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EDUCATION POLICY ANALYSIS ARCHIVES A peer-reviewed scholarly journal Editor: Gene V Glass College of Education Arizona State University Copyright is retained by the first or sole au thor, who grants right of first publication to the Education Policy Analysis Archives EPAA is a project of the Education Policy Studies Laboratory. Articles are in dexed in the Directory of Open Access Journals ( Volume 12 Number 53 September 24, 2004 ISSN 1068-2341 Effects of High-School Size on Student Outcomes: Response to Howley and Howley Valerie E. Lee University of Michigan Citation: Lee, V. E. (2004, September 24) Effects of high-school size on student outcomes: Response to Howley and Howley. Education Policy Analysis Archives, 12(53). Retrieved [date] from ht tp:// Abstract I take issue with several points in the Howleys' reanalysis of "High School Size: Which Works Best and for Whom?" (Lee & Smith, 1997). That the original sample of NELS schools might have underrepresented small rural public schools would not bias results, as they claim. Their assertion that our conclusions about an ideal high-school size privileged excellence over equity ignores the fact that our mult ilevel analyses explored the two outcomes simultaneously. Neither do I agree that our claim about "ideal size" (600-900) was too narrow, as our paper was clear that our focus was on achievement and its equitable distri bution. Perhaps the most important area of disagreement concerns non-linear relationships between school size and achievement gains. Ignoring the skewed distribution of school size, without either transforming or categor izing the variable produces findings that spuriously favor the smallest schools. Our recent involvement as expert witnesses on opposite sides in a court case may have motivated the Howleys' attempt to discredit our work. Finally, I argue that research attempting to establish a direct link between school size and student outcomes may be misguided. Rather, school size influences student outcomes only indirectly, through the academic and social organization of


Response to Howley & Howley 2 schools. Considerable evidence links these organizational factors to student outcomes (especially learning and its equitable distribution). In their article "School Size and the Influence of Socioeconomic Status on Student Achievement: Confronting the Threat of Size Bias in National Data Sets," Craig and Aimee Howley (Vol. 12 No. 52 in this journal) took exception to several issues in a paper I co-authored with Julia B. Smith about high school size. They also provided some evidence to support their claims of "size bias," as well as another study that claims be nefits for smaller schools. My comments here are organized around four issues relevant to research on the effects of school size on student outcomes. First, I respond to specific criticisms the authors raised about the Lee and Smith (1997) study. The second issue concerns the evidence offered by the au thors in their study using similar data to those used by Smith and me. Third, I describe the context within which the Howleys’ and I have interacted recently, as it may have motivated their criticism of my work. Fourth, I briefly discuss a broader framework within which I suggest that re search linking school size to student outcomes should be seen. Issue 1: The Howleys' Critique of the Lee and Smith (1997) Study The Howleys summarized our three majo r conclusions by citing our exact words. Though they agreed with the firs t conclusion ("high schools should be smaller than many are"), they took issue with the conclusion that "high schools ca n be too small." They also found our offering an "ideal" high-school size as problematic. I organize my response around five areas mentioned in their micro-analysis of our work: (1) that NELS is unrepresentative of small schools; (2) that we emphasized excellence at the expense of equity in our conclusions; (3) that we inappropriately drew conclusions about an "ideal" size for high schools; (4) that our use of weights did not adequately adjust for the non-random sampling of schools in NELS; and (5) that rural schools were undersampled in NELS. I address another area, imp lied but not stated directly: (6) that our results are incorrect because our analyses were structured so differently from other studies about school size. Though their discussion of our work faults both the data we used (over which we had no control) and our analyses of the data and the c onclusions we drew from our results (both of which we did control), their critique seems aimed at un dermining our work and the respect researchers and policy makers should afford it. Area 1: NELS School Sample The NELS:88 school sampling frame started in 1988 with U.S. schools including 8th grades; no high schools were sampled. According to the National Center for Education Statistics (NCES), schools with 8th grades in them were sampled from a national frame of about 39,000 schools (public and private) drawn from a school data list complied by Quality Education Data, Inc. (QED), which "contained information about whet her a school was urban, suburban, or rural" (NCES, 1994, p.23). The longitudinal NELS:88 desi gn, with students surveyed and tested every two years (i.e., in 8th grade, 10th grade, and 12th grade), needed to capture the phenomenon that virtually all students changed schools sometime between 8th and 12th grade. One difficulty of the NELS design, one that had to be confronted by many analysts who wanted to follow the sample of NELS:88 students through secondary school, was that


Education Policy Analysis Archives Vol. 12 No. 53 3 secondary schools were not directly sampled by NELS :88. Rather, high schools in the NELS study were those the NELS-sampled students chose. Al though rich survey data about the NELS high schools (from principals and teachers) were collect ed, the NELS data files never provided school weights for high schools in the study (although weights for the base-year schools weights were included). Our 1997 study focused on NELS high schools, although the Howleys' discussion focuses exclusively on base-year (i.e., middle-grad e) schools. Their comparison of public schools in the Common Core of Data (CCD) and the NELS base -year school sample in Table 1 of their does suggest some pattern of undersampling of the smallest public schools. Their title suggests that this underrepresentation of the smallest middle-grade pu blic schools in NELS introduces bias into any studies (like ours) that used NELS to investigate th e effects of school size on learning. Although the NELS study sampled schools at the outset (i.e., when students were in 8th grade), and didn't sample high schools, the underrepresentation of small school s at the high school level may have persisted. Although virtually all students went to a differe nt high school than the middle-grade school they attended, and high schools are typically larger than middle-grade schools, it may be that small school size is related to the area where the schools are lo cated -so that "smallness" or "largeness" may be somewhat consistent as students move to s econdary school. What is not clear (and possibly misleading) is that such undersampling of the sma llest U.S. public middle-grade schools would bias the results of such analyses. Area 2: Privileging Excellence Over Equity The Howleys claimed that Smith and I inappropriately used "authorial privilege" in our conclusions about equity and excellence, in that we did not provide sufficient justification for our conclusions. We explored four outcomes in our mu ltilevel study: gains in achievement in reading and mathematics over the four years the students were in high school and the relationship between SES and achievement gains in these two subjects. We characterized these measures as "excellence" (achievement gains) and "equity" (the SES/achievement gain slope). Although these outcomes were estimated simultaneously in the same multilevel models, our presentation of results in the body of the paper in graphic form may have suggested that we analyzed these outcomes separately (the text of the paper did explain this). Numerical results both weighted and un-weighted, were included in Appendices B-2 and B-3 of the study. Readers wh o looked only at the graphs might assume that equity and excellence were separate outcomes, as separate graphs presented the achievement gains (Figure 2) and the SES/achievement slopes (Figure 3) as functions of school size. These results, estimated as Hierarchical Linear Models (HLM), included statistical adjustments for both student characteristics (gender, minority status, SES, init ial ability) and school characteristics (school SES, minority concentration, sector). For any study, authors must consider carefully the audience to whom the results might be relevant. In our case, two distinct audiences seemed reasonable: policy makers and school professionals on the one hand and researchers on the other. The technical expertise of these two audiences is rather different. Our purpose in pr esenting results in graphic form was to make analyses that were quite complex more accessi ble for non-technical readers. The many, many inquiries we have received from school people and policy makers, starting from our first presentation of the study's results (at the 1996 AE RA meeting in New York) and continuing up to the present time, suggest that the graphs told our "story" to this audience well. However, the graphic presentation was perhaps misleading. We included all results in Appendices to allow for full scrutiny by reviewers and readers with more technical unde rstanding. It is unclear whether the Howleys scrutinized the numerical results at the end of the paper.


Response to Howley & Howley 4 Among the several criticisms about our st udy raised by the Howleys, their claim that we seemingly disregarded equity disturbs me the most. Identifying and encouraging educational structures and organizations that are simultaneousl y linked to excellence and equity has characterized almost all of my research, from my dissertation (Lee, 1985), through my work with Anthony Bryk focusing on Catholic schools (Bryk, Lee, & Ho lland; 1993; Lee & Bryk, 1988; 1989), including several studies about school restructuring (summarized in Lee, 2002), and guiding my recent research on young children (Lee & Burkam, 2002). School factors that are associated with a socially equitable distribution of achievem ent without also being linked to higher achievement would imply that in such schools students of different SES leve ls or minority groups would achieve equally -at low levels. That is, equity without excellence is not something we should encourage in schools. Social equity in the distribution of outcomes is only useful if everyone -high-SES or low SES, minority or non-minority -does well. Although the conclusions in our pape r were drawn from our findings, we meant them to rise beyond the results. They represented th e meaning we drew from our work. The evidence for our conclusions lay in our results. Drawing conclusions is, quite rightly, "authorial privilege." These conclusions were located in the Discussion section of the paper, where authors typically interpret their findings more broadly. Had the reviewers of this paper felt we had "gone beyond the data," they surely would have required us to scale back ou r conclusions. That the Howleys don't agree with some of our conclusions does not render them groundless. Area 3: "Ideal" Size Too Narrowly Defined The Howleys also took issue with our identifying an "ideal" size range (600-900 students) for three reasons: (1) that our outcome set was too narrow, (2) that the smallest high schools were not included in the ideal range; and (3) that private schools were included in our study. Regarding the first reason, they suggested that our use of the term "ideal" was inappropriate because our study was narrow, focusing only on size effects on achievement. Our focus in the 1997 study was on gains in achievement; we included only NELS students with test scores at both 8th and 12th grade who had remained in the same high school. Ou r analysis was admittedly narrow in that sense; we explored size effects on achievement gains only for students whose exposure to their schools was maximized. Many other important educational outcome s surely could be influenced by school size, and I have pursued these in several studies. My colleague and I explored dropping out as a function of school social organization and structure (size and sector) in a subset of NELS high schools in urban and suburban areas (Lee & Burkam, 2003). Another colleague and I used multilevel methods to explore size effects on teachers' attitudes in Chicago elementary (K-8) schools (Lee & Loeb, 2000). A qualitative study compared large and sma ll public high schools in terms of social relations and curriculum (Lee, Smerdon, Alfeld-Liro, & Brow n, 2000). It is surely possible that different studies may come to different "ideal size" concl usions, based on the dependent variable of interest. We clearly defined the outcomes in the 1997 study: achievement gain (and its equitable distribution by SES) over the four years students spent in hi gh school, and we selected our sample of students accordingly. Readers would recognize its focus on achievement. We suspect that school professionals and policy makers would "privilege" achievement over other outcomes (if, perhaps, "ideal sizes" differed for other outcomes), especially in the contemporary climate of achievementrelated mandates from No Child Left Behind The second reason for the Howleys objecti on to our "ideal" size designation centers on our finding that secondary schools smaller than 600 were not "ideal" in terms of size. I believe that nationally representative longitudin al data provide an excellent (perhaps the best) venue for policy-


Education Policy Analysis Archives Vol. 12 No. 53 5 relevant research in education. The numbers of sm all high schools in our study using NELS data are reasonable. The numbers of schools in the various size categories (from Table 1 of Lee & Smith, 1997) do differ, but the smallest category (enrollin g 300 or fewer students) contains 75 schools (and 912 students in those schools). The next-smalle st category (301-600) contains 67 schools and 830 students. The Howleys statement that there is "much more error embedded in findings, and therefore, in conclusions, about smaller schools th an is acknowledged" (p.10) seems groundless. Whatever error accrues is reflected in statistical testing (reported in Appendix B-2) and not in parameter estimates. If the Howleys are referring to sampling error, this doesn't seem problematic; the numbers of small schools and students are actually substantial. Regarding the third reason, the Howleys suggest that many of the schools in our "ideal size" range (600-900) are private sc hools, and that this might bias our findings that favor schools in that range. There are more private schools in that size cate gory; but the large majority (75.5 percent) of the 148 schools in that category are public. More over, schools in the smaller size categories are almost all public (95 percent of schools enrolling 30 0 or fewer students are public; 92.5 percent of schools with 301-600 students are public -see Lee & Smith, 1997, Table 1). The Howleys argue that "the issue of size is arguably confounded with sector" (p.13); I disagree. All of our HLM analyses included statistical adjustments for school sector (Catholic and elite independent schools each compared to public schools). Moreover, our HLMs also included statistical adjustment for school average SES and minority composition, on which public and private (as well as small and large) schools differ. 1 The reason to include such controls is precisely to avoid such a bias. Area 4: Weighting The concept of weighting in multivariate analysis is theoretically simple: weights are the inverse of the probability of being sampled. Weig hts adjust for non-random sampling; over-sampled units get weighted down and under-sampled units get weighted up. The concept is simple, but the process of creating weights is not. Researchers typ ically rely on those who collect the data to supply weights. Virtually all NCES longitudinal datasets re quire the use of weights for multivariate analysis, to compensate for non-random sample selection. Although NELS students as 8th graders were selected close to randomly within schools, the original sample of schools was not random. Not only was the original 8th-grade school sample strati fied by location, certain types of schools were purposely oversampled (i.e., private schools). All documentation that accompanies NELS data (e.g., NCES, 1994) suggests that analyses must be weigh ted. Multilevel analyses (in our case, students nested in schools) allow weighting at different le vels. Because of the original near-random sampling of students within schools, we assumed that sample s of students within high schools was also close to random (without evidence to the contrary). Thus, the within-school portion of our HLMs were unweighted. However, we needed weights for the between-school HLM analyses. As quantitative researchers like Smith and me recognize, the great value of nationally representative longitudinal data in strengthen ing generalizable causal in ferences and the also necessity of using multilevel methods to conduct school-effects studies, we faced a serious dilemma. In our several published studies using NELS sec ondary schools, we described several decisions in choosing our samples of students and schools. Fo r the 1997 study, we selected only high schools with at least 5 original NELS students in them. 2 We also included only students who were 12th graders in 1992 (i.e., those who had neither dropped out, transferred, nor repeated a grade in high school), and we constructed our own school weigh ts (which we used in all of our high-school studies with NELS data). Not being sampling st atisticians ourselves, we sought advice from colleagues at the University of Michigan's Institute fo r Social Research (ISR), which is internationally recognized for expertise in sampling theory. After the publication of our first NELS high-school


Response to Howley & Howley 6 study (Lee & Smith, 1995), other NELS resear chers asked us to "lend" them our weights; we declined. Rather, we explained how we had constructed NELS school weights and suggested they make their own. The Howleys stated that "the National Center for Education Statistics has in fact recommended against using school-level weights for any but school-level analyses" (pp.10-11); exactly what we did. They also stated (p.10) that "despite weighting and adjustments of mean standard errors for design effects, much more error is embedded in findings, and therefore conclusions, about smaller schools than is acknow ledged." Why? We included no adjustment for design effects; 2-level HLMs render the need for design effects unnecessary with NELS (because of the parallel between studentswithin-school sampling and analysis). If there were larger errors accruing to estimates for smaller schools, as the Howleys suggest (but which I question), this would influence statistical testing rather than parameter estimates. The major results in our study, presented in graphic form, did not report statistical testing. However, the p-values associated with statistical testing of size comparisons are available in the Ap pendices (to which the Howleys do not refer). The Howleys imply that somehow we have tried to mislead readers; this I disagree with most strenuously. Neither Smith nor I are sampling statisti cians, nor to my knowledge are the Howleys. Thus, we all should follow the recommendations from NCES about analyses of their datasets. We were certain that school weights were necessary, an d we did our best to create weights based on the information available about the high schools in fi rst and second follow-ups of NELS. We checked our procedures with colleagues who knew more about sampling and weights than we did. We weighted our analysis at the school level, within a multilevel analysis framework. 3 Although researchers could surely question the method we us ed to create our school weights, we have not heard such criticism. Moreover, as we worried that our results might be influenced by the school weights we created, we reported the size effects fr om unweighted HLMs in Appendix B-3 of our paper. The pattern of results did not change, alth ough the magnitude of some coefficients did. Area 5: Why So Few Small Rural Schools? The Howleys’ discussion of base-year NELS schools is actually not directly relevant to our study, in that we did not examine base-year school effects in this study. Julia Smith and I did publish a study of that NELS students as 8th grader s (Lee & Smith, 1993). In that case, we felt it was inappropriate to explore school size directly, as the variation in the grade-level composition of the base-year NELS schools clouded the issue (e.g., K-8, K-12, 6-8, 7-9). In th at study, we captured "size" with the number of 8th graders in the schoo l. In their analyses of NELS base-year data, the Howleys also used 8th-grade cohort size. However, even at the high-school level in our study, there were sufficient numbers of schools in even the smallest size categories to sustain analysis. It is unclear why an underrepresentation of smaller high schools (if it exists) would bias the results of our study. Their use of the word "bias" in the title of their paper suggests that results of such a study would not be correct. Were that the case, I wonder why the Howleys them selves used the NELS data for analyses. Perhaps there is an under-representation of small mi ddle-grade schools in the NELS base-year school sample. Why, however, would this lead to biased results? From the totality of their paper (parti cularly the Discussion), I infer that that the Howleys believe that small rural schools are actually quite different from (and probably much better than) other small schools. They imply that the effects of school size might be different for rural than suburban or urban schools. This hypothesis, which the descriptive results presented in Table 6 of their study suggest, could be tested directly usi ng NELS data to expl ore size-by-urbanicity


Education Policy Analysis Archives Vol. 12 No. 53 7 interactions. With the same data and structure of our 1997 study, one could create a series of interaction terms for the size categories and test th em, just as we tested size-by-school SES and sizeby-minority composition interactions. In their own analyses of base-year NELS data, they did not include school-level urbanicity-by-grade cohort size interactions, nor did their analysis include even a first-order dummyvariable indicator for rural and small-town schools. It is not appropriate to proclaim as fact an interesting and testable hypothesis. Small rural school s may, indeed, differentially influence students' achievement gains, the social di stribution of achievement, or ma ny other outcomes. The technology to test interactions is well developed (e.g., Cohen, Cohen, West, & Aiken (2003), Chapters 7 and 9). The Howleys obviously understand interactions, as they included them in their own study. If small school size is hypothesized to be differentially effe ctive for schools in rural areas, the data should support this statistically. Area 6: Structure of Our Analyses Multilevel questions, multilevel methods A large volume of the research on the size of educational units has explored data aggregated to the school level. That is, such studies have chosen to structure their analyses with "school" (or perhaps "district") as the single unit of analysis. In such analyses, student outcomes (e.g., achievement, ac hievement gains, dropout rates) have also been aggregated to the school level, as have other st udent characteristics (e.g., student-SES, gender, ability, minority status). In several instances, SES has been captured as many schools and districts do, by the proportion of students in the school rece iving lunch subsidies. Though this approach may seem to make intuitive sense -after all, school si ze is inherently a school characteristic –a schoollevel analysis is actually inappropriate for several reasons. First, student outcomes (and background characteristics) accrue to individu als. When these variables are aggr egated to the school level, they mean something different (creating a mistake that is called either ecological fallacy" or "aggregation bias"). More importantly, aggregat ion essentially discards the large majority of the variance in the outcome of interest (in U.S. data on achievement, typically only 20-25 percent of the total variance lies systematically between schools). Using only school -level aggregates essentially discards 75-80 percent of the variation. Moreover, by doing that researchers are unable to explore within-school relationships between achievement and student backgrou nd -essentially relegating all exploration of inequality to between-school analyses. More than three decades ago, Jencks and his colleagues informed us that the large majority of the inequitable distribution of educational resources lies within, not between, schools (Jencks et al., 1972). Arguments about the proper structure of what has come to be called "school effects research" have been made frequently in other venues, as well as in the Lee and Smith (1997) study. Readers who are in terested in this issue should surely consult the major source (Raudenbush & Bryk, 2002). To me, the question of appropriate m ethodology is simple: if you are asking a multilevel question, you need multilevel methods. Many questions in educational research are inherently multilevel; children experience their education in groups: reading groups, classrooms, schools, districts. The question of how school size influences student outcomes is inherently multilevel. Thus, statements about the consistency of findings in school-size studies rings a bit hollow. Almost all of those studies were condu cted using data aggregated to the school level. Exceptions are the Howleys study described in thei r paper and the Bickel and Howley (2000) study. Distribution of school size Perhaps a more intuitive (but equally important) technical issue surrounds the form of the independent variable of focus. Most school size studies (especially those that focus on schools in a particular state), use si ze as a continuous variable. However, school size is rarely normally distributed. Rather, it is positively skewed, with a long right-hand tail (similar to the


Response to Howley & Howley 8 distribution of family income). There are generally more small than large schools (even though most students attend larger schools). Such a non-norma l distribution typically results in a non-linear relationship between size and achievement (even if achievement is normally distributed, which it usually is). A glance at Figure 1 in the Lee and Smith (1997) study shows a distinct non-linear relationship. Multivariate analysis techniques such as OLS regression and HLM assume normally distributed continuous variables (or dummy-coded independent variables) and linear bivariate relationships. Quantitative researchers exploring the size/achievement relationship have three options. They can either (1) transform the school si ze variable to make it normally distributed (typically a logarithmic transformation will do the tr ick); or (2) create a series of categories and use them as dummy-coded indicators in the analysis; or (3) leave the continuous variable nontransformed and include a quadratic term in the analysis to test for non-linear effects. In our 1997 paper we pursued the second option, precisely beca use we wanted to know "which size high school works best?" In other studies (Lee & Smith, 1993 ; 1995; Lee, Smith, and Croninger, 1997) we chose the first option, using size in its logarithmic tran sformation. Many other studies of school size have used school size (or grade cohort size or even school district size) without correcting for the nonnormal distribution. To non-technical readers, this ma y seem like an esoteric point, but to me it is not. Many of these studies have also used data aggregated to the school or district level (e.g., Howley, 1995). Although I have discussed some of thes e issues at length, my purpose here is not to engage in a lengthy debate about the best (or acceptable) way to investigate the effects of school size on student outcomes. Rather, I have responded to what I consider to be several inappropriate criticisms directed to a study I stand behind strong ly. I contend also that these two methodological issues undermine the validity of many school-size st udies. Later in this paper, I offer a possible explanation for what I consider to be unwarranted cr iticisms raised about our study, when I describe the context of my cont act with Craig Howley. Issue 2: Research Provided in Their Article School and Grade--Cohort Si ze in Middle-Grade Schools Similar to our study with the base-yea r NELS data (Lee & Smith, 1993), in the study described in their article, the Howleys used the indi cator of the number of 8th graders in the NELS' base-year schools, rather than the total en rollment of the school (i.e., school size). However they refer often to small schools when they mean schools with small 8th grades I can think of contexts where a seemingly small 8th grade cohort might exist in a re latively large school: if the school offered a wide grade range (e.g., K-12 or K-8). The distribution of grade grouping by school enrollment size in NELS base-year schools (including private schools) is described elsewhere (see Figure 2.2, p.23 in Lee, 2002). Clearly, the base-year NELS schools offered many different grade configurations. The authors focused only on the public NELS middle-grade schools, grouping them into those they labeled "small schools" and "large schools," using the cut-point of 84 (i.e., they used the CCD to determine that the av erage middle-grade public school in the U.S. enrolled 84 8th graders in 1987-88). However, they then referred to "smaller or larger school size" (p.19). More accurately, they should refer to "schools with smalle r or larger 8th-grade cohorts." My point here is simple: grade cohort size and school size are diffe rent structural features of schools. Either is interesting, but they are not the same thing. They ar e especially different in schools that include 8th grades, as the grade groupings are so varied.


Education Policy Analysis Archives Vol. 12 No. 53 9 Rather than offering policy conclusions about the size of schools that enroll young adolescents, the results of the Howleys’ study migh t be more useful to policy makers interested in decisions about how the schools that young adolescents attend should be configured (i.e., the grades they should include). Their results say something positive about schools with fewer 8th-graders; quite likely these are schools that include more grade levels. Such schools are more likely to be located (and results more positive) in rural areas and small towns. Much has been written recently about troubled large middle schools or junior high sc hools, many of which are located in large cities. There is new research supporting the K-8 organizational form. Process vs. Structure In their cross-sectional analysis of base-year data from NELS:88 collected on 8th grade students in middle-grade schools, the Howleys tell us that they are interested in the "structural ramifications of size" rather than "hypothetical influence of size on process" (p.14). To me, that means that rather than attempting to investigate how students who attend schools of different sizes are influenced by their schools' sizes, they are simp ly exploring issues of selectivity. i.e., which types of students attend schools of different sizes (or wi th 8th grades of different sizes). Because they explore data from the base year of NELS, they may not investigate achievement gains. However, they have quite appropriately incl uded a statistical control as a proxy measure of students' ability -their self-reported grades si nce 6th grade -the same statistical control that Smith and I used in our 1993 study using NELS base-year data. They refer to this as "prior achievement" (p.16), which it is not. The majority of research on school size has used such a design cross-sectional data with schools or districts as the unit of analysis. The distinction between process and structure, given their multilevel analyses and inclusion of a proxy control for ability, is unclear. They seem to be backing away from inferring causality in the introductory sections of their paper, but their analyses and conclusions seem to me to be constructed to infer causality. Which is it? Centering Decisions in Multilevel Models For their multilevel analysis, the Howleys used the SPSS mixed-models analysis methodology, whereas we made use of HLM (Raudenbush & Bryk, 2002). They also included adjustments for design effects, something that is not needed with HLM; the stratification in sampling (students within schools) is the same as the stratification in analysis. As I am unfamiliar with this particular SPSS procedure, I do not make direct comparisons between their analyses results and ours. However, in their text and in footnote a of Table 10, they suggest that they followed the same centering procedures as we did in our 1997 study. As recommended by Raudenbush and Bryk (2002), we centered the intercept and the SES/achievement gain slopes around the grand mean, and other control variables (gender, ability, minority status) around the school means. In their analyses they have investigated as outcomes at Level-2 not only the intercept (8-grade achievement) but two social distributi onal outcomes: the SES/achievement slope and the self-reported grades/achievement slope. If these slope s are to be investigated at Level-2 as functions of school size (as their models suggest), then th ese slopes must be centered around the grand mean and they must be allowed to vary between schools. These are standard centering decision rules in HLM. As they claimed to have followed the same procedures we have, one would assume that their models would be similar (which they seem not to be).


Response to Howley & Howley 10 Structure of Their Multilevel Models In Table 10, the authors present results of a multilevel analysis of 8th-grade mathematics achievement. Although I am very familiar with multilevel analyses (and teach courses in this methodology), I find it difficult to make sense of their results. For example, what is the withinschool model, and what is the between-school model? Perhaps these results could be presented more clearly. Do PRIOR2 and WHITE2 represent sc hool-level aggregates of within-school variables that measure students' race and prior achievement? From footnote c of Table 10, I surmise that "size" is divided into deciles (absent decile 1) an d treated as a continuous variable. Is this still grade cohort size? Why use the deciles rather than the co ntinuous measure? What is the distribution of this 9-level measure? In our NELS study, our decisi on to use school-size categories was made because (a) the distribution of high-school size was defini tely not normal and (b) we wanted to identify an "ideal" size. The Howleys have also categorized sc hool size (9 categories), but they have used this as a continuous variable. They report that this is the same measure they have used in their analyses in Tables 8 and 9 (footnote c Table 10). However, the analyses in Tables 8 and 9 did use school size categories, whereas in the results from the multilevel analyses presented in Table 10 they appear to have used this as a continuous variable. We have no idea whether this variable, used this way, satisfies the distributional requirement of their methodology. 4 Summary of Questions About Their Analyses Query 1: Might there be non-linear size effects? Actually, this question is at the heart of the Lee and Smith (1997), and our findings on this issue are those that the Howleys objected to most strongly. Readers would not know the answer to this question from the analyses offered here. The Howleys used a 9-level continuous variable to represent grade-cohort size in their study. Why were these categories used? They did not show us th e distribution of this variable, nor did they explore the possibility of a non-linear cohort size effect. Without knowing if the quasi-continuous va riable they used as an indicator of 8th-grade cohort size is normally distributed, we cannot judg e whether estimating a linear effect of gradecohort size on achievement is appropriate, or whether this unusual variable has in fact masked a possible non-linear effect. The distribution of school size in U.S. schools (elementary, middle-grade, or secondary schools) is definitely non-linear; there are many more small schools than larger schools. Given that our 1997 study indicated a definite a non-linear effect, and because the Howleys were particularly critical about that finding from our st udy, I believe that this issue must be addressed before we can be confident in their results and conclusions. They state (p.26), "contrary to the assertion of Lee and Smith (1997), these results do not disclose any lower limits for school size." First, we did not assert this; rather, we supported our conclusions on this issue with empirical results. Second, the Howleys study surely did not disclose any lower limits for schools size (a) because they did not structure their analysis so such disclosures would be manifested, and (b) because they didn't actually study school size. Query 2: Is school size equi valent to grade-cohort size? Although the issue of the link between grade-cohort size and student achievemen t is interesting, particularly in middle-grade schools, it is a different issue from school size. Se veral studies of school size have used grade-cohort size as a size proxy, precisely because schools co uld contain different grade configurations (or to combine elementary, middle, and high schools in a particular state in the same analysis). The Howleys made this decision in their study, reasonable one given the substantial variation in grade levels in U.S. middle-grade schools sampled in NELS. However, their equating of grade cohort


Education Policy Analysis Archives Vol. 12 No. 53 11 effects with school size effects is inappropriate. They should change their language, and also discuss the policy implications based on different grade configurations for U.S. schools that enroll 8th graders. Query 3: Are policy conclusions about school size appropriate? Even if we could have confidence in the Howleys results, are "efforts to build and sustained smaller schools... warranted on the basis of these findings," as they state (p.26)? Their study was not focused on school size or small schools, it was focused on sch ools with different sized grade cohorts. Moreover, the focus of their study was on middle-grade school s, but the conclusions offered would seem to apply to schools of all levels. It could very well be the case that size effects at one level of schooling were not generalizable to another. To their credit the final paragraph of their paper does discuss grade-cohort size; however, it refers to high schools rather than the middle-grade schools they studied. Issue 3: The Context Normally, in the academic world we take critiques of our published work in stride -believing that reasonable people can disagree. The Lee and Smith (1997) paper has been cited widely, and I have been asked about it often by sc hool and district personnel who are in positions to make important decisions about how big or small their high schools should be. These queries have led me to recognize that high school size (and rese arch about it) is more relevant to policy makers than much of my research on other topics. In fa ct, the relevance of this issue has extended most recently into another policy arena: the courts. Within the last year, the Howleys and I were invited to serve as expert witnesses on opposite sides of a lawsuit focusing on high school si ze in Lincoln County, West Virginia. I agreed, quite reluctantly, to serve as an expert for the defe nse. The State of West Virginia had taken control of the schools in Lincoln Country in 2000 due to extreme poverty in the county and very weak school performance in the county's schools compared to the rest of the state. Last year the state recommended that four very small high schools be closed and one larger higher school (with a projected enrollment of about 800 students) be constr ucted -a classic case of school consolidation. An advocacy group, "Challenge West Virginia," sued the State to enjoin it from pursuing these actions, and the Howleys agreed to serve as expert witnesses for prosecution. Even though depositions have been collected and the tr ial postponed several times, the case may be over without going to trial. Earlier this year the judge assigned to the case ruled in favor of the State, and construction of the new high school is underway (scheduled to be opened for business in the 200506 school year). The Lee and Smith (1997) study was offered by the State in support of their actions. The Howleys work (including this new article) was offered as evidence. A few of my other studies on the topic of schools size wer e also offered in evidence. Obviously, a legal setting is by nature adversarial. In this context, it is difficult for me to overlook both the timing and the unusually critic al nature of the Howleys 2004 article. I have seldom experienced such micro-level criticis m of my work. I appreciate the effort by Education Policy Analysis Archives and its editor, Gene V Glass, to present readers with different viewpoints about what seems to have developed as a contentious deba te about an issue of educational policy. In fact, I would like readers to see this issue in a somewhat different context. Issue 4: A Causal Link? It is quite appealing in educational resear ch to focus on issues that translate into direct policy levers over which schools, districts, states, and nations actually have control. This is the


Response to Howley & Howley 12 essence of policy-related research. The enrollment size of a school represents such a lever, in that schools are built (and money allocated) based on st udent head counts. Thus, it may seem reasonable for policy makers to ask, "Which size school wo rks best?" Of course, this requires that those exploring the issues define what "works" means; not unusually, this has been defined in terms of student achievement, or even more appropriately, student learning. If one wants to explore a relationship between school size and student lear ning, moreover, it may be reasonable to define learning in terms of how much the same student' s achievement changes over the period he or she has been enrolled in his or her school. However appealing might be the policy i ssue that links school size and student learning, researchers might challenge the validity of such a question. Is it really appropriate to posit a causal link between these two factors? I agree with the Howleys suggestion that research and writings that focus on small schools often confound issues of peda gogical and curricular changes and size per se. However, this suggestion raises an even more important and appropriate question: “Why would anyone think that school size would exert a direct effect on student achievement or learning?” Julia Smith and I raised this same issue toward the end of our 1997 article. We stated: "...we suspect that size acts as a facilitating or deb ilitative factor for other organizational forms or practices that, in turn, promote student learning" (p.218). I teach several courses that focus on quantitative methodology for conducting social science research. From almost the first day in any of the courses I teach (or those I took in graduate school), fledgling researchers are cautioned that "c orrelation does not imply causality." This caution is typically followed with a few examples that illustrate this point, usually with an obvious "third variable" that might explain a spurious link between the two variables in question. We researchers try to keep these cautions in mind, even as we frequently conduct solid correlational research. We are mindful of the need to discount alternative explanations for our findings -by introducing appropriate statistical controls, using longitudinal designs, employing appropriate statistical methods, and many other ways to increase the validity of our studies. In the case of efforts to link school size with student outcomes (particularly learning), we would be wise to revisit the cautions about corre lation and causality. Were we really to identify a residual causal link between school size and student le arning (i.e., gains in achievement over the time students have attended the schools), we might want to control for other school and classroom characteristics that might be confounded with si ze -variables that describe, for example, the curriculum, instruction, student engagement, or social relations among school members. Our 1997 study did not include statistical adjustment for such forms or practices. Our controls were limited to those describing de mographic characteristics of students (SES, race/ethnicity, gender) and structural or compositi onal measures of schools (average SES, minority concentration, school sector). That is, we mainly included statistical controls for selectivity bias In other research (Lee, Burkam, Chow-Hoy, Smerdon, & Geverdt, 1998; Lee & Smith, 1995; Lee, Smith, & Croninger, 1997), we did find residual sc hool size effects even after taking into account many other factors that captured the social an d academic organization of schools. However, we never claimed that our research models were exha ustive. Our major focus in those studies was on issues other than size. Why would we expect school size to influence student learning (or other student outcomes)? It seems logical to think that basic organizational structures are different in smaller than larger schools. School members may relate to one another through more productive and sustained encounters in smaller schools. The ability to offer a full curriculum may be constrained in very small schools. Small schools in rural areas may have troubl e attracting faculty with sufficient expertise to prepare students for a productive future. It may seem reasonable, even logical, to differentiate


Education Policy Analysis Archives Vol. 12 No. 53 13 students by ability in larger schools, thus facilitatin g social stratification through ability grouping and tracking. The list could go on and on. The important issue in such studies is unlikely to be school size per se. Rather, size facilitates or constrains how people relate to one another, the offerings that schools can muster, the web of human relationships that surrounds adults' efforts to facilitate the academic development of the young people they serve. The very fractious co urt case in West Virginia may be missing the point. And we who study school size as though it influences student outcomes directly may also be missing something very important. Notes 1. Because the private school effects are captured by two dummy-coded variables (one coded 1 for Catholic schools, 0 for public schools, another coded 1 for elite private schools, 0 for public schools), technically the size effects in our study are for schools who are coded 0 on all school-level control variables (average SES, school minority con centration, the two sector dummies). That is, the size effects reported in our study are for public schools with average SES and minority enrollments below 40 percent. Even if the private schools were smaller than the public schools (but mostly not the very smallest schools), the size effects in our study are estimated net of school sector, average SES, and minority concentration. 2. NCES made the identical decision when they created the High School Effectiveness Study (HSES), that included only high schools attended by NELS stud ents that were (a) in the 30 largest MSAs (Metropolitan Sampling Areas) in the U.S. (i.e., rural schools were excluded), and (b) enrolled at least 5 original NELS students. In these high sc hools, NCES staff increased within-school sample sizes (which they tested and surveyed). See Scott et al. (1996) for more detail about HSES sampling. 3. NCES did provide school weights with the HSES data (see footnote 2)in fact they provided three of them. My research team and I were asked to conduct a study using HSES data and write a working paper for them (Lee et al, 1998). Because th e HSES data specifically excluded rural schools, we believed that they were not ideal for studying the full range of school size effects. The Lee and Burkam (2003) paper used the HSES data as well, where size was also explored. 4. Although it is not relevant to the issue of school or grade-cohort size, I find the Howleys’ interpretation of the SES2 X PRIOR1 interaction confusing. Because this interaction effect is positive, I would interpret this as indicating that schools with higher average SES are particularly stratifying, in that the relationship between 8th-gr aders' self-reported grades and their mathematics achievement is even stronger than in schools of lower average SES.


Response to Howley & Howley 14References Bickel, R. & Howley, C. (2000). The influence of scale on school performance: A multi-level extension of the Matthew Principle. Education Policy Analysis Archives, 8 (22), May 10, 2000. Bryk, A.S., Lee, V.E., & Holland, P.B. (1993). Catholic Schools and the Common Good Cambridge, MA: Harvard University Press. Cohen, J., Cohen, P., West, S.G., & Aiken, L.S. (2003). Applied Multiple Regression/Correlation Analysis for the Behavioral Science (3rd Ed.). Mahwah, N.J.: Erlbaum. Howley, C. (1995). The Matthew Principl e: A West Virginia replication? Education Policy Analysis Archives, 3 (18), November 15, 1995. Howley, C. B. & Howley, A. A. (2004, Sep tember 24). School size and the influence of socioeconomic status on student achievem ent: Confronting the threat of size bias in national data sets. Education Policy Analysis Archives, 12 (52). Retrieved [date] from Jencks, C., Smith, M., Acland, H., Bane, M.J., Cohen, D.K., Gintis, H., Heyns, B., & Michelson, S. (1972). Inequality: A Reassessment of the Effect of Family and Schooling in America New York: Basic Books. Lee, V.E. (2002). Restructuring High Schools for E quity and Excellence: What Works New York: Teachers College Press. Lee, V.E. (1985). Investigating the Relationship Between Social Cl ass and Academic Achievement in Catholic and Public Schools: The Role of the Ac ademic Organization of the School. Cambridge, MA: Harvard Graduate School of Education, unpublished doctoral dissertation. Lee, V.E. & Bryk, A.S. (1989). A multilevel mode l of the social distribution of high school achievement. Sociology of Education, 62 (3), 172-192. Lee, V.E. & Bryk, A.S. (1988). Curriculum tracki ng as mediating the social distribution of high school achievement. Sociology of Education, 62 (2), 78-94. Lee, V.E. & Burkam, D.T. (2003). Dropping out of high school: The role of school organization and structure. American Educational Research Journal, 40 (2), 353-393. Lee, V.E. & Burkam, D.T. (2002). Inequality at the Starting Gate: Social Background Differences in Achievement as Children Begin School Washington, D. C.: Economic Policy Institute. Lee, V.E., Burkam, D.T., Chow-Hoy, T., Sm erdon, B.A., & Geverdt, D. (1998). High School Curriculum Structure: Effects on Coursetaking and Achi evement in Mathematics for High School Graduates National Center for Education Statistics Working Paper Series (Working Paper No. 98-09). U.S. Department of Education, Office of Educational Research and Improvement.


Education Policy Analysis Archives Vol. 12 No. 53 15 Lee, V.E. & Loeb, S. (2000). School size in Chicago elementary schools: effects on teachers' attitudes and students' achievement. American Educational Research Journal, 37 (1), 3-31. Lee, V.E., Smerdon, B.A., Alfeld-Liro, C., & Brown, S.L. (2000). Inside large and small high schools: Curriculum and social relations. American Educational Research Journal, 22 (2), 147-171. Lee, V.E. & Smith, J.B. (1997). High sc hool size: Which works best, and for whom? Educational Evaluation and Policy Analysis, 19 (3), 205-227. Lee, V.E. & Smith, J.B. (1995). Effects of school restructuring and size on gains in achievement and engagement for early secondary school students. Sociology of Education, 68 (4), 241-270. Lee, V.E. & Smith, J.B. (1993). Effects of school restructuring on the achievement and engagement of middle-grade students. Sociology of Education, 66 (2), 164-187. Lee, V.E., Smith, J.B., & Croninger, R.G. (19 95). How high school organization influences the equitable distribution of learning in mathematics and science. Sociology of Education, 70 (2), 128150. National Center for Educational Statistics (1994 September). National Education Longitudinal Study of 1988. Second Follow-Up: Student Component Data File User's Manual (NCES 94-374. Washington, D. C.: U.S. Department of Education, O ffice of Educational Research and Improvement. Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: A pplications and Data Analysis Methods (2nd Edition). Newbury Park, CA: SAGE. Scott, L.A., Ingels, S.J., Pulliam, P., Sehra, S., Taylor, J.R., & Jerovic, D. (1996). National Education Longitudinal Study of 1988. High School Ef fectiveness Study, Data File User's Manual Washington, D. C: National Center for Education Statistics U.S. Department of Education, March 1996. About the Author Valerie E. Lee School of Education University of Michigan Email: Valerie E. Lee is Professor of Education at the University of Michigan and a Faculty Associate at the University's Institute for Social Research. She teaches courses in quantitative research methods (including multilevel analysis) and the sociology of education. Her research focuses on issues of social equity in education, and her most recent work has considered these issues in the early elementary grades, using a multi-method approach to address the same research questions.


Response to Howley & Howley 16Education Policy Analysis Archives http:// Editor: Gene V Glass, Arizona State University Production Assistant: Chris Murr ell, Arizona State University General questions about appropriateness of topics or particular articles may be addressed to the Editor, Gene V Glass, gl or reach him at College of Education, Arizona State University, Tempe, AZ 85287-2411. The Commentary Editor is Casey D. Cobb: EPAA Editorial Board Michael W. Apple University of Wisconsin David C. Berliner Arizona State University Greg Camilli Rutgers University Linda Darling-Hammond Stanford University Sherman Dorn University of South Florida Mark E. Fetler California Commission on Teacher Credentialing Gustavo E. Fischman Arizona State Univeristy Richard Garlikov Birmingham, Alabama Thomas F. Green Syracuse University Aimee Howley Ohio University Craig B. Howley Appalachia Educational Laboratory William Hunter University of Ontario Institute of Technology Patricia Fey Jarvis Seattle, Washington Daniel Kalls Ume University Benjamin Levin University of Manitoba Thomas Mauhs-Pugh Green Mountain College Les McLean University of Toronto Heinrich Mintrop University of California, Berkeley Michele Moses Arizona State University Gary Orfield Harvard University Anthony G. Rud Jr. Purdue University Jay Paredes Scribner University of Missouri Michael Scriven University of Auckland Lorrie A. Shepard University of Colorado, Boulder Robert E. Stake University of Illinois—UC Kevin Welner University of Colorado, Boulder Terrence G. Wiley Arizona State University John Willinsky University of British Columbia


Education Policy Analysis Archives Vol. 12 No. 53 17AAPE Editorial Board Associate Editors Gustavo E. Fischman & Pablo Gentili Arizona State University & Universida de do Estado do Rio de Janeiro Founding Associate Editor for Spanish Language (1998—2003) Roberto Rodrguez Gmez Hugo Aboites Universidad Autnoma Metropolitana-Xochimilco Adrin Acosta Universidad de Guadalajara Mxico Claudio Almonacid Avila Universidad Metropolitana de Ciencias de la Educacin, Chile Dalila Andrade de Oliveira Universidade Federal de Minas Gerais, Belo Horizonte, Brasil Alejandra Birgin Ministerio de Educacin, Argentina Teresa Bracho Centro de Investigacin y Docencia Econmica-CIDE Alejandro Canales Universidad Nacional Autnoma de Mxico Ursula Casanova Arizona State University, Tempe, Arizona Sigfredo Chiroque Instituto de Pedagoga Popular, Per Erwin Epstein Loyola University, Chicago, Illinois Mariano Fernndez Enguita Universidad de Salamanca. Espaa Gaudncio Frigotto Universidade Estadual do Rio de Janeiro, Brasil Rollin Kent Universidad Autnoma de Puebla. Puebla, Mxico Walter Kohan Universidade Estadual do Rio de Janeiro, Brasil Roberto Leher Universidade Estadual do Rio de Janeiro, Brasil Daniel C. Levy University at Albany, SUNY, Albany, New York Nilma Limo Gomes Universidade Federal de Minas Gerais, Belo Horizonte Pia Lindquist Wong California State University, Sacramento, California Mara Loreto Egaa Programa Interdisciplinario de Investigacin en Educacin, Chile Mariano Narodowski Universidad Torcuato Di Tella, Argentina Iolanda de Oliveira Universidade Federal Fluminense, Brasil Grover Pango Foro Latinoamericano de Polticas Educativas, Per Vanilda Paiva Universidade Estadual do Rio de Janeiro, Brasil Miguel Pereira Catedratico Universidad de Granada, Espaa Angel Ignacio Prez Gmez Universidad de Mlaga Mnica Pini Universidad Nacional de San Martin, Argentina Romualdo Portella do Oliveira Universidade de So Paulo Diana Rhoten Social Science Research Council, New York, New York Jos Gimeno Sacristn Universidad de Valencia, Espaa Daniel Schugurensky Ontario Institute for Studies in Education, Canada Susan Street Centro de Investigaciones y Estudios Superiores en Antropologia Social Occidente, Guadalajara, Mxico Nelly P. Stromquist University of Southern California, Los Angeles, California Daniel Suarez Laboratorio de Politicas Publicas-Universidad de Buenos Aires, Argentina Antonio Teodoro Universidade Lusfona Lisboa, Carlos A. Torres University of California, Los Angeles Jurjo Torres Santom Universidad de la Corua, Espaa Lilian do Valle Universidade Estadual do Rio de Janeiro, Brasil

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