#57081 | AsPredicted

'Feelings about COVID-19 and Political Attitudes'
(AsPredicted #57081)

Created:       01/31/2021 07:39 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?
The COVID-19 pandemic can elicit various emotions (Pedrosa et.al., 2020). In turn, perceived emotions about COVID-19 can impact individuals’ attitudes towards political figures and their voting behaviors. In this study, we examine whether we can replicate our previous results and if feelings about COVID-19 predict an arguably less relevant attitude, views on impeaching President Trump.
H1: Feeling hopeful/optimistic about COVID-19 will predict positive feelings toward Trump and voting for Trump.
H2: Feeling negative emotion and ambivalent about COVID-19 will predict negative feelings toward Trump and not voting for Trump.
H3: Feelings neutral about COVID-19 will predict positive feelings toward Trump and voting for Trump.
H4: We also examine whether feeling relaxed/calm do NOT significantly predict feelings toward Trump and voting behavior.
H5: If feelings about COVID-19 shape an overall view of Trump and extend to his other actions, then H1 to H3 should replicate when it comes to predicting views about the impeachment of Trump (hope/optimism and neutrality against impeachment, negative emotions and ambivalence for impeachment).
H6: We will also examine whether these patterns extend to feelings about Biden
• if feeling hopeful/optimistic and neutral about COVID-19, then feeling negative about Biden
• if feeling negative and ambivalent about COVID-19, then feeling positive about Biden.
H7. We predict that neutral feelings about COVID-19 will positively predict not voting at all.
We will test whether these associations hold over and above various COVID-19 related covariates (noted below in more detail) that may impact voting decisions. Note, we do not predict that feelings will predict feelings about Trump and voting once political orientation is taken into account.

3) Describe the key dependent variable(s) specifying how they will be measured.
Voting decision: Trump, Biden, did not vote
Feelings Thermometer for Trump and Biden
Support for impeachment
To what extent do you support the House of Representatives impeaching President Donald Trump? To what extent are you in favor of the Senate finding President Donald Trump to be guilty during his impeachment trial?
COVID-19 affect: Rated the following feelings:
• Optimistic • Hopeful • Neutral • Indifferent • Anxious • Fearful • Depressed • Sad • Calm • Relaxed • Mixed feelings • Conflicted • Tired • Fatigued

Integrated COVID-19 Threat Scale:
5 items assessing Realistic Threat (e.g., “How much of a threat, if any, is the coronavirus outbreak for your personal health?”)
5 items assessing Symbolic Threat (e.g., “How much of a threat, if any, is the coronavirus outbreak for what it means to be American?”)

COVID-19 Attitudes
10 items, such as “Social distancing will help stop the spread of coronavirus. To help stop the spread of the coronavirus, I should wear a mask when going out.”

COVID-19 stress
5 items such as “In the last month, how often have you felt nervous and “stressed” because of the COVID-19 pandemic?”

Political Orientation
How would you describe your political views on social issues? economic issues?

Three attention checks
3 items to check whether participants are paying attention to the survey such as “This is attention check. Please select 1 =Never.”

4) How many and which conditions will participants be assigned to?
This is a correlational study (so 0 condition).

5) Specify exactly which analyses you will conduct to examine the main question/hypothesis.
Data preparation
Among positive emotions (i.e., Optimistic, Hopeful, Calm, Relaxed), Optimistic/Hopeful and Calm/Relaxed will be separately aggregated. Negative emotions (i.e., Anxious, Fearful, Depressed, Sad, Tired, Fatigued) will be aggregated. Neutral emotions (i.e., Neutral, Indifferent) and ambivalent emotions (i.e., Mixed feelings, Conflicted) will be separately aggregated.

Whether voted or not
Attitudes towards Trump
Attitudes towards Biden
Voting decision (only among those who voted)

Regressions will be conducted entering the following variables:
Step 1:
COVID-19 stress
COVID-19 realistic threat
COVID-19 symbolic threat
COVID-19 attitudes

Step 2:
Negative emotion
Neutral emotion
Ambivalent emotion

Step 3: (Note, political orientation will probably eliminate emotion effects)
Political orientation
Note: For analyses predicting Attitudes towards Trump and Biden, we will control for whether participants voted or not.

6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations.
Data exclusion:
To ensure that participants pay full attention to our measures, we included 3 attention check measures. If participants get more than 2/3 wrong on attention check, they will be excluded. Respondents who spend less than 1 minute and more than 30 minutes will be excluded.

We will run our analyses with and without data points with leverage values that exceed a value of 3p/n (where p is the number of parameters including the intercept and n is the sample size), studentized deleted residuals exceeding |3|, and Cook’s D exceeding 1.

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.

Respondents will be recruited using an online survey platform. Assuming an effect size of f2 = .02, we would need 387 participants. We will collect at least 450 subjects to account for potential exclusion.

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

Secondary analyses: Negative emotions will be further separated into Anxious/Fearful, Depressed/Sad, and Tired/Fatigued. We will run all our analyses (noted above) with these separated negative emotion variables. We predict that Anxious/Fearful (but not Depressed/sad and Tired/Fatigued) will predict DVs.