The Value of Exploratory Structural Equation Modeling in Identifying Factor Overlap in the Mental Health Continuum-Short Form (MHC-SF): A Study with a New Zealand Sample

Abstract

This study is the first to investigate the factor structure of the Mental Health Continuum-Short Form (MHC-SF) in New Zealand. Towards this end, traditional Confirmatory Factor Analysis (CFA) and the new method of Exploratory Structural Equation Modeling (ESEM) were used. Both ESEM and CFA supported the tripartite model of mental well-being in comparison to the one-and two-factor models; however, ESEM provided better fit with the data. Moreover, interfactor correlations were considerably lower in ESEM than they were in CFA, indicating greater factor distinctiveness in ESEM. ESEM also revealed a number of important cross-loadings for items in the measurement model of the MHC-SF. The results supported full metric and full scalar invariance of the MHC-SF across gender. The attenuated correlations among well-being factors obtained by ESEM here provide an important insight about the ongoing controversy regarding the failure of empirical research to identify distinct eudaimonic and hedonic factors in well-being measures. An overreliance on CFA methods may have led the field to rely on inflated estimates of shared variance between eudaimonia and hedonia.

Using Bifactor EFA, Bifactor CFA and Exploratory Structural Equation Modeling to Validate Factor Structure of the Meaning in Life Questionnaire, Greek Version

“…This may not necessarily suggest a predominant general Meaning-in-life factor be present, since unidimensionality based only on bifactor analysis is unstable (Joshanloo et al, 2017;Joshanloo & Jovanovic, 2016).…”

“…Even trivial, unaccounted secondary loadings can inflate factor correlations leading to misspecification (Marsh et al, 2014;Asparouhov & Muthen 2009;Howard et al, 2016). Therefore, unidimensionality based only on bifactor analysis is often questionable (Joshanloo et al, 2017;Joshanloo & Jovanovic, 2016). Thus, we suggest that the two-factor ESEM model with covariances be preferable to the bifactor model with covariances (see Figure 1 for more details).…”

“…The marginally better fit statistics of the bifactor solution further support this suggestion. However, Bifactor models always tend to support unidimensionality (Joshanloo, Jose, & Kielpikowski, 2017). Thus, we decided to examine the MLQ factor structure further with Confirmatory Factor Analysis.…”

“…However, bifactor analysis has received some criticism (Reise et al, 2013;Reise, 2012;Joshanloo et al, 2017). More specifically, it seems that relying solely on the results of bifactor analysis to decide whether a psychological scale is unidimensional or multidimensional may be questionable (Joshanloo et al, 2017).…”

“…More specifically, it seems that relying solely on the results of bifactor analysis to decide whether a psychological scale is unidimensional or multidimensional may be questionable (Joshanloo et al, 2017).…”