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Using Bayes Factors to Test Hypotheses in Developmental Research

Williams, Matt N.; Arnling Bååth, Rasmus LU and Philipp, Michael C. (2017) In Research in Human Development
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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organization
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type
Contribution to journal
publication status
epub
subject
in
Research in Human Development
pages
17 pages
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
2018-01-16 13:23:05
@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},
  pages        = {17},
  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},
  year         = {2017},
}