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Showing 1 to 15 of 48 results Save | Export
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Caspar J. Van Lissa; Eli-Boaz Clapper; Rebecca Kuiper – Research Synthesis Methods, 2024
The product Bayes factor (PBF) synthesizes evidence for an informative hypothesis across heterogeneous replication studies. It can be used when fixed- or random effects meta-analysis fall short. For example, when effect sizes are incomparable and cannot be pooled, or when studies diverge significantly in the populations, study designs, and…
Descriptors: Hypothesis Testing, Evaluation Methods, Replication (Evaluation), Sample Size
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Blair P. Lloyd; Jessica N. Torelli; Marney S. Pollack; Emily S. Weaver – Journal of Behavioral Education, 2024
For students with severe or complex challenging behavior, incorporating hypothesis testing as a component of functional behavior assessment (FBA) is often warranted. Several hypothesis testing strategies (i.e., functional analysis, antecedent analysis, concurrent operant analysis) can confirm whether and how features of a student's environment…
Descriptors: Behavior Disorders, Severe Disabilities, Functional Behavioral Assessment, Environmental Influences
Elizabeth Talbott; Andres De Los Reyes; Devin M. Kearns; Jeannette Mancilla-Martinez; Mo Wang – Exceptional Children, 2023
Evidence-based assessment (EBA) requires that investigators employ scientific theories and research findings to guide decisions about what domains to measure, how and when to measure them, and how to make decisions and interpret results. To implement EBA, investigators need high-quality assessment tools along with evidence-based processes. We…
Descriptors: Evidence Based Practice, Evaluation Methods, Special Education, Educational Research
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Barrenechea, Rodrigo; Mahoney, James – Sociological Methods & Research, 2019
This article develops a set-theoretic approach to Bayes's theorem and Bayesian process tracing. In the approach, hypothesis testing is the procedure whereby one updates beliefs by narrowing the range of states of the world that are regarded as possible, thus diminishing the domain in which the actual world can reside. By explicitly connecting…
Descriptors: Bayesian Statistics, Hypothesis Testing, Qualitative Research, Research Methodology
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Lortie-Forgues, Hugues; Inglis, Matthew – Educational Researcher, 2019
In this response, we first show that Simpson's proposed analysis answers a different and less interesting question than ours. We then justify the choice of prior for our Bayes factors calculations, but we also demonstrate that the substantive conclusions of our article are not substantially affected by varying this choice.
Descriptors: Randomized Controlled Trials, Bayesian Statistics, Educational Research, Program Evaluation
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Yang, Shitao; Black, Ken – Teaching Statistics: An International Journal for Teachers, 2019
Summary Employing a Wald confidence interval to test hypotheses about population proportions could lead to an increase in Type I or Type II errors unless the hypothesized value, p0, is used in computing its standard error rather than the sample proportion. Whereas the Wald confidence interval to estimate a population proportion uses the sample…
Descriptors: Error Patterns, Evaluation Methods, Error of Measurement, Measurement Techniques
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Marmolejo-Ramos, Fernando; Cousineau, Denis – Educational and Psychological Measurement, 2017
The number of articles showing dissatisfaction with the null hypothesis statistical testing (NHST) framework has been progressively increasing over the years. Alternatives to NHST have been proposed and the Bayesian approach seems to have achieved the highest amount of visibility. In this last part of the special issue, a few alternative…
Descriptors: Hypothesis Testing, Bayesian Statistics, Evaluation Methods, Statistical Inference
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How, Meng-Leong; Hung, Wei Loong David – Education Sciences, 2019
Artificial intelligence-enabled adaptive learning systems (AI-ALS) are increasingly being deployed in education to enhance the learning needs of students. However, educational stakeholders are required by policy-makers to conduct an independent evaluation of the AI-ALS using a small sample size in a pilot study, before that AI-ALS can be approved…
Descriptors: Stakeholders, Artificial Intelligence, Bayesian Statistics, Probability
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Dittrich, Dino; Leenders, Roger Th. A. J.; Mulder, Joris – Sociological Methods & Research, 2019
Currently available (classical) testing procedures for the network autocorrelation can only be used for falsifying a precise null hypothesis of no network effect. Classical methods can be neither used for quantifying evidence for the null nor for testing multiple hypotheses simultaneously. This article presents flexible Bayes factor testing…
Descriptors: Correlation, Bayesian Statistics, Networks, Evaluation Methods
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
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Gorbunova, Tatiana N. – European Journal of Contemporary Education, 2017
The subject of the research is to build methodologies to evaluate the student knowledge by testing. The author points to the importance of feedback about the mastering level in the learning process. Testing is considered as a tool. The object of the study is to create the test system models for defence practice problems. Special attention is paid…
Descriptors: Testing, Evaluation Methods, Feedback (Response), Simulation
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Raykov, Tenko; Marcoulides, George A.; Millsap, Roger E. – Educational and Psychological Measurement, 2013
A multiple testing method for examining factorial invariance for latent constructs evaluated by multiple indicators in distinct populations is outlined. The procedure is based on the false discovery rate concept and multiple individual restriction tests and resolves general limitations of a popular factorial invariance testing approach. The…
Descriptors: Testing, Statistical Analysis, Factor Analysis, Statistical Significance
Pane, John F.; Baird, Matthew – RAND Corporation, 2014
The purpose of this document is to describe the methods RAND used to analyze achievement for 23 personalized learning (PL) schools for the 2012-13 through 2013-14 academic years. This work was performed at the request of the Bill & Melinda Gates Foundation (BMGF), as part of a multi-year evaluation contract. The 23 schools were selected from a…
Descriptors: Individualized Instruction, Outcome Measures, Academic Achievement, Achievement Gains
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Morey, Richard D.; Rouder, Jeffrey N. – Psychological Methods, 2011
Psychological theories are statements of constraint. The role of hypothesis testing in psychology is to test whether specific theoretical constraints hold in data. Bayesian statistics is well suited to the task of finding supporting evidence for constraint, because it allows for comparing evidence for 2 hypotheses against each another. One issue…
Descriptors: Evidence, Intervals, Testing, Hypothesis Testing
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Iverson, Geoffrey J.; Wagenmakers, Eric-Jan; Lee, Michael D. – Psychological Methods, 2010
The purpose of the recently proposed "p[subscript rep]" statistic is to estimate the probability of concurrence, that is, the probability that a replicate experiment yields an effect of the same sign (Killeen, 2005a). The influential journal "Psychological Science" endorses "p[subscript rep]" and recommends its use…
Descriptors: Effect Size, Evaluation Methods, Probability, Experiments
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