CV19 Ageism study all cleaning and results
A Thorpe
01/03/2021
1 Data Quality Checks
1.1 Exclusions (Qualtrics filters)
(For the tables, the column ‘n’ refers to the number of participants and the percentage is next to this.)
Below are some of the tags that Qualtrics put on certain participants for us to check. We can see here that a total of 2747 people accessed the survey. For the manuscript we will report 2344 (explained below).
2046 did not get assigned any tag. 400 did not consent, 155 who responded either that they will not provide their best answers or could not promise to, 16 were under the age of 18, 130 were white (this actually is for when we asked them to get us 200 more white participants. They used this to filter out 130 participants who did not indicate that they were white. I won’t include this in the manuscript as is technically a quota filter implemented by Qualtrics. We do mention the quota filters in the methods section).
| term | n | percent |
|---|---|---|
| 2046 | 74.5% | |
| No_consent | 400 | 14.6% |
| not_best_answers | 155 | 5.6% |
| underage | 16 | 0.6% |
| white | 130 | 4.7% |
Qualtrics also included a variable “gc” for us to filter participants:
- Those that don’t have a gc value (""), are either responses that were gathered previously, or responses in progress that never finished the survey on their own (n=343)
- Those with a gc value equal to 1 designate the Good Completes. (n=1703)
- Those with a gc value equal to 2 are Screen Out responses. (n=546)
- Those with a gc value equal to 3 are Over Quota responses which are complete responses that came in after the Total sample size quota was filled. (n=0)
- Those with a gc value equal to 4 are those that have failed a quality check. (n=155)
I will double check on “speeders” (anyone who finished in less than 6 minutes) as it appears did we not have any.
| gc | n |
|---|---|
| 343 | |
| 1 | 1703 |
| 2 | 546 |
| 4 | 155 |
We will filter out all except the good completes so our final sample is 1703.
#df %>% filter(gc == "4") %>% group_by(gc, term) %>% tally() %>% dplyr::mutate(percent = scales::percent(n/sum(n), accuracy = 0.1)) %>% arrange(desc(n)) %>% print(n = Inf)
df <- df %>%filter(gc == "1")1.2 Timing
1.2.1 Overall
Median completion timing for the study is 15 minutes and 1 second.
## [1] "15M 1S"Mean completion timing for current study is 20 minutes and 15 seconds
## [1] "20M 15.4680000000001S"1.2.2 Messages
Summary results of total time taken on the CDC message:
## vars n mean sd median trimmed mad min max range skew
## X1 1 546 57.69 137.5 20.36 35.27 23.62 1.91 2349.12 2347.21 10.74
## kurtosis se
## X1 155.57 5.88Written in time format as the mean,
## [1] "57.69S"standard deviation,
## [1] "2M 17.5S"and median
## [1] "20.36S"Summary results of total time taken on the tailored message
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 575 76.01 96.78 51.45 58.44 48.7 4.58 979.64 975.06 4.67 30.69
## se
## X1 4.04Written in time format as the mean,
## [1] "1M 16.01S"standard deviation,
## [1] "1M 36.78S"and median
## [1] "51.45S"1.3 Group and quota checks
1.3.1 Groups
Study groups assignment to the three groups (control;CDC;KS) looks good with around 33% in each.
| Group | n | percent |
|---|---|---|
| Control | 582 | 34.2% |
| CDC | 546 | 32.1% |
| KS | 575 | 33.8% |
Framing groups assignment to the two question framings (out of 100…how many will die vs how many will recover) again looks good as exactly 50/50.
| Frame | n | percent |
|---|---|---|
| How many die framing | 852 | 50.0% |
| How many recover framing | 851 | 50.0% |
1.3.2 Quotas
Age quota looks good. We asked for 30.5%, 34.4%, and 35.2%.
| Age_group | n | percent |
|---|---|---|
| 18 to 34 | 511 | 30.0% |
| 35 to 54 | 571 | 33.5% |
| 55 and older | 593 | 34.8% |
| NA | 28 | 1.6% |
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1675 45.12 17.09 42 44.61 20.76 18 93 75 0.23 -1 0.42Gender quota looks fine too. Close enough to 50/50.
| GenderCHR | n | percent |
|---|---|---|
| Female | 820 | 48.2% |
| Male or Other Gender | 883 | 51.8% |
Full breakdown here.
## # A tibble: 7 x 3
## Gender n percent
## <chr> <int> <chr>
## 1 Female 820 48.2%
## 2 Male 841 49.4%
## 3 Non-binary / Third gender 14 0.8%
## 4 Prefer not to say 6 0.4%
## 5 Prefer to self-describe 10 0.6%
## 6 Transgender man / Transman 9 0.5%
## 7 Transgender woman / Transwoman 3 0.2%Income quota looks good and close to the 40%, 33% and 27% that we asked for.
| Income_group | n | percent |
|---|---|---|
| $0 - $49k | 861 | 50.6% |
| $50K to $99K | 569 | 33.4% |
| $100K and more | 271 | 15.9% |
| NA | 2 | 0.1% |
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1701 5.96 3.71 5 5.91 4.45 1 12 11 0.11 -1.46 0.09Race/Ethnicity quota it is a bit hard to tell right now. I have spoken to Qualtrics to try and get more information.
We asked for:
- Non-Hispanic White 62.3%
- Non-Hispanic Black 12.4%
- Hispanic 17.3%
- Asian/Other Race 8%
Here are the full results from the Race question that we put in (this was a multiple choice question and participants could mark as many as they wished) combined with our question about whether they are Hispanic or Non-Hispanic. This is what I used to work out the figures that are in the manuscript.
| HispCHR | RaceCHR | n | percent |
|---|---|---|---|
| Non-hispanic | 2 | 0.1% | |
| Non-hispanic | American Indian or Alaskan Native | 28 | 1.9% |
| Non-hispanic | American Indian or Alaskan Native & Asian or Asian American | 1 | 0.1% |
| Non-hispanic | American Indian or Alaskan Native & Asian or Asian American & Black or African American | 1 | 0.1% |
| Non-hispanic | American Indian or Alaskan Native & Asian or Asian American & White or European American | 2 | 0.1% |
| Non-hispanic | American Indian or Alaskan Native & Black or African American | 7 | 0.5% |
| Non-hispanic | American Indian or Alaskan Native & Black or African American & White or European American | 1 | 0.1% |
| Non-hispanic | American Indian or Alaskan Native & Other | 1 | 0.1% |
| Non-hispanic | American Indian or Alaskan Native & White or European American | 10 | 0.7% |
| Non-hispanic | Asian or Asian American | 59 | 4.1% |
| Non-hispanic | Asian or Asian American & Black or African American | 2 | 0.1% |
| Non-hispanic | Asian or Asian American & Native Hawaiian or other Pacific Islander | 1 | 0.1% |
| Non-hispanic | Asian or Asian American & White or European American | 3 | 0.2% |
| Non-hispanic | Black or African American | 314 | 21.8% |
| Non-hispanic | Black or African American & Other | 2 | 0.1% |
| Non-hispanic | Black or African American & White or European American | 5 | 0.3% |
| Non-hispanic | Native Hawaiian or other Pacific Islander | 4 | 0.3% |
| Non-hispanic | Other | 37 | 2.6% |
| Non-hispanic | White or European American | 959 | 66.6% |
| Non-hispanic | White or European American & Other | 2 | 0.1% |
| Hispanic | 2 | 0.8% | |
| Hispanic | American Indian or Alaskan Native | 10 | 3.9% |
| Hispanic | American Indian or Alaskan Native & Black or African American | 1 | 0.4% |
| Hispanic | American Indian or Alaskan Native & Other | 1 | 0.4% |
| Hispanic | American Indian or Alaskan Native & White or European American | 3 | 1.2% |
| Hispanic | Asian or Asian American | 17 | 6.7% |
| Hispanic | Asian or Asian American & White or European American | 1 | 0.4% |
| Hispanic | Black or African American | 39 | 15.3% |
| Hispanic | Black or African American & Native Hawaiian or other Pacific Islander | 1 | 0.4% |
| Hispanic | Black or African American & White or European American | 2 | 0.8% |
| Hispanic | Native Hawaiian or other Pacific Islander | 3 | 1.2% |
| Hispanic | Other | 73 | 28.6% |
| Hispanic | White or European American | 101 | 39.6% |
| Hispanic | White or European American & Other | 1 | 0.4% |
| American Indian or Alaskan Native | 1 | 14.3% | |
| American Indian or Alaskan Native & Black or African American | 1 | 14.3% | |
| Asian or Asian American | 1 | 14.3% | |
| Black or African American | 2 | 28.6% | |
| White or European American | 2 | 28.6% |
| HispCHR | RaceCHR | count | freq |
|---|---|---|---|
| Non-hispanic | 2 | 2 (0.1%) | |
| Non-hispanic | American Indian or Alaskan Native | 28 | 28 (1.6%) |
| Non-hispanic | American Indian or Alaskan Native & Asian or Asian American | 1 | 1 (0.1%) |
| Non-hispanic | American Indian or Alaskan Native & Asian or Asian American & Black or African American | 1 | 1 (0.1%) |
| Non-hispanic | American Indian or Alaskan Native & Asian or Asian American & White or European American | 2 | 2 (0.1%) |
| Non-hispanic | American Indian or Alaskan Native & Black or African American | 7 | 7 (0.4%) |
| Non-hispanic | American Indian or Alaskan Native & Black or African American & White or European American | 1 | 1 (0.1%) |
| Non-hispanic | American Indian or Alaskan Native & Other | 1 | 1 (0.1%) |
| Non-hispanic | American Indian or Alaskan Native & White or European American | 10 | 10 (0.6%) |
| Non-hispanic | Asian or Asian American | 59 | 59 (3.5%) |
| Non-hispanic | Asian or Asian American & Black or African American | 2 | 2 (0.1%) |
| Non-hispanic | Asian or Asian American & Native Hawaiian or other Pacific Islander | 1 | 1 (0.1%) |
| Non-hispanic | Asian or Asian American & White or European American | 3 | 3 (0.2%) |
| Non-hispanic | Black or African American | 314 | 314 (18.4%) |
| Non-hispanic | Black or African American & Other | 2 | 2 (0.1%) |
| Non-hispanic | Black or African American & White or European American | 5 | 5 (0.3%) |
| Non-hispanic | Native Hawaiian or other Pacific Islander | 4 | 4 (0.2%) |
| Non-hispanic | Other | 37 | 37 (2.2%) |
| Non-hispanic | White or European American | 959 | 959 (56.3%) |
| Non-hispanic | White or European American & Other | 2 | 2 (0.1%) |
| Hispanic | 2 | 2 (0.1%) | |
| Hispanic | American Indian or Alaskan Native | 10 | 10 (0.6%) |
| Hispanic | American Indian or Alaskan Native & Black or African American | 1 | 1 (0.1%) |
| Hispanic | American Indian or Alaskan Native & Other | 1 | 1 (0.1%) |
| Hispanic | American Indian or Alaskan Native & White or European American | 3 | 3 (0.2%) |
| Hispanic | Asian or Asian American | 17 | 17 (1.0%) |
| Hispanic | Asian or Asian American & White or European American | 1 | 1 (0.1%) |
| Hispanic | Black or African American | 39 | 39 (2.3%) |
| Hispanic | Black or African American & Native Hawaiian or other Pacific Islander | 1 | 1 (0.1%) |
| Hispanic | Black or African American & White or European American | 2 | 2 (0.1%) |
| Hispanic | Native Hawaiian or other Pacific Islander | 3 | 3 (0.2%) |
| Hispanic | Other | 73 | 73 (4.3%) |
| Hispanic | White or European American | 101 | 101 (5.9%) |
| Hispanic | White or European American & Other | 1 | 1 (0.1%) |
| American Indian or Alaskan Native | 1 | 1 (0.1%) | |
| American Indian or Alaskan Native & Black or African American | 1 | 1 (0.1%) | |
| Asian or Asian American | 1 | 1 (0.1%) | |
| Black or African American | 2 | 2 (0.1%) | |
| White or European American | 2 | 2 (0.1%) |
2 Participant Descriptives
2.1 Region
Q1. What state do you live in?
Response scale: List of all states (re-coded by region)
We did not set a quota for regions, but it is interesting to see how much these differ based on census data. I have worked out some rough estimates of how off we are below (seems mainly over-represented in NE and under-represented in MW).
- Midwest: (-6.3%)
- Northeast: (+7.7%)
- South: (-2.5%)
- West: (-0.9%)
| region | n | percent |
|---|---|---|
| south | 593 | 34.8% |
| northeast | 432 | 25.4% |
| west | 396 | 23.3% |
| midwest | 258 | 15.1% |
| 24 | 1.4% |
- Q2. How would you best describe the place where you live?
| RuralSub | n | percent |
|---|---|---|
| Suburban near large city | 574 | 33.7% |
| large city more than 1million | 355 | 20.8% |
| Rural | 305 | 17.9% |
| Small (less than 100,000) | 232 | 13.6% |
| Mid sized city (100,000 to 1million) | 221 | 13.0% |
| Other | 13 | 0.8% |
| 3 | 0.2% |
2.2 CV19: Health
- Q1. Have you been diagnosed with COVID-19?
| HadCov1 | n | percent |
|---|---|---|
| No, I Haven’t had COVID-19 | 1393 | 81.8% |
| Yes, I currently have COVID-19 | 176 | 10.3% |
| Yes, I had COVID-19 and I recovered | 134 | 7.9% |
- Q2. Do you have a pre-existing health condition (respiratory illness, cancer, heart disease, high blood pressure, diabetes etc.) that makes you more vulnerable to COVID-19?
| CoVatRisk | n | percent |
|---|---|---|
| Yes | 817 | 48.0% |
| No | 786 | 46.2% |
| Unsure | 99 | 5.8% |
| 1 | 0.1% |
2.3 CV19: Exposure
- Q1. Has anyone you care about been diagnosed with COVID-19? select all that apply?
| KnowCov1 | n | percent |
|---|---|---|
| No | 803 | 47.2% |
| Yes immediate family | 232 | 13.6% |
| yes, friend | 217 | 12.7% |
| yes other family | 184 | 10.8% |
| yes, co-worker | 39 | 2.3% |
| yes, other family & friend | 39 | 2.3% |
| No & yes, friend | 29 | 1.7% |
| No & yes, other family | 25 | 1.5% |
| yes, friend & co-worker | 24 | 1.4% |
| Yes, immediate family & friend | 23 | 1.4% |
| Yes, immediate family & other family | 12 | 0.7% |
| Yes, immediate family & other family & friend | 12 | 0.7% |
| yes, other family & friend & co-worker | 11 | 0.6% |
| No & yes, co-worker | 9 | 0.5% |
| Yes, immediate family & friend & co-worker | 9 | 0.5% |
| Yes, immediate family & other family & friend & co-worker | 8 | 0.5% |
| No & Yes, immediate family | 7 | 0.4% |
| yes, other family & co-worker | 7 | 0.4% |
| Yes, immediate family & other family & co-worker | 3 | 0.2% |
| No & yes, friend & co-worker | 2 | 0.1% |
| Yes, immediate family & co-worker | 2 | 0.1% |
| 1 | 0.1% | |
| No & Yes immediate family & co-worker | 1 | 0.1% |
| No & Yes immediate family & other family | 1 | 0.1% |
| No & Yes immediate family & other family & friend & co-worker | 1 | 0.1% |
| No & yes other family & co-worker | 1 | 0.1% |
| No & yes other family & friend | 1 | 0.1% |
- Q2. To the best of your knowledge, are there currently any people diagnosed with COVID-19 in your city or town?
| KnowCov2 | n | percent |
|---|---|---|
| Yes | 1206 | 70.8% |
| No | 312 | 18.3% |
| Don’t know | 185 | 10.9% |
2.4 Medical work
- Q1. Do you work in a medical field?
| MedWork | n | percent |
|---|---|---|
| No | 1545 | 90.7% |
| Yes | 136 | 8.0% |
| 22 | 1.3% |
For those who said they work in a medical field (n=129) we asked them to specify their area of work. Only 68 gave written answer. The responses are below in a table with each row and each column representing a different response.
| Home health care | Healthy |
| Hospitality | Adverting and marketing research |
| Good | Doctors |
| Home health care | yes |
| Nurse | Foi o que |
| Doctor | No |
| Home healthcare | Call cenrer |
| Dentist | Modesto |
| Cardiology | Pharmacy |
| Y | Clinical tech |
| Nursing | Healthcare |
| Clinical lab | CPR |
| RN | LPN |
| Administration | Cna |
| nurse | Nurse |
| Pediatrics | Retired LPN |
| Sleep apnea | coloda |
| Nursing home | Audiologist |
| Youth care worker just got laid off | Nurse |
| Home health | Care giver of elderly |
| 1 | ok |
| doctors | like it |
| good | urban |
| Good. | Critical care and Rapid Response Team |
| bvhvhvh | Endocrine |
| Alsome | mental health |
| Medical | xdcgb |
| good | gftrf |
| it departmant | pharmacy |
| I don’t but my mom does, she’s a nurse for premature babies | Social Work in a Nursing Home |
| Nursing | Customer service |
| Hospital in the ER | Business office manager of skilled nursing facility |
| Emergency medical services | 200 |
| 2 | Medical assistant |
| Pharmacy | 1 |
| CNA | Cna |
| Healthcare | Retired RN |
| Med surg | Home health care |
2.5 Politics
2.5.1 Affiliation
- Q1. Which political party are you affiliated with?
| PolParty | n | percent |
|---|---|---|
| 3 | 0.2% | |
| Conservative third party | 15 | 0.9% |
| Democrat | 832 | 48.9% |
| Independent | 308 | 18.1% |
| Liberal third party | 27 | 1.6% |
| No political affiliation | 112 | 6.6% |
| Republican | 406 | 23.8% |
2.5.2 CV19: Response
We asked: “Indicate whether you approve or disapprove of each individual or group’s handling of the COVID-19 pandemic. There are no right or wrong answers. Please answer in a way that reflects your own personal beliefs.”
- Q1. President Trump
- Q2. Centers for Disease Control and Prevention (CDC)
- Q3. U.S. Senate
- Q4. U.S. House of Representatives
- Q5. Your State Governor
Response scale: Strongly disapprove(1), Disapprove(2), Neither approve nor disapprove(3), Approve(4), Strongly approve(5).
Below are the descriptive responses for these items overall.
## vars n mean sd median trimmed mad min max range skew
## PolApproval_1 1 1696 2.66 1.61 2 2.58 1.48 1 5 4 0.28
## PolApproval_2 2 1700 3.52 1.18 4 3.61 1.48 1 5 4 -0.53
## PolApproval_3 3 1699 2.98 1.29 3 2.98 1.48 1 5 4 -0.04
## PolApproval_4 4 1700 3.09 1.26 3 3.11 1.48 1 5 4 -0.15
## PolApproval_5 5 1698 3.33 1.37 4 3.41 1.48 1 5 4 -0.42
## kurtosis se
## PolApproval_1 -1.55 0.04
## PolApproval_2 -0.58 0.03
## PolApproval_3 -1.01 0.03
## PolApproval_4 -0.97 0.03
## PolApproval_5 -1.05 0.03- Q1. In general, do you think the government is doing not enough or too much to address the spread of COVID-19?
Response scale (slider): Not enough (0) ——Just right (50) —— Too much (100).
Below are the descriptive responses for this item overall and then split by group.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1506 56.14 28.45 55 56.6 35.58 1 100 99 -0.1 -1.06 0.73##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 512 56.89 28.56 56 57.48 35.58 1 100 99 -0.11 -1.07 1.26
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 480 56.54 28.13 54 56.85 35.58 1 100 99 -0.07 -1.12 1.28
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 514 55.01 28.67 54 55.43 34.1 1 100 99 -0.12 -1.02 1.262.5.3 Outlook
Currently, I’m just looking at overall alignments, but we can also use these to subset.
- Q1. How would you describe your political outlook with regard to economic issues?
- Q2. How would you describe your political outlook with regard to social issues?
Response scale (slider): Very liberal(1), Liberal(2), Slightly liberal(3), Moderate(4), Slightly conservative(5), Conservative(6), Very conservative(7).
Below are the descriptive responses for these items overall.
## vars n mean sd median trimmed mad min max range skew kurtosis
## PolEcon 1 1694 4.17 1.93 4 4.21 2.97 1 7 6 -0.11 -1.00
## PolSoc 2 1698 4.10 1.86 4 4.13 2.97 1 7 6 -0.05 -0.96
## se
## PolEcon 0.05
## PolSoc 0.052.6 Religion
- Q1. How religious are you?
Response scale: Not at all religious(1), — (2), — (3), — (4), — (5), — (6), Very religious(7)
Descriptive statistics for this item overall shown below.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1703 4.78 2.08 5 4.97 2.97 1 7 6 -0.58 -0.94 0.05- Q2. How would you describe your religious views?
Response scale: Very traditional(1), — (2), — (3), — (4), — (5), — (6), Very Progressive(7), Not at all religious (8)
Around 12% of respondents said that they were not religious at all.
| Relig2 | n | percent |
|---|---|---|
| 1 | 216 | 12.7% |
| 2 | 130 | 7.6% |
| 3 | 156 | 9.2% |
| 4 | 217 | 12.7% |
| 5 | 211 | 12.4% |
| 6 | 171 | 10.0% |
| 7 | 385 | 22.6% |
| 8 | 217 | 12.7% |
Descriptive statistics for this item overall shown below. For this I excluded those who said they are not religious at all.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1486 4.43 2.12 5 4.54 2.97 1 7 6 -0.27 -1.25 0.062.7 Literacy
- 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).
Below are the descriptive statistics for the health literacy item overall and then split by group assignment.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1701 2.44 1.46 2 2.3 1.48 1 5 4 0.54 -1.09 0.04##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 580 2.47 1.47 2 2.33 1.48 1 5 4 0.49 -1.16 0.06
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 546 2.43 1.48 2 2.29 1.48 1 5 4 0.55 -1.12 0.06
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 575 2.41 1.44 2 2.27 1.48 1 5 4 0.57 -1.01 0.062.8 Numeracy
- 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).
Below are the descriptive statistics for the SNS items overall and then split by group assignment.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## Num1 1 1702 4.10 1.59 4 4.23 1.48 1 6 5 -0.46 -0.85 0.04
## Num2 2 1702 4.52 1.48 5 4.71 1.48 1 6 5 -0.78 -0.36 0.04
## Num3 3 1703 4.47 1.38 5 4.62 1.48 1 6 5 -0.64 -0.37 0.03##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## Num1 1 582 4.09 1.57 4 4.22 1.48 1 6 5 -0.43 -0.90 0.07
## Num2 2 582 4.49 1.49 5 4.67 1.48 1 6 5 -0.72 -0.49 0.06
## Num3 3 582 4.50 1.34 5 4.62 1.48 1 6 5 -0.54 -0.61 0.06
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## Num1 1 546 4.08 1.60 4 4.23 1.48 1 6 5 -0.47 -0.86 0.07
## Num2 2 545 4.46 1.50 5 4.65 1.48 1 6 5 -0.76 -0.41 0.06
## Num3 3 546 4.45 1.38 5 4.61 1.48 1 6 5 -0.67 -0.31 0.06
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## Num1 1 574 4.11 1.59 4 4.25 1.48 1 6 5 -0.47 -0.79 0.07
## Num2 2 575 4.61 1.45 5 4.82 1.48 1 6 5 -0.86 -0.17 0.06
## Num3 3 575 4.46 1.41 5 4.63 1.48 1 6 5 -0.71 -0.26 0.06The reliability looks good. Cronbach’s alpha is .84.
##
## Reliability analysis
## Call: psych::alpha(x = df[140:142])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.84 0.85 0.79 0.65 5.5 0.0065 4.4 1.3 0.65
##
## lower alpha upper 95% confidence boundaries
## 0.83 0.84 0.86
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## Num1 0.78 0.79 0.65 0.65 3.7 0.010 NA 0.65
## Num2 0.78 0.78 0.64 0.64 3.6 0.011 NA 0.64
## Num3 0.79 0.79 0.66 0.66 3.8 0.010 NA 0.66
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## Num1 1702 0.88 0.87 0.78 0.71 4.1 1.6
## Num2 1702 0.88 0.88 0.78 0.72 4.5 1.5
## Num3 1703 0.86 0.87 0.77 0.71 4.5 1.4
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 miss
## Num1 0.09 0.08 0.17 0.20 0.21 0.25 0
## Num2 0.05 0.06 0.13 0.18 0.23 0.35 0
## Num3 0.03 0.06 0.15 0.23 0.24 0.30 0Below are the descriptive statistics for the SNS overall and then split by group assignment.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1703 4.36 1.3 4.67 4.47 1.48 1 6 5 -0.54 -0.55 0.03##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 582 4.36 1.28 4.33 4.45 1.48 1 6 5 -0.39 -0.8 0.05
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 546 4.33 1.3 4.67 4.44 1.48 1 6 5 -0.6 -0.43 0.06
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 575 4.39 1.31 4.67 4.52 1.48 1 6 5 -0.62 -0.44 0.052.9 MinMax
Below are the descriptive statistics for the MinMax facts scale overall and then split by group assignment.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1703 3.92 1.61 4 4.03 1.48 1 6 5 -0.34 -0.95 0.04##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 582 3.88 1.61 4 3.97 1.48 1 6 5 -0.32 -1 0.07
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 546 3.94 1.6 4 4.05 1.48 1 6 5 -0.35 -0.92 0.07
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 575 3.95 1.61 4 4.07 1.48 1 6 5 -0.35 -0.95 0.073 Outcome Measures (Descriptives and reliability)
3.1 CV19-OAS
Items for the COVID-19 and older adults scale (CV19-OAS)
- Q1. Only older adults really need to worry about getting COVID-19.
- Q2. All adults over the age of 50 are at high risk from COVID-19.
- Q3. People of all ages can be infected by COVID-19. (R) Denotes reverse coded question
- Q4. Only older adults need to follow COVID-19 health guidelines (e.g., distancing and mask wearing).
- Q5. A teenager dying from COVID-19 is more tragic than an older person dying from COVID-19.
Response scale: Strongly disagree (1), Disagree(2), Agree(3), Strongly agree(4)
Descriptive statistics for these items shown below.
## vars n mean sd median trimmed mad min max range skew
## Ageism_CVBelief_1 1 1702 2.17 1.15 2 2.09 1.48 1 4 3 0.44
## Ageism_CVBelief_2 2 1701 3.04 0.90 3 3.15 1.48 1 4 3 -0.72
## Ageism_CVBelief_3 3 1703 1.50 0.75 1 1.36 0.00 1 4 3 1.62
## Ageism_CVBelief_4 4 1702 2.02 1.15 2 1.90 1.48 1 4 3 0.61
## Ageism_CVBelief_5 5 1701 2.25 1.08 2 2.19 1.48 1 4 3 0.30
## kurtosis se
## Ageism_CVBelief_1 -1.27 0.03
## Ageism_CVBelief_2 -0.21 0.02
## Ageism_CVBelief_3 2.43 0.02
## Ageism_CVBelief_4 -1.15 0.03
## Ageism_CVBelief_5 -1.21 0.03The reliability of these items is not ideal, but okay I think. Cronbach’s Alpha is .63. The highest it can get is .71 by dropping the item “People of all ages can be infected by COVID-19”.
##
## Reliability analysis
## Call: psych::alpha(x = df[, c(14:18)])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.63 0.57 0.61 0.21 1.3 0.012 2.2 0.65 0.17
##
## lower alpha upper 95% confidence boundaries
## 0.6 0.63 0.65
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## Ageism_CVBelief_1 0.44 0.37 0.43 0.13 0.58 0.019 0.072
## Ageism_CVBelief_2 0.67 0.63 0.62 0.30 1.73 0.012 0.060
## Ageism_CVBelief_3 0.71 0.69 0.66 0.36 2.27 0.011 0.033
## Ageism_CVBelief_4 0.43 0.36 0.41 0.12 0.55 0.020 0.066
## Ageism_CVBelief_5 0.49 0.41 0.48 0.15 0.69 0.018 0.080
## med.r
## Ageism_CVBelief_1 0.15
## Ageism_CVBelief_2 0.30
## Ageism_CVBelief_3 0.36
## Ageism_CVBelief_4 0.13
## Ageism_CVBelief_5 0.15
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## Ageism_CVBelief_1 1702 0.80 0.78 0.730 0.5976 2.2 1.15
## Ageism_CVBelief_2 1701 0.42 0.43 0.202 0.1552 3.0 0.90
## Ageism_CVBelief_3 1703 0.24 0.31 0.032 0.0046 1.5 0.75
## Ageism_CVBelief_4 1702 0.81 0.79 0.763 0.6171 2.0 1.15
## Ageism_CVBelief_5 1701 0.75 0.74 0.655 0.5382 2.2 1.08
##
## Non missing response frequency for each item
## 1 2 3 4 miss
## Ageism_CVBelief_1 0.39 0.24 0.17 0.20 0
## Ageism_CVBelief_2 0.08 0.15 0.43 0.35 0
## Ageism_CVBelief_3 0.61 0.30 0.05 0.04 0
## Ageism_CVBelief_4 0.48 0.18 0.17 0.17 0
## Ageism_CVBelief_5 0.32 0.28 0.23 0.17 0Below are the descriptive statistics for the CV19-OAS scale overall.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1703 2.2 0.65 2.2 2.17 0.89 1 4 3 0.31 -0.82 0.023.2 CV19: Age CFR
Q6a. If 100 people between 50-64 years old got COVID-19, how many of them do you think would die from it?
Q7a. If 100 people between 65 years or older got COVID-19, how many of them do you think would die from it?
Q6b. If 100 people between 50-64 years old got COVID-19, how many of them do you think would recover from it? (R)
Q7b. If 100 people between 65 years or older got COVID-19, how many of them do you think would recover from it? (R)
Response scale: Slider from 0 to 100.
I believe this is essentially a proxy for what people think the age-related CFR is. Below are the initial descriptive results for each questions.
## vars n mean sd median trimmed mad min max range
## Ageism_CVBelief6a_1 1 839 44.71 31.46 44 43.24 41.51 1 100 99
## Ageism_CVBelief7a_1 2 843 53.83 30.70 55 54.10 40.03 1 100 99
## Ageism_CVBelief6b_1 3 848 65.04 25.05 67 66.37 29.65 3 100 97
## Ageism_CVBelief7b_1 4 844 57.61 28.35 59 58.33 35.58 1 100 99
## skew kurtosis se
## Ageism_CVBelief6a_1 0.24 -1.19 1.09
## Ageism_CVBelief7a_1 -0.07 -1.29 1.06
## Ageism_CVBelief6b_1 -0.33 -0.92 0.86
## Ageism_CVBelief7b_1 -0.16 -1.13 0.98I reversed the scale for the 6b and 7b questions so they represent number expected to die instead of recover. Now it is a bit easier for us to interpret the overall means.
## vars n mean sd median trimmed mad min max range
## Ageism_CVBelief6a_1 1 839 44.71 31.46 44 43.24 41.51 1 100 99
## Ageism_CVBelief7a_1 2 843 53.83 30.70 55 54.10 40.03 1 100 99
## Ageism_CVBelief6b_1 3 848 34.96 25.05 33 33.63 29.65 0 97 97
## Ageism_CVBelief7b_1 4 844 42.39 28.35 41 41.67 35.58 0 99 99
## skew kurtosis se
## Ageism_CVBelief6a_1 0.24 -1.19 1.09
## Ageism_CVBelief7a_1 -0.07 -1.29 1.06
## Ageism_CVBelief6b_1 0.33 -0.92 0.86
## Ageism_CVBelief7b_1 0.16 -1.13 0.983.3 CV19: Priority preferences
- Q1. For the sake of the economy we should all get back to work, even if it means that more older people will die from COVID-19.
Response scale: Strongly disagree(1), Disagree (2), Somewhat disagree(3), Somewhat agree(4), Agree(5), Strongly agree(6).
Below is the mean response for this preference item overall.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1673 3.16 1.83 3 3.08 2.97 1 6 5 0.23 -1.39 0.04- Q2: Using the scale below please indicate your preference between the two options
Response scale: Prefer to protect those at higher risk from COVID-19 (e.g., older adults) (1), — (2), — (3), — (4), — (5), Prefer to keep the economy going(6).
Below is the mean response for this preference overall.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1684 2.9 1.76 3 2.76 2.97 1 6 5 0.45 -1.1 0.04- Q3: Using the scale below please indicate your preference between the two options
Response scale: Prefer to protect those at higher risk from COVID-19 (e.g., older adults) (1), — (2), — (3), — (4), — (5), Prefer to get life ‘back to normal’(6).
Below is the mean response for this preference overall.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1668 2.88 1.71 3 2.73 2.97 1 6 5 0.45 -1.03 0.043.4 CV19: Willing (Stay home)
- Q1. I am willing to do another period of staying at home and avoiding contact with other people this winter, if the number of COVID-19 infections starts increasing again.
Response scale: Strongly disagree (1), Disagree(2), Somewhat disagree(3), Somewhat agree (4), Agree (5), Strongly agree (6)
Below is the mean response for this preference overall.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1702 4.79 1.41 5 5.04 1.48 1 6 5 -1.22 0.73 0.033.5 CV19: Willing (Guidelines)
- Q1. How willing or unwilling are you to follow COVID-19 guidelines (e.g., distancing and mask wearing)?
Response scale: Very unwilling (1), — (2), — (3), — (4), Very willing (5)
Below is the mean response for this preference overall.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1651 4.23 1.17 5 4.47 0 1 5 4 -1.41 0.92 0.033.6 CV19: Behavioral Intent_Risk
We asked: How frequently, if at all, do you plan to do the following things in the next month?
- 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
Below are the descriptive statistics for these items.
## vars n mean sd median trimmed mad min max range skew
## AdhereIntent_1 1 1702 2.66 1.73 2 2.45 1.48 1 6 5 0.69
## AdhereIntent_2 2 1700 3.23 1.57 3 3.17 1.48 1 6 5 0.10
## AdhereIntent_3 3 1697 2.60 1.73 2 2.38 1.48 1 6 5 0.69
## AdhereIntent_4 4 1697 2.87 1.66 3 2.73 1.48 1 6 5 0.44
## AdhereIntent_5 5 1699 2.84 1.74 2 2.68 1.48 1 6 5 0.48
## kurtosis se
## AdhereIntent_1 -0.83 0.04
## AdhereIntent_2 -1.07 0.04
## AdhereIntent_3 -0.88 0.04
## AdhereIntent_4 -1.05 0.04
## AdhereIntent_5 -1.11 0.04The reliability of these items is good. Cronbach’s Alpha is .83.
##
## Reliability analysis
## Call: psych::alpha(x = df[, c(27:31)])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.93 0.93 0.92 0.74 14 0.0025 2.8 1.5 0.75
##
## lower alpha upper 95% confidence boundaries
## 0.93 0.93 0.94
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## AdhereIntent_1 0.92 0.92 0.89 0.73 11 0.0033 0.00205 0.73
## AdhereIntent_2 0.93 0.93 0.91 0.77 13 0.0028 0.00031 0.76
## AdhereIntent_3 0.91 0.91 0.89 0.73 11 0.0034 0.00120 0.73
## AdhereIntent_4 0.92 0.92 0.89 0.73 11 0.0033 0.00177 0.74
## AdhereIntent_5 0.92 0.92 0.90 0.74 12 0.0031 0.00152 0.73
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## AdhereIntent_1 1702 0.90 0.90 0.87 0.84 2.7 1.7
## AdhereIntent_2 1700 0.85 0.85 0.80 0.77 3.2 1.6
## AdhereIntent_3 1697 0.91 0.91 0.88 0.85 2.6 1.7
## AdhereIntent_4 1697 0.90 0.90 0.87 0.84 2.9 1.7
## AdhereIntent_5 1699 0.89 0.89 0.85 0.82 2.8 1.7
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 miss
## AdhereIntent_1 0.39 0.15 0.17 0.11 0.07 0.11 0
## AdhereIntent_2 0.18 0.18 0.18 0.23 0.14 0.09 0
## AdhereIntent_3 0.42 0.15 0.13 0.13 0.08 0.10 0
## AdhereIntent_4 0.29 0.21 0.15 0.16 0.11 0.09 0
## AdhereIntent_5 0.34 0.16 0.14 0.14 0.10 0.11 0Below are the descriptive statistics for the risky behavioral intentions scale overall.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1702 2.84 1.5 2.6 2.71 1.78 1 6 5 0.57 -0.81 0.043.7 CV19: Behavioral Intent_Protect
We asked: How frequently, if at all, do you plan to do the following things in the next month?
- 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)
Below are the descriptive statistics for these items.
## vars n mean sd median trimmed mad min max range skew
## AdhereIntent_6 1 1697 5.05 1.34 6 5.32 0.00 1 6 5 -1.46
## AdhereIntent_7 2 1697 5.17 1.31 6 5.46 0.00 1 6 5 -1.64
## AdhereIntent_8 3 1699 4.67 1.50 5 4.91 1.48 1 6 5 -0.99
## kurtosis se
## AdhereIntent_6 1.25 0.03
## AdhereIntent_7 1.81 0.03
## AdhereIntent_8 -0.03 0.04The reliability of these items is good. Cronbach’s Alpha is .77.
##
## Reliability analysis
## Call: psych::alpha(x = df[, c(32:34)])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.77 0.78 0.72 0.54 3.5 0.0099 5 1.1 0.53
##
## lower alpha upper 95% confidence boundaries
## 0.75 0.77 0.79
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## AdhereIntent_6 0.69 0.69 0.53 0.53 2.3 0.0148 NA 0.53
## AdhereIntent_7 0.57 0.58 0.40 0.40 1.4 0.0205 NA 0.40
## AdhereIntent_8 0.80 0.80 0.67 0.67 4.1 0.0096 NA 0.67
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## AdhereIntent_6 1697 0.82 0.83 0.72 0.60 5.0 1.3
## AdhereIntent_7 1697 0.88 0.88 0.82 0.71 5.2 1.3
## AdhereIntent_8 1699 0.80 0.78 0.58 0.51 4.7 1.5
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 miss
## AdhereIntent_6 0.03 0.05 0.06 0.10 0.23 0.53 0
## AdhereIntent_7 0.03 0.04 0.06 0.10 0.16 0.62 0
## AdhereIntent_8 0.06 0.05 0.09 0.17 0.21 0.42 0Below are the descriptive statistics for the protective behavioral intentions scale overall.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1699 4.96 1.15 5.33 5.15 0.99 1 6 5 -1.26 1.07 0.033.8 CV19: Attitudes (Guidelines)
- Q1. The guidelines for slowing the spread of COVID-19 (e.g., distancing and mask wearing) are…
Response scale: Too restrictive(1), — (2), — (3), the right balance(4), — (5), —(6), Not restrictive enough(7)
Below are the descriptive statistics for this measure overall.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1690 4.21 1.78 4 4.26 1.48 1 7 6 -0.12 -0.47 0.04- Q2. Following COVID-19 guidelines (e.g., distancing and mask wearing) is an effective method for slowing the spread of COVID-19
Response scale: Strongly disagree(1), Disagree(2), Somewhat disagree(3), Somewhat agree(4), Agree(5), Strongly agree(6)
Below are the descriptive statistics for this measure overall.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1701 4.78 1.39 5 5.01 1.48 1 6 5 -1.12 0.48 0.03- Q3. Following COVID-19 guidelines (e.g., distancing and mask wearing) is an effective method to avoid getting COVID-19
Response scale: Strongly disagree(1), Disagree(2), Somewhat disagree(3), Somewhat agree(4), Agree(5), Strongly agree(6)
Below are the descriptive statistics for this measure overall.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1700 4.77 1.28 5 4.96 1.48 1 6 5 -1.07 0.6 0.03- Q4. Following COVID-19 guidelines (e.g., distancing and mask wearing) is currently the most effective method available for saving lives
Response scale: Strongly disagree(1), Disagree(2), Somewhat disagree(3), Somewhat agree(4), Agree(5), Strongly agree(6)
Below are the descriptive statistics for this measure overall.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1699 4.83 1.3 5 5.04 1.48 1 6 5 -1.15 0.8 0.033.9 CV19: Collective trust
(noted by Aaron that this may not be an accurate heading for these questions and we might describe it differently in any write ups)
- Q1. How willing or unwilling do you think other people are to follow COVID-19 guidelines (e.g., distancing and mask wearing)?
Response scale: Very unwilling(1), — (2), — (3), — (4), Very willing(5)
- Q2. Do you think that most people under the age of 50 are willing to follow COVID-19 guidelines (e.g., distancing and mask wearing) when in public to help protect older adults?
- Q3. Do you think that people under the age of 50 have a moral obligation to follow COVID-19 guidelines (e.g., distancing and mask wearing) when in public to help protect older adults?
Response scale: Strongly disagree(1), Disagree (2), Somewhat disagree(3), Somewhat agree(4), Agree(5), Strongly agree(6).
- Q4. In your opinion, to what extent can people in general be trusted to follow COVID-19 guidelines (e.g., distancing and mask wearing)?
Response scale: Slider from 0 to 100.
Below are the descriptive statistics for these items overall.
## vars n mean sd median trimmed mad min max range skew
## Coll_Trust1_1 1 1679 3.48 1.21 3 3.55 1.48 1 5 4 -0.26
## Coll_Trust2 2 1702 4.14 1.34 4 4.23 1.48 1 6 5 -0.47
## Coll_Trust3 3 1701 4.75 1.29 5 4.93 1.48 1 6 5 -1.04
## Coll_Trust4_1 4 1669 62.27 24.02 63 63.22 25.20 1 100 99 -0.31
## kurtosis se
## Coll_Trust1_1 -0.88 0.03
## Coll_Trust2 -0.39 0.03
## Coll_Trust3 0.57 0.03
## Coll_Trust4_1 -0.51 0.593.12 CV19: Societal risk
- Q1 How serious of a threat do you think COVID-19 is to the U.S.?
Response scale: Not a serious threat at all(1), — (2), — (3), — (4), — (5), — (6), A very serious threat(7).
Below are the descriptive statistics for this item overall.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1702 5.88 1.54 7 6.19 0 1 7 6 -1.37 1 0.04- Q2 Compared to now, do you think the COVID-19 pandemic in the U.S. will be better or worse in six months?
Response scale: Much worse(1), — (2), — (3), — (4), — (5), — (6), Much better(7).
Below are the descriptive statistics for this item overall.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1700 4.61 1.83 5 4.76 1.48 1 7 6 -0.44 -0.69 0.043.13 CV19: Individual risk
- Q1. How likely does it feel that you will get COVID-19 within the next month?
- Q2. How certain do you feel about your chances of getting COVID-19 in the next month?
- Q3. How worried are you that you will get COVID-19 in the next month?
- Q4. If you were to get COVID-19, how sick do you think you would feel?
- Q5. If you were to get COVID-19, how likely do you think it is that you would experience the most serious symptoms (e.g., severe shortness of breath, high fever)?
- Q6. If you were to get COVID-19, how worried are you that you would experience the most serious symptoms (e.g., severe shortness breath, high fever)?
- Q7. If you were to get COVID-19, how likely do you think it is that you would die?
- Q8. How likely do you think it is that you would get COVID-19 but not have any symptoms?
- Q9. How worried are you that you will spread COVID-19 if you were infected but didn’t have any symptoms?
Response scale: Very unlikely/Not certain at all/Not sick at all/Not worried at all(1), — (2), — (3), — (4), — (5), — (6), Very likely/ Very certain/ Very sick/Very worried(7).
Below are the descriptive statistics for these items overall.
## vars n mean sd median trimmed mad min max range skew kurtosis
## IndivRisk1 1 1392 3.15 1.91 3 2.96 2.97 1 7 6 0.55 -0.77
## IndivRisk2 2 1391 3.75 1.99 4 3.69 2.97 1 7 6 0.12 -1.16
## IndivRisk3 3 1388 4.00 2.10 4 4.00 2.97 1 7 6 0.02 -1.30
## IndivRisk4 4 1392 4.70 1.84 5 4.84 1.48 1 7 6 -0.41 -0.82
## IndivRisk5 5 1393 4.52 1.91 5 4.64 1.48 1 7 6 -0.32 -0.96
## IndivRisk6 6 1391 4.66 1.97 5 4.82 2.97 1 7 6 -0.42 -0.99
## IndivRisk7 7 1390 3.86 2.00 4 3.82 2.97 1 7 6 0.02 -1.19
## IndivRisk8 8 1391 4.01 1.87 4 4.02 1.48 1 7 6 -0.06 -0.98
## IndivRisk9 9 1392 4.66 1.98 5 4.83 2.97 1 7 6 -0.43 -0.98
## se
## IndivRisk1 0.05
## IndivRisk2 0.05
## IndivRisk3 0.06
## IndivRisk4 0.05
## IndivRisk5 0.05
## IndivRisk6 0.05
## IndivRisk7 0.05
## IndivRisk8 0.05
## IndivRisk9 0.05The reliability of these items is ideal for a scale. Cronbach’s Alpha is .86.
##
## Reliability analysis
## Call: psych::alpha(x = df[, c(56:64)])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.87 0.87 0.89 0.43 6.8 0.0047 4.1 1.4 0.42
##
## lower alpha upper 95% confidence boundaries
## 0.86 0.87 0.88
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## IndivRisk1 0.86 0.86 0.87 0.43 6.1 0.0051 0.036 0.46
## IndivRisk2 0.87 0.87 0.89 0.46 6.8 0.0046 0.032 0.48
## IndivRisk3 0.85 0.85 0.87 0.42 5.7 0.0055 0.036 0.36
## IndivRisk4 0.85 0.85 0.87 0.42 5.8 0.0054 0.025 0.38
## IndivRisk5 0.85 0.85 0.86 0.41 5.5 0.0056 0.023 0.36
## IndivRisk6 0.85 0.85 0.86 0.41 5.5 0.0056 0.023 0.38
## IndivRisk7 0.85 0.85 0.87 0.41 5.5 0.0057 0.030 0.36
## IndivRisk8 0.88 0.88 0.89 0.48 7.4 0.0044 0.025 0.48
## IndivRisk9 0.86 0.86 0.88 0.43 6.1 0.0052 0.036 0.38
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## IndivRisk1 1392 0.68 0.68 0.63 0.59 3.2 1.9
## IndivRisk2 1391 0.56 0.56 0.48 0.44 3.7 2.0
## IndivRisk3 1388 0.76 0.76 0.72 0.68 4.0 2.1
## IndivRisk4 1392 0.74 0.75 0.73 0.66 4.7 1.8
## IndivRisk5 1393 0.80 0.81 0.81 0.74 4.5 1.9
## IndivRisk6 1391 0.80 0.80 0.80 0.73 4.7 2.0
## IndivRisk7 1390 0.80 0.80 0.78 0.73 3.9 2.0
## IndivRisk8 1391 0.48 0.48 0.38 0.35 4.0 1.9
## IndivRisk9 1392 0.69 0.69 0.63 0.59 4.7 2.0
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## IndivRisk1 0.27 0.17 0.14 0.19 0.09 0.05 0.09 0.18
## IndivRisk2 0.19 0.13 0.12 0.20 0.13 0.10 0.12 0.18
## IndivRisk3 0.17 0.13 0.12 0.16 0.12 0.11 0.19 0.18
## IndivRisk4 0.07 0.08 0.10 0.19 0.18 0.15 0.23 0.18
## IndivRisk5 0.09 0.09 0.11 0.18 0.18 0.14 0.21 0.18
## IndivRisk6 0.10 0.08 0.11 0.15 0.16 0.16 0.25 0.18
## IndivRisk7 0.18 0.11 0.13 0.18 0.15 0.12 0.13 0.18
## IndivRisk8 0.14 0.10 0.12 0.24 0.16 0.12 0.12 0.18
## IndivRisk9 0.10 0.08 0.10 0.16 0.16 0.16 0.25 0.18Below are the descriptive statistics for the individual risk scale overall.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1393 4.15 1.37 4.11 4.16 1.32 1 7 6 -0.1 -0.34 0.043.14 Trust in the CDC
Q1. How confident are you that the CDC has been providing accurate information to the public about COVID-19? Q2. How confident are you that the CDC is responding effectively to protect the health of the public against COVID-19? *Q3. In general, how much do you trust the CDC?
Response scale: Not at all confident/Not at all(1), — (2), — (3), — (4), Very confident/Completely(5).
Below are the descriptive statistics for these items overall.
## vars n mean sd median trimmed mad min max range skew kurtosis
## CDCTrust1 1 1701 3.64 1.26 4 3.79 1.48 1 5 4 -0.66 -0.52
## CDCTrust2 2 1701 3.63 1.19 4 3.76 1.48 1 5 4 -0.66 -0.35
## CDCTrust3 3 1701 3.61 1.23 4 3.74 1.48 1 5 4 -0.62 -0.53
## se
## CDCTrust1 0.03
## CDCTrust2 0.03
## CDCTrust3 0.03The reliability of these items is ideal. Cronbach’s Alpha is .91.
##
## Reliability analysis
## Call: psych::alpha(x = df[, c(65:67)])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.92 0.92 0.88 0.79 11 0.0034 3.6 1.1 0.79
##
## lower alpha upper 95% confidence boundaries
## 0.91 0.92 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CDCTrust1 0.88 0.88 0.78 0.78 7.1 0.0060 NA 0.78
## CDCTrust2 0.89 0.89 0.80 0.80 7.9 0.0054 NA 0.80
## CDCTrust3 0.88 0.88 0.79 0.79 7.5 0.0057 NA 0.79
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CDCTrust1 1701 0.93 0.93 0.88 0.84 3.6 1.3
## CDCTrust2 1701 0.92 0.92 0.86 0.83 3.6 1.2
## CDCTrust3 1701 0.93 0.93 0.87 0.83 3.6 1.2
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## CDCTrust1 0.09 0.08 0.23 0.28 0.32 0
## CDCTrust2 0.08 0.08 0.25 0.32 0.28 0
## CDCTrust3 0.08 0.10 0.24 0.30 0.29 0Below are the descriptive statistics for the trust in CDC scale overall.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1702 3.63 1.14 4 3.75 1.48 1 5 4 -0.68 -0.3 0.033.15 Ageism facts
- Q1 The majority of old people (age 65+) are senile (have defective memory, or are disoriented or demented). Correct answer = False
- Q2 The five senses (sight, hearing, taste, touch and smell) tend to decline in old age. Correct answer = True
- Q3 The majority of old people feel miserable most of the time. Correct answer = False
- Q4 Physical strength tends to decline in old age. Correct answer = True
- Q5 At least one-tenth of the aged are living in long-stay institutions such as nursing homes. Correct answer = False
- Q6 Over three-fourths of the aged are healthy enough to carry out their normal activities without help. Correct answer = True
- Q7 Older workers usually cannot work as effectively as younger workers. Correct answer = False
- Q8 Older people usually take longer to learn something new. Correct answer = True
- Q9 The majority of old people are unable to adapt to change. Correct answer = False
- Q10 Old people tend to react more slowly than younger people. Correct answer = True
- Q11 Depression is more frequent among the elderly than among younger people. Correct answer = False
- Q12 In general, old people tend to be pretty much alike. Correct answer = False
Response scale: False(1), True (2).
I re-coded these variables to be 0 for incorrect and 1 for correct. So a mean of 1 for an item would indicate that everyone got the item correct.
Descriptive statistics for these items shown below.
## vars n mean sd median trimmed mad min max range skew
## Ageism_Facts1_1 1 1700 0.68 0.47 1 0.72 0 0 1 1 -0.76
## Ageism_Facts1_2 2 1699 0.76 0.43 1 0.83 0 0 1 1 -1.22
## Ageism_Facts1_3 3 1700 0.67 0.47 1 0.71 0 0 1 1 -0.71
## Ageism_Facts1_4 4 1698 0.82 0.39 1 0.89 0 0 1 1 -1.63
## Ageism_Facts1_5 5 1702 0.34 0.48 0 0.31 0 0 1 1 0.65
## Ageism_Facts1_6 6 1700 0.74 0.44 1 0.80 0 0 1 1 -1.09
## Ageism_Facts2_1 7 1695 0.51 0.50 1 0.52 0 0 1 1 -0.05
## Ageism_Facts2_2 8 1696 0.54 0.50 1 0.55 0 0 1 1 -0.16
## Ageism_Facts2_3 9 1694 0.53 0.50 1 0.54 0 0 1 1 -0.13
## Ageism_Facts2_4 10 1695 0.68 0.47 1 0.72 0 0 1 1 -0.76
## Ageism_Facts2_5 11 1694 0.52 0.50 1 0.53 0 0 1 1 -0.09
## Ageism_Facts2_6 12 1694 0.58 0.49 1 0.60 0 0 1 1 -0.33
## kurtosis se
## Ageism_Facts1_1 -1.42 0.01
## Ageism_Facts1_2 -0.51 0.01
## Ageism_Facts1_3 -1.50 0.01
## Ageism_Facts1_4 0.66 0.01
## Ageism_Facts1_5 -1.58 0.01
## Ageism_Facts1_6 -0.81 0.01
## Ageism_Facts2_1 -2.00 0.01
## Ageism_Facts2_2 -1.98 0.01
## Ageism_Facts2_3 -1.98 0.01
## Ageism_Facts2_4 -1.42 0.01
## Ageism_Facts2_5 -1.99 0.01
## Ageism_Facts2_6 -1.89 0.01The reliability of these items is awful. Cronbach’s Alpha is .18. The highest it can get is .26 by dropping the item “Over three-fourths of the aged are healthy enough to carry out their normal activities without help.”.
## Warning in psych::alpha(df[, c(77:88)]): 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 ( Ageism_Facts1_2 Ageism_Facts1_4 Ageism_Facts2_2 Ageism_Facts2_4 ) 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##
## Reliability analysis
## Call: psych::alpha(x = df[, c(77:88)])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.43 0.43 0.51 0.059 0.75 0.02 0.61 0.17 0.061
##
## lower alpha upper 95% confidence boundaries
## 0.39 0.43 0.47
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## Ageism_Facts1_1 0.31 0.32 0.41 0.040 0.46 0.024 0.032 0.036
## Ageism_Facts1_2 0.44 0.43 0.51 0.065 0.76 0.019 0.039 0.078
## Ageism_Facts1_3 0.31 0.32 0.41 0.040 0.46 0.024 0.032 0.020
## Ageism_Facts1_4 0.42 0.41 0.50 0.060 0.71 0.020 0.039 0.071
## Ageism_Facts1_5 0.45 0.45 0.53 0.070 0.83 0.019 0.038 0.062
## Ageism_Facts1_6 0.40 0.40 0.49 0.057 0.66 0.021 0.041 0.036
## Ageism_Facts2_1 0.36 0.37 0.46 0.051 0.59 0.022 0.032 0.036
## Ageism_Facts2_2 0.55 0.54 0.58 0.097 1.18 0.016 0.026 0.090
## Ageism_Facts2_3 0.35 0.35 0.45 0.048 0.55 0.023 0.033 0.036
## Ageism_Facts2_4 0.47 0.47 0.54 0.074 0.88 0.018 0.036 0.078
## Ageism_Facts2_5 0.39 0.40 0.49 0.056 0.66 0.021 0.036 0.062
## Ageism_Facts2_6 0.33 0.34 0.43 0.044 0.51 0.024 0.032 0.020
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## Ageism_Facts1_1 1700 0.61 0.60 0.625 0.434 0.68 0.47
## Ageism_Facts1_2 1699 0.26 0.29 0.130 0.053 0.76 0.43
## Ageism_Facts1_3 1700 0.60 0.59 0.616 0.426 0.67 0.47
## Ageism_Facts1_4 1698 0.30 0.35 0.213 0.116 0.82 0.39
## Ageism_Facts1_5 1702 0.24 0.23 0.031 0.015 0.34 0.48
## Ageism_Facts1_6 1700 0.37 0.39 0.258 0.169 0.74 0.44
## Ageism_Facts2_1 1695 0.49 0.47 0.417 0.277 0.51 0.50
## Ageism_Facts2_2 1696 -0.10 -0.10 -0.383 -0.325 0.54 0.50
## Ageism_Facts2_3 1694 0.53 0.51 0.469 0.321 0.53 0.50
## Ageism_Facts2_4 1695 0.16 0.17 -0.024 -0.064 0.68 0.47
## Ageism_Facts2_5 1694 0.42 0.40 0.284 0.196 0.52 0.50
## Ageism_Facts2_6 1694 0.56 0.55 0.534 0.368 0.58 0.49
##
## Non missing response frequency for each item
## 0 1 miss
## Ageism_Facts1_1 0.32 0.68 0.00
## Ageism_Facts1_2 0.24 0.76 0.00
## Ageism_Facts1_3 0.33 0.67 0.00
## Ageism_Facts1_4 0.18 0.82 0.00
## Ageism_Facts1_5 0.66 0.34 0.00
## Ageism_Facts1_6 0.26 0.74 0.00
## Ageism_Facts2_1 0.49 0.51 0.00
## Ageism_Facts2_2 0.46 0.54 0.00
## Ageism_Facts2_3 0.47 0.53 0.01
## Ageism_Facts2_4 0.32 0.68 0.00
## Ageism_Facts2_5 0.48 0.52 0.01
## Ageism_Facts2_6 0.42 0.58 0.01Below are the descriptive statistics for the ageism facts scale overall.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1703 0.61 0.17 0.58 0.62 0.25 0.08 1 0.92 -0.16 -0.5 0## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1703 0.39 0.17 0.42 0.38 0.25 0 0.92 0.92 0.16 -0.5 03.16 Ageism Fraboni
- Q1. Many old people are stingy and hoard their money and possessions.
- Q2. Many old people are not interested in making new friends, preferring instead the circle of friends they have had for years.
- Q3. Many old people just live in the past.
- Q4. Most old people should not be trusted to take care of infants.
- Q5. Many old people are happiest when they are with people their own age.
- Q6. Most old people would be considered to have poor personal hygiene.
- Q7. Most old people can be irritating because they tell the same stories over and over again.
- Q8. Old people complain more than other people do.
- Q9. I would prefer not to go to an open house at a senior’s club, if invited.
- Q10. Teenage suicide is more tragic than suicide among the old.
- Q11. I sometimes avoid eye contact with old people when I see them.
- Q12. I don’t like it when old people try to make conversation with me.
- Q13. Complex and interesting conversation cannot be expected from most old people.
- Q14. Feeling depressed when around old people is probably a common feeling.
- Q15. Old people should find friends their own age.
- Q16. Old people should feel welcome at the social gatherings of young people. (R)
- Q17. Old people don’t really need to use our community sports facilities.
- Q18. It is best that old people live where they won’t bother anyone.
- Q19. The company of most old people is quite enjoyable. (R)
- Q20. It is sad to hear about the plight of the old in our society these days. (R)
- Q21. Old people should be encouraged to speak out politically. (R)
- Q22. Most old people are interesting, individualistic people. (R)
- Q23. I personally would not want to spend much time with an old person.
Response scale: Strongly disagree(1), Disagree(2). Agree(3), Strongly agree(4).
Descriptive statistics for these items shown below.
## vars n mean sd median trimmed mad min max range skew
## Ageism_Fabroni1_1 1 1703 2.38 0.97 2 2.35 1.48 1 4 3 0.17
## Ageism_Fabroni1_2 2 1702 2.51 0.93 3 2.52 1.48 1 4 3 -0.04
## Ageism_Fabroni1_3 3 1701 2.44 0.95 2 2.43 1.48 1 4 3 0.12
## Ageism_Fabroni1_4 4 1702 2.28 0.99 2 2.23 1.48 1 4 3 0.28
## Ageism_Fabroni1_5 5 1703 2.70 0.86 3 2.73 1.48 1 4 3 -0.13
## Ageism_Fabroni1_6 6 1701 2.27 1.00 2 2.22 1.48 1 4 3 0.30
## Ageism_Fabroni2_1 7 1702 2.43 0.95 2 2.41 1.48 1 4 3 0.10
## Ageism_Fabroni2_2 8 1698 2.45 0.94 2 2.44 1.48 1 4 3 0.10
## Ageism_Fabroni2_3 9 1697 2.43 0.94 2 2.42 1.48 1 4 3 0.12
## Ageism_Fabroni2_4 10 1700 2.47 1.04 2 2.47 1.48 1 4 3 0.02
## Ageism_Fabroni2_5 11 1700 2.12 1.04 2 2.03 1.48 1 4 3 0.48
## Ageism_Fabroni2_6 12 1700 2.09 1.05 2 1.98 1.48 1 4 3 0.57
## Ageism_Fabroni3_1 13 1701 2.19 1.04 2 2.11 1.48 1 4 3 0.37
## Ageism_Fabroni3_2 14 1697 2.27 0.97 2 2.21 1.48 1 4 3 0.32
## Ageism_Fabroni3_3 15 1695 2.29 0.99 2 2.24 1.48 1 4 3 0.28
## Ageism_Fabroni3_4 16 1701 1.89 0.83 2 1.78 1.48 1 4 3 0.84
## Ageism_Fabroni3_5 17 1700 2.22 1.00 2 2.15 1.48 1 4 3 0.38
## Ageism_Fabroni3_6 18 1701 2.09 1.05 2 1.98 1.48 1 4 3 0.53
## Ageism_Fabroni4_1 19 1703 1.78 0.74 2 1.68 0.00 1 4 3 0.90
## Ageism_Fabroni4_2 20 1701 1.86 0.77 2 1.78 1.48 1 4 3 0.70
## Ageism_Fabroni4_3 21 1703 1.81 0.75 2 1.72 1.48 1 4 3 0.75
## Ageism_Fabroni4_4 22 1702 1.75 0.77 2 1.64 1.48 1 4 3 0.96
## Ageism_Fabroni4_5 23 1699 2.14 1.08 2 2.05 1.48 1 4 3 0.48
## kurtosis se
## Ageism_Fabroni1_1 -0.95 0.02
## Ageism_Fabroni1_2 -0.85 0.02
## Ageism_Fabroni1_3 -0.90 0.02
## Ageism_Fabroni1_4 -0.97 0.02
## Ageism_Fabroni1_5 -0.69 0.02
## Ageism_Fabroni1_6 -0.98 0.02
## Ageism_Fabroni2_1 -0.91 0.02
## Ageism_Fabroni2_2 -0.88 0.02
## Ageism_Fabroni2_3 -0.89 0.02
## Ageism_Fabroni2_4 -1.16 0.03
## Ageism_Fabroni2_5 -0.98 0.03
## Ageism_Fabroni2_6 -0.91 0.03
## Ageism_Fabroni3_1 -1.07 0.03
## Ageism_Fabroni3_2 -0.85 0.02
## Ageism_Fabroni3_3 -0.96 0.02
## Ageism_Fabroni3_4 0.31 0.02
## Ageism_Fabroni3_5 -0.92 0.02
## Ageism_Fabroni3_6 -0.96 0.03
## Ageism_Fabroni4_1 0.94 0.02
## Ageism_Fabroni4_2 0.22 0.02
## Ageism_Fabroni4_3 0.43 0.02
## Ageism_Fabroni4_4 0.78 0.02
## Ageism_Fabroni4_5 -1.05 0.03The reliability of these items is ideal Cronbach’s Alpha is .94.
## Warning in psych::alpha(df[, c(89:111)]): 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 ( Ageism_Fabroni3_4 Ageism_Fabroni4_1 Ageism_Fabroni4_2 Ageism_Fabroni4_3 ) 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##
## Reliability analysis
## Call: psych::alpha(x = df[, c(89:111)])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.94 0.93 0.95 0.36 13 0.002 2.2 0.61 0.5
##
## lower alpha upper 95% confidence boundaries
## 0.93 0.94 0.94
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## Ageism_Fabroni1_1 0.93 0.92 0.95 0.35 12 0.0021 0.089
## Ageism_Fabroni1_2 0.93 0.92 0.95 0.36 12 0.0021 0.091
## Ageism_Fabroni1_3 0.93 0.92 0.95 0.35 12 0.0021 0.089
## Ageism_Fabroni1_4 0.93 0.92 0.95 0.35 12 0.0021 0.089
## Ageism_Fabroni1_5 0.93 0.93 0.95 0.36 12 0.0020 0.091
## Ageism_Fabroni1_6 0.93 0.92 0.95 0.35 12 0.0022 0.088
## Ageism_Fabroni2_1 0.93 0.92 0.95 0.35 12 0.0021 0.088
## Ageism_Fabroni2_2 0.93 0.92 0.95 0.35 12 0.0021 0.089
## Ageism_Fabroni2_3 0.93 0.92 0.95 0.35 12 0.0021 0.090
## Ageism_Fabroni2_4 0.93 0.92 0.95 0.36 12 0.0021 0.092
## Ageism_Fabroni2_5 0.93 0.92 0.94 0.35 12 0.0022 0.088
## Ageism_Fabroni2_6 0.93 0.92 0.94 0.35 12 0.0022 0.089
## Ageism_Fabroni3_1 0.93 0.92 0.94 0.35 12 0.0022 0.088
## Ageism_Fabroni3_2 0.93 0.92 0.95 0.35 12 0.0022 0.088
## Ageism_Fabroni3_3 0.93 0.92 0.95 0.35 12 0.0021 0.089
## Ageism_Fabroni3_4 0.94 0.93 0.95 0.39 14 0.0018 0.081
## Ageism_Fabroni3_5 0.93 0.92 0.95 0.35 12 0.0022 0.089
## Ageism_Fabroni3_6 0.93 0.92 0.94 0.35 12 0.0022 0.088
## Ageism_Fabroni4_1 0.94 0.93 0.95 0.39 14 0.0018 0.083
## Ageism_Fabroni4_2 0.94 0.93 0.95 0.39 14 0.0018 0.083
## Ageism_Fabroni4_3 0.94 0.93 0.95 0.39 14 0.0019 0.085
## Ageism_Fabroni4_4 0.94 0.93 0.95 0.38 14 0.0019 0.087
## Ageism_Fabroni4_5 0.93 0.92 0.95 0.35 12 0.0021 0.090
## med.r
## Ageism_Fabroni1_1 0.49
## Ageism_Fabroni1_2 0.51
## Ageism_Fabroni1_3 0.49
## Ageism_Fabroni1_4 0.49
## Ageism_Fabroni1_5 0.52
## Ageism_Fabroni1_6 0.49
## Ageism_Fabroni2_1 0.49
## Ageism_Fabroni2_2 0.49
## Ageism_Fabroni2_3 0.50
## Ageism_Fabroni2_4 0.50
## Ageism_Fabroni2_5 0.49
## Ageism_Fabroni2_6 0.49
## Ageism_Fabroni3_1 0.49
## Ageism_Fabroni3_2 0.49
## Ageism_Fabroni3_3 0.49
## Ageism_Fabroni3_4 0.52
## Ageism_Fabroni3_5 0.49
## Ageism_Fabroni3_6 0.49
## Ageism_Fabroni4_1 0.52
## Ageism_Fabroni4_2 0.52
## Ageism_Fabroni4_3 0.52
## Ageism_Fabroni4_4 0.52
## Ageism_Fabroni4_5 0.49
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## Ageism_Fabroni1_1 1703 0.737 0.727 0.717 0.703 2.4 0.97
## Ageism_Fabroni1_2 1702 0.645 0.636 0.616 0.604 2.5 0.93
## Ageism_Fabroni1_3 1701 0.743 0.735 0.726 0.710 2.4 0.95
## Ageism_Fabroni1_4 1702 0.773 0.764 0.756 0.742 2.3 0.99
## Ageism_Fabroni1_5 1703 0.603 0.596 0.570 0.561 2.7 0.86
## Ageism_Fabroni1_6 1701 0.821 0.811 0.809 0.795 2.3 1.00
## Ageism_Fabroni2_1 1702 0.759 0.749 0.742 0.728 2.4 0.95
## Ageism_Fabroni2_2 1698 0.746 0.738 0.727 0.714 2.5 0.94
## Ageism_Fabroni2_3 1697 0.698 0.689 0.671 0.661 2.4 0.94
## Ageism_Fabroni2_4 1700 0.662 0.652 0.627 0.617 2.5 1.04
## Ageism_Fabroni2_5 1700 0.827 0.817 0.818 0.801 2.1 1.04
## Ageism_Fabroni2_6 1700 0.819 0.810 0.810 0.793 2.1 1.05
## Ageism_Fabroni3_1 1701 0.820 0.810 0.808 0.793 2.2 1.04
## Ageism_Fabroni3_2 1697 0.820 0.812 0.811 0.796 2.3 0.97
## Ageism_Fabroni3_3 1695 0.791 0.783 0.776 0.763 2.3 0.99
## Ageism_Fabroni3_4 1701 0.044 0.076 0.025 -0.016 1.9 0.83
## Ageism_Fabroni3_5 1700 0.802 0.794 0.789 0.775 2.2 1.00
## Ageism_Fabroni3_6 1701 0.844 0.835 0.837 0.821 2.1 1.05
## Ageism_Fabroni4_1 1703 0.093 0.134 0.095 0.041 1.8 0.74
## Ageism_Fabroni4_2 1701 0.094 0.131 0.086 0.039 1.9 0.77
## Ageism_Fabroni4_3 1703 0.146 0.187 0.148 0.094 1.8 0.75
## Ageism_Fabroni4_4 1702 0.203 0.242 0.206 0.150 1.8 0.77
## Ageism_Fabroni4_5 1699 0.787 0.777 0.771 0.756 2.1 1.08
##
## Non missing response frequency for each item
## 1 2 3 4 miss
## Ageism_Fabroni1_1 0.20 0.36 0.28 0.15 0
## Ageism_Fabroni1_2 0.15 0.33 0.36 0.15 0
## Ageism_Fabroni1_3 0.17 0.38 0.30 0.16 0
## Ageism_Fabroni1_4 0.25 0.36 0.25 0.14 0
## Ageism_Fabroni1_5 0.08 0.33 0.40 0.19 0
## Ageism_Fabroni1_6 0.26 0.36 0.24 0.15 0
## Ageism_Fabroni2_1 0.18 0.36 0.31 0.15 0
## Ageism_Fabroni2_2 0.16 0.37 0.31 0.15 0
## Ageism_Fabroni2_3 0.17 0.37 0.30 0.15 0
## Ageism_Fabroni2_4 0.22 0.29 0.30 0.20 0
## Ageism_Fabroni2_5 0.35 0.31 0.20 0.14 0
## Ageism_Fabroni2_6 0.37 0.32 0.17 0.14 0
## Ageism_Fabroni3_1 0.32 0.31 0.23 0.14 0
## Ageism_Fabroni3_2 0.23 0.39 0.24 0.13 0
## Ageism_Fabroni3_3 0.24 0.37 0.25 0.15 0
## Ageism_Fabroni3_4 0.35 0.47 0.12 0.06 0
## Ageism_Fabroni3_5 0.28 0.37 0.22 0.14 0
## Ageism_Fabroni3_6 0.38 0.30 0.19 0.13 0
## Ageism_Fabroni4_1 0.37 0.51 0.08 0.04 0
## Ageism_Fabroni4_2 0.34 0.49 0.13 0.04 0
## Ageism_Fabroni4_3 0.36 0.50 0.11 0.03 0
## Ageism_Fabroni4_4 0.41 0.46 0.09 0.04 0
## Ageism_Fabroni4_5 0.36 0.30 0.18 0.16 0Below are the descriptive statistics for the ageism Fraboni scale overall.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1703 2.21 0.61 2.17 2.21 0.71 1 3.57 2.57 0.06 -0.89 0.013.18 Self values
- Q1. How important is it to you to put your own needs before the needs of others?
- Q2. How important is it to you to do what you want, regardless of what other people might want?
Response scale: Not important at all(1), A little important(2), Somewhat important(3), Quite important(4), Extremely important(5).
Below are the descriptive statistics for these items overall.
## vars n mean sd median trimmed mad min max range skew
## Self_Values_1 1 1703 3.19 1.32 3 3.24 1.48 1 5 4 -0.14
## Self_Values_2 2 1702 2.99 1.40 3 2.99 1.48 1 5 4 -0.03
## kurtosis se
## Self_Values_1 -1.09 0.03
## Self_Values_2 -1.26 0.03##
## Reliability analysis
## Call: psych::alpha(x = df[, c(115:116)])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.77 0.77 0.63 0.63 3.4 0.011 3.1 1.2 0.63
##
## lower alpha upper 95% confidence boundaries
## 0.75 0.77 0.79
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## Self_Values_1 0.60 0.63 0.4 0.63 1.7 NA 0 0.63
## Self_Values_2 0.66 0.63 0.4 0.63 1.7 NA 0 0.63
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## Self_Values_1 1703 0.90 0.9 0.72 0.63 3.2 1.3
## Self_Values_2 1702 0.91 0.9 0.72 0.63 3.0 1.4
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## Self_Values_1 0.14 0.18 0.27 0.20 0.22 0
## Self_Values_2 0.21 0.18 0.22 0.21 0.18 0Below are the descriptive statistics for the self values scale overall.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1703 3.09 1.23 3 3.11 1.48 1 5 4 -0.05 -1.02 0.033.19 Death anxiety
- Q1. The thought of death seldom enters my mind. (R)
- Q2. I am very much afraid to die.
- Q3. I am often distressed by the way time flies so very rapidly.
- Q4. The subject of life after death troubles me a lot.
- Q5. I often think about how short life really is.
- Q6. I feel that the future holds nothing for me to fear. (R)
- Q7. I fear dying a painful death.
- Q8. I am preoccupied with the fear of having a serious illness.
- Q9. I see death as a passage to an eternal and blessed place. (R)
Response scale (slider): Strongly disagree(1), Disagree(2), Uncertain or Not applicable(3), Agree(4), Strongly agree(5).
Below are the descriptive statistics for these items overall.
## vars n mean sd median trimmed mad min max range skew
## D_Anxiety_1 1 1703 2.83 1.29 3 2.79 1.48 1 5 4 0.17
## D_Anxiety_2 2 1701 3.14 1.29 3 3.17 1.48 1 5 4 -0.20
## D_Anxiety_3 3 1702 3.41 1.21 4 3.50 1.48 1 5 4 -0.47
## D_Anxiety_4 4 1701 2.97 1.33 3 2.96 1.48 1 5 4 -0.01
## D_Anxiety_5 5 1698 3.62 1.14 4 3.74 1.48 1 5 4 -0.75
## D_Anxiety_6 6 1700 2.75 1.25 3 2.69 1.48 1 5 4 0.27
## D_Anxiety_7 7 1698 3.42 1.26 4 3.52 1.48 1 5 4 -0.47
## D_Anxiety_8 8 1699 2.95 1.32 3 2.94 1.48 1 5 4 0.03
## D_Anxiety_9 9 1700 2.36 1.16 2 2.24 1.48 1 5 4 0.62
## kurtosis se
## D_Anxiety_1 -1.11 0.03
## D_Anxiety_2 -1.09 0.03
## D_Anxiety_3 -0.73 0.03
## D_Anxiety_4 -1.16 0.03
## D_Anxiety_5 -0.17 0.03
## D_Anxiety_6 -0.93 0.03
## D_Anxiety_7 -0.80 0.03
## D_Anxiety_8 -1.17 0.03
## D_Anxiety_9 -0.31 0.03The reliability of these items is ideal. Cronbach’s Alpha is .84.
## Warning in psych::alpha(df[, c(128:136)]): 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 ( D_Anxiety_1 D_Anxiety_6 D_Anxiety_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##
## Reliability analysis
## Call: psych::alpha(x = df[, c(128:136)])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.56 0.55 0.69 0.12 1.2 0.015 3.1 0.59 0.095
##
## lower alpha upper 95% confidence boundaries
## 0.53 0.56 0.59
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## D_Anxiety_1 0.65 0.64 0.74 0.182 1.78 0.011 0.15 0.38
## D_Anxiety_2 0.42 0.42 0.59 0.082 0.71 0.021 0.13 -0.13
## D_Anxiety_3 0.44 0.43 0.61 0.085 0.75 0.020 0.14 -0.11
## D_Anxiety_4 0.42 0.41 0.58 0.080 0.70 0.021 0.13 -0.13
## D_Anxiety_5 0.49 0.48 0.64 0.103 0.91 0.018 0.15 -0.11
## D_Anxiety_6 0.67 0.66 0.75 0.194 1.92 0.011 0.14 0.38
## D_Anxiety_7 0.44 0.43 0.61 0.085 0.74 0.020 0.14 -0.11
## D_Anxiety_8 0.44 0.43 0.60 0.087 0.76 0.020 0.13 -0.11
## D_Anxiety_9 0.62 0.62 0.72 0.168 1.62 0.013 0.16 0.38
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## D_Anxiety_1 1703 0.034 0.039 -0.18 -0.21 2.8 1.3
## D_Anxiety_2 1701 0.720 0.710 0.72 0.56 3.1 1.3
## D_Anxiety_3 1702 0.685 0.687 0.67 0.53 3.4 1.2
## D_Anxiety_4 1701 0.731 0.720 0.74 0.57 3.0 1.3
## D_Anxiety_5 1698 0.561 0.572 0.51 0.39 3.6 1.1
## D_Anxiety_6 1700 -0.046 -0.039 -0.27 -0.27 2.8 1.2
## D_Anxiety_7 1698 0.692 0.688 0.66 0.53 3.4 1.3
## D_Anxiety_8 1699 0.689 0.677 0.68 0.52 3.0 1.3
## D_Anxiety_9 1700 0.119 0.133 -0.07 -0.10 2.4 1.2
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## D_Anxiety_1 0.17 0.29 0.20 0.22 0.12 0
## D_Anxiety_2 0.14 0.20 0.21 0.29 0.16 0
## D_Anxiety_3 0.09 0.15 0.21 0.35 0.19 0
## D_Anxiety_4 0.18 0.21 0.23 0.23 0.15 0
## D_Anxiety_5 0.07 0.11 0.19 0.42 0.22 0
## D_Anxiety_6 0.18 0.29 0.25 0.18 0.11 0
## D_Anxiety_7 0.10 0.14 0.22 0.32 0.22 0
## D_Anxiety_8 0.17 0.23 0.22 0.23 0.15 0
## D_Anxiety_9 0.27 0.31 0.27 0.08 0.07 0Below are the descriptive statistics for the death anxiety scale overall.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1703 3.05 0.59 3.11 3.08 0.49 1 5 4 -0.46 0.45 0.01- Q1. How worried are you about getting older?
- Q2. How worried are you about dying?
Response scale (slider): Not worried at all(1), — (2), — (3), — (4), Very worried(5).
Below are the descriptive statistics for these items overall.
## vars n mean sd median trimmed mad min max range skew kurtosis
## O_Worry 1 1703 3.13 1.42 3 3.16 1.48 1 5 4 -0.16 -1.23
## D_Worry 2 1701 3.06 1.41 3 3.08 1.48 1 5 4 -0.10 -1.25
## se
## O_Worry 0.03
## D_Worry 0.034 Research Q1
Is there a relationship between attitudes towards/knowledge about older people and willingness/intentions/attitudes regarding COVID-19 health guidelines and behaviors?
To answer this question, we analyzed results from respondents in the control condition. This was to ensure that we could estimate the relationships of interest as they were naturally distributed in the sample without concern about whether responses were influenced by the public health messages. The sample size for the control group (n=582) was sensitive enough to detect a conventionally small effect (f2=.02), for a linear multiple regression analysis (fixed model, single regression coefficient, 13 predictors), assuming α=.05 and 1-β=.95, two-tailed.
Outcome measures
CV19-OAS
- (e.g., “All adults over the age of 50 are at high risk from COVID-19.”). Higher scores correspond to more misconceptions
Fraboni ageism
- (e.g., “Many old people are stingy and hoard their money and possessions.”)). Higher scores correspond to more negative attitudes towards older adults
Facts of aging
- (e.g., “Physical strength tends to decline in old age.”). Higher scores correspond to better knowledge
and our measures related to willingness to follow guidelines, intentions to engage in COVID relevant behaviors, and attitudes towards guidelines (listed below).
Ageism_CVPriority1
- (“For the sake of the economy we should all get back to work, even if it means that more older people will die from COVID-19.”). Higher scores correspond to greater agreement
Ageism_CVPriority2_1
- (“Please indicate your preference between the two options.”). Lower scores correspond to preference to protect those at higher risk from COVID-19 (e.g., older adults). Higher scores correspond to preference to keep the economy going
Ageism_CVPriority2_1
- (“Please indicate your preference between the two options.”). Lower scores correspond to preference to protect those at higher risk from COVID-19 (e.g., older adults). Higher scores correspond to preference to get life back to normal
Adhere_willing
- (“I am willing to do another period of staying at home and avoiding contact with other people this winter, if the number of COVID-19 infections starts increasing again.”). Higher scores correspond to greater willingness
AdhereAttitude1_1
- (“How willing or unwilling are you to follow COVID-19 guidelines (e.g., distancing and mask wearing)?”)). Higher scores correspond to greater willingness
AdhereAttitude2
- (“The guidelines for slowing the spread of COVID-19 (e.g., distancing and mask wearing) are…”). Higher scores correspond to belief they are not restrictive enough; lower scores correspond to belief they are too restrictive; midpoint(4) is that they are just right
AdhereAttitude3_1
- (“Following COVID-19 guidelines (e.g., distancing and mask wearing) is an effective method for slowing the spread of COVID-19”). Higher scores correspond to greater agreement
AdhereAttitude3_2
- (“Following COVID-19 guidelines (e.g., distancing and mask wearing) is An effective method to avoid getting COVID-19”). Higher scores correspond to greater agreement
AdhereAttitude3_2
- (“Following COVID-19 guidelines (e.g., distancing and mask wearing) is currently the most effective method available for saving lives”). Higher scores correspond to greater agreement
BehavIntRisk_Avg
- (e.g., “Going to gatherings of 10 or more people.”). Five items, higher scores correspond to plans to more frequently engage in these riskier behaviors in the next month
BehavIntPrtct_Avg
- (e.g., “Practicing good hygiene such as washing your hands, especially after touching frequently used items or surfaces.”). Three items, higher scores correspond to plans to more frequently engage in these protective behaviors in the next month
Prosocial_Avg
- (e.g., “Go shopping for an older person.”). Six items, higher scores correspond to plans to more frequently engage in these behaviors
IndivRisk_Avg
- (e.g., “How likely does it feel that you will get COVID-19 within the next month?”). Nine items, higher scores correspond to greater perceived likelihood and severity of getting COVID-19
4.1 Correlations
For a pure test we look at the results just in the control group alone. The three dependent variables are the CV19-OAS (attitudes and misconceptions towards older adults in the context of COVID-19), FSA (Fraboni Scale of Ageism), and FAQ (Facts on Aging quiz). Bonferroni correction applied to all correlations
| CV19-OAS: correlation estimate (r) | CV19-OAS: Bonferroni adjusted p | FSA: correlation estimate (r) | FSA: Bonferroni adjusted p | FAQ: correlation estimate (r) | FAQ: Bonferroni adjusted p | |
|---|---|---|---|---|---|---|
| Ageism_CVPriority1 | 0.51 | 0.00 | 0.54 | 0.00 | 0.32 | 0.00 |
| Ageism_CVPriority2_1 | 0.31 | 0.00 | 0.27 | 0.00 | 0.12 | 0.11 |
| Ageism_CVPriority2_2 | 0.29 | 0.00 | 0.28 | 0.00 | 0.13 | 0.03 |
| Adhere_willing | 0.10 | 0.29 | 0.10 | 0.26 | 0.02 | 1.00 |
| AdhereAttitude1_1 | -0.08 | 1.00 | -0.04 | 1.00 | -0.16 | 0.00 |
| AdhereAttitude2 | -0.28 | 0.00 | -0.27 | 0.00 | -0.22 | 0.00 |
| AdhereAttitude3_1 | 0.00 | 1.00 | -0.03 | 1.00 | -0.14 | 0.01 |
| AdhereAttitude3_2 | 0.05 | 1.00 | 0.09 | 0.79 | -0.05 | 1.00 |
| AdhereAttitude3_3 | 0.06 | 1.00 | 0.08 | 0.99 | -0.04 | 1.00 |
| BehavIntRisk_Avg | 0.58 | 0.00 | 0.60 | 0.00 | 0.42 | 0.00 |
| BehavIntPrtct_Avg | -0.09 | 0.68 | -0.12 | 0.09 | -0.22 | 0.00 |
| Prosocial_Avg | 0.11 | 0.22 | 0.07 | 1.00 | -0.05 | 1.00 |
| IndivRisk_Avg | 0.26 | 0.00 | 0.35 | 0.00 | 0.23 | 0.00 |
4.2 Regression
Again we look at the results just in the control group alone. The CV19-OAS (attitudes and misconceptions towards older adults in the context of COVID-19), FSA (Fraboni Scale of Ageism), and FAQ (Facts on Aging quiz), were each regressed on attitudes and intentions regarding infection control guidelines and COVID-19 risk perceptions.
| CV19-OAS | FSA | FAQ | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Beta (95% CI) | Statistic | p value | Beta (95% CI) | Statistic | p value | Beta (95% CI) | Statistic | p value |
| (Intercept) |
0.00 (-0.08 – 0.08) |
7.50 | <0.001 |
0.00 (-0.07 – 0.07) |
9.60 | <0.001 |
-0.00 (-0.08 – 0.08) |
7.36 | <0.001 |
| Priority: Back to work |
0.28 (0.17 – 0.39) |
4.94 | <0.001 |
0.29 (0.19 – 0.39) |
5.52 | <0.001 |
0.07 (-0.05 – 0.19) |
1.21 | 0.227 |
| Priority: Economy |
0.09 (-0.02 – 0.20) |
1.61 | 0.109 |
0.01 (-0.09 – 0.11) |
0.16 | 0.871 |
-0.06 (-0.17 – 0.06) |
-0.95 | 0.342 |
| Priority: Normal |
-0.02 (-0.13 – 0.09) |
-0.38 | 0.703 |
0.00 (-0.10 – 0.10) |
0.01 | 0.992 |
-0.01 (-0.13 – 0.11) |
-0.17 | 0.863 |
| Guidelines restrictive |
0.01 (-0.08 – 0.11) |
0.30 | 0.765 |
0.02 (-0.07 – 0.11) |
0.46 | 0.642 |
0.00 (-0.10 – 0.10) |
0.04 | 0.964 |
| Guidelines effective prevent getting CV19 |
-0.02 (-0.16 – 0.12) |
-0.26 | 0.792 |
0.12 (-0.02 – 0.25) |
1.73 | 0.083 |
0.08 (-0.07 – 0.23) |
1.05 | 0.296 |
| Guidelines effective save lives |
0.11 (-0.02 – 0.23) |
1.65 | 0.100 |
0.06 (-0.06 – 0.17) |
0.95 | 0.342 |
0.02 (-0.11 – 0.15) |
0.29 | 0.773 |
| Guidelines effective slow spread |
0.00 (-0.15 – 0.15) |
0.01 | 0.992 |
-0.16 (-0.30 – -0.02) |
-2.27 | 0.024 |
-0.10 (-0.26 – 0.07) |
-1.18 | 0.239 |
| Willingness to stay home again |
0.21 (0.10 – 0.32) |
3.73 | <0.001 |
0.20 (0.10 – 0.30) |
3.85 | <0.001 |
0.16 (0.04 – 0.28) |
2.65 | 0.008 |
| Willingness to follow guidelines |
-0.07 (-0.17 – 0.04) |
-1.30 | 0.194 |
0.05 (-0.04 – 0.15) |
1.05 | 0.294 |
-0.06 (-0.17 – 0.06) |
-0.98 | 0.328 |
| Risky Behavioral intent scale |
0.34 (0.23 – 0.44) |
6.55 | <0.001 |
0.39 (0.30 – 0.49) |
8.29 | <0.001 |
0.34 (0.24 – 0.45) |
6.29 | <0.001 |
| Protective Behavioral intent scale |
-0.14 (-0.24 – -0.04) |
-2.70 | 0.007 |
-0.19 (-0.29 – -0.10) |
-4.12 | <0.001 |
-0.24 (-0.35 – -0.13) |
-4.42 | <0.001 |
| Prosocial scale |
-0.05 (-0.14 – 0.04) |
-1.14 | 0.253 |
-0.09 (-0.17 – -0.01) |
-2.13 | 0.034 |
-0.10 (-0.19 – -0.01) |
-2.15 | 0.032 |
| Individual risk |
0.04 (-0.06 – 0.13) |
0.78 | 0.433 |
0.12 (0.03 – 0.21) |
2.75 | 0.006 |
0.12 (0.02 – 0.22) |
2.41 | 0.016 |
| Observations | 429 | 429 | 429 | ||||||
| R2 / R2 adjusted | 0.346 / 0.326 | 0.439 / 0.421 | 0.253 / 0.230 | ||||||
VIF scores are less than 3.8 for all models
## Ageism_CVPriority1 Ageism_CVPriority2_1 Ageism_CVPriority2_2
## 2.042947 1.973152 1.966206
## AdhereAttitude2 AdhereAttitude3_2 AdhereAttitude3_3
## 1.489788 3.256179 2.572886
## AdhereAttitude3_1 Adhere_willing AdhereAttitude1_1
## 3.823082 1.973162 1.768511
## BehavIntRisk_Avg BehavIntPrtct_Avg Prosocial_Avg
## 1.664469 1.649452 1.224190
## IndivRisk_Avg
## 1.4392534.2.1 Regression (Moderated by age)
What I have done here is run the same model, but added age (mean centered) and then also added all the interaction terms of the outcome measures with age.
| CV19-OAS | FSA | FAQ | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Beta (95% CI) | Statistic | std. Statistic | p | std. p | Beta (95% CI) | Statistic | std. Statistic | p | std. p | Beta (95% CI) | Statistic | std. Statistic | p | std. p |
| (Intercept) |
-0.03 (-0.12 – 0.05) |
78.81 | -0.79 | <0.001 | 0.430 |
-0.03 (-0.11 – 0.05) |
95.17 | -0.71 | <0.001 | 0.481 |
-0.00 (-0.09 – 0.09) |
49.63 | -0.03 | <0.001 | 0.978 |
| Priority: Back to work |
0.31 (0.19 – 0.42) |
5.21 | 5.27 | <0.001 | <0.001 |
0.28 (0.18 – 0.39) |
5.30 | 5.23 | <0.001 | <0.001 |
0.09 (-0.03 – 0.21) |
1.52 | 1.53 | 0.130 | 0.127 |
| Priority: Economy |
0.10 (-0.01 – 0.21) |
1.76 | 1.74 | 0.079 | 0.083 |
0.04 (-0.07 – 0.14) |
0.55 | 0.66 | 0.581 | 0.508 |
-0.04 (-0.15 – 0.08) |
-0.69 | -0.65 | 0.492 | 0.517 |
| Priority: Normal |
-0.01 (-0.12 – 0.10) |
-0.10 | -0.17 | 0.922 | 0.869 |
-0.01 (-0.11 – 0.10) |
-0.05 | -0.10 | 0.960 | 0.917 |
-0.00 (-0.12 – 0.12) |
0.09 | -0.02 | 0.929 | 0.982 |
| Guidelines restrictive |
-0.01 (-0.11 – 0.08) |
-0.39 | -0.29 | 0.695 | 0.771 |
0.00 (-0.09 – 0.09) |
0.08 | 0.01 | 0.939 | 0.995 |
-0.01 (-0.11 – 0.10) |
-0.12 | -0.10 | 0.901 | 0.920 |
| Guidelines effective prevent getting CV19 |
-0.03 (-0.18 – 0.12) |
-0.38 | -0.38 | 0.704 | 0.706 |
0.09 (-0.05 – 0.23) |
1.24 | 1.25 | 0.217 | 0.211 |
0.03 (-0.12 – 0.18) |
0.47 | 0.39 | 0.639 | 0.697 |
| Guidelines effective save lives |
0.14 (0.01 – 0.28) |
2.08 | 2.07 | 0.038 | 0.039 |
0.07 (-0.06 – 0.20) |
1.12 | 1.07 | 0.264 | 0.284 |
0.08 (-0.07 – 0.22) |
1.07 | 1.05 | 0.287 | 0.294 |
| Guidelines effective slow spread |
-0.04 (-0.21 – 0.12) |
-0.40 | -0.52 | 0.693 | 0.605 |
-0.16 (-0.31 – 0.00) |
-1.99 | -1.96 | 0.047 | 0.050 |
-0.08 (-0.25 – 0.09) |
-0.91 | -0.93 | 0.365 | 0.352 |
| Willingness to stay home again |
0.21 (0.09 – 0.32) |
3.63 | 3.58 | <0.001 | <0.001 |
0.22 (0.11 – 0.33) |
4.00 | 4.00 | <0.001 | <0.001 |
0.15 (0.03 – 0.27) |
2.52 | 2.53 | 0.012 | 0.012 |
| Willingness to follow guidelines |
-0.06 (-0.16 – 0.05) |
-1.04 | -1.07 | 0.298 | 0.286 |
0.07 (-0.03 – 0.17) |
1.33 | 1.31 | 0.184 | 0.191 |
-0.05 (-0.16 – 0.06) |
-0.88 | -0.87 | 0.381 | 0.385 |
| Risky Behavioral intent scale |
0.31 (0.20 – 0.41) |
5.76 | 5.61 | <0.001 | <0.001 |
0.33 (0.23 – 0.43) |
6.64 | 6.45 | <0.001 | <0.001 |
0.24 (0.13 – 0.35) |
4.29 | 4.25 | <0.001 | <0.001 |
| Protective Behavioral intent scale |
-0.07 (-0.19 – 0.04) |
-1.55 | -1.30 | 0.123 | 0.194 |
-0.16 (-0.27 – -0.06) |
-3.23 | -3.09 | 0.001 | 0.002 |
-0.18 (-0.29 – -0.06) |
-3.15 | -3.03 | 0.002 | 0.003 |
| Prosocial scale |
-0.04 (-0.13 – 0.05) |
-0.93 | -0.93 | 0.352 | 0.352 |
-0.09 (-0.17 – -0.00) |
-2.05 | -2.02 | 0.041 | 0.045 |
-0.11 (-0.21 – -0.02) |
-2.47 | -2.42 | 0.014 | 0.016 |
| Individual risk |
0.02 (-0.07 – 0.12) |
0.37 | 0.46 | 0.714 | 0.645 |
0.12 (0.03 – 0.21) |
2.67 | 2.63 | 0.008 | 0.009 |
0.12 (0.02 – 0.22) |
2.50 | 2.45 | 0.013 | 0.015 |
| Age |
-0.09 (-0.18 – -0.00) |
-1.88 | -1.99 | 0.061 | 0.047 |
-0.16 (-0.24 – -0.07) |
-3.40 | -3.56 | 0.001 | <0.001 |
-0.31 (-0.40 – -0.21) |
-5.64 | -6.41 | <0.001 | <0.001 |
| AgeXPriority: Back to work |
0.10 (-0.03 – 0.23) |
1.54 | 1.54 | 0.125 | 0.125 |
-0.02 (-0.14 – 0.10) |
-0.33 | -0.33 | 0.741 | 0.741 |
0.02 (-0.11 – 0.16) |
0.37 | 0.37 | 0.715 | 0.715 |
| AgeXPriority: Economy |
-0.01 (-0.13 – 0.12) |
-0.12 | -0.12 | 0.904 | 0.904 |
0.10 (-0.02 – 0.22) |
1.71 | 1.71 | 0.088 | 0.088 |
0.03 (-0.10 – 0.16) |
0.47 | 0.47 | 0.640 | 0.640 |
| AgeXPriority: Normal |
-0.07 (-0.19 – 0.06) |
-1.05 | -1.05 | 0.296 | 0.296 |
-0.05 (-0.17 – 0.07) |
-0.83 | -0.83 | 0.407 | 0.407 |
-0.11 (-0.24 – 0.02) |
-1.69 | -1.69 | 0.092 | 0.092 |
| AgeXGuidelines restrictive |
0.08 (-0.03 – 0.19) |
1.47 | 1.47 | 0.144 | 0.144 |
-0.05 (-0.16 – 0.05) |
-1.04 | -1.04 | 0.297 | 0.297 |
0.02 (-0.09 – 0.13) |
0.36 | 0.36 | 0.721 | 0.721 |
| AgeXGuidelines effective prevent getting CV19 |
0.00 (-0.15 – 0.15) |
0.01 | 0.01 | 0.995 | 0.995 |
0.03 (-0.11 – 0.17) |
0.41 | 0.41 | 0.681 | 0.681 |
-0.10 (-0.25 – 0.06) |
-1.22 | -1.22 | 0.225 | 0.225 |
| AgeXGuidelines effective save lives |
0.03 (-0.10 – 0.17) |
0.47 | 0.47 | 0.640 | 0.640 |
-0.03 (-0.15 – 0.10) |
-0.41 | -0.41 | 0.681 | 0.681 |
0.01 (-0.13 – 0.14) |
0.09 | 0.09 | 0.927 | 0.927 |
| AgeXGuidelines effective slow spread |
-0.18 (-0.35 – -0.01) |
-2.09 | -2.09 | 0.037 | 0.037 |
-0.01 (-0.18 – 0.15) |
-0.18 | -0.18 | 0.855 | 0.855 |
-0.06 (-0.24 – 0.11) |
-0.72 | -0.72 | 0.474 | 0.474 |
| AgeXWillingness to stay home again |
0.00 (-0.13 – 0.13) |
0.01 | 0.01 | 0.993 | 0.993 |
0.04 (-0.08 – 0.16) |
0.60 | 0.60 | 0.549 | 0.549 |
0.04 (-0.09 – 0.18) |
0.67 | 0.67 | 0.506 | 0.506 |
| AgeXWillingness to follow guidelines |
-0.02 (-0.12 – 0.09) |
-0.30 | -0.30 | 0.762 | 0.762 |
-0.03 (-0.13 – 0.07) |
-0.52 | -0.52 | 0.604 | 0.604 |
0.01 (-0.10 – 0.12) |
0.23 | 0.23 | 0.819 | 0.819 |
| AgeXRisky Behavioral intent scale |
-0.08 (-0.20 – 0.05) |
-1.14 | -1.14 | 0.254 | 0.254 |
-0.09 (-0.21 – 0.03) |
-1.51 | -1.51 | 0.133 | 0.133 |
0.01 (-0.12 – 0.14) |
0.13 | 0.13 | 0.897 | 0.897 |
| AgeXProtective Behavioral intent scale |
0.19 (0.05 – 0.32) |
2.77 | 2.77 | 0.006 | 0.006 |
0.05 (-0.08 – 0.17) |
0.72 | 0.72 | 0.475 | 0.475 |
0.04 (-0.10 – 0.17) |
0.51 | 0.51 | 0.608 | 0.608 |
| AgeXProsocial scale |
0.01 (-0.08 – 0.09) |
0.18 | 0.18 | 0.855 | 0.855 |
0.04 (-0.04 – 0.12) |
0.97 | 0.97 | 0.335 | 0.335 |
0.06 (-0.03 – 0.15) |
1.41 | 1.41 | 0.160 | 0.160 |
| AgeXIndividual risk |
0.08 (-0.02 – 0.17) |
1.50 | 1.50 | 0.135 | 0.135 |
-0.03 (-0.13 – 0.06) |
-0.73 | -0.73 | 0.467 | 0.467 |
-0.04 (-0.14 – 0.06) |
-0.73 | -0.73 | 0.466 | 0.466 |
| Observations | 424 | 424 | 424 | ||||||||||||
| R2 / R2 adjusted | 0.389 / 0.347 | 0.464 / 0.428 | 0.350 / 0.306 | ||||||||||||
5 Research Q2
Do the public health messages (CDC & ours) influence willingness/intentions/attitudes regarding COVID-19 health guidelines?
To answer this question, we ran omnibus one-way ANOVA models to test the effect of group assignment (control; CDC message; tailored message) on our outcome measures and also analyzed whether age moderated the effect of the messages. For each model, we conducted two orthogonal planned contrasts, with Bonferroni correction (αadj=.025), to compare both public health message groups to the control group (contrast 1: control vs CDC and our message) and to compare the two message groups (contrast 2: CDC vs our message).
## CvsCD_KS CDCvsKS
## Control -2 0
## CDC 1 -1
## KS 1 1We ran follow up independent samples t-tests when the planned contrasts revealed a significant effect and applied Bonferroni correction for multiple comparisons. The final sample (n=1,703) was sufficient to detect a conventionally small effect size (f=.10), for one-way ANOVA (fixed effects, omnibus, 3 groups), with 1-β=0.95 and two-sided α=.025.
5.1 Priority
5.1.1 Back to work
Omnibus ANOVA test: CV19 priority preference (get to work even if means more older people will die)
| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| Group | 2 | 9.122 | 4.561 | 1.359 | 0.257 |
| Residuals | 1670 | 5604.625 | 3.356 |
Effect sizes:
| term | df | sumsq | meansq | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group | Group | 2 | 9.122 | 4.561 | 1.359 | 0.257 | 0.002 | 0.002 | 0 | 0 | 0 | 0.04 | 0.294 |
| …2 | Residuals | 1670 | 5604.625 | 3.356 |
Contrasts:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 3.159 | 0.045 | 70.509 | 0.000 |
| GroupCvsCD_KS | -0.052 | 0.031 | -1.644 | 0.100 |
| GroupCDCvsKS | -0.006 | 0.055 | -0.108 | 0.914 |
| Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
|---|---|---|---|
| 1673 | 1.832 | 0.002 | 0 |
Planned contrast effect sizes
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c1 | Group.c1 | 9.083 | 9.083 | 1 | 2.708 | 0.1 | 0.002 | 0.002 | 0.001 | 0.001 | 0.001 | 0.04 | 0.377 |
| …2 | Residuals | 5604.665 | 3.354 | 1671 |
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c2 | Group.c2 | 0.039 | 0.039 | 1 | 0.012 | 0.914 | 0 | 0 | -0.001 | -0.001 | -0.001 | 0.003 | 0.051 |
| …2 | Residuals | 3701.279 | 3.377 | 1096 |
Contrast v2
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 9 4.561 1.359 0.257
## Group: Control vs. CDC_KS 1 9 9.083 2.706 0.100
## Group: CDC vs KS 1 0 0.039 0.012 0.914
## Residuals 1670 5605 3.356
## 30 observations deleted due to missingnesslsmeans contrast (unadjusted)
| contrast | estimate | SE | df | lower.CL | upper.CL | t.ratio | p.value |
|---|---|---|---|---|---|---|---|
| Control vs CDC and KS | -0.310 | 0.189 | 1670 | -0.680 | 0.060 | -1.644 | 0.100 |
| CDC vs KS | -0.012 | 0.111 | 1670 | -0.229 | 0.205 | -0.108 | 0.914 |
Show least squares means and CIs around means
| Group | lsmean | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Control | 3.263 | 0.076 | 1670 | 3.113 | 3.412 |
| CDC | 3.114 | 0.079 | 1670 | 2.959 | 3.269 |
| KS | 3.102 | 0.077 | 1670 | 2.950 | 3.253 |
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 575 3.26 1.82 3 3.2 2.97 1 6 5 0.14 -1.43 0.08
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 537 3.11 1.82 3 3.02 2.97 1 6 5 0.27 -1.36 0.08
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 561 3.1 1.85 3 3 2.97 1 6 5 0.3 -1.36 0.08
Here is the moderated model. I just included age (mean centered) as an interaction term. Basically, "lm(DV ~ Group*AgeR, df)"
##
## Call:
## lm(formula = Ageism_CVPriority1 ~ Group * AgeR, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6792 -1.7271 -0.2377 1.6376 3.4155
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.1559024 0.0447532 70.518 < 2e-16 ***
## GroupCvsCD_KS -0.0521692 0.0314222 -1.660 0.0971 .
## GroupCDCvsKS 0.0053884 0.0551950 0.098 0.9222
## AgeR -0.0164966 0.0026176 -6.302 0.000000000376 ***
## GroupCvsCD_KS:AgeR -0.0002279 0.0018219 -0.125 0.9005
## GroupCDCvsKS:AgeR -0.0035093 0.0032552 -1.078 0.2812
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.814 on 1640 degrees of freedom
## (57 observations deleted due to missingness)
## Multiple R-squared: 0.02645, Adjusted R-squared: 0.02348
## F-statistic: 8.911 on 5 and 1640 DF, p-value: 0.00000002315## 2.5 % 97.5 %
## (Intercept) 3.068122920 3.243681808
## GroupCvsCD_KS -0.113801015 0.009462690
## GroupCDCvsKS -0.102871651 0.113648432
## AgeR -0.021630715 -0.011362491
## GroupCvsCD_KS:AgeR -0.003801428 0.003345679
## GroupCDCvsKS:AgeR -0.009894115 0.0028756085.1.2 Economy
Omnibus ANOVA test: CV19 priority preference (get to work even if means more older people will die)
| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| Group | 2 | 28.97 | 14.485 | 4.702 | 0.009 |
| Residuals | 1681 | 5178.64 | 3.081 |
Effect sizes:
| term | df | sumsq | meansq | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group | Group | 2 | 28.97 | 14.485 | 4.702 | 0.009 | 0.006 | 0.006 | 0.004 | 0.004 | 0.004 | 0.075 | 0.79 |
| …2 | Residuals | 1681 | 5178.64 | 3.081 |
Contrasts:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 2.903 | 0.043 | 67.857 | 0.000 |
| GroupCvsCD_KS | -0.089 | 0.030 | -2.974 | 0.003 |
| GroupCDCvsKS | -0.038 | 0.053 | -0.713 | 0.476 |
| Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
|---|---|---|---|
| 1684 | 1.755 | 0.006 | 0.004 |
Planned contrast effect sizes
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c1 | Group.c1 | 27.4 | 27.40 | 1 | 8.898 | 0.003 | 0.005 | 0.005 | 0.005 | 0.005 | 0.005 | 0.073 | 0.847 |
| …2 | Residuals | 5180.2 | 3.08 | 1682 |
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c2 | Group.c2 | 1.565 | 1.565 | 1 | 0.516 | 0.473 | 0 | 0 | 0 | 0 | 0 | 0.022 | 0.111 |
| …2 | Residuals | 3367.493 | 3.037 | 1109 |
Contrast v2
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 29 14.485 4.702 0.0092 **
## Group: Control vs. CDC_KS 1 27 27.404 8.895 0.0029 **
## Group: CDC vs KS 1 2 1.565 0.508 0.4761
## Residuals 1681 5179 3.081
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 19 observations deleted due to missingnesslsmeans contrast (unadjusted)
| contrast | estimate | SE | df | lower.CL | upper.CL | t.ratio | p.value |
|---|---|---|---|---|---|---|---|
| Control vs CDC and KS | -0.537 | 0.181 | 1681 | -0.891 | -0.183 | -2.974 | 0.003 |
| CDC vs KS | -0.075 | 0.105 | 1681 | -0.282 | 0.132 | -0.713 | 0.476 |
Show least squares means and CIs around means
| Group | lsmean | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Control | 3.082 | 0.073 | 1681 | 2.938 | 3.226 |
| CDC | 2.851 | 0.075 | 1681 | 2.704 | 2.999 |
| KS | 2.776 | 0.074 | 1681 | 2.631 | 2.921 |
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 573 3.08 1.78 3 2.98 2.97 1 6 5 0.33 -1.18 0.07
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 544 2.85 1.76 3 2.69 2.97 1 6 5 0.48 -1.1 0.08
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 567 2.78 1.72 3 2.6 2.97 1 6 5 0.52 -0.99 0.07
Exploratory: Age moderation
##
## Call:
## lm(formula = Ageism_CVPriority2_1 ~ Group * AgeR, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2065 -1.7232 -0.1141 1.2092 3.4214
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.8936106 0.0432264 66.941 < 2e-16 ***
## GroupCvsCD_KS -0.0848113 0.0304309 -2.787 0.00538 **
## GroupCDCvsKS -0.0275402 0.0531736 -0.518 0.60458
## AgeR -0.0063828 0.0025330 -2.520 0.01183 *
## GroupCvsCD_KS:AgeR -0.0005494 0.0017726 -0.310 0.75666
## GroupCDCvsKS:AgeR 0.0006742 0.0031340 0.215 0.82969
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.757 on 1650 degrees of freedom
## (47 observations deleted due to missingness)
## Multiple R-squared: 0.008968, Adjusted R-squared: 0.005965
## F-statistic: 2.986 on 5 and 1650 DF, p-value: 0.0109## 2.5 % 97.5 %
## (Intercept) 2.808826219 2.978394892
## GroupCvsCD_KS -0.144498678 -0.025124000
## GroupCDCvsKS -0.131835086 0.076754617
## AgeR -0.011350973 -0.001414561
## GroupCvsCD_KS:AgeR -0.004026103 0.002927393
## GroupCDCvsKS:AgeR -0.005472792 0.0068212535.1.3 Normal
Omnibus ANOVA test: CV19 priority preference (get to work even if means more older people will die)
| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| Group | 2 | 32.83 | 16.41 | 5.66 | 0.004 |
| Residuals | 1665 | 4828.90 | 2.90 |
Effect sizes:
| term | df | sumsq | meansq | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group | Group | 2 | 32.83 | 16.41 | 5.66 | 0.004 | 0.007 | 0.007 | 0.006 | 0.006 | 0.006 | 0.082 | 0.863 |
| …2 | Residuals | 1665 | 4828.90 | 2.90 |
Contrasts:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 2.879 | 0.042 | 69.027 | 0.000 |
| GroupCvsCD_KS | -0.098 | 0.029 | -3.348 | 0.001 |
| GroupCDCvsKS | 0.019 | 0.051 | 0.369 | 0.712 |
| Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
|---|---|---|---|
| 1668 | 1.703 | 0.007 | 0.006 |
Planned contrast effect sizes
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c1 | Group.c1 | 32.44 | 32.436 | 1 | 11.19 | 0.001 | 0.007 | 0.007 | 0.006 | 0.006 | 0.006 | 0.082 | 0.917 |
| …2 | Residuals | 4829.30 | 2.899 | 1666 |
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c2 | Group.c2 | 0.395 | 0.395 | 1 | 0.14 | 0.709 | 0 | 0 | -0.001 | -0.001 | -0.001 | 0.011 | 0.066 |
| …2 | Residuals | 3093.146 | 2.822 | 1096 |
Contrast v2
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 33 16.42 5.660 0.003550 **
## Group: Control vs. CDC_KS 1 32 32.44 11.184 0.000843 ***
## Group: CDC vs KS 1 0 0.39 0.136 0.712309
## Residuals 1665 4829 2.90
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 35 observations deleted due to missingnesslsmeans contrast (unadjusted)
| contrast | estimate | SE | df | lower.CL | upper.CL | t.ratio | p.value |
|---|---|---|---|---|---|---|---|
| Control vs CDC and KS | -0.589 | 0.176 | 1665 | -0.934 | -0.244 | -3.348 | 0.001 |
| CDC vs KS | 0.038 | 0.103 | 1665 | -0.164 | 0.240 | 0.369 | 0.712 |
Show least squares means and CIs around means
| Group | lsmean | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Control | 3.075 | 0.071 | 1665 | 2.936 | 3.215 |
| CDC | 2.762 | 0.073 | 1665 | 2.618 | 2.906 |
| KS | 2.800 | 0.072 | 1665 | 2.659 | 2.941 |
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 570 3.08 1.75 3 2.97 2.97 1 6 5 0.3 -1.18 0.07
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 538 2.76 1.69 3 2.58 2.97 1 6 5 0.56 -0.92 0.07
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 560 2.8 1.67 3 2.63 2.97 1 6 5 0.49 -0.94 0.07
Exploratory: Age moderation
##
## Call:
## lm(formula = Ageism_CVPriority2_2 ~ Group * AgeR, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2069 -1.6814 -0.0693 1.1687 3.5166
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.8774608 0.0421183 68.319 < 2e-16 ***
## GroupCvsCD_KS -0.0956208 0.0296114 -3.229 0.00127 **
## GroupCDCvsKS 0.0262946 0.0518782 0.507 0.61233
## AgeR -0.0063822 0.0024676 -2.586 0.00978 **
## GroupCvsCD_KS:AgeR -0.0006433 0.0017250 -0.373 0.70923
## GroupCDCvsKS:AgeR -0.0002102 0.0030562 -0.069 0.94517
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.704 on 1635 degrees of freedom
## (62 observations deleted due to missingness)
## Multiple R-squared: 0.0108, Adjusted R-squared: 0.007773
## F-statistic: 3.57 on 5 and 1635 DF, p-value: 0.003253## 2.5 % 97.5 %
## (Intercept) 2.794849277 2.960072317
## GroupCvsCD_KS -0.153701069 -0.037540512
## GroupCDCvsKS -0.075460189 0.128049354
## AgeR -0.011222136 -0.001542174
## GroupCvsCD_KS:AgeR -0.004026768 0.002740082
## GroupCDCvsKS:AgeR -0.006204665 0.0057842565.2 Guidelines
5.2.1 Too restrictive
Omnibus ANOVA test: The guidelines for slowing the spread of COVID-19 (e.g., distancing and mask wearing) are too restrictive; just right; not restrictive enough.
| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| Group | 2 | 6.476 | 3.238 | 1.02 | 0.361 |
| Residuals | 1687 | 5358.208 | 3.176 |
Effect sizes:
| term | df | sumsq | meansq | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group | Group | 2 | 6.476 | 3.238 | 1.02 | 0.361 | 0.001 | 0.001 | 0 | 0 | 0 | 0.035 | 0.229 |
| …2 | Residuals | 1687 | 5358.208 | 3.176 |
Contrasts:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 4.210 | 0.043 | 97.074 | 0.000 |
| GroupCvsCD_KS | 0.035 | 0.030 | 1.141 | 0.254 |
| GroupCDCvsKS | -0.047 | 0.053 | -0.876 | 0.381 |
| Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
|---|---|---|---|
| 1690 | 1.782 | 0.001 | 0 |
Planned contrast effect sizes
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c1 | Group.c1 | 4.04 | 4.040 | 1 | 1.272 | 0.26 | 0.001 | 0.001 | 0 | 0 | 0 | 0.027 | 0.204 |
| …2 | Residuals | 5360.64 | 3.176 | 1688 |
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c2 | Group.c2 | 2.437 | 2.437 | 1 | 0.788 | 0.375 | 0.001 | 0.001 | 0 | 0 | 0 | 0.027 | 0.144 |
| …2 | Residuals | 3434.578 | 3.091 | 1111 |
Contrast v2
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 6 3.238 1.020 0.361
## Group: Control vs. CDC_KS 1 4 4.040 1.272 0.260
## Group: CDC vs KS 1 2 2.437 0.767 0.381
## Residuals 1687 5358 3.176
## 13 observations deleted due to missingnesslsmeans contrast (unadjusted)
| contrast | estimate | SE | df | lower.CL | upper.CL | t.ratio | p.value |
|---|---|---|---|---|---|---|---|
| Control vs CDC and KS | 0.209 | 0.183 | 1687 | -0.150 | 0.567 | 1.141 | 0.254 |
| CDC vs KS | -0.094 | 0.107 | 1687 | -0.303 | 0.116 | -0.876 | 0.381 |
Show least squares means and CIs around means
| Group | lsmean | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Control | 4.140 | 0.074 | 1687 | 3.995 | 4.286 |
| CDC | 4.292 | 0.077 | 1687 | 4.141 | 4.442 |
| KS | 4.198 | 0.075 | 1687 | 4.052 | 4.344 |
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 577 4.14 1.83 4 4.17 1.48 1 7 6 -0.14 -0.57 0.08
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 542 4.29 1.78 4 4.36 1.48 1 7 6 -0.11 -0.47 0.08
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 571 4.2 1.74 4 4.25 1.48 1 7 6 -0.1 -0.38 0.07
Exploratory: Age moderation
##
## Call:
## lm(formula = AdhereAttitude2 ~ Group * AgeR, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7471 -0.5921 -0.0838 1.0094 3.2556
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.2165458 0.0432150 97.571 < 2e-16 ***
## GroupCvsCD_KS 0.0281966 0.0303920 0.928 0.354
## GroupCDCvsKS -0.0582234 0.0532127 -1.094 0.274
## AgeR 0.0149079 0.0025292 5.894 0.00000000455 ***
## GroupCvsCD_KS:AgeR -0.0000022 0.0017644 -0.001 0.999
## GroupCDCvsKS:AgeR 0.0013968 0.0031387 0.445 0.656
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.761 on 1657 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.02195, Adjusted R-squared: 0.019
## F-statistic: 7.437 on 5 and 1657 DF, p-value: 0.0000006552## 2.5 % 97.5 %
## (Intercept) 4.131784088 4.301307555
## GroupCvsCD_KS -0.031414118 0.087807299
## GroupCDCvsKS -0.162594609 0.046147765
## AgeR 0.009947079 0.019868648
## GroupCvsCD_KS:AgeR -0.003462921 0.003458520
## GroupCDCvsKS:AgeR -0.004759354 0.0075529795.2.2 Prevent CV19
Omnibus ANOVA test: Following COVID-19 guidelines (e.g., distancing and mask wearing) is an effective method for preventing getting COVID-19
Strongly disagree(1), Disagree(2), Somewhat disagree(3), Somewhat agree(4), Agree(5), Strongly agree(6)
| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| Group | 2 | 6.16 | 3.080 | 1.879 | 0.153 |
| Residuals | 1697 | 2782.13 | 1.639 |
Effect sizes:
| term | df | sumsq | meansq | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group | Group | 2 | 6.16 | 3.080 | 1.879 | 0.153 | 0.002 | 0.002 | 0.001 | 0.001 | 0.001 | 0.047 | 0.393 |
| …2 | Residuals | 1697 | 2782.13 | 1.639 |
Contrasts:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 4.768 | 0.031 | 153.471 | 0.000 |
| GroupCvsCD_KS | 0.042 | 0.022 | 1.932 | 0.054 |
| GroupCDCvsKS | -0.007 | 0.038 | -0.184 | 0.854 |
| Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
|---|---|---|---|
| 1700 | 1.28 | 0.002 | 0.001 |
Planned contrast effect sizes
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c1 | Group.c1 | 6.105 | 6.105 | 1 | 3.726 | 0.054 | 0.002 | 0.002 | 0.002 | 0.002 | 0.002 | 0.047 | 0.488 |
| …2 | Residuals | 2782.184 | 1.639 | 1698 |
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c2 | Group.c2 | 0.055 | 0.055 | 1 | 0.036 | 0.85 | 0 | 0 | -0.001 | -0.001 | -0.001 | 0.006 | 0.054 |
| …2 | Residuals | 1720.401 | 1.540 | 1117 |
Contrast v2
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 6.2 3.080 1.879 0.1531
## Group: Control vs. CDC_KS 1 6.1 6.105 3.724 0.0538 .
## Group: CDC vs KS 1 0.1 0.055 0.034 0.8544
## Residuals 1697 2782.1 1.639
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 3 observations deleted due to missingnesslsmeans contrast (unadjusted)
| contrast | estimate | SE | df | lower.CL | upper.CL | t.ratio | p.value |
|---|---|---|---|---|---|---|---|
| Control vs CDC and KS | 0.253 | 0.131 | 1697 | -0.004 | 0.510 | 1.932 | 0.054 |
| CDC vs KS | -0.014 | 0.077 | 1697 | -0.164 | 0.136 | -0.184 | 0.854 |
Show least squares means and CIs around means
| Group | lsmean | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Control | 4.683 | 0.053 | 1697 | 4.579 | 4.787 |
| CDC | 4.817 | 0.055 | 1697 | 4.709 | 4.924 |
| KS | 4.803 | 0.053 | 1697 | 4.698 | 4.908 |
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 581 4.68 1.35 5 4.89 1.48 1 6 5 -1.06 0.47 0.06
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 546 4.82 1.23 5 5 1.48 1 6 5 -1.06 0.59 0.05
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 573 4.8 1.25 5 4.98 1.48 1 6 5 -1.05 0.61 0.05
Exploratory: Age moderation. Plots shown following significant interaction
##
## Call:
## lm(formula = AdhereAttitude3_2 ~ Group * AgeR, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2508 -0.6716 0.2812 1.0373 1.5674
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.7765255 0.0309515 154.323 < 2e-16 ***
## GroupCvsCD_KS 0.0415431 0.0217587 1.909 0.0564 .
## GroupCDCvsKS -0.0168321 0.0381269 -0.441 0.6589
## AgeR 0.0101718 0.0018125 5.612 0.0000000234 ***
## GroupCvsCD_KS:AgeR 0.0029501 0.0012640 2.334 0.0197 *
## GroupCDCvsKS:AgeR 0.0004722 0.0022499 0.210 0.8338
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.264 on 1666 degrees of freedom
## (31 observations deleted due to missingness)
## Multiple R-squared: 0.02356, Adjusted R-squared: 0.02063
## F-statistic: 8.04 on 5 and 1666 DF, p-value: 0.0000001671## 2.5 % 97.5 %
## (Intercept) 4.7158175840 4.837233406
## GroupCvsCD_KS -0.0011342281 0.084220433
## GroupCDCvsKS -0.0916137480 0.057949453
## AgeR 0.0066168573 0.013726814
## GroupCvsCD_KS:AgeR 0.0004708257 0.005429348
## GroupCDCvsKS:AgeR -0.0039406197 0.004885118
5.2.3 Save lives
Omnibus ANOVA test: Following COVID-19 guidelines (e.g., distancing and mask wearing) is an effective method for slowing the spread of COVID-19
Strongly disagree(1), Disagree(2), Somewhat disagree(3), Somewhat agree(4), Agree(5), Strongly agree(6)
| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| Group | 2 | 10.57 | 5.286 | 3.145 | 0.043 |
| Residuals | 1696 | 2850.59 | 1.681 |
Effect sizes:
| term | df | sumsq | meansq | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group | Group | 2 | 10.57 | 5.286 | 3.145 | 0.043 | 0.004 | 0.004 | 0.003 | 0.003 | 0.003 | 0.061 | 0.606 |
| …2 | Residuals | 1696 | 2850.59 | 1.681 |
Contrasts:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 4.830 | 0.031 | 153.506 | 0.000 |
| GroupCvsCD_KS | 0.054 | 0.022 | 2.459 | 0.014 |
| GroupCDCvsKS | 0.018 | 0.039 | 0.462 | 0.644 |
| Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
|---|---|---|---|
| 1699 | 1.296 | 0.004 | 0.003 |
Planned contrast effect sizes
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c1 | Group.c1 | 10.21 | 10.21 | 1 | 6.079 | 0.014 | 0.004 | 0.004 | 0.003 | 0.003 | 0.003 | 0.06 | 0.693 |
| …2 | Residuals | 2850.95 | 1.68 | 1697 |
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c2 | Group.c2 | 0.358 | 0.358 | 1 | 0.223 | 0.637 | 0 | 0 | -0.001 | -0.001 | -0.001 | 0.014 | 0.076 |
| …2 | Residuals | 1791.757 | 1.606 | 1116 |
Contrast v2
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 10.6 5.286 3.145 0.0433 *
## Group: Control vs. CDC_KS 1 10.2 10.213 6.077 0.0138 *
## Group: CDC vs KS 1 0.4 0.358 0.213 0.6445
## Residuals 1696 2850.6 1.681
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 4 observations deleted due to missingnesslsmeans contrast (unadjusted)
| contrast | estimate | SE | df | lower.CL | upper.CL | t.ratio | p.value |
|---|---|---|---|---|---|---|---|
| Control vs CDC and KS | 0.326 | 0.133 | 1696 | 0.066 | 0.586 | 2.459 | 0.014 |
| CDC vs KS | 0.036 | 0.078 | 1696 | -0.116 | 0.188 | 0.462 | 0.644 |
Show least squares means and CIs around means
| Group | lsmean | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Control | 4.721 | 0.054 | 1696 | 4.616 | 4.827 |
| CDC | 4.866 | 0.055 | 1696 | 4.757 | 4.975 |
| KS | 4.902 | 0.054 | 1696 | 4.796 | 5.008 |
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 581 4.72 1.35 5 4.93 1.48 1 6 5 -1.11 0.66 0.06
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 546 4.87 1.25 5 5.06 1.48 1 6 5 -1.14 0.84 0.05
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 572 4.9 1.28 5 5.12 1.48 1 6 5 -1.2 0.84 0.05
Exploratory: Age moderation
##
## Call:
## lm(formula = AdhereAttitude3_3 ~ Group * AgeR, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.316 -0.684 0.316 1.034 1.581
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.838197 0.031154 155.302 < 2e-16 ***
## GroupCvsCD_KS 0.054539 0.021897 2.491 0.0128 *
## GroupCDCvsKS 0.001868 0.038382 0.049 0.9612
## AgeR 0.013092 0.001824 7.176 0.00000000000107 ***
## GroupCvsCD_KS:AgeR 0.002144 0.001272 1.686 0.0921 .
## GroupCDCvsKS:AgeR 0.002292 0.002265 1.012 0.3117
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.272 on 1665 degrees of freedom
## (32 observations deleted due to missingness)
## Multiple R-squared: 0.036, Adjusted R-squared: 0.03311
## F-statistic: 12.44 on 5 and 1665 DF, p-value: 0.00000000000717## 2.5 % 97.5 %
## (Intercept) 4.7770931402 4.899301466
## GroupCvsCD_KS 0.0115903323 0.097488392
## GroupCDCvsKS -0.0734130964 0.077149830
## AgeR 0.0095139167 0.016670541
## GroupCvsCD_KS:AgeR -0.0003507873 0.004639408
## GroupCDCvsKS:AgeR -0.0021505199 0.0067346165.2.4 Slow spread
Omnibus ANOVA test: Following COVID-19 guidelines (e.g., distancing and mask wearing) is an effective method for slowing the spread of COVID-19
Strongly disagree(1), Disagree(2), Somewhat disagree(3), Somewhat agree(4), Agree(5), Strongly agree(6)
| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| Group | 2 | 12.79 | 6.397 | 3.303 | 0.037 |
| Residuals | 1698 | 3288.16 | 1.936 |
Effect sizes:
| term | df | sumsq | meansq | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group | Group | 2 | 12.79 | 6.397 | 3.303 | 0.037 | 0.004 | 0.004 | 0.003 | 0.003 | 0.003 | 0.062 | 0.628 |
| …2 | Residuals | 1698 | 3288.16 | 1.936 |
Contrasts:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 4.785 | 0.034 | 141.770 | 0.000 |
| GroupCvsCD_KS | 0.054 | 0.024 | 2.286 | 0.022 |
| GroupCDCvsKS | -0.050 | 0.042 | -1.210 | 0.226 |
| Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
|---|---|---|---|
| 1701 | 1.392 | 0.004 | 0.003 |
Planned contrast effect sizes
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c1 | Group.c1 | 9.958 | 9.958 | 1 | 5.141 | 0.023 | 0.003 | 0.003 | 0.002 | 0.002 | 0.002 | 0.055 | 0.621 |
| …2 | Residuals | 3290.994 | 1.937 | 1699 |
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c2 | Group.c2 | 2.835 | 2.835 | 1 | 1.546 | 0.214 | 0.001 | 0.001 | 0 | 0 | 0 | 0.037 | 0.237 |
| …2 | Residuals | 2048.888 | 1.834 | 1117 |
Contrast v2
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 13 6.397 3.303 0.0370 *
## Group: Control vs. CDC_KS 1 10 9.958 5.142 0.0235 *
## Group: CDC vs KS 1 3 2.835 1.464 0.2264
## Residuals 1698 3288 1.936
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 2 observations deleted due to missingnesslsmeans contrast (unadjusted)
| contrast | estimate | SE | df | lower.CL | upper.CL | t.ratio | p.value |
|---|---|---|---|---|---|---|---|
| Control vs CDC and KS | 0.325 | 0.142 | 1698 | 0.046 | 0.604 | 2.286 | 0.022 |
| CDC vs KS | -0.101 | 0.083 | 1698 | -0.264 | 0.063 | -1.210 | 0.226 |
Show least squares means and CIs around means
| Group | lsmean | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Control | 4.677 | 0.058 | 1698 | 4.564 | 4.790 |
| CDC | 4.890 | 0.060 | 1698 | 4.773 | 5.007 |
| KS | 4.789 | 0.058 | 1698 | 4.675 | 4.903 |
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 582 4.68 1.46 5 4.91 1.48 1 6 5 -1.07 0.25 0.06
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 545 4.89 1.31 5 5.1 1.48 1 6 5 -1.21 0.87 0.06
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 574 4.79 1.4 5 5.02 1.48 1 6 5 -1.08 0.32 0.06
Exploratory: Age moderation
##
## Call:
## lm(formula = AdhereAttitude3_1 ~ Group * AgeR, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4694 -0.6392 0.3972 1.0171 1.7864
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.799540 0.033056 145.195 <2e-16 ***
## GroupCvsCD_KS 0.051853 0.023231 2.232 0.0257 *
## GroupCDCvsKS -0.072093 0.040732 -1.770 0.0769 .
## AgeR 0.017556 0.001936 9.069 <2e-16 ***
## GroupCvsCD_KS:AgeR 0.001176 0.001350 0.871 0.3840
## GroupCDCvsKS:AgeR 0.002127 0.002403 0.885 0.3762
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.351 on 1667 degrees of freedom
## (30 observations deleted due to missingness)
## Multiple R-squared: 0.05228, Adjusted R-squared: 0.04943
## F-statistic: 18.39 on 5 and 1667 DF, p-value: < 2.2e-16## 2.5 % 97.5 %
## (Intercept) 4.734704368 4.864375149
## GroupCvsCD_KS 0.006289194 0.097417324
## GroupCDCvsKS -0.151984792 0.007797953
## AgeR 0.013758795 0.021352421
## GroupCvsCD_KS:AgeR -0.001472117 0.003823297
## GroupCDCvsKS:AgeR -0.002586277 0.0068405585.3 Willing
5.3.1 Stay home
Omnibus ANOVA test: CV19 willingness (to do another period of staying at home in Winter if cases rise)
| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| Group | 2 | 18.89 | 9.445 | 4.751 | 0.009 |
| Residuals | 1699 | 3377.81 | 1.988 |
Effect sizes:
| term | df | sumsq | meansq | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group | Group | 2 | 18.89 | 9.445 | 4.751 | 0.009 | 0.006 | 0.006 | 0.004 | 0.004 | 0.004 | 0.075 | 0.794 |
| …2 | Residuals | 1699 | 3377.81 | 1.988 |
Contrasts:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 4.791 | 0.034 | 140.127 | 0.000 |
| GroupCvsCD_KS | 0.072 | 0.024 | 2.982 | 0.003 |
| GroupCDCvsKS | 0.031 | 0.042 | 0.738 | 0.460 |
| Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
|---|---|---|---|
| 1702 | 1.41 | 0.006 | 0.004 |
Planned contrast effect sizes
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c1 | Group.c1 | 17.81 | 17.807 | 1 | 8.959 | 0.003 | 0.005 | 0.005 | 0.005 | 0.005 | 0.005 | 0.073 | 0.849 |
| …2 | Residuals | 3378.89 | 1.988 | 1700 |
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c2 | Group.c2 | 1.084 | 1.084 | 1 | 0.573 | 0.449 | 0.001 | 0.001 | 0 | 0 | 0 | 0.023 | 0.118 |
| …2 | Residuals | 2113.016 | 1.890 | 1118 |
Contrast v2
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 19 9.445 4.751 0.00876 **
## Group: Control vs. CDC_KS 1 18 17.807 8.957 0.00280 **
## Group: CDC vs KS 1 1 1.084 0.545 0.46047
## Residuals 1699 3378 1.988
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation deleted due to missingnesslsmeans contrast (unadjusted)
| contrast | estimate | SE | df | lower.CL | upper.CL | t.ratio | p.value |
|---|---|---|---|---|---|---|---|
| Control vs CDC and KS | 0.430 | 0.144 | 1699 | 0.147 | 0.712 | 2.982 | 0.003 |
| CDC vs KS | 0.062 | 0.084 | 1699 | -0.103 | 0.228 | 0.738 | 0.460 |
Show least squares means and CIs around means
| Group | lsmean | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Control | 4.648 | 0.058 | 1699 | 4.533 | 4.762 |
| CDC | 4.832 | 0.060 | 1699 | 4.713 | 4.950 |
| KS | 4.894 | 0.059 | 1699 | 4.778 | 5.009 |
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 582 4.65 1.48 5 4.89 1.48 1 6 5 -1.07 0.23 0.06
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 546 4.83 1.42 5 5.09 1.48 1 6 5 -1.26 0.81 0.06
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 574 4.89 1.33 5 5.13 1.48 1 6 5 -1.33 1.24 0.06
Exploratory: Age moderation
##
## Call:
## lm(formula = Adhere_willing ~ Group * AgeR, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1917 -0.7787 0.2879 1.1550 1.4848
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.7971171 0.0344228 139.359 < 2e-16 ***
## GroupCvsCD_KS 0.0732758 0.0241950 3.029 0.00249 **
## GroupCDCvsKS 0.0186262 0.0424099 0.439 0.66058
## AgeR 0.0058217 0.0020164 2.887 0.00394 **
## GroupCvsCD_KS:AgeR 0.0003193 0.0014062 0.227 0.82039
## GroupCDCvsKS:AgeR 0.0039874 0.0025032 1.593 0.11137
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 1668 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.01261, Adjusted R-squared: 0.009651
## F-statistic: 4.261 on 5 and 1668 DF, p-value: 0.0007468## 2.5 % 97.5 %
## (Intercept) 4.7296005926 4.864633565
## GroupCvsCD_KS 0.0258200433 0.120731473
## GroupCDCvsKS -0.0645560922 0.101808485
## AgeR 0.0018667609 0.009776709
## GroupCvsCD_KS:AgeR -0.0024387321 0.003077341
## GroupCDCvsKS:AgeR -0.0009223315 0.0088970705.3.2 Follow Guidelines
Omnibus ANOVA test: CV19 willingness (to do another period of staying at home in Winter if cases rise)
| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| Group | 2 | 2.245 | 1.122 | 0.82 | 0.44 |
| Residuals | 1648 | 2254.486 | 1.368 |
Effect sizes:
| term | df | sumsq | meansq | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group | Group | 2 | 2.245 | 1.122 | 0.82 | 0.44 | 0.001 | 0.001 | 0 | 0 | 0 | 0.032 | 0.192 |
| …2 | Residuals | 1648 | 2254.486 | 1.368 |
Contrasts:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 4.227 | 0.029 | 146.780 | 0.000 |
| GroupCvsCD_KS | 0.025 | 0.020 | 1.214 | 0.225 |
| GroupCDCvsKS | -0.015 | 0.035 | -0.429 | 0.668 |
| Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
|---|---|---|---|
| 1651 | 1.17 | 0.001 | 0 |
Planned contrast effect sizes
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c1 | Group.c1 | 1.993 | 1.993 | 1 | 1.458 | 0.227 | 0.001 | 0.001 | 0 | 0 | 0 | 0.03 | 0.227 |
| …2 | Residuals | 2254.737 | 1.367 | 1649 |
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c2 | Group.c2 | 0.251 | 0.251 | 1 | 0.187 | 0.665 | 0 | 0 | -0.001 | -0.001 | -0.001 | 0.013 | 0.072 |
| …2 | Residuals | 1458.248 | 1.343 | 1086 |
Contrast v2
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 2.2 1.1223 0.820 0.440
## Group: Control vs. CDC_KS 1 2.0 1.9934 1.457 0.228
## Group: CDC vs KS 1 0.3 0.2513 0.184 0.668
## Residuals 1648 2254.5 1.3680
## 52 observations deleted due to missingnesslsmeans contrast (unadjusted)
| contrast | estimate | SE | df | lower.CL | upper.CL | t.ratio | p.value |
|---|---|---|---|---|---|---|---|
| Control vs CDC and KS | 0.147 | 0.121 | 1648 | -0.091 | 0.386 | 1.214 | 0.225 |
| CDC vs KS | -0.030 | 0.071 | 1648 | -0.170 | 0.109 | -0.429 | 0.668 |
Show least squares means and CIs around means
| Group | lsmean | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Control | 4.178 | 0.049 | 1648 | 4.081 | 4.274 |
| CDC | 4.267 | 0.051 | 1648 | 4.167 | 4.366 |
| KS | 4.236 | 0.049 | 1648 | 4.139 | 4.333 |
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 563 4.18 1.19 5 4.41 0 1 5 4 -1.31 0.63 0.05
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 529 4.27 1.16 5 4.52 0 1 5 4 -1.48 1.13 0.05
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 559 4.24 1.16 5 4.48 0 1 5 4 -1.43 1.03 0.05
Exploratory: Age moderation
##
## Call:
## lm(formula = AdhereAttitude1_1 ~ Group * AgeR, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9021 -0.5462 0.4862 0.8116 1.2208
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.2337180 0.0283498 149.339 <2e-16 ***
## GroupCvsCD_KS 0.0254209 0.0199400 1.275 0.203
## GroupCDCvsKS -0.0321206 0.0349044 -0.920 0.358
## AgeR 0.0142210 0.0016482 8.628 <2e-16 ***
## GroupCvsCD_KS:AgeR -0.0001221 0.0011494 -0.106 0.915
## GroupCDCvsKS:AgeR 0.0024136 0.0020460 1.180 0.238
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.141 on 1617 degrees of freedom
## (80 observations deleted due to missingness)
## Multiple R-squared: 0.04691, Adjusted R-squared: 0.04396
## F-statistic: 15.92 on 5 and 1617 DF, p-value: 2.475e-15## 2.5 % 97.5 %
## (Intercept) 4.178111906 4.289324149
## GroupCvsCD_KS -0.013690006 0.064531767
## GroupCDCvsKS -0.100583290 0.036342062
## AgeR 0.010988190 0.017453747
## GroupCvsCD_KS:AgeR -0.002376527 0.002132315
## GroupCDCvsKS:AgeR -0.001599545 0.0064267435.4 Behavioral intentions
5.4.1 Risk increasing
Omnibus ANOVA test: How frequently, if at all, do you plan to do the following things in the next month?
(e.g., going to gatherings of 10 or more people)
| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| Group | 2 | 23.09 | 11.547 | 5.151 | 0.006 |
| Residuals | 1699 | 3808.57 | 2.242 |
Effect sizes:
| term | df | sumsq | meansq | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group | Group | 2 | 23.09 | 11.547 | 5.151 | 0.006 | 0.006 | 0.006 | 0.005 | 0.005 | 0.005 | 0.078 | 0.827 |
| …2 | Residuals | 1699 | 3808.57 | 2.242 |
Contrasts:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 2.839 | 0.036 | 78.201 | 0.000 |
| GroupCvsCD_KS | -0.082 | 0.026 | -3.200 | 0.001 |
| GroupCDCvsKS | -0.009 | 0.045 | -0.209 | 0.835 |
| Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
|---|---|---|---|
| 1702 | 1.497 | 0.006 | 0.005 |
Planned contrast effect sizes
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c1 | Group.c1 | 23 | 23.00 | 1 | 10.27 | 0.001 | 0.006 | 0.006 | 0.005 | 0.005 | 0.005 | 0.078 | 0.893 |
| …2 | Residuals | 3809 | 2.24 | 1700 |
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c2 | Group.c2 | 0.098 | 0.098 | 1 | 0.044 | 0.834 | 0 | 0 | -0.001 | -0.001 | -0.001 | 0.006 | 0.055 |
| …2 | Residuals | 2473.896 | 2.213 | 1118 |
Contrast v2
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 23 11.547 5.151 0.00588 **
## Group: Control vs. CDC_KS 1 23 22.997 10.259 0.00139 **
## Group: CDC vs KS 1 0 0.098 0.044 0.83471
## Residuals 1699 3809 2.242
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation deleted due to missingnesslsmeans contrast (unadjusted)
| contrast | estimate | SE | df | lower.CL | upper.CL | t.ratio | p.value |
|---|---|---|---|---|---|---|---|
| Control vs CDC and KS | -0.490 | 0.153 | 1699 | -0.790 | -0.189 | -3.200 | 0.001 |
| CDC vs KS | -0.019 | 0.090 | 1699 | -0.194 | 0.157 | -0.209 | 0.835 |
Show least squares means and CIs around means
| Group | lsmean | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Control | 3.002 | 0.062 | 1699 | 2.881 | 3.124 |
| CDC | 2.767 | 0.064 | 1699 | 2.641 | 2.893 |
| KS | 2.748 | 0.062 | 1699 | 2.626 | 2.871 |
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 582 3 1.52 2.8 2.9 1.78 1 6 5 0.45 -0.93 0.06
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 546 2.77 1.48 2.4 2.63 1.48 1 6 5 0.62 -0.74 0.06
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 574 2.75 1.49 2.4 2.6 1.48 1 6 5 0.66 -0.73 0.06
Exploratory: Age moderation
##
## Call:
## lm(formula = BehavIntRisk_Avg ~ Group * AgeR, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7038 -1.0880 -0.2833 0.9014 3.6812
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.829922260 0.034765759 81.400 < 2e-16 ***
## GroupCvsCD_KS -0.077087260 0.024436016 -3.155 0.00164 **
## GroupCDCvsKS 0.004238658 0.042832433 0.099 0.92118
## AgeR -0.027563545 0.002036505 -13.535 < 2e-16 ***
## GroupCvsCD_KS:AgeR -0.000003949 0.001420175 -0.003 0.99778
## GroupCDCvsKS:AgeR -0.001888044 0.002528115 -0.747 0.45528
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.421 on 1668 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.1056, Adjusted R-squared: 0.1029
## F-statistic: 39.38 on 5 and 1668 DF, p-value: < 2.2e-16## 2.5 % 97.5 %
## (Intercept) 2.761733144 2.898111375
## GroupCvsCD_KS -0.125015749 -0.029158770
## GroupCDCvsKS -0.079772329 0.088249646
## AgeR -0.031557921 -0.023569170
## GroupCvsCD_KS:AgeR -0.002789462 0.002781564
## GroupCDCvsKS:AgeR -0.006846658 0.0030705695.4.2 Risk reducing
Omnibus ANOVA test: How frequently, if at all, do you plan to do the following things in the next month?
(e.g., Wearing a mask over your nose and mouth when you are in a public place (e.g., store))
| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| Group | 2 | 3.691 | 1.845 | 1.399 | 0.247 |
| Residuals | 1696 | 2237.984 | 1.320 |
Effect sizes:
| term | df | sumsq | meansq | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group | Group | 2 | 3.691 | 1.845 | 1.399 | 0.247 | 0.002 | 0.002 | 0 | 0 | 0 | 0.041 | 0.302 |
| …2 | Residuals | 1696 | 2237.984 | 1.320 |
Contrasts:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 4.963 | 0.028 | 178.032 | 0.000 |
| GroupCvsCD_KS | 0.030 | 0.020 | 1.521 | 0.129 |
| GroupCDCvsKS | -0.025 | 0.034 | -0.716 | 0.474 |
| Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
|---|---|---|---|
| 1699 | 1.149 | 0.002 | 0 |
Planned contrast effect sizes
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c1 | Group.c1 | 3.014 | 3.014 | 1 | 2.285 | 0.131 | 0.001 | 0.001 | 0.001 | 0.001 | 0.001 | 0.037 | 0.327 |
| …2 | Residuals | 2238.661 | 1.319 | 1697 |
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c2 | Group.c2 | 0.676 | 0.676 | 1 | 0.515 | 0.473 | 0 | 0 | 0 | 0 | 0 | 0.021 | 0.111 |
| …2 | Residuals | 1463.595 | 1.313 | 1115 |
Contrast v2
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 3.7 1.8454 1.399 0.247
## Group: Control vs. CDC_KS 1 3.0 3.0145 2.284 0.131
## Group: CDC vs KS 1 0.7 0.6764 0.513 0.474
## Residuals 1696 2238.0 1.3196
## 4 observations deleted due to missingnesslsmeans contrast (unadjusted)
| contrast | estimate | SE | df | lower.CL | upper.CL | t.ratio | p.value |
|---|---|---|---|---|---|---|---|
| Control vs CDC and KS | 0.179 | 0.117 | 1696 | -0.052 | 0.409 | 1.521 | 0.129 |
| CDC vs KS | -0.049 | 0.069 | 1696 | -0.184 | 0.086 | -0.716 | 0.474 |
Show least squares means and CIs around means
| Group | lsmean | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Control | 4.904 | 0.048 | 1696 | 4.810 | 4.997 |
| CDC | 5.018 | 0.049 | 1696 | 4.921 | 5.114 |
| KS | 4.968 | 0.048 | 1696 | 4.874 | 5.063 |
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 582 4.9 1.15 5.33 5.08 0.99 1 6 5 -1.15 0.73 0.05
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 546 5.02 1.11 5.33 5.2 0.99 1 6 5 -1.29 1.18 0.05
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 571 4.97 1.18 5.33 5.17 0.99 1 6 5 -1.34 1.26 0.05
Exploratory: Age moderation
##
## Call:
## lm(formula = BehavIntPrtct_Avg ~ Group * AgeR, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2958 -0.5875 0.3140 0.8249 1.4557
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.9754887 0.0273265 182.076 <2e-16 ***
## GroupCvsCD_KS 0.0306301 0.0191989 1.595 0.111
## GroupCDCvsKS -0.0321408 0.0336812 -0.954 0.340
## AgeR 0.0143961 0.0016032 8.979 <2e-16 ***
## GroupCvsCD_KS:AgeR 0.0001172 0.0011167 0.105 0.916
## GroupCDCvsKS:AgeR -0.0010377 0.0019925 -0.521 0.603
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.116 on 1665 degrees of freedom
## (32 observations deleted due to missingness)
## Multiple R-squared: 0.04824, Adjusted R-squared: 0.04538
## F-statistic: 16.88 on 5 and 1665 DF, p-value: 2.653e-16## 2.5 % 97.5 %
## (Intercept) 4.921890832 5.029086656
## GroupCvsCD_KS -0.007026323 0.068286612
## GroupCDCvsKS -0.098202752 0.033921127
## AgeR 0.011251503 0.017540631
## GroupCvsCD_KS:AgeR -0.002073096 0.002307504
## GroupCDCvsKS:AgeR -0.004945753 0.0028702835.4.3 Pro-social
Omnibus ANOVA test: Prosocial behaviors
| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| Group | 2 | 8.006 | 4.003 | 1.872 | 0.154 |
| Residuals | 1647 | 3522.804 | 2.139 |
Effect sizes:
| term | df | sumsq | meansq | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group | Group | 2 | 8.006 | 4.003 | 1.872 | 0.154 | 0.002 | 0.002 | 0.001 | 0.001 | 0.001 | 0.048 | 0.392 |
| …2 | Residuals | 1647 | 3522.804 | 2.139 |
Contrasts:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 5.438 | 0.036 | 150.977 | 0.000 |
| GroupCvsCD_KS | 0.049 | 0.025 | 1.922 | 0.055 |
| GroupCDCvsKS | 0.008 | 0.044 | 0.188 | 0.851 |
| Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
|---|---|---|---|
| 1650 | 1.463 | 0.002 | 0.001 |
Planned contrast effect sizes
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c1 | Group.c1 | 7.93 | 7.930 | 1 | 3.71 | 0.054 | 0.002 | 0.002 | 0.002 | 0.002 | 0.002 | 0.047 | 0.487 |
| …2 | Residuals | 3522.88 | 2.138 | 1648 |
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c2 | Group.c2 | 0.076 | 0.076 | 1 | 0.037 | 0.847 | 0 | 0 | -0.001 | -0.001 | -0.001 | 0.006 | 0.054 |
| …2 | Residuals | 2222.831 | 2.041 | 1089 |
lsmeans contrast (adjusted)
## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## Control vs CDC and KS 0.2925 0.1522 1647 -0.0489 0.634 1.922 0.1096
## CDC vs KS 0.0167 0.0886 1647 -0.1821 0.215 0.188 0.8508
##
## Confidence level used: 0.95
## Conf-level adjustment: bonferroni method for 2 estimates
## P value adjustment: holm method for 2 testsShow least squares means and CIs around means
| Group | lsmean | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Control | 5.340 | 0.062 | 1647 | 5.219 | 5.462 |
| CDC | 5.478 | 0.064 | 1647 | 5.354 | 5.603 |
| KS | 5.495 | 0.062 | 1647 | 5.374 | 5.616 |
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis
## ProSocial_1 1 497 5.18 1.98 6 5.45 1.48 1 7 6 -0.81 -0.58
## ProSocial_2 2 510 5.10 1.86 5 5.33 2.97 1 7 6 -0.73 -0.51
## ProSocial_3 3 479 5.28 1.83 6 5.54 1.48 1 7 6 -0.84 -0.34
## ProSocial_4 4 504 5.41 1.86 6 5.72 1.48 1 7 6 -1.06 0.04
## ProSocial_5 5 504 5.38 1.81 6 5.66 1.48 1 7 6 -0.98 -0.09
## ProSocial_6 6 495 5.29 1.80 6 5.55 1.48 1 7 6 -0.93 -0.12
## se
## ProSocial_1 0.09
## ProSocial_2 0.08
## ProSocial_3 0.08
## ProSocial_4 0.08
## ProSocial_5 0.08
## ProSocial_6 0.08
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis
## ProSocial_1 1 471 5.17 2.00 6 5.46 1.48 1 7 6 -0.85 -0.51
## ProSocial_2 2 493 5.24 1.77 5 5.48 2.97 1 7 6 -0.80 -0.27
## ProSocial_3 3 456 5.44 1.74 6 5.71 1.48 1 7 6 -1.04 0.17
## ProSocial_4 4 478 5.54 1.74 6 5.85 1.48 1 7 6 -1.20 0.57
## ProSocial_5 5 468 5.47 1.75 6 5.77 1.48 1 7 6 -1.09 0.29
## ProSocial_6 6 473 5.55 1.68 6 5.83 1.48 1 7 6 -1.13 0.49
## se
## ProSocial_1 0.09
## ProSocial_2 0.08
## ProSocial_3 0.08
## ProSocial_4 0.08
## ProSocial_5 0.08
## ProSocial_6 0.08
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis
## ProSocial_1 1 498 5.23 1.94 6 5.50 1.48 1 7 6 -0.87 -0.42
## ProSocial_2 2 509 5.19 1.79 6 5.43 1.48 1 7 6 -0.82 -0.27
## ProSocial_3 3 484 5.46 1.75 6 5.74 1.48 1 7 6 -1.02 0.03
## ProSocial_4 4 510 5.63 1.71 6 5.95 1.48 1 7 6 -1.31 0.88
## ProSocial_5 5 510 5.53 1.74 6 5.82 1.48 1 7 6 -1.10 0.22
## ProSocial_6 6 502 5.55 1.67 6 5.82 1.48 1 7 6 -1.11 0.41
## se
## ProSocial_1 0.09
## ProSocial_2 0.08
## ProSocial_3 0.08
## ProSocial_4 0.08
## ProSocial_5 0.08
## ProSocial_6 0.07##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 559 5.34 1.53 5.67 5.54 1.58 1 7 6 -0.91 0.11 0.06
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 529 5.48 1.42 5.83 5.67 1.24 1 7 6 -1.14 1.01 0.06
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 562 5.49 1.44 5.92 5.69 1.36 1 7 6 -1.05 0.56 0.06
Exploratory: Age moderation. Plots shown following significant interaction
##
## Call:
## lm(formula = Prosocial_Avg ~ Group * AgeR, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9880 -0.8018 0.3299 1.1608 2.0031
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.443113 0.035911 151.570 < 2e-16 ***
## GroupCvsCD_KS 0.051493 0.025292 2.036 0.04192 *
## GroupCDCvsKS -0.009240 0.044157 -0.209 0.83427
## AgeR 0.009711 0.002089 4.648 0.00000362 ***
## GroupCvsCD_KS:AgeR 0.001889 0.001456 1.297 0.19481
## GroupCDCvsKS:AgeR 0.007099 0.002595 2.736 0.00629 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.445 on 1617 degrees of freedom
## (80 observations deleted due to missingness)
## Multiple R-squared: 0.02174, Adjusted R-squared: 0.01872
## F-statistic: 7.187 on 5 and 1617 DF, p-value: 0.000001155## 2.5 % 97.5 %
## (Intercept) 5.3726748925 5.513550794
## GroupCvsCD_KS 0.0018839856 0.101101717
## GroupCDCvsKS -0.0958507379 0.077370462
## AgeR 0.0056135599 0.013809398
## GroupCvsCD_KS:AgeR -0.0009674414 0.004744545
## GroupCDCvsKS:AgeR 0.0020093163 0.012189439
5.5 Risk perceptions
Omnibus ANOVA test: Individual susceptibility and likelihood of getting CV19
(higher score correspond to more risk)
| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| Group | 2 | 0.151 | 0.076 | 0.04 | 0.961 |
| Residuals | 1390 | 2623.983 | 1.888 |
Effect sizes:
| term | df | sumsq | meansq | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group | Group | 2 | 0.151 | 0.076 | 0.04 | 0.961 | 0 | 0 | -0.001 | -0.001 | -0.001 | 0.008 | 0.056 |
| …2 | Residuals | 1390 | 2623.983 | 1.888 |
Contrasts:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 4.147 | 0.037 | 112.635 | 0.000 |
| GroupCvsCD_KS | -0.007 | 0.026 | -0.277 | 0.782 |
| GroupCDCvsKS | -0.003 | 0.045 | -0.057 | 0.954 |
| Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
|---|---|---|---|
| 1393 | 1.374 | 0 | -0.001 |
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 0.2 0.0757 0.040 0.961
## Group: Control vs. CDC_KS 1 0.1 0.1452 0.077 0.782
## Group: CDC vs KS 1 0.0 0.0062 0.003 0.954
## Residuals 1390 2624.0 1.8878
## 310 observations deleted due to missingnessPlanned contrast effect sizes
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c1 | Group.c1 | 0.145 | 0.145 | 1 | 0.077 | 0.781 | 0 | 0 | -0.001 | -0.001 | -0.001 | 0.007 | 0.059 |
| …2 | Residuals | 2623.989 | 1.886 | 1391 |
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c2 | Group.c2 | 0.006 | 0.006 | 1 | 0.003 | 0.955 | 0 | 0 | -0.001 | -0.001 | -0.001 | 0.002 | 0.05 |
| …2 | Residuals | 1748.207 | 1.894 | 923 |
Show least squares means and CIs around means
| Group | lsmean | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Control | 4.161 | 0.064 | 1390 | 4.037 | 4.286 |
| CDC | 4.142 | 0.064 | 1390 | 4.016 | 4.269 |
| KS | 4.137 | 0.063 | 1390 | 4.013 | 4.261 |
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 468 4.16 1.37 4.11 4.17 1.32 1 7 6 -0.04 -0.3 0.06
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 455 4.14 1.39 4.22 4.16 1.32 1 7 6 -0.13 -0.46 0.07
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 470 4.14 1.36 4.11 4.16 1.32 1 7 6 -0.14 -0.27 0.06
Exploratory: Age moderation
##
## Call:
## lm(formula = IndivRisk_Avg ~ Group * AgeR, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2192 -0.9320 0.0047 0.9337 2.9546
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.1458147 0.0371954 111.461 <2e-16 ***
## GroupCvsCD_KS -0.0100602 0.0262305 -0.384 0.701
## GroupCDCvsKS -0.0134342 0.0456768 -0.294 0.769
## AgeR 0.0001202 0.0021349 0.056 0.955
## GroupCvsCD_KS:AgeR 0.0018224 0.0014935 1.220 0.223
## GroupCDCvsKS:AgeR 0.0008935 0.0026423 0.338 0.735
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.374 on 1372 degrees of freedom
## (325 observations deleted due to missingness)
## Multiple R-squared: 0.001276, Adjusted R-squared: -0.002363
## F-statistic: 0.3507 on 5 and 1372 DF, p-value: 0.882## 2.5 % 97.5 %
## (Intercept) 4.072848764 4.218780666
## GroupCvsCD_KS -0.061516402 0.041395976
## GroupCDCvsKS -0.103038180 0.076169764
## AgeR -0.004067922 0.004308236
## GroupCvsCD_KS:AgeR -0.001107504 0.004752207
## GroupCDCvsKS:AgeR -0.004289916 0.006076927Here are the posthoc Tukey multiple comparisons of means (95% family-wise confidence level) with a plot to show.
5.6 Older Adults
5.6.1 CV19-OAS
Omnibus ANOVA test: CV19-OAS by group
| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| Group | 2 | 0.865 | 0.433 | 1.038 | 0.354 |
| Residuals | 1700 | 708.252 | 0.417 |
Effect sizes:
| term | df | sumsq | meansq | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group | Group | 2 | 0.865 | 0.433 | 1.038 | 0.354 | 0.001 | 0.001 | 0 | 0 | 0 | 0.035 | 0.233 |
| …2 | Residuals | 1700 | 708.252 | 0.417 |
Contrasts:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 2.197 | 0.016 | 140.411 | 0.000 |
| GroupCvsCD_KS | -0.004 | 0.011 | -0.391 | 0.696 |
| GroupCDCvsKS | 0.027 | 0.019 | 1.393 | 0.164 |
| Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
|---|---|---|---|
| 1703 | 0.645 | 0.001 | 0 |
Planned contrast effect sizes
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c1 | Group.c1 | 0.057 | 0.057 | 1 | 0.136 | 0.712 | 0 | 0 | -0.001 | -0.001 | -0.001 | 0.009 | 0.066 |
| …2 | Residuals | 709.061 | 0.417 | 1701 |
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c2 | Group.c2 | 0.808 | 0.808 | 1 | 1.914 | 0.167 | 0.002 | 0.002 | 0.001 | 0.001 | 0.001 | 0.041 | 0.283 |
| …2 | Residuals | 472.668 | 0.422 | 1119 |
Contrast v2
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 0.9 0.4326 1.038 0.354
## Group: Control vs. CDC_KS 1 0.1 0.0569 0.137 0.712
## Group: CDC vs KS 1 0.8 0.8083 1.940 0.164
## Residuals 1700 708.3 0.4166lsmeans contrast (unadjusted)
| contrast | estimate | SE | df | lower.CL | upper.CL | t.ratio | p.value |
|---|---|---|---|---|---|---|---|
| Control vs CDC and KS | -0.026 | 0.066 | 1700 | -0.155 | 0.104 | -0.391 | 0.696 |
| CDC vs KS | 0.054 | 0.039 | 1700 | -0.022 | 0.129 | 1.393 | 0.164 |
Show least squares means and CIs around means
| Group | lsmean | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Control | 2.206 | 0.027 | 1700 | 2.153 | 2.258 |
| CDC | 2.166 | 0.028 | 1700 | 2.112 | 2.220 |
| KS | 2.220 | 0.027 | 1700 | 2.167 | 2.272 |
Show standard means and sds by group
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 582 2.21 0.64 2.2 2.18 0.89 1 3.6 2.6 0.28 -0.77 0.03
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 546 2.17 0.66 2 2.14 0.59 1 3.6 2.6 0.36 -0.89 0.03
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 575 2.22 0.64 2.2 2.19 0.89 1 4 3 0.29 -0.78 0.03
Exploratory: Age moderation
##
## Call:
## lm(formula = AgeismMyth_Avg ~ Group * AgeR, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.44651 -0.48166 -0.08682 0.44685 1.95660
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.1938184 0.0152943 143.440 <2e-16 ***
## GroupCvsCD_KS -0.0028177 0.0107516 -0.262 0.7933
## GroupCDCvsKS 0.0325963 0.0188403 1.730 0.0838 .
## AgeR -0.0094017 0.0008961 -10.491 <2e-16 ***
## GroupCvsCD_KS:AgeR -0.0001934 0.0006249 -0.309 0.7570
## GroupCDCvsKS:AgeR -0.0004827 0.0011124 -0.434 0.6644
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6253 on 1669 degrees of freedom
## (28 observations deleted due to missingness)
## Multiple R-squared: 0.06338, Adjusted R-squared: 0.06058
## F-statistic: 22.59 on 5 and 1669 DF, p-value: < 2.2e-16## 2.5 % 97.5 %
## (Intercept) 2.163820281 2.223816489
## GroupCvsCD_KS -0.023905816 0.018270347
## GroupCDCvsKS -0.004356858 0.069549493
## AgeR -0.011159347 -0.007644006
## GroupCvsCD_KS:AgeR -0.001419139 0.001032373
## GroupCDCvsKS:AgeR -0.002664630 0.0016992135.6.2 Fraboni Ageism
Omnibus ANOVA test: CV19 priority preference (get to work even if means more older people will die)
| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| Group | 2 | 0.48 | 0.240 | 0.646 | 0.524 |
| Residuals | 1700 | 631.60 | 0.372 |
Effect sizes:
| term | df | sumsq | meansq | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group | Group | 2 | 0.48 | 0.240 | 0.646 | 0.524 | 0.001 | 0.001 | 0 | 0 | 0 | 0.028 | 0.159 |
| …2 | Residuals | 1700 | 631.60 | 0.372 |
Contrasts:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 2.212 | 0.015 | 149.73 | 0.000 |
| GroupCvsCD_KS | -0.009 | 0.010 | -0.86 | 0.390 |
| GroupCDCvsKS | -0.013 | 0.018 | -0.73 | 0.465 |
| Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
|---|---|---|---|
| 1703 | 0.61 | 0.001 | 0 |
Planned contrast effect sizes
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c1 | Group.c1 | 0.282 | 0.282 | 1 | 0.759 | 0.384 | 0 | 0 | 0 | 0 | 0 | 0.021 | 0.14 |
| …2 | Residuals | 631.795 | 0.371 | 1701 |
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c2 | Group.c2 | 0.198 | 0.198 | 1 | 0.52 | 0.471 | 0 | 0 | 0 | 0 | 0 | 0.022 | 0.111 |
| …2 | Residuals | 426.267 | 0.381 | 1119 |
Contrast v2
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 0.5 0.2400 0.646 0.524
## Group: Control vs. CDC_KS 1 0.3 0.2819 0.759 0.384
## Group: CDC vs KS 1 0.2 0.1982 0.533 0.465
## Residuals 1700 631.6 0.3715lsmeans contrast (unadjusted)
| contrast | estimate | SE | df | lower.CL | upper.CL | t.ratio | p.value |
|---|---|---|---|---|---|---|---|
| Control vs CDC and KS | -0.054 | 0.062 | 1700 | -0.176 | 0.069 | -0.86 | 0.390 |
| CDC vs KS | -0.027 | 0.036 | 1700 | -0.098 | 0.045 | -0.73 | 0.465 |
Show least squares means and CIs around means
| Group | lsmean | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Control | 2.230 | 0.025 | 1700 | 2.181 | 2.280 |
| CDC | 2.217 | 0.026 | 1700 | 2.166 | 2.268 |
| KS | 2.190 | 0.025 | 1700 | 2.140 | 2.240 |
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 582 2.23 0.59 2.17 2.22 0.64 1 3.39 2.39 0.08 -0.91 0.02
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 546 2.22 0.6 2.17 2.21 0.64 1 3.35 2.35 0.03 -0.82 0.03
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 575 2.19 0.63 2.13 2.18 0.71 1 3.57 2.57 0.08 -0.95 0.03
Exploratory: Age moderation
##
## Call:
## lm(formula = AgeismFraboni_Avg ~ Group * AgeR, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.45247 -0.38124 -0.01317 0.39308 1.37556
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.2093980 0.0139605 158.260 <2e-16 ***
## GroupCvsCD_KS -0.0066457 0.0098140 -0.677 0.498
## GroupCDCvsKS -0.0078410 0.0171973 -0.456 0.648
## AgeR -0.0125092 0.0008180 -15.293 <2e-16 ***
## GroupCvsCD_KS:AgeR -0.0005269 0.0005704 -0.924 0.356
## GroupCDCvsKS:AgeR -0.0019263 0.0010154 -1.897 0.058 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5708 on 1669 degrees of freedom
## (28 observations deleted due to missingness)
## Multiple R-squared: 0.1265, Adjusted R-squared: 0.1239
## F-statistic: 48.35 on 5 and 1669 DF, p-value: < 2.2e-16## 2.5 % 97.5 %
## (Intercept) 2.182015998 2.23677992934
## GroupCvsCD_KS -0.025894705 0.01260326925
## GroupCDCvsKS -0.041571495 0.02588947399
## AgeR -0.014113568 -0.01090480083
## GroupCvsCD_KS:AgeR -0.001645770 0.00059194449
## GroupCDCvsKS:AgeR -0.003917919 0.000065352935.6.3 Ageism Fact
Omnibus ANOVA test: CV19 priority preference (get to work even if means more older people will die)
| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| Group | 2 | 0.009 | 0.005 | 0.15 | 0.86 |
| Residuals | 1700 | 51.737 | 0.030 |
Effect sizes:
| term | df | sumsq | meansq | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group | Group | 2 | 0.009 | 0.005 | 0.15 | 0.86 | 0 | 0 | -0.001 | -0.001 | -0.001 | 0.013 | 0.073 |
| …2 | Residuals | 1700 | 51.737 | 0.030 |
Contrasts:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 0.386 | 0.004 | 91.245 | 0.000 |
| GroupCvsCD_KS | -0.002 | 0.003 | -0.540 | 0.589 |
| GroupCDCvsKS | 0.000 | 0.005 | -0.085 | 0.932 |
| Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
|---|---|---|---|
| 1703 | 0.174 | 0 | -0.001 |
Planned contrast effect sizes
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c1 | Group.c1 | 0.009 | 0.009 | 1 | 0.294 | 0.588 | 0 | 0 | 0 | 0 | 0 | 0.013 | 0.084 |
| …2 | Residuals | 51.738 | 0.030 | 1701 |
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c2 | Group.c2 | 0.00 | 0.000 | 1 | 0.007 | 0.933 | 0 | 0 | -0.001 | -0.001 | -0.001 | 0.003 | 0.051 |
| …2 | Residuals | 34.95 | 0.031 | 1119 |
Contrast v2
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 0.01 0.004576 0.150 0.860
## Group: Control vs. CDC_KS 1 0.01 0.008931 0.293 0.588
## Group: CDC vs KS 1 0.00 0.000221 0.007 0.932
## Residuals 1700 51.74 0.030434lsmeans contrast (unadjusted)
| contrast | estimate | SE | df | lower.CL | upper.CL | t.ratio | p.value |
|---|---|---|---|---|---|---|---|
| Control vs CDC and KS | -0.010 | 0.018 | 1700 | -0.045 | 0.025 | -0.540 | 0.589 |
| CDC vs KS | -0.001 | 0.010 | 1700 | -0.021 | 0.020 | -0.085 | 0.932 |
Show least squares means and CIs around means
| Group | lsmean | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Control | 0.389 | 0.007 | 1700 | 0.375 | 0.403 |
| CDC | 0.385 | 0.007 | 1700 | 0.370 | 0.399 |
| KS | 0.384 | 0.007 | 1700 | 0.370 | 0.398 |
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 582 0.39 0.17 0.42 0.39 0.25 0 0.92 0.92 0.09 -0.54 0.01
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 546 0.38 0.17 0.39 0.38 0.21 0 0.92 0.92 0.19 -0.57 0.01
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 575 0.38 0.18 0.42 0.38 0.25 0 0.92 0.92 0.19 -0.43 0.01
Exploratory: Age moderation
##
## Call:
## lm(formula = AgeismFact_Avg ~ Group * AgeR, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.42152 -0.11179 -0.00056 0.10671 0.55727
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.38410138 0.00387432 99.140 <2e-16 ***
## GroupCvsCD_KS -0.00069544 0.00272357 -0.255 0.798
## GroupCDCvsKS 0.00321718 0.00477258 0.674 0.500
## AgeR -0.00421834 0.00022701 -18.582 <2e-16 ***
## GroupCvsCD_KS:AgeR -0.00006077 0.00015831 -0.384 0.701
## GroupCDCvsKS:AgeR -0.00001896 0.00028180 -0.067 0.946
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1584 on 1669 degrees of freedom
## (28 observations deleted due to missingness)
## Multiple R-squared: 0.1718, Adjusted R-squared: 0.1694
## F-statistic: 69.26 on 5 and 1669 DF, p-value: < 2.2e-16## 2.5 % 97.5 %
## (Intercept) 0.3765023484 0.3917004083
## GroupCvsCD_KS -0.0060374098 0.0046465296
## GroupCDCvsKS -0.0061436893 0.0125780465
## AgeR -0.0046635833 -0.0037730878
## GroupCvsCD_KS:AgeR -0.0003712753 0.0002497343
## GroupCDCvsKS:AgeR -0.0005716756 0.00053376005.7 Death/Aging anxiety
5.7.1 Death anxiety scale
Omnibus ANOVA test: death anxiety scale
| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| Group | 2 | 0.041 | 0.021 | 0.06 | 0.942 |
| Residuals | 1700 | 584.366 | 0.344 |
Effect sizes:
| term | df | sumsq | meansq | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group | Group | 2 | 0.041 | 0.021 | 0.06 | 0.942 | 0 | 0 | -0.001 | -0.001 | -0.001 | 0.008 | 0.059 |
| …2 | Residuals | 1700 | 584.366 | 0.344 |
Contrasts:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 3.050 | 0.014 | 214.604 | 0.000 |
| GroupCvsCD_KS | -0.003 | 0.010 | -0.326 | 0.744 |
| GroupCDCvsKS | -0.002 | 0.018 | -0.110 | 0.913 |
| Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
|---|---|---|---|
| 1703 | 0.586 | 0 | -0.001 |
Planned contrast effect sizes
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c1 | Group.c1 | 0.037 | 0.037 | 1 | 0.108 | 0.743 | 0 | 0 | -0.001 | -0.001 | -0.001 | 0.008 | 0.062 |
| …2 | Residuals | 584.370 | 0.344 | 1701 |
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c2 | Group.c2 | 0.004 | 0.004 | 1 | 0.012 | 0.914 | 0 | 0 | -0.001 | -0.001 | -0.001 | 0.003 | 0.051 |
| …2 | Residuals | 399.197 | 0.357 | 1119 |
Show least squares means and CIs around means
| Group | lsmean | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Control | 3.057 | 0.024 | 1700 | 3.009 | 3.104 |
| CDC | 3.049 | 0.025 | 1700 | 3.000 | 3.098 |
| KS | 3.045 | 0.024 | 1700 | 2.997 | 3.093 |
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 582 3.06 0.56 3.11 3.09 0.49 1.11 5 3.89 -0.49 0.64 0.02
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 546 3.05 0.58 3.11 3.08 0.49 1 4.56 3.56 -0.42 0.17 0.02
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 575 3.04 0.61 3.11 3.08 0.49 1 5 4 -0.45 0.45 0.03
Exploratory: Age moderation
##
## Call:
## lm(formula = DeathAnxty_Avg ~ Group * AgeR, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.10064 -0.34485 0.05506 0.38800 2.05563
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.0472683 0.0138863 219.444 <2e-16 ***
## GroupCvsCD_KS -0.0002433 0.0097618 -0.025 0.980
## GroupCDCvsKS 0.0022274 0.0171058 0.130 0.896
## AgeR -0.0090451 0.0008136 -11.117 <2e-16 ***
## GroupCvsCD_KS:AgeR -0.0008155 0.0005674 -1.437 0.151
## GroupCDCvsKS:AgeR -0.0007545 0.0010100 -0.747 0.455
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5678 on 1669 degrees of freedom
## (28 observations deleted due to missingness)
## Multiple R-squared: 0.0702, Adjusted R-squared: 0.06742
## F-statistic: 25.2 on 5 and 1669 DF, p-value: < 2.2e-16## 2.5 % 97.5 %
## (Intercept) 3.020031919 3.074504718
## GroupCvsCD_KS -0.019389974 0.018903340
## GroupCDCvsKS -0.031323819 0.035778519
## AgeR -0.010640950 -0.007449242
## GroupCvsCD_KS:AgeR -0.001928425 0.000297394
## GroupCDCvsKS:AgeR -0.002735512 0.001226584Here are the posthoc Tukey multiple comparisons of means (95% family-wise confidence level) with a plot to show.
Group:
diff lwr upr p adj CDC-Control -0.008 -0.090 0.074 0.973 KS-Control -0.012 -0.093 0.069 0.938 KS-CDC -0.004 -0.086 0.078 0.993
5.7.2 Death worry single item
Omnibus ANOVA test: Death worry single item
| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| Group | 2 | 3.481 | 1.741 | 0.871 | 0.419 |
| Residuals | 1698 | 3393.913 | 1.999 |
Effect sizes:
| term | df | sumsq | meansq | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group | Group | 2 | 3.481 | 1.741 | 0.871 | 0.419 | 0.001 | 0.001 | 0 | 0 | 0 | 0.032 | 0.201 |
| …2 | Residuals | 1698 | 3393.913 | 1.999 |
Contrasts:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 3.063 | 0.034 | 89.321 | 0.000 |
| GroupCvsCD_KS | -0.007 | 0.024 | -0.296 | 0.767 |
| GroupCDCvsKS | -0.054 | 0.042 | -1.281 | 0.200 |
| Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
|---|---|---|---|
| 1701 | 1.414 | 0.001 | 0 |
Planned contrast effect sizes
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c1 | Group.c1 | 0.199 | 0.199 | 1 | 0.1 | 0.752 | 0 | 0 | -0.001 | -0.001 | -0.001 | 0.008 | 0.061 |
| …2 | Residuals | 3397.195 | 2.000 | 1699 |
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c2 | Group.c2 | 3.282 | 3.282 | 1 | 1.635 | 0.201 | 0.001 | 0.001 | 0.001 | 0.001 | 0.001 | 0.038 | 0.248 |
| …2 | Residuals | 2242.393 | 2.008 | 1117 |
Show least squares means and CIs around means
| Group | lsmean | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Control | 3.077 | 0.059 | 1698 | 2.962 | 3.192 |
| CDC | 3.110 | 0.061 | 1698 | 2.991 | 3.229 |
| KS | 3.002 | 0.059 | 1698 | 2.886 | 3.117 |
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 582 3.08 1.41 3 3.1 1.48 1 5 4 -0.09 -1.24 0.06
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 545 3.11 1.41 3 3.14 1.48 1 5 4 -0.12 -1.25 0.06
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 574 3 1.42 3 3 1.48 1 5 4 -0.1 -1.28 0.06
Exploratory: Age moderation
##
## Call:
## lm(formula = D_Worry ~ Group * AgeR, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.66377 -1.15388 0.08944 1.09087 2.66792
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.0611724 0.0337534 90.692 <2e-16 ***
## GroupCvsCD_KS -0.0031167 0.0237205 -0.131 0.895
## GroupCDCvsKS -0.0525942 0.0415919 -1.265 0.206
## AgeR -0.0183459 0.0019770 -9.280 <2e-16 ***
## GroupCvsCD_KS:AgeR -0.0012357 0.0013785 -0.896 0.370
## GroupCDCvsKS:AgeR 0.0008143 0.0024545 0.332 0.740
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 1667 degrees of freedom
## (30 observations deleted due to missingness)
## Multiple R-squared: 0.05062, Adjusted R-squared: 0.04777
## F-statistic: 17.78 on 5 and 1667 DF, p-value: < 2.2e-16## 2.5 % 97.5 %
## (Intercept) 2.994968955 3.127375873
## GroupCvsCD_KS -0.049641867 0.043408369
## GroupCDCvsKS -0.134172010 0.028983550
## AgeR -0.022223518 -0.014468269
## GroupCvsCD_KS:AgeR -0.003939459 0.001468147
## GroupCDCvsKS:AgeR -0.003999832 0.005628513Here are the posthoc Tukey multiple comparisons of means (95% family-wise confidence level) with a plot to show.
Group:
diff lwr upr p adj CDC-Control 0.033 -0.165 0.23 0.920 KS-Control -0.076 -0.271 0.12 0.635 KS-CDC -0.108 -0.307 0.09 0.406
5.7.3 Worry about getting older single item check
Omnibus ANOVA test: worry about getting older
| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| Group | 2 | 4.455 | 2.228 | 1.108 | 0.33 |
| Residuals | 1700 | 3417.904 | 2.011 |
Effect sizes:
| term | df | sumsq | meansq | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group | Group | 2 | 4.455 | 2.228 | 1.108 | 0.33 | 0.001 | 0.001 | 0 | 0 | 0 | 0.036 | 0.246 |
| …2 | Residuals | 1700 | 3417.904 | 2.011 |
Contrasts:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 3.125 | 0.034 | 90.926 | 0.000 |
| GroupCvsCD_KS | -0.022 | 0.024 | -0.890 | 0.373 |
| GroupCDCvsKS | -0.050 | 0.042 | -1.179 | 0.238 |
| Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
|---|---|---|---|
| 1703 | 1.418 | 0.001 | 0 |
Planned contrast effect sizes
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c1 | Group.c1 | 1.659 | 1.659 | 1 | 0.825 | 0.364 | 0 | 0 | 0 | 0 | 0 | 0.022 | 0.149 |
| …2 | Residuals | 3420.701 | 2.011 | 1701 |
| term | sumsq | meansq | df | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group.c2 | Group.c2 | 2.797 | 2.797 | 1 | 1.353 | 0.245 | 0.001 | 0.001 | 0 | 0 | 0 | 0.035 | 0.214 |
| …2 | Residuals | 2312.406 | 2.066 | 1119 |
Show least squares means and CIs around means
| Group | lsmean | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Control | 3.168 | 0.059 | 1700 | 3.053 | 3.284 |
| CDC | 3.154 | 0.061 | 1700 | 3.035 | 3.273 |
| KS | 3.054 | 0.059 | 1700 | 2.938 | 3.170 |
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 582 3.17 1.38 3 3.21 1.48 1 5 4 -0.21 -1.15 0.06
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 546 3.15 1.43 3 3.19 1.48 1 5 4 -0.2 -1.25 0.06
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 575 3.05 1.45 3 3.07 1.48 1 5 4 -0.08 -1.29 0.06
Exploratory: Age moderation
##
## Call:
## lm(formula = O_Worry ~ Group * AgeR, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6579 -1.2079 0.1157 1.1709 2.6031
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.120551521 0.033956850 91.898 <2e-16 ***
## GroupCvsCD_KS -0.020251561 0.023871002 -0.848 0.396
## GroupCDCvsKS -0.045895994 0.041829758 -1.097 0.273
## AgeR -0.017309321 0.001989624 -8.700 <2e-16 ***
## GroupCvsCD_KS:AgeR -0.001550339 0.001387514 -1.117 0.264
## GroupCDCvsKS:AgeR 0.000008015 0.002469862 0.003 0.997
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 1669 degrees of freedom
## (28 observations deleted due to missingness)
## Multiple R-squared: 0.04536, Adjusted R-squared: 0.0425
## F-statistic: 15.86 on 5 and 1669 DF, p-value: 2.729e-15## 2.5 % 97.5 %
## (Intercept) 3.053949018 3.187154023
## GroupCvsCD_KS -0.067071820 0.026568698
## GroupCDCvsKS -0.127940311 0.036148324
## AgeR -0.021211742 -0.013406900
## GroupCvsCD_KS:AgeR -0.004271791 0.001171112
## GroupCDCvsKS:AgeR -0.004836338 0.004852369Here are the posthoc Tukey multiple comparisons of means (95% family-wise confidence level) with a plot to show.
Group:
diff lwr upr p adj CDC-Control -0.015 -0.213 0.184 0.984 KS-Control -0.114 -0.310 0.081 0.355 KS-CDC -0.100 -0.299 0.099 0.466
5.8 Follow-up tests
First row is Control vs CDC. Second row is Control vs Kitchen Sink.
| Var | M_C | sd_C | M_XI | sd_XI | Diff_est | CI_low | CI_high | t_statistic | test_dfs | p_value | p_value_Holm | p_value_Bonf | Effect_D |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Ageism_CVPriority2_1 | 3.082 | 1.779 | 2.851 | 1.764 | 0.231 | 0.023 | 0.439 | 2.178 | 1113 | 0.030 | 0.119 | 0.356 | 0.130 |
| Ageism_CVPriority2_1 | 3.082 | 1.779 | 2.776 | 1.722 | 0.306 | 0.103 | 0.509 | 2.951 | 1137 | 0.003 | 0.032 | 0.039 | 0.175 |
| Ageism_CVPriority2_2 | 3.075 | 1.747 | 2.762 | 1.692 | 0.313 | 0.111 | 0.516 | 3.033 | 1105 | 0.002 | 0.030 | 0.030 | 0.182 |
| Ageism_CVPriority2_2 | 3.075 | 1.747 | 2.800 | 1.668 | 0.275 | 0.076 | 0.475 | 2.711 | 1127 | 0.007 | 0.054 | 0.082 | 0.161 |
| Adhere_willing | 4.648 | 1.475 | 4.832 | 1.425 | -0.184 | -0.353 | -0.014 | -2.127 | 1125 | 0.034 | 0.119 | 0.403 | 0.127 |
| Adhere_willing | 4.648 | 1.475 | 4.894 | 1.325 | -0.246 | -0.408 | -0.084 | -2.983 | 1144 | 0.003 | 0.032 | 0.035 | 0.175 |
| AdhereAttitude3_1 | 4.677 | 1.460 | 4.890 | 1.307 | -0.213 | -0.375 | -0.051 | -2.582 | 1123 | 0.010 | 0.060 | 0.119 | 0.154 |
| AdhereAttitude3_1 | 4.677 | 1.460 | 4.789 | 1.398 | -0.112 | -0.277 | 0.053 | -1.335 | 1153 | 0.182 | 0.182 | 1.000 | 0.079 |
| AdhereAttitude3_3 | 4.721 | 1.351 | 4.866 | 1.248 | -0.145 | -0.297 | 0.007 | -1.874 | 1125 | 0.061 | 0.122 | 0.734 | 0.112 |
| AdhereAttitude3_3 | 4.721 | 1.351 | 4.902 | 1.285 | -0.181 | -0.333 | -0.029 | -2.330 | 1150 | 0.020 | 0.100 | 0.240 | 0.137 |
| BehavIntRisk_Avg | 3.002 | 1.516 | 2.767 | 1.485 | 0.235 | 0.060 | 0.411 | 2.635 | 1124 | 0.009 | 0.060 | 0.102 | 0.157 |
| BehavIntRisk_Avg | 3.002 | 1.516 | 2.748 | 1.490 | 0.254 | 0.081 | 0.428 | 2.875 | 1154 | 0.004 | 0.037 | 0.049 | 0.169 |
6 Research Q3
Does the framing of the question (how many would die vs. how many would recover) influence how people think COVID-19 would impact older adults?
6.1 Framing effects
6.1.1 50-64
I ran a Welch’s independent samples t-test to test whether participants estimates of number of adults aged 50-64 (out of 100 who got COVID) who would die differed depending on how the question was framed (recover vs. die).
We found that asking how many adults will die (46 out of 100) results in higher estimates than asking how many recover (36 out of 100). On average, both groups overestimate how many will die compared to current CFR (1.3) for this age group which is corresponding to 1 out of 100 (according to CDC data).
## Warning in independentSamplesTTest(formula = fiftytosixtyfour_die ~ Frame, : 16
## case(s) removed due to missingness##
## Welch's independent samples t-test
##
## Outcome variable: fiftytosixtyfour_die
## Grouping variable: Frame
##
## Descriptive statistics:
## How many die framing How many recover framing
## mean 44.708 34.959
## std dev. 31.461 25.054
##
## Hypotheses:
## null: population means equal for both groups
## alternative: different population means in each group
##
## Test results:
## t-statistic: 7.036
## degrees of freedom: 1597.34
## p-value: <.001
##
## Other information:
## two-sided 95% confidence interval: [7.031, 12.467]
## estimated effect size (Cohen's d): 0.3436.1.2 65 and older
I then ran the same test for the questions asking about adults over 65 years old.
Again, asking how many will die results in higher estimates (55 out of 100) than asking how many will recover (42 out of 100). Again, both groups on average overestimate how many will die compared to current CFR (9.9) for this age group which is corresponding to 10 out of 100.
## Warning in independentSamplesTTest(formula = sixtyfiveorolder_die ~ Frame, : 16
## case(s) removed due to missingness##
## Welch's independent samples t-test
##
## Outcome variable: sixtyfiveorolder_die
## Grouping variable: Frame
##
## Descriptive statistics:
## How many die framing How many recover framing
## mean 53.827 42.392
## std dev. 30.699 28.346
##
## Hypotheses:
## null: population means equal for both groups
## alternative: different population means in each group
##
## Test results:
## t-statistic: 7.948
## degrees of freedom: 1674.082
## p-value: <.001
##
## Other information:
## two-sided 95% confidence interval: [8.613, 14.257]
## estimated effect size (Cohen's d): 0.387Check whether estimates differ based on the group for fifty to sixty four years old. Combined, in the die framing, and in the recover framing.
Check whether estimates differ based on the group for sixty or older. Combined, in the die framing, and in the recover framing.
6.1.3 Tragic death
We asked if teenage suicide is more tragic than suicide among the old (M=2.48, SD=1.04) and if a teenager dying from COVID-19 is more tragic than an older person dying from COVID-19 (M=2.25, SD=1.08).
There was a significant difference - on average the death of a teenager was seen as more tragic than the death of an older person when it is a result of suicide rather than when it is caused by COVID-19.
## Warning in pairedSamplesTTest(formula = ~Ageism_CVBelief_5 +
## Ageism_Fabroni2_4, : 5 case(s) removed due to missingness##
## Paired samples t-test
##
## Variables: Ageism_CVBelief_5 , Ageism_Fabroni2_4
##
## Descriptive statistics:
## Ageism_CVBelief_5 Ageism_Fabroni2_4 difference
## mean 2.249 2.475 -0.226
## std dev. 1.084 1.037 1.052
##
## Hypotheses:
## null: population means equal for both measurements
## alternative: different population means for each measurement
##
## Test results:
## t-statistic: -8.839
## degrees of freedom: 1697
## p-value: <.001
##
## Other information:
## two-sided 95% confidence interval: [-0.276, -0.176]
## estimated effect size (Cohen's d): 0.2147 Debrief
In the debrief we said:
*If you have any final comments for us about the survey, please leave them in the textbox below
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| The questions were saddening in regards to various opinions in regards to the elderly. |
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| new retirement age 55 |
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| Pmccau7514@aol.com |
| other than severe diseases, Geriatrics is a sad place to work, death is anticipated more frequently than the Middle age groups, due to the Life Expectancy. the other end is the neonates, they too die, and while this is heart breaking, EVERYLIFE is important no matter the issues experienced, I was Licensed in 1977, 4-1977 Thank you, for allowing me to participate:) |
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| I am 74 and pretty much knew where this survey was going. I am in the high risk group myself, but still say I don’t want the rest of the population, which is younger than 74, to give up their life, liberty, and way to make a living for me. I say NO LOCKDOWN AGAIN…I’ll take my chances. Live YOUR life. |
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| Covid 19 An epidemic size virus. I have been asked more questions about this for so long. This disease is more common in the elderly. But everyone, big or small, has to be careful. |
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| you have questions |
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| well survey |
| its great |
| study was about COVID-19 |
| 12 |
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| 1 |
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| g |
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| Good smattering of questions. |
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| 1 |
| 1 |
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| The covid-19 virus really affected us as a whole |
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| 1 |
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| 30 |
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| As long as people, places, cities and states do not comply with COVID precautions, we’re in big trouible! |
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| You don’t have a question on why the cdc doesn’t have a clue |
| Very interesting metrics; I would be interested in seeing the results, if you can achieve a large enough sample set |
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| Very unique and interesting survey, enjoyed providing feedback |
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| There are several means by which you can gather customer information: comments, social media, live sessions, emails, in-product … |
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| 1 |
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| this is very exccelent survey |
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| COVID-19 affects different people in different ways. Infected people have had a wide range of symptoms reported – from mild symptoms to severe illness. |
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| Nick |
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| Na |
8 Plots
8.1 Regression
First plot…
Same plot but showing density distributions 
9 Exploratory Qs
9.1 Age as predictor (C)
Here checking to see if age predicts the outcomes measures.
| Age | |||
|---|---|---|---|
| Predictors | Beta (95% CI) | Statistic | p value |
| (Intercept) |
-0.00 (-0.09 – 0.09) |
6.75 | <0.001 |
| Priority: Back to work |
0.02 (-0.11 – 0.15) |
0.30 | 0.764 |
| Priority: Economy |
0.04 (-0.09 – 0.16) |
0.58 | 0.563 |
| Priority: Normal |
0.07 (-0.06 – 0.19) |
1.03 | 0.301 |
| Guidelines restrictive |
-0.03 (-0.14 – 0.07) |
-0.61 | 0.542 |
| Guidelines effective prevent getting CV19 |
-0.23 (-0.39 – -0.08) |
-2.90 | 0.004 |
| Guidelines effective save lives |
0.08 (-0.06 – 0.22) |
1.11 | 0.267 |
| Guidelines effective slow spread |
0.12 (-0.05 – 0.29) |
1.38 | 0.169 |
| Willingness to stay home again |
-0.04 (-0.17 – 0.08) |
-0.65 | 0.518 |
| Willingness to follow guidelines |
0.08 (-0.04 – 0.20) |
1.36 | 0.176 |
| Risky Behavioral intent scale |
-0.36 (-0.48 – -0.25) |
-6.32 | <0.001 |
| Protective Behavioral intent scale |
0.18 (0.07 – 0.29) |
3.10 | 0.002 |
| Prosocial scale |
0.04 (-0.06 – 0.14) |
0.77 | 0.441 |
| Individual risk |
0.02 (-0.08 – 0.13) |
0.45 | 0.654 |
| Observations | 424 | ||
| R2 / R2 adjusted | 0.176 / 0.149 | ||
9.2 Risk mediator of intentions
9.2.1 Risk mediator (all)
From Laura: “you might find that the CDC intervention reduces intentions to social distance among younger adults, but the mediator is perceived risk not attitudes toward older adults. For my peace of mind let’s make sure to test this alternative mediator”
Risk perceptions predicted behavioral intentions for the risky behaviors
##
## Call:
## lm(formula = BehavIntRisk_Avg ~ IndivRisk_Avg, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1463 -1.1355 -0.2791 1.0173 4.0318
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.77190 0.11641 15.222 < 2e-16 ***
## IndivRisk_Avg 0.19634 0.02666 7.364 0.000000000000306 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.364 on 1390 degrees of freedom
## (311 observations deleted due to missingness)
## Multiple R-squared: 0.03754, Adjusted R-squared: 0.03685
## F-statistic: 54.22 on 1 and 1390 DF, p-value: 0.0000000000003056However, risk perception was not predicted by group. I think theoretically we are not justified to run the mediation.
Just to be sure, running the mediation model anyway.
##
## Call:
## lm(formula = BehavIntRisk_Avg ~ Group + IndivRisk_Avg, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1776 -1.1312 -0.2800 0.9824 4.0718
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.77493 0.11603 15.297 < 2e-16 ***
## GroupCvsCD_KS -0.08214 0.02571 -3.195 0.00143 **
## GroupCDCvsKS -0.03989 0.04472 -0.892 0.37265
## IndivRisk_Avg 0.19554 0.02658 7.357 0.000000000000321 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.359 on 1388 degrees of freedom
## (311 observations deleted due to missingness)
## Multiple R-squared: 0.04515, Adjusted R-squared: 0.04308
## F-statistic: 21.88 on 3 and 1388 DF, p-value: 0.00000000000007635## $`Mod1: Y~X`
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.58539432 0.03712839 69.633897 0.000000000
## predCvsCD_KS -0.08374515 0.02619633 -3.196827 0.001420779
## predCDCvsKS -0.04098819 0.04557201 -0.899416 0.368587041
##
## $`Mod2: Y~X+M`
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.77492976 0.11603311 15.2967528 6.222939e-49
## predCvsCD_KS -0.08214012 0.02571023 -3.1948425 1.430535e-03
## predCDCvsKS -0.03988637 0.04472500 -0.8918137 3.726474e-01
## med 0.19553817 0.02657874 7.3569398 3.207467e-13
##
## $`Mod3: M~X`
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.144789551 0.03678497 112.6761804 0.0000000
## predCvsCD_KS -0.008208263 0.02595403 -0.3162616 0.7518515
## predCDCvsKS -0.005634830 0.04515049 -0.1248011 0.9006991
##
## $Indirect.Effect
## [1] 0.0003273978
##
## $SE
## [1] 0.001098379
##
## $z.value
## [1] 0.2980736
##
## $N
## [1] 1392## [1] 1.234353## Control CDC KS
## 3.002320 2.766850 2.748171## Control CDC KS
## 4.161206 4.142216 4.137051Omnibus ANOVA test: Risky behaviors by group controlling for individual risk perception
(e.g., going to gatherings of 10 or more people)
| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| Group | 2 | 21.26 | 10.629 | 5.752 | 0.003 |
| IndivRisk_Avg | 1 | 100.01 | 100.014 | 54.125 | 0.000 |
| Residuals | 1388 | 2564.82 | 1.848 |
Effect sizes:
| term | df | sumsq | meansq | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group | Group | 2 | 21.26 | 10.629 | 5.752 | 0.003 | 0.008 | 0.008 | 0.007 | 0.007 | 0.007 | 0.091 | 0.869 |
| IndivRisk_Avg | IndivRisk_Avg | 1 | 100.01 | 100.014 | 54.125 | 0.000 | 0.037 | 0.038 | 0.037 | 0.037 | 0.037 | 0.197 | 1.000 |
| …3 | Residuals | 1388 | 2564.82 | 1.848 |
Contrasts:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 1.775 | 0.116 | 15.297 | 0.000 |
| GroupCvsCD_KS | -0.082 | 0.026 | -3.195 | 0.001 |
| GroupCDCvsKS | -0.040 | 0.045 | -0.892 | 0.373 |
| IndivRisk_Avg | 0.196 | 0.027 | 7.357 | 0.000 |
| Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
|---|---|---|---|
| 1392 | 1.359 | 0.045 | 0.043 |
Show least squares means and CIs around means
| Group | lsmean | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Control | 2.750 | 0.063 | 1388 | 2.626 | 2.873 |
| CDC | 2.543 | 0.064 | 1388 | 2.418 | 2.668 |
| KS | 2.463 | 0.063 | 1388 | 2.340 | 2.587 |
9.2.2 Risky mediator (young)
Running the same tests but with participants under the age of 34.
Omnibus ANOVA test: Risky behaviors by group (young participants)
(higher score correspond to more risk)
| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| Group | 2 | 1.534 | 0.767 | 0.432 | 0.65 |
| Residuals | 390 | 692.905 | 1.777 |
Effect sizes:
| term | df | sumsq | meansq | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group | Group | 2 | 1.534 | 0.767 | 0.432 | 0.65 | 0.002 | 0.002 | -0.003 | -0.003 | -0.003 | 0.047 | 0.121 |
| …2 | Residuals | 390 | 692.905 | 1.777 |
Contrasts:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 3.997 | 0.067 | 59.415 | 0.000 |
| GroupCvsCD_KS | -0.043 | 0.047 | -0.920 | 0.358 |
| GroupCDCvsKS | 0.009 | 0.083 | 0.112 | 0.911 |
| Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
|---|---|---|---|
| 393 | 1.333 | 0.002 | -0.003 |
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 1.5 0.7668 0.432 0.650
## Group: Control vs. CDC_KS 1 1.5 1.5112 0.851 0.357
## Group: CDC vs KS 1 0.0 0.0224 0.013 0.911
## Residuals 390 692.9 1.7767
## 118 observations deleted due to missingnessShow least squares means and CIs around means
| Group | lsmean | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Control | 4.084 | 0.115 | 390 | 3.858 | 4.309 |
| CDC | 3.944 | 0.116 | 390 | 3.717 | 4.172 |
| KS | 3.963 | 0.119 | 390 | 3.729 | 4.197 |
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 135 4.08 1.24 4 4.08 1.15 1 7 6 0.01 0.08 0.11
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 133 3.94 1.3 4.11 3.97 1.15 1 7 6 -0.16 -0.02 0.11
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 125 3.96 1.46 3.89 3.97 1.32 1 7 6 -0.05 -0.45 0.13##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 582 3 1.52 2.8 2.9 1.78 1 6 5 0.45 -0.93 0.06
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 546 2.77 1.48 2.4 2.63 1.48 1 6 5 0.62 -0.74 0.06
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 574 2.75 1.49 2.4 2.6 1.48 1 6 5 0.66 -0.73 0.06##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 178 3.15 1.29 3.1 3.1 1.33 1 6 5 0.27 -0.65 0.1
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 168 2.89 1.29 3 2.82 1.48 1 6 5 0.35 -0.7 0.1
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 165 2.98 1.37 2.8 2.91 1.48 1 6 5 0.4 -0.83 0.11Here are the posthoc Tukey multiple comparisons of means (95% family-wise confidence level) with a plot to show.
Group:
diff lwr upr p adj CDC-Control -0.140 -0.523 0.244 0.668 KS-Control -0.121 -0.510 0.268 0.745 KS-CDC 0.019 -0.372 0.409 0.993
Risk is associated with the risky behavioral intentions.
##
## Call:
## lm(formula = BehavIntRisk_Avg ~ IndivRisk_Avg, data = df_Young)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2946 -1.0430 -0.0783 0.8665 3.4828
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.15432 0.20250 10.638 < 2e-16 ***
## IndivRisk_Avg 0.16289 0.04806 3.389 0.000772 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.267 on 391 degrees of freedom
## (118 observations deleted due to missingness)
## Multiple R-squared: 0.02854, Adjusted R-squared: 0.02606
## F-statistic: 11.49 on 1 and 391 DF, p-value: 0.0007721Again, because risk is not predicted by group I don’t think we are justified to run this. But for robustness sake, here is the mediation model.
##
## Call:
## lm(formula = BehavIntRisk_Avg ~ Group + IndivRisk_Avg, data = df_Young)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2134 -1.0679 -0.1081 0.8865 3.5213
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.16738 0.20249 10.704 < 2e-16 ***
## GroupCvsCD_KS -0.07197 0.04486 -1.604 0.10946
## GroupCDCvsKS 0.02409 0.07882 0.306 0.76003
## IndivRisk_Avg 0.15920 0.04807 3.312 0.00101 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.265 on 389 degrees of freedom
## (118 observations deleted due to missingness)
## Multiple R-squared: 0.0352, Adjusted R-squared: 0.02776
## F-statistic: 4.731 on 3 and 389 DF, p-value: 0.002963## $`Mod1: Y~X`
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.80370987 0.06467840 43.3484754 3.261012e-151
## predCvsCD_KS -0.07888581 0.04538209 -1.7382586 8.295449e-02
## predCDCvsKS 0.02557594 0.07982032 0.3204189 7.488224e-01
##
## $`Mod2: Y~X+M`
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.16737875 0.20248681 10.7038021 1.314652e-23
## predCvsCD_KS -0.07197172 0.04486169 -1.6043026 1.094589e-01
## predCDCvsKS 0.02409239 0.07882078 0.3056603 7.600268e-01
## med 0.15919862 0.04807263 3.3116268 1.014307e-03
##
## $`Mod3: M~X`
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.997089471 0.06727432 59.4147870 1.559738e-197
## predCvsCD_KS -0.043430573 0.04720354 -0.9200703 3.581048e-01
## predCDCvsKS 0.009318881 0.08302398 0.1122432 9.106883e-01
##
## $Indirect.Effect
## [1] -0.001046346
##
## $SE
## [1] 0.003607193
##
## $z.value
## [1] -0.2900721
##
## $N
## [1] 393## [1] 0.7717611## Control CDC KS
## 3.148315 2.886905 2.981818## Control CDC KS
## 4.083951 3.944340 3.962978Omnibus ANOVA test: Risky behaviors by group controlling for individual risk perception (young participants)
(e.g., going to gatherings of 10 or more people)
| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| Group | 2 | 5.166 | 2.583 | 1.613 | 0.201 |
| IndivRisk_Avg | 1 | 17.561 | 17.561 | 10.967 | 0.001 |
| Residuals | 389 | 622.901 | 1.601 |
Effect sizes:
| term | df | sumsq | meansq | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group | Group | 2 | 5.166 | 2.583 | 1.613 | 0.201 | 0.008 | 0.008 | 0.003 | 0.003 | 0.003 | 0.091 | 0.343 |
| IndivRisk_Avg | IndivRisk_Avg | 1 | 17.561 | 17.561 | 10.967 | 0.001 | 0.027 | 0.027 | 0.025 | 0.025 | 0.025 | 0.168 | 0.912 |
| …3 | Residuals | 389 | 622.901 | 1.601 |
Contrasts:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 2.167 | 0.202 | 10.704 | 0.000 |
| GroupCvsCD_KS | -0.072 | 0.045 | -1.604 | 0.109 |
| GroupCDCvsKS | 0.024 | 0.079 | 0.306 | 0.760 |
| IndivRisk_Avg | 0.159 | 0.048 | 3.312 | 0.001 |
| Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
|---|---|---|---|
| 393 | 1.265 | 0.035 | 0.028 |
Show least squares means and CIs around means
| Group | lsmean | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Control | 2.948 | 0.109 | 389 | 2.734 | 3.162 |
| CDC | 2.708 | 0.110 | 389 | 2.492 | 2.924 |
| KS | 2.756 | 0.113 | 389 | 2.533 | 2.979 |
9.3 Social risk perceptions
Omnibus ANOVA test: How serious of a threat do you think COVID-19 is to the U.S.?
(higher score correspond to more risk)
| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| Group | 2 | 7.873 | 3.937 | 1.658 | 0.191 |
| Residuals | 1699 | 4033.860 | 2.374 |
Effect sizes:
| term | df | sumsq | meansq | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group | Group | 2 | 7.873 | 3.937 | 1.658 | 0.191 | 0.002 | 0.002 | 0.001 | 0.001 | 0.001 | 0.044 | 0.352 |
| …2 | Residuals | 1699 | 4033.860 | 2.374 |
Contrasts:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 5.885 | 0.037 | 157.496 | 0.000 |
| GroupCvsCD_KS | 0.046 | 0.026 | 1.767 | 0.077 |
| GroupCDCvsKS | -0.021 | 0.046 | -0.465 | 0.642 |
| Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
|---|---|---|---|
| 1702 | 1.541 | 0.002 | 0.001 |
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 8 3.937 1.658 0.1908
## Group: Control vs. CDC_KS 1 7 7.360 3.100 0.0785 .
## Group: CDC vs KS 1 1 0.513 0.216 0.6420
## Residuals 1699 4034 2.374
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation deleted due to missingnessShow least squares means and CIs around means
| Group | lsmean | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Control | 5.792 | 0.064 | 1699 | 5.666 | 5.917 |
| CDC | 5.952 | 0.066 | 1699 | 5.823 | 6.082 |
| KS | 5.910 | 0.064 | 1699 | 5.784 | 6.036 |
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 581 5.79 1.63 7 6.1 0 1 7 6 -1.28 0.63 0.07
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 546 5.95 1.48 7 6.25 0 1 7 6 -1.43 1.26 0.06
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 575 5.91 1.51 7 6.2 0 1 7 6 -1.37 1.1 0.06Here are the posthoc Tukey multiple comparisons of means (95% family-wise confidence level) with a plot to show.
Group:
diff lwr upr p adj CDC-Control 0.161 -0.055 0.376 0.187 KS-Control 0.118 -0.095 0.330 0.395 KS-CDC -0.043 -0.259 0.173 0.888
Omnibus ANOVA test: Compared to now, do you think the COVID-19 pandemic in the U.S. will be better or worse in six months?
(higher score correspond to more optimistic)
| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| Group | 2 | 17.82 | 8.908 | 2.668 | 0.07 |
| Residuals | 1697 | 5665.50 | 3.339 |
Effect sizes:
| term | df | sumsq | meansq | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group | Group | 2 | 17.82 | 8.908 | 2.668 | 0.07 | 0.003 | 0.003 | 0.002 | 0.002 | 0.002 | 0.056 | 0.532 |
| …2 | Residuals | 1697 | 5665.50 | 3.339 |
Contrasts:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 4.611 | 0.044 | 104.018 | 0.000 |
| GroupCvsCD_KS | -0.072 | 0.031 | -2.310 | 0.021 |
| GroupCDCvsKS | 0.003 | 0.055 | 0.047 | 0.962 |
| Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
|---|---|---|---|
| 1700 | 1.827 | 0.003 | 0.002 |
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 18 8.908 2.668 0.0697 .
## Group: Control vs. CDC_KS 1 18 17.809 5.334 0.0210 *
## Group: CDC vs KS 1 0 0.008 0.002 0.9622
## Residuals 1697 5665 3.339
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 3 observations deleted due to missingnessShow least squares means and CIs around means
| Group | lsmean | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Control | 4.755 | 0.076 | 1697 | 4.606 | 4.904 |
| CDC | 4.537 | 0.078 | 1697 | 4.383 | 4.690 |
| KS | 4.542 | 0.076 | 1697 | 4.392 | 4.691 |
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 580 4.76 1.82 5 4.91 1.48 1 7 6 -0.46 -0.67 0.08
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 546 4.54 1.81 5 4.66 1.48 1 7 6 -0.42 -0.67 0.08
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 574 4.54 1.85 5 4.68 1.48 1 7 6 -0.44 -0.74 0.08Here are the posthoc Tukey multiple comparisons of means (95% family-wise confidence level) with a plot to show.
Group:
diff lwr upr p adj CDC-Control -0.219 -0.474 0.037 0.111 KS-Control -0.213 -0.466 0.039 0.117 KS-CDC 0.005 -0.251 0.261 0.999
##
## Welch's independent samples t-test
##
## Outcome variable: SocietalRisk2
## Grouping variable: Group
##
## Descriptive statistics:
## Control CDC
## mean 4.755 4.537
## std dev. 1.821 1.809
##
## Hypotheses:
## null: population means equal for both groups
## alternative: different population means in each group
##
## Test results:
## t-statistic: 2.02
## degrees of freedom: 1120.716
## p-value: 0.044
##
## Other information:
## two-sided 95% confidence interval: [0.006, 0.431]
## estimated effect size (Cohen's d): 0.12##
## Welch's independent samples t-test
##
## Outcome variable: SocietalRisk2
## Grouping variable: Group
##
## Descriptive statistics:
## Control KS
## mean 4.755 4.542
## std dev. 1.821 1.851
##
## Hypotheses:
## null: population means equal for both groups
## alternative: different population means in each group
##
## Test results:
## t-statistic: 1.974
## degrees of freedom: 1151.176
## p-value: 0.049
##
## Other information:
## two-sided 95% confidence interval: [0.001, 0.425]
## estimated effect size (Cohen's d): 0.1169.4 Extra regressions
9.4.1 Behavior intent (all items)
| Ageism Myth Avg | Ageism Fraboni Avg | Ageism Fact Avg | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Beta (95% CI) | Statistic | p value | Beta (95% CI) | Statistic | p value | Beta (95% CI) | Statistic | p value |
| (Intercept) |
-0.00 (-0.08 – 0.08) |
7.83 | <0.001 |
0.00 (-0.07 – 0.07) |
11.02 | <0.001 |
0.00 (-0.08 – 0.08) |
9.27 | <0.001 |
| Priority: Back to work |
0.27 (0.16 – 0.39) |
4.75 | <0.001 |
0.27 (0.17 – 0.38) |
5.33 | <0.001 |
0.05 (-0.07 – 0.16) |
0.84 | 0.401 |
| Priority: Economy |
0.09 (-0.02 – 0.20) |
1.57 | 0.117 |
0.03 (-0.07 – 0.13) |
0.58 | 0.564 |
-0.03 (-0.14 – 0.09) |
-0.46 | 0.646 |
| Priority: Normal |
-0.01 (-0.12 – 0.10) |
-0.25 | 0.800 |
0.00 (-0.10 – 0.10) |
0.03 | 0.973 |
-0.01 (-0.12 – 0.10) |
-0.20 | 0.844 |
| Willingness to stay home again |
0.18 (0.07 – 0.30) |
3.23 | 0.001 |
0.14 (0.04 – 0.24) |
2.67 | 0.008 |
0.07 (-0.04 – 0.18) |
1.21 | 0.228 |
| Willingness to follow guidelines |
-0.05 (-0.16 – 0.05) |
-1.02 | 0.308 |
0.06 (-0.04 – 0.15) |
1.19 | 0.236 |
-0.03 (-0.14 – 0.07) |
-0.62 | 0.532 |
| Guidelines restrictive |
0.00 (-0.09 – 0.10) |
0.09 | 0.925 |
-0.00 (-0.09 – 0.08) |
-0.09 | 0.929 |
-0.03 (-0.13 – 0.07) |
-0.63 | 0.532 |
| Guidelines effective slow spread |
0.03 (-0.12 – 0.19) |
0.42 | 0.674 |
-0.11 (-0.25 – 0.03) |
-1.55 | 0.122 |
0.01 (-0.15 – 0.17) |
0.10 | 0.920 |
| Guidelines effective prevent getting CV19 |
-0.06 (-0.20 – 0.09) |
-0.78 | 0.436 |
0.04 (-0.09 – 0.17) |
0.56 | 0.573 |
-0.02 (-0.17 – 0.12) |
-0.29 | 0.774 |
| Guidelines effective save lives |
0.10 (-0.03 – 0.23) |
1.54 | 0.124 |
0.06 (-0.05 – 0.17) |
1.04 | 0.297 |
-0.00 (-0.13 – 0.13) |
-0.05 | 0.962 |
| Intent: 10 more |
0.17 (0.03 – 0.32) |
2.44 | 0.015 |
0.15 (0.02 – 0.28) |
2.35 | 0.020 |
0.08 (-0.06 – 0.23) |
1.14 | 0.255 |
| Intent: opt shops |
0.04 (-0.08 – 0.15) |
0.65 | 0.517 |
-0.04 (-0.15 – 0.06) |
-0.79 | 0.428 |
-0.02 (-0.14 – 0.10) |
-0.37 | 0.714 |
| Intent: opt travel |
0.10 (-0.05 – 0.26) |
1.33 | 0.186 |
0.20 (0.06 – 0.33) |
2.81 | 0.005 |
0.28 (0.12 – 0.43) |
3.53 | <0.001 |
| Intent: opt social |
0.00 (-0.14 – 0.15) |
0.05 | 0.958 |
0.04 (-0.09 – 0.17) |
0.65 | 0.517 |
-0.09 (-0.23 – 0.06) |
-1.15 | 0.252 |
| Intent: eat in restaurants |
0.05 (-0.08 – 0.18) |
0.79 | 0.432 |
0.07 (-0.05 – 0.18) |
1.13 | 0.259 |
0.10 (-0.03 – 0.23) |
1.46 | 0.146 |
| Intent: good hygeine (R) |
-0.08 (-0.19 – 0.02) |
-1.52 | 0.129 |
-0.23 (-0.33 – -0.14) |
-4.86 | <0.001 |
-0.27 (-0.38 – -0.17) |
-5.03 | <0.001 |
| Intent: mask in public (R) |
-0.09 (-0.22 – 0.04) |
-1.43 | 0.153 |
-0.03 (-0.14 – 0.09) |
-0.48 | 0.629 |
-0.14 (-0.27 – -0.01) |
-2.05 | 0.041 |
| Intent: mask outdoors (R) |
0.03 (-0.08 – 0.14) |
0.55 | 0.585 |
0.08 (-0.01 – 0.18) |
1.72 | 0.086 |
0.19 (0.08 – 0.29) |
3.39 | 0.001 |
| Prosocial scale |
-0.04 (-0.13 – 0.04) |
-0.99 | 0.324 |
-0.07 (-0.15 – 0.01) |
-1.75 | 0.081 |
-0.08 (-0.17 – 0.01) |
-1.77 | 0.078 |
| Individual risk |
0.03 (-0.07 – 0.12) |
0.55 | 0.586 |
0.11 (0.02 – 0.19) |
2.50 | 0.013 |
0.10 (0.00 – 0.20) |
2.06 | 0.040 |
| Observations | 428 | 428 | 428 | ||||||
| R2 / R2 adjusted | 0.356 / 0.326 | 0.482 / 0.458 | 0.342 / 0.311 | ||||||
VIF scores are less than 5 for all models
## Ageism_CVPriority1 Ageism_CVPriority2_1 Ageism_CVPriority2_2
## 2.042947 1.973152 1.966206
## AdhereAttitude2 AdhereAttitude3_2 AdhereAttitude3_3
## 1.489788 3.256179 2.572886
## AdhereAttitude3_1 Adhere_willing AdhereAttitude1_1
## 3.823082 1.973162 1.768511
## BehavIntRisk_Avg BehavIntPrtct_Avg Prosocial_Avg
## 1.664469 1.649452 1.224190
## IndivRisk_Avg
## 1.4392539.5 CDC Trust by group
Omnibus ANOVA test: Trust in CDC
| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| Group | 2 | 2.258 | 1.129 | 0.873 | 0.418 |
| Residuals | 1699 | 2197.541 | 1.293 |
Effect sizes:
| term | df | sumsq | meansq | statistic | p.value | etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | power | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group | Group | 2 | 2.258 | 1.129 | 0.873 | 0.418 | 0.001 | 0.001 | 0 | 0 | 0 | 0.032 | 0.201 |
| …2 | Residuals | 1699 | 2197.541 | 1.293 |
Contrasts:
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 3.626 | 0.028 | 131.484 | 0.000 |
| GroupCvsCD_KS | 0.015 | 0.019 | 0.754 | 0.451 |
| GroupCDCvsKS | 0.037 | 0.034 | 1.074 | 0.283 |
| Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
|---|---|---|---|
| 1702 | 1.137 | 0.001 | 0 |
lsmeans contrast (unadjusted)
| contrast | estimate | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Control vs CDC and KS | 0.088 | 0.116 | 1699 | -0.14 | 0.316 |
| CDC vs KS | 0.073 | 0.068 | 1699 | -0.06 | 0.206 |
| t.ratio | p.value |
|---|---|
| 0.754 | 0.451 |
| 1.074 | 0.283 |
lsmeans contrast (adjusted)
## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## Control vs CDC and KS 0.0876 0.116 1699 -0.1731 0.348 0.754 0.5658
## CDC vs KS 0.0730 0.068 1699 -0.0795 0.226 1.074 0.5658
##
## Confidence level used: 0.95
## Conf-level adjustment: bonferroni method for 2 estimates
## P value adjustment: holm method for 2 testsShow least squares means and CIs around means
| Group | lsmean | SE | df | lower.CL | upper.CL |
|---|---|---|---|---|---|
| Control | 3.597 | 0.047 | 1699 | 3.504 | 3.689 |
| CDC | 3.604 | 0.049 | 1699 | 3.509 | 3.700 |
| KS | 3.677 | 0.047 | 1699 | 3.584 | 3.770 |
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis
## CDCTrust1 1 581 3.60 1.28 4 3.74 1.48 1 5 4 -0.61 -0.64
## CDCTrust2 2 581 3.62 1.19 4 3.75 1.48 1 5 4 -0.64 -0.39
## CDCTrust3 3 582 3.57 1.27 4 3.71 1.48 1 5 4 -0.61 -0.62
## se
## CDCTrust1 0.05
## CDCTrust2 0.05
## CDCTrust3 0.05
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis
## CDCTrust1 1 546 3.63 1.26 4 3.78 1.48 1 5 4 -0.65 -0.55
## CDCTrust2 2 546 3.60 1.22 4 3.73 1.48 1 5 4 -0.64 -0.42
## CDCTrust3 3 545 3.58 1.23 4 3.70 1.48 1 5 4 -0.56 -0.62
## se
## CDCTrust1 0.05
## CDCTrust2 0.05
## CDCTrust3 0.05
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis
## CDCTrust1 1 574 3.69 1.24 4 3.85 1.48 1 5 4 -0.72 -0.38
## CDCTrust2 2 574 3.66 1.18 4 3.79 1.48 1 5 4 -0.69 -0.25
## CDCTrust3 3 574 3.68 1.19 4 3.81 1.48 1 5 4 -0.68 -0.36
## se
## CDCTrust1 0.05
## CDCTrust2 0.05
## CDCTrust3 0.05##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 582 3.6 1.16 4 3.72 1.48 1 5 4 -0.65 -0.39 0.05
## ------------------------------------------------------------
## group: CDC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 546 3.6 1.14 3.83 3.72 1.24 1 5 4 -0.66 -0.35 0.05
## ------------------------------------------------------------
## group: KS
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 574 3.68 1.12 4 3.8 1.48 1 5 4 -0.73 -0.15 0.05Here are the posthoc Tukey multiple comparisons of means (95% family-wise confidence level) with a plot to show.
Group:
diff lwr upr p adj CDC-Control 0.007 -0.152 0.166 0.994 KS-Control 0.080 -0.077 0.237 0.453 KS-CDC 0.073 -0.086 0.233 0.530
9.6 Preferences by group
Endorsement of age-related perceptions with agreement with protect people vs keep economy
## # A tibble: 3 x 2
## Group r
## <fct> <dbl>
## 1 Control 0.307
## 2 CDC 0.309
## 3 KS 0.264Endorsement of age-related perceptions with agreement with protect people vs back to normal
## # A tibble: 3 x 2
## Group r
## <fct> <dbl>
## 1 Control 0.285
## 2 CDC 0.321
## 3 KS 0.246Endorsement of age-related perceptions with agreement we should all get back to work, even if it means that more older people will die from COVID-19
## # A tibble: 3 x 2
## Group r
## <fct> <dbl>
## 1 Control 0.515
## 2 CDC 0.559
## 3 KS 0.551
3.17 Social values
Response scale: Not important at all(1), A little important(2), Somewhat important(3), Quite important(4), Extremely important(5).
Descriptive statistics for these items shown below.
The reliability of these items is ideal Cronbach’s Alpha is .86.
Below are the descriptive statistics for the social values scale overall.