Variability and instability are commonplace

Today, I present two studies that use clever methodologies to present alternatives to traditional notions. First, Ringwald, Shields, Kushner, Herzhoff & Tackett (2024) published “Cross-Situational Variability in Childhood Personality States” in Journal of Personality and Social Psychology.  Here’s the edited abstract:

Personality variability is an important individual difference construct that is the focus of major psychological theories and relates to socioemotional functioning. Although cross-situational personality variability has been studied extensively in adult populations, little is known about variability in children’s personality. In this study, we aimed to address this gap in knowledge by evaluating whether cross-situational variability is a potentially meaningful individual difference in youth. We used a “thin slice” approach in which research assistants viewed videos of 324 children (Mage = 9.92) completing 15 standardized tasks and rated youth’s Big Five personality states. Cross-situational variability in each personality state was estimated by calculating within-person standard deviations across tasks. Results showed that (a) there is substantial variability in children’s personality states; (b) children who are variable in one personality domain tend to be variable in other domains; and (c) more variable children are described by their parents as being less competent, less agreeable, less conscientious, and more neurotic. However, associations with parent-rated external criterion were generally small in magnitude, and key psychometric properties of the thin slice personality variability index are not well-established. Our study adds tentative but promising evidence that individual differences in cross-situational personality variability are not only present in childhood but may be consequential. 

I love this study because too many people think of personality in terms of stable “traits.” Here, in a relatively large sample, we see evidence for substantial variability in elementary school children in general, with helpful evidence that higher variability is viewed as problematic by parents. This may mean that different informants may characterize children differently, not because they’re biased but because the child functions differently in different settings. The next study tackles a whole different population Mildiner Moraga et al. (2024) published “Evidence for Mood Instability in Patients with Bipolar Disorder: Applying multilevel hidden Markov modeling to intensive longitudinal ecological momentary assessment data” in Journal of Psychopathology and Clinical Science. Here are the edited abstract and impact statements:

Bipolar disorder (BD) is a chronic psychiatric condition characterized by large episodic changes in mood and energy. Recently, BD has been proposed to be conceptualized as chronic cyclical mood instability, as opposed to the traditional view of alternating discrete episodes with stable periods in-between. Recognizing this mood instability may improve care and call for high-frequency measures coupled with advanced statistical models. To uncover empirically derived mood states, a multilevel hidden Markov model (HMM) was applied to 4-month ecological momentary assessment data in 20 patients with BD, yielding ∼9,820 assessments in total. Ecological momentary assessment data comprised self-report questionnaires (5 × daily) measuring manic and depressive constructs. Manic and depressive symptoms were also assessed weekly using the Altman Self-Rating Mania Scale and the Quick Inventory for Depressive Symptomatology Self-Report. Alignment between HMM-uncovered momentary mood states and weekly questionnaires was assessed with a multilevel linear model. HMM uncovered four mood states: neutral, elevated, mixed, and lowered, which aligned with weekly symptom scores. On average, patients remained < 25 hr in one state. In almost half of the patients, mood instability was observed. Switching between mood states, three patterns were identified: patients switching predominantly between (a) neutral and lowered states, (b) neutral and elevated states, and (c) mixed, elevated, and lowered states. In all, elevated and lowered mood states were interspersed by mixed states. The results indicate that chronic mood instability is a key feature of BD, even in “relatively” euthymic periods. This should be considered in theoretical and clinical conceptualizations of the disorder. 

The article investigates bipolar disorder with advanced statistical methods and adds evidence challenging the traditional view of alternating discrete mood episodes with stable periods in-between. Instead, it suggests that chronic mood instability may be a significant aspect of bipolar disorder and calls for high-frequency assessments using advanced statistical models to better understand and improve care for individuals with the disorder. 

This study has far fewer participants but more data. I like the three patterns they identify and suspect others may be identified in larger samples. This work, like the previous study, challenges traditional notions about stability of personality.