Multitrait–multimethod matrices in consumer research: Critique and new developments
Open accessAbstract
In this article we address problems with recently proposed methods for examining construct validity and we introduce alternatives. With respect to the firstorder confirmatory factor analysis model, shortcomings in the application of the method to multitrait‐multimethod data are considered. The correlated uniqueness model is then presented as an alternative when ill‐defined solutions arise in first‐order confirmatory factor analyses or when method and trait variance are confounded. Procedures are then developed for investigating important hypotheses in the application of the direct product model. These procedures were not considered in previous treatments. Finally, panel models are shown to be adaptable to certain investigations of construct validity.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Common method bias can be addressed with a multitrait-multimethod study.
“which can be addressed with a multitrait–multimethod study in which the same research participants are measured on multiple constructs using multiple methods or instruments (Bagozzi and Yi 1993).”
From Nonattachment Predicts Empathy, Rejection Sensitivity, and Symptom Reduction After a Mindfulness-Based Intervention Among Young Adults with a History of Childhood Maltreatment (2020) · cites this paper as Bagozzi and Yi 1993
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 35 · Cited by 61
Research works printed but not linked: 1
- Wothke, W. (1984). The estimation of trait and method components in multitrait-multimethod measurement. Unpublished doctoral dissertation, University of Chicago.
- Authors
- Bagozzi, R. P., & Yi, Y.
- Affiliations
- University of Michigan (US)
- Seoul National University (KR)
- Corresponding author
- Bagozzi, Richard P.
- Venue
- Journal of Consumer Psychology
- Publisher
- Wiley
- Published
- 1993-01
- Volume
- 2
- Issue
- 2
- Pages
- 143-170
- ISSN
- 1057-7408 (ISSN-L)
- 1532-7663
- OpenAlex
- W2040825146
- Cited by
- 61
- References
- 35
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2022: 3 · 2021: 3 · 2020: 9 · 2019: 3 · 2018: 6 · 2017: 1 · 2016: 2 · 2015: 5 · 2014: 3 · 2013: 2 · 2012: 2
- FWCI
- 1.21
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Consumer Psychology (publishedVersion)
- Deep Blue (University of Michigan) (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Social Sciences › Psychology › Social Psychology › Cognitive and psychological constructs research
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- construct validity, method variance, trait variation, panel data models, consumer research
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
