the exception of the EH category, the variable pre-data willingness to reintegrate, generally showed a weak correlation with the other variables.

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66 66 Table 15 presents a summary of the multiple regression results for each of the disability categories. For each analysis, the overall R2 was significant ( p <.001) indicating that a statistically significant portion of the outcome variable’s variance was related to the set of predictor variables. The adjusted R2 ranged from 0.462 (EMR) to 0.669 (EH) indicating little shrinkage. Thus, it was concluded that these models were appropriate for determining variance associated with this set of predictor variables on the outcome variable. Table 15 Squared Semi-partial Proportions Correlations and R2 Variable SLD OHI EMR EH Willingness Pre-Data .02 .26** .22** .06 Grade Equivalent Rating .07 .10* .00 .36** Percentile Rank Rating .02 .04 .01 .03 WCPM Rating .01 .00 .03 .02 Peer Comparison Rating .10* .04 .00 .03 R2 0.515** 0.551** 0.511** 0.699** Adj. R2 0.465 0.505 0.462 0.669 *p <.05, ** p <.01 In all analyses, one or two of the predictor variables had statistically significant beta weights and squared semi-partial correlations, and thus emerged as accounting for higher unique contributions than others variables to the variance in willingness to reintegrate ratings. The highly influencing factor(s) varied between disability categories.

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67 67 In the OHI and EMR analyses, the pre-data willingnessratings, or anecdotal information, accounted for the largest portion of the variance. For EH, influence ratings for the grade equivalent data accounted for the largest portion of the variance, while for SLD, influence ratings for peer comparison data accounted for the largest portion of the variance. Relationships between variables with high correlations were also compared. With regard to data from a standardized norm-referenced test, the unique contribution of the influence of grade equivalent data on the special educators’ willingness to reintegrate was higher than that for the percentile rank data in three of four analyses. When considering CBM data, the influence of peer comparison data made a larger contribution to willingness to reintegrate than the influence of words correct per minute data in the SLD vignette analysis while the amounts were similar in the other three analyses. In two of four analyses, the information from the standardized test (grade equivalent and percentile rank added together ) had higher contributions than CBM data (words correct per minute and peer comparison added together).

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68 68 Chapter 5 Discussion This chapter begins with a review of the study’s results with discussion of the effect performance data has on willingness to reintegrate and the influence of various factors used in reintegration decision making. The results of this study are then compared with other research on the topic of reintegration. Additional findings, including the effect of disability category and comparison of different types of information used in reintegration decision-making are considered. Significance of the contribution, implications for practice, and suggestions for further study complete the chapter. This study found that academic performance data have an effect on special educators’ willingness to reintegrate students beyond their prior willingness. As mentioned in Chapter 2, students with CBM scores within the range of low achieving peers were considered to be potential candidates for reintegration, and those with CBM scores below the low achieving peers were considered to be unlikely candidates for reintegration. In this study, participants were initially presented with only anecdotal information about four hypothetical special education students and asked to rate their willingness to reintegrate the student into the general education classroom for reading. Next, participants were provided with data from a published norm-referenced test of

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69 69 reading achievement, and CBM data comparing the target student with low readers in the general education class and again asked to rate their willingness to reintegrate the student into the general education classroom for reading. These data were designed to depict the student as a potential candidate (PC) or unlikely candidate (UC) for reintegration. ANOVA procedures were used to determine if there were statistically significant differences in means between groups. When comparing group means at the pre-data and post-data time points, the difference in mean willingness ratings was statistically significant at post-data for the Specific Learning Disability, Other Health Impaired, and Educable Mentally Retarded categories. The difference in mean willingness ratings was statistically significant at pre-data for the Emotionally Handicapped category. When considering the change across time from pre-data to post-data, two conditions demonstrated statistically significant changes. Willingness ratings for the student in the Specific Learning Disability category decreased significantly when presented as an unlikely candidate (UC) for reintegration while willingness ratings for the Emotionally Handicapped category increased si gnificantly when presented as a potential candidate (PC) for reintegration. Overall it appears that the presentation of academic performance data designed to depict the student as a potential candidate (PC) or unlikely candidate (UC) for reintegration had an effect on special educators’ willingness to reintegrate. However the nature of this effect is reflected differently between disability categories. Further analyses were conducted through multiple regression to determine which variables had the greatest influence on reintegration decisions. The adjusted R2 value, or

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70 70 proportion of the variance in the outcome variable explained by the set of predictors, ranged from 0.462 (EMR) to 0.669 (EH). The percentage of influence for each of the five variables varied as a function of disability category. The influence of grade equivalent data on special educators’ willingness to reintegrate accounted for the largest percentage of unique variance (36%) in the ratings of their willingness to reintegrate an EH student. For the SLD student, the influence of peer comparison data from CBM scores accounted for the largest proportion of unique variance (10%) in special educators’ ratings for their willingness to reintegrate. Though academic performance data did significantly affect the post-data willingness to reintegrate, the contribution of anecdotal information regarding non-academic skills was still strong. In two of the four vignettes, pre-data willingness, or anecdotal information, accounted for the largest unique variance in the regression model (OHI = 26% and EMR = 22%). Comparison with Previous Research Studies examining PC or UC status. A similar study published by Rodden-Nord, Shinn, and Good (1992) involved analysis of ratings by general education teachers. Results were similar to the current study in that the mean willingness ratings increased for PC students, and decreased for UC students after academic performance data were provided. Though these studies examined similar variables and applications, comparisons should be made with caution. The Rodden-Nord, et al. (1992) study surveyed general education teachers who were rating actual students, not vignettes of hypothetical students. Contrasting results were found relative to factors influencing

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71 71 decision making. In the current study, pre-data willingness, or anecdotal information, accounted for the largest percentage of variance while in the Rodden-Nord study, CBM reading performance data accounted for the most variance in teachers’ post-data willingness to reintegrate. Other studies with actual students have also been conducted by Shinn and colleagues who studied reintegration procedures primarily with students identified with a learning disability (Shinn, et al. 1993; Shinn, et al. 1996; Shinn, et al., 1997). The findings of these studies with actual students with learning disabilities support findings from the current study. For SLD students, comparison of CBM scores with low reading group peers in the general education class has a significant effect on reintegration decisions. Attitudinal studies on reintegration. Previous research on the attitudes of special educators has indicated that the special education setting is preferred for students with mild disabilities. Knoff (1985) reported that both general and special educators perceived special education classroom settings as more effective and more preferred for students with mild disabilities than the general education classroom. More recently, Cook, Semmel, and Gerber (1999) reported that special educators indicated a lack of support for the ideal that students with disabilities improve their academic achievement when placed in the general education classroom with consultative services. Data in this sample were contradictory to previous research on special educator attitudes. For this sample, the distribution of the pre-data willingness ratings to reintegrate was negatively skewed with a mean of 5.513 on a scale of 1 to 7 indicating

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72 72 general support for reintegration. These skewed results could be the result of the regional limitations of the sample selection. Another hypothesis is experience with reintegration. Over half of the special educators (59%) in the current study reported that within the past two years, they had a student reintegrated into the general education curriculum in subjects in which they previously received special education services. Of those who had participated in reintegration, an average of 6.85 students were reintegrated within the past two years. This sample’s level of experience with reintegration may have been responsible for high pre-data willingness ratings endorsed by participants. This hypothesis is consistent with Grier (2001), who found a significant association between general education teachers’ attitudes towards including special education students into general education classrooms and the teachers’ overall breadth of experience (i.e., practice with the inclusion of more types of disabilities). Recent paradigm shifts or legislative changes promoting reintegration may also be responsible for the change in trend. The previous attitudinal studies presented were published between seven and twenty years ago. Since that time, revisions to the Individuals with Disabilities Education Act (IDEA) in 1997 and in 2004 have placed increasing emphasis on inclusion and reintegration practices. More than ever, schools now are expected to educate students in the regular classroom to the maximum extent possible. Additional Findings Disability category. Though disability category was not a variable of interest in this study, it did have a statistically significant interaction with time in the ANOVA

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73 73 analysis for both the EMR and the OHI categories. This finding suggests that special educators’ decisions may have been influenced by information on disability category presented in the vignette. Willingness ratings for EMR were consistently lower than OHI, for example. Special education classification, or labeling, may influence attitudes and expectations for a child, thus affecting a mainstreaming program’s success or failure (Knoff, 1985). Research has shown that students with behavioral disorders have the highest rejection rate of mainstreamed students and a general bias exists against mainstreaming children with behavioral disorders (Downing et al., 1990). Results for both research questions appeared to vary as a function of disability category. In research question one, there were different patterns of significance observed across disability categories. For research question two, different variables accounted for the largest proportion of unique variance across the four disability categories. For EMR and OHI, anecdotal information was more influential than academic data. It is possible that preconceived ideas about the nature of these disabilities, or attributions of the labels may be responsible for the difference in results. Teachers’ attitudes may become less positive as the special needs of the student become more severe in nature (Grier, 2001). As noted previously, in previous studies with students with learning disabilities, CBM data has been influential in reintegration decision-making (Shinn, et al. 1993; Shinn, et al. 1996; Shinn, et al., 1997). For this study, peer comparison using CBM data was the most influential variable for the SLD student. In contrast, grade equivalent data was the most influential variable for the EH student. Thus, it appears that for EH students, an index of achievement relative to local

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74 74 peers was less important than a more global index of achievement level represented by scores on a published norm-referenced test. Teachers may conceive of the data from the published norm-referenced test to be more of an absolute achievement level, not relative to local peers. Use of published norm-referenced tests also promotes the belief that the problem is located within the child (Witt, et al., 1998) instead of within the child’s educational environment. Grade equivalent scores versus percentile ranks. In three of four multiple regression analyses (see Table 15), data using grade equivalents was found to be more influential than data reported in terms of percentile ranks. This difference was most extremely pronounced for the EH vignette where grade equivalent data was the largest unique contributor with 36% while percentile rank data accounted for only 3% of unique variance. This finding suggests that when interpreting information from norm-referenced tests, special educators are more likely to use grade equivalent scores than percentile ranks in decision making. Grade equivalent scores are likely one of the most popular methods of presenting norm-referenced scores. People without a background in tests and measurement may gravitate to grade equivalent scores because they appear to be easily understood, yet they are often misinterpreted. Disadvantages of grade equivalent scores have been summarized by Witt, Elliott, Daly, Gresham, and Kramer (1988). First, grade equivalent scores do not provide equal units of measurement. This means that the increase in reading achievement from grade 5.0 to 6.0 is not the same as the increase between two different grade levels on the same test. Also, grade equivalent scores may not have the

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75 75 same meaning for students of different ages. In addition to interpretation errors, grade equivalents are obtained by interpolation and extrapolation of scores in the sample, which reduces the statistical soundness of the data (Sattler, 2001). For these reasons, some professional organizations and even test publishers have argued against the presentation and use of grade equivalent scores (Witt, et al., 1998; AGS, 2006). Norm-referenced tests are designed to give information about a student’s performance in reference to a normative sample. Percentiles are a common way of expressing a student’s relative standing in a distribution of scores and are also generally easily understood. However, they do not have the same disadvantages as grade equivalent scores as mentioned above. Data from published norm-referenced tests versus CBM data. In three of four multiple regression analyses, the unique contributions of data from CBM (words correct per minute and peer comparison) were less than that of data from the published normreferenced test (grade equivalent and percentile rank). In the most extreme case (EH), this difference was 39% for published norm-referenced test to 5% for CBM data. These data suggest that special educators (at least those in this sample) are more heavily influenced by data from published norm-referenced tests than by CBM data. Published norm-referenced tests are widely used in special education for decisions involving eligibility and labeling. Many states specify scores from standardized tests required to identify a student as eligible for special education services. These tests have also received much attention in terms of technical data and research and provide information easily communicated with people unfamiliar with tests. Though published

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76 76 norm-referenced tests are often used in identification and labeling, they typically provide information that is too general to be useful in programming and planning classroom teaching activities (Witt, et al., 1998). Because norm-referenced tests are designed to compare one student with another, they tend to promote and reinforce the belief that the problem is located within the child (Witt, et al., 1998). If the problem is viewed to be within the child, educators may be less likely to investigate interventions targeting environmental factors such as teaching and curriculum modifications. Special educators may be inclined to attribute lack of progress to low ability or achievement levels as determined by published norm-referenced tests, rather than to instructional methods. As discussed in the literature review, the reliability and validity evidence for CBM has also been tested and supported. In addition to technical adequacy, CBM also has advantages over published norm-referenced tests. Use of repeated measures allows the examiner to review the student’s performance at any stage in the decision-making process. Also, using time series analysis, it is possible to examine functional relationships between the data and instructional intervention, or change in placement. Because norm-referenced tests are not designed to be administered repeatedly, the examiner must rely on summative, instead of formative, evaluation. In addition, normreferenced tests are more expensive and time consuming to administer. Specific to reintegration decisions, previous research has advocated for the use of CBM because it can be used in deciding to attempt a reintegrating trial as well as in evaluating its success (Shinn, Powell-Smith, & Good, 1996).

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77 77 Even more recently, CBM has been used to test student’s responsiveness to treatment (Deno, 2003). Response to Intervention (RtI) has gained favor within policymaking groups as an alternative to traditional standardized testing through published norm-referenced tests. RtI may serve to reduce the number of false positive errors made in special education referrals that occur due to insufficient pre-referral intervention and data monitoring (VanDerHeyden, Witt, & Barnett, 2005). Data from the current study indicate that teachers consider data from published norm-referenced tests more than they consider CBM data. The use of data from published norm-referenced tests has historical roots, in that these tests have been used as the legal basis for eligibility determination for a number of years. Given that the technical adequacy of CBM has been established, and the research on its utilization for responsible reintegration and special education eligibility determination, special educators should be encouraged to use CBM data as a part of data-based decisionmaking. Significance and Contribution Previously it was thought that general education teachers might be a hindrance to the reintegration process because of resistance against having students with disabilities in their classes (Knoff, 1985). The few other studies that have addressed special educators’ attitudes towards reintegration have presented evidence that hesitancy by special educators may actually be part of the problem as well (Knoff, 1985; Cook et al., 1999). Candidates for reintegration have typically been identified by the special education teacher’s judgment (Rodden-Nord, et al. 1992; Ball, 1997). In the past, special

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78 78 educators’ ratings have been very conservative in their judgments of student readiness for reintegration (Rodden-Nord et al., 1992). The more contemporary sample of participants in the current study expressed willingness to reintegrate hypothetical students presented in vignettes. Though special educators today appear more willing to consider reintegration and have practiced reintegration, the system could still benefit from specific procedures. Because the use of CBM is becoming more widespread in both general and special education, these data could be used to develop such systematic procedures. The use of numerical data such as CBM scores reduces subjectivity, and is more valid and reliable than special educator nomination alone. CBM data are designed to be sensitive to change and can be administered frequently. These qualities are not present with published norm-referenced tests. Furthermore, this study found that special educators are more likely to consider grade equivalent scores, extrapolated and interpolated data, than percentile ranks which are more statistically sound. Formative evaluation in the form of CBM has been supported through previous research as an appropriate source of data in responsible reintegration practice (Shinn, et al., 1996; Shinn, et al., 1997). Parents often look to the special educator for guidance regarding placement decisions (Green & Shinn, 1994; Ball, 1997), thus their recommendation is premium. Because the opinion of the special educator is so influential, it should be based on data obtained from appropriate sources. CBM data have proven to be an important and appropriate data source in responsible reintegration (Fuchs, et al., 1992; Shinn, PowellSmith, & Good, 1996).

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79 79 Implications for Practice CBM data have been used for various assessment purposes including screening, pre-referral evaluation, placement in special programs, formative evaluation, and evaluation of reintegration and inclusion (Deno, 2003). For some years, CBM has been viewed as a choice measurement tool in the Problem-Solving Model (Deno, 2002; Shinn, 2002). More recently, CBM has also emerged as a metric of choice in the Response to Intervention model, a specification of problem-solving and a promising alternative to the current model of identification and eligibility assessment in special education (VanDerHeyden et al., 2005). As the number of uses for CBM is expanding, its acceptability and uses are increasing. Recent research has explored the use of CBM data to predict success on high-stakes assessment. Thus, instead of belonging solely to special education, the use of CBM is now supported in general education as well. Some districts are now using CBM data to screen all students for potential reading problems and to plan and track intervention programs. Because CBM is becoming more widely administered, the data are readily available for use in reintegration decision-making. On a positive note, from results of this study, it appears that special educators are becoming more willing to consider reintegration of students with mild disabilities. Teachers have typically been the referral source for special education placement decisions including reintegration. However, Deno (2003) questions the validity of teachers as ‘tests’ of student success. Inherently, subjective opinions contain more bias than the numerical data gathered from a standardized, reliable, and valid method. By developing systematic procedures using CBM data, reintegration trials can be initiated

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80 80 appropriately and monitored for effectiveness. Special educators are encouraged to use this information to promote responsible reintegration. School psychologists and other assessment specialists are in an ideal position to strive for insuring that special educators select the optimal information desired for the decision-making task at hand. When published norm-referenced tests are used, the most psychometrically sound score interpretations should be emphasized (i.e., reduced emphasis on grade equivalents from norm-referenced tests). Schools may consider educating general and special education teachers on the variety of uses of CBM data and how to use these data for decision-making. Limitations and Delimitations This study focused only on reading instruction for elementary-age students. Therefore, the results should not be generalized to other grade levels or subject areas. Also, the results of this study may be specific to the local education agencies where the study was conducted and may not be applicable to wider populations. Because convenience sampling procedures were used and the participants were volunteers, the responses given by them may not reflect the population as a whole. As discussed in Chapter 3, this was a self-selection sample and some of the potential participants (particularly those who would have to complete the surveys on their own time) elected not to participate. This study examined the special educators’ attitudes towards reintegration, not their actual behavior. The educators were not obligated to reintegrate any children. Hypothetical cases, as opposed to actual classroom data, were used for the purposes of this study which was another limitation. The educators decisions

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81 81 may have been different if they were to consider an actual student, or if they were required to actually reintegrate the student. Due to the design of the survey instruments (i.e., there were two forms of the survey), each participant did not respond to all status by disability category conditions. Suggestions for Future Research Future research should replicate this study with a different population of educators and students as the results may be specific to the geographic area or grade levels of instruction of the educators. Also, studies should be conducted with actual reintegration cases instead of hypothetical vignettes. Educators’ actual reintegration practices may be different from their responses to a hypothetical student. Due to the differences found in results between students identified with different disabilities, other researchers may wish to investigate the role of disability category in reintegration decisions and to explore the influence of non-academic skills. Conclusions Results of this study indicate that academic performance data do have a statistically significant impact on the reintegration decisions of special educators, which concurs with previous research (e.g., Rodden-Nord, et. al. 1992). However, this sample was different from those in previous studies in that they were generally willing to attempt reintegration and that they had previous experience in reintegration. The influence of factors affecting reintegration decisions appear to vary as a function of disability category. Results of this analysis have many implications for practice. Psychologists and other assessment professionals should assure that teachers

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82 82 are selecting the best possible data for decision-making and interpreting it correctly. CBM data may be used to develop systematic procedures for nominating students for possible reintegration.

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83 83 References Cook, B. G., Semmel, M. I., & Gerber, M. M. (1999). Attitudes of principals and special education teachers toward the inclusion of students with mild disabilities: Critical differences of opinion. Remedial and Special Education, 20 (4), 199-207, 243. Deno, S. (1985). Curriculum-based measurement: The emerging alternative. Exceptional Children, 52, 219-232. Deno, S. (1986). Formative evaluation of individual programs: A new role for school psychologists. School Psychology Review, 15, 358-374. Deno, S.L. (1989). Curriculum-based measurement and special education services: A fundamental and direct relationship. In M.R. Shinn (Ed.) Curriculum-Based Measurement: Assessing Special Children (pp. 1-17). New York: Guilford. Shinn, M.R. (2003). Problem-solving as “best practice.” In A. Thomas and J. Grimes (Eds.) Best practices in school psychology IV (pp. 671-698). Washington, DC: National Association of School Psychologists. Deno, S. L. (2003). Developments in curriculum-based measurement. Journal of Special Education, 37 (3), 184-192.

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84 84 Downing, J.A., Simpson, R.L., & Myles, B.S. (1990). Regular and special educator perceptions of nonacademic skills n eeded by mainstreamed students with behavioral disorders and learning disabilities. Behavioral Disorders, 15 (4), 217266. Fuchs, L.S., & Fuchs, D. (1984). Criterion-referenced assessment without measurement: How accurate for special education. Remedial and Special Education, 5 (4), 2932. Fuchs, D. Fuchs, L.S., & Fernstrom, P. (1992). Case-by-case reintegration of students with learning disabilities. Elementary School Journal 92, 261-281. Fuchs, D. Roberts, P.H., Fuchs, L.S., & Bowers, J. (1996). Reintegrating students with learning disabilities into the mainstream: A two-year study. Learning Disabilities Research and Practice 11 (4), 214-229 Glass, G.V. & Hopkins, K.D. (1996). Statistical Methods in Education and Psychology (3rd.) Boston: Allyn and Bacon. Green, S.K. & Shinn, M.R. (1994). Parent attitudes about special education and reintegration: What is the role of student outcomes? Exceptional Children 61 (3), 269-281. Grier, J.E.C. (2001). The Relationship between teachers’ problem-solving skills and their attitudes towards inclusion Unpublished doctoral dissertation. Tampa: University of South Florida.

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85 85 Knoff, H. M. (1985). Attitudes toward mainstreaming: A status report and comparison of regular and special educators in New York and Massachusetts. Psychology in the Schools, 22 (4), 410-418. Lytle, J., & Penn, W. (1986). Special education: Views from America’s cities. Philadelphia: Research for Better Schools. Martson, D.B. (1989). A curriculum-based measurement approach to assessing academic performance: What is it and why do it? In M.R. Shinn (Ed.) Curriculum-Based Measurement: Assessing Special Children (pp. 18-78). New York: Guilford. Pearson Assessments (n.d.) Interpretation Problems of Age and Grade Equivalents. Retrieved September 6, 2006, from http://ags.pearsonassessments.com/assessments/age_grade.asp Powell-Smith, K.A. & Ball, P.L. (2002). Best Practices in Reintegration and Special Education Exit Decisions. In A. Thomas and J. Grimes (Eds.) Best Practices in School Psychology IV (pp. 535-551). Washington, DC: National Association of School Psychologists. Powell-Smith, K.A. & Stewart, L.H. (1998). The use of curriculum-based measurement in the reintegration of students with mild disabilities. In M.R. Shinn (Ed.) Advanced applications of curriculum-based measurement (pp. 254-297). New York: Guilford.

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86 86 Rodden-Nord, M.K. (1990). An examination of the effects of achievement information on general education teachers’ willingness to rein tegrate mildly handicapped students into general e ducation classrooms. Unpublished doctoral dissertation. Eugene: University of Oregon. Rodden-Nord, K., Shinn, M.R., & Good, R.H. (1992). Effects of classroom performance data on general education teachers’ willingness to reintegrate mildly handicapped students. School Psychology Review 21, 138-154. Sattler, J. M. (2001). Assessment of children: Cognitive applications (4th ed.) Jerome M. Sattler, Publisher, Inc. Shinn, M.R. (1986). Does anyone care what happens after the referral-test-place placement process? The systematic evaluation of special education effectiveness. School Psychology Review, 15, 49-58. Shinn, M.R. (2003). Best practices in using curriculum-based measurement in a problem-solving model. In A. Thomas and J. Grimes (Eds.) Best Practices in School Psychology IV (pp. 671-698). Washington, DC: National Association of School Psychologists. Shinn, M.R., Habedank, L., Rodden-Nord, K., & Knutson, N. (1993). Using curriculumbased measurement to identify potential candidates for reintegration into general education. The Journal of Special Education, 27 (2), 202-221. Shinn, M.R., Powell-Smith, K.A., and Good, R.H. (1996). Evaluating the effects of responsible reintegration into general education for students with mild disabilities on a case-by-case basis. School Psychology Review, 25 (4), 519-539.

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87 87 Shinn, M.R., Powell-Smith, K.A., Good, R.H., & Baker, S.K. (1997). The effects of reintegration into general education reading instruction for students with mild disabilities. Exceptional Children, 64 (1), 59-79. Stevens, J. P. (1999). Intermediate statistics: A modern approach (2nd ed.) Lawrence Erlbaum Associates, Publishers. VanDerHeyden, A. M., Witt, J. C., & Barnett, D. W. (2005). The emergence and possible futures of response to intervention. Journal of Psychoeducational Assessment, 23 (4), 339-361. Witt, J.C., Elliott, S.N., Daly III, E.J., Grehsam, F.M., & Kramer, J.J. (1998). Assessment of At-Risk and Special Needs Children (2nd ed.). Boston: McGraw Hill.

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88 88 Appendices

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89 89 Appendix A: Informed Consent Informed Consent Social and Behavioral Sciences University of South Florida Information for People Who Take Part in Research Studies The following information is being presented to help you decide whether or not you want to take part in a minimal risk research study. Please read this carefully. If you do not understand anything, ask the person in charge of the study. Title of Study: Special Education Teachers’ Perceptions of Reintegrating Special Education Students into General Education Classrooms Principal Investigator: Brandi L. Tanner Study Location(s): Hillsborough, Polk, and Hernando County Schools You are being asked to participate because you are a special education teacher of students with mild disabilities in grades one through five. General Information about the Research Study The purpose of this research study is to determine what factors influence the willingness of special education teachers to reintegrate students into general education settings. Plan of Study For this study, you will be asked to read a series of vignettes and respond to related surveys. The entire process will be part of a single administration session to last approximately 45 minutes to one hour. Payment for Participation You will not be paid for participation in this study. Benefits of Being a Part of this Research Study You will not directly benefit from participating in the survey. However, by taking part in this research study, you may increase our overall knowledge of factors that affect special education teachers’ decision-making process concerning reintegrating students with disabilities into general education classes. Risks of Being a Part of this Research Study Because you will remain anonymous, and because the nature of the questions asked is not likely to cause any discomfort, participation should present no more than minimal risk. Confidentiality of Your Records Your privacy and research records will be kept confidential to the extent of the law. Authorized research personnel, employees of the Department of Health and Human Services, and the USF Institutional Review Board and its staff, and any other individuals acting on behalf of USF, may inspect the records from this research project.

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90 90 Appendix A: (Continued) The results of this study may be published. However, the data obtained from you will be combined with data from others in the publication. Responses will be collected anonymously. The published results will not include your name or any other information that would personally identify you in any way. The data will be recorded in a database on the principal investigator's computer. Each participant will receive a research ID number that will be used to keep track of survey data. The names of each participant will be kept in a separate file on a disk that will be locked in a filing cabinet. The files will be deleted from the computer and disk and destroyed once seven years has elapsed. Volunteering to Be Part of this Research Study Your decision to participate in this research study is completely voluntary. You are free to participate in this research study or to withdraw at any time. Your decision to participate (or not participate) will in no way affect your job status. There will be no penalty or loss of benefits you are entitled to receive, if you stop taking part in the study. Questions and Contacts € If you have any questions about this research study, contact Brandi Tanner at (813) 624-3568 or Dr. Kelly Powell-Smith at (813) 974-9698. € If you have questions about your rights as a person who is taking part in a research study, you may contact the Division of Research Compliance of the University of South Florida at (813) 974-5638. Consent to Take Part in This Research Study By signing this form I agree that: € I have fully read or have had read and explained to me this informed consent form describing this research project. € I have had the opportunity to question one of the persons in charge of this research and have received satisfactory answers. € I understand that I am being asked to participate in research. I understand the risks and benefits, and I freely give my consent to participate in the research project outlined in this form, under the conditions indicated in it. € I have been given a signed copy of this informed consent form, which is mine to keep. Investigator Statement: I certify that participants have been provided with an informed consent form that has been approved by the University of South Florida’s Institutional Review Board and that explains the nature, demands, risks, and benefits involved in participating in this study. I further certify that a phone number has been provided in the event of additional questions. _________________________ _________________________

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91 91 Appendix B: Standardized Survey Administration Procedures and Directions 1. Distribution of Informed Consent “You are being asked to participate in a survey of special educators of elementary-aged students for a graduate research project titled Special Educators’ Perceptions of Reintegrating Special E ducation Students Into General Education Classrooms. Your participation is voluntary and will contribute to the body of knowledge about reintegration of special education students into general education classrooms. You will not be asked to provide any identifying information on any of the study materials. Instead a code number will be assigned to ensure anonymity. Your decision to participate (or not to participate) will in no way affect your job status. If you agree to participate in the study, please sign the informed consent document and it will be collected by a member of the research team.” 2. Distribution of SETS-R1. “The first survey contains four vignettes. Please use the information presented to answer the related questions following each. Answer each question independently and do not consult with others regarding your responses. Also, please answer the demographic questions on the last sheet. When you have finished with the first survey, please raise your had and we will collect this survey from you and give you part two of the survey. When you have completed part two you are finished. Please give your materials to a member of our research team. This survey session will last approximately 30 minutes. Thank you very much for your time.

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92 92 Appendix C: Special Education Teacher Survey on Reintegration 1 Special Education Teacher Survey on Reintegration – 1 Research ID # Kevin is a fourth grade student who has been served in special education under the category of Specific Learning Disability for the past two years. He receives reading instruction in the resource room and is in the general education classroom the remainder of the day. Kevin follows classroom rules and interacts well with other students. His handwriting is messy and he takes longer than the typical student to complete his work. 1. Given your current knowledge of Kevin, what do you think is the most appropriate reading placement for him? a. The lowest reading group in the general education classroom b. The lowest reading group in the general education classroom with collaboration from the special education teacher c. Special education resource room d. A self-contained special education placement e. Other 2. How willing or unwilling would you be to attempt to reintegrate Kevin into the general education classroom for reading instruction? Very Unwilling Unwilling Somewhat Unwilling Neutral Somewhat Willing Willing Very Willing 1 2 3 4 5 6 7 3. What is the single most important factor influencing your decision to attempt to reintegrate Kevin into the general education classroom for reading instruction?

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93 93 Appendix C: (Continued) Special Education Teacher Survey on Reintegration – 1 Research ID # Sarah is a third grade student who is receiving special education services under the category of Other Health Impaired She was diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) last school year and began receiving reading instruction in the resource room at that time. Sarah works quickly and does most of her in class work with little assistance. She is frequently out of her seat and talks out of turn. 1. Given your current knowledge of Sarah, what do you think is the most appropriate reading placement for her? a. The lowest reading group in the general education classroom b. The lowest reading group in the general education classroom with collaboration from the special education teacher c. Special education resource room d. A self-contained special education placement e. Other 2. How willing or unwilling would you be to attempt to reintegrate Sarah into the general education classroom for reading instruction? Very Unwilling Unwilling Somewhat Unwilling Neutral Somewhat Willing Willing Very Willing 1 2 3 4 5 6 7 3. What is the single most important factor influencing your decision to attempt to reintegrate Sarah into the general education classroom for reading instruction?

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94 94 Appendix C: (Continued) Special Education Teacher Survey on Reintegration – 1 Research ID # Derrick has received special education services since first grade under the category of Educable Mentally Retarded He is now in the fifth grade and receives reading and math instruction in the resource room. Derrick follows classroom rules and stays on task. His work is often not of acceptable quality and he requires more assistance that the typical student to complete his in class work. 1. Given your current knowledge of Derrick, what do you think is the most appropriate reading placement for him? a. The lowest reading group in the general education classroom b. The lowest reading group in the general education classroom with collaboration from the special education teacher c. Special education resource room d. A self-contained special education placement e. Other 2. How willing or unwilling would you be to attempt to reintegrate Derrick into the general education classroom for reading instruction? Very Unwilling Unwilling Somewhat Unwilling Neutral Somewhat Willing Willing Very Willing 1 2 3 4 5 6 7 3. What is the single most important factor influencing your decision to attempt to reintegrate Derrick into the general education classroom for reading instruction?

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95 95 Appendix C: (Continued) Special Education Teacher Survey on Reintegration – 1 Research ID # Sonya is in the fourth grade and has received reading and social skills instruction in the resource room for three years. She is served under the special education category of Emotionally Handicapped Sonya does most in class work correctly and remains on task independently while working. She frequently gents into fights and has difficulty making her needs known in an appropriate manner. 1. Given your current knowledge of Sonya, what do you think is the most appropriate reading placement for her? a. The lowest reading group in the general education classroom b. The lowest reading group in the general education classroom with collaboration from the special education teacher c. Special education resource room d. A self-contained special education placement e. Other 2. How willing or unwilling would you be to attempt to reintegrate Sonya into the general education classroom for reading instruction? Very Unwilling Unwilling Somewhat Unwilling Neutral Somewhat Willing Willing Very Willing 1 2 3 4 5 6 7 3. What is the single most important factor influencing your decision to attempt to reintegrate Sonya into the general education classroom for reading instruction?

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96 96 Appendix C: (Continued) Special Education Teacher Survey on Reintegration – 1 Research ID # Special Education Teacher Demographic Information Please answer the items on this page as best you can. The information is for research purposes and will be kept confidential. Answering questions is voluntary. However, the information will be helpful in understanding the results of the research. 1. What grade(s) do you teach? 2. What subject(s) do you teach? 3. How many students do you teach? 4. How many reading groups do you teach? 5. How large is a typical reading group? 6. Do you participate in collaborative teaching? If yes, how many hours per week? 7. How many of your students receive special education services less that 50% of the day? 8. How many of your students receive special education services more than 50% of the day? 9. How many of your students in the last two years have been reintegrated into the general education curriculum in subjects in which they had previously received special education services? 10. Please indicate the degrees or other training you have received : a. Bachelors in b. Masters in c. Specialist in d. Doctorate in e. Other 11. How many years of experience do you have in special education? 12. What populations (e.g. LD, EBD, EMR, etc.)? 13. How many years of experience do you have teaching general education?

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97 97 Appendix D: Special Education Teacher Survey on Reintegration – 2 Form A Special Education Teacher Survey on Reintegration – 2 Research ID # A Kevin is a fourth grade student who has been served in special education under the category of Specific Learning Disability for the past two years. He receives reading instruction in the resource room and is in the general education classroom the remainder of the day. Kevin follows classroom rules and interacts well with other students. His handwriting is messy and he takes longer than the typical student to complete his work. Recently, Kevin was given a brief reading test in which he read three stories aloud. Kevin’s general education teacher identified five students who are low readers in Kevin’s general education classroom. These students read the same three stories out loud. This type of test has been shown to be a good measure of students’ reading skills. The picture to the left shows Kevin’s score on the reading test compared to the other students. By looking at the picture, you can see if Kevin’s score is similar to or below the other readers. The square is Kevin’s reading score. The diamonds are the reading scores of the five low readers. Scores near the top of the box mean more words were read correctly than scores lower in the box. Kevin read 65 words correctly in one minute. The highest score earned by the five low readers was 95 words read correctly in one minute. The lowest score was 52 words read correctly in one minute. So, Kevin’s reading score was higher than the reading scores of two low readers in the classroom. Woodcock-Johnson III Tests of Achievement Broad Reading Cluster The Woodcock-Johnson III Tests of Achievement is a comprehensive, individually administered battery of tests that measures skills in several academic areas. Subtests in the battery combine to form cluster scores. The Broad Reading Cluster is made up of several subtests that measure several important aspects of reading. Cluster scores have a mean of 100 and a standard deviation of 15. Kevin obtained the following scores: Standard Score 80 Percentile Rank 13 Grade Equivalent 3.0 CBM Data95 92 66 58 52 65 0 20 40 60 80 100 120 140 160Words Read Correct Per Minute Low Reading Group Peers Kevin

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98 98 Appendix D: (Continued) Special Education Teacher Survey on Reintegration – 2 Research ID # A For each of the items listed below, indicate how the information provided affected your willingness to attempt to reintegrate Kevin into the general education classroom for reading instruction. A rating of 1 indicates the information greatly decreased your willingness. A rating of 7 indicates the information greatly increased your willingness. Greatly Decreased Willingness Decision not influenced Greatly Increased Willingness 1. Kevin earned a grade equivalent of 3.0 on the Broad Reading cluster of the Woodcock-Johnson III Test of Achievement. 1 2 3 4 5 6 7 2. Kevin performed at the 13th percentile on the Broad Reading cluster of the Woodcock-Johnson III Test of Achievement. 1 2 3 4 5 6 7 3. Kevin read 65 words in a minute in the same curriculum as students in the low reading group in the general education classroom. 1 2 3 4 5 6 7 4. Kevin read at a rate which placed him above 2 other readers in the low reading group in the general education classroom. 1 2 3 4 5 6 7 5. Given your current knowledge of Kevin, what do you think is the most appropriate reading placement for him? a. The lowest reading group in the general education classroom b. The lowest reading group in the general education classroom with collaboration from the special education teacher c. Special education resource room d. A self-contained special education placement e. Other 6. How willing or unwilling would you be to attempt to reintegrate Kevin into the general education classroom for reading instruction? 7. What is the single most important factor influencing your decision to attempt to reintegrate Kevin into the general education classroom for reading instruction? Very Unwilling Unwilling Somewhat Unwilling Neutral S omewhat Willing Willing Very Willing 1 2 3 4 5 6 7

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99 99 Appendix D: (Continued) Special Education Teacher Survey on Reintegration – 2 Research ID # A Sarah is a third grade student who is receiving special education services under the category of Other Health Impaired She was diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) last school year and began receiving reading instruction in the resource room at that time. Sarah works quickly and does most of her in class work with little assistance. She is frequently out of her seat and talks out of turn. Recently, Sarah was given a brief reading test in which she read three stories aloud. Sarah’s general education teacher identified three students who are low readers in Sarah’s general education classroom. These students read the same three stories out loud. This type of test has been shown to be a good measure of students’ reading skills. The picture to the left shows Sarah’s score on the reading test compared to the other students. By looking at the picture, you can see if Sarah’s score is similar to or below the other readers. The square is Sarah’s reading score. The diamonds are the reading scores of the three low readers. Scores near the top of the box mean more words were read correctly than scores lower in the box. Sarah read 66 words correctly in one minute. The highest score earned by the three low readers was 92 words read correctly in one minute. The lowest score was 68 words read correctly in one minute. So, Sarah’s reading score was higher than no low readers in the classroom. Woodcock-Johnson III Test of Achievement Broad Reading Cluster The Woodcock-Johnson III Tests of Achievement is a comprehensive, individually administered battery of tests that measures skills in several academic areas. Subtests in the battery combine to form cluster scores. The Broad Reading Cluster is made up of several subtests that measure several important aspects of reading. Cluster scores have a mean of 100 and a standard deviation of 15. Sarah obtained the following scores: Standard Score 101 Percentile Rank 52 Grade Equivalent 4.1 CBM Data92 78 68 66 0 20 40 60 80 100 120 140 160Words Read Correct Per Minute Low Reading Group Peers Sarah

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100 100 Appendix D: (Continued) Special Education Teacher Survey on Reintegration – 2 Research ID # A For each of the items listed below, indicate how the information provided affected your willingness to attempt to reintegrate Sarah into the general education classroom for reading instruction. A rating of 1 indicates the information greatly decreased your willingness. A rating of 7 indicates the information greatly increased your willingness. Greatly Decreased Willingness Decision not influenced Greatly Increased Willingness 1. Sarah earned a grade equivalent of 4.1 on the Broad Reading cluster of the Woodcock-Johnson III Test of Achievement. 1 2 3 4 5 6 7 2. Sarah performed at the 52nd percentile on the Broad Reading cluster of the Woodcock-Johnson III Test of Achievement. 1 2 3 4 5 6 7 3. Sarah read 68 words in a minute in the same curriculum as students in the low reading group in the general education classroom. 1 2 3 4 5 6 7 4. Sarah read at a rate which placed her above no other readers in the low reading group in the general education classroom. 1 2 3 4 5 6 7 5. Given your current knowledge of Sarah, what do you think is the most appropriate reading placement for her? a. The lowest reading group in the general education classroom b. The lowest reading group in the general education classroom with collaboration from the special education teacher c. Special education resource room d. A self-contained special education placement e. Other 6. How willing or unwilling would you be to attempt to reintegrate Sarah into the general education classroom for reading instruction? 7. What is the single most important factor influencing your decision to attempt to reintegrate Sarah into the general education classroom for reading instruction? Very Unwilling Unwilling Somewhat Unwilling Neutral Somewhat Willing Willing Very Willing 1 2 3 4 5 6 7

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101 101 Appendix D: (Continued) Special Education Teacher Survey on Reintegration – 2 Research ID # A Derrick has received special education services since first grade under the category of Educable Mentally Retarded He is now in the fifth grade and receives reading and math instruction in the resource room. Derrick follows classroom rules and stays on task. His work is often not of acceptable quality and he requires more assistance that the typical student to complete his in class work. Recently, Derrick was given a brief reading test in which he read three stories aloud. Derrick’s general education teacher identified four students who are low readers in Derrick’s general education classroom. These students read the same three stories out loud. This type of test has been shown to be a good measure of students’ reading skills. The picture to the left shows Derrick’s score on the reading test compared to the other students. By looking at the picture, you can see if Derrick’s score is similar to or below the other readers. The square is Derrick’s reading score. The diamonds are the reading scores of the four low readers. Scores near the top of the box mean more words were read correctly than scores lower in the box. Derrick read 59 words correctly in one minute. The highest score earned by the four low readers was 142 words read correctly in one minute. The lowest score was 71 words read correctly in one minute. So, Derrick’s reading score was higher than no low readers in the classroom. Woodcock-Johnson III Test of Achievement Broad Reading Cluster The Woodcock-Johnson III Tests of Achievement is a comprehensive, individually administered battery of tests that measures skills in several academic areas. Subtests in the battery combine to form cluster scores. The Broad Reading Cluster is made up of several subtests that measure several important aspects of reading. Cluster scores have a mean of 100 and a standard deviation of 15. Derrick obtained the following scores: Standard Score 82 Percentile Rank 12 Grade Equivalent 3.7 CBM Data142 99 85 71 59 0 20 40 60 80 100 120 140 160Words Read Correct Per Minute Low Reading Group Peers Derrick

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102 102 Appendix D: (Continued) Special Education Teacher Survey on Reintegration – 2 Research ID # A For each of the items listed below, indicate how the information provided affected your willingness to attempt to reintegrate Derrick into the general education classroom for reading instruction. A rating of 1 indicates the information greatly decreased your willingness. A rating of 7 indicates the information greatly increased your willingness. Greatly Decreased Willingness Decision not influenced Greatly Increased Willingness 1. Derrick earned a grade equivalent of 3.7 on the Broad Reading cluster of the Woodcock-Johnson III Test of Achievement. 1 2 3 4 5 6 7 2. Derrick performed at the 12th percentile on the Broad Reading cluster of the Woodcock-Johnson III Test of Achievement. 1 2 3 4 5 6 7 3. Derrick read 59 words in a minute in the same curriculum as students in the low reading group in the general education classroom. 1 2 3 4 5 6 7 4. Derrick read at a rate which placed him above no other readers in the low reading group in the general education classroom. 1 2 3 4 5 6 7 5. Given your current knowledge of Derrick, what do you think is the most appropriate reading placement for him? a. The lowest reading group in the general education classroom b. The lowest reading group in the general education classroom with collaboration from the special education teacher c. Special education resource room d. A self-contained special education placement e. Other 6. How willing or unwilling would you be to attempt to reintegrate Derrick into the general education classroom for reading instruction? 7. What is the single most important factor influencing your decision to attempt to reintegrate Derrick into the general education classroom for reading instruction? Very Unwilling Unwilling Somewhat Unwilling Neutral Somewhat Willing Willing Very Willing 1 2 3 4 5 6 7

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103 103 Appendix D: (Continued) Special Education Teacher Survey on Reintegration – 2 Research ID # A Sonya is in the fourth grade and has received reading and social skills instruction in the resource room for three years. She is served under the special education category of Emotionally Handicapped Sonya does most in-class work correctly and remains on task independently while working. She frequently gets into fights and has difficulty making her needs known in an appropriate manner. Recently, Sonya was given a brief reading test in which she read three stories aloud. Sonya’s general education teacher identified five students who are low readers in Sonya’s general education classroom. These students read the same three stories out loud. This type of test has been shown to be a good measure of students’ reading skills. The picture to the left shows Sonya’s score on the reading test compared to the other students. By looking at the picture, you can see if Sonya’s score is similar to or below the other readers. The square is Sonya’s reading score. The diamonds are the reading scores of the three low readers. Scores near the top of the box mean more words were read correctly than scores lower in the box. Sonya read 119 words correctly in one minute. The highest score earned by the three low readers was 137 words read correctly in one minute. The lowest score was 72 words read correctly in one minute. So, Sonya’s reading score was higher than four low readers in the classroom. Woodcock-Johnson III Test of Achievement Broad Reading Cluster The Woodcock-Johnson III Tests of Achievement is a comprehensive, individually administered battery of tests that measures skills in several academic areas. Subtests in the battery combine to form cluster scores. The Broad Reading Cluster is made up of several subtests that measure several important aspects of reading. Cluster scores have a mean of 100 and a standard deviation of 15. Sonya obtained the following scores: Standard Score 100 Percentile Rank 50 Grade Equivalent 5.0 CBM Data137 115 105 82 72 119 0 20 40 60 80 100 120 140 160Words Read Correct Per Minute Low Reading Group Peers Sonya

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104 104 Appendix D: (Continued) Special Education Teacher Survey on Reintegration – 2 Research ID # A For each of the items listed below, indicate how the information provided affected your willingness to attempt to reintegrate Sonya into the general education classroom for reading instruction. A rating of 1 indicates the information greatly decreased your willingness. A rating of 7 indicates the information greatly increased your willingness. Greatly Decreased Willingness Decision not influenced Greatly Increased Willingness 1. Sonya earned a grade equivalent of 5.0 on the Broad Reading cluster of the Woodcock-Johnson III Test of Achievement. 1 2 3 4 5 6 7 2. Sonya performed at the 50th percentile on the Broad Reading cluster of the Woodcock-Johnson III Test of Achievement. 1 2 3 4 5 6 7 3. Sonya read 119 words in a minute in the same curriculum as students in the low reading group in the general education classroom. 1 2 3 4 5 6 7 4. Sonya read at a rate which placed her above four other readers in the low reading group in the general education classroom. 1 2 3 4 5 6 7 5. Given your current knowledge of Sonya, what do you think is the most appropriate reading placement for her? a. The lowest reading group in the general education classroom b. The lowest reading group in the general education classroom with collaboration from the special education teacher c. Special education resource room d. A self-contained special education placement e. Other 6. How willing or unwilling would you be to attempt to reintegrate Sonya into the general education classroom for reading instruction? 7. What is the single most important factor affecting your willingness to attempt to reintegrate Sonya into the general education classroom for reading instruction? Very Unwilling Unwilling Somewhat Unwilling Neutral Somewhat Willing Willing Very Willing 1 2 3 4 5 6 7

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105 105 Appendix D: (Continued) Special Education Teacher Survey on Reintegration – 2 Research ID # A Student Factors Affecting Reintegration Decisions How important is each of the following factors in determining your willingness to attempt to reintegrate a student into the general education classroom for reading instruction? A rating of 1 indicates the factor is totally unimportant A rating of 7 indicates the factor is very important. Totally Unimportant Somewhat Important Very Important 1. The student follows classroom rules. 1 2 3 4 5 6 7 2. The student does most in-class work correctly, and needs little assistance. 1 2 3 4 5 6 7 3. The student’s work is acceptable quality. 1 2 3 4 5 6 7 4. The student makes his or her needs known in an appropriate manner. 1 2 3 4 5 6 7 5. The student copes with failure in an appropriate manner. 1 2 3 4 5 6 7 6. The student does not frequently talk out. 1 2 3 4 5 6 7 7. The student behaves maturely. 1 2 3 4 5 6 7 8. The student interacts well with others. 1 2 3 4 5 6 7 9. The student is not frequently out of seat. 1 2 3 4 5 6 7 10. The student doesn’t need frequent reminders to stay on task. 1 2 3 4 5 6 7 11. The student remains on task for at least ten minutes while working alone. 1 2 3 4 5 6 7 12. The student’s handwriting is legible. 1 2 3 4 5 6 7 13. The student does not often get into fights. 1 2 3 4 5 6 7 14. The student takes no longer than the typical student to complete his or her work. 1 2 3 4 5 6 7 15. The student has met or made progress on goals and objectives on the IEP. 1 2 3 4 5 6 7

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106 106 Appendix D: (Continued) Special Education Teacher Survey on Reintegration – 2 Research ID # A Committee Influence in Reintegration Decisions How would the following information affect your willingness to attempt to reintegrate a student into the general education classroom for reading instruction? A rating of 1 indicates the information would greatly decrease your willingness. A rating of 7 indicates the information would greatly increase your willingness. Would Decrease Willingness Somewhat Important Would Increase Willingness 16. The principal has asked that you attempt to reintegrate the student into the general education classroom. 1 2 3 4 5 6 7 17. The student’s general education teacher has asked that you attempt to reintegrate the student into the general education classroom. 1 2 3 4 5 6 7 18. The student’s parent has asked that you attempt to reintegrate the student into the general education classroom. 1 2 3 4 5 6 7 19. The student has asked that you attempt to reintegrate him or her into the general education classroom. 1 2 3 4 5 6 7

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107 107 CBM Data95 92 68 72 80 65 0 20 40 60 80 100 120 140 160Words Read Correct Per Minute Low Reading Group Peers Kevin Appendix E: Special Education Teacher Survey on Reintegration – 2 Form B Special Education Teacher Survey on Reintegration – 2 Research ID # B Kevin is a fourth grade student who has been served in special education under the category of Specific Learning Disability for the past two years. He receives reading instruction in the resource room and is in the general education classroom the remainder of the day. Kevin follows classroom rules and interacts well with other students. His handwriting is messy and he takes longer than the typical student to complete his work. Recently, Kevin was given a brief reading test in which he read three stories aloud. Kevin’s general education teacher identified five students who are low readers in Kevin’s general education classroom. These students read the same three stories out loud. This type of test has been shown to be a good measure of students’ reading skills. The picture to the left shows Kevin’s score on the reading test compared to the other students. By looking at the picture, you can see if Kevin’s score is similar to or below the other readers. The square is Kevin’s reading score. The diamonds are the reading scores of the five low readers. Scores near the top of the box mean more words were read correctly than scores lower in the box. Kevin read 65 words correctly in one minute. The highest score earned by the five low readers was 95 words read correctly in one minute. The lowest score was 68 words read correctly in one minute. So, Kevin’s reading score was higher than the reading scores of no low readers in the classroom. Woodcock-Johnson III Tests of Achievement Broad Reading Cluster The Woodcock-Johnson III Tests of Achievement is a comprehensive, individually administered battery of tests that measures skills in several academic areas. Subtests in the battery combine to form cluster scores. The Broad Reading Cluster is made up of several subtests that measure several important aspects of reading. Cluster scores have a mean of 100 and a standard deviation of 15. Kevin obtained the following scores: Standard Score 80 Percentile Rank 13 Grade Equivalent 3.0

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108 108 Appendix E: (Continued) Special Education Teacher Survey on Reintegration – 2 Research ID # B For each of the items listed below, indicate how the information provided influenced your willingness to reintegrate Kevin into the general education classroom for reading instruction. A rating of 1 indicates the information greatly decreased your willingness. A rating of 7 indicates the information greatly increased your willingness. Greatly Decreased Willingness Decision not influenced Greatly Increased Willingness 8. Kevin earned a grade equivalent of 3.0 on the Broad Reading cluster of the Woodcock-Johnson III Test of Achievement. 1 2 3 4 5 6 7 9. Kevin performed at the 13th percentile on the Broad Reading cluster of the Woodcock-Johnson III Test of Achievement. 1 2 3 4 5 6 7 10. Kevin read 65 words in a minute in the same curriculum as students in the low reading group in the general education classroom. 1 2 3 4 5 6 7 11. Kevin read at a rate which placed him above no other readers in the low reading group in the general education classroom. 1 2 3 4 5 6 7 12. Given your current knowledge of Kevin, what do you think is the most appropriate reading placement for him? f. The lowest reading group in the general education classroom g. The lowest reading group in the general education classroom with collaboration from the special education teacher h. Special education resource room i. A self-contained special education placement j. Other 13. How willing or unwilling would you be to attempt to reintegrate Kevin into the general education classroom for reading instruction? 14. What is the single most important factor influencing your decision in item 6 above? Very Unwilling Unwilling Somewhat Unwilling Neutral Somewhat Willing Willing Very Willing 1 2 3 4 5 6 7

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109 109 CBM Data57 78 62 66 0 20 40 60 80 100 120 140 160Words Read Correct Per Minute Low Reading Group Peers Sarah Appendix E: (Continued) Special Education Teacher Survey on Reintegration – 2 Research ID # B Sarah is a third grade student who is receiving special education services under the category of Other Health Impaired She was diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) last school year and began receiving reading instruction in the resource room at that time. Sarah works quickly and does most of her in class work with little assistance. She is frequently out of her seat and talks out of turn. Recently, Sarah was given a brief reading test in which she read three stories aloud. Sarah’s general education teacher identified three students who are low readers in Sarah’s general education classroom. These students read the same three stories out loud. This type of test has been shown to be a good measure of students’ reading skills. The picture to the left shows Sarah’s score on the reading test compared to the other students. By looking at the picture, you can see if Sarah’s score is similar to or below the other readers. The square is Sarah’s reading score. The diamonds are the reading scores of the three low readers. Scores near the top of the box mean more words were read correctly than scores lower in the box. Sarah read 66 words correctly in one minute. The highest score earned by the three low readers was 78 words read correctly in one minute. The lowest score was 57 words read correctly in one minute. So, Sarah’s reading score was higher than two low readers in the classroom. Woodcock-Johnson III Test of Achievement Broad Reading Cluster The Woodcock-Johnson III Tests of Achievement is a comprehensive, individually administered battery of tests that measures skills in several academic areas. Subtests in the battery combine to form cluster scores. The Broad Reading Cluster is made up of several subtests that measure several important aspects of reading. Cluster scores have a mean of 100 and a standard deviation of 15. Sarah obtained the following scores: Standard Score 101 Percentile Rank 52 Grade Equivalent 4.1

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110 110 Appendix E: (Continued) Special Education Teacher Survey on Reintegration – 2 Research ID # B For each of the items listed below, indicate how the information provided influenced your willingness to reintegrate Sarah into the general education classroom for reading instruction. A rating of 1 indicates the information greatly decreased your willingness. A rating of 7 indicates the information greatly increased your willingness. Greatly Decreased Willingness Decision not influenced Greatly Increased Willingness 8. Sarah earned a grade equivalent of 4.1 on the Broad Reading cluster of the Woodcock-Johnson III Test of Achievement. 1 2 3 4 5 6 7 9. Sarah performed at the 52nd percentile on the Broad Reading cluster of the Woodcock-Johnson III Test of Achievement. 1 2 3 4 5 6 7 10. Sarah read 66 words in a minute in the same curriculum as students in the low reading group in the general education classroom. 1 2 3 4 5 6 7 11. Sarah read at a rate which placed her above two other readers in the low reading group in the general education classroom. 1 2 3 4 5 6 7 12. Given your current knowledge of Sarah, what do you think is the most appropriate reading placement for her? a. The lowest reading group in the general education classroom b. The lowest reading group in the general education classroom with collaboration from the special education teacher c. Special education resource room d. A self-contained special education placement e. Other 13. How willing or unwilling would you be to attempt to reintegrate Sarah into the general education classroom for reading instruction? 14. What is the single most important factor influencing your decision in item 6 above? Very Unwilling Unwilling Somewhat Unwilling Neutral Somewhat Willing Willing Very Willing 1 2 3 4 5 6 7

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111 111 CBM Data50 99 85 71 59 0 20 40 60 80 100 120Words Read Correct Per Minute Low Reading Group Peers Derrick Appendix E: (Continued) Special Education Teacher Survey on Reintegration – 2 Research ID # B Derrick has received special education services since first grade under the category of Educable Mentally Retarded He is now in the fifth grade and receives reading and math instruction in the resource room. Derrick follows classroom rules and stays on task. His work is often not of acceptable quality and he requires more assistance that the typical student to complete his in class work. Recently, Derrick was given a brief reading test in which he read three stories aloud. Derrick’s general education teacher identified four students who are low readers in Derrick’s general education classroom. These students read the same three stories out loud. This type of test has been shown to be a good measure of students’ reading skills. The picture to the left shows Derrick’s score on the reading test compared to the other students. By looking at the picture, you can see if Derrick’s score is similar to or below the other readers. The square is Derrick’s reading score. The diamonds are the reading scores of the four low readers. Scores near the top of the box mean more words were read correctly than scores lower in the box. Derrick read 59 words correctly in one minute. The highest score earned by the four low readers was 99 words read correctly in one minute. The lowest score was 50 words read correctly in one minute. So, Derrick’s reading score was higher than one low readers in the classroom. Woodcock-Johnson III Test of Achievement Broad Reading Cluster The Woodcock-Johnson III Tests of Achievement is a comprehensive, individually administered battery of tests that measures skills in several academic areas. Subtests in the battery combine to form cluster scores. The Broad Reading Cluster is made up of several subtests that measure several important aspects of reading. Cluster scores have a mean of 100 and a standard deviation of 15. Derrick obtained the following scores: Standard Score 82 Percentile Rank 12 Grade Equivalent 3.7

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112 112 Appendix E: (Continued) Special Education Teacher Survey on Reintegration – 2 Research ID # B For each of the items listed below, indicate how the information provided influenced your willingness to reintegrate Derrick into the general education classroom for reading instruction. A rating of 1 indicates the information greatly decreased your willingness. A rating of 7 indicates the information greatly increased your willingness. Greatly Decreased Willingness Decision not influenced Greatly Increased Willingness 8. Derrick earned a grade equivalent of 3.7 on the Broad Reading cluster of the Woodcock-Johnson III Test of Achievement. 1 2 3 4 5 6 7 9. Derrick performed at the 12th percentile on the Broad Reading cluster of the Woodcock-Johnson III Test of Achievement. 1 2 3 4 5 6 7 10. Derrick read 59 words in a minute in the same curriculum as students in the low reading group in the general education classroom. 1 2 3 4 5 6 7 11. Derrick read at a rate which placed him above one other reader in the low reading group in the general education classroom. 1 2 3 4 5 6 7 12. Given your current knowledge of Derrick, what do you think is the most appropriate reading placement for him? a. The lowest reading group in the general education classroom b. The lowest reading group in the general education classroom with collaboration from the special education teacher c. Special education resource room d. A self-contained special education placement e. Other 13. How willing or unwilling would you be to attempt to reintegrate Derrick into the general education classroom for reading instruction? 14. What is the single most important factor influencing your decision in item 6 above? Very Unwilling Unwilling Somewhat Unwilling Neutral Somewhat Willing Willing Very Willing 1 2 3 4 5 6 7

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113 113 CBM Data137 148 142 130 125 119 0 20 40 60 80 100 120 140 160Words Read Correct Per Minute Low Reading Group Peers Sonya Appendix E: (Continued) Special Education Teacher Survey on Reintegration – 2 Research ID # B Sonya is in the fourth grade and has received reading and social skills instruction in the resource room for three years. She is served under the special education category of Emotionally Handicapped Sonya does most in-class work correctly and remains on task independently while working. She frequently gets into fights and has difficulty making her needs known in an appropriate manner. Recently, Sonya was given a brief reading test in which she read three stories aloud. Sonya’s general education teacher identified five students who are low readers in Sonya’s general education classroom. These students read the same three stories out loud. This type of test has been shown to be a good measure of students’ reading skills. The picture to the left shows Sonya’s score on the reading test compared to the other students. By looking at the picture, you can see if Sonya’s score is similar to or below the other readers. The square is Sonya’s reading score. The diamonds are the reading scores of the five low readers. Scores near the top of the box mean more words were read correctly than scores lower in the box. Sonya read 119 words correctly in one minute. The highest score earned by the five low readers was 148 words read correctly in one minute. The lowest score was 125 words read correctly in one minute. So, Sonya’s reading score was higher than no low readers in the classroom. Woodcock-Johnson III Test of Achievement Broad Reading Cluster The Woodcock-Johnson III Tests of Achievement is a comprehensive, individually administered battery of tests that measures skills in several academic areas. Subtests in the battery combine to form cluster scores. The Broad Reading Cluster is made up of several subtests that measure several important aspects of reading. Cluster scores have a mean of 100 and a standard deviation of 15. Sonya obtained the following scores: Standard Score 100 Percentile Rank 50 Grade Equivalent 5.0

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114 114 Appendix E: (Continued) Special Education Teacher Survey on Reintegration – 2 Research ID # B For each of the items listed below, indicate how the information provided influenced your willingness to reintegrate Sonya into the general education classroom for reading instruction. A rating of 1 indicates the information greatly decreased your willingness. A rating of 7 indicates the information greatly increased your willingness. Greatly Decreased Willingness Decision not influenced Greatly Increased Willingness 8. Sonya earned a grade equivalent of 5.0 on the Broad Reading cluster of the Woodcock-Johnson III Test of Achievement. 1 2 3 4 5 6 7 9. Sonya performed at the 50th percentile on the Broad Reading cluster of the Woodcock-Johnson III Test of Achievement. 1 2 3 4 5 6 7 10. Sonya read 119 words in a minute in the same curriculum as students in the low reading group in the general education classroom. 1 2 3 4 5 6 7 11. Sonya read at a rate which placed her above no other readers in the low reading group in the general education classroom. 1 2 3 4 5 6 7 12. Given your current knowledge of Sonya, what do you think is the most appropriate reading placement for her? a. The lowest reading group in the general education classroom b. The lowest reading group in the general education classroom with collaboration from the special education teacher c. Special education resource room d. A self-contained special education placement e. Other 13. How willing or unwilling would you be to attempt to reintegrate Sonya into the general education classroom for reading instruction? 14. What is the single most important factor influencing your decision in item 6 above? Very Unwilling Unwilling Somewhat Unwilling Neutral Somewhat Willing Willing Very Willing 1 2 3 4 5 6 7

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115 115 Appendix E: (Continued) Special Education Teacher Survey on Reintegration – 2 Research ID # B Student Factors Affecting Reintegration Decisions How important is each of the following factors in determining your willingness to attempt to reintegrate a student into the general education classroom for reading instruction? A rating of 1 indicates the factor is totally unimportant A rating of 7 indicates the factor is very important. Totally Unimportant Somewhat Important Very Important 20. The student follows classroom rules. 1 2 3 4 5 6 7 21. The student does most in-class work correctly, and needs little assistance. 1 2 3 4 5 6 7 22. The student’s work is acceptable quality. 1 2 3 4 5 6 7 23. The student makes his or her needs known in an appropriate manner. 1 2 3 4 5 6 7 24. The student copes with failure in an appropriate manner. 1 2 3 4 5 6 7 25. The student does not frequently talk out. 1 2 3 4 5 6 7 26. The student behaves maturely. 1 2 3 4 5 6 7 27. The student interacts well with others. 1 2 3 4 5 6 7 28. The student is not frequently out of seat. 1 2 3 4 5 6 7 29. The student doesn’t need frequent reminders to stay on task. 1 2 3 4 5 6 7 30. The student remains on task for at least ten minutes while working alone. 1 2 3 4 5 6 7 31. The student’s handwriting is legible. 1 2 3 4 5 6 7 32. The student does not often get into fights. 1 2 3 4 5 6 7 33. The student takes no longer than the typical student to complete his or her work. 1 2 3 4 5 6 7 34. The student has met or made progress on goals and objectives on the IEP. 1 2 3 4 5 6 7

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116 116 Appendix E: (Continued) Special Education Teacher Survey on Reintegration – 2 Research ID # B Committee Influence in Reintegration Decisions How would the following information influence your willingness to attempt to reintegrate a student into the general education classroom for reading instruction? A rating of 1 indicates the information would greatly decrease your willingness. A rating of 7 indicates the information would greatly increase your willingness. Would Decrease Willingness Somewhat Important Would Increase Willingness 35. The principal has asked that you attempt to reintegrate the student into the general education classroom. 1 2 3 4 5 6 7 36. The student’s general education teacher has asked that you attempt to reintegrate the student into the general education classroom. 1 2 3 4 5 6 7 37. The student’s parent has asked that you attempt to reintegrate the student into the general education classroom. 1 2 3 4 5 6 7 38. The student has asked that you attempt to reintegrate him or her into the general education classroom. 1 2 3 4 5 6 7