To send content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about sending content to .
To send content items to your Kindle, first ensure email@example.com
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about sending to your Kindle.
Note you can select to send to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
In their chapter, Bach and Presnall-Shvorin (this volume) introduce guidelines for incorporating empirically-driven trait models of personality pathology, codified in the DSM-5 and ICD-11, into therapeutic practice. Though the authors of this commentary are supportive of the effort to bridge research with clinical practice, they suggest that a mechanistic model which accounts for personality processes underlying descriptive traits could offer greater precision than traits alone. Furthermore, they argue that clinical dysfunction can only be meaningfully defined and treated with an understanding of dynamic, contextualized aspects of personality. To illustrate how a mechanistic model could complement and extend Bach and Presnall’s recommendations, the authors present a case conceptualization using cybernetic theory. Finally, they review how idiographic data gleaned from ambulatory assessment methods provide insight into pathological processes ideal for therapeutic intervention. To achieve a generalizable approach flexible enough to adapt to the individual, they encourage the development of treatment models that go beyond traits to mechanistically link stable and dynamic personality features into a unified framework.
Most of what clinical psychology concerns itself with is directly unobservable. Concepts like neuroticism and depression, but also learning and development, represent dispositions, states, or processes that must be inferred and cannot (currently) be directly measured. Latent variable modeling, as a statistical framework, encompasses a range of techniques that involve estimating the presence and effect of unobserved variables from observed data. This chapter provides a nontechnical overview of latent variable modeling in clinical psychology. Dimensional latent variable models are emphasized, although categorical and hybrid models are touched on briefly. Challenges with specific models, such as the bifactor model are discussed. Examples draw from the psychopathology literature.
The scientific discipline of clinical psychology has witnessed paradigm changes in the prevailing conceptualization of psychopathology and in the rigor of experimental methods to test psychosocial treatments. In parallel, neuroscience approaches to mental illness have become increasingly prominent and technologies to measure psychological constructs over time and across contexts are becoming ubiquitous in psychological research. Altogether, these changes have pushed clinical scientists to incorporate novel research methodologies and analytic approaches. Modern studies of clinical phenomena are often theoretically integrative and assess constructs across levels of measurement, ranging from the molecular to the behavioral. These shifts are fundamental, and necessitate changes in the way modern clinical psychologists design studies, collect data, and draw scientific conclusions. This book is intended to serve as a guide for the next generation of clinical psychologists, who will benefit from greater training in statistics, study design, developmental psychopathology, and multimethod approaches.
This book integrates philosophy of science, data acquisition methods, and statistical modeling techniques to present readers with a forward-thinking perspective on clinical science. It reviews modern research practices in clinical psychology that support the goals of psychological science, study designs that promote good research, and quantitative methods that can test specific scientific questions. It covers new themes in research including intensive longitudinal designs, neurobiology, developmental psychopathology, and advanced computational methods such as machine learning. Core chapters examine significant statistical topics, for example missing data, causality, meta-analysis, latent variable analysis, and dyadic data analysis. A balanced overview of observational and experimental designs is also supplied, including preclinical research and intervention science. This is a foundational resource that supports the methodological training of the current and future generations of clinical psychological scientists.
An ongoing challenge in understanding and treating personality disorders (PDs) is a significant heterogeneity in disorder expression, stemming from variability in underlying dynamic processes. These processes are commonly discussed in clinical settings, but are rarely empirically studied due to their personalized, temporal nature. The goal of the current study was to combine intensive longitudinal data collection with person-specific temporal network models to produce individualized symptom-level structures of personality pathology. These structures were then linked to traditional PD diagnoses and stress (to index daily functioning).
Using about 100 daily assessments of internalizing and externalizing domains underlying PDs (i.e. negative affect, detachment, impulsivity, hostility), a temporal network mapping approach (i.e. group iterative multiple model estimation) was used to create person-specific networks of the temporal relations among domains for 91 individuals (62.6% female) with a PD. Network characteristics were then associated with traditional PD symptomatology (controlling for mean domain levels) and with daily variation in clinically-relevant phenomena (i.e. stress).
Features of the person-specific networks predicted paranoid, borderline, narcissistic, and obsessive-PD symptom counts above average levels of the domains, in ways that align with clinical conceptualizations. They also predicted between-person variation in stress across days.
Relations among behavioral domains thought to underlie heterogeneity in PDs were indeed associated with traditional diagnostic constructs and with daily functioning (i.e. stress) in person-specific networks. Findings highlight the importance of leveraging data and models that capture person-specific, dynamic processes, and suggest that person-specific networks may have implications for precision medicine.
Trait impulsivity is thought to play a key role in predicting behaviors on the externalizing spectrum, such as drug and alcohol use and aggression. Research suggests that impulsivity may not be a unitary construct, but rather multidimensional in nature with dimensions varying across self-report assessments and laboratory behavioral tasks. Few studies with large samples have included a range of impulsivity-related measures and assessed several externalizing behaviors to clarify the predictive validity of these assessments on important life outcomes.
Community adults (N = 1295) between the ages of 30 and 54 completed a multidimensional assessment of impulsivity-related traits (including 54 self-report scales of personality traits implicated in impulsive behaviors, and four behavioral tasks purporting to assess a construct similar to impulsivity) and reported on five externalizing behavioral outcomes (i.e. drug, alcohol, and cigarette use, and physical and verbal aggression). We ran an exploratory factor analysis on the trait scales, and then a structural equation model predicting the externalizing behaviors from the three higher-order personality factors (i.e. Disinhibition v. Constraint/Conscientiousness, Neuroticism/Negative Emotionality, and Extraversion/Positive Emotionality) and the four behavioral tasks.
Relations between the self-report factors and behavioral tasks were small or nonexistent. Associations between the self-report factors and the externalizing outcomes were generally medium to large, but relationships between the behavioral tasks and externalizing outcomes were either nonexistent or small.
These results partially replicate and extend recent meta-analytic findings reported by Sharma et al. (2014) to further clarify the predictive validity of impulsivity-related trait scales and laboratory behavioral tasks on externalizing behaviors.
Email your librarian or administrator to recommend adding this to your organisation's collection.