Application of Bayesian posterior probabilistic inference in educational trials

[1]  Xiao-Li Meng,et al.  Posterior Predictive $p$-Values , 1994 .

[2]  Zhiyong Zhang,et al.  Investigating Ceiling Effects in Longitudinal Data Analysis , 2008, Multivariate behavioral research.

[3]  M. Lee,et al.  Bayesian Benefits for the Pragmatic Researcher , 2016 .

[4]  L. Hedges,et al.  Intraclass Correlation Values for Planning Group-Randomized Trials in Education , 2007 .

[5]  Scott D. Brown,et al.  A simple introduction to Markov Chain Monte–Carlo sampling , 2016, Psychonomic bulletin & review.

[6]  Jeffrey N. Rouder,et al.  Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications , 2017, Psychonomic Bulletin & Review.

[7]  B. Thompson What Future Quantitative Social Science Research Could Look Like: Confidence Intervals for Effect Sizes , 2002 .

[8]  Gesine Reinert,et al.  Distances between nested densities and a measure of the impact of the prior in Bayesian statistics , 2015 .

[9]  G. Cumming Replication and p Intervals: p Values Predict the Future Only Vaguely, but Confidence Intervals Do Much Better , 2008, Perspectives on psychological science : a journal of the Association for Psychological Science.

[10]  Adetayo Kasim,et al.  Same difference? Understanding variation in the estimation of effect sizes from educational trials , 2016 .

[11]  Sarah Depaoli,et al.  Just Another Gibbs Sampler (JAGS) , 2016 .

[12]  Regina Nuzzo,et al.  Scientific method: Statistical errors , 2014, Nature.

[13]  Ahnalee M. Brincks,et al.  Modeling Site Effects in the Design and Analysis of Multi-site Trials , 2011, The American journal of drug and alcohol abuse.

[14]  David Gal,et al.  Abandon Statistical Significance , 2017, The American Statistician.

[15]  Karl J. Friston,et al.  Posterior probability maps and SPMs , 2003, NeuroImage.

[16]  Ziheng Yang,et al.  Branch-length prior influences Bayesian posterior probability of phylogeny. , 2005, Systematic biology.

[17]  I. Cuthill,et al.  Effect size, confidence interval and statistical significance: a practical guide for biologists , 2007, Biological reviews of the Cambridge Philosophical Society.

[18]  Andrew B. Lawson,et al.  Bayesian Biostatistics: Lesaffre/Bayesian Biostatistics , 2012 .

[19]  Satoshi Morita,et al.  Evaluating the Impact of Prior Assumptions in Bayesian Biostatistics , 2010, Statistics in biosciences.

[20]  Larry V. Hedges,et al.  What Are Effect Sizes and Why Do We Need Them , 2008 .

[21]  Christoph König,et al.  Bayesian statistics in educational research: a look at the current state of affairs , 2018 .

[22]  Stanley Pogrow How Effect Size (Practical Significance) Misleads Clinical Practice: The Case for Switching to Practical Benefit to Assess Applied Research Findings , 2019, The American Statistician.

[23]  I. Belitskaya-Levy,et al.  The debate about p-values , 2015, Shanghai archives of psychiatry.

[24]  Hariharan Swaminathan,et al.  An effect size measure and Bayesian analysis of single-case designs. , 2014, Journal of school psychology.

[25]  U. Hahn The Bayesian boom: good thing or bad? , 2014, Front. Psychol..

[26]  Andrew Gelman,et al.  General methods for monitoring convergence of iterative simulations , 1998 .

[27]  N. Lazar,et al.  Moving to a World Beyond “p < 0.05” , 2019, The American Statistician.

[28]  Antonis Rokas,et al.  Comparing bootstrap and posterior probability values in the four-taxon case. , 2003, Systematic biology.

[29]  Torrin M. Liddell,et al.  The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective , 2016, Psychonomic bulletin & review.

[30]  A. Gelfand,et al.  Efficient parametrisations for normal linear mixed models , 1995 .

[31]  Nathan P. Lemoine,et al.  Moving beyond noninformative priors: why and how to choose weakly informative priors in Bayesian analyses , 2019, Oikos.

[32]  Andrew Gelman,et al.  The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo , 2011, J. Mach. Learn. Res..

[33]  Steven N. Goodman,et al.  Why is Getting Rid of P-Values So Hard? Musings on Science and Statistics , 2019, The American Statistician.

[34]  B. Thompson Effect sizes, confidence intervals, and confidence intervals for effect sizes , 2007 .

[35]  S. Higgins Improving Learning , 2018 .

[36]  A. Gelfand,et al.  Identifiability, Improper Priors, and Gibbs Sampling for Generalized Linear Models , 1999 .

[37]  L. Hedges Effect Sizes in Cluster-Randomized Designs , 2007 .

[38]  D. Curran‐Everett,et al.  The fickle P value generates irreproducible results , 2015, Nature Methods.

[39]  B. Rannala,et al.  The Bayesian revolution in genetics , 2004, Nature Reviews Genetics.

[40]  Brian A. Nosek,et al.  Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015 , 2018, Nature Human Behaviour.

[41]  Mark W. Lipsey,et al.  Empirical Benchmarks for Interpreting Effect Sizes in Research , 2008 .

[42]  Tomasz Burzykowski,et al.  A Bayesian Framework Allowing Incorporation of Retrospective Information in Prospective Diagnostic Biomarker-Validation Designs , 2019, Statistics in Biopharmaceutical Research.