The Performance of Multilevel Models When Outcome Data Are Incomplete

Wanchen Chang, Keenan A. Pituch

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

When data for multiple outcomes are collected in a multilevel design, researchers can select a univariate or multivariate analysis to examine group-mean differences. When correlated outcomes are incomplete, a multivariate multilevel model (MVMM) may provide greater power than univariate multilevel models (MLMs). For a two-group multilevel design with two correlated outcomes, a simulation study was conducted to compare the performance of MVMM to MLMs. The results showed that MVMM and MLM performed similarly when data were complete or missing completely at random. However, when outcome data were missing at random, MVMM continued to provide unbiased estimates, whereas MLM produced grossly biased estimates and severely inflated Type I error rates. As such, this study provides further support for using MVMM rather than univariate analyses, particularly when outcome data are incomplete.

Original languageEnglish (US)
Pages (from-to)1-16
Number of pages16
JournalJournal of Experimental Education
Volume87
Issue number1
DOIs
StatePublished - Jan 2 2019
Externally publishedYes

Keywords

  • HLM
  • missing data
  • multivariate
  • simulation studies
  • univariate

ASJC Scopus subject areas

  • Education
  • Developmental and Educational Psychology

Fingerprint Dive into the research topics of 'The Performance of Multilevel Models When Outcome Data Are Incomplete'. Together they form a unique fingerprint.

Cite this