scholarly journals A cross-cultural study of purposive “traits of action”: Measurement invariance of scales based on the action–trait theory of human motivation using exploratory structural equation modeling

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Larry C. Bernard ◽  
Jan Cieciuch ◽  
Andrew Lac ◽  
Barbara Žuro ◽  
Dino Krupić ◽  
...  
Keyword(s):  
Structural Equation Modeling ◽  
Theoretical Model ◽  
Measurement Invariance ◽  
Structural Equation ◽  
English Language ◽  
Equation Modeling ◽  
Exploratory Structural Equation Modeling ◽  
Human Motivation ◽  
Trait Theory ◽  
Cross Cultural Study

The Action–Trait theory of human motivation posits that individual differences in predispositional traits of action may account for variance in contemporary purposeful human behavior. Prior research has supported the theory, psychometric properties of scales designed to assess the motive dimensions of the theory, and the utility of these scales to predict an array of behaviors, but this is the first study to evaluate the cross-linguistical invariance of the 15-factor theoretical model. This study evaluated translations of the English language 60-item Quick AIM in 5 samples – Croatian (N = 614), French (N = 246), German (N = 154), Polish (M = 314), and U.S. English (N = 490) – recruited from 4 countries (Croatia, Poland, Switzerland, and the U.S.). Exploratory structural equation modeling (ESEM) supported the theoretical model on which the traits of action are based and scrutinized the measurement invariance (configural, metric, scalar invariance) of the scale across the languages.

Is Self-Compassion Universal? Support for the Measurement Invariance of the Self-Compassion Scale Across Populations

Assessment ◽  
2020 ◽  
Vol 28 (1) ◽  
pp. 169-185 ◽  
Author(s):  
István Tóth-Király ◽  
Kristin D. Neff
Keyword(s):  
Structural Equation Modeling ◽  
Measurement Invariance ◽  
Structural Equation ◽  
Secondary Data ◽  
The Self ◽  
Equation Modeling ◽  
Exploratory Structural Equation Modeling ◽  
Self Compassion ◽  
Wide Range ◽  
Compassion Scale

The Self-Compassion Scale (SCS) is a widely used measure to assess the trait of self-compassion, and, so far, it has been implicitly assumed that it functions the same way across different groups. This assumption needs to be explicitly tested to ascertain that no measurement biases exist. To address this issue, the present study sought to systematically examine the generalizability of the bifactor exploratory structural equation modeling operationalization of the SCS via tests of measurement invariance across a wide range of populations, varying according to features such as student or community status, gender, age, and language. Secondary data were used for this purpose and included a total of 18 samples and 12 different languages ( N = 10,997). Multigroup analyses revealed evidence for the configural, weak, strong, strict, and latent variance–covariance of the bifactor exploratory structural equation modeling operationalization of the SCS across different groups. These findings suggest that the SCS provides an assessment of self-compassion that is psychometrically equivalent across groups. However, findings comparing latent mean invariance found that levels of self-compassion differed across groups.

Testing Measurement Invariance Across Spanish and English Versions of the Physical Self-Description Questionnaire: An Application of Exploratory Structural Equation Modeling

2014 ◽  
Vol 36 (2) ◽  
pp. 179-188 ◽  
Author(s):  
Inés Tomás ◽  
Herbert W. Marsh ◽  
Vicente González-Romá ◽  
Víctor Valls ◽  
Benjamin Nagengast
Keyword(s):  
Structural Equation Modeling ◽  
Measurement Invariance ◽  
Structural Equation ◽  
Cross Cultural ◽  
Spanish Version ◽  
Equation Modeling ◽  
Exploratory Structural Equation Modeling ◽  
Alternative Approach ◽  
Cultural Comparisons ◽  
Sport And Exercise

Test of measurement invariance across translated versions of questionnaires is a critical prerequisite to comparing scores on the different versions. In this study, we used exploratory structural equation modeling (ESEM) as an alternative approach to evaluate the measurement invariance of the Spanish version of the Physical Self-Description Questionnaire (PSDQ). The two versions were administered to large samples of Australian and Spanish adolescents. First, we compared the CFA and ESEM approaches and showed that ESEM fitted the data much better and resulted in substantially more differentiated factors. We then tested measurement invariance with a 13-model ESEM taxonomy. Results justified using the Spanish version of the PSDQ to carry out cross-cultural comparisons in sport and exercise psychology research. Overall, the study can stimulate research on physical self-concept across countries and foster better cross-cultural comparisons.

Psychometric Properties of the Body Checking Questionnaire (BCQ) and of the Body Checking Cognitions Scale (BCCS): A Bifactor-Exploratory Structural Equation Modeling Approach

Assessment ◽  
2019 ◽  
pp. 107319111985841
Author(s):  
Christophe Maïano ◽  
Alexandre J. S. Morin ◽  
Annie Aimé ◽  
Geneviève Lepage ◽  
Stéphane Bouchard
Keyword(s):  
Structural Equation Modeling ◽  
Psychometric Properties ◽  
Measurement Invariance ◽  
Structural Equation ◽  
Eating Attitudes ◽  
The Body ◽  
Equation Modeling ◽  
Exploratory Structural Equation Modeling ◽  
Body Checking ◽  
Two Measures

This research sought to assess the psychometric properties of the French versions of the Body Checking Questionnaire and the Body Checking Cognitions Scale among community samples. A total sample of 922 adolescents and adults was involved in a series of two studies. The results from the first study supported factor validity and reliability of responses obtained on these two measures, and showed that both measures were best represented by a bifactor-exploratory structural equation modeling representation of the data. The results from the second study replicated these conclusions, while also supporting the measurement invariance of the bifactor-exploratory structural equation modeling solution and the equivalence of the correlations among the two measures (i.e., convergent validity) across samples. This second study also supported the criterion-related validity of ratings on both measures with measures of global self-esteem, physical appearance, social physique anxiety, fear of negative appearance evaluation, and disturbed eating attitudes and behaviors. Finally, the results of this last study also supported the measurement invariance and lack of differential item functioning of both measures in relation to sex, age, diagnosis of eating disorders, and body mass index.

Analyzing Observed Composite Differences Across Groups

Methodology ◽  
2013 ◽  
Vol 9 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Holger Steinmetz
Keyword(s):  
Monte Carlo ◽  
Structural Equation Modeling ◽  
Measurement Invariance ◽  
Structural Equation ◽  
Monte Carlo Study ◽  
Equation Modeling ◽  
Factor Loadings ◽  
Latent Mean Differences ◽  
Partial Invariance ◽  
Mean Differences

Although the use of structural equation modeling has increased during the last decades, the typical procedure to investigate mean differences across groups is still to create an observed composite score from several indicators and to compare the composite’s mean across the groups. Whereas the structural equation modeling literature has emphasized that a comparison of latent means presupposes equal factor loadings and indicator intercepts for most of the indicators (i.e., partial invariance), it is still unknown if partial invariance is sufficient when relying on observed composites. This Monte-Carlo study investigated whether one or two unequal factor loadings and indicator intercepts in a composite can lead to wrong conclusions regarding latent mean differences. Results show that unequal indicator intercepts substantially affect the composite mean difference and the probability of a significant composite difference. In contrast, unequal factor loadings demonstrate only small effects. It is concluded that analyses of composite differences are only warranted in conditions of full measurement invariance, and the author recommends the analyses of latent mean differences with structural equation modeling instead.

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