'Congruence of self-perception and metaperception in relation to depressive symptoms and the valence of traits' (AsPredicted #89784)
Author(s) This pre-registration is currently anonymous to enable blind peer-review. It has 2 authors.
Pre-registered on 2022/03/03 - 07:03 AM (PT)
1) Have any data been collected for this study already? No, no data have been collected for this study yet.
2) What's the main question being asked or hypothesis being tested in this study? Metaperception describes our beliefs about how other people perceive us. Recent studies have shown that people can distinguish between self-perception and metaperception and are aware that the way they perceive themselves can differ from how other people perceive them ("meta-insight"). In terms of depression, there is evidence that depressive symptoms are linked to a more negative self-perception and a negatively biased metaperception. However, the question of whether depressive symptoms have an influence on the congruence between self-perception and metaperception remains open. This is important because it sheds light on how depressive symptoms influence how people form their expectations on how they are perceived by others. Further, with regard to the literature on the optimism bias and depressive realism, it is of interest whether congruence between self-and meta-perception differs as a function of the valence of traits.
Thus, we aim to examine how depressive symptoms influence the congruence between self- and meta-perception and how this may interact with the valence of traits (positive vs. negative).
3) Describe the key dependent variable(s) specifying how they will be measured. Our main DV is the congruence between self- and meta-perception as assessed with a set of 20 negative and 20 positive traits. Self-perception is measured for each trait using a self-developed item. Metaperception will be measured using an item adapted from Kelly and Sharot (2021). Depressive symptoms will be measured by a German version of the BDI-II (Hautzinger et al., 2009).
4) How many and which conditions will participants be assigned to? no experimental conditions
5) Specify exactly which analyses you will conduct to examine the main question/hypothesis. We will first compute a mixed model with depressive symptoms (continuous variable: BDI-II score), valence of trait (binary variable: positive vs. negative) as well as subjective positive and negative valence (each as continuous variable; VAS ranging from "0" to "100") and relevance of traits (metric: VAS ranging from "0" to "100") as fixed effects. Subjects and traits will be specified as random intercepts. Depressive symptoms and valence are defined as variables of interest and subjective valence (positive and negative) and relevance will be defined as control variables. In a second step, we will specify another model with the interaction between depressive symptoms and valence as additional variable of interest and will use model comparison based on information criteria to examine the additional exploratory value of the interaction effect.
6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations. We will exclude participants that discontinue with the study before entering 2/3 of all data points or are +/- 3 standard deviations above or below the mean on the dependent variable.
7) How many observations will be collected or what will determine sample size? No need to justify decision, but be precise about exactly how the number will be determined. We conducted a sensitivity-analysis for a linear multiple regression: fixed model, R^2 increase using G*Power. Given six predictors (depressive symptoms, valence, subjective positive valence, subjective negative valance, relevance and interaction of depressive symptoms and valence), one tested predictor (interaction of depressive symptoms and valence), an anticipated sample size of N=150, an α of 0.05 and a power of 1-β=0.80, we can detect a minimum effect size of f^2=0.053.
8) Anything else you would like to pre-register? (e.g., secondary analyses, variables collected for exploratory purposes, unusual analyses planned?) We want to compute an additional model with confidence regarding self- and metaperception (each as metric variable; VAS ranging from "0" to "100") as additional fixed effects and run a model comparison with the models of the main analysis to see if there is an additional exploratory value of confidence.
