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John Deke; Mariel Finucane; Dan Thal – Society for Research on Educational Effectiveness, 2022
Background/Context: Methodological background: Meta-analysis typically depends on the assumption that true effects follow the normal distribution. While assuming normality of effect "estimates" is often supported by a central limit theorem, normality for the distribution of interventions' "true" effects is a computational…
Descriptors: Bayesian Statistics, Meta Analysis, Regression (Statistics), Research Design
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Lee, Daniel Y.; Harring, Jeffrey R. – Journal of Educational and Behavioral Statistics, 2023
A Monte Carlo simulation was performed to compare methods for handling missing data in growth mixture models. The methods considered in the current study were (a) a fully Bayesian approach using a Gibbs sampler, (b) full information maximum likelihood using the expectation-maximization algorithm, (c) multiple imputation, (d) a two-stage multiple…
Descriptors: Monte Carlo Methods, Research Problems, Statistical Inference, Bayesian Statistics
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Martinková, Patrícia; Bartoš, František; Brabec, Marek – Journal of Educational and Behavioral Statistics, 2023
Inter-rater reliability (IRR), which is a prerequisite of high-quality ratings and assessments, may be affected by contextual variables, such as the rater's or ratee's gender, major, or experience. Identification of such heterogeneity sources in IRR is important for the implementation of policies with the potential to decrease measurement error…
Descriptors: Interrater Reliability, Bayesian Statistics, Statistical Inference, Hierarchical Linear Modeling
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Kreitchmann, Rodrigo S.; Sorrel, Miguel A.; Abad, Francisco J. – Educational and Psychological Measurement, 2023
Multidimensional forced-choice (FC) questionnaires have been consistently found to reduce the effects of socially desirable responding and faking in noncognitive assessments. Although FC has been considered problematic for providing ipsative scores under the classical test theory, item response theory (IRT) models enable the estimation of…
Descriptors: Measurement Techniques, Questionnaires, Social Desirability, Adaptive Testing
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Huang, Hening – Research Synthesis Methods, 2023
Many statistical methods (estimators) are available for estimating the consensus value (or average effect) and heterogeneity variance in interlaboratory studies or meta-analyses. These estimators are all valid because they are developed from or supported by certain statistical principles. However, no estimator can be perfect and must have error or…
Descriptors: Statistical Analysis, Computation, Measurement Techniques, Meta Analysis
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Hecht, Martin; Voelkle, Manuel C. – International Journal of Behavioral Development, 2021
The analysis of cross-lagged relationships is a popular approach in prevention research to explore the dynamics between constructs over time. However, a limitation of commonly used cross-lagged models is the requirement of equally spaced measurement occasions that prevents the usage of flexible longitudinal designs and complicates cross-study…
Descriptors: Models, Longitudinal Studies, Prevention, Time
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Xing, Wanli; Du, Dongping; Bakhshi, Ali; Chiu, Kuo-Chun; Du, Hanxiang – IEEE Transactions on Learning Technologies, 2021
Predictive modeling in online education is a popular topic in learning analytics research and practice. This study proposes a novel predictive modeling method to improve model transferability over time within the same course and across different courses. The research gaps addressed are limited evidence showing whether a predictive model built on…
Descriptors: Electronic Learning, Bayesian Statistics, Prediction, Models
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Luo, Yong – Measurement: Interdisciplinary Research and Perspectives, 2021
To date, only frequentist model-selection methods have been studied with mixed-format data in the context of IRT model-selection, and it is unknown how popular Bayesian model-selection methods such as DIC, WAIC, and LOO perform. In this study, we present the results of a comprehensive simulation study that compared the performances of eight…
Descriptors: Item Response Theory, Test Format, Selection, Methods
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Weber, Frank; Knapp, Guido; Glass, Änne; Kundt, Günther; Ickstadt, Katja – Research Synthesis Methods, 2021
There exists a variety of interval estimators for the overall treatment effect in a random-effects meta-analysis. A recent literature review summarizing existing methods suggested that in most situations, the Hartung-Knapp/Sidik-Jonkman (HKSJ) method was preferable. However, a quantitative comparison of those methods in a common simulation study…
Descriptors: Meta Analysis, Computation, Intervals, Statistical Analysis
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Lee, Hyung Rock; Sung, Jaeyun; Lee, Sunbok – International Journal of Assessment Tools in Education, 2021
Conventional estimators for indirect effects using a difference in coefficients and product of coefficients produce the same results for continuous outcomes. However, for binary outcomes, the difference in coefficient estimator systematically underestimates the indirect effects because of a scaling problem. One solution is to standardize…
Descriptors: Statistical Analysis, Computation, Regression (Statistics), Scaling
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Cui, Zhongmin – Educational Measurement: Issues and Practice, 2021
Commonly used machine learning applications seem to relate to big data. This article provides a gentle review of machine learning and shows why machine learning can be applied to small data too. An example of applying machine learning to screen irregularity reports is presented. In the example, the support vector machine and multinomial naïve…
Descriptors: Artificial Intelligence, Man Machine Systems, Data, Bayesian Statistics
Aki Vehtari; Andrew Gelman; Daniel Simpson; Bob Carpenter; Paul-Christian Burkner – Grantee Submission, 2021
Markov chain Monte Carlo is a key computational tool in Bayesian statistics, but it can be challenging to monitor the convergence of an iterative stochastic algorithm. In this paper we show that the convergence diagnostic [R-hat] of Gelman and Rubin (1992) has serious flaws. Traditional [R-hat] will fail to correctly diagnose convergence failures…
Descriptors: Markov Processes, Monte Carlo Methods, Bayesian Statistics, Efficiency
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Betsy Wolf – Society for Research on Educational Effectiveness, 2021
The What Works Clearinghouse (WWC) seeks to provide practitioners information about "what works in education." One challenge in understanding "what works" to practitioners is that effect sizes--the degree to which an intervention produces positive (or negative) outcomes--are not comparable across different interventions, in…
Descriptors: Effect Size, Outcome Measures, Intervention, Educational Research
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Winnie Wing-Yee Tse; Hok Chio Lai – Society for Research on Educational Effectiveness, 2021
Background: Power analysis and sample size planning are key components in designing cluster randomized trials (CRTs), a common study design to test treatment effect by randomizing clusters or groups of individuals. Sample size determination in two-level CRTs requires knowledge of more than one design parameter, such as the effect size and the…
Descriptors: Sample Size, Bayesian Statistics, Randomized Controlled Trials, Research Design
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Nesra Yannier; Scott E. Hudson; Henry Chang; Kenneth R. Koedinger – International Journal of Artificial Intelligence in Education, 2024
Adaptivity in advanced learning technologies offer the possibility to adapt to different student backgrounds, which is difficult to do in a traditional classroom setting. However, there are mixed results on the effectiveness of adaptivity based on different implementations and contexts. In this paper, we introduce AI adaptivity in the context of a…
Descriptors: Artificial Intelligence, Computer Software, Feedback (Response), Outcomes of Education
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