Inferentialism has emerged as a valuable theoretical resource in mathematics education. As a theory of meaning about the use and content of concepts, it offers a fresh perspective on traditional epistemological and linguistic questions in the field. Despite its emergence, important inferentialist ideas still need to be operationalized. In this…

Sensitivity analyses can inform evidence-based education policy by quantifying the hypothetical conditions necessary to change an inference. Perhaps the most prevalent index used for sensitivity analyses is Oster's (2019) Coefficient of Proportionality (COP). Oster's COP leverages changes in estimated effects and R[superscript 2] when observed…

As adults, we do not expect ignorant agents to behave randomly or always get things wrong. Instead, we expect them to act reasonably, guided by past experiences. We test whether 4-to-6-year-olds share this intuition and use it to infer others' knowledge, or whether they rely on a simple "ignorance = error" heuristic identified in past…

All scientists must cope with variability in data to make inferences about the world. However, in observation-based geology, how scientists cope with variability is particularly consequential because it determines what become data in the first place, with observations that are deemed "too variable" potentially being ignored or minimized.…

When planning a cluster randomized trial, evaluators often have access to an enumerated cohort representing the target population of clusters. Practicalities of conducting the trial, such as the need to oversample clusters with certain characteristics in order to improve trial economy or support inferences about subgroups of clusters, may preclude…

This primer systematizes the emerging literature on causal inference using deep neural networks under the potential outcomes framework. It provides an intuitive introduction to building and optimizing custom deep learning models and shows how to adapt them to estimate/predict heterogeneous treatment effects. It also discusses ongoing work to…

Matching methods are widely used in educational research to estimate causal effects when randomization is not feasible. These techniques pair treated units (such as schools receiving an intervention) with similar control units based on observable characteristics. However, current statistical inference procedures for these methods can produce…

In the last years special "ovals" appear increasingly often in diagrams and applets for discussing crucial items of statistical inference (when dealing with confidence intervals for an unknown probability p; approximation of the binomial distribution by the normal distribution; especially in German literature, see e.g. [Meyer,…

Background: Double/debiased machine learning (DML) methods have been proposed to overcome the regularization bias from the naive approach of ML methods (Chernozhukov et al., 2018). DML methods use a partialling-out approach which removes the effect of confounders from both the treatment and outcome and then regresses the residualized outcome on…

This note is concerned with the study of temporal development in several indices reflecting clustering effects in multilevel designs that are frequently utilized in educational and behavioral research. A latent variable method-based approach is outlined, which can be used to point and interval estimate the growth or decline in important functions…

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…

Recent progress made in conversational AI lays emphasis on the need for development of language models that possess solid logical reasoning skills and further extrapolated capabilities. An examination into this phenomenon investigates how well the Capybara dataset can improve one's ability to reason using language-based systems. Multiple…

This theoretical, methodological, and empirical networking study investigated the potential of inferentialism to contribute to the study of meaning and collective argumentation in mathematics. I carefully attended to my worldview and explicated my philosophical process for identifying inferentialism as my theory of choice. I then drew on Prediger…

Under the noisy-channel framework of language comprehension, comprehenders infer the speaker's intended meaning by integrating the perceived utterance with their knowledge of the language, the world, and the kinds of errors that can occur in communication. Previous research has shown that, when sentences are improbable under the meaning prior…

Given decision-makers often prioritize causal research that identifies the impact of treatments on the people they serve, a key question in education research is, "Does it work?". Today, however, researchers are paying increasing attention to successive questions that are equally important from a practical standpoint--not only does it…