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
library(reshape2) # melt
library(nparcomp) # gao_cs
## Warning: package 'nparcomp' was built under R version 3.4.2
## Loading required package: multcomp
## Warning: package 'multcomp' was built under R version 3.4.2
## Loading required package: mvtnorm
## Loading required package: survival
## Loading required package: TH.data
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## Loading required package: MASS
##
## Attaching package: 'TH.data'
## The following object is masked from 'package:MASS':
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## geyser
library(car) # leveneTest and Anova Type III
## Warning: package 'car' was built under R version 3.4.2
library(heplots) # etasquared
## Warning: package 'heplots' was built under R version 3.4.2
library(MASS) # lda
library(psy) # cronbach
library(igraph) # network graphs
## Warning: package 'igraph' was built under R version 3.4.2
##
## Attaching package: 'igraph'
## The following objects are masked from 'package:stats':
##
## decompose, spectrum
## The following object is masked from 'package:base':
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## union
library(lsr) # partial eta squared
library(psych) # KMO
##
## Attaching package: 'psych'
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##
## wkappa
## The following object is masked from 'package:car':
##
## logit
library(biotools) # M Box test
## Warning: package 'biotools' was built under R version 3.4.2
## Loading required package: rpanel
## Warning: package 'rpanel' was built under R version 3.4.2
## Loading required package: tcltk
## Package `rpanel', version 1.1-3: type help(rpanel) for summary information
## Loading required package: tkrplot
## Loading required package: lattice
## Loading required package: SpatialEpi
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## Loading required package: sp
## Warning: package 'sp' was built under R version 3.4.2
##
## Attaching package: 'SpatialEpi'
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## normalize
## ---
## biotools version 3.1
##
##
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## boxM
library(vcd) # goodfit
## Warning: package 'vcd' was built under R version 3.4.2
## Loading required package: grid
library(agricolae)
## Warning: package 'agricolae' was built under R version 3.4.2
##
## Attaching package: 'agricolae'
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## similarity
library(lavaan) # SEM4
## Warning: package 'lavaan' was built under R version 3.4.2
## This is lavaan 0.5-23.1097
## lavaan is BETA software! Please report any bugs.
##
## Attaching package: 'lavaan'
## The following object is masked from 'package:psych':
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## cor2cov
library(semPlot) # SEM graph
## Warning: package 'semPlot' was built under R version 3.4.2
library(Hmisc) # correlation matrix
## Warning: package 'Hmisc' was built under R version 3.4.2
## Loading required package: Formula
## Loading required package: ggplot2
##
## Attaching package: 'ggplot2'
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## %+%, alpha
##
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## describe
## The following objects are masked from 'package:base':
##
## format.pval, round.POSIXt, trunc.POSIXt, units
library(MVN) # multivariate normality
## Warning: package 'MVN' was built under R version 3.4.2
## sROC 0.1-2 loaded
##
## Attaching package: 'MVN'
## The following object is masked from 'package:psych':
##
## mardia
library(mvoutlier) # multivariate outlier
## Warning: package 'mvoutlier' was built under R version 3.4.2
## Loading required package: sgeostat
library(fitdistrplus)
## Warning: package 'fitdistrplus' was built under R version 3.4.2
library(logspline)
## Pretest 1 - Validation SEC Categorization of Products
cat("\014") # cleans screen
rm(list=ls(all=TRUE)) # remove variables in working memory
setwd("C:/Users/Erik Ernesto Vazquez/Downloads/IJEC Data recollection") # sets working directory
Pretest<-read.csv("Main_study__3x3_United_States.csv", skip=2, header=F) # reads raw data from Qualtrics
NamesandHeaders<-read.csv("Main_study__3x3_United_States.csv") # assigns headers and names to data frame
names(Pretest)<-names(NamesandHeaders)
Pretest$V6<-as.character(Pretest$V6)
Pretest<-Pretest[which(!duplicated(Pretest$V6)&Pretest$t2.frmwrk_3>0&Pretest$t12_3>0),] # This procedure displays a freq. table and a bar plot showing grouping' without IPs duplicates
framework.wide=data.frame(Pretest[1],Pretest[34:36],Pretest[596:598],Pretest[603:604])
names(framework.wide)<-c("Subject","Credence","Experience","Search","Age","Gender","Income","Education","RE")
summary(framework.wide[1:90,2:4])
## Credence Experience Search
## Min. :1.000 Min. :1.000 Min. :1.0
## 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:3.0
## Median :6.500 Median :5.000 Median :4.0
## Mean :5.878 Mean :4.833 Mean :4.2
## 3rd Qu.:8.000 3rd Qu.:7.000 3rd Qu.:6.0
## Max. :9.000 Max. :9.000 Max. :9.0
framework.wide.sample<-framework.wide[1:90,]
framework.long.sample<-melt(framework.wide.sample,id.vars=c("Subject","Age","Gender","Income","Education","RE"),measure.vars=c("Credence", "Experience", "Search" ),variable.name="Framework", value.name="Measurement")
framework.long.sample1<-subset(framework.long.sample,framework.long.sample$Framework!="Credence")
leveneTest(framework.long.sample1$Measurement~framework.long.sample1$Framework,center=mean)
## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 1 1.6707 0.1978
## 178
t.test(framework.wide.sample$Search,framework.wide.sample$Experience,paired=T,var.equal=T)
##
## Paired t-test
##
## data: framework.wide.sample$Search and framework.wide.sample$Experience
## t = -2.46, df = 89, p-value = 0.01582
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.144878 -0.121789
## sample estimates:
## mean of the differences
## -0.6333333
mean(framework.wide.sample$Search)
## [1] 4.2
sd(framework.wide.sample$Search)
## [1] 2.017897
mean(framework.wide.sample$Experience)
## [1] 4.833333
sd(framework.wide.sample$Experience)
## [1] 2.189095
framework.long.sample2<-subset(framework.long.sample,framework.long.sample$Framework!="Search")
leveneTest(framework.long.sample2$Measurement~framework.long.sample2$Framework,center=mean)
## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 1 0.2658 0.6068
## 178
t.test(framework.wide.sample$Credence,framework.wide.sample$Experience,paired=T,var.equal=T)
##
## Paired t-test
##
## data: framework.wide.sample$Credence and framework.wide.sample$Experience
## t = 3.5085, df = 89, p-value = 0.0007089
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.4529485 1.6359404
## sample estimates:
## mean of the differences
## 1.044444
mean(framework.wide.sample$Credence)
## [1] 5.877778
sd(framework.wide.sample$Credence)
## [1] 2.302064
## Pretest 2 - Validation of perceived tie strength and content vividness
cat("\014") # cleans screen
rm(list=ls(all=TRUE)) # remove variables in working memory
setwd("C:/Users/Erik Ernesto Vazquez/Downloads/IJEC Data recollection") # sets working directory
MainStudy<-read.csv("Pretest Analysis Tie Strength and Media Richness.csv", header=T) # reads raw data from Qualtrics
MainStudy<-subset(MainStudy,MainStudy$X1_15>0&MainStudy$X2_15>0&MainStudy$X3_15>0&MainStudy$X4_15>0&MainStudy$X5_15>0)
table(MainStudy$V3)
##
## 9 10
## 40 36
MainStudyF<-subset(MainStudy,MainStudy$V3==9)
MainStudyM<-subset(MainStudy,MainStudy$V3==10)
MainStudy<-rbind(MainStudyF[1:36,],MainStudyM[1:36,])
table(MainStudy$V3)
##
## 9 10
## 36 36
##Reliability Media Richness
MainStudyMelt1<-melt(MainStudy,id.vars=c("ResponseId","X3","V3","X1_1","X1_2","X1_3",
"X1_4","X1_5","X1_6","X1_7",
"X1_15"),
measure.vars=c("X1_1","X1_2","X1_3",
"X1_4","X1_5","X1_6","X1_7",
"X1_15"),
variable.name="MediaRichness1", value.name="MRItem1")
MainStudyMelt2<-melt(MainStudy,id.vars=c("ResponseId","X3","V3","X2_1","X2_2","X2_3",
"X2_4","X2_5","X2_6","X2_7",
"X2_15"),
measure.vars=c("X2_1","X2_2","X2_3",
"X2_4","X2_5","X2_6","X2_7",
"X2_15"),
variable.name="MediaRichness2", value.name="MRItem2")
MainStudyMelt3<-melt(MainStudy,id.vars=c("ResponseId","X3","V3","X3_1","X3_2","X3_3",
"X3_4","X3_5","X3_6","X3_7",
"X3_15"),
measure.vars=c("X3_1","X3_2","X3_3",
"X3_4","X3_5","X3_6","X3_7",
"X3_15"),
variable.name="MediaRichness3", value.name="MRItem3")
cronbach(cbind(MainStudyMelt1$MRItem1,MainStudyMelt2$MRItem2,MainStudyMelt3$MRItem3)) ## Cronabch 0.81
## $sample.size
## [1] 576
##
## $number.of.items
## [1] 3
##
## $alpha
## [1] 0.8151994
## Reliability Tie Strength
MainStudyMelt4<-melt(MainStudy,id.vars=c("ResponseId","X3","V3","X4_1","X4_2","X4_3",
"X4_4","X4_5","X4_6","X4_7",
"X4_15"),
measure.vars=c("X4_1","X4_2","X4_3",
"X4_4","X4_5","X4_6","X4_7",
"X4_15"),
variable.name="TieStr1", value.name="TieStrItem1")
MainStudyMelt5<-melt(MainStudy,id.vars=c("ResponseId","X3","V3","X5_1","X5_2","X5_3",
"X5_4","X5_5","X5_6","X5_7",
"X5_15"),
measure.vars=c("X5_1","X5_2","X5_3",
"X5_4","X5_5","X5_6","X5_7",
"X5_15"),
variable.name="TieStr2", value.name="TieStrItem2")
cronbach(cbind(MainStudyMelt4$TieStrItem1,MainStudyMelt5$TieStrItem2)) ## Cronabch 0.89
## $sample.size
## [1] 576
##
## $number.of.items
## [1] 2
##
## $alpha
## [1] 0.8877107
validity<-data.frame(cbind(MainStudyMelt1$MRItem1,MainStudyMelt2$MRItem2,MainStudyMelt3$MRItem3,MainStudyMelt4$TieStrItem1,MainStudyMelt5$TieStrItem2))
mardiaTest(validity)
## Mardia's Multivariate Normality Test
## ---------------------------------------
## data : validity
##
## g1p : 2.269525
## chi.skew : 217.8744
## p.value.skew : 2.756997e-28
##
## g2p : 39.00506
## z.kurtosis : 5.744359
## p.value.kurt : 9.226987e-09
##
## chi.small.skew : 219.3894
## p.value.small : 1.447419e-28
##
## Result : Data are not multivariate normal.
## ---------------------------------------
KMO(validity)
## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = validity)
## Overall MSA = 0.82
## MSA for each item =
## X1 X2 X3 X4 X5
## 0.88 0.85 0.86 0.77 0.76
factanal(validity,2,rotation="varimax")
##
## Call:
## factanal(x = validity, factors = 2, rotation = "varimax")
##
## Uniquenesses:
## X1 X2 X3 X4 X5
## 0.503 0.323 0.329 0.290 0.093
##
## Loadings:
## Factor1 Factor2
## X1 0.295 0.640
## X2 0.293 0.769
## X3 0.489 0.657
## X4 0.752 0.379
## X5 0.889 0.343
##
## Factor1 Factor2
## SS loadings 1.767 1.695
## Proportion Var 0.353 0.339
## Cumulative Var 0.353 0.692
##
## Test of the hypothesis that 2 factors are sufficient.
## The chi square statistic is 0.22 on 1 degree of freedom.
## The p-value is 0.637
summary(prcomp(validity)) ## Two components explain 82% of the variance
## Importance of components%s:
## PC1 PC2 PC3 PC4 PC5
## Standard deviation 4.5709 2.0228 1.49136 1.38419 1.18419
## Proportion of Variance 0.6844 0.1340 0.07286 0.06276 0.04594
## Cumulative Proportion 0.6844 0.8184 0.89130 0.95406 1.00000
screeplot(prcomp(validity),type="lines")
biplot(prcomp(validity,scale.=T),cex=0.5,xlabs=rep(".",nrow(validity)))
rcorr(as.matrix(validity))
## X1 X2 X3 X4 X5
## X1 1.00 0.58 0.57 0.46 0.48
## X2 0.58 1.00 0.65 0.51 0.52
## X3 0.57 0.65 1.00 0.62 0.66
## X4 0.46 0.51 0.62 1.00 0.80
## X5 0.48 0.52 0.66 0.80 1.00
##
## n= 576
##
##
## P
## X1 X2 X3 X4 X5
## X1 0 0 0 0
## X2 0 0 0 0
## X3 0 0 0 0
## X4 0 0 0 0
## X5 0 0 0 0
MainStudy$MRFacebook<-(MainStudy$X3_1+MainStudy$X1_1+MainStudy$X2_1)/3-38
MainStudy$MRTwitter<-(MainStudy$X3_2+MainStudy$X1_2+MainStudy$X2_2)/3-38
MainStudy$MRYouTube<-(MainStudy$X3_3+MainStudy$X1_3+MainStudy$X2_3)/3-38
MainStudy$MRInstagram<-(MainStudy$X3_4+MainStudy$X1_4+MainStudy$X2_4)/3-38
MainStudy$MRPinterest<-(MainStudy$X3_5+MainStudy$X1_5+MainStudy$X2_5)/3-38
MainStudy$MRSnapChat<-(MainStudy$X3_6+MainStudy$X1_6+MainStudy$X2_6)/3-38
MainStudy$MRLinkedIn<-(MainStudy$X3_7+MainStudy$X1_7+MainStudy$X2_7)/3-38
MainStudy$MRSecondLife<-(MainStudy$X3_15+MainStudy$X1_15+MainStudy$X2_15)/3-38
MainStudy$TSFacebook<-(MainStudy$X4_1+MainStudy$X5_1)/2-38
MainStudy$TSTwitter<-(MainStudy$X4_2+MainStudy$X5_2)/2-38
MainStudy$TSYouTube<-(MainStudy$X4_3+MainStudy$X5_3)/2-38
MainStudy$TSInstagram<-(MainStudy$X4_4+MainStudy$X5_4)/2-38
MainStudy$TSPinterest<-(MainStudy$X4_5+MainStudy$X5_5)/2-38
MainStudy$TSSnapChat<-(MainStudy$X4_6+MainStudy$X5_6)/2-38
MainStudy$TSLinkedIn<-(MainStudy$X4_7+MainStudy$X5_7)/2-38
MainStudy$TSSecondLife<-(MainStudy$X4_15+MainStudy$X5_15)/2-38
summary(MainStudy)
## StartDate EndDate Status IPAddress
## 9/28/2017 5:29: 3 9/28/2017 2:13: 2 Min. :0 103.78.22.181 : 2
## 9/28/2017 1:15: 2 9/28/2017 2:26: 2 1st Qu.:0 117.198.169.251: 2
## 9/28/2017 1:44: 2 9/28/2017 3:43: 2 Median :0 1.22.132.15 : 1
## 9/28/2017 2:05: 2 9/28/2017 5:02: 2 Mean :0 103.204.47.33 : 1
## 9/28/2017 2:14: 2 9/28/2017 5:06: 2 3rd Qu.:0 103.25.47.134 : 1
## 9/28/2017 3:20: 2 9/28/2017 5:29: 2 Max. :0 103.88.77.3 : 1
## (Other) :59 (Other) :60 (Other) :64
## Progress Duration..in.seconds. Finished RecordedDate
## Min. :100 Min. : 372.0 Min. :1 9/28/2017 2:13: 2
## 1st Qu.:100 1st Qu.: 424.8 1st Qu.:1 9/28/2017 2:26: 2
## Median :100 Median : 492.0 Median :1 9/28/2017 3:43: 2
## Mean :100 Mean : 587.4 Mean :1 9/28/2017 5:02: 2
## 3rd Qu.:100 3rd Qu.: 616.8 3rd Qu.:1 9/28/2017 5:06: 2
## Max. :100 Max. :1753.0 Max. :1 9/28/2017 5:29: 2
## (Other) :60
## ResponseId RecipientLastName RecipientFirstName
## R_10NI2vQEEZ0E2Fs: 1 Mode:logical Mode:logical
## R_10T8rIxyUdDqUvY: 1 NA's:72 NA's:72
## R_1BRuaNPDmjgU8BI: 1
## R_1CazBZ3AMwO2Xwb: 1
## R_1f2xJMMytF6btHR: 1
## R_1fZNr0g0fVep5ki: 1
## (Other) :66
## RecipientEmail ExternalReference LocationLatitude LocationLongitude
## Mode:logical Mode:logical Min. : 8.00 Min. :-122.68
## NA's:72 NA's:72 1st Qu.:13.08 1st Qu.: 72.15
## Median :13.08 Median : 78.80
## Mean :19.93 Mean : 40.43
## 3rd Qu.:22.69 3rd Qu.: 80.28
## Max. :53.75 Max. : 121.02
##
## DistributionChannel UserLanguage t0_First.Click t0_Last.Click
## anonymous:72 : 0 Min. : 0.000 Min. : 0.000
## EN:72 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 0.000 Median : 0.000
## Mean : 1.623 Mean : 3.466
## 3rd Qu.: 0.000 3rd Qu.: 0.000
## Max. :36.752 Max. :59.745
##
## t0_Page.Submit t0_Click.Count X0B_Browser X0B_Version
## Min. : 15.93 Min. :0.000 Chrome :55 61.0.3163.100:32
## 1st Qu.: 17.81 1st Qu.:0.000 Firefox :13 55 : 9
## Median : 20.45 Median :0.000 Edge : 1 60.0.3112.113: 7
## Mean : 38.54 Mean :0.625 Opera : 1 60.0.3112.90 : 3
## 3rd Qu.: 28.79 3rd Qu.:0.000 Safari iPad : 1 61.0.3163.98 : 3
## Max. :416.32 Max. :9.000 Safari iPhone: 1 49.0.2623.112: 2
## (Other) : 0 (Other) :16
## X0B_Operating.System X0B_Resolution V1 V2
## Windows NT 6.1 :33 1366x768 :29 Min. :10 Min. :9
## Windows NT 10.0:16 1280x1024:11 1st Qu.:10 1st Qu.:9
## Windows NT 6.3 : 4 1280x800 : 7 Median :10 Median :9
## Android 6.0.1 : 3 360x640 : 5 Mean :10 Mean :9
## Macintosh : 3 1024x768 : 3 3rd Qu.:10 3rd Qu.:9
## Windows NT 5.1 : 3 1440x900 : 3 Max. :10 Max. :9
## (Other) :10 (Other) :14
## V3 V4 tV_First.Click tV_Last.Click
## Min. : 9.0 4,5,6,7,8,9,10:15 Min. : 1.014 Min. : 9.257
## 1st Qu.: 9.0 4,5,6,7,8,10 : 7 1st Qu.: 2.751 1st Qu.: 17.727
## Median : 9.5 4 : 6 Median : 3.827 Median : 21.335
## Mean : 9.5 4,5,6 : 4 Mean : 10.537 Mean : 28.527
## 3rd Qu.:10.0 4,5,6,7,9,10 : 4 3rd Qu.: 5.038 3rd Qu.: 26.106
## Max. :10.0 4,6,7 : 4 Max. :391.223 Max. :413.915
## (Other) :32
## tV_Page.Submit tV_Click.Count X1_1 X1_2
## Min. : 9.938 Min. : 4.00 Min. :39.00 Min. :39.00
## 1st Qu.: 19.006 1st Qu.: 7.00 1st Qu.:44.00 1st Qu.:43.00
## Median : 23.508 Median :10.00 Median :45.00 Median :45.00
## Mean : 30.599 Mean :10.75 Mean :44.81 Mean :44.19
## 3rd Qu.: 28.643 3rd Qu.:11.00 3rd Qu.:46.00 3rd Qu.:45.00
## Max. :414.291 Max. :32.00 Max. :47.00 Max. :47.00
##
## X1_3 X1_4 X1_5 X1_6
## Min. :40.00 Min. :39.00 Min. :39.0 Min. :39.00
## 1st Qu.:44.00 1st Qu.:43.00 1st Qu.:43.0 1st Qu.:42.00
## Median :45.00 Median :45.00 Median :44.0 Median :43.00
## Mean :44.92 Mean :44.32 Mean :44.1 Mean :43.43
## 3rd Qu.:46.25 3rd Qu.:46.00 3rd Qu.:46.0 3rd Qu.:45.00
## Max. :47.00 Max. :47.00 Max. :47.0 Max. :47.00
##
## X1_7 X1_15 t1_First.Click t1_Last.Click
## Min. :39.00 Min. :39.00 Min. : 0.836 Min. : 4.026
## 1st Qu.:41.00 1st Qu.:41.75 1st Qu.: 3.895 1st Qu.: 20.640
## Median :43.00 Median :43.00 Median : 5.879 Median : 43.196
## Mean :43.24 Mean :43.04 Mean : 10.092 Mean : 45.490
## 3rd Qu.:45.00 3rd Qu.:45.00 3rd Qu.: 9.114 3rd Qu.: 59.918
## Max. :47.00 Max. :47.00 Max. :124.207 Max. :127.734
##
## t1_Page.Submit t1_Click.Count X2_1 X2_2
## Min. : 60.99 Min. : 1.0 Min. :40.00 Min. :39.00
## 1st Qu.: 62.56 1st Qu.: 8.0 1st Qu.:44.00 1st Qu.:42.75
## Median : 65.69 Median :11.0 Median :45.00 Median :44.00
## Mean :102.22 Mean :13.9 Mean :44.79 Mean :43.82
## 3rd Qu.: 93.35 3rd Qu.:16.0 3rd Qu.:46.00 3rd Qu.:45.00
## Max. :981.92 Max. :65.0 Max. :47.00 Max. :47.00
##
## X2_3 X2_4 X2_5 X2_6
## Min. :40.00 Min. :39.00 Min. :39.00 Min. :39.00
## 1st Qu.:44.00 1st Qu.:42.75 1st Qu.:43.00 1st Qu.:41.00
## Median :46.00 Median :44.00 Median :45.00 Median :43.00
## Mean :45.19 Mean :43.81 Mean :44.01 Mean :42.64
## 3rd Qu.:47.00 3rd Qu.:45.00 3rd Qu.:46.00 3rd Qu.:44.00
## Max. :47.00 Max. :47.00 Max. :47.00 Max. :47.00
##
## X2_7 X2_15 Q774_First.Click Q774_Last.Click
## Min. :39.00 Min. :39.0 Min. : 0.814 Min. : 2.105
## 1st Qu.:42.00 1st Qu.:41.0 1st Qu.: 3.990 1st Qu.: 16.200
## Median :43.00 Median :43.0 Median : 7.019 Median : 34.456
## Mean :43.25 Mean :42.6 Mean : 9.585 Mean : 45.695
## 3rd Qu.:45.00 3rd Qu.:45.0 3rd Qu.:10.986 3rd Qu.: 57.972
## Max. :47.00 Max. :47.0 Max. :64.105 Max. :555.504
##
## Q774_Page.Submit Q774_Click.Count X3_1 X3_2
## Min. : 61.14 Min. : 1.00 Min. :39.00 Min. :39.00
## 1st Qu.: 62.77 1st Qu.: 8.00 1st Qu.:43.00 1st Qu.:41.00
## Median : 68.04 Median :10.00 Median :44.50 Median :43.00
## Mean : 95.79 Mean :13.11 Mean :44.26 Mean :42.93
## 3rd Qu.: 89.30 3rd Qu.:14.00 3rd Qu.:46.00 3rd Qu.:45.00
## Max. :555.58 Max. :50.00 Max. :47.00 Max. :47.00
##
## X3_3 X3_4 X3_5 X3_6
## Min. :39.00 Min. :39.00 Min. :39.00 Min. :39.00
## 1st Qu.:43.00 1st Qu.:41.00 1st Qu.:41.75 1st Qu.:39.00
## Median :45.00 Median :43.00 Median :43.00 Median :41.00
## Mean :44.47 Mean :42.88 Mean :43.08 Mean :41.83
## 3rd Qu.:46.00 3rd Qu.:45.00 3rd Qu.:45.00 3rd Qu.:44.00
## Max. :47.00 Max. :47.00 Max. :47.00 Max. :47.00
##
## X3_7 X3_15 Q776_First.Click Q776_Last.Click
## Min. :39.00 Min. :39.00 Min. : 0.849 Min. : 2.925
## 1st Qu.:40.75 1st Qu.:39.00 1st Qu.: 2.430 1st Qu.: 17.806
## Median :43.00 Median :41.00 Median : 4.425 Median : 31.653
## Mean :42.60 Mean :41.40 Mean : 8.686 Mean : 39.014
## 3rd Qu.:44.25 3rd Qu.:43.25 3rd Qu.: 8.939 3rd Qu.: 59.646
## Max. :47.00 Max. :46.00 Max. :72.620 Max. :116.506
##
## Q776_Page.Submit Q776_Click.Count X4_1 X4_2
## Min. : 61.14 Min. : 1.00 Min. :40.00 Min. :39.00
## 1st Qu.: 62.51 1st Qu.: 8.00 1st Qu.:44.75 1st Qu.:43.00
## Median : 68.57 Median :10.00 Median :46.00 Median :44.00
## Mean : 95.09 Mean :13.90 Mean :45.32 Mean :43.94
## 3rd Qu.: 87.00 3rd Qu.:16.25 3rd Qu.:47.00 3rd Qu.:46.00
## Max. :683.66 Max. :51.00 Max. :47.00 Max. :47.00
##
## X4_3 X4_4 X4_5 X4_6
## Min. :39.00 Min. :39.00 Min. :39.00 Min. :39.00
## 1st Qu.:42.00 1st Qu.:43.00 1st Qu.:40.00 1st Qu.:40.00
## Median :44.00 Median :44.00 Median :43.00 Median :42.00
## Mean :43.68 Mean :43.71 Mean :42.54 Mean :42.12
## 3rd Qu.:46.00 3rd Qu.:45.25 3rd Qu.:45.00 3rd Qu.:44.00
## Max. :47.00 Max. :47.00 Max. :47.00 Max. :47.00
##
## X4_7 X4_15 Q778_First.Click Q778_Last.Click
## Min. :39.00 Min. :39.00 Min. : 0.144 Min. : 2.295
## 1st Qu.:42.00 1st Qu.:39.00 1st Qu.: 3.066 1st Qu.: 22.445
## Median :44.00 Median :41.00 Median : 6.181 Median : 41.587
## Mean :43.75 Mean :41.72 Mean : 7.821 Mean : 42.552
## 3rd Qu.:45.00 3rd Qu.:44.00 3rd Qu.:10.763 3rd Qu.: 57.941
## Max. :47.00 Max. :47.00 Max. :65.250 Max. :121.689
##
## Q778_Page.Submit Q778_Click.Count X5_1 X5_2
## Min. : 61.13 Min. : 1.00 Min. :40.00 Min. :39.00
## 1st Qu.: 62.02 1st Qu.: 9.00 1st Qu.:44.00 1st Qu.:41.75
## Median : 63.72 Median :10.00 Median :46.00 Median :44.00
## Mean : 77.26 Mean :15.54 Mean :45.21 Mean :43.46
## 3rd Qu.: 77.73 3rd Qu.:16.00 3rd Qu.:47.00 3rd Qu.:46.00
## Max. :251.05 Max. :67.00 Max. :47.00 Max. :47.00
##
## X5_3 X5_4 X5_5 X5_6
## Min. :39.00 Min. :39.00 Min. :39.00 Min. :39.00
## 1st Qu.:41.00 1st Qu.:41.00 1st Qu.:39.75 1st Qu.:39.00
## Median :44.00 Median :44.00 Median :42.50 Median :42.50
## Mean :43.42 Mean :43.19 Mean :42.39 Mean :42.21
## 3rd Qu.:46.00 3rd Qu.:45.00 3rd Qu.:45.00 3rd Qu.:45.00
## Max. :47.00 Max. :47.00 Max. :47.00 Max. :47.00
##
## X5_7 X5_15 Q780_First.Click Q780_Last.Click
## Min. :39.00 Min. :39.00 Min. : 0.882 Min. : 3.221
## 1st Qu.:42.00 1st Qu.:39.00 1st Qu.: 3.765 1st Qu.: 21.674
## Median :43.50 Median :41.00 Median : 7.328 Median : 41.085
## Mean :43.39 Mean :41.46 Mean : 9.370 Mean : 46.418
## 3rd Qu.:45.00 3rd Qu.:43.00 3rd Qu.:12.346 3rd Qu.: 59.800
## Max. :47.00 Max. :47.00 Max. :44.253 Max. :196.035
##
## Q780_Page.Submit Q780_Click.Count X3 X4
## Min. : 60.93 Min. : 1.0 Min. :1949 Min. :1.000
## 1st Qu.: 61.88 1st Qu.: 8.0 1st Qu.:1981 1st Qu.:1.000
## Median : 64.85 Median :10.0 Median :1988 Median :3.000
## Mean : 90.92 Mean :15.0 Mean :1985 Mean :3.014
## 3rd Qu.: 75.79 3rd Qu.:15.5 3rd Qu.:1991 3rd Qu.:5.000
## Max. :505.77 Max. :70.0 Max. :1996 Max. :7.000
##
## t3_First.Click t3_Last.Click t3_Page.Submit t3_Click.Count
## Min. : 1.404 Min. : 3.197 Min. : 5.587 Min. : 2.000
## 1st Qu.: 2.281 1st Qu.: 7.391 1st Qu.: 8.845 1st Qu.: 2.000
## Median : 3.377 Median : 9.220 Median : 11.625 Median : 2.000
## Mean : 7.622 Mean : 14.777 Mean : 17.139 Mean : 3.083
## 3rd Qu.: 4.912 3rd Qu.: 14.014 3rd Qu.: 16.764 3rd Qu.: 3.000
## Max. :203.935 Max. :205.335 Max. :208.775 Max. :16.000
##
## X5 X6 X6_6_TEXT t4_First.Click
## Min. : 2.00 Min. :1.000 :56 Min. : 1.234
## 1st Qu.:12.00 1st Qu.:2.000 Asian : 4 1st Qu.: 2.728
## Median :12.00 Median :2.000 Indian : 4 Median : 3.502
## Mean :11.69 Mean :3.181 South Asian : 3 Mean : 5.535
## 3rd Qu.:13.00 3rd Qu.:5.000 asian : 2 3rd Qu.: 5.088
## Max. :15.00 Max. :6.000 Asian/Indian: 1 Max. :71.134
## (Other) : 2
## t4_Last.Click t4_Page.Submit t4_Click.Count mTurkCode
## Min. : 3.004 Min. : 3.834 Min. : 2.000 Min. : 0.00
## 1st Qu.: 5.456 1st Qu.: 7.167 1st Qu.: 2.000 1st Qu.:21.75
## Median : 7.705 Median : 9.520 Median : 3.000 Median :44.00
## Mean :10.741 Mean :15.380 Mean : 3.792 Mean :47.75
## 3rd Qu.:12.225 3rd Qu.:15.404 3rd Qu.: 3.250 3rd Qu.:77.00
## Max. :75.028 Max. :85.504 Max. :16.000 Max. :99.00
##
## MRFacebook MRTwitter MRYouTube MRInstagram
## Min. :2.333 Min. :1.000 Min. :2.333 Min. :2.000
## 1st Qu.:5.667 1st Qu.:4.333 1st Qu.:6.000 1st Qu.:4.333
## Median :6.667 Median :6.000 Median :7.000 Median :6.333
## Mean :6.620 Mean :5.648 Mean :6.861 Mean :5.667
## 3rd Qu.:7.667 3rd Qu.:7.000 3rd Qu.:8.000 3rd Qu.:7.000
## Max. :9.000 Max. :8.667 Max. :9.000 Max. :9.000
##
## MRPinterest MRSnapChat MRLinkedIn MRSecondLife
## Min. :1.667 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.250 1st Qu.:2.917 1st Qu.:3.667 1st Qu.:2.667
## Median :5.833 Median :4.667 Median :5.000 Median :3.833
## Mean :5.731 Mean :4.634 Mean :5.028 Mean :4.347
## 3rd Qu.:7.333 3rd Qu.:6.333 3rd Qu.:6.417 3rd Qu.:6.333
## Max. :9.000 Max. :8.667 Max. :8.667 Max. :8.667
##
## TSFacebook TSTwitter TSYouTube TSInstagram
## Min. :2.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:6.500 1st Qu.:4.375 1st Qu.:4.000 1st Qu.:3.875
## Median :7.500 Median :6.000 Median :5.500 Median :6.000
## Mean :7.264 Mean :5.701 Mean :5.549 Mean :5.451
## 3rd Qu.:8.500 3rd Qu.:7.500 3rd Qu.:7.625 3rd Qu.:7.000
## Max. :9.000 Max. :9.000 Max. :9.000 Max. :9.000
##
## TSPinterest TSSnapChat TSLinkedIn TSSecondLife
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:4.500 1st Qu.:1.000
## Median :4.500 Median :4.000 Median :5.750 Median :3.000
## Mean :4.465 Mean :4.167 Mean :5.569 Mean :3.590
## 3rd Qu.:6.125 3rd Qu.:6.500 3rd Qu.:7.000 3rd Qu.:5.625
## Max. :9.000 Max. :9.000 Max. :9.000 Max. :9.000
##
MainStudyMelt1<-melt(MainStudy,id.vars=c("ResponseId","X3","V3","TSFacebook","TSTwitter","TSYouTube","TSInstagram","TSPinterest","TSSnapChat","TSLinkedIn","TSSecondLife"),measure.vars=c("TSFacebook","TSTwitter","TSYouTube","TSInstagram","TSPinterest","TSSnapChat","TSLinkedIn","TSSecondLife"),variable.name="SMP", value.name="TieStrength")
MainStudyMelt2<-melt(MainStudy,id.vars=c("ResponseId","X3","V3","MRFacebook","MRTwitter","MRYouTube","MRInstagram","MRPinterest","MRSnapChat","MRLinkedIn","MRSecondLife"),measure.vars=c("MRFacebook","MRTwitter","MRYouTube","MRInstagram","MRPinterest","MRSnapChat","MRLinkedIn","MRSecondLife"),variable.name="SMP", value.name="MediaRichness")
MainStudyMelt<-cbind(MainStudyMelt1,MainStudyMelt2)
summary(aov(TieStrength~SMP,MainStudyMelt))
## Df Sum Sq Mean Sq F value Pr(>F)
## SMP 7 650 92.86 17.39 <2e-16 ***
## Residuals 568 3034 5.34
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
aov.out<-aov(TieStrength~SMP,MainStudyMelt)
TukeyHSD(aov.out) ## Differences in SMP by TS
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = TieStrength ~ SMP, data = MainStudyMelt)
##
## $SMP
## diff lwr upr p adj
## TSTwitter-TSFacebook -1.56250000 -2.73426192 -0.39073808 0.0014572
## TSYouTube-TSFacebook -1.71527778 -2.88703970 -0.54351586 0.0002716
## TSInstagram-TSFacebook -1.81250000 -2.98426192 -0.64073808 0.0000861
## TSPinterest-TSFacebook -2.79861111 -3.97037303 -1.62684919 0.0000000
## TSSnapChat-TSFacebook -3.09722222 -4.26898414 -1.92546030 0.0000000
## TSLinkedIn-TSFacebook -1.69444444 -2.86620637 -0.52268252 0.0003446
## TSSecondLife-TSFacebook -3.67361111 -4.84537303 -2.50184919 0.0000000
## TSYouTube-TSTwitter -0.15277778 -1.32453970 1.01898414 0.9999281
## TSInstagram-TSTwitter -0.25000000 -1.42176192 0.92176192 0.9981329
## TSPinterest-TSTwitter -1.23611111 -2.40787303 -0.06434919 0.0303139
## TSSnapChat-TSTwitter -1.53472222 -2.70648414 -0.36296030 0.0019446
## TSLinkedIn-TSTwitter -0.13194444 -1.30370637 1.03981748 0.9999735
## TSSecondLife-TSTwitter -2.11111111 -3.28287303 -0.93934919 0.0000018
## TSInstagram-TSYouTube -0.09722222 -1.26898414 1.07453970 0.9999967
## TSPinterest-TSYouTube -1.08333333 -2.25509525 0.08842859 0.0938639
## TSSnapChat-TSYouTube -1.38194444 -2.55370637 -0.21018252 0.0086198
## TSLinkedIn-TSYouTube 0.02083333 -1.15092859 1.19259525 1.0000000
## TSSecondLife-TSYouTube -1.95833333 -3.13009525 -0.78657141 0.0000138
## TSPinterest-TSInstagram -0.98611111 -2.15787303 0.18565081 0.1729199
## TSSnapChat-TSInstagram -1.28472222 -2.45648414 -0.11296030 0.0203084
## TSLinkedIn-TSInstagram 0.11805556 -1.05370637 1.28981748 0.9999876
## TSSecondLife-TSInstagram -1.86111111 -3.03287303 -0.68934919 0.0000475
## TSSnapChat-TSPinterest -0.29861111 -1.47037303 0.87315081 0.9943018
## TSLinkedIn-TSPinterest 1.10416667 -0.06759525 2.27592859 0.0814233
## TSSecondLife-TSPinterest -0.87500000 -2.04676192 0.29676192 0.3111142
## TSLinkedIn-TSSnapChat 1.40277778 0.23101586 2.57453970 0.0071067
## TSSecondLife-TSSnapChat -0.57638889 -1.74815081 0.59537303 0.8093067
## TSSecondLife-TSLinkedIn -1.97916667 -3.15092859 -0.80740475 0.0000105
## TSPinterest-TSFacebook 0
## TSSnapChat-TSFacebook 0
## TSSecondLife-TSFacebook 0
## TSSecondLife-TSTwitter 0.0000018
## TSSecondLife-TSLinkedIn 0.0000105
## TSSecondLife-TSYouTube 0.0000138
## TSSecondLife-TSInstagram 0.0000475
## TSInstagram-TSFacebook 0.0000861
## TSYouTube-TSFacebook 0.0002716
## TSLinkedIn-TSFacebook 0.0003446
## TSTwitter-TSFacebook 0.0014572
## TSSnapChat-TSTwitter 0.0019446
## TSLinkedIn-TSSnapChat 0.0071067
## TSSnapChat-TSYouTube 0.0086198
## TSSnapChat-TSInstagram 0.0203084
## TSPinterest-TSTwitter 0.0303139
## TSLinkedIn-TSPinterest 0.0814233
## TSPinterest-TSYouTube 0.0938639
plot(TieStrength~SMP,MainStudyMelt)
aggregate(MainStudyMelt$TieStrength,list(MainStudyMelt$SMP),mean)
## Group.1 x
## 1 TSFacebook 7.263889
## 2 TSTwitter 5.701389
## 3 TSYouTube 5.548611
## 4 TSInstagram 5.451389
## 5 TSPinterest 4.465278
## 6 TSSnapChat 4.166667
## 7 TSLinkedIn 5.569444
## 8 TSSecondLife 3.590278
aggregate(MainStudyMelt$TieStrength,list(MainStudyMelt$SMP),sd)
## Group.1 x
## 1 TSFacebook 1.642251
## 2 TSTwitter 2.303793
## 3 TSYouTube 2.442522
## 4 TSInstagram 2.236319
## 5 TSPinterest 2.489876
## 6 TSSnapChat 2.527316
## 7 TSLinkedIn 2.175901
## 8 TSSecondLife 2.536114
## Max TSFacebook 7.26
## Min TSSecondLife 3.59
(7.26+3.59)/2
## [1] 5.425
mean(MainStudyMelt$TieStrength)
## [1] 5.219618
## Mid point range 5.425 - 5.21
## Candidates TSInstagram 5.45, TSYouTube 5.54
t.test(MainStudy$TSTwitter,MainStudy$TSPinterest)
##
## Welch Two Sample t-test
##
## data: MainStudy$TSTwitter and MainStudy$TSPinterest
## t = 3.092, df = 141.15, p-value = 0.002396
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.4457941 2.0264281
## sample estimates:
## mean of x mean of y
## 5.701389 4.465278
t.test(MainStudy$MRTwitter,MainStudy$MRPinterest)
##
## Welch Two Sample t-test
##
## data: MainStudy$MRTwitter and MainStudy$MRPinterest
## t = -0.28028, df = 141.77, p-value = 0.7797
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.6710859 0.5044193
## sample estimates:
## mean of x mean of y
## 5.648148 5.731481
t.test(MainStudy$TSFacebook,MainStudy$TSYouTube)
##
## Welch Two Sample t-test
##
## data: MainStudy$TSFacebook and MainStudy$TSYouTube
## t = 4.945, df = 124.3, p-value = 2.412e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.028743 2.401812
## sample estimates:
## mean of x mean of y
## 7.263889 5.548611
t.test(MainStudy$MRFacebook,MainStudy$MRYouTube)
##
## Welch Two Sample t-test
##
## data: MainStudy$MRFacebook and MainStudy$MRYouTube
## t = -0.9353, df = 141.76, p-value = 0.3512
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.7495677 0.2680863
## sample estimates:
## mean of x mean of y
## 6.620370 6.861111
## There are at least three levels of TS but Facebook has also high MR
summary(aov(MediaRichness~SMP,MainStudyMelt))
## Df Sum Sq Mean Sq F value Pr(>F)
## SMP 7 394.3 56.33 16.99 <2e-16 ***
## Residuals 568 1883.5 3.32
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
aov.out<-aov(MediaRichness~SMP,MainStudyMelt)
TukeyHSD(aov.out) ## Differences in SMP by MR
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = MediaRichness ~ SMP, data = MainStudyMelt)
##
## $SMP
## diff lwr upr p adj
## TSTwitter-TSFacebook -0.97222222 -1.8955546 -0.04888988 0.0308806
## TSYouTube-TSFacebook 0.24074074 -0.6825916 1.16407309 0.9934449
## TSInstagram-TSFacebook -0.95370370 -1.8770360 -0.03037136 0.0372048
## TSPinterest-TSFacebook -0.88888889 -1.8122212 0.03444346 0.0688485
## TSSnapChat-TSFacebook -1.98611111 -2.9094435 -1.06277876 0.0000000
## TSLinkedIn-TSFacebook -1.59259259 -2.5159249 -0.66926025 0.0000060
## TSSecondLife-TSFacebook -2.27314815 -3.1964805 -1.34981580 0.0000000
## TSYouTube-TSTwitter 1.21296296 0.2896306 2.13629531 0.0018546
## TSInstagram-TSTwitter 0.01851852 -0.9048138 0.94185086 1.0000000
## TSPinterest-TSTwitter 0.08333333 -0.8399990 1.00666568 0.9999942
## TSSnapChat-TSTwitter -1.01388889 -1.9372212 -0.09055654 0.0199749
## TSLinkedIn-TSTwitter -0.62037037 -1.5437027 0.30296198 0.4530346
## TSSecondLife-TSTwitter -1.30092593 -2.2242583 -0.37759358 0.0005614
## TSInstagram-TSYouTube -1.19444444 -2.1177768 -0.27111210 0.0023593
## TSPinterest-TSYouTube -1.12962963 -2.0529620 -0.20629728 0.0053116
## TSSnapChat-TSYouTube -2.22685185 -3.1501842 -1.30351951 0.0000000
## TSLinkedIn-TSYouTube -1.83333333 -2.7566657 -0.91000099 0.0000001
## TSSecondLife-TSYouTube -2.51388889 -3.4372212 -1.59055654 0.0000000
## TSPinterest-TSInstagram 0.06481481 -0.8585175 0.98814716 0.9999990
## TSSnapChat-TSInstagram -1.03240741 -1.9557398 -0.10907506 0.0163411
## TSLinkedIn-TSInstagram -0.63888889 -1.5622212 0.28444346 0.4128352
## TSSecondLife-TSInstagram -1.31944444 -2.2427768 -0.39611210 0.0004319
## TSSnapChat-TSPinterest -1.09722222 -2.0205546 -0.17388988 0.0078243
## TSLinkedIn-TSPinterest -0.70370370 -1.6270360 0.21962864 0.2851823
## TSSecondLife-TSPinterest -1.38425926 -2.3075916 -0.46092691 0.0001677
## TSLinkedIn-TSSnapChat 0.39351852 -0.5298138 1.31685086 0.9000453
## TSSecondLife-TSSnapChat -0.28703704 -1.2103694 0.63629531 0.9813487
## TSSecondLife-TSLinkedIn -0.68055556 -1.6038879 0.24277679 0.3280487
## MRSnapChat-MRFacebook 0
## MRSecondLife-MRFacebook 0
## MRSnapChat-MRYouTube 0
## MRSecondLife-MRYouTube 0
## MRLinkedIn-MRYouTube 0.0000001
## MRLinkedIn-MRFacebook 0.000006
## MRSecondLife-MRPinterest 0.0001677
## MRSecondLife-MRInstagram 0.0004319
## MRSecondLife-MRTwitter 0.0005614
## MRYouTube-MRTwitter 0.0018546
## MRInstagram-MRYouTube 0.0023593
## MRPinterest-MRYouTube 0.0053116
## MRSnapChat-MRPinterest 0.0078243
## MRSnapChat-MRInstagram 0.0163411
## MRSnapChat-MRTwitter 0.0199749
## MRTwitter-MRFacebook 0.0308806
## MRInstagram-MRFacebook 0.0372048
## MRPinterest-MRFacebook 0.0688485
plot(MediaRichness~SMP,MainStudyMelt)
aggregate(MainStudyMelt$MediaRichness,list(MainStudyMelt$SMP),mean)
## Group.1 x
## 1 TSFacebook 6.620370
## 2 TSTwitter 5.648148
## 3 TSYouTube 6.861111
## 4 TSInstagram 5.666667
## 5 TSPinterest 5.731481
## 6 TSSnapChat 4.634259
## 7 TSLinkedIn 5.027778
## 8 TSSecondLife 4.347222
aggregate(MainStudyMelt$MediaRichness,list(MainStudyMelt$SMP),sd)
## Group.1 x
## 1 TSFacebook 1.575741
## 2 TSTwitter 1.747691
## 3 TSYouTube 1.512338
## 4 TSInstagram 1.715254
## 5 TSPinterest 1.819421
## 6 TSSnapChat 2.035029
## 7 TSLinkedIn 1.994319
## 8 TSSecondLife 2.081619
## Max MRYouTube 6.86
## Min MRSecondLife 4.34
(6.86+4.34)/2
## [1] 5.6
mean(MainStudyMelt$MediaRichness)
## [1] 5.56713
## Mid point range 5.6 - 5.56
## Candidates MRTwitter 5.64, MRInstagram 5.66, MRPinterest 5.73
t.test(MainStudy$MRYouTube,MainStudy$MRFacebook)
##
## Welch Two Sample t-test
##
## data: MainStudy$MRYouTube and MainStudy$MRFacebook
## t = 0.9353, df = 141.76, p-value = 0.3512
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.2680863 0.7495677
## sample estimates:
## mean of x mean of y
## 6.861111 6.620370
t.test(MainStudy$MRYouTube,MainStudy$MRInstagram)
##
## Welch Two Sample t-test
##
## data: MainStudy$MRYouTube and MainStudy$MRInstagram
## t = 4.4321, df = 139.81, p-value = 1.871e-05
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.6616278 1.7272611
## sample estimates:
## mean of x mean of y
## 6.861111 5.666667
t.test(MainStudy$MRInstagram,MainStudy$MRLinkedIn)
##
## Welch Two Sample t-test
##
## data: MainStudy$MRInstagram and MainStudy$MRLinkedIn
## t = 2.0609, df = 138.89, p-value = 0.04118
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.02595048 1.25182729
## sample estimates:
## mean of x mean of y
## 5.666667 5.027778
t.test(MainStudy$TSYouTube,MainStudy$TSInstagram)
##
## Welch Two Sample t-test
##
## data: MainStudy$TSYouTube and MainStudy$TSInstagram
## t = 0.24911, df = 140.91, p-value = 0.8036
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.6743427 0.8687871
## sample estimates:
## mean of x mean of y
## 5.548611 5.451389
t.test(MainStudy$TSYouTube,MainStudy$TSLinkedIn)
##
## Welch Two Sample t-test
##
## data: MainStudy$TSYouTube and MainStudy$TSLinkedIn
## t = -0.054041, df = 140.14, p-value = 0.957
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.7829993 0.7413326
## sample estimates:
## mean of x mean of y
## 5.548611 5.569444
t.test(MainStudy$TSLinkedIn,MainStudy$TSInstagram)
##
## Welch Two Sample t-test
##
## data: MainStudy$TSLinkedIn and MainStudy$TSInstagram
## t = 0.32105, df = 141.89, p-value = 0.7486
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.6088611 0.8449722
## sample estimates:
## mean of x mean of y
## 5.569444 5.451389
## There are at least three levels of MR with the same TS
## YouTube, Instagram and LinkedIn
hist(MainStudyMelt$TieStrength)
plot(density(MainStudyMelt$TieStrength))
screeplot(prcomp(cbind(MainStudyMelt$SMP,MainStudyMelt$TieStrength),type="lines"))
## Warning: In prcomp.default(cbind(MainStudyMelt$SMP, MainStudyMelt$TieStrength),
## type = "lines") :
## extra argument 'type' will be disregarded
summary(prcomp(cbind(MainStudyMelt$SMP,MainStudyMelt$TieStrength)))
## Importance of components%s:
## PC1 PC2
## Standard deviation 2.8084 1.9438
## Proportion of Variance 0.6761 0.3239
## Cumulative Proportion 0.6761 1.0000
## At least two components
mydata<-data.frame(MainStudyMelt$TieStrength)
wss <- (nrow(mydata)-1)*sum(apply(mydata,2,var))
for (i in 2:15) wss[i] <- sum(kmeans(mydata,
centers=i)$withinss)
plot(1:15, wss, type="b", xlab="Number of Clusters",
ylab="Within groups sum of squares")
wss<-wss/sum(wss)*100
for (i in 2:15)
wss[i]<-wss[i]+wss[i-1]
plot(1:15, wss, type="b", xlab="Number of Clusters",
ylab="% Var explained")
wss
## [1] 62.75067 78.75270 85.37342 90.22994 93.00741 94.83905 96.08708
## [8] 96.76071 97.96615 98.49386 99.11522 99.47383 99.69592 99.88560
## [15] 100.00000
## 3 clusters explain more than 80% of the variance
abline(v=3,lty=2)
d <- dist(mydata,method="euclidean") # distance matrix
fit <- hclust(d, method="ward")
## The "ward" method has been renamed to "ward.D"; note new "ward.D2"
plot(fit) # display dendogram all raw data
groups <- cutree(fit,k=3) # cut tree into 3 clusters
rect.hclust(fit,k=3,border="red") # draw dendogram with red borders around the 4 clusters
mydata2<-aggregate(MainStudyMelt$TieStrength,list(MainStudyMelt$SMP),mean)
rownames(mydata2)<-c("Facebook","Twitter","YouTube","Instagram","Pinterest",
"SnapChat","LinkedIn","SecondLife")
d<-dist(mydata2,method="euclidean") # distance matrix
## Warning in dist(mydata2, method = "euclidean"): NAs introduced by coercion
fit <- hclust(d, method="ward")
## The "ward" method has been renamed to "ward.D"; note new "ward.D2"
plot(fit,ylab="Tie strength") # display dendogram mean by SMP
groups <- cutree(fit,k=3) # cut tree into 4 clusters
rect.hclust(fit,k=3,border="red") # draw dendogram with red borders around the 4 clusters
hist(MainStudyMelt$MediaRichness)
plot(density(MainStudyMelt$MediaRichness))
screeplot(prcomp(cbind(MainStudyMelt$SMP,MainStudyMelt$MediaRichness),type="lines"))
## Warning: In prcomp.default(cbind(MainStudyMelt$SMP, MainStudyMelt$MediaRichness),
## type = "lines") :
## extra argument 'type' will be disregarded
summary(prcomp(cbind(MainStudyMelt$SMP,MainStudyMelt$MediaRichness)))
## Importance of components%s:
## PC1 PC2
## Standard deviation 2.5182 1.6969
## Proportion of Variance 0.6877 0.3123
## Cumulative Proportion 0.6877 1.0000
## At least two components
mydata<-data.frame(MainStudyMelt$MediaRichness)
wss <- (nrow(mydata)-1)*sum(apply(mydata,2,var))
for (i in 2:15) wss[i] <- sum(kmeans(mydata,
centers=i)$withinss)
plot(1:15, wss, type="b", xlab="Number of Clusters",
ylab="Within groups sum of squares")
wss<-wss/sum(wss)*100
for (i in 2:15)
wss[i]<-wss[i]+wss[i-1]
plot(1:15, wss, type="b", xlab="Number of Clusters",
ylab="% Var explained")
wss
## [1] 54.74077 70.86496 79.32655 84.24533 88.17038 90.93079 93.11597
## [8] 95.11800 96.26178 97.24250 98.05502 98.59362 99.08845 99.56719
## [15] 100.00000
## 3 clusters explain more than 80% of the variance
abline(v=3,lty=2)
d <- dist(mydata,method="euclidean") # distance matrix
fit <- hclust(d, method="ward")
## The "ward" method has been renamed to "ward.D"; note new "ward.D2"
plot(fit) # display dendogram all raw data
groups <- cutree(fit,k=3) # cut tree into 3 clusters
rect.hclust(fit,k=3,border="red") # draw dendogram with red borders around the 4 clusters
mydata2<-aggregate(MainStudyMelt$MediaRichness,list(MainStudyMelt$SMP),mean)
rownames(mydata2)<-c("Facebook","Twitter","YouTube","Instagram","Pinterest",
"SnapChat","LinkedIn","SecondLife")
d<-dist(mydata2,method="euclidean") # distance matrix
## Warning in dist(mydata2, method = "euclidean"): NAs introduced by coercion
fit <- hclust(d, method="ward")
## The "ward" method has been renamed to "ward.D"; note new "ward.D2"
plot(fit,ylab="Content vividness") # display dendogram mean by SMP
groups <- cutree(fit,k=3) # cut tree into 4 clusters
rect.hclust(fit,k=3,border="red") # draw dendogram with red borders around the 4 clusters
## STUDY 1 e-retailer seconday data
cat("\014") # cleans screen
rm(list=ls(all=TRUE)) # remove variables in working memory
setwd("C:/Users/Erik Ernesto Vazquez/Documents") # sets working directory
X<-read.csv("E-Retailer.csv", skip=0, header=T) # reads raw data from Qualtrics
X<-subset(X,X$Merchandise.Category=="Apparel/Accessories"|
X$Merchandise.Category=="Computers/Electronics"|
X$Merchandise.Category=="Health/Beauty"|
X$Merchandise.Category=="Food/Drug")
X$ProdCat<-ifelse(X$Merchandise.Category=="Computers/Electronics","Search",
ifelse(X$Merchandise.Category=="Apparel/Accessories","Experience",
"Credence"))
X$ProdCatLvl<-ifelse(X$Merchandise.Category=="Computers/Electronics",1,
ifelse(X$Merchandise.Category=="Apparel/Accessories",2,
3))
X<-data.frame(X$Company.Name,X$ProdCat,X$ProdCatLvl,
X$X2011.Monthly.Visits,X$X2011.Conversion.Rate)
MainStudy<-na.omit(X)
aggregate(MainStudy$X.X2011.Monthly.Visits,list(MainStudy$X.ProdCat),mean)
## Group.1 x
## 1 Credence 2457035
## 2 Experience 3589244
## 3 Search 15271531
aggregate(MainStudy$X.X2011.Monthly.Visits,list(MainStudy$X.ProdCat),sd)
## Group.1 x
## 1 Credence 4702492
## 2 Experience 4944603
## 3 Search 72400684
summary(lm(X.X2011.Monthly.Visits~X.ProdCatLvl,MainStudy)) ## H1a Partially Approved
##
## Call:
## lm(formula = X.X2011.Monthly.Visits ~ X.ProdCatLvl, data = MainStudy)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12065709 -5139369 -3311586 999120 487903840
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18410015 6948316 2.65 0.0086 **
## X.ProdCatLvl -6313855 3287770 -1.92 0.0560 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 32710000 on 236 degrees of freedom
## Multiple R-squared: 0.01539, Adjusted R-squared: 0.01121
## F-statistic: 3.688 on 1 and 236 DF, p-value: 0.05601
aggregate(MainStudy$X.X2011.Conversion.Rate,list(MainStudy$X.ProdCat),mean)
## Group.1 x
## 1 Credence 0.05784314
## 2 Experience 0.02956835
## 3 Search 0.02250000
aggregate(MainStudy$X.X2011.Conversion.Rate,list(MainStudy$X.ProdCat),sd)
## Group.1 x
## 1 Credence 0.04244119
## 2 Experience 0.01735689
## 3 Search 0.01344809
summary(lm(X.X2011.Conversion.Rate~X.ProdCatLvl,MainStudy)) ## H1a Approved
##
## Call:
## lm(formula = X.X2011.Conversion.Rate ~ X.ProdCatLvl, data = MainStudy)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.041836 -0.013977 -0.006117 0.006023 0.138164
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.001742 0.005295 -0.329 0.742
## X.ProdCatLvl 0.017859 0.002506 7.128 1.24e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02493 on 236 degrees of freedom
## Multiple R-squared: 0.1771, Adjusted R-squared: 0.1736
## F-statistic: 50.8 on 1 and 236 DF, p-value: 1.235e-11
## STUDY 2 primary data MTurk
cat("\014") # cleans screen
rm(list=ls(all=TRUE)) # remove variables in working memory
setwd("C:/Users/Erik Ernesto Vazquez/Documents") # sets working directory
MainStudy<-read.csv("Main Study 1158 April 2017.csv", skip=2, header=F) # reads raw data from Qualtrics
names(MainStudy)<-names(read.csv("Main Study 1158 April 2017.csv")) # assigns headers and names to data frame
MainStudy<-subset(MainStudy,MainStudy$X60<5) ## Non-repeated measures
MainStudy1<-subset(MainStudy,MainStudy$X207=="YouTube-Search")
MainStudy2<-subset(MainStudy,MainStudy$X207=="YouTube-Experience")
MainStudy3<-subset(MainStudy,MainStudy$X207=="YouTube-Credence")
MainStudy4<-subset(MainStudy,MainStudy$X207=="Facebook-Search")
MainStudy5<-subset(MainStudy,MainStudy$X207=="Facebook-Experience")
MainStudy6<-subset(MainStudy,MainStudy$X207=="Facebook-Credence")
MainStudy7<-subset(MainStudy,MainStudy$X207=="Twitter-Search")
MainStudy8<-subset(MainStudy,MainStudy$X207=="Twitter-Experience")
MainStudy9<-subset(MainStudy,MainStudy$X207=="Twitter-Credence")
MainStudy1<-MainStudy1[order(MainStudy1$X188),]
MainStudy2<-MainStudy2[order(MainStudy2$X188),]
MainStudy3<-MainStudy3[order(MainStudy3$X188,MainStudy3$X202,MainStudy3$X117),]
MainStudy4<-MainStudy4[order(MainStudy4$X188),]
MainStudy5<-MainStudy5[order(MainStudy5$X188),]
MainStudy6<-MainStudy6[order(MainStudy6$X188),]
MainStudy7<-MainStudy7[order(MainStudy7$X188,MainStudy7$X187),]
MainStudy8<-MainStudy8[order(MainStudy8$X188,MainStudy8$X187),]
MainStudy9<-MainStudy9[order(MainStudy9$X188,MainStudy9$X187),]
MainStudy<-rbind(MainStudy1[1:50,],MainStudy1[120:71,],
MainStudy2[1:50,],MainStudy2[123:74,],
MainStudy3[1:47,],MainStudy3[48:97,],MainStudy3[7,],MainStudy3[15,],MainStudy3[17,],
MainStudy4[1:50,],MainStudy4[122:73,],
MainStudy5[1:50,],MainStudy5[110:61,],
MainStudy6[1:50,],MainStudy6[119:70,],
MainStudy7[1:50,],MainStudy7[58:107,],
MainStudy8[1:50,],MainStudy8[52:101,],
MainStudy9[1:50,],MainStudy9[72:121,])
write.csv(MainStudy,file="MainStudy.csv")
table(MainStudy$X188,MainStudy$X207)
##
## Facebook-Credence Facebook-Experience Facebook-Search Twitter-Credence
## 1 50 50 50 50
## 2 50 50 50 50
##
## Twitter-Experience Twitter-Search YouTube-Credence YouTube-Experience
## 1 50 50 50 50
## 2 50 50 50 50
##
## YouTube-Search
## 1 50
## 2 50
aggregate(MainStudy$X117,list(MainStudy$X207),mean)
## Group.1 x
## 1 Facebook-Credence 62.05320
## 2 Facebook-Experience 59.73800
## 3 Facebook-Search 60.32524
## 4 Twitter-Credence 63.94979
## 5 Twitter-Experience 59.04020
## 6 Twitter-Search 62.52855
## 7 YouTube-Credence 59.51666
## 8 YouTube-Experience 61.98686
## 9 YouTube-Search 65.29600
aggregate(scale(MainStudy$X117),list(MainStudy$X207),mean)
## Group.1 V1
## 1 Facebook-Credence 0.02642300
## 2 Facebook-Experience -0.10971201
## 3 Facebook-Search -0.07518198
## 4 Twitter-Credence 0.13794352
## 5 Twitter-Experience -0.15074302
## 6 Twitter-Search 0.05437384
## 7 YouTube-Credence -0.12272692
## 8 YouTube-Experience 0.02252217
## 9 YouTube-Search 0.21710139
aggregate(MainStudy$X117,list(MainStudy$X207),sd)
## Group.1 x
## 1 Facebook-Credence 16.85373
## 2 Facebook-Experience 16.71092
## 3 Facebook-Search 18.32444
## 4 Twitter-Credence 17.18115
## 5 Twitter-Experience 17.59770
## 6 Twitter-Search 18.11519
## 7 YouTube-Credence 15.11658
## 8 YouTube-Experience 16.33752
## 9 YouTube-Search 16.18982
summary(aov(X187~X207,MainStudy)) ## Age
## Df Sum Sq Mean Sq F value Pr(>F)
## X207 8 856 107.00 1.115 0.35
## Residuals 891 85509 95.97
chisq.test(MainStudy$X207,MainStudy$X188) ## Gender
##
## Pearson's Chi-squared test
##
## data: MainStudy$X207 and MainStudy$X188
## X-squared = 0, df = 8, p-value = 1
summary(aov(X189~X207,MainStudy)) ## Income
## Df Sum Sq Mean Sq F value Pr(>F)
## X207 8 16 1.995 0.5 0.857
## Residuals 891 3554 3.989
summary(aov(X194~X207,MainStudy)) ## Education
## Df Sum Sq Mean Sq F value Pr(>F)
## X207 8 27 3.432 0.922 0.498
## Residuals 891 3318 3.724
summary(aov(X202~X207,MainStudy)) ## Location 1
## Df Sum Sq Mean Sq F value Pr(>F)
## X207 8 146 18.22 0.616 0.765
## Residuals 891 26353 29.58
summary(aov(X203~X207,MainStudy)) ## Location 2
## Df Sum Sq Mean Sq F value Pr(>F)
## X207 8 1672 209.0 0.352 0.945
## Residuals 891 528906 593.6
summary(aov(X117~X207,MainStudy)) ## StimuliTime
## Df Sum Sq Mean Sq F value Pr(>F)
## X207 8 3638 454.8 1.581 0.126
## Residuals 891 256376 287.7
summary(aov(X60~X207,MainStudy)) ## BrandFam
## Df Sum Sq Mean Sq F value Pr(>F)
## X207 8 3.5 0.4344 1.025 0.415
## Residuals 891 377.8 0.4240
summary(aov(X187~X206,MainStudy)) ## Age
## Df Sum Sq Mean Sq F value Pr(>F)
## X206 2 69 34.61 0.36 0.698
## Residuals 897 86296 96.20
chisq.test(MainStudy$X206,MainStudy$X188) ## Gender
##
## Pearson's Chi-squared test
##
## data: MainStudy$X206 and MainStudy$X188
## X-squared = 0, df = 2, p-value = 1
summary(aov(X189~X206,MainStudy)) ## Income
## Df Sum Sq Mean Sq F value Pr(>F)
## X206 2 1 0.343 0.086 0.917
## Residuals 897 3570 3.979
summary(aov(X194~X206,MainStudy)) ## Education
## Df Sum Sq Mean Sq F value Pr(>F)
## X206 2 5 2.434 0.654 0.52
## Residuals 897 3341 3.724
summary(aov(X202~X206,MainStudy)) ## Location 1
## Df Sum Sq Mean Sq F value Pr(>F)
## X206 2 49 24.28 0.823 0.439
## Residuals 897 26450 29.49
summary(aov(X203~X206,MainStudy)) ## Location 2
## Df Sum Sq Mean Sq F value Pr(>F)
## X206 2 131 65.3 0.11 0.895
## Residuals 897 530448 591.4
summary(aov(X117~X206,MainStudy)) ## StimuliTime
## Df Sum Sq Mean Sq F value Pr(>F)
## X206 2 934 467.0 1.617 0.199
## Residuals 897 259080 288.8
summary(aov(X60~X206,MainStudy)) ## BrandFam
## Df Sum Sq Mean Sq F value Pr(>F)
## X206 2 1.1 0.5344 1.261 0.284
## Residuals 897 380.2 0.4239
summary(aov(X187~X205,MainStudy)) ## Age
## Df Sum Sq Mean Sq F value Pr(>F)
## X205 2 58 29.15 0.303 0.739
## Residuals 897 86307 96.22
chisq.test(MainStudy$X205,MainStudy$X188) ## Gender
##
## Pearson's Chi-squared test
##
## data: MainStudy$X205 and MainStudy$X188
## X-squared = 0, df = 2, p-value = 1
summary(aov(X189~X205,MainStudy)) ## Income
## Df Sum Sq Mean Sq F value Pr(>F)
## X205 2 8 3.803 0.958 0.384
## Residuals 897 3563 3.972
summary(aov(X194~X205,MainStudy)) ## Education
## Df Sum Sq Mean Sq F value Pr(>F)
## X205 2 13 6.341 1.707 0.182
## Residuals 897 3333 3.716
summary(aov(X202~X205,MainStudy)) ## Location 1
## Df Sum Sq Mean Sq F value Pr(>F)
## X205 2 48 23.75 0.806 0.447
## Residuals 897 26451 29.49
summary(aov(X203~X205,MainStudy)) ## Location 2
## Df Sum Sq Mean Sq F value Pr(>F)
## X205 2 686 343.2 0.581 0.56
## Residuals 897 529892 590.7
summary(aov(X117~X205,MainStudy)) ## StimuliTime
## Df Sum Sq Mean Sq F value Pr(>F)
## X205 2 391 195.3 0.675 0.51
## Residuals 897 259624 289.4
summary(aov(X60~X205,MainStudy)) ## BrandFam
## Df Sum Sq Mean Sq F value Pr(>F)
## X205 2 0.1 0.0544 0.128 0.88
## Residuals 897 381.2 0.4249
aov.out<-aov(X60~X207,MainStudy)
TukeyHSD(aov.out)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = X60 ~ X207, data = MainStudy)
##
## $X207
## diff lwr upr
## Facebook-Experience-Facebook-Credence 4.000000e-02 -0.2463386 0.3263386
## Facebook-Search-Facebook-Credence 1.700000e-01 -0.1163386 0.4563386
## Twitter-Credence-Facebook-Credence -1.000000e-02 -0.2963386 0.2763386
## Twitter-Experience-Facebook-Credence 1.000000e-01 -0.1863386 0.3863386
## Twitter-Search-Facebook-Credence 7.000000e-02 -0.2163386 0.3563386
## YouTube-Credence-Facebook-Credence 7.000000e-02 -0.2163386 0.3563386
## YouTube-Experience-Facebook-Credence 1.600000e-01 -0.1263386 0.4463386
## YouTube-Search-Facebook-Credence 1.000000e-02 -0.2763386 0.2963386
## Facebook-Search-Facebook-Experience 1.300000e-01 -0.1563386 0.4163386
## Twitter-Credence-Facebook-Experience -5.000000e-02 -0.3363386 0.2363386
## Twitter-Experience-Facebook-Experience 6.000000e-02 -0.2263386 0.3463386
## Twitter-Search-Facebook-Experience 3.000000e-02 -0.2563386 0.3163386
## YouTube-Credence-Facebook-Experience 3.000000e-02 -0.2563386 0.3163386
## YouTube-Experience-Facebook-Experience 1.200000e-01 -0.1663386 0.4063386
## YouTube-Search-Facebook-Experience -3.000000e-02 -0.3163386 0.2563386
## Twitter-Credence-Facebook-Search -1.800000e-01 -0.4663386 0.1063386
## Twitter-Experience-Facebook-Search -7.000000e-02 -0.3563386 0.2163386
## Twitter-Search-Facebook-Search -1.000000e-01 -0.3863386 0.1863386
## YouTube-Credence-Facebook-Search -1.000000e-01 -0.3863386 0.1863386
## YouTube-Experience-Facebook-Search -1.000000e-02 -0.2963386 0.2763386
## YouTube-Search-Facebook-Search -1.600000e-01 -0.4463386 0.1263386
## Twitter-Experience-Twitter-Credence 1.100000e-01 -0.1763386 0.3963386
## Twitter-Search-Twitter-Credence 8.000000e-02 -0.2063386 0.3663386
## YouTube-Credence-Twitter-Credence 8.000000e-02 -0.2063386 0.3663386
## YouTube-Experience-Twitter-Credence 1.700000e-01 -0.1163386 0.4563386
## YouTube-Search-Twitter-Credence 2.000000e-02 -0.2663386 0.3063386
## Twitter-Search-Twitter-Experience -3.000000e-02 -0.3163386 0.2563386
## YouTube-Credence-Twitter-Experience -3.000000e-02 -0.3163386 0.2563386
## YouTube-Experience-Twitter-Experience 6.000000e-02 -0.2263386 0.3463386
## YouTube-Search-Twitter-Experience -9.000000e-02 -0.3763386 0.1963386
## YouTube-Credence-Twitter-Search -2.220446e-16 -0.2863386 0.2863386
## YouTube-Experience-Twitter-Search 9.000000e-02 -0.1963386 0.3763386
## YouTube-Search-Twitter-Search -6.000000e-02 -0.3463386 0.2263386
## YouTube-Experience-YouTube-Credence 9.000000e-02 -0.1963386 0.3763386
## YouTube-Search-YouTube-Credence -6.000000e-02 -0.3463386 0.2263386
## YouTube-Search-YouTube-Experience -1.500000e-01 -0.4363386 0.1363386
## p adj
## Facebook-Experience-Facebook-Credence 0.9999661
## Facebook-Search-Facebook-Credence 0.6511750
## Twitter-Credence-Facebook-Credence 1.0000000
## Twitter-Experience-Facebook-Credence 0.9762166
## Twitter-Search-Facebook-Credence 0.9978162
## YouTube-Credence-Facebook-Credence 0.9978162
## YouTube-Experience-Facebook-Credence 0.7231143
## YouTube-Search-Facebook-Credence 1.0000000
## Facebook-Search-Facebook-Experience 0.8934258
## Twitter-Credence-Facebook-Experience 0.9998145
## Twitter-Experience-Facebook-Experience 0.9992813
## Twitter-Search-Facebook-Experience 0.9999964
## YouTube-Credence-Facebook-Experience 0.9999964
## YouTube-Experience-Facebook-Experience 0.9304868
## YouTube-Search-Facebook-Experience 0.9999964
## Twitter-Credence-Facebook-Search 0.5756534
## Twitter-Experience-Facebook-Search 0.9978162
## Twitter-Search-Facebook-Search 0.9762166
## YouTube-Credence-Facebook-Search 0.9762166
## YouTube-Experience-Facebook-Search 1.0000000
## YouTube-Search-Facebook-Search 0.7231143
## Twitter-Experience-Twitter-Credence 0.9576724
## Twitter-Search-Twitter-Credence 0.9944689
## YouTube-Credence-Twitter-Credence 0.9944689
## YouTube-Experience-Twitter-Credence 0.6511750
## YouTube-Search-Twitter-Credence 0.9999999
## Twitter-Search-Twitter-Experience 0.9999964
## YouTube-Credence-Twitter-Experience 0.9999964
## YouTube-Experience-Twitter-Experience 0.9992813
## YouTube-Search-Twitter-Experience 0.9878511
## YouTube-Credence-Twitter-Search 1.0000000
## YouTube-Experience-Twitter-Search 0.9878511
## YouTube-Search-Twitter-Search 0.9992813
## YouTube-Experience-YouTube-Credence 0.9878511
## YouTube-Search-YouTube-Credence 0.9992813
## YouTube-Search-YouTube-Experience 0.7887943
Demographics<-cbind(MainStudy[187:189],MainStudy[194:195],MainStudy[202:207])
Demographics$Age<-2014-MainStudy$X187
Demographics$Income<-MainStudy$X189
Demographics$Education<-MainStudy$X194
Demographics$Location1<-Demographics$X202
Demographics$Location2<-Demographics$X203
Demographics$AgeRange<-ifelse(Demographics$Age<21,1,ifelse(Demographics$Age>50,5,ifelse(Demographics$Age>20&Demographics$Age<29,2,ifelse(Demographics$Age>28&Demographics$Age<35,3,4))))
Demographics$IncomeRange<-ifelse(Demographics$Income<3,1,ifelse(Demographics$Income>7,5,ifelse(Demographics$Income>2&Demographics$Income<5,2,ifelse(Demographics$Income>4&Demographics$Income<7,3,4))))
Demographics$EducationRange<-ifelse(Demographics$Education<8,1,ifelse(Demographics$Education>12,5,ifelse(Demographics$Education==8,2,ifelse(Demographics$Education==12,4,3))))
nrow(Demographics)
## [1] 900
ftable(Demographics$AgeRange~Demographics$X207)
## Demographics$AgeRange 1 2 3 4 5
## Demographics$X207
## Facebook-Credence 4 39 21 28 8
## Facebook-Experience 4 36 26 26 8
## Facebook-Search 6 37 21 32 4
## Twitter-Credence 0 43 18 33 6
## Twitter-Experience 5 46 18 28 3
## Twitter-Search 0 44 17 31 8
## YouTube-Credence 4 46 20 26 4
## YouTube-Experience 6 35 18 35 6
## YouTube-Search 7 36 20 30 7
aggregate(Demographics$Age,list(Demographics$X207),mean)
## Group.1 x
## 1 Facebook-Credence 33.05
## 2 Facebook-Experience 33.09
## 3 Facebook-Search 32.20
## 4 Twitter-Credence 33.81
## 5 Twitter-Experience 30.54
## 6 Twitter-Search 33.23
## 7 YouTube-Credence 31.20
## 8 YouTube-Experience 32.66
## 9 YouTube-Search 32.62
aggregate(Demographics$Age,list(Demographics$X207),sd)
## Group.1 x
## 1 Facebook-Credence 10.517302
## 2 Facebook-Experience 10.157442
## 3 Facebook-Search 9.443078
## 4 Twitter-Credence 9.908363
## 5 Twitter-Experience 8.559265
## 6 Twitter-Search 9.946173
## 7 YouTube-Credence 9.467648
## 8 YouTube-Experience 9.677862
## 9 YouTube-Search 10.349235
ftable(Demographics$IncomeRange~Demographics$X207)
## Demographics$IncomeRange 1 2 3 4 5
## Demographics$X207
## Facebook-Credence 17 26 45 11 1
## Facebook-Experience 28 28 25 17 2
## Facebook-Search 22 31 33 12 2
## Twitter-Credence 19 36 31 9 5
## Twitter-Experience 18 28 37 11 6
## Twitter-Search 24 26 27 16 7
## YouTube-Credence 26 32 28 13 1
## YouTube-Experience 28 21 33 15 3
## YouTube-Search 25 24 34 10 7
aggregate(Demographics$Income,list(Demographics$X207),mean)
## Group.1 x
## 1 Facebook-Credence 4.38
## 2 Facebook-Experience 4.13
## 3 Facebook-Search 4.20
## 4 Twitter-Credence 4.29
## 5 Twitter-Experience 4.53
## 6 Twitter-Search 4.48
## 7 YouTube-Credence 4.15
## 8 YouTube-Experience 4.23
## 9 YouTube-Search 4.34
aggregate(Demographics$Income,list(Demographics$X207),sd)
## Group.1 x
## 1 Facebook-Credence 1.830052
## 2 Facebook-Experience 2.082418
## 3 Facebook-Search 1.974586
## 4 Twitter-Credence 1.810714
## 5 Twitter-Experience 1.971796
## 6 Twitter-Search 2.171812
## 7 YouTube-Credence 1.929960
## 8 YouTube-Experience 2.073668
## 9 YouTube-Search 2.099639
ftable(Demographics$Income~Demographics$X207)
## Demographics$Income 1 2 3 4 5 6 7 8 9
## Demographics$X207
## Facebook-Credence 13 4 11 15 31 14 11 1 0
## Facebook-Experience 11 17 15 13 15 10 17 1 1
## Facebook-Search 14 8 14 17 18 15 12 2 0
## Twitter-Credence 4 15 15 21 24 7 9 4 1
## Twitter-Experience 8 10 12 16 24 13 11 4 2
## Twitter-Search 11 13 8 18 17 10 16 5 2
## YouTube-Credence 11 15 6 26 16 12 13 0 1
## YouTube-Experience 12 16 10 11 22 11 15 3 0
## YouTube-Search 11 14 9 15 24 10 10 5 2
ftable(Demographics$EducationRange~Demographics$X207)
## Demographics$EducationRange 1 2 3 4 5
## Demographics$X207
## Facebook-Credence 2 8 45 32 13
## Facebook-Experience 0 14 43 29 14
## Facebook-Search 0 10 41 33 16
## Twitter-Credence 2 12 43 28 15
## Twitter-Experience 1 10 35 40 14
## Twitter-Search 2 8 43 30 17
## YouTube-Credence 3 13 35 37 12
## YouTube-Experience 5 15 42 25 13
## YouTube-Search 5 9 39 31 16
aggregate(Demographics$Education,list(Demographics$X207),mean)
## Group.1 x
## 1 Facebook-Credence 10.58
## 2 Facebook-Experience 10.61
## 3 Facebook-Search 10.81
## 4 Twitter-Credence 10.49
## 5 Twitter-Experience 10.81
## 6 Twitter-Search 10.67
## 7 YouTube-Credence 10.46
## 8 YouTube-Experience 10.21
## 9 YouTube-Search 10.56
aggregate(Demographics$Education,list(Demographics$X207),sd)
## Group.1 x
## 1 Facebook-Credence 1.837763
## 2 Facebook-Experience 1.879689
## 3 Facebook-Search 1.709717
## 4 Twitter-Credence 2.081642
## 5 Twitter-Experience 1.846071
## 6 Twitter-Search 1.885913
## 7 YouTube-Credence 2.012185
## 8 YouTube-Experience 2.031544
## 9 YouTube-Search 2.051459
ftable(Demographics$Education~Demographics$X207)
## Demographics$Education 1 5 6 7 8 9 10 11 12 13 14 15
## Demographics$X207
## Facebook-Credence 0 0 1 1 8 34 2 9 32 11 1 1
## Facebook-Experience 0 0 0 0 14 27 7 9 29 9 3 2
## Facebook-Search 0 0 0 0 10 23 9 9 33 15 1 0
## Twitter-Credence 1 0 0 1 12 29 5 9 28 12 2 1
## Twitter-Experience 0 0 0 1 10 26 6 3 40 11 1 2
## Twitter-Search 0 0 0 2 8 32 6 5 30 13 3 1
## YouTube-Credence 0 1 1 1 13 31 3 1 37 10 0 2
## YouTube-Experience 0 0 1 4 15 33 4 5 25 9 2 2
## YouTube-Search 0 1 0 4 9 28 9 2 31 11 3 2
aggregate(Demographics$Location1,list(Demographics$X207),mean)
## Group.1 x
## 1 Facebook-Credence 36.79035
## 2 Facebook-Experience 37.14975
## 3 Facebook-Search 37.33771
## 4 Twitter-Credence 37.03470
## 5 Twitter-Experience 36.62884
## 6 Twitter-Search 37.80277
## 7 YouTube-Credence 37.34193
## 8 YouTube-Experience 37.81276
## 9 YouTube-Search 37.67040
aggregate(Demographics$Location1,list(Demographics$X207),sd)
## Group.1 x
## 1 Facebook-Credence 6.154053
## 2 Facebook-Experience 5.607674
## 3 Facebook-Search 5.496958
## 4 Twitter-Credence 4.865277
## 5 Twitter-Experience 5.355676
## 6 Twitter-Search 5.547963
## 7 YouTube-Credence 5.225834
## 8 YouTube-Experience 4.847807
## 9 YouTube-Search 5.719205
aggregate(Demographics$Location2,list(Demographics$X207),mean)
## Group.1 x
## 1 Facebook-Credence -86.74644
## 2 Facebook-Experience -87.63357
## 3 Facebook-Search -88.75820
## 4 Twitter-Credence -90.68666
## 5 Twitter-Experience -87.78349
## 6 Twitter-Search -90.70321
## 7 YouTube-Credence -88.57208
## 8 YouTube-Experience -90.53339
## 9 YouTube-Search -88.93982
aggregate(Demographics$Location2,list(Demographics$X207),sd)
## Group.1 x
## 1 Facebook-Credence 24.82919
## 2 Facebook-Experience 25.62070
## 3 Facebook-Search 26.75240
## 4 Twitter-Credence 23.05074
## 5 Twitter-Experience 27.10145
## 6 Twitter-Search 26.45972
## 7 YouTube-Credence 23.55533
## 8 YouTube-Experience 15.74071
## 9 YouTube-Search 24.19343
aggregate(MainStudy$X117,list(MainStudy$X207),mean)
## Group.1 x
## 1 Facebook-Credence 62.05320
## 2 Facebook-Experience 59.73800
## 3 Facebook-Search 60.32524
## 4 Twitter-Credence 63.94979
## 5 Twitter-Experience 59.04020
## 6 Twitter-Search 62.52855
## 7 YouTube-Credence 59.51666
## 8 YouTube-Experience 61.98686
## 9 YouTube-Search 65.29600
aggregate(MainStudy$X117,list(MainStudy$X207),sd)
## Group.1 x
## 1 Facebook-Credence 16.85373
## 2 Facebook-Experience 16.71092
## 3 Facebook-Search 18.32444
## 4 Twitter-Credence 17.18115
## 5 Twitter-Experience 17.59770
## 6 Twitter-Search 18.11519
## 7 YouTube-Credence 15.11658
## 8 YouTube-Experience 16.33752
## 9 YouTube-Search 16.18982
aggregate(MainStudy$X60,list(MainStudy$X207),mean)
## Group.1 x
## 1 Facebook-Credence 1.30
## 2 Facebook-Experience 1.34
## 3 Facebook-Search 1.47
## 4 Twitter-Credence 1.29
## 5 Twitter-Experience 1.40
## 6 Twitter-Search 1.37
## 7 YouTube-Credence 1.37
## 8 YouTube-Experience 1.46
## 9 YouTube-Search 1.31
aggregate(MainStudy$X60,list(MainStudy$X207),sd)
## Group.1 x
## 1 Facebook-Credence 0.5595814
## 2 Facebook-Experience 0.6699917
## 3 Facebook-Search 0.7971540
## 4 Twitter-Credence 0.6558979
## 5 Twitter-Experience 0.6513389
## 6 Twitter-Search 0.5252224
## 7 YouTube-Credence 0.6912878
## 8 YouTube-Experience 0.6878454
## 9 YouTube-Search 0.5807519
## 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(Demographics$Location2)
lat<-as.character(Demographics$Location1)
coord<-mapproject(lon, lat, proj="gilbert", orientation=c(90, 0, 225))
points(coord, pch=20, cex=0.8, col="black")
cronbach(cbind(MainStudy$X126,MainStudy$X127,MainStudy$X128,MainStudy$X134,MainStudy$X135)) ## Quality Alpha 0.82 Good
## $sample.size
## [1] 900
##
## $number.of.items
## [1] 5
##
## $alpha
## [1] 0.9084386
MainStudy$Quality<-(MainStudy$X126+MainStudy$X127+MainStudy$X128+MainStudy$X134+MainStudy$X135)/5
2014-mean(MainStudy$X187)
## [1] 32.48889
sd(MainStudy$X187)
## [1] 9.801415
aggregate(2014-MainStudy$X187,list(MainStudy$X207),mean)
## Group.1 x
## 1 Facebook-Credence 33.05
## 2 Facebook-Experience 33.09
## 3 Facebook-Search 32.20
## 4 Twitter-Credence 33.81
## 5 Twitter-Experience 30.54
## 6 Twitter-Search 33.23
## 7 YouTube-Credence 31.20
## 8 YouTube-Experience 32.66
## 9 YouTube-Search 32.62
aggregate(MainStudy$X187,list(MainStudy$X207),sd)
## Group.1 x
## 1 Facebook-Credence 10.517302
## 2 Facebook-Experience 10.157442
## 3 Facebook-Search 9.443078
## 4 Twitter-Credence 9.908363
## 5 Twitter-Experience 8.559265
## 6 Twitter-Search 9.946173
## 7 YouTube-Credence 9.467648
## 8 YouTube-Experience 9.677862
## 9 YouTube-Search 10.349235
aggregate(MainStudy$Quality,list(MainStudy$X207),mean)
## Group.1 x
## 1 Facebook-Credence 5.402
## 2 Facebook-Experience 5.792
## 3 Facebook-Search 5.336
## 4 Twitter-Credence 5.254
## 5 Twitter-Experience 5.732
## 6 Twitter-Search 6.126
## 7 YouTube-Credence 5.514
## 8 YouTube-Experience 5.970
## 9 YouTube-Search 6.276
aggregate(MainStudy$Quality,list(MainStudy$X207),sd)
## Group.1 x
## 1 Facebook-Credence 1.2363174
## 2 Facebook-Experience 1.0909805
## 3 Facebook-Search 1.6572778
## 4 Twitter-Credence 1.4269711
## 5 Twitter-Experience 1.2398175
## 6 Twitter-Search 1.2819351
## 7 YouTube-Credence 1.2408062
## 8 YouTube-Experience 1.0852082
## 9 YouTube-Search 0.9972812
## Brand Familiarity
aggregate(MainStudy$X60,list(MainStudy$X207),mean)
## Group.1 x
## 1 Facebook-Credence 1.30
## 2 Facebook-Experience 1.34
## 3 Facebook-Search 1.47
## 4 Twitter-Credence 1.29
## 5 Twitter-Experience 1.40
## 6 Twitter-Search 1.37
## 7 YouTube-Credence 1.37
## 8 YouTube-Experience 1.46
## 9 YouTube-Search 1.31
aggregate(MainStudy$X60,list(MainStudy$X207),sd)
## Group.1 x
## 1 Facebook-Credence 0.5595814
## 2 Facebook-Experience 0.6699917
## 3 Facebook-Search 0.7971540
## 4 Twitter-Credence 0.6558979
## 5 Twitter-Experience 0.6513389
## 6 Twitter-Search 0.5252224
## 7 YouTube-Credence 0.6912878
## 8 YouTube-Experience 0.6878454
## 9 YouTube-Search 0.5807519
mean(MainStudy$X60)
## [1] 1.367778
sd(MainStudy$X60)
## [1] 0.6512293
## Effects of content richness and product cat on quality
aggregate(MainStudy$Quality,list(MainStudy$X207),mean)
## Group.1 x
## 1 Facebook-Credence 5.402
## 2 Facebook-Experience 5.792
## 3 Facebook-Search 5.336
## 4 Twitter-Credence 5.254
## 5 Twitter-Experience 5.732
## 6 Twitter-Search 6.126
## 7 YouTube-Credence 5.514
## 8 YouTube-Experience 5.970
## 9 YouTube-Search 6.276
aggregate(MainStudy$Quality,list(MainStudy$X207),sd)
## Group.1 x
## 1 Facebook-Credence 1.2363174
## 2 Facebook-Experience 1.0909805
## 3 Facebook-Search 1.6572778
## 4 Twitter-Credence 1.4269711
## 5 Twitter-Experience 1.2398175
## 6 Twitter-Search 1.2819351
## 7 YouTube-Credence 1.2408062
## 8 YouTube-Experience 1.0852082
## 9 YouTube-Search 0.9972812
aov.out<-aov(Quality~X207,MainStudy)
TukeyHSD(aov.out)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Quality ~ X207, data = MainStudy)
##
## $X207
## diff lwr upr
## Facebook-Experience-Facebook-Credence 0.390 -0.16611075 0.94611075
## Facebook-Search-Facebook-Credence -0.066 -0.62211075 0.49011075
## Twitter-Credence-Facebook-Credence -0.148 -0.70411075 0.40811075
## Twitter-Experience-Facebook-Credence 0.330 -0.22611075 0.88611075
## Twitter-Search-Facebook-Credence 0.724 0.16788925 1.28011075
## YouTube-Credence-Facebook-Credence 0.112 -0.44411075 0.66811075
## YouTube-Experience-Facebook-Credence 0.568 0.01188925 1.12411075
## YouTube-Search-Facebook-Credence 0.874 0.31788925 1.43011075
## Facebook-Search-Facebook-Experience -0.456 -1.01211075 0.10011075
## Twitter-Credence-Facebook-Experience -0.538 -1.09411075 0.01811075
## Twitter-Experience-Facebook-Experience -0.060 -0.61611075 0.49611075
## Twitter-Search-Facebook-Experience 0.334 -0.22211075 0.89011075
## YouTube-Credence-Facebook-Experience -0.278 -0.83411075 0.27811075
## YouTube-Experience-Facebook-Experience 0.178 -0.37811075 0.73411075
## YouTube-Search-Facebook-Experience 0.484 -0.07211075 1.04011075
## Twitter-Credence-Facebook-Search -0.082 -0.63811075 0.47411075
## Twitter-Experience-Facebook-Search 0.396 -0.16011075 0.95211075
## Twitter-Search-Facebook-Search 0.790 0.23388925 1.34611075
## YouTube-Credence-Facebook-Search 0.178 -0.37811075 0.73411075
## YouTube-Experience-Facebook-Search 0.634 0.07788925 1.19011075
## YouTube-Search-Facebook-Search 0.940 0.38388925 1.49611075
## Twitter-Experience-Twitter-Credence 0.478 -0.07811075 1.03411075
## Twitter-Search-Twitter-Credence 0.872 0.31588925 1.42811075
## YouTube-Credence-Twitter-Credence 0.260 -0.29611075 0.81611075
## YouTube-Experience-Twitter-Credence 0.716 0.15988925 1.27211075
## YouTube-Search-Twitter-Credence 1.022 0.46588925 1.57811075
## Twitter-Search-Twitter-Experience 0.394 -0.16211075 0.95011075
## YouTube-Credence-Twitter-Experience -0.218 -0.77411075 0.33811075
## YouTube-Experience-Twitter-Experience 0.238 -0.31811075 0.79411075
## YouTube-Search-Twitter-Experience 0.544 -0.01211075 1.10011075
## YouTube-Credence-Twitter-Search -0.612 -1.16811075 -0.05588925
## YouTube-Experience-Twitter-Search -0.156 -0.71211075 0.40011075
## YouTube-Search-Twitter-Search 0.150 -0.40611075 0.70611075
## YouTube-Experience-YouTube-Credence 0.456 -0.10011075 1.01211075
## YouTube-Search-YouTube-Credence 0.762 0.20588925 1.31811075
## YouTube-Search-YouTube-Experience 0.306 -0.25011075 0.86211075
## p adj
## Facebook-Experience-Facebook-Credence 0.4192655
## Facebook-Search-Facebook-Credence 0.9999904
## Twitter-Credence-Facebook-Credence 0.9960400
## Twitter-Experience-Facebook-Credence 0.6518033
## Twitter-Search-Facebook-Credence 0.0018343
## YouTube-Credence-Facebook-Credence 0.9994629
## YouTube-Experience-Facebook-Credence 0.0409829
## YouTube-Search-Facebook-Credence 0.0000426
## Facebook-Search-Facebook-Experience 0.2099053
## Twitter-Credence-Facebook-Experience 0.0669387
## Twitter-Experience-Facebook-Experience 0.9999954
## Twitter-Search-Facebook-Experience 0.6364544
## YouTube-Credence-Facebook-Experience 0.8290689
## YouTube-Experience-Facebook-Experience 0.9863260
## YouTube-Search-Facebook-Experience 0.1469837
## Twitter-Credence-Facebook-Search 0.9999487
## Twitter-Experience-Facebook-Search 0.3972076
## Twitter-Search-Facebook-Search 0.0003822
## YouTube-Credence-Facebook-Search 0.9863260
## YouTube-Experience-Facebook-Search 0.0122796
## YouTube-Search-Facebook-Search 0.0000065
## Twitter-Experience-Twitter-Credence 0.1591300
## Twitter-Search-Twitter-Credence 0.0000450
## YouTube-Credence-Twitter-Credence 0.8762935
## YouTube-Experience-Twitter-Credence 0.0021969
## YouTube-Search-Twitter-Credence 0.0000005
## Twitter-Search-Twitter-Experience 0.4045090
## YouTube-Credence-Twitter-Experience 0.9523703
## YouTube-Experience-Twitter-Experience 0.9220249
## YouTube-Search-Twitter-Experience 0.0608634
## YouTube-Credence-Twitter-Search 0.0186954
## YouTube-Experience-Twitter-Search 0.9943145
## YouTube-Search-Twitter-Search 0.9956550
## YouTube-Experience-YouTube-Credence 0.2099053
## YouTube-Search-YouTube-Credence 0.0007565
## YouTube-Search-YouTube-Experience 0.7398261
summary(aov(Quality~X205+X206,MainStudy))
## Df Sum Sq Mean Sq F value Pr(>F)
## X205 2 25.2 12.620 7.751 0.00046 ***
## X206 2 47.5 23.729 14.574 5.91e-07 ***
## Residuals 895 1457.2 1.628
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
aggregate(MainStudy$Quality,list(MainStudy$X205),mean)
## Group.1 x
## 1 Facebook 5.510
## 2 Twitter 5.704
## 3 YouTube 5.920
aggregate(MainStudy$Quality,list(MainStudy$X205),sd)
## Group.1 x
## 1 Facebook 1.360221
## 2 Twitter 1.361923
## 3 YouTube 1.152110
aggregate(MainStudy$Quality,list(MainStudy$X206),mean)
## Group.1 x
## 1 Credence 5.390000
## 2 Experience 5.831333
## 3 Search 5.912667
aggregate(MainStudy$Quality,list(MainStudy$X206),sd)
## Group.1 x
## 1 Credence 1.304392
## 2 Experience 1.141591
## 3 Search 1.397647
aov.out<-aov(Quality~X205+X206,MainStudy)
TukeyHSD(aov.out)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Quality ~ X205 + X206, data = MainStudy)
##
## $X205
## diff lwr upr p adj
## Twitter-Facebook 0.194 -0.0505869 0.4385869 0.1504328
## YouTube-Facebook 0.410 0.1654131 0.6545869 0.0002637
## YouTube-Twitter 0.216 -0.0285869 0.4605869 0.0960073
##
## $X206
## diff lwr upr p adj
## Experience-Credence 0.44133333 0.1967464 0.6859202 0.0000744
## Search-Credence 0.52266667 0.2780798 0.7672536 0.0000019
## Search-Experience 0.08133333 -0.1632536 0.3259202 0.7150230
MainStudy$TieStr<-ifelse(MainStudy$X205=="Facebook","Strong","Weak")
MainStudy$TieStrLvl<-ifelse(MainStudy$X205=="Facebook",3,
ifelse(MainStudy$X205=="Twitter",2,1))
MainStudy$MR<-ifelse(MainStudy$X205=="Twitter","Poor","Rich")
MainStudy$MRLvl<-ifelse(MainStudy$X205=="Twitter",1,
ifelse(MainStudy$X205=="Facebook",2,3))
MainStudy$Pure<-ifelse(MainStudy$X205=="YouTube","Mix","Pure")
MainStudyX<-subset(MainStudy,MainStudy$X206=="Search")
aggregate(MainStudyX$Quality,list(MainStudyX$MR),mean)
## Group.1 x
## 1 Poor 6.126
## 2 Rich 5.806
aggregate(MainStudyX$Quality,list(MainStudyX$MR),sd)
## Group.1 x
## 1 Poor 1.281935
## 2 Rich 1.443323
## Cohen d 0.23442944671866184 and effect size r 0.11641770326988482
aggregate(MainStudyX$Quality,list(MainStudyX$TieStr),mean)
## Group.1 x
## 1 Strong 5.336
## 2 Weak 6.201
aggregate(MainStudyX$Quality,list(MainStudyX$TieStr),sd)
## Group.1 x
## 1 Strong 1.657278
## 2 Weak 1.148037
## Cohen d 0.6067703798001213 and effect size r 0.29031841991493496
## Cohen suggests that d values of 0.2, 0.5, and 0.8
## represent small, medium, and large effect sizes respectively
## Hence, small-medium effect from vividness vs medium-large effect from tie str
## H2a approved
t.test(Quality~TieStr,MainStudyX) ## H2b approved
##
## Welch Two Sample t-test
##
## data: Quality by TieStr
## t = -4.6873, df = 147.97, p-value = 6.229e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.2296777 -0.5003223
## sample estimates:
## mean in group Strong mean in group Weak
## 5.336 6.201
summary(lm(Quality~TieStrLvl,MainStudyX))
##
## Call:
## lm(formula = Quality ~ TieStrLvl, data = MainStudyX)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4427 -0.7977 0.0173 1.0173 3.0873
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.85267 0.20560 33.329 < 2e-16 ***
## TieStrLvl -0.47000 0.09518 -4.938 1.32e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.346 on 298 degrees of freedom
## Multiple R-squared: 0.07564, Adjusted R-squared: 0.07254
## F-statistic: 24.39 on 1 and 298 DF, p-value: 1.316e-06
t.test(Quality~MR,MainStudyX)
##
## Welch Two Sample t-test
##
## data: Quality by MR
## t = 1.9529, df = 220.25, p-value = 0.0521
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.002930428 0.642930428
## sample estimates:
## mean in group Poor mean in group Rich
## 6.126 5.806
summary(lm(Quality~MRLvl,MainStudyX))
##
## Call:
## lm(formula = Quality ~ MRLvl, data = MainStudyX)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9127 -0.8377 0.0123 1.0123 3.1623
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.7627 0.2137 26.973 <2e-16 ***
## MRLvl 0.0750 0.0989 0.758 0.449
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.399 on 298 degrees of freedom
## Multiple R-squared: 0.001926, Adjusted R-squared: -0.001423
## F-statistic: 0.5751 on 1 and 298 DF, p-value: 0.4488
MainStudyX<-subset(MainStudy,MainStudy$X206=="Experience")
aggregate(MainStudyX$Quality,list(MainStudyX$MR),mean)
## Group.1 x
## 1 Poor 5.732
## 2 Rich 5.881
aggregate(MainStudyX$Quality,list(MainStudyX$MR),sd)
## Group.1 x
## 1 Poor 1.239818
## 2 Rich 1.089022
## Cohen d 0.12769328761125776 and effect size r 0.06371690827553132
aggregate(MainStudyX$Quality,list(MainStudyX$TieStr),mean)
## Group.1 x
## 1 Strong 5.792
## 2 Weak 5.851
aggregate(MainStudyX$Quality,list(MainStudyX$TieStr),sd)
## Group.1 x
## 1 Strong 1.090980
## 2 Weak 1.168256
## Cohen d 0.052199519508090444 and effect size r 0.02609087474810489
## Cohen suggests that d values of 0.2, 0.5, and 0.8
## represent small, medium, and large effect sizes respectively
## Hence, small effect from vividness vs small effect from tie str
## H3a approved
t.test(Quality~TieStr,MainStudyX) ## H3b rejected ** tie strength does not affect
##
## Welch Two Sample t-test
##
## data: Quality by TieStr
## t = -0.43115, df = 210.62, p-value = 0.6668
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.3287609 0.2107609
## sample estimates:
## mean in group Strong mean in group Weak
## 5.792 5.851
summary(lm(Quality~TieStrLvl,MainStudyX))
##
## Call:
## lm(formula = Quality ~ TieStrLvl, data = MainStudyX)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8313 -0.7423 -0.1313 0.7909 3.2577
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.00933 0.17432 34.473 <2e-16 ***
## TieStrLvl -0.08900 0.08069 -1.103 0.271
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.141 on 298 degrees of freedom
## Multiple R-squared: 0.004066, Adjusted R-squared: 0.0007235
## F-statistic: 1.216 on 1 and 298 DF, p-value: 0.2709
t.test(Quality~MR,MainStudyX)
##
## Welch Two Sample t-test
##
## data: Quality by MR
## t = -1.0209, df = 177.01, p-value = 0.3087
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.4370254 0.1390254
## sample estimates:
## mean in group Poor mean in group Rich
## 5.732 5.881
summary(lm(Quality~MRLvl,MainStudyX))
##
## Call:
## lm(formula = Quality ~ MRLvl, data = MainStudyX)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8313 -0.7123 -0.1123 0.8497 3.1687
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.59333 0.17404 32.139 <2e-16 ***
## MRLvl 0.11900 0.08056 1.477 0.141
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.139 on 298 degrees of freedom
## Multiple R-squared: 0.007268, Adjusted R-squared: 0.003937
## F-statistic: 2.182 on 1 and 298 DF, p-value: 0.1407
MainStudyX<-subset(MainStudy,MainStudy$X206=="Credence")
aggregate(MainStudyX$Quality,list(MainStudyX$MR),mean)
## Group.1 x
## 1 Poor 5.254
## 2 Rich 5.458
aggregate(MainStudyX$Quality,list(MainStudyX$MR),sd)
## Group.1 x
## 1 Poor 1.426971
## 2 Rich 1.236723
## Cohen d 0.15278155887796593 and effect size r 0.07616885908703643
aggregate(MainStudyX$Quality,list(MainStudyX$TieStr),mean)
## Group.1 x
## 1 Strong 5.402
## 2 Weak 5.384
aggregate(MainStudyX$Quality,list(MainStudyX$TieStr),sd)
## Group.1 x
## 1 Strong 1.236317
## 2 Weak 1.340121
## Cohen d 0.013961452515901696 and effect size r 0.00698055617689004
## Cohen suggests that d values of 0.2, 0.5, and 0.8
## represent small, medium, and large effect sizes respectively
## Hence, small effect from vividness vs very small effect from tie str
## H4a rejected ** although there is a trend in the direction of the hypothesis*
t.test(Quality~MR,MainStudyX) ## H4b rejected ** although there is a trend in the direction of the hypothesis
##
## Welch Two Sample t-test
##
## data: Quality by MR
## t = -1.2189, df = 175.04, p-value = 0.2245
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.5343062 0.1263062
## sample estimates:
## mean in group Poor mean in group Rich
## 5.254 5.458
summary(lm(Quality~MRLvl,MainStudyX))
##
## Call:
## lm(formula = Quality ~ MRLvl, data = MainStudyX)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3900 -0.5200 -0.1900 0.7575 3.7400
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.13000 0.19892 25.789 <2e-16 ***
## MRLvl 0.13000 0.09208 1.412 0.159
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.302 on 298 degrees of freedom
## Multiple R-squared: 0.006644, Adjusted R-squared: 0.003311
## F-statistic: 1.993 on 1 and 298 DF, p-value: 0.1591
t.test(Quality~TieStr,MainStudyX)
##
## Welch Two Sample t-test
##
## data: Quality by TieStr
## t = 0.11555, df = 212.93, p-value = 0.9081
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.2890496 0.3250496
## sample estimates:
## mean in group Strong mean in group Weak
## 5.402 5.384
summary(lm(Quality~TieStrLvl,MainStudyX))
##
## Call:
## lm(formula = Quality ~ TieStrLvl, data = MainStudyX)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.390 -0.446 -0.134 0.768 3.610
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.50200 0.19946 27.584 <2e-16 ***
## TieStrLvl -0.05600 0.09233 -0.607 0.545
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.306 on 298 degrees of freedom
## Multiple R-squared: 0.001233, Adjusted R-squared: -0.002119
## F-statistic: 0.3678 on 1 and 298 DF, p-value: 0.5446