Effect sizes are underappreciated and often misinterpreted—the most common mistakes being to describe them in ways that are uninformative (e.g., using arbitrary standards) or misleading (e.g., squaring effect-size rs). We propose that effect sizes can be usefully evaluated by comparing them with well-understood benchmarks or by considering them in terms of concrete consequences. In that light, we conclude that when reliably estimated (a critical consideration), an effect-size r of .05 indicates an effect that is very small for the explanation of single events but potentially consequential in the not-very-long run, an effect-size r of .10 indicates an effect that is still small at the level of single events but potentially more ultimately consequential, an effect-size r of .20 indicates a medium effect that is of some explanatory and practical use even in the short run and therefore even more important, and an effect-size r of .30 indicates a large effect that is potentially powerful in both the short and the long run. A very large effect size ( r = .40 or greater) in the context of psychological research is likely to be a gross overestimate that will rarely be found in a large sample or in a replication. Our goal is to help advance the treatment of effect sizes so that rather than being numbers that are ignored, reported without interpretation, or interpreted superficially or incorrectly, they become aspects of research reports that can better inform the application and theoretical development of psychological research.

■ Abstract Personality has consequences. Measures of personality have contemporaneous and predictive relations to a variety of important outcomes. Using the Big Five factors as heuristics for organizing the research literature, numerous consequential relations are identified. Personality dispositions are associated with happiness, physical and psychological health, spirituality, and identity at an individual level; associated with the quality of relationships with peers, family, and romantic others at an interpersonal level; and associated with occupational choice, satisfaction, and performance, as well as community involvement, criminal activity, and political ideology at a social institutional level.

Two hypotheses concerning the hostile and depressive components of envy were tested: that hostile feelings are associated with a subjective belief that the envy-producing difference is unfair and that depressive feelings are associated with a sense of inferiority evoked by the envied person's advantage. Subjects wrote autobiographical accounts of experiences of envy and then indicated how unfair (in both a subjective and an objective sense) they believed the envied person's advantage was, how inferior the advantage made them feel, and how hostile and depressed they felt. Multiple regression analysis generally supported the hypotheses. Hostile feelings were predicted by subjective injustice beliefs and objective injustice beliefs but not by inferiority beliefs. Depressive feelings, however, were predicted largely by inferiority beliefs but also by subjective injustice beliefs. Envy, especially in its typically hostile form, may need to be understood as resulting in part from a subjective, yet robust, sense of injustice.

It is common practice in psychological statistics to use the square of the correlation as a coefficient of determination or a percentage measure of variance accounted for. This use of the correlation coefficient requires the adoption of a particular model and attendant assumptions. In a variety of circumstances where the square of the correlation coefficient is used, the required assumptions are not tenable. An alternative, less well-known interpretation of the correlation coefficient is described. In this model, the absolute value of the correlation provides a coefficient of determination. Similarities and differences between these two models are described, and conditions for the appropriate use of each model are discussed. The correlation coefficient and not the correlation squared is recommended for use as an effect size indicator, because evaluating effect size in terms of variance accounted for may lead to interpretations that grossly underestimate the magnitude of a relation.

Personality types are construed as constellations of features that uniquely define discrete groups of individuals. Types are conceptually convenient because they summarize many traits in a single label, but until recently most researchers agreed that there was little evidence for the existence of discrete personality types. Several groups of researchers have now proposed replicable, empirical person clusters based on measures of the Five‐Factor Model. We consider several methodological artifacts that might be responsible for these types, and conclude that these artifacts may contribute to the replicability of types, but cannot entirely account for it. The present research attempts to replicate these types in four large and diverse adult samples: the Baltimore Longitudinal Study of Aging (N = 1856); the East Baltimore Epidemiologic Catchment Area study (N = 486); the University of North Carolina Alumni Heart Study (N = 2420); and an HIV risk reduction intervention study (N = 274). A clear replication (kappa = 0.60) of the proposed types was found in only one sample by one standard of comparison. The failure of the three personality types to replicate in three of the four samples leads to the conclusion that they are not robust empirical entities. Type membership predicted psychosocial functioning and ego resiliency and control, but only because it summarized trait standing; dimensional trait measures were consistently better predictors. Nevertheless, while the types do not refer to distinct, homogeneous classes of persons, they do have utility as convenient labels summarizing combinations of traits that relate to important outcomes. Published in 2002 by John Wiley & Sons, Ltd.