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Equating and scaling in the context of small sample exams, such as credentialing exams for highly specialized professions, has received increased attention in recent research. Investigators have proposed a variety of both classical and Rasch-based approaches to the problem. This study attempts to extend past research by (1) directly comparing classical and Rasch techniques of equating exam scores when sample sizes are small ( N≤ 100 per exam form) and (2) attempting to pool multiple forms’ worth of data to improve estimation in the Rasch framework. We simulated multiple years of a small-sample exam program by resampling from a larger certification exam program’s real data. Results showed that combining multiple administrations’ worth of data via the Rasch model can lead to more accurate equating compared to classical methods designed to work well in small samples. WINSTEPS-based Rasch methods that used multiple exam forms’ data worked better than Bayesian Markov Chain Monte Carlo methods, as the prior distribution used to estimate the item difficulty parameters biased predicted scores when there were difficulty differences between exam forms.

Bi-factor confirmatory factor models have been influential in research on cognitive abilities because they often better fit the data than correlated factors and higher-order models. They also instantiate a perspective that differs from that offered by other models. Motivated by previous work that hypothesized an inherent statistical bias of fit indices favoring the bi-factor model, we compared the fit of correlated factors, higher-order, and bi-factor models via Monte Carlo methods. When data were sampled from a true bi-factor structure, each of the approximate fit indices was more likely than not to identify the bi-factor solution as the best fitting. When samples were selected from a true multiple correlated factors structure, approximate fit indices were more likely overall to identify the correlated factors solution as the best fitting. In contrast, when samples were generated from a true higher-order structure, approximate fit indices tended to identify the bi-factor solution as best fitting. There was extensive overlap of fit values across the models regardless of true structure. Although one model may fit a given dataset best relative to the other models, each of the models tended to fit the data well in absolute terms. Given this variability, models must also be judged on substantive and conceptual grounds.

Common estimators of interrater reliability include Pearson product-moment correlation coefficients, Spearman rank-order correlations, and the generalizability coefficient. The purpose of this study was to examine the accuracy of estimators of interrater reliability when varying the true reliability, number of scale categories, and number of essays rated. This research used Monte Carlo methods to draw samples from known population models to examine the accuracy of select estimators of interrater reliability between two raters. In addition to the estimates shown above, we included the polychoric correlation coefficient based on its alignment with the context in which student language assessments are rated. Although each estimator produced an estimate close to the population parameter, polychoric correlations provided the closest estimates with mean and median bias equal to 0.00 (SD = 0.05) across all conditions. The use of Pearson product-moment and Spearman rank-order correlation coefficients might result...

The purpose is to explore factors associated with community college student persistence in academic program areas through the modeling of student profiles (i.e., classes) using selected variables from the Education Longitudinal Study of 2002 (ELS:2002) conducted by the National Center for Education Statistics that has followed a national sample of students from the tenth grade with follow-ups 2 and 4 years later. To this end, we used multiple-group latent class analysis in order to identify underlying classes of students and to evaluate the equivalence of the latent class solution across those students who persisted and those who did not. A four-class solution was identified that was determined to be invariant across student groups although the proportions of persisters and nonpersisters were different across classes. Using the final class solution for persisting students, we found that class membership was moderately associated with which Career Cluster students pursued.

The purpose of the current study was to compare the use of virtual discussion boards in various educational settings in the United States and Costa Rica. Participants included professors of education, in-service and pre-service teachers in the United States and Costa Rica where a survey was used that included demographic, knowledge, attitude, and behavioral questions regarding the use of virtual discussion boards. Results indicated that sixty-two percent of the participants used discussion boards in an educational setting. Instructors reported creating discussion board prompts that were constructivist in nature, and responses were frequently assessed for reflection, application, or collaboration. Findings show implications for educators in Costa Rica the United States due to the extensive rural landscape that perpetuates a need for alternative forms of communication and distance learning as well as to provide a comparison to how this technology is used in United States educational settings.