Recurrent event analyses have found a wide range of applications in biomedicine, public health, and engineering, among others, where study subjects may experience a sequence of event of interest during follow-up. The R package reReg offers a comprehensive collection of practical and easy-to-use tools for regression analysis of recurrent events,…

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…

Given the increased focus of educational research on what works for whom and under what circumstances over the last decade, educational researchers are increasingly turning toward mixture models to identify heterogeneous subgroups among students. Such data are inherently nested, as students are nested within classrooms and schools. Yet there has…

With the prevalence of missing data in social science research, it is necessary to use methods for handling missing data. One framework in which data with missing values can still be used for parameter estimation is the Bayesian framework. In this tutorial, different missing data mechanisms including Missing Completely at Random, Missing at…

This paper explores three simple data manipulation techniques (synthesis, augmentation, curriculum) for improving abstractive summarization models without the need for any additional data. We introduce a method of data synthesis with paraphrasing, a data augmentation technique with sample mixing, and curriculum learning with two new difficulty…

This paper presents a tool for creating student models in logistic regression. Creating student models has typically been done by expert selection of the appropriate terms, beginning with models as simple as IRT or AFM but more recently with highly complex models like BestLR. While alternative methods exist to select the appropriate predictors for…

Estimation of causal effects requires making comparisons across groups of observations exposed and not exposed to a a treatment or cause (intervention, program, drug, etc). To interpret differences between groups causally we need to ensure that they have been constructed in such a way that the comparisons are "fair." This can be…

Randomized A/B tests allow causal estimation without confounding but are often under-powered. This paper uses a new dataset, including over 250 randomized comparisons conducted in an online learning platform, to illustrate a method combining data from A/B tests with log data from users who were not in the experiment. Inference remains exact and…

A wide range of machine-learning-based approaches have been developed in the past decade, increasing our ability to accurately model nonlinear and nonadditive response surfaces. This has improved performance for inferential tasks such as estimating average treatment effects in situations where standard parametric models may not fit the data well.…

Data in social and behavioral sciences typically contain measurement errors and do not have predefined metrics. Structural equation modeling (SEM) is widely used for the analysis of such data, where the scales of the manifest and latent variables are often subjective. This article studies how the model, parameter estimates, their standard errors…

This paper explores the effect of using multitask learning for abstractive summarization in the context of small training corpora. In particular, we incorporate four different tasks (extractive summarization, language modeling, concept detection, and paraphrase detection) both individually and in combination, with the goal of enhancing the target…

Emergent technologies present platforms for educational researchers to conduct randomized controlled trials (RCTs) and collect rich data on study students' performance, behavior, learning processes, and outcomes in authentic learning environments. As educational research increasingly uses methods and data collection from such platforms, it is…

The purpose of this paper is to demonstrate and evaluate the use of "Bayesian dynamic borrowing"(Viele et al, in Pharm Stat 13:41-54, 2014) as a means of systematically utilizing historical information with specific applications to large-scale educational assessments. Dynamic borrowing via Bayesian hierarchical models is a special case…

Composite scores are an exceptionally important psychometric tool for behavioral science research applications. A prototypical example occurs with self-report data, where researchers routinely use questionnaires with multiple items that tap into different features of a target construct. Item-level missing data are endemic to composite score…

We assessed whether an adaptation of value-added analysis (VAA) can provide evidence on the relative effectiveness of interventions implemented in a large school district. We analyzed two datasets, one documenting interventions received by underperforming students, and one documenting interventions received by students in schools benefiting from…