#49797 | AsPredicted

As Predicted:Rape myths acceptance, rape evaluation, and personal experience with rape (#49797)


Created:       10/16/2020 04:38 AM (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?
Hypothesis 1: People with experience with rape (victims, rape-impacted people) accept rape myths less than unimpacted people. Therefore, I expect the main effect of previous experience with rape on rape labelling and victim blaming.
Hypothesis 2: People label uncertain rape scenarios less as rape and blame the victim more, than in case of certain rape scenarios. Therefore, I expect the main effect of certainty of the scenarios on rape labelling and victim blaming.
Hypothesis 3: The main effect of rape myths acceptance on rape labelling and victim blaming, that rape myths acceptance negatively predicts them. Hypothesis 4: Rape myths acceptance is a stronger predictor of rape labeling and victim blaming, when the case is uncertain than when it is certain, therefore, I expect a two-way interaction effect between rape myths acceptance and the certainty of the scenario.
Hypothesis 5: People with experience with rape (victims, rape-impacted people) label scenarios more likely as rape and blame the victim less than unimpacted people, therefore, I expect a two-way interaction effect between rape myths acceptance and previous experience with rape
Hypothesis 6: I hypothesize that rape myths acceptance has the strongest effect on rape labeling (negative) and victim blaming (positive), in case of unimpacted people (in comparison to rape impacted and victims), in case of uncertain rape scenario. Therefore, I expect the main effect of rape myths acceptance (positive on victim blaming, negative on rape labeling), a main effect of previous experience (victims and impacted blame the victim less and label it as rape more than unimpacted people), and a main effect of certainty (uncertain with higher victim blaming and lower rape labeling than certain) of the case on victim blaming and rape labeling. Furthermore, I expect a three-way interaction between rape myths acceptance (between subject), previous experience with rape (between subject), and certainty of the scenario (within subject).

3) Describe the key dependent variable(s) specifying how they will be measured.
Rape labeling measured with 1 item on a 7-point Likert scale: I think what happened was rape.
Victim blaming measured with 1 item on a 7-point Likert scale: I think [name of victim] is responsible for what happened.
Rape myths acceptance measured with the Hungarian Updated Illinois Rape Myths Acceptance Scale.
As IVs we will measure rape myths acceptance with the Hungarian version of the Updated Illinois Rape Myths Acceptance Scale (McMahon & Farmer, 2011) and the Ambivalent Sexism Scale (Glick & Fiske, 1996). Furthermore, we will ask participants previous experience with rape and other demographical information.

4) How many and which conditions will participants be assigned to?
We use within subject design where participants read two rape scenarios (uncertain and certain) in a randomized order.

5) Specify exactly which analyses you will conduct to examine the main question/hypothesis.
H1: ANOVA to compare rape myths acceptance among victims, rape-impacted, and unimpacted people.
H2: Paired sample t-test to compare rape labeling and victim blaming between uncertain and certain rape cases
H3, H4, H5, H6: Three-way mixed ANOVA with a within (uncertain-certain) and between (victim, impacted, unimpacted people), and between subject (level of RMA) design on dependent variable victim blaming and labeling.

6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations.
Attention check: We will implement an attention check and test results without participants who failed the attention check. Careless response: We will also implement a seriousness-check and exclude participants who indicated that they filled out the questionnaire carelessly. We will also exclude those participants, whose open format responses indicate that they did not take the survey seriously.
Attention check: We will implement an attention check and test results without participants who failed the attention check. Careless response: We will also implement a seriousness-check and exclude participants who indicated that they filled out the questionnaire carelessly. We will also exclude those participants, whose open format responses indicate that they did not take the survey seriously.
Missing values: In case missing values are 5% or less we will include them in the data, but participants who have 20% or more missing values on the main variables will be excluded.
Outliers: we will not exclude statistical outliers, but will exclude participants who are not part of the targeted population (participants under 18 years).

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 offer participation in the survey until October 30th 2020 with the target number of 357. Because there is no previous effect size to rely on, we will let the study run until the end date, even if we have enough participants before that. We use G*Power (Faul, Erdfelder, Lang, & Buchner, 2007) to determine the sample sizes necessary to detect small-sized effects with a statistical power ≈ 80% and a significance level of α = .05, . Sample size is composed of that calculated required sample size + 10% additional participants to compensate for potential participant exclusion.

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

Secondary analysis with Ambivalent Sexism Scale, examining its role in the evaluation of rape cases.