A correlation coefficient taking positive values is introduced. It is more easily understood than other correlation measures especially in social science contexts. Estimation issues are addressed. A data application is given.

We provide tools for identification and exploration of data with very large variability having power law tails. Such data describe extreme features of processes such as fire losses, flood, drought, financial gain/loss, hurricanes, population of cities, among others. Prediction and quantification of extreme events are at the forefront of the…

This tutorial uses publicly available data from drug makers and the Food and Drug Administration to guide learners to estimate the confidence intervals of COVID-19 vaccine efficacy rates with a Bayesian framework. Under the classical approach, there is no probability associated with a parameter, and the meaning of confidence intervals can be…

Large-scale analyses are a powerful and increasingly common tool for investigating a range of public health and social concerns (Pienta, O'Rourke, & Franks, 2011). This series will provide a primer on large-scale secondary analysis in school psychology, with this article focusing on considerations for researchers interested in applying and…

This paper provides introductory statistics instructors with the capacity to use engaging National Basketball Association (NBA) data within a web application to either strengthen students' understanding or introduce the concept of variance and one-way analysis of variance. Using engaging data within the classroom provides context to data that…

A randomized experiment that measures the impact of a social policy in a sample of the population reveals whether the policy will work on average with universal application. An experiment that includes only the subset of the population that volunteers for the intervention generates narrower "proof-of-concept" evidence of whether the…

This article describes five elementary statistics projects involving the COVID-19 data made available to the public in csv files by the Centers for Disease Control and Prevention. The first project examined data available at the beginning of the COVID surge in New York City in spring, 2020, and used the correlation coefficient to estimate the…

Policymakers and institutional leaders in higher education too often make decisions based on descriptive data analyses or even anecdote when better analysis options could produce more nuanced and more valuable results. Employing the setting of higher education program evaluation at a midwestern regional public university, for this study we…

Monte Carlo simulation is a useful tool for researchers to estimated accuracy of a statistical model. It is usually used for investigating parameter estimation procedure or violation of assumption for some given conditions. To run a simulation either the paid software or open source but free program such as R is need to be used. For that,…

A potential negative consequence of high-stakes testing is inappropriate test behaviour involving individuals and/or institutions. Inappropriate test behaviour and test collusion can result in aberrant response patterns and anomalous test scores and invalidate the intended interpretation and use of test results. A variety of statistical techniques…

Despite the centrality of data analysis to the discipline, sociology departments are currently falling short of teaching both undergraduate and graduate students crucial computing and statistical software skills. We argue that sociology instructors must intentionally and explicitly teach computing skills alongside statistical concepts to prepare…

The growing fiscal cost of K-12 teacher pension plans and pension-induced labor market distortions have led to calls for teacher pension reforms. Dynamic structural econometric models are a useful way to analyze the fiscal and staffing consequences of current and alternative retirement plans. This article lays out the benefits of the structural…

This paper aims to unsettle assumptions of generalizability and representativeness in quantitative research using queer framings and positionalities. We argue that generalizability and representativeness are tools of supremacist dominance that reinforce harmful and essentialist categories of identities for the false purpose of statistical…

Social science datasets usually have missing cases, and missing values. All such missing data has the potential to bias future research findings. However, many research reports ignore the issue of missing data, only consider some aspects of it, or do not report how it is handled. This paper rehearses the damage caused by missing data. The paper…

Examples of the impact of statistical theory on assessment practice are provided from the perspective of a statistician trained in theoretical statistics who began to work on assessments. Goodness of fit of item-response models is examined in terms of restricted likelihood-ratio tests and generalized residuals. Minimum discriminant information…