The Latent Structure of Youth Responses to Peer Provocation

This study examined whether the three categories often applied to children’s behavior—aggressive, avoidant, and assertive—actually capture the structure of a naturalistic sample of youth behavior coded at a more micro level. A sample of lower-income youth (N = 392; M age = 12.69, SD = 0.95) completed a new multiple-choice measure asking them to select responses to scenarios depicting physical, verbal, and relational provocation by a peer. Youth responses to the vignettes showed the expected associations with self-reported aggression and regulation of anger, providing preliminary evidence for the convergent validity of the measure. Factor analysis confirmed that responses loaded on three factors: aggression, avoidance, and assertion. Model fit was adequate (RMSEA = .028) and cross-validated in a second sample (RMSEA = .039). Several types of responses loaded on two factors suggesting that some strategies that youth use to manage provocation are not “pure” examples of these broadband categories. Implications for conceptualization and measurement of youth social behavior are discussed

Evaluation of the Psychological Clinical Science Accreditation System (PCSAS) Program Review

The Psychological Clinical Science Accreditation System (PCSAS) accredits PhD programs in clinical psychology that adhere to the clinical science training model. An evaluation of the psychometric properties of a key judgment in review of applicant programs was undertaken. Results show that program reviewers’ judgments of the career outcomes of program graduates, a fundamental criterion in evaluating the success of a program applying for PCSAS accreditation, demonstrate high reliability and validity, in turn providing evidence that PCSAS-accredited programs achieve what they claim to achieve. Their convergence on central features of successful clinical science training underscores what it means to function as a psychological clinical scientist. In turn, the expertise of psychological clinical scientists can be invaluable in a wide range of research and practice settings, providing diverse career choices for PCSAS-accredited program graduates

Alcohol alters men’s perceptual and decisional processing of women’s sexual interest

The current investigation examines the etiology of men’s errors in sexual perception after moderate alcohol use. Sensitivity and bias estimates, derived from multidimensional signal detection analysis, revealed that men’s alcohol-influenced performance was associated with declining sensitivity to the distinction between women’s friendliness and sexual interest. However, sensitivity to the distinction between conservative and provocative clothing was unaffected. Similarly, an alcohol dose led to an increased bias to respond that women’s ambiguous cues were sexual interest (rather than friendliness) but did not influence response thresholds for clothing style. Thus, there was specificity to the perceptual and decisional changes associated with alcohol use rather than a simple degradation of men’s capacity to process all dating-relevant cues in the environment. Given the link between alcohol use, sexual misperception, and acquaintance-initiated sexual coercion, understanding the etiology of sexual misper-ception in the context of alcohol use may inform sexual coercion prevention efforts

Evaluating the accuracy of alcohol expectancies relative to subjective response to alcohol

Introduction: Although limited in empirical support, Alcohol Expectancy (AE) theory posits that AEs may overestimate subjective response (SR) to the positive effects of alcohol, which, in turn, confers alcohol-related risk (e.g., Darkes & Goldman, 1993). The recent development of the Anticipated Effects of Alcohol Scale (AEAS; Morean, Corbin, & Treat, 2012) and the Subjective Effects of Alcohol Scale (SEAS; Morean, Corbin, & Treat, 2013) now permits direct AE-SR comparisons using psychometrically sound assessments designed for this purpose. Methods: We ran secondary data analyses (Morean et al., 2012, 2013) evaluating measurement invariance of AEs and SR; AE-SR relationships; the accuracy of AEs; and relations between AE-SR discrepancies and binge drinking, driving after drinking, and alcohol-related problems in a sample of 102 young adults (mean age 22.81 [2.25]; 74.5% male; 76.5% Caucasian) who consumed alcohol in a simulated bar setting (target blood alcohol level = .08 g/dL). Results: The AEAS and SEAS were scalar measurement invariant and that AEs generally overestimated SR (mean Cohen\u27s d = .48). Relative to SR, inflated high arousal negative AEs (e.g., aggressive) were associated with frequent binge drinking and alcohol-related problems, whereas exaggerated low arousal negative AEs (e.g., woozy) served protective functions. As blood alcohol levels rose, inflated low arousal positive AEs (e.g., relaxed) and low arousal negative AEs (e.g., wobbly) were associated with less frequent driving after drinking. Conclusions: Challenging AE-SR discrepancies for high arousal effects may have utility in treatment and prevention efforts, whereas maintaining overestimates of low arousal effects may serve protective functions. (C) 2015 Elsevier Ltd. All rights reserved

Differences in Subjective Response to Alcohol by Gender, Family History, Heavy Episodic Drinking, and Cigarette Use: Refining and Broadening the Scope of Measurement

Objective: Subjective response to alcohol (SR) has been shown to differ by gender, family history of alcoholism, drinking status, and cigarette smoking status. However, the requisite statistical basis for making mean-level comparisons (scalar measurement invariance; MI) has not been established for any SR measure, making it impossible to determine whether observed differences reflect true differences or measurement bias. Secondary data analyses were conducted to evaluate (a) MI of the Subjective Effects of Alcohol Scale (SEAS) by gender, family history, heavy drinking status, and cigarette smoking status using multigroup confirmatory factor analysis; and (b) the impact of these group-level variables on SR using multivariate general linear modeling. A central strength, the SEAS assesses novel high arousal negative (HIGH-; e.g., aggressive) and low arousal positive effects (LOW+; e.g., relaxed) in addition to commonly assessed high arousal positive [HIGH+; e.g., sociable] and low arousal negative effects [LOW; e.g., woozy]). Method: A total of 215 young adults reported on SR during a placebo-controlled alcohol administration study in a simulated bar setting (target blood alcohol concentration = .08%). Results: Scalar MI was achieved for each group. After consuming alcohol, family history positive individuals reported stronger HIGH- effects and female smokers reported weaker LOW+ effects than their counterparts. Heavy episodic drinkers and family history positive females reported weaker LOW- effects than their counterparts. Conclusions: The SEAS permits meaningful SR comparisons within several important groups. SR differences largely were observed on the novel SEAS subscales, highlighting the importance of assessing a full range of SR

Unhealthy and Unaware? Misjudging Social Comparative Standing for Health-Relevant Behavior

People sometimes modify their behavior based on whether they believe they do more or less of that behavior than others. But are people’s perceptions of their social-comparative status for behaviors generally accurate? The current research assessed accuracy and bias in perceived social-comparative status for a number of health-related behaviors. In two studies, participants estimated their social-comparative percentile regarding behavior frequency for 20 behaviors—pre-classified according to a 2 (healthy or unhealthy) x 2 (generally common or uncommon) design. Participants also reported their absolute frequency of engagement for the behaviors, and these reports were used to approximate people’s actual percentiles. Subjective percentile estimates were overly favorable for both healthy and unhealthy behaviors and were biased by a behavior’s general commonness/rarity. People who were least healthy regarding a behavior tended to be the most miscalibrated in their percentile estimation for that behavior. There was also support for a noise-plus-bias model of people’s percentile estimates