library(MASS) # lda
library(psy) # cronbach
cat("\014") # cleans screen
rm(list=ls(all=TRUE)) # remove variables in working memory
setwd("C:/Users/evazquez/Downloads") # sets working directory
MainStudy<-read.csv("Base de datos FINAL PEF.csv", skip=2, header=F) # reads raw data from Qualtrics
NamesandHeaders<-read.csv("Base de datos FINAL PEF.csv") # assigns headers and names to data frame
names(MainStudy)<-names(NamesandHeaders)
cronbach(cbind(MainStudy$Niv_conoc,MainStudy$Confiabilidad,MainStudy$Atractivo))
## $sample.size
## [1] 996
##
## $number.of.items
## [1] 3
##
## $alpha
## [1] 0.8500476
MainStudy$Credibilidad.final2<-(MainStudy$Niv_conoc+MainStudy$Confiabilidad+MainStudy$Atractivo)/3
summary(MainStudy$Credibilidad.final2)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 3.000 3.667 3.592 4.000 5.000 119
summary(aov(Credibilidad.final2~as.factor(TEMA)+as.factor(Edad)+as.factor(Gastosem)+as.factor(Ocupacion),MainStudy))
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(TEMA) 2 93.4 46.72 75.116 < 2e-16 ***
## as.factor(Edad) 3 25.5 8.50 13.667 1.01e-08 ***
## as.factor(Gastosem) 3 13.5 4.51 7.249 8.30e-05 ***
## as.factor(Ocupacion) 3 1.6 0.54 0.873 0.455
## Residuals 886 551.0 0.62
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 217 observations deleted due to missingness
aov.out<-aov(Credibilidad.final2~as.factor(TEMA)+as.factor(Edad)+as.factor(Gastosem)+as.factor(Ocupacion),MainStudy)
TukeyHSD(aov.out) ## Two levels of products ease to evaluate quality | Shopping vs non-shopping OR Credence vs Non-Credence
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Credibilidad.final2 ~ as.factor(TEMA) + as.factor(Edad) + as.factor(Gastosem) + as.factor(Ocupacion), data = MainStudy)
##
## $`as.factor(TEMA)`
## diff lwr upr p adj
## 2-1 0.3958951 0.2445975 0.5471928 0
## 3-1 -0.3933507 -0.5448999 -0.2418015 0
## 3-2 -0.7892458 -0.9404163 -0.6380754 0
##
## $`as.factor(Edad)`
## diff lwr upr p adj
## 2-1 -0.32324409 -0.4940063 -0.15248186 0.0000078
## 3-1 -0.72718334 -1.1684347 -0.28593204 0.0001437
## 4-1 -0.75496112 -1.1962124 -0.31370981 0.0000704
## 3-2 -0.40393925 -0.8256315 0.01775302 0.0661174
## 4-2 -0.43171703 -0.8534093 -0.01002476 0.0424573
## 4-3 -0.02777778 -0.6137533 0.55819778 0.9993507
##
## $`as.factor(Gastosem)`
## diff lwr upr p adj
## 2-1 -0.17755156 -0.33122179 -0.02388133 0.0159489
## 3-1 0.04922831 -0.16289393 0.26135054 0.9328751
## 4-1 -0.28194101 -0.59757128 0.03368926 0.0989655
## 3-2 0.22677987 0.02417403 0.42938570 0.0211176
## 4-2 -0.10438945 -0.41370441 0.20492552 0.8210553
## 4-3 -0.33116932 -0.67330533 0.01096670 0.0619265
##
## $`as.factor(Ocupacion)`
## diff lwr upr p adj
## 2-1 -0.06635473 -0.2135807 0.08087124 0.6522435
## 3-1 -0.09649839 -0.5278225 0.33482573 0.9393054
## 4-1 0.09416883 -0.3371553 0.52549295 0.9432680
## 3-2 -0.03014366 -0.4532303 0.39294303 0.9978109
## 4-2 0.16052356 -0.2625631 0.58361024 0.7628573
## 4-3 0.19066722 -0.3953083 0.77664277 0.8365784
MainStudy.sinNAs<-subset(MainStudy,MainStudy$Credibilidad.final2!="NA")
aggregate(MainStudy.sinNAs$Credibilidad.final2,list(MainStudy.sinNAs$TEMA),mean)
## Group.1 x
## 1 1 3.586171
## 2 2 3.954500
## 3 3 3.233135
aggregate(MainStudy.sinNAs$Credibilidad.final2,list(MainStudy.sinNAs$TEMA),sd)
## Group.1 x
## 1 1 0.8264027
## 2 2 0.8058403
## 3 3 0.7855747
summary(lm(Credibilidad.final2~as.numeric(ER)+as.numeric(Average.Likes)+as.numeric(Ranking)+as.numeric(posts),MainStudy))
##
## Call:
## lm(formula = Credibilidad.final2 ~ as.numeric(ER) + as.numeric(Average.Likes) +
## as.numeric(Ranking) + as.numeric(posts), data = MainStudy)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.79528 -0.56265 0.02306 0.57811 1.68973
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.291e+00 1.114e-01 29.536 < 2e-16 ***
## as.numeric(ER) -2.443e-02 1.345e-02 -1.816 0.0697 .
## as.numeric(Average.Likes) 1.401e-06 3.356e-07 4.174 3.25e-05 ***
## as.numeric(Ranking) 7.584e-03 6.174e-03 1.228 0.2196
## as.numeric(posts) 3.818e-05 7.311e-06 5.223 2.15e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8299 on 991 degrees of freedom
## (119 observations deleted due to missingness)
## Multiple R-squared: 0.06814, Adjusted R-squared: 0.06438
## F-statistic: 18.12 on 4 and 991 DF, p-value: 2.266e-14
summary(lm(Credibilidad.final2~as.factor(TEMA)+as.factor(Edad)+as.factor(Gastosem)+as.factor(Ocupacion)+as.numeric(ER)+as.numeric(Average.Likes)+as.numeric(Ranking)+as.numeric(posts),MainStudy))
##
## Call:
## lm(formula = Credibilidad.final2 ~ as.factor(TEMA) + as.factor(Edad) +
## as.factor(Gastosem) + as.factor(Ocupacion) + as.numeric(ER) +
## as.numeric(Average.Likes) + as.numeric(Ranking) + as.numeric(posts),
## data = MainStudy)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.64737 -0.50258 0.03228 0.52937 1.99086
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.372e+00 1.540e-01 28.391 < 2e-16 ***
## as.factor(TEMA)2 3.582e-01 7.144e-02 5.014 6.44e-07 ***
## as.factor(TEMA)3 -5.191e-01 7.779e-02 -6.674 4.41e-11 ***
## as.factor(Edad)2 -3.515e-01 7.161e-02 -4.909 1.09e-06 ***
## as.factor(Edad)3 -4.442e-01 2.212e-01 -2.008 0.04499 *
## as.factor(Edad)4 -2.838e-01 2.654e-01 -1.069 0.28523
## as.factor(Gastosem)2 -1.814e-01 5.937e-02 -3.056 0.00231 **
## as.factor(Gastosem)3 3.580e-02 8.875e-02 0.403 0.68679
## as.factor(Gastosem)4 -5.577e-01 2.074e-01 -2.689 0.00731 **
## as.factor(Ocupacion)2 -7.750e-02 6.309e-02 -1.228 0.21964
## as.factor(Ocupacion)3 -1.452e-01 2.459e-01 -0.590 0.55507
## as.factor(Ocupacion)4 1.100e-01 1.811e-01 0.607 0.54382
## as.numeric(ER) -1.320e-02 1.336e-02 -0.988 0.32322
## as.numeric(Average.Likes) -8.196e-07 3.998e-07 -2.050 0.04067 *
## as.numeric(Ranking) -1.377e-02 6.506e-03 -2.117 0.03456 *
## as.numeric(posts) 9.431e-06 7.950e-06 1.186 0.23581
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7753 on 882 degrees of freedom
## (217 observations deleted due to missingness)
## Multiple R-squared: 0.2262, Adjusted R-squared: 0.213
## F-statistic: 17.18 on 15 and 882 DF, p-value: < 2.2e-16
summary(lm(Credibilidad.final2~as.factor(TEMA)+as.integer(Edad)+as.integer(Gastosem)+as.factor(Ocupacion)+as.numeric(ER)+as.numeric(Average.Likes)+as.numeric(Ranking)+as.numeric(posts),MainStudy))
##
## Call:
## lm(formula = Credibilidad.final2 ~ as.factor(TEMA) + as.integer(Edad) +
## as.integer(Gastosem) + as.factor(Ocupacion) + as.numeric(ER) +
## as.numeric(Average.Likes) + as.numeric(Ranking) + as.numeric(posts),
## data = MainStudy)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.71532 -0.47454 0.01736 0.56629 2.06045
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.581e+00 1.840e-01 24.904 < 2e-16 ***
## as.factor(TEMA)2 3.584e-01 7.204e-02 4.974 7.87e-07 ***
## as.factor(TEMA)3 -5.193e-01 7.845e-02 -6.620 6.24e-11 ***
## as.integer(Edad) -2.738e-01 6.136e-02 -4.462 9.17e-06 ***
## as.integer(Gastosem) -5.071e-02 3.801e-02 -1.334 0.1825
## as.factor(Ocupacion)2 -6.702e-02 6.325e-02 -1.060 0.2896
## as.factor(Ocupacion)3 -1.148e-01 1.922e-01 -0.597 0.5505
## as.factor(Ocupacion)4 2.849e-01 1.732e-01 1.645 0.1003
## as.numeric(ER) -1.321e-02 1.347e-02 -0.981 0.3268
## as.numeric(Average.Likes) -8.205e-07 4.032e-07 -2.035 0.0421 *
## as.numeric(Ranking) -1.379e-02 6.562e-03 -2.102 0.0358 *
## as.numeric(posts) 9.426e-06 8.017e-06 1.176 0.2400
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7819 on 886 degrees of freedom
## (217 observations deleted due to missingness)
## Multiple R-squared: 0.2094, Adjusted R-squared: 0.1996
## F-statistic: 21.34 on 11 and 886 DF, p-value: < 2.2e-16