title: Measurement Invariance Conventions and Reporting: The State of the Art and Future Directions for Psychological Research.
abstract: 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; χ2is 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.
authors.affiliation: Eunice Kennedy Shriver National Institute of Child Health and Human Development.
authors.forename: Diane L
authors.initials: DL
authors.lastname: Putnick
authors.affiliation: Eunice Kennedy Shriver National Institute of Child Health and Human Development.
authors.forename: Marc H
authors.initials: MH
authors.lastname: Bornstein
keywords: Comparative psychology; confirmatory factor analysis; measurement invariance; multiple-group analysis; structural equation modeling