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Merkle, Edgar C.; Fitzsimmons, Ellen; Uanhoro, James; Goodrich, Ben – Grantee Submission, 2021
Structural equation models comprise a large class of popular statistical models, including factor analysis models, certain mixed models, and extensions thereof. Model estimation is complicated by the fact that we typically have multiple interdependent response variables and multiple latent variables (which may also be called random effects or…
Descriptors: Bayesian Statistics, Structural Equation Models, Psychometrics, Factor Analysis
Sinharay, Sandip – Educational Measurement: Issues and Practice, 2019
Test score users often demand the reporting of subscores due to their potential diagnostic, remedial, and instructional benefits. Therefore, there is substantial pressure on testing programs to report subscores. However, professional standards require that subscores have to satisfy minimum quality standards before they can be reported. In this…
Descriptors: Testing, Scores, Item Response Theory, Evaluation Methods
Nagy, Gabriel; Brunner, Martin; Lüdtke, Oliver; Greiff, Samuel – Journal of Experimental Education, 2017
We present factor extension procedures for confirmatory factor analysis that provide estimates of the relations of common and unique factors with external variables that do not undergo factor analysis. We present identification strategies that build upon restrictions of the pattern of correlations between unique factors and external variables. The…
Descriptors: Factor Analysis, Evaluation Methods, Identification, Correlation
Raykov, Tenko; Marcoulides, George A. – Educational and Psychological Measurement, 2018
This article outlines a procedure for examining the degree to which a common factor may be dominating additional factors in a multicomponent measuring instrument consisting of binary items. The procedure rests on an application of the latent variable modeling methodology and accounts for the discrete nature of the manifest indicators. The method…
Descriptors: Measurement Techniques, Factor Analysis, Item Response Theory, Likert Scales
McGill, Ryan J.; Dombrowski, Stefan C. – Communique, 2017
Factor analysis is a versatile class of psychometric techniques used by researchers to provide insight into the psychological dimensions (factors) that may account for the relationships among variables in a given dataset. The primary goal of a factor analysis is to determine a more parsimonious set of variables (i.e., fewer than the number of…
Descriptors: Factor Analysis, School Psychology, Psychometrics, Predictor Variables
Osborne, Jason W. – Practical Assessment, Research & Evaluation, 2015
Exploratory factor analysis (EFA) is one of the most commonly-reported quantitative methodology in the social sciences, yet much of the detail regarding what happens during an EFA remains unclear. The goal of this brief technical note is to explore what "rotation" is, what exactly is rotating, and why we use rotation when performing…
Descriptors: Factor Analysis, Social Sciences, Engineering Education, Evaluation Methods
Guasch, Marc; Haro, Juan; Boada, Roger – Psicologica: International Journal of Methodology and Experimental Psychology, 2017
With the increasing refinement of language processing models and the new discoveries about which variables can modulate these processes, stimuli selection for experiments with a factorial design is becoming a tough task. Selecting sets of words that differ in one variable, while matching these same words into dozens of other confounding variables…
Descriptors: Factor Analysis, Language Processing, Design, Cluster Grouping
Lewis, Todd F. – Measurement and Evaluation in Counseling and Development, 2017
American Educational Research Association (AERA) standards stipulate that researchers show evidence of the internal structure of instruments. Confirmatory factor analysis (CFA) is one structural equation modeling procedure designed to assess construct validity of assessments that has broad applicability for counselors interested in instrument…
Descriptors: Educational Research, Factor Analysis, Structural Equation Models, Construct Validity
Raykov, Tenko; Marcoulides, George A.; Millsap, Roger E. – Educational and Psychological Measurement, 2013
A multiple testing method for examining factorial invariance for latent constructs evaluated by multiple indicators in distinct populations is outlined. The procedure is based on the false discovery rate concept and multiple individual restriction tests and resolves general limitations of a popular factorial invariance testing approach. The…
Descriptors: Testing, Statistical Analysis, Factor Analysis, Statistical Significance
Jennrich, Robert I.; Bentler, Peter M. – Psychometrika, 2012
Bi-factor analysis is a form of confirmatory factor analysis originally introduced by Holzinger and Swineford ("Psychometrika" 47:41-54, 1937). The bi-factor model has a general factor, a number of group factors, and an explicit bi-factor structure. Jennrich and Bentler ("Psychometrika" 76:537-549, 2011) introduced an exploratory form of bi-factor…
Descriptors: Factor Structure, Factor Analysis, Models, Comparative Analysis
Drummond, Gordon B.; Vowler, Sarah L. – Advances in Physiology Education, 2012
These authors have previously described how to use the "t" test to compare two groups. In this article, they describe the use of a different test, analysis of variance (ANOVA) to compare more than two groups. ANOVA is a test of group differences: do at least two of the means differ from each other? ANOVA assumes (1) normal distribution…
Descriptors: Test Results, Statistical Analysis, Multivariate Analysis, Evaluation Methods
Drummond, Gordon B.; Vowler, Sarah L. – Advances in Physiology Education, 2012
In this article, the authors consider the possibility that groups could be different, because of the different conditions of a factor. This is as far as the analysis can extend: the consideration is restricted to groups characterized by the different category of the factor being considered. In many biological experiments, the factor considered may…
Descriptors: Regression (Statistics), Science Experiments, Biology, Factor Analysis
Wall, Melanie M.; Guo, Jia; Amemiya, Yasuo – Multivariate Behavioral Research, 2012
Mixture factor analysis is examined as a means of flexibly estimating nonnormally distributed continuous latent factors in the presence of both continuous and dichotomous observed variables. A simulation study compares mixture factor analysis with normal maximum likelihood (ML) latent factor modeling. Different results emerge for continuous versus…
Descriptors: Sample Size, Simulation, Form Classes (Languages), Diseases
Lee, Chun-Ting; Zhang, Guangjian; Edwards, Michael C. – Multivariate Behavioral Research, 2012
Exploratory factor analysis (EFA) is often conducted with ordinal data (e.g., items with 5-point responses) in the social and behavioral sciences. These ordinal variables are often treated as if they were continuous in practice. An alternative strategy is to assume that a normally distributed continuous variable underlies each ordinal variable.…
Descriptors: Personality Traits, Intervals, Monte Carlo Methods, Factor Analysis
Maydeu-Olivares, Alberto; Cai, Li; Hernandez, Adolfo – Structural Equation Modeling: A Multidisciplinary Journal, 2011
Linear factor analysis (FA) models can be reliably tested using test statistics based on residual covariances. We show that the same statistics can be used to reliably test the fit of item response theory (IRT) models for ordinal data (under some conditions). Hence, the fit of an FA model and of an IRT model to the same data set can now be…
Descriptors: Factor Analysis, Research Methodology, Statistics, Item Response Theory