# Definindo as variáveis (Tr é o grupo, Y é o escore de cada subescala, X são as variáveis independentes)
Tr <- cbind(Grupo)
Ypreocupacao <- cbind(Preocupacao)
Ycontrole <- cbind(Controle)
Ycuriosidade <- cbind(Curiosidade)
Yconfianca <- cbind(Confianca)
Ycuriosidade <- cbind(Curiosidade)
Ycooperacao <- cbind(Cooperacao)
Ycolaboration <- cbind(Colaboration)
Ytask <- cbind(Task_Performance)
Yemotional <- cbind(Emotional_Regulation)
Yengaging <- cbind(Engaging_With_Others)
Yopen <- cbind(Open_Mindedness)
X <- cbind(Idade, Residencia, Renda, Escolaridade_cuida, Ano)
# Variaveis para uso posterior
var1 <- Idade
var2 <- Residencia
var3 <- Renda
var4 <- Escolaridade_cuida
var5 <- Ano
var6 <- Periodo# Descritivas gerais, sem divisão por grupo
summary(Ypreocupacao)## Preocupacao
## Min. :1.833
## 1st Qu.:3.333
## Median :3.833
## Mean :3.773
## 3rd Qu.:4.333
## Max. :5.000summary(Ycontrole)## Controle
## Min. :1.667
## 1st Qu.:3.458
## Median :3.917
## Mean :3.870
## 3rd Qu.:4.500
## Max. :5.000summary(Ycuriosidade)## Curiosidade
## Min. :1.500
## 1st Qu.:3.167
## Median :3.800
## Mean :3.674
## 3rd Qu.:4.167
## Max. :5.000summary(Yconfianca)## Confianca
## Min. :2.167
## 1st Qu.:3.500
## Median :4.000
## Mean :3.997
## 3rd Qu.:4.500
## Max. :5.000summary(Ycooperacao)## Cooperacao
## Min. :1.833
## 1st Qu.:3.500
## Median :4.000
## Mean :3.975
## 3rd Qu.:4.500
## Max. :5.000summary(Ycolaboration)## Colaboration
## Min. :2.500
## 1st Qu.:3.583
## Median :3.917
## Mean :3.880
## 3rd Qu.:4.167
## Max. :5.000summary(Ytask)## Task_Performance
## Min. :2.333
## 1st Qu.:3.667
## Median :4.067
## Mean :4.073
## 3rd Qu.:4.467
## Max. :5.000summary(Yemotional)## Emotional_Regulation
## Min. :1.778
## 1st Qu.:2.889
## Median :3.556
## Mean :3.484
## 3rd Qu.:4.000
## Max. :5.000summary(Yengaging)## Engaging_With_Others
## Min. :1.778
## 1st Qu.:3.222
## Median :3.556
## Mean :3.626
## 3rd Qu.:4.111
## Max. :5.000summary(Yopen)## Open_Mindedness
## Min. :1.444
## 1st Qu.:3.333
## Median :3.667
## Mean :3.697
## 3rd Qu.:4.222
## Max. :5.000summary(X)## Idade Residencia Renda Escolaridade_cuida
## Min. :14.00 Min. :1.0 Min. :1.000 Min. :1.00
## 1st Qu.:16.00 1st Qu.:1.0 1st Qu.:2.000 1st Qu.:2.00
## Median :17.00 Median :1.0 Median :2.000 Median :4.00
## Mean :16.57 Mean :1.5 Mean :2.215 Mean :3.77
## 3rd Qu.:17.00 3rd Qu.:2.0 3rd Qu.:3.000 3rd Qu.:5.00
## Max. :19.00 Max. :3.0 Max. :4.000 Max. :7.00
## Ano
## Min. :1.0
## 1st Qu.:2.0
## Median :3.0
## Mean :2.5
## 3rd Qu.:3.0
## Max. :4.0# Propensity score model
glm1 <- glm(Tr ~ X, family=binomial(link = "probit"), data=mydata)
summary(glm1)##
## Call:
## glm(formula = Tr ~ X, family = binomial(link = "probit"), data = mydata)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.14321 -0.74610 -0.00363 0.84149 1.73452
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -15.78516 2.10367 -7.504 6.21e-14 ***
## XIdade 0.99860 0.13937 7.165 7.78e-13 ***
## XResidencia -0.10866 0.16891 -0.643 0.520
## XRenda -0.05495 0.15740 -0.349 0.727
## XEscolaridade_cuida -0.06804 0.06689 -1.017 0.309
## XAno -0.09185 0.17110 -0.537 0.591
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 277.26 on 199 degrees of freedom
## Residual deviance: 186.92 on 194 degrees of freedom
## AIC: 198.92
##
## Number of Fisher Scoring iterations: 6Trocar o polo da residência???
library(MatchIt)## Warning: package 'MatchIt' was built under R version 3.5.3#install.packages("tidyr")
library(tidyr)
na.omit(mydata)## # A tibble: 200 x 113
## Protocolo Grupo Idade Sexo Residencia Renda Escolaridade_cuida Ano
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 17 1 2 3 5 3
## 2 2 1 17 2 2 2 5 2
## 3 3 1 19 1 2 2 2 3
## 4 4 1 16 1 2 2 5 3
## 5 5 1 17 2 1 2 4 2
## 6 6 1 17 2 1 3 3 2
## 7 7 1 17 2 2 2 4 3
## 8 8 1 18 2 1 2 2 4
## 9 9 1 17 2 1 3 2 2
## 10 10 1 17 2 2 2 3 2
## # ... with 190 more rows, and 105 more variables: Periodo <dbl>,
## # Trabalha <dbl>, Tipo_contrato <chr>, Tempo <chr>, Local <chr>,
## # Trabalhou <dbl>, CAAS_1 <dbl>, CAAS_2 <dbl>, CAAS_3 <chr>,
## # CAAS_4 <dbl>, CAAS_5 <dbl>, CAAS_6 <dbl>, CAAS_7 <chr>, CAAS_8 <dbl>,
## # CAAS_9 <dbl>, CAAS_10 <chr>, CAAS_11 <chr>, CAAS_12 <dbl>,
## # CAAS_13 <chr>, CAAS_14 <dbl>, CAAS_15 <chr>, CAAS_16 <chr>,
## # CAAS_17 <dbl>, CAAS_18 <dbl>, CAAS_19 <dbl>, CAAS_20 <dbl>,
## # CAAS_21 <chr>, CAAS_22 <chr>, CAAS_23 <dbl>, CAAS_24 <chr>,
## # CAAS_25 <chr>, CAAS_26 <chr>, CAAS_27 <dbl>, CAAS_28 <dbl>,
## # CAAS_29 <dbl>, CAAS_30 <chr>, CAAS_31 <dbl>, CAAS_32 <chr>,
## # CAAS_33 <chr>, CAAS_34 <chr>, CAAS_35 <dbl>, SENNA_1_1 <dbl>,
## # SENNA_1_2 <dbl>, SENNA_1_3 <dbl>, SENNA_1_4 <chr>, SENNA_1_5 <chr>,
## # SENNA_1_6 <chr>, SENNA_1_7 <dbl>, SENNA_1_8 <chr>, SENNA_1_9 <dbl>,
## # SENNA_1_10 <dbl>, SENNA_1_11 <dbl>, SENNA_1_12 <chr>,
## # SENNA_1_13 <dbl>, SENNA_1_14 <chr>, SENNA_1_15 <chr>,
## # SENNA_1_16 <dbl>, SENNA_1_17 <chr>, SENNA_1_18 <dbl>,
## # SENNA_1_19 <dbl>, SENNA_1_20 <dbl>, SENNA_1_21 <dbl>,
## # SENNA_1_22 <dbl>, SENNA_1_23 <chr>, SENNA_1_24 <dbl>,
## # SENNA_1_25 <chr>, SENNA_1_26 <dbl>, SENNA_1_27 <dbl>,
## # SENNA_1_28 <chr>, SENNA_1_29 <dbl>, SENNA_1_30 <chr>,
## # SENNA_1_31 <dbl>, SENNA_1_32 <dbl>, SENNA_1_33 <dbl>,
## # SENNA_1_34 <dbl>, SENNA_1_35 <dbl>, SENNA_1_36 <dbl>, SENNA_2_1 <chr>,
## # SENNA_2_2 <chr>, SENNA_2_3 <dbl>, SENNA_2_4 <chr>, SENNA_2_5 <dbl>,
## # SENNA_2_6 <dbl>, SENNA_2_7 <chr>, SENNA_2_8 <dbl>, SENNA_2_9 <dbl>,
## # SENNA_2_10 <dbl>, SENNA_2_11 <dbl>, SENNA_2_12 <dbl>,
## # SENNA_2_13 <dbl>, SENNA_2_14 <chr>, SENNA_2_15 <chr>,
## # SENNA_2_16 <dbl>, SENNA_2_17 <dbl>, SENNA_2_18 <dbl>,
## # Preocupacao <dbl>, Controle <dbl>, Curiosidade <dbl>, Confianca <dbl>,
## # Cooperacao <dbl>, ... drop_na(mydata)## # A tibble: 200 x 113
## Protocolo Grupo Idade Sexo Residencia Renda Escolaridade_cuida Ano
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 17 1 2 3 5 3
## 2 2 1 17 2 2 2 5 2
## 3 3 1 19 1 2 2 2 3
## 4 4 1 16 1 2 2 5 3
## 5 5 1 17 2 1 2 4 2
## 6 6 1 17 2 1 3 3 2
## 7 7 1 17 2 2 2 4 3
## 8 8 1 18 2 1 2 2 4
## 9 9 1 17 2 1 3 2 2
## 10 10 1 17 2 2 2 3 2
## # ... with 190 more rows, and 105 more variables: Periodo <dbl>,
## # Trabalha <dbl>, Tipo_contrato <chr>, Tempo <chr>, Local <chr>,
## # Trabalhou <dbl>, CAAS_1 <dbl>, CAAS_2 <dbl>, CAAS_3 <chr>,
## # CAAS_4 <dbl>, CAAS_5 <dbl>, CAAS_6 <dbl>, CAAS_7 <chr>, CAAS_8 <dbl>,
## # CAAS_9 <dbl>, CAAS_10 <chr>, CAAS_11 <chr>, CAAS_12 <dbl>,
## # CAAS_13 <chr>, CAAS_14 <dbl>, CAAS_15 <chr>, CAAS_16 <chr>,
## # CAAS_17 <dbl>, CAAS_18 <dbl>, CAAS_19 <dbl>, CAAS_20 <dbl>,
## # CAAS_21 <chr>, CAAS_22 <chr>, CAAS_23 <dbl>, CAAS_24 <chr>,
## # CAAS_25 <chr>, CAAS_26 <chr>, CAAS_27 <dbl>, CAAS_28 <dbl>,
## # CAAS_29 <dbl>, CAAS_30 <chr>, CAAS_31 <dbl>, CAAS_32 <chr>,
## # CAAS_33 <chr>, CAAS_34 <chr>, CAAS_35 <dbl>, SENNA_1_1 <dbl>,
## # SENNA_1_2 <dbl>, SENNA_1_3 <dbl>, SENNA_1_4 <chr>, SENNA_1_5 <chr>,
## # SENNA_1_6 <chr>, SENNA_1_7 <dbl>, SENNA_1_8 <chr>, SENNA_1_9 <dbl>,
## # SENNA_1_10 <dbl>, SENNA_1_11 <dbl>, SENNA_1_12 <chr>,
## # SENNA_1_13 <dbl>, SENNA_1_14 <chr>, SENNA_1_15 <chr>,
## # SENNA_1_16 <dbl>, SENNA_1_17 <chr>, SENNA_1_18 <dbl>,
## # SENNA_1_19 <dbl>, SENNA_1_20 <dbl>, SENNA_1_21 <dbl>,
## # SENNA_1_22 <dbl>, SENNA_1_23 <chr>, SENNA_1_24 <dbl>,
## # SENNA_1_25 <chr>, SENNA_1_26 <dbl>, SENNA_1_27 <dbl>,
## # SENNA_1_28 <chr>, SENNA_1_29 <dbl>, SENNA_1_30 <chr>,
## # SENNA_1_31 <dbl>, SENNA_1_32 <dbl>, SENNA_1_33 <dbl>,
## # SENNA_1_34 <dbl>, SENNA_1_35 <dbl>, SENNA_1_36 <dbl>, SENNA_2_1 <chr>,
## # SENNA_2_2 <chr>, SENNA_2_3 <dbl>, SENNA_2_4 <chr>, SENNA_2_5 <dbl>,
## # SENNA_2_6 <dbl>, SENNA_2_7 <chr>, SENNA_2_8 <dbl>, SENNA_2_9 <dbl>,
## # SENNA_2_10 <dbl>, SENNA_2_11 <dbl>, SENNA_2_12 <dbl>,
## # SENNA_2_13 <dbl>, SENNA_2_14 <chr>, SENNA_2_15 <chr>,
## # SENNA_2_16 <dbl>, SENNA_2_17 <dbl>, SENNA_2_18 <dbl>,
## # Preocupacao <dbl>, Controle <dbl>, Curiosidade <dbl>, Confianca <dbl>,
## # Cooperacao <dbl>, ...# Average treatment on the treated effect
rrpreocupacao <- Match(Y = Ypreocupacao, Tr = Tr, X = glm1$fitted)
rrcontrole <- Match(Y = Ycontrole, Tr = Tr, X = glm1$fitted)
rrcuriosidade <- Match(Y = Ycuriosidade, Tr = Tr, X = glm1$fitted)
rrconfianca <- Match(Y = Yconfianca, Tr = Tr, X = glm1$fitted)
rrcooperacao <- Match(Y = Ycooperacao, Tr = Tr, X = glm1$fitted)
rrcolaboration <- Match(Y = Ycolaboration, Tr = Tr, X = glm1$fitted)
rrtask <- Match(Y = Ytask, Tr = Tr, X = glm1$fitted)
rremotional <- Match(Y = Yemotional, Tr = Tr, X = glm1$fitted)
rrengaging <- Match(Y = Yengaging, Tr = Tr, X = glm1$fitted)
rropen <- Match(Y = Yopen, Tr = Tr, X = glm1$fitted)
summary(rrpreocupacao)##
## Estimate... 0.51928
## AI SE...... 0.16998
## T-stat..... 3.0549
## p.val...... 0.0022516
##
## Original number of observations.............. 200
## Original number of treated obs............... 100
## Matched number of observations............... 100
## Matched number of observations (unweighted). 155summary(rrcontrole)##
## Estimate... 0.276
## AI SE...... 0.14867
## T-stat..... 1.8565
## p.val...... 0.063379
##
## Original number of observations.............. 200
## Original number of treated obs............... 100
## Matched number of observations............... 100
## Matched number of observations (unweighted). 155summary(rrcuriosidade)##
## Estimate... 0.60222
## AI SE...... 0.16266
## T-stat..... 3.7023
## p.val...... 0.00021362
##
## Original number of observations.............. 200
## Original number of treated obs............... 100
## Matched number of observations............... 100
## Matched number of observations (unweighted). 155summary(rrconfianca)##
## Estimate... 0.44383
## AI SE...... 0.13959
## T-stat..... 3.1795
## p.val...... 0.0014754
##
## Original number of observations.............. 200
## Original number of treated obs............... 100
## Matched number of observations............... 100
## Matched number of observations (unweighted). 155summary(rrcooperacao)##
## Estimate... 0.44289
## AI SE...... 0.15343
## T-stat..... 2.8866
## p.val...... 0.0038941
##
## Original number of observations.............. 200
## Original number of treated obs............... 100
## Matched number of observations............... 100
## Matched number of observations (unweighted). 155summary(rrcolaboration)##
## Estimate... 0.3573
## AI SE...... 0.10662
## T-stat..... 3.3511
## p.val...... 0.00080502
##
## Original number of observations.............. 200
## Original number of treated obs............... 100
## Matched number of observations............... 100
## Matched number of observations (unweighted). 155summary(rrtask)##
## Estimate... 0.60489
## AI SE...... 0.11521
## T-stat..... 5.2502
## p.val...... 1.5194e-07
##
## Original number of observations.............. 200
## Original number of treated obs............... 100
## Matched number of observations............... 100
## Matched number of observations (unweighted). 155summary(rremotional)##
## Estimate... 0.418
## AI SE...... 0.20105
## T-stat..... 2.0791
## p.val...... 0.037606
##
## Original number of observations.............. 200
## Original number of treated obs............... 100
## Matched number of observations............... 100
## Matched number of observations (unweighted). 155summary(rrengaging)##
## Estimate... 0.43503
## AI SE...... 0.14545
## T-stat..... 2.9908
## p.val...... 0.0027824
##
## Original number of observations.............. 200
## Original number of treated obs............... 100
## Matched number of observations............... 100
## Matched number of observations (unweighted). 155summary(rropen)##
## Estimate... 0.37578
## AI SE...... 0.15523
## T-stat..... 2.4208
## p.val...... 0.015488
##
## Original number of observations.............. 200
## Original number of treated obs............... 100
## Matched number of observations............... 100
## Matched number of observations (unweighted). 155