"The Times They Are a-Changin'" says the old Bob Dylan song. But it is not just the times that are a-changin'. For statistical literacy, the very earth is moving under our feet (apologies to Carole King). The seismic forces are (i) new forms of communication and discourse and (ii) new forms of data, data display and human interaction…

An introduction to digital stochastic simulations for modeling a variety of physical and chemical processes is presented. Despite the importance of stochastic simulations in chemistry, the prevalence of turn-key software solutions can impose a layer of abstraction between the user and the underlying approach obscuring the methodology being…

The quantitative strand of the Young People's Attitudes towards Religious Diversity Project, conducted by the Warwick Religions and Education Research Unit, set out to capture data from over 2000 students living in each of the four nations of the United Kingdom (England, Northern Ireland, Scotland and Wales) and from London as a special case.…

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…

There are many well-known or new methods to adjust statistical tests for multiplicity. This article provides an illustration helping lecturers or consultants to remember the differences of three important multiplicity adjustment methods and to explain them to non-statisticians.

A strategy to facilitate understanding of spatial randomness is described, using student activities developed in sequence: looking at spatial patterns, simulating approximate spatial randomness using a grid of equally-likely squares, using binomial probabilities for approximations and predictions and then comparing with given Poisson…

Context is at the core of any statistical investigation, yet many statistics tasks barely require students to go beyond superficial consideration of the contexts the tasks are situated in. In this article, I discuss a framework for evaluating the level of interaction with context a task requires of students and how to modify tasks to increase the…

Critical scholarship in the social sciences usually focuses on the social sphere outside of the academy. However, for higher education researchers, critical research is often undertaken within and for the academy, which in turn may endanger one's career if the dominant power structures are threatened. How might we enact and perform critical…

Functional Data Analysis (FDA) has attracted substantial attention for the last two decades. Within FDA, classifying curves into two or more categories is consistently of interest to scientists, but multi-class prediction within FDA is challenged in that most classification tools have been limited to binary response applications. The functional…

Classical statistical tests are used in many disciplines such as education and psychology. Such tests are based on certain assumptions (e.g., normality and homoscedasticity) that are must to be met in order to produce accurate results. Violation of such assumptions is a common problem researchers encounter, particularly when analyzing real data.…

In recent years, organizations have shown increasing willingness to adopt agile scrum method (ASM) to meet the demand of modern-day software development; that is to deliver faster and better software, with a built-in flexibility to absorb last minute changes in requirements. This research study was undertaken to uncover the underlying factors that…

The analysis of variance (ANOVA) F test is a commonly used method to test the mean equality among two or more populations. A critical assumption of ANOVA is homogeneity of variance (HOV), that is, the compared groups have equal variances. Although it is encouraged to test HOV as part of the regular ANOVA procedure, the efficacy of the initial HOV…

As states consider non-academic measures of school quality for their accountability plans under the Every Student Succeeds Act, few are choosing to focus on the school climate in which students learn. That's not a surprise, given concerns about the validity and usefulness of existing climate surveys. However, the authors argue, a recent study…

There has been increasing interest in exploring heterogeneous treatment effects using machine learning (ML) methods such as causal forests, Bayesian additive regression trees, and targeted maximum likelihood estimation. However, there is little work on applying these methods to estimate treatment effects in latent classes defined by…

The use of advanced quantitative methods within mixed methods research has been investigated in a limited capacity. In particular, hierarchical linear models are a popular approach to account for multilevel data, such as students within schools, but its use and value as the quantitative strand in a mixed methods study remains unknown. This article…