Relations among mock jurors' attitudes, trial evidence, and their selections of an insanity defense verdict: a path analytic approach.
This study examined an important question relevant to the domain of the insanity defense: What are the interrelationships among important evidential and attitudinal factors which influence how jurors decide their final verdicts? To answer this question, a mock trial in which the insanity defense was argued was presented to 224 college undergraduates by means of an audiotape and slide show. Following the presentation, participants were asked to answer a series of questions regarding the trial. A path model was specified with four evidential factors as endogenous variables, i.e., evaluation of the defendant's mental status, belief that the defendant could be rehabilitated, beliefs regarding the accuracy of the expert witnesses, and mock-jurors' predeliberation verdicts. In addition, three attitudinal factors were specified as exogenous variables, i.e., attitudes toward the insanity defense, attitudes towards due process vs crime control, and attitudes towards the death penalty. The path model was consistent with previous literature, suggesting that jurors' attitudes toward the death penalty and the insanity defense had a direct effect on how they evaluated the accuracy of the expert testimony and their evaluation of the defendant's over-all mental status. In turn, mock jurors' evaluations of the defendant's mental status had a direct effect on their selections of verdict. Importantly, mock jurors' evaluations of the evidential factors, particularly the mental status of the defendant, were a stronger predictor of their selections of verdict than were their initial attitudes.
Poulson
R L
RL
Department of Psychology, East Carolina University, Greenville, NC 27858-4353, USA.
Brondino
M J
MJ
Brown
H
H
Braithwaite
R L
RL
D001290:Attitude; D002203:Capital Punishment; D016009:Chi-Square Distribution; D003416:Criminal Law; D003657:Decision Making; D006801:Humans; D007298:Insanity Defense; D016013:Likelihood Functions; D012044:Regression Analysis