library(reshape2) # melt
## Warning: package 'reshape2' was built under R version 3.4.3
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 3.4.3
## Loading required package: lattice
## Loading required package: survival
## Loading required package: Formula
## Loading required package: ggplot2
## Warning: package 'ggplot2' was built under R version 3.4.3
##
## 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, 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
library(lavaan)
## 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':
##
## cor2cov
library(semPlot)
## Warning: package 'semPlot' was built under R version 3.4.4
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("MainStudyNov2.csv", header=T)
## MainStudy<-read.csv("MonaMourMainStudyUS.csv", header=T) # reads raw data from Qualtrics
## MainStudy<-read.csv("MonaMourMainStudyMX.csv", header=T) # reads raw data from Qualtrics
MainStudy1<-subset(MainStudy,MainStudy$Q40_Page.Submit!=""&MainStudy$FL_17_DO!="")
MainStudy1<-subset(MainStudy1,MainStudy1$Q4==3|MainStudy1$Q4==4) ## Only Desktop and Laptop PC
## recoding mobile and tablet (reverse)
MainStudy2<-subset(MainStudy,MainStudy$Q40_Page.Submit!=""&MainStudy$FL_17_DO!="")
MainStudy2<-subset(MainStudy2,MainStudy2$Q4==1|MainStudy2$Q4==2) ## Only Mobile and Tablet
## Reversing Answers
MainStudy1$Q19_1<-10-MainStudy1$Q19_1
MainStudy1$Q19_2<-10-MainStudy1$Q19_2
MainStudy1$Q19_3<-10-MainStudy1$Q19_3
MainStudy1$Q19_4<-10-MainStudy1$Q19_4
MainStudy1$Q21_7<-10-MainStudy1$Q21_7
MainStudy1$Q21_8<-10-MainStudy1$Q21_8
MainStudy1$Q21_9<-10-MainStudy1$Q21_9
MainStudy<-rbind(MainStudy1,MainStudy2)
MainStudy<-subset(MainStudy,MainStudy$FL_17_DO!="Block2"&MainStudy$FL_17_DO!="Block5")
table(MainStudy$Q4)
##
## 1 2 3 4
## 37 15 197 110
table(MainStudy$FL_17_DO)
##
## Block1 Block2 Block3 Block4 Block5 Block6
## 0 93 0 89 89 0 88
## Demographics
summary(MainStudy$Q22)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 4.000 3.953 5.000 9.000
summary(MainStudy$Q23)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.00 9.00 12.00 11.01 12.00 15.00
summary(MainStudy$Q24)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.000 1.000 1.067 1.000 2.000
summary(MainStudy$Q25)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.000 1.000 2.457 5.000 6.000
summary(MainStudy$Q26)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 19.00 26.00 31.00 34.72 41.00 70.00
summary(MainStudy$LocationLatitude)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.286 24.395 35.934 32.143 40.903 57.133
summary(MainStudy$LocationLongitude)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -158.02 -89.16 -78.72 -40.92 72.74 140.60
##Filtering NAs
MainStudy<-subset(MainStudy,is.na(MainStudy$Q26)==FALSE)
mean(MainStudy$Q26)
## [1] 34.72423
sd(MainStudy$Q26)
## [1] 11.35335
table(MainStudy$FL_17_DO)
##
## Block1 Block2 Block3 Block4 Block5 Block6
## 0 93 0 89 89 0 88
## Verifying equivalency of groups
summary(aov(as.numeric(Q22)~as.factor(FL_17_DO),MainStudy))
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(FL_17_DO) 3 1.9 0.650 0.169 0.918
## Residuals 355 1368.2 3.854
summary(aov(as.numeric(Q23)~as.factor(FL_17_DO),MainStudy))
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(FL_17_DO) 3 5.4 1.815 0.405 0.749
## Residuals 355 1589.5 4.478
summary(aov(as.numeric(Q24)~as.factor(FL_17_DO),MainStudy))
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(FL_17_DO) 3 0.017 0.00564 0.089 0.966
## Residuals 355 22.379 0.06304
chisq.test(MainStudy$FL_17_DO,MainStudy$Q25)
## Warning in chisq.test(MainStudy$FL_17_DO, MainStudy$Q25): Chi-squared
## approximation may be incorrect
##
## Pearson's Chi-squared test
##
## data: MainStudy$FL_17_DO and MainStudy$Q25
## X-squared = 18.118, df = 15, p-value = 0.2565
ftable(MainStudy$Q25,MainStudy$FL_17_DO)
## Block1 Block2 Block3 Block4 Block5 Block6
##
## 1 0 60 0 49 46 0 48
## 2 0 5 0 10 8 0 8
## 3 0 1 0 4 5 0 9
## 4 0 3 0 1 1 0 2
## 5 0 19 0 16 15 0 12
## 6 0 5 0 9 14 0 9
MainStudy<-subset(MainStudy,MainStudy$Q25!=4)
MainStudy<-subset(MainStudy,MainStudy$Q25!=3)
chisq.test(MainStudy$FL_17_DO,MainStudy$Q25)
##
## Pearson's Chi-squared test
##
## data: MainStudy$FL_17_DO and MainStudy$Q25
## X-squared = 8.8476, df = 9, p-value = 0.4515
summary(aov(as.numeric(Q22)~as.factor(FL_17_DO),MainStudy))
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(FL_17_DO) 3 2 0.661 0.168 0.918
## Residuals 329 1294 3.932
summary(aov(as.numeric(Q23)~as.factor(FL_17_DO),MainStudy))
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(FL_17_DO) 3 2.9 0.982 0.235 0.872
## Residuals 329 1374.9 4.179
summary(aov(as.numeric(Q24)~as.factor(FL_17_DO),MainStudy))
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(FL_17_DO) 3 0.048 0.01585 0.278 0.841
## Residuals 329 18.751 0.05699
summary(aov(as.numeric(Q26)~as.factor(FL_17_DO),MainStudy))
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(FL_17_DO) 3 167 55.75 0.42 0.739
## Residuals 329 43675 132.75
summary(aov(as.numeric(LocationLatitude)~as.factor(FL_17_DO),MainStudy))
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(FL_17_DO) 3 319 106.4 0.721 0.54
## Residuals 329 48553 147.6
summary(aov(as.numeric(LocationLongitude)~as.factor(FL_17_DO),MainStudy))
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(FL_17_DO) 3 5055 1685 0.308 0.82
## Residuals 329 1801385 5475
## Measures
cronbach(cbind(MainStudy$Q19_1,MainStudy$Q19_2,MainStudy$Q19_3,MainStudy$Q19_4))
## $sample.size
## [1] 333
##
## $number.of.items
## [1] 4
##
## $alpha
## [1] 0.8926269
cronbach(cbind(MainStudy$Q19_1,MainStudy$Q19_2,MainStudy$Q19_4))
## $sample.size
## [1] 333
##
## $number.of.items
## [1] 3
##
## $alpha
## [1] 0.9053645
cronbach(cbind(MainStudy$Q21_7,MainStudy$Q21_8,MainStudy$Q21_9))
## $sample.size
## [1] 333
##
## $number.of.items
## [1] 3
##
## $alpha
## [1] 0.9666211
MainStudy$Auth<-(MainStudy$Q19_1+MainStudy$Q19_2+MainStudy$Q19_4)/3
MainStudy$PurchInt<-(MainStudy$Q21_7+MainStudy$Q21_8+MainStudy$Q21_9)/3
boxplot(Auth~FL_17_DO,MainStudy,range=3)$out ## no outliers
## numeric(0)
MainStudy$UrbanLvl<-ifelse(MainStudy$FL_17_DO=="Block1"|
MainStudy$FL_17_DO=="Block2"|
MainStudy$FL_17_DO=="Block3",1,2)
MainStudy$MCLvl<-ifelse(MainStudy$FL_17_DO=="Block1"|
MainStudy$FL_17_DO=="Block4",1,
(ifelse(MainStudy$FL_17_DO=="Block2"|
MainStudy$FL_17_DO=="Block5",2,3)))
table(MainStudy$FL_17_DO)
##
## Block1 Block2 Block3 Block4 Block5 Block6
## 0 89 0 84 83 0 77
table(MainStudy$UrbanLvl)
##
## 1 2
## 173 160
table(MainStudy$MCLvl)
##
## 1 3
## 172 161
MainStudy[is.na(MainStudy)]<-0
MainStudy$StimuliTime<-MainStudy$Q32_Page.Submit+MainStudy$Q33_Page.Submit+
MainStudy$Q34_Page.Submit+MainStudy$Q35_Page.Submit+MainStudy$Q36_Page.Submit+
MainStudy$Q37_Page.Submit
t.test(as.numeric(MainStudy$StimuliTime)~MainStudy$UrbanLvl) ## Equivalent StimuliTime
##
## Welch Two Sample t-test
##
## data: as.numeric(MainStudy$StimuliTime) by MainStudy$UrbanLvl
## t = -0.5602, df = 217.88, p-value = 0.5759
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.820023 1.014388
## sample estimates:
## mean in group 1 mean in group 2
## 9.438445 9.841263
t.test(as.numeric(MainStudy$StimuliTime)~MainStudy$MCLvl) ## Equivalent StimuliTime
##
## Welch Two Sample t-test
##
## data: as.numeric(MainStudy$StimuliTime) by MainStudy$MCLvl
## t = -0.3161, df = 204.14, p-value = 0.7523
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.643109 1.189054
## sample estimates:
## mean in group 1 mean in group 3
## 9.522227 9.749255
## SEM
datamatrix<-as.data.frame(cbind(MainStudy$Q19_1,MainStudy$Q19_2,MainStudy$Q19_4,
MainStudy$Q21_7,MainStudy$Q21_8,MainStudy$Q21_9,MainStudy$UrbanLvl,
MainStudy$MCLvl))
mymodel<-'
# measurement model
Authencity=~V1+V2+V3
PurchaseIntent=~V4+V5+V6
# regressions
PurchaseIntent~Authencity
Authencity~V7+V8
'
fit.path<-sem(mymodel,data=datamatrix)
fitMeasures(fit.path)
## npar fmin chisq
## 15.000 0.019 12.564
## df pvalue baseline.chisq
## 18.000 0.817 1953.721
## baseline.df baseline.pvalue cfi
## 27.000 0.000 1.000
## tli nnfi rfi
## 1.004 1.004 0.990
## nfi pnfi ifi
## 0.994 0.662 1.003
## rni logl unrestricted.logl
## 1.003 -4167.335 -4161.053
## aic bic ntotal
## 8364.671 8421.793 333.000
## bic2 rmsea rmsea.ci.lower
## 8374.212 0.000 0.000
## rmsea.ci.upper rmsea.pvalue rmr
## 0.031 0.996 0.074
## rmr_nomean srmr srmr_bentler
## 0.074 0.019 0.019
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.019 0.019 0.019
## srmr_mplus srmr_mplus_nomean cn_05
## 0.019 0.019 766.144
## cn_01 gfi agfi
## 923.470 0.988 0.975
## pgfi mfi ecvi
## 0.494 1.008 0.128
summary(fit.path,fit.measures=T,standardized=T)
## lavaan (0.5-23.1097) converged normally after 44 iterations
##
## Number of observations 333
##
## Estimator ML
## Minimum Function Test Statistic 12.564
## Degrees of freedom 18
## P-value (Chi-square) 0.817
##
## Model test baseline model:
##
## Minimum Function Test Statistic 1953.721
## Degrees of freedom 27
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.004
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4167.335
## Loglikelihood unrestricted model (H1) -4161.053
##
## Number of free parameters 15
## Akaike (AIC) 8364.671
## Bayesian (BIC) 8421.793
## Sample-size adjusted Bayesian (BIC) 8374.212
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent Confidence Interval 0.000 0.031
## P-value RMSEA <= 0.05 0.996
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.019
##
## Parameter Estimates:
##
## Information Expected
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Authencity =~
## V1 1.000 1.796 0.905
## V2 0.891 0.045 19.686 0.000 1.600 0.828
## V3 0.958 0.044 21.653 0.000 1.720 0.886
## PurchaseIntent =~
## V4 1.000 2.376 0.935
## V5 1.027 0.027 38.301 0.000 2.440 0.971
## V6 0.993 0.028 35.408 0.000 2.360 0.951
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PurchaseIntent ~
## Authencity 0.405 0.075 5.404 0.000 0.306 0.306
## Authencity ~
## V7 0.423 0.204 2.071 0.038 0.235 0.118
## V8 0.186 0.102 1.824 0.068 0.104 0.104
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .V1 0.713 0.108 6.613 0.000 0.713 0.181
## .V2 1.176 0.118 9.992 0.000 1.176 0.315
## .V3 0.812 0.106 7.671 0.000 0.812 0.215
## .V4 0.812 0.083 9.789 0.000 0.812 0.126
## .V5 0.367 0.064 5.772 0.000 0.367 0.058
## .V6 0.593 0.070 8.434 0.000 0.593 0.096
## .Authencity 3.147 0.307 10.249 0.000 0.976 0.976
## .PurchaseIntent 5.118 0.457 11.189 0.000 0.906 0.906
semPaths(fit.path,"std",edge.label.cex=1,curvePivot=T,covAtResiduals=F,residuals=F,fade=F)
aggregate(MainStudy$Auth,list(MainStudy$UrbanLvl),mean)
## Group.1 x
## 1 1 5.751445
## 2 2 6.129167
aggregate(MainStudy$Auth,list(MainStudy$UrbanLvl),sd)
## Group.1 x
## 1 1 1.849787
## 2 2 1.715628
aggregate(MainStudy$Auth,list(MainStudy$MCLvl),mean)
## Group.1 x
## 1 1 5.76938
## 2 3 6.10766
aggregate(MainStudy$Auth,list(MainStudy$MCLvl),sd)
## Group.1 x
## 1 1 1.748418
## 2 3 1.830529
aggregate(MainStudy$Auth,list(MainStudy$FL_17_DO),mean)
## Group.1 x
## 1 Block1 5.524345
## 2 Block3 5.992063
## 3 Block4 6.032129
## 4 Block6 6.233766
aggregate(MainStudy$Auth,list(MainStudy$FL_17_DO),sd)
## Group.1 x
## 1 Block1 1.742145
## 2 Block3 1.938807
## 3 Block4 1.727048
## 4 Block6 1.708309
## Hypotheses testing
t.test(MainStudy$Auth~MainStudy$UrbanLvl) ## H1 Accepted
##
## Welch Two Sample t-test
##
## data: MainStudy$Auth by MainStudy$UrbanLvl
## t = -1.9332, df = 331, p-value = 0.05406
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.762071995 0.006628836
## sample estimates:
## mean in group 1 mean in group 2
## 5.751445 6.129167
t.test(MainStudy$Auth~MainStudy$MCLvl) ## H2 Accepted
##
## Welch Two Sample t-test
##
## data: MainStudy$Auth by MainStudy$MCLvl
## t = -1.7221, df = 326.89, p-value = 0.08599
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.7247118 0.0481506
## sample estimates:
## mean in group 1 mean in group 3
## 5.76938 6.10766
summary(lm(PurchInt~UrbanLvl+MCLvl,MainStudy))
##
## Call:
## lm(formula = PurchInt ~ UrbanLvl + MCLvl, data = MainStudy)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2266 -2.2266 -0.2266 1.9010 5.0774
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.68247 0.49528 7.435 9.08e-13 ***
## UrbanLvl 0.17638 0.26779 0.659 0.511
## MCLvl 0.06379 0.13387 0.477 0.634
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.441 on 330 degrees of freedom
## Multiple R-squared: 0.001991, Adjusted R-squared: -0.004058
## F-statistic: 0.3291 on 2 and 330 DF, p-value: 0.7198
summary(lm(PurchInt~Auth,MainStudy))
##
## Call:
## lm(formula = PurchInt ~ Auth, data = MainStudy)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2999 -2.0280 0.1014 1.7045 5.9742
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.68808 0.44201 3.819 0.00016 ***
## Auth 0.40132 0.07132 5.627 3.91e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.331 on 331 degrees of freedom
## Multiple R-squared: 0.0873, Adjusted R-squared: 0.08455
## F-statistic: 31.66 on 1 and 331 DF, p-value: 3.913e-08
summary(lm(PurchInt~Auth+UrbanLvl+MCLvl,MainStudy)) ## H3 Accepted fully mediates
##
## Call:
## lm(formula = PurchInt ~ Auth + UrbanLvl + MCLvl, data = MainStudy)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3153 -2.0453 0.0942 1.7119 5.9805
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.663384 0.597938 2.782 0.00572 **
## Auth 0.400830 0.072267 5.546 6e-08 ***
## UrbanLvl 0.024392 0.257933 0.095 0.92472
## MCLvl -0.004332 0.128796 -0.034 0.97319
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.338 on 329 degrees of freedom
## Multiple R-squared: 0.08733, Adjusted R-squared: 0.07901
## F-statistic: 10.49 on 3 and 329 DF, p-value: 1.308e-06
## Interaction effects
summary(aov(Auth~UrbanLvl*MCLvl,MainStudy)) ## There are not interaction effects
## Df Sum Sq Mean Sq F value Pr(>F)
## UrbanLvl 1 11.9 11.859 3.732 0.0542 .
## MCLvl 1 9.6 9.608 3.024 0.0830 .
## UrbanLvl:MCLvl 1 1.5 1.470 0.462 0.4969
## Residuals 329 1045.5 3.178
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(lm(Auth~UrbanLvl*MCLvl,MainStudy)) ## There are not interaction effects
##
## Call:
## lm(formula = Auth ~ UrbanLvl * MCLvl, data = MainStudy)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2338 -1.0321 0.1423 1.4329 3.4757
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.6497 0.6750 6.888 2.88e-11 ***
## UrbanLvl 0.6408 0.4316 1.485 0.139
## MCLvl 0.3669 0.3057 1.200 0.231
## UrbanLvl:MCLvl -0.1330 0.1956 -0.680 0.497
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.783 on 329 degrees of freedom
## Multiple R-squared: 0.02147, Adjusted R-squared: 0.01255
## F-statistic: 2.406 on 3 and 329 DF, p-value: 0.06724
## 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$LocationLatitude)
coord<-mapproject(lon, lat, proj="gilbert", orientation=c(90, 0, 225))
points(coord, pch=20, cex=0.8, col="black")
## Demographics
table(MainStudy$FL_17_DO)
##
## Block1 Block2 Block3 Block4 Block5 Block6
## 0 89 0 84 83 0 77
aggregate(MainStudy$Q22,list(MainStudy$FL_17_DO),mean)
## Group.1 x
## 1 Block1 4.056180
## 2 Block3 3.976190
## 3 Block4 3.843373
## 4 Block6 3.974026
aggregate(MainStudy$Q22,list(MainStudy$FL_17_DO),sd)
## Group.1 x
## 1 Block1 1.866934
## 2 Block3 1.975611
## 3 Block4 2.033152
## 4 Block6 2.064576
aggregate(MainStudy$Q23,list(MainStudy$FL_17_DO),mean)
## Group.1 x
## 1 Block1 11.05618
## 2 Block3 11.11905
## 3 Block4 10.86747
## 4 Block6 11.05195
aggregate(MainStudy$Q23,list(MainStudy$FL_17_DO),sd)
## Group.1 x
## 1 Block1 2.069033
## 2 Block3 2.107958
## 3 Block4 2.288737
## 4 Block6 1.621371
aggregate(MainStudy$Q24,list(MainStudy$FL_17_DO),mean)
## Group.1 x
## 1 Block1 1.078652
## 2 Block3 1.059524
## 3 Block4 1.048193
## 4 Block6 1.051948
aggregate(MainStudy$Q24,list(MainStudy$FL_17_DO),sd)
## Group.1 x
## 1 Block1 0.2707195
## 2 Block3 0.2380235
## 3 Block4 0.2154753
## 4 Block6 0.2233774
aggregate(MainStudy$Q25,list(MainStudy$FL_17_DO),mean)
## Group.1 x
## 1 Block1 2.191011
## 2 Block3 2.416667
## 3 Block4 2.662651
## 4 Block6 2.311688
aggregate(MainStudy$Q25,list(MainStudy$FL_17_DO),sd)
## Group.1 x
## 1 Block1 1.870078
## 2 Block3 1.970923
## 3 Block4 2.120039
## 4 Block6 1.961872
aggregate(MainStudy$Q26,list(MainStudy$FL_17_DO),mean)
## Group.1 x
## 1 Block1 35.55056
## 2 Block3 36.13095
## 3 Block4 35.40964
## 4 Block6 34.14286
aggregate(MainStudy$Q26,list(MainStudy$FL_17_DO),sd)
## Group.1 x
## 1 Block1 11.37782
## 2 Block3 12.17026
## 3 Block4 11.03656
## 4 Block6 11.47161
aggregate(MainStudy$LocationLatitude,list(MainStudy$FL_17_DO),mean)
## Group.1 x
## 1 Block1 32.83436
## 2 Block3 34.04834
## 3 Block4 31.41157
## 4 Block6 32.07949
aggregate(MainStudy$LocationLatitude,list(MainStudy$FL_17_DO),sd)
## Group.1 x
## 1 Block1 12.27932
## 2 Block3 11.41858
## 3 Block4 12.45835
## 4 Block6 12.42631
aggregate(MainStudy$LocationLongitude,list(MainStudy$FL_17_DO),mean)
## Group.1 x
## 1 Block1 -45.37594
## 2 Block3 -44.95078
## 3 Block4 -35.89819
## 4 Block6 -40.20810
aggregate(MainStudy$LocationLongitude,list(MainStudy$FL_17_DO),sd)
## Group.1 x
## 1 Block1 71.90488
## 2 Block3 74.20781
## 3 Block4 76.42499
## 4 Block6 73.48378
aggregate(MainStudy$StimuliTime,list(MainStudy$FL_17_DO),mean)
## Group.1 x
## 1 Block1 9.579112
## 2 Block3 9.289405
## 3 Block4 9.461229
## 4 Block6 10.250909
aggregate(MainStudy$StimuliTime,list(MainStudy$FL_17_DO),sd)
## Group.1 x
## 1 Block1 3.933450
## 2 Block3 3.625392
## 3 Block4 2.467222
## 4 Block6 11.770746
ftable(MainStudy$Q25,MainStudy$FL_17_DO)
## Block1 Block2 Block3 Block4 Block5 Block6
##
## 1 0 60 0 49 46 0 48
## 2 0 5 0 10 8 0 8
## 5 0 19 0 16 15 0 12
## 6 0 5 0 9 14 0 9