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Su, Hsu-Lin; Chen, Po-Hsi – Educational and Psychological Measurement, 2023
The multidimensional mixture data structure exists in many test (or inventory) conditions. Heterogeneity also relatively exists in populations. Still, some researchers are interested in deciding to which subpopulation a participant belongs according to the participant's factor pattern. Thus, in this study, we proposed three analysis procedures…
Descriptors: Data Analysis, Correlation, Classification, Factor Structure
Olasunkanmi James Kehinde; Jeff Walls; Amanda Mayeaux; Allison Comeaux – Journal of Professional Capital and Community, 2024
Purpose: The purpose of this study is to propose and explore a conceptualization of decisional capital that is suitable for early career teachers. Design/methodology/approach: This study uses exploratory factor analysis on a sample of early career teachers to examine a literature-derived conceptualization of decisional capital. Findings: The…
Descriptors: Beginning Teachers, Decision Making, Human Capital, Factor Structure
Adrian Quintero; Emmanuel Lesaffre; Geert Verbeke – Journal of Educational and Behavioral Statistics, 2024
Bayesian methods to infer model dimensionality in factor analysis generally assume a lower triangular structure for the factor loadings matrix. Consequently, the ordering of the outcomes influences the results. Therefore, we propose a method to infer model dimensionality without imposing any prior restriction on the loadings matrix. Our approach…
Descriptors: Bayesian Statistics, Factor Analysis, Factor Structure, Sampling
Timothy R. Konold; Elizabeth A. Sanders – Measurement: Interdisciplinary Research and Perspectives, 2024
Compared to traditional confirmatory factor analysis (CFA), exploratory structural equation modeling (ESEM) has been shown to result in less structural parameter bias when cross-loadings (CLs) are present. However, when model fit is reasonable for CFA (over ESEM), CFA should be preferred on the basis of parsimony. Using simulations, the current…
Descriptors: Structural Equation Models, Factor Analysis, Factor Structure, Goodness of Fit
Dombrowski, Stefan C.; McGill, Ryan J.; Watkins, Marley W.; Canivez, Gary L.; Pritchard, Alison E.; Jacobson, Lisa A. – Contemporary School Psychology, 2022
The Wechsler Intelligence Scale for Children's (WISC) factorial\theoretical structure has undergone numerous substantive changes since it was first developed, and each of these changes has subsequently been questioned by assessment experts. Given remaining questions about the structure of the latest revision, the WISC-V, the present study used…
Descriptors: Children, Intelligence Tests, Factor Structure, Factor Analysis
Gilbert, Kacey; Benson, Nicholas F.; Kranzler, John H. – Contemporary School Psychology, 2023
Despite the fact that the digital administration format of Wechsler Intelligence Scale for Children-Fifth Edition (WISC-V) was published in 2016, no research to date has examined its factor structure using all 10 of the primary subtests to measure intellectual ability. The purpose of this study, therefore, was to use exploratory and confirmatory…
Descriptors: Computer Assisted Testing, Children, Intelligence Tests, Factor Structure
Ella Bjerga Pettersen; Sigrun K. Ertesvåg; Sanni Pöysä; Grete Sørensen Vaaland; Tuomo Erkki Virtanen – Scandinavian Journal of Educational Research, 2024
Context is considered to greatly impact student engagement. However, little is known about the association between students' situational engagement in a particular lesson and their overall engagement with school and learning over time. The current study aims to validate the InSitu measure of situational engagement in a Norwegian context and to…
Descriptors: Learner Engagement, Secondary School Students, Foreign Countries, Measures (Individuals)
Naoto Yamashita – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Matrix decomposition structural equation modeling (MDSEM) is introduced as a novel approach in structural equation modeling, contrasting with traditional structural equation modeling (SEM). MDSEM approximates the data matrix using a model generated by the hypothetical model and addresses limitations faced by conventional SEM procedures by…
Descriptors: Structural Equation Models, Factor Structure, Robustness (Statistics), Matrices
Sara Dhaene; Yves Rosseel – Structural Equation Modeling: A Multidisciplinary Journal, 2024
In confirmatory factor analysis (CFA), model parameters are usually estimated by iteratively minimizing the Maximum Likelihood (ML) fit function. In optimal circumstances, the ML estimator yields the desirable statistical properties of asymptotic unbiasedness, efficiency, normality, and consistency. In practice, however, real-life data tend to be…
Descriptors: Factor Analysis, Factor Structure, Maximum Likelihood Statistics, Computation
Liu, Xiaoling; Cao, Pei; Lai, Xinzhen; Wen, Jianbing; Yang, Yanyun – Educational and Psychological Measurement, 2023
Percentage of uncontaminated correlations (PUC), explained common variance (ECV), and omega hierarchical ([omega]H) have been used to assess the degree to which a scale is essentially unidimensional and to predict structural coefficient bias when a unidimensional measurement model is fit to multidimensional data. The usefulness of these indices…
Descriptors: Correlation, Measurement Techniques, Prediction, Regression (Statistics)
Rebecca E. Knoph; Joshua F. Lawrence; David J. Francis – Scientific Studies of Reading, 2024
Purpose: There are many aspects of words that can influence our lexical processing, and the words we are exposed to influence our opportunities for language and reading development. The purpose of this study is to establish a more comprehensive understanding of the lexical challenges and opportunities students face. Method: We explore the latent…
Descriptors: Academic Language, Lexicology, Language Acquisition, Vocabulary Development
Sena Dogruyol; Bilge Bakir Aygar; Nezaket Bilge Uzun; Asena Yucedaglar – Journal on Educational Psychology, 2024
The Satisfaction with Life Scale (SWLS), a popular and widely used measurement tool in cross-cultural research, evaluates life satisfaction. Even though numerous studies have demonstrated factorial validity across a range of samples and cultures, the topic of factorial invariance across various subgroups is still up for debate. There are…
Descriptors: Measures (Individuals), Life Satisfaction, Factor Structure, Models
Chunhua Cao; Xinya Liang – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Cross-loadings are common in multiple-factor confirmatory factor analysis (CFA) but often ignored in measurement invariance testing. This study examined the impact of ignoring cross-loadings on the sensitivity of fit measures (CFI, RMSEA, SRMR, SRMRu, AIC, BIC, SaBIC, LRT) to measurement noninvariance. The manipulated design factors included the…
Descriptors: Goodness of Fit, Error of Measurement, Sample Size, Factor Analysis
Sung, Sooyeon; Fenoglio, Angela; Wolff, Jason J.; Schultz, Robert T.; Botteron, Kelly N.; Dager, Stephen R.; Estes, Annette M.; Hazlett, Heather C.; Zwaigenbaum, Lonnie; Piven, Joseph; Elison, Jed T. – Child Development, 2022
Using the Infant Behavior Questionnaire--Revised in a longitudinal sample of infant siblings of autistic children (HR; n = 427, 171 female, 83.4% White) and a comparison group of low-risk controls (LR, n = 200, 86 female, 81.5% White), collected between 2007 and 2017, this study identified an invariant factor structure of temperament traits across…
Descriptors: Factor Structure, Infants, Behavior, Siblings
Paliwal, Deepshikha; Kumar, Ritesh – European Journal of Psychology and Educational Research, 2022
This study was conducted to explore the five-factor structure of the Need for Closure scale on Indian samples using exploratory and confirmatory factor analysis. Data were initially collected from 450 samples which were reduced to 235 cases later based on the lie score criteria of the Need for Closure Scale. To rule out the problems caused by all…
Descriptors: Factor Analysis, Factor Structure, Foreign Countries, Psychological Patterns

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