#61719 | AsPredicted

'Predictors of getting a COVID-19 vaccine'
(AsPredicted #61719)


Author(s)
This pre-registration is currently anonymous to enable blind peer-review.
It has 3 authors.
Pre-registered on
03/23/2021 07:36 PM (PT)

1) Have any data been collected for this study already?
It's complicated. We have already collected some data but explain in Question 8 why readers may consider this a valid pre-registration nevertheless.

2) What's the main question being asked or hypothesis being tested in this study?
RQ1: Do factors associated with early eligibility for COVID-19 vaccination predict getting a COVID-19 vaccination?
H1: Specifically, we expect that older age, greater proportion of state vaccinated, veteran status, worse health status, lower health literacy, lower numerical literacy, and non-Hispanic white race, will be associated with having received at least one dose of a vaccine.
RQ2: Do individual differences further predict getting a COVID-19 vaccination?
H2: Specifically, we expect that greater worry about COVID-19, greater perceived risk from COVID-19, greater confidence in vaccines, greater intentions to get a COVID-19 vaccine, greater trust in health care, greater belief in science, less belief in conspiracies, more liberal political views, and medical maximising will be associated with getting at least one dose of a COVID-19 vaccination while accounting for factors associated with early eligibility for COVID-19 vaccination.

3) Describe the key dependent variable(s) specifying how they will be measured.
Primary dependent variable:
• Respondents' vaccination status measured at wave 2 and wave 3. This is a single item measured with three options (1=No; 2=Yes, 1 dose; or 3=Yes, 2 doses). For analyses, responses 2 and 3 will be combined together resulting in a single item measure with two options (0=No; 1=Yes).
Factors associated with early eligibility for COVID-19 vaccination:
• Age: single item with age specified ranges (e.g., 18-34), measured at wave 1.
• Proportion of state vaccinated: Proportion score calculated from publicly available data for each US state. Scores will be calculated at times corresponding with the start of data collection for waves 2 and 3.
• Health status: summed total of items from the Charlson Comorbidity Index, measured at wave 1.
• Veteran status: single item (0=Non-veteran; 1=Veteran) measured at wave 1.
• Health literacy: single item measured at wave 1.
• Numerical literacy: 3 items measured at wave 1. Assuming good reliability (Cronbach's α≥.70), items will be averaged for analyses. If α<0.70, we will choose the item "How good are you at figuring out how much a shirt will cost if it is 25% off?".
• Race/ethnicity: single-item with multiple options, measured at wave 1 to be dummy coded as (0=any other race; 1=non-Hispanic white)
Individual difference measures provided in section four.

4) How many and which conditions will participants be assigned to?
As this is a survey-based observational study no assignments will be made.
Individual difference measures:
• Worry about COVID-19: single item measured at wave 1.
• Perceived risk from COVID-19: 3 items measured at wave 1. Assuming good reliability (Cronbach's α≥.70), items will be averaged for analyses. If α<0.7, we will choose the item "In your opinion, how likely is it that you will get COVID-19 during the next month?".
• Confidence in vaccines: measured at wave 2 with the Emory Vaccine confidence Index. Assuming good reliability (Cronbach's α≥.70), items will be averaged. If α<0.70, we will omit the item with the lowest correlation to the summated score for all other items (Corrected Item-Total Correlation) and re-run Cronbach's α. We will repeat this procedure [hereafter 'reliability process 1'] until the internal consistency of the scale reaches the a-priori threshold of α=0.70. If we do not reach the α=0.70 threshold with this procedure, we will choose three items "Centers for Disease Control and Prevention (CDC), the federal government agency that makes recommendations about who should get licensed vaccines"; "Vaccines recommended for children are safe"; "It is important for everyone to get the recommended vaccines for their child(ren)". We also included two separate items about the importance of everyone getting a COVID-19 vaccine and Flu vaccine.
• Intentions to get a COVID-19 vaccine: single item measured at wave 1.
• Trust in healthcare: 9 items measured at wave 1. Assuming good reliability (Cronbach's α≥.70), items will be averaged. If α<0.70, we will perform reliability process 1. If we do not reach the α=0.70 threshold with this procedure, we will choose one item "The Health Care System does its best to make patients health better".
• Belief in science: 6 items measured at wave 1. Assuming good reliability (Cronbach's α≥.70), items will be averaged. If α<0.70, we will perform reliability process 1. If we do not reach the α=0.70 threshold with this procedure, we will choose one item "I am concerned by the amount of influence that scientists have in society".
• Conspiracy beliefs: 4 items measured at wave 3. Assuming good reliability (Cronbach's α≥.70), items will be averaged. If α<0.70, we will perform reliability process 1. If we do not reach the α=0.70 threshold with this procedure, we will choose a different general conspiracy belief item "I think that the official version of the events given by the authorities very often hides the truth", which is measured on a 9-point scale.
• Political views: single item measured at wave 2.
• Medical maximising: single item measured at wave 1.

5) Specify exactly which analyses you will conduct to examine the main question/hypothesis.
Respondent characteristics and relevant study measures will be summarized using descriptive statistics (means and standard deviations for continuous variables; frequencies and proportions for categorical variables). Nonnormal summary measures (median and interquartile range for continuous variables; Fischer's exact tests for categorical variables) will be used as appropriate.
Where appropriate (e.g., for standard scales and provided α ≥.70), study measures will be averaged to create summary variables. Measures exhibiting substantial skewness may be transformed to better approximate normality and any measures that display substantial multicollinearity (e.g., VIF score > 10) may be removed or replaced to improve the stability of coefficient estimates.
We will run correlations to assess whether factors associated with early eligibility for a COVID-19 vaccination and individual difference measures are associated with getting a COVID-19 vaccination at wave 2 (all measures and planned transformations are listed in section three).
We will then run a baseline multiple regression model in which we assess whether factors associated with early eligibility for a COVID-19 vaccination predict getting at least one dose of a COVID-19 vaccination at wave 2.
Using a hierarchical approach, we will then add the individual difference measures to the baseline model. We will examine significant predictors for each model and assess differences in explained variance.
We will repeat these analyses with getting a COVID-19 vaccination at wave 3 as the dependent variable.

6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations.
We will only analyse data from respondents who responded to all three waves.

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.

This is a three-wave longitudinal survey. We will attempt to recontact all 2000 people who completed wave 1. We estimate approximately a 50% recontact rate but the final sample size will be determined by the number that chooses to participate out of the initial 2000 participants. No inferential analyses have been conducted although we did look to identify whether the analysis plan would be feasible given the proportion of respondents vaccinated at waves 2 and 3 and given the number of respondents who completed all three waves. The survey will close on the 23rd of March, 2021 at 23:59 Mountain Time.

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

Only data for waves 1 and 2 have been fully collected. Data collection for wave 3 is ongoing at the time of this pre-registration.

Version of AsPredicted Questions: 2.00
Bundle
This pre-registration is part of a bundle. PDFs for each pre-registration in the bundle include links to all other pre-registrations in the bundle. The bundle includes:

#61718 - https://aspredicted.org/gr8jq.pdf - Title: 'Predictors of COVID-19 risk behaviors over time'