'Negotiation Norms'
(AsPredicted #48289)

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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?
Do boys and girls have the same or different perceptions of negotiation norms? What factors (differentially) predict boys’ and girls’ negotiation behavior?

3) Describe the key dependent variable(s) specifying how they will be measured.
We will measure children’s:

hypothetical behavior: “What would you do? Would you take the candy or ask for more?” coding 4-point scale: 0 - take the candy to 3 - ask for 3 more items

anticipated backlash: “Do you think [protagonist] would like you less for asking for more candy or the same?” coding 3-point scale: 0 - the same to 2 - a lot less; “Do you think [protagonist] would be annoyed that you asked for more or not mind?” coding three point scale: 0 - the same to 2 - really annoyed

descriptive norms of negotiation: “Now let's think about other kids your age. If other kids decided to [task] and were given a piece of candy as a thank you, how many of those kids would ask for more [item]? Do you think none of them would ask for more candy, some of them would, half of them would, most of them would, or all of them would?” coding 5-point scale: none - 0, half - 0.5, all - 1

prescriptive norms of negotiation: “Do you think it’s okay for the child to ask for more candies or is it not okay to ask for more [item]?”, “Do you think it’s rude for the child to ask for more candy or is it not rude to ask for more candy?”, “Do you think this child deserves more candy or does not deserve more candy?” coding, 4-point scale: -1.5 - definitely sure [not okay/rude/not deserve], 1.5 - definitely sure [okay/not rude/deserve]

expected effectiveness of negotiation in the present: “Now, let’s imagine that you decide to ask for three more candies. How many extra candies do you think your teacher would give you?” coding 4-point scale: 0 - no extra candies, 3 - three extra candies

future effect of negotiation: “How many candies do you think your teacher would give you for spending that hour a few weeks from now [doing task]?” coding 5-point scale: 0 - no candies, 4 - four candies

4) How many and which conditions will participants be assigned to?
There will be 2 within subject conditions: male and female protagonists. These protagonists are the individuals with which children will be negotiating. Children will see 2 male (1 teacher, 1 neighbor) and 2 female (1 teacher, 1 neighbor) protagonists across four stories and answer the same questions (see section 3) for each story. Each story will be paired with a male or female protagonist counterbalanced across participants.

5) Specify exactly which analyses you will conduct to examine the main question/hypothesis.
We will perform three main analyses. The first will investigate whether children’s hypothetical behavior differs by their gender, age, and condition (protagonistGender):
lmer(behavior ~ childGender*protagonistGender*childAge + (1|ID) + (1|story))

We will then investigate whether children’s perceptions of negotiation differ by their gender, age and condition (protagonistGender):
lmer(perceptions ~ childGender*protagonistGender*childAge + (1|ID) + (1|story))

Then, children’s perception of negotiation will be included along with their gender, protagonist gender, and their age in a regression predicting their behavior.
lmer(behavior ~ perceptions*childGender*protagonistGender*childAge + (1|ID) + (1|story))

We will run contrast analyses to unpack any above interactions.

6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations.
Based on lab policies, we will exclude any participants where parents tell children how to respond or if the child wants to stop participating before completing at least 3 out of 4 stories. Because data will be collected over the online platform Zoom, we will also exclude participants whose sessions encountered major technical issues (e.g. sound level, connectivity issues).

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 aim to collect data from 128 participants: 16 girls and 16 boys per year of age ranging from 6 to 9 years of age.

8) Anything else you would like to pre-register?
(e.g., secondary analyses, variables collected for exploratory purposes, unusual analyses planned?)
For secondary analyses, we will also look into children’s reasons for why they chose [not] to engage in negotiation. We will devise a coding scheme based on children’s responses. Using this coding scheme, we will examine whether children’s explanations are predicted by the child’s gender, protagonist gender, and age using statistical models similar to those outlined above in section 5. Additionally, we will examine how their explanations are related to their negotiation behavior and negotiation perceptions.

We will also collect demographic data such as children’s race/ethnicity. We will look to see whether individual children’s decision to negotiate and perceptions of negotiation are predicated on any of these demographic factors.

At the end of the sessions, we will ask children whether they like candy (the resource being negotiated for). Children’s responses to this question will be included as a moderator in the analyses with children’s negotiation behavior as a DV. We will also analyze children’s negotiation behavior separately for the subset of children who report liking candy.

For all analyses, we will test whether condition order (male protagonists first vs female protagonists first) or story order relates to children’s negotiation behavior and perception of negotiation. We will also analyse the responses to the first block separately.

For all analyses, we may include condition (protagonist gender) as a random slope under the participant ID and story random intercepts in the model. Assuming the models converge, we will compare model fit using standard measures of model fit including AIC to determine appropriate model specifications.

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