Analyzing longitudinal data with multilevel models: An example with individuals living with lower extremity intra-articular fractures.
Year: 2008
Type: article
Abstract: The use and quality of longitudinal research designs has increased over the past two decades, and new approaches for analyzing longitudinal data, including multi-level modeling (MLM) and latent growth modeling (LGM), have been developed. The purpose of this paper is to demonstrate the use of MLM and its advantages in analyzing longitudinal data. Data from a sample of individuals with intra-articular fractures of the lower extremity from the University of Alabama at Birmingham's Injury Control Research Center is analyzed using both SAS PROC MIXED and SPSS MIXED. We start our presentation with a discussion of data preparation for MLM analyses. We then provide example analyses of different growth models, including a simple linear growth model and a model with a time-invariant covariate, with interpretation for all the parameters in the models. More complicated growth models with different between- and within-individual covariance structures and nonlinear models are discussed. Finally, information related to MLM analysis such as online resources is provided at the end of the paper.
Source: Rehabilitation Psychology
DOI: 10.1037/a0012765
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Funders: U.S. Department of Health and Human Services, University of Alabama at Birmingham, University of Alabama, Centers for Disease Control and Prevention, National Institute of Child Health and Human Development, National Center for Injury Prevention and Control, Eunice Kennedy Shriver National Institute of Child Health and Human Development
OpenAlex ID: W2159072822