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Tihomir Asparouhov; Bengt Muthén – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Penalized structural equation models (PSEM) is a new powerful estimation technique that can be used to tackle a variety of difficult structural estimation problems that can not be handled with previously developed methods. In this paper we describe the PSEM framework and illustrate the quality of the method with simulation studies.…
Descriptors: Structural Equation Models, Computation, Factor Analysis, Measurement Techniques
Tomek, Sara; Robinson, Cecil – Measurement: Interdisciplinary Research and Perspectives, 2021
Typical longitudinal growth models assume constant functional growth over time. However, there are often conditions where trajectories may not be constant over time. For example, trajectories of psychological behaviors may vary based on a participant's age, or conversely, participants may experience an intervention that causes trajectories to…
Descriptors: Growth Models, Statistical Analysis, Hierarchical Linear Modeling, Computation
Li, Wei; Konstantopoulos, Spyros – Journal of Experimental Education, 2019
Education experiments frequently assign students to treatment or control conditions within schools. Longitudinal components added in these studies (e.g., students followed over time) allow researchers to assess treatment effects in average rates of change (e.g., linear or quadratic). We provide methods for a priori power analysis in three-level…
Descriptors: Research Design, Statistical Analysis, Sample Size, Effect Size
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
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