The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Method Variance and Marker Variables: A Review and Comprehensive CFA Marker Technique
Abstract
Lindell and Whitney introduced a partial correlation technique, now referred to as the correlational marker technique, for controlling method variance using a marker variable that is theoretically unrelated to substantive variables in a study. This article (a) first reviews their specific analysis plan, and then (b) reviews empirical studies that have followed all or part of this plan. The authors also (c) describe a structural equation method that has been applied to the analysis of marker variables and (d) review empirical studies using this analytical approach. Next, the authors (e) develop a comprehensive confirmatory factor analysis (CFA) marker technique analysis plan, and (f) demonstrate this plan with an empirical example. Finally, the authors (g) describe how marker variables can be examined along with other method variance processes, (h) discuss the important role of theory in the critical step of selecting marker variables, and (i) discuss assumptions and limitations of the Comprehensive CFA Marker Technique.
Overview
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
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003) · Journal of Applied Psychology · doi:10.1037/0021-9010.88.5.879 · Cited by 82,194
Structural equation modeling in practice: A review and recommended two-step approach
Anderson, J. C., & Gerbing, D. W. (1988) · Psychological Bulletin · doi:10.1037/0033-2909.103.3.411 · Cited by 41,941
Self-Reports in Organizational Research: Problems and Prospects
Podsakoff, P. M., & Organ, D. W. (1986) · Journal of Management · doi:10.1177/014920638601200408 · Cited by 17,102
Practical issues in structural modeling
BENTLER, P. M., & CHOU, C.-P. (1987) · Sociological Methods & Research · doi:10.1177/0049124187016001004 · Cited by 5,804
Common methods bias: does common methods variance really bias results?
Doty, D. H., & Glick, W. H. (1998) · Organizational Research Methods · doi:10.1177/109442819814002 · Cited by 1,380 · cited 2×
“Doty and Glick (1998) reanalyzed data from 28 multitrait multimethod studies using the SEM approach.”
MethodResultMore Statistical and Methodological Myths and Urban Legends
Lance, C. E. (2011) · Organizational Research Methods · doi:10.1177/1094428110391814 · Cited by 88
Cited by
Sources of method bias in social science research and recommendations on how to control it
Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012) · Annual Review of Psychology · doi:10.1146/annurev-psych-120710-100452 · Cited by 15,744
Common methods variance detection in business research
Fuller, C. M., Simmering, M. J., Atinc, G., Atinc, Y., & Babin, B. J. (2016) · Journal of Business Research · doi:10.1016/j.jbusres.2015.12.008 · Cited by 3,519
Common Method Bias in Marketing: Causes, Mechanisms, and Procedural Remedies
MacKenzie, S. B., & Podsakoff, P. M. (2012) · Journal of Retailing · doi:10.1016/j.jretai.2012.08.001 · Cited by 3,051
Common method bias: It’ s bad, it’ s complex, it’ s widespread, and it’ s not easy to fix
Podsakoff, P. M., Podsakoff, N. P., Williams, L. J., Huang, C., & Yang, J. (2024) · Annual Review of Organizational Psychology and Organizational Behavior · doi:10.1146/annurev-orgpsych-110721-040030 · Cited by 1,899
Self-citationUnderstanding and managing the threat of common method bias: Detection, prevention and control
Kock, F., Berbekova, A., & Assaf, A. G. (2021) · Tourism Management · doi:10.1016/j.tourman.2021.104330 · Cited by 1,692
Metadata
- Authors
- Williams, L. J., Hartman, N., & Cavazotte, F.
- Venue
- Organizational Research Methods
- Publisher
- SAGE Publications
- Published
- 2010-04-29
- Volume
- 13
- Issue
- 3
- Pages
- 477-514
- ISSN
- 1094-4281 (ISSN-L)
- 1552-7425
- OpenAlex
- W2161537835
- Cited by
- 1,599
- References
- 6
- FWCI
- 23.55
- Type
- journal article
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Method Variance and Marker Variables: A Review and Comprehensive CFA Marker Technique. https://app.cochat.ai/papers/10.1177/1094428110366036
- The paper
- Williams, L. J., Hartman, N., & Cavazotte, F. (2010). Method Variance and Marker Variables: A Review and Comprehensive CFA Marker Technique. Organizational Research Methods. https://doi.org/10.1177/1094428110366036
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
