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23
Exploring models of school performance: From theory to practice
 In
, 2005
"... Our purpose in this report is to present and discuss competing accountability approaches, or models, designed to systematically indicate how a school’s students are performing academically. Within the framework of the current federally mandated accountability legislation, increased interest in model ..."
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Our purpose in this report is to present and discuss competing accountability approaches, or models, designed to systematically indicate how a school’s students are performing academically. Within the framework of the current federally mandated accountability legislation, increased interest in models measuring school performance has caused educational policymakers to consider several key issues. These issues include whether results from different accountability models yield different inferences about a school’s performance; what assumptions underlie each of the models; how different models are implemented; and ultimately which model is best suited for a particular context. We address these issues by building a framework for accountability models and then explicitly comparing and contrasting these competing models. In order to
Beyond multilevel regression modeling: Multilevel analysis in a general latent variable framework
 In J. Hox & J.K. Roberts (eds), The Handbook of Advanced Multilevel Analysis
, 2011
"... 1 Multilevel modeling is often treated as if it concerns only regression analysis and growth modeling. Multilevel modeling, however, is relevant for nested data not only with regression and growth analysis but with all types of statistical analyses. This chapter has two aims. First, it shows that a ..."
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Cited by 8 (4 self)
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1 Multilevel modeling is often treated as if it concerns only regression analysis and growth modeling. Multilevel modeling, however, is relevant for nested data not only with regression and growth analysis but with all types of statistical analyses. This chapter has two aims. First, it shows that already in the traditional multilevel analysis areas of regression and growth there are several new modeling opportunities that should be considered. Second, it gives an overview with examples of multilevel modeling for path analysis, factor analysis, structural equation modeling, and growth mixture modeling. Examples include two extensions of twolevel regression analysis with measurement error in the level 2 covariate and a level 1 mixture; twolevel path analysis and structural equation modeling; twolevel exploratory factor analysis of classroom misbehavior; twolevel growth modeling using a twopart model for heavy drinking development; an unconventional approach to threelevel growth modeling of math achievement; and multilevel latent class mediation of high school dropout using multilevel growth mixture modeling of math achievement development. 2 1
Modeling heterogeneity in relationships between initial status and rates of change: Treating latent variable regression coefficients as random coefficients in a threelevel hierarchical model. Accepted for publication
 in Journal of Educational and Behavioral Statistics
, 2006
"... In studies of change in education and numerous other fields, interest often centers on how differences in the status of individuals at the start of a time period of substantive interest relate to differences in subsequent change. In this report we present a fully Bayesian approach to estimating thre ..."
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In studies of change in education and numerous other fields, interest often centers on how differences in the status of individuals at the start of a time period of substantive interest relate to differences in subsequent change. In this report we present a fully Bayesian approach to estimating threelevel hierarchical models in which latent variable regression coefficients capturing the relationship between initial status and rates of change within each of J schools (Bwj, j = 1, …, J) are treated as varying across schools. Through analyses of data from the Longitudinal Study of American Youth, we show how modeling differences in Bwj as a function of school characteristics can broaden the kinds of questions we can address in school effects research. We illustrate the possibility of conducting sensitivity analyses employing t distributional assumptions at each level of
Exploring StudentTeacher Interactions in Longitudinal Achievement Data
, 2008
"... This article develops a model for longitudinal student achievement data designed to estimate heterogeneity in teacher effects across students of different achivement levels. The model specifies interactions between teacher effects and students’ predicted scores on a test, estimating both average eff ..."
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This article develops a model for longitudinal student achievement data designed to estimate heterogeneity in teacher effects across students of different achivement levels. The model specifies interactions between teacher effects and students’ predicted scores on a test, estimating both average effects of individual teachers and interaction terms indicating whether individual teachers are differentially effective with students of different predicted scores. Using various longitudinal data sources, we find evidence of these interactions that are of relatively consistent but modest magnitude across different contexts, accounting for about 10 % of the total variation in teacher effects across all students. However, the amount that the interactions matter in practice depends on how different are the groups of students taught by different teachers. Using empirical estimates of the heterogeneity of students across teachers, we find that the interactions account for about 3%4 % of total variation in teacher effects on different classes, with somewhat larger values in middle school mathematics. Our findings suggest that ignoring these interactions is not likely to introduce appreciable bias in estimated teacher effects for most teachers in most settings. The results of this study should be of interest to policymakers concerned about the validity of VAM teacher effect estimates.
Modeling Interactions Between Latent and Observed Continuous Variables Using MaximumLikelihood Estimation In Mplus
, 2003
"... Modeling with random slopes is used in random coefficient regression, multilevel regression, and growth modeling. Random slopes can be seen as continuous latent variables. Recently, a flexible modeling framework has been implemented in the Mplus program to do modeling with such latent variables comb ..."
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Modeling with random slopes is used in random coefficient regression, multilevel regression, and growth modeling. Random slopes can be seen as continuous latent variables. Recently, a flexible modeling framework has been implemented in the Mplus program to do modeling with such latent variables combined with modeling of psychometric constructs, typically referred to as factors, measured by multiple indicators. This note shows how such a framework can handle interactions between latent continuous and observed continuous indicators. Three examples are given: a Monte Carlo simulation to estimate power to detect the interaction; a psychological example; and a growth modeling example. Mplus input, output, and data are available at the Mplus web site, www.statmodel.com/mplus/examples/webnote.html. 1 1
Individual Growth and School Success
, 2004
"... or by any means, electronic or mechanical, including photocopying, recording, or by ..."
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or by any means, electronic or mechanical, including photocopying, recording, or by
The Study of School Effectiveness as a Problem in Research Design
"... islation there is an increased focus on the evaluation of school effectiveness and the identification of both exemplary and failing schools. In order to accomplish the promise of this focus, methodologies must be brought to bear that can disentangle the impact a school has on its students from othe ..."
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islation there is an increased focus on the evaluation of school effectiveness and the identification of both exemplary and failing schools. In order to accomplish the promise of this focus, methodologies must be brought to bear that can disentangle the impact a school has on its students from other influences on student learning. NCLB and other recent federal mandates and programs place strong emphasis on “evidence based ” or “scientifically based ” research to better understand educational programs and interventions that are effective in promoting student learning. Scientifically based research “…means research that involves the application of rigorous, systematic, and objective procedures to obtain reliable and valid knowledge relevant to education activities and programs ” (NCLB, 2001). In some discussions, scientifically based research is equated with experiments using random assignment or randomized clinical trials. For example, criteria used by the federal What Works Clearinghouse (WWC, n.d.) for reviewing research studies assign higher ratings to randomized 2 STEVENS clinical trials than quasiexperiments and appear to essentially exclude from consideration research that uses case study and other research designs. Throughout these recent discussions of scientifically based evidence, there is relatively little consideration of a wide array of other methods of experimental and statistical control. The purpose of this chapter is to draw attention to certain research design issues inherent in the study of school effectiveness and to examine the way in which these issues relate to NCLB methods for evaluating school effectiveness using adequate yearly progress (AYP). It is also our intent to contrast those methods with alternative research designs, especially longitudinal designs, and offer preliminary evidence on the performance of alternative designs in controlling for covariates that may represent threats to internal validity.
Implementer’s Guide to Growth Models A paper commissioned by the CCSSO Accountability Systems and Reporting State Collaborative on Assessment and Student Standards
"... organization of public officials who head departments of elementary and secondary education in ..."
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organization of public officials who head departments of elementary and secondary education in
POLICYMAKERS ’ GUIDE TO GROWTH MODELS FOR SCHOOL ACCOUNTABILITY: HOW DO ACCOUNTABILITY MODELS DIFFER? A paper commissioned by the CCSSO Accountability Systems and Reporting
"... of public officials who head departments of elementary and secondary education in the states, the District ..."
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of public officials who head departments of elementary and secondary education in the states, the District
Teacher Effect Change Model: Latent Variable Regression in 5Level Hierarchical Models for Evaluating Teacher Preparation Programs
"... The main goal of this deliverable is to use available data from California State University, Northridge (CSUN) to explore a model that in the future could be used to evaluate the effects of various teacher preparation programs on student learning, as measured by standardized test scores. This delive ..."
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The main goal of this deliverable is to use available data from California State University, Northridge (CSUN) to explore a model that in the future could be used to evaluate the effects of various teacher preparation programs on student learning, as measured by standardized test scores. This deliverable first provides important