Supplemental Material for Rejection Sensitivity Across Sex, Sexual Orientation, and Age: Measurement Invariance and Latent Mean Differences

2022 ◽  
Keyword(s):  
Sexual Orientation ◽  
Measurement Invariance ◽  
Rejection Sensitivity ◽  
Latent Mean Differences ◽  
Mean Differences

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.

Psychometric Properties and Measurement Invariance Across Sex of the Wong and Law Emotional Intelligence Scale in Chinese adolescents

2020 ◽  
pp. 003329412097263
Author(s):  
Meilin Di ◽  
Xinping Deng ◽  
Jingjing Zhao ◽  
Feng Kong
Keyword(s):  
Emotional Intelligence ◽  
Measurement Invariance ◽  
Factor Model ◽  
Chinese Adolescents ◽  
Female Adolescents ◽  
Intelligence Scale ◽  
Male Adolescents ◽  
Latent Mean Differences ◽  
Emotional Appraisal ◽  
Mean Differences

The aim of this study was to validate the Wong and Law Emotional Intelligence Scale (WLEIS) in Chinese adolescents by examining factor structure, sex-related measurement invariance and latent mean differences, reliability, and criterion-related validity.A total of 1674 Chinese adolescents aged 12–17 years old (Mage = 15.26, SD = .58) were recruited. Results of confirmatory factor analysis (CFA) supported the four-factor model of the WLEIS as the best fit to the data. Multi-group CFA suggested that the measurement structure of the WLEIS was invariant across sex. Testing for the latent mean differences showed that male adolescents scored lower on the Other-Emotional Appraisal subscale, but scored higher on the Regulation of Emotion subscale than female adolescents. Furthermore, all the subscales of male and female adolescents had satisfactory composite reliability. Finally, the WLEIS had favorable criterion-related validity with self-esteem, life satisfaction, positive affect, negative affect and perceived stress. These findings suggested that the WLEIS is a reliable and valid instrument for assessing trait emotional intelligence of adolescents and can be generalized across sex.

Homework Expectancy Value Scale: Measurement Invariance and Latent Mean Differences Across Gender

2017 ◽  
Vol 36 (8) ◽  
pp. 863-868 ◽  
Author(s):  
Fuyi Yang ◽  
Jianzhong Xu
Keyword(s):  
Middle School ◽  
Academic Achievement ◽  
Emotion Regulation ◽  
Middle School Students ◽  
Measurement Invariance ◽  
School Students ◽  
Expectancy Value ◽  
Factor Analytic ◽  
Latent Mean Differences ◽  
Mean Differences

This study validated the Homework Expectancy Value Scale (HEVS) based on 918 middle school students from China. The factor analytic results showed that the HEVS consisted of two distinct yet related subscales: Expectancy and Value. Results further revealed that latent factor means for the HEVS were invariant across gender. Finally, consistent with theoretical expectations, Expectancy and Value were negatively correlated with homework distraction, and positively correlated with homework effort, emotion regulation, completion, and academic achievement.

Measurement Invariance of the Ruminative Responses Scale Across Gender

Assessment ◽  
2018 ◽  
Vol 27 (3) ◽  
pp. 508-517 ◽  
Author(s):  
Mark A. Whisman ◽  
Regina Miranda ◽  
David M. Fresco ◽  
Richard G. Heimberg ◽  
Elizabeth L. Jeglic ◽  
...  
Keyword(s):  
United States ◽  
Measurement Invariance ◽  
Covariance Structure ◽  
The United States ◽  
Confirmatory Factor ◽  
Latent Mean Differences ◽  
Using Data ◽  
Mean Differences ◽  
Brooding And Reflection ◽  
Multigroup Confirmatory Factor Analysis

Although women demonstrate higher levels of rumination than men, it is unknown whether instruments used to measure rumination have the same psychometric properties for women and men. To examine this question, we evaluated measurement invariance of the brooding and reflection subscales from the Ruminative Responses Scale (RRS) by gender, using data from four samples of undergraduates from three universities within the United States ( N = 4,205). A multigroup confirmatory factor analysis revealed evidence for configural, metric, and scalar invariance of the covariance structure of the 10-item version of the RRS. There were statistically significant latent mean differences between women and men, with women scoring significantly higher than men on both brooding and reflection. These findings suggest that the 10-item version of the RRS provides an assessment of rumination that is psychometrically equivalent across gender. Consequently, gender differences in brooding and reflection likely reflect valid differences between women and men.

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