Testing for Measurement and Structural Equivalence in Large-Scale Cross-Cultural Studies: Addressing the Issue of Nonequivalence

Abstract

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Using a Multilevel Structural Equation Modeling Approach to Explain Cross-Cultural Measurement Noninvariance

“…Results in many of these studies were able to demonstrate that the assumption that item intercepts (i.e., the expected item score for a respondent with a zero score on the latent variable) are equal across groups is particularly problematic. However, this type of research has largely neglected investigating why invariance is absent (for a notable exception, see Byrne & van de Vijver, 2010; for studies tackling a similar question within a multidimensional scaling [MDS] framework, see Fontaine, Poortinga, Delbeke, & Schwartz, 2008;Fischer, Milfont, & Gouveia, 2011). This neglect is unfortunate because findings of noninvariance may reveal meaningful cross-cultural differences.…”

"Cultural Values, Emotional Intelligence, and Conflict Handling Styles: A Global Study"

“…We followed the recommendations in the literature (Byrne, 2008;Byrne & van De Vijver, 2010;Hult et al, 2008) and conducted confirmatory factor analysis (CFA) for each cultural cluster independently to assess the reliability and validity of all constructs and to obtain the best fitting measurement model for each cultural cluster, which is necessary to attain an acceptable baseline multigroup model for the multi-group confirmatory factor analysis (MGCFA). Given the small sample sizes for the majority of the countries we used the cultural clusters instead of the individual countries for the CFA and the MGCFA, which is a limitation that is further discussed in the limitation section.…”

Investigating the Structure and Meaning of Public Service Motivation across Populations: Developing an International Instrument and Addressing Issues of Measurement Invariance

“…The items of CPV1 may be regarded as action-oriented by the respondents because dedication to public service, community, and common good is underlined in these 8 Although using ordinal scales violates the maximum likelihood (ML) assumption that the observed variables are continuous and normally distributed, recent evidence suggests that robust ML is relatively robust to violations of the multivariate normality assumption and is generally endorsed for most uses (Byrne and van de Vijver 2010;Iacobucci 2010;Olsson et al 2000;Yang-Wallentin, Jöreskog, and Luo 2010). Given that there is also an argument that suggests that diagonally weighted least squares (DWLS) estimation may be better than ML (Coursey and Pandey 2007;Kim 2011;Moynihan, Pandey, and Wright 2012), we also conducted all of the analysis using DWLS.…”