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Marcoulides, Katerina M. – International Journal of Behavioral Development, 2021
The purpose of this research note is to introduce a latent growth curve reconstruction approach based on the Tabu search algorithm. The approach algorithmically enables researchers to optimally determine at both the individual and the group levels the order of the polynomial needed to represent the latent growth curve model. The procedure is…
Descriptors: Growth Models, Computation, Mathematics, Longitudinal Studies
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Marcoulides, Katerina M. – Measurement: Interdisciplinary Research and Perspectives, 2019
Longitudinal data analysis has received widespread interest throughout educational, behavioral, and social science research, with latent growth curve modeling currently being one of the most popular methods of analysis. Despite the popularity of latent growth curve modeling, limited attention has been directed toward understanding the issues of…
Descriptors: Reliability, Longitudinal Studies, Growth Models, Structural Equation Models
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Soland, James; Thum, Yeow Meng – Journal of Research on Educational Effectiveness, 2022
Sources of longitudinal achievement data are increasing thanks partially to the expansion of available interim assessments. These tests are often used to monitor the progress of students, classrooms, and schools within and across school years. Yet, few statistical models equipped to approximate the distinctly seasonal patterns in the data exist,…
Descriptors: Academic Achievement, Longitudinal Studies, Data Use, Computation
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Liu, Haiyan; Zhang, Zhiyong; Grimm, Kevin J. – Grantee Submission, 2016
Growth curve modeling provides a general framework for analyzing longitudinal data from social, behavioral, and educational sciences. Bayesian methods have been used to estimate growth curve models, in which priors need to be specified for unknown parameters. For the covariance parameter matrix, the inverse Wishart prior is most commonly used due…
Descriptors: Bayesian Statistics, Computation, Statistical Analysis, Growth Models
Choi, Kilchan; Kim, Jinok – Journal of Educational and Behavioral Statistics, 2019
This article proposes a latent variable regression four-level hierarchical model (LVR-HM4) that uses a fully Bayesian approach. Using multisite multiple-cohort longitudinal data, for example, annual assessment scores over grades for students who are nested within cohorts within schools, the LVR-HM4 attempts to simultaneously model two types of…
Descriptors: Regression (Statistics), Hierarchical Linear Modeling, Longitudinal Studies, Cohort Analysis
Schulte, Ann C.; Stevens, Joseph J.; Nese, Joseph F. T.; Yel, Nedim; Tindal, Gerald; Elliott, Stephen N. – National Center on Assessment and Accountability for Special Education, 2018
This technical report is one of a series of four technical reports that describe the results of a study comparing eight alternative models for estimating school academic achievement using data from the Arizona, North Carolina, Oregon, and Pennsylvania accountability systems. The purpose of these reports was to evaluate a broad range of models…
Descriptors: School Effectiveness, Models, Computation, Comparative Analysis
Nese, Joseph F. T.; Stevens, Joseph J.; Schulte, Ann C.; Tindal, Gerald; Yel, Nedim; Anderson, Daniel; Matta, Tyler; Elliott, Stephen N. – National Center on Assessment and Accountability for Special Education, 2018
This technical report is one of a series of four technical reports that describe the results of a study comparing eight alternative models for estimating school academic achievement using data from the Arizona, North Carolina, Oregon, and Pennsylvania accountability systems. The purpose of these reports was to evaluate a broad range of models…
Descriptors: School Effectiveness, Models, Computation, Comparative Analysis
Stevens, Joseph J.; Nese, Joseph F. T.; Schulte, Ann C.; Tindal, Gerald; Yel, Nedim; Anderson, Daniel; Matta, Tyler; Elliott, Stephen N. – National Center on Assessment and Accountability for Special Education, 2017
This technical report is one of a series of four technical reports that describe the results of a study comparing eight alternative models for estimating school academic achievement using data from the Arizona, North Carolina, Oregon, and Pennsylvania accountability systems. The purpose of these reports was to evaluate a broad range of models…
Descriptors: School Effectiveness, Models, Computation, Comparative Analysis
Schulte, Ann C.; Nese, Joseph F. T.; Stevens, Joseph J.; Yel, Nedim; Tindal, Gerald; Anderson, Daniel; Elliott, Stephen N. – National Center on Assessment and Accountability for Special Education, 2017
This technical report is one of a series of four technical reports that describe the results of a study comparing eight alternative models for estimating school academic achievement using data from the Arizona, North Carolina, Oregon, and Pennsylvania accountability systems. The purpose of these reports was to evaluate a broad range of models…
Descriptors: School Effectiveness, Models, Computation, Comparative Analysis
Schmitt, Lisa; Hutchins, Shaun – Online Submission, 2016
This report provides an overview of the process used to derive a school's growth level and summarizes 2015 math and reading/ELA growth levels for all AISD elementary, middle and high schools. Additionally, longitudinal data are provided for each school level.
Descriptors: School Districts, Academic Achievement, Elementary School Students, Middle School Students
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Braun, Henry; Qu, Yanxuan – ETS Research Report Series, 2008
This paper reports on a study conducted to investigate the consistency of the results between 2 approaches to estimating school effectiveness through value-added modeling. Estimates of school effects from the layered model employing item response theory (IRT) scaled data are compared to estimates derived from a discrete growth model based on the…
Descriptors: Value Added Models, School Effectiveness, Robustness (Statistics), Computation