*Analyses Code for COVID Messaging Study *Author: Rachel Silverman *Created: Aug 2020 *Last modified: 26 Oct 2020 ****************************************************************************************************************** *set working directory cd "/Users/rsilverman/Desktop/Covid19MessagingStudy/" *import data use "AnalysisData/preliminary_analysis_23July2020_total_Public.dta", clear *group gender for analysis gen male=. replace male=1 if gender=="male" replace male=0 if gender=="female" gen female=0 if male==1 replace female=1 if male==0 *make a variable indicating Young Adult. gen YA=0 replace YA=1 if agegroup=="18-24" replace YA=. if agegroup=="" *group elderly together for analysis gen agegroup2=agegroup replace agegroup2="70+" if agegroup=="70-79"| agegroup=="80+" *Indicate missing race data replace race="" if Q37_1==. & Q37_2==. & Q37_3==. & Q37_4==. & Q37_5==. & Q37_6==. & Q37_7==. & Q37_8==. replace race_simple="" if Q37_1==. & Q37_2==. & Q37_3==. & Q37_4==. & Q37_5==. & Q37_6==. & Q37_7==. & Q37_8==. replace race_standard="" if Q37_1==. & Q37_2==. & Q37_3==. & Q37_4==. & Q37_5==. & Q37_6==. & Q37_7==. & Q37_8==. *group race for analyses gen white=. replace white=1 if race_standard=="White non-Hispanic" replace white=0 if race_standard!="" & race_standard!="White non-Hispanic" gen white_0=1 if white==0 replace white_0=0 if white==1 *group masking for analysis gen mask=0 replace mask=1 if Q40_14==1 gen mask_0=1 if mask==0 replace mask_0=0 if mask==1 *group distancing for analysis gen distancing=0 replace distancing=1 if Q40_5==1 gen distancing_0=1 if distancing==0 replace distancing_0=0 if distancing==1 *group political affiliation/identity for analysis gen republican=. replace republican=0 if simple_politicalparty==2 replace republican=1 if simple_politicalparty==1 replace republican=2 if simple_politicalparty==9 gen politicstxt="a. dem" if simple_politicalparty==2 replace politicstxt="b. repub" if simple_politicalparty==1 replace politicstxt="c. inde/other/none" if simple_politicalparty==9 *group education for analysis gen college_deg=. replace college_deg=1 if education==5 replace college_deg=1 if education==6 replace college_deg=1 if education==7 replace college_deg=0 if education !=. & college_deg==. tab college_deg education, missing *group info sourses gen NIH=0 replace NIH=1 if Q3_25==1 gen no_info=0 replace no_info=1 if Q3_27==1 *rename income variable to be informative rename Q17 house_income *create date variable for when survey was completed generate surveymonth=month(StartDate) *Make indicator variables for region tab Region, gen(region_num) gen Southwest=. replace Southwest=1 if Region=="Southwest" replace Southwest=0 if Region!="Southwest" & Region!="" replace Region="a. Southwest" if Region=="Southwest" *Make binary income variable for analysis tab house_income, missing gen income_under100k=. replace income_under100k=0 if house_income==11 replace income_under100k=1 if house_income!=11 & house_income!=. tab income_under100k, missing gen income_under40k=. replace income_under40k=0 if house_income>=3 & house_income!=. replace income_under40k=1 if house_income!=. & income_under40k==. gen income_under60k=. replace income_under60k=0 if house_income>=7 & house_income!=. replace income_under60k=1 if house_income!=. & income_under60k==. gen income_under80k=. replace income_under80k=0 if house_income>=9 & house_income!=. replace income_under80k=1 if house_income!=. & income_under80k==. ***In Manuscript Text*** *Eligibility tab1 Q1 Q45 Q47, missing drop if Q47==. *Completion tab Progress *Survey month hist StartDate tab surveymonth *concern about being infected and sick tab1 Q30 Q54 Q56 Q53, missing *Eligibility tab1 Q1 Q45 Q47, missing drop if Q47==. *drop if there were no survey responses completed: drop if Progress<10 & Q28_1_1==. tab Progress ****************** *Make clean analysis variables: *Race/Ethnicity gen race_cat="NHWhite" if race_standard=="White non-Hispanic" replace race_cat="Black" if race_standard=="Black" replace race_cat="Asian" if race_standard=="Asian" replace race_cat="Other" if race_standard=="Multiracial" | race_standard=="Middle Eastern" | race_standard=="AmInAlsNtv" replace race_cat="Hispanic" if race_standard=="Hispanic only/white" replace race_cat="Hispanic" if hispanic=="Hispanic" & race_standard=="" tab race_cat race_standard, missing *Income gen income_cat="" replace income_cat="under 60" if income_under60k==1 replace income_cat="60 to 100" if income_under60k==0 & income_under100k==1 replace income_cat="100 and up" if income_under100k==0 tab income_cat house_income, missing gen nocollege_deg=1 if college_deg==0 replace nocollege_deg=0 if college_deg==1 gen missing_employment_info=1 if Q19_1==. & Q19_2==. & Q19_3==. & Q19_5==. & Q19_6==. & Q19_12==. & Q19_14==. & Q19_7==. tab missing_employment_info, missing gen info_noreponse=1 if Q3_1==. & Q3_2==. & Q3_3==. & Q3_16==. & Q3_17==. & Q3_19==. & Q3_20==. & Q3_21==. & Q3_22==. & Q3_23==. & Q3_24==. & Q3_25==. & Q3_26==. & Q3_27==. & Q3_28==. gen impact_noresponse=1 if Q4_1==. & Q4_2==. & Q4_17==. & Q4_3==. & Q4_4==. & Q4_5==. & Q4_6==. & Q4_7==. & Q4_8==. & Q4_9==. & Q4_10==. & Q4_11==. & Q4_12==. & Q4_18==. & Q4_19==. & Q4_13==. & Q4_14==. & Q4_20==. & Q4_15==. gen age60=. replace age60=1 if agegroup2=="60-69" | agegroup2=="70+" replace age60=0 if agegroup2!="" & age60==. tab agegroup age60, missing gen believe_noresponse=1 if Q28_1_2==. & Q28_2_2==. & Q28_3_2==. & Q28_4_2==. & Q28_5_2==. & Q28_6_2==. & Q28_7_2==. & Q28_8_2==. & Q28_9_2==. & Q28_10_2==. & Q28_11_2==. & Q28_12_2==. & Q28_13_2==. & Q28_14_2==. & Q28_15_2==. & Q28_16_2==. & Q28_17_2==. & Q28_9_2==. gen affected_noresponse=1 if Q28_1_4==. & Q28_2_4==. & Q28_3_4==. & Q28_4_4==. & Q28_5_4==. & Q28_6_4==. & Q28_7_4==. & Q28_8_4==. & Q28_9_4==. & Q28_10_4==. & Q28_11_4==. & Q28_12_4==. & Q28_13_4==. & Q28_14_4==. & Q28_15_4==. & Q28_16_4==. & Q28_17_4==. gen believeaffected_noresponse=1 if affected_noresponse==1 & believe_noresponse==1 gen behaviors_noresponse=1 if Q40_2==. & Q40_3==. & Q40_4==. & Q40_5==. & Q40_6==. & Q40_20==. & Q40_19==. & Q40_18==. & Q40_22==. & Q40_7==. & Q40_8==. & Q40_9==. & Q40_15==. &Q40_10==. & Q40_21==. & Q40_11==. & Q40_14==. & Q40_16==. & Q40_17==. & Q40_23==. & Q40_24==. & Q40_25==. & Q40_1==. gen NIH_0=1 if NIH==0 replace NIH_0=0 if NIH==1 replace NIH_0=. if info_noreponse==1 replace NIH=. if info_noreponse==1 replace pseudoscientific=. if believeaffected_noresponse==1 **************** save "AnalysisData/preliminary_analysis_23July2020_TOTALPublic.dta", replace ******************** *Make Tables and Figures use "AnalysisData/preliminary_analysis_23July2020_TOTALPublic.dta", clear ***Figure 1*** Map Data tab CityCounty, missing ***Table 1*** Sumarize paticipant characteristics *Geography tab Region, missing *Demographics tab1 gender agegroup race_standard race_cat hispanic orientation politicalparty education house_income, missing *Employment & employment change tab1 Q19_1 Q19_2 Q19_3 Q19_5 Q19_6 Q19_12 Q19_14 Q19_7 Q35, missing ***Figure 2*** Trusted Info Sources *Output for Figures: replace NIH=. if info_noreponse==1 tab info_noreponse, missing *A. Overall tab1 Q3_1 Q3_2 Q3_3 Q3_16 Q3_17 Q3_19 Q3_20 Q3_21 Q3_22 Q3_23 Q3_24 Q3_25 Q3_26 Q3_27 if info_noreponse==., missing *B. how does gender influence info sources? tab2 gender Q3_1 Q3_2 Q3_3 Q3_16 Q3_17 Q3_19 Q3_20 Q3_21 Q3_22 Q3_23 Q3_24 Q3_25 Q3_26 Q3_27 if info_noreponse==., missing firstonly row chi2 tab2 gender Q3_1 Q3_2 Q3_3 Q3_16 Q3_17 Q3_19 Q3_20 Q3_21 Q3_22 Q3_23 Q3_24 Q3_25 Q3_26 Q3_27 if info_noreponse==. & gender!="other" & gender!="", missing firstonly row chi2 *C. Age tab2 agegroup Q3_1 Q3_2 Q3_3 Q3_16 Q3_17 Q3_19 Q3_20 Q3_21 Q3_22 Q3_23 Q3_24 Q3_25 Q3_26 Q3_27 if agegroup!="" & info_noreponse==., missing firstonly row chi2 tab2 agegroup Q3_1 Q3_2 Q3_3 Q3_16 Q3_17 Q3_19 Q3_20 Q3_21 Q3_22 Q3_23 Q3_24 Q3_25 Q3_26 Q3_27 if info_noreponse==., missing firstonly row chi2 tab2 YA Q3_1 Q3_2 Q3_3 Q3_16 Q3_17 Q3_19 Q3_20 Q3_21 Q3_22 Q3_23 Q3_24 Q3_25 Q3_26 Q3_27 if agegroup!="" & info_noreponse==., missing firstonly row chi2 *D. how does race influence info sources? **small nubmers of some minority groups, just group white and others for now. tab2 white Q3_1 Q3_2 Q3_3 Q3_16 Q3_17 Q3_19 Q3_20 Q3_21 Q3_22 Q3_23 Q3_24 Q3_25 Q3_26 Q3_27 if white!=. & info_noreponse==., missing firstonly row chi2 tab2 race_standard Q3_1 Q3_2 Q3_3 Q3_16 Q3_17 Q3_19 Q3_20 Q3_21 Q3_22 Q3_23 Q3_24 Q3_25 Q3_26 Q3_27 if race_standard!="" & info_noreponse==., missing firstonly row chi2 tab2 race_cat Q3_1 Q3_2 Q3_3 Q3_16 Q3_17 Q3_19 Q3_20 Q3_21 Q3_22 Q3_23 Q3_24 Q3_25 Q3_26 Q3_27 if race_cat!="" & info_noreponse==., missing firstonly row chi2 *E. How does politics influence info sources? tab2 politicstxt Q3_1 Q3_2 Q3_3 Q3_16 Q3_17 Q3_19 Q3_20 Q3_21 Q3_22 Q3_23 Q3_24 Q3_25 Q3_26 Q3_27 if info_noreponse==., missing firstonly row chi2 *just compare dems to republicans tab2 politicstxt Q3_1 Q3_2 Q3_3 Q3_16 Q3_17 Q3_19 Q3_20 Q3_21 Q3_22 Q3_23 Q3_24 Q3_25 Q3_26 Q3_27 if politicstxt!="c. inde/other/none" & info_noreponse==., missing firstonly row chi2 *F. Education tab2 college_deg Q3_1 Q3_2 Q3_3 Q3_16 Q3_17 Q3_19 Q3_20 Q3_21 Q3_22 Q3_23 Q3_24 Q3_25 Q3_26 Q3_27 if college_deg!=. & info_noreponse==., missing firstonly row chi2 *G. Income tab2 income_cat Q3_1 Q3_2 Q3_3 Q3_16 Q3_17 Q3_19 Q3_20 Q3_21 Q3_22 Q3_23 Q3_24 Q3_25 Q3_26 Q3_27 if income_cat!="" & info_noreponse==., missing firstonly row chi2 *H. Region: tab2 Region Q3_1 Q3_2 Q3_3 Q3_16 Q3_17 Q3_19 Q3_20 Q3_21 Q3_22 Q3_23 Q3_24 Q3_25 Q3_26 Q3_27 if info_noreponse==., missing firstonly row chi2 tab2 Region Q3_1 Q3_2 Q3_3 Q3_16 Q3_17 Q3_19 Q3_20 Q3_21 Q3_22 Q3_23 Q3_24 Q3_25 Q3_26 Q3_27 if Region!="" & info_noreponse==., missing firstonly row chi2 ***Figure 3***Perception of seriousness and what things impacted this belief *A. Overall tab1 Q4_1 Q4_2 Q4_17 Q4_3 Q4_4 Q4_5 Q4_6 Q4_7 Q4_8 Q4_9 Q4_10 Q4_11 Q4_12 Q4_18 Q4_19 Q4_13 Q4_14 Q4_20 Q4_15 if impact_noresponse==., missing *B. Gender tab2 gender Q4_1 Q4_2 Q4_17 Q4_3 Q4_4 Q4_5 Q4_6 Q4_7 Q4_8 Q4_9 Q4_10 Q4_11 Q4_12 Q4_18 Q4_19 Q4_13 Q4_14 Q4_20 Q4_15 if impact_noresponse==. & gender!="other" & gender!="", missing firstonly row chi2 *C. AgeGroup tab2 agegroup2 Q4_1 Q4_2 Q4_17 Q4_3 Q4_4 Q4_5 Q4_6 Q4_7 Q4_8 Q4_9 Q4_10 Q4_11 Q4_12 Q4_18 Q4_19 Q4_13 Q4_14 Q4_20 Q4_15 if impact_noresponse==. & agegroup2!="", missing firstonly row chi2 tab2 YA Q4_1 Q4_2 Q4_17 Q4_3 Q4_4 Q4_5 Q4_6 Q4_7 Q4_8 Q4_9 Q4_10 Q4_11 Q4_12 Q4_18 Q4_19 Q4_13 Q4_14 Q4_20 Q4_15 if impact_noresponse==. & agegroup2!="", missing firstonly row chi2 tab agegroup2 YA, missing *D. Race/Ethnicity tab2 race_cat Q4_1 Q4_2 Q4_17 Q4_3 Q4_4 Q4_5 Q4_6 Q4_7 Q4_8 Q4_9 Q4_10 Q4_11 Q4_12 Q4_18 Q4_19 Q4_13 Q4_14 Q4_20 Q4_15 if impact_noresponse==. & race_standard!="" & race_cat!="Hispanic", missing firstonly row chi2 tab white race_cat, missing tab2 white Q4_1 Q4_2 Q4_17 Q4_3 Q4_4 Q4_5 Q4_6 Q4_7 Q4_8 Q4_9 Q4_10 Q4_11 Q4_12 Q4_18 Q4_19 Q4_13 Q4_14 Q4_20 Q4_15 if impact_noresponse==. & race_standard!="" & race_cat!="Hispanic", missing firstonly row chi2 tab1 Q4_1 Q4_2 Q4_17 Q4_3 Q4_4 Q4_5 Q4_6 Q4_7 Q4_8 Q4_9 Q4_10 Q4_11 Q4_12 Q4_18 Q4_19 Q4_13 Q4_14 Q4_20 Q4_15 if impact_noresponse==. & hispanic=="Hispanic", missing tab1 Q37_1 Q37_2 Q37_3 Q37_4 Q37_5 Q37_6 Q37_7 Q37_8, missing tab race, missing tab race *E. Politics tab2 politicstxt Q4_1 Q4_2 Q4_17 Q4_3 Q4_4 Q4_5 Q4_6 Q4_7 Q4_8 Q4_9 Q4_10 Q4_11 Q4_12 Q4_18 Q4_19 Q4_13 Q4_14 Q4_20 Q4_15 if impact_noresponse==. , missing firstonly row chi2 *just compare dems to republicans tab2 politicstxt Q4_1 Q4_2 Q4_17 Q4_3 Q4_4 Q4_5 Q4_6 Q4_7 Q4_8 Q4_9 Q4_10 Q4_11 Q4_12 Q4_18 Q4_19 Q4_13 Q4_14 Q4_20 Q4_15 if impact_noresponse==. & politicstxt!="c. inde/other/none" & politicstxt!="", missing firstonly row chi2 *just compare dems to republicans tab2 politicstxt Q4_1 Q4_2 Q4_17 Q4_3 Q4_4 Q4_5 Q4_6 Q4_7 Q4_8 Q4_9 Q4_10 Q4_11 Q4_12 Q4_18 Q4_19 Q4_13 Q4_14 Q4_20 Q4_15 if impact_noresponse==. & politicstxt!="", missing firstonly row chi2 *F. Education tab college_deg education, missing tab2 college_deg Q4_1 Q4_2 Q4_17 Q4_3 Q4_4 Q4_5 Q4_6 Q4_7 Q4_8 Q4_9 Q4_10 Q4_11 Q4_12 Q4_18 Q4_19 Q4_13 Q4_14 Q4_20 Q4_15 if impact_noresponse==. & college_deg!=., missing firstonly row chi2 *G. Income tab2 income_cat Q4_1 Q4_2 Q4_17 Q4_3 Q4_4 Q4_5 Q4_6 Q4_7 Q4_8 Q4_9 Q4_10 Q4_11 Q4_12 Q4_18 Q4_19 Q4_13 Q4_14 Q4_20 Q4_15 if impact_noresponse==. & income_cat!="", missing firstonly row chi2 *H. Region tab2 Region Q4_1 Q4_2 Q4_17 Q4_3 Q4_4 Q4_5 Q4_6 Q4_7 Q4_8 Q4_9 Q4_10 Q4_11 Q4_12 Q4_18 Q4_19 Q4_13 Q4_14 Q4_20 Q4_15 if impact_noresponse==. & Region!="", missing firstonly row chi2 *Other cross tabs: *By perceived seriousness tab2 veryserious Q30, missing tab2 veryserious gender agegroup YA race_standard white hispanic orientation politicalparty simple_politicalparty education college_deg house_income Region, missing firstonly col chi2 tab2 veryserious gender agegroup YA race_standard white hispanic orientation politicalparty simple_politicalparty education college_deg house_income Region, firstonly col chi2 tab2 veryserious gender agegroup YA race_standard white hispanic orientation politicalparty simple_politicalparty education college_deg house_income Region if gender!="other", firstonly col chi2 tab2 veryserious male, col chi2 tab2 veryserious simple_politicalparty, col chi2 tab2 veryserious simple_politicalparty, col chi2 tab2 veryserious YA, col chi2 tab2 veryserious agegroup2, col chi2 tab2 veryserious age60, col chi2 tab2 veryserious orientation, col chi2 tab2 veryserious college_deg, col chi2 tab2 veryserious house_income, col chi2 tab2 veryserious income_under40k, col chi2 tab2 veryserious income_under60k, col chi2 tab2 veryserious income_under100k, col chi2 tab2 veryserious race_standard, col chi2 tab2 veryserious Q3_1 Q3_2 Q3_3 Q3_16 Q3_17 Q3_19 Q3_20 Q3_21 Q3_22 Q3_23 Q3_24 Q3_25 Q3_26 Q3_27, missing firstonly row chi2 ***Figure 4****Messages Believed and Affected Behaviors tab1 Q28_1_2 Q28_1_4 Q28_2_2 Q28_2_4 Q28_3_2 Q28_3_4 Q28_4_2 Q28_4_4 Q28_5_2 Q28_5_4 Q28_6_2 Q28_6_4 Q28_7_2 Q28_7_4 Q28_8_2 Q28_8_4 Q28_9_2 Q28_9_4 Q28_10_2 Q28_10_4 Q28_11_2 Q28_11_4 Q28_12_2 Q28_12_4 Q28_13_2 Q28_13_4 Q28_14_2 Q28_14_4 Q28_15_2 Q28_15_4 Q28_16_2 Q28_16_4 Q28_17_2 Q28_17_4 if believeaffected_noresponse==., missing ***Figure 5***Alternative messages *what are these alt beliefs for reference tab pseudoscientific if believeaffected_noresponse==., missing tab1 Q28_4_2 Q28_10_2 Q28_12_2 Q28_13_2 Q28_14_2 Q28_15_2 Q28_16_2 if believeaffected_noresponse==. , missing *By pseudoscientific/conspiracy beliefs (increase sales; population control; natural remedies; hoax; endtimes; bioweapon; social security) tab2 pseudoscientific gender agegroup race_standard race_cat hispanic white orientation politicalparty simple_politicalparty politicstxt education college_deg house_income if believeaffected_noresponse==., missing firstonly col chi2 *A. Gender tab2 pseudoscientific gender if believeaffected_noresponse==. & gender!="" & gender!="other", missing firstonly col chi2 *B. Age tab2 pseudoscientific agegroup if believeaffected_noresponse==. & agegroup!="", missing firstonly col chi2 tab2 pseudoscientific YA if believeaffected_noresponse==. & agegroup!="", missing firstonly col chi2 *C. Race tab2 pseudoscientific white if believeaffected_noresponse==. & white!=., missing firstonly col chi2 tab2 pseudoscientific race_cat if believeaffected_noresponse==. & race_cat!="", missing firstonly col chi2 *D. Politics tab2 pseudoscientific politicstxt if believeaffected_noresponse==. & politicstxt!="" & politicstxt!="c. inde/other/none", missing firstonly col chi2 *E. Education tab2 pseudoscientific college_deg if believeaffected_noresponse==. & education!=., missing firstonly col chi2 *F. Income tab2 pseudoscientific income_cat if believeaffected_noresponse==. & income_cat!="", missing firstonly col chi2 *G. Region tab2 pseudoscientific Region if believeaffected_noresponse==. & Region!="", missing firstonly col chi2 *H. Info sources & pseudoscientific beliefs tab2 pseudoscientific Q3_1 Q3_2 Q3_3 Q3_16 Q3_17 Q3_19 Q3_20 Q3_21 Q3_22 Q3_23 Q3_24 Q3_25 Q3_26 Q3_27 if believeaffected_noresponse==. & info_noreponse==., missing firstonly row chi2 *text tab2 pseudoscientific agegroup college_deg education NIH no_info if believeaffected_noresponse==. , missing firstonly row chi2 ***Figure 6***Behavior changes *A. Overall tab1 Q40_2 Q40_3 Q40_4 Q40_5 Q40_6 Q40_20 Q40_19 Q40_18 Q40_22 Q40_7 Q40_8 Q40_9 Q40_15 Q40_10 Q40_21 Q40_11 Q40_14 Q40_16 Q40_17 Q40_23 Q40_24 Q40_25 Q40_1 if behaviors_noresponse==., missing **By behavior changes: mask wearing and social distancing behevior changes *A-H* *Mask tab2 Q40_14 gender agegroup agegroup2 race_standard race_cat white hispanic orientation politicalparty simple_politicalparty education college_deg house_income income_cat Region if behaviors_noresponse==., missing firstonly col chi2 *Distancing tab2 Q40_5 gender agegroup agegroup2 race_standard race_cat hispanic orientation politicalparty simple_politicalparty education house_income income_cat Region if behaviors_noresponse==., missing firstonly col chi2 *Mask and distancing messaging belief by demographics: *Mask message question tab2 Q28_9_2 gender agegroup agegroup2 race_standard hispanic orientation politicalparty education house_income, missing firstonly col chi2 *Distancing message question tab2 Q28_7_2 gender agegroup race_standard hispanic orientation politicalparty education house_income, missing firstonly col chi2 *I-J* *how are info sources related to masks and distancing tab2 mask Q3_1 Q3_2 Q3_3 Q3_16 Q3_17 Q3_19 Q3_20 Q3_21 Q3_22 Q3_23 Q3_24 Q3_25 Q3_26 Q3_27 if info_noreponse==. & behaviors_noresponse==., missing firstonly col chi2 tab2 distancing Q3_1 Q3_2 Q3_3 Q3_16 Q3_17 Q3_19 Q3_20 Q3_21 Q3_22 Q3_23 Q3_24 Q3_25 Q3_26 Q3_27 if info_noreponse==. & behaviors_noresponse==., missing firstonly col chi2 * tab2 mask Q3_1 Q3_2 Q3_3 Q3_16 Q3_17 Q3_19 Q3_20 Q3_21 Q3_22 Q3_23 Q3_24 Q3_25 Q3_26 Q3_27 if info_noreponse==. & behaviors_noresponse==., missing firstonly row chi2 tab2 distancing Q3_1 Q3_2 Q3_3 Q3_16 Q3_17 Q3_19 Q3_20 Q3_21 Q3_22 Q3_23 Q3_24 Q3_25 Q3_26 Q3_27 if info_noreponse==. & behaviors_noresponse==., missing firstonly row chi2 ** tab2 mask NIH no_info, missing firstonly col chi2 tab2 distancing NIH no_info, missing firstonly col chi2 ***Added June 2022: *How were events related to masking and distancing: tab2 mask Q4_1 Q4_2 Q4_17 Q4_3 Q4_4 Q4_5 Q4_6 Q4_7 Q4_8 Q4_9 Q4_10 Q4_11 Q4_12 Q4_18 Q4_19 Q4_13 Q4_14 Q4_20 Q4_15 if impact_noresponse==. & behaviors_noresponse==., missing firstonly col chi2 tab2 distancing Q4_1 Q4_2 Q4_17 Q4_3 Q4_4 Q4_5 Q4_6 Q4_7 Q4_8 Q4_9 Q4_10 Q4_11 Q4_12 Q4_18 Q4_19 Q4_13 Q4_14 Q4_20 Q4_15 if impact_noresponse==. & behaviors_noresponse==., missing firstonly col chi2 * tab2 mask Q4_1 Q4_2 Q4_17 Q4_3 Q4_4 Q4_5 Q4_6 Q4_7 Q4_8 Q4_9 Q4_10 Q4_11 Q4_12 Q4_18 Q4_19 Q4_13 Q4_14 Q4_20 Q4_15 if impact_noresponse==. & behaviors_noresponse==., missing firstonly row chi2 tab2 distancing Q4_1 Q4_2 Q4_17 Q4_3 Q4_4 Q4_5 Q4_6 Q4_7 Q4_8 Q4_9 Q4_10 Q4_11 Q4_12 Q4_18 Q4_19 Q4_13 Q4_14 Q4_20 Q4_15 if impact_noresponse==. & behaviors_noresponse==., missing firstonly row chi2 **by politics tab politicalparty tab2 mask Q4_1 Q4_2 Q4_17 Q4_3 Q4_4 Q4_5 Q4_6 Q4_7 Q4_8 Q4_9 Q4_10 Q4_11 Q4_12 Q4_18 Q4_19 Q4_13 Q4_14 Q4_20 Q4_15 if impact_noresponse==. & behaviors_noresponse==. &politicalparty==1, missing firstonly row chi2 ** tab2 mask Q4_1 Q4_2 Q4_17 Q4_3 Q4_4 Q4_5 Q4_6 Q4_7 Q4_8 Q4_9 Q4_10 Q4_11 Q4_12 Q4_18 Q4_19 Q4_13 Q4_14 Q4_20 Q4_15 if impact_noresponse==. & behaviors_noresponse==. &politicalparty==2, missing firstonly row chi2 ** tab2 mask Q4_1 Q4_2 Q4_17 Q4_3 Q4_4 Q4_5 Q4_6 Q4_7 Q4_8 Q4_9 Q4_10 Q4_11 Q4_12 Q4_18 Q4_19 Q4_13 Q4_14 Q4_20 Q4_15 if impact_noresponse==. & behaviors_noresponse==. &politicalparty>=3 & politicalparty!=., missing firstonly row chi2 **by gender tab2 mask Q4_1 Q4_2 Q4_17 Q4_3 Q4_4 Q4_5 Q4_6 Q4_7 Q4_8 Q4_9 Q4_10 Q4_11 Q4_12 Q4_18 Q4_19 Q4_13 Q4_14 Q4_20 Q4_15 if impact_noresponse==. & behaviors_noresponse==. & gender=="female", missing firstonly row chi2 * tab2 mask Q4_1 Q4_2 Q4_17 Q4_3 Q4_4 Q4_5 Q4_6 Q4_7 Q4_8 Q4_9 Q4_10 Q4_11 Q4_12 Q4_18 Q4_19 Q4_13 Q4_14 Q4_20 Q4_15 if impact_noresponse==. & behaviors_noresponse==. & gender=="male", missing firstonly row chi2 ******************************************************** *Regression* *Table 2* *unadjusted xi: logistic mask_0 Southwest, robust xi: logistic mask_0 male, robust xi: logistic mask_0 YA, robust xi: logistic mask_0 white, robust xi: logistic mask_0 i.politicstxt , robust xi: logistic mask_0 nocollege_deg, robust xi: logistic mask_0 income_under100k, robust xi: logistic mask_0 NIH_0, robust xi: logistic mask_0 pseudoscientific, robust *adjusted xi: logistic mask_0 Southwest male YA white i.politicstxt nocollege_deg income_under100k NIH_0 pseudoscientific, robust *unadjusted xi: logistic distancing_0 Southwest, robust xi: logistic distancing_0 male, robust xi: logistic distancing_0 YA, robust xi: logistic distancing_0 white, robust xi: logistic distancing_0 i.politicstxt , robust xi: logistic distancing_0 nocollege_deg, robust xi: logistic distancing_0 income_under100k, robust xi: logistic distancing_0 NIH_0, robust xi: logistic distancing_0 pseudoscientific, robust *adjusted xi: logistic distancing_0 Southwest male YA white i.politicstxt nocollege_deg income_under100k NIH_0 pseudoscientific, robust *Other models: *unadjusted: xi: logistic mask_0 i.Region, robust logistic mask_0 region_num1 region_num2 region_num3 region_num4, robust logistic mask_0 Southwest, robust xi: logistic mask_0 i.agegroup2, robust xi: logistic mask_0 YA, robust logistic mask_0 male, robust logistic mask_0 white, robust xi: logistic mask_0 i.politicstxt, robust xi: logistic mask_0 nocollege_deg, robust xi: logistic mask_0 income_under100k, robust logistic mask_0 white, robust xi: logistic mask_0 i.politicstxt, robust logistic mask_0 male, robust xi: logistic mask_0 i.agegroup2, robust xi: logistic mask_0 income_under100k, robust xi: logistic mask_0 income_under60k, robust xi: logistic mask_0 income_under40k, robust logistic mask_0 college_deg, robust logistic mask_0 NIH, robust logistic mask_0 pseudoscientific, robust logistic mask_0 region_num1 region_num2 region_num3 region_num4, robust logistic mask region_num1 region_num2 region_num3 region_num4, robust logistic mask_0 Southwest, robust xi: logistic mask_0 white i.politicstxt male i.agegroup2 income_under100k college_deg NIH pseudoscientific Southwest, robust xi: logistic mask_0 white i.politicstxt male YA income_under100k college_deg NIH_0 pseudoscientific Southwest, robust xi: logistic mask_0 white i.politicstxt male YA income_under100k NIH_0 pseudoscientific Southwest, robust xi: logistic mask_0 white i.politicstxt male YA income_under80k NIH_0 pseudoscientific Southwest, robust xi: logistic mask_0 white i.politicstxt male YA income_under60k NIH_0 pseudoscientific Southwest, robust xi: logistic mask_0 white i.politicstxt male YA income_under40k NIH_0 pseudoscientific Southwest, robust xi: logistic mask_0 white i.politicstxt male YA i.house_income NIH_0 pseudoscientific Southwest, robust xi: logistic mask_0 white, robust xi: logistic mask_0 male, robust xi: logistic mask_0 YA, robust xi: logistic mask_0 income_under100k, robust xi: logistic mask_0 college_deg, robust xi: logistic mask_0 NIH_0, robust xi: logistic mask_0 pseudoscientific, robust xi: logistic mask_0 Southwest, robust xi: logistic distancing_0 white i.politicstxt male i.agegroup2 income_under100k college_deg NIH pseudoscientific Southwest, robust xi: logistic distancing_0 white i.politicstxt male YA income_under100k college_deg NIH_0 pseudoscientific Southwest, robust xi: logistic distancing_0 white i.politicstxt male YA income_under100k NIH_0 pseudoscientific Southwest, robust xi: logistic distancing_0 white i.politicstxt male YA income_under60k NIH_0 pseudoscientific Southwest, robust xi: logistic distancing_0 white i.politicstxt male YA income_under40k NIH_0 pseudoscientific Southwest, robust xi: logistic distancing_0 white, robust xi: logistic distancing_0 i.politicstxt, robust xi: logistic distancing_0 male, robust xi: logistic distancing_0 YA, robust xi: logistic distancing_0 income_under100k, robust xi: logistic distancing_0 college_deg, robust xi: logistic distancing_0 NIH_0, robust xi: logistic distancing_0 pseudoscientific, robust xi: logistic distancing_0 Southwest, robust xi: logistic mask_0 white i.politicstxt male YA income_under100k college_deg NIH_0 pseudoscientific Southwest, robust xi: logistic distancing_0 white i.politicstxt male YA income_under100k college_deg NIH_0 pseudoscientific Southwest, robust ******************************************************************************************************************************************** **Odds ratios of no mask wearing: *unadjusted no mask: logistic mask_0 white, robust xi: logistic mask_0 i.politicstxt, robust logistic mask_0 male, robust xi: logistic mask_0 i.agegroup2, robust xi: logistic mask_0 income_under100k, robust logistic mask_0 college_deg, robust logistic mask_0 NIH, robust logistic mask_0 pseudoscientific, robust logistic mask_0 region_num1 region_num2 region_num3 region_num4, robust logistic mask region_num1 region_num2 region_num3 region_num4, robust logistic mask_0 Southwest, robust *Adjusted no mask xi: logistic mask_0 white i.politicstxt male i.agegroup2 income_under100k college_deg NIH pseudoscientific Southwest, robust xi: logistic mask_0 white i.politicstxt male i.agegroup2 income_under60k college_deg NIH pseudoscientific Southwest, robust xi: logistic mask_0 white i.politicstxt male i.agegroup2 income_under40k college_deg NIH pseudoscientific Southwest, robust *unadjusted distancing: tab2 distancing male white republican, missing firstonly chi2 col *Odds ratios of not distancing: *unadjusted no mask: logistic distancing_0 white, robust xi: logistic distancing_0 i.politicstxt, robust logistic distancing_0 male, robust *Adjusted no mask xi: logistic distancing_0 white i.politicstxt male i.agegroup2, robust ******************************************************************************************************************************************************