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1. Empirical prior distributions for Bayesian meta-analyses of binary and time to event outcomes

Author

Bartoš, František, Otte, Willem M., Gronau, Quentin F., Timmers, Bram, Ly, Alexander, and Wagenmakers, Eric-Jan

Subjects
Statistics - Methodology, 62F15, G.3
Abstract

Bayesian model-averaged meta-analysis allows quantification of evidence for both treatment effectiveness $\mu$ and across-study heterogeneity $\tau$. We use the Cochrane Database of Systematic Reviews to develop discipline-wide empirical prior distributions for $\mu$ and $\tau$ for meta-analyses of binary and time-to-event clinical trial outcomes. First, we use 50% of the database to estimate parameters of different required parametric families. Second, we use the remaining 50% of the database to select the best-performing parametric families and explore essential assumptions about the presence or absence of the treatment effectiveness and across-study heterogeneity in real data. We find that most meta-analyses of binary outcomes are more consistent with the absence of the meta-analytic effect or heterogeneity while meta-analyses of time-to-event outcomes are more consistent with the presence of the meta-analytic effect or heterogeneity. Finally, we use the complete database - with close to half a million trial outcomes - to propose specific empirical prior distributions, both for the field in general and for specific medical subdisciplines. An example from acute respiratory infections demonstrates how the proposed prior distributions can be used to conduct a Bayesian model-averaged meta-analysis in the open-source software R and JASP.

Published
2023

2. Evidential Calibration of Confidence Intervals

Author

Pawel, Samuel, Ly, Alexander, and Wagenmakers, Eric-Jan

Subjects
Statistics - Methodology
Abstract

We present a novel and easy-to-use method for calibrating error-rate based confidence intervals to evidence-based support intervals. Support intervals are obtained from inverting Bayes factors based on a parameter estimate and its standard error. A $k$ support interval can be interpreted as "the observed data are at least $k$ times more likely under the included parameter values than under a specified alternative". Support intervals depend on the specification of prior distributions for the parameter under the alternative, and we present several types that allow different forms of external knowledge to be encoded. We also show how prior specification can to some extent be avoided by considering a class of prior distributions and then computing so-called minimum support intervals which, for a given class of priors, have a one-to-one mapping with confidence intervals. We also illustrate how the sample size of a future study can be determined based on the concept of support. Finally, we show how the bound for the type I error rate of Bayes factors leads to a bound for the coverage of support intervals. An application to data from a clinical trial illustrates how support intervals can lead to inferences that are both intuitive and informative.

Published
2022
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3. History and Nature of the Jeffreys-Lindley Paradox

Author

Wagenmakers, Eric-Jan and Ly, Alexander

Subjects
Statistics - Methodology, Mathematics - Statistics Theory, Statistics - Other Statistics
Abstract

The Jeffreys-Lindley paradox exposes a rift between Bayesian and frequentist hypothesis testing that strikes at the heart of statistical inference. Contrary to what most current literature suggests, the paradox was central to the Bayesian testing methodology developed by Sir Harold Jeffreys in the late 1930s. Jeffreys showed that the evidence against a point-null hypothesis $\mathcal{H}_0$ scales with $\sqrt{n}$ and repeatedly argued that it would therefore be mistaken to set a threshold for rejecting $\mathcal{H}_0$ at a constant multiple of the standard error. Here we summarize Jeffreys's early work on the paradox and clarify his reasons for including the $\sqrt{n}$ term. The prior distribution is seen to play a crucial role; by implicitly correcting for selection, small parameter values are identified as relatively surprising under $\mathcal{H}_1$. We highlight the general nature of the paradox by presenting both a fully frequentist and a fully Bayesian version. We also demonstrate that the paradox does not depend on assigning prior mass to a point hypothesis, as is commonly believed., Comment: To be published in Archive for History of Exact Sciences

Published
2021

4. Bayesian Model-Averaged Meta-Analysis in Medicine

Author

Bartoš, František, Gronau, Quentin F., Timmers, Bram, Otte, Willem M., Ly, Alexander, and Wagenmakers, Eric-Jan

Subjects
Statistics - Methodology, 62F15, G.3
Abstract

We outline a Bayesian model-averaged meta-analysis for standardized mean differences in order to quantify evidence for both treatment effectiveness $\delta$ and across-study heterogeneity $\tau$. We construct four competing models by orthogonally combining two present-absent assumptions, one for the treatment effect and one for across-study heterogeneity. To inform the choice of prior distributions for the model parameters, we used 50% of the Cochrane Database of Systematic Reviews to specify rival prior distributions for $\delta$ and $\tau$. The relative predictive performance of the competing models and rival prior distributions was assessed using the remaining 50\% of the Cochrane Database. On average, $\mathcal{H}_1^r$ -- the model that assumes the presence of a treatment effect as well as across-study heterogeneity -- outpredicted the other models, but not by a large margin. Within $\mathcal{H}_1^r$, predictive adequacy was relatively constant across the rival prior distributions. We propose specific empirical prior distributions, both for the field in general and for each of 46 specific medical subdisciplines. An example from oral health demonstrates how the proposed prior distributions can be used to conduct a Bayesian model-averaged meta-analysis in the open-source software R and JASP. The preregistered analysis plan is available at https://osf.io/zs3df/.

Published
2021
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5. Generic E-Variables for Exact Sequential k-Sample Tests that allow for Optional Stopping

Author

Turner, Rosanne, Ly, Alexander, and Grünwald, Peter

Subjects
Statistics - Methodology, Computer Science - Machine Learning, Mathematics - Statistics Theory
Abstract

We develop E-variables for testing whether two or more data streams come from the same source or not, and more generally, whether the difference between the sources is larger than some minimal effect size. These E-variables lead to exact, nonasymptotic tests that remain safe, i.e. keep their type-I error guarantees, under flexible sampling scenarios such as optional stopping and continuation. In special cases our E-variables also have an optimal 'growth' property under the alternative. While the construction is generic, we illustrate it through the special case of k x 2 contingency tables, where we also allow for the incorporation of different restrictions on a composite alternative. Comparison to p-value analysis in simulations and a real-world example show that E-variables, through their flexibility, often allow for early stopping of data collection, thereby retaining similar power as classical methods, while also retaining the option of extending or combining data afterwards.

Published
2021

6. Bayes Factors for Peri-Null Hypotheses

Author

Ly, Alexander and Wagenmakers, Eric-Jan

Subjects
Mathematics - Statistics Theory, Statistics - Methodology, 62F03, 62F15 (Primary), 62F05 (Secondary)
Abstract

A perennial objection against Bayes factor point-null hypothesis tests is that the point-null hypothesis is known to be false from the outset. We examine the consequences of approximating the sharp point-null hypothesis by a hazy `peri-null' hypothesis instantiated as a narrow prior distribution centered on the point of interest. The peri-null Bayes factor then equals the point-null Bayes factor multiplied by a correction term which is itself a Bayes factor. For moderate sample sizes, the correction term is relatively inconsequential; however, for large sample sizes the correction term becomes influential and causes the peri-null Bayes factor to be inconsistent and approach a limit that depends on the ratio of prior ordinates evaluated at the maximum likelihood estimate. We characterize the asymptotic behavior of the peri-null Bayes factor and briefly discuss suggestions on how to construct peri-null Bayes factor hypothesis tests that are also consistent., Comment: Accepted for publication in Test

Published
2021

7. Default Bayes Factors for Testing the (In)equality of Several Population Variances

Author

Dablander, Fabian, Bergh, Don van den, Wagenmakers, Eric-Jan, and Ly, Alexander

Subjects
Statistics - Methodology
Abstract

Testing the (in)equality of variances is an important problem in many statistical applications. We develop default Bayes factor tests to assess the (in)equality of two or more population variances, as well as a test for whether the population variances equal a specific value. The resulting test can be used to check assumptions for commonly used procedures such as the $t$-test or ANOVA, or test substantive hypotheses concerning variances directly. We show that our Bayes factor fulfills a number of desiderata. Researchers may have directed hypotheses such as $\sigma_{1}^{2} > \sigma_{2}^{2}$, they may want to extend $\mathcal{H}_{0}$ to have a null-region, or wish to combine hypotheses about equality with hypotheses about inequality, for example $\sigma_{1}^{2} = \sigma_{2}^{2} > (\sigma_{3}^{2}, \sigma_{4}^{2})$. We extend our Bayes factor test to allow for these deviations from our proposed default and illustrate it on a number of practical examples. Our procedure is implemented in the R package $bfvartest$.

Published
2020

8. Crowdsourcing hypothesis tests: Making transparent how design choices shape research results.

Author

Landy, Justin F, Jia, Miaolei Liam, Ding, Isabel L, Viganola, Domenico, Tierney, Warren, Dreber, Anna, Johannesson, Magnus, Pfeiffer, Thomas, Ebersole, Charles R, Gronau, Quentin F, Ly, Alexander, van den Bergh, Don, Marsman, Maarten, Derks, Koen, Wagenmakers, Eric-Jan, Proctor, Andrew, Bartels, Daniel M, Bauman, Christopher W, Brady, William J, Cheung, Felix, Cimpian, Andrei, Dohle, Simone, Donnellan, M Brent, Hahn, Adam, Hall, Michael P, Jiménez-Leal, William, Johnson, David J, Lucas, Richard E, Monin, Benoît, Montealegre, Andres, Mullen, Elizabeth, Pang, Jun, Ray, Jennifer, Reinero, Diego A, Reynolds, Jesse, Sowden, Walter, Storage, Daniel, Su, Runkun, Tworek, Christina M, Van Bavel, Jay J, Walco, Daniel, Wills, Julian, Xu, Xiaobing, Yam, Kai Chi, Yang, Xiaoyu, Cunningham, William A, Schweinsberg, Martin, Urwitz, Molly, The Crowdsourcing Hypothesis Tests Collaboration, and Uhlmann, Eric L

Subjects
conceptual replications, crowdsourcing, forecasting, research robustness, scientific transparency, Crowdsourcing Hypothesis Tests Collaboration, Crowdsourcing, stimulus sampling, replication, Psychology, Cognitive Sciences, Marketing, Social Psychology
Abstract

To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer five original research questions related to moral judgments, negotiations, and implicit cognition. Participants from 2 separate large samples (total N > 15,000) were then randomly assigned to complete 1 version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: Materials from different teams rendered statistically significant effects in opposite directions for 4 of 5 hypotheses, with the narrowest range in estimates being d = -0.37 to + 0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for 2 hypotheses and a lack of support for 3 hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, whereas considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Published
2020

9. Bayesian Rank-Based Hypothesis Testing for the Rank Sum Test, the Signed Rank Test, and Spearman's $\rho$

Author

van Doorn, Johnny, Ly, Alexander, Marsman, Maarten, and Wagenmakers, Eric-Jan

Subjects
Statistics - Methodology
Abstract

Bayesian inference for rank-order problems is frustrated by the absence of an explicit likelihood function. This hurdle can be overcome by assuming a latent normal representation that is consistent with the ordinal information in the data: the observed ranks are conceptualized as an impoverished reflection of an underlying continuous scale, and inference concerns the parameters that govern the latent representation. We apply this generic data-augmentation method to obtain Bayes factors for three popular rank-based tests: the rank sum test, the signed rank test, and Spearman's $\rho_s$., Comment: 33 pages, 12 figures

Published
2017

10. A Tutorial on Fisher Information

Author

Ly, Alexander, Marsman, Maarten, Verhagen, Josine, Grasman, Raoul, and Wagenmakers, Eric-Jan

Subjects
Mathematics - Statistics Theory, 62-01, 62B10 (Primary), 62F03, 62F12, 62F15, 62B10 (Secondary)
Abstract

In many statistical applications that concern mathematical psychologists, the concept of Fisher information plays an important role. In this tutorial we clarify the concept of Fisher information as it manifests itself across three different statistical paradigms. First, in the frequentist paradigm, Fisher information is used to construct hypothesis tests and confidence intervals using maximum likelihood estimators; second, in the Bayesian paradigm, Fisher information is used to define a default prior; lastly, in the minimum description length paradigm, Fisher information is used to measure model complexity.

Published
2017

11. Informed Bayesian T-Tests

Author

Gronau, Quentin F., Ly, Alexander, and Wagenmakers, Eric-Jan

Subjects
Statistics - Methodology
Abstract

Across the empirical sciences, few statistical procedures rival the popularity of the frequentist t-test. In contrast, the Bayesian versions of the t-test have languished in obscurity. In recent years, however, the theoretical and practical advantages of the Bayesian t-test have become increasingly apparent and various Bayesian t-tests have been proposed, both objective ones (based on general desiderata) and subjective ones (based on expert knowledge). Here we propose a flexible t-prior for standardized effect size that allows computation of the Bayes factor by evaluating a single numerical integral. This specification contains previous objective and subjective t-test Bayes factors as special cases. Furthermore, we propose two measures for informed prior distributions that quantify the departure from the objective Bayes factor desiderata of predictive matching and information consistency. We illustrate the use of informed prior distributions based on an expert prior elicitation effort.

Published
2017

12. A Tutorial on Bridge Sampling

Author

Gronau, Quentin F., Sarafoglou, Alexandra, Matzke, Dora, Ly, Alexander, Boehm, Udo, Marsman, Maarten, Leslie, David S., Forster, Jonathan J., Wagenmakers, Eric-Jan, and Steingroever, Helen

Subjects
Statistics - Computation
Abstract

The marginal likelihood plays an important role in many areas of Bayesian statistics such as parameter estimation, model comparison, and model averaging. In most applications, however, the marginal likelihood is not analytically tractable and must be approximated using numerical methods. Here we provide a tutorial on bridge sampling (Bennett, 1976; Meng & Wong, 1996), a reliable and relatively straightforward sampling method that allows researchers to obtain the marginal likelihood for models of varying complexity. First, we introduce bridge sampling and three related sampling methods using the beta-binomial model as a running example. We then apply bridge sampling to estimate the marginal likelihood for the Expectancy Valence (EV) model---a popular model for reinforcement learning. Our results indicate that bridge sampling provides accurate estimates for both a single participant and a hierarchical version of the EV model. We conclude that bridge sampling is an attractive method for mathematical psychologists who typically aim to approximate the marginal likelihood for a limited set of possibly high-dimensional models.

Published
2017

13. Bayesian Estimation of Kendall's tau Using a Latent Normal Approach

Author

van Doorn, Johnny, Ly, Alexander, Marsman, Maarten, and Wagenmakers, Eric-Jan

Subjects
Statistics - Methodology
Abstract

The rank-based association between two variables can be modeled by introducing a latent normal level to ordinal data. We demonstrate how this approach yields Bayesian inference for Kendall's rank correlation coefficient, improving on a recent Bayesian solution from asymptotic properties of the test statistic., Comment: 13 pages, 2 figures

Published
2017

14. A test of the diffusion model explanation for the worst performance rule using preregistration and blinding

Author

Dutilh, Gilles, Vandekerckhove, Joachim, Ly, Alexander, Matzke, Dora, Pedroni, Andreas, Frey, Renato, Rieskamp, Jörg, and Wagenmakers, Eric-Jan

Subjects
Biological Psychology, Cognitive and Computational Psychology, Psychology, Clinical Research, Behavioral and Social Science, Mental health, Adolescent, Adult, Female, Humans, Intelligence, Male, Memory, Short-Term, Models, Psychological, Psychomotor Performance, Young Adult, Response time model, Preregistration, Predictions, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

People with higher IQ scores also tend to perform better on elementary cognitive-perceptual tasks, such as deciding quickly whether an arrow points to the left or the right Jensen (2006). The worst performance rule (WPR) finesses this relation by stating that the association between IQ and elementary-task performance is most pronounced when this performance is summarized by people's slowest responses. Previous research has shown that the WPR can be accounted for in the Ratcliff diffusion model by assuming that the same ability parameter-drift rate-mediates performance in both elementary tasks and higher-level cognitive tasks. Here we aim to test four qualitative predictions concerning the WPR and its diffusion model explanation in terms of drift rate. In the first stage, the diffusion model was fit to data from 916 participants completing a perceptual two-choice task; crucially, the fitting happened after randomly shuffling the key variable, i.e., each participant's score on a working memory capacity test. In the second stage, after all modeling decisions were made, the key variable was unshuffled and the adequacy of the predictions was evaluated by means of confirmatory Bayesian hypothesis tests. By temporarily withholding the mapping of the key predictor, we retain flexibility for proper modeling of the data (e.g., outlier exclusion) while preventing biases from unduly influencing the results. Our results provide evidence against the WPR and suggest that it may be less robust and less ubiquitous than is commonly believed.

Published
2017

15. Evidential Calibration of Confidence Intervals

Author

Pawel, Samuel; https://orcid.org/0000-0003-2779-320X, Ly, Alexander; https://orcid.org/0000-0003-3925-3833, Wagenmakers, Eric-Jan; https://orcid.org/0000-0003-1596-1034, Pawel, Samuel; https://orcid.org/0000-0003-2779-320X, Ly, Alexander; https://orcid.org/0000-0003-3925-3833, and Wagenmakers, Eric-Jan; https://orcid.org/0000-0003-1596-1034

Published
2024

16. Analytic Posteriors for Pearson's Correlation Coefficient

Author

Ly, Alexander, Marsman, Maarten, and Wagenmakers, Eric-Jan

Subjects
Mathematics - Statistics Theory, Statistics - Computation, 62H20, 62E15, 62F15
Abstract

Pearson's correlation is one of the most common measures of linear dependence. Recently, Bernardo (2015) introduced a flexible class of priors to study this measure in a Bayesian setting. For this large class of priors we show that the (marginal) posterior for Pearson's correlation coefficient and all of the posterior moments are analytic. Our results are available in the open-source software package JASP.

Published
2015

17. Data from a pre-publication independent replication initiative examining ten moral judgement effects.

Author

Tierney, Warren, Schweinsberg, Martin, Jordan, Jennifer, Kennedy, Deanna M, Qureshi, Israr, Sommer, S Amy, Thornley, Nico, Madan, Nikhil, Vianello, Michelangelo, Awtrey, Eli, Zhu, Luke Lei, Diermeier, Daniel, Heinze, Justin E, Srinivasan, Malavika, Tannenbaum, David, Bivolaru, Eliza, Dana, Jason, Davis-Stober, Clintin P, du Plessis, Christilene, Gronau, Quentin F, Hafenbrack, Andrew C, Liao, Eko Yi, Ly, Alexander, Marsman, Maarten, Murase, Toshio, Schaerer, Michael, Tworek, Christina M, Wagenmakers, Eric-Jan, Wong, Lynn, Anderson, Tabitha, Bauman, Christopher W, Bedwell, Wendy L, Brescoll, Victoria, Canavan, Andrew, Chandler, Jesse J, Cheries, Erik, Cheryan, Sapna, Cheung, Felix, Cimpian, Andrei, Clark, Mark A, Cordon, Diana, Cushman, Fiery, Ditto, Peter H, Amell, Alice, Frick, Sarah E, Gamez-Djokic, Monica, Grady, Rebecca Hofstein, Graham, Jesse, Gu, Jun, Hahn, Adam, Hanson, Brittany E, Hartwich, Nicole J, Hein, Kristie, Inbar, Yoel, Jiang, Lily, Kellogg, Tehlyr, Legate, Nicole, Luoma, Timo P, Maibeucher, Heidi, Meindl, Peter, Miles, Jennifer, Mislin, Alexandra, Molden, Daniel C, Motyl, Matt, Newman, George, Ngo, Hoai Huong, Packham, Harvey, Ramsay, P Scott, Ray, Jennifer L, Sackett, Aaron M, Sellier, Anne-Laure, Sokolova, Tatiana, Sowden, Walter, Storage, Daniel, Sun, Xiaomin, Van Bavel, Jay J, Washburn, Anthony N, Wei, Cong, Wetter, Erik, Wilson, Carlos T, Darroux, Sophie-Charlotte, and Uhlmann, Eric Luis

Subjects
Humans, Reproducibility of Results, Morals
Abstract

We present the data from a crowdsourced project seeking to replicate findings in independent laboratories before (rather than after) they are published. In this Pre-Publication Independent Replication (PPIR) initiative, 25 research groups attempted to replicate 10 moral judgment effects from a single laboratory's research pipeline of unpublished findings. The 10 effects were investigated using online/lab surveys containing psychological manipulations (vignettes) followed by questionnaires. Results revealed a mix of reliable, unreliable, and culturally moderated findings. Unlike any previous replication project, this dataset includes the data from not only the replications but also from the original studies, creating a unique corpus that researchers can use to better understand reproducibility and irreproducibility in science.

Published
2016

18. The pipeline project: Pre-publication independent replications of a single laboratory's research pipeline

Author

Schweinsberg, Martin, Madan, Nikhil, Vianello, Michelangelo, Sommer, S Amy, Jordan, Jennifer, Tierney, Warren, Awtrey, Eli, Zhu, Luke Lei, Diermeier, Daniel, Heinze, Justin E, Srinivasan, Malavika, Tannenbaum, David, Bivolaru, Eliza, Dana, Jason, Davis-Stober, Clintin P, du Plessis, Christilene, Gronau, Quentin F, Hafenbrack, Andrew C, Liao, Eko Yi, Ly, Alexander, Marsman, Maarten, Murase, Toshio, Qureshi, Israr, Schaerer, Michael, Thornley, Nico, Tworek, Christina M, Wagenmakers, Eric-Jan, Wong, Lynn, Anderson, Tabitha, Bauman, Christopher W, Bedwell, Wendy L, Brescoll, Victoria, Canavan, Andrew, Chandler, Jesse J, Cheries, Erik, Cheryan, Sapna, Cheung, Felix, Cimpian, Andrei, Clark, Mark A, Cordon, Diana, Cushman, Fiery, Ditto, Peter H, Donahue, Thomas, Frick, Sarah E, Gamez-Djokic, Monica, Grady, Rebecca Hofstein, Graham, Jesse, Gu, Jun, Hahn, Adam, Hanson, Brittany E, Hartwich, Nicole J, Hein, Kristie, Inbar, Yoel, Jiang, Lily, Kellogg, Tehlyr, Kennedy, Deanna M, Legate, Nicole, Luoma, Timo P, Maibuecher, Heidi, Meindl, Peter, Miles, Jennifer, Mislin, Alexandra, Molden, Daniel C, Motyl, Matt, Newman, George, Ngo, Hoai Huong, Packham, Harvey, Ramsay, Philip S, Ray, Jennifer L, Sackett, Aaron M, Sellier, Anne-Laure, Sokolova, Tatiana, Sowden, Walter, Storage, Daniel, Sun, Xiaomin, Van Bavel, Jay J, Washburn, Anthony N, Wei, Cong, Wetter, Erik, Wilson, Carlos T, Darroux, Sophie-Charlotte, and Uhlmann, Eric Luis

Subjects
Heart Disease, Cardiovascular, Crowdsourcing science, Replication, Reproducibility, Research transparency, Methodology, Meta-science, Psychology, Cognitive Sciences, Social Psychology
Abstract

This crowdsourced project introduces a collaborative approach to improving the reproducibility of scientific research, in which findings are replicated in qualified independent laboratories before (rather than after) they are published. Our goal is to establish a non-adversarial replication process with highly informative final results. To illustrate the Pre-Publication Independent Replication (PPIR) approach, 25 research groups conducted replications of all ten moral judgment effects which the last author and his collaborators had “in the pipeline” as of August 2014. Six findings replicated according to all replication criteria, one finding replicated but with a significantly smaller effect size than the original, one finding replicated consistently in the original culture but not outside of it, and two findings failed to find support. In total, 40% of the original findings failed at least one major replication criterion. Potential ways to implement and incentivize pre-publication independent replication on a large scale are discussed.

Published
2016

20. What Are the Odds? Modern Relevance and Bayes Factor Solutions for MacAlister's Problem from the 1881 'Educational Times'

Author

Jamil, Tahira, Marsman, Maarten, Ly, Alexander, Morey, Richard D., and Wagenmakers, Eric-Jan

Abstract

In 1881, Donald MacAlister posed a problem in the "Educational Times" that remains relevant today. The problem centers on the statistical evidence for the effectiveness of a treatment based on a comparison between two proportions. A brief historical sketch is followed by a discussion of two default Bayesian solutions, one based on a one-sided test between independent rates, and one on a one-sided test between dependent rates. We demonstrate the current-day relevance of MacAlister's original question with a modern-day example about the effectiveness of an educational program.

Published
2017
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22. Evidential Calibration of Confidence Intervals.

Author

Pawel, Samuel, Ly, Alexander, and Wagenmakers, Eric-Jan

Subjects
FALSE positive error, BAYESIAN analysis, CALIBRATION, ERROR rates
Abstract

We present a novel and easy-to-use method for calibrating error-rate based confidence intervals to evidence-based support intervals. Support intervals are obtained from inverting Bayes factors based on a parameter estimate and its standard error. A k support interval can be interpreted as "the observed data are at least k times more likely under the included parameter values than under a specified alternative." Support intervals depend on the specification of prior distributions for the parameter under the alternative, and we present several types that allow different forms of external knowledge to be encoded. We also show how prior specification can to some extent be avoided by considering a class of prior distributions and then computing so-called minimum support intervals which, for a given class of priors, have a one-to-one mapping with confidence intervals. We also illustrate how the sample size of a future study can be determined based on the concept of support. Finally, we show how the bound for the Type I error rate of Bayes factors leads to a bound for the coverage of support intervals. An application to data from a clinical trial illustrates how support intervals can lead to inferences that are both intuitive and informative. [ABSTRACT FROM AUTHOR]

Published
2024
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25. Replication Bayes factors from evidence updating

Author

Marsman, Maarten, Wagenmakers , Eric-Jan, Etz , Alexander, Ly, Alexander, FMG, and Psychologische Methodenleer (Psychologie, FMG)

Subjects
General method, Computer science, Experimental psychology, Replication, Experimental and Cognitive Psychology, Machine learning, computer.software_genre, Social and Behavioral Sciences, 050105 experimental psychology, Article, 03 medical and health sciences, 0302 clinical medicine, Arts and Humanities (miscellaneous), Replication (statistics), Developmental and Educational Psychology, Psychology, Humans, 0501 psychology and cognitive sciences, General Psychology, Statistical hypothesis testing, Theory and Philosophy, business.industry, 05 social sciences, Bayes factor, Bayes Theorem, PsyArXiv|Social and Behavioral Sciences|Theory and Philosophy of Science, FOS: Psychology, PsyArXiv|Social and Behavioral Sciences, Meta-analysis, Hypothesis testing, Research Design, Evidence synthesis, Data Interpretation, Statistical, bepress|Social and Behavioral Sciences, bepress|Social and Behavioral Sciences|Psychology|Theory and Philosophy, Psychology (miscellaneous), Artificial intelligence, business, computer, 030217 neurology & neurosurgery
Abstract

We describe a general method that allows experimenters to quantify the evidence from the data of a direct replication attempt given data already acquired from an original study. These so-called replication Bayes factors are a reconceptualization of the ones introduced by Verhagen and Wagenmakers (Journal of Experimental Psychology: General, 143(4), 1457–1475 2014) for the common t test. This reconceptualization is computationally simpler and generalizes easily to most common experimental designs for which Bayes factors are available.

Published
2019

27. sj-pdf-1-amp-10.1177_2515245921992035 – Supplemental material for A Cautionary Note on Estimating Effect Size

Author

Bergh, Don Van Den, Haaf, Julia M., Ly, Alexander, Rouder, Jeffrey N., and Eric-Jan Wagenmakers

Subjects
FOS: Psychology, FOS: Clinical medicine, 170199 Psychology not elsewhere classified, 110319 Psychiatry (incl. Psychotherapy)
Abstract

Supplemental material, sj-pdf-1-amp-10.1177_2515245921992035 for A Cautionary Note on Estimating Effect Size by Don van den Bergh, Julia M. Haaf, Alexander Ly, Jeffrey N. Rouder and Eric-Jan Wagenmakers in Advances in Methods and Practices in Psychological Science

Published
2021
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28. A tutorial on conducting and interpreting a bayesian ANOVA in JASP Tutoriel pour réaliser et interpréter une analyse de variance bayésienne dans JASP

Author

Bergh, Don, Doorn, Johnny, Marsman, Maarten, Draws, Tim, Kesteren, Erik-Jan, Derks, Koen, Dablander, Fabian, Gronau, Quentin, Kucharský, Šimon, Komarlu Narendra Gupta, Akash, Sarafoglou, Alexandra, Voelkel, Jan, Stefan, Angelika, Ly, Alexander, Hinne, Max, Matzke, Dora, Wagenmakers, Eric-Jan, and Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands

Subjects
Analysis of Variance, Bayes Factor, Hypothesis Test, Tutorial, JASP, Posterior distribution
Abstract

Analysis of variance (ANOVA) is the standard procedure for statistical inference in factorial designs. Typically, ANOVAs are executed using frequentist statistics, where p-values determine statistical significance in an all-or-none fashion. In recent years, the Bayesian approach to statistics is increasingly viewed as a legitimate alternative to the p-value. However, the broad adoption of Bayesian statistics-and Bayesian ANOVA in particular-is frustrated by the fact that Bayesian concepts are rarely taught in applied statistics courses. Consequently, practitioners may be unsure how to conduct a Bayesian ANOVA and interpret the results. Here we provide a guide for executing and interpreting a Bayesian ANOVA with JASP, an open-source statistical software program with a graphical user interface. We explain the key concepts of the Bayesian ANOVA using two empirical examples.

Published
2020

29. Utvikling av et sirkulært økonomisk system for Skogmo Industripark Case: Materialstrømmer og avfall av byggevarer

Author

Ly, Alexander Tri Vinh and Hermansen, John Eilif

Abstract

Året 2020 er et avgjørende år for å ivareta livet på jorda og nå FNs bærekraftsmål innen 2030. Hensikten med studien var å utforske hvordan Skogmo Industripark kan utvikle til en attraktiv, konkurransedyktig, og bærekraftig industripark med muligens anvendelse av industrielle økolo-gisk og sirkulært økonomisk perspektiv. Studien gikk ut på å få operative materialer til å gå tilbake til ressurs og økonomien hvor det ville bli lønnsomt for bedriftene til å samarbeide bedre i et fellesskap. Der ble det kartlagt over ressursstrømmer på Skogmo og konstruert et leveransekjedesystem av betong mellom små og mellomstore bedrifter. Deretter ble det vurdert muligheter for bedre ressursutnyttelse og kobling av et ufullstendig sirkulært økonomisk system fantes i regionen. Videre ble det undersøkt ulike synsvinkler og arbeid hos avfallsbedrifter i Midt-Norge for å identifisere samarbeidsmuligheter for et sirkulært økonomisk perspektiv i regionen. Tilslutt ble det identifisert og analysert virkemidler og barrierer for oppnåelse av FNs bærekraftsmålene for Skogmo Industripark slik at de bidra en del av strategien for det grønne skiftet i Norge. Metoden ble gjennomført som en virksomhetsorientert forskning der dataene ble innsamlet i form av omfattende gjennomgang av dokumenter fra offentlige institusjoner og empirier fra aktørene i Skogmo Industripark og Midt-Norge. Resultatet viste at små og mellomstore bedrifter i Skogmo Industripark hadde et velfungerende leveransekjedesystem med gode verdinettverk, men genererte store mengder spillressurser etter-som systemet var lineært og ga miljøpåvirkning. Samtidig viste det seg at samarbeidsmuligheter mellom avfallsbedrifter og næringsklynger i regionen var gode på immaterielle verdier, men var svake på materielle ettersom det fantes ingen resirkulerbare løsninger på den tekniske syklusen i et lukket sløyfesystem, men i et åpent sløyfesystem. Det ble funnet ut at mennesker og organisasjonen utgjorde de største barrierene for virkemidle-ne og ville være krevende for arbeidet mot oppnåelse av FNs bærekraftsmål i Skogmo Industri-park. For å håndtere barrierene og utfordringene, ble det funnet ut at samfunnet må utvikles i samme retning og virksomhetene i Skogmo må ha større samarbeid, åpenhet, og tillit noen gang. Prosessen kommer til være krevende, men kan gi gode effekter tilslutt. The year 2020 is a crucial year for safeguarding life on earth and achieving the UN's sustaina-bility goals by 2030. The purpose of the study was to explore how Skogmo Industrial Park can develop into an at-tractive, competitive, and sustainable industrial park with the possible application of an industri-al ecological and circular economic perspective. The study aimed to get operational materials to go back to resources and the economy where it would be profitable for companies to collaborate better in a community. There, resource flows at Skogmo were mapped and a concrete supply chain system was constructed between small and medium-sized companies. Subsequently, opportunities for better resource utilization and con-nection of an incomplete circular economic system that existed in the region were assessed. Furthermore, different points of view and work at waste companies in Central Norway were examined to identify collaboration opportunities for a circular economic perspective in region. Finally, instruments and barriers to achieving the UN's sustainability goals for Skogmo Indus-trial Park were identified and analyzed so that they contribute to part of the strategy for the green shift in Norway. The method was carried out as business-oriented research where the data was collected in the form of a comprehensive review of documents from public institutions and empirical data from the actors in Central Norway and Skogmo Industrial Park. The results showed that small and medium-sized companies in Skogmo Industrial Park had a well-functioning supply chain system with good value networks but generated large amounts of waste resources as the system was linear and gave environmental impacts. At the same time, it turned out that co-operation opportunities between waste companies and business clusters in the region were good on intangible assets but were weak on tangible as there were no recyclable solutions to the technical cycle in a closed loop system but in an open loop system. It was found that people and the organization were the biggest barriers to the instruments and would be demanding for the work towards achieving the UN's sustainability goals in Skogmo Industrial Park. To deal with the barriers and challenges, it was found that society must develop in the same direction and the businesses in Skogmo must have greater cooperation, openness, and trust ever. The process will be demanding but can have a good development effect.

Published
2020

30. Crowdsourcing hypothesis tests: Making transparent how design choices shape research results.

Author

Landy, Justin F., Jia, Miaolei (Liam), Ding, Isabel L., Viganola, Domenico, Tierney, Warren, Dreber, Anna, Johannesson, Magnus, Pfeiffer, Thomas, Ebersole, Charles R., Gronau, Quentin F., Ly, Alexander, van den Bergh, Don, Marsman, Maarten, Derks, Koen, Wagenmakers, Eric-Jan, Proctor, Andrew, Bartels, Daniel M., Bauman, Christopher W., Brady, William J., Cheung, Felix, Cimpian, Andrei, Dohle, Simone, Donnellan, M. Brent, Hahn, Adam, Hall, Michael P., Jiménez-Leal, William, Johnson, David J., Lucas, Richard E., Monin, Benoît, Montealegre, Andres, Mullen, Elizabeth, Pang, Jun, Ray, Jennifer, Reinero, Diego A., Reynolds, Jesse, Sowden, Walter, Storage, Daniel, Su, Runkun, Tworek, Christina M., Van Bavel, Jay J., Walco, Daniel, Wills, Julian, Xu, Xiaobing, Yam, Kai Chi, Yang, Xiaoyu, Cunningham, William A., Schweinsberg, Martin, Urwitz, Molly, The Crowdsourcing Hypothesis Tests Collaboration, Uhlmann, Eric L., Landy, Justin F., Jia, Miaolei (Liam), Ding, Isabel L., Viganola, Domenico, Tierney, Warren, Dreber, Anna, Johannesson, Magnus, Pfeiffer, Thomas, Ebersole, Charles R., Gronau, Quentin F., Ly, Alexander, van den Bergh, Don, Marsman, Maarten, Derks, Koen, Wagenmakers, Eric-Jan, Proctor, Andrew, Bartels, Daniel M., Bauman, Christopher W., Brady, William J., Cheung, Felix, Cimpian, Andrei, Dohle, Simone, Donnellan, M. Brent, Hahn, Adam, Hall, Michael P., Jiménez-Leal, William, Johnson, David J., Lucas, Richard E., Monin, Benoît, Montealegre, Andres, Mullen, Elizabeth, Pang, Jun, Ray, Jennifer, Reinero, Diego A., Reynolds, Jesse, Sowden, Walter, Storage, Daniel, Su, Runkun, Tworek, Christina M., Van Bavel, Jay J., Walco, Daniel, Wills, Julian, Xu, Xiaobing, Yam, Kai Chi, Yang, Xiaoyu, Cunningham, William A., Schweinsberg, Martin, Urwitz, Molly, The Crowdsourcing Hypothesis Tests Collaboration, and Uhlmann, Eric L.

Published
2020

31. What to do with all these Bayes factors : How to make Bayesian reports in deception research more informative

Author

Mac Giolla, Erik, Ly, Alexander, Mac Giolla, Erik, and Ly, Alexander

Abstract

Bayes factors quantify the evidence in support of the null (absence of an effect) or the alternative hypothesis (presence of an effect). Based on commonly used cut-offs, Bayes factors between 1/3 and 3 are interpreted as evidentially weak, and one typically concludes there is an absence of evidence. In this commentary on Warmelink, Subramanian, Tkacheva, and McLatchie (Legal Criminol Psychol 24, 2019, 258), we discuss how a Bayesian report can be made more informative. Firstly, this implies a departure from the labels provided by commonly used cut-offs when reporting Bayes factors. Instead, we encourage researchers to report the value of the Bayes factors, or to convert these values into nominal support for the hypotheses. Secondly, researchers can provide recommendations to design follow-up studies by examining the posterior distribution of the magnitude of the effect size. Lastly, we show how individual Bayes factors can be evaluated in the context of large-scale meta-analyses. © 2019 British Psychological Society

Published
2020
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32. Crowdsourcing hypothesis tests: Making transparent how design choices shape research results

Author

Landy, J, Jia, M, Ding, I, Viganola, D, Tierney, W, Dreber, A, Johannesson, M, Pfeiffer, T, Ebersole, C, Gronau, Q, Ly, A, van den Bergh, D, Marsman, M, Derks, K, Wagenmakers, E, Proctor, A, Bartels, D, Bauman, C, Brady, W, Cheung, F, Cimpian, A, Dohle, S, Donnellan, M, Hahn, A, Hall, M, Jiménez-Leal, W, Johnson, D, Lucas, R, Monin, B, Montealegre, A, Mullen, E, Pang, J, Ray, J, Reinero, D, Reynolds, J, Sowden, W, Storage, D, Su, R, Tworek, C, Van Bavel, J, Walco, D, Wills, J, Xu, X, Yam, K, Yang, X, Cunningham, W, Schweinsberg, M, Urwitz, M, The Crowdsourcing Hypothesis Tests, C, Uhlmann, E, Mari, S, Landy, Justin F, Jia, Miaolei Liam, Ding, Isabel L, Viganola, Domenico, Tierney, Warren, Dreber, Anna, Johannesson, Magnus, Pfeiffer, Thomas, Ebersole, Charles R, Gronau, Quentin F, Ly, Alexander, van den Bergh, Don, Marsman, Maarten, Derks, Koen, Wagenmakers, Eric-Jan, Proctor, Andrew, Bartels, Daniel M, Bauman, Christopher W, Brady, William J, Cheung, Felix, Cimpian, Andrei, Dohle, Simone, Donnellan, M Brent, Hahn, Adam, Hall, Michael P, Jiménez-Leal, William, Johnson, David J, Lucas, Richard E, Monin, Benoît, Montealegre, Andres, Mullen, Elizabeth, Pang, Jun, Ray, Jennifer, Reinero, Diego A, Reynolds, Jesse, Sowden, Walter, Storage, Daniel, Su, Runkun, Tworek, Christina M, Van Bavel, Jay J, Walco, Daniel, Wills, Julian, Xu, Xiaobing, Yam, Kai Chi, Yang, Xiaoyu, Cunningham, William A, Schweinsberg, Martin, Urwitz, Molly, The Crowdsourcing Hypothesis Tests Collaboration, Uhlmann, Eric L, Landy, J, Jia, M, Ding, I, Viganola, D, Tierney, W, Dreber, A, Johannesson, M, Pfeiffer, T, Ebersole, C, Gronau, Q, Ly, A, van den Bergh, D, Marsman, M, Derks, K, Wagenmakers, E, Proctor, A, Bartels, D, Bauman, C, Brady, W, Cheung, F, Cimpian, A, Dohle, S, Donnellan, M, Hahn, A, Hall, M, Jiménez-Leal, W, Johnson, D, Lucas, R, Monin, B, Montealegre, A, Mullen, E, Pang, J, Ray, J, Reinero, D, Reynolds, J, Sowden, W, Storage, D, Su, R, Tworek, C, Van Bavel, J, Walco, D, Wills, J, Xu, X, Yam, K, Yang, X, Cunningham, W, Schweinsberg, M, Urwitz, M, The Crowdsourcing Hypothesis Tests, C, Uhlmann, E, Mari, S, Landy, Justin F, Jia, Miaolei Liam, Ding, Isabel L, Viganola, Domenico, Tierney, Warren, Dreber, Anna, Johannesson, Magnus, Pfeiffer, Thomas, Ebersole, Charles R, Gronau, Quentin F, Ly, Alexander, van den Bergh, Don, Marsman, Maarten, Derks, Koen, Wagenmakers, Eric-Jan, Proctor, Andrew, Bartels, Daniel M, Bauman, Christopher W, Brady, William J, Cheung, Felix, Cimpian, Andrei, Dohle, Simone, Donnellan, M Brent, Hahn, Adam, Hall, Michael P, Jiménez-Leal, William, Johnson, David J, Lucas, Richard E, Monin, Benoît, Montealegre, Andres, Mullen, Elizabeth, Pang, Jun, Ray, Jennifer, Reinero, Diego A, Reynolds, Jesse, Sowden, Walter, Storage, Daniel, Su, Runkun, Tworek, Christina M, Van Bavel, Jay J, Walco, Daniel, Wills, Julian, Xu, Xiaobing, Yam, Kai Chi, Yang, Xiaoyu, Cunningham, William A, Schweinsberg, Martin, Urwitz, Molly, The Crowdsourcing Hypothesis Tests Collaboration, and Uhlmann, Eric L

Abstract

To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer five original research questions related to moral judgments, negotiations, and implicit cognition. Participants from 2 separate large samples (total N > 15,000) were then randomly assigned to complete 1 version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: Materials from different teams rendered statistically significant effects in opposite directions for 4 of 5 hypotheses, with the narrowest range in estimates being d = -0.37 to + 0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for 2 hypotheses and a lack of support for 3 hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, whereas considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim.

Published
2020

33. A tutorial on bridge sampling

Author

Marsman, Maarten, Gronau , Quentin, Matzke , Dora, Leslie, David, Steingroever , Helen, Forster, Jonathan, Boehm , Udo, Sarafoglou , Alexandra, Wagenmakers , Eric-Jan, Ly , Alexander, Psychologische Methodenleer (Psychologie, FMG), and FMG

Subjects
FOS: Computer and information sciences, MetaArXiv|Social and Behavioral Sciences|Psychology|Quantitative Psychology, bepress|Social and Behavioral Sciences|Psychology|Quantitative Psychology, Social and Behavioral Sciences, Statistics - Computation, 01 natural sciences, Article, 050105 experimental psychology, Hierarchical database model, 010104 statistics & probability, Reinforcement learning, Statistics, Psychology, 0501 psychology and cognitive sciences, Normalizing constant, 0101 mathematics, Computation (stat.CO), General Psychology, Mathematics, bepress|Social and Behavioral Sciences|Psychology, MetaArXiv|Social and Behavioral Sciences, BITSS|Social and Behavioral Sciences|Psychology, BITSS|Social and Behavioral Sciences|Psychology|Quantitative Psychology, Estimation theory, Applied Mathematics, 05 social sciences, Predictive accuracy, Sampling (statistics), Quantitative Psychology, Bayes factor, Marginal likelihood, FOS: Psychology, Bayesian statistics, MetaArXiv|Social and Behavioral Sciences|Psychology, bepress|Social and Behavioral Sciences, BITSS|Social and Behavioral Sciences, Hierarchical model, Algorithm
Abstract

The marginal likelihood plays an important role in many areas of Bayesian statistics such as parameter estimation, model comparison, and model averaging. In most applications, however, the marginal likelihood is not analytically tractable and must be approximated using numerical methods. Here we provide a tutorial on bridge sampling (Bennett, 1976; Meng & Wong, 1996), a reliable and relatively straightforward sampling method that allows researchers to obtain the marginal likelihood for models of varying complexity. First, we introduce bridge sampling and three related sampling methods using the beta-binomial model as a running example. We then apply bridge sampling to estimate the marginal likelihood for the Expectancy Valence (EV) model—a popular model for reinforcement learning. Our results indicate that bridge sampling provides accurate estimates for both a single participant and a hierarchical version of the EV model. We conclude that bridge sampling is an attractive method for mathematical psychologists who typically aim to approximate the marginal likelihood for a limited set of possibly high-dimensional models., Highlights • We provide a tutorial on bridge sampling for estimating marginal likelihoods. • We use the beta-binomial model as a running example. • We estimate the marginal likelihood for the Expectancy Valence (EV) model. • We obtain accurate results for individual-level and hierarchical EV model versions.

Published
2017

36. Bayes factors for research workers

Author

Ly, Alexander, Wagenmakers, Eric-Jan, Marsman, Maarten, and Psychologische Methodenleer (Psychologie, FMG)

Abstract

In this dissertation we advocate the use of Bayes factors in empirical research to replace or complement standard null hypothesis tests based on p-values. These Bayes factors were specifically designed to quantify the evidence for or against the existence of an effect. This was done by comparing two models with the same distributional assumptions, where the alternative model is an extension of the null model by incorporating one extra parameter. Instead of returning a decision to “reject” or “not reject”, a Bayes factor BF10(d) returns a non-negative number that represents the evidence provided by the observed data d for the model that includes the effect. The returned number can be seen as a refinement of the binary decision with BF10(d) = ∞ and BF10(d) = 0 corresponding to definite rejection and acceptance of the null, respectively. Moreover, the Bayes factor allows its users to forgo the binary decision and acknowledge uncertainty, so that the evidence can be updated continually in light of new data, directly and easily. For empirical scientists to be able to use these Bayes factors, we implemented them in Jeffreys’s Amazing Statistics Program, JASP, which is freely available and open-source (url: https://jasp-stats.org).

Published
2018

38. JASP: Graphical Statistical Software for Common Statistical Designs

Author

Love, Jonathon, primary, Selker, Ravi, additional, Marsman, Maarten, additional, Jamil, Tahira, additional, Dropmann, Damian, additional, Verhagen, Josine, additional, Ly, Alexander, additional, Gronau, Quentin F., additional, Smíra, Martin, additional, Epskamp, Sacha, additional, Matzke, Dora, additional, Wild, Anneliese, additional, Knight, Patrick, additional, Rouder, Jeffrey N., additional, Morey, Richard D., additional, and Wagenmakers, Eric-Jan, additional

Published
2019
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39. Informed Bayesian t-Tests.

Author

Gronau, Quentin F., Ly, Alexander, and Wagenmakers, Eric-Jan

Subjects
POPULARITY, TECHNICAL specifications, DIVERGENCE theorem
Abstract

Across the empirical sciences, few statistical procedures rival the popularity of the frequentist t -test. In contrast, the Bayesian versions of the t -test have languished in obscurity. In recent years, however, the theoretical and practical advantages of the Bayesian t -test have become increasingly apparent and various Bayesian t-tests have been proposed, both objective ones (based on general desiderata) and subjective ones (based on expert knowledge). Here, we propose a flexible t-prior for standardized effect size that allows computation of the Bayes factor by evaluating a single numerical integral. This specification contains previous objective and subjective t-test Bayes factors as special cases. Furthermore, we propose two measures for informed prior distributions that quantify the departure from the objective Bayes factor desiderata of predictive matching and information consistency. We illustrate the use of informed prior distributions based on an expert prior elicitation effort. for this article are available online. [ABSTRACT FROM AUTHOR]

Published
2020
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41. Synthesis and Characterization of an Alumina Forming Nanolaminated Boride: MoAlB

Author

Kota, Sankalp, Zapata-Solvas, Eugenio, Ly, Alexander, Lu, Jun, Elkassabany, Omar, Huon, Amanda, Lee, William E., Hultman, Lars, May, Steve J., Barsoum, Michel W., Kota, Sankalp, Zapata-Solvas, Eugenio, Ly, Alexander, Lu, Jun, Elkassabany, Omar, Huon, Amanda, Lee, William E., Hultman, Lars, May, Steve J., and Barsoum, Michel W.

Abstract

The MAlB phases are nanolaminated, ternary transition metal borides that consist of a transition metal boride sublattice interleaved by monolayers or bilayers of pure aluminum. However, their synthesis and properties remain largely unexplored. Herein, we synthesized dense, predominantly single-phase samples of one such compound, MoAlB, using a reactive hot pressing method. High-resolution scanning transmission electron microscopy confirmed the presence of two Al layers in between a Mo-B sublattice. Unique among the transition metal borides, MoAlB forms a dense, mostly amorphous, alumina scale when heated in air. Like other alumina formers, the oxidation kinetics follow a cubic time-dependence. At room temperature, its resistivity is low (0.36-0.49 mu Omega m) and - like a metal - drops linearly with decreasing temperatures. It is also a good thermal conductor (35 Wm(-1)K(-1) at 26 degrees C). In the 25-1300 degrees C temperature range, its thermal expansion coefficient is 9.5 x 10(-6) K-1. Preliminary results suggest the compound is stable to at least 1400 degrees C in inert atmospheres. Moderately low Vickers hardness values of 10.6 +/- 0.3 GPa, compared to other transition metal borides, and ultimate compressive strengths up to 1940 +/- 103 MPa were measured at room temperature. These results are encouraging and warrant further study of this compound for potential use at high temperatures., Funding Agencies|Leverhulme Trust; Army Research Office [W911NF-11-1-0525]

Published
2016
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42. Data Descriptor: Data from a pre-publication independent replication initiative examining ten moral judgement effects

Author

Tierney, Warren, Schweinsberg, Martin, Jordan, Jennifer, Kennedy, Deanna M., Qureshi, Israr, Sommer, S. Amy, Thornley, Nico, Madan, Nikhil, Vianello, Michelangelo, Awtrey, Eli, Zhu, Luke Lei, Diermeier, Daniel, Heinze, Justin E., Srinivasan, Malavika, Tannenbaum, David, Bivolaru, Eliza, Dana, Jason, Davis-Stober, Clintin P., du Plessis, Christilene, Gronau, Quentin F., Hafenbrack, Andrew C., Liao, Eko Yi, Ly, Alexander, Marsman, Maarten, Murase, Toshio, Schaerer, Michael, Tworek, Christina M., Wagenmakers, Eric-Jan, Wong, Lynn, Anderson, Tabitha, Bauman, Christopher W., Bedwell, Wendy L., Brescoll, Victoria, Canavan, Andrew, Chandler, Jesse J., Cheries, Erik, Cheryan, Sapna, Cheung, Felix, Cimpian, Andrei, Clark, Mark A., Cordon, Diana, Cushman, Fiery, Ditto, Peter H., Amell, Alice, Frick, Sarah E., Gamez-Djokic, Monica, Grady, Rebecca Hofstein, Graham, Jesse, Gu, Jun, Hahn, Adam, Hanson, Brittany E., Hartwich, Nicole J., Hein, Kristie, Inbar, Yoel, Jiang, Lily, Kellogg, Tehlyr, Legate, Nicole, Luoma, Timo P., Maibeucher, Heidi, Meindl, Peter, Miles, Jennifer, Mislin, Alexandra, Molden, Daniel C., Motyl, Matt, Newman, George, Packham, Harvey, Ramsay, P. Scott, Ray, Jennifer L., Sackett, Aaron M., Sellier, Anne-Laure, Sokolova, Tatiana, Sowden, Walter, Storage, Daniel, Sun, Xiaomin, Van Bavel, Jay J., Washburn, Anthony N., Wei, Cong, Wetter, Erik, Wilson, Carlos T., Darroux, Sophie-Charlotte, Uhlmann, Eric Luis, Tierney, Warren, Schweinsberg, Martin, Jordan, Jennifer, Kennedy, Deanna M., Qureshi, Israr, Sommer, S. Amy, Thornley, Nico, Madan, Nikhil, Vianello, Michelangelo, Awtrey, Eli, Zhu, Luke Lei, Diermeier, Daniel, Heinze, Justin E., Srinivasan, Malavika, Tannenbaum, David, Bivolaru, Eliza, Dana, Jason, Davis-Stober, Clintin P., du Plessis, Christilene, Gronau, Quentin F., Hafenbrack, Andrew C., Liao, Eko Yi, Ly, Alexander, Marsman, Maarten, Murase, Toshio, Schaerer, Michael, Tworek, Christina M., Wagenmakers, Eric-Jan, Wong, Lynn, Anderson, Tabitha, Bauman, Christopher W., Bedwell, Wendy L., Brescoll, Victoria, Canavan, Andrew, Chandler, Jesse J., Cheries, Erik, Cheryan, Sapna, Cheung, Felix, Cimpian, Andrei, Clark, Mark A., Cordon, Diana, Cushman, Fiery, Ditto, Peter H., Amell, Alice, Frick, Sarah E., Gamez-Djokic, Monica, Grady, Rebecca Hofstein, Graham, Jesse, Gu, Jun, Hahn, Adam, Hanson, Brittany E., Hartwich, Nicole J., Hein, Kristie, Inbar, Yoel, Jiang, Lily, Kellogg, Tehlyr, Legate, Nicole, Luoma, Timo P., Maibeucher, Heidi, Meindl, Peter, Miles, Jennifer, Mislin, Alexandra, Molden, Daniel C., Motyl, Matt, Newman, George, Packham, Harvey, Ramsay, P. Scott, Ray, Jennifer L., Sackett, Aaron M., Sellier, Anne-Laure, Sokolova, Tatiana, Sowden, Walter, Storage, Daniel, Sun, Xiaomin, Van Bavel, Jay J., Washburn, Anthony N., Wei, Cong, Wetter, Erik, Wilson, Carlos T., Darroux, Sophie-Charlotte, and Uhlmann, Eric Luis

Abstract

We present the data from a crowdsourced project seeking to replicate findings in independent laboratories before (rather than after) they are published. In this Pre-Publication Independent Replication (PPIR) initiative, 25 research groups attempted to replicate 10 moral judgment effects from a single laboratory's research pipeline of unpublished findings. The 10 effects were investigated using online/lab surveys containing psychological manipulations (vignettes) followed by questionnaires. Results revealed a mix of reliable, unreliable, and culturally moderated findings. Unlike any previous replication project, this dataset includes the data from not only the replications but also from the original studies, creating a unique corpus that researchers can use to better understand reproducibility and irreproducibility in science.

Published
2016

46. Turning the hands of time again: a purely confirmatory replication study and a Bayesian analysis

Author

Wagenmakers, Eric-Jan, primary, Beek, Titia F., additional, Rotteveel, Mark, additional, Gierholz, Alex, additional, Matzke, Dora, additional, Steingroever, Helen, additional, Ly, Alexander, additional, Verhagen, Josine, additional, Selker, Ravi, additional, Sasiadek, Adam, additional, Gronau, Quentin F., additional, Love, Jonathon, additional, and Pinto, Yair, additional

Published
2015
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47. Crowdsourcing hypothesis tests: making transparent how design choices shape research results

Author

Landy, Justin F., Jia, Miaolei (Liam), Ding, Isabel L., Viganola, Domenico, Tierney, Warren, Dreber, Anna, Johannesson, Magnus, Pfeiffer, Thomas, Ebersole, Charles R., Gronau, Quentin F., Ly, Alexander, van den Bergh, Don, Marsman, Maaten, Derks, Koen, Wagenmakers, Eric-Jan, Proctor, Andrew, Bartels, Daniel M., Bauman, Christopher W., Brady, William J., Cheung, Felix, Cimpian, Andrei, Dohle, Simone, Donnellan, Brent M., Hahn, Adam, Hall, Michael P., Jiménez-Leal, William, Johnson, David J., Lucas, Richard E., Monin, Benoit, Montealegre, Andres, Mullen, Elizabeth, Pang, Jun, Ray, Jennifer, Reinero, Diego A., Reynolds, Jesse, Sowden, Walter, Storage, Daniel, Su, Runkun, Tworek, Christina M., Van Bavel, Jay J., Walco, Daniel, Wills, Julian, Xu, Xiaobing, Yam, Chi Kai, Yang, Xiaoyu, Cunningham, William A., Schweinsberg, Martin, Urwitz, Molly, Uhlmann, Eric Luis, Landy, Justin F., Jia, Miaolei (Liam), Ding, Isabel L., Viganola, Domenico, Tierney, Warren, Dreber, Anna, Johannesson, Magnus, Pfeiffer, Thomas, Ebersole, Charles R., Gronau, Quentin F., Ly, Alexander, van den Bergh, Don, Marsman, Maaten, Derks, Koen, Wagenmakers, Eric-Jan, Proctor, Andrew, Bartels, Daniel M., Bauman, Christopher W., Brady, William J., Cheung, Felix, Cimpian, Andrei, Dohle, Simone, Donnellan, Brent M., Hahn, Adam, Hall, Michael P., Jiménez-Leal, William, Johnson, David J., Lucas, Richard E., Monin, Benoit, Montealegre, Andres, Mullen, Elizabeth, Pang, Jun, Ray, Jennifer, Reinero, Diego A., Reynolds, Jesse, Sowden, Walter, Storage, Daniel, Su, Runkun, Tworek, Christina M., Van Bavel, Jay J., Walco, Daniel, Wills, Julian, Xu, Xiaobing, Yam, Chi Kai, Yang, Xiaoyu, Cunningham, William A., Schweinsberg, Martin, Urwitz, Molly, and Uhlmann, Eric Luis

Abstract

peer-reviewed, To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer five original research questions related to moral judgments, negotiations, and implicit cognition. Participants from two separate large samples (total N > 15,000) were then randomly assigned to complete one version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: materials from different teams rendered statistically significant effects in opposite directions for four out of five hypotheses, with the narrowest range in estimates being d = -0.37 to +0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for two hypotheses, and a lack of support for three hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, while considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim.

48. Crowdsourcing hypothesis tests: making transparent how design choices shape research results

Author

Landy, Justin F., Jia, Miaolei (Liam), Ding, Isabel L., Viganola, Domenico, Tierney, Warren, Dreber, Anna, Johannesson, Magnus, Pfeiffer, Thomas, Ebersole, Charles R., Gronau, Quentin F., Ly, Alexander, van den Bergh, Don, Marsman, Maaten, Derks, Koen, Wagenmakers, Eric-Jan, Proctor, Andrew, Bartels, Daniel M., Bauman, Christopher W., Brady, William J., Cheung, Felix, Cimpian, Andrei, Dohle, Simone, Donnellan, Brent M., Hahn, Adam, Hall, Michael P., Jiménez-Leal, William, Johnson, David J., Lucas, Richard E., Monin, Benoit, Montealegre, Andres, Mullen, Elizabeth, Pang, Jun, Ray, Jennifer, Reinero, Diego A., Reynolds, Jesse, Sowden, Walter, Storage, Daniel, Su, Runkun, Tworek, Christina M., Van Bavel, Jay J., Walco, Daniel, Wills, Julian, Xu, Xiaobing, Yam, Chi Kai, Yang, Xiaoyu, Cunningham, William A., Schweinsberg, Martin, Urwitz, Molly, Uhlmann, Eric Luis, Landy, Justin F., Jia, Miaolei (Liam), Ding, Isabel L., Viganola, Domenico, Tierney, Warren, Dreber, Anna, Johannesson, Magnus, Pfeiffer, Thomas, Ebersole, Charles R., Gronau, Quentin F., Ly, Alexander, van den Bergh, Don, Marsman, Maaten, Derks, Koen, Wagenmakers, Eric-Jan, Proctor, Andrew, Bartels, Daniel M., Bauman, Christopher W., Brady, William J., Cheung, Felix, Cimpian, Andrei, Dohle, Simone, Donnellan, Brent M., Hahn, Adam, Hall, Michael P., Jiménez-Leal, William, Johnson, David J., Lucas, Richard E., Monin, Benoit, Montealegre, Andres, Mullen, Elizabeth, Pang, Jun, Ray, Jennifer, Reinero, Diego A., Reynolds, Jesse, Sowden, Walter, Storage, Daniel, Su, Runkun, Tworek, Christina M., Van Bavel, Jay J., Walco, Daniel, Wills, Julian, Xu, Xiaobing, Yam, Chi Kai, Yang, Xiaoyu, Cunningham, William A., Schweinsberg, Martin, Urwitz, Molly, and Uhlmann, Eric Luis

Abstract

peer-reviewed, To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer five original research questions related to moral judgments, negotiations, and implicit cognition. Participants from two separate large samples (total N > 15,000) were then randomly assigned to complete one version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: materials from different teams rendered statistically significant effects in opposite directions for four out of five hypotheses, with the narrowest range in estimates being d = -0.37 to +0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for two hypotheses, and a lack of support for three hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, while considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim.

49. Crowdsourcing hypothesis tests: making transparent how design choices shape research results

Author

Landy, Justin F., Jia, Miaolei (Liam), Ding, Isabel L., Viganola, Domenico, Tierney, Warren, Dreber, Anna, Johannesson, Magnus, Pfeiffer, Thomas, Ebersole, Charles R., Gronau, Quentin F., Ly, Alexander, van den Bergh, Don, Marsman, Maaten, Derks, Koen, Wagenmakers, Eric-Jan, Proctor, Andrew, Bartels, Daniel M., Bauman, Christopher W., Brady, William J., Cheung, Felix, Cimpian, Andrei, Dohle, Simone, Donnellan, Brent M., Hahn, Adam, Hall, Michael P., Jiménez-Leal, William, Johnson, David J., Lucas, Richard E., Monin, Benoit, Montealegre, Andres, Mullen, Elizabeth, Pang, Jun, Ray, Jennifer, Reinero, Diego A., Reynolds, Jesse, Sowden, Walter, Storage, Daniel, Su, Runkun, Tworek, Christina M., Van Bavel, Jay J., Walco, Daniel, Wills, Julian, Xu, Xiaobing, Yam, Chi Kai, Yang, Xiaoyu, Cunningham, William A., Schweinsberg, Martin, Urwitz, Molly, Uhlmann, Eric Luis, Landy, Justin F., Jia, Miaolei (Liam), Ding, Isabel L., Viganola, Domenico, Tierney, Warren, Dreber, Anna, Johannesson, Magnus, Pfeiffer, Thomas, Ebersole, Charles R., Gronau, Quentin F., Ly, Alexander, van den Bergh, Don, Marsman, Maaten, Derks, Koen, Wagenmakers, Eric-Jan, Proctor, Andrew, Bartels, Daniel M., Bauman, Christopher W., Brady, William J., Cheung, Felix, Cimpian, Andrei, Dohle, Simone, Donnellan, Brent M., Hahn, Adam, Hall, Michael P., Jiménez-Leal, William, Johnson, David J., Lucas, Richard E., Monin, Benoit, Montealegre, Andres, Mullen, Elizabeth, Pang, Jun, Ray, Jennifer, Reinero, Diego A., Reynolds, Jesse, Sowden, Walter, Storage, Daniel, Su, Runkun, Tworek, Christina M., Van Bavel, Jay J., Walco, Daniel, Wills, Julian, Xu, Xiaobing, Yam, Chi Kai, Yang, Xiaoyu, Cunningham, William A., Schweinsberg, Martin, Urwitz, Molly, and Uhlmann, Eric Luis

Abstract

peer-reviewed, To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer five original research questions related to moral judgments, negotiations, and implicit cognition. Participants from two separate large samples (total N > 15,000) were then randomly assigned to complete one version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: materials from different teams rendered statistically significant effects in opposite directions for four out of five hypotheses, with the narrowest range in estimates being d = -0.37 to +0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for two hypotheses, and a lack of support for three hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, while considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim.

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