Science educators now broadly recognize the multimodal nature of learning in science, where learners make meanings within modes (linguistic, mathematical, visual, and actional) by using the conventions of different sign systems or grammars in these modes. However, how teachers guide students to link and infer new meanings across modes, called…

Missing data are a pervasive problem in data analysis. Three common methods for addressing the problem are (a) complete-case analysis, where only units that are complete on the variables in an analysis are included; (b) weighting, where the complete cases are weighted by the inverse of an estimate of the probability of being complete; and (c)…

Research in the social and behavioral sciences relies on a wide range of experimental and quasi-experimental designs to estimate the causal effects of specific programs, policies, and events. In this paper we highlight measurement issues relevant to evaluating the validity of causal estimation and generalization. These issues impact all four…

We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning (AutoDML). These popular "doubly robust" or "de-biased machine learning estimators" combine outcome modeling with balancing weights -- weights that achieve covariate balance directly in lieu of estimating and…

In describing how people generalize from observed samples of data to novel cases, theories of inductive inference have emphasized the learner's reliance on the contents of the sample. More recently, a growing body of literature suggests that different assumptions about how a data sample was generated can lead the learner to draw qualitatively…

According to Bayesian/predictive coding models of autism, autistic individuals may have difficulties learning probabilistic cue-outcome associations, but empirical evidence has been mixed. The target cues used in previous studies were often straightforward and might not reflect real-life learning of such associations which requires learners to…

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,…

Research substantiates that inferencing is a critical component to making sense of texts. The ability to make logical inferences is a key characteristic of proficient comprehenders that can be developed before children become fluent readers. This article argues for teaching inferencing via teacher or parent read-alouds to help young readers…

Since statistical inference is a probabilistic generalization about a population analyzed on the basis of a sample, inferential reasoning demands producing reasons ("statistical" and "contextual") to substantiate and validate generalizations. To convey an understanding of students' inferential reasoning, we present a…

We examined knowledge-based inference in 6-, 8- and 10-year-olds. Participants listened to texts where the number of clues for an inference was manipulated and then judged whether single-word probes (target inference, competing inference, literal word from the text and an unrelated concept) were related to the story. Accuracy and response times…

How do children learn about the structure of the social world? We tested whether children would extract patterns from an agent's social choices to make inferences about multiple groups' relative social standing. In Experiment 1, 4- to 6-year-old children (N = 36; tested in Central New York) saw an agent and three groups ("Group-A,"…

Background: The generalizability of a study's results continues to be at the forefront of concerns in evaluation research in education (Tipton & Olsen, 2018). Over the past decade, statisticians have developed methods, mainly based on propensity scores, to improve generalizations in the absence of random sampling (Stuart et al., 2011; Tipton,…

We consider the statistical inference for noisy incomplete binary (or 1-bit) matrix. Despite the importance of uncertainty quantification to matrix completion, most of the categorical matrix completion literature focuses on point estimation and prediction. This paper moves one step further toward the statistical inference for binary matrix…

The limitations of Cohen's ? are reviewed and an alternative G-index is recommended for assessing nominal-scale agreement. Maximum likelihood estimates, standard errors, and confidence intervals for a two-rater G-index are derived for one-group and two-group designs. A new G-index of agreement for multirater designs is proposed. Statistical…

Children rely on their Approximate Number System to intuitively perceive number. Such adaptations often exhibit sensitivity to real-world statistics. This study investigates a potential manifestation of the ANS's sensitivity to real-world statistics: a negative power-law distribution of objects in natural scenes should be reflected in children's…