Suchergebnisse

This investigation was conceptually framed within the theory of marginal deviations ( Caprara & Zimbardo, 1996 ) and sought evidence for the general hypothesis that some children who initially show marginal behavioral problems may, over time, develop more serious problems depending partly on other personal and behavioral characteristics. To this end, the findings of two studies conducted, respectively, with American elementary school children and Italian middle school students are reviewed. These two studies show that hyperactivity, cognitive difficulties, low social preference, and lack of prosocial behavior increase a child's risk for growth in aggressive behavior over several school years. More importantly, they also show that equivalent levels of these risk factors have a greater impact on the development of children who, early on, were marginally aggressive.

At the level of manifest categorical variables, a large number of coefficients and models for the examination of rater agreement has been proposed and used. The most popular of these is Cohen's κ. In this article, a new coefficient, κ s , is proposed as an alternative measure of rater agreement. Both κ and κ s allow researchers to determine whether agreement in groups of two or more raters is significantly beyond chance. Stouffer's z is used to test the null hypothesis that κ s = 0. The coefficient κ s allows one, in addition to evaluating rater agreement in a fashion parallel to κ, to (1) examine subsets of cells in agreement tables, (2) examine cells that indicate disagreement, (3) consider alternative chance models, (4) take covariates into account, and (5) compare independent samples. Results from a simulation study are reported, which suggest that (a) the four measures of rater agreement, Cohen's κ, Brennan and Prediger's κ n , raw agreement, and κ s are sensitive to the same data characteristics when evaluating rater agreement and (b) both the z-statistic for Cohen's κ and Stouffer's z for κ s are unimodally and symmetrically distributed, but slightly heavy-tailed. Examples use data from verbal processing and applicant selection.