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…

There is a longstanding debate on whether the analysis of covariance (ANCOVA) or the change score approach is more appropriate when analyzing non-experimental longitudinal data. In this article, we use a structural modeling perspective to clarify that the ANCOVA approach is based on the assumption that all relevant covariates are measured (i.e.,…

Nonresponse bias is a widely prevalent problem for data on education. We develop a ten-step exemplar to guide nonresponse bias analysis (NRBA) in cross-sectional studies and apply these steps to the Early Childhood Longitudinal Study, Kindergarten Class of 2010-2011. A key step is the construction of indices of nonresponse bias based on proxy…

Q methodology is a unique, yet underutilized methodology designed specifically to scientifically study subjectivity. Q, as it is most often referred to, is an appropriate methodology whenever a researcher is interested in uncovering and describing the multiple divergent viewpoints on any topic. Such discovery of viewpoints provides insight into…

In this digital ITEMS module, Dr. Jeffrey Harring and Ms. Tessa Johnson introduce the linear mixed effects (LME) model as a flexible general framework for simultaneously modeling continuous repeated measures data with a scientifically defensible function that adequately summarizes both individual change as well as the average response. The module…

In mixed methods approaches, statistical models are used to identify "nested" cases for intensive, small-n investigation for a range of purposes, including notably the examination of causal mechanisms. This article shows that under a commonsense interpretation of causal effects, large-n models allow no reliable conclusions about effect…

This article develops new closed-form variance expressions for power analyses for commonly used difference-in-differences (DID) and comparative interrupted time series (CITS) panel data estimators. The main contribution is to incorporate variation in treatment timing into the analysis. The power formulas also account for other key design features…

Social scientists are often interested in estimating the marginal effects of a time-varying treatment on an end-of-study continuous outcome. With observational data, estimating these effects is complicated by the presence of time-varying confounders affected by prior treatments, which may lead to bias in conventional regression and matching…

We propose cross-disciplinary learning as a construct that can guide instruction and assessment in programs that feature sequential learning across multiple science disciplines. Crossdisciplinary learning combines insights from interdisciplinary learning, transfer, and resources frameworks and highlights the processes of resource activation,…

To fully appreciate genetics, one must understand the link between genotype (DNA sequence) and phenotype (observable characteristics). Advances in high-throughput genomic sequencing technologies and applications, so-called "-omics," have made genetic sequencing readily available across fields in biology from applications in…

Longitudinal models provide researchers with a framework for investigating key aspects of change over time, but rarely is "time" itself modeled as a focal parameter of interest. Rather than treat time as purely an index of measurement occasions, the proposed Time to Criterion (T2C) growth model allows for modeling individual variability…

This paper proposes the use of the tools of statistical meta-analysis as a method of conflict resolution with respect to experiments in cognitive linguistics. With the help of statistical meta-analysis, the effect size of similar experiments can be compared, a well-founded and robust synthesis of the experimental data can be achieved, and possible…

Randomized evaluations of educational technology produce log data as a bi-product: highly granular data on student and teacher usage. These datasets could shed light on causal mechanisms, effect heterogeneity, or optimal use. However, there are methodological challenges: implementation is not randomized and is only defined for the treatment group,…

Zinc oxide is a common additive in food, cosmetics, and medicines. Here, we describe the validation of methods to analyze two pharmaceutical samples containing ZnO--talc powder and dermal ointment--by ethylenediaminetetraacetic acid (EDTA) titration. The methods developed were implemented as a practical class to students of a Quantitative Analysis…

Aim: The main aim of this commentary was to connect the insights from the contributions of the special issue on the intersection between depth and the regulation of strategy use. The seven contributions in this special issue stem from three perspectives: self-regulated learning (SRL), model of domain learning (MDL), or the student approaches to…