A new, more powerful approach to multitrait^multimethod analyses: Application of second-order confirmatory factor analysis.
Abstract
The advantages of applying confirmatory factor analysis (CFA) to multitrait-multimethod (MTMM) data are widely recognized. However, because CFA. as traditionally applied to MTMM data, incorporates single indicators of each scale (i.e., each trait-method combination), important weaknesses are the failure to (a) correct appropriately for measurement error in scale scores, (b) separate error due to law internal consistency from uniqueness due to weak trait or method effects, (c) test whether items or subscales accurately reflect the intended factor structure, and (d) test for correlated uniquenesses. However, when the analysis begins with multiple indicators of each scale (i.e., items or subscales), second-order factor analysis can be used to address each of these problems. In this approach, first-order factors defined by multiple items or subscales are posited for each scale, and the method and trait factors are posited as second-order factors.
Cites (2)
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- A comparison of some methodologies for the factor analysis of non‐normal Likert variablesMuthén, Bengt, Kaplan, David · 1985 · British Journal of Mathematical and Statistical Psychology
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- A Brief Tutorial on the Development of Measures for Use in Survey QuestionnairesHinkin, Timothy R. · 1998 · Organizational Research Methods
- Validation Guidelines for IS Positivist ResearchStraub, Detmar, Gefen, David · 2004 · Communications of the Association for Information Systems
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Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A new, more powerful approach to multitrait^multimethod analyses: Application of second-order confirmatory factor analysis. https://app.cochat.ai/papers/10.1037//0021-9010.73.1.107
- The paper
- Marsh, H. W., & Hocevar, D. (1988). A new, more powerful approach to multitrait^multimethod analyses: Application of second-order confirmatory factor analysis. Journal of Applied Psychology. https://doi.org/10.1037//0021-9010.73.1.107
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