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How Replicable Are Links Between Personality Traits and Consequential Life Outcomes? The Life Outcomes of Personality Replication Project
The Big Five personality traits have been linked to dozens of life outcomes. However, metascientific research has raised questions about the replicability of behavioral science. The Life Outcomes of Personality Replication (LOOPR) Project was therefore conducted to estimate the replicability of the personality-outcome literature. Specifically, I conducted preregistered, high-powered (median N = 1,504) replications of 78 previously published trait–outcome associations. Overall, 87% of the replication attempts were statistically significant in the expected direction. The replication effects were typically 77% as strong as the corresponding original effects, which represents a significant decline in effect size. The replicability of individual effects was predicted by the effect size and design of the original study, as well as the sample size and statistical power of the replication. These results indicate that the personality-outcome literature provides a reasonably accurate map of trait–outcome associations but also that it stands to benefit from efforts to improve replicability.
Journal Article
User's guide for the SCID-5-AMPD : structured clinical interview for the DSM-5 alternative model for personality disorders
The paramount tool for the use of SCID-5-AMPD, the User's Guide for the SCID-5-AMPD provides readers with an essential manual to effectively understand and use the three SCID-5-AMPD modules. Integrating an overview of the DSM-5 Alternative Model, this companion guide provides instructions for each SCID-5-AMPD module and features completed samples of all modules in full, with corresponding sample patient cases and commentary-- back cover
Book
Psychotherapies for borderline personality disorder: a focused systematic review and meta-analysis
A recently updated Cochrane review supports the efficacy of psychotherapy for borderline personality disorder (BPD).
To evaluate the effects of standalone and add-on psychotherapeutic treatments more concisely.
We applied the same methods as the 2020 Cochrane review, but focused on adult samples and comparisons of active treatments and unspecific control conditions. Standalone treatments (i.e. necessarily including individual psychotherapy as either the sole or one of several treatment components) and add-on interventions (i.e. complementing any ongoing individual BPD treatment) were analysed separately. Primary outcomes were BPD severity, self-harm, suicide-related outcomes and psychosocial functioning. Secondary outcomes were remaining BPD diagnostic criteria, depression and attrition.
Thirty-one randomised controlled trials totalling 1870 participants were identified. Among standalone treatments, statistically significant effects of low overall certainty were observed for dialectical behaviour therapy (self-harm: standardised mean difference (SMD) -0.54,
= 0.006; psychosocial functioning: SMD -0.51,
= 0.01) and mentalisation-based treatment (self-harm: risk ratio 0.51,
< 0.0007; suicide-related outcomes: risk ratio 0.10,
< 0.0001). For adjunctive interventions, moderate-quality evidence of beneficial effects was observed for DBT skills training (BPD severity: SMD -0.66,
= 0.002; psychosocial functioning: SMD -0.45,
= 0.002), and statistically significant low-certainty evidence was observed for the emotion regulation group (BPD severity: mean difference -8.49,
< 0.00001), manual-assisted cognitive therapy (self-harm: mean difference -3.03,
= 0.03; suicide-related outcomes: SMD -0.96,
= 0.005) and the systems training for emotional predictability and problem-solving (BPD severity: SMD -0.48,
= 0.002).
There is reasonable evidence to conclude that psychotherapeutic interventions are helpful for individuals with BPD. Replication studies are needed to enhance the certainty of findings.
Journal Article
Generalized Network Psychometrics: Combining Network and Latent Variable Models
We introduce the network model as a formal psychometric model, conceptualizing the covariance between psychometric indicators as resulting from pairwise interactions between observable variables in a network structure. This contrasts with standard psychometric models, in which the covariance between test items arises from the influence of one or more common latent variables. Here, we present two generalizations of the network model that encompass latent variable structures, establishing network modeling as parts of the more general framework of structural equation modeling (SEM). In the first generalization, we model the covariance structure of latent variables as a network. We term this framework latent network modeling (LNM) and show that, with LNM, a unique structure of conditional independence relationships between latent variables can be obtained in an explorative manner. In the second generalization, the residual variance–covariance structure of indicators is modeled as a network. We term this generalization residual network modeling (RNM) and show that, within this framework, identifiable models can be obtained in which local independence is structurally violated. These generalizations allow for a general modeling framework that can be used to fit, and compare, SEM models, network models, and the RNM and LNM generalizations. This methodology has been implemented in the free-to-use software package
lvnet
, which contains confirmatory model testing as well as two exploratory search algorithms: stepwise search algorithms for low-dimensional datasets and penalized maximum likelihood estimation for larger datasets. We show in simulation studies that these search algorithms perform adequately in identifying the structure of the relevant residual or latent networks. We further demonstrate the utility of these generalizations in an empirical example on a personality inventory dataset.
Journal Article
Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists
The 21st century marks the emergence of “big data” with a rapid increase in the availability of datasets with multiple measurements. In neuroscience, brain-imaging datasets are more commonly accompanied by dozens or hundreds of phenotypic subject descriptors on the behavioral, neural, and genomic level. The complexity of such “big data” repositories offer new opportunities and pose new challenges for systems neuroscience. Canonical correlation analysis (CCA) is a prototypical family of methods that is useful in identifying the links between variable sets from different modalities. Importantly, CCA is well suited to describing relationships across multiple sets of data, such as in recently available big biomedical datasets. Our primer discusses the rationale, promises, and pitfalls of CCA.
•Introduction to the feature of canonical correlation analysis and its applications in combining two or more domains of data, such as behavioural and neuroimaging measures.•The utility of different variations the pros/cons of CCA.•Tips on application of CCA on rich phenotype datasets such as UK Biobank and HCP.
Journal Article
Latent traits of impulsivity and compulsivity: toward dimensional psychiatry
The concepts of impulsivity and compulsivity are commonly used in psychiatry. Little is known about whether different manifest measures of impulsivity and compulsivity (behavior, personality, and cognition) map onto underlying latent traits; and if so, their inter-relationship.
A total of 576 adults were recruited using media advertisements. Psychopathological, personality, and cognitive measures of impulsivity and compulsivity were completed. Confirmatory factor analysis was used to identify the optimal model.
The data were best explained by a two-factor model, corresponding to latent traits of impulsivity and compulsivity, respectively, which were positively correlated with each other. This model was statistically superior to the alternative models of their being one underlying factor ('disinhibition') or two anticorrelated factors. Higher scores on the impulsive and compulsive latent factors were each significantly associated with worse quality of life (both p < 0.0001).
This study supports the existence of latent functionally impairing dimensional forms of impulsivity and compulsivity, which are positively correlated. Future work should examine the neurobiological and neurochemical underpinnings of these latent traits; and explore whether they can be used as candidate treatment targets. The findings have implications for diagnostic classification systems, suggesting that combining categorical and dimensional approaches may be valuable and clinically relevant.
Journal Article
Statistically Controlling for Confounding Constructs Is Harder than You Think
Social scientists often seek to demonstrate that a construct has incremental validity over and above other related constructs. However, these claims are typically supported by measurement-level models that fail to consider the effects of measurement (un)reliability. We use intuitive examples, Monte Carlo simulations, and a novel analytical framework to demonstrate that common strategies for establishing incremental construct validity using multiple regression analysis exhibit extremely high Type I error rates under parameter regimes common in many psychological domains. Counterintuitively, we find that error rates are highest--in some cases approaching 100%--when sample sizes are large and reliability is moderate. Our findings suggest that a potentially large proportion of incremental validity claims made in the literature are spurious. We present a web application (http://jakewestfall.org/ivy/) that readers can use to explore the statistical properties of these and other incremental validity arguments. We conclude by reviewing SEM-based statistical approaches that appropriately control the Type I error rate when attempting to establish incremental validity.
Journal Article
Assessing the Big Five personality traits using real-life static facial images
There is ample evidence that morphological and social cues in a human face provide signals of human personality and behaviour. Previous studies have discovered associations between the features of artificial composite facial images and attributions of personality traits by human experts. We present new findings demonstrating the statistically significant prediction of a wider set of personality features (all the Big Five personality traits) for both men and women using real-life static facial images. Volunteer participants (N = 12,447) provided their face photographs (31,367 images) and completed a self-report measure of the Big Five traits. We trained a cascade of artificial neural networks (ANNs) on a large labelled dataset to predict self-reported Big Five scores. The highest correlations between observed and predicted personality scores were found for conscientiousness (0.360 for men and 0.335 for women) and the mean effect size was 0.243, exceeding the results obtained in prior studies using ‘selfies’. The findings strongly support the possibility of predicting multidimensional personality profiles from static facial images using ANNs trained on large labelled datasets. Future research could investigate the relative contribution of morphological features of the face and other characteristics of facial images to predicting personality.
Journal Article
The Conceptualization and Assessment of Schizotypal Traits: A Comparison of the FFSI and PID-5
The purpose of the present study was to compare the cognitive and perceptual aberration scales from the Five-Factor Schizotypal Inventory (FFSI; Edmundson, Lynam, Miller, Gore, & Widiger, 2011) and the Personality Inventory for DSM-5 (Krueger, Derringer, Markon, Watson, & Skodol,, 2012), as well as to address more generally the validity of the FFSI as a measure of both schizotypal personality traits and the FFM. Two independent samples were obtained, including 259 college students (55 of whom were preselected with elevated scores on a measure of schizotypal personality disorder [STPD]) and 346 adult MTurk participants (43% of whom had been or were currently in mental health treatment). Administered were the FFSI, the PID-5 Psychoticism scales, and alternative measures of general personality, openness, STPD, and schizotypal cognitive-perceptual aberrations. The results of the study are discussed with respect to the validity of the FFSI and PID-5 schizotypal cognitive and perception scales.
Journal Article