Katherine L. Collison

Differences Among Dark Triad Components: A Meta-Analytic Investigation.

Since its emergence 14 years ago the dark triad (DT), composed of narcissism, psychopathy, and Machiavellianism, has become an increasingly popular research focus. Yet questions remain over whether the DT components are sufficiently distinct from another. We examined the nomological networks of each DT component through a meta-analysis of the available literature on the DT. We conducted 3 separate analyses—an examination of the average intercorrelations among the DT components (k = 156), an examination of similarities in each DT component’s nomological network (k = 159), and an examination of the effect sizes between DT components and 15 outcome categories (k range = 7 to 42). Our results indicate that the nomological networks of psychopathy and Machiavellianism overlap substantially while narcissism demonstrated differential relations compared with psychopathy and Machiavellianism. These results remained relatively constant after controlling for DT assessment approach. We argue that the current literature on Machiavellianism may be better understood as a secondary psychopathy literature. Future directions for DT research are discussed in light of our meta-analytic results.

Examining the Effects of Controlling for Shared Variance among the Dark Triad Using Meta-analytic Structural Equation Modelling: Dark Triad metaSEM

Multivariate procedures (e.g. structural equation modelling) are essential to personality psychology, but interpretive difficulties can arise when examining the relations between residualized variables (i.e. the residual content of a variable after its overlap with other variables has been statistically controlled for) and outcomes of interest. These issues have been the focus of recent debate within the research literature on the Dark Triad, which is a collection of interrelated but theoretically distinct personality constructs made up of narcissism, Machiavellianism and psychopathy. The present paper highlights previous work on the issue of partialling and also makes use of recent developments surrounding meta–analytic structural equation modelling to reliably assess the impact of partialling on the empirical profiles of the Dark Triad components. The results show that numerous interpretive difficulties arise after partialling the overlap among the Dark Triad components, most notably for narcissism and Machiavellianism. The results are discussed in the context of contemporary Dark Triad research in addition to discussing the implications for structural equation modelling methods in personality psychology more generally. Recommendations are made for how future research can mitigate the interpretive difficulties that may arise from partialling. Copyright

Screening for depression in hospitalized medical patients

&NA; Depression among hospitalized patients is often unrecognized, undiagnosed, and therefore untreated. Little is known about the feasibility of screening for depression during hospitalization, or whether depression is associated with poorer outcomes, longer hospital stays, and higher readmission rates. We searched PubMed and PsycINFO for published, peer‐reviewed articles in English (1990‐2016) using search terms designed to capture studies that tested the performance of depression screening tools in inpatient settings and studies that examined associations between depression detected during hospitalization and clinical or utilization outcomes. Two investigators reviewed each full‐text article and extracted data. The prevalence of depression ranged from 5% to 60%, with a median of 33%, among hospitalized patients. Several screening tools identified showed high sensitivity and specificity, even when self‐administered by patients or when abbreviated versions were administered by individuals without formal training. With regard to outcomes, studies from several individual hospitals found depression to be associated with poorer functional outcomes, worse physical health, and returns to the hospital after discharge. These findings suggest that depression screening may be feasible in the inpatient setting, and that more research is warranted to determine whether screening for and treating depression during hospitalization can improve patient outcomes.

Using Dominance Analysis to Decompose Narcissism and Its Relation to Aggression and Externalizing Outcomes

Research on narcissism has shown it to be multidimensional construct. As such, the relations the larger construct bear with certain outcomes may mask heterogeneity apparent at the more basic trait level. This article used the Five Factor Narcissism Inventory, a Five-Factor Model–based measure of narcissism that allows for multiple levels of analysis, to examine the relative importance of narcissistic traits in relation to aggression, externalizing behavior, and self-esteem outcomes in two independent samples. The relative importance of the narcissism factors was determined through the use of dominance analysis—a relatively underused method for determining relative importance among a set of related predictors. The results showed that antagonism, compared with agentic extraversion and neuroticism, was the dominant predictor across all forms of aggressive behavior. Additional analyses showed that subscales within the broader factor of antagonism also showed differential importance relative to one another for certain aggression outcomes. The results are discussed in the context of the relation between narcissism and aggression and highlight the utility of using extensions of regression-based analyses to explore the heterogeneity within personality constructs.

Amazon Mechanical Turk as a platform for borderline personality disorder research

Researchers investigating the psychological processes underlying specific mental health problems often have difficulties achieving large enough samples for adequately powered studies. This can be particularly problematic when studying psychopathology with low base rates in typical samples (i.e., undergraduate and community). A relatively new approach to recruitment and testing employs online crowdsourcing to rapidly measure the characteristics and behavior of large numbers of people. We tested the feasibility of researching borderline personality disorder (BPD) in this manner using one large crowdsourcing site, Amazon Mechanical Turk (MTurk). Specifically, we examined prevalence rates of psychopathology in a large MTurk sample, as well as the demographic, psychosocial, and psychiatric characteristics of individuals who met criteria for BPD. These characteristics were compared across three groups: those who met criteria for BPD currently, those who met criteria for remitted BPD, and those who had never met criteria for BPD. The results suggest that MTurk may be ideally suited for studying individuals with a wide range of pathology, from healthy to intensely symptomatic to remitted.

Development and preliminary validation of a five factor model measure of machiavellianism.

Machiavellianism is characterized by planfulness, the ability to delay gratification, and interpersonal antagonism (i.e., manipulativeness and callousness). Although its theoretically positive relations with facets of Conscientiousness should help distinguish Machiavellianism from psychopathy, current measurements of Machiavellianism are indistinguishable from those of psychopathy in large part because of their assessment of low Conscientiousness. The goal of the present study was to create a measure of Machiavellianism that is more in line with theory using an expert-derived profile based on the 30 facets of the five-factor model (FFM) and then test the validity of that measure by comparing it with relevant constructs. Previously collected expert ratings of the prototypical Machiavellian individual on FFM facets yielded a profile of 13 facets including low Agreeableness and high Conscientiousness. Items were written to represent each facet, resulting in a 201-item Five Factor Machiavellianism Inventory (FFMI). Across 2 studies, with a total of 710 participants recruited via Mechanical Turk, the FFMI was reduced to its final 52-item form and was shown to relate as expected to measures of Big Five personality traits, current Machiavellianism measures, psychopathy, narcissism, ambition, and impulsivity. The FFMI is a promising alternative Machiavellianism measure.

Using Bayesian methods to update and expand the meta-analytic evidence of the five-factor model's relation to antisocial behavior

The Five Factor Model (FFM) of personality is the dominant hierarchical model of personality. Previous work has demonstrated the importance of the FFM domains and facets in understanding a variety of antisocial behaviors ranging from non-violent antisocial behavior to a variety of aggression outcomes. The aim of the present meta-analysis was to quantitatively summarize the empirical work that has examined these relations, as well as update and expand previous work in this area using Bayesian meta-analytic methods. A comprehensive search of available literature on the FFM and antisocial behavior was conducted and posterior distributions of effect sizes were computed for the FFM domains (across 12 antisocial outcomes). The meta-analytic results supported the primary importance of (low) Agreeableness and (low) Conscientiousness in predicting antisocial behavior across antisocial outcomes, with the exception of the outcome related to child molestation. The importance of Neuroticism was more dependent on the specific antisocial outcome under examination. The results are discussed in the context of the descriptive research on the FFM and antisocial behavior, and how Bayesian methods provide additional utility in estimation and prediction compared to more common frequentist methods. Furthermore, we recommend that future work on the FFM and antisocial behavior move towards process-level analyses to further examine how traits are implicated in different forms of antisocial behavior.

Analysis of Patient-reported Outcomes of Quality of Life and Functioning Before and After Treatment of Major Depressive Disorder Comorbid With Alcohol Use Disorders.

OBJECTIVE Alcohol use disorders (AUDs) are common among persons with major depressive disorder (MDD) and have an adverse impact on course of illness and patient outcomes. The aim of this study was to examine whether AUD adversely impacted patient-centered outcomes in a sample of research subjects evaluated as part of a large clinical trial for depression. The outcomes of interest to this post hoc analysis are quality of life (QOL), functioning, and depressive symptom severity. METHODS We analyzed 2280 adult MDD outpatient research subjects using data from the Sequenced Treatment Alternatives to Relieve Depression trial. We compared entry and post-selective serotonin reuptake inhibitors (SSRI) treatment QOL, functioning, and depressive symptom severity scores between 121comorbid MDD with AUD (MDD + AUD) subjects and 2159 MDD-no-AUD subjects, and also differences between subjects categorized as remitters versus nonremitters within each group at exit. RESULTS At entry, MDD + AUD subjects reported similar QOL, functioning, and depressive symptom severity compared with the MDD-no-AUD subjects. After treatment with citalopram, both groups showed significant improvements throughout treatment; however, 36% to 55% of subjects still suffered from severely impaired QOL and functioning at exit. CONCLUSIONS The overall study population demonstrated a significant response to treatment with large effect sizes in depressive symptom reduction, but to a lesser extent in QOL and functioning. Findings suggest that subjects with MDD + AUD benefited equally as MDD-no-AUD from treatment with selective serotonin reuptake inhibitors (SSRI) medication, yet both groups continue to experience reduced QOL and functioning after treatment. Monitoring QOL and functioning is critical to determine whether interventions that improve clinical outcomes also impact patient-centered outcomes, and our analysis suggests that there is a pressing need for innovative interventions that effectively improve these outcomes.

Examining the Impact of Patient-Reported Hope for Improvement and Patient Satisfaction with Clinician/Treatment on the Outcome of Major Depressive Disorder Treatment

Aims This analysis aims at examining if patient-reported variables such as hope for improvement and patient satisfaction with clinician/treatment could influence the outcome major depressive disorder (MDD) treatment, namely depression remission, in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial. Study Design Retrospective cohort study. Place and Duration of Study The STAR*D study was conducted at 18 primary care and 23 psychiatric care settings in the United States from 2001–2007 and was funded by the National Institute of Mental health (NIMH). The analysis contained in this manuscript was conceptualized at the Cedars-Sinai Department of Psychiatry and Behavioral Neurosciences and performed at the UCLA School of Public Health. Methodology Using data from STAR*D, the current study used logistic regression and survival analyses to examine the relationship between depressive symptoms remission and two sets of self-reported factors: Hope for improvement and, Patient satisfaction with treatment/clinician. Results First, more than 90% of STAR*D patients reported having high hope for improvement (agree or strongly agree) and more than 66% endorsed high satisfaction with clinicians and more than 50% expressed high satisfaction with treatments (very or mostly satisfied). Second, hope for improvement was predictive of depression remission (p<0.05). Third, satisfaction with clinician/treatment, did not predict remission. Conclusion This study shows the impact that patients’ subjective hope for improvement can have on predicting depression remission in contrast to satisfaction with clinician/treatment. Future studies should prospectively incorporate patients’ subjective attitudes regarding hope for improvement and satisfaction with clinicians and treatments as mediators and moderators of MDD treatment success.