BASIE is a framework for interpreting impact estimates from evaluations. It is an alternative to null hypothesis significance testing. This guide walks researchers through the key steps of applying BASIE, including selecting prior evidence, reporting impact estimates, interpreting impact estimates, and conducting sensitivity analyses. The guide…

This paper addresses a common challenge with computational cognitive models: identifying parameter values that are both theoretically plausible and generate predictions that match well with empirical data. While computational models can offer deep explanations of cognition, they are computationally complex and often out of reach of traditional…

Recent years have seen increasing interest in incorporating resampling methods into introductory statistics courses and the high school mathematics curriculum. While the use of permutation tests for data from experiments is a step forward, the use of simple bootstrap methods for sampling situations is more problematical. This article demonstrates…

Respondent-driven sampling (RDS), a link-tracing sampling and inference method for studying hard-to-reach populations, has been shown to produce asymptotically unbiased population estimates when its assumptions are satisfied. However, some of the assumptions are prohibitively difficult to reach in the field, and the violation of a crucial…

Hinterecker, Knauff, and Johnson-Laird (2016) compared the adequacy of the probabilistic new paradigm in reasoning with the recent revision of mental models theory (MMT) for explaining a novel class of inferences containing the modal term "possibly." For example, "the door is closed or the window is open or both," therefore,…

Machine learning is a popular topic in data analysis and modeling. Many different machine learning algorithms have been developed and implemented in a variety of programming languages over the past 20 years. In this article, we first provide an overview of machine learning and clarify its difference from statistical inference. Then, we review…

Psychometric modeling has become a frequently used statistical tool in research on scientific reasoning. We review psychometric modeling practices in this field, including model choice, model testing, and researchers' inferences based on their psychometric practices. A review of 11 empirical research studies reveals that the predominant…

For binary experimental data, we discuss randomization-based inferential procedures that do not need to invoke any modeling assumptions. We also introduce methods for likelihood and Bayesian inference based solely on the physical randomization without any hypothetical super population assumptions about the potential outcomes. These estimators have…

Several studies investigated preschoolers' ability to compute scalar and ad-hoc implicatures, but only one compared children's performance with both kinds of implicature with the same task, a picture selection task. In Experiment 1 (N = 58, age: 4;2-6;0), we first show that the truth value judgment task, traditionally employed to investigate…

Given the questionnaire design and the nature of the problem, partially ordered data that are neither completely ordered nor completely unordered are frequently encountered in social, behavioral, and medical studies. However, early developments in partially ordered data analysis are very limited and restricted only to cross-sectional data. In this…

Eye-tracking technology can create a record of the location and duration of visual fixations as a test-taker reads test questions. Although the cognitive process the test-taker is using cannot be directly observed, eye-tracking data can support inferences about these unobserved cognitive processes. This type of information has the potential to…

This research is motivated by the importance of developing ways of thinking (WoT) in geometry learning at the Sambas District Elementary School, West Kalimantan, Indonesia. In general, this study aims to describe realistic mathematics teaching to WoT elementary school students in completing geometry material related to the design of the Sambas…

Statisticians have developed propensity score methods to improve generalizations from studies that do not employ random sampling. However, these methods rely on assumptions whose plausibility may be questionable. We introduce and discuss bounding, an approach that is based on alternative assumptions that may be more plausible. The bounding…

Tinkering is often viewed as arbitrary practice that should be avoided. However, tinkering can be performed as part of a sound reasoning process. In this ethnomethodological study, we investigated tinkering as a reasoning process that construes logical inferences. This is a new asset-based approach that can be applied in computer science…

Comprehension is a primary ingredient of reading success--but most educators aren't taught how to deliver structured comprehension instruction in their classrooms. K-8 teachers will find the guidance they need in this groundbreaking professional resource from Nancy Hennessy, former IDA President and an expert on reading comprehension. Meticulously…