Author(s) Gordon Kraft-Todd (Boston College) - gordon.kraft-todd@bc.edu Max Kleiman-Weiner (Massachusetts Institute of Technology) - maxkw@mit.edu Liane Young (Boston College) - liane.young@bc.edu
Pre-registered on 2021/11/12 - 07:14 AM (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? Here, we are exploring how virtuous (generous vs. impartial) actors' moral goodness and trait generosity are affected by the observability of the actors' behavior (private vs. public) and their stipulated motivation (no motive vs. selfish vs. selfless). Specifically, we predict:
• H1) within the "no motive" conditions, actors' moral goodness (and trait virtue) will be rated lower in the public compared to the private condition, and this effect will be greater in the "generosity" than in the "impartiality" conditions.
• H2) within the "selfish" and "selfless" motive stipulated conditions, actors' moral goodness (and trait virtue) will be rated lower in the selfish compared to the selfless conditions (for both the "generosity" and "impartiality" conditions).
• H3) the main effect of observability (i.e. public vs. private) in the "selfish" and "selfless" motive stipulated conditions will be substantially reduced compared to the "no motive" conditions.
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 Jen?" 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 generosity/impartiality ratings ("How [generous/impartial] is Jen?" measured with a 100-point unmarked slider scale with anchors at 0 "not at all" and 100 "very much").
4) How many and which conditions will participants be assigned to? We will randomly assign participants to one of 12 between-subjects conditions in a 2 (virtue: generosity vs. impartiality) x 2 (observability: public vs. private) x 3 (stipulated actor motivation: selfish vs. selfless vs. none) factorial design.
5) Specify exactly which analyses you will conduct to examine the main question/hypothesis. • H1) We will conduct a multivariate regression comparing participants' moral goodness and trait generosity ratings across the two conditions (public vs. private) in the "no motive" stipulated condition (for both generosity and impartiality).
• H2) We will conduct a multivariate regression predicting participants' moral goodness and trait virtue ratings by the interaction of observability (public vs. private) and stipulated actor motivation (selfish vs. selfless; for both generosity and impartiality).
• H3) We will conduct a Wald test to compare the size of the main effect of observability in the "no motive" conditions to the size of the main effect of observability in the "selfish" and "selfless" motive stipulated conditions (for both generosity and impartiality).
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]"). As a robustness check, we will test to ensure the results of the tests reported above hold in the subpopulation of our sample who correctly answer comprehension questions.
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 power calculations, we employed the Superpower package in R software (Lakens & Caldwell, 2021) based on pilot data employing the design described here. With a desired effect size of d=.22 (for the virtue*observability interaction on the moral goodness dependent measure in the "no motive" conditions), we determined a sample size of N=250 per cell was required to achieve power at 93% with an alpha level of .05 (total N=3000).
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).
