Classification of Antisocial Behaviors Along Severity and Frequency Parameters

Antisocial behaviors were systematically classified along a hierarchy of seriousness or severity that also accounted for frequency of occurrence and heterogeneity of behaviors. Items from the CD and ODD schedules of the NIMH DISC-IV and from the Elliot Delinquency Scales were listed at specified frequencies. Nine mental health clinicians rated the level of seriousness of each alternative on a scale from 0 (trivial) to 5 (very serious). Reliability of the ratings was assessed. Over two thirds of the items showed excellent agreement among the raters, 8% showed poor agreement and 21% showed fair to moderate agreement. The overall reliability of a single rater’s score was 0.56 and the reliability of the average was 0.84. The classification meets high psychometric standards for reliability and has face validity. The final output provides a classification along a spectrum that takes into account severity, frequency of occurrence during the previous year, and presence of multiple behaviors. It is useful for classification purposes and for longitudinal tracking of antisocial behavior.     

Epidemiological Measurement of Children's and Adolescents' Exposure to Community Violence: Working with the Current State of the Science

A number of measures have been used in epidemiological studies of children's exposure to community violence, yet the quality of these instruments is not uniformly good. This paper undertakes a systematic review of the most commonly used (or most promising) self-report or interview-administered instruments, with regard to their conceptual bases and psychometric properties. Based on the review, recommendations are made for working with the current state of the science in order to move the field forward. A key recommendation is for sounder conceptualization of work in the field and greater transparency in the reporting of research, in order to facilitate the comparability of studies.     

Quitting smoking: The importance of non-smoker identity in predicting smoking behaviour and responses to a smoking ban

Objective: We examined how ‘smoker’ and ‘non-smoker’ self- and group-identities and socio-economic status (SES) may predict smoking behaviour and responses to antismoking measures (i.e. the Dutch smoking ban in hospitality venues). We validated a measure of responses to the smoking ban.

Design: Longitudinal online survey study with one-year follow-up (N = 623 at T1 in 2011; N = 188 at T2 in 2012) among daily smokers.

Main outcome measures: Intention to quit, quit attempts and ‘rejecting’, ‘victimizing’, ‘socially conscious smoking’ and ‘active quitting’ responses to the smoking ban.

Results: Non-smoker identities are more important than smoker identities in predicting intention to quit, quit attempts and responses to the smoking ban, even when controlling for other important predictors such as nicotine dependence. Smokers with stronger non-smoker identities had stronger intentions to quit, were more likely to attempt to quit between measurements, and showed less negative and more positive responses to the smoking ban. The association between non-smoker self-identity and intention to quit was stronger among smokers with lower than higher SES.

Conclusion: Antismoking measures might be more effective if they would focus also on the identity of smokers, and help smokers to increase identification with non-smoking and non-smokers.     

On the relationship between implicit and explicit self-esteem: The moderating role of dismissing attachment

Recently, dual models of social cognition have distinguished the implicit (affective) and the explicit (propositional) levels of self-esteem. Many studies have found that implicit and explicit self-esteem are weakly or even not correlated. Concerning the moderating factors of such a relationship, other studies stressed the importance of emotional awareness in the translation of implicit self-evaluations into a propositional format. If so, the defensive strategy which characterizes dismissing attachment should determine a fictitious explicit self-image, by impairing the awareness of self-related emotional experiences. At the implicit level, instead, such defensive processes should not operate. We therefore hypothesized that dismissing attachment would increase the implicit-explicit self-esteem discordance. Participants were 145 university students with a mean age of 20.4 (SD = 2.75). Moderated multiple regression analyses were conducted to test for the interactive effects of the Self-Esteem Implicit Association Test and the Attachment Style Questionnaire scales on the Rosenberg Self-Esteem Scale. Findings revealed an interaction effect only for the relationships as secondary scale, confirming that dismissing style, but not other attachment dimensions, increases implicit-explicit self-esteem discordance.     

Fundamental Beliefs,Origin Explanations and Perceived Effectiveness of Protection Measures: Exploring Laypersons' Chains of Reasoning About Influenza

Laypersons' chains of reasoning in explaining recent influenza outbreaks are investigated. Drawing on social representations theory, fundamental worldviews, that is, the belief in a dangerous world (BDW), are postulated to anchor explanations of disease origins, which in turn affect perceived effectiveness of protection measures. Our study, based on a longitudinal survey among the general public in Switzerland, showed that the lower people's BDW scores, the more they appeal to natural origins to explain outbreaks and the more they perceive official protection measures as effective. The higher people's BDW scores, the more they explain outbreaks via hygienic origins, which are linked with out‐group discrimination measures, and conspiracy origins, which are linked with lower perceived effectiveness of aid intervention measures. Copyright © 2013 John Wiley & Sons, Ltd.     

Eyewitness identification discriminability: ROC analysis versus logistic regression

To reach conclusions regarding the respective accuracy of two conditions, eyewitness researchers evaluate correct and false identification rates computed across participants. Two approaches typically are employed. One approach relies on ratio-based probative value measures; but Wixted and Mickes (2012) and Gronlund, Wixted, and Mickes (2014) showed that these measures fail to disentangle an assessment of accuracy (i.e., discriminability between guilty and innocent suspects) from response bias (i.e., a willingness to make a response). Our focus is on a second approach, logistic regression analyses of the correct and of the false identification rates. Logistic regression also fails to disentangle discriminability from bias. Therefore, it only can denote the most accurate condition in limited circumstances. The best approach for reaching the proper conclusion regarding which condition is most accurate is to use receiver operator characteristic (ROC) analysis. Simulated ROC data illustrate the problem with a reliance on logistic regression to assess accuracy.     

Mother-Infant Interaction Kinect Analysis (MIIKA): An automatic kinematic-based methodology for the investigation of interpersonal distance during early exchanges

Interpersonal distance is a core aspect of mother-child interaction. While conventional measures based on human coders do not fully capture the dynamics of this feature, computational methods provide automatic measures which can detect even small changes and more accurate estimates both spatially and temporally. Using RGB-D sensors (Microsoft Kinect V2), the present study describes a setup to automatically examine interpersonal distance during mother-child interactions, termed Mother-Infant Interaction Kinect Analysis (MIIKA). First, the laboratory setting and the data extraction method are described. By using an ad-hoc algorithm for kinematic data extraction, MIIKA returns three metrics: barycenter position (distance and velocity of approach and separation), movements (number of small, medium and large approaches and separations) and contributions (proportional contributions of mother and child to approaches and separations). Secondly, preliminary MIIKA metrics are described for a non-clinical mother-child dyad as an exemplification of the protocol. As interpersonal distance can be affected by contingent situations, we detected mother-infant full skeleton during three interactional contexts characterized by different kinds of dyadic exchanges: a free play session, a task-oriented activity and an emotionally arousing condition. Results highlighted similarities and differences between the three interactional contexts. MIIKA appears to be a promising setup to automatically examine interpersonal distance in early mother-child interactions.     

Latent Growth Curve and Repeated Measures ANOVA Contrasts: What the Models are Telling You

The relationship between the latent growth curve and repeated measures ANOVA models is often misunderstood. Although a number of investigators have looked into the similarities and differences among these models, a cursory reading of the literature can give the impression that they are very different models. Here we show that each model represents a set of contrasts on the occasion means. We demonstrate that the fixed effects parameters of the estimated basis vector latent growth curve model are merely a transformation of the repeated measures ANOVA fixed effects parameters. We further show that differences in fit in models that estimate the same means structure can be due to the different error covariance structures implied by the model. We show these relationships both algebraically and through using data from a simulation.