Bayesian estimations of complex regression models with high-dimensional parameter spaces require advanced priors, capable of addressing both sparsity and multicollinearity in the data. The Dirichlet-horseshoe, a new prior distribution that combines and expands on the concepts of the regularized horseshoe and the Dirichlet-Laplace priors, is a…

This dissertation used synthetic datasets, semi-synthetic datasets, and a real-world dataset from an educational intervention to compare the performance of 15 machine learning and multiple imputation methods to estimate the individual treatment effect (ITE). In addition, it examined the performance of five evaluation metrics that can be used to…

To provide practice and assessment of computational thinking, we need specific problems students can solve. There are many such problems, but they are hard to find. Learning environments and assessments often use only specific types of problems and thus do not cover computational thinking in its whole scope. We provide an extensive catalog of…

The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit with panel data. Two challenges arise with higher frequency data (e.g., monthly versus yearly): (1) achieving excellent pre-treatment fit is typically more challenging; and (2) overfitting to noise is more likely. Aggregating data…

Computational Thinking (CT) is considered a core 21st century digital skill. The aspect of assessment is crucial and knowing what, who, when, how, and where to assess is important for assessment design. In this study, we conducted an umbrella review to gain insights regarding CT assessment in higher education. In total, we analyzed 11 reviews,…

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…

Computational thinking (CT) is increasingly integrated into education, promoting problem-solving skills essential for the 21st century. However, little is known about how teachers assess CT in K-12 classrooms. This qualitative study explores elementary teachers' approaches to CT assessment after professional development in integrating CT into core…

The use of the aggregate scoring method for scoring concordance tests requires the weighting of test items to be derived from the performance of a group of experts who take the test under the same conditions as the examinees. However, the average score of experts constituting the reference panel remains a critical issue in the use of these tests.…

Green development is an effective mode of economic growth and social development aiming at efficiency, harmony, and sustainability, which has become an important trend. The development of green industry is taken by many countries as an important measure to promote economic restructuring and highlighting environmental awareness. The green chemistry…

This paper argues for three aspects of tolerance with respect to QCA research: tolerance with respect to different approaches to QCA; producing QCA research with tolerance (work that is resistant to criticism); and for QCA researchers to be clear about the tolerance of the solutions they present -- especially in terms of calibration and truth…

Background and Context: Computational thinking (CT) has been increasingly added to K-12 curricula, prompting teachers to grade more and more CT artifacts. This has led to a rise in automated CT assessment tools. Objective: This study examines the scope and characteristics of publications that use machine learning (ML) approaches to assess…

Despite the increasing adaption of automated text analysis in communication studies, its strengths and weaknesses in framing analysis are so far unknown. Fewer efforts have been made to automatic detection of networked frames. Drawing on the recent developments in this field, we harness a comparative exploration, using Latent Dirichlet Allocation…

Multiple measures, such as multiple content domains or multiple types of performance, are used in various testing programs to classify examinees for screening or selection. Despite the popular usages of multiple measures, there is little research on classification consistency and accuracy of multiple measures. Accordingly, this study introduces an…

Back random responding (BRR) behavior is one of the commonly observed careless response behaviors. Accurately detecting BRR behavior can improve test validities. Yu and Cheng (2019) showed that the change point analysis (CPA) procedure based on weighted residual (CPA-WR) performed well in detecting BRR. Compared with the CPA procedure, the…

Once research questions are posed, researchers must answer many a priori questions regarding research design before analysis can be performed and any conclusions can be made, including sample selection criteria, data collection method, model specification, analysis and estimation technique. The choices made by researchers along this forking path…