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Some of the material in is restricted to members of the community. By logging in, you may be able to gain additional access to certain collections or items. If you have questions about access or logging in, please use the form on the Contact Page.
Value-Added Models (VAMs) require consistent longitudinal data that includes student test scores coming from sequential years. However, longitudinal data is usually incomplete for several reasons, including year-to-year changes in...
Coefficient Omega was introduced by McDonald (1978) as a reliability coefficient of composite scores for the congeneric model. Interval estimation (Neyman, 1937) on coefficient Omega provides a range of plausible values which is likely...
The Comparison of Standard Error Methods in the Marginal Maximum Likelihood Estimation of the Two-Parameter Logistic Item Response Model When the Distribution of the Latent Trait Is Nonnormal
A Monte Carlo simulation study was conducted to investigate the accuracy of several item parameter standard error (SE) estimation methods in item response theory (IRT) when the marginal maximum likelihood (MML) estimation method was used...
In structural equation modeling (SEM), researchers use the model chi-square statistic and model-fit indexes to evaluate model-data fit. Root mean square error of approximation (RMSEA), comparative fit index (CFI), and Tucker-Lewis index ...
Dimensions of Undergraduate Research: Research Engagement, Researcher Role-Identity Salience, Awareness, Interest, and Career Attitudes Among Diverse Undergraduate Majors
This study investigated dimensions of undergraduate research among diverse academic majors at a large Southeastern public research university. Undergraduates' engagement in research, awareness of research opportunities, interests in...
This study evaluates a CART-based value-added model and compares it with commonly used multiple regression, hierarchical linear model, and student growth percentiles models. The comparisons are done in terms of prediction accuracy, ...
In factor analysis, determining the number of factors underlying measurement indicators is important. An incorrect decision on the number of factors may mislead practitioners in terms of estimating parameters in factor analysis, ...
Measurement invariance analysis is important when test scores are used to make a group-wise comparison. Multiple-group IRT modeling is one of the commonly used methods for measurement invariance examination. One essential step in the...
Checking that models adequately present data is an essential component of applied statistical inference. Psychometricans increasingly use complex models to analyze test takers responses. The appeal of using complex cognitive diagnostic...
With the latest developments in computer based testing, implementing equating techniques that incorporate automated essay scoring systems such as e-rater are encouraging potential new directions for equating mixed-format tests of writing...
Mixture IRT modeling allows the detection of latent classes and different item parameter profile patterns across latent classes. In Rasch mixture model estimation, latent classes are assumed to follow a normal distribution with means...
The Impact of Unbalanced Designs on the Performance of Parametric and Nonparametric DIF Procedures: A Comparison of Mantel Haenszel, Logistic Regression, SIBTEST, and IRTLR Procedures
The current study examined the impact of unbalanced sample sizes between focal and reference groups on the Type I error rates and DIF detection rates (power) of five DIF procedures (MH, LR, general IRTLR, IRTLR-b, and SIBTEST). Five...
Educators use various statistical techniques to explain relationships between latent and observable variables. One way to model these relationships is to use Bayesian networks as a scoring model. However, adjusting the conditional...
Despite years of attempted mathematics education reform, there is little evidence that prospective teachers entering the profession are significantly better positioned than their in-service peers to implement reform-based mathematics...
The nonequivalent-groups anchor-test (NEAT) data-collection design is commonly used in large-scale assessments. Under this design, different test groups take different test forms. Each test form has its own unique items and all test...
In the current study, I intended to simulate single case research design (SCRD) data to investigate the impact of the presence of autocorrelation on analysis of SCRD for Bayesian method under a variety of simulation conditions. The...
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