Estimating prescription medicine use is challenging due to recall bias associated with surveys and coverage bias in administrative data. This study assesses how making operational improvements and combining both survey and administrative data sources can increase data quality on filled prescriptions. We use data from the Medicare Current…

Multivariate Functional Principal Component Analysis (MFPCA) is a valuable tool for exploring relationships and identifying shared patterns of variation in multivariate functional data. However, interpreting these functional principal components (PCs) can sometimes be challenging due to issues such as roughness and sparsity. In this dissertation,…

Systematic reviews are a time-consuming yet effective approach to understanding research trends. While researchers have investigated how to speed up the process of screening studies for potential inclusion, few have focused on to what extent we can use algorithms to extract data instead of human coders. In this study, we explore to what extent…

The five SEERNet digital learning platforms (DLPs) present unique opportunities for researchers by offering tools, processes, and infrastructure to make research more efficient, scalable, and relevant. However, conducting research within a DLP may require a shift in a researcher's orientation or mindset in how they think about potential research…

This study is a meta-analysis of overall recruitment rate (ORR) in probability-based online panel research. In this study, we included 23 general population probability-based online panels (out of 28 identified and described) covering 15 countries and provided a comprehensive overview of their methodological approaches to recruitment. We…

This paper presents a unique advanced statistical approach based on Artificial Intelligence (AI) to examine factors affective on phonological awareness and print awareness of preschool children. Artificial Neural Network (ANN) models were created and correlations between the independent and dependent (outcome) variables were analyzed. The ANN…

In a clustered observational study, a treatment is assigned to groups and all units within the group are exposed to the treatment. We develop a new method for statistical adjustment in clustered observational studies using approximate balancing weights, a generalization of inverse propensity score weights that solve a convex optimization problem…

Balancing speed and quality during crises pose challenges for ensuring the value and utility of data in social science research. The COVID-19 pandemic in particular underscores the need for high-quality data and rapid dissemination. Given the importance of behavioural measures and compliance with measures to contain the pandemic, social science…

Higher education is awash with data that, when refined, facilitates data-informed decisions. Such decision-making is much more prevalent in support of undergraduate education given the much larger number of undergraduates pursuing higher education in contrast to the much smaller proportion of graduate students. A simple extension of current…

During the COVID-19 pandemic, organizations increased virtual programming and adoption of online technologies. This article outlines the University of California assessment of tools for gathering data on participant learning outcomes from virtual educational programs. After assessing colleagues' experiences and searching for new web applications,…

Undergraduate economics degrees awarded by U.S. colleges and universities increased almost 12 percent from 2013 through 2015, then stabilized at a little above the 2015 level until 2018, after which they began an accelerating decline over the past five years to end back at a little below 2015 levels.

There have been numerous iterations of naming convention specified for myalgic encephalomyelitis (ME) and chronic fatigue syndrome (CFS). As health care turns to "big data" analytics to gain insights, the Google Trends database was mined to ascertain worldwide trends of public interest in several ME- and CFS-related search categories…

Survey methodology is the dominant approach among universities in the United States for reporting employment outcomes for recent graduates. However, past studies have shown that survey methodology may yield upwardly biased results, which can result in overreporting of employment rates and salary outcomes. This case study describes the development…

We report the results of a convergent validity study examining the externalizing subscale (SRSS-E5, five items) of the adapted Student Risk Screening Scale for Internalizing and Externalizing (SRSS-IE 9) with the externalizing subscale of the Teacher Report Form (TRF) with two samples of K-12 students. Results of logistic regression and receiver…

The data sets used in statistics education have changed over time, from mathematically "well-behaved" ones that facilitated computation, to more context-rich sources and now, with the increasing influence of data science practices, to "found" data, often from open data sites. As data sources change, it is important for…