Title |
Bayesian inference for psychology, part IV: parameter estimation and Bayes factors
|
---|---|
Published in |
Psychonomic Bulletin & Review, February 2018
|
DOI | 10.3758/s13423-017-1420-7 |
Pubmed ID | |
Authors |
Jeffrey N. Rouder, Julia M. Haaf, Joachim Vandekerckhove |
Abstract |
In the psychological literature, there are two seemingly different approaches to inference: that from estimation of posterior intervals and that from Bayes factors. We provide an overview of each method and show that a salient difference is the choice of models. The two approaches as commonly practiced can be unified with a certain model specification, now popular in the statistics literature, called spike-and-slab priors. A spike-and-slab prior is a mixture of a null model, the spike, with an effect model, the slab. The estimate of the effect size here is a function of the Bayes factor, showing that estimation and model comparison can be unified. The salient difference is that common Bayes factor approaches provide for privileged consideration of theoretically useful parameter values, such as the value corresponding to the null hypothesis, while estimation approaches do not. Both approaches, either privileging the null or not, are useful depending on the goals of the analyst. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Netherlands | 2 | 12% |
China | 1 | 6% |
Ireland | 1 | 6% |
Sweden | 1 | 6% |
Germany | 1 | 6% |
Canada | 1 | 6% |
France | 1 | 6% |
Denmark | 1 | 6% |
Australia | 1 | 6% |
Other | 0 | 0% |
Unknown | 7 | 41% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 9 | 53% |
Members of the public | 7 | 41% |
Practitioners (doctors, other healthcare professionals) | 1 | 6% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 197 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 44 | 22% |
Researcher | 30 | 15% |
Student > Master | 19 | 10% |
Student > Bachelor | 17 | 9% |
Professor > Associate Professor | 12 | 6% |
Other | 42 | 21% |
Unknown | 33 | 17% |
Readers by discipline | Count | As % |
---|---|---|
Psychology | 84 | 43% |
Neuroscience | 16 | 8% |
Linguistics | 10 | 5% |
Agricultural and Biological Sciences | 6 | 3% |
Medicine and Dentistry | 6 | 3% |
Other | 28 | 14% |
Unknown | 47 | 24% |