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Tenko Raykov – Structural Equation Modeling: A Multidisciplinary Journal, 2024
This note demonstrates that measurement invariance does not guarantee meaningful and valid group comparisons in multiple-population settings. The article follows on a recent critical discussion by Robitzsch and Lüdtke, who argued that measurement invariance was not a pre-requisite for such comparisons. Within the framework of common factor…
Descriptors: Error of Measurement, Prerequisites, Factor Analysis, Evaluation Methods
Jeroen D. Mulder; Kim Luijken; Bas B. L. Penning de Vries; Ellen L. Hamaker – Structural Equation Modeling: A Multidisciplinary Journal, 2024
The use of structural equation models for causal inference from panel data is critiqued in the causal inference literature for unnecessarily relying on a large number of parametric assumptions, and alternative methods originating from the potential outcomes framework have been recommended, such as inverse probability weighting (IPW) estimation of…
Descriptors: Structural Equation Models, Time on Task, Time Management, Causal Models
Suyoung Kim; Sooyong Lee; Jiwon Kim; Tiffany A. Whittaker – Structural Equation Modeling: A Multidisciplinary Journal, 2024
This study aims to address a gap in the social and behavioral sciences literature concerning interaction effects between latent factors in multiple-group analysis. By comparing two approaches for estimating latent interactions within multiple-group analysis frameworks using simulation studies and empirical data, we assess their relative merits.…
Descriptors: Social Science Research, Behavioral Sciences, Structural Equation Models, Statistical Analysis
Emma Somer; Carl Falk; Milica Miocevic – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Factor Score Regression (FSR) is increasingly employed as an alternative to structural equation modeling (SEM) in small samples. Despite its popularity in psychology, the performance of FSR in multigroup models with small samples remains relatively unknown. The goal of this study was to examine the performance of FSR, namely Croon's correction and…
Descriptors: Scores, Structural Equation Models, Comparative Analysis, Sample Size
Ke-Hai Yuan; Yongfei Fang – Grantee Submission, 2023
Observational data typically contain measurement errors. Covariance-based structural equation modelling (CB-SEM) is capable of modelling measurement errors and yields consistent parameter estimates. In contrast, methods of regression analysis using weighted composites as well as a partial least squares approach to SEM facilitate the prediction and…
Descriptors: Structural Equation Models, Regression (Statistics), Weighted Scores, Comparative 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
Ke-Hai Yuan; Zhiyong Zhang – Grantee Submission, 2024
Data in social and behavioral sciences typically contain measurement errors and also do not have predefined metrics. Structural equation modeling (SEM) is commonly used to analyze such data. This article discuss issues in latent-variable modeling as compared to regression analysis with composite-scores. Via logical reasoning and analytical results…
Descriptors: Error of Measurement, Measurement Techniques, Social Science Research, Behavioral Science Research
Ben Kelcey; Fangxing Bai; Amota Ataneka; Yanli Xie; Kyle Cox – Society for Research on Educational Effectiveness, 2024
We develop a structural after measurement (SAM) method for structural equation models (SEMs) that accommodates missing data. The results show that the proposed SAM missing data estimator outperforms conventional full information (FI) estimators in terms of convergence, bias, and root-mean-square-error in small-to-moderate samples or large samples…
Descriptors: Structural Equation Models, Research Problems, Error of Measurement, Maximum Likelihood Statistics
Wang, Ze – Large-scale Assessments in Education, 2022
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…
Descriptors: Comparative Analysis, Evaluation Methods, Grade 8, Mathematics Achievement
Sideridis, Georgios D.; Tsaousis, Ioannis; Alamri, Abeer A. – Educational and Psychological Measurement, 2020
The main thesis of the present study is to use the Bayesian structural equation modeling (BSEM) methodology of establishing approximate measurement invariance (A-MI) using data from a national examination in Saudi Arabia as an alternative to not meeting strong invariance criteria. Instead, we illustrate how to account for the absence of…
Descriptors: Bayesian Statistics, Structural Equation Models, Foreign Countries, Error of Measurement
Hsiao, Yu-Yu; Kwok, Oi-Man; Lai, Mark H. C. – Educational and Psychological Measurement, 2018
Path models with observed composites based on multiple items (e.g., mean or sum score of the items) are commonly used to test interaction effects. Under this practice, researchers generally assume that the observed composites are measured without errors. In this study, we reviewed and evaluated two alternative methods within the structural…
Descriptors: Error of Measurement, Testing, Scores, Models
Devlieger, Ines; Mayer, Axel; Rosseel, Yves – Educational and Psychological Measurement, 2016
In this article, an overview is given of four methods to perform factor score regression (FSR), namely regression FSR, Bartlett FSR, the bias avoiding method of Skrondal and Laake, and the bias correcting method of Croon. The bias correcting method is extended to include a reliable standard error. The four methods are compared with each other and…
Descriptors: Regression (Statistics), Comparative Analysis, Structural Equation Models, Monte Carlo Methods
Smith, Carrie E.; Cribbie, Robert A. – Structural Equation Modeling: A Multidisciplinary Journal, 2013
When structural equation modeling (SEM) analyses are conducted, significance tests for all important model relationships (parameters including factor loadings, covariances, etc.) are typically conducted at a specified nominal Type I error rate ([alpha]). Despite the fact that many significance tests are often conducted in SEM, rarely is…
Descriptors: Structural Equation Models, Error of Measurement, Statistical Analysis, Comparative Analysis
Levy, Roy – Educational Psychologist, 2016
In this article, I provide a conceptually oriented overview of Bayesian approaches to statistical inference and contrast them with frequentist approaches that currently dominate conventional practice in educational research. The features and advantages of Bayesian approaches are illustrated with examples spanning several statistical modeling…
Descriptors: Bayesian Statistics, Models, Educational Research, Innovation
Cho, Sun-Joo; Preacher, Kristopher J.; Bottge, Brian A. – Grantee Submission, 2015
Multilevel modeling (MLM) is frequently used to detect group differences, such as an intervention effect in a pre-test--post-test cluster-randomized design. Group differences on the post-test scores are detected by controlling for pre-test scores as a proxy variable for unobserved factors that predict future attributes. The pre-test and post-test…
Descriptors: Structural Equation Models, Hierarchical Linear Modeling, Intervention, Program Effectiveness
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