Trait Versus State: The Differential Impact of Personality Traits, Coping Behaviors and Cognitions on Depression and Anxiety

Overview

The present study aims to investigate the differential impact of different types of variables on depression and anxiety. In particular, the impact of trait-variables (i.e., personality measured with the brief Big Five Inventory (BFI-10)) are contrasted against state-variables, including unhelpful coping behavior (measured with CAS-1) and cognitions including positive metacognitions and negative metacognitions (both measured with CAS-1). Personality is defined as a set of traits that is relatively consistent over time and situation, and is subsequently less susceptible as a target mechanisms for therapy. Still, researchers have devoted great efforts toward describing personality traits as related to different psychopathological disorders. Consequently, the present study aims to investigate the differential associations of traits (i.e., personality) versus state variables (metacognitions and coping behaviors) on depression and anxiety. The findings of the present study will provide important insights in finding important associations between trait and state variables in relation to psychopathology, providing an important foundation for further directional investigations with temporal data Hypothesis 1: Neuroticism measured with (BFI-10), positive metacognitions, negative metacognitions, and unhelpful coping strategies (the latter three measured with CAS-1), will predict higher levels of depression and anxiety. Research Question 1: How and to what extent are different traits related measured with BFI-10 related to depression and anxiety in the present pandemic sample? Research Question 2: Are the trait or state variables most strongly associated with depressive and anxiety symptoms? This question will be investigated using part correlations in the multiple regression analyses.

Hypothesis 1: Neuroticism measured with (BFI-10), positive metacognitions, negative metacognitions, and unhelpful coping strategies (the latter three measured with CAS-1), will predict higher levels of depression and anxiety. Research Question 1: How and to what extent are different traits related measured with BFI-10 related to depression and anxiety in the present pandemic sample? Research Question 2: Are the trait or state variables most strongly associated with depressive and anxiety symptoms? This question will be investigated using part correlations in the multiple regression analyses. Statistical analysis: Two hierarchical regression analyses will be conducted. 1) with PHQ-9 as the dependent variable; and 2) the second with GAD-7 at as the dependent variable. Both analyses will include the following variables and the following steps: In step 1, the demographic variables age, gender, and education will be included in the model. In step 2 two, the trait-variables (i.e., all five personality traits measured with BFI-10) will be included. In the final step, the three state-variables positive metacognitions, negative metacognitions (CAS-1), and unhelpful coping strategies (CAS-1) will be included. Part correlations will be reported for each regression analysis, presenting the effect size of the hypothesized predictors on depression and anxiety. A part (semi-partial) correlation gives the least biased and easiest interpretable estimate of the strength of a predictive relationship (Dudgeon, 2016). It is the correlation between the outcome and the aspects of the predictor unique from all the other predictors. As a type of correlation, its size can be evaluated according to Cohen's (1988) criteria: small \>=0.10, medium \>=0.30, large \>=0.50. Both analysis include the following 5 trait-predictor variables: Neuroticism (BFI-10), Openness (BFI-10), Conscientiousness (BFI-10), Extroversion-Introversion (BFI-10), and Agreeableness (BFI-10). The state-predictor variables are as mentioned: positive metacognitions (CAS-1 subscale); negative metacognitions (CAS-1 subscale); unhelpful coping behaviors (CAS-1 subscale). Multicollinearity and other statistical assumptions will be checked using examined. Multicollinearity will be assessed with common guidelines (VIF \< 5 and Tolerance \> 0.2; Hocking, 2003; O'Brian, 2007). If any further analyses or questions are addressed in the forthcoming paper that are not pre-specified in this pre-registered protocol, they will be explicitly defined as exploratory. Sensitivity analyses and random subsample replications of the main findings will be conducted following selection of a random sample of participants that ensure a proportionate ratio between the collected sample and the adult population of Norway. Sample size and power calculation: The present study is part of a larger project with the first part aiming to investigate information source predictors of mental health through regression analyses, and the second part aiming to examine directional relations amongst specific sources of information and their centrality through complex systems approaches (i.e., network analysis). Consequently, power calculations are based on power required for network analyses. Following power analysis guidelines by Fried \& Cramer (2017), it is recommended that the number of participants are three times larger than the number of estimated parameters. However, more conservative recommendations by Roscoe (1975) for multivariate research, recommends sample size that is ten times larger than the number of estimated parameters. Thus, following these two approaches respectively, between 1305 to 4350 participants are required. Data will be collected for three weeks, and participants are based on a representative and random sample of Norwegian adults, randomly selected and provided equal opportunity to partake in the study, providing digital consent. Missing data: The TSD system (Services for Sensitive Data), a platform used in Norway to store person-sensitive data verifies participants officially through a kind of national ID number to give them full right to withdraw their data at any time, following the European GDPR (General Data Protection Regulation) laws. Accordingly, participants are allowed to withdraw their own data at any time. The survey includes mandatory fields of response. Participation is voluntarily, and withdrawal of provided data is possible at any moment. The investigators do not expect participants to withdraw their data and thus expect no missing data. However, if participants do withdraw their data, the investigators will conduct state-of-art missing data analyses and investigate whether data is missing at random.