Universität zu Köln

Rouder, Jeffrey N., Haaf, Julia M. and Aust, Frederik ORCID: 0000-0003-4900-788X (2018). From theories to models to predictions: A Bayesian model comparison approach. Commun. Monogr., 85 (1). S. 41 - 57. ABINGDON: ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD. ISSN 1479-5787

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Abstract

A key goal in research is to use data to assess competing hypotheses or theories. An alternative to the conventional significance testing is Bayesian model comparison. The main idea is that competing theories are represented by statistical models. In the Bayesian framework, these models then yield predictions about data even before the data are seen. How well the data match the predictions under competing models may be calculated, and the ratio of these matches - the Bayes factor - is used to assess the evidence for one model compared to another. We illustrate the process of going from theories to models and to predictions in the context of two hypothetical examples about how exposure to media affects attitudes toward refugees.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Rouder, Jeffrey N. UNSPECIFIED UNSPECIFIED UNSPECIFIED Haaf, Julia M. UNSPECIFIED UNSPECIFIED UNSPECIFIED Aust, Frederik UNSPECIFIED orcid.org/0000-0003-4900-788X UNSPECIFIED
URN:	urn:nbn:de:hbz:38-203270
DOI:	10.1080/03637751.2017.1394581
Journal or Publication Title:	Commun. Monogr.
Volume:	85
Number:	1
Page Range:	S. 41 - 57
Date:	2018
Publisher:	ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
Place of Publication:	ABINGDON
ISSN:	1479-5787
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language STATISTICAL POWER Multiple languages Communication Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/20327