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Psychometrika

, Volume 84, Issue 1, pp 212–235 | Cite as

Simplified Estimation and Testing in Unbalanced Repeated Measures Designs

  • Martin SpiessEmail author
  • Pascal Jordan
  • Mike Wendt
Article
  • 105 Downloads

Abstract

In this paper we propose a simple estimator for unbalanced repeated measures design models where each unit is observed at least once in each cell of the experimental design. The estimator does not require a model of the error covariance structure. Thus, circularity of the error covariance matrix and estimation of correlation parameters and variances are not necessary. Together with a weak assumption about the reason for the varying number of observations, the proposed estimator and its variance estimator are unbiased. As an alternative to confidence intervals based on the normality assumption, a bias-corrected and accelerated bootstrap technique is considered. We also propose the naive percentile bootstrap for Wald-type tests where the standard Wald test may break down when the number of observations is small relative to the number of parameters to be estimated. In a simulation study we illustrate the properties of the estimator and the bootstrap techniques to calculate confidence intervals and conduct hypothesis tests in small and large samples under normality and non-normality of the errors. The results imply that the simple estimator is only slightly less efficient than an estimator that correctly assumes a block structure of the error correlation matrix, a special case of which is an equi-correlation matrix. Application of the estimator and the bootstrap technique is illustrated using data from a task switch experiment based on an experimental within design with 32 cells and 33 participants.

Keywords

repeated measures design unbalanced design generalized estimating equations bootstrap Wald test task switching paradigm 

Supplementary material

11336_2018_9620_MOESM1_ESM.pdf (228 kb)
Supplementary material 1 (pdf 227 KB)
11336_2018_9620_MOESM2_ESM.zip (109 kb)
Supplementary material 2 (zip 109 KB)

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Copyright information

© The Psychometric Society 2018

Authors and Affiliations

  1. 1.Department of PsychologyUniversity of HamburgHamburgGermany
  2. 2.Faculty of Human SciencesMedical School HamburgHamburgGermany

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