scholarly journals Psychometric Evaluation of the Overexcitability Questionnaire-Two Applying Bayesian Structural Equation Modeling (BSEM) and Multiple-Group BSEM-Based Alignment with Approximate Measurement Invariance

2015 ◽  
Vol 6 ◽  
Author(s):  
Niki De Bondt ◽  
Peter Van Petegem
Keyword(s):  
Structural Equation Modeling ◽  
Measurement Invariance ◽  
Structural Equation ◽  
Psychometric Evaluation ◽  
Equation Modeling ◽  
Multiple Group ◽  
Bayesian Structural Equation Modeling ◽  
Approximate Measurement

scholarly journals Accounting for Differential Item Functioning Using Bayesian Approximate Measurement Invariance

2019 ◽  
Vol 80 (4) ◽  
pp. 638-664 ◽  
Author(s):  
Georgios D. Sideridis ◽  
Ioannis Tsaousis ◽  
Abeer A. Alamri
Keyword(s):  
Measurement Invariance ◽  
Structural Equation ◽  
Equation Modeling ◽  
P Value ◽  
National Examination ◽  
Item Functioning ◽  
Using Data ◽  
Mean Differences ◽  
Bayesian Structural Equation Modeling ◽  
Approximate Measurement

The main thesis of the present study is to use the Bayesian structural equation modeling (BSEM) methodology of establishing approximate measurement invariance (A-MI) using data from a national examination in Saudi Arabia as an alternative to not meeting strong invariance criteria. Instead, we illustrate how to account for the absence of measurement invariance using relative compared to exact criteria. A secondary goal was to compare latent means across groups using invariant parameters only and through utilizing exact and relative evaluative-MI protocol suggested equivalence of the thresholds using prior variances equal to 0.10. Subsequent differences between groups were evaluated using effect size criteria and the prior-posterior predictive p-value (PPPP), which proved to be invaluable in attesting for differences that are beyond zero, some meaningless nonzero estimate, and the three commonly used indices of effect sizes described by Cohen in 1988 (i.e., .20, .50, and .80). Results substantiated the use of the PPPP for evaluating mean differences across groups when utilizing nonexact evaluative criteria.

Further Insights Into the German Version of the Multidimensional Assessment of Interoceptive Awareness (MAIA)

2019 ◽  
Vol 35 (3) ◽  
pp. 317-325 ◽  
Author(s):  
Dorota Reis
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Equation Modeling ◽  
Interoceptive Awareness ◽  
Multidimensional Assessment ◽  
New Instrument ◽  
Confirmatory Factor ◽  
Complex Models ◽  
Approximate Zero ◽  
Bayesian Structural Equation Modeling

Abstract. Interoception is defined as an iterative process that refers to receiving, accessing, appraising, and responding to body sensations. Recently, following an extensive process of development, Mehling and colleagues (2012) proposed a new instrument, the Multidimensional Assessment of Interoceptive Awareness (MAIA), which captures these different aspects of interoception with eight subscales. The aim of this study was to reexamine the dimensionality of the MAIA by applying maximum likelihood confirmatory factor analysis (ML-CFA), exploratory structural equation modeling (ESEM), and Bayesian structural equation modeling (BSEM). ML-CFA, ESEM, and BSEM were examined in a sample of 320 German adults. ML-CFA showed a poor fit to the data. ESEM yielded a better fit and contained numerous significant cross-loadings, of which one was substantial (≥ .30). The BSEM model with approximate zero informative priors yielded an excellent fit and confirmed the substantial cross-loading found in ESEM. The study demonstrates that ESEM and BSEM are flexible techniques that can be used to improve our understanding of multidimensional constructs. In addition, BSEM can be seen as less exploratory than ESEM and it might also be used to overcome potential limitations of ESEM with regard to more complex models relative to the sample size.

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.

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.

Contact, Perceived Threat, and Attitudes Toward Assimilation and Multiculturalism: Evidence From a Majority and Minority Perspective in Luxembourg

2018 ◽  
Vol 50 (2) ◽  
pp. 285-310 ◽  
Author(s):  
Marie-Sophie Callens ◽  
Bart Meuleman ◽  
Valentová Marie
Keyword(s):  
Structural Equation Modeling ◽  
Host Country ◽  
Structural Equation ◽  
Equation Modeling ◽  
Perceived Threat ◽  
Threat Perceptions ◽  
Multiple Group ◽  
Integration Of Immigrants ◽  
European Values ◽  
European Values Study

In this article, we study how attitudes toward the integration of immigrants (multiculturalism and assimilation) are formed through the interplay between immigration-related threat perceptions, intergroup contacts, and the different migratory backgrounds of residents in a host country. The analysis is conducted using Multiple Group Structural Equation Modeling on data from the 2008 Luxembourg European Values Study. Our findings indicate that stronger perceptions of threat are related to more support for assimilation among all residents and to less support for multiculturalism among native residents and culturally close immigrants. More contact with natives is associated with more support for assimilation among culturally close immigrants and with more threat perceptions among culturally distant immigrants.

scholarly journals Effect of practical training on the learning motivation profile of Japanese pharmacy students using structural equation modeling

2017 ◽  
Vol 14 ◽  
pp. 2 ◽  
Author(s):  
Shigeo Yamamura ◽  
Rieko Takehira
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Training Programs ◽  
Group Analysis ◽  
Learning Motivation ◽  
Self Determination ◽  
Pharmacy Students ◽  
Equation Modeling ◽  
Practical Training ◽  
Multiple Group

Purpose: To establish a model of Japanese pharmacy students’ learning motivation profile and investigate the effects of pharmaceutical practical training programs on their learning motivation. Methods: The Science Motivation Questionnaire II was administered to pharmacy students in their 4th (before practical training), 5th (before practical training at clinical sites), and 6th (after all practical training) years of study at Josai International University in April, 2016. Factor analysis and multiple-group structural equation modeling were conducted for data analysis. Results: A total of 165 students participated. The learning motivation profile was modeled with 4 factors (intrinsic, career, self-determination, and grade motivation), and the most effective learning motivation was grade motivation. In the multiple-group analysis, the fit of the model with the data was acceptable, and the estimated mean value of the factor of ‘self-determination’ in the learning motivation profile increased after the practical training programs (P= 0.048, Cohen’s d= 0.43). Conclusion: Practical training programs in a 6-year course were effective for increasing learning motivation, based on ‘self-determination’ among Japanese pharmacy students. The results suggest that practical training programs are meaningful not only for providing clinical experience but also for raising learning motivation.

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