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Latent Growth Curve and Repeated Measures Anova Contrasts

What the Models are Telling You

Bibliographic Data

ID19289637
AuthorsMICHAEL J ROVINE (0000-0003-4624-891X, Human Development and Quantitative Methods Division, University of Pennsylvania's Graduate School of Education, corresponding author), Paul A Mcdermott (Human Development and Quantitative Methods Division, University of Pennsylvania's Graduate School of Education)
Year2018
Volume53
Issue1
Pages90-101
Publication date2018-01-02
Open AccessNo
TypeARTICLE
VenueMultivariate Behavioral Research (JOURNAL)
Journal identifiersISSN: 0027-3171 • E-ISSN: 1532-7906
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/00273171.2017.1387511
PMID29220588
OpenAlexW2771566799
LanguageEN
Citations received6
References cited18

The relationship between the latent growth curve and repeated measures ANOVA models is often misunderstood. Although a number of investigators have looked into the similarities and differences among these models, a cursory reading of the literature can give the impression that they are very different models. Here we show that each model represents a set of contrasts on the occasion means. We demonstrate that the fixed effects parameters of the estimated basis vector latent growth curve model are merely a transformation of the repeated measures ANOVA fixed effects parameters. We further show that differences in fit in models that estimate the same means structure can be due to the different error covariance structures implied by the model. We show these relationships both algebraically and through using data from a simulation

Analysis of covariance · Analysis of variance · Covariance · Econometrics · Growth curve (statistics) · Latent growth modeling · Repeated measures design · Set (abstract data type) · Statistics · Transformation (genetics) · Computer Science · Mathematics · Spatial and Panel Data Analysis · Statistical Methods and Bayesian Inference · Statistical Methods and Inference

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Citations per year1.2
Citation span2021 - 2025 (5)
Highly citedNo

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