Abstract
Critics of positive psychology have questioned the validity of positive psychological assessment measures (PPAMs), which negatively affects the credibility and public perception of the discipline. Psychometric evaluations of PPAMs have shown that various instruments produce inconsistent factor structures between groups/contexts/times frames, that their predictive validity is questionable, and that popular PPAMs are culturally biased. Further, it would seem positive psychological researchers prioritize date-model-fit over measurement quality. To address these analytical challenges, more innovative and robust approaches toward the validation and evaluation of PPAMs are required to enhance the discipline's credibility and to advance positive psychological science. Exploratory Structural Equation Modeling (ESEM) has recently emerged as a promising alternative to overcome some of these challenges by incorporating the best elements from exploratory- and confirmatory factor analyses. ESEM is still a relatively novel approach, and estimating these models in statistical software packages can be complex and tedious. Therefore, the purpose of this paper is to provide novice researchers with a practical tutorial on how to estimate ESEM with a convenient online tool for Mplus. Specifically, we aim to demonstrate the use of ESEM through an illustrative example by using a popular positive psychological instrument: the Mental Health Continuum-SF. By using the MHC-SF as an example, we aim to provide (a) a brief overview of ESEM (and different ESEM models/approaches), (b) guidelines for novice researchers on how to estimate, compare, report, and interpret ESEM, and (c) a step-by-step tutorial on how to run ESEM analyses in Mplus with the De Beer and Van Zy ESEM syntax generator. The results of this study highlight the value of ESEM, over and above that of traditional confirmatory factor analytical approaches. The results also have practical implications for measuring mental health with the MHC-SF, illustrating that a bifactor ESEM Model fits the data significantly better than any other theoretical model.
Highlights
Positive psychology emerged in the late 1990s to counterbalance the dominating psychopathological focus of the time (1)
The results of this study highlight the value of Exploratory Structural Equation Modeling (ESEM), over and above that of traditional confirmatory factor analytical approaches
Unlike the confirmatory factor analysis (CFA) models, the results showed that Model 4 [χ2(1,802) = 634.78; df = 52; Comparative Fit Index (CFI) = 0.94; Tucker-Lewis Index (TLI) = 0.90; Root-Means-Square Error of Approximation (RMSEA) = 0.08 [0.073, 0.084]; SRMR = 0.03; FIGURE 4 | Uploading ESEM output and specifying cross-loadings option
Summary
Positive psychology emerged in the late 1990s to counterbalance the dominating psychopathological focus of the time (1). Psychometric evaluations of PPAMs have shown that various instruments produce inconsistent factor structures, that reliability estimates vary significantly between groups/contexts/times frames, that the predictive validity is questionable and that popular PPAMs are culturally biased [cf (9–11)]. These challenges apply to all self-report psychometric instruments aimed at measuring psychological phenomena, it is damaging to the discipline as it fuels current scientific critiques of positive psychology [cf (12, 13)]. These critiques, in turn, negatively affect the credibility and public perception of the discipline
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