----------------------------------------------------------------------------------------------------------------- log: C:\Users\Javier\Documents\dia2cursoencup.log log type: text opened on: 27 Jul 2009, 14:00:26 . . use "C:\Users\Javier\Documents\ENCUP_2008stata.dta", clear . . ** CURSO ENCUP / Javier Aparicio . ** DIA 2 (27 jul 2009) . ** . ** Creación y recodificación de variables (generate, recode, label var) . ** Prueba Chi cuadrada para tablas de contingencia . ** Modelos logit para variables dependientes binarias . ** Modelos oprobit para variables dependientes ordinales . ** . . // Nota: Todo lo que aparece a la derecha de "//" o "*" es un comentario . . . ** Explorando los datos . desc ent - p6r // describe desde la variable ent hasta p6r storage display value variable name type format label variable label ------------------------------------------------------------------------------- ent byte %8.0g ent entidad federativa par byte %8.0g par parentesco ¿qué es ud del jefe(a) de este hogar? sex byte %8.0g sex sexo eda byte %8.0g edad f_nac long %dD_m_Y fecha de nacimiento alf byte %8.0g alf ¿sabe leer y escribir un recado? i_niv byte %8.0g i_niv ¿hasta qué año o grado aprobó en la escuela? i_anio byte %8.0g años en que terminó ese ciclo a_esc byte %8.0g a_esc ¿asiste actualmente a la escuela? e_con byte %8.0g e_con ¿actualmente... c_act byte %8.0g c_act ¿la semana pasada... ocu byte %8.0g ocu ¿cuál es el nombre del oficio, puesto o cargo que desempeñó en su trabajo princi a_eco byte %8.0g a_eco ¿a qué se dedica la empresa, negocio o institución para la que trabajó o ayudó l pos byte %8.0g pos ¿en su trabajo de la semana pasada ud es: i_per byte %8.0g i_per ¿cada cuándo obtiene sus ingresos o le pagan? ing long %12.0g ingreso tloc byte %8.0g tamaño de la localidad p1 byte %8.0g p1 1. usted tiene credencial de elector? p2 byte %8.0g p2 2. ¿cree usted que méxico vive o no en una democracia? p3 byte %8.0g p3 3. ¿usted diría que está muy satisfecho, algo satisfecho, poco satisfecho o nada p4 byte %8.0g p4 4. ¿usted diría que está muy satisfecho, algo satisfecho, poco satisfecho o nada p5 byte %8.0g p5 5. en términos generales, ¿qué tan satisfecho está con su vida en general? p6a byte %8.0g p6a 6a. ¿qué tanta confianza le inspira… el presidente de la república p6b byte %8.0g p6b 6b. ¿qué tanta confianza le inspira… el gobernador/ jefe de gobierno p6c byte %8.0g p6c 6c. ¿qué tanta confianza le inspira… el presidente municipal/ jefe delegacional p6d byte %8.0g p6d 6d. ¿qué tanta confianza le inspira… los jueces y juzgados p6e byte %8.0g p6e 6e. ¿qué tanta confianza le inspira… los partidos políticos p6f byte %8.0g p6f 6f. ¿qué tanta confianza le inspira… los empresarios p6g byte %8.0g p6g 6g. ¿qué tanta confianza le inspira… los medios de comunicación p6h byte %8.0g p6h 6h. ¿qué tanta confianza le inspira… la iglesia p6i byte %8.0g p6i 6i. ¿qué tanta confianza le inspira… los maestros p6j byte %8.0g p6j 6j. ¿qué tanta confianza le inspira… los sindicatos p6k byte %8.0g p6k 6k. ¿qué tanta confianza le inspira… las organizaciones campesinas p6l byte %8.0g p6l 6l. ¿qué tanta confianza le inspira… los movimientos vecinales p6m byte %8.0g p6m 6m. ¿qué tanta confianza le inspira… las organizaciones de mujeres p6n byte %8.0g p6n 6n. ¿qué tanta confianza le inspira… las organizaciones de estudiantes universit p6o byte %8.0g p6o 6o. ¿qué tanta confianza le inspira… las organizaciones de profesionistas p6p byte %8.0g p6p 6p. ¿qué tanta confianza le inspira… las organizaciones que tratan con niños en p6q byte %8.0g p6q 6q. ¿qué tanta confianza le inspira… las organizaciones que se relacionan con el p6r byte %8.0g p6r 6r. ¿qué tanta confianza le inspira… las organizaciones que se relacionan con la . summ ent - p6r Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- ent | 4383 15.99475 8.189323 1 32 par | 4383 1.828656 1.094603 1 8 sex | 4383 1.563769 .4959734 1 2 eda | 4383 42.25165 16.18892 18 96 f_nac | 4383 2194.003 5908.785 -17397 11276 -------------+-------------------------------------------------------- alf | 4383 1.091718 .3100071 1 9 i_niv | 4383 3.336756 3.515076 0 99 i_anio | 4311 3.324055 1.736218 0 9 a_esc | 4383 1.939767 .2866649 1 9 e_con | 4383 4.369154 1.626508 1 9 -------------+-------------------------------------------------------- c_act | 4383 3.491444 3.310682 1 99 ocu | 2599 4.527895 1.766784 1 9 a_eco | 2599 3.896114 1.688382 1 9 pos | 2598 2.887606 .9774471 1 9 i_per | 2595 1.639306 1.028955 1 5 -------------+-------------------------------------------------------- ing | 2310 3634.205 12854.74 4 200000 tloc | 4383 2.284736 1.26179 1 4 p1 | 4383 1.058636 .3260802 1 9 p2 | 4383 2.69587 2.901218 1 9 p3 | 4383 3.615104 2.32236 1 9 -------------+-------------------------------------------------------- p4 | 4383 3.211043 1.836281 1 9 p5 | 4383 1.994068 1.396174 1 9 p6a | 4383 2.431896 1.40575 1 9 p6b | 4383 2.549395 1.519165 1 9 p6c | 4383 2.796486 1.504327 1 9 -------------+-------------------------------------------------------- p6d | 4383 3.405886 2.05298 1 9 p6e | 4383 3.31394 1.577166 1 9 p6f | 4383 3.283824 1.973751 1 9 p6g | 4383 2.669861 1.668655 1 9 p6h | 4383 1.991102 1.32599 1 9 -------------+-------------------------------------------------------- p6i | 4383 2.297741 1.434278 1 9 p6j | 4383 3.548026 2.174478 1 9 p6k | 4383 3.459046 2.355083 1 9 p6l | 4383 3.402236 2.211328 1 9 p6m | 4383 3.174766 2.317069 1 9 -------------+-------------------------------------------------------- p6n | 4383 3.491216 2.443025 1 9 p6o | 4383 3.492357 2.528229 1 9 p6p | 4383 3.104495 2.256091 1 9 p6q | 4383 3.161533 2.323536 1 9 p6r | 4383 3.314168 2.510152 1 9 . . . ** Generando variables "de trabajo" para no contaminar las originales . . gen democ = p2 // democ es un "clon" de p2 . recode democ 1=1 2=0 9=. // recodifico democ para que sea binaria 0/1 (democ: 2176 changes made) . . gen satisfdem = p3 // satisfdem es un clon de p3 . . * etiqueta de variables . label var democ "mexico es democ? 1=si 0=no" . label values democ p2 // etiqueta de valores . label var satisfdem "satisfecho c/dem? 1=muy 2=algo ... 4=nada" . label values satisfdem p3 // etiqueta de valores . . tab democ p2 | 2. ¿cree usted que | méxico vive o no en mexico es democ? 1=si | una democracia? 0=no | si no | Total ----------------------+----------------------+---------- 0 | 0 1,425 | 1,425 si | 2,207 0 | 2,207 ----------------------+----------------------+---------- Total | 2,207 1,425 | 3,632 . tab democ p2, nolabel // verificando "clonacion" | 2. ¿cree usted que mexico es | méxico vive o no en democ? | una democracia? 1=si 0=no | 1 2 | Total -----------+----------------------+---------- 0 | 0 1,425 | 1,425 1 | 2,207 0 | 2,207 -----------+----------------------+---------- Total | 2,207 1,425 | 3,632 . . tab satisfdem p3 satisfecho c/dem? | 3. ¿usted diría que está muy satisfecho, algo 1=muy 2=algo ... | satisfecho, poco satisfecho o nada 4=nada | muy satis algo sati poco sati nada sati no sabe o | Total ----------------------+-------------------------------------------------------+---------- muy satisfecho | 291 0 0 0 0 | 291 algo satisfecho | 0 1,215 0 0 0 | 1,215 poco satisfecho | 0 0 1,454 0 0 | 1,454 nada satisfecho | 0 0 0 809 0 | 809 no sabe o no responde | 0 0 0 0 614 | 614 ----------------------+-------------------------------------------------------+---------- Total | 291 1,215 1,454 809 614 | 4,383 . tab satisfdem p3, nolabel // verificando "clonacion" satisfecho | c/dem? | 1=muy | 3. ¿usted diría que está muy satisfecho, algo 2=algo ... | satisfecho, poco satisfecho o nada 4=nada | 1 2 3 4 9 | Total -----------+-------------------------------------------------------+---------- 1 | 291 0 0 0 0 | 291 2 | 0 1,215 0 0 0 | 1,215 3 | 0 0 1,454 0 0 | 1,454 4 | 0 0 0 809 0 | 809 9 | 0 0 0 0 614 | 614 -----------+-------------------------------------------------------+---------- Total | 291 1,215 1,454 809 614 | 4,383 . . * Recodificando la variable sex . generate mujer = sex==2 // 1 si sex==2, 0 en caso contrario . tab mujer mujer | Freq. Percent Cum. ------------+----------------------------------- 0 | 1,912 43.62 43.62 1 | 2,471 56.38 100.00 ------------+----------------------------------- Total | 4,383 100.00 . label var mujer "1=mujer, 0=hombre" . . * Recodificando confianza en la iglesia (p6h) . tab p6h 6h. ¿qué tanta | confianza le inspira… | la iglesia | Freq. Percent Cum. ----------------------+----------------------------------- mucha | 1,953 44.56 44.56 algo | 1,295 29.55 74.10 poca | 736 16.79 90.90 nada | 323 7.37 98.27 no sabe o no responde | 76 1.73 100.00 ----------------------+----------------------------------- Total | 4,383 100.00 . tab p6h, nolab // noten que la escala es de "desconfianza" 6h. ¿qué | tanta | confianza | le inspira… | la iglesia | Freq. Percent Cum. ------------+----------------------------------- 1 | 1,953 44.56 44.56 2 | 1,295 29.55 74.10 3 | 736 16.79 90.90 4 | 323 7.37 98.27 9 | 76 1.73 100.00 ------------+----------------------------------- Total | 4,383 100.00 . . gen configle = -p6h // ahora va de -4 a -1 . tab configle configle | Freq. Percent Cum. ------------+----------------------------------- -9 | 76 1.73 1.73 -4 | 323 7.37 9.10 -3 | 736 16.79 25.90 -2 | 1,295 29.55 55.44 -1 | 1,953 44.56 100.00 ------------+----------------------------------- Total | 4,383 100.00 . . replace configle = . if p6h == 9 // elimino los NS/NC (76 real changes made, 76 to missing) . . label var configle "confianza en iglesia -4=nada ... -1=mucha" . tab configle confianza | en iglesia | -4=nada ... | -1=mucha | Freq. Percent Cum. ------------+----------------------------------- -4 | 323 7.50 7.50 -3 | 736 17.09 24.59 -2 | 1,295 30.07 54.66 -1 | 1,953 45.34 100.00 ------------+----------------------------------- Total | 4,307 100.00 . hist configle (bin=36, start=-4, width=.08333333) . . * Recodificando el nivel de educacion (i_niv) . tab i_niv, miss ¿hasta qué año o grado | aprobó en la escuela? | Freq. Percent Cum. ----------------------------+----------------------------------- ninguno | 386 8.81 8.81 preescolar | 7 0.16 8.97 primaria | 1,492 34.04 43.01 secundaria | 1,053 24.02 67.03 preparatoria o bachillerato | 597 13.62 80.65 normal | 45 1.03 81.68 carrera técnica | 176 4.02 85.69 profesional | 580 13.23 98.93 maestria | 37 0.84 99.77 doctorado | 6 0.14 99.91 no sabe | 4 0.09 100.00 ----------------------------+----------------------------------- Total | 4,383 100.00 . tab i_niv, nolabel // hay muchas categorias... ¿hasta qué | año o grado | aprobó en | la escuela? | Freq. Percent Cum. ------------+----------------------------------- 0 | 386 8.81 8.81 1 | 7 0.16 8.97 2 | 1,492 34.04 43.01 3 | 1,053 24.02 67.03 4 | 597 13.62 80.65 5 | 45 1.03 81.68 6 | 176 4.02 85.69 7 | 580 13.23 98.93 8 | 37 0.84 99.77 9 | 6 0.14 99.91 99 | 4 0.09 100.00 ------------+----------------------------------- Total | 4,383 100.00 . . ** Para simplificar el análisis, generamos . ** una "dummy" para quienes tienen "prepa o más" . generate prepaomas = i_niv >= 4 // 1=si i_niv es mayor o igual a 4, 0 en caso contrario . replace prepa = . if i_niv==99 // elimino los NS/NC (4 real changes made, 4 to missing) . . label var prepaomas "1=estudio prepa o mas, 0=sin prepa" . tab i_niv prepaomas , miss // verifico que la nueva variable esté bien ¿hasta qué año o | 1=estudio prepa o mas, 0=sin grado aprobó en la | prepa escuela? | 0 1 . | Total ----------------------+---------------------------------+---------- ninguno | 386 0 0 | 386 preescolar | 7 0 0 | 7 primaria | 1,492 0 0 | 1,492 secundaria | 1,053 0 0 | 1,053 preparatoria o bachil | 0 597 0 | 597 normal | 0 45 0 | 45 carrera técnica | 0 176 0 | 176 profesional | 0 580 0 | 580 maestria | 0 37 0 | 37 doctorado | 0 6 0 | 6 no sabe | 0 0 4 | 4 ----------------------+---------------------------------+---------- Total | 2,938 1,441 4 | 4,383 . . tab ing // es una variable continua! ingreso | Freq. Percent Cum. ------------+----------------------------------- 4 | 1 0.04 0.04 9 | 16 0.69 0.74 35 | 1 0.04 0.78 50 | 7 0.30 1.08 60 | 1 0.04 1.13 70 | 1 0.04 1.17 75 | 1 0.04 1.21 80 | 3 0.13 1.34 99 | 1 0.04 1.39 100 | 26 1.13 2.51 120 | 3 0.13 2.64 130 | 1 0.04 2.68 140 | 4 0.17 2.86 150 | 21 0.91 3.77 160 | 3 0.13 3.90 200 | 38 1.65 5.54 210 | 1 0.04 5.58 240 | 1 0.04 5.63 250 | 25 1.08 6.71 260 | 2 0.09 6.80 280 | 1 0.04 6.84 300 | 104 4.50 11.34 335 | 1 0.04 11.39 350 | 17 0.74 12.12 360 | 4 0.17 12.29 380 | 1 0.04 12.34 400 | 76 3.29 15.63 420 | 3 0.13 15.76 440 | 1 0.04 15.80 450 | 17 0.74 16.54 480 | 8 0.35 16.88 490 | 2 0.09 16.97 500 | 119 5.15 22.12 520 | 2 0.09 22.21 528 | 1 0.04 22.25 540 | 2 0.09 22.34 549 | 1 0.04 22.38 550 | 6 0.26 22.64 560 | 3 0.13 22.77 570 | 1 0.04 22.81 600 | 126 5.45 28.27 650 | 16 0.69 28.96 700 | 92 3.98 32.94 710 | 1 0.04 32.99 720 | 4 0.17 33.16 740 | 1 0.04 33.20 750 | 20 0.87 34.07 760 | 2 0.09 34.16 770 | 1 0.04 34.20 800 | 130 5.63 39.83 840 | 2 0.09 39.91 844 | 1 0.04 39.96 850 | 8 0.35 40.30 860 | 1 0.04 40.35 880 | 1 0.04 40.39 900 | 63 2.73 43.12 950 | 4 0.17 43.29 960 | 1 0.04 43.33 980 | 3 0.13 43.46 988 | 1 0.04 43.51 990 | 1 0.04 43.55 999 | 2 0.09 43.64 1000 | 178 7.71 51.34 1050 | 3 0.13 51.47 1067 | 1 0.04 51.52 1080 | 2 0.09 51.60 1100 | 30 1.30 52.90 1120 | 1 0.04 52.94 1150 | 3 0.13 53.07 1200 | 110 4.76 57.84 1214 | 1 0.04 57.88 1250 | 4 0.17 58.05 1260 | 1 0.04 58.10 1300 | 32 1.39 59.48 1350 | 2 0.09 59.57 1400 | 17 0.74 60.30 1470 | 1 0.04 60.35 1500 | 132 5.71 66.06 1507 | 1 0.04 66.10 1550 | 1 0.04 66.15 1560 | 1 0.04 66.19 1570 | 1 0.04 66.23 1600 | 21 0.91 67.14 1650 | 1 0.04 67.19 1700 | 13 0.56 67.75 1750 | 1 0.04 67.79 1800 | 27 1.17 68.96 1850 | 1 0.04 69.00 1900 | 4 0.17 69.18 2000 | 113 4.89 74.07 2100 | 7 0.30 74.37 2130 | 1 0.04 74.42 2160 | 1 0.04 74.46 2200 | 9 0.39 74.85 2250 | 2 0.09 74.94 2300 | 8 0.35 75.28 2400 | 6 0.26 75.54 2450 | 1 0.04 75.58 2500 | 51 2.21 77.79 2550 | 1 0.04 77.84 2600 | 7 0.30 78.14 2700 | 8 0.35 78.48 2750 | 1 0.04 78.53 2800 | 9 0.39 78.92 2850 | 1 0.04 78.96 2900 | 3 0.13 79.09 3000 | 84 3.64 82.73 3200 | 8 0.35 83.07 3400 | 1 0.04 83.12 3500 | 33 1.43 84.55 3600 | 2 0.09 84.63 3700 | 4 0.17 84.81 3800 | 4 0.17 84.98 4000 | 55 2.38 87.36 4100 | 1 0.04 87.40 4200 | 5 0.22 87.62 4300 | 2 0.09 87.71 4500 | 14 0.61 88.31 4600 | 1 0.04 88.35 4800 | 1 0.04 88.40 5000 | 42 1.82 90.22 5300 | 1 0.04 90.26 5400 | 1 0.04 90.30 5500 | 2 0.09 90.39 5800 | 1 0.04 90.43 6000 | 22 0.95 91.39 6200 | 1 0.04 91.43 6300 | 1 0.04 91.47 6500 | 3 0.13 91.60 7000 | 24 1.04 92.64 7500 | 9 0.39 93.03 8000 | 28 1.21 94.24 8500 | 1 0.04 94.29 9000 | 6 0.26 94.55 9999 | 12 0.52 95.06 10000 | 25 1.08 96.15 11000 | 3 0.13 96.28 12000 | 16 0.69 96.97 14000 | 3 0.13 97.10 15000 | 6 0.26 97.36 16000 | 1 0.04 97.40 17000 | 3 0.13 97.53 17500 | 1 0.04 97.58 18000 | 2 0.09 97.66 20000 | 11 0.48 98.14 24000 | 1 0.04 98.18 25000 | 1 0.04 98.23 30000 | 3 0.13 98.35 40000 | 2 0.09 98.44 48000 | 1 0.04 98.48 80000 | 1 0.04 98.53 99999 | 32 1.39 99.91 100000 | 1 0.04 99.96 200000 | 1 0.04 100.00 ------------+----------------------------------- Total | 2,310 100.00 . histogram ing // la grafica indica gran sesgo positivo (bin=33, start=4, width=6060.4848) . . * Transformando ingreso en log(ingreso) para "normalizarla" un poco . generate loging = log(ing) (2073 missing values generated) . label var loging "log(ingreso)" . histogram loging // ahora "se ve" mucho mejor! (bin=33, start=1.3862944, width=.32787207) . . . * Creando algunas variables "interactivas" . gen edaing = eda * ing // controla por "rucos ricos vs. jovenes pobres" (2073 missing values generated) . gen edamujer = eda * mujer // controla por "abuelitas vs. chicas" . . * Vale la pena "subir" mis nuevas variables al inicio de la base: . order democ - edamujer . . * Guardo la base modificada con un NUEVO nombre (para no contaminar la original) . save encupmodificada, replace file encupmodificada.dta saved . . . ************ ANALISIS ************* . . tab democ mexico es democ? 1=si | 0=no | Freq. Percent Cum. ----------------------+----------------------------------- 0 | 1,425 39.23 39.23 si | 2,207 60.77 100.00 ----------------------+----------------------------------- Total | 3,632 100.00 . tab eda edad | Freq. Percent Cum. ------------+----------------------------------- 18 | 73 1.67 1.67 19 | 84 1.92 3.58 20 | 93 2.12 5.70 21 | 85 1.94 7.64 22 | 91 2.08 9.72 23 | 82 1.87 11.59 24 | 82 1.87 13.46 25 | 99 2.26 15.72 26 | 101 2.30 18.02 27 | 82 1.87 19.90 28 | 112 2.56 22.45 29 | 98 2.24 24.69 30 | 107 2.44 27.13 31 | 99 2.26 29.39 32 | 120 2.74 32.12 33 | 121 2.76 34.88 34 | 118 2.69 37.58 35 | 114 2.60 40.18 36 | 106 2.42 42.60 37 | 102 2.33 44.92 38 | 119 2.72 47.64 39 | 96 2.19 49.83 40 | 117 2.67 52.50 41 | 84 1.92 54.41 42 | 106 2.42 56.83 43 | 101 2.30 59.14 44 | 84 1.92 61.05 45 | 76 1.73 62.79 46 | 83 1.89 64.68 47 | 70 1.60 66.28 48 | 85 1.94 68.22 49 | 90 2.05 70.27 50 | 68 1.55 71.82 51 | 60 1.37 73.19 52 | 70 1.60 74.79 53 | 66 1.51 76.29 54 | 68 1.55 77.85 55 | 56 1.28 79.12 56 | 58 1.32 80.45 57 | 54 1.23 81.68 58 | 59 1.35 83.03 59 | 40 0.91 83.94 60 | 37 0.84 84.78 61 | 29 0.66 85.44 62 | 46 1.05 86.49 63 | 43 0.98 87.47 64 | 38 0.87 88.34 65 | 30 0.68 89.03 66 | 34 0.78 89.80 67 | 28 0.64 90.44 68 | 49 1.12 91.56 69 | 31 0.71 92.27 70 | 35 0.80 93.06 71 | 39 0.89 93.95 72 | 28 0.64 94.59 73 | 31 0.71 95.30 74 | 23 0.52 95.82 75 | 23 0.52 96.35 76 | 24 0.55 96.90 77 | 15 0.34 97.24 78 | 23 0.52 97.76 79 | 8 0.18 97.95 80 | 12 0.27 98.22 81 | 14 0.32 98.54 82 | 10 0.23 98.77 83 | 11 0.25 99.02 84 | 9 0.21 99.22 85 | 8 0.18 99.41 86 | 5 0.11 99.52 87 | 5 0.11 99.63 88 | 4 0.09 99.73 89 | 5 0.11 99.84 90 | 1 0.02 99.86 91 | 2 0.05 99.91 92 | 2 0.05 99.95 94 | 1 0.02 99.98 96 | 1 0.02 100.00 ------------+----------------------------------- Total | 4,383 100.00 . tab eda democ, chi | mexico es democ? 1=si | 0=no edad | 0 si | Total -----------+----------------------+---------- 18 | 28 36 | 64 19 | 28 50 | 78 20 | 35 49 | 84 21 | 36 39 | 75 22 | 32 46 | 78 23 | 30 42 | 72 24 | 23 45 | 68 25 | 36 50 | 86 26 | 26 62 | 88 27 | 27 39 | 66 28 | 27 60 | 87 29 | 33 52 | 85 30 | 41 56 | 97 31 | 29 52 | 81 32 | 41 58 | 99 33 | 37 75 | 112 34 | 39 62 | 101 35 | 31 71 | 102 36 | 27 61 | 88 37 | 31 56 | 87 38 | 40 66 | 106 39 | 25 56 | 81 40 | 39 66 | 105 41 | 25 49 | 74 42 | 44 49 | 93 43 | 31 57 | 88 44 | 27 37 | 64 45 | 22 40 | 62 46 | 30 41 | 71 47 | 30 29 | 59 48 | 26 48 | 74 49 | 21 50 | 71 50 | 26 34 | 60 51 | 20 32 | 52 52 | 23 31 | 54 53 | 21 31 | 52 54 | 29 30 | 59 55 | 16 27 | 43 56 | 23 22 | 45 57 | 22 15 | 37 58 | 22 22 | 44 59 | 15 18 | 33 60 | 10 19 | 29 61 | 15 10 | 25 62 | 17 17 | 34 63 | 13 21 | 34 64 | 11 15 | 26 65 | 7 13 | 20 66 | 9 17 | 26 67 | 11 16 | 27 68 | 17 22 | 39 69 | 7 15 | 22 70 | 10 7 | 17 71 | 16 13 | 29 72 | 6 13 | 19 73 | 11 15 | 26 74 | 7 9 | 16 75 | 4 9 | 13 76 | 10 8 | 18 77 | 2 8 | 10 78 | 6 8 | 14 79 | 2 3 | 5 80 | 2 7 | 9 81 | 4 6 | 10 82 | 3 2 | 5 83 | 2 4 | 6 84 | 3 4 | 7 85 | 1 1 | 2 86 | 0 3 | 3 87 | 0 5 | 5 88 | 1 2 | 3 89 | 1 2 | 3 91 | 1 1 | 2 92 | 1 0 | 1 94 | 1 0 | 1 96 | 0 1 | 1 -----------+----------------------+---------- Total | 1,425 2,207 | 3,632 Pearson chi2(75) = 79.0471 Pr = 0.352 . tab eda democ, row chi +----------------+ | Key | |----------------| | frequency | | row percentage | +----------------+ | mexico es democ? 1=si | 0=no edad | 0 si | Total -----------+----------------------+---------- 18 | 28 36 | 64 | 43.75 56.25 | 100.00 -----------+----------------------+---------- 19 | 28 50 | 78 | 35.90 64.10 | 100.00 -----------+----------------------+---------- 20 | 35 49 | 84 | 41.67 58.33 | 100.00 -----------+----------------------+---------- 21 | 36 39 | 75 | 48.00 52.00 | 100.00 -----------+----------------------+---------- 22 | 32 46 | 78 | 41.03 58.97 | 100.00 -----------+----------------------+---------- 23 | 30 42 | 72 | 41.67 58.33 | 100.00 -----------+----------------------+---------- 24 | 23 45 | 68 | 33.82 66.18 | 100.00 -----------+----------------------+---------- 25 | 36 50 | 86 | 41.86 58.14 | 100.00 -----------+----------------------+---------- 26 | 26 62 | 88 | 29.55 70.45 | 100.00 -----------+----------------------+---------- 27 | 27 39 | 66 | 40.91 59.09 | 100.00 -----------+----------------------+---------- 28 | 27 60 | 87 | 31.03 68.97 | 100.00 -----------+----------------------+---------- 29 | 33 52 | 85 | 38.82 61.18 | 100.00 -----------+----------------------+---------- 30 | 41 56 | 97 | 42.27 57.73 | 100.00 -----------+----------------------+---------- 31 | 29 52 | 81 | 35.80 64.20 | 100.00 -----------+----------------------+---------- 32 | 41 58 | 99 | 41.41 58.59 | 100.00 -----------+----------------------+---------- 33 | 37 75 | 112 | 33.04 66.96 | 100.00 -----------+----------------------+---------- 34 | 39 62 | 101 | 38.61 61.39 | 100.00 -----------+----------------------+---------- 35 | 31 71 | 102 | 30.39 69.61 | 100.00 -----------+----------------------+---------- 36 | 27 61 | 88 | 30.68 69.32 | 100.00 -----------+----------------------+---------- 37 | 31 56 | 87 | 35.63 64.37 | 100.00 -----------+----------------------+---------- 38 | 40 66 | 106 | 37.74 62.26 | 100.00 -----------+----------------------+---------- 39 | 25 56 | 81 | 30.86 69.14 | 100.00 -----------+----------------------+---------- 40 | 39 66 | 105 | 37.14 62.86 | 100.00 -----------+----------------------+---------- 41 | 25 49 | 74 | 33.78 66.22 | 100.00 -----------+----------------------+---------- 42 | 44 49 | 93 | 47.31 52.69 | 100.00 -----------+----------------------+---------- 43 | 31 57 | 88 | 35.23 64.77 | 100.00 -----------+----------------------+---------- 44 | 27 37 | 64 | 42.19 57.81 | 100.00 -----------+----------------------+---------- 45 | 22 40 | 62 | 35.48 64.52 | 100.00 -----------+----------------------+---------- 46 | 30 41 | 71 | 42.25 57.75 | 100.00 -----------+----------------------+---------- 47 | 30 29 | 59 | 50.85 49.15 | 100.00 -----------+----------------------+---------- 48 | 26 48 | 74 | 35.14 64.86 | 100.00 -----------+----------------------+---------- 49 | 21 50 | 71 | 29.58 70.42 | 100.00 -----------+----------------------+---------- 50 | 26 34 | 60 | 43.33 56.67 | 100.00 -----------+----------------------+---------- 51 | 20 32 | 52 | 38.46 61.54 | 100.00 -----------+----------------------+---------- 52 | 23 31 | 54 | 42.59 57.41 | 100.00 -----------+----------------------+---------- 53 | 21 31 | 52 | 40.38 59.62 | 100.00 -----------+----------------------+---------- 54 | 29 30 | 59 | 49.15 50.85 | 100.00 -----------+----------------------+---------- 55 | 16 27 | 43 | 37.21 62.79 | 100.00 -----------+----------------------+---------- 56 | 23 22 | 45 | 51.11 48.89 | 100.00 -----------+----------------------+---------- 57 | 22 15 | 37 | 59.46 40.54 | 100.00 -----------+----------------------+---------- 58 | 22 22 | 44 | 50.00 50.00 | 100.00 -----------+----------------------+---------- 59 | 15 18 | 33 | 45.45 54.55 | 100.00 -----------+----------------------+---------- 60 | 10 19 | 29 | 34.48 65.52 | 100.00 -----------+----------------------+---------- 61 | 15 10 | 25 | 60.00 40.00 | 100.00 -----------+----------------------+---------- 62 | 17 17 | 34 | 50.00 50.00 | 100.00 -----------+----------------------+---------- 63 | 13 21 | 34 | 38.24 61.76 | 100.00 -----------+----------------------+---------- 64 | 11 15 | 26 | 42.31 57.69 | 100.00 -----------+----------------------+---------- 65 | 7 13 | 20 | 35.00 65.00 | 100.00 -----------+----------------------+---------- 66 | 9 17 | 26 | 34.62 65.38 | 100.00 -----------+----------------------+---------- 67 | 11 16 | 27 | 40.74 59.26 | 100.00 -----------+----------------------+---------- 68 | 17 22 | 39 | 43.59 56.41 | 100.00 -----------+----------------------+---------- 69 | 7 15 | 22 | 31.82 68.18 | 100.00 -----------+----------------------+---------- 70 | 10 7 | 17 | 58.82 41.18 | 100.00 -----------+----------------------+---------- 71 | 16 13 | 29 | 55.17 44.83 | 100.00 -----------+----------------------+---------- 72 | 6 13 | 19 | 31.58 68.42 | 100.00 -----------+----------------------+---------- 73 | 11 15 | 26 | 42.31 57.69 | 100.00 -----------+----------------------+---------- 74 | 7 9 | 16 | 43.75 56.25 | 100.00 -----------+----------------------+---------- 75 | 4 9 | 13 | 30.77 69.23 | 100.00 -----------+----------------------+---------- 76 | 10 8 | 18 | 55.56 44.44 | 100.00 -----------+----------------------+---------- 77 | 2 8 | 10 | 20.00 80.00 | 100.00 -----------+----------------------+---------- 78 | 6 8 | 14 | 42.86 57.14 | 100.00 -----------+----------------------+---------- 79 | 2 3 | 5 | 40.00 60.00 | 100.00 -----------+----------------------+---------- 80 | 2 7 | 9 | 22.22 77.78 | 100.00 -----------+----------------------+---------- 81 | 4 6 | 10 | 40.00 60.00 | 100.00 -----------+----------------------+---------- 82 | 3 2 | 5 | 60.00 40.00 | 100.00 -----------+----------------------+---------- 83 | 2 4 | 6 | 33.33 66.67 | 100.00 -----------+----------------------+---------- 84 | 3 4 | 7 | 42.86 57.14 | 100.00 -----------+----------------------+---------- 85 | 1 1 | 2 | 50.00 50.00 | 100.00 -----------+----------------------+---------- 86 | 0 3 | 3 | 0.00 100.00 | 100.00 -----------+----------------------+---------- 87 | 0 5 | 5 | 0.00 100.00 | 100.00 -----------+----------------------+---------- 88 | 1 2 | 3 | 33.33 66.67 | 100.00 -----------+----------------------+---------- 89 | 1 2 | 3 | 33.33 66.67 | 100.00 -----------+----------------------+---------- 91 | 1 1 | 2 | 50.00 50.00 | 100.00 -----------+----------------------+---------- 92 | 1 0 | 1 | 100.00 0.00 | 100.00 -----------+----------------------+---------- 94 | 1 0 | 1 | 100.00 0.00 | 100.00 -----------+----------------------+---------- 96 | 0 1 | 1 | 0.00 100.00 | 100.00 -----------+----------------------+---------- Total | 1,425 2,207 | 3,632 | 39.23 60.77 | 100.00 Pearson chi2(75) = 79.0471 Pr = 0.352 . . scatter democ eda // scatterplot entre democ y edad . scatter democ eda, jitter(5) // scatterplot "agitado" . . scatter democ eda || lfit democ eda // scatterplot + regresion simple . scatter democ eda, jitter(5) || lfit democ eda . . reg democ eda // regresion lineal SIMPLE Source | SS df MS Number of obs = 3632 -------------+------------------------------ F( 1, 3630) = 3.18 Model | .758664511 1 .758664511 Prob > F = 0.0745 Residual | 865.148549 3630 .238332934 R-squared = 0.0009 -------------+------------------------------ Adj R-squared = 0.0006 Total | 865.907214 3631 .238476236 Root MSE = .48819 ------------------------------------------------------------------------------ democ | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- eda | -.0009258 .0005189 -1.78 0.074 -.0019432 .0000916 _cons | .645821 .0228745 28.23 0.000 .6009729 .690669 ------------------------------------------------------------------------------ . logit democ eda // modelo LOGIT simple Iteration 0: log likelihood = -2432.6622 Iteration 1: log likelihood = -2431.0752 Iteration 2: log likelihood = -2431.0751 Logistic regression Number of obs = 3632 LR chi2(1) = 3.17 Prob > chi2 = 0.0748 Log likelihood = -2431.0751 Pseudo R2 = 0.0007 ------------------------------------------------------------------------------ democ | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- eda | -.0038695 .0021701 -1.78 0.075 -.0081229 .0003838 _cons | .597365 .0960997 6.22 0.000 .409013 .7857169 ------------------------------------------------------------------------------ . . ** La prueba chi cuadrada ayuda a explorar "relaciones de pares" . tab democ mujer , row chi +----------------+ | Key | |----------------| | frequency | | row percentage | +----------------+ mexico es democ? 1=si | 1=mujer, 0=hombre 0=no | 0 1 | Total ----------------------+----------------------+---------- 0 | 651 774 | 1,425 | 45.68 54.32 | 100.00 ----------------------+----------------------+---------- si | 1,002 1,205 | 2,207 | 45.40 54.60 | 100.00 ----------------------+----------------------+---------- Total | 1,653 1,979 | 3,632 | 45.51 54.49 | 100.00 Pearson chi2(1) = 0.0280 Pr = 0.867 . tab democ prepaomas , row chi +----------------+ | Key | |----------------| | frequency | | row percentage | +----------------+ | 1=estudio prepa o mexico es democ? 1=si | mas, 0=sin prepa 0=no | 0 1 | Total ----------------------+----------------------+---------- 0 | 827 595 | 1,422 | 58.16 41.84 | 100.00 ----------------------+----------------------+---------- si | 1,447 759 | 2,206 | 65.59 34.41 | 100.00 ----------------------+----------------------+---------- Total | 2,274 1,354 | 3,628 | 62.68 37.32 | 100.00 Pearson chi2(1) = 20.4399 Pr = 0.000 . ** pero si hay muchas categorias es complicado: . tab eda democ , row chi +----------------+ | Key | |----------------| | frequency | | row percentage | +----------------+ | mexico es democ? 1=si | 0=no edad | 0 si | Total -----------+----------------------+---------- 18 | 28 36 | 64 | 43.75 56.25 | 100.00 -----------+----------------------+---------- 19 | 28 50 | 78 | 35.90 64.10 | 100.00 -----------+----------------------+---------- 20 | 35 49 | 84 | 41.67 58.33 | 100.00 -----------+----------------------+---------- 21 | 36 39 | 75 | 48.00 52.00 | 100.00 -----------+----------------------+---------- 22 | 32 46 | 78 | 41.03 58.97 | 100.00 -----------+----------------------+---------- 23 | 30 42 | 72 | 41.67 58.33 | 100.00 -----------+----------------------+---------- 24 | 23 45 | 68 | 33.82 66.18 | 100.00 -----------+----------------------+---------- 25 | 36 50 | 86 | 41.86 58.14 | 100.00 -----------+----------------------+---------- 26 | 26 62 | 88 | 29.55 70.45 | 100.00 -----------+----------------------+---------- 27 | 27 39 | 66 | 40.91 59.09 | 100.00 -----------+----------------------+---------- 28 | 27 60 | 87 | 31.03 68.97 | 100.00 -----------+----------------------+---------- 29 | 33 52 | 85 | 38.82 61.18 | 100.00 -----------+----------------------+---------- 30 | 41 56 | 97 | 42.27 57.73 | 100.00 -----------+----------------------+---------- 31 | 29 52 | 81 | 35.80 64.20 | 100.00 -----------+----------------------+---------- 32 | 41 58 | 99 | 41.41 58.59 | 100.00 -----------+----------------------+---------- 33 | 37 75 | 112 | 33.04 66.96 | 100.00 -----------+----------------------+---------- 34 | 39 62 | 101 | 38.61 61.39 | 100.00 -----------+----------------------+---------- 35 | 31 71 | 102 | 30.39 69.61 | 100.00 -----------+----------------------+---------- 36 | 27 61 | 88 | 30.68 69.32 | 100.00 -----------+----------------------+---------- 37 | 31 56 | 87 | 35.63 64.37 | 100.00 -----------+----------------------+---------- 38 | 40 66 | 106 | 37.74 62.26 | 100.00 -----------+----------------------+---------- 39 | 25 56 | 81 | 30.86 69.14 | 100.00 -----------+----------------------+---------- 40 | 39 66 | 105 | 37.14 62.86 | 100.00 -----------+----------------------+---------- 41 | 25 49 | 74 | 33.78 66.22 | 100.00 -----------+----------------------+---------- 42 | 44 49 | 93 | 47.31 52.69 | 100.00 -----------+----------------------+---------- 43 | 31 57 | 88 | 35.23 64.77 | 100.00 -----------+----------------------+---------- 44 | 27 37 | 64 | 42.19 57.81 | 100.00 -----------+----------------------+---------- 45 | 22 40 | 62 | 35.48 64.52 | 100.00 -----------+----------------------+---------- 46 | 30 41 | 71 | 42.25 57.75 | 100.00 -----------+----------------------+---------- 47 | 30 29 | 59 | 50.85 49.15 | 100.00 -----------+----------------------+---------- 48 | 26 48 | 74 | 35.14 64.86 | 100.00 -----------+----------------------+---------- 49 | 21 50 | 71 | 29.58 70.42 | 100.00 -----------+----------------------+---------- 50 | 26 34 | 60 | 43.33 56.67 | 100.00 -----------+----------------------+---------- 51 | 20 32 | 52 | 38.46 61.54 | 100.00 -----------+----------------------+---------- 52 | 23 31 | 54 | 42.59 57.41 | 100.00 -----------+----------------------+---------- 53 | 21 31 | 52 | 40.38 59.62 | 100.00 -----------+----------------------+---------- 54 | 29 30 | 59 | 49.15 50.85 | 100.00 -----------+----------------------+---------- 55 | 16 27 | 43 | 37.21 62.79 | 100.00 -----------+----------------------+---------- 56 | 23 22 | 45 | 51.11 48.89 | 100.00 -----------+----------------------+---------- 57 | 22 15 | 37 | 59.46 40.54 | 100.00 -----------+----------------------+---------- 58 | 22 22 | 44 | 50.00 50.00 | 100.00 -----------+----------------------+---------- 59 | 15 18 | 33 | 45.45 54.55 | 100.00 -----------+----------------------+---------- 60 | 10 19 | 29 | 34.48 65.52 | 100.00 -----------+----------------------+---------- 61 | 15 10 | 25 | 60.00 40.00 | 100.00 -----------+----------------------+---------- 62 | 17 17 | 34 | 50.00 50.00 | 100.00 -----------+----------------------+---------- 63 | 13 21 | 34 | 38.24 61.76 | 100.00 -----------+----------------------+---------- 64 | 11 15 | 26 | 42.31 57.69 | 100.00 -----------+----------------------+---------- 65 | 7 13 | 20 | 35.00 65.00 | 100.00 -----------+----------------------+---------- 66 | 9 17 | 26 | 34.62 65.38 | 100.00 -----------+----------------------+---------- 67 | 11 16 | 27 | 40.74 59.26 | 100.00 -----------+----------------------+---------- 68 | 17 22 | 39 | 43.59 56.41 | 100.00 -----------+----------------------+---------- 69 | 7 15 | 22 | 31.82 68.18 | 100.00 -----------+----------------------+---------- 70 | 10 7 | 17 | 58.82 41.18 | 100.00 -----------+----------------------+---------- 71 | 16 13 | 29 | 55.17 44.83 | 100.00 -----------+----------------------+---------- 72 | 6 13 | 19 | 31.58 68.42 | 100.00 -----------+----------------------+---------- 73 | 11 15 | 26 | 42.31 57.69 | 100.00 -----------+----------------------+---------- 74 | 7 9 | 16 | 43.75 56.25 | 100.00 -----------+----------------------+---------- 75 | 4 9 | 13 | 30.77 69.23 | 100.00 -----------+----------------------+---------- 76 | 10 8 | 18 | 55.56 44.44 | 100.00 -----------+----------------------+---------- 77 | 2 8 | 10 | 20.00 80.00 | 100.00 -----------+----------------------+---------- 78 | 6 8 | 14 | 42.86 57.14 | 100.00 -----------+----------------------+---------- 79 | 2 3 | 5 | 40.00 60.00 | 100.00 -----------+----------------------+---------- 80 | 2 7 | 9 | 22.22 77.78 | 100.00 -----------+----------------------+---------- 81 | 4 6 | 10 | 40.00 60.00 | 100.00 -----------+----------------------+---------- 82 | 3 2 | 5 | 60.00 40.00 | 100.00 -----------+----------------------+---------- 83 | 2 4 | 6 | 33.33 66.67 | 100.00 -----------+----------------------+---------- 84 | 3 4 | 7 | 42.86 57.14 | 100.00 -----------+----------------------+---------- 85 | 1 1 | 2 | 50.00 50.00 | 100.00 -----------+----------------------+---------- 86 | 0 3 | 3 | 0.00 100.00 | 100.00 -----------+----------------------+---------- 87 | 0 5 | 5 | 0.00 100.00 | 100.00 -----------+----------------------+---------- 88 | 1 2 | 3 | 33.33 66.67 | 100.00 -----------+----------------------+---------- 89 | 1 2 | 3 | 33.33 66.67 | 100.00 -----------+----------------------+---------- 91 | 1 1 | 2 | 50.00 50.00 | 100.00 -----------+----------------------+---------- 92 | 1 0 | 1 | 100.00 0.00 | 100.00 -----------+----------------------+---------- 94 | 1 0 | 1 | 100.00 0.00 | 100.00 -----------+----------------------+---------- 96 | 0 1 | 1 | 0.00 100.00 | 100.00 -----------+----------------------+---------- Total | 1,425 2,207 | 3,632 | 39.23 60.77 | 100.00 Pearson chi2(75) = 79.0471 Pr = 0.352 . . ** Un modelo LOGIT multiple puede estimar "al mismo tiempo" el efecto . ** de edad y género en la confianza en la democracia... . . logit democ eda mujer // añado una 2a variable p/controlar por género Iteration 0: log likelihood = -2432.6622 Iteration 1: log likelihood = -2431.0724 Iteration 2: log likelihood = -2431.0723 Logistic regression Number of obs = 3632 LR chi2(2) = 3.18 Prob > chi2 = 0.2040 Log likelihood = -2431.0723 Pseudo R2 = 0.0007 ------------------------------------------------------------------------------ democ | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- eda | -.003861 .0021731 -1.78 0.076 -.0081202 .0003981 mujer | .0050875 .0683587 0.07 0.941 -.1288932 .1390681 _cons | .5942437 .1048472 5.67 0.000 .388747 .7997404 ------------------------------------------------------------------------------ . logit democ eda mujer prepaomas // añado una 3a var p/controlar educacion Iteration 0: log likelihood = -2429.356 Iteration 1: log likelihood = -2414.4807 Iteration 2: log likelihood = -2414.4748 Logistic regression Number of obs = 3628 LR chi2(3) = 29.76 Prob > chi2 = 0.0000 Log likelihood = -2414.4748 Pseudo R2 = 0.0061 ------------------------------------------------------------------------------ democ | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- eda | -.0069523 .0022655 -3.07 0.002 -.0113925 -.0025121 mujer | -.0096474 .0687219 -0.14 0.888 -.1443399 .1250451 prepaomas | -.3750747 .0728076 -5.15 0.000 -.5177749 -.2323746 _cons | .8745052 .1188843 7.36 0.000 .6414962 1.107514 ------------------------------------------------------------------------------ . logit democ eda mujer prepaomas ing // añado ingreso... Iteration 0: log likelihood = -1358.5141 Iteration 1: log likelihood = -1356.1034 Iteration 2: log likelihood = -1356.1031 Logistic regression Number of obs = 2015 LR chi2(4) = 4.82 Prob > chi2 = 0.3061 Log likelihood = -1356.1031 Pseudo R2 = 0.0018 ------------------------------------------------------------------------------ democ | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- eda | -.0038415 .0035867 -1.07 0.284 -.0108713 .0031884 mujer | -.1081296 .0940133 -1.15 0.250 -.2923922 .0761331 prepaomas | -.1583851 .0957828 -1.65 0.098 -.3461159 .0293456 ing | 1.44e-06 3.54e-06 0.41 0.684 -5.51e-06 8.39e-06 _cons | .6477268 .1671614 3.87 0.000 .3200964 .9753572 ------------------------------------------------------------------------------ . logit democ eda mujer prepaomas loging // añado log(ingreso) Iteration 0: log likelihood = -1358.5141 Iteration 1: log likelihood = -1355.8854 Iteration 2: log likelihood = -1355.8851 Logistic regression Number of obs = 2015 LR chi2(4) = 5.26 Prob > chi2 = 0.2618 Log likelihood = -1355.8851 Pseudo R2 = 0.0019 ------------------------------------------------------------------------------ democ | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- eda | -.0036163 .0035889 -1.01 0.314 -.0106504 .0034178 mujer | -.1197587 .0949076 -1.26 0.207 -.305774 .0662567 prepaomas | -.1171093 .104036 -1.13 0.260 -.3210161 .0867975 loging | -.0314383 .0404875 -0.78 0.437 -.1107923 .0479157 _cons | .8570251 .3174587 2.70 0.007 .2348174 1.479233 ------------------------------------------------------------------------------ . . * Noten como los coeficientes estimados, y su significancia, "cambian" conforme . * añado o ignoro variables: esto es el "sesgo de variable omitida" . . scatter eda loging . corr eda loging // edad e ingreso no tienen una alta correlacion (obs=2310) | eda loging -------------+------------------ eda | 1.0000 loging | -0.0510 1.0000 . . . ** Probando "efectos interactivos" . . logit democ eda mujer prepaomas // modelo sin interacciones Iteration 0: log likelihood = -2429.356 Iteration 1: log likelihood = -2414.4807 Iteration 2: log likelihood = -2414.4748 Logistic regression Number of obs = 3628 LR chi2(3) = 29.76 Prob > chi2 = 0.0000 Log likelihood = -2414.4748 Pseudo R2 = 0.0061 ------------------------------------------------------------------------------ democ | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- eda | -.0069523 .0022655 -3.07 0.002 -.0113925 -.0025121 mujer | -.0096474 .0687219 -0.14 0.888 -.1443399 .1250451 prepaomas | -.3750747 .0728076 -5.15 0.000 -.5177749 -.2323746 _cons | .8745052 .1188843 7.36 0.000 .6414962 1.107514 ------------------------------------------------------------------------------ . . * controlando por la interaccion entre edad y genero . logit democ eda mujer prepaomas edamujer Iteration 0: log likelihood = -2429.356 Iteration 1: log likelihood = -2414.1235 Iteration 2: log likelihood = -2414.1174 Logistic regression Number of obs = 3628 LR chi2(4) = 30.48 Prob > chi2 = 0.0000 Log likelihood = -2414.1174 Pseudo R2 = 0.0063 ------------------------------------------------------------------------------ democ | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- eda | -.0088421 .0031823 -2.78 0.005 -.0150793 -.0026049 mujer | -.1628888 .1938781 -0.84 0.401 -.5428828 .2171052 prepaomas | -.3753414 .0728123 -5.15 0.000 -.518051 -.2326318 edamujer | .00369 .0043648 0.85 0.398 -.0048649 .0122449 _cons | .9547068 .1522993 6.27 0.000 .6562057 1.253208 ------------------------------------------------------------------------------ . . * controlando por la interaccion entre edad e ingreso . logit democ eda mujer prepaomas loging edaing Iteration 0: log likelihood = -1358.5141 Iteration 1: log likelihood = -1355.3465 Iteration 2: log likelihood = -1355.346 Logistic regression Number of obs = 2015 LR chi2(5) = 6.34 Prob > chi2 = 0.2749 Log likelihood = -1355.346 Pseudo R2 = 0.0023 ------------------------------------------------------------------------------ democ | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- eda | -.0040798 .003619 -1.13 0.260 -.0111729 .0030133 mujer | -.1238603 .0950222 -1.30 0.192 -.3101003 .0623797 prepaomas | -.1065437 .1046259 -1.02 0.309 -.3116068 .0985193 loging | -.0611763 .0498151 -1.23 0.219 -.1588121 .0364595 edaing | 1.07e-07 1.05e-07 1.02 0.306 -9.80e-08 3.12e-07 _cons | 1.069196 .3790281 2.82 0.005 .3263147 1.812078 ------------------------------------------------------------------------------ . . . . *** MODELOS OLOGIT para VARIABLES ORDINALES . . ** La "confianza en la iglesia" va de -4 nada, -3 poca ... a -1 (mucha) . . tab prepaomas configle, row chi +----------------+ | Key | |----------------| | frequency | | row percentage | +----------------+ 1=estudio | prepa o | mas, 0=sin | confianza en iglesia -4=nada ... -1=mucha prepa | -4 -3 -2 -1 | Total -----------+--------------------------------------------+---------- 0 | 192 446 729 1,510 | 2,877 | 6.67 15.50 25.34 52.49 | 100.00 -----------+--------------------------------------------+---------- 1 | 131 290 564 441 | 1,426 | 9.19 20.34 39.55 30.93 | 100.00 -----------+--------------------------------------------+---------- Total | 323 736 1,293 1,951 | 4,303 | 7.51 17.10 30.05 45.34 | 100.00 Pearson chi2(3) = 182.8801 Pr = 0.000 . * la chi cuadrada me dice que educacion y confianza en la iglesia . * NO son independientes . . tab mujer configle, row chi +----------------+ | Key | |----------------| | frequency | | row percentage | +----------------+ 1=mujer, | confianza en iglesia -4=nada ... -1=mucha 0=hombre | -4 -3 -2 -1 | Total -----------+--------------------------------------------+---------- 0 | 169 342 596 768 | 1,875 | 9.01 18.24 31.79 40.96 | 100.00 -----------+--------------------------------------------+---------- 1 | 154 394 699 1,185 | 2,432 | 6.33 16.20 28.74 48.73 | 100.00 -----------+--------------------------------------------+---------- Total | 323 736 1,295 1,953 | 4,307 | 7.50 17.09 30.07 45.34 | 100.00 Pearson chi2(3) = 30.0689 Pr = 0.000 . * la chi cuadrada me dice que genero y confianza en la iglesia . * NO son independientes . . ** Pero podemos estimar conjuntamente el efecto . ** de estas variables en la confianza en la iglesia: . . ologit configle prepaomas loging Iteration 0: log likelihood = -2821.3903 Iteration 1: log likelihood = -2795.8527 Iteration 2: log likelihood = -2795.8236 Ordered logistic regression Number of obs = 2275 LR chi2(2) = 51.13 Prob > chi2 = 0.0000 Log likelihood = -2795.8236 Pseudo R2 = 0.0091 ------------------------------------------------------------------------------ configle | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- prepaomas | -.3336762 .0869209 -3.84 0.000 -.5040381 -.1633143 loging | -.1317322 .0339939 -3.88 0.000 -.198359 -.0651054 -------------+---------------------------------------------------------------- /cut1 | -3.429031 .2454634 -3.91013 -2.947931 /cut2 | -2.176132 .2378637 -2.642336 -1.709927 /cut3 | -.7510125 .2335165 -1.208696 -.2933285 ------------------------------------------------------------------------------ . ologit configle prepaomas loging mujer Iteration 0: log likelihood = -2821.3903 Iteration 1: log likelihood = -2793.9337 Iteration 2: log likelihood = -2793.9013 Ordered logistic regression Number of obs = 2275 LR chi2(3) = 54.98 Prob > chi2 = 0.0000 Log likelihood = -2793.9013 Pseudo R2 = 0.0097 ------------------------------------------------------------------------------ configle | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- prepaomas | -.3598052 .0879556 -4.09 0.000 -.5321951 -.1874154 loging | -.122655 .0342735 -3.58 0.000 -.1898299 -.0554801 mujer | .1584939 .0809154 1.96 0.050 -.0000973 .3170852 -------------+---------------------------------------------------------------- /cut1 | -3.315106 .2519004 -3.808821 -2.82139 /cut2 | -2.0615 .2446118 -2.540931 -1.58207 /cut3 | -.63487 .2406696 -1.106574 -.1631662 ------------------------------------------------------------------------------ . ologit configle prepaomas loging eda mujer Iteration 0: log likelihood = -2821.3903 Iteration 1: log likelihood = -2793.8912 Iteration 2: log likelihood = -2793.8581 Ordered logistic regression Number of obs = 2275 LR chi2(4) = 55.06 Prob > chi2 = 0.0000 Log likelihood = -2793.8581 Pseudo R2 = 0.0098 ------------------------------------------------------------------------------ configle | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- prepaomas | -.3648813 .0896421 -4.07 0.000 -.5405766 -.189186 loging | -.1222394 .0342885 -3.57 0.000 -.1894435 -.0550353 eda | -.0008958 .0030493 -0.29 0.769 -.0068723 .0050808 mujer | .1570229 .0810684 1.94 0.053 -.0018683 .3159141 -------------+---------------------------------------------------------------- /cut1 | -3.350733 .2795699 -3.89868 -2.802786 /cut2 | -2.097023 .2728458 -2.631791 -1.562255 /cut3 | -.6702683 .2691023 -1.197699 -.1428375 ------------------------------------------------------------------------------ . . * A partir del último modelo, ¿qué variables explican mejor . * la confianza en la iglesia? . *** . * A mayor educación o ingreso, menor confianza . * En general, las mujeres confian más en la iglesia que los hombres . * La edad no parece afectar la confianza en la iglesia . . * listo! ahora solo hay que cerrar la bitacora . . log close log: C:\Users\Javier\Documents\dia2cursoencup.log log type: text closed on: 27 Jul 2009, 14:00:46 -----------------------------------------------------------------------------------------------------------------