Random graphs (96): Categorical variables

use S003A S002 X001 C041 C039 C038 C037 if inlist(S002, 4, 5) ///
    using WVS_Longitudinal_1981-2014_stata_v_2014_11_25.dta, clear

// Declare missing values
mvdecode C041 C039 C038 C037, mv(-5 -4 -3 -2 -1)

// Factor analysis
factor C041 C039 C038 C037, pcf
alpha C041 C039 C038 C037, item
local alph = round(`r(alpha)', .01)

// Prepare variables
scores nonworkethic = mean(C041 C039 C038 C037), nv(3) 
generate workethic = 5 - nonworkethic 
gen male = (X001 == 1) if X001 != -2
drop nonworkethic X001


// Create descriptive plot    
foreach var of varlist C041 C039 C038 C037 {
 recode `var' (1 = 4 "Strongly agree") ///
              (2 = 3 "Agree") ///
              (3 = 2 "Neither agree nor disagree") ///
              (4 = 1 "Disagree") ///
              (5 = 0 "Strongly disagree"), gen(`var'_rec)
 numlabel `var'_rec, add mask("# ")
 twoway histogram `var'_rec, discrete horizontal yla(0/4, valuelabel) percent ///
                             title("`: variable label `var''") ///
                             ytitle("") ///
                             name(`var', replace) nodraw
 drop `var'_rec
}
graph combine C041 C039 C038 C037, col(2) row(2) altshrink title("Work ethic is measured as the average of four items:") ///
      caption("In a PCA, all items load on one dimension; explained variance = 49%, Cronbach's alpha = `alph'", span) note("{it:Source:} WVS 1999-2009, pooled", span) name(figure1, replace)
drop C041 C039 C038 C037


// Calculate country differences
preserve   
  statsby mean_ = _b[_cons] ///
          loci  = (_b[_cons] - 1.96 * _se[_cons]) ///
          hici  = (_b[_cons] + 1.96 * _se[_cons]) ///
        , by(S003A) clear: ///
          regress workethic

  // Sort coefficients by size
  egen order_ = rank(mean_), unique
  labmask order_, value(S003A) decode

  // Plot
  twoway (rcap mean_ mean_ order_, horizontal) ///
         (rspike loci hici order_, horizontal) ///
        , legend(off) ylabel(1/63, valuelabels ang(h) labsize(*.8)) ///
          xlabel(0 (1) 4, grid format(%6.1f)) name(all, replace)  ///
          xmtick(0 (.5) 4) ///
          ytitle("") xtitle("Work ethic across countries") ///
          title("{bf:A}", justification(left) bexpand span) ///
          xscale(alt) ysize(10) nodraw
restore  

// Country differences--men only
preserve   
  statsby mean_ = _b[_cons] ///
          loci  = (_b[_cons] - 1.96 * _se[_cons]) ///
          hici  = (_b[_cons] + 1.96 * _se[_cons]) ///
        , by(S003A) clear: ///
          regress workethic if male == 1

  // Sort coefficients by size
  egen order_ = rank(mean_), unique
  labmask order_, value(S003A) decode

  // Plot
  twoway (rcap mean_ mean_ order_, horizontal) ///
         (rspike loci hici order_, horizontal) ///
        , legend(off) ylabel(1/63, valuelabels ang(h) labsize(*.8)) ///
          xlabel(0 (1) 4, grid format(%6.1f)) name(men, replace)  ///
          xmtick(0 (.5) 4) ///
          ytitle("") xtitle("Men's work ethic across countries") ///
          title("{bf:B}", justification(left) bexpand span) ///
          xscale(alt) ysize(10) nodraw
restore  

// Calculate gender gap
preserve   
  statsby mean_ = _b[male] ///
          loci  = (_b[male] - 1.96 * _se[male]) ///
          hici  = (_b[male] + 1.96 * _se[male]) ///
        , by(S003A) clear: ///
          regress workethic male

  // Sort coefficients by size
  egen order_ = rank(mean_), unique
  labmask order_, value(S003A) decode

  // Plot
  twoway (rcap mean_ mean_ order_, horizontal) ///
         (rspike loci hici order_, horizontal) ///
        , legend(off) ylabel(1/63, valuelabels ang(h) labsize(*.8)) ///
          xlabel(-.25 (.25) .5, grid format(%6.2f)) name(gendergap, replace)  ///
          xmtick(-.25 (.1) .5) ///
          text(63 .5  "Men" "higher", place(sw)) ///
          text( 1 -.25 "Women" "higher", place(ne)) ///    
          ytitle("") xtitle("Gender gap in work ethic") ///
          title("{bf:C}", justification(left) bexpand span) ///
          xscale(alt) ysize(10) nodraw
restore  

graph combine all men gendergap, note(" " "{it:Source:} WVS 1999-2009, pooled", span) col(3) ysize(12) xsize(18) altshrink ///
                                 title("Work ethic in cross-national comparison", span) name(figure2, replace)

Random graphs (62): Histograms

qui summarize challenging
local m = round(r(mean), .1)
local sd = round(r(mean), .1)
local n = r(N)
numlabel challenging, add mask("# ")
twoway histogram challenging, discrete yla(, valuelabel) horizontal gap(20)  ///
                              freq ytitle("") start(1) xlabel(0/4, grid) ///
                              title("How" "{bf:challenging}" "was this paper?", span) ///
                              xsize(3) note("Mean = `m', {it:SD} = `sd', {it:N} = `n'", span) ///
                              name(challenging, replace)

qui summarize interesting
local m = round(r(mean), .1)
local sd = round(r(mean), .1)
local n = r(N)
numlabel interesting, add mask("# ")
twoway histogram interesting, discrete yla(, valuelabel) gap(20) horizontal ///
                              freq ytitle("") start(1) xlabel(0/4, grid) ///
                              title("How" "{bf:interesting}" "was this paper?", span) ///
                              xsize(3) note("Mean = `m', {it:SD} = `sd', {it:N} = `n'", span) ///
                              name(interesting, replace)

qui summarize recommendable
local m = round(r(mean), .1)
local sd = round(r(mean), .1)
local n = r(N)
numlabel recommendable, add mask("# ")
twoway histogram recommendable, discrete yla(, valuelabel) gap(20) horizontal ///
                                freq ytitle("") start(1) xlabel(0/4, grid) ///
                                title("How likely are you to" "{bf:recommend}" "this paper to a friend?", span) ///
                                xsize(3) note("Mean = `m', {it:SD} = `sd', {it:N} = `n'", span) ///
                                name(recommendable, replace)
         
graph combine challenging interesting recommendable, row(1)

Random graphs (58): Descriptives of a categorical variable

numlabel health, add mask("[#] ")
twoway histogram health, discrete yla(, valuelabel) gap(20) horizontal percent 
numlabel health, remove mask("[#] ")