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("PretestCognitive.csv", header=T) # reads raw data from Qualtrics
MainStudy<-subset(MainStudy,MainStudy$Q30_Page.Submit>0) ## Valid responses
## Reliability of Scale Media Quality
MainStudyMelt1MediaQuality<-melt(MainStudy,id.vars=c("ResponseId","Q31_1","Q29_1","Q30_1"),
measure.vars=c("Q31_1","Q29_1","Q30_1"),
variable.name="BA", value.name="Item1")
MainStudyMelt2MediaQuality<-melt(MainStudy,id.vars=c("ResponseId","Q31_2","Q29_2","Q30_2"),
measure.vars=c("Q31_2","Q29_2","Q30_2"),
variable.name="BA", value.name="Item2")
MainStudyMelt3MediaQuality<-melt(MainStudy,id.vars=c("ResponseId","Q31_3","Q29_3","Q30_3"),
measure.vars=c("Q31_3","Q29_3","Q30_3"),
variable.name="BA", value.name="Item3")
MainStudyMelt4MediaQuality<-melt(MainStudy,id.vars=c("ResponseId","Q31_4","Q29_4","Q30_4"),
measure.vars=c("Q31_4","Q29_4","Q30_4"),
variable.name="BA", value.name="Item3")
cronbach(cbind(MainStudyMelt1MediaQuality$Item1,MainStudyMelt2MediaQuality$Item2,MainStudyMelt3MediaQuality$Item3,MainStudyMelt4MediaQuality$Item4)) ## Cronbach 0.95
## $sample.size
## [1] 690
##
## $number.of.items
## [1] 3
##
## $alpha
## [1] 0.9566172
## Reliability of Scale Cognitive Load
cronbach(cbind(MainStudy$Q45_1,MainStudy$Q45_2,MainStudy$Q45_3)) ## Cronbach 0.80
## $sample.size
## [1] 230
##
## $number.of.items
## [1] 3
##
## $alpha
## [1] 0.8020035
## Clean NAs
MainStudy[is.na(MainStudy)]<-0
## Computing Scale scores (items averages)
MainStudy$MediaQualityAvg1HD<-(MainStudy$Q31_1+MainStudy$Q31_2+MainStudy$Q31_3+MainStudy$Q31_4)/4
MainStudy$MediaQualityAvg2MD<-(MainStudy$Q29_1+MainStudy$Q29_2+MainStudy$Q29_3+MainStudy$Q29_4)/4
MainStudy$MediaQualityAvg3LD<-(MainStudy$Q30_1+MainStudy$Q30_2+MainStudy$Q30_3+MainStudy$Q30_4)/4
MainStudy$CognitiveLoad<-(MainStudy$Q45_1+MainStudy$Q45_2+MainStudy$Q45_3)/3
## Analysis
table(MainStudy$FL_144_DO)
##
## Block1onedigit Block2fivedigits Block3ninedigits
## 0 80 73 77
aggregate(MainStudy$CognitiveLoad,list(MainStudy$FL_144_DO),mean)
## Group.1 x
## 1 Block1onedigit 3.254167
## 2 Block2fivedigits 4.018265
## 3 Block3ninedigits 5.017316
aggregate(MainStudy$CognitiveLoad,list(MainStudy$FL_144_DO),sd)
## Group.1 x
## 1 Block1onedigit 2.052917
## 2 Block2fivedigits 2.150144
## 3 Block3ninedigits 1.759599
summary(aov(MainStudy$CognitiveLoad~MainStudy$FL_144_DO))
## Df Sum Sq Mean Sq F value Pr(>F)
## MainStudy$FL_144_DO 2 122.5 61.24 15.43 5.23e-07 ***
## Residuals 227 901.1 3.97
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
aov.out<-aov(CognitiveLoad~as.factor(FL_144_DO),MainStudy)
TukeyHSD(aov.out)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = CognitiveLoad ~ as.factor(FL_144_DO), data = MainStudy)
##
## $`as.factor(FL_144_DO)`
## diff lwr upr p adj
## Block2fivedigits-Block1onedigit 0.7640982 0.003281035 1.524915 0.0487508
## Block3ninedigits-Block1onedigit 1.7631494 1.012736149 2.513563 0.0000002
## Block3ninedigits-Block2fivedigits 0.9990512 0.231195088 1.766907 0.0067716
t.test(MainStudy$MediaQualityAvg1HD,MainStudy$MediaQualityAvg2MD)
##
## Welch Two Sample t-test
##
## data: MainStudy$MediaQualityAvg1HD and MainStudy$MediaQualityAvg2MD
## t = 4.9449, df = 435.09, p-value = 1.09e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.4866095 1.1286079
## sample estimates:
## mean of x mean of y
## 7.210870 6.403261
t.test(MainStudy$MediaQualityAvg2MD,MainStudy$MediaQualityAvg3LD)
##
## Welch Two Sample t-test
##
## data: MainStudy$MediaQualityAvg2MD and MainStudy$MediaQualityAvg3LD
## t = 8.6654, df = 433.61, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.389209 2.204269
## sample estimates:
## mean of x mean of y
## 6.403261 4.606522
t.test(MainStudy$MediaQualityAvg1HD,MainStudy$MediaQualityAvg3LD)
##
## Welch Two Sample t-test
##
## data: MainStudy$MediaQualityAvg1HD and MainStudy$MediaQualityAvg3LD
## t = 13.563, df = 382.98, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 2.226802 2.981894
## sample estimates:
## mean of x mean of y
## 7.210870 4.606522
## Age
summary(MainStudy$Q25)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1949 1977 1985 1982 1990 1999
2017-1982
## [1] 35
## Gender
summary(as.factor(MainStudy$Q40))
## 1 2
## 109 121
109/(109+121)
## [1] 0.473913
## Verifying equivalency of groups
aggregate(MainStudy$Q25,list(MainStudy$FL_144_DO),mean)
## Group.1 x
## 1 Block1onedigit 1982.662
## 2 Block2fivedigits 1981.370
## 3 Block3ninedigits 1982.221
aggregate(MainStudy$Q25,list(MainStudy$FL_144_DO),sd)
## Group.1 x
## 1 Block1onedigit 11.39058
## 2 Block2fivedigits 11.81984
## 3 Block3ninedigits 10.41712
summary(aov(as.numeric(Q25)~as.factor(FL_144_DO),MainStudy)) ## Age
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(FL_144_DO) 2 65 32.67 0.26 0.771
## Residuals 227 28556 125.80
aov.out<-aov(Q25~as.factor(FL_144_DO),MainStudy)
TukeyHSD(aov.out)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Q25 ~ as.factor(FL_144_DO), data = MainStudy)
##
## $`as.factor(FL_144_DO)`
## diff lwr upr p adj
## Block2fivedigits-Block1onedigit -1.2926370 -5.575551 2.990277 0.7566331
## Block3ninedigits-Block1onedigit -0.4417208 -4.666067 3.782625 0.9670140
## Block3ninedigits-Block2fivedigits 0.8509162 -3.471622 5.173455 0.8879878
chisq.test(MainStudy$FL_144_DO,MainStudy$Q40,simulate.p.value=T) ## Gender
##
## Pearson's Chi-squared test with simulated p-value (based on 2000
## replicates)
##
## data: MainStudy$FL_144_DO and MainStudy$Q40
## X-squared = 0.31103, df = NA, p-value = 0.8526
## Location of the sample
map(database="world", ylim=c(36,40), xlim=c(-99,-95), col="white", fill=TRUE, projection="gilbert", orientation= c(90,0,225))
lon<-as.character(MainStudy$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")
