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Showing 1 to 15 of 89 results Save | Export
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Karl Schweizer; Andreas Gold; Dorothea Krampen; Stefan Troche – Educational and Psychological Measurement, 2024
Conceptualizing two-variable disturbances preventing good model fit in confirmatory factor analysis as item-level method effects instead of correlated residuals avoids violating the principle that residual variation is unique for each item. The possibility of representing such a disturbance by a method factor of a bifactor measurement model was…
Descriptors: Correlation, Factor Analysis, Measurement Techniques, Item Analysis
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Tenko Raykov; Christine DiStefano; Lisa Calvocoressi – Educational and Psychological Measurement, 2024
This note demonstrates that the widely used Bayesian Information Criterion (BIC) need not be generally viewed as a routinely dependable index for model selection when the bifactor and second-order factor models are examined as rival means for data description and explanation. To this end, we use an empirically relevant setting with…
Descriptors: Bayesian Statistics, Models, Decision Making, Comparative Analysis
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James Ohisei Uanhoro – Educational and Psychological Measurement, 2024
Accounting for model misspecification in Bayesian structural equation models is an active area of research. We present a uniquely Bayesian approach to misspecification that models the degree of misspecification as a parameter--a parameter akin to the correlation root mean squared residual. The misspecification parameter can be interpreted on its…
Descriptors: Bayesian Statistics, Structural Equation Models, Simulation, Statistical Inference
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Markus T. Jansen; Ralf Schulze – Educational and Psychological Measurement, 2024
Thurstonian forced-choice modeling is considered to be a powerful new tool to estimate item and person parameters while simultaneously testing the model fit. This assessment approach is associated with the aim of reducing faking and other response tendencies that plague traditional self-report trait assessments. As a result of major recent…
Descriptors: Factor Analysis, Models, Item Analysis, Evaluation Methods
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Jang, Yoona; Hong, Sehee – Educational and Psychological Measurement, 2023
The purpose of this study was to evaluate the degree of classification quality in the basic latent class model when covariates are either included or are not included in the model. To accomplish this task, Monte Carlo simulations were conducted in which the results of models with and without a covariate were compared. Based on these simulations,…
Descriptors: Classification, Models, Prediction, Sample Size
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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
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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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Wang, Yan; Kim, Eunsook; Ferron, John M.; Dedrick, Robert F.; Tan, Tony X.; Stark, Stephen – Educational and Psychological Measurement, 2021
Factor mixture modeling (FMM) has been increasingly used to investigate unobserved population heterogeneity. This study examined the issue of covariate effects with FMM in the context of measurement invariance testing. Specifically, the impact of excluding and misspecifying covariate effects on measurement invariance testing and class enumeration…
Descriptors: Role, Error of Measurement, Monte Carlo Methods, Models
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Thompson, Yutian T.; Song, Hairong; Shi, Dexin; Liu, Zhengkui – Educational and Psychological Measurement, 2021
Conventional approaches for selecting a reference indicator (RI) could lead to misleading results in testing for measurement invariance (MI). Several newer quantitative methods have been available for more rigorous RI selection. However, it is still unknown how well these methods perform in terms of correctly identifying a truly invariant item to…
Descriptors: Measurement, Statistical Analysis, Selection, Comparative Analysis
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Huang, Hung-Yu – Educational and Psychological Measurement, 2023
The forced-choice (FC) item formats used for noncognitive tests typically develop a set of response options that measure different traits and instruct respondents to make judgments among these options in terms of their preference to control the response biases that are commonly observed in normative tests. Diagnostic classification models (DCMs)…
Descriptors: Test Items, Classification, Bayesian Statistics, Decision Making
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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
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Son, Sookyoung; Lee, Hyunjung; Jang, Yoona; Yang, Junyeong; Hong, Sehee – Educational and Psychological Measurement, 2019
The purpose of the present study is to compare nonnormal distributions (i.e., t, skew-normal, skew-t with equal skew and skew-t with unequal skew) in growth mixture models (GMMs) based on diverse conditions of a number of time points, sample sizes, and skewness for intercepts. To carry out this research, two simulation studies were conducted with…
Descriptors: Statistical Distributions, Statistical Analysis, Structural Equation Models, Comparative Analysis
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Yesiltas, Gonca; Paek, Insu – Educational and Psychological Measurement, 2020
A log-linear model (LLM) is a well-known statistical method to examine the relationship among categorical variables. This study investigated the performance of LLM in detecting differential item functioning (DIF) for polytomously scored items via simulations where various sample sizes, ability mean differences (impact), and DIF types were…
Descriptors: Simulation, Sample Size, Item Analysis, Scores
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Komboz, Basil; Strobl, Carolin; Zeileis, Achim – Educational and Psychological Measurement, 2018
Psychometric measurement models are only valid if measurement invariance holds between test takers of different groups. Global model tests, such as the well-established likelihood ratio (LR) test, are sensitive to violations of measurement invariance, such as differential item functioning and differential step functioning. However, these…
Descriptors: Item Response Theory, Models, Tests, Measurement
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Raykov, Tenko; Marcoulides, George A.; Akaeze, Hope O. – Educational and Psychological Measurement, 2017
This note is concerned with examining the relationship between within-group and between-group variances in two-level nested designs. A latent variable modeling approach is outlined that permits point and interval estimation of their ratio and allows their comparison in a multilevel study. The procedure can also be used to test various hypotheses…
Descriptors: Comparative Analysis, Models, Statistical Analysis, Hierarchical Linear Modeling
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