Read about the latest research published in Clinical Psychological Science:
When Did Posttraumatic Stress Disorder Get So Many Factors? Confirmatory Factor Models Since DSM–5
Andrew Rasmussen, Jay Verkuilen, Nuwan Jayawickreme, Zebing Wu, and Sydne T. McCluskey
Confirmatory factor analysis (CFA) is the statistical procedure commonly used to test the validity of different models of posttraumatic stress disorder (PTSD). CFA models allow the identification of symptom dimensions (or factors) that characterize PTSD. In the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM–5), PTSD is characterized by four dimensions (e.g., intrusion, avoidance, negative alterations in mood and cognition, and hyperarousal), but since the publication of the DSM–5, CFA models of PTSD have become increasingly multidimensional, identifying six and even seven factors. To clarify why the models of PTSD are becoming increasingly multidimensional, Rasmussen and colleagues examined 23 publications that used CFA and DSM–5symptoms to study PTSD. They found that researchers have been finding support for multidimensionality of PTSD in their CFA analyses mostly because they have not been following basic premises concerning factor identification in CFA. More specifically, the multidimensional models may fit the data, but their factors are underidentified (i.e., composed of fewer than three symptoms) and are correlated with each other, which means they might not be meaningfully distinct. Two positive aspects of the CFA studies examined were that two thirds of them used external data to validate their factors and that most of them relied on large sample sizes. However, the authors suggest the need to improve CFA practices in the PTSD literature, and they offer some practical suggestions about how to do so (e.g., avoid underidentified factors).
Mathematically Modeling Emotion Regulation Abnormalities During Psychotic Experiences in Schizophrenia
Gregory P. Strauss, Farnaz Zamani Esfahlani, Katherine Frost Visser, Elizabeth K. Dickinson, June Gruber, and Hiroki Sayama
People diagnosed with psychotic disorders often experience symptoms, such as hallucinations or paranoia, that are usually associated with negative emotions that the individuals attempt to manage and regulate. This study examined how individuals with schizophrenia or schizoaffective disorder (SZ) regulate emotions during the presence and absence of psychotic symptoms. For 6 days, four times per day, outpatients with SZ received a message on a mobile device and were required to file the survey immediately. In each survey, participants (a) rated the intensity of their current positive and negative emotions; (b) reported how much they were using emotion regulation strategies; (c) provided information about their current whereabouts, activity, and companions; and (d) reported any psychotic symptoms they could be experiencing. When participants were experiencing psychotic symptoms, they rated their emotions as more negative and reported using emotion-regulation strategies; the type of strategy used depended on the symptom and context. However, emotion regulation at one report time did not result in decreased negative emotion in the next report, especially when negative emotion resulted from auditory hallucinations. During psychotic symptoms, emotions were more densely connected to each other, and denser networks of emotions were more difficult to regulate. These results suggest that emotion regulation failures in SZ result from problems in selecting and implementing regulation strategies but not from identifying the emotions, although denser connections among individual emotions may make it particularly difficult to implement regulation strategies effectively.
Thomas and Sharp argue that the dominant approaches to understanding psychological processes, despite helping to elaborate laws between hypothetical psychological constructs and observable data, do not describe links between psychological and biological phenomena or how to integrate them in unified explanations of the psychological constructs. To make up for this shortcoming, they propose the use of mechanistic science. This approach has the goal of explaining how particular psychological phenomena are implemented in living systems, which can be achieved by identifying mechanisms (i.e., structures defined by their component parts, operations, and organization), the functioning of which results in a particular phenomenon. This approach requires that psychological scientists constrain their conceptions of psychological functions to those that might plausibly be implemented by mechanisms in living systems. Thomas and Sharp give the example of biologists applying this approach to move their field from a descriptive to a causal science. They also explain in depth how to apply mechanistic science, giving examples that include a mechanistic explanation of vision processes and a mechanistic framework applied to psychopathology research. The authors suggest that mechanistic science can complement existing research approaches rather than replacing them and that it can facilitate collaboration across fields by providing a common framework for guiding future scientific investigation. Moreover, demonstrating how psychological processes are implemented in biological systems can lead to a better understanding of psychological phenomena and, consequently, more effective interventions in psychopathology.