Define Proxy Measure

Here are the three potential proxy measures that we tested in the survey:

  1. Change in self-rated vaccination likelihood after treatment: vax_chance_post - vax_chance_pre
    • Question asked:
      • how likely are you to get vaccinated? (scale of 1 to 5)
      • If you did get [the treatment], how likely are you to get vaccinated? (scale of 1 to 5)
    • Data Cleaning Required:
      • participants who answered best_treatment with “nothing would help” should be assigned a vax_chance_post equal to their vax_chance_pre (they are essentially saying that nothing would help them change their mind)
  2. Openness to getting vaccine in the future: vax_future
    • Question asked:
      • would you ever consider getting a vaccine in the future? (no way!; maybe; of course!;)
  3. Confidence in getting vaccine in the future (unipolar measures of confidence): vax_conf_pre_yes vax_conf_pre_no
    • Questions:
      • how sure are you that you will get the vaccine? (scale of 1 to 5)
      • how sure are you that you won’t get the vaccine? (scale of 1 to 5)

Questions on Proxy Treatment Measures

Objective: We need to decide whether or not we have a good proxy measure for treatment effect for our pilot. If we’re not confidence in the proxy measure, we’ll likely have to get more of Susan/Dean’s feedback on a better design.

  • Association between proxy measures: A plausible hypothesis is that the three measurements above should be roughly associated with each other, as they measure some shared aspects of motivation/likelihood/confidence of getting the vaccine (vax_conf_pre_no should be negatively associated). Are the three proxy measures listed above associated with one another and what is the strength of the association? If there are not associated with each other, why do we think that is? After doing this analysis, are we confident in using one of these measures as a proxy measure for our large pilot? Why or why not?

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
## -4.0000  0.0000  0.0000  0.2367  1.0000  4.0000    2935

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
## -4.0000  0.0000  0.0000  0.2153  1.0000  4.0000    2920

Questions on Relationship Between Covariates and Proxy Treatment Measures

Objective: We want to begin testing and hypothesizing which covariates are most important in predicting treatment effects. Impediments particularly are a key covariate we should test (to see if there are differences in proxy measures of treatment effect, given that a large part of the survey is designed to identify their impediment).

Quartile Analysis (continuous variables)

What covariates are likely to vary with our proxy treatment effects? One ideas is to order our participants by proxy measure of treatment effects (e.g., vax_chance_post - vax_chance_pre - but we should test for all three), and then separate them into quartile groups. For each quartile group, we can see what the average value of a covariate is, and how much that varies from the average value of a covariate across the entire survey population. Covariates that vary in the top/bot quartile are likely candidates for predicting our proxy treatment effects. Ultimately, we’re trying to answer: which covariates are likely to not have any impact on treatment effects, and what covariates look promising?

We mapped binary and ordinal variables to continuous variables.

Details on the mapping:

  • female: 1 if female, 0 if male
  • black: 1 if black or african, 0 otherwise
  • income: 0 if the participant is unemployed, 1 if income < R5,000, 2 if income in R5,000 – R9,999, …, 6 if income > R100,000
  • education: 1 if the participant’s education < high school, 2 if education is high school, …, 6 if education is a graduate degree
  • christian: 1 if the participant identifies as christian, 0 otherwise
  • religiosity: 1 if the participant is not very religious, 2 if somewhat religious, 3 if very religious
  • politics: 1 if the participant is conservative, 2 if moderate, 3 if liberal
  • location: 1 if the participant lives in rural, 2 if suburban, 3 if urban,
  • ability: 1 if the participant has the ability to get vax, 0 if not
  • motivation: 1 if the participant has the motivation to get vax, 0 if not

Vax Chance Change

Openness to Vaccination Future

Confidence Level (Vaccine Yes)

Confidence Level (Vaccine No)

Categorical variables on proxy measures

Do any of our categorical variables have a significant difference (or approaching statistically significance) in proxy treatment measures? If so, what are the effect sizes?

Causal Forest

What are the top covariates that predict variation in our three different measures for proxy treatment effects? If we haven’t done so, we should test to see if we get any results from a causal forest.

Vax Chance Change

Openness to Vaccination Future

Confidence Level (Vaccine Yes)

Confidence Level (Vaccine No)