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Goretzko, David – Educational and Psychological Measurement, 2022

Determining the number of factors in exploratory factor analysis is arguably the most crucial decision a researcher faces when conducting the analysis. While several simulation studies exist that compare various so-called factor retention criteria under different data conditions, little is known about the impact of missing data on this process.…

Descriptors: Factor Analysis, Research Problems, Data, Prediction

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

Son, Sookyoung; Hong, Sehee – Educational and Psychological Measurement, 2021

The purpose of this two-part study is to evaluate methods for multiple group analysis when the comparison group is at the within level with multilevel data, using a multilevel factor mixture model (ML FMM) and a multilevel multiple-indicators multiple-causes (ML MIMIC) model. The performance of these methods was evaluated integrally by a series of…

Descriptors: Hierarchical Linear Modeling, Factor Analysis, Structural Equation Models, Groups

Montoya, Amanda K.; Edwards, Michael C. – Educational and Psychological Measurement, 2021

Model fit indices are being increasingly recommended and used to select the number of factors in an exploratory factor analysis. Growing evidence suggests that the recommended cutoff values for common model fit indices are not appropriate for use in an exploratory factor analysis context. A particularly prominent problem in scale evaluation is the…

Descriptors: Goodness of Fit, Factor Analysis, Cutting Scores, Correlation

Levy, Roy; Xia, Yan; Green, Samuel B. – Educational and Psychological Measurement, 2021

A number of psychometricians have suggested that parallel analysis (PA) tends to yield more accurate results in determining the number of factors in comparison with other statistical methods. Nevertheless, all too often PA can suggest an incorrect number of factors, particularly in statistically unfavorable conditions (e.g., small sample sizes and…

Descriptors: Bayesian Statistics, Statistical Analysis, Factor Structure, Probability

Park, Sung Eun; Ahn, Soyeon; Zopluoglu, Cengiz – Educational and Psychological Measurement, 2021

This study presents a new approach to synthesizing differential item functioning (DIF) effect size: First, using correlation matrices from each study, we perform a multigroup confirmatory factor analysis (MGCFA) that examines measurement invariance of a test item between two subgroups (i.e., focal and reference groups). Then we synthesize, across…

Descriptors: Item Analysis, Effect Size, Difficulty Level, Monte Carlo Methods

Ferrando, Pere J.; Lorenzo-Seva, Urbano – Educational and Psychological Measurement, 2021

Unit-weight sum scores (UWSSs) are routinely used as estimates of factor scores on the basis of solutions obtained with the nonlinear exploratory factor analysis (EFA) model for ordered-categorical responses. Theoretically, this practice results in a loss of information and accuracy, and is expected to lead to biased estimates. However, the…

Descriptors: Scores, Factor Analysis, Automation, Fidelity

Wind, Stefanie A.; Schumacker, Randall E. – Educational and Psychological Measurement, 2021

Researchers frequently use Rasch models to analyze survey responses because these models provide accurate parameter estimates for items and examinees when there are missing data. However, researchers have not fully considered how missing data affect the accuracy of dimensionality assessment in Rasch analyses such as principal components analysis…

Descriptors: Item Response Theory, Data, Factor Analysis, Accuracy

Raykov, Tenko; Calvocoressi, Lisa – Educational and Psychological Measurement, 2021

A procedure for evaluating the average R-squared index for a given set of observed variables in an exploratory factor analysis model is discussed. The method can be used as an effective aid in the process of model choice with respect to the number of factors underlying the interrelationships among studied measures. The approach is developed within…

Descriptors: Factor Analysis, Structural Equation Models, Statistical Analysis, Selection

Xia, Yan – Educational and Psychological Measurement, 2021

Despite the existence of many methods for determining the number of factors, none outperforms the others under every condition. This study compares traditional parallel analysis (TPA), revised parallel analysis (RPA), Kaiser's rule, minimum average partial, sequential X[superscript 2], and sequential root mean square error of approximation,…

Descriptors: Statistical Analysis, Factor Analysis, Accuracy, Goodness of Fit

Revuelta, Javier; Franco-Martínez, Alicia; Ximénez, Carmen – Educational and Psychological Measurement, 2021

Situational judgment tests have gained popularity in educational and psychological measurement and are widely used in personnel assessment. A situational judgment item presents a hypothetical scenario and a list of actions, and the individuals are asked to select their most likely action for that scenario. Because actions have no explicit order,…

Descriptors: Factor Analysis, Situational Tests, Statistical Analysis, Sex Stereotypes

Ferrando, Pere J.; Navarro-González, David – Educational and Psychological Measurement, 2021

Item response theory "dual" models (DMs) in which both items and individuals are viewed as sources of differential measurement error so far have been proposed only for unidimensional measures. This article proposes two multidimensional extensions of existing DMs: the M-DTCRM (dual Thurstonian continuous response model), intended for…

Descriptors: Item Response Theory, Error of Measurement, Models, Factor Analysis

Beauducel, André; Hilger, Norbert – Educational and Psychological Measurement, 2021

Methods for optimal factor rotation of two-facet loading matrices have recently been proposed. However, the problem of the correct number of factors to retain for rotation of two-facet loading matrices has rarely been addressed in the context of exploratory factor analysis. Most previous studies were based on the observation that two-facet loading…

Descriptors: Factor Analysis, Statistical Analysis, Correlation, Models

Beauducel, André; Kersting, Martin – Educational and Psychological Measurement, 2020

We investigated by means of a simulation study how well methods for factor rotation can identify a two-facet simple structure. Samples were generated from orthogonal and oblique two-facet population factor models with 4 (2 factors per facet) to 12 factors (6 factors per facet). Samples drawn from orthogonal populations were submitted to factor…

Descriptors: Factor Structure, Factor Analysis, Sample Size, Intelligence

Shi, Dexin; Maydeu-Olivares, Alberto – Educational and Psychological Measurement, 2020

We examined the effect of estimation methods, maximum likelihood (ML), unweighted least squares (ULS), and diagonally weighted least squares (DWLS), on three population SEM (structural equation modeling) fit indices: the root mean square error of approximation (RMSEA), the comparative fit index (CFI), and the standardized root mean square residual…

Descriptors: Structural Equation Models, Computation, Maximum Likelihood Statistics, Least Squares Statistics