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Combining Latent Variable Modeling Techniques with Cross-classified Mixed Effect Models for Use in Longitudinal Research
Principal Investigator: Leslie Hawley
Funding Agency: Office of Research and Economic Development—Layman Award
Award Date: Jun 1, 2015
End Date: May 31, 2016
One of the most difficult methodological challenges is measuring human growth and contextual aspects, which add an additional degree of complexity. In particular, there is a gap in the literature regarding the ability to model both changes in context over time (e.g., students who change schools) and multivariate outcome measures (e.g., multiple items measuring a single construct).
Ignoring changes in environmental context over the course of a longitudinal study can lead to an underestimation of standard errors. Additionally, measuring a construct with a single summary score as opposed to using multivariate measures can lead to increased measurement error. The combination of these two aspects is important for longitudinal research; these situations increase the degree of model error, which decreases the power to detect significant effects.
This project evaluates the efficacy of blending a particular type of hierarchical linear model with multivariate latent variable modeling techniques. The aim is to evaluate two types of modeling approaches (i.e., models that either ignore or account for changes in context) across a series of univariate and multivariate measurement model structures.