Toward Improving the Modeling of MTMM Data: A Response to Geiser, Koch, and Eid (2014)

Laura Castro-Schilo, Keith F. Widaman, Kevin J. Grimm

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Geiser, Koch, and Eid (2014) expressed their views on an article we published describing findings from a simulation study and an empirical study of multitrait–multimethod (MTMM) data. Geiser and colleagues raised concerns with (a) our use of the term bias, (b) our statement that the correlated trait–correlated method minus one [CT–C(M–1)] model is not in line with Campbell and Fiske’s (1959) conceptualization of MTMM data, (c) our selection of a data-generating model for our simulation study, and (d) our preference for the correlated trait–correlated method (CT–CM) model over the CT–C(M–1) model. Here, we respond to and elaborate on issues raised by Geiser et al. We maintain our position on each of these issues and point to the interpretational challenges of the CT–C(M–1) model. But, we clarify our opinion that none of the existing structural models for MTMM data are flawless; each has its strengths and each has its weaknesses. We further remind readers of the goal, findings, and implications of our recently published article.

Original languageEnglish (US)
Pages (from-to)524-533
Number of pages10
JournalStructural Equation Modeling
Volume21
Issue number4
DOIs
StatePublished - Oct 2 2014
Externally publishedYes

Keywords

  • CT-C(M-1) model
  • CT-CM model
  • construct validity
  • multitrait–multimethod (MTMM) analysis
  • structural equation modeling

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Modeling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)

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