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Jeff Coon; Paulina N. Silva; Alexander Etz; Barbara W. Sarnecka – Journal of Cognition and Development, 2025
Bayesian methods offer many advantages when applied to psychological research, yet they may seem esoteric to researchers who are accustomed to traditional methods. This paper aims to lower the barrier of entry for developmental psychologists who are interested in using Bayesian methods. We provide worked examples of how to analyze common study…
Descriptors: Developmental Psychology, Bayesian Statistics, Research Methodology, Psychological Studies
Deke, John; Finucane, Mariel; Thal, Daniel – National Center for Education Evaluation and Regional Assistance, 2022
BASIE is a framework for interpreting impact estimates from evaluations. It is an alternative to null hypothesis significance testing. This guide walks researchers through the key steps of applying BASIE, including selecting prior evidence, reporting impact estimates, interpreting impact estimates, and conducting sensitivity analyses. The guide…
Descriptors: Bayesian Statistics, Educational Research, Data Interpretation, Hypothesis Testing
Hicks, Tyler; Rodríguez-Campos, Liliana; Choi, Jeong Hoon – American Journal of Evaluation, 2018
To begin statistical analysis, Bayesians quantify their confidence in modeling hypotheses with priors. A prior describes the probability of a certain modeling hypothesis apart from the data. Bayesians should be able to defend their choice of prior to a skeptical audience. Collaboration between evaluators and stakeholders could make their choices…
Descriptors: Bayesian Statistics, Evaluation Methods, Statistical Analysis, Hypothesis Testing
Kuiper, Rebecca M.; Hoijtink, Herbert – Psychological Methods, 2010
This article discusses comparisons of means using exploratory and confirmatory approaches. Three methods are discussed: hypothesis testing, model selection based on information criteria, and Bayesian model selection. Throughout the article, an example is used to illustrate and evaluate the two approaches and the three methods. We demonstrate that…
Descriptors: Models, Testing, Hypothesis Testing, Probability
Azevedo, Ana, Ed.; Azevedo, José, Ed. – IGI Global, 2019
E-assessments of students profoundly influence their motivation and play a key role in the educational process. Adapting assessment techniques to current technological advancements allows for effective pedagogical practices, learning processes, and student engagement. The "Handbook of Research on E-Assessment in Higher Education"…
Descriptors: Higher Education, Computer Assisted Testing, Multiple Choice Tests, Guides
Klugkist, Irene; Laudy, Olav; Hoijtink, Herbert – Psychological Methods, 2010
In this article, a Bayesian model selection approach is introduced that can select the best of a set of inequality and equality constrained hypotheses for contingency tables. The hypotheses are presented in terms of cell probabilities allowing researchers to test (in)equality constrained hypotheses in a format that is directly related to the data.…
Descriptors: Bayesian Statistics, Models, Selection, Probability
Weiss, David J. – 1983
During 1975-1979 this research into the potential of computerized adaptive testing to reduce errors in the measurement of human capabilities used Marine recruits for a live-testing validity comparison of computerized adaptive and conventional tests. The program purposes were to: (1) identify the most useful computer-based adaptive testing…
Descriptors: Ability, Adaptive Testing, Adults, Bayesian Statistics
Wainer, Howard – Journal of Educational and Behavioral Statistics, 2010
In this essay, the author tries to look forward into the 21st century to divine three things: (i) What skills will researchers in the future need to solve the most pressing problems? (ii) What are some of the most likely candidates to be those problems? and (iii) What are some current areas of research that seem mined out and should not distract…
Descriptors: Research Skills, Researchers, Internet, Access to Information
Peer reviewedMcClure, John; Suen, Hoi K. – Topics in Early Childhood Special Education, 1994
This article compares three models that have been the foundation for approaches to the analysis of statistical significance in early childhood research--the Fisherian and the Neyman-Pearson models (both considered "classical" approaches), and the Bayesian model. The article concludes that all three models have a place in the analysis of research…
Descriptors: Bayesian Statistics, Early Childhood Education, Educational Research, Hypothesis Testing
PDF pending restorationvan der Linden, Wim J. – 1984
The classification problem in educational testing is a decision problem. One must assign subjects to one of several available treatments on the basis of test scores, where the success of each treatment is measured by a different criterion. Examples of classification decisions include individualized instruction, counseling, and clinical settings.…
Descriptors: Bayesian Statistics, Classification, Cutting Scores, Decision Making
Peer reviewedConquest, Loveday L. – Environmental Monitoring and Assessment, 1993
Presents two statistical topics and examples of their use in natural resource monitoring. The first topic deals with use of correlated observations in calculations of variance estimates for a regional mean, required sample size determination, and confidence intervals. The second topic concerns the use of Bayesian techniques in hypothesis testing.…
Descriptors: Bayesian Statistics, Environmental Education, Environmental Research, Evaluation Methods

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