Assessing psychopathic personality traits with the MMPI-2

In this study, we examined the utility of the MMPI-2 (Butcher et al., 2001) in assessing psychopathic personality traits. We explored whether MMPI-2 scales that measure affective and interpersonal traits add to the instrument's social deviance measures in assessing global psychopathy and its two facets. Our study of 281 male and female college students indicates that the MMPI-2 Social Deviance scales (e.g., Clinical Scales 4 and 9, ASP) predict substantial variance in the social deviance factor and affiliated subscales of the Psychopathic Personality Inventory (PPI; Lilienfeld & Andrews, 1996), whereas MMPI-2 measures of affective and interpersonal functioning predict substantial variance in the affective-interpersonal PPI factor. In addition, the results of two regression models indicate that the Restructured Clinical scales provide the most parsimonious assessment of psychopathic personality traits.     

Mental health aspects of Arab-Israeli adolescents from polygamous versus monogamous families

The authors considered the mental health consequences of polygamy in a sample of 101 Arab Muslim adolescents (19 from polygamous and 82 from monogamous families) at Juarish (Ramla), Israel. The respondents completed the Self-Esteem Scale (SE; M. Rosenberg, 1979), the Brief Symptom Inventory (BSI; L. Derogatis & N. Melsavados, 1983; L. Derogatis & P. Spencer, 1982), and the McMaster Family Assessment Device (FAD; N. B. Epstein, M. N. Baldwin, & D. S. Bishop, 1983). The respondents from polygamous families had lower SE scores, statistically significant higher scores in 2 BSI dimensions, higher scores in all other BSI dimensions, and higher levels of self-reported family dysfunction. The respondents from polygamous families reported lower levels of socioeconomic status, academic achievement, and parental academic attainment. Those variables may have had a more direct impact on mental health than did parental marital status. The data also indicated that perceived family functioning was the best predictor of mental health.     

Effects of geometric distortions on face-recognition performance

The importance of 'configural' processing for face recognition is now well established, but it remains unclear precisely what it entails. Through four experiments we attempted to clarify the nature of configural processing by investigating the effects of various affine transformations on the recognition of familiar faces. Experiment 1 showed that recognition was markedly impaired by inversion of faces, somewhat impaired by shearing or horizontally stretching them, but unaffected by vertical stretching of faces to twice their normal height. In experiment 2 we investigated vertical and horizontal stretching in more detail, and found no effects of either transformation. Two further experiments were performed to determine whether participants were recognising stretched faces by using configural information. Experiment 3 showed that nonglobal vertical stretching of faces (stretching either the top or the bottom half while leaving the remainder undistorted) impaired recognition, implying that configural information from the stretched part of the face was influencing the process of recognition--ie that configural processing involves global facial properties. In experiment 4 we examined the effects of Gaussian blurring on recognition of undistorted and vertically stretched faces. Faces remained recognisable even when they were both stretched and blurred, implying that participants were basing their judgments on configural information from these stimuli, rather than resorting to some strategy based on local featural details. The tolerance of spatial distortions in human face recognition suggests that the configural information used as a basis for face recognition is unlikely to involve information about the absolute position of facial features relative to each other, at least not in any simple way.     

Methods for obtaining menstrual-cycle data in menstrual-synchrony studies: commentary on Schank (2001)

J. C. Schank (2001) claimed to identify 2 categories of error-measurement error and recall biases-in the methodology used in studies of menstrual synchrony. This commentary focuses on the 2nd of these, recall biases, and other errors that he argued may result from "allowing participants to fill out menstrual-cycle-onset calendars" (p. 3). J. C. Schank's detailed discussion of various types of recall biases is inappropriate and misleading given that the majority of menstrual-synchrony studies have used prospective menstrual calendars. The use of menstrual calendars is a well-accepted method in a number of research areas and demands little of participants. J. C. Schank questioned the validity of menstrual calendar data on the basis of assumptions about recall bias for which he had no evidence.     

The appropriateness of the MMPI-2 K correction

The extent to which the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) K scale serves as a suppressor variable that influences the validity of clinical scale scores was examined using 274 male and 425 female outpatients from a community mental health center and 105 male and 247 female clients from a university psychological clinic. Hierarchical regression analyses were performed with MMPI-2 K scale scores and clinical scale scores as predictors and therapist ratings as criteria. In most cases, the K scale did not act as a suppressor Optimal K weights were different from the traditional K corrections. In most instances, there were no significant differences between correlations of therapist ratings with K-corrected and uncorrected clinical scale scores. The results do not support the K scale as a suppressor variable in these settings, although additional research is needed in settings where higher levels of defensiveness are common.     

Missing data: our view of the state of the art

Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.