Alternative Models for Small Samples in Psychological Research: Applying Linear Mixed Effects Models and Generalized Estimating Equations to Repeated Measures Data

Chelsea Muth, Karen L. Bales, Katie Hinde, Nicole Maninger, Sally P. Mendoza, Emilio Ferrer

Research output: Contribution to journalArticle

28 Scopus citations

Abstract

Unavoidable sample size issues beset psychological research that involves scarce populations or costly laboratory procedures. When incorporating longitudinal designs these samples are further reduced by traditional modeling techniques, which perform listwise deletion for any instance of missing data. Moreover, these techniques are limited in their capacity to accommodate alternative correlation structures that are common in repeated measures studies. Researchers require sound quantitative methods to work with limited but valuable measures without degrading their data sets. This article provides a brief tutorial and exploration of two alternative longitudinal modeling techniques, linear mixed effects models and generalized estimating equations, as applied to a repeated measures study (n = 12) of pairmate attachment and social stress in primates. Both techniques provide comparable results, but each model offers unique information that can be helpful when deciding the right analytic tool.

Original languageEnglish (US)
Pages (from-to)64-87
Number of pages24
JournalEducational and Psychological Measurement
Volume76
Issue number1
DOIs
StatePublished - Feb 1 2016
Externally publishedYes

Keywords

  • generalized estimating equations
  • linear mixed effects models
  • longitudinal data
  • repeated measures ANOVA
  • small sample

ASJC Scopus subject areas

  • Education
  • Developmental and Educational Psychology
  • Applied Psychology
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Alternative Models for Small Samples in Psychological Research: Applying Linear Mixed Effects Models and Generalized Estimating Equations to Repeated Measures Data'. Together they form a unique fingerprint.

  • Cite this