Community adolescents reported high rates of NSSI, engaged in to influence behaviors of others and to manage internal emotions. Intervention efforts should be tailored to reducing individual issues that contribute to NSSI and building alternative skills for positive coping, communication, stress management, and strong social support.

Reporting effect sizes in scientific articles is increasingly widespread and encouraged by journals; however, choosing an effect size for analyses such as mixed-effects regression modeling and hierarchical linear modeling can be difficult. One relatively uncommon, but very informative, standardized measure of effect size is Cohen’s f2, which allows an evaluation of local effect size, i.e., one variable’s effect size within the context of a multivariate regression model. Unfortunately, this measure is often not readily accessible from commonly used software for repeated-measures or hierarchical data analysis. In this guide, we illustrate how to extract Cohen’s f2 for two variables within a mixed-effects regression model using PROC MIXED in SAS® software. Two examples of calculating Cohen’s f2 for different research questions are shown, using data from a longitudinal cohort study of smoking development in adolescents. This tutorial is designed to facilitate the calculation and reporting of effect sizes for single variables within mixed-effects multiple regression models, and is relevant for analyses of repeated-measures or hierarchical/multilevel data that are common in experimental psychology, observational research, and clinical or intervention studies.

This study used semi-parametric group-based modeling to explore unconditional and conditional trajectories of self-reported depressed mood from age 12 to 25. Drawing on data from the National Longitudinal Study of Adolescent Health (N=11,559), four distinct trajectories were identified: no depressed mood, stable low depressed mood, early high declining depressed mood, and late escalating depressed mood. Baseline risk factors associated with greater likelihood of membership in depressed mood trajectory groups compared to the no depressed mood group included being female, Black/African American, Hispanic/Latino American, or Asian American, lower SES, using alcohol, tobacco, or other drugs on a weekly basis, and delinquent behavior. Baseline protective factors associated with greater likelihood of membership in the no depressed mood group compared to the depressed mood trajectory groups included two-parent family structure, feeling connected to parents, peers, or school, and self esteem. With the exception of delinquent behavior, risk and protective factors also distinguished the likelihood of membership among several of the three depressed mood groups. The results add to basic etiologic research regarding developmental pathways of depressed mood in adolescence and young adulthood.

Understanding temporal change in human behavior and psychological processes is a central issue in the behavioral sciences. With technological advances, intensive longitudinal data (ILD) are increasingly generated by studies of human behavior that repeatedly administer assessments over time. ILD offer unique opportunities to describe temporal behavioral changes in detail and identify related environmental and psychosocial antecedents and consequences. Traditional analytical approaches impose strong parametric assumptions about the nature of change in the relationship between time-varying covariates and outcomes of interest. This paper introduces time-varying effect models (TVEM) that explicitly model changes in the association between ILD covariates and ILD outcomes over time in a flexible manner. In this article, we describes unique research questions that the TVEM addresses, outline the model-estimation procedure, share a SAS macro for implementing the model, demonstrate model utility with a simulated example, and illustrate model applications in ILD collected as part of a smoking-cessation study to explore the relationship between smoking urges and self-efficacy during the course of the pre- and post- cessation period.

The present study sought to extend prior research by using data from the National Comorbidity Survey Replication (NCS-R) to examine the relationship between number of lifetime traumas, PTSD and fifteen self-reported chronic medical conditions. The goal was to determine whether the commonly found relationship between PTSD symptomatology and physical health were better explained by the number of lifetime traumas experienced. The NCS-R is a representative U.S. household survey that assessed lifetime experience of a variety of traumas, lifetime diagnosis of PTSD and fifteen chronic medical conditions (e.g. pain conditions, cardiovascular disorders, etc.). Two major findings emerged: 1) there was a graded relationship between trauma exposure, PTSD, and the majority of chronic medical conditions where individuals with PTSD had the highest likelihood of chronic medical condition and non-traumatized individuals had the lowest risk and; 2) with the exception of headaches, the relationship between PTSD and chronic medical conditions was explained by the number of lifetime traumas experienced when analyses were subset to traumatized individuals. The present study supports prior research suggesting that multiple traumas have a cumulative effect on physical health. The impact of trauma on health may be independent of PTSD symptomatology. Keywords trauma; PTSD; chronic medical conditions; National Comorbidity Survey-Replication Posttraumatic stress disorder (PTSD) affects 6.8% to 7.8% of the U.S. population during their lifetime (Cox, 2002;Kessler, Berglund, Demler, Jin, & Walters, 2005) and is recognized as an expensive form of psychiatric disability (Marciniak, Lage, Dunayevich, Russell, Bowman, Landbloom et al., 2005;Walker, Katon, Russo, Ciechanowski, Newman, & Wagner, 2003). According to one estimate, the annual cost of anxiety disorders in the U.S. was approximately $42.3 billion in mental and physical health services, with PTSD ranking as the highest in terms of per person health care expenditures (Greenberg, Sisitsky, Kessler, Finkelstein, Berndt, Davidson et al., 1999). Further, the majority of the economic burden (54%) is due to direct nonpsychiatric health care costs such as primary care and emergency room visits (Greenberg et al., 1999). Numerous studies have shown a consistent association between PTSD and both self-reported measures of physical health such as chronic pain, back problems and general physical complaints (Asmundson, Coons, Taylor, & Katz, 2002;Beckham, Crawford, Feldman, Kirby, Hertzberg, Davidson et al., 1997;McWilliams, Cox, & Enns, 2003; RoyByrne, Smith, Goldberg, Afari, & Buchwald, 2004 Goldberg, 2006;Vedantham, Brunet, Boyer, Weiss, Metzler, & Marmar, 2001;Wagner, Wolfe, Rotnitsky, Proctor, & Erickson, 2000;Wolfe, Schnurr, Brown, & Furey, 1994;Zoellner, Goodwin, & Foa, 2000) as well as a range of physician-diagnosed disorders such as cardiovascular/circulatory, autoimmune, musculoskeletal, digestive and respiratory disorders (Boscarino, 1997(Boscarino, , 2004Goodwin & Davidson, 2005;Ka...

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This paper reports the results of a high-risk study of children under age 18 of parents who served as probands in a family study of comorbidity of substance abuse and anxiety disorders. There was a strong degree of specificity of familial aggregation of both the anxiety disorders and substance disorders. Rates of conduct disorder and depression were elevated among offspring of all affected parents. Inclusion of co-parent disorders in the evaluation of familial transmission in the present study strengthened the findings regarding the specificity of transmission of the anxiety disorders and the links between both parental substance abuse and antisocial personality with child conduct disorder.