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A Call for, and Beginner’s Guide to, Measurement Invariance Testing in Evolutionary Psychology

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Abstract

Measurement invariance is a statistical property of an instrument (e.g., a test or questionnaire) indicating that it measures the same construct(s) in the same way across subgroups of respondents. Given the extensive research in evolutionary psychology devoted to sex differences and cross-cultural comparisons, measurement invariance testing is crucial not only because it protects against erroneous inference, but also because it provides nuanced information about group similarities and differences. In this article, we draw attention to the importance of, and present a beginner’s guide to, measurement invariance testing. We define measurement invariance formally, summarize sources of non-invariance and the rationale for measurement invariance testing, and describe the two frameworks typically used to test for invariance. We then review evidence of a relative lack of measurement invariance testing in evolutionary psychology and conclude with a case example with MPlus syntax. By testing for measurement invariance, researchers studying evolutionary psychology can strengthen their field and enhance the popularity of their constructs in other disciplines.

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Notes

  1. This syntax also works in MPlus 8.

  2. Metric invariance is tested by specifying MODEL = METRIC in the ANALYSIS command. If the scalar invariant model fit substantially worse than the baseline configural model, Richardson, Chen et al. (2017) could have used this command to free all the thresholds (i.e., constraining only factor loadings to equality across the sexes) to discover if threshold variance between the groups accounted for the misfit. If the metric invariant model also fit more poorly than the baseline model, they could have observed modification indices for loadings that might vary between the groups. The authors could have freed any non-invariant loadings and proceeded to test, by specifying MODEL = SCALAR in the ANALYSIS command, whether the thresholds associated with invariant loadings were themselves invariant. Readers can find additional information about the MPlus commands for testing invariance in the MPlus User Guides, available via www.statmodel.com.

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Authors and Affiliations

  1. University of Cincinnati, Cincinnati, OH, USA

    Shanshan Wang & George B. Richardson

  2. University of Nevada, Las Vegas, Las Vegas, NV, USA

    Ching-Chen Chen & Chia-Liang Dai

  3. School of Human Services, College of Education, Criminal Justice, and Human Services [460R Teachers-Dyer Complex], Cincinnati, OH, USA

    George B. Richardson

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  1. Shanshan Wang
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Wang, S., Chen, CC., Dai, CL. et al. A Call for, and Beginner’s Guide to, Measurement Invariance Testing in Evolutionary Psychology. Evolutionary Psychological Science 4, 166–178 (2018). https://doi.org/10.1007/s40806-017-0125-5

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