'Children's perceptions of harm motivated by different group memberships' (AsPredicted #59,255)
Author(s) Vivian Liu (New York University) - vl845@nyu.edu Andrei Cimpian (New York University) - andrei.cimpian@nyu.edu
Pre-registered on 2021/02/24 13:14 (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? This study evaluates children’s perceptions of intergroup harm based on a group reason by looking at different types of groups. Children will be told about three transgressions (taking someone’s eraser, knocking over someone’s block tower, or sneaking up and scaring someone) that happened because of a difference in group membership. The type of group membership (social category vs. task group) will vary across participants. Children will then be asked to evaluate each transgression on a set of dimensions (detailed below). We hypothesize that younger children will evaluate group-based harm similarly across both types of group membership, whereas older children will distinguish between the two and rate harm based on social category as more problematic.
3) Describe the key dependent variable(s) specifying how they will be measured. 1. Act: How bad is what A did to B? (1-4 scale)
2. Rating of Aggressor: Is A a good person or a bad person? (1-4 scale)
3. Rating of Victim: Is B a good person or a bad person? (1-4 scale)
4. Punishment: Should A be punished? (1-3 scale)
5. Victim-Blaming: Did B do something bad before this? (1-4 scale)
6. Future Ingroup Harm: The next day A was playing with another ingroup member. Will A do the same thing? (1-4 scale)
7. Future Outgroup Harm: The next day A was playing with another outgroup member. Will A do the same thing? (1-4 scale)
4) How many and which conditions will participants be assigned to? There are 2 between-subject conditions:
1. Social category harm: script describes intergroup harm due to difference in social category (i.e., “because I don’t like [social category name].”)
2. Team harm: script describes intergroup harm due to difference in task group (i.e., “because I don’t like [team name].”)
5) Specify exactly which analyses you will conduct to examine the main question/hypothesis. We will investigate the effects of condition on a battery of measures (see above) using a series of regression models (one per measure). We will factor in age (continuous), condition (categorical), and their interaction as predictor variables to explore developmental changes. Children’s gender, race, and socioeconomic status may be included as moderators. We will also investigate if there are any significant correlations between the 7 DVs, and potentially aggregate across subsets of DVs (e.g., the ratings of the act, aggressor, and severity of punishment, which were strongly correlated in prior work) and/or include some DVs as covariates.
6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations. Participants will be excluded if (1) they needed the script to be repeated two times or more for any comprehension check question, (2) they did not finish the entire study, or (3) their parents or another person (e.g., a sibling) intervened/interrupted.
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 will initially collect data from 120 children after exclusions (60 per condition). Following recommendations in Lakens (2014), we will use a sequential analysis approach. If the effects are smaller than anticipated and there are no significant differences on our measures of interest at N = 120, we will do a one-time sample size increase to 180. The alpha value for statistical significance at the first examination (i.e., after 120 participants) will be .0333 and, if applicable, after the one-time increase to 180 the alpha will be .0303. These values were determined using an overall two-sided alpha of .05 and a linear spending function with two time points.
8) Anything else you would like to pre-register? (e.g., secondary analyses, variables collected for exploratory purposes, unusual analyses planned?) We may conduct additional analyses involving (1) demographic moderators (gender, race, SES, etc.), and (2) some of the measures as moderators (depending on the results of preliminary reliability analyses and inspection of correlation matrices).
