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Courtenay A. Barrett; Kathrin E. Maki; Steven R. Chesnut – Journal of Learning Disabilities, 2025
Schools conduct comprehensive psychoeducational evaluations to identify students with specific learning disabilities (SLDs) and determine whether they qualify for special education services. This decision-making process is complex and research has documented many factors influencing SLD identification decisions. One such factor may be…
Descriptors: Learning Disabilities, Beliefs, Disability Identification, School Psychologists
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
Bilge Bal-Sezerel; Deniz Arslan; Ugur Sak – Measurement: Interdisciplinary Research and Perspectives, 2025
In this study, the factorial invariance of the ASIS (Anadolu-Sak Intelligence Scale) was examined across time. Data were obtained from there groups of first-grade students who were administered the ASIS in 2020, 2021, and 2022. The analyses were conducted using multisample confirmatory factor analyses. Factorial invariance was tested with six…
Descriptors: Intelligence Tests, Grade 1, Factor Structure, Scores
Abdullah Faruk Kiliç; Meltem Acar Güvendir; Gül Güler; Tugay Kaçak – Measurement: Interdisciplinary Research and Perspectives, 2025
In this study, the extent to wording effects impact structure and factor loadings, internal consistency and measurement invariance was outlined. The modified form, which includes items that semantically reversed, explains %21.5 more variance than the original form. Also, reversed items' factor loadings are higher. As a result of CFA, indexes…
Descriptors: Test Items, Factor Structure, Test Reliability, Semantics
Bodo Przibilla; Chiara Enderle; Gino Casale; David Scheer; Anett Platte; Conny Melzer; Tatjana Leidig – European Journal of Education, 2025
Based on previous research, it can be assumed that teachers' self-efficacy (TSE) varies across situations, domains and individual students' behaviors confronting teachers with particularly challenging tasks. The construct of student-specific TSE is considered informative theoretical basis for understanding the relationship between…
Descriptors: Psychometrics, German, Foreign Countries, Teacher Effectiveness
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
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
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)
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
Laura M. Crothers; Taylor Steeves; Jered B. Kolbert; James B. Schreiber; Ara J. Schmitt; Brianna Drischler; Kelly Paulson; Jessica Cowley; Amelia Klass; Athena Vafiadis; Kayla Perfetto – Contemporary School Psychology, 2025
In this exploratory study, we adapted items from a previously developed measure of job satisfaction, the Measure of Job Satisfaction (MJS), an instrument first developed for use with community nurses in the UK, to create a brief, 15-item instrument (Job Satisfaction--Brief) applicable to practitioners of school psychology from Pennsylvania (N =…
Descriptors: Job Satisfaction, School Psychology, School Psychologists, Factor Structure
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)