title: A comparison of traditional approaches to hierarchical linear modeling when analyzing longitudinal data.
abstract: Longitudinal designs typically involve repeated time-ordered observations for each individual (or unit). Such designs are uniquely suited to studying changes over time within individuals, and relating these to individual characteristics to identify processes and causes of intra- individual changes and interindividual differences in physiologic and psychological development. The purpose of this paper is to compare and contrast univariate and multivariate ANOVA with repeated measures to hierarchical linear modeling as approaches to analyzing such longitudinal data. This will enable researchers to choose the approach that best meets their research needs, and it will enable them to compare research results that are reported using one analytical approach with results that are reported using the other approach.
authors.affiliation: School of Nursing, State University of New York at Buffalo, 820 Kimball Tower, Buffalo, NY 14214, USA.
authors.forename: Y W
authors.initials: YW
authors.lastname: Wu
authors.forename: R R
authors.initials: RR
authors.lastname: Clopper
authors.forename: P J
authors.initials: PJ
authors.lastname: Wooldridge
mesh_terms: D000704:Analysis of Variance; D019382:Human Growth Hormone / Q000627:therapeutic use; D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies; D012107:Research Design
chemical_list: D019382:Human Growth Hormone