'Survey Experiment on Media Polls about Public Ignorance - Study 2'
(AsPredicted #67762)

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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 examines the effects of news coverage of surveys designed to make the public look ignorant. Headlines about public ignorance…
H1. … cause respondents to rate Americans as less intelligent.
H1a. … have a larger effect on perceptions of out-partisans' intelligence (e.g., Democrats' perceptions of Republicans) than on perceptions of in-partisans' intelligence (e.g., Democrats' perceptions of Democrats).
H2. … cause respondents to view Americans as less qualified to vote.
H3. … reduce confidence in the U.S. system of democracy.
H4. … increase support for restricting uninformed peoples' ability to vote.
H5. … increase support for other restrictive voting rules that are not directly related to uninformed peoples' ability to vote.

3) Describe the key dependent variable(s) specifying how they will be measured.
• H1 will be measured using a battery of trait ratings: intelligent, patriotic, honest, selfish, open-minded. Each is measured on a 4-point scale. We only expect effects on intelligence.
• H2 will be measured by two items: confidence in Americans to cast informed votes (4-point scale) and agreement with the statement "most Americans are well-qualified to vote" (5-point scale).
• H3 will be measured using one item on confidence in the U.S. system of democracy (4-point scale).
• H4 will be measured using (a) a three-item battery on support for restrictions on uninformed voters and (b) an item on whether uninformed voters or people who do not vote are a greater threat to American democracy. We will interpret the latter as tapping a tradeoffs people plausibly weigh in their mind when they decide whether to support the former.
• H5 will be measured using (a) a four-item battery on support for restrictions on uninformed voters and (b) an item on whether voter fraud or voter suppression is a greater threat to American democracy. We will interpret the latter as tapping a tradeoffs people plausibly weigh in their mind when they decide whether to support the former.
• For H2, H4, and H5, we will report effects on the separate measures, as well as on indices that combine the specified items. For H4 and H5, the items designed to measure what is the greatest threat (denoted (b) just above) will not be included in the indices.

4) How many and which conditions will participants be assigned to?
Using simple random assignment, we will allocate respondents to one of two conditions: a treatment group, a placebo control group, or a pure control group that sees no stimulus at all.

5) Specify exactly which analyses you will conduct to examine the main question/hypothesis.
We will estimate treatment effects using OLS with robust standard errors using lm_robust() in R. Our main model will be:
Yi = a + b*treatment_i + b*placebo_i + Σ b*x_i + epsilon_i
where treatment_i is an indicator for treatment status (0 = control, 1 = treatment) and x_i is a pretreatment covariate. We will use LASSO to select pretreatment covariates that predict the outcome variable in the control group, with the tuning parameter (i.e., lambda or the penalty) selected by cross-validation. We will also report unadjusted estimates (i.e., excluding the b*x_i terms).

As all hypotheses specify directional expectations, we will conduct one-sided tests. We will use a significance threshold of p < 0.05 as a reference point, not a sharp threshold.

6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations.
So that all treatment effect estimates represent the same group of respondents, we will exclude all respondents who do not reach the final page of outcome questions (on perceptions of Democrats' and Republicans' intelligence).

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.
3,900

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

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