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. // Ben Jann, jann@soz.unibe.ch, 2. Mai 2014

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. version 12.1 . 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 -----------+----------------------+---------- 0 | 32 32 | 64 | 59.26 58.18 | 58.72 -----------+----------------------+---------- 1 | 22 23 | 45 | 40.74 41.82 | 41.28 -----------+----------------------+---------- Total | 54 55 | 109 | 100.00 100.00 | 100.00 Pearson chi2(1) = 0.0130 Pr = 0.909 Fisher's exact = 1.000 1-sided Fisher's exact = 0.532 . ci helped if lang==1, binomial // Schweizerdeutsch -- Binomial Exact -- Variable | Obs Mean Std. Err. [95% Conf. Interval] -------------+--------------------------------------------------------------- helped | 54 .4074074 .0668645 .2756799 .5496559 . ci helped if lang==2, binomial // Hochdeutsch -- Binomial Exact -- Variable | Obs Mean Std. Err. [95% Conf. Interval] -------------+--------------------------------------------------------------- helped | 55 .4181818 .0665112 .2865452 .5589416 .

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. // Ohne Akzent

. tab helped lang if accent!=1, col exact chi2 +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ | Sprache helped | Schweizer Hochdeuts | Total -----------+----------------------+---------- 0 | 28 29 | 57 | 56.00 58.00 | 57.00 -----------+----------------------+---------- 1 | 22 21 | 43 | 44.00 42.00 | 43.00 -----------+----------------------+---------- Total | 50 50 | 100 | 100.00 100.00 | 100.00 Pearson chi2(1) = 0.0408 Pr = 0.840 Fisher's exact = 1.000 1-sided Fisher's exact = 0.500 . ci helped if lang==1 & accent!=1, binomial // Schweizerdeutsch -- Binomial Exact -- Variable | Obs Mean Std. Err. [95% Conf. Interval] -------------+--------------------------------------------------------------- helped | 50 .44 .0701997 .2999072 .5874559 . ci helped if lang==2 & accent!=1, binomial // Hochdeutsch -- Binomial Exact -- Variable | Obs Mean Std. Err. [95% Conf. Interval] -------------+--------------------------------------------------------------- helped | 50 .42 .0697997 .2818822 .5679396 . . // Ohne Akzent, unter Kontrolle von Datum . logit helped i.lang i.datum if accent!=1 Iteration 0: log likelihood = -68.331491 Iteration 1: log likelihood = -68.127312 Iteration 2: log likelihood = -68.127295 Iteration 3: log likelihood = -68.127295 Logistic regression Number of obs = 100 LR chi2(2) = 0.41 Prob > chi2 = 0.8153 Log likelihood = -68.127295 Pseudo R2 = 0.0030 ------------------------------------------------------------------------------ helped | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- lang | Hochdeutsch | -.0819123 .404813 -0.20 0.840 -.8753313 .7115066 | datum | 5/11/2010 | -.2452231 .4048663 -0.61 0.545 -1.038746 .5483003 _cons | -.1194536 .3482085 -0.34 0.732 -.8019297 .5630225 ------------------------------------------------------------------------------ . margins lang, expression(invlogit(predict(xb))*100) Predictive margins Number of obs = 100 Model VCE : OIM Expression : invlogit(predict(xb))*100 -----------------------------------------------------------------------------------

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| Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] ------------------+---------------------------------------------------------------- lang | Schweizerdeutsch | 44 7.006991 6.28 0.000 30.26655 57.73345 Hochdeutsch | 42 6.967213 6.03 0.000 28.34451 55.65549 ----------------------------------------------------------------------------------- . margins, dydx(lang) expression(invlogit(predict(xb))*100) Average marginal effects Number of obs = 100 Model VCE : OIM Expression : invlogit(predict(xb))*100 dy/dx w.r.t. : 2.lang ------------------------------------------------------------------------------ | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- lang | Hochdeutsch | -2 9.881295 -0.20 0.840 -21.36698 17.36698 ------------------------------------------------------------------------------ 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 i.lang##i.sex##i.alter i.datum if accent!=1 Iteration 0: log likelihood = -68.331491 Iteration 1: log likelihood = -65.097316 Iteration 2: log likelihood = -65.088093 Iteration 3: log likelihood = -65.08809 Logistic regression Number of obs = 100 LR chi2(8) = 6.49 Prob > chi2 = 0.5929 Log likelihood = -65.08809 Pseudo R2 = 0.0475 -------------------------------------------------------------------------------------------------- helped | Coef. Std. Err. z P>|z| [95% Conf. Interval] ---------------------------------+---------------------------------------------------------------- lang | Hochdeutsch | 1.313487 .8990069 1.46 0.144 -.4485341 3.075508 | sex | weiblich | 1.532698 1.002678 1.53 0.126 -.4325142 3.49791 | lang#sex | Hochdeutsch#weiblich | -2.285128 1.294231 -1.77 0.077 -4.821774 .2515172 | alter | Alter > 45 | .5629611 .8222944 0.68 0.494 -1.048706 2.174629 | lang#alter | Hochdeutsch#Alter > 45 | -2.127767 1.20008 -1.77 0.076 -4.479881 .2243473 | sex#alter | weiblich#Alter > 45 | -1.653816 1.263614 -1.31 0.191 -4.130455 .8228226 | lang#sex#alter | Hochdeutsch#weiblich#Alter > 45 | 2.947981 1.750546 1.68 0.092 -.4830266 6.378988 | datum | 5/11/2010 | -.0199897 .4421005 -0.05 0.964 -.8864907 .8465114 _cons | -.837323 .7243437 -1.16 0.248 -2.25701 .5823645 -------------------------------------------------------------------------------------------------- . margins lang, over(sex) expression(invlogit(predict(xb))*100) Predictive margins Number of obs = 100 Model VCE : OIM

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Expression : invlogit(predict(xb))*100 over : sex -------------------------------------------------------------------------------------------- | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] ---------------------------+---------------------------------------------------------------- sex#lang | männlich#Schweizerdeutsch | 37.61276 8.754247 4.30 0.000 20.45475 54.77077 männlich#Hochdeutsch | 39.96719 9.254673 4.32 0.000 21.82836 58.10601 weiblich#Schweizerdeutsch | 53.85781 11.20817 4.81 0.000 31.8902 75.82542 weiblich#Hochdeutsch | 39.81842 9.878582 4.03 0.000 20.45675 59.18008 -------------------------------------------------------------------------------------------- . margins, dydx(lang) over(sex) expression(invlogit(predict(xb))*100) Average marginal effects Number of obs = 100 Model VCE : OIM Expression : invlogit(predict(xb))*100 dy/dx w.r.t. : 2.lang over : sex ------------------------------------------------------------------------------ | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- 2.lang | sex | männlich | 2.354426 12.79427 0.18 0.854 -22.72188 27.43073 weiblich | -14.03939 15.10117 -0.93 0.353 -43.63715 15.55837 ------------------------------------------------------------------------------ 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, "%9.3f") . local b = string(`b', "%9.1f") . local se = string(`se', "%9.1f") . di "Effekt-Differenz: `b' (`se'), p = `p'" Effekt-Differenz: 16.4 (20.1), p = 0.414 . margins lang, over(alter) expression(invlogit(predict(xb))*100) Predictive margins Number of obs = 100 Model VCE : OIM Expression : invlogit(predict(xb))*100 over : alter ---------------------------------------------------------------------------------------------- | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -----------------------------+---------------------------------------------------------------- alter#lang | Alter < 45#Schweizerdeutsch | 48.29375 10.73869 4.50 0.000 27.24632 69.34119 Alter < 45#Hochdeutsch | 52.2221 9.466506 5.52 0.000 33.66809 70.77611 Alter > 45#Schweizerdeutsch | 41.75085 8.935986 4.67 0.000 24.23663 59.26506 Alter > 45#Hochdeutsch | 29.4066 9.494323 3.10 0.002 10.79806 48.01513 ---------------------------------------------------------------------------------------------- . margins, dydx(lang) over(alter) expression(invlogit(predict(xb))*100) Average marginal effects Number of obs = 100 Model VCE : OIM Expression : invlogit(predict(xb))*100 dy/dx w.r.t. : 2.lang

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over : alter ------------------------------------------------------------------------------ | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- 2.lang | alter | Alter < 45 | 3.928349 14.34847 0.27 0.784 -24.19413 32.05082 Alter > 45 | -12.34425 13.04045 -0.95 0.344 -37.90307 13.21457 ------------------------------------------------------------------------------ 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, "%9.3f") . local b = string(`b', "%9.1f") . local se = string(`se', "%9.1f") . di "Effekt-Differenz: `b' (`se'), p = `p'" Effekt-Differenz: 16.3 (19.4), p = 0.402 . . . /*---------------------------------------------------------------------------*/ . /* 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 | 37 24 28 | 89 | 37.00 24.00 28.00 | 29.67 -----------+---------------------------------+---------- ja | 63 76 72 | 211 | 63.00 76.00 72.00 | 70.33 -----------+---------------------------------+---------- Total | 100 100 100 | 300 | 100.00 100.00 100.00 | 100.00 Pearson chi2(2) = 4.2494 Pr = 0.119 Fisher's exact = 0.128

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. ci ankunft if ort==1, binomial // Deutschschweiz (St. Gallen) -- Binomial Exact -- Variable | Obs Mean Std. Err. [95% Conf. Interval] -------------+--------------------------------------------------------------- ankunft | 100 .63 .0482804 .5276484 .7244334 . ci ankunft if ort==2, binomial // Westschweiz (Genf) -- Binomial Exact -- Variable | Obs Mean Std. Err. [95% Conf. Interval] -------------+--------------------------------------------------------------- ankunft | 100 .76 .0427083 .6642645 .8397754 . ci ankunft if ort==3, binomial // Deutschland (Berlin) -- Binomial Exact -- Variable | Obs Mean Std. Err. [95% Conf. Interval] -------------+--------------------------------------------------------------- ankunft | 100 .72 .0448999 .621333 .8052064 . . // Unter Kontrolle von Stadtkreis, Beschriftung Brief und Post-it, Zeit (kubisch) . logit ankunft i.ort i.kreis i.adr i.postit c.Zeit##c.Zeit##c.Zeit Iteration 0: log likelihood = -182.40404 Iteration 1: log likelihood = -166.51657 Iteration 2: log likelihood = -166.14843 Iteration 3: log likelihood = -166.147 Iteration 4: log likelihood = -166.147 Logistic regression Number of obs = 300 LR chi2(21) = 32.51 Prob > chi2 = 0.0519 Log likelihood = -166.147 Pseudo R2 = 0.0891 --------------------------------------------------------------------------------------- ankunft | Coef. Std. Err. z P>|z| [95% Conf. Interval] ----------------------+---------------------------------------------------------------- ort | Westschweiz (Genf) | .654344 .3296086 1.99 0.047 .008323 1.300365 Deutschland (Berlin) | .4125646 .32204 1.28 0.200 -.2186222 1.043751 | kreis | 2 | -3.256194 2.489578 -1.31 0.191 -8.135678 1.623289 3 | -1.435434 1.765592 -0.81 0.416 -4.89593 2.025062 4 | -2.871502 1.212724 -2.37 0.018 -5.248397 -.4946077 5 | .6459303 .9210069 0.70 0.483 -1.15921 2.451071 6 | .5515064 1.128019 0.49 0.625 -1.65937 2.762383 7 | .0630833 2.364838 0.03 0.979 -4.571914 4.69808 8 | -.1113475 1.19358 -0.09 0.926 -2.450721 2.228026 9 | -.7125778 1.317441 -0.54 0.589 -3.294714 1.869558 10 | -1.395484 1.082083 -1.29 0.197 -3.516327 .7253588 11 | -1.591951 1.455583 -1.09 0.274 -4.444843 1.26094 | adr | 2 | .428283 .6546344 0.65 0.513 -.8547768 1.711343 3 | 1.086495 .595786 1.82 0.068 -.0812238 2.254214 4 | -.5371418 .6202804 -0.87 0.387 -1.752869 .6785855 | postit | 2 | .4796866 .4628779 1.04 0.300 -.4275375 1.386911 3 | .6761229 .5327526 1.27 0.204 -.368053 1.720299 4 | .0299437 .5029338 0.06 0.953 -.9557885 1.015676 | Zeit | -.2438517 .9927667 -0.25 0.806 -2.189639 1.701935 | c.Zeit#c.Zeit | .0109556 .0986738 0.11 0.912 -.1824416 .2043528 | c.Zeit#c.Zeit#c.Zeit | .0003123 .0029285 0.11 0.915 -.0054275 .0060521 | _cons | 1.139959 3.574223 0.32 0.750 -5.865389 8.145307 ---------------------------------------------------------------------------------------

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. margins ort, expression(invlogit(predict(xb))*100) Predictive margins Number of obs = 300 Model VCE : OIM Expression : invlogit(predict(xb))*100 ---------------------------------------------------------------------------------------------- | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -----------------------------+---------------------------------------------------------------- ort | Deutschschweiz (St. Gallen) | 63.54379 4.559329 13.94 0.000 54.60767 72.47991 Westschweiz (Genf) | 75.90772 4.088507 18.57 0.000 67.89439 83.92104 Deutschland (Berlin) | 71.67481 4.328823 16.56 0.000 63.19047 80.15915 ---------------------------------------------------------------------------------------------- . margins, dydx(ort) expression(invlogit(predict(xb))*100) post Average marginal effects Number of obs = 300 Model VCE : OIM Expression : invlogit(predict(xb))*100 dy/dx w.r.t. : 2.ort 3.ort --------------------------------------------------------------------------------------- | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] ----------------------+---------------------------------------------------------------- ort | Westschweiz (Genf) | 12.36393 6.126374 2.02 0.044 .3564595 24.3714 Deutschland (Berlin) | 8.131023 6.305721 1.29 0.197 -4.227963 20.49001 --------------------------------------------------------------------------------------- Note: dy/dx for factor levels is the discrete change from the base level. . test 2.ort 3.ort ( 1) 2.ort = 0 ( 2) 3.ort = 0 chi2( 2) = 4.13 Prob > chi2 = 0.1269 . . // Nach Automarke, unter Kontrolle von Stadtkreis, Beschriftung Brief und Post-it, Zeit (kubisch) . logit ankunft i.ort##i.markedeutsch i.kreis i.adr i.postit c.Zeit##c.Zeit##c.Zeit Iteration 0: log likelihood = -182.40404 Iteration 1: log likelihood = -164.94148 Iteration 2: log likelihood = -164.44164 Iteration 3: log likelihood = -164.43822 Iteration 4: log likelihood = -164.43822 Logistic regression Number of obs = 300 LR chi2(24) = 35.93 Prob > chi2 = 0.0557 Log likelihood = -164.43822 Pseudo R2 = 0.0985 ---------------------------------------------------------------------------------------- ankunft | Coef. Std. Err. z P>|z| [95% Conf. Interval] -----------------------+---------------------------------------------------------------- ort | Westschweiz (Genf) | .8153296 .4611715 1.77 0.077 -.0885498 1.719209 Deutschland (Berlin) | .3318824 .4318464 0.77 0.442 -.514521 1.178286 | markedeutsch | Deutsche Automarke | -.3910035 .4598176 -0.85 0.395 -1.292229 .5102223 | ort#markedeutsch | Westschweiz (Genf) #| Deutsche Automarke | -.4081593 .6891058 -0.59 0.554 -1.758782 .9424633 Deutschland (Berlin) #|

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Deutsche Automarke | .1398974 .6858915 0.20 0.838 -1.204425 1.48422 | kreis | 2 | -2.81123 2.541264 -1.11 0.269 -7.792016 2.169557 3 | -1.155125 1.786473 -0.65 0.518 -4.656548 2.346298 4 | -2.823571 1.210328 -2.33 0.020 -5.19577 -.4513724 5 | .7686153 .9359056 0.82 0.412 -1.065726 2.602957 6 | .6004407 1.133225 0.53 0.596 -1.62064 2.821522 7 | .0146295 2.377239 0.01 0.995 -4.644674 4.673933 8 | -.1013974 1.205507 -0.08 0.933 -2.464148 2.261353 9 | -.5681543 1.328719 -0.43 0.669 -3.172395 2.036087 10 | -1.158688 1.095802 -1.06 0.290 -3.30642 .9890435 11 | -1.39661 1.472797 -0.95 0.343 -4.283239 1.490018 | adr | 2 | .3852023 .6640209 0.58 0.562 -.9162547 1.686659 3 | .994264 .6080367 1.64 0.102 -.197466 2.185994 4 | -.5303448 .6262338 -0.85 0.397 -1.75774 .6970508 | postit | 2 | .4981536 .4664247 1.07 0.286 -.416022 1.412329 3 | .7428164 .5370246 1.38 0.167 -.3097325 1.795365 4 | .0011947 .5031286 0.00 0.998 -.9849191 .9873086 | Zeit | -.2099785 1.005578 -0.21 0.835 -2.180876 1.760919 | c.Zeit#c.Zeit | .006864 .0998572 0.07 0.945 -.1888526 .2025806 | c.Zeit#c.Zeit#c.Zeit | .0003929 .0029642 0.13 0.895 -.0054167 .0062025 | _cons | 1.266577 3.607093 0.35 0.725 -5.803196 8.33635 ---------------------------------------------------------------------------------------- . margins ort, over(markedeutsch) expression(invlogit(predict(xb))*100) Predictive margins Number of obs = 300 Model VCE : OIM Expression : invlogit(predict(xb))*100 over : markedeutsch ---------------------------------------------------------------------------------------------- | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -----------------------------+---------------------------------------------------------------- markedeutsch#ort | Nicht-deutsche Automarke #| Deutschschweiz (St. Gallen) | 67.07067 5.987376 11.20 0.000 55.33563 78.80571 Nicht-deutsche Automarke #| Westschweiz (Genf) | 80.80739 4.742613 17.04 0.000 71.51204 90.10274 Nicht-deutsche Automarke #| Deutschland (Berlin) | 73.23357 5.246005 13.96 0.000 62.95159 83.51555 Deutsche Automarke #| Deutschschweiz (St. Gallen) | 58.91381 7.232529 8.15 0.000 44.73831 73.0893 Deutsche Automarke #| Westschweiz (Genf) | 67.60277 7.511862 9.00 0.000 52.87979 82.32575 Deutsche Automarke #| Deutschland (Berlin) | 68.89676 7.733703 8.91 0.000 53.73898 84.05454 ---------------------------------------------------------------------------------------------- . margins, dydx(ort) over(markedeutsch) expression(invlogit(predict(xb))*100) Average marginal effects Number of obs = 300 Model VCE : OIM Expression : invlogit(predict(xb))*100 dy/dx w.r.t. : 2.ort 3.ort over : markedeutsch ------------------------------------------------------------------------------------------- | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] --------------------------+----------------------------------------------------------------

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2.ort | markedeutsch | Nicht-deutsche Automarke | 13.73672 7.676959 1.79 0.074 -1.309843 28.78328 Deutsche Automarke | 8.688961 10.50582 0.83 0.408 -11.90206 29.27999 --------------------------+---------------------------------------------------------------- 3.ort | markedeutsch | Nicht-deutsche Automarke | 6.162897 8.023664 0.77 0.442 -9.563196 21.88899 Deutsche Automarke | 9.982954 10.74514 0.93 0.353 -11.07714 31.04305 ------------------------------------------------------------------------------------------- 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 -----------+----------------------+---------- 0 | 87 78 | 165 | 54.37 51.32 | 52.88 -----------+----------------------+---------- 1 | 73 74 | 147 | 45.62 48.68 | 47.12 -----------+----------------------+---------- Total | 160 152 | 312 | 100.00 100.00 | 100.00 Pearson chi2(1) = 0.2928 Pr = 0.588 Fisher's exact = 0.650 1-sided Fisher's exact = 0.334 . ci contact if scarf==0, binomial // ohne Kopftuch -- Binomial Exact -- Variable | Obs Mean Std. Err. [95% Conf. Interval] -------------+--------------------------------------------------------------- contact | 160 .45625 .0393769 .3774077 .536743 . ci contact if scarf==1, binomial // mit Kopftuch -- Binomial Exact -- Variable | Obs Mean Std. Err. [95% Conf. Interval] -------------+--------------------------------------------------------------- contact | 152 .4868421 .0405413 .4050458 .5691613 .

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. // Gespräch: unter Kontrolle von Ort/Datum und Studentin

. logit contact i.scarf i.student i.place Iteration 0: log likelihood = -215.7424 Iteration 1: log likelihood = -214.5941 Iteration 2: log likelihood = -214.59398 Iteration 3: log likelihood = -214.59398 Logistic regression Number of obs = 312 LR chi2(3) = 2.30 Prob > chi2 = 0.5131 Log likelihood = -214.59398 Pseudo R2 = 0.0053 ------------------------------------------------------------------------------- contact | Coef. Std. Err. z P>|z| [95% Conf. Interval] --------------+---------------------------------------------------------------- scarf | mit Kopftuch | .2001945 .234531 0.85 0.393 -.2594777 .6598668 | student | Studentin B | .0212222 .234531 0.09 0.928 -.4384501 .4808945 | place | Bellevue | .3299981 .234531 1.41 0.159 -.1296742 .7896703 _cons | -.3920423 .2588958 -1.51 0.130 -.8994687 .1153842 ------------------------------------------------------------------------------- . margins scarf, expression(invlogit(predict(xb))*100) Predictive margins Number of obs = 312 Model VCE : OIM Expression : invlogit(predict(xb))*100 -------------------------------------------------------------------------------- | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] ---------------+---------------------------------------------------------------- scarf | ohne Kopftuch | 44.70669 3.964609 11.28 0.000 36.9362 52.47718 mit Kopftuch | 49.65764 4.098879 12.11 0.000 41.62398 57.69129 -------------------------------------------------------------------------------- . margins, dydx(scarf) expression(invlogit(predict(xb))*100) Average marginal effects Number of obs = 312 Model VCE : OIM Expression : invlogit(predict(xb))*100 dy/dx w.r.t. : 1.scarf ------------------------------------------------------------------------------- | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] --------------+---------------------------------------------------------------- scarf | mit Kopftuch | 4.950947 5.780311 0.86 0.392 -6.378255 16.28015 ------------------------------------------------------------------------------- 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 i.scarf##i.sex##i.alter i.student i.place Iteration 0: log likelihood = -215.7424 Iteration 1: log likelihood = -210.89894 Iteration 2: log likelihood = -210.8961 Iteration 3: log likelihood = -210.8961 Logistic regression Number of obs = 312 LR chi2(9) = 9.69 Prob > chi2 = 0.3759 Log likelihood = -210.8961 Pseudo R2 = 0.0225

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--------------------------------------------------------------------------------------------------- contact | Coef. Std. Err. z P>|z| [95% Conf. Interval] ----------------------------------+---------------------------------------------------------------- scarf | mit Kopftuch | .6830921 .4016523 1.70 0.089 -.104132 1.470316 | sex | weiblich | .4751529 .4014346 1.18 0.237 -.3116444 1.26195 | scarf#sex | mit Kopftuch#weiblich | -.6745988 .5843887 -1.15 0.248 -1.81998 .470782 | alter | Alter > 50 | -.1384507 .5229208 -0.26 0.791 -1.163357 .8864551 | scarf#alter | mit Kopftuch#Alter > 50 | -.5416789 .6951536 -0.78 0.436 -1.904155 .8207971 | sex#alter | weiblich#Alter > 50 | -.4532013 .6827351 -0.66 0.507 -1.791337 .8849349 | scarf#sex#alter | mit Kopftuch#weiblich#Alter > 50 | .4002897 .9676959 0.41 0.679 -1.49636 2.296939 | student | Studentin B | .0389594 .2408904 0.16 0.872 -.4331772 .511096 | place | Bellevue | .3884016 .2408904 1.61 0.107 -.0837349 .8605382 _cons | -.5438911 .33931 -1.60 0.109 -1.208927 .1211442 --------------------------------------------------------------------------------------------------- . margins scarf, over(sex) expression(invlogit(predict(xb))*100) Predictive margins Number of obs = 312 Model VCE : OIM Expression : invlogit(predict(xb))*100 over : sex ----------------------------------------------------------------------------------------- | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] ------------------------+---------------------------------------------------------------- sex#scarf | männlich#ohne Kopftuch | 40.63286 5.727539 7.09 0.000 29.40709 51.85863 männlich#mit Kopftuch | 52.81712 5.404103 9.77 0.000 42.22527 63.40897 weiblich#ohne Kopftuch | 47.90853 5.347598 8.96 0.000 37.42743 58.38963 weiblich#mit Kopftuch | 46.73029 6.00327 7.78 0.000 34.96409 58.49648 ----------------------------------------------------------------------------------------- . margins, dydx(scarf) over(sex) expression(invlogit(predict(xb))*100) Average marginal effects Number of obs = 312 Model VCE : OIM Expression : invlogit(predict(xb))*100 dy/dx w.r.t. : 1.scarf over : sex ------------------------------------------------------------------------------ | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- 1.scarf | sex | männlich | 12.18426 7.929844 1.54 0.124 -3.357945 27.72647 weiblich | -1.17824 8.09368 -0.15 0.884 -17.04156 14.68508 ------------------------------------------------------------------------------ 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)

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, "%9.3f") . local b = string(`b', "%9.1f") . local se = string(`se', "%9.1f") . di "Effekt-Differenz: `b' (`se'), p = `p'" Effekt-Differenz: 13.4 (11.2), p = 0.232 . margins scarf, over(alter) expression(invlogit(predict(xb))*100) Predictive margins Number of obs = 312 Model VCE : OIM Expression : invlogit(predict(xb))*100 over : alter ------------------------------------------------------------------------------------------- | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] --------------------------+---------------------------------------------------------------- alter#scarf | Alter < 50#ohne Kopftuch | 47.00216 4.919128 9.55 0.000 37.36085 56.64348 Alter < 50#mit Kopftuch | 56.08313 5.178172 10.83 0.000 45.9341 66.23216 Alter > 50#ohne Kopftuch | 39.65665 6.517956 6.08 0.000 26.8817 52.43161 Alter > 50#mit Kopftuch | 39.50375 6.47041 6.11 0.000 26.82197 52.18552 ------------------------------------------------------------------------------------------- . margins, dydx(scarf) over(alter) expression(invlogit(predict(xb))*100) Average marginal effects Number of obs = 312 Model VCE : OIM Expression : invlogit(predict(xb))*100 dy/dx w.r.t. : 1.scarf over : alter ------------------------------------------------------------------------------ | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- 1.scarf | alter | Alter < 50 | 9.080968 7.188159 1.26 0.206 -5.007565 23.1695 Alter > 50 | -.1529079 9.308459 -0.02 0.987 -18.39715 18.09134 ------------------------------------------------------------------------------ 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, "%9.3f") . local b = string(`b', "%9.1f") . local se = string(`se', "%9.1f") . di "Effekt-Differenz: `b' (`se'), p = `p'" Effekt-Differenz: 9.2 (11.7), p = 0.428 . . // Unterschrift . tab sign scarf, col exact chi2 +-------------------+ | Key | |-------------------| | frequency | | column percentage |

13

+-------------------+ Initiative | wurde | unterschri | Kopftuch eben | ohne Kopf mit Kopft | Total -----------+----------------------+---------- 0 | 133 122 | 255 | 83.12 80.26 | 81.73 -----------+----------------------+---------- 1 | 27 30 | 57 | 16.88 19.74 | 18.27 -----------+----------------------+---------- Total | 160 152 | 312 | 100.00 100.00 | 100.00 Pearson chi2(1) = 0.4276 Pr = 0.513 Fisher's exact = 0.559 1-sided Fisher's exact = 0.306 . ci sign if scarf==0, binomial // ohne Kopftuch -- Binomial Exact -- Variable | Obs Mean Std. Err. [95% Conf. Interval] -------------+--------------------------------------------------------------- sign | 160 .16875 .0296093 .1142505 .2359431 . ci sign if scarf==1, binomial // mit Kopftuch -- Binomial Exact -- Variable | Obs Mean Std. Err. [95% Conf. Interval] -------------+--------------------------------------------------------------- sign | 152 .1973684 .0322831 .1373027 .2696096 . . // Unterschrift: unter Kontrolle von Ort/Datum und Studentin . logit sign i.scarf i.student i.place Iteration 0: log likelihood = -148.34087 Iteration 1: log likelihood = -146.96589 Iteration 2: log likelihood = -146.95159 Iteration 3: log likelihood = -146.95159 Logistic regression Number of obs = 312 LR chi2(3) = 2.78 Prob > chi2 = 0.4270 Log likelihood = -146.95159 Pseudo R2 = 0.0094 ------------------------------------------------------------------------------- sign | Coef. Std. Err. z P>|z| [95% Conf. Interval] --------------+---------------------------------------------------------------- scarf | mit Kopftuch | .1628745 .2977984 0.55 0.584 -.4207996 .7465487 | student | Studentin B | -.3710924 .2977984 -1.25 0.213 -.9547666 .2125817 | place | Bellevue | -.2744656 .2977984 -0.92 0.357 -.8581398 .3092086 _cons | -1.232144 .3037464 -4.06 0.000 -1.827476 -.6368116 ------------------------------------------------------------------------------- . margins scarf, expression(invlogit(predict(xb))*100) Predictive margins Number of obs = 312 Model VCE : OIM Expression : invlogit(predict(xb))*100 -------------------------------------------------------------------------------- | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] ---------------+----------------------------------------------------------------

14

scarf | ohne Kopftuch | 17.07418 3.003058 5.69 0.000 11.18829 22.96006 mit Kopftuch | 19.48474 3.189589 6.11 0.000 13.23326 25.73622 -------------------------------------------------------------------------------- . margins, dydx(scarf) expression(invlogit(predict(xb))*100) Average marginal effects Number of obs = 312 Model VCE : OIM Expression : invlogit(predict(xb))*100 dy/dx w.r.t. : 1.scarf ------------------------------------------------------------------------------- | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] --------------+---------------------------------------------------------------- scarf | mit Kopftuch | 2.410566 4.404623 0.55 0.584 -6.222336 11.04347 ------------------------------------------------------------------------------- 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 i.scarf##i.sex##i.alter i.student i.place Iteration 0: log likelihood = -148.34087 Iteration 1: log likelihood = -141.86617 Iteration 2: log likelihood = -141.46069 Iteration 3: log likelihood = -141.45548 Iteration 4: log likelihood = -141.45548 Logistic regression Number of obs = 312 LR chi2(9) = 13.77 Prob > chi2 = 0.1307 Log likelihood = -141.45548 Pseudo R2 = 0.0464 --------------------------------------------------------------------------------------------------- sign | Coef. Std. Err. z P>|z| [95% Conf. Interval] ----------------------------------+---------------------------------------------------------------- scarf | mit Kopftuch | .4416656 .5413104 0.82 0.415 -.6192834 1.502614 | sex | weiblich | .9282393 .5173174 1.79 0.073 -.0856843 1.942163 | scarf#sex | mit Kopftuch#weiblich | -.4593452 .7191415 -0.64 0.523 -1.868837 .9501463 | alter | Alter > 50 | -1.081203 1.10628 -0.98 0.328 -3.249472 1.087067 | scarf#alter | mit Kopftuch#Alter > 50 | .2510451 1.309895 0.19 0.848 -2.316302 2.818392 | sex#alter | weiblich#Alter > 50 | .312129 1.245948 0.25 0.802 -2.129885 2.754143 | scarf#sex#alter | mit Kopftuch#weiblich#Alter > 50 | .1779504 1.544687 0.12 0.908 -2.849581 3.205482 | student | Studentin B | -.3193547 .3060978 -1.04 0.297 -.9192954 .2805859 | place | Bellevue | -.2314987 .3060978 -0.76 0.449 -.8314394 .3684419 _cons | -1.591749 .4517191 -3.52 0.000 -2.477102 -.7063955 --------------------------------------------------------------------------------------------------- . margins scarf, over(sex) expression(invlogit(predict(xb))*100) Predictive margins Number of obs = 312 Model VCE : OIM

15

Expression : invlogit(predict(xb))*100 over : sex ----------------------------------------------------------------------------------------- | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] ------------------------+---------------------------------------------------------------- sex#scarf | männlich#ohne Kopftuch | 10.42966 3.487877 2.99 0.003 3.593547 17.26577 männlich#mit Kopftuch | 15.82281 3.98514 3.97 0.000 8.012085 23.63354 weiblich#ohne Kopftuch | 22.35607 4.471623 5.00 0.000 13.59185 31.12029 weiblich#mit Kopftuch | 24.57784 5.152052 4.77 0.000 14.48 34.67568 ----------------------------------------------------------------------------------------- . margins, dydx(scarf) over(sex) expression(invlogit(predict(xb))*100) Average marginal effects Number of obs = 312 Model VCE : OIM Expression : invlogit(predict(xb))*100 dy/dx w.r.t. : 1.scarf over : sex ------------------------------------------------------------------------------ | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- 1.scarf | sex | männlich | 5.393154 5.307958 1.02 0.310 -5.010252 15.79656 weiblich | 2.221773 6.847015 0.32 0.746 -11.19813 15.64168 ------------------------------------------------------------------------------ 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, "%9.3f") . local b = string(`b', "%9.1f") . local se = string(`se', "%9.1f") . di "Effekt-Differenz: `b' (`se'), p = `p'" Effekt-Differenz: 3.2 (8.6), p = 0.713 . margins scarf, over(alter) expression(invlogit(predict(xb))*100) Predictive margins Number of obs = 312 Model VCE : OIM Expression : invlogit(predict(xb))*100 over : alter ------------------------------------------------------------------------------------------- | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] --------------------------+---------------------------------------------------------------- alter#scarf | Alter < 50#ohne Kopftuch | 20.12671 3.893073 5.17 0.000 12.49643 27.75699 Alter < 50#mit Kopftuch | 23.15368 4.4025 5.26 0.000 14.52494 31.78242 Alter > 50#ohne Kopftuch | 10.05202 3.888645 2.58 0.010 2.430417 17.67363 Alter > 50#mit Kopftuch | 15.19643 4.65523 3.26 0.001 6.072343 24.32051 ------------------------------------------------------------------------------------------- . margins, dydx(scarf) over(alter) expression(invlogit(predict(xb))*100) Average marginal effects Number of obs = 312 Model VCE : OIM Expression : invlogit(predict(xb))*100

16

dy/dx w.r.t. : 1.scarf over : alter ------------------------------------------------------------------------------ | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- 1.scarf | alter | Alter < 50 | 3.026968 5.910626 0.51 0.609 -8.557647 14.61158 Alter > 50 | 5.144404 6.091496 0.84 0.398 -6.794708 17.08352 ------------------------------------------------------------------------------ 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, "%9.3f") . local b = string(`b', "%9.1f") . local se = string(`se', "%9.1f") . di "Effekt-Differenz: `b' (`se'), p = `p'" Effekt-Differenz: -2.1 (8.5), p = 0.803 . . . /*---------------------------------------------------------------------------*/ . /* 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 | 4 6 | 10 | 8.00 12.00 | 10.00 -----------+----------------------+---------- ja | 46 44 | 90 | 92.00 88.00 | 90.00 -----------+----------------------+---------- Total | 50 50 | 100 | 100.00 100.00 | 100.00 Pearson chi2(1) = 0.4444 Pr = 0.505 Fisher's exact = 0.741 1-sided Fisher's exact = 0.370 . ci received if address==1, binomial // Benjamin Zürcher -- Binomial Exact -- Variable | Obs Mean Std. Err. [95% Conf. Interval] -------------+--------------------------------------------------------------- received | 50 .92 .0383667 .8076572 .977772 . ci received if address==2, binomial // Mohammed Al-Muttalib

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-- Binomial Exact -- Variable | Obs Mean Std. Err. [95% Conf. Interval] -------------+--------------------------------------------------------------- received | 50 .88 .0459565 .7568987 .9546647 . . // Unter Kontrolle von Verlustdatum und Knotenpunkt . logit received i.address i.datum i.junction Iteration 0: log likelihood = -32.508297 Iteration 1: log likelihood = -29.833474 Iteration 2: log likelihood = -28.985694 Iteration 3: log likelihood = -28.984004 Iteration 4: log likelihood = -28.984003 Logistic regression Number of obs = 100 LR chi2(5) = 7.05 Prob > chi2 = 0.2171 Log likelihood = -28.984003 Pseudo R2 = 0.1084 --------------------------------------------------------------------------------------- received | Coef. Std. Err. z P>|z| [95% Conf. Interval] ----------------------+---------------------------------------------------------------- address | Mohammed Al-Muttalib | -.4666117 .7060092 -0.66 0.509 -1.850364 .9171409 | datum | 5/4/2010 | .3935737 .7849865 0.50 0.616 -1.144972 1.932119 | junction | Central | -1.057392 1.206154 -0.88 0.381 -3.42141 1.306625 Hauptbahnhof | -.4317787 1.447321 -0.30 0.765 -3.268475 2.404918 Milchbuck | -2.274156 1.144283 -1.99 0.047 -4.516909 -.0314035 | _cons | 3.454334 1.148425 3.01 0.003 1.203462 5.705205 --------------------------------------------------------------------------------------- . margins address, expression(invlogit(predict(xb))*100) Predictive margins Number of obs = 100 Model VCE : OIM Expression : invlogit(predict(xb))*100 --------------------------------------------------------------------------------------- | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] ----------------------+---------------------------------------------------------------- address | Benjamin Zürcher | 91.93062 3.742543 24.56 0.000 84.59537 99.26587 Mohammed Al-Muttalib | 88.08776 4.376413 20.13 0.000 79.51015 96.66537 --------------------------------------------------------------------------------------- . margins, dydx(address) expression(invlogit(predict(xb))*100) Average marginal effects Number of obs = 100 Model VCE : OIM Expression : invlogit(predict(xb))*100 dy/dx w.r.t. : 2.address --------------------------------------------------------------------------------------- | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] ----------------------+---------------------------------------------------------------- address | Mohammed Al-Muttalib | -3.842855 5.762352 -0.67 0.505 -15.13686 7.451148 --------------------------------------------------------------------------------------- Note: dy/dx for factor levels is the discrete change from the base level. . . . /*---------------------------------------------------------------------------*/

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. /* Experiment 5: Initiativbewerbungen bei Unternehmen */

. /*---------------------------------------------------------------------------*/

.

. use eth-fs2010-bewerbungen, clear . fre reaction reaction -- Art der Reaktion -------------------------------------------------------------------------- | Freq. Percent Valid Cum. -----------------------------+-------------------------------------------- Valid 1 Prüfung | 1 0.33 0.47 0.47 2 Stellenangebot | 3 1.00 1.40 1.86 3 Weiterleitung | 2 0.67 0.93 2.79 4 negativ | 204 68.00 94.88 97.67 5 weitere Unterlagen | 5 1.67 2.33 100.00 Total | 215 71.67 100.00 Missing . | 85 28.33 Total | 300 100.00 -------------------------------------------------------------------------- . 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 -----------+----------------------+---------- 0 | 33 52 | 85 | 22.00 34.67 | 28.33 -----------+----------------------+---------- 1 | 117 98 | 215 | 78.00 65.33 | 71.67 -----------+----------------------+---------- Total | 150 150 | 300 | 100.00 100.00 | 100.00 Pearson chi2(1) = 5.9261 Pr = 0.015 Fisher's exact = 0.021 1-sided Fisher's exact = 0.010 . ci reaktion if name==1, binomial // Mark -- Binomial Exact -- Variable | Obs Mean Std. Err. [95% Conf. Interval] -------------+--------------------------------------------------------------- reaktion | 150 .78 .0338231 .7051459 .8434627 . ci reaktion if name==2, binomial // Dukan -- Binomial Exact -- Variable | Obs Mean Std. Err. [95% Conf. Interval] -------------+--------------------------------------------------------------- reaktion | 150 .6533333 .0388578 .571386 .7290896

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. . // Reaktion: unter Kontrolle von Briefversion . logit reaktion i.name i.letter Iteration 0: log likelihood = -178.82221 Iteration 1: log likelihood = -173.33306 Iteration 2: log likelihood = -173.27741 Iteration 3: log likelihood = -173.27739 Iteration 4: log likelihood = -173.27739 Logistic regression Number of obs = 300 LR chi2(5) = 11.09 Prob > chi2 = 0.0496 Log likelihood = -173.27739 Pseudo R2 = 0.0310 ---------------------------------------------------------------------------------- reaktion | Coef. Std. Err. z P>|z| [95% Conf. Interval] -----------------+---------------------------------------------------------------- name | Dukan Jovanovic | -.6427634 .2636525 -2.44 0.015 -1.159513 -.1260141 | letter | 2 | .0758305 .389496 0.19 0.846 -.6875677 .8392288 3 | .7826264 .4254355 1.84 0.066 -.0512117 1.616465 4 | .5825298 .4122443 1.41 0.158 -.2254541 1.390514 5 | .2334171 .3950715 0.59 0.555 -.5409089 1.007743 | _cons | .9560106 .3097104 3.09 0.002 .3489894 1.563032 ---------------------------------------------------------------------------------- . margins name, expression(invlogit(predict(xb))*100) Predictive margins Number of obs = 300 Model VCE : OIM Expression : invlogit(predict(xb))*100 ---------------------------------------------------------------------------------- | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -----------------+---------------------------------------------------------------- name | Mark Muggli | 78 3.358226 23.23 0.000 71.418 84.582 Dukan Jovanovic | 65.33333 3.848075 16.98 0.000 57.79125 72.87542 ---------------------------------------------------------------------------------- . margins, dydx(name) expression(invlogit(predict(xb))*100) Average marginal effects Number of obs = 300 Model VCE : OIM Expression : invlogit(predict(xb))*100 dy/dx w.r.t. : 2.name ---------------------------------------------------------------------------------- | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] -----------------+---------------------------------------------------------------- name | Dukan Jovanovic | -12.66667 5.107383 -2.48 0.013 -22.67695 -2.65638 ---------------------------------------------------------------------------------- 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 i.name##i.sector##i.size i.letter Iteration 0: log likelihood = -178.82221 Iteration 1: log likelihood = -170.80981 Iteration 2: log likelihood = -170.59364 Iteration 3: log likelihood = -170.59261 Iteration 4: log likelihood = -170.59261

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Logistic regression Number of obs = 300 LR chi2(11) = 16.46 Prob > chi2 = 0.1249 Log likelihood = -170.59261 Pseudo R2 = 0.0460 --------------------------------------------------------------------------------------- reaktion | Coef. Std. Err. z P>|z| [95% Conf. Interval] ----------------------+---------------------------------------------------------------- name | Dukan Jovanovic | -.2357193 .4250904 -0.55 0.579 -1.068881 .5974426 3.sector | .0298337 .6178729 0.05 0.961 -1.181175 1.240842 | name#sector | Dukan Jovanovic#3 | .2142694 .8330305 0.26 0.797 -1.41844 1.846979 | size | Mitarbeiter >= 100 | .4287625 .4779705 0.90 0.370 -.5080424 1.365567 | name#size | Dukan Jovanovic #| Mitarbeiter >= 100 | -.4641409 .6483726 -0.72 0.474 -1.734928 .8066461 | sector#size | 3#Mitarbeiter >= 100 | .5304256 .8929127 0.59 0.552 -1.219651 2.280502 | name#sector#size | Dukan Jovanovic #| 3 #| Mitarbeiter >= 100 | -1.315977 1.169783 -1.12 0.261 -3.60871 .9767559 | letter | 2 | .0742722 .3947996 0.19 0.851 -.6995209 .8480652 3 | .7458765 .4309988 1.73 0.084 -.0988656 1.590619 4 | .6042918 .4169973 1.45 0.147 -.213008 1.421591 5 | .2449953 .4006357 0.61 0.541 -.5402362 1.030227 | _cons | .6397112 .405127 1.58 0.114 -.154323 1.433746 --------------------------------------------------------------------------------------- . margins name, over(sector) expression(invlogit(predict(xb))*100) Predictive margins Number of obs = 300 Model VCE : OIM Expression : invlogit(predict(xb))*100 over : sector ------------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------------+---------------------------------------------------------------- sector#name | 2#Mark Muggli | 75.69354 4.276329 17.70 0.000 67.31209 84.07499 2#Dukan Jovanovic | 66.96809 4.678336 14.31 0.000 57.79872 76.13746 3#Mark Muggli | 81.50115 5.441243 14.98 0.000 70.83651 92.16579 3#Dukan Jovanovic | 61.31697 6.758039 9.07 0.000 48.07146 74.56248 ------------------------------------------------------------------------------------ . margins, dydx(name) over(sector) expression(invlogit(predict(xb))*100) Average marginal effects Number of obs = 300 Model VCE : OIM Expression : invlogit(predict(xb))*100 dy/dx w.r.t. : 2.name over : sector ------------------------------------------------------------------------------ | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- 2.name |

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sector | 2 | -8.725447 6.338034 -1.38 0.169 -21.14777 3.696872 3 | -20.18418 8.675629 -2.33 0.020 -37.1881 -3.180261 ------------------------------------------------------------------------------ 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, "%9.3f") . local b = string(`b', "%9.1f") . local se = string(`se', "%9.1f") . di "Effekt-Differenz: `b' (`se'), p = `p'" Effekt-Differenz: 11.5 (10.7), p = 0.286 . margins name, over(size) expression(invlogit(predict(xb))*100) Predictive margins Number of obs = 300 Model VCE : OIM Expression : invlogit(predict(xb))*100 over : size ------------------------------------------------------------------------------------------------- | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] --------------------------------+---------------------------------------------------------------- size#name | Mitarbeiter < 100#Mark Muggli | 72.44611 5.358108 13.52 0.000 61.94441 82.94781 Mitarbeiter < 100 #| Dukan Jovanovic | 68.77232 5.126286 13.42 0.000 58.72498 78.81966 Mitarbeiter >= 100#Mark Muggli | 82.74406 4.149367 19.94 0.000 74.61145 90.87667 Mitarbeiter >= 100 #| Dukan Jovanovic | 61.44529 5.747473 10.69 0.000 50.18045 72.71013 ------------------------------------------------------------------------------------------------- . margins, dydx(name) over(size) expression(invlogit(predict(xb))*100) Average marginal effects Number of obs = 300 Model VCE : OIM Expression : invlogit(predict(xb))*100 dy/dx w.r.t. : 2.name over : size ------------------------------------------------------------------------------------- | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] --------------------+---------------------------------------------------------------- 2.name | size | Mitarbeiter < 100 | -3.673788 7.426343 -0.49 0.621 -18.22915 10.88158 Mitarbeiter >= 100 | -21.29876 7.097217 -3.00 0.003 -35.20905 -7.388473 ------------------------------------------------------------------------------------- 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, "%9.3f") . local b = string(`b', "%9.1f") . local se = string(`se', "%9.1f") . di "Effekt-Differenz: `b' (`se'), p = `p'" Effekt-Differenz: 17.6 (10.3), p = 0.087

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. // Interesse

. tab interesse name, col exact chi2 +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ | Name des Bewerbers interesse | Mark Mugg Dukan Jov | Total -----------+----------------------+---------- 0 | 141 148 | 289 | 94.00 98.67 | 96.33 -----------+----------------------+---------- 1 | 9 2 | 11 | 6.00 1.33 | 3.67 -----------+----------------------+---------- Total | 150 150 | 300 | 100.00 100.00 | 100.00 Pearson chi2(1) = 4.6241 Pr = 0.032 Fisher's exact = 0.061 1-sided Fisher's exact = 0.030 . ci interesse if name==1, binomial // Mark -- Binomial Exact -- Variable | Obs Mean Std. Err. [95% Conf. Interval] -------------+--------------------------------------------------------------- interesse | 150 .06 .0193907 .0278 .1108415 . ci interesse if name==2, binomial // Dukan -- Binomial Exact -- Variable | Obs Mean Std. Err. [95% Conf. Interval] -------------+--------------------------------------------------------------- interesse | 150 .0133333 .009365 .0016188 .047333 . . capture log close