Within-Person Variability Score-Based Causal Inference: A Two-Step Estimation for Joint Effects of Time-Varying Treatments

Depression and Personality Traits Across Adolescence-Within-Person Analyses of a Birth Cohort

Depressive symptoms and personality traits covary in adolescents, but our understanding of the nature of this relation is limited. Whereas a predisposition explanation posits that specific personality traits increase the vulnerability for developing depression, a scar explanation proposes that depression may alter premorbid personality. Attempts to test these explanatory models have relied on analyses that conflate within-person changes and between-person differences, which limits the implications that can be drawn. Moreover, research on the early adolescent years is lacking. The present study therefore examined within-person associations between depressive symptoms and Big Five personality traits across ages 10 to 16. Children (n = 817; 49.9% boys) and parents from two birth cohorts in Trondheim, Norway, were assessed biennially with clinical interviews capturing symptoms of major depressive disorder and dysthymia, and self-reported Big Five personality traits. Analyses were conducted using a random intercept cross-lagged panel model, which accounts for all unmeasured time-invariant confounding effects. Increased Neuroticism predicted an increased number of depressive symptoms-and increased depressive symptoms predicted increased Neuroticism-across ages 10 to 14. Moreover, increased depressive symptoms forecast reduced Extraversion across ages 10 to 16, and reduced Conscientiousness from ages 12 to 14. Increases in Neuroticism may contribute to the development of depressive symptoms-in line with the predisposition model. As regards the scar model, depression may have an even wider impact on personality traits: increasing Neuroticism and reducing Extraversion and Conscientiousness. These effects may already be present in the earliest adolescent years.

Why the Cross-Lagged Panel Model Is Almost Never the Right Choice

The cross-lagged panel model (CLPM) is a widely used technique for examining reciprocal causal effects using longitudinal data. Critics of the CLPM have noted that by failing to account for certain person-level associations, estimates of these causal effects can be biased. Because of this, models that incorporate stable-trait components (e.g., the random-intercept CLPM) have become popular alternatives. Debates about the merits of the CLPM have continued, however, with some researchers arguing that the CLPM is more appropriate than modern alternatives for examining common psychological questions. In this article, I discuss the ways that these defenses of the CLPM fail to acknowledge well-known limitations of the model. I propose some possible sources of confusion regarding these models and provide alternative ways of thinking about the problems with the CLPM. I then show in simulated data that with realistic assumptions, the CLPM is very likely to find spurious cross-lagged effects when they do not exist and can sometimes underestimate these effects when they do exist.