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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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Yan Xia; Selim Havan – Educational and Psychological Measurement, 2024
Although parallel analysis has been found to be an accurate method for determining the number of factors in many conditions with complete data, its application under missing data is limited. The existing literature recommends that, after using an appropriate multiple imputation method, researchers either apply parallel analysis to every imputed…
Descriptors: Data Interpretation, Factor Analysis, Statistical Inference, Research Problems
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Fujimoto, Ken A.; Neugebauer, Sabina R. – Educational and Psychological Measurement, 2020
Although item response theory (IRT) models such as the bifactor, two-tier, and between-item-dimensionality IRT models have been devised to confirm complex dimensional structures in educational and psychological data, they can be challenging to use in practice. The reason is that these models are multidimensional IRT (MIRT) models and thus are…
Descriptors: Bayesian Statistics, Item Response Theory, Sample Size, Factor Structure
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Leth-Steensen, Craig; Gallitto, Elena – Educational and Psychological Measurement, 2016
A large number of approaches have been proposed for estimating and testing the significance of indirect effects in mediation models. In this study, four sets of Monte Carlo simulations involving full latent variable structural equation models were run in order to contrast the effectiveness of the currently popular bias-corrected bootstrapping…
Descriptors: Mediation Theory, Structural Equation Models, Monte Carlo Methods, Simulation
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Bishara, Anthony J.; Hittner, James B. – Educational and Psychological Measurement, 2015
It is more common for educational and psychological data to be nonnormal than to be approximately normal. This tendency may lead to bias and error in point estimates of the Pearson correlation coefficient. In a series of Monte Carlo simulations, the Pearson correlation was examined under conditions of normal and nonnormal data, and it was compared…
Descriptors: Research Methodology, Monte Carlo Methods, Correlation, Simulation
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Magis, David; De Boeck, Paul – Educational and Psychological Measurement, 2012
The identification of differential item functioning (DIF) is often performed by means of statistical approaches that consider the raw scores as proxies for the ability trait level. One of the most popular approaches, the Mantel-Haenszel (MH) method, belongs to this category. However, replacing the ability level by the simple raw score is a source…
Descriptors: Test Bias, Data, Error of Measurement, Raw Scores
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Tian, Wei; Cai, Li; Thissen, David; Xin, Tao – Educational and Psychological Measurement, 2013
In item response theory (IRT) modeling, the item parameter error covariance matrix plays a critical role in statistical inference procedures. When item parameters are estimated using the EM algorithm, the parameter error covariance matrix is not an automatic by-product of item calibration. Cai proposed the use of Supplemented EM algorithm for…
Descriptors: Item Response Theory, Computation, Matrices, Statistical Inference
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Paek, Insu; Wilson, Mark – Educational and Psychological Measurement, 2011
This study elaborates the Rasch differential item functioning (DIF) model formulation under the marginal maximum likelihood estimation context. Also, the Rasch DIF model performance was examined and compared with the Mantel-Haenszel (MH) procedure in small sample and short test length conditions through simulations. The theoretically known…
Descriptors: Test Bias, Test Length, Statistical Inference, Geometric Concepts
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Chan, Wai – Educational and Psychological Measurement, 2009
A typical question in multiple regression analysis is to determine if a set of predictors gives the same degree of predictor power in two different populations. Olkin and Finn (1995) proposed two asymptotic-based methods for testing the equality of two population squared multiple correlations, [rho][superscript 2][subscript 1] and…
Descriptors: Multiple Regression Analysis, Intervals, Correlation, Computation
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Barchard, Kimberly A.; Hakstian, A. Ralph – Educational and Psychological Measurement, 1997
The distinction between Type 1 and Type 12 sampling in connection with measurement data is discussed, and a method is presented for simulating data arising from Type 12 sampling. A Monte Carlo study is described that shows conditions under which precise confidence level control under Type 12 sampling is maintained. (SLD)
Descriptors: Models, Monte Carlo Methods, Sampling, Simulation