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Daniel Seddig – Structural Equation Modeling: A Multidisciplinary Journal, 2024
The latent growth model (LGM) is a popular tool in the social and behavioral sciences to study development processes of continuous and discrete outcome variables. A special case are frequency measurements of behaviors or events, such as doctor visits per month or crimes committed per year. Probability distributions for such outcomes include the…
Descriptors: Growth Models, Statistical Analysis, Structural Equation Models, Crime
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McNeish, Daniel; Peña, Armando; Vander Wyst, Kiley B.; Ayers, Stephanie L.; Olson, Micha L.; Shaibi, Gabriel Q. – Prevention Science, 2023
Growth mixture models (GMMs) are applied to intervention studies with repeated measures to explore heterogeneity in the intervention effect. However, traditional GMMs are known to be difficult to estimate, especially at sample sizes common in single-center interventions. Common strategies to coerce GMMs to converge involve post hoc adjustments to…
Descriptors: Prevention, Intervention, Growth Models, Program Effectiveness
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Rohit Batra; Silvia A. Bunge; Emilio Ferrer – Structural Equation Modeling: A Multidisciplinary Journal, 2022
Studying development processes, as they unfold over time, involves collecting repeated measures from individuals and modeling the changes over time. One methodological challenge in this type of longitudinal data is separating retest effects, due to the repeated assessments, from developmental processes such as maturation or age. In this article,…
Descriptors: Children, Adolescents, Longitudinal Studies, Test Reliability
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Serang, Sarfaraz – New Directions for Child and Adolescent Development, 2021
Longitudinal research is often interested in identifying correlates of heterogeneity in change. This paper compares three approaches for doing so: the mixed-effects model (latent growth curve model), the growth mixture model, and structural equation model trees. Each method is described, with special focus given to how each structures…
Descriptors: Longitudinal Studies, National Surveys, Growth Models, Structural Equation Models
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Daria Khanolainen; Maria Psyridou; Kenneth Eklund; Tuija Aro; Minna Torppa – Scientific Studies of Reading, 2024
Purpose: Reading fluency establishes the basis for the strong literacy skills needed for academic success. We aim to trace how reading fluency develops from childhood to adulthood and identify factors that influence this development. Method: In this study, 200 families were followed. All participating children (N = 200, 47% female) were ethnic…
Descriptors: Reading Fluency, Growth Models, Student Development, Children
McNeish, Daniel; Peña, Armando; Vander Wyst, Kiley B.; Ayers, Stephanie L.; Olson, Micha L.; Shaibi, Gabriel Q. – Grantee Submission, 2021
Growth mixture models (GMMs) are applied to intervention studies with repeated measures to explore heterogeneity in the intervention effect. However, traditional GMMs are known to be difficult to estimate, especially at sample sizes common in single-center interventions. Common strategies to coerce GMMs to converge involve post-hoc adjustments to…
Descriptors: Prevention, Intervention, Growth Models, Program Effectiveness