// Open ISSP 2005 use COUNTRY WRKHRS V51 using "ZA4350_v2-0-0.dta", clear // Prepare variables rename COUNTRY country // Job satisfaction generate jobsatis = 7 - V51 rename WRKHRS workhrs // Average working hours per country bys country: egen avghrs = mean(workhrs) // Center working hours center workhrs, inplace // Fit models eststo clear eststo: regress jobsatis workhrs avghrs // OLS regression eststo: mixed jobsatis workhrs avghrs || country: // RE regression qui matrix foo = e(N_g) // Number of individuals qui estadd scalar nc = foo[1,1] // Number of individuals // Prepare data for meta-regression preserve // Generate country-level data set of average working hours egen pickone = tag(country) keep if pickone keep country avghrs tempfile temp save `temp', replace restore // Average job satisfaction per country, controlling for individual working hours statsby _b[_cons] _se[_cons] e(N), clear by(country): regress jobsatis workhrs // Merge with average working hours merge 1:1 country using `temp' eststo: regress _stat_1 avghrs // OLS regression two-step eststo: vwls _stat_1 avghrs, sd(_stat_2) // FE meta-regression eststo: metareg _stat_1 avghrs, wsse(_stat_2) // RE meta-regressionesttab, keep(main:) mtitles("OLS" "Multilevel" "OLS two-step" "FE meta-regression" "RE meta-regression") b(3) se(3) /// coeflabel(workhrs "Working hours" /// avghrs "Average working hours" /// _cons "Intercept") /// stats(nc N, labels("No. countries") fmt(%12.0gc %12.0gc)) varwidth(25) modelwidth(20) eqlabels("", none)
Showing posts with label International Social Survey Program. Show all posts
Showing posts with label International Social Survey Program. Show all posts
Aug 2, 2018
Meta-regression
Jul 27, 2017
Random graphs (105): Scatterplot
// Open ISSP data
use C_ALPHAN SEX V51 WEIGHT using ZA4350_v2-0-0.dta, clear
// Fix country variable
replace C_ALPHAN = "BE" if C_ALPHAN == "BE-FLA"
replace C_ALPHAN = "DE" if C_ALPHAN == "DE-E"
replace C_ALPHAN = "DE" if C_ALPHAN == "DE-W"
replace C_ALPHAN = "UK" if C_ALPHAN == "GB-GBN"
// Recode job satisfaction
recode V51 (7 = 0 "Completely dissatisfied") ///
(6 = 1 "Very dissatisfied") ///
(5 = 2 "Fairly dissatisfied") ///
(4 = 3 "Neither satisfied nor dissatisfied") ///
(3 = 4 "Fairly satisfied") ///
(2 = 5 "Very satisfied") ///
(1 = 6 "Completely satisfied"), gen(jobsat)
label var jobsat "Job satisfaction"
// Recode sex
gen female = (SEX == 2) if !missing(SEX)
label define female 0 "Male" 1 "Female"
label val female female
label var female "Sex"
drop if missing(female)
// Get rid of old variables
drop SEX V51
// Collapse and reshape data
collapse jobsat [pw = WEIGHT], by(C_ALPHAN female)
reshape wide jobsat, i(C_ALPHAN) j(female)
label var jobsat0 "Job satisfaction: men"
label var jobsat1 "Job satisfaction: women"
// Plot
twoway (scatter jobsat0 jobsat1 if jobsat0 > jobsat1, mlabel(C_ALPHAN) mlabpos(9)) ///
(scatter jobsat0 jobsat1 if jobsat0 < jobsat1, mlabel(C_ALPHAN) mlabpos(6)) ///
(function y = x, range(3.5 5.1)) ///
, xlabel(3.5 (.5) 5.0, format(%6.2f)) ///
ylabel(3.5 (.5) 5.0, format(%6.2f)) ///
ytitle("Job satisfaction: men") ///
xtitle("Job satisfaction: women") ///
title("Cross-national variation in the gender gap in job satisfaction", span) ///
note(" " "{it:Source:} International Social Survey Program (ISSP) 2005, doi:10.4232/1.11648", span) ///
legend(off) name(figure1, replace)
Sep 10, 2016
Latex tables using -esttab-
// Table 1
eststo clear
bysort male country: eststo: estpost summarize hwhrs phwhrs gap ///
workhours pworkhours ///
genderroles femaleemployment ///
married higherincome phigherincome ///
age preschool schoolage ///
retired pretired, listwise
esttab using test.tex, main(mean) aux(sd) label nodepvar nostar nonote nonumbers ///
b(%9.2f) varwidth(35) compress booktabs ///
title("Descriptive statistics: Means and standard deviations by gender and country") ///
mgroups("Women" "Men", pattern(1 0 0 1 0 0) ///
prefix(\multicolumn{@span}{c}{) suffix(}) ///
span erepeat(\cmidrule(lr){@span})) replace
// Table 2
eststo clear
levelsof country, local(country)
foreach i of local country {
capture drop touse
mark touse
markout touse hwhrs workhours pworkhours phwhrs ///
genderroles femaleemployment ///
i.relativeincome pretired retired ///
age preschool schoolage married
eststo: regress hwhrs workhours pworkhours phwhrs ///
if female == 1 & country == `i' & touse
eststo: regress hwhrs workhours pworkhours phwhrs ///
genderroles femaleemployment ///
i.relativeincome pretired retired ///
age preschool schoolage married ///
if female == 1 & country == `i' & touse
}
esttab using test.tex, se ar2 label varwidth(35) b(%9.2f) compress nodepvars order(_cons) booktabs ///
mtitles("Germany" "Germany" "USA" "USA" "Finland" "Finland") ///
title("Determinants of weekly housework hours of women in West Germany, USA, and Finland. OLS coefficients") ///
append
Apr 30, 2016
Random graphs (79): Means with confidence intervals
use ZA5900_v3-0-0.dta, replace
renvars, lower // Switch variable names to lower case
// Fix country variable
generate cntry = c_alphan
replace cntry = "GB" if cntry == "GB-GBN"
replace cntry = "DE" if cntry == "DE-E" | cntry == "DE-W"
replace cntry = "BE" if cntry == "BE-BRU" | cntry == "BE-WAL" | cntry == "BE-FLA"
// Generate country name variable
kountry cntry, from(iso2c)
encode NAMES_STD, gen(country)
drop NAMES_STD
// Happiness variable
recode v55 (1 = 6) (2 = 5) (3 = 4) (4 = 3) (5 = 2) (6 = 1) (7 = 0) (0 8 9 = .), gen(lsat)
label define lsat 0 "Completely unhappy" ///
1 "Very unhappy" ///
2 "Fairly unhappy" ///
3 "Neither happy nor unhappy" ///
4 "Fairly happy" ///
5 "Very happy" ///
6 "Completely happy"
label val lsat lsat
label var lsat "Happiness"
// Plot means by country
preserve
statsby mean_ = _b[_cons] ///
loci = (_b[_cons] - 1.96 * _se[_cons]) ///
hici = (_b[_cons] + 1.96 * _se[_cons]) ///
, by(country) total clear: ///
regress lsat
replace country = 1000 if country == .
label define country 1000 "{bf: Total}", modify
egen order_ = rank(mean_), unique
labmask order_, value(country) decode
twoway (rcap mean_ mean_ order_, horizontal) ///
(rspike loci hici order_, horizontal) ///
, legend(off) ylabel(1/41, valuelabels ang(h) labsize(*.8)) ///
xlabel(3.5 (.5) 5.0, grid format(%6.1f)) name(lsat, replace) ///
xmtick(3.5 (.25) 5.0, grid) ///
ytitle("") xtitle("Average happiness") xscale(alt) ysize(8) ///
note(" " ///
"{it:Note:} Happiness ranges from 0 ('Completely unhappy') to 6 ('Completely happy')" ///
"{it:Source:} ISSP 2012, doi:10.4232/1.12339" , span size(*.8))
restore
Mar 21, 2016
Dropping one group at a time
// Interaction plot excluding one country at a time
foreach n of numlist 1/35 { /// Countries are numbered from 1 to 35
preserve
qui drop if country == `n' // Drop one country
local naam: label country `n' // Save name in a local
*di "`naam'"
qui mixed happiness c.workfamconf##c.gdppc || country: workfamconf, cov(uns)
predict u1 u0, reffect
capture drop pickone
egen pickone = tag(country)
qui keep if pickone
gen wfceffect = u1 + _b[workfamconf] + _b[c.workfamconf#c.gdppc] * gdppc
qui sum gdppc // get standard deviation
local gdpmin2sd = r(mean) - 2*r(sd)
local gdpminsd = r(mean) - r(sd)
local gdpmean = r(mean)
local gdpplusd = r(mean) + r(sd)
local gdpplu2sd = r(mean) + 2*r(sd)
twoway (scatter wfceffect gdp, mlabel(country) mlabpos(0) msymbol(none) mlabsize(*.8)) ///
(function y = _b[workfamconf] + _b[c.workfamconf#c.gdppc] * x, range(gdppc)) ///
, ytitle("WFC coefficient", size(*.6)) ///
xlabel(`gdpmin2sd' "-2 SD" `gdpminsd' "-1 SD" ///
`gdpmean' `"Avg."' ///
`gdpplusd' "+1 SD" `gdpplu2sd' "+2 SD", labsize(*.8)) ///
title("Excluding `naam'") ///
legend(off) ///
name(figurewo`n', replace) xsize(3) ysize(2.5) nodraw
restore
}
graph combine figurewo1 figurewo2 ///
figurewo3 figurewo4 ///
figurewo5 figurewo6 ///
figurewo7 figurewo8 ///
figurewo9 figurewo10 ///
figurewo11 figurewo12 ///
figurewo13 figurewo14 ///
figurewo15 figurewo16 , col(4) row(4) xsize(12) ysize(10)
// And so on ...
Oct 6, 2015
Random graphs (54): Cross-level interactions
use deleteme.dta
// Cross-level interaction model
qui mixed lsat c.wfc##c.gdp || country: wfc , cov(uns)
estimates store cross_lvl
qui estadd scalar dev = -2*e(ll) // Deviance
qui matrix foo = e(N_g) // Number of level 2 units
qui estadd scalar nc = foo[1,1] // Number of level 2 units
qui estadd scalar v2 = exp(2*[lns1_1_1]_b[_cons]) // Slope variance
qui estadd scalar v1 = exp(2*[lns1_1_2]_b[_cons]) // Intercept variance
qui estadd scalar cov = tanh([atr1_1_1_2]_b[_cons]) * /// Slope-intercept covariance
exp([lns1_1_1]_b[_cons]) * ///
exp([lns1_1_2]_b[_cons])
qui estadd scalar v_e = exp(2*[lnsig_e]_b[_cons]) // Residual variance
// Create table esttab cross_lvl /// , se /// stats(v1 v2 v_e cov dev nc N, /// Add variance components to table labels("Var(Intercept)" /// "Var(Slope)" /// "Var(Residual)" /// "Cov(Int., Slope)" /// "Deviance" /// "No. clusters" /// "No. individuals")) /// coeflabel(wfc "Work-family conflict" /// gdp "GDP" /// c.wfc#c.gdp "Work-family conflict X GDP" /// _cons "Intercept") /// mtitles("Cross-level interaction") /// nonumbers varwidth(28) modelwidth(24) /// keep(lsat:) // Drop variance components in weird shapes // Interaction plot (A) preserve estimates restore cross_lvl qui sum wfc // get standard deviation and mean local wfcminsd = r(mean) - r(sd) local wfcmean = r(mean) local wfcplusd = r(mean) + r(sd) qui sum gdp // get standard deviation local gdpmin2sd = r(mean) - 2*r(sd) local gdpminsd = r(mean) - r(sd) local gdpmean = r(mean) local gdpplusd = r(mean) + r(sd) local gdpplu2sd = r(mean) + 2*r(sd) margins, at(gdp = (`gdpmin2sd' `gdpminsd' `gdpmean' `gdpplusd' `gdpplu2sd') /// wfc = (`wfcminsd' `wfcmean' `wfcplusd')) vsquish marginsplot, x(gdp) noci /// xlabel(`gdpmin2sd' "-2 SD" /// `gdpminsd' "-1 SD" /// `gdpmean' `""Average" "GDP""' /// `gdpplusd' "+1 SD" /// `gdpplu2sd' "+2 SD") /// xtitle("") /// ylabel(, format(%6.1f)) /// ytitle("Predicted happiness") /// plotopts(msymbol(none)) /// // Turn off markers plot1opts(lpattern(longdash)) /// // Define line types here plot2opts(lpattern(solid)) /// plot3opts(lpattern(shortdash)) /// legend(subtitle("Work{c 150}family conflict" , size(small)) /// order(1 "- 1 SD" 2 "Mean" 3 "+ 1 SD") size(small) /// pos(11) ring(0)) /// title("(A) Interaction plot") /// name(xlvl_plot1, replace) restore // Interaction plot (B) preserve estimates restore cross_lvl predict u1 u0, reffect capture drop pickone egen pickone = tag(country) keep if pickone gen wfceffect = u1 + _b[wfc] + _b[c.wfc#c.gdp] * gdp qui sum gdp // get standard deviation local gdpmin2sd = r(mean) - 2*r(sd) local gdpminsd = r(mean) - r(sd) local gdpmean = r(mean) local gdpplusd = r(mean) + r(sd) local gdpplu2sd = r(mean) + 2*r(sd) twoway (scatter wfceffect gdp, mlabel(country) mlabpos(0) msymbol(none)) /// (function y = _b[wfc] + _b[c.wfc#c.gdp] * x, range(gdp)) /// , ytitle("Work{c 150}family conflict coefficient") /// xlabel(`gdpmin2sd' "-2 SD" `gdpminsd' "-1 SD" /// `gdpmean' `""Average" "GDP""' /// `gdpplusd' "+1 SD" `gdpplu2sd' "+2 SD") /// title("(B) Interaction plot") /// legend(order(2 "Work{c 150}family conflict coefficient by GDP") /// pos(7) ring(0) size(small)) /// name(xlvl_plot2, replace) restore // Combine graphs graph combine xlvl_plot1 xlvl_plot2, row(1) ysize(3) xsize(5.5) altshrink
Oct 15, 2014
Random graphs (33): Regression parameters
use V4 V56 SEX using "ZA5900_v2-0-0.dta", replace // ISSP 2012 data
renvars, lower // Switch variable names to lower case
kountry v4, from(iso3n) to(iso2c) // Generate country abbreviation variable
replace _ISO2C_ = "RU" if v4 == 643
encode _ISO2C_, gen(country)
drop if cntry == "ZA" // v56 missing in ZA
// Prepare variables
recode sex (9 = .)
recode v56 (0 8 9 = .), gen(jobsat_rev)
generate jobsat = 7 - jobsat_rev
label define jobsat 6 "Completely satisfied" ///
3 "Neither satisfied nor dissatisfied" ///
0 "Completely dissatisfied"
label value jobsat jobsat
preserve
// Save parameters
statsby gendergap = _b[sex] ///
loci = (_b[sex] - 1.96 * _se[sex]) ///
hici = (_b[sex] + 1.96 * _se[sex]) ///
, by(country) clear total: ///
regress jobsat sex
replace country = 1000 if country == . // Label parameter from total sample
label define country 1000 "{bf: Total}", modify // Label parameter from total sample
egen order_ = rank(-gendergap), unique
labmask order_, value(country) decode
twoway (dot gendergap order_, msize(vsmall)) ///
(rcap loci hici order_) ///
, legend(off) xlabel(1/37, valuelabels ang(v)) ///
yline(0) note("{it:Source:} ISSP 2012 (Family and Changing Gender Roles IV), own calculations", span) ///
ylabel(,format(%6.1f)) name(gendergap, replace) ///
xtitle(" ") ytitle("Gender gap in job satisfaction")
restore
Jul 16, 2014
Random graphs (28): Plotting categorical variables
foreach x of varlist V39 V40 V41 {
// Recode each variable:
recode `x' (1/5 = 2 "Valid answer") ///
( .c = 1 "Can't choose") ///
( .n = 0 "No answer") ///
, gen(`x'_mis)
label var `x'_mis "Recode of `x'"
// Plot each variable:
catplot `x'_mis, over(C_ALPHAN, label(ang(v)))
stack asyvars perc(C_ALPHAN) ///
legend(pos(1) row(1)) recast(bar) ///
ytitle("Percentage") b1title("") ///
title("Recode of `x'") ///
name(`x'_mis, replace)
}
grc1leg V39_mis V40_mis V41_mis, col(1) name(combined, replace)
graph display combined, ysize(11) xsize(7)
May 28, 2014
Calculate coefficient alpha by country
use "ZA5900_v1-0-0.dta", replace
renvars, lower // Switch variable names to lower case
kountry v4, from(iso3n) to(iso2c) // Generate country abbreviation variable
encode _ISO2C_, gen(country)
fre v51-v54 // Work-family conflict items
// Factor analysis yields one-dimensional solution:
factor v51-v54, pcf
// Calculate Cronbach's alpha for each country of the sample:
preserve
statsby alpha_ = r(alpha) ///
, by(country) clear total: /// -total- adds row for total sample
alpha v51-v54
replace country = 25 if country == . // Label row for total sample
label define country 25 "All", modify // Label row for total sample
sort alpha_ // Make list
list, sep(0)
// Create graph
egen order_ = rank(-alpha_), unique
labmask order_, value(country) decode
twoway (dot alpha_ order_) ///
, yline(.70) ///
xlab(1/25, valuelabels ang(v)) xtitle("Country") ///
ylabel(, format(%6.2f)) ytitle("Cronbach's alpha of work{c 150}family conflict scale") ///
note("{it: Note:} Horizontal line denotes conventional cut-off value for Cronbach's alpha", span)
restore
Oct 8, 2013
Random graphs (16): Plots with confidence intervals
preserve
reg srh i.country [pweight = weight]
predict means, xb
predict semeans, stdp
gen loci = means - 1.96 * semeans
gen hici = means + 1.96 * semeans
keep if pickone
egen order_ = rank(-means), unique
labmask order_, value(country) decode
twoway (dot means order_) (rcap loci hici order_) ///
, legend(off) xlab(1/29, valuelabels ang(v)) ///
ylabel(, format(%6.1f)) ///
ytitle("Self-rated health") ///
xtitle("") ///
note(`"Note: SRH ranges from 0 ("poor") to 4 ("excellent")"', span)
restore
preserve logit srh_bin i.country [pweight = weight] predict lr_index, xb predict se_index, stdp generate p_hat = exp(lr_index)/(1+exp(lr_index)) // Predicted probabilities // (following http://www.stata.com/support/faqs/...) gen lb = lr_index - invnormal(0.975)*se_index gen ub = lr_index + invnormal(0.975)*se_index gen plb = exp(lb)/(1+exp(lb)) gen pub = exp(ub)/(1+exp(ub)) replace p_hat = p_hat * 100 replace plb = plb * 100 replace pub = pub * 100 keep if pickone egen order_ = rank(p_hat), unique labmask order_, value(country) decode twoway (dot p_hat order_) (rcap plb pub order_) /// , legend(off) xlab(1/29, valuelabels ang(v)) /// ylabel(, format(%6.0f)) /// ytitle("% Poor health") xtitle("")
Oct 5, 2012
Random graphs (4): Plotting intercept and slope variation
use V3 V51 ISCO88 using "C:\Users\User\work\data sets\issp - work orientations\work orientations iii (2005)\ZA4350_F1.dta", clear // Generate variables gen jobsatisf = 7 - V51 iskoisei isei, isko(ISCO88) drop V51 ISCO88 preserve// Calculating intercept and slope variation using OLS statsby inter = _b[_cons] /// slope = _b[isei] /// , by(V3) /// saving(ols, replace): /// regress jobsatisf isei merge m:1 V3 using ols drop _merge // Visualizing intercept and slope variation gen yhat_ols = inter + slope*isei separate jobsatisf, by(V3) separate yhat_ols, by(V3) twoway (line yhat_ols1-yhat_ols43 isei, sort(V3 isei)) /// (lfit jobsatisf isei, clwidth(vvthick) clcolor(black)) /// , legend(off) ytitle("Job satisfaction") xtitle("ISEI") /// xlabel(16 25 50 75 90) ylabel(,format(%6.1f)) /// caption("{it:Source:} ISSP 2005 (Work Orientations III), own calculations", span) /// name(one, replace) restore// Calculating intercept and slope variation using a random effects model center isei, inplace mixed jobsatisf isei || V3: isei, var predict u1 u0, reffects // Visualizing intercept and slope variation gen predRandomSlope = (_b[_cons] + u0) + ((_b[isei] + u1) * isei) twoway (line predRandomSlope isei, connect(ascending) sort(V3 isei)), /// ytitle("Job satisfaction") /// xtitle("ISEI (centered)") /// xlabel(-25 0 25 50) /// ylabel(,format(%6.1f)) /// caption("{it:Source:} ISSP 2005 (Work Orientations III), own calculations", span) /// name(two, replace)
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