Exploratory Structural Equation Modeling Framework Research Articles

Exploratory Structural Equation Modeling Analysis of the Self-Compassion Scale

The present research investigated the construct validity and psychometric properties of the Self-Compassion Scale (SCS) with a new and advanced statistical procedure, exploratory structural equation modeling (ESEM), in order to contribute to the ongoing discussion about its dimensionality by employing a bifactor-ESEM framework. A Hungarian online representative sample (N = 505, N female = 265, M age = 44.37) filled out the Hungarian version of the SCS. Confirmatory factor analysis (CFA) and ESEM methods were employed, and first-order and bifactor solutions were examined and compared. The bifactor ESEM model demonstrated the best fit to the data with the joint presence of the general self-compassion factor and the specific factors. Internal consistency was adequate in all cases. Reliability indices—omega and omega hierarchical—showed that not all specific factors had unique contributions over and above the general factor. High levels of gender invariance were also achieved with females having lower general self-compassion and self-judgment latent means, while having higher self-kindness scores. The findings shed new light on the underlying theory behind the SCS and proved the usefulness of the bifactor ESEM framework in the investigation of multidimensional constructs.

Assessment of Emotional Avoidance in Adolescents: Psychometric Properties of a New Multidimensional Measure

Emotional avoidance—which includes cognitive and attentional processes that function as means of avoiding or dampening emotional experiences—may be a transdiagnostic risk and maintenance factor for emotional disorders. However, existing measures generally assess separate aspects of emotional avoidance, and few multidimensional measures exist for adolescents. The aim of the current study was to establish the psychometric properties of a new multidimensional measure of emotional avoidance for adolescents--the Emotional Avoidance Strategy Inventory for Adolescents (EASI-A; Fairholme et al. 2008)--employing an exploratory structural equation modeling framework. Analyses yielded a three-factor structure for the EASI-A, including subscales measuring Avoidance of Emotion Expression, Avoidance of Thoughts and Feelings, and Distraction. Moderate correlations were observed between the EASI-A and a measure of thought suppression, and the EASI-A was significantly associated with anxiety and depression symptoms. The EASI-A is a new, psychometrically sound measure that will aid in the assessment of avoidance as a vulnerability factor for psychopathology and as an outcome and mechanism for treatments of emotional disorders in adolescents.

Using Bifactor Exploratory Structural Equation Modeling to Test for a Continuum Structure of Motivation

This paper explores the nature of workplace motivation by testing the continuum structure of motivation proposed by self-determination theory through the application of relatively new and advanced methodological techniques. Specifically, we demonstrate the usefulness of the overarching bifactor exploratory structural equation modeling framework in organizational psychology and discuss implications of such models over more traditional confirmatory factor analyses. This framework is applied to responses obtained from 1,124 Canadian employees who completed a multidimensional measure of workplace motivation. The results support a continuum of self-regulation and illustrate the importance of accounting for quality of motivation in addition to its global quantity. Indeed, the results showed that specific types of motivation explained variance in covariates over and above the variance already explained by the global quantity of self-determination. The current study further demonstrates the limitation of the commonly used relative autonomy index and offers alternate conceptualizations of human motivation.

Beyond congruence measures for the evaluation of personality factor structure replicability: An exploratory structural equation modeling approach

The present study aims to illustrate an encompassing approach to the evaluation of personality factor structure replicability based on novel exploratory structural equation modeling (ESEM) methods. This approach comprises formal tests of measurement invariance applied to the flexible ESEM framework and overcomes the limitations of congruence measures that have traditionally been used to assess factor replicability in personality research. On the basis of 1566 responses to the widely-used NEO Five-Factor-Inventory (NEO-FFI), we demonstrate this ESEM approach in the context of examining the invariance of the NEO-FFI factor structure across gender. The approach is shown to converge with traditional congruence measures and extend these measures for examining factorial structure consistency. In addition, more general replicative data supporting the validity of the NEO-FFI are reported. We discuss the ESEM approach as a viable alternative to the congruence approach and acknowledge some important limitations of the method.

Application of Exploratory Structural Equation Modeling to Evaluate the Academic Motivation Scale

In this research, the authors examined the construct validity of scores of the Academic Motivation Scale using exploratory structural equation modeling. Study 1 and Study 2 involved 1,416 college students and 4,498 high school students, respectively. First, results of both studies indicated that the factor structure tested with exploratory structural equation modeling provides better fit to the data than the one tested with confirmatory factor analysis. Second, the factor structure was gender invariant in the exploratory structural equation modeling framework. Third, the pattern of convergent and divergent correlations among Academic Motivation Scale factors was more in line with theoretical expectations when computed with exploratory structural equation modeling rather than confirmatory factor analysis. Fourth, the configuration of convergent and divergent correlations connecting each Academic Motivation Scale factors to a validity criterion was more in line with theoretical expectations with exploratory structural equation modeling than with confirmatory factor analysis.

Coaching competency and (exploratory) structural equation modeling: A substantive-methodological synergy

ObjectivesThe purpose of this manuscript was to provide a substantive-methodological synergy of potential importance to future research in the psychology of sport and exercise. DesignThe substantive focus was the emerging role for, and particularly the measurement of, athletes' evaluations of their coach's competency within conceptual models of effective coaching. The methodological focus was exploratory structural equation modeling (ESEM), a methodology that integrates the advantages of exploratory factor analysis and confirmatory factor analysis (CFA) within the general structural equation model. MethodsThe synergy was a demonstration of when a new and flexible methodological framework, ESEM, may be preferred as compared to a more familiar and restrictive methodological framework, CFA, by reanalyzing existing data. ResultsESEM analysis on extant datasets suggested that for responses to the Athletes' Perceptions of Coaching Competency Scale II – High School Teams (APCCS II-HST), a CFA model based on the relevant literature plus one post hoc modification, offered a viable alternative to a more complex ESEM model. For responses to the Coaching Competency Scale (CCS), a CFA model based on the relevant literature did not offer a viable alternative to a more complex ESEM model. ConclusionsThe ESEM framework should be strongly considered in subsequent studies validity studies - for new and/or existing instruments in the psychology of sport and exercise. A key consideration for deciding between ESEM and the accompanying rotation criterion and CFA in future validity studies should be level of a priori measurement theory.

On Correlated Errors

On Correlated Errors

An Investigation of the Factor Structure and Convergent and Discriminant Validity of the Five-Factor Model Rating Form

The Five-Factor Model Rating Form (FFMRF) is a one-page measure designed to provide an efficient assessment of the higher order domains of the Five Factor Model (FFM) as well as the more specific, lower order facets proposed by McCrae and Costa. Although previous research has suggested that the FFMRF's assessment of the lower order facets converge reasonably with other FFM measures, the structural validity of the domain-level assessment has not yet been evaluated. The current study employed an exploratory structural equation modeling framework to investigate the fit of a five-factor solution within a combined sample of 757 participants. This was a novel analysis using a combined sample drawn from three previously published studies and was composed primarily of undergraduates but also included a smaller clinical subsample. Results indicated that the FFMRF is well accommodated within a five-factor solution. Furthermore, the FFMRF domain scores evinced large correlations with domain scores from the NEO Personality Inventory-Revised. The results suggest that the FFMRF might hold promise as a choice for those seeking a brief measure that provides a valid assessment of both the broad and specific traits of the FFM.