Using Bayes Factors to Test Hypotheses in Developmental Research
(2017) In Research in Human Development 14(4). p.321-337- Abstract
This article discusses the concept of Bayes factors as inferential tools that can serve as an alternative to null hypothesis significance testing in the day-to-day work of developmental researchers. A Bayes factor indicates the degree to which data observed should increase (or decrease) the credibility of one hypothesis in comparison to another. Bayes factor analyses can be used to compare many types of models but are particularly helpful when comparing a point null hypothesis to a directional or nondirectional alternative hypothesis. A key advantage of this approach is that a Bayes factor analysis makes it clear when a set of observed data is more consistent with the null hypothesis than the alternative. Bayes factor alternatives to... (More)
This article discusses the concept of Bayes factors as inferential tools that can serve as an alternative to null hypothesis significance testing in the day-to-day work of developmental researchers. A Bayes factor indicates the degree to which data observed should increase (or decrease) the credibility of one hypothesis in comparison to another. Bayes factor analyses can be used to compare many types of models but are particularly helpful when comparing a point null hypothesis to a directional or nondirectional alternative hypothesis. A key advantage of this approach is that a Bayes factor analysis makes it clear when a set of observed data is more consistent with the null hypothesis than the alternative. Bayes factor alternatives to common tests used by developmental psychologists are available in easy-to-use software. However, we note that analysis using Bayes factors is a less general approach than Bayesian estimation/modeling, and is not the right tool for every research question.
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- author
- Williams, Matt N. ; Arnling Bååth, Rasmus LU and Philipp, Michael C.
- organization
- publishing date
- 2017-10-04
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Research in Human Development
- volume
- 14
- issue
- 4
- pages
- 321 - 337
- publisher
- Taylor & Francis
- external identifiers
-
- scopus:85030553275
- wos:000417604300004
- ISSN
- 1542-7609
- DOI
- 10.1080/15427609.2017.1370964
- language
- English
- LU publication?
- yes
- id
- b8460bc3-e04a-47a2-a879-3ff8d4ae975a
- date added to LUP
- 2017-10-16 10:37:54
- date last changed
- 2024-01-14 07:36:30
@article{b8460bc3-e04a-47a2-a879-3ff8d4ae975a,
abstract = {{<p>This article discusses the concept of Bayes factors as inferential tools that can serve as an alternative to null hypothesis significance testing in the day-to-day work of developmental researchers. A Bayes factor indicates the degree to which data observed should increase (or decrease) the credibility of one hypothesis in comparison to another. Bayes factor analyses can be used to compare many types of models but are particularly helpful when comparing a point null hypothesis to a directional or nondirectional alternative hypothesis. A key advantage of this approach is that a Bayes factor analysis makes it clear when a set of observed data is more consistent with the null hypothesis than the alternative. Bayes factor alternatives to common tests used by developmental psychologists are available in easy-to-use software. However, we note that analysis using Bayes factors is a less general approach than Bayesian estimation/modeling, and is not the right tool for every research question.</p>}},
author = {{Williams, Matt N. and Arnling Bååth, Rasmus and Philipp, Michael C.}},
issn = {{1542-7609}},
language = {{eng}},
month = {{10}},
number = {{4}},
pages = {{321--337}},
publisher = {{Taylor & Francis}},
series = {{Research in Human Development}},
title = {{Using Bayes Factors to Test Hypotheses in Developmental Research}},
url = {{http://dx.doi.org/10.1080/15427609.2017.1370964}},
doi = {{10.1080/15427609.2017.1370964}},
volume = {{14}},
year = {{2017}},
}