1 Data Checks

We only included participants with a gc score of 1. Those with a gc value equal to 1 designate the Good Completes on the filter supplied by Qualtrics.

For the VA surveys we had 1060 at wave 1, 746 at wave 2, and 688 at wave 3.

Wave 1 Wave 2 Wave 3
1060 746 688

For the non-VA surveys we had 1025 at wave 1, 512 at wave 2, and 388 at wave 3.

Wave 1 Wave 2 Wave 3
1025 512 388

Overall we had 2085 at wave 1, 1258 at wave 2, and 1076 at wave 3.

Wave 1 Wave 2 Wave 3
2085 1258 1076

total of 930 respondents completed waves 1,2, and 3. This represents the total sample included in these analyses.

gc n
1 930

2 Demographics

2.1 Vaccination status

Checking vaccination status in January and March 2021

JanVax_Status n percent
0 Doses 765 82.3%
1 Dose 160 17.2%
2 Doses 5 0.5%
MarVax_Status n percent
0 Doses 310 33.3%
1 Dose 206 22.2%
2 Doses 414 44.5%

2.2 Had CV19

Checking the number of respondents who said they had had COVID in December 2020, January 2021, and in March 2021.

DecCV_Status n percent
haven’t had COVID 899 96.7%
yes, currently have COVID 4 0.4%
yes, had COVID and recovered 27 2.9%
JanCV_Status n percent
haven’t had COVID 882 94.8%
yes, currently have COVID 1 0.1%
yes, had COVID and recovered 47 5.1%
MarCV_Status n percent
haven’t had COVID 881 94.7%
yes, currently have COVID 3 0.3%
yes, had COVID and recovered 46 4.9%

2.3 Veteran status

The majority of respondents were Veterans.

Veteran_Fct n percent
Non-Veteran 346 37.2%
Veteran 584 62.8%

2.4 Age

Age of sample based on survey labels, collapsed into fewer age nands, and split by younger than 55 or 55+

age1 n percent
2 0.2%
18 to 24 10 1.1%
25 to 34 27 2.9%
35 to 44 39 4.2%
45 to 54 48 5.2%
55 to 64 116 12.5%
65 to 74 475 51.1%
75 to 84 198 21.3%
85 or older 15 1.6%
age2 n percent
18 to 34 37 4.0%
35 to 54 87 9.4%
55 to 74 591 63.5%
75 or older 213 22.9%
NA 2 0.2%
age_FCT1 n percent
Younger than 55 126 13.5%
55 or older 804 86.5%

2.5 Gender

Gender of sample based on raw coding, grouped by survey labels

Gender of sample with different groupings

Gender_CHR n percent
Female 193 20.8%
Male 735 79.0%
Non-binary/third gender 2 0.2%

2.6 Income

Overall descriptives for sample income and then frequencies

##    vars   n mean   sd median trimmed  mad min max range  skew kurtosis   se
## X1    1 930 6.38 2.35      7    6.59 1.48   1  10     9 -0.68    -0.39 0.08
income2a n percent
$0 - $49k 206 22.2%
$50K to $99K 362 38.9%
$100K and more 325 34.9%
Prefer to not say 37 4.0%

2.7 Race/Ethnicity

Race and ethnicity frequencies

LatinxCHR RaceCHR count freq
Non-hispanic American Indian or Alaskan Native 4 4 (0.4%)
Non-hispanic Asian or Asian American 26 26 (2.8%)
Non-hispanic Black or African American 64 64 (6.9%)
Non-hispanic Multiple 8 8 (0.9%)
Non-hispanic Native Hawaiian or other Pacific Islander 2 2 (0.2%)
Non-hispanic Other 13 13 (1.4%)
Non-hispanic White or European American 720 720 (77.4%)
No response White or European American 1 1 (0.1%)
Hispanic Asian or Asian American 1 1 (0.1%)
Hispanic Black or African American 5 5 (0.5%)
Hispanic Multiple 2 2 (0.2%)
Hispanic Other 4 4 (0.4%)
Hispanic White or European American 80 80 (8.6%)
NonHispanicWhite_Yes n percent
No 210 22.6%
Yes 720 77.4%

2.8 Rural/Urban

How participants best described the place where they live

ruralUrban_fct n percent
Rural 151 16.2%
Small (less than 100,000) 159 17.1%
Suburban near large city 457 49.1%
Mid sized city (100,000 to 1million) 90 9.7%
large city more than 1million 70 7.5%
Other 3 0.3%
Urban_Chr n percent
Rural 151 16.2%
Urban 776 83.4%
NA 3 0.3%

2.9 % State Vaccinated

Looking at proportions of the state vaccinated and then ranking states for both January and March. Data from: https://www.kff.org/coronavirus-covid-19/issue-brief/state-covid-19-data-and-policy-actions/

stateCHR StateVaxProp_Jan StateVaxRank_Jan StateVaxProp_Mar StateVaxRank_Mar n
NA NA NA NA 2
Alabama 0.4 49 14.6 49 10
Arizona 3 33 19.9 17 29
Arkansas 0.1 50 16.3 46 5
California 9.2 1 18.6 25 76
Colorado 4.1 15 18.2 28 20
Connecticut 5 6 24.9 2 13
Delaware 1.4 45 18.1 29 4
District of Columbia 9.1 2 20.9 8 2
Florida 4.5 7 17.6 35 88
Georgia 0.9 47 13.5 50 20
Hawaii 0.6 48 19.7 18 9
Idaho 2.7 37 16.6 45 7
Illinois 2.3 42 18.8 24 38
Indiana 3.6 21 17.1 41 16
Iowa 3.7 20 20.4 12 10
Kansas 2.9 34 16.9 43 3
Kentucky 1.4 45 18.9 23 8
Louisiana 4.2 11 17.6 35 7
Maine 4.2 11 21.1 7 1
Maryland 3.6 21 17.8 32 12
Massachusetts 2.5 40 21.8 6 29
Michigan 3.6 21 17.6 35 18
Minnesota 2.9 34 20.2 14 21
Mississippi 3.5 26 17 42 6
Missouri 2.4 41 17.2 40 16
Montana 3.6 21 20.4 12 5
Nebraska 4 17 19.2 21 4
Nevada 2.2 43 17.7 33 18
New Hampshire 4.2 11 20 15 3
New Jersey 3.5 26 19.2 21 22
New Mexico 7 3 25.2 1 7
New York 3.9 18 19.3 20 52
North Carolina NA 37 17.7 33 38
North Dakota NA 4 23.4 4 3
Ohio 3.9 18 17.6 35 35
Oklahoma 3.6 21 20.9 8 6
Oregon 4.1 15 18 30 10
Pennsylvania 2.7 37 17.9 31 41
Puerto Rico NA NA NA NA 2
Rhode Island 3.4 30 22 5 5
South Carolina 2.1 44 16.8 44 24
South Dakota 4.3 10 24 3 2
Tennessee 4.2 11 15.7 48 15
Texas 3.5 26 15.8 47 73
Utah 4.4 9 17.4 39 1
Vermont 4.5 7 20.8 10 4
Virginia 3.3 31 18.5 26 35
Washington 3.3 31 18.5 26 30
West Virginia 6 4 20.6 11 3
Wisconsin 2.9 34 19.7 18 22

2.10 CCI

Descriptives for total number of comorbidities.

##    vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 930 1.47 1.37      1    1.31 1.48   0   7     7 0.83     0.16 0.05

3 Descriptives & reliability

3.1 Risk behaviors

We asked: How frequently, if at all, do you plan to do the following things in the next month?

Risk increasing behaviors

  • Q1. Going to gatherings of 10 or more people
  • Q2. Going on optional shopping trips
  • Q3. Going on optional travel
  • Q4. Having optional social visits
  • Q5. Eating inside restaurants, bars and food courts

Risk decreasing behaviors

  • Q6. Practicing good hygiene such as washing your hands, especially after touching frequently used items or surfaces
  • Q7. Wearing a mask over your nose and mouth when you are in a public place (e.g., store)
  • Q8. Wearing a mask over your nose and mouth when you are outdoors

Response scale: Never (1), Very rarely (2), Rarely (3), Occasionally (4), Frequently (5), Very frequently (6)

3.1.1 Wave 1

The reliability of the wave 1 risk increasing behavior items is good. Cronbach’s Alpha is .83.

## $total
##  raw_alpha std.alpha   G6(smc) average_r     S/N         ase     mean       sd
##  0.8289232 0.8356725 0.8061625 0.5042343 5.08541 0.008804863 2.027312 0.956754
##   median_r
##  0.5246565

Overall descriptives of risk increasing behaviors at wave 1 as a scale

##    vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 930 2.03 0.96    1.8    1.89 0.89   1   6     5 1.18     1.08 0.03

The reliability of the wave 1 risk decreasing behavior items is not good Cronbach’s Alpha is .54. We initially planned to remove the item with the lowest correlation to the summated score for all other items, but as this would result in different variables at each wave, we instead used the second contingency plan and used the item “Wearing a mask over your nose and mouth when you are in a public place (e.g., a store)” for Risk-decreasing behaviors at all three waves.

## $total
##  raw_alpha std.alpha   G6(smc) average_r      S/N        ase     mean        sd
##  0.5446453 0.6227492 0.5252931 0.3549437 1.650757 0.02203419 5.110753 0.8682035
##   median_r
##  0.3715421

Overall descriptives of wearing a mask over your nose and mouth when you are in a public place (e.g., a store) at wave 1

##    vars   n mean   sd median trimmed mad min max range  skew kurtosis   se
## X1    1 930 5.74 0.75      6    5.93   0   1   6     5 -3.99    18.24 0.02

3.1.2 Wave 2

The reliability of the wave 2 risk increasing behavior items again is good. Cronbach’s Alpha is .84.

## $total
##  raw_alpha std.alpha   G6(smc) average_r      S/N         ase     mean
##  0.8308709 0.8352593 0.8086059 0.5034828 5.070144 0.008700963 2.063118
##         sd  median_r
##  0.9823112 0.5231782

Overall descriptives of risk increasing behaviors at wave 2 as a scale

##    vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 930 2.06 0.98    1.8    1.93 0.89   1   6     5 1.05     0.71 0.03

Just showing that the reliability of the wave 2 risk decreasing behavior items is not good Cronbach’s Alpha is .56 so we use the mask item.

## $total
##  raw_alpha std.alpha   G6(smc) average_r      S/N        ase     mean        sd
##   0.558658 0.6401004 0.5474184 0.3721949 1.778553 0.02209038 5.104659 0.8817352
##   median_r
##  0.3566065

Overall descriptives for the mask item at wave 2

##    vars   n mean   sd median trimmed mad min max range  skew kurtosis   se
## X1    1 930 5.73 0.79      6    5.93   0   1   6     5 -3.82    16.48 0.03

3.1.3 Wave 3

The reliability of the wave 3 risk increasing behavior items is good again. Cronbach’s Alpha is .87.

## $total
##  raw_alpha std.alpha   G6(smc) average_r      S/N         ase    mean       sd
##  0.8660147  0.868964 0.8448845 0.5701324 6.631488 0.006924479 2.31828 1.127038
##   median_r
##  0.5776737

Overall descriptives of risk increasing behaviors at wave 3 as a scale

##    vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 930 2.32 1.13      2     2.2 1.19   1   6     5 0.91     0.29 0.04

Just showing again that the reliability of the wave 3 risk decreasing behavior items is not good Cronbach’s Alpha is .53 so we use one item.

## $total
##  raw_alpha std.alpha   G6(smc) average_r      S/N        ase     mean        sd
##  0.5322676 0.6083205 0.5130136 0.3411094 1.553108 0.02317739 5.029032 0.8769494
##   median_r
##  0.3664434

Overall descriptives for the mask item at wave 3

##    vars   n mean   sd median trimmed mad min max range  skew kurtosis   se
## X1    1 930 5.69 0.82      6    5.89   0   1   6     5 -3.52    14.08 0.03

Showing responses to all the individual risk behavior at each wave

First, bar plots for all the risk increasing items showing total responses. Overall, there is a general trend of the frequency of engaging in these behaviors increasing over time.

Using line plots for all the risk increasing items the trend is a bit clearer. As you look towards the right the green line rises above the others.

Pirate plot for all reported mask wearing when in public for each wave. We can see this stays consistently high with our respondents.

3.2 Health literacy

Measured at wave 1 (Dec 2020)

  • Q1. How often do you have someone (like a family member, friend, hospital/clinic worker or caregiver) help you read instructions, pamphlets or other written health materials from your doctor or pharmacy?

Response scale (slider): Never(1), Rarely(2), Sometimes(3), Often(4), Always(5).

Overall descriptives

##    vars   n mean   sd median trimmed mad min max range skew kurtosis   se
## X1    1 930 1.24 0.68      1    1.05   0   1   5     4 3.36    11.88 0.02

3.3 Numeracy

Measured at wave 1 (Dec 2020)

  • Q1. How good are you at working with fractions?
  • Q2. How good are you at figuring out how much a shirt will cost if it is 25% off?

Response scale (slider): Not at all good(1), — (2), — (3), — (4), — (5) Extremely good (6).

  • Q3. How often do you find numerical information to be useful?

Response scale (slider): Never(1), — (2), — (3), — (4), — (5) Very often (6).

The reliability of the numeracy items is good. Cronbach’s Alpha is .87.

## Warning in psych::alpha(df[, c(17:19)]): Some items were negatively correlated with the total scale and probably 
## should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option
## Some items ( pharmacyInstructions ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option
## $total
##  raw_alpha std.alpha  G6(smc) average_r      S/N        ase     mean        sd
##   0.475826 0.3134905 0.493991 0.1321062 0.456644 0.02190755 3.729391 0.7910568
##    median_r
##  -0.1571531

Overall descriptives

##    vars   n mean   sd median trimmed  mad min  max range  skew kurtosis   se
## X1    1 930 3.73 0.79      4    3.84 0.49   1 5.67  4.67 -1.13     1.21 0.03

For individual numeracy items

3.4 COVID-19 worry

Measured at wave 1 (Dec 2020)

  • Q1. How worried are you about getting COVID-19?

Response scale (slider): Not at all worried (1), (2), (3), (4), Very worried (5).

Overall descriptives

##    vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 930 2.92 1.29      3     2.9 1.48   1   5     4 0.19    -1.05 0.04

3.5 CV19 risk perception

Measured at wave 1 (Dec 2020)

  • Q1. In your opinion, how likely is it that you will get COVID-19 during the next month?
  • Q2. If you were to get COVID-19, how likely do you think it is that you would need to be hospitalized?
  • Q3. If you were to get COVID-19, how likely do you think it is that you would die?

Response scale (slider): Not at all likely(1), — (2), — (3), — (4), Very likely (5).

The reliability of the risk perception items is good. Cronbach’s Alpha is .79.

## $total
##  raw_alpha std.alpha   G6(smc) average_r     S/N        ase     mean        sd
##  0.7429823 0.7483663 0.6742347 0.4978264 2.97403 0.01392076 2.642294 0.9402066
##   median_r
##  0.5390999

Overall descriptives

##    vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 930 2.64 0.94   2.67    2.63 0.99   1   5     4 0.15    -0.66 0.03

For individual risk items

3.6 Working from home

Measured at wave 1 (Dec 2020)

  • Q1. Since COVID-19 began, have you been able to work from home?

Response scale (slider): None of the time(1), A little bit of the time (2), About half the time (3), Most of the time (4), All of the time (5).

Overall descriptives

##    vars   n mean   sd median trimmed mad min max range skew kurtosis   se
## X1    1 923 2.68 1.89      1     2.6   0   1   5     4 0.32    -1.83 0.06
workathome n percent
1 490 52.7%
2 34 3.7%
3 18 1.9%
4 40 4.3%
5 341 36.7%
NA 7 0.8%
WFH_Chr n percent
None of the time 490 52.7%
A little bit of the time 34 3.7%
About half the time 18 1.9%
Most of the time 40 4.3%
All of the time 341 36.7%
NA 7 0.8%
WFH_Fct n percent
No 490 52.7%
Yes 433 46.6%
NA 7 0.8%

3.7 Internet quality

Measured at wave 1 (Dec 2020)

  • Q1. Is your Internet connection good enough that you can work from home or to do video calls with family and friends?

Response scale: No (0), Yes (1).

Overall descriptives

Internet_Fct n percent
No 46 4.9%
Yes 798 85.8%
NA 86 9.2%

3.8 Grocery deliveries

Measured at wave 1 (Dec 2020)

  • Q1. Do you order groceries or other necessities for delivery?

Response scale: Never, I can’t afford to (1), Never, it’s not available where I live (2), Never, I prefer to shop in person (3), Never, I have friends or family who do it for me (4), Yes, I do sometimes (5), Yes, I do most of the time (6), Yes, I do all of the time (7).

“Never, I can’t afford to” (1), “Never, it’s not available where I live” (2), “Never, I prefer to shop in person” (3), “Never, I have friends or family who do it for me” (4), “Yes, I do sometimes” (5), “Yes, I do most of the time” (6), “Yes, I do all of the time” (7)

Overall descriptives

Groceries_Fct n percent
No 693 74.5%
Yes 237 25.5%

3.9 Healthcare trust

Measured at wave 1 (Dec 2020)

Please indicate how much you agree or disagree with each statement. There are no right or wrong answers. Please answer in a way that reflects your own personal beliefs:

  • The Health Care System does its best to make patients health better
  • The Health Care System covers up its mistakes
  • Patients receive high quality medical care from the Health Care System
  • The Health Care System makes too many mistakes
  • The Health Care System puts making money above patients’ needs
  • The Health Care System gives excellent medical care
  • Patients get the same medical treatment from the Health Care System no matter what the patient’s race or ethnicity
  • The Health Care System lies to make money
  • The Health Care System experiments on patients without them knowing

Response scale (slider): Strongly disagree (1), Disagree (2), Somewhat disagree (3), Neither agree nor disagree (4), Somewhat agree (5), Agree (6), Strongly agree (7).

The reliability of the Healthcare trust items is good. Cronbach’s Alpha is .89.

## $total
##  raw_alpha std.alpha   G6(smc) average_r      S/N         ase    mean       sd
##  0.8885886 0.8932149 0.9101051 0.4817042 8.364602 0.005484399 4.32957 1.071551
##  median_r
##  0.449045

Overall descriptives

##    vars   n mean   sd median trimmed  mad  min  max range skew kurtosis   se
## X1    1 930 4.33 1.07   4.22    4.32 1.15 0.78 6.44  5.67 0.01    -0.34 0.04

Reported trust in healthcare for each item

3.10 Belief in science

Measured at wave 1 (Dec 2020)

Please indicate how much you agree or disagree with each statement. There are no right or wrong answers. Please answer in a way that reflects your own personal beliefs:

  • People trust scientists a lot more than they should
  • People don’t realize just how flawed a lot of scientific research really is
  • A lot of scientific theories are dead wrong
  • Sometimes I think we put too much faith in science
  • Our society places too much emphasis on science
  • I am concerned by the amount of influence that scientists have in society

Response scale (slider): Strongly disagree (1), Disagree (2), Somewhat disagree (3), Neither agree nor disagree (4), Somewhat agree (5), Agree (6), Strongly agree (7).

The reliability of the (lack of) Belief in science items is good. Cronbach’s Alpha is .96.

## Warning in psych::alpha(df[, c(36:41)]): Some items were negatively correlated with the total scale and probably 
## should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option
## Some items ( healthcare.trust_7 healthcare.trust_8 healthcare.trust_9 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option
## $total
##  raw_alpha std.alpha   G6(smc) average_r      S/N        ase     mean      sd
##   0.509794 0.5023495 0.7119137 0.1440118 1.009442 0.02659103 3.842473 0.89981
##    median_r
##  0.06169046

Overall descriptives

##    vars   n mean  sd median trimmed  mad  min max range skew kurtosis   se
## X1    1 930 3.84 0.9   3.67    3.83 0.74 1.17 6.5  5.33  0.2     0.26 0.03

Reported belief in science for each item

3.11 Conspiracy theories

Measured at wave 3 (March 2021)

Below are things that some people might believe. Please indicate whether you personally think each statement is true or false.

  • The virus causing COVID-19 was purposefully released by a government or person.
  • COVID-19 is actually a biological weapon being tested.
  • The current COVID-19 outbreak is actually a form of population control to reduce the number of people in the infected countries.
  • The COVID- 19 vaccine is a microchip so the government can track you.

Response scale (slider): Definitely false (1), Probably false (2), Unsure (3), Probably true (4),Definitely true (5)

The reliability of the Belief in conspiracy theories items is good. Cronbach’s Alpha is .88.

## $total
##  raw_alpha std.alpha   G6(smc) average_r      S/N         ase     mean
##  0.8770891 0.8864585 0.8788258 0.6612281 7.807355 0.006153219 1.755108
##         sd  median_r
##  0.9398848 0.6769861

Overall descriptives

##    vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 930 1.76 0.94   1.25     1.6 0.37   1   5     4 1.12     0.31 0.03

Reported belief in each conspiracy theory item

Specific responses to the individual conspiracy items

3.12 Liberal/Conservative

Measured at wave 2 (Jan 2021)

  • Q1. Here is a 7-point scale on which the political views that people might hold are arranged from extremely liberal (left) to extremely conservative (right). Where would you place yourself on this scale?

Response scale (slider): Extremely liberal (1), Moderately liberal (2), Slightly liberal (3), Neutral (4), Slightly conservative (5), Moderately conservative (6), Extremely conservative (7).

Overall descriptives

##    vars   n mean   sd median trimmed  mad min max range  skew kurtosis   se
## X1    1 928 4.44 1.76      5    4.51 1.48   1   7     6 -0.34     -0.9 0.06

3.13 Gov response

Measured at wave 1 (Dec 2020)

  • Q1. In general, do you think the FEDERAL government is doing not enough or too much to limit the spread of coronavirus?
  • Q2. In general, do you think your STATE government is doing not enough or too much to limit the spread of coronavirus?

Response scale: Not nearly enough (1), Not enough (2), Just right (3), Too much (4), Way too much (5).

  • Q3. How angry are you about how the FEDERAL government responded to the COVID-19 pandemic?
  • Q4. How angry are you about how your STATE’s government responded to the COVID-19 pandemic?

Response scale: Not at all angry (1), — (2), — (3), — (4), Very angry (5).

Overall descriptives

The reliability of the Gov response items is poor. Cronbach’s Alpha is .38.

## Warning in psych::alpha(df[, c(45:48)]): Some items were negatively correlated with the total scale and probably 
## should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option
## Some items ( angrygovtFed angrygovtState ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option
## $total
##   raw_alpha  std.alpha   G6(smc)   average_r        S/N       ase     mean
##  -0.3790706 -0.3010816 0.3088346 -0.06140456 -0.2314087 0.0763153 2.680376
##         sd   median_r
##  0.5822356 -0.1309113

If we do not reach the α=0.70 threshold with this procedure, we will select two separate items:

  • Q2. In general, do you think your STATE government is doing not enough or too much to limit the spread of coronavirus?

Response scale: Not nearly enough (1), Not enough (2), Just right (3), Too much (4), Way too much (5).

##    vars   n mean  sd median trimmed  mad min max range skew kurtosis   se
## X1    1 930 2.63 1.1      3    2.57 1.48   1   5     4 0.26    -0.31 0.04

and

  • Q4. How angry are you about how your STATE’s government responded to the COVID-19 pandemic?

Response scale: Not at all angry (1), — (2), — (3), — (4), Very angry (5).

##    vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 930 2.78 1.44      3    2.73 1.48   1   5     4  0.2    -1.26 0.05

4 Analyses

4.1 Correlations

Here are all the correlations we planned to run for each of the three waves December (wave 1), January (wave 2), and March (wave 3). All p values are adjusted using bonferroni correction.

4.1.1 December

Correlations with risk increasing behaviors

  r (lower) correlation estimate (r) r (upper) Raw p value Bonferroni adjusted p
Age -0.15 -0.09 -0.03 0.06 0.11
Urban -0.13 -0.07 0 0.31 0.8
Total comorbidities -0.11 -0.05 0.02 0.78 1
Veteran -0.07 -0.01 0.06 1 1
Health literacy 0.02 0.08 0.14 0.12 0.28
Numeracy -0.04 0.02 0.09 1 1
Non-Hispanic White 0 0.06 0.12 0.42 1
Worry about getting COVID-19 -0.42 -0.37 -0.31 0 0
COVID-19 risk perceptions -0.38 -0.32 -0.26 0 0
Work from home -0.15 -0.08 -0.02 0.11 0.22
Good internet -0.08 -0.02 0.05 1 1
Grocies delivered -0.28 -0.22 -0.16 0 0
Trust in healthcare -0.08 -0.02 0.05 1 1
(lack of) Belief in science 0.27 0.33 0.38 0 0
Belief in conspiracy theories 0.28 0.34 0.39 0 0
Conservative beliefs 0.27 0.33 0.38 0 0
State too much 0.26 0.32 0.38 0 0
Angry with State 0.06 0.13 0.19 0 0

Correlations with mask wearing

  r (lower) correlation estimate (r) r (upper) Raw p value Bonferroni adjusted p
Age 0.12 0.19 0.25 0 0
Urban -0.01 0.05 0.12 0.54 1
Total comorbidities -0.01 0.06 0.12 0.54 1
Veteran -0.03 0.03 0.09 0.89 1
Health literacy -0.19 -0.13 -0.07 0 0
Numeracy 0.1 0.17 0.23 0 0
Non-Hispanic White -0.01 0.05 0.12 0.54 1
Worry about getting COVID-19 0.16 0.22 0.28 0 0
COVID-19 risk perceptions 0.14 0.2 0.27 0 0
Work from home -0.03 0.03 0.1 0.89 1
Good internet 0.01 0.08 0.15 0.16 0.35
Grocies delivered -0.03 0.03 0.1 0.89 1
Trust in healthcare 0.09 0.16 0.22 0 0
(lack of) Belief in science -0.23 -0.16 -0.1 0 0
Belief in conspiracy theories -0.38 -0.32 -0.26 0 0
Conservative beliefs -0.17 -0.11 -0.04 0.01 0.02
State too much -0.28 -0.21 -0.15 0 0
Angry with State -0.13 -0.06 0 0.39 1

4.1.2 January

Correlations with risk increasing behaviors

  r (lower) correlation estimate (r) r (upper) Raw p value Bonferroni adjusted p
Age -0.2 -0.13 -0.07 0 0
Urban -0.12 -0.05 0.01 0.52 1
Total comorbidities -0.13 -0.06 0 0.33 0.98
Veteran -0.09 -0.03 0.04 1 1
Health literacy 0 0.07 0.13 0.31 0.69
Numeracy -0.09 -0.02 0.04 1 1
Non-Hispanic White -0.02 0.04 0.11 0.85 1
Worry about getting COVID-19 -0.4 -0.35 -0.29 0 0
COVID-19 risk perceptions -0.37 -0.31 -0.25 0 0
Work from home -0.15 -0.09 -0.02 0.06 0.12
Good internet -0.09 -0.03 0.04 1 1
Grocies delivered -0.27 -0.21 -0.15 0 0
Trust in healthcare -0.13 -0.07 0 0.32 0.83
(lack of) Belief in science 0.24 0.3 0.36 0 0
Belief in conspiracy theories 0.32 0.37 0.43 0 0
Conservative beliefs 0.24 0.3 0.36 0 0
State too much 0.27 0.33 0.38 0 0
Angry with State 0.05 0.12 0.18 0 0.01

Correlations with mask wearing

  r (lower) correlation estimate (r) r (upper) Raw p value Bonferroni adjusted p
Age 0.17 0.23 0.29 0 0
Urban 0.05 0.11 0.17 0.01 0.01
Total comorbidities -0.06 0.01 0.07 1 1
Veteran 0.05 0.11 0.17 0.01 0.01
Health literacy -0.17 -0.1 -0.04 0.01 0.03
Numeracy 0.02 0.08 0.15 0.06 0.19
Non-Hispanic White -0.06 0 0.07 1 1
Worry about getting COVID-19 0.09 0.16 0.22 0 0
COVID-19 risk perceptions 0.08 0.14 0.2 0 0
Work from home -0.07 0 0.06 1 1
Good internet 0.05 0.12 0.19 0.01 0.01
Grocies delivered -0.09 -0.02 0.04 1 1
Trust in healthcare 0.1 0.16 0.22 0 0
(lack of) Belief in science -0.19 -0.12 -0.06 0 0
Belief in conspiracy theories -0.35 -0.29 -0.23 0 0
Conservative beliefs -0.15 -0.08 -0.02 0.06 0.18
State too much -0.21 -0.15 -0.08 0 0
Angry with State -0.18 -0.11 -0.05 0.01 0.01

4.1.3 March

Correlations with risk increasing behaviors

  r (lower) correlation estimate (r) r (upper) Raw p value Bonferroni adjusted p
Age -0.11 -0.05 0.01 0.85 1
Urban -0.12 -0.06 0 0.52 1
Total comorbidities -0.11 -0.04 0.02 1 1
Veteran -0.05 0.01 0.07 1 1
Health literacy -0.03 0.04 0.1 1 1
Numeracy -0.08 -0.01 0.05 1 1
Non-Hispanic White 0.01 0.08 0.14 0.19 0.37
Worry about getting COVID-19 -0.41 -0.35 -0.29 0 0
COVID-19 risk perceptions -0.37 -0.32 -0.26 0 0
Work from home -0.16 -0.1 -0.04 0.02 0.04
Good internet -0.05 0.02 0.08 1 1
Grocies delivered -0.25 -0.18 -0.12 0 0
Trust in healthcare -0.09 -0.03 0.04 1 1
(lack of) Belief in science 0.23 0.29 0.35 0 0
Belief in conspiracy theories 0.28 0.34 0.4 0 0
Conservative beliefs 0.24 0.3 0.36 0 0
State too much 0.24 0.3 0.35 0 0
Angry with State 0.08 0.15 0.21 0 0

Correlations with mask wearing

  r (lower) correlation estimate (r) r (upper) Raw p value Bonferroni adjusted p
Age 0.1 0.16 0.23 0 0
Urban 0.06 0.12 0.18 0 0
Total comorbidities -0.03 0.04 0.1 0.97 1
Veteran 0.01 0.08 0.14 0.1 0.3
Health literacy -0.14 -0.07 -0.01 0.11 0.41
Numeracy 0.04 0.11 0.17 0.01 0.02
Non-Hispanic White -0.04 0.02 0.08 1 1
Worry about getting COVID-19 0.16 0.22 0.28 0 0
COVID-19 risk perceptions 0.14 0.2 0.26 0 0
Work from home -0.05 0.02 0.08 1 1
Good internet 0.03 0.1 0.16 0.03 0.08
Grocies delivered -0.05 0.01 0.08 1 1
Trust in healthcare 0.08 0.15 0.21 0 0
(lack of) Belief in science -0.24 -0.18 -0.11 0 0
Belief in conspiracy theories -0.4 -0.34 -0.28 0 0
Conservative beliefs -0.19 -0.13 -0.06 0 0
State too much -0.25 -0.19 -0.13 0 0
Angry with State -0.18 -0.11 -0.05 0.01 0.01

4.2 Regressions

Here are all the regressions we planned to run for December (wave 1), January (wave 2), and March (wave 3). These were done in a stepwise (hierarchical) process with varaibles that we would associate with early eligibility entered first (step 1 output) and other variables added after (step 2 output).

4.2.1 December

Predicting risk increasing behaviors

  Step 1 Step 2
Predictors Estimates p value Estimates p value
(Intercept) 2.25
(1.80 – 2.69) 		<0.001 1.18
(0.59 – 1.77) 		<0.001
35 to 54 -0.52
(-0.89 – -0.15) 		0.005 -0.43
(-0.75 – -0.12) 		0.006
55 to 74 -0.43
(-0.77 – -0.09) 		0.013 -0.33
(-0.62 – -0.04) 		0.027
75 or older -0.60
(-0.96 – -0.23) 		0.001 -0.39
(-0.71 – -0.08) 		0.014
Urban -0.14
(-0.30 – 0.03) 		0.107 0.00
(-0.15 – 0.15) 		0.986
Veteran 0.08
(-0.06 – 0.22) 		0.286 -0.03
(-0.16 – 0.10) 		0.625
Total comorbidities -0.02
(-0.07 – 0.02) 		0.323 -0.00
(-0.05 – 0.04) 		0.845
Health literacy 0.12
(0.03 – 0.22) 		0.013 0.13
(0.04 – 0.22) 		0.004
Numeracy 0.02
(-0.06 – 0.10) 		0.662 0.05
(-0.03 – 0.12) 		0.214
Non-Hispanic White 0.16
(0.01 – 0.31) 		0.036 0.11
(-0.03 – 0.24) 		0.120
Worry about getting COVID-19 -0.17
(-0.26 – -0.08) 		<0.001
COVID-19 risk perceptions -0.02
(-0.14 – 0.11) 		0.788
Work from home -0.03
(-0.14 – 0.08) 		0.601
Good internet -0.00
(-0.25 – 0.25) 		0.999
Grocies delivered -0.28
(-0.41 – -0.16) 		<0.001
Trust in healthcare -0.06
(-0.13 – 0.01) 		0.088
(lack of) Belief in science 0.15
(0.07 – 0.24) 		<0.001
Belief in conspiracy theories 0.13
(0.05 – 0.20) 		0.001
Conservative beliefs 0.05
(0.01 – 0.09) 		0.011
State too much 0.15
(0.09 – 0.20) 		<0.001
Angry with State 0.09
(0.05 – 0.13) 		<0.001
Observations 925 834
R2 / R2 adjusted 0.031 / 0.022 0.339 / 0.323

Predicting mask wearing

  Step 1 Step 2
Predictors Estimates p value Estimates p value
(Intercept) 4.62
(4.29 – 4.95) 		<0.001 4.87
(4.40 – 5.34) 		<0.001
35 to 54 0.65
(0.38 – 0.92) 		<0.001 0.59
(0.34 – 0.84) 		<0.001
55 to 74 0.68
(0.42 – 0.93) 		<0.001 0.55
(0.32 – 0.78) 		<0.001
75 or older 0.76
(0.50 – 1.03) 		<0.001 0.59
(0.34 – 0.84) 		<0.001
Urban 0.05
(-0.08 – 0.17) 		0.456 -0.03
(-0.15 – 0.09) 		0.640
Veteran -0.08
(-0.19 – 0.02) 		0.127 -0.05
(-0.15 – 0.06) 		0.381
Total comorbidities 0.03
(-0.00 – 0.07) 		0.072 0.02
(-0.01 – 0.06) 		0.221
Health literacy -0.10
(-0.17 – -0.03) 		0.006 -0.09
(-0.16 – -0.02) 		0.013
Numeracy 0.15
(0.09 – 0.21) 		<0.001 0.13
(0.07 – 0.19) 		<0.001
Non-Hispanic White 0.00
(-0.11 – 0.12) 		0.936 0.02
(-0.09 – 0.13) 		0.670
Worry about getting COVID-19 0.09
(0.02 – 0.16) 		0.010
COVID-19 risk perceptions -0.01
(-0.11 – 0.09) 		0.861
Work from home -0.00
(-0.09 – 0.09) 		0.994
Good internet 0.14
(-0.06 – 0.34) 		0.177
Grocies delivered -0.05
(-0.15 – 0.05) 		0.345
Trust in healthcare 0.08
(0.02 – 0.13) 		0.005
(lack of) Belief in science -0.07
(-0.13 – 0.00) 		0.059
Belief in conspiracy theories -0.09
(-0.15 – -0.04) 		0.002
Conservative beliefs 0.01
(-0.02 – 0.04) 		0.446
State too much -0.10
(-0.14 – -0.05) 		<0.001
Angry with State -0.04
(-0.07 – -0.01) 		0.015
Observations 925 834
R2 / R2 adjusted 0.082 / 0.073 0.223 / 0.204

4.2.2 January

Predicting risk increasing behaviors

  Step 1 Step 2
Predictors Estimates p value Estimates p value
(Intercept) 2.48
(2.03 – 2.94) 		<0.001 1.24
(0.63 – 1.86) 		<0.001
35 to 54 -0.41
(-0.79 – -0.03) 		0.033 -0.31
(-0.63 – 0.01) 		0.060
55 to 74 -0.46
(-0.80 – -0.11) 		0.010 -0.34
(-0.64 – -0.04) 		0.027
75 or older -0.68
(-1.06 – -0.31) 		<0.001 -0.46
(-0.78 – -0.13) 		0.006
Urban -0.09
(-0.27 – 0.08) 		0.281 0.06
(-0.09 – 0.22) 		0.425
Veteran 0.08
(-0.07 – 0.22) 		0.287 -0.02
(-0.15 – 0.11) 		0.744
Total comorbidities -0.03
(-0.08 – 0.02) 		0.213 -0.01
(-0.06 – 0.03) 		0.595
Health literacy 0.10
(-0.00 – 0.19) 		0.052 0.09
(0.00 – 0.18) 		0.043
Numeracy -0.03
(-0.11 – 0.06) 		0.532 0.01
(-0.07 – 0.08) 		0.894
Non-Hispanic White 0.15
(-0.00 – 0.30) 		0.058 0.11
(-0.03 – 0.25) 		0.132
Worry about getting COVID-19 -0.15
(-0.24 – -0.05) 		0.002
COVID-19 risk perceptions 0.01
(-0.12 – 0.14) 		0.857
Work from home -0.05
(-0.16 – 0.07) 		0.401
Good internet 0.01
(-0.25 – 0.27) 		0.944
Grocies delivered -0.26
(-0.39 – -0.13) 		<0.001
Trust in healthcare -0.07
(-0.14 – 0.00) 		0.058
(lack of) Belief in science 0.15
(0.06 – 0.24) 		0.001
Belief in conspiracy theories 0.17
(0.10 – 0.25) 		<0.001
Conservative beliefs 0.04
(0.00 – 0.08) 		0.037
State too much 0.16
(0.10 – 0.22) 		<0.001
Angry with State 0.08
(0.04 – 0.12) 		<0.001
Observations 925 834
R2 / R2 adjusted 0.031 / 0.022 0.313 / 0.296

Predicting mask wearing

  Step 1 Step 2
Predictors Estimates p value Estimates p value
(Intercept) 4.77
(4.42 – 5.13) 		<0.001 5.12
(4.60 – 5.64) 		<0.001
35 to 54 0.56
(0.27 – 0.85) 		<0.001 0.50
(0.23 – 0.78) 		<0.001
55 to 74 0.83
(0.57 – 1.10) 		<0.001 0.69
(0.44 – 0.95) 		<0.001
75 or older 0.94
(0.65 – 1.23) 		<0.001 0.72
(0.44 – 1.00) 		<0.001
Urban 0.18
(0.04 – 0.31) 		0.009 0.13
(-0.00 – 0.27) 		0.051
Veteran 0.01
(-0.10 – 0.13) 		0.800 0.03
(-0.08 – 0.14) 		0.578
Total comorbidities -0.02
(-0.06 – 0.01) 		0.239 -0.02
(-0.06 – 0.01) 		0.203
Health literacy -0.08
(-0.15 – -0.00) 		0.047 -0.04
(-0.12 – 0.03) 		0.253
Numeracy 0.05
(-0.01 – 0.11) 		0.131 0.03
(-0.04 – 0.09) 		0.423
Non-Hispanic White -0.07
(-0.19 – 0.05) 		0.245 -0.08
(-0.20 – 0.04) 		0.201
Worry about getting COVID-19 0.07
(-0.01 – 0.15) 		0.093
COVID-19 risk perceptions -0.01
(-0.12 – 0.10) 		0.848
Work from home -0.06
(-0.16 – 0.04) 		0.250
Good internet 0.21
(-0.01 – 0.43) 		0.066
Grocies delivered -0.11
(-0.23 – -0.00) 		0.043
Trust in healthcare 0.07
(0.01 – 0.13) 		0.017
(lack of) Belief in science -0.06
(-0.14 – 0.01) 		0.098
Belief in conspiracy theories -0.14
(-0.21 – -0.08) 		<0.001
Conservative beliefs 0.02
(-0.01 – 0.06) 		0.195
State too much -0.07
(-0.12 – -0.02) 		0.007
Angry with State -0.05
(-0.09 – -0.02) 		0.003
Observations 925 834
R2 / R2 adjusted 0.085 / 0.076 0.198 / 0.178

4.2.3 March

Predicting risk increasing behaviors

  Step 1 Step 2
Predictors Estimates p value Estimates p value
(Intercept) 2.54
(2.01 – 3.07) 		<0.001 1.09
(0.38 – 1.80) 		0.003
35 to 54 -0.28
(-0.72 – 0.15) 		0.204 -0.20
(-0.57 – 0.18) 		0.308
55 to 74 -0.26
(-0.66 – 0.14) 		0.199 -0.11
(-0.46 – 0.24) 		0.525
75 or older -0.39
(-0.82 – 0.04) 		0.073 -0.16
(-0.54 – 0.22) 		0.404
Urban -0.15
(-0.34 – 0.05) 		0.149 0.01
(-0.17 – 0.20) 		0.878
Veteran 0.10
(-0.06 – 0.27) 		0.227 -0.03
(-0.19 – 0.12) 		0.672
Total comorbidities -0.03
(-0.09 – 0.02) 		0.270 -0.01
(-0.06 – 0.04) 		0.696
Health literacy 0.08
(-0.03 – 0.19) 		0.169 0.09
(-0.02 – 0.19) 		0.103
Numeracy -0.03
(-0.13 – 0.07) 		0.544 0.01
(-0.08 – 0.10) 		0.858
Non-Hispanic White 0.23
(0.05 – 0.41) 		0.011 0.17
(0.00 – 0.33) 		0.050
Worry about getting COVID-19 -0.16
(-0.27 – -0.06) 		0.003
COVID-19 risk perceptions -0.04
(-0.18 – 0.11) 		0.630
Work from home -0.09
(-0.22 – 0.04) 		0.184
Good internet 0.19
(-0.12 – 0.49) 		0.234
Grocies delivered -0.23
(-0.38 – -0.08) 		0.003
Trust in healthcare -0.06
(-0.14 – 0.02) 		0.164
(lack of) Belief in science 0.14
(0.04 – 0.25) 		0.006
Belief in conspiracy theories 0.19
(0.10 – 0.27) 		<0.001
Conservative beliefs 0.06
(0.01 – 0.11) 		0.010
State too much 0.15
(0.08 – 0.22) 		<0.001
Angry with State 0.12
(0.07 – 0.17) 		<0.001
Observations 925 834
R2 / R2 adjusted 0.018 / 0.008 0.295 / 0.277

Predicting mask wearing

  Step 1 Step 2
Predictors Estimates p value Estimates p value
(Intercept) 4.79
(4.41 – 5.16) 		<0.001 5.24
(4.71 – 5.77) 		<0.001
35 to 54 0.37
(0.06 – 0.68) 		0.018 0.30
(0.02 – 0.58) 		0.036
55 to 74 0.47
(0.19 – 0.75) 		0.001 0.30
(0.04 – 0.56) 		0.025
75 or older 0.57
(0.27 – 0.88) 		<0.001 0.33
(0.04 – 0.61) 		0.024
Urban 0.22
(0.08 – 0.36) 		0.003 0.17
(0.03 – 0.30) 		0.016
Veteran 0.02
(-0.10 – 0.14) 		0.737 0.06
(-0.06 – 0.17) 		0.330
Total comorbidities 0.01
(-0.03 – 0.05) 		0.647 0.00
(-0.03 – 0.04) 		0.812
Health literacy -0.08
(-0.16 – 0.00) 		0.051 -0.05
(-0.12 – 0.03) 		0.251
Numeracy 0.09
(0.02 – 0.16) 		0.009 0.06
(-0.01 – 0.13) 		0.085
Non-Hispanic White -0.01
(-0.14 – 0.11) 		0.839 0.01
(-0.11 – 0.14) 		0.813
Worry about getting COVID-19 0.11
(0.03 – 0.19) 		0.007
COVID-19 risk perceptions -0.02
(-0.13 – 0.09) 		0.676
Work from home -0.04
(-0.14 – 0.06) 		0.441
Good internet 0.18
(-0.05 – 0.41) 		0.122
Grocies delivered -0.10
(-0.21 – 0.01) 		0.083
Trust in healthcare 0.08
(0.02 – 0.14) 		0.013
(lack of) Belief in science -0.08
(-0.16 – 0.00) 		0.050
Belief in conspiracy theories -0.17
(-0.24 – -0.11) 		<0.001
Conservative beliefs 0.02
(-0.01 – 0.05) 		0.242
State too much -0.09
(-0.14 – -0.04) 		0.001
Angry with State -0.06
(-0.10 – -0.02) 		0.001
Observations 925 834
R2 / R2 adjusted 0.051 / 0.042 0.211 / 0.192

multicollinearity checks seem okay with all VIFs < 5 (We set a very high threshold of >10 in the pre-reg)

## MODEL INFO:
## Observations: 925 (5 missing obs. deleted)
## Dependent Variable: RiskIncrW1
## Type: OLS linear regression 
## 
## MODEL FIT:
## F(9,915) = 3.29, p = 0.00
## R² = 0.03
## Adj. R² = 0.02 
## 
## Standard errors: OLS
## ----------------------------------------------------------------------
##                                     Est.   S.E.   t val.      p    VIF
## -------------------------------- ------- ------ -------- ------ ------
## (Intercept)                         2.25   0.23     9.87   0.00       
## age2_FCT35 to 54                   -0.52   0.19    -2.78   0.01   1.43
## age2_FCT55 to 74                   -0.43   0.17    -2.50   0.01   1.43
## age2_FCT75 or older                -0.60   0.18    -3.23   0.00   1.43
## Urban_FctUrban                     -0.14   0.08    -1.62   0.11   1.02
## Veteran_FctVeteran                  0.08   0.07     1.07   0.29   1.25
## CCI_ttl                            -0.02   0.02    -0.99   0.32   1.06
## pharmacyInstructions                0.12   0.05     2.49   0.01   1.10
## Numeracy_Avg                        0.02   0.04     0.44   0.66   1.09
## NonHispanicWhite_FctHispanic        0.16   0.08     2.10   0.04   1.06
## White                                                                 
## ----------------------------------------------------------------------
## MODEL INFO:
## Observations: 834 (96 missing obs. deleted)
## Dependent Variable: RiskIncrW1
## Type: OLS linear regression 
## 
## MODEL FIT:
## F(20,813) = 20.85, p = 0.00
## R² = 0.34
## Adj. R² = 0.32 
## 
## Standard errors: OLS
## ----------------------------------------------------------------------
##                                     Est.   S.E.   t val.      p    VIF
## -------------------------------- ------- ------ -------- ------ ------
## (Intercept)                         1.18   0.30     3.93   0.00       
## age2_FCT35 to 54                   -0.43   0.16    -2.75   0.01   1.58
## age2_FCT55 to 74                   -0.33   0.15    -2.22   0.03   1.58
## age2_FCT75 or older                -0.39   0.16    -2.45   0.01   1.58
## Urban_FctUrban                      0.00   0.08     0.02   0.99   1.05
## Veteran_FctVeteran                 -0.03   0.06    -0.49   0.62   1.35
## CCI_ttl                            -0.00   0.02    -0.20   0.85   1.18
## pharmacyInstructions                0.13   0.04     2.89   0.00   1.19
## Numeracy_Avg                        0.05   0.04     1.24   0.21   1.18
## NonHispanicWhite_FctHispanic        0.11   0.07     1.56   0.12   1.10
## White                                                                 
## worried.self                       -0.17   0.05    -3.68   0.00   4.55
## CV19Risk_Avg                       -0.02   0.06    -0.27   0.79   4.50
## WFH_FctYes                         -0.03   0.06    -0.52   0.60   1.06
## Internet_FctYes                    -0.00   0.13    -0.00   1.00   1.15
## Groceries_FctYes                   -0.28   0.06    -4.45   0.00   1.06
## HealthcareTrust_Avg                -0.06   0.03    -1.71   0.09   1.77
## BeliefinScience_Avg                 0.15   0.04     3.50   0.00   2.01
## Conspiracy_Avg                      0.13   0.04     3.44   0.00   1.59
## liberal.conservative                0.05   0.02     2.54   0.01   1.52
## stategovtoomuch                     0.15   0.03     5.07   0.00   1.35
## angrygovtState                      0.09   0.02     4.58   0.00   1.14
## ----------------------------------------------------------------------

5 Plots

5.1 Correlation

5.1.1 Risk increasing behaviors

Here are the results of the correlations with risk increasing behaviors for each wave in the order they were input in the regression

I have also re-ordered them to be in direction of the estimate.

5.1.2 Mask wearing

Here are the results of the correlations with mask wearing for each wave in the order they were input in the regression

I have also re-ordered them to be in direction of the estimate.

5.2 Regression

5.2.1 December

Risk increasing

For the step 1 regression in December We find that older age, health literacy, and race/ethnicity are predicting frequency of doing risk increasing behaviors.

With the step 2 regression of risk increasing behaviors in December, we find that worry about getting COVID, getting groceries delivered, belief in science, conspiracy beliefs, political views, and views about state response are predicting frequency of doing risk increasing behaviors when accounting for primary factors.

Mask wearing

For the step 1 regression in December We find that older age, health literacy, and numeracy are predicting frequency of wearing masks in public.

With the step 2 regression of mask wearing in December, we find that worry about getting COVID, trust in healthcare, conspiracy beliefs, political views, and views about state response are predicting frequency of mask wearing when accounting for primary factors.

5.2.2 January

Risk increasing

For the step 1 regression in January We find only older age predicting frequency of doing risk increasing behaviors.

With the step 2 regression of risk increasing behaviors in January, we find that worry about getting COVID, getting groceries delivered, belief in science, conspiracy beliefs, political views, and views about state response are predicting frequency of doing risk increasing behaviors when accounting for primary factors.

Mask wearing

For the step 1 regression in January We find that older age and living in in an urban place are predicting frequency of wearing masks in public.

With the step 2 regression of mask wearing in January, we find that trust in healthcare, conspiracy beliefs, and views about state response are predicting frequency of mask wearing when accounting for primary factors.

5.2.3 March

Risk increasing

For the step 1 regression in March We find only race/ethnicity is predicting frequency of doing risk increasing behaviors.

With the step 2 regression of risk increasing behaviors in March, we find that worry about getting COVID, getting groceries delivered, belief in science, conspiracy beliefs, political views, and views about state response are predicting frequency of doing risk increasing behaviors when accounting for primary factors.

Mask wearing

For the step 1 regression in March We find that older age, living in an urban place, and numeracy are predicting frequency of wearing masks in public.

With the step 2 regression of mask wearing in March, we find that worry about getting COVID, trust in healthcare, conspiracy beliefs, and views about state response are predicting frequency of mask wearing when accounting for primary factors.