Bayes factor testing of equality and order constraints on measures of association in social research

Joris Mulder*, John Gelissen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Measures of association play a central role in the social sciences to quantify the strength of a linear relationship between the variables of interest. In many applications researchers can translate scientific expectations to hypotheses with equality and/or order constraints on these measures of association. In this paper a Bayes factor test is proposed for testing multiple hypotheses with constraints on the measures of association between ordinal and/or continuous variables, possibly after correcting for certain covariates. This test can be used to obtain a direct answer to the research question how much evidence there is in the data for a social science theory relative to competing theories. The stand-alone software package ‘BCT’ allows users to apply the methodology in an easy manner. The methodology will also be available in the R package ‘BFpack’. An empirical application from leisure studies about the associations between life, leisure and relationship satisfaction and an application about the differences about egalitarian justice beliefs across countries are used to illustrate the methodology.
Original languageEnglish
Number of pages37
JournalJournal of Applied Statistics
DOIs
Publication statusE-pub ahead of print - 2022

Keywords

  • BCT
  • BFpack
  • Bayesian hypothesis testing
  • HYPOTHESES
  • INEQUALITY
  • MODEL SELECTION
  • QUALITY
  • measures of association
  • social sciences
  • uniform priors

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