library(reshape2) # melt
library(MASS) # lda
library(psy) # cronbach
library(psych) # KMO
##
## Attaching package: 'psych'
## The following object is masked from 'package:psy':
##
## wkappa
library(Hmisc) # correlation matrix
## Warning: package 'Hmisc' was built under R version 3.4.2
## Loading required package: lattice
## Loading required package: survival
## Loading required package: Formula
## Loading required package: ggplot2
##
## Attaching package: 'ggplot2'
## The following objects are masked from 'package:psych':
##
## %+%, alpha
##
## Attaching package: 'Hmisc'
## The following object is masked from 'package:psych':
##
## describe
## The following objects are masked from 'package:base':
##
## format.pval, round.POSIXt, trunc.POSIXt, units
library(psych) #KMO
library(Hmisc) # correlation matrix
library(mapproj) # map
## Warning: package 'mapproj' was built under R version 3.4.2
## Loading required package: maps
## Warning: package 'maps' was built under R version 3.4.2
cat("\014") # cleans screen
rm(list=ls(all=TRUE)) # remove variables in working memory
setwd("C:/Users/Erik Ernesto Vazquez/Downloads") # sets working directory
MainStudy<-read.csv("MainStudyCognitiveLoadDataset.csv", header=T) # reads raw data from Qualtrics
MainStudy<-subset(MainStudy,MainStudy$Q30_Page.Submit>0) ## Valid responses
## Clean NAs
MainStudy[is.na(MainStudy)]<-0
## Reliability of Scale Purchase Intent
cronbach(cbind(MainStudy$Q33_25,MainStudy$Q35_1,MainStudy$Q34_1))
## $sample.size
## [1] 1544
##
## $number.of.items
## [1] 3
##
## $alpha
## [1] 0.8993857
## Reliability of Scale Product Quality
cronbach(cbind(MainStudy$Q36_1,MainStudy$Q36_2,MainStudy$Q36_6,MainStudy$Q36_5,
MainStudy$Q36_3,MainStudy$Q36_4,MainStudy$Q36_10,MainStudy$Q37_1))
## $sample.size
## [1] 1544
##
## $number.of.items
## [1] 8
##
## $alpha
## [1] 0.6180858
cronbach(cbind(MainStudy$Q36_1,MainStudy$Q36_2,MainStudy$Q36_6,MainStudy$Q36_5))
## $sample.size
## [1] 1544
##
## $number.of.items
## [1] 4
##
## $alpha
## [1] 0.8988028
## Computing Scale scores (items averages)
MainStudy$PurchInt<-(MainStudy$Q33_25+MainStudy$Q35_1+MainStudy$Q34_1)/3
MainStudy$ProdQuality<-(MainStudy$Q36_1+MainStudy$Q36_2+MainStudy$Q36_6+MainStudy$Q36_5)/4
## Verifying equivalency of groups
MainStudy$Group<-paste(MainStudy$FL_144_DO,MainStudy$FL_148_DO)
table(MainStudy$Group,MainStudy$Q40) ## Gender
##
## 1 2
## Block1onedigit Block4Mediacontenthigh 82 80
## Block1onedigit Block5mediacontentmedium 69 99
## Block1onedigit Block6mediacontentlow 91 106
## Block2fivedigits Block4Mediacontenthigh 80 101
## Block2fivedigits Block5mediacontentmedium 71 89
## Block2fivedigits Block6mediacontentlow 75 85
## Block3ninedigits Block4Mediacontenthigh 85 91
## Block3ninedigits Block5mediacontentmedium 88 98
## Block3ninedigits Block6mediacontentlow 63 91
chisq.test(MainStudy$Group,MainStudy$Q40) ## Gender
##
## Pearson's Chi-squared test
##
## data: MainStudy$Group and MainStudy$Q40
## X-squared = 5.507, df = 8, p-value = 0.7023
aggregate(MainStudy$Q25,list(MainStudy$FL_144_DO),mean)
## Group.1 x
## 1 Block1onedigit 1959.211
## 2 Block2fivedigits 1949.359
## 3 Block3ninedigits 1977.316
aggregate(MainStudy$Q25,list(MainStudy$FL_144_DO),sd)
## Group.1 x
## 1 Block1onedigit 210.74278
## 2 Block2fivedigits 248.82376
## 3 Block3ninedigits 87.82643
summary(aov(as.numeric(Q25)~as.factor(FL_144_DO),MainStudy)) ## Age is equivalent among groups
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(FL_144_DO) 2 205189 102595 2.712 0.0667 .
## Residuals 1541 58290060 37826
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
aov.out<-aov(as.numeric(Q25)~as.factor(FL_144_DO),MainStudy)
TukeyHSD(aov.out) ## Age is balanced among groups
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = as.numeric(Q25) ~ as.factor(FL_144_DO), data = MainStudy)
##
## $`as.factor(FL_144_DO)`
## diff lwr upr p adj
## Block2fivedigits-Block1onedigit -9.851345 -38.3215829 18.61889 0.6957866
## Block3ninedigits-Block1onedigit 18.105265 -10.1520377 46.36257 0.2897023
## Block3ninedigits-Block2fivedigits 27.956610 -0.6611393 56.57436 0.0572414
aggregate(MainStudy$Q25,list(MainStudy$FL_148_DO),mean)
## Group.1 x
## 1 Block4Mediacontenthigh 1947.243
## 2 Block5mediacontentmedium 1969.261
## 3 Block6mediacontentlow 1969.881
aggregate(MainStudy$Q25,list(MainStudy$FL_148_DO),sd)
## Group.1 x
## 1 Block4Mediacontenthigh 259.1341
## 2 Block5mediacontentmedium 151.4395
## 3 Block6mediacontentlow 151.9439
summary(aov(as.numeric(Q25)~as.factor(FL_148_DO),MainStudy)) ## Age is equivalent among groups
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(FL_148_DO) 2 171851 85926 2.27 0.104
## Residuals 1541 58323398 37848
aov.out<-aov(as.numeric(Q25)~as.factor(FL_148_DO),MainStudy)
TukeyHSD(aov.out) ## Age is balanced among groups
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = as.numeric(Q25) ~ as.factor(FL_148_DO), data = MainStudy)
##
## $`as.factor(FL_148_DO)`
## diff lwr
## Block5mediacontentmedium-Block4Mediacontenthigh 22.0179258 -6.382692
## Block6mediacontentlow-Block4Mediacontenthigh 22.6378517 -5.804621
## Block6mediacontentlow-Block5mediacontentmedium 0.6199258 -27.891097
## upr p adj
## Block5mediacontentmedium-Block4Mediacontenthigh 50.41854 0.1637567
## Block6mediacontentlow-Block4Mediacontenthigh 51.08032 0.1486306
## Block6mediacontentlow-Block5mediacontentmedium 29.13095 0.9985665
## Analysis
table(MainStudy$FL_144_DO)
##
## Block1onedigit Block2fivedigits Block3ninedigits
## 0 527 501 516
aggregate(MainStudy$PurchInt,list(MainStudy$FL_144_DO),mean)
## Group.1 x
## 1 Block1onedigit 5.707147
## 2 Block2fivedigits 5.753826
## 3 Block3ninedigits 5.755814
aggregate(MainStudy$PurchInt,list(MainStudy$FL_144_DO),sd)
## Group.1 x
## 1 Block1onedigit 2.149082
## 2 Block2fivedigits 2.014544
## 3 Block3ninedigits 2.075547
summary(aov(MainStudy$PurchInt~MainStudy$FL_144_DO))
## Df Sum Sq Mean Sq F value Pr(>F)
## MainStudy$FL_144_DO 2 1 0.395 0.091 0.913
## Residuals 1541 6677 4.333
aov.out<-aov(PurchInt~as.factor(FL_144_DO),MainStudy)
TukeyHSD(aov.out)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = PurchInt ~ as.factor(FL_144_DO), data = MainStudy)
##
## $`as.factor(FL_144_DO)`
## diff lwr upr
## Block2fivedigits-Block1onedigit 0.046678307 -0.2580329 0.3513895
## Block3ninedigits-Block1onedigit 0.048666578 -0.2537656 0.3510988
## Block3ninedigits-Block2fivedigits 0.001988272 -0.3043017 0.3082782
## p adj
## Block2fivedigits-Block1onedigit 0.9312922
## Block3ninedigits-Block1onedigit 0.9244620
## Block3ninedigits-Block2fivedigits 0.9998721
## There are no significant differences in purchase intent as a consequence of cognitive load, H1 is rejected
table(MainStudy$FL_148_DO)
##
## Block4Mediacontenthigh Block5mediacontentmedium
## 0 519 514
## Block6mediacontentlow
## 511
aggregate(MainStudy$ProdQuality,list(MainStudy$FL_148_DO),mean)
## Group.1 x
## 1 Block4Mediacontenthigh 6.498073
## 2 Block5mediacontentmedium 6.542802
## 3 Block6mediacontentlow 6.112524
aggregate(MainStudy$ProdQuality,list(MainStudy$FL_148_DO),sd)
## Group.1 x
## 1 Block4Mediacontenthigh 1.622640
## 2 Block5mediacontentmedium 1.450236
## 3 Block6mediacontentlow 1.696250
summary(aov(MainStudy$ProdQuality~MainStudy$FL_148_DO))
## Df Sum Sq Mean Sq F value Pr(>F)
## MainStudy$FL_148_DO 2 57 28.686 11.3 1.34e-05 ***
## Residuals 1541 3910 2.537
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
aov.out<-aov(ProdQuality~as.factor(FL_148_DO),MainStudy)
TukeyHSD(aov.out)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = ProdQuality ~ as.factor(FL_148_DO), data = MainStudy)
##
## $`as.factor(FL_148_DO)`
## diff lwr
## Block5mediacontentmedium-Block4Mediacontenthigh 0.04472834 -0.1878164
## Block6mediacontentlow-Block4Mediacontenthigh -0.38554876 -0.6184362
## Block6mediacontentlow-Block5mediacontentmedium -0.43027709 -0.6637258
## upr p adj
## Block5mediacontentmedium-Block4Mediacontenthigh 0.2772731 0.8938660
## Block6mediacontentlow-Block4Mediacontenthigh -0.1526613 0.0003155
## Block6mediacontentlow-Block5mediacontentmedium -0.1968284 0.0000485
## There are significant differences in perceived quality of the product as a consequence of media quality, H2 accepted (partially)
summary(lm(MainStudy$PurchInt~MainStudy$ProdQuality))
##
## Call:
## lm(formula = MainStudy$PurchInt ~ MainStudy$ProdQuality)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0721 -0.7684 0.3260 1.0075 5.2639
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.46478 0.16774 2.771 0.00566 **
## MainStudy$ProdQuality 0.82592 0.02548 32.416 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.605 on 1542 degrees of freedom
## Multiple R-squared: 0.4053, Adjusted R-squared: 0.4049
## F-statistic: 1051 on 1 and 1542 DF, p-value: < 2.2e-16
## Prod quality affects purchase intention, H3 accepted
## Location of the sample
map(database="world", ylim=c(36,40), xlim=c(-99,-95), col="white", fill=TRUE, projection="gilbert", orientation= c(90,0,225))
lon<-as.character(MainStudy$LocationLongitude)
lat<-as.character(MainStudy$LocationLatitude)
coord<-mapproject(lon, lat, proj="gilbert", orientation=c(90, 0, 225))
points(coord, pch=20, cex=0.8, col="black")
