***Stata version 8 SE ***kta, December 4, 2003 **Be sure file handles reflect correct folders log using "C:\randomization monte carlo.log", replace ****Interactive Model use "C:\NHrep_household.dta", clear set more off gen phn_sim=. gen vst_sim=. gen mail_sim=. gen chi2_p=. gen df_p=. gen chi2_v=. gen df_v=. gen chi2_m=. gen df_m=. local a=1 while `a'<=1000 { replace phn_sim=uniform() replace vst_sim=uniform() replace mail_sim=uniform() recode phn_sim 0/0.072=1 *=0 recode vst_sim 0/0.198=1 *=0 recode mail_sim 0/0.405=1 *=0 xi: logit phn_sim i.WARD*PERSONS i.WARD*AGE1 i.WARD*V96_1_0 i.WARD*V96_1_1 i.WARD*MAJPTY1 i.PERSONS*V96_1_1 i.PERSONS*V96_1_0 i.PERSONS*AGE1 i.PERSONS*MAJPTY1 i.MAJPTY1*AGE1 i.V96_1_1*AGE1 i.V96_1_0*AGE1 i.MAJPTY1*V96_1_1 i.MAJPTY1*V96_1_0 AGE2 MAJPTY2 AGE2MISS V96_2_1 V96_2_0 AGE1MISS local chi2=e(chi2) local df=e(df_m) replace chi2_p=`chi2' in `a' replace df_p=`df' in `a' xi: logit vst_sim i.WARD*PERSONS i.WARD*AGE1 i.WARD*V96_1_0 i.WARD*V96_1_1 i.WARD*MAJPTY1 i.PERSONS*V96_1_1 i.PERSONS*V96_1_0 i.PERSONS*AGE1 i.PERSONS*MAJPTY1 i.MAJPTY1*AGE1 i.V96_1_1*AGE1 i.V96_1_0*AGE1 i.MAJPTY1*V96_1_1 i.MAJPTY1*V96_1_0 AGE2 MAJPTY2 AGE2MISS V96_2_1 V96_2_0 AGE1MISS local chi2=e(chi2) local df=e(df_m) replace chi2_v=`chi2' in `a' replace df_v=`df' in `a' xi: logit mail_sim i.WARD*PERSONS i.WARD*AGE1 i.WARD*V96_1_0 i.WARD*V96_1_1 i.WARD*MAJPTY1 i.PERSONS*V96_1_1 i.PERSONS*V96_1_0 i.PERSONS*AGE1 i.PERSONS*MAJPTY1 i.MAJPTY1*AGE1 i.V96_1_1*AGE1 i.V96_1_0*AGE1 i.MAJPTY1*V96_1_1 i.MAJPTY1*V96_1_0 AGE2 MAJPTY2 AGE2MISS V96_2_1 V96_2_0 AGE1MISS local chi2=e(chi2) local df=e(df_m) replace chi2_m=`chi2' in `a' replace df_m=`df' in `a' local a=`a'+1 } sum chi2* sum df* **Chi-sqare p-value = 0.10, 0.05 for 188 df (see http://www.fourmilab.ch/rpkp/experiments/analysis/chiCalc.html) gen sig10_p=0 replace sig10_p=1 if chi2_p>=213.2390 replace sig10_p=. if chi2_p==. gen sig05_p=0 replace sig05_p=1 if chi2_p>=220.9908 replace sig05_p=. if chi2_p==. gen sig10_v=0 replace sig10_v=1 if chi2_v>=213.2390 replace sig10_v=. if chi2_v==. gen sig05_p=0 replace sig05_v=1 if chi2_v>=220.9908 replace sig05_v=. if chi2_v==. gen sig10_m=0 replace sig10_m=1 if chi2_m>=213.2390 replace sig10_m=. if chi2_m==. gen sig05_m=0 replace sig05_m=1 if chi2_m>=220.9908 replace sig05_m=. if chi2_m==. tab sig10_p tab sig05_p tab sig10_v tab sig05_v tab sig10_m tab sig05_m ******Conduct same Monte Carlo on one-treatment sample use "C:\NHrep_household.dta", clear gen onetreat=0 replace onetreat=1 if MAILGRP==0 & PERSNGRP==0 & PHONGOTV==0 replace onetreat=1 if MAILGRP==1 & PERSNGRP==0 & PHONGOTV==0 replace onetreat=1 if MAILGRP==0 & PERSNGRP==1 & PHONGOTV==0 replace onetreat=1 if MAILGRP==0 & PERSNGRP==0 & PHONGOTV==1 drop if onetreat==0 set more off gen phn_sim=. gen vst_sim=. gen mail_sim=. gen chi2_p=. gen df_p=. gen chi2_v=. gen df_v=. gen chi2_m=. gen df_m=. local a=1 while `a'<=1000 { replace phn_sim=uniform() replace vst_sim=uniform() replace mail_sim=uniform() recode phn_sim 0/0.072=1 *=0 recode vst_sim 0/0.198=1 *=0 recode mail_sim 0/0.405=1 *=0 xi: logit phn_sim i.WARD*PERSONS i.WARD*AGE1 i.WARD*V96_1_0 i.WARD*V96_1_1 i.WARD*MAJPTY1 i.PERSONS*V96_1_1 i.PERSONS*V96_1_0 i.PERSONS*AGE1 i.PERSONS*MAJPTY1 i.MAJPTY1*AGE1 i.V96_1_1*AGE1 i.V96_1_0*AGE1 i.MAJPTY1*V96_1_1 i.MAJPTY1*V96_1_0 AGE2 MAJPTY2 AGE2MISS V96_2_1 V96_2_0 AGE1MISS local chi2=e(chi2) local df=e(df_m) replace chi2_p=`chi2' in `a' replace df_p=`df' in `a' xi: logit vst_sim i.WARD*PERSONS i.WARD*AGE1 i.WARD*V96_1_0 i.WARD*V96_1_1 i.WARD*MAJPTY1 i.PERSONS*V96_1_1 i.PERSONS*V96_1_0 i.PERSONS*AGE1 i.PERSONS*MAJPTY1 i.MAJPTY1*AGE1 i.V96_1_1*AGE1 i.V96_1_0*AGE1 i.MAJPTY1*V96_1_1 i.MAJPTY1*V96_1_0 AGE2 MAJPTY2 AGE2MISS V96_2_1 V96_2_0 AGE1MISS local chi2=e(chi2) local df=e(df_m) replace chi2_v=`chi2' in `a' replace df_v=`df' in `a' xi: logit mail_sim i.WARD*PERSONS i.WARD*AGE1 i.WARD*V96_1_0 i.WARD*V96_1_1 i.WARD*MAJPTY1 i.PERSONS*V96_1_1 i.PERSONS*V96_1_0 i.PERSONS*AGE1 i.PERSONS*MAJPTY1 i.MAJPTY1*AGE1 i.V96_1_1*AGE1 i.V96_1_0*AGE1 i.MAJPTY1*V96_1_1 i.MAJPTY1*V96_1_0 AGE2 MAJPTY2 AGE2MISS V96_2_1 V96_2_0 AGE1MISS local chi2=e(chi2) local df=e(df_m) replace chi2_m=`chi2' in `a' replace df_m=`df' in `a' local a=`a'+1 } sum chi2* sum df* **Chi-sqare p-value = 0.10, 0.05 for 188 df (see http://www.fourmilab.ch/rpkp/experiments/analysis/chiCalc.html) gen sig10_p=0 replace sig10_p=1 if chi2_p>=213.2390 replace sig10_p=. if chi2_p==. gen sig05_p=0 replace sig05_p=1 if chi2_p>=220.9908 replace sig05_p=. if chi2_p==. gen sig10_v=0 replace sig10_v=1 if chi2_v>=213.2390 replace sig10_v=. if chi2_v==. gen sig05_p=0 replace sig05_v=1 if chi2_v>=220.9908 replace sig05_v=. if chi2_v==. gen sig10_m=0 replace sig10_m=1 if chi2_m>=213.2390 replace sig10_m=. if chi2_m==. gen sig05_m=0 replace sig05_m=1 if chi2_m>=220.9908 replace sig05_m=. if chi2_m==. tab sig10_p tab sig05_p tab sig10_v tab sig05_v tab sig10_m tab sig05_m ******Conduct same Monte Carlo on 10% of sample use "C:\NHrep_household.dta", clear sample 10 set more off gen phn_sim=. gen vst_sim=. gen mail_sim=. gen chi2_p=. gen df_p=. gen chi2_v=. gen df_v=. gen chi2_m=. gen df_m=. local a=1 while `a'<=1000 { replace phn_sim=uniform() replace vst_sim=uniform() replace mail_sim=uniform() recode phn_sim 0/0.072=1 *=0 recode vst_sim 0/0.198=1 *=0 recode mail_sim 0/0.405=1 *=0 xi: logit phn_sim i.WARD*PERSONS i.WARD*AGE1 i.WARD*V96_1_0 i.WARD*V96_1_1 i.WARD*MAJPTY1 i.PERSONS*V96_1_1 i.PERSONS*V96_1_0 i.PERSONS*AGE1 i.PERSONS*MAJPTY1 i.MAJPTY1*AGE1 i.V96_1_1*AGE1 i.V96_1_0*AGE1 i.MAJPTY1*V96_1_1 i.MAJPTY1*V96_1_0 AGE2 MAJPTY2 AGE2MISS V96_2_1 V96_2_0 AGE1MISS local chi2=e(chi2) local df=e(df_m) replace chi2_p=`chi2' in `a' replace df_p=`df' in `a' xi: logit vst_sim i.WARD*PERSONS i.WARD*AGE1 i.WARD*V96_1_0 i.WARD*V96_1_1 i.WARD*MAJPTY1 i.PERSONS*V96_1_1 i.PERSONS*V96_1_0 i.PERSONS*AGE1 i.PERSONS*MAJPTY1 i.MAJPTY1*AGE1 i.V96_1_1*AGE1 i.V96_1_0*AGE1 i.MAJPTY1*V96_1_1 i.MAJPTY1*V96_1_0 AGE2 MAJPTY2 AGE2MISS V96_2_1 V96_2_0 AGE1MISS local chi2=e(chi2) local df=e(df_m) replace chi2_v=`chi2' in `a' replace df_v=`df' in `a' xi: logit mail_sim i.WARD*PERSONS i.WARD*AGE1 i.WARD*V96_1_0 i.WARD*V96_1_1 i.WARD*MAJPTY1 i.PERSONS*V96_1_1 i.PERSONS*V96_1_0 i.PERSONS*AGE1 i.PERSONS*MAJPTY1 i.MAJPTY1*AGE1 i.V96_1_1*AGE1 i.V96_1_0*AGE1 i.MAJPTY1*V96_1_1 i.MAJPTY1*V96_1_0 AGE2 MAJPTY2 AGE2MISS V96_2_1 V96_2_0 AGE1MISS local chi2=e(chi2) local df=e(df_m) replace chi2_m=`chi2' in `a' replace df_m=`df' in `a' local a=`a'+1 } sum chi2* sum df* **Chi-sqare p-value = 0.10, 0.05 (df varies due to dropped variables from small cell sizes in interactions) (see http://www.fourmilab.ch/rpkp/experiments/analysis/chiCalc.html) gen sig10_p=0 replace sig10_p=1 if df_p== 160 & chi2_p>= 183.3105 replace sig10_p=1 if df_p== 161 & chi2_p>= 184.3822 replace sig10_p=1 if df_p== 162 & chi2_p>= 185.4536 replace sig10_p=1 if df_p== 164 & chi2_p>= 187.5959 replace sig10_p=1 if df_p== 165 & chi2_p>= 188.6666 replace sig10_p=1 if df_p== 166 & chi2_p>= 189.7372 replace sig10_p=1 if df_p== 167 & chi2_p>= 190.8076 replace sig10_p=1 if df_p== 168 & chi2_p>= 191.8777 replace sig10_p=1 if df_p== 169 & chi2_p>= 192.9476 replace sig10_p=1 if df_p== 170 & chi2_p>= 194.0174 replace sig10_p=1 if df_p== 171 & chi2_p>= 195.0869 replace sig10_p=1 if df_p== 172 & chi2_p>= 196.156 replace sig10_p=1 if df_p== 173 & chi2_p>= 197.2253 replace sig10_p=1 if df_p== 174 & chi2_p>= 198.2942 replace sig10_p=1 if df_p== 175 & chi2_p>= 199.363 replace sig10_p=1 if df_p== 176 & chi2_p>= 200.4315 replace sig10_p=1 if df_p== 177 & chi2_p>= 201.4998 replace sig10_p=1 if df_p== 178 & chi2_p>= 202.5679 replace sig10_p=1 if df_p== 179 & chi2_p>= 203.6359 replace sig10_p=1 if df_p== 180 & chi2_p>= 204.7036 replace sig10_p=1 if df_p== 181 & chi2_p>= 205.7712 replace sig10_p=1 if df_p== 182 & chi2_p>= 206.8386 replace sig10_p=1 if df_p== 183 & chi2_p>= 207.9058 replace sig10_p=1 if df_p== 184 & chi2_p>= 208.9728 replace sig10_p=1 if df_p== 185 & chi2_p>= 210.0396 replace sig10_p=1 if df_p== 186 & chi2_p>= 211.1062 replace sig10_p=1 if df_p== 187 & chi2_p>= 212.172 replace sig10_p=1 if df_p== 188 & chi2_p>= 213.239 replace sig10_p=. if chi2_p==. gen sig05_p=0 replace sig05_p=1 if df_p== 160 & chi2_p>= 190.5164 replace sig05_p=1 if df_p== 161 & chi2_p>= 191.6084 replace sig05_p=1 if df_p== 162 & chi2_p>= 192.7 replace sig05_p=1 if df_p== 164 & chi2_p>= 194.8825 replace sig05_p=1 if df_p== 165 & chi2_p>= 195.9733 replace sig05_p=1 if df_p== 166 & chi2_p>= 197.0639 replace sig05_p=1 if df_p== 167 & chi2_p>= 198.1541 replace sig05_p=1 if df_p== 168 & chi2_p>= 199.2441 replace sig05_p=1 if df_p== 169 & chi2_p>= 200.3339 replace sig05_p=1 if df_p== 170 & chi2_p>= 201.4233 replace sig05_p=1 if df_p== 171 & chi2_p>= 202.5125 replace sig05_p=1 if df_p== 172 & chi2_p>= 203.6015 replace sig05_p=1 if df_p== 173 & chi2_p>= 204.6902 replace sig05_p=1 if df_p== 174 & chi2_p>= 205.7786 replace sig05_p=1 if df_p== 175 & chi2_p>= 206.8667 replace sig05_p=1 if df_p== 176 & chi2_p>= 207.9547 replace sig05_p=1 if df_p== 177 & chi2_p>= 209.0423 replace sig05_p=1 if df_p== 178 & chi2_p>= 210.1298 replace sig05_p=1 if df_p== 179 & chi2_p>= 211.2169 replace sig05_p=1 if df_p== 180 & chi2_p>= 212.3039 replace sig05_p=1 if df_p== 181 & chi2_p>= 213.3906 replace sig05_p=1 if df_p== 182 & chi2_p>= 214.477 replace sig05_p=1 if df_p== 183 & chi2_p>= 215.5632 replace sig05_p=1 if df_p== 184 & chi2_p>= 216.6492 replace sig05_p=1 if df_p== 185 & chi2_p>= 217.7349 replace sig05_p=1 if df_p== 186 & chi2_p>= 218.8204 replace sig05_p=1 if df_p== 187 & chi2_p>= 219.906 replace sig05_p=1 if df_p== 188 & chi2_p>= 220.991 replace sig05_p=. if chi2_p==. gen sig10_v=0 replace sig10_v=1 if df_v== 160 & chi2_v>= 183.3105 replace sig10_v=1 if df_v== 161 & chi2_v>= 184.3822 replace sig10_v=1 if df_v== 162 & chi2_v>= 185.4536 replace sig10_v=1 if df_v== 164 & chi2_v>= 187.5959 replace sig10_v=1 if df_v== 165 & chi2_v>= 188.6666 replace sig10_v=1 if df_v== 166 & chi2_v>= 189.7372 replace sig10_v=1 if df_v== 167 & chi2_v>= 190.8076 replace sig10_v=1 if df_v== 168 & chi2_v>= 191.8777 replace sig10_v=1 if df_v== 169 & chi2_v>= 192.9476 replace sig10_v=1 if df_v== 170 & chi2_v>= 194.0174 replace sig10_v=1 if df_v== 171 & chi2_v>= 195.0869 replace sig10_v=1 if df_v== 172 & chi2_v>= 196.156 replace sig10_v=1 if df_v== 173 & chi2_v>= 197.2253 replace sig10_v=1 if df_v== 174 & chi2_v>= 198.2942 replace sig10_v=1 if df_v== 175 & chi2_v>= 199.363 replace sig10_v=1 if df_v== 176 & chi2_v>= 200.4315 replace sig10_v=1 if df_v== 177 & chi2_v>= 201.4998 replace sig10_v=1 if df_v== 178 & chi2_v>= 202.5679 replace sig10_v=1 if df_v== 179 & chi2_v>= 203.6359 replace sig10_v=1 if df_v== 180 & chi2_v>= 204.7036 replace sig10_v=1 if df_v== 181 & chi2_v>= 205.7712 replace sig10_v=1 if df_v== 182 & chi2_v>= 206.8386 replace sig10_v=1 if df_v== 183 & chi2_v>= 207.9058 replace sig10_v=1 if df_v== 184 & chi2_v>= 208.9728 replace sig10_v=1 if df_v== 185 & chi2_v>= 210.0396 replace sig10_v=1 if df_v== 186 & chi2_v>= 211.1062 replace sig10_v=1 if df_v== 187 & chi2_v>= 212.172 replace sig10_v=1 if df_v== 188 & chi2_v>= 213.239 replace sig10_v=. if chi2_v==. gen sig05_v=0 replace sig05_v=1 if df_v== 160 & chi2_v>= 190.5164 replace sig05_v=1 if df_v== 161 & chi2_v>= 191.6084 replace sig05_v=1 if df_v== 162 & chi2_v>= 192.7 replace sig05_v=1 if df_v== 164 & chi2_v>= 194.8825 replace sig05_v=1 if df_v== 165 & chi2_v>= 195.9733 replace sig05_v=1 if df_v== 166 & chi2_v>= 197.0639 replace sig05_v=1 if df_v== 167 & chi2_v>= 198.1541 replace sig05_v=1 if df_v== 168 & chi2_v>= 199.2441 replace sig05_v=1 if df_v== 169 & chi2_v>= 200.3339 replace sig05_v=1 if df_v== 170 & chi2_v>= 201.4233 replace sig05_v=1 if df_v== 171 & chi2_v>= 202.5125 replace sig05_v=1 if df_v== 172 & chi2_v>= 203.6015 replace sig05_v=1 if df_v== 173 & chi2_v>= 204.6902 replace sig05_v=1 if df_v== 174 & chi2_v>= 205.7786 replace sig05_v=1 if df_v== 175 & chi2_v>= 206.8667 replace sig05_v=1 if df_v== 176 & chi2_v>= 207.9547 replace sig05_v=1 if df_v== 177 & chi2_v>= 209.0423 replace sig05_v=1 if df_v== 178 & chi2_v>= 210.1298 replace sig05_v=1 if df_v== 179 & chi2_v>= 211.2169 replace sig05_v=1 if df_v== 180 & chi2_v>= 212.3039 replace sig05_v=1 if df_v== 181 & chi2_v>= 213.3906 replace sig05_v=1 if df_v== 182 & chi2_v>= 214.477 replace sig05_v=1 if df_v== 183 & chi2_v>= 215.5632 replace sig05_v=1 if df_v== 184 & chi2_v>= 216.6492 replace sig05_v=1 if df_v== 185 & chi2_v>= 217.7349 replace sig05_v=1 if df_v== 186 & chi2_v>= 218.8204 replace sig05_v=1 if df_v== 187 & chi2_v>= 219.906 replace sig05_v=1 if df_v== 188 & chi2_v>= 220.991 replace sig05_v=. if chi2_v==. gen sig10_m=0 replace sig10_m=1 if df_m== 160 & chi2_m>= 183.3105 replace sig10_m=1 if df_m== 161 & chi2_m>= 184.3822 replace sig10_m=1 if df_m== 162 & chi2_m>= 185.4536 replace sig10_m=1 if df_m== 164 & chi2_m>= 187.5959 replace sig10_m=1 if df_m== 165 & chi2_m>= 188.6666 replace sig10_m=1 if df_m== 166 & chi2_m>= 189.7372 replace sig10_m=1 if df_m== 167 & chi2_m>= 190.8076 replace sig10_m=1 if df_m== 168 & chi2_m>= 191.8777 replace sig10_m=1 if df_m== 169 & chi2_m>= 192.9476 replace sig10_m=1 if df_m== 170 & chi2_m>= 194.0174 replace sig10_m=1 if df_m== 171 & chi2_m>= 195.0869 replace sig10_m=1 if df_m== 172 & chi2_m>= 196.156 replace sig10_m=1 if df_m== 173 & chi2_m>= 197.2253 replace sig10_m=1 if df_m== 174 & chi2_m>= 198.2942 replace sig10_m=1 if df_m== 175 & chi2_m>= 199.363 replace sig10_m=1 if df_m== 176 & chi2_m>= 200.4315 replace sig10_m=1 if df_m== 177 & chi2_m>= 201.4998 replace sig10_m=1 if df_m== 178 & chi2_m>= 202.5679 replace sig10_m=1 if df_m== 179 & chi2_m>= 203.6359 replace sig10_m=1 if df_m== 180 & chi2_m>= 204.7036 replace sig10_m=1 if df_m== 181 & chi2_m>= 205.7712 replace sig10_m=1 if df_m== 182 & chi2_m>= 206.8386 replace sig10_m=1 if df_m== 183 & chi2_m>= 207.9058 replace sig10_m=1 if df_m== 184 & chi2_m>= 208.9728 replace sig10_m=1 if df_m== 185 & chi2_m>= 210.0396 replace sig10_m=1 if df_m== 186 & chi2_m>= 211.1062 replace sig10_m=1 if df_m== 187 & chi2_m>= 212.172 replace sig10_m=1 if df_m== 188 & chi2_m>= 213.239 replace sig10_m=. if chi2_m==. gen sig05_m=0 replace sig05_m=1 if df_m== 160 & chi2_m>= 190.5164 replace sig05_m=1 if df_m== 161 & chi2_m>= 191.6084 replace sig05_m=1 if df_m== 162 & chi2_m>= 192.7 replace sig05_m=1 if df_m== 164 & chi2_m>= 194.8825 replace sig05_m=1 if df_m== 165 & chi2_m>= 195.9733 replace sig05_m=1 if df_m== 166 & chi2_m>= 197.0639 replace sig05_m=1 if df_m== 167 & chi2_m>= 198.1541 replace sig05_m=1 if df_m== 168 & chi2_m>= 199.2441 replace sig05_m=1 if df_m== 169 & chi2_m>= 200.3339 replace sig05_m=1 if df_m== 170 & chi2_m>= 201.4233 replace sig05_m=1 if df_m== 171 & chi2_m>= 202.5125 replace sig05_m=1 if df_m== 172 & chi2_m>= 203.6015 replace sig05_m=1 if df_m== 173 & chi2_m>= 204.6902 replace sig05_m=1 if df_m== 174 & chi2_m>= 205.7786 replace sig05_m=1 if df_m== 175 & chi2_m>= 206.8667 replace sig05_m=1 if df_m== 176 & chi2_m>= 207.9547 replace sig05_m=1 if df_m== 177 & chi2_m>= 209.0423 replace sig05_m=1 if df_m== 178 & chi2_m>= 210.1298 replace sig05_m=1 if df_m== 179 & chi2_m>= 211.2169 replace sig05_m=1 if df_m== 180 & chi2_m>= 212.3039 replace sig05_m=1 if df_m== 181 & chi2_m>= 213.3906 replace sig05_m=1 if df_m== 182 & chi2_m>= 214.477 replace sig05_m=1 if df_m== 183 & chi2_m>= 215.5632 replace sig05_m=1 if df_m== 184 & chi2_m>= 216.6492 replace sig05_m=1 if df_m== 185 & chi2_m>= 217.7349 replace sig05_m=1 if df_m== 186 & chi2_m>= 218.8204 replace sig05_m=1 if df_m== 187 & chi2_m>= 219.906 replace sig05_m=1 if df_m== 188 & chi2_m>= 220.991 replace sig05_m=. if chi2_m==. tab sig10_p tab sig05_p tab sig10_v tab sig05_v tab sig10_m tab sig05_m ****Additive Model use "C:\NHrep_household.dta", clear set more off gen phn_sim=. gen vst_sim=. gen mail_sim=. gen chi2_p=. gen df_p=. gen chi2_v=. gen df_v=. gen chi2_m=. gen df_m=. local a=1 while `a'<=1000 { replace phn_sim=uniform() replace vst_sim=uniform() replace mail_sim=uniform() recode phn_sim 0/0.072=1 *=0 recode vst_sim 0/0.198=1 *=0 recode mail_sim 0/0.405=1 *=0 xi: logit phn_sim PERSONS AGE1 V96_1_0 V96_1_1 MAJPTY1 i.WARD AGE2 MAJPTY2 AGE2MISS V96_2_1 V96_2_0 AGE1MISS local chi2=e(chi2) local df=e(df_m) replace chi2_p=`chi2' in `a' replace df_p=`df' in `a' xi: logit vst_sim PERSONS AGE1 V96_1_0 V96_1_1 MAJPTY1 i.WARD AGE2 MAJPTY2 AGE2MISS V96_2_1 V96_2_0 AGE1MISS local chi2=e(chi2) local df=e(df_m) replace chi2_v=`chi2' in `a' replace df_v=`df' in `a' xi: logit mail_sim PERSONS AGE1 V96_1_0 V96_1_1 MAJPTY1 i.WARD AGE2 MAJPTY2 AGE2MISS V96_2_1 V96_2_0 AGE1MISS local chi2=e(chi2) local df=e(df_m) replace chi2_m=`chi2' in `a' replace df_m=`df' in `a' local a=`a'+1 } sum chi2* sum df* **Chi-sqare p-value = 0.10, 0.05 for 39 df (see http://www.fourmilab.ch/rpkp/experiments/analysis/chiCalc.html) gen sig10_p=0 replace sig10_p=1 if chi2_p>=50.6597 replace sig10_p=. if chi2_p==. gen sig05_p=0 replace sig05_p=1 if chi2_p>=54.5722 replace sig05_p=. if chi2_p==. gen sig10_v=0 replace sig10_v=1 if chi2_v>=50.6597 replace sig10_v=. if chi2_v==. gen sig05_v=0 replace sig05_v=1 if chi2_v>=54.5722 replace sig05_v=. if chi2_v==. gen sig10_m=0 replace sig10_m=1 if chi2_m>=50.6597 replace sig10_m=. if chi2_m==. gen sig05_m=0 replace sig05_m=1 if chi2_m>=54.5722 replace sig05_m=. if chi2_m==. tab sig10_p tab sig05_p tab sig10_v tab sig05_v tab sig10_m tab sig05_m ******Conduct same Monte Carlo on one-treatment sample use "C:\NHrep_household.dta", clear gen onetreat=0 replace onetreat=1 if MAILGRP==0 & PERSNGRP==0 & PHONGOTV==0 replace onetreat=1 if MAILGRP==1 & PERSNGRP==0 & PHONGOTV==0 replace onetreat=1 if MAILGRP==0 & PERSNGRP==1 & PHONGOTV==0 replace onetreat=1 if MAILGRP==0 & PERSNGRP==0 & PHONGOTV==1 drop if onetreat==0 set more off gen phn_sim=. gen vst_sim=. gen mail_sim=. gen chi2_p=. gen df_p=. gen chi2_v=. gen df_v=. gen chi2_m=. gen df_m=. local a=1 while `a'<=1000 { replace phn_sim=uniform() replace vst_sim=uniform() replace mail_sim=uniform() recode phn_sim 0/0.072=1 *=0 recode vst_sim 0/0.198=1 *=0 recode mail_sim 0/0.405=1 *=0 xi: logit phn_sim PERSONS AGE1 V96_1_0 V96_1_1 MAJPTY1 i.WARD AGE2 MAJPTY2 AGE2MISS V96_2_1 V96_2_0 AGE1MISS local chi2=e(chi2) local df=e(df_m) replace chi2_p=`chi2' in `a' replace df_p=`df' in `a' xi: logit vst_sim PERSONS AGE1 V96_1_0 V96_1_1 MAJPTY1 i.WARD AGE2 MAJPTY2 AGE2MISS V96_2_1 V96_2_0 AGE1MISS local chi2=e(chi2) local df=e(df_m) replace chi2_v=`chi2' in `a' replace df_v=`df' in `a' xi: logit mail_sim PERSONS AGE1 V96_1_0 V96_1_1 MAJPTY1 i.WARD AGE2 MAJPTY2 AGE2MISS V96_2_1 V96_2_0 AGE1MISS local chi2=e(chi2) local df=e(df_m) replace chi2_m=`chi2' in `a' replace df_m=`df' in `a' local a=`a'+1 } sum chi2* sum df* **Chi-sqare p-value = 0.10, 0.05 for 39 df (see http://www.fourmilab.ch/rpkp/experiments/analysis/chiCalc.html) gen sig10_p=0 replace sig10_p=1 if chi2_p>=50.6597 replace sig10_p=. if chi2_p==. gen sig05_p=0 replace sig05_p=1 if chi2_p>=54.5722 replace sig05_p=. if chi2_p==. gen sig10_v=0 replace sig10_v=1 if chi2_v>=50.6597 replace sig10_v=. if chi2_v==. gen sig05_v=0 replace sig05_v=1 if chi2_v>=54.5722 replace sig05_v=. if chi2_v==. gen sig10_m=0 replace sig10_m=1 if chi2_m>=50.6597 replace sig10_m=. if chi2_m==. gen sig05_m=0 replace sig05_m=1 if chi2_m>=54.5722 replace sig05_m=. if chi2_m==. tab sig10_p tab sig05_p tab sig10_v tab sig05_v tab sig10_m tab sig05_m ******Conduct same Monte Carlo on 10% of sample use "C:\NHrep_household.dta", clear sample 10 set more off gen phn_sim=. gen vst_sim=. gen mail_sim=. gen chi2_p=. gen df_p=. gen chi2_v=. gen df_v=. gen chi2_m=. gen df_m=. local a=1 while `a'<=1000 { replace phn_sim=uniform() replace vst_sim=uniform() replace mail_sim=uniform() recode phn_sim 0/0.072=1 *=0 recode vst_sim 0/0.198=1 *=0 recode mail_sim 0/0.405=1 *=0 xi: logit phn_sim PERSONS AGE1 V96_1_0 V96_1_1 MAJPTY1 i.WARD AGE2 MAJPTY2 AGE2MISS V96_2_1 V96_2_0 AGE1MISS local chi2=e(chi2) local df=e(df_m) replace chi2_p=`chi2' in `a' replace df_p=`df' in `a' xi: logit vst_sim PERSONS AGE1 V96_1_0 V96_1_1 MAJPTY1 i.WARD AGE2 MAJPTY2 AGE2MISS V96_2_1 V96_2_0 AGE1MISS local chi2=e(chi2) local df=e(df_m) replace chi2_v=`chi2' in `a' replace df_v=`df' in `a' xi: logit mail_sim PERSONS AGE1 V96_1_0 V96_1_1 MAJPTY1 i.WARD AGE2 MAJPTY2 AGE2MISS V96_2_1 V96_2_0 AGE1MISS local chi2=e(chi2) local df=e(df_m) replace chi2_m=`chi2' in `a' replace df_m=`df' in `a' local a=`a'+1 } sum chi2* sum df* **Chi-sqare p-value = 0.10, 0.05 (df varies due to dropped variables) (see http://www.fourmilab.ch/rpkp/experiments/analysis/chiCalc.html) gen sig10_p=0 replace sig10_p=1 if df_p==35 & chi2_p>=46.0587 replace sig10_p=1 if df_p==36 & chi2_p>=47.2121 replace sig10_p=1 if df_p==37 & chi2_p>=48.3634 replace sig10_p=1 if df_p==38 & chi2_p>=49.5125 replace sig10_p=1 if df_p==39 & chi2_p>=50.6597 replace sig10_p=. if chi2_p==. gen sig05_p=0 replace sig05_p=1 if df_p==35 & chi2_p>=49.8018 replace sig05_p=1 if df_p==36 & chi2_p>=50.9984 replace sig05_p=1 if df_p==37 & chi2_p>=52.1923 replace sig05_p=1 if df_p==38 & chi2_p>=53.3835 replace sig05_p=1 if df_p==39 & chi2_p>=54.5722 replace sig05_p=. if chi2_p==. gen sig10_v=0 replace sig10_v=1 if df_v==35 & chi2_v>=46.0587 replace sig10_v=1 if df_v==36 & chi2_v>=47.2121 replace sig10_v=1 if df_v==37 & chi2_v>=48.3634 replace sig10_v=1 if df_v==38 & chi2_v>=49.5125 replace sig10_v=1 if df_v==39 & chi2_v>=50.6597 replace sig10_v=. if chi2_v==. gen sig05_v=0 replace sig05_v=1 if df_v==35 & chi2_v>=49.8018 replace sig05_v=1 if df_v==36 & chi2_v>=50.9984 replace sig05_v=1 if df_v==37 & chi2_v>=52.1923 replace sig05_v=1 if df_v==38 & chi2_v>=53.3835 replace sig05_v=1 if df_v==39 & chi2_v>=54.5722 replace sig05_v=. if chi2_v==. gen sig10_m=0 replace sig10_m=1 if df_m==35 & chi2_m>=46.0587 replace sig10_m=1 if df_m==36 & chi2_m>=47.2121 replace sig10_m=1 if df_m==37 & chi2_m>=48.3634 replace sig10_m=1 if df_m==38 & chi2_m>=49.5125 replace sig10_m=1 if df_m==39 & chi2_m>=50.6597 replace sig10_m=. if chi2_m==. gen sig05_m=0 replace sig05_m=1 if df_m==35 & chi2_m>=49.8018 replace sig05_m=1 if df_m==36 & chi2_m>=50.9984 replace sig05_m=1 if df_m==37 & chi2_m>=52.1923 replace sig05_m=1 if df_m==38 & chi2_m>=53.3835 replace sig05_m=1 if df_m==39 & chi2_m>=54.5722 replace sig05_m=. if chi2_m==. tab sig10_p tab sig05_p tab sig10_v tab sig05_v tab sig10_m tab sig05_m log close