Multivariate hierarchical linear modeling in randomized field experiments

Richard L. Tate, Keenan A. Pituch

Research output: Contribution to journalReview articlepeer-review

25 Scopus citations

Abstract

The hierarchical linear model (HLM) is now commonly accepted as a useful modeling approach for multilevel data resulting from randomized field experiments. When multiple outcomes of interest exist, a multivariate extension of the conventional univariate HLM offers advantages over the usual application of separate HLM analyses for each of the outcomes. In this article, the authors review these advantages, discuss the device that allows the univariate HLM procedure to model multiple outcomes, and present a series of multivariate models that would be useful in addressing typical questions in field experiments. In addition to the multivariate multilevel versions of basic analysis of variance (ANOVA) or analysis of covariance (ANCOVA) designs, the authors present more complex models that allow the testing of moderation and mediation of the treatment effect. The various analyses are illustrated with computer generated data for a hypothetical scenario.

Original languageEnglish (US)
Pages (from-to)317-337
Number of pages21
JournalJournal of Experimental Education
Volume75
Issue number4
DOIs
StatePublished - 2007
Externally publishedYes

Keywords

  • Field experiments
  • Hierarchical linear models
  • Mediation
  • Multilevel models
  • Multivariate
  • Randomized moderation

ASJC Scopus subject areas

  • Education
  • Developmental and Educational Psychology

Fingerprint

Dive into the research topics of 'Multivariate hierarchical linear modeling in randomized field experiments'. Together they form a unique fingerprint.

Cite this