Author(s) This pre-registration is currently anonymous to enable blind peer-review. It has 3 authors.
Pre-registered on 2022/05/10 01:37 (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? Does perceived threat due to the pandemic moderate the relation between Political Covid-19 Conspiracy (PCC) and perceived societal polarization (i.e., the perceived homogeneity of the society and procedural justice)?
Hypothesis:
PCC at t(k) predicts lower perceived homogeneity of the society and lower procedural justice at t(k+1), more so the higher the perceived threat at t(k).
3) Describe the key dependent variable(s) specifying how they will be measured. Ingroup homogeneity (2 items, Hutchison et al., 2016)
Procedural justice (4 items, Tyler et al., 1985)
4) How many and which conditions will participants be assigned to? There is no manipulation. The following two concepts are measured as predictors:
Belief in a Political Covid-19 Conspiracy (PCC) will be measured with 5 items, adapted from Pummerer et al., 2022.
Threat due to the pandemic is assessed separately for health and work/income using 9 items respectively (suggested by Klemm et al., 2019): 6 for cognitive and affective evaluation, and 3 for vulnerability.
5) Specify exactly which analyses you will conduct to examine the main question/hypothesis. We will conduct a random-intercept cross-lagged panel model (Hamaker et al., 2015) using lavaan (Rosseel, 2012) in order to examine whether the interaction of PCC with all threat indices at t(k) predicts a change in procedural justice and ingroup homogeneity (tk) to t(k+1).
The treatment of threat will be determined based on an analysis for internal consistency within scales and correlation between scales at t1. First, internal consistency will be computed for the four threat indices (evaluation and vulnerability separately for health and work/income). Single items will be excluded from computation of the indices, if the internal consistency increases as an outcome above .7 - otherwise all items will be averaged to compute the four threat indices.
Second, bivariate correlations will be computed to decide which indices will be included in the analysis reported above. If evaluations of heath and work/income correlate r > .4 at one measurement point, the analysis will be conducted separately once for health and once for work/income (otherwise both will be entered into same analysis).
Finally, if vulnerability correlates r < .4 with all other predictors at all time points, vulnerability and its interactions with PCC and evaluation will be considered as additional predictors in the analyses.
6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations. For missing data within a wave or missing waves between two completed ones, we will use full information maximum likelihood estimation.
To ensure data quality, we will implement several measures:
We will only include participants who indicate that they speak German fluently.
Participants will be excluded if (a) they indicate that they did not answer the questions honestly, (b) if they fail two attention checks, or (c) if they do not agree that their data can be used for research. In these cases, we will exclude the data from this individual for the specific timepoint.
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. The survey is part of a longitudinal study. We plan 5 waves each in 3 months apart, starting May 2022. Participants will be recruited by respondi. In the first wave, we recruit approximately 5000 individuals, representative for the German population based on quota sampling in age, gender and education (not crossed). All those 5000 participants are invited to participate in the following waves.
8) Anything else you would like to pre-register? (e.g., secondary analyses, variables collected for exploratory purposes, unusual analyses planned?) This preregistration is part of a larger project. Other parts of the survey are preregistered under Rosmarin1 and Rosmarin3.
