We consider the cascading effects of researcher decisions throughout the process of quantifying racially polarized voting (RPV). We contrast three methods of estimating precinct racial composition, Bayesian Improved Surname Geocoding (BISG), fully Bayesian BISG, and Citizen Voting Age Population (CVAP), and two algorithms for performing ecological…

Sensitivity analysis, a statistical method crucial for validating inferences across disciplines, quantifies the conditions that could alter conclusions (Razavi et al., 2021). One line of work is rooted in linear models and foregrounds the sensitivity of inferences to the strength of omitted variables (Cinelli & Hazlett, 2019; Frank, 2000). A…

The import or force of the result of a statistical test has long been portrayed as consistent with deductive reasoning. The simplest form of deductive argument has a first premise with conditional form, such as p[right arrow]q, which means that "if p is true, then q must be true." Given the first premise, one can either affirm or deny…

We consider a class of multiple-group individually-randomized group trials (IRGTs) that introduces a (partially) cross-classified structure in the treatment condition (only). The novel feature of this design is that the nature of the treatment induces a clustering structure that involves two or more non-nested groups among individuals in the…

A simulation study was conducted to examine the efficacy of nine frequently-used HOV tests, including Levene's tests with squared residuals and with absolute residuals, Brown and Forsythe (BF) test, Bootstrap BF test, O'Brien test, Z-variance test, Box-Scheffé (BS) test, Bartlett test, and Pseudo jackknife test under comprehensive simulation…

Purpose: Multisite mediation studies are a cornerstone in mapping out developmental processes because they probe the mechanisms of a treatment while creating key opportunities to learn from and about variation in those mechanisms across sites. Despite the prevalence of multisite studies, a significant gap in the literature is how to plan such…

The estimation of RxC ecological inference contingency tables from aggregate data is one of the most salient and challenging problems in the field of quantitative social sciences, with major solutions proposed from both the ecological regression and the mathematical programming frameworks. In recent decades, there has been a drive to find…

Providing a rich context has become a sine qua non of principled teaching of applied statistical thinking. With increasing opportunities to access secondary data, there should be increasing opportunity to work with rich context. We review the contextual information provided in 41 data sets suitable for introductory tertiary statistics teaching,…

Several marginal effect size (ES) statistics suitable for quantifying the magnitude of differential item functioning (DIF) have been proposed in the area of item response theory; for instance, the Differential Functioning of Items and Tests (DFIT) statistics, signed and unsigned item difference in the sample statistics (SIDS, UIDS, NSIDS, and…

Many studies demonstrate that detecting statistical regularities in linguistic input plays a key role in language acquisition. Yet, it is unclear to what extent statistical learning is involved in more naturalistic settings, when young children have to acquire meaningful grammatical elements. In the present study, we address these points, by…

In causal mediation studies that decompose an average treatment effect into indirect and direct effects, examples of post-treatment confounding are abundant. In the presence of treatment-by-mediator interactions, past research has generally considered it infeasible to adjust for a post-treatment confounder of the mediator-outcome relationship due…

Beginning with debates about the effects of smoking on lung cancer, sensitivity analyses characterizing the hypothetical unobserved conditions that can alter statistical inferences have had profound impacts on public policy. One of the most ascendant techniques for sensitivity analysis is Oster's (2019) coefficient of proportionality, which…

Combining observational and experimental data for causal inference can improve treatment effect estimation. However, many observational data sets cannot be released due to data privacy considerations, so one researcher may not have access to both experimental and observational data. Nonetheless, a small amount of risk of disclosing sensitive…

For randomized trials that use text as an outcome, traditional approaches for assessing treatment impact require that each document first be manually coded for constructs of interest by trained human raters. This process, the current standard, is both time-consuming and limiting: even the largest human coding efforts are typically constrained to…

When analyzing treatment effects on test scores, researchers face many choices and competing guidance for scoring tests and modeling results. This study examines the impact of scoring choices through simulation and an empirical application. Results show that estimates from multiple methods applied to the same data will vary because two-step models…