To date, only frequentist model-selection methods have been studied with mixed-format data in the context of IRT model-selection, and it is unknown how popular Bayesian model-selection methods such as DIC, WAIC, and LOO perform. In this study, we present the results of a comprehensive simulation study that compared the performances of eight…

A mixture of logit models is proposed that discriminates between responses to rating questions that are affected by a tendency to prefer middle or extremes of the scale regardless of the content of the item (response styles) and purely content-driven preferences. Explanatory variables are used to characterize the content-driven way of answering as…

International students in the United States face unique challenges of adjusting to college life in a foreign country due to additional stressors of language, differing academic and study habits, and being socially isolated from their home environment. Therefore, the purpose of this study was to examine the levels of acculturative stress of…

There exists a variety of interval estimators for the overall treatment effect in a random-effects meta-analysis. A recent literature review summarizing existing methods suggested that in most situations, the Hartung-Knapp/Sidik-Jonkman (HKSJ) method was preferable. However, a quantitative comparison of those methods in a common simulation study…

The estimation of high-dimensional latent regression item response theory (IRT) models is difficult because of the need to approximate integrals in the likelihood function. Proposed solutions in the literature include using stochastic approximations, adaptive quadrature, and Laplace approximations. We propose using a second-order Laplace…

Summary statistics and data visualizations are often used to explore data and draw preliminary conclusions. Although valuable, these tools do not always reveal the underlying patterns and trends in the data and can sometimes be misleading. We describe an approach for teaching the need for more advanced statistical analysis using multiple linear…

As we take advantage of new technologies that allow us to streamline the coding process of large qualitative datasets, we must consider whether human cognitive bias may introduce statistical bias in the process. Our research group analyzes large sets of student responses by developing computer models that are trained using human-coded responses…

Objective: Our master's program in biostatistics requires a qualifying examination (QE). A curriculum review led us to question whether to replace a closed-book format with an open-book one. Our goal was to improve the QE. Methods: This is a case study and commentary, where we describe the evolution of the QE, both in its goals and its content.…

Commonly used machine learning applications seem to relate to big data. This article provides a gentle review of machine learning and shows why machine learning can be applied to small data too. An example of applying machine learning to screen irregularity reports is presented. In the example, the support vector machine and multinomial naïve…

This study explores the performance of classical methods for detecting publication bias--namely, Egger's regression test, Funnel Plot test, Begg's Rank Correlation and Trim and Fill method--in meta-analysis of studies that report multiple effects. Publication bias, outcome reporting bias, and a combination of these were generated. Egger's…

The statistical problem-solving process is key to the statistics curriculum at the school level, post-secondary, and in statistical practice. The process has four main components: formulate questions, collect data, analyze data, and interpret results. The Pre-K-12 Guidelines for Assessment and Instruction in Statistics Education (GAISE) emphasizes…

Compared to their written counterpart, oral assessments provide a wealth of information about student understanding. Instead of deciphering a static response, oral assessments provide instructors the opportunity to probe student explanations, obtaining a more complete picture of their understanding. Moreover, students explaining their conceptual…

Understanding the abstract principles of statistical experimental design can challenge undergraduate students, especially when learned in a lecture setting. This article presents a concrete and easily replicated example of experimental design principles in action through a hands-on learning activity for students enrolled in an experimental design…

Psychology research often focuses on interactions, and this has deep implications for inference from non-representative samples. For the goal of estimating average treatment effects, we propose to fit a model allowing treatment to interact with background variables and then average over the distribution of these variables in the population. This…

Markov chain Monte Carlo is a key computational tool in Bayesian statistics, but it can be challenging to monitor the convergence of an iterative stochastic algorithm. In this paper we show that the convergence diagnostic [R-hat] of Gelman and Rubin (1992) has serious flaws. Traditional [R-hat] will fail to correctly diagnose convergence failures…