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Xu, Jun; Bauldry, Shawn G.; Fullerton, Andrew S. – Sociological Methods & Research, 2022
We first review existing literature on cumulative logit models along with various ways to test the parallel lines assumption. Building on the traditional frequentist framework, we introduce a method of Bayesian assessment of null values to provide an alternative way to examine the parallel lines assumption using highest density intervals and…
Descriptors: Bayesian Statistics, Evaluation Methods, Models, Intervals
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Van Lissa, Caspar J.; van Erp, Sara; Clapper, Eli-Boaz – Research Synthesis Methods, 2023
When meta-analyzing heterogeneous bodies of literature, meta-regression can be used to account for potentially relevant between-studies differences. A key challenge is that the number of candidate moderators is often high relative to the number of studies. This introduces risks of overfitting, spurious results, and model non-convergence. To…
Descriptors: Bayesian Statistics, Regression (Statistics), Maximum Likelihood Statistics, Meta Analysis
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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
Zhang, Zhiyong; Jiang, Kaifeng; Liu, Haiyan; Oh, In-Sue – Grantee Submission, 2018
To answer the call of introducing more Bayesian techniques to organizational research (e.g., Kruschke, Aguinis, & Joo, 2012; Zyphur & Oswald, 2013), we propose a Bayesian approach for meta-analysis with power prior in this article. The primary purpose of this method is to allow meta-analytic researchers to control the contribution of each…
Descriptors: Bayesian Statistics, Meta Analysis, Correlation, Statistical Analysis
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Villanueva Manjarres, Andrés; Moreno Sandoval, Luis Gabriel; Salinas Suárez, Martha Janneth – Digital Education Review, 2018
Educational Data Mining is an emerging discipline which seeks to develop methods to explore large amounts of data from educational settings, in order to understand students' behavior, interests and results in a better way. In recent years there have been various works related to this specialty and multiple data mining techniques derived from this…
Descriptors: Information Retrieval, Data Analysis, Educational Environment, Research Methodology
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Gray, Geraldine; McGuinness, Colm; Owende, Philip; Carthy, Aiden – Journal of Learning Analytics, 2014
Increasing college participation rates, and diversity in student population, is posing a challenge to colleges in their attempts to facilitate learners achieve their full academic potential. Learning analytics is an evolving discipline with capability for educational data analysis that could enable better understanding of learning process, and…
Descriptors: Psychometrics, Data Analysis, Academic Achievement, Postsecondary Education
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Weitzman, R. A. – Journal of Educational and Behavioral Statistics, 2006
Focusing on a single sample obtained randomly with replacement from a single population, this article examines the regression of population on sample proportions and develops an unbiased estimator of the square of the correlation between them. This estimator turns out to be the regression coefficient. Use of the squared-correlation estimator as a…
Descriptors: Sample Size, Intervals, Credibility, Computation
Elmore, Patricia B.; Woehlke, Paula L. – 1996
A content analysis was conducted of three educational research journals published by the American Educational Research Association to review the quantitative and qualitative techniques used in educational research. All articles appearing in these three journals from 1988 through 1995 (total n=1,715) were considered. Research methods were…
Descriptors: Analysis of Covariance, Analysis of Variance, Bayesian Statistics, Content Analysis