'Sequential Collaboration: Gender Effects on Change Rates'
(AsPredicted #136,018)
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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?
Hypothesis 1: Individuals who identify as male change presented judgments more often than individuals who identify as female (main effect of participant's gender).
Hypothesis 2: Previous judgments supposedly provided by females are corrected more often than previous judgments supposedly provided by males (main effect of previously changing person's gender)
Hypothesis 3: More accurate previous judgments are less likeliy changed than less accurate previous judgments.
Hypothesis 4: With increasing expertise, the difference in change rate between individuals identifying as male and individuals identifying as female becomes smaller (interaction effect of participant's gender with expertise)
Hypothesis 5: With increasing expertise, the difference in change rate between previous judgments that were supposedly provided by females and previous judgments that were supposedly provided by males becomes smaller (interaction effect of previously changing person's gender with expertise).

3) Describe the key dependent variable(s) specifying how they will be measured.
Change rate: Probability of a participant to make a change to a presented judgment of a previous participant

4) How many and which conditions will participants be assigned to?
UV1 (within): female vs. male vs. unknown gender of the individual providing the previous judgment
UV2 (between) : gender (male vs. female) of the participant
Expertise: Participants' expertise is measured with 17 items for which they provide independent individual judgments before they complete the remaining 40 items for which they are presented with the judgment of a previous participant

5) Specify exactly which analyses you will conduct to examine the main question/hypothesis.
The analysis is conducted using generalized hierarchical linear models with participants' gender and gender of the previous participant as independent variables, and whether an entry is changed or not as dependent variable (coded with 0, 1). Participants' expertise is entered into the model as a fixed effect. Moreover, we account for the nested structure of our data by including random intercepts for item and participant.

6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations.
When indicating the subjects' own gender, it is possible to select diverse. Since we assume that we will not be able to collect enough diverse participants to carry out a meaningful statistical analysis, these participants will be excluded from the statistical analysis of the hypotheses and only collected for potential explorative analyses.
Participants are excluded if at least 15% of their judgments are positioned outside of the countries of interest (colored white instead of grey for countries which are not of interest) or if at least 15% of their judgments end in time out after 40 seconds. Only single judgments are excluded due to time-out or placement if this does not apply to at least 15% of their judgments.
Mover, participants are excluded who are suspected to look up answers (based on a pretest using cheating instructions) or who repeated click on the same location on the map.

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.
For an effect size of f = 0.25, alpha = .05, and beta = 0.8, a power analysis for a between subjest effect in an repeated measures ANOVA revealed a required sample of at least 86 participants, 43 participants in each condition, respectively.

8) Anything else you would like to pre-register?
(e.g., secondary analyses, variables collected for exploratory purposes, unusual analyses planned?)
We perform exploratory analyses on the change magnitude and the accuracy of judgments, self-reposted expertise of participants. Moreover, we explore possible interactions between participants' gender, gender of the previous participant, accuracy of the presented judgment, and participants' expertise. Last, if at least 15 participants identifying as diverse complete the study, we will conduct an exploratory analysis on their judgments.