Author(s) Gordon Kraft-Todd (Boston College) - gordon.kraft-todd@bc.edu Liane Young (Boston College) - liane.young@bc.edu Max Kleiman-Weiner (Massachusetts Institute of Technology) - maxkw@mit.edu
Pre-registered on 2020/09/21 - 05:34 PM (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? In this study, we are exploring how observability (i.e. public vs private display) of virtues (generosity and impartiality) affects perceptions of the actor’s moral goodness and trait ratings of the respective virtue. Specifically, we predict: H1) Public displays of virtue will be perceived as worse than private displays of virtue (i.e. virtue discounting); H2) Virtue discounting of generosity will be greater than virtue discounting of impartiality; and H3) Observers will infer more selfish motivations for public displays of generosity compared to public displays of impartiality.
3) Describe the key dependent variable(s) specifying how they will be measured. We have two primary dependent measures: moral goodness (“How morally good is [your friend]?” measured with a 100-point unmarked slider scale with anchors at: 0 “extremely morally bad”; 50 “neither morally bad nor morally good”; and 100 “extremely morally good”), and trait ratings (“How [generous/impartial] is [your friend]?” measured with a 100-point unmarked slider scale with anchors at 0 “not at all” and 100 “very much”). We have six secondary dependent measures which will be answered on 100-point unmarked slider scales with anchors at 0 “not at all” and 100 “very much”. Two assess the perceived benefit participants perceived to various parties relative to the actor: 1st-party benefit (“How much do you think [your friend] will personally benefit from behaving this way?”); and 3rd-pary benefit (“How much do you think another person would benefit from interacting with [your friend]?”). Four assess participants’ perceived motivations for the actor’s behaviors (“How much do you think [your friend] is motivated to act [generously/impartially]…”): reputational (“…because she is trying to make others think she is [generous/impartial]?”); authentic (“…because she wants to be [generous/impartial]?”); norm-signaling (“…because she wants others to be [generous/impartial], and she is trying to lead by example?”); and moral (“…because she thinks it is the right thing to do?”).
4) How many and which conditions will participants be assigned to? Subjects will be randomly assigned to 1 of 4 between-subjects conditions in a 2 (virtue: generosity vs impartiality) x 2 (observability: public vs private) factorial design. Additionally, each subject will respond to 3 scenarios (within the 4-cell between-subjects randomization) which were matched on 9 dimensions using an independent sample of participant ratings across the virtue dimension in a previous study.
5) Specify exactly which analyses you will conduct to examine the main question/hypothesis. For H1 and H2, we will conduct multilevel mixed-effect models with both of our manipulations (virtue and observability) and their interaction as fixed effects as well as the random intercepts of participant and scenario. A significant effect of the observability manipulation would provide support for H1. If we observe a significant interaction of the virtue and observability manipulations, we will test for H2 by comparing coefficients of the observability manipulation for generosity compared to impartiality by computing pairwise comparisons of estimated marginal cell means corrected for multiple comparisons using Scheffe’s adjustment. Observing a coefficient on observability for generosity that is significantly larger for than impartiality would provide support for H2. For H3, we will construct multiple mediation models using structural equation modeling with standardized variables. For one set of models, we will enter all six secondary dependent measures separately as mediators of our observability manipulation on both of our primary dependent measures for each virtue that has a significant simple effect of observability in the multivariate regressions described above. We may construct a second set of models, depending on the outcome of a factor analysis of the six secondary dependent measures with varimax rotation and iterated principal factors. If a sufficiently small (<=3) number of factors explain a sufficiently large (>=70%) proportion of variance, we will also enter these factor scores as mediators of our observability manipulation on both of our primary dependent measures for each virtue that has a significant simple effect of observability in the multivariate regressions described above.
6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations. We will exclude observations that fail basic attention checks (e.g. "What is your favorite color? [Please select blue]").
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. n=100 per condition (n=400 total)
8) Anything else you would like to pre-register? (e.g., secondary analyses, variables collected for exploratory purposes, unusual analyses planned?) We will also collect basic demographics (gender, age, race, income, education, and political affiliation).
