The <i>g</i> Factor and Cognitive Test Session Behavior

Frisby, Craig L.; Wang, Ze

doi:10.1177/0734282915621440

Cited by 2 publications

(1 citation statement)

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“…Measurement invariance testing has been increasingly included as part of scale development, scale validation, and comparisons of groups on latent constructs (e.g., Frisby & Wang, 2016;Wang, Bergin, & Bergin, 2014). In general, measurement invariance testing procedures investigate models by sequentially adding equality constraints across groups on model parameters.…”

Section: Measurement Invariancementioning

confidence: 99%

Measurement Invariance of the Career Futures Inventory–Revised Across General and Client Samples

Park

Rottinghaus

Wang

et al. 2018

Journal of Career Assessment

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Establishing measurement invariance has been emphasized as an important scale validation procedure for group comparisons. The 28-item Career Futures Inventory–Revised (CFI-R) is a widely used measure of career adaptability that has demonstrated initial validity with various samples. The purpose of the present study is to further examine the validity of the CFI-R by testing measurement invariance between a general university student sample and a client sample. First, a five-factor confirmatory factor analysis model was tested with each group. Then, measurement invariance tests were conducted through subsequently examining configural invariance, metric invariance, and scalar invariance. Test of invariance was achieved until partial scalar invariance, suggesting that the CFI-R is similarly applicable to both clinical and nonclinical samples. In addition, the comparisons of latent means between two groups revealed that clients showed significantly lower latent means than general students for four factors: Career Agency, Occupational Awareness, Support, and Work–Life Balance.

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Section: Measurement Invariancementioning

confidence: 99%

Measurement Invariance of the Career Futures Inventory–Revised Across General and Client Samples

Park

Rottinghaus

Wang

et al. 2018

Journal of Career Assessment

Self Cite

View full text Add to dashboard Cite

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A comparison of three approaches to covariate effects on latent factors

Wang

2022

Large-scale Assess Educ

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In educational and psychological research, it is common to use latent factors to represent constructs and then to examine covariate effects on these latent factors. Using empirical data, this study applied three approaches to covariate effects on latent factors: the multiple-indicator multiple-cause (MIMIC) approach, multiple group confirmatory factor analysis (MG-CFA) approach, and the structural equation model trees (SEM Trees) approach. The MIMIC approach directly models covariate effects on latent factors. The MG-CFA approach allows testing of measurement invariance before latent factor means could be compared. The more recently developed SEM Trees approach partitions the sample into homogenous subsets based on the covariate space; model parameters are estimated separately for each subgroup. We applied the three approaches using an empirical dataset extracted from the eighth-grade U.S. data from the Trends in International Mathematics and Science Study 2019 database. All approaches suggested differences among mathematics achievement categories for the latent factor of mathematics self-concept. In addition, language spoken at home did not seem to affect students’ mathematics self-concept. Despite these general findings, the three approaches provided different pieces of information regarding covariate effects. For all models, we appropriately considered the complex data structure and sampling weights following recent recommendations for analyzing large-scale assessment data.

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The g Factor and Cognitive Test Session Behavior

Cited by 2 publications

References 24 publications

Measurement Invariance of the Career Futures Inventory–Revised Across General and Client Samples

Measurement Invariance of the Career Futures Inventory–Revised Across General and Client Samples

A comparison of three approaches to covariate effects on latent factors

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