The methodological controversy surrounding ordinal outcome data has posed a distinct challenge to the conceptualization, design, and conduct of research in the social and behavioral sciences for more than 75 years. Accordingly, this study sought to supply a comprehensive and multidisciplinary perspective of the debate and in so doing lay the…

Many ethical concerns in online big data research stem from a pervasive assumption that data are disembodied and place-less. While some scholars have begun addressing the ethical dilemmas of big data, few offer approaches or tools that fully grapple with the situatedness of online data and its ethical implications. We draw on feminist new…

The COVID-19 pandemic caused tremendous upheaval in schooling. In addition to devasting effects on students, these disruptions had consequences for researchers conducting studies on education programs and policies. Given the likelihood of future large-scale disruptions, it is important for researchers to plan resilient studies and think critically…

The unstructured multiple-attempt (MA) item response data in virtual learning environments (VLEs) are often from student-selected assessment data sets, which include missing data, single-attempt responses, multiple-attempt responses, and unknown growth ability across attempts, leading to a complex and complicated scenario for using this kind of…

Agent-based models (ABMs) often rely on psychometric constructs such as 'opinions', 'stubbornness', 'happiness', etc. The measurement process for these constructs is quite different from the one used in physics as there is no standardized unit of measurement for opinion or happiness. Consequently, measurements are usually affected by 'psychometric…

Simple matrix sampling planned missing (SMS PD) design, introduce missing data patterns that lead to covariances between variables that are not jointly observed, and create difficulties for analyses other than mean and variance estimations. Based on prior research, we adopted a new multigroup confirmatory factor analysis (CFA) approach to handle…

In education settings, treatments are often non-randomly assigned to clusters, such as schools or classrooms, while outcomes are measured for students. This research design is called the clustered observational study (COS). We examine the consequences of common support violations in the COS context. Common support violations occur when the…

The aim of this paper is to introduce a software that is appropriate for the generalizability theory for not only balanced but also unbalanced data sets. Because it is possible to have unbalanced data sets while conducting a study, the researchers have devised an easy solution, other than deleting data, to balance the design to cope with this…

In this study, it was aimed to examine the effect of missing data in different patterns and sizes on test equating methods under the NEAT design for different factors. For this purpose, as part of this study, factors such as sample size, average difficulty level difference between the test forms, difference between the ability distribution,…

The COVID-19 pandemic caused tremendous upheaval in schooling. In addition to its devasting effects on students' academic development, the disruptions to schooling had important consequences for researchers conducting effectiveness studies on educational programs during this era. Given the likelihood of future large-scale disruptions, it is…

In education, machine learning (ML), especially deep learning (DL) in recent years, has been extensively used to improve both teaching and learning. Despite the rapid advancement of ML and its application in education, a few challenges remain to be addressed. In this thesis, in particular, we focus on two such challenges: (i) data scarcity and…

Determining the number of factors in exploratory factor analysis is arguably the most crucial decision a researcher faces when conducting the analysis. While several simulation studies exist that compare various so-called factor retention criteria under different data conditions, little is known about the impact of missing data on this process.…

There is an increased focus on fostering integrity in research by through creating an open culture where research integrity dilemmas can be discussed. We describe a pilot intervention study that used Moral Case Deliberation (MCD), a method that originated in clinical ethics support, to discuss research integrity dilemmas with researchers. Our…

Scholars who conduct process tracing often face the problem of missing data. The inability to document key steps in their causal chains makes it difficult to validate theoretical models. In this article, we conceptualize "missingness" as it relates to process tracing, describe different scenarios in which it is pervasive, and present…

The various empirical models built for enrollment management, operations, and program evaluation purposes may have lost their predictive power as a result of the recent collective impact of COVID restrictions, widespread social upheaval, and the shift in educational preferences. This statistical artifact is known as model drifting, data-shift,…