. // Ben Jann, 2. Mai version clear all. . set linesize set type double. . set more off

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1 // Ben Jann, 2 Mai 2014 version 121 clear all set linesize 100 set type double set more off /* */ /* Experiment 1: "Häsch mer zwöi Stutz?" */ /* */ use eth-fs2010-hilfeleistung, clear gen byte helped = (help==1) if help< recode language (2=1 "Schweizerdeutsch") (1=2 "Hochdeutsch"), gen(lang) (109 differences between language and lang) lab var lang "Sprache" encode date, gen(datum) recode age (1 2 = 1 "Alter < 45") (3 4 = 2 "Alter > 45"), gen(alter) (96 differences between age and alter) // Alle Beobachtungen tab helped lang, col exact chi2 Key frequency column percentage Sprache helped Schweizer Hochdeuts Total Total Pearson chi2(1) = Pr = 0909 Fisher's exact = sided Fisher's exact = 0532 ci helped if lang==1, binomial // Schweizerdeutsch Variable Obs Mean Std Err [95% Conf Interval] helped ci helped if lang==2, binomial // Hochdeutsch Variable Obs Mean Std Err [95% Conf Interval] helped

2 // Ohne Akzent tab helped lang if accent!=1, col exact chi2 Key frequency column percentage Sprache helped Schweizer Hochdeuts Total Total Pearson chi2(1) = Pr = 0840 Fisher's exact = sided Fisher's exact = 0500 ci helped if lang==1 & accent!=1, binomial // Schweizerdeutsch Variable Obs Mean Std Err [95% Conf Interval] helped ci helped if lang==2 & accent!=1, binomial // Hochdeutsch Variable Obs Mean Std Err [95% Conf Interval] helped // Ohne Akzent, unter Kontrolle von Datum logit helped ilang idatum if accent!=1 Iteration 0: log likelihood = Iteration 1: log likelihood = Iteration 2: log likelihood = Iteration 3: log likelihood = Logistic regression Number of obs = 100 LR chi2(2) = 041 Prob > chi2 = Log likelihood = Pseudo R2 = helped Coef Std Err z P>z [95% Conf Interval] - lang Hochdeutsch datum 5/11/ _cons margins lang, expression(invlogit(predict(xb))*100) Predictive margins Number of obs =

3 Delta-method Margin Std Err z P>z [95% Conf Interval] lang Schweizerdeutsch Hochdeutsch margins, dydx(lang) expression(invlogit(predict(xb))*100) Average marginal effects Number of obs = 100 dy/dx wrt : 2lang Delta-method dy/dx Std Err z P>z [95% Conf Interval] - lang Hochdeutsch Note: dy/dx for factor levels is the discrete change from the base level // Ohne Akzent, unter Kontrolle von Datum, nach Geschlecht und Alter logit helped ilang##isex##ialter idatum if accent!=1 Iteration 0: log likelihood = Iteration 1: log likelihood = Iteration 2: log likelihood = Iteration 3: log likelihood = Logistic regression Number of obs = 100 LR chi2(8) = 649 Prob > chi2 = Log likelihood = Pseudo R2 = helped Coef Std Err z P>z [95% Conf Interval] lang Hochdeutsch sex weiblich lang#sex Hochdeutsch#weiblich alter Alter > lang#alter Hochdeutsch#Alter > sex#alter weiblich#alter > lang#sex#alter Hochdeutsch#weiblich#Alter > datum 5/11/ _cons margins lang, (sex) expression(invlogit(predict(xb))*100) Predictive margins Number of obs = 100 3

4 : sex Delta-method Margin Std Err z P>z [95% Conf Interval] sex#lang männlich#schweizerdeutsch männlich#hochdeutsch weiblich#schweizerdeutsch weiblich#hochdeutsch margins, dydx(lang) (sex) expression(invlogit(predict(xb))*100) Average marginal effects Number of obs = 100 dy/dx wrt : 2lang : sex Delta-method dy/dx Std Err z P>z [95% Conf Interval] - 2lang sex männlich weiblich Note: dy/dx for factor levels is the discrete change from the base level local b = el(r(b),1,3)-el(r(b),1,4) local se = sqrt(el(r(v),3,3)+el(r(v),4,4)-2*el(r(v),3,4)) local p = string((1-normal(abs(`b'/`se')))*2, "%93f") local b = string(`b', "%91f") local se = string(`se', "%91f") di "Effekt-Differenz: `b' (`se'), p = `p'" Effekt-Differenz: 164 (201), p = 0414 margins lang, (alter) expression(invlogit(predict(xb))*100) Predictive margins Number of obs = 100 : alter Delta-method Margin Std Err z P>z [95% Conf Interval] alter#lang Alter < 45#Schweizerdeutsch Alter < 45#Hochdeutsch Alter > 45#Schweizerdeutsch Alter > 45#Hochdeutsch margins, dydx(lang) (alter) expression(invlogit(predict(xb))*100) Average marginal effects Number of obs = 100 dy/dx wrt : 2lang 4

5 : alter Delta-method dy/dx Std Err z P>z [95% Conf Interval] - 2lang alter Alter < Alter > Note: dy/dx for factor levels is the discrete change from the base level local b = el(r(b),1,3)-el(r(b),1,4) local se = sqrt(el(r(v),3,3)+el(r(v),4,4)-2*el(r(v),3,4)) local p = string((1-normal(abs(`b'/`se')))*2, "%93f") local b = string(`b', "%91f") local se = string(`se', "%91f") di "Effekt-Differenz: `b' (`se'), p = `p'" Effekt-Differenz: 163 (194), p = 0402 /* */ /* Experiment 2: Verlorene Briefe hinter dem Scheibenwischer */ /* */ use eth-fs2010-mtu, clear generate markedeutsch = inlist(marke,2,3,12,16,18,23,28) lab def markedeutsch 1 "Deutsche Automarke" 0 "Nicht-deutsche Automarke" lab val markedeutsch markedeutsch generate Zeit = 1 + floor(((real(substr(zeit,1,2))-10)*60 + real(substr(zeit,4,2)))/30) // Rohe Differenz tab ankunft ort, col exact chi2 Key frequency column percentage Enumerating sample-space combinations: stage 3: enumerations = 1 stage 2: enumerations = 15 stage 1: enumerations = 0 Brief ist Ort der Empfängeradresse angekommen Deutschsc Westschwe Deutschla Total nein ja Total Pearson chi2(2) = Pr = 0119 Fisher's exact =

6 ci ankunft if ort==1, binomial // Deutschschweiz (St Gallen) Variable Obs Mean Std Err [95% Conf Interval] ankunft ci ankunft if ort==2, binomial // Westschweiz (Genf) Variable Obs Mean Std Err [95% Conf Interval] ankunft ci ankunft if ort==3, binomial // Deutschland (Berlin) Variable Obs Mean Std Err [95% Conf Interval] ankunft // Unter Kontrolle von Stadtkreis, Beschriftung Brief und Post-it, Zeit (kubisch) logit ankunft iort ikreis iadr ipostit czeit##czeit##czeit Iteration 0: log likelihood = Iteration 1: log likelihood = Iteration 2: log likelihood = Iteration 3: log likelihood = Iteration 4: log likelihood = Logistic regression Number of obs = 300 LR chi2(21) = 3251 Prob > chi2 = Log likelihood = Pseudo R2 = ankunft Coef Std Err z P>z [95% Conf Interval] ort Westschweiz (Genf) Deutschland (Berlin) kreis adr postit Zeit czeit#czeit czeit#czeit#czeit _cons

7 margins ort, expression(invlogit(predict(xb))*100) Predictive margins Number of obs = Delta-method Margin Std Err z P>z [95% Conf Interval] ort Deutschschweiz (St Gallen) Westschweiz (Genf) Deutschland (Berlin) margins, dydx(ort) expression(invlogit(predict(xb))*100) post Average marginal effects Number of obs = 300 dy/dx wrt : 2ort 3ort Delta-method dy/dx Std Err z P>z [95% Conf Interval] ort Westschweiz (Genf) Deutschland (Berlin) Note: dy/dx for factor levels is the discrete change from the base level test 2ort 3ort ( 1) 2ort = 0 ( 2) 3ort = 0 chi2( 2) = 413 Prob > chi2 = // Nach Automarke, unter Kontrolle von Stadtkreis, Beschriftung Brief und Post-it, Zeit (kubisch) logit ankunft iort##imarkedeutsch ikreis iadr ipostit czeit##czeit##czeit Iteration 0: log likelihood = Iteration 1: log likelihood = Iteration 2: log likelihood = Iteration 3: log likelihood = Iteration 4: log likelihood = Logistic regression Number of obs = 300 LR chi2(24) = 3593 Prob > chi2 = Log likelihood = Pseudo R2 = ankunft Coef Std Err z P>z [95% Conf Interval] ort Westschweiz (Genf) Deutschland (Berlin) markedeutsch Deutsche Automarke ort#markedeutsch Westschweiz (Genf) # Deutsche Automarke Deutschland (Berlin) # 7

8 Deutsche Automarke kreis adr postit Zeit czeit#czeit czeit#czeit#czeit _cons margins ort, (markedeutsch) expression(invlogit(predict(xb))*100) Predictive margins Number of obs = 300 : markedeutsch Delta-method Margin Std Err z P>z [95% Conf Interval] markedeutsch#ort Nicht-deutsche Automarke # Deutschschweiz (St Gallen) Nicht-deutsche Automarke # Westschweiz (Genf) Nicht-deutsche Automarke # Deutschland (Berlin) Deutsche Automarke # Deutschschweiz (St Gallen) Deutsche Automarke # Westschweiz (Genf) Deutsche Automarke # Deutschland (Berlin) margins, dydx(ort) (markedeutsch) expression(invlogit(predict(xb))*100) Average marginal effects Number of obs = 300 dy/dx wrt : 2ort 3ort : markedeutsch Delta-method dy/dx Std Err z P>z [95% Conf Interval]

9 2ort markedeutsch Nicht-deutsche Automarke Deutsche Automarke ort markedeutsch Nicht-deutsche Automarke Deutsche Automarke Note: dy/dx for factor levels is the discrete change from the base level /* */ /* Experiment 3: Unterschriftensammlung für Initiative mit Kopftuch */ /* */ use eth-fs2010-kopftuch, clear keep if valid (16 observations deleted) generate contact = (talk==1) (sign==1) if talk< & sign< lab var contact "Kontakt kam zustande" recode age (1 2 = 1 "Alter < 50") (3 4 = 2 "Alter > 50"), gen(alter) (252 differences between age and alter) // Gespräch tab contact scarf, col exact chi2 Key frequency column percentage Kontakt kam Kopftuch zustande ohne Kopf mit Kopft Total Total Pearson chi2(1) = Pr = 0588 Fisher's exact = sided Fisher's exact = 0334 ci contact if scarf==0, binomial // ohne Kopftuch Variable Obs Mean Std Err [95% Conf Interval] contact ci contact if scarf==1, binomial // mit Kopftuch Variable Obs Mean Std Err [95% Conf Interval] contact

10 // Gespräch: unter Kontrolle von Ort/Datum und Studentin logit contact iscarf istudent iplace Iteration 0: log likelihood = Iteration 1: log likelihood = Iteration 2: log likelihood = Iteration 3: log likelihood = Logistic regression Number of obs = 312 LR chi2(3) = 230 Prob > chi2 = Log likelihood = Pseudo R2 = contact Coef Std Err z P>z [95% Conf Interval] -- scarf mit Kopftuch student Studentin B place Bellevue _cons margins scarf, expression(invlogit(predict(xb))*100) Predictive margins Number of obs = Delta-method Margin Std Err z P>z [95% Conf Interval] --- scarf ohne Kopftuch mit Kopftuch margins, dydx(scarf) expression(invlogit(predict(xb))*100) Average marginal effects Number of obs = 312 dy/dx wrt : 1scarf - Delta-method dy/dx Std Err z P>z [95% Conf Interval] -- scarf mit Kopftuch Note: dy/dx for factor levels is the discrete change from the base level // Gespräch: nach Geschlecht und Alter, unter Kontrolle von Ort/Datum und Studentin logit contact iscarf##isex##ialter istudent iplace Iteration 0: log likelihood = Iteration 1: log likelihood = Iteration 2: log likelihood = Iteration 3: log likelihood = Logistic regression Number of obs = 312 LR chi2(9) = 969 Prob > chi2 = Log likelihood = Pseudo R2 =

11 contact Coef Std Err z P>z [95% Conf Interval] scarf mit Kopftuch sex weiblich scarf#sex mit Kopftuch#weiblich alter Alter > scarf#alter mit Kopftuch#Alter > sex#alter weiblich#alter > scarf#sex#alter mit Kopftuch#weiblich#Alter > student Studentin B place Bellevue _cons margins scarf, (sex) expression(invlogit(predict(xb))*100) Predictive margins Number of obs = 312 : sex Delta-method Margin Std Err z P>z [95% Conf Interval] sex#scarf männlich#ohne Kopftuch männlich#mit Kopftuch weiblich#ohne Kopftuch weiblich#mit Kopftuch margins, dydx(scarf) (sex) expression(invlogit(predict(xb))*100) Average marginal effects Number of obs = 312 dy/dx wrt : 1scarf : sex Delta-method dy/dx Std Err z P>z [95% Conf Interval] - 1scarf sex männlich weiblich Note: dy/dx for factor levels is the discrete change from the base level local b = el(r(b),1,3)-el(r(b),1,4) 11

12 local se = sqrt(el(r(v),3,3)+el(r(v),4,4)-2*el(r(v),3,4)) local p = string((1-normal(abs(`b'/`se')))*2, "%93f") local b = string(`b', "%91f") local se = string(`se', "%91f") di "Effekt-Differenz: `b' (`se'), p = `p'" Effekt-Differenz: 134 (112), p = 0232 margins scarf, (alter) expression(invlogit(predict(xb))*100) Predictive margins Number of obs = 312 : alter Delta-method Margin Std Err z P>z [95% Conf Interval] alter#scarf Alter < 50#ohne Kopftuch Alter < 50#mit Kopftuch Alter > 50#ohne Kopftuch Alter > 50#mit Kopftuch margins, dydx(scarf) (alter) expression(invlogit(predict(xb))*100) Average marginal effects Number of obs = 312 dy/dx wrt : 1scarf : alter Delta-method dy/dx Std Err z P>z [95% Conf Interval] - 1scarf alter Alter < Alter > Note: dy/dx for factor levels is the discrete change from the base level local b = el(r(b),1,3)-el(r(b),1,4) local se = sqrt(el(r(v),3,3)+el(r(v),4,4)-2*el(r(v),3,4)) local p = string((1-normal(abs(`b'/`se')))*2, "%93f") local b = string(`b', "%91f") local se = string(`se', "%91f") di "Effekt-Differenz: `b' (`se'), p = `p'" Effekt-Differenz: 92 (117), p = 0428 // Unterschrift tab sign scarf, col exact chi2 Key frequency column percentage 12

13 Initiative wurde unterschri Kopftuch eben ohne Kopf mit Kopft Total Total Pearson chi2(1) = Pr = 0513 Fisher's exact = sided Fisher's exact = 0306 ci sign if scarf==0, binomial // ohne Kopftuch Variable Obs Mean Std Err [95% Conf Interval] sign ci sign if scarf==1, binomial // mit Kopftuch Variable Obs Mean Std Err [95% Conf Interval] sign // Unterschrift: unter Kontrolle von Ort/Datum und Studentin logit sign iscarf istudent iplace Iteration 0: log likelihood = Iteration 1: log likelihood = Iteration 2: log likelihood = Iteration 3: log likelihood = Logistic regression Number of obs = 312 LR chi2(3) = 278 Prob > chi2 = Log likelihood = Pseudo R2 = sign Coef Std Err z P>z [95% Conf Interval] -- scarf mit Kopftuch student Studentin B place Bellevue _cons margins scarf, expression(invlogit(predict(xb))*100) Predictive margins Number of obs = Delta-method Margin Std Err z P>z [95% Conf Interval]

14 scarf ohne Kopftuch mit Kopftuch margins, dydx(scarf) expression(invlogit(predict(xb))*100) Average marginal effects Number of obs = 312 dy/dx wrt : 1scarf - Delta-method dy/dx Std Err z P>z [95% Conf Interval] -- scarf mit Kopftuch Note: dy/dx for factor levels is the discrete change from the base level // Unterschrift: nach Geschlecht und Alter, unter Kontrolle von Ort/Datum und Studentin logit sign iscarf##isex##ialter istudent iplace Iteration 0: log likelihood = Iteration 1: log likelihood = Iteration 2: log likelihood = Iteration 3: log likelihood = Iteration 4: log likelihood = Logistic regression Number of obs = 312 LR chi2(9) = 1377 Prob > chi2 = Log likelihood = Pseudo R2 = sign Coef Std Err z P>z [95% Conf Interval] scarf mit Kopftuch sex weiblich scarf#sex mit Kopftuch#weiblich alter Alter > scarf#alter mit Kopftuch#Alter > sex#alter weiblich#alter > scarf#sex#alter mit Kopftuch#weiblich#Alter > student Studentin B place Bellevue _cons margins scarf, (sex) expression(invlogit(predict(xb))*100) Predictive margins Number of obs =

15 : sex Delta-method Margin Std Err z P>z [95% Conf Interval] sex#scarf männlich#ohne Kopftuch männlich#mit Kopftuch weiblich#ohne Kopftuch weiblich#mit Kopftuch margins, dydx(scarf) (sex) expression(invlogit(predict(xb))*100) Average marginal effects Number of obs = 312 dy/dx wrt : 1scarf : sex Delta-method dy/dx Std Err z P>z [95% Conf Interval] - 1scarf sex männlich weiblich Note: dy/dx for factor levels is the discrete change from the base level local b = el(r(b),1,3)-el(r(b),1,4) local se = sqrt(el(r(v),3,3)+el(r(v),4,4)-2*el(r(v),3,4)) local p = string((1-normal(abs(`b'/`se')))*2, "%93f") local b = string(`b', "%91f") local se = string(`se', "%91f") di "Effekt-Differenz: `b' (`se'), p = `p'" Effekt-Differenz: 32 (86), p = 0713 margins scarf, (alter) expression(invlogit(predict(xb))*100) Predictive margins Number of obs = 312 : alter Delta-method Margin Std Err z P>z [95% Conf Interval] alter#scarf Alter < 50#ohne Kopftuch Alter < 50#mit Kopftuch Alter > 50#ohne Kopftuch Alter > 50#mit Kopftuch margins, dydx(scarf) (alter) expression(invlogit(predict(xb))*100) Average marginal effects Number of obs =

16 dy/dx wrt : 1scarf : alter Delta-method dy/dx Std Err z P>z [95% Conf Interval] - 1scarf alter Alter < Alter > Note: dy/dx for factor levels is the discrete change from the base level local b = el(r(b),1,3)-el(r(b),1,4) local se = sqrt(el(r(v),3,3)+el(r(v),4,4)-2*el(r(v),3,4)) local p = string((1-normal(abs(`b'/`se')))*2, "%93f") local b = string(`b', "%91f") local se = string(`se', "%91f") di "Effekt-Differenz: `b' (`se'), p = `p'" Effekt-Differenz: -21 (85), p = 0803 /* */ /* Experiment 4: Verlorene Briefe im Tram */ /* */ use eth-fs2010-lostletter, clear encode datelost, gen(datum) // Rohe Differenz tab received address, col exact chi2 Key frequency column percentage Brief Adressierung erhalten Benjamin Mohammed Total nein ja Total Pearson chi2(1) = Pr = 0505 Fisher's exact = sided Fisher's exact = 0370 ci received if address==1, binomial // Benjamin Zürcher Variable Obs Mean Std Err [95% Conf Interval] received ci received if address==2, binomial // Mohammed Al-Muttalib 16

17 Variable Obs Mean Std Err [95% Conf Interval] received // Unter Kontrolle von Verlustdatum und Knotenpunkt logit received iaddress idatum ijunction Iteration 0: log likelihood = Iteration 1: log likelihood = Iteration 2: log likelihood = Iteration 3: log likelihood = Iteration 4: log likelihood = Logistic regression Number of obs = 100 LR chi2(5) = 705 Prob > chi2 = Log likelihood = Pseudo R2 = received Coef Std Err z P>z [95% Conf Interval] address Mohammed Al-Muttalib datum 5/4/ junction Central Hauptbahnhof Milchbuck _cons margins address, expression(invlogit(predict(xb))*100) Predictive margins Number of obs = Delta-method Margin Std Err z P>z [95% Conf Interval] address Benjamin Zürcher Mohammed Al-Muttalib margins, dydx(address) expression(invlogit(predict(xb))*100) Average marginal effects Number of obs = 100 dy/dx wrt : 2address Delta-method dy/dx Std Err z P>z [95% Conf Interval] address Mohammed Al-Muttalib Note: dy/dx for factor levels is the discrete change from the base level /* */ 17

18 /* Experiment 5: Initiativbewerbungen bei Unternehmen */ /* */ use eth-fs2010-bewerbungen, clear fre reaction reaction -- Art der Reaktion Freq Percent Valid Cum Valid 1 Prüfung Stellenangebot Weiterleitung negativ weitere Unterlagen Total Missing Total generate reaktion = reaction< generate interesse = inlist(reaction, 1, 2, 3, 5) lab def sector 2 "Industrie" 3 "Finanzsektor" generate size = employees>1 if employees< lab def size 0 "Mitarbeiter < 100" 1 "Mitarbeiter >= 100" lab val size size // Reaktion tab reaktion name, col exact chi2 Key frequency column percentage Name des Bewerbers reaktion Mark Mugg Dukan Jov Total Total Pearson chi2(1) = Pr = 0015 Fisher's exact = sided Fisher's exact = 0010 ci reaktion if name==1, binomial // Mark Variable Obs Mean Std Err [95% Conf Interval] reaktion ci reaktion if name==2, binomial // Dukan Variable Obs Mean Std Err [95% Conf Interval] reaktion

19 // Reaktion: unter Kontrolle von Briefversion logit reaktion iname iletter Iteration 0: log likelihood = Iteration 1: log likelihood = Iteration 2: log likelihood = Iteration 3: log likelihood = Iteration 4: log likelihood = Logistic regression Number of obs = 300 LR chi2(5) = 1109 Prob > chi2 = Log likelihood = Pseudo R2 = reaktion Coef Std Err z P>z [95% Conf Interval] name Dukan Jovanovic letter _cons margins name, expression(invlogit(predict(xb))*100) Predictive margins Number of obs = Delta-method Margin Std Err z P>z [95% Conf Interval] name Mark Muggli Dukan Jovanovic margins, dydx(name) expression(invlogit(predict(xb))*100) Average marginal effects Number of obs = 300 dy/dx wrt : 2name ---- Delta-method dy/dx Std Err z P>z [95% Conf Interval] name Dukan Jovanovic Note: dy/dx for factor levels is the discrete change from the base level // Reaktion: nach Sektor und Betriebsgrösse, unter Kontrolle von Briefversion logit reaktion iname##isector##isize iletter Iteration 0: log likelihood = Iteration 1: log likelihood = Iteration 2: log likelihood = Iteration 3: log likelihood = Iteration 4: log likelihood =

20 Logistic regression Number of obs = 300 LR chi2(11) = 1646 Prob > chi2 = Log likelihood = Pseudo R2 = reaktion Coef Std Err z P>z [95% Conf Interval] name Dukan Jovanovic sector name#sector Dukan Jovanovic# size Mitarbeiter >= name#size Dukan Jovanovic # Mitarbeiter >= sector#size 3#Mitarbeiter >= name#sector#size Dukan Jovanovic # 3 # Mitarbeiter >= letter _cons margins name, (sector) expression(invlogit(predict(xb))*100) Predictive margins Number of obs = 300 : sector Delta-method Margin Std Err z P>z [95% Conf Interval] sector#name 2#Mark Muggli #Dukan Jovanovic #Mark Muggli #Dukan Jovanovic margins, dydx(name) (sector) expression(invlogit(predict(xb))*100) Average marginal effects Number of obs = 300 dy/dx wrt : 2name : sector Delta-method dy/dx Std Err z P>z [95% Conf Interval] - 2name 20

21 sector Note: dy/dx for factor levels is the discrete change from the base level local b = el(r(b),1,3)-el(r(b),1,4) local se = sqrt(el(r(v),3,3)+el(r(v),4,4)-2*el(r(v),3,4)) local p = string((1-normal(abs(`b'/`se')))*2, "%93f") local b = string(`b', "%91f") local se = string(`se', "%91f") di "Effekt-Differenz: `b' (`se'), p = `p'" Effekt-Differenz: 115 (107), p = 0286 margins name, (size) expression(invlogit(predict(xb))*100) Predictive margins Number of obs = 300 : size Delta-method Margin Std Err z P>z [95% Conf Interval] size#name Mitarbeiter < 100#Mark Muggli Mitarbeiter < 100 # Dukan Jovanovic Mitarbeiter >= 100#Mark Muggli Mitarbeiter >= 100 # Dukan Jovanovic margins, dydx(name) (size) expression(invlogit(predict(xb))*100) Average marginal effects Number of obs = 300 dy/dx wrt : 2name : size Delta-method dy/dx Std Err z P>z [95% Conf Interval] name size Mitarbeiter < Mitarbeiter >= Note: dy/dx for factor levels is the discrete change from the base level local b = el(r(b),1,3)-el(r(b),1,4) local se = sqrt(el(r(v),3,3)+el(r(v),4,4)-2*el(r(v),3,4)) local p = string((1-normal(abs(`b'/`se')))*2, "%93f") local b = string(`b', "%91f") local se = string(`se', "%91f") di "Effekt-Differenz: `b' (`se'), p = `p'" Effekt-Differenz: 176 (103), p =

22 // Interesse tab interesse name, col exact chi2 Key frequency column percentage Name des Bewerbers interesse Mark Mugg Dukan Jov Total Total Pearson chi2(1) = Pr = 0032 Fisher's exact = sided Fisher's exact = 0030 ci interesse if name==1, binomial // Mark Variable Obs Mean Std Err [95% Conf Interval] interesse ci interesse if name==2, binomial // Dukan Variable Obs Mean Std Err [95% Conf Interval] interesse capture log close 22