Here - Tilburg University

Presenter
Wesel van, Floryt; Dept. Methodology & Statistics, Utrecht University
Authors
Floryt van Wesel and Hennie Boeije; Dept. of Methodology and Statistics,
Utrecht University
Title
Priors & Prejudice: Using existing knowledge in social science research
Abstract
Using Bayesian statistics implies the use of prior distributions. These prior
distributions can contain information about the topic at hand that is already
known from previous research. In this presentation we discuss how to acquire
such existing information on the one hand and how to translate this information
into a statistical prior distribution on the other hand. For the information part of
the prior distribution we use three sources to formulate a single integrated
theoretical model. The sources are: meta-analysis, qualitative synthesis and
expert elicitation. The results that emerge from each individual source will be
used to formulate an inequality constrained hypothesis. The three hypotheses
are then integrated leading to an overall hypothesis representing the integrated
model. This overall hypothesis is the existing information that determines the
first part of the prior distribution. As the hypothesis does not contain any
numerical information to base a statistical prior distribution on, we update a
non-informative prior with a small part of the data, called a training sample. The
final prior distribution consists of a combination of the information in the
inequality constrained hypothesis and the training sample. The case we use to
exemplify this procedure is that of factors influencing the development of post-
traumatic stress disorder (PTSD) in children who have gone through trauma.