Recently, the rise of generative AI tools such as "ChatGPT" have prompted deep and wide considerations about teaching and learning, student success, research and development, and the use of data for informed institutional decision making. In this volume, authors discuss specific concepts, considerations for use, and some specific tools…

Missing data are common with large scale assessments (LSAs). A typical approach to handling missing data with LSAs is the use of listwise deletion, despite decades of research showing that approach can be a suboptimal strategy resulting in biased estimates. In order to help researchers account for missing data, we provide a tutorial using R and…

Personalized assessments are of increasing interest because of their potential to lead to more equitable decisions about the examinees. However, one obstacle to the widespread use of personalized assessments is the lack of a measurement toolkit that can be used to analyze data from these assessments. This article takes one step toward building…

In a ranking created by using data from multiple data sources, including CareerBuilder, GitHub, Google, Hacker News, the IEEE, Reddit, Stack Overflow, and Twitter, Python was shown to be the top programming language of 2023. Created in 1990, Python has seen a recent uptick in popularity driven primarily by its ability to sustain the use of…

Often, eye-tracking researchers define areas of interest (AOIs) to analyze eye-tracking data. Although AOIs can be defined with systematic methods, researchers in organic chemistry education eye-tracking research often define them manually, as the semantic composition of the stimulus must be considered. Still, defining appropriate AOIs during data…

The development of research methodology in any scientific field is closely tied to technological advancements that allow for deeper and more sophisticated data analysis. To help readers understanding the scope of current lexical availability research, this chapter provides a description of the tools and research resources specifically developed…

This article presents a general-purpose method for detecting cheating in online courses, which combines anomaly detection and supervised machine learning. Using features that are rooted in psychometrics and learning analytics literature, and capture anomalies in learner behavior and response patterns, we demonstrate that a classifier that is…

The COVID-19 pandemic uniquely affected nearly all the subject matter in a typical principles of macroeconomics class. Fluctuations in the basic macroeconomic data in the COVID era were staggering and offer new teaching opportunities. In addition, because the recession was primarily driven by supply side shocks, the entire episode offers a unique…

External audit is undergoing rapid changes where more and more routine tasks are automated with analytics and artificial intelligence (AI) instruments. The paper addresses a research problem of mapping data analytics to audit tasks and develops a framework aligning audit phases and AI and using data analytics in teaching audit with AI. The paper…

With the growing availability of large-scale text-based data sets, there is an increasing need for an accessible and systematic way to analyze qualitative texts. This article introduces and details the contextual text coding (CTC) method as a mixed-methods approach to large-scale qualitative data analysis. The method is particularly useful for…

Transparency is once again a central issue of debate across types of qualitative research. Work on how to conduct qualitative data analysis, on the other hand, walks us through the step-by-step process on how to code and understand the data we've collected. Although there are a few exceptions, less focus is on transparency regarding…

Quantile regression (QR) provides an alternative to linear regression (LR) that allows for the estimation of relationships across the distribution of an outcome. However, as highlighted in recent research on the motherhood penalty across the wage distribution, different procedures for conditional and unconditional quantile regression (CQR, UQR)…

Although there is growing social science research examining how generative AI models can be effectively and systematically applied to text-based tasks, whether and how these models can be used to analyze images remain open questions. In this article, we introduce a framework for analyzing images with generative multimodal models, which consists of…

The practice of statistics has the power to motivate and support learning across the STEM (Science, Technology, Engineering, and Mathematics) disciplines. When connected with meaningful science contexts, statistical problem solving through the collection of data and subsequent data analysis, supported by contemporary graphing technology, presents…

Principal Component Analysis (PCA) is a powerful statistical technique for reducing the complexity of data and making patterns and relationships within the data more easily understandable. By using PCA, students can learn to identify the most important features of a data set, visualize relationships between variables, and make informed decisions…