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Statistical techniques for juror and jury research
Author(s) -
Wright Daniel B.,
Strubler Kevin A.,
Vallano Jonathan P.
Publication year - 2011
Publication title -
legal and criminological psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.65
H-Index - 57
eISSN - 2044-8333
pISSN - 1355-3259
DOI - 10.1348/135532510x487655
Subject(s) - jury , categorical variable , psychology , jury instructions , thriving , social psychology , applied psychology , computer science , law , machine learning , political science , psychotherapist
Juror and jury research is a thriving area of investigation in legal psychology. The basic ANOVA and regression, well‐known by psychologists, are inappropriate for analysing many types of data from this area of research. This paper describes statistical techniques suitable for some of the main questions asked by jury researchers. First, we discuss how to examine manipulations that may affect levels of reasonable doubt and how to measure reasonable doubt using the coefficients estimated from a logistic regression. Second, we compare models designed for analysing the data like those which often arise in research where jurors first make categorical judgments (e.g., negligent or not, guilty or not) and then dependent on their response may make another judgment (e.g., award, punishment). We concentrate on zero‐inflated and hurdle models. Third, we examine how to take into account that jurors are part of a jury using multilevel modelling. We illustrate each of the techniques using software that can be downloaded for free from the Internet (the package R) and provide a web page that gives further details for running these analyses.