The nonequivalent groups with anchor test (NEAT) design is widely used in test equating. Under this design, two groups of examinees are administered different test forms with each test form containing a subset of common items. Because test takers from different groups are assigned only one test form, missing score data emerge by design rendering…

Common sense is a dynamic concept and it is natural that our (statistical) common sense lags behind the development of statistical science. What is not so easy to understand is why common sense lags behind as much as it does. We conduct a survey among Japanese students and provide examples and tentative explanations of a number of statistical…

There are two schools of thought in statistical analysis, frequentist, and Bayesian. Though the two approaches produce similar estimations and predictions in large-sample studies, their interpretations are different. Bland Altman analysis is a statistical method that is widely used for comparing two methods of measurement. It was originally…

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…

The analysis of covariance (ANCOVA) is a useful statistical procedure that incorporates covariate features into the adjustment of treatment effects. The consequences of omitted prognostic covariates on the statistical inferences of ANCOVA are well documented in the literature. However, the corresponding influence on sample-size calculations for…

The purpose of this article is to provide educators with resources to help students establish a deeper understanding of the application and role of statistical analysis within the design and innovation process. Quantitative analyses are often taught and applied through design activities, especially during testing or experimenting phases of design.…

Stochastic independence is not an easy notion. Because it is part of the probability structure, it can have some surprising non-properties, which is beneficial for teachers and students to see illustrated. Neither is the relationship to causal independence an easy one.

Two approaches for the statistical analysis of social network generation are widely used; the tie-oriented exponential random graph model (ERGM) and the stochastic actor-oriented model (SAOM) or Siena model. While the choice for either model by empirical researchers often seems arbitrary, there are important differences between these models that…

Propensity score analysis is a statistical method that balances pre-existing differences across treatment conditions achieving a similar condition as randomization and thus, allowing the estimation of causal effects in non-randomized experimental designs. The four stages in propensity score analysis are (1) propensity score estimation, (2)…

The natural experiment proposed in this article extracts three stories from boxes of "100 paper clips". The activity requires students to apply three lessons from inferential statistics, starting with a hypothesis test and including confidence intervals as well as tolerance intervals.

The contrary of stochastic independence splits up into two cases: pairs of events being favourable or being unfavourable. Examples show that both notions have quite unexpected properties, some of them being opposite to intuition. For example, transitivity does not hold. Stochastic dependence is also useful to explain cases of Simpson's paradox.

Network meta-analysis (NMA) is gaining popularity for comparing multiple treatments in a single analysis. Generalized linear mixed models provide a unifying framework for NMA, allow us to analyze datasets with dichotomous, continuous or count endpoints, and take into account multiarm trials, potential heterogeneity between trials and network…

Across all social science disciplines, but in particular public administration, there is a shared concern about the costs of using traditional random samples to generate data, and its impact on researchers' ability to engage in "quality" research. As a result of these costs, more academics, practitioners, and students are turning to…

"p" values are commonly reported in quantitative research, but are often misunderstood and misinterpreted by research consumers. Our aim in this article is to provide special educators with guidance for appropriately interpreting "p" values, with the broader goal of improving research consumers' understanding and interpretation…

In order to investigate causality in situations where random assignment is not possible, propensity scores can be used in regression adjustment, stratification, inverse-probability treatment weighting, or matching. The basic concepts behind propensity scores have been extensively described. When data are longitudinal or missing, the estimation and…