Publication Date
In 2025 | 2 |
Since 2024 | 6 |
Since 2021 (last 5 years) | 19 |
Since 2016 (last 10 years) | 89 |
Since 2006 (last 20 years) | 203 |
Descriptor
Factor Analysis | 214 |
Goodness of Fit | 214 |
Structural Equation Models | 214 |
Foreign Countries | 106 |
Correlation | 70 |
Statistical Analysis | 57 |
Questionnaires | 48 |
Measures (Individuals) | 44 |
Factor Structure | 43 |
Student Attitudes | 31 |
Reliability | 29 |
More ▼ |
Source
Author
Marsh, Herbert W. | 5 |
Morin, Alexandre J. S. | 4 |
Akilli, Mustafa | 3 |
Nagengast, Benjamin | 3 |
Arens, A. Katrin | 2 |
Asparouhov, Tihomir | 2 |
Boz, Yezdan | 2 |
Buhner, Markus | 2 |
Canivez, Gary L. | 2 |
Chan, Melvin | 2 |
Craven, Rhonda G. | 2 |
More ▼ |
Publication Type
Education Level
Higher Education | 59 |
Postsecondary Education | 41 |
Secondary Education | 28 |
Elementary Education | 23 |
High Schools | 19 |
Middle Schools | 13 |
Junior High Schools | 9 |
Elementary Secondary Education | 7 |
Grade 9 | 6 |
Grade 5 | 4 |
Grade 8 | 4 |
More ▼ |
Audience
Researchers | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
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
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
Njål Foldnes; Jonas Moss; Steffen Grønneberg – Structural Equation Modeling: A Multidisciplinary Journal, 2025
We propose new ways of robustifying goodness-of-fit tests for structural equation modeling under non-normality. These test statistics have limit distributions characterized by eigenvalues whose estimates are highly unstable and biased in known directions. To take this into account, we design model-based trend predictions to approximate the…
Descriptors: Goodness of Fit, Structural Equation Models, Robustness (Statistics), Prediction
Sergio Dominguez-Lara; Mario A. Trógolo; Rodrigo Moreta-Herrera; Diego Vaca-Quintana; Manuel Fernández-Arata; Ana Paredes-Proaño – Journal of Psychoeducational Assessment, 2025
Academic engagement plays a crucial role in students' learning and performance. One of the most popular measures for assessing this construct is the Utrecht Work Engagement Scale for Students (UWES-S), which is based on a tridimensional conceptualization consisting of dedication, vigor, and absorption. However, prior research on its factor…
Descriptors: Learner Engagement, College Students, Foreign Countries, Factor Analysis
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
Schamberger, Tamara; Schuberth, Florian; Henseler, Jörg – International Journal of Behavioral Development, 2023
Research in human development often relies on composites, that is, composed variables such as indices. Their composite nature renders these variables inaccessible to conventional factor-centric psychometric validation techniques such as confirmatory factor analysis (CFA). In the context of human development research, there is currently no…
Descriptors: Individual Development, Factor Analysis, Statistical Analysis, Structural Equation Models
Sideridis, Georgios D.; Jaffari, Fathima – Measurement and Evaluation in Counseling and Development, 2022
The present study describes an R function that implements six corrective procedures developed by Bartlett, Swain, and Yuan in the correction of 21 statistics associated with the omnibus Chi-square test, the residuals, or fit indices in confirmatory factor analysis (CFA) and structural equation modeling (SEM).
Descriptors: Statistical Analysis, Goodness of Fit, Factor Analysis, Structural Equation Models
Daniel McNeish – Grantee Submission, 2023
Scale validation is vital to psychological research because it ensures that scores from measurement scales represent the intended construct. Factor analysis fit indices are commonly used to provide quantitative evidence that a proposed factor structure is plausible. However, there is mismatch between guidelines for evaluating fit of factor models…
Descriptors: Factor Analysis, Goodness of Fit, Validity, Likert Scales
E. Damiano D'Urso; Jesper Tijmstra; Jeroen K. Vermunt; Kim De Roover – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Measurement invariance (MI) is required for validly comparing latent constructs measured by multiple ordinal self-report items. Non-invariances may occur when disregarding (group differences in) an acquiescence response style (ARS; an agreeing tendency regardless of item content). If non-invariance results solely from neglecting ARS, one should…
Descriptors: Error of Measurement, Structural Equation Models, Construct Validity, Measurement Techniques
Jin, Ying – International Journal of Behavioral Development, 2020
This research examines the performance of the previously proposed cutoff values of alternative fit indices (i.e., change in comparative fit index [(delta)CFI], change in Tucker-Lewis index [(delta)TLI], and change in root mean squared error of approximation [(delta)RMSEA]) to evaluate measurement invariance for exploratory structural equation…
Descriptors: Structural Equation Models, Goodness of Fit, Measurement, Factor Analysis
Fu, Yuanshu; Wen, Zhonglin; Wang, Yang – Educational and Psychological Measurement, 2022
Composite reliability, or coefficient omega, can be estimated using structural equation modeling. Composite reliability is usually estimated under the basic independent clusters model of confirmatory factor analysis (ICM-CFA). However, due to the existence of cross-loadings, the model fit of the exploratory structural equation model (ESEM) is…
Descriptors: Comparative Analysis, Structural Equation Models, Factor Analysis, Reliability
Haiyan Liu; Sarah Depaoli; Lydia Marvin – Structural Equation Modeling: A Multidisciplinary Journal, 2022
The deviance information criterion (DIC) is widely used to select the parsimonious, well-fitting model. We examined how priors impact model complexity (pD) and the DIC for Bayesian CFA. Study 1 compared the empirical distributions of pD and DIC under multivariate (i.e., inverse Wishart) and separation strategy (SS) priors. The former treats the…
Descriptors: Structural Equation Models, Bayesian Statistics, Goodness of Fit, Factor Analysis
Fatih Orcan – International Journal of Assessment Tools in Education, 2023
Among all, Cronbach's Alpha and McDonald's Omega are commonly used for reliability estimations. The alpha uses inter-item correlations while omega is based on a factor analysis result. This study uses simulated ordinal data sets to test whether the alpha and omega produce different estimates. Their performances were compared according to the…
Descriptors: Statistical Analysis, Monte Carlo Methods, Correlation, Factor Analysis
Bang Quan Zheng; Peter M. Bentler – Structural Equation Modeling: A Multidisciplinary Journal, 2022
Chi-square tests based on maximum likelihood (ML) estimation of covariance structures often incorrectly over-reject the null hypothesis: [sigma] = [sigma(theta)] when the sample size is small. Reweighted least squares (RLS) avoids this problem. In some models, the vector of parameter must contain means, variances, and covariances, yet whether RLS…
Descriptors: Maximum Likelihood Statistics, Structural Equation Models, Goodness of Fit, Sample Size
Rai, Abha; Lee, Sunwoo; Jang, Jungwoo; Lee, Eunhye; Okech, David – Journal of Teaching in Social Work, 2022
The use of structural equation modeling (SEM) techniques in social work has increased over the last two decades. We therefore conducted a systematic review to understand the extent to which SEM is utilized in social work research, given that statistical training is now becoming a part of social work doctoral education. For our review, we utilized…
Descriptors: Structural Equation Models, Social Work, Social Science Research, Experiential Learning