Four meta-analyses were conducted to examine gender differences in personality in the literature (1958-1992) and in normative data for well-known personality inventories (1940-1992). Males were found to be more assertive and had slightly higher self-esteem than females. Females were higher than males in extraversion, anxiety, trust, and, especially, tender-mindedness (e.g., nurturance). There were no noteworthy sex differences in social anxiety, impulsiveness, activity, ideas (e.g., reflectiveness), locus of control, and orderliness. Gender differences in personality traits were generally constant across ages, years of data collection, educational levels, and nations.

Meta-analysis was used to examine findings in 2 related areas: experimental research on the physical attractiveness stereotype and correlational studies of characteristics associated with physical attractiveness. The experimental literature found that physically attractive people were perceived as more sociable, dominant, sexually warm, mentally healthy, intelligent, and socially skilled than physically unattractive people. Yet, the correlational literature indicated generally trivial relationships between physical attractiveness and measures of personality and mental ability, although good-looking people were less lonely, less socially anxious, more popular, more socially skilled, and more sexually experienced than unattractive people. Self-ratings of physical attractiveness were positively correlated with a wider range of attributes than was actual physical attractiveness.Do good-looking people differ from unattractive people and, if so, why? Now consider self-perceptions of physical attractiveness. Do people who view themselves as physically appealing different from their counterparts who hold modest opinions of their own physical appearance and, if so, why? This article examines and integrates theories and empirical findings from the physical attractiveness literature to address these interesting questions. Conceptualization and Measurement of AttractivenessWhat is physical attractiveness? Social scientists, like laymen, believe that beauty is denned by social consensus (Berscheid & Walster, 1974;Hatfield & Sprecher, 1986). Accordingly, researchers measure physical attractiveness by use of judges, with each judge asked to provide an independent rating of the physical attractiveness of each subject, a procedure strikingly similar to the notorious 1 -to-10 attractiveness-rating scale often used in the "real world" when people first observe strangers of the opposite sex. These assessments are then averaged over judges by subject to yield physical attractiveness ratings (e.g., Walster, . Since the mid-1960s, scores of studies have correlated such pooled physical attractiveness judgments (sometimes called objective physical attractiveness) with other characteristics, including personality traits, cognitive ability, popularity, social skills, and sexual experience I want to thank Ronald Mazzella for assistance in the preparation of data for meta-analysis, two anonymous reviewers for their helpful comments on a draft of this article, and the following researchers for providing me with unpublished findings for this review:

The use of growth-modeling analysis (GMA)--including Hierarchical Linear Models, Latent Growth Models, and General Estimating Equations--to evaluate interventions in psychology, psychiatry, and prevention science has grown rapidly over the last decade. However, an effect size associated with the difference between the trajectories of the intervention and control groups that captures the treatment effect is rarely reported. This article first reviews two classes of formulas for effect sizes associated with classical repeated-measures designs that use the standard deviation of either change scores or raw scores for the denominator. It then broadens the scope to subsume GMA, and demonstrates that the independent groups, within-subjects, pretest-posttest control-group, and GMA designs all estimate the same effect size when the standard deviation of raw scores is uniformly used. Finally, it is shown that the correct effect size for treatment efficacy in GMA--the difference between the estimated means of the two groups at end of study (determined from the coefficient for the slope difference and length of study) divided by the baseline standard deviation--is not reported in clinical trials.Keywords effect size; growth modeling; hierarchical linear models; clinical trials Social scientists contribute to human well-being through the development of interventions to prevent and to treat a wide range of psychological, educational, and behavioral problems. Intervention research is a broad interdisciplinary field, with contributors from clinical psychology, psychiatry, applied developmental psychology, prevention science, and education. An important objective of these investigators is to conduct clinical trials to examine the efficacy of psychosocial and psychopharmacological treatments. Thus, controlled clinical trials are needed to answer two questions: Is a particular intervention effective? How powerful are its effects? The former is addressed in data analysis through tests of statistical significance, and the latter through calculation of effect sizes (McGrath & Meyer, 2006). Growth-Modeling Designs for Controlled Clinical TrialsTraditionally, data from controlled clinical trials have been examined with classical statistical techniques, such as analysis of variance (ANOVA), which use Ordinary Least Squares (OLS) and the General Linear Model (GLM). Over the last decade, however, growth-modeling analysis (GMA)--based on Generalized Least Squares (GLS) and the expectationmaximization (EM) algorithm (Dempster, Laird, & Rubin, 1977)--has emerged as a competing (c) 2009 APA, all rights reserved.Correspondence concerning this article should be addressed to Alan Feingold, Oregon Social Learning Center, 10 Shelton McMurphey Blvd., Eugene, OR, 97401−4928. E-mail: E-mail: alanf@oslc.org.. statistical framework for use in the evaluation of intervention efficacy. GMA compares temporal trajectories (growth curves) between the treatment and comparison groups, with the difference in the slopes for linear trend a common test of...

Evolutionary and sociocultural theories of mate selection preferences contend that men place greater value on physical attractiveness than do women. Thus, meta-analyses were conducted of findings from 5 research paradigms that have examined the hypothesis: (a) questionnaire studies, (b) analyses of lonely hearts advertisments, (c) studies that correlate attractiveness with opposite-sex popularity, (d) studies that correlate attractiveness with liking by a dyadic interaction partner, and (e) experiments that manipulate the attractiveness and similarity of an opposite-sex stranger. The anticipated sex difference emerged in all five meta-analyses, although it was larger in research that examined self-reports than in research that examined social behavior.It is a popular belief that being physically attractive is of greater importance for women than for men, particularly in attracting the opposite sex. Research on the value placed on physical appearance began in the 1930s and 1940s by sociologists who constructed mate selection questionnaires (Baber, 1936;Christensen, 1947;Hill, 1945;Strauss, 1946). Respondents were asked to judge the importance of different characteristics for their ideal mate, and more men than women reported that "good looks" were necessary (see review by Powers, 1971).Two correlational paradigms were subsequently developed to address the same question. The attractiveness-popularity paradigm examines the correlation between people's physical attractiveness and their popularity with the opposite sex (e.g, Berscheid, Dion, Walster, & Walster, 1971). The dyadic interaction paradigm consists of the arrangement of encounters (e.g, blind dates) between single men and women. Individuals' physical attractiveness is correlated with postinteraction reports of how much they are liked by their partners (e.g, Walster,

A meta‐analysis of experimental research on mock juror judgments was conducted to assess the effects of physical attractiveness, race, socioeconomic status (SES), and gender of both defendants and victims to test the theory that jurors use characteristics that are correlated with criminal behavior as cues to infer guilt and to recommend punishment. In general, it was advantageous for defendants to be physically attractive, female, and of high SES, although these advantages were nil for some crimes. There were no overall effects of race on mock jurors' judgments, but the effect of defendant race on punishment was strongly moderated by type of crime. Effects of victim characteristics on jurors' judgments were generally inconsequential, although defendants were at a disadvantage when the victim was female.