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Showing 1 to 15 of 29 results Save | Export
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Hyunjung Lee; Heining Cham – Educational and Psychological Measurement, 2024
Determining the number of factors in exploratory factor analysis (EFA) is crucial because it affects the rest of the analysis and the conclusions of the study. Researchers have developed various methods for deciding the number of factors to retain in EFA, but this remains one of the most difficult decisions in the EFA. The purpose of this study is…
Descriptors: Factor Structure, Factor Analysis, Monte Carlo Methods, Goodness of Fit
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Fatih Orçan – International Journal of Assessment Tools in Education, 2025
Factor analysis is a statistical method to explore the relationships among observed variables and identify latent structures. It is crucial in scale development and validity analysis. Key factors affecting the accuracy of factor analysis results include the type of data, sample size, and the number of response categories. While some studies…
Descriptors: Factor Analysis, Factor Structure, Item Response Theory, Sample Size
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Kim, Hyung Jin; Lee, Won-Chan – Journal of Educational Measurement, 2022
Orlando and Thissen (2000) introduced the "S - X[superscript 2]" item-fit index for testing goodness-of-fit with dichotomous item response theory (IRT) models. This study considers and evaluates an alternative approach for computing "S - X[superscript 2]" values and other factors associated with collapsing tables of observed…
Descriptors: Goodness of Fit, Test Items, Item Response Theory, Computation
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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
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Sedat Sen; Allan S. Cohen – Educational and Psychological Measurement, 2024
A Monte Carlo simulation study was conducted to compare fit indices used for detecting the correct latent class in three dichotomous mixture item response theory (IRT) models. Ten indices were considered: Akaike's information criterion (AIC), the corrected AIC (AICc), Bayesian information criterion (BIC), consistent AIC (CAIC), Draper's…
Descriptors: Goodness of Fit, Item Response Theory, Sample Size, Classification
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Su, Shiyang; Wang, Chun; Weiss, David J. – Educational and Psychological Measurement, 2021
S-X[superscript 2] is a popular item fit index that is available in commercial software packages such as "flex"MIRT. However, no research has systematically examined the performance of S-X[superscript 2] for detecting item misfit within the context of the multidimensional graded response model (MGRM). The primary goal of this study was…
Descriptors: Statistics, Goodness of Fit, Test Items, Models
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Diaz, Emily; Brooks, Gordon; Johanson, George – International Journal of Assessment Tools in Education, 2021
This Monte Carlo study assessed Type I error in differential item functioning analyses using Lord's chi-square (LC), Likelihood Ratio Test (LRT), and Mantel-Haenszel (MH) procedure. Two research interests were investigated: item response theory (IRT) model specification in LC and the LRT and continuity correction in the MH procedure. This study…
Descriptors: Test Bias, Item Response Theory, Statistical Analysis, Comparative Analysis
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Sauder, Derek; DeMars, Christine – Applied Measurement in Education, 2020
We used simulation techniques to assess the item-level and familywise Type I error control and power of an IRT item-fit statistic, the "S-X"[superscript 2]. Previous research indicated that the "S-X"[superscript 2] has good Type I error control and decent power, but no previous research examined familywise Type I error control.…
Descriptors: Item Response Theory, Test Items, Sample Size, Test Length
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Lenhard, Wolfgang; Lenhard, Alexandra – Educational and Psychological Measurement, 2021
The interpretation of psychometric test results is usually based on norm scores. We compared semiparametric continuous norming (SPCN) with conventional norming methods by simulating results for test scales with different item numbers and difficulties via an item response theory approach. Subsequently, we modeled the norm scores based on random…
Descriptors: Test Norms, Scores, Regression (Statistics), Test Items
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Ames, Allison J.; Leventhal, Brian C.; Ezike, Nnamdi C. – Measurement: Interdisciplinary Research and Perspectives, 2020
Data simulation and Monte Carlo simulation studies are important skills for researchers and practitioners of educational and psychological measurement, but there are few resources on the topic specific to item response theory. Even fewer resources exist on the statistical software techniques to implement simulation studies. This article presents…
Descriptors: Monte Carlo Methods, Item Response Theory, Simulation, Computer Software
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Ravand, Hamdollah; Baghaei, Purya – International Journal of Testing, 2020
More than three decades after their introduction, diagnostic classification models (DCM) do not seem to have been implemented in educational systems for the purposes they were devised. Most DCM research is either methodological for model development and refinement or retrofitting to existing nondiagnostic tests and, in the latter case, basically…
Descriptors: Classification, Models, Diagnostic Tests, Test Construction
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Dai, Ting; Du, Yang; Cromley, Jennifer G.; Fechter, Tia M.; Nelson, Frank – AERA Online Paper Repository, 2019
Certain planned-missing designs (e.g., simple-matrix sampling) cause zero covariances between variables not jointly observed, making it impossible to do analyses beyond mean estimations without specialized analyses. We tested a multigroup confirmatory factor analysis (CFA) approach by Cudeck (2000), which obtains a model-estimated…
Descriptors: Factor Analysis, Educational Research, Research Design, Data Analysis
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DiStefano, Christine; McDaniel, Heather L.; Zhang, Liyun; Shi, Dexin; Jiang, Zhehan – Educational and Psychological Measurement, 2019
A simulation study was conducted to investigate the model size effect when confirmatory factor analysis (CFA) models include many ordinal items. CFA models including between 15 and 120 ordinal items were analyzed with mean- and variance-adjusted weighted least squares to determine how varying sample size, number of ordered categories, and…
Descriptors: Factor Analysis, Effect Size, Data, Sample Size
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Willse, John T. – Measurement and Evaluation in Counseling and Development, 2017
This article provides a brief introduction to the Rasch model. Motivation for using Rasch analyses is provided. Important Rasch model concepts and key aspects of result interpretation are introduced, with major points reinforced using a simulation demonstration. Concrete guidelines are provided regarding sample size and the evaluation of items.
Descriptors: Item Response Theory, Test Results, Test Interpretation, Simulation
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Suh, Youngsuk – Journal of Educational Measurement, 2016
This study adapted an effect size measure used for studying differential item functioning (DIF) in unidimensional tests and extended the measure to multidimensional tests. Two effect size measures were considered in a multidimensional item response theory model: signed weighted P-difference and unsigned weighted P-difference. The performance of…
Descriptors: Effect Size, Goodness of Fit, Statistical Analysis, Statistical Significance
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