Partially specified correlation matrices (not to be confused with matrices with missing data or EM-correlation matrices) can appear in research settings such as integrative data analyses, quantitative systematic reviews or whenever the study design only allows for the collection of certain variables. Although approaches to fill in these missing…

Critical quantitative methods provide opportunities for Queer Theory to challenge, re-define, and re-claim the historically privileged research tradition. In this paper, I begin by summarizing the various binaries that oppress research and individuality. I then engage with Queer Theory and my own intersectional positionality to propose a nonbinary…

In this essay, we focus on methodological issues that reviewers and editors commonly encounter when evaluating empirical articles in scholarship of teaching and learning in management education. We organize our discussion around three stages--design, analysis, and reporting. The essay identifies which types of issues are likely to receive…

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…

Assessing sensitivity to unmeasured confounding is an important step in observational studies, which typically estimate effects under the assumption that all confounders are measured. In this paper, we develop a sensitivity analysis framework for balancing weights estimators, an increasingly popular approach that solves an optimization problem to…

Meta-analysis is a widely used methodology to combine evidence from different sources examining a common research phenomenon, to obtain a quantitative summary of the studied phenomenon. In the medical field, multiple studies investigate the effectiveness of new treatments and meta-analysis is largely performed to generate the summary (average)…

Multivariate analysis of variance (MANOVA) is a statistical procedure commonly used in fields such as education and psychology. However, MANOVA's popularity may actually be for the wrong reasons. The large majority of published research using MANOVA focus on univariate research questions rather than on the multivariate questions that MANOVA is…

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…

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…

It is widely believed that regression models for binary responses are problematic if we want to compare estimated coefficients from models for different groups or with different explanatory variables. This concern has two forms. The first arises if the binary model is treated as an estimate of a model for an unobserved continuous response and the…

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…

This paper presents an application of the Kalman filter in signal processing in instrumentation systems when the conditions of the environment generate a large amount of interference for the acquisition of signals from measurement systems. The unwanted interferences make important use of the instrumentation system resources and do not represent…

The COVID epidemic has provided an excellent example of the need to call on a wide variety of statistical tools to address a global problem, and can give students insights into some of the dimensions of data science. Here, we describe some of the characteristics of data that students encounter as citizens. We set out some teaching ideas, which…

In systematic reviews, meta-analyses are routinely applied to summarize the results of the relevant studies for a specific research question. If one can assume that in all studies the same true effect is estimated, the application of a meta-analysis with common effect (commonly referred to as fixed-effect meta-analysis) is adequate. If…

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,…