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Jason A. Schoeneberger; Christopher Rhoads – American Journal of Evaluation, 2025
Regression discontinuity (RD) designs are increasingly used for causal evaluations. However, the literature contains little guidance for conducting a moderation analysis within an RDD context. The current article focuses on moderation with a single binary variable. A simulation study compares: (1) different bandwidth selectors and (2) local…
Descriptors: Regression (Statistics), Causal Models, Evaluation Methods, Multivariate Analysis
Bo Zhang; Jing Luo; Susu Zhang; Tianjun Sun; Don C. Zhang – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Oblique bifactor models, where group factors are allowed to correlate with one another, are commonly used. However, the lack of research on the statistical properties of oblique bifactor models renders the statistical validity of empirical findings questionable. Therefore, the present study took the first step to examine the statistical properties…
Descriptors: Correlation, Predictor Variables, Monte Carlo Methods, Statistical Bias
John M. LaVelle; Clayton L. Stephenson; Scott I. Donaldson; Justin D. Hackett – American Journal of Evaluation, 2024
Psychological theory suggests that evaluators' individual values and traits play a fundamental role in evaluation practice, though few empirical studies have explored those constructs in evaluators. This paper describes an empirical study on evaluators' individual, work, and political values, as well as their personality traits to predict…
Descriptors: Evaluators, Values, Political Attitudes, Work Attitudes
Ismail Cuhadar; Ömür Kaya Kalkan – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Simulation studies are needed to investigate how many score categories are sufficient to treat ordered categorical data as continuous, particularly for bifactor models. The current simulation study aims to address such needs by investigating the performance of estimation methods in the bifactor models with ordered categorical data. Results support…
Descriptors: Predictor Variables, Structural Equation Models, Sample Size, Evaluation Methods
Playfoot, David; Wilkinson, Laura L.; Mead, Jessica – Assessment & Evaluation in Higher Education, 2023
This paper reports a series of studies that assessed the performance of students on continuous assessment components from two courses in an undergraduate psychology programme. Data were collected from two consecutive cohorts of students (total N = 576) and the grades of students were compared based on additional learning needs (ALN; ALN versus No…
Descriptors: Evaluation Methods, Inclusion, Predictor Variables, Undergraduate Students
Nazanin Nezami; Parian Haghighat; Denisa Gándara; Hadis Anahideh – Grantee Submission, 2024
The education sector has been quick to recognize the power of predictive analytics to enhance student success rates. However, there are challenges to widespread adoption, including the lack of accessibility and the potential perpetuation of inequalities. These challenges present in different stages of modeling, including data preparation, model…
Descriptors: Evaluation Methods, College Students, Success, Predictor Variables
Corinne Huggins-Manley; Anthony W. Raborn; Peggy K. Jones; Ted Myers – Journal of Educational Measurement, 2024
The purpose of this study is to develop a nonparametric DIF method that (a) compares focal groups directly to the composite group that will be used to develop the reported test score scale, and (b) allows practitioners to explore for DIF related to focal groups stemming from multicategorical variables that constitute a small proportion of the…
Descriptors: Nonparametric Statistics, Test Bias, Scores, Statistical Significance
Mingya Huang; David Kaplan – Journal of Educational and Behavioral Statistics, 2025
The issue of model uncertainty has been gaining interest in education and the social sciences community over the years, and the dominant methods for handling model uncertainty are based on Bayesian inference, particularly, Bayesian model averaging. However, Bayesian model averaging assumes that the true data-generating model is within the…
Descriptors: Bayesian Statistics, Hierarchical Linear Modeling, Statistical Inference, Predictor Variables
Abdessamad Chanaa; Nour-eddine El Faddouli – Journal of Education and Learning (EduLearn), 2024
Adaptive online learning can be realized through the evaluation of the learning process. Monitoring and supervising learners' cognitive levels and adjusting learning strategies can increasingly improve the quality of online learning. This analysis is made possible by real-time measurement of learners' cognitive levels during the online learning…
Descriptors: Electronic Learning, Evaluation Methods, Artificial Intelligence, Taxonomy
Wenyi Lu; Joseph Griffin; Troy D. Sadler; James Laffey; Sean P. Goggins – Journal of Learning Analytics, 2025
Game-based learning (GBL) is increasingly recognized as an effective tool for teaching diverse skills, particularly in science education, due to its interactive, engaging, and motivational qualities, along with timely assessments and intelligent feedback. However, more empirical studies are needed to facilitate its wider application in school…
Descriptors: Game Based Learning, Predictor Variables, Evaluation Methods, Educational Games
Berna Yüner – Journal of Theoretical Educational Science, 2023
In parallel with the increase in social expectations regarding education and its outcomes, studies on school effectiveness continue unabated. The ability of educational organizations to provide qualitatively higher education has become the focus of the researches. In this direction, school governance, the adaptation of governance principles to…
Descriptors: School Effectiveness, Predictor Variables, Foreign Countries, Evaluation Methods
Conor O. Chandler; Irina Proskorovsky – Research Synthesis Methods, 2024
In health technology assessment, matching-adjusted indirect comparison (MAIC) is the most common method for pairwise comparisons that control for imbalances in baseline characteristics across trials. One of the primary challenges in MAIC is the need to properly account for the additional uncertainty introduced by the matching process. Limited…
Descriptors: Predictor Variables, Influence of Technology, Evaluation Methods, Methods Research
Caroline F. Rowland; Amy Bidgood; Gary Jones; Andrew Jessop; Paula Stinson; Julian M. Pine; Samantha Durrant; Michelle S. Peter – Language Learning, 2025
A strong predictor of children's language is performance on non-word repetition (NWR) tasks. However, the basis of this relationship remains unknown. Some suggest that NWR tasks measure phonological working memory, which then affects language growth. Others argue that children's knowledge of language/language experience affects NWR performance. A…
Descriptors: Vocabulary Development, Comparative Analysis, Computational Linguistics, Language Skills
Suping Yi; Rustam Shadiev; Yanyan Zhang – Interactive Learning Environments, 2024
This study reviewed thirty-seven articles on intercultural learning supported by technology. The results are reported in terms of strength of evidence and relationship among research variables. The results indicated the following strength of evidence: (1) moderate evidence showed higher frequency of the technology usage in higher education or…
Descriptors: Literature Reviews, Multicultural Education, Technology Uses in Education, Predictor Variables
Ying Zhan; Daner Sun; Ho Man Kong; Ye Zeng – British Journal of Educational Technology, 2024
There is a global trend in the increased adoption of e-assessment in school classrooms to enhance learning. Teachers, as classroom-based assessment designers and implementers, play a vital role in such assessment change. However, little is known about school teachers' classroom-based e-assessment practices and the underlying reasons. To address…
Descriptors: Elementary School Teachers, Student Evaluation, Evaluation Methods, Educational Technology