Measurement Invariance Conventions and Reporting: The State of the Art and Future Directions for Psychological Research

By D. Putnick, Marc H. Bornstein. Developmental Review, 2016

3,596 citations recorded by the source corpus.

Measurement invariance assesses the psychometric equivalence of a construct across groups or across time. Measurement noninvariance suggests that a construct has a different structure or meaning to different groups or on different measurement occasions in the same group, and so the construct cannot be meaningfully tested or construed across groups or across time. Hence, prior to testing mean differences across groups or measurement occasions (e.g., boys and girls, pretest and posttest), or differential relations of the construct across groups, it is essential to assess the invariance of the construct. Conventions and reporting on measurement invariance are still in flux, and researchers are often left with limited understanding and inconsistent advice. Measurement invariance is tested and established in different steps. This report surveys the state of measurement invariance testing and reporting, and details the results of a literature review of studies that tested invariance. Most tests of measurement invariance include configural, metric, and scalar steps; a residual invariance step is reported for fewer tests. Alternative fit indices (AFIs) are reported as model fit criteria for the vast majority of tests; χ2 is reported as the single index in a minority of invariance tests. Reporting AFIs is associated with higher levels of achieved invariance. Partial invariance is reported for about one-third of tests. In general, sample size, number of groups compared, and model size are unrelated to the level of invariance achieved. Implications for the future of measurement invariance testing, reporting, and best practices are discussed.

Subfields: Applied Psychology, Computer Networks and Communications, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/5a80ba5e06f1efbdcdbee39123c4241e3b5ee38b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Applied Psychology

In Computer Networks and Communications

In Management Science and Operations Research

Citations in the corpus

Ostracism breeds depression: Longitudinal associations between ostracism and depression over a three-year-period (2021, positive): “We detected no violation of metric invariance as deviations between model ﬁt indices (CFI, TLI, and RMSEA) did not exceed the critical value of .01 ( Putnick and Bornstein, 2016 ).”
Towards psychological herd immunity: Cross-cultural evidence for two prebunking interventions against COVID-19 misinformation (2021, background): “Additionally, failing to reach a certain level of invariance should not prevent analyses so long as researchers note this limitation, which we do here (Putnick and Bornstein, 2016).”
Shared identity and shared information in social media: development and validation of the identity bubble reinforcement scale (2018, methodology): “A nonsignificant change in the chi-square statistic (with 95% confidence interval) and a decrease in CFI of less than −.01 were considered as evidence for the tested measurement invariance (Putnick & Bornstein, 2016).”
Best-Practice Recommendations for Producers, Evaluators, and Users of Methodological Literature Reviews (2020, methodology): “…and Lance’s article was published over 20 years ago and before recent methodological advancements, such as the use of Bayesian methods to assess measurement invariance (Kim et al., 2017), or more recent reviews of measurement invariance (e.g., Putnick & Bornstein, 2016; Schmitt & Kuljanin, 2008).”
“Mom, dad, look at me”: The development of the Parental Phubbing Scale (2020, methodology): “Strong measurement invariance was evaluated constraining items intercepts to be equivalent across groups, to check whether latent mean differences accounted for all the mean differences in the shared variance of the observable items (Putnick & Bornstein, 2016).”
Susceptibility to misinformation about COVID-19 around the world (2020, result): “We thus find support for weak measurement invariance and, in line with Putnick & Bornstein’s [59] recommendation, proceed with the analysis and interpret this as a minor limitation to our study. royalsocietypublishing.org/journal/rsos R.Soc.”