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

By F. Guay, Alexandre J. S. Morin, David Litalien, Pierre Valois, Robert J. Vallerand. 2015

231 citations recorded by the source corpus.

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.

Subfields: Experimental and Cognitive Psychology, Management Science and Operations Research, Social Psychology

View Paper: https://www.semanticscholar.org/paper/f61682a22cdebae973118a02fbb19220ec4323fb

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In Experimental and Cognitive Psychology

In Management Science and Operations Research

In Social Psychology

Citations in the corpus

Motivation for PhD studies : scale development and validation (2015, methodology): “Following recommendations fromMarsh et al. (2009, 2010) (also see Guay et al., 2014; Morin et al., 2013), we used an oblique Geomin rotation with an epsilon value of 0.5.”
Learning Directed Acyclic Graphs with Penalized Neighbourhood Regression (2015, background): “, 2013), network modeling (Horvath, 2011), and psychology (Guay et al., 2015; Morin et al., 2015).”
Students' ICT self-efficacy and computer and information literacy: Determinants and relationships (2018, methodology): “However, according to Guay et al. (2014) a chi-square test is sample sensitive, and therefore the ratio of chi-square to its degree of freedom, i.e. 𝜒 2 /d.f. < 3, is also used to evaluate the fit.”
The Academic Motivation Scale: Dimensionality, Reliability, and Construct Validity Among Vocational Students (2016, background): “Two recent studies have presented an overview of the literature regarding the reliability and the correlational pattern of AMS subscales (Can, 2015; Guay et al., 2015).”
Using Bifactor Exploratory Structural Equation Modeling to Test for a Continuum Structure of Motivation (2018, background): “First, SDT’s continuum can be evidenced by the observation of ICM-CFA or ESEM factor correlations corresponding to the expected simplex pattern, showing larger correlations between conceptually adjacent motivation factors and smaller or negative correlations between conceptually distant factors (Gua”
Evidence of a continuum structure of academic self-determination: A two-study test using a bifactor-ESEM representation of academic motivation (2017, methodology): “Some psychological scales like the AMS are expected to include both sources of construct- relevant multidimensionality, as they assess both conceptually-adjacent constructs (as estimated by Guay et al., 2015) and the presence of an overarching construct (as estimated by Chemolli & Gagné, 2014).”
Different motivations for knowledge sharing and hiding: The role of motivating work design (2019, background): “…fails to account for sources of construct-relevant psychometric multidimensionality, and might thus produce biased parameter estimates as a result (Morin et al., 2016), and (2) when cross loadings are not estimated, latent factor correlations end up being overestimated (Guay et al., 2014).”