#64246 | AsPredicted

As Predicted:COVID-19 vaccination messaging study (#64246)


Created:       04/23/2021 03:14 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?
We plan to run an online experiment with a sample of Americans who haven't yet gotten the COVID-19 vaccine to measure the effect of 4 different text messages aimed at encouraging individuals to schedule a COVID-19 vaccination appointment at their local healthcare provider. In particular, we will ask participants to imagine their healthcare provider sends them a message encouraging them to get the COVID-19 vaccine. In a 2x2 design, we vary whether the messages contain the link to a video providing information about COVID-19 (e.g., the prevalence of COVID-19 and the effectiveness of the COVID-19 vaccine) and whether the messages use ownership language aimed at facilitating people with follow-through. We will investigate a) the perceived persuasiveness of the messages and the extent to which these messages affect b) people's likelihood of scheduling a COVID-19 vaccination appointment and c) the extent to which people want the vaccine. We will randomize whether we ask these questions in the present tense (e.g., How likely are you to schedule a vaccination appointment today after reading this message from your healthcare provider?) or whether we ask participants how they would act (e.g., how likely would you be to schedule a vaccination appointment after receiving this message from your healthcare provider?). We will explore the underlying mechanisms.

3) Describe the key dependent variable(s) specifying how they will be measured.
Version 1
1a-How persuasive do you think the message is? (1 = not at all, 7 = very persuasive)
2a-How likely are you to schedule a vaccination appointment today after reading this message from your healthcare provider? (1 = not at all likely, 7= very likely)
3a-How much do you want the vaccine now after reading this message from your healthcare provider? (1-not at all, 7-very much)
If the DVs #2a and #3a are highly correlated (greater than .50), we will compute a composite score.

Version 2
1b-How persuasive do you think the message is? (1 = not at all, 7 = very persuasive)
2b-How likely would you be to schedule a vaccination appointment after receiving this message from your healthcare provider? (1 = not at all likely, 7= very likely)
3b-How much would you want the vaccine after receiving this message from your healthcare provider? (1-not at all, 7-very much)
If the DVs #2b and #3b are highly correlated (greater than .50), we will compute a composite score.

4) How many and which conditions will participants be assigned to?
Participants will be randomly assigned to one of four conditions.
In the simple text condition, the message is, "[Your first name], you can get the COVID-19 vaccine at our clinics. Make a vaccination appointment here: [link to the appointment website]"
In the Simple Text+Video, the message is, "[Your first name], you can get the COVID-19 vaccine at our clinics. Please watch this important 2 min video: [Link to the video]. Make a vaccination appointment here: [Link to the appointment website]" People will watch an educational video about covid-19.
In the Ownership Text, the message is, "[Your first name], a COVID-19 vaccine has just been made available to you at our clinics. Claim your dose today by making a vaccination appointment here: [link to the appointment website]".
In the Ownership Text+Video, the message is, "[Your first name], a COVID-19 vaccine has just been made available to you at our clinics. Please take 2 simple steps: 1. Watch this important 2 min video: [Link to the video]. 2. Claim your dose today by making a vaccination appointment here: [Link to the appointment website]". People will watch an educational video about covid-19.

Within each condition, we will randomize whether participants respond to the first set of DVs (version 1) or the second set (Version 2)

5) Specify exactly which analyses you will conduct to examine the main question/hypothesis.
For the main analysis, we will run an ordinary least squares regression (OLS) to predict the outcome variables described in section 3 (separately for Version 1 and Version 2). The predictor variables will include (1) an indicator for random assignment to the message with the Video, (2) an indicator for random assignment to the message with Ownership Text.

We will include the following control variables:
-Indicators for participant gender (male, female, other/unknown)
-Participant age (If age is missing, we will use average age and add a dummy for missing age)
-Indicators for participant race/ethnicity (Black non-Hispanic, Hispanic, Asian, white non-Hispanic, other/unknown)

6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations.
A Captcha question will be used to filter out bots from taking the study. Participants recruited through Amazon Mechanical Turk (18 or older) based in the United States will be included in our data analysis if they satisfy the following criteria:
-Pass an attention check question assessing whether participants are reading instructions closely
-Have not already received the COVID-19 vaccine or had an appointment
-Answer our primary dependent variables (Section 3).
-Report having no problems with watching the video
-Report either having not taken a similar study on Prolific or that they cannot remember (i.e., excluding people who report having taken a similar study)

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 post the study every day on Amazon Mechanical Turk from 9am PST to 7pm PST for 7 consecutive days (except for the first day, we will post the study right after the pre-registration is submitted). We will stop data collection once the 7 days are over or when we recruit 1000 non-vaccinated people that satisfy our selection criteria (Section 6).

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

For robustness, we will conduct the same OLS regression analyses specified above but include participants who reported having problems with watching the video or that reported having taken a similar study on Prolific.

We will collect the following measures:
1) Participants' belief in how likely they would catch the COVID-19 with (1a) versus without (1b) COVID-19 vaccine (ranging from -100% to 100%) and the difference between these measures (1c)
2) Participants' belief in how likely they would develop symptomatic COVID-19 with (2a) versus without (2b) COVID-19 vaccine (ranging from -100% to 100%) and the difference between these measures (2c)
3) Anticipated regret for not taking the COVID-19 vaccine (1-7 scale);
4) How worried participants are about passing COVID-19 to other people (1-7 scale);
5) Perceptions of trust in the vaccine (concerns about side effects, worries about long term impact, concerns about the vaccine being authorized too quickly, trust in the research and development of the vaccine, safety of the vaccine)
6) Extent to which patients feel that the vaccine is already theirs (1-7 scale)

We will use OLS regressions to predict variables 1a-5 with an indicator for random assignment to the message with the Video as well as control variables described in Section 5. We will use an OLS regression to predict variable 6 with an indicator for random assignment to the message with Ownership Text as well as control variables described in Section 5. For these analyses, we will collapse people who respond to Version 1 and Version 2 of outcome variables.

We will conduct mediation analyses and see which items predict intentions to get the COVID-19 vaccine (separately for Version 1 and Version 2 of the intention measures).

We will explore the following moderators:
-Whether the participant received a flu shot either in 2019-20 flu season or the 2020-2021 flu season
-The political party that the participant identifies with (democratic vs. republican)
-Education (Below bachelor or bachelor/above bachelor)
-How often the participant wears masks when going outside (above vs. below median)
-How often the participant washes hands when coming back home (above vs. below median)
-Whether the participant has children under 18