Determining the dimensionality and gender invariance of the MACE work-to-family enrichment scale using bifactor and approximate invariance tests

Abstract

Orientation: Uncertainty about which measurement model of the MACE work-to-family enrichment scale (MACE-W2FE) is best supported by the data called for clarification. Research purpose:The main aim of our study was to get clarity on the dimensionality of the MACE-W2FE. The secondary aim was to test for approximate invariance of the measure for gender groups.Motivation for the study: Variations in the reported measurement models for the MACE-W2FE between studies are not conducive for theory development and called for clarification. Previous models reported were a multidimensional model and a secondorder model. Approximate measurement invariance is a prerequisite for study differences between gender groups.Research approach/design and method: We did seek to resolve the problem by using bifactor model analysis, factor strength indices and local indicator misspecification analyses using a sample of 786 South African employees. Invariance was tested using the alignment optimisation method.Main findings: In this study, we solved a substantive research problem by determining that the data from the study best supported a single breadth factor or first-order factor model that was essentially unidimensional. The invariance tests across gender groups confirmed approximate configural, measurement and scalar invariances for the unidimensional model. Practical/managerial implications: Researchers and practitioners may include the MACE-W2FE in studies as a single-aggregated score without negligible loss in measurement precision. Contribution/value-add:The extended confirmatory factor analyses we conducted proved valuable in resolving the MACE-W2FE's dimensionality vacillations, thereby enhancing the validity of inferences made from scale scores.

Replicating the essentially unidimensional model of the MACE work-to-family enrichment scale: Going beyond goodness-of-fit indices

“…In most studies the WFE was used as a first-order factor model, while in other studies a second-order model was added to avoid the biasing effects of multicollinearity in the analyses (De Klerk et al, 2015;Koekemoer et al, 2017;Marais et al, 2014;Van Zyl, 2020). Except for De Klerk (2013), none of these earlier studies tested for a first-order unidimensional model of the MACE-W2FE, despite strong empirical evidence suggesting that a substantive general factor existed (Schaap & Koekemoer, 2021). According to Reise et al (2010), first-order unidimensional models for measures of psychological constructs rarely display good GoF, whereas first-order multidimensional models and hierarchical models almost always display good GoF.…”

“…The MACE (the acronym MACE represents the names of the authors), a measure of work-to-family enrichment (WFE) based on Greenhaus and Powell's (2006) theory of WFE, was used to demonstrate the usefulness of BSEM and local indicator misspecification analysis in testing a measures dimensionality. According to Schaap and Koekemoer (2021), resolving the dimensionality vacillations of the MACE reported across studies is important. In this study the theory and findings published in Schaap and Koekemoer (2021) has been tested using alternative samples, different measurement formats and a different statistical theorem known as Bayes theory.…”

“…According to Schaap and Koekemoer (2021), resolving the dimensionality vacillations of the MACE reported across studies is important. In this study the theory and findings published in Schaap and Koekemoer (2021) has been tested using alternative samples, different measurement formats and a different statistical theorem known as Bayes theory. This approach is known as 'conceptual replication' (p. 5) which tests the robustness and generalisability of a theoretical claim to alternative methodological procedures, format of the measure and samples (Zwaan et al, 2017).…”

“…Moreover, GoF indices have been shown to be overly sensitive in testing essentially unidimensional models because these models rarely show acceptable model fit, even when the model misspecifications are minor and are thus unlikely to have any substantive consequences (Reise et al, 2013a). Schaap and Koekemoer (2021) recently found support for an essentially unidimensional model of the MACE-W2FE using bifactor and local indicator misspecification analyses in SEM with the frequentist-based maximum likelihood (ML) estimation, after the unidimensional model was rejected by the GoF indices. However, these findings may not generalise to other samples and measurement formats or when different and more refined methods of analyses are used.…”

“…In order to determine the extent to which the MACE-W2FE versions reflect a single latent variable for two study samples, the 'substantive-methodology synergy' (p. 14) approach, coined by Hau (2007, cited in Schaap &Koekemoer, 2021) has been used. This approach involves applying innovative and high-precision statistical techniques (in particular, BSEM) to complex substantive issues in order to advance theory.…”