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date: 03 April 2020

Abstract and Keywords

Longitudinal data analysis is an increasingly popular approach because evaluating change is of central interest in many areas of research. Using advanced statistical techniques such as multilevel modeling (MLM) and structural equation modeling (SEM), longitudinal data analysis allows for the simultaneous evaluation of intra-individual change and interindividual differences in intra-individual change. This chapter presents an overview of both MLM and SEM approaches to evaluating change with different functional forms for continuous panel data, including linear, curvilinear, nonlinear, and spline curve models. This chapter also covers a variety of longitudinal models that take advantage of the flexibility of SEM over MLM, including autoregressive cross-lagged, latent difference, fully latent, parallel process, and second-order curve models. The chapter closes with a discussion of the advantages and disadvantages of MLM and SEM in modeling change, along with a brief review of advances in longitudinal data analysis.

Keywords: Longitudinal data analysis, multilevel modeling, structural equation modeling, repeated measures, latent curve model, change trajectory, parallel process curve model

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