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Testing measurement invariance of composites using partial least squares
Abstract
Purpose: – Research on international marketing usually involves comparing different groups of respondents. When using structural equation modeling (SEM), group comparisons can be misleading unless researchers establish the invariance of their measures. While methods have been proposed to analyze measurement invariance in common factor models, research lacks an approach in respect of composite models. The purpose of this paper is to present a novel three-step procedure to analyze the measurement invariance of composite models (MICOM) when using variance-based SEM, such as partial least squares (PLS) path modeling. Design/methodology/approach: – A simulation study allows us to assess the suitability of the MICOM procedure to analyze the measurement invariance in PLS applications. Findings: – The MICOM procedure appropriately identifies no, partial, and full measurement invariance. Research limitations/implications: – The statistical power of the proposed tests requires further research, and researchers using the MICOM procedure should take potential type-II errors into account. Originality/value: – The research presents a novel procedure to assess the measurement invariance in the context of composite models. Researchers in international marketing and other disciplines need to conduct this kind of assessment before undertaking multigroup analyses. They can use MICOM procedure as a standard means to assess the measurement invariance.
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“Next, measurement invariance was assessed using a stepwise approach (Henseler et al., 2016), confirming metric invariance (Δχ²(6) = 2.3485, p = 0.885; ΔCFI = 0.002).”
From Exploring the influence of flagship store experience and consumer interactions on purchase behaviour (2025) · cites this paper as Henseler et al., 2016
In the citing paper
“Measurement invariance was assessed using the MICOM procedure, including configural and compositional invariance, as well as equality of composite means and variances (Henseler et al., 2016).”
From From search results to generative answers: A mixed-methods study of credibility assessment and trust formation in AI Overviews (2027) · cites this paper as Henseler et al., 2016
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A new criterion for assessing discriminant validity in variance-based structural equation modeling
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Gelman, A., & Stern, H. (2006) · The American Statistician · doi:10.1198/000313006x152649 · Cited by 1,407 · cited 1×
“At this stage, the significance of the indicator weights is irrelevant, because differences in their significance (an indicator may be significant in one group, but not in another) do not imply that the coefficients differ significantly (Gelman and Stern, 2006).”
BackgroundSarstedt, M., Henseler, J., & Ringle, C. M. (2011) · Advances in International Marketing · doi:10.1108/s1474-7979(2011)0000022012 · Cited by 1,326
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“According to Henseler et al. (2016), compositional invariance is established when the original correlation is equal to or greater than the 5% quantile of the permutation distribution; the permutation p value can also be used as supplementary evidence, with values above 0.05 indicating that…”
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Metadata
- Authors
- Henseler, J., Ringle, C. M., & Sarstedt, M.
- Venue
- International Marketing Review
- Publisher
- Emerald
- Published
- 2016-05-09
- Volume
- 33
- Issue
- 3
- Pages
- 405-431
- ISSN
- 0265-1335
- Cited by
- 2,964
- References
- 9
- Language
- en
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.
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