In this article, we consider how to make data more meaningful to students through the choice of data and the activities we use them in drawing upon students lived experiences more in the teaching of statistics and data science courses. In translating scholarship around culturally relevant pedagogy from the fields of education and mathematics…

Data reported from memory can be unreliable. A simple activity lets students experience this firsthand.

Missing data are a pervasive problem in data analysis. Three common methods for addressing the problem are (a) complete-case analysis, where only units that are complete on the variables in an analysis are included; (b) weighting, where the complete cases are weighted by the inverse of an estimate of the probability of being complete; and (c)…

Grouped and right-censored (GRC) counts have been used in a wide range of attitudinal and behavioural surveys yet they cannot be readily analyzed or assessed by conventional statistical models. This study develops a unified regression framework for the design and optimality of GRC counts in surveys. To process infinitely many grouping schemes for…

Weighted least squares (WLS), weighted least squares mean-and-variance-adjusted (WLSMV), unweighted least squares mean-and-variance-adjusted (ULSMV), maximum likelihood (ML), robust maximum likelihood (MLR) and Bayesian estimation methods were compared in mixed item response type data via Monte Carlo simulation. The percentage of polytomous items,…

The use of real contexts and history in the teaching of statistical principles holds much attraction in the classroom. This is especially so when the examples used are in situations that are quite out of the ordinary but can lead to further investigation by the students. Previous instances of these have been used with great effect with sporting…

In this paper, the objective is to discuss some aspects of statistical literacy in the context of conducting an educative project of opinion survey in the perspective of the teaching-learning approach of the Nossa Escola Pesquisa Sua Opinião (Nepso) Program or "Our School Survey Your Opinion," in English. This program, with centers…

Biostatisticians apply statistical methods to solve problems in the biological sciences. Successful practioners of biostatistics have advanced technical knowledge, are skilled communicators, and can seamlesslessly integrate with interdisciplinary scientific teams. Despite the breadth of skills required for success in this field, most biostatistics…

This paper addresses the question of whether peer collaboration affects students' performance of regression modelling tasks, an experimental study consisting of a test was conducted in a computing laboratory. Collaborating groups of students were randomly assigned to one of three experimental conditions: pre-task discussion (i.e., group members…

We developed and tested strategies for using spatial representations to help students understand core probability concepts, including the multiplication rule for computing a joint probability from a marginal and conditional probability, interpreting an odds value as the ratio of two probabilities, and Bayesian inference. The general goal of these…

A number of machine learning methods can be employed in the prediction of suicide attempts. However, many models do not predict new cases well in cases with unbalanced data. The present study improved prediction of suicide attempts via the use of a generative adversarial network.

Interoception, the body's perception of its own internal states, is thought to be altered in autism, though results of empirical studies have been inconsistent. The current study systematically reviewed and meta-analyzed the extant literature comparing interoceptive outcomes between autistic (AUT) and neurotypical (NT) individuals, determining…

A common problem in the analysis of multiple data sources, including individual participant data meta-analysis (IPD-MA), is the misclassification of binary variables. Misclassification may lead to biased estimators of model parameters, even when the misclassification is entirely random. We aimed to develop statistical methods that facilitate…

In order to detect a wide range of aberrant behaviors, it can be useful to incorporate information beyond the dichotomous item scores. In this paper, we extend the l[subscript z] and l*[subscript z] person-fit statistics so that unusual behavior in item scores and unusual behavior in item distractors can be used as indicators of aberrance. Through…

According to Bayesian/predictive coding models of autism, autistic individuals may have difficulties learning probabilistic cue-outcome associations, but empirical evidence has been mixed. The target cues used in previous studies were often straightforward and might not reflect real-life learning of such associations which requires learners to…