Abstract
Longitudinal data are used in the research of growth, development, and change. Such data consist of the same subjects repeatedly measured over time. Multivariate analysis of variance and repeated measures ANOVA have historically been the most widely used tools for analyzing longitudinal data. However, there are some limitations in utilizing these two methods in analyzing longitudinal data. The longitudinal data typically need some structured covariance models, and the residual errors often exhibit heteroscedasticity and dependence. They also possess a hierarchical data structure in the sense that the repeated measurements are viewed as a separate level nested within an individual. The main objective of this study was to investigate hierarchical linear models/linear mixed-effects models for longitudinal data. The visual-search dataset is used to demonstrate the advantages of utilizing the methodology. Several recommendations and advantages on the application of linear mixed-effects models in longitudinal modeling are discussed.
| Keywords: | linear mixed-effects models; hierarchical linear models; random effects; longitudinal data; heteroscedasticity; dependence |
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