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3 results

  • Chapter 5 - Psychological Processes

  • from Part III - The Fair Process Effect
  • Kees van den Bos, Utrecht University
  • Book:
    The Fair Process Effect
    Published online:
    26 October 2023
    Print publication:
    09 November 2023, pp 54-64

  • Summary

    Chapter 5 focuses on the psychology of the fair process effect. The chapter notes that although the effect is often there, it is important to study it in the context in which it is supposed to appear. After all, the relevance of various criteria and psychological processes may vary across different contexts and the different people present in those contexts. The chapter also notes that it is important to specify which types of dependent variables, such as trust or protest intentions, are most likely to be affected by the fair process effect under consideration. Relevant moderators (such as personal uncertainty) and mediators (such as perceived legitimacy) are discussed as well. The chapter ends with discussing the systemic aspects that are important when understanding the functioning of the fair process effect and how to prevent the effect from being misused in system-justifying ways.

  • Chapter eight - Behavior Genetic Research Methods

  • from Part two - Procedural Possibilities
  • Edited by Harry T. Reis, University of Rochester, New York, Charles M. Judd, University of Colorado Boulder
  • Book:
    Handbook of Research Methods in Social and Personality Psychology
    Published online:
    05 June 2014
    Print publication:
    24 February 2014, pp 159-187

  • Summary

    Research design is inextricably linked to data analysis. Research designs can be divided into three fundamental categories: experimental design, quasi-experimental design, and nonexperimental or passive observational design. This chapter focuses almost exclusively on experimental design, for two main reasons. In an experimental design there are always at least two factors such as fixed and random factors or independent variables, variables that are considered to influence the dependent variable response. The chapter discusses a series of questions that must be answered regarding each individual factor in a design. It considers the possible ways that multiple factors included in a single experiment may be interrelated. Multiple factors in a design can be crossed, nested, or confounded. Issues involving the dependent measures in a study are also part of experimental design, and these choices can influence statistical conclusion validity (power), internal validity, construct validity, and external validity.

  • Asymptotic normality of some Graph-Related statistics

  • Pierre Baldi, Yosef Rinott
  • Journal:
    Journal of Applied Probability / Volume 26 / Issue 1 / March 1989
    Published online by Cambridge University Press:
    14 July 2016, pp. 171-175
    Print publication:
    March 1989

  • Petrovskaya and Leontovich (1982) proved a central limit theorem for sums of dependent random variables indexed by a graph. We apply this theorem to obtain asymptotic normality for the number of local maxima of a random function on certain graphs and for the number of edges having the same color at both endpoints in randomly colored graphs. We briefly motivate these problems, and conclude with a simple proof of the asymptotic normality of certain U-statistics.