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ABSTRACT



Many studies testing the effects of procedural justice judgments rely on cross-sectional data. The shortcomings of such a strategy are clear and alternative methodologies are needed. Using a factorial vignette design, this study tests a variety

ABSTRACT



Many studies testing the effects of procedural justice judgments rely on cross-sectional data. The shortcomings of such a strategy are clear and alternative methodologies are needed. Using a factorial vignette design, this study tests a variety of hypotheses derived from the process-based model of regulation, most of which involve the posited outcomes of procedural justice judgments during police-citizen encounters. This technique allows the researcher to manipulate police process during citizen encounters via hypothetical scenarios. Experimental stimuli are used as independent variables in the regression models. The results show that participants who were administered vignettes characterized by procedural injustice had lower levels of encounter satisfaction, decision acceptance, immediate compliance and greater expectations that police handle similar situations in the future differently relative to individuals who did not receive the negative stimulus. These effects are statistically significant across encounters involving traffic stops and noise complaints. As anticipated, the effect of procedural injustice often proved more salient regardless of whether participants were administered vignettes where they received a citation. Given the utility of the vignette design, future researchers are encouraged to apply the design to additional causal questions derived from the process-based model.
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    Title
    • The effect of procedural justice during police-citizen encounters: a factorial vignette-based study
    Contributors
    Date Created
    2016
    Resource Type
  • Text
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    Note
    • thesis
      Partial requirement for: M.S., Arizona State University, 2016
    • bibliography
      Includes bibliographical references
    • Field of study: Criminology

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    by Ryan D. Mays

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