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Showing 1 to 15 of 128 results Save | Export
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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
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Philipp Sterner; Kim De Roover; David Goretzko – Structural Equation Modeling: A Multidisciplinary Journal, 2025
When comparing relations and means of latent variables, it is important to establish measurement invariance (MI). Most methods to assess MI are based on confirmatory factor analysis (CFA). Recently, new methods have been developed based on exploratory factor analysis (EFA); most notably, as extensions of multi-group EFA, researchers introduced…
Descriptors: Error of Measurement, Measurement Techniques, Factor Analysis, Structural Equation Models
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Xijuan Zhang; Hao Wu – Structural Equation Modeling: A Multidisciplinary Journal, 2024
A full structural equation model (SEM) typically consists of both a measurement model (describing relationships between latent variables and observed scale items) and a structural model (describing relationships among latent variables). However, often researchers are primarily interested in testing hypotheses related to the structural model while…
Descriptors: Structural Equation Models, Goodness of Fit, Robustness (Statistics), Factor Structure
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Abdolvahab Khademi; Craig S. Wells; Maria Elena Oliveri; Ester Villalonga-Olives – SAGE Open, 2023
The most common effect size when using a multiple-group confirmatory factor analysis approach to measurement invariance is [delta]CFI and [delta]TLI with a cutoff value of 0.01. However, this recommended cutoff value may not be ubiquitously appropriate and may be of limited application for some tests (e.g., measures using dichotomous items or…
Descriptors: Factor Analysis, Factor Structure, Error of Measurement, Test Items
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Hyunjung Lee; Heining Cham – Educational and Psychological Measurement, 2024
Determining the number of factors in exploratory factor analysis (EFA) is crucial because it affects the rest of the analysis and the conclusions of the study. Researchers have developed various methods for deciding the number of factors to retain in EFA, but this remains one of the most difficult decisions in the EFA. The purpose of this study is…
Descriptors: Factor Structure, Factor Analysis, Monte Carlo Methods, Goodness of Fit
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Teck Kiang Tan – Practical Assessment, Research & Evaluation, 2024
The procedures of carrying out factorial invariance to validate a construct were well developed to ensure the reliability of the construct that can be used across groups for comparison and analysis, yet mainly restricted to the frequentist approach. This motivates an update to incorporate the growing Bayesian approach for carrying out the Bayesian…
Descriptors: Bayesian Statistics, Factor Analysis, Programming Languages, Reliability
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Mohammad Mehdi Latifi; Dariush Tahmasebi Aghbelaghi; Sajad Khani Pordanjani – European Journal of Education, 2025
The present study sought to assess the psychometric properties of the Iranian adaptation of the Vietnam Teacher Resilience Scale for Asia (VITRS), referred to as the Iranian Teachers' Resilience Scale (ITRS) and to examine its measurement invariance across middle and high school teachers in Iran. In total, 700 participants completed the…
Descriptors: Resilience (Psychology), Error of Measurement, Factor Analysis, Teacher Attitudes
Kush, Joseph M.; Konold, Timothy R.; Bradshaw, Catherine P. – Educational and Psychological Measurement, 2022
Multilevel structural equation modeling (MSEM) allows researchers to model latent factor structures at multiple levels simultaneously by decomposing within- and between-group variation. Yet the extent to which the sampling ratio (i.e., proportion of cases sampled from each group) influences the results of MSEM models remains unknown. This article…
Descriptors: Structural Equation Models, Factor Structure, Statistical Bias, Error of Measurement
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Cristian Zanon; Nan Zhao; Nursel Topkaya; Ertugrul Sahin; David L. Vogel; Melissa M. Ertl; Samineh Sanatkar; Hsin-Ya Liao; Mark Rubin; Makilim N. Baptista; Winnie W. S. Mak; Fatima Rashed Al-Darmaki; Georg Schomerus; Ying-Fen Wang; Dalia Nasvytiene – International Journal of Testing, 2025
Examinations of the internal structure of the Depression, Anxiety, and Stress Scale-21 (DASS-21) have yielded inconsistent conclusions within and across cultural contexts. This study examined the dimensionality and reliability of the DASS-21 across three theoretically plausible factor structures (i.e., unidimensional, oblique three-factor, and…
Descriptors: Anxiety, Depression (Psychology), Psychometrics, Cultural Context
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Yanjing Cao; Chenchen Xu; Shan Lu; Qi Li; Jing Xiao – Psychology in the Schools, 2025
The patient health questionnaire-9 (PHQ-9) is widely utilized in assessing individuals' depression levels. Nevertheless, research regarding its factor structure and measurement invariance remains inadequate. The aim of this study was to delve into the factor structure of the PHQ-9 and to further investigate its measurement invariance across gender…
Descriptors: Factor Structure, Error of Measurement, Factor Analysis, Age Differences
Emily A. Brown – ProQuest LLC, 2024
Previous research has been limited regarding the measurement of computational thinking, particularly as a learning progression in K-12. This study proposes to apply a multidimensional item response theory (IRT) model to a newly developed measure of computational thinking utilizing both selected response and open-ended polytomous items to establish…
Descriptors: Models, Computation, Thinking Skills, Item Response Theory
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Lee, Bitna; Sohn, Wonsook – Educational and Psychological Measurement, 2022
A Monte Carlo study was conducted to compare the performance of a level-specific (LS) fit evaluation with that of a simultaneous (SI) fit evaluation in multilevel confirmatory factor analysis (MCFA) models. We extended previous studies by examining their performance under MCFA models with different factor structures across levels. In addition,…
Descriptors: Goodness of Fit, Factor Structure, Monte Carlo Methods, Factor Analysis
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Alkan, Muhammet Fatih; Mavis Sevim, Fazilet Özge; Evers, Arnoud T. – Journal of Psychoeducational Assessment, 2023
Teacher autonomy positively impacts various profession-related variables, including professional self-efficacy, motivation, job satisfaction, organizational commitment, teacher success, and job performance. The development and adaptation of sound instruments will contribute to achieving a complete understanding of teachers' autonomous behavior and…
Descriptors: Factor Structure, Error of Measurement, Professional Autonomy, Behavior Rating Scales
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Carolien Rieffe; Zijian Li; Yung-Ting Tsou – European Journal of Developmental Psychology, 2024
How parents value and address emotions with their children is essential for children's emotion socialization. This study developed and validated the short Parent-Child Emotion Communication questionnaire (PEC), which measures the extent to which parents appreciate emotions, consciously discuss their own and their child's emotions with their…
Descriptors: Parent Child Relationship, Questionnaires, Socialization, Emotional Response
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Blaine G. Robbins – Sociological Methods & Research, 2024
The Stranger Face Trust scale (SFT) and Imaginary Stranger Trust scale (IST) are two new self-report measures of generalized trust that assess trust in strangers--both real and imaginary--across four trust domains. Prior research has established the reliability and validity of SFT and IST, but a number of measurement validation tests remain.…
Descriptors: Attitude Measures, Trust (Psychology), Stranger Reactions, Pretests Posttests
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