'Information on previous contributors' expertise in sequential collaboration'
(AsPredicted #188,758)
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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?
Replication:
Hypothesis 1: With increasing individual expertise, contributors a) are more likely to change a presented judgment, b) make larger changes to the presented judgments.
Hypothesis 2: With increasing distance of the presented to the correct judgment (presented deviation), contributors a) are more likely to change a presented judgment, b) make larger changes to the presented judgments.
Hypothesis 3: Individuals' expertise and presented deviation interact such that presented judgments are a) more likely adjusted and b) these changes are larger the higher individuals' expertise and the larger presented deviation become.

Information of previous contributor:
Hypothesis 4: If the previous contributor is a novice, contributors a) are more likely to change a presented judgment, b) make larger changes compared to an expert as previous contributor.

3) Describe the key dependent variable(s) specifying how they will be measured.
Change probability: Probability to adjust a presented judgment modeled from the dichotomous answer 1 – judgment adjusted / 0 – judgment maintained
Change magnitude: Euclidean distance between the presented and the provided judgment; if no judgment is provided, change magnitude is 0

4) How many and which conditions will participants be assigned to?
Participants complete a city-location task with 17 cities being positioned independently and 40 cities being positioned applying sequential collaboration with the judgments of a previous participant.

Individuals' expertise: Expertise is measured with the 17 cities positioned independently and computed as -1*mean(Euclidean distance between provided and correct position).
Presented deviation: Euclidean distance between the presented and the correct position.
Presented expertise: Instructions whether the presented judgments were provided by a participant showing high or average performance.

5) Specify exactly which analyses you will conduct to examine the main question/hypothesis.
All hypotheses are assessed using (generalized) linear models with random intercepts for items and participants and a random slope for presented deviation. For both dependent variables, a simple model is computed with only individuals' expertise and presented deviation as predictors. This model is tested against a complex model also including presented expertise. If the complex model does not show better model fit than the simple model, the simple model will be used to evaluate Hypotheses 1-3, and Hypothesis 4 is considered as not supported. If the complex model shows better model fit than the simple model, the complex model is used to evaluate the hypotheses. In this case, we will additionally test exploratorily for an interaction between presented expertise and individuals' expertise.

6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations.
Participants are excluded during the study if they a) do not accept the consent, b) answer less than 3 of 4 control questions for the manipulation of the previous participant correctly, and c) if they switch (browser) windows more than five times.
Participants are excluded from data analysis, if they position judgments extremely accurate as cheating is suspected (less than 13 pixels from correct answers) or at the same position across maps (25 pixels from mean of all judgments); these values were identified in a small sample with respective conditions. Moreover, they are excluded if they position more than 20 % of their judgments outside the highlighted area of interest.

Single judgments are excluded if they are timed out after 40 seconds.

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 sample size is determined with a power analysis based on data from Mayer, Broß, and Heck (2023). Random effect, residual variance, and fixed effects for individuals' expertise and presented deviation are taken from this data.
For the effect of change magnitude, we assume a SESOI of 5 pixels, corresponding to the size of the dot that is positioned on the map to indicate the city position. To achieve a power of .90, a sample size of 800 participants is required. To account for exclusions, we recruit at least 880 participants.
Since generalized models including logit link functions have less power due to variance restrictions, the smallest odds ratio that can be tested with a power of .90 is 2.23, the smallest odds ratio that can be tested with a power of .80 is 2.01.

8) Anything else you would like to pre-register?
(e.g., secondary analyses, variables collected for exploratory purposes, unusual analyses planned?)
Nothing else to pre-register.