*********************************** ************************************ ***CONFIDENCE INTERVALS AND HYPOTHESIS TESTING ************************************ ************************************ ************************************ ***CLEAR MEMORY ************************************ clear all ************************************ ***WINDOWS ************************************ ***Start saving results window log using "C:\course\programs\Stata03.log", replace text ***Shortcut for folders global data = "C:\course\data" global output = "C:\course\output" ************************************ ***MACINTOSH ************************************ ***Start saving results window log using "/course/programs/Stata03.log", replace text ***Shortcut for folders global data = "/course/data" global output = "/course/output" ************************************ ***OPENING COMMANDS ************************************ ***Tell Stata to not pause for "more" messages set more off ***Change directory cd "$data" ***Open 2018 ACS (only Texas) use "ACS2018TX.dta", clear ************************************ ***GENERATE VARIABLES ************************************ ***Sex gen female=. replace female=0 if sex==1 // Male replace female=1 if sex==2 // Female label define female 0 "Male" 1 "Female" label values female female ***Race/ethnicity gen raceth=. replace raceth=1 if race==1 & hispan==0 // White replace raceth=2 if race==2 & hispan==0 // Black replace raceth=3 if hispan>=1 & hispan<=4 // Hispanic replace raceth=4 if (race==4 | race==5 | race==6) & hispan==0 // Asian replace raceth=5 if race==3 & hispan==0 // Native American replace raceth=6 if (race==7 | race==8 | race==9) & hispan==0 // Other label define raceth 1 "White" 2 "African American" 3 "Hispanic" /// 4 "Asian" 5 "Native American" 6 "Ohter races" label values raceth raceth ***Age egen agegr = cut(age), at(0,16,20,25,35,45,55,65,100) label define agecode 0 "0-15" 16 "16-19" 20 "20-24" 25 "25-34" /// 35 "35-44" 45 "45-54" 55 "55-64" 65 "65-100" label values agegr agegr ***Educational attainment gen educgr=. replace educgr=1 if educ>=0 & educ<=5 // Less than high school replace educgr=2 if educ==6 // High school replace educgr=3 if educ==7 | educ==8 // Some college replace educgr=4 if educ==10 // College replace educgr=5 if educ==11 // 5+ years of college, graduate school label define educgr 1 "Less than high school" 2 "High school" /// 3 "Some college" 4 "College" 5 "Graduate school" label values educgr educgr ***Marital status gen marital=. replace marital=1 if marst==1 | marst==2 // Married replace marital=2 if marst>=3 & marst<=5 // Separated, divorced, widowed replace marital=3 if marst==6 // Never married, single label define marital 1 "Married" 2 "Separated, divorced, widowed" 3 "Never married" label values marital marital ***Wage and salary income gen income=. replace income=incwage if incwage!=999999 ***Migration status gen migrant=. replace migrant=1 if migrate1d==10 | migrate1d==23 // same house or within PUMA replace migrant=2 if migrate1d>=24 & migrate1d<=32 // internal migrant replace migrant=3 if migrate1d==40 // international migrant label define migrant 1 "Non-migrant" 2 "Internal migrant" 3 "International migrant" label values migrant migrant ***Internal migration status (domestic migration) gen dommig=. replace dommig=0 if migrant==1 // non-migrant replace dommig=1 if migrant==2 // internal migrant label define dommig 0 "Non-migrant" 1 "Internal migrant" label values dommig dommig tab migrant dommig, m ***International migration status gen intmig=. replace intmig=0 if migrant==1 // non-migrant replace intmig=1 if migrant==3 // international migrant label define intmig 0 "Non-migrant" 1 "International migrant" label values intmig intmig tab migrant intmig, m ************************************ ***COMPLEX SAMPLE DESIGN ************************************ svyset cluster [pweight=perwt], strata(strata) ************************************ ***CONFIDENCE INTERVAL FOR MEANS ************************************ ************************************ ***Wage and salary income ***Focus on those with some income ***(exclude those with zero income) ************************************ ***Summary statistics mean income if income!=0 [fweight=perwt] ***Mean with survey design is similar. ***This procedure is used to correct standard error svy, subpop(if income!=. & income!=0): mean income ************************************ ***Different confidence levels ************************************ ***90% confidence level svy, subpop(if income!=. & income!=0): mean income, level(90) ***95% confidence level svy, subpop(if income!=. & income!=0): mean income ***Standard deviation estat sd ***99% confidence level svy, subpop(if income!=. & income!=0): mean income, level(99) ************************************ ***CONFIDENCE INTERVAL FOR PROPORTIONS ************************************ ************************************ ***Migration status ************************************ ***Frequency distribution tab migrant [fweight=perwt] ***Frequency distribution with survey design is similar. ***This procedure is used to correct standard error svy: prop migrant ************************************ ***Different confidence levels ************************************ ***90% confidence level svy: prop migrant, level(90) ***95% confidence level svy: prop migrant ***99% confidence level svy: prop migrant, level(99) ************************************ ***TWO-SAMPLE t-TEST ************************************ ***Mean of wage and salary income by sex table sex if income!=0 [fweight=perwt], c(mean income) ***t-test of personal income by sex ***Weights are not allowed table sex if income!=0, c(mean income) ttest income if income!=0, by(sex) ************************************ ***TWO-SAMPLE TEST OF PROPORTIONS ************************************ ***Migration status by sex ***Independent variable (sex): column ***Dependent variable (migrant): row tab migrant sex [fweight=perwt] ***Percentage distribution is estimated ***within categories of independent variable (sex) ***Add up to 100% within each sex category tab migrant sex [fweight=perwt], col nofreq ***Proportion is tested for a dummy dependent variable ***by categories of a dummy independent variable (sex) ***Need to create variable only for internal migrant vs. non-migrant ***as well as only for international migrant vs. non-migrant ************************************ ***Internal migration ************************************ ***Proportion of internal migrants by sex tab dommig sex [fweight=perwt], col nofreq table sex [fweight=perwt], c(mean dommig) ***Sample size for internal migration test count if dommig!=. & sex!=. ***Test of proportions of internal migrants by sex ***Weights are not allowed tab dommig sex, col nofreq prtest dommig, by(sex) ************************************ ***International migration ************************************ ***Proportion of international migrants by sex tab intmig sex [fweight=perwt], col nofreq table sex [fweight=perwt], c(mean intmig) ***Sample size for international migration test count if intmig!=. & sex!=. ***Test of proportions of international migrants by sex ***Weights are not allowed tab intmig sex, col nofreq prtest intmig, by(sex) ************************************ ***CLOSING COMMANDS ************************************ ***Save data save "Stata03.dta", replace ***Save log log close