Statistics anxiety is a reality among many students that may affect their academic and professional lives. Although there are instructional strategies proven to demonstrate advanced statistical concepts, a method to mitigate statistics anxiety in introductory courses is lacking. This research examines the effectiveness of three interventions used…

Conventional advice discourages controlling for postoutcome variables in regression analysis. By contrast, we show that controlling for commonly available postoutcome (i.e., future) values of the treatment variable can help detect, reduce, and even remove omitted variable bias (unobserved confounding). The premise is that the same unobserved…

The methodology of single-case experimental designs (SCED) has been expanding its efforts toward rigorous design tactics to address a variety of research questions related to intervention effectiveness. Effect size indicators appropriate to quantify the magnitude and the direction of interventions have been recommended and intensively studied for…

Single-Case Experimental Designs (SCEDs) have lately been recognized as a valuable alternative tolarge group studies. SCEDs form a great tool for the evaluation of treatment effectiveness in heterogeneous and low-incidence conditions, which are common in the field of communication disorders. Mediation analysis is indispensable in treatment…

We develop a structural after measurement (SAM) method for structural equation models (SEMs) that accommodates missing data. The results show that the proposed SAM missing data estimator outperforms conventional full information (FI) estimators in terms of convergence, bias, and root-mean-square-error in small-to-moderate samples or large samples…

Ordinary least squares (OLS) regression provides optimal linear predictions of a dependent variable, y, given an independent variable, x, but OLS regressions are not symmetric or reversible. In order to get optimal linear predictions of x given y, a separate OLS regression in that direction would be needed. This report provides a least squares…

In late November 2020, there was a flurry of media coverage of two companies' claims of 95% efficacy rates of newly developed COVID-19 vaccines, but information about the confidence interval was not reported. This paper presents a way of teaching the concept of hypothesis testing and the construction of confidence intervals using numbers announced…

Mathematics education forms the foundational framework for cultivating crucial skills and competencies vital for navigating an ever more intricate and numerical society. Despite its significance, students often encounter challenges and express reluctance towards certain mathematical topics, thus warranting a deeper exploration into the factors…

The concept of statistical sense provides an understanding of the goals of statistics education and helps to clarify the design of activities that promote the development of statistical literacy, reasoning and thinking. The new approaches to statistics in schools mean special attention must be paid to teacher training. This training should enable…

Undergraduate students outside Statistics major frequently take only one class of Basic Statistics or Introductory Statistics during their studies. As a consequence, many of them have little knowledge about advanced statistical methods, such as logistic regression, nonparametric procedures, and multivariate analysis, although they might need to…

This study aims to estimate the latent interaction effect in the CLPM model through a two-step multiple imputation analysis. The estimation of within x within and between x within latent interaction under the CLPM model framework is compared between the one-step Bayesian LMS method and the two-step multiple imputation analysis through a simulation…

This study explores the use of generative AI, specifically ChatGPT, in statistical data analysis and its implications for statistics education at universities of applied sciences. This paper begins with first discussing the future division of labor between humans and machines in the context of statistical data analyses following the widespread…

Social science theories often postulate systems of causal relationships among variables, which are commonly represented using directed acyclic graphs (DAGs). As non-parametric causal models, DAGs require no assumptions about the functional form of the hypothesized relationships. Nevertheless, to simplify empirical evaluation, researchers typically…

This case study analyzes the application of parametric and nonparametric statistical analyses with the multiple linear regression model in education and agricultural education research. The fields of education and agricultural education heavily rely on parametric analyses. We questioned the validity of the extensive use of such approaches after…

This study investigates the attitudes of undergraduate health science students towards statistics before and after completing an introductory statistics course. Given the critical role that attitudes play in students' academic performance, understanding how these attitudes evolve during the course is essential for improving teaching strategies and…