This study investigated how gifted children with dyslexia might be able to mask literacy problems and the role of possible compensatory mechanisms. The sample consisted of 121 Dutch primary school children that were divided over four groups (typically developing [TD] children, children with dyslexia, gifted children, gifted children with…

Considering the increasing importance of adaptive approaches in CALL systems, this study implemented a machine learning based student modeling middleware with Bayesian networks. The profiling approach of the student modeling system is based on Felder and Silverman's Learning Styles Model and Felder and Soloman's Index of Learning Styles…

This article provides GP-CORE norms for a South African university sample, which are compared to published data obtained from a United Kingdom university sample. The measure appears to be both reliable and valid for this multilingual and multicultural South African sample. The profiles of the psychological distress reported by white South African…

Propensity score analysis (PSA) has been used in a variety of settings, such as education, epidemiology, and sociology. Most typically, propensity score analysis has been implemented within the conventional frequentist perspective of statistics. This perspective, as is well known, does not account for uncertainty in either the parameters of the…

The present study compared the performance of five missing data treatment methods within a Cross-Classified Random Effects Model environment under various levels and patterns of missing data given a specified sample size. Prior research has shown the varying effect of missing data treatment options within the context of numerous statistical…

The aim of this study was to improve the criterion-related test score interpretation of a text-based assessment of scientific reasoning competencies in higher education by evaluating factors which systematically affect item difficulty. To provide evidence about the specific demands which test items of various difficulty make on pre-service…

Causal inference in observational studies typically requires making comparisons between groups that are dissimilar. For instance, researchers investigating the role of a prolonged duration of breastfeeding on child outcomes may be forced to make comparisons between women with substantially different characteristics on average. In the extreme there…

Many university leaders and faculty have the goal of promoting learning that connects across domains and prepares students with skills for their whole lives. However, as assessment emerges in higher education, many assessments focus on knowledge and skills that are specific to a single domain. Reworking assessment in higher education to focus on…

We present an intelligent tutoring system that lets students of Chinese learn words and grammatical constructions. It relies on a Bayesian, linguistically motivated cognitive model that represents the learner's knowledge. This model is dynamically updated given observations about the learner's behaviour in the exercises, and employed at runtime to…

In practice, the sum of the item scores is often used as a basis for comparing subjects. For items that have more than two ordered score categories, only the partial credit model (PCM) and special cases of this model imply that the subjects are stochastically ordered on the common latent variable. However, the PCM is very restrictive with respect…

Imputation methods are popular for the handling of missing data in psychology. The methods generally consist of predicting missing data based on observed data, yielding a complete data set that is amiable to standard statistical analyses. In the context of Bayesian factor analysis, this article compares imputation under an unrestricted…

This study replicates and extends a 2004 content analysis of three major higher education journals. The original study examined the methodological characteristics of all published research in these journals from 1996 to 2000, recommending that higher education programs adjust their graduate training to better match the heavily quantitative and…

Generalized fiducial inference is a powerful tool for many difficult problems. Based on an extension of R. A. Fisher's work, we used generalized fiducial inference for two extreme value problems and a multiple comparison procedure. The first extreme value problem is dealing with the generalized Pareto distribution. The generalized Pareto…

The purpose of this study is to compare the maximum likelihood (ML) and Bayesian estimation methods for polychoric correlation (PCC) under diverse conditions using a Monte Carlo simulation. Two new Bayesian estimates, maximum a posteriori (MAP) and expected a posteriori (EAP), are compared to ML, the classic solution, to estimate PCC. Different…

Errors in detecting randomness are often explained in terms of biases and misconceptions. We propose and provide evidence for an account that characterizes the contribution of the inherent statistical difficulty of the task. Our account is based on a Bayesian statistical analysis, focusing on the fact that a random process is a special case of…