| Title |
Default “Gunel and Dickey” Bayes factors for contingency tables
|
|---|---|
| Published in |
Behavior Research Methods, June 2016
|
| DOI | 10.3758/s13428-016-0739-8 |
| Pubmed ID | |
| Authors |
Tahira Jamil, Alexander Ly, Richard D. Morey, Jonathon Love, Maarten Marsman, Eric-Jan Wagenmakers |
| Abstract |
The analysis of R×C contingency tables usually features a test for independence between row and column counts. Throughout the social sciences, the adequacy of the independence hypothesis is generally evaluated by the outcome of a classical p-value null-hypothesis significance test. Unfortunately, however, the classical p-value comes with a number of well-documented drawbacks. Here we outline an alternative, Bayes factor method to quantify the evidence for and against the hypothesis of independence in R×C contingency tables. First we describe different sampling models for contingency tables and provide the corresponding default Bayes factors as originally developed by Gunel and Dickey (Biometrika, 61(3):545-557 (1974)). We then illustrate the properties and advantages of a Bayes factor analysis of contingency tables through simulations and practical examples. Computer code is available online and has been incorporated in the "BayesFactor" R package and the JASP program ( jasp-stats.org ). |
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 8 | 21% |
| Netherlands | 3 | 8% |
| United Kingdom | 2 | 5% |
| Canada | 2 | 5% |
| Mexico | 1 | 3% |
| Brazil | 1 | 3% |
| Indonesia | 1 | 3% |
| Switzerland | 1 | 3% |
| Australia | 1 | 3% |
| Other | 5 | 13% |
| Unknown | 14 | 36% |
Demographic breakdown
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|---|---|---|
| Members of the public | 29 | 74% |
| Scientists | 7 | 18% |
| Science communicators (journalists, bloggers, editors) | 2 | 5% |
| Practitioners (doctors, other healthcare professionals) | 1 | 3% |
Mendeley readers
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Chile | 2 | 2% |
| Bosnia and Herzegovina | 1 | <1% |
| Belgium | 1 | <1% |
| Unknown | 109 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 30 | 27% |
| Student > Master | 18 | 16% |
| Researcher | 15 | 13% |
| Professor | 7 | 6% |
| Student > Bachelor | 7 | 6% |
| Other | 25 | 22% |
| Unknown | 11 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 49 | 43% |
| Medicine and Dentistry | 10 | 9% |
| Mathematics | 5 | 4% |
| Computer Science | 5 | 4% |
| Neuroscience | 4 | 4% |
| Other | 22 | 19% |
| Unknown | 18 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 21. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 April 2020.
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#1,798,448
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Outputs from Behavior Research Methods
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#32,365
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Outputs of similar age from Behavior Research Methods
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