#32193 | AsPredicted

'Synchronic Purposes'
(AsPredicted #32193)


Created:       12/03/2019 03:29 PM (PT)

This is an anonymized version of the pre-registration.  It was created by the author(s) to use during peer-review.
A non-anonymized version (containing author names) should be made available by the authors when the work it supports is made public.

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?
The moral valence of legislative intentions exerts influence over attributions of purpose to legal rules.

3) Describe the key dependent variable(s) specifying how they will be measured.
Agreement on a 7 point likert scale with statements describing either the good or the bad purpose presented in the vignette.

4) How many and which conditions will participants be assigned to?
16 conditions in a mixed design study. The study follows a 2 (majority wants moral purpose/minority wants moral purpose) x 2 (moral purpose as DV/immoral purpose as DV) between x 4 (scenario) within subjects design. Each participant will receive 4 vignettes in a random order, each with a different combination of the crossed factors (majority wants moral purpose/minority wants moral purpose x moral purpose as DV/immoral purpose as DV) and under a different scenario. Participants will also answer comprehension checks about each scenario and demographic questions.

5) Specify exactly which analyses you will conduct to examine the main question/hypothesis.
Mixed effects linear model predicting agreement with the DV with moral valence, whether the purpose was supported by the majority (0, 1), and the interaction between them as fixed effects, and scenario and respondent id as random effects, with random slopes for moral valence across scenarios. R code using package lmerTest should resemble the following command: model <- lmer("dv ~ moral * majority + (1 + moral | scenario) + (1 | ResponseId)"...).

We will then run type 2 ANOVA's of the model and comparing the main model against an alternative model without the additive term for morality (R formula should resemble "dv ~ majority + moral:majority + (1 | scenario) + (1 | ResponseId)").

6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations.
We will exclude responses that failed comprehension checks for each vignette (if someone fails 1 out of 4 comprehension checks, we will keep his answer for the 3 scenarios where he got it right).

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.

150

8) Anything else you would like to pre-register?
(e.g., secondary analyses, variables collected for exploratory purposes, unusual analyses planned?)

Null results will be taken to mean that there are no effects of morality over the attribution of purposes to legal rules.