As evidence from evaluation and experimental studies continue to influence decision and policymaking, applied researchers and practitioners require tools to derive valid and credible inferences. Over the past several decades, research in causal inference has progressed with the development and application of propensity scores. Since their…

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)…

The causal view of categories assumes that categories are represented by features and their causal relations. To study the effect of causal knowledge on categorization, researchers have used Bayesian causal models. Within that framework, categorization may be viewed as dependent on a likelihood computation (i.e., the likelihood of an exemplar with…

A computer science camp for pre-collegiate students was operated during the summers of 2022 and 2023. The effect the camp had on attitudes was quantitatively assessed using a survey instrument. However, enrollment at the summer camp was small, which meant the well-known Pearson's Chi-Squared to measure the significance of results was not applied.…

Literature screening is the process of identifying all relevant records from a pool of candidate paper records in systematic review, meta-analysis, and other research synthesis tasks. This process is time consuming, expensive, and prone to human error. Screening prioritization methods attempt to help reviewers identify most relevant records while…

The primary objective of this investigation is the formulation of random intercept latent profile transition analysis (RI-LPTA). Our simulation investigation suggests that the election between LPTA and RI-LPTA for examination has negligible impact on the estimation of transition probability parameters when the population parameters are generated…

Evidence of the internal consistency of standard-setting judgments is a critical part of the validity argument for tests used to make classification decisions. The bookmark standard-setting procedure is a popular approach to establishing performance standards, but there is relatively little research that reflects on the internal consistency of the…

Propensity score matching and weighting methods are applied to balance covariates and reduce selection bias in the analysis of observational study data, and ultimately estimate a treatment effect. We wish to evaluate the impact of a Supplemental Instruction (SI) program on student success in an Introductory Statistics course. In such student…

The object of research is a statistics exam that contains problem tasks. One examiner performed two exam evaluation methods to repeatedly evaluate the exam. The goal was to compare the methods for objectivity. One of the two exam evaluation methods we call a serial evaluation method. The serial evaluation method assumes evaluation of all exam…

Count responses with grouping and right censoring have long been used in surveys to study a variety of behaviors, status, and attitudes. Yet grouping or right-censoring decisions of count responses still rely on arbitrary choices made by researchers. We develop a new method for evaluating grouping and right-censoring decisions of count responses…

IXL is an end-to-end teaching and learning solution that engages learners in grades Pre-K through 12 with a comprehensive curriculum and a first-of-its-kind assessment suite. A core component of IXL's assessment suite is the IXL Diagnostic, an interim assessment designed by a team of educators and mathematicians that uses Item Response Theory…

To address response style or bias in rating scales, forced-choice items are often used to request that respondents rank their attitudes or preferences among a limited set of options. The rating scales used by raters to render judgments on ratees' performance also contribute to rater bias or errors; consequently, forced-choice items have recently…

Modeling and predicting student learning in computer-based environments often relies solely on sequences of accuracy data. Previous research suggests that it does not only matter what we learn, but also how we learn. The detection and analysis of learning behavior becomes especially important, when dealing with open-ended exploration environments,…

This study introduces a novel differential item functioning (DIF) method based on propensity score matching that tackles two challenges in analyzing performance assessment data, that is, continuous task scores and lack of a reliable internal variable as a proxy for ability or aptitude. The proposed DIF method consists of two main stages. First,…

In educational contexts, students often self-select into specific interventions (e.g., courses, majors, extracurricular programming). When students self-select into an intervention, systematic group differences may impact the validity of inferences made regarding the effect of the intervention. Propensity score methods are commonly used to reduce…