library(reshape2) # melt
library(MASS) # lda
library(psy) # cronbach
library(psych) # KMO
##
## Attaching package: 'psych'
## The following object is masked from 'package:psy':
##
## wkappa
library(Hmisc) # correlation matrix
## Warning: package 'Hmisc' was built under R version 3.4.2
## Loading required package: lattice
## Loading required package: survival
## Loading required package: Formula
## Loading required package: ggplot2
##
## Attaching package: 'ggplot2'
## The following objects are masked from 'package:psych':
##
## %+%, alpha
##
## Attaching package: 'Hmisc'
## The following object is masked from 'package:psych':
##
## describe
## The following objects are masked from 'package:base':
##
## format.pval, round.POSIXt, trunc.POSIXt, units
library(psych) #KMO
library(Hmisc) # correlation matrix
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("DataSetChuy.csv", header=T) # reads raw data from Qualtrics
cronbach(cbind(MainStudy$IC1,MainStudy$IC2,MainStudy$IC3,MainStudy$IC4,MainStudy$IC5))
## $sample.size
## [1] 89
##
## $number.of.items
## [1] 5
##
## $alpha
## [1] 0.8581088
cronbach(cbind(MainStudy$VT1,MainStudy$VT2,MainStudy$VT3,MainStudy$VT4,MainStudy$VT5))
## $sample.size
## [1] 89
##
## $number.of.items
## [1] 5
##
## $alpha
## [1] 0.8753848
cronbach(cbind(MainStudy$PE1,MainStudy$PE2,MainStudy$PE3,MainStudy$PE4,MainStudy$PE5))
## $sample.size
## [1] 89
##
## $number.of.items
## [1] 5
##
## $alpha
## [1] 0.8711415
cronbach(cbind(MainStudy$P1,MainStudy$P2,MainStudy$P3,MainStudy$P4,MainStudy$P5))
## $sample.size
## [1] 89
##
## $number.of.items
## [1] 5
##
## $alpha
## [1] 0.9030035
cronbach(cbind(MainStudy$IC1,MainStudy$IC2,MainStudy$IC3,MainStudy$IC3,MainStudy$IC5,
MainStudy$VT1,MainStudy$VT2,MainStudy$VT3,MainStudy$VT4,MainStudy$VT5,
MainStudy$PE1,MainStudy$PE2,MainStudy$PE3,MainStudy$PE4,MainStudy$PE5,
MainStudy$P1,MainStudy$P2,MainStudy$P3,MainStudy$P4,MainStudy$P5))
## $sample.size
## [1] 89
##
## $number.of.items
## [1] 20
##
## $alpha
## [1] 0.9540903
## Variables
MainStudy$IC<-MainStudy$IC1+MainStudy$IC2+MainStudy$IC3+MainStudy$IC4+MainStudy$IC5
MainStudy$VT<-MainStudy$VT1+MainStudy$VT2+MainStudy$VT3+MainStudy$VT4+MainStudy$VT5
MainStudy$PE<-MainStudy$PE1+MainStudy$PE2+MainStudy$PE3+MainStudy$PE4+MainStudy$PE5
MainStudy$P<-MainStudy$P1+MainStudy$P2+MainStudy$P3+MainStudy$P4+MainStudy$P5
# VALIDITY
validity<-cbind(MainStudy$IC1,MainStudy$IC2,MainStudy$IC3,MainStudy$IC4,MainStudy$IC5,
MainStudy$VT1,MainStudy$VT2,MainStudy$VT3,MainStudy$VT4,MainStudy$VT5,
MainStudy$PE1,MainStudy$PE2,MainStudy$PE3,MainStudy$PE4,MainStudy$PE5,
MainStudy$P1,MainStudy$P2,MainStudy$P3,MainStudy$P4,MainStudy$P5)
factanal(validity,12,rotation="varimax") ## 10 items explain more than 80% of the variance
##
## Call:
## factanal(x = validity, factors = 12, rotation = "varimax")
##
## Uniquenesses:
## [1] 0.170 0.224 0.005 0.255 0.065 0.005 0.170 0.289 0.005 0.005 0.343
## [12] 0.100 0.276 0.163 0.297 0.217 0.163 0.140 0.005 0.138
##
## Loadings:
## Factor1 Factor2 Factor3 Factor4 Factor5 Factor6 Factor7 Factor8
## [1,] 0.163 0.430 0.299 0.283 0.247 0.225 0.274
## [2,] 0.392 0.160 0.245 0.510 0.193 0.335
## [3,] 0.122 0.164 0.178 0.896 0.143 0.217 0.198
## [4,] 0.328 0.390 0.221 0.202 0.142 0.135 0.146 0.115
## [5,] 0.229 0.261 0.197 0.241 0.157 0.806 0.103 0.147
## [6,] 0.205 0.908 0.151 0.145 0.132 0.174
## [7,] 0.162 0.769 0.147 0.104 0.180 0.306
## [8,] 0.260 0.621 0.263 0.283 0.201 0.114
## [9,] 0.103 0.288 0.880 0.221 0.123
## [10,] 0.271 0.155 0.792 0.267 0.152 0.116 0.120
## [11,] 0.342 0.257 0.118 0.209 0.101 0.131 0.605 0.114
## [12,] 0.675 0.201 0.151 0.117 0.222 0.204
## [13,] 0.739 0.226 0.167 0.105 0.100 0.220
## [14,] 0.752 0.211 0.156 0.226 0.187 0.140 0.225
## [15,] 0.570 0.133 0.181 0.312 0.332 0.257
## [16,] 0.265 0.262 0.111 0.241 0.320 0.457 0.402 0.141
## [17,] 0.412 0.185 0.119 0.230 0.187 0.151 0.691
## [18,] 0.318 0.352 0.222 0.323 0.411 0.151 0.233
## [19,] 0.352 0.275 0.203 0.234 0.741 0.231 0.122 0.204
## [20,] 0.317 0.426 0.272 0.157 0.503 0.154 0.273 0.221
## Factor9 Factor10 Factor11 Factor12
## [1,] 0.373 0.349
## [2,] 0.272 0.201
## [3,]
## [4,] 0.564
## [5,]
## [6,] 0.120
## [7,] 0.124 0.141
## [8,] 0.147 0.151
## [9,] 0.133 0.177
## [10,] 0.255 -0.258
## [11,]
## [12,] 0.512
## [13,]
## [14,] 0.173
## [15,] 0.144 0.139
## [16,] 0.226 0.167
## [17,] 0.134
## [18,] 0.104 0.459
## [19,] 0.174
## [20,] 0.253
##
## Factor1 Factor2 Factor3 Factor4 Factor5 Factor6 Factor7
## SS loadings 3.149 3.068 2.075 1.699 1.590 1.420 1.059
## Proportion Var 0.157 0.153 0.104 0.085 0.079 0.071 0.053
## Cumulative Var 0.157 0.311 0.415 0.500 0.579 0.650 0.703
## Factor8 Factor9 Factor10 Factor11 Factor12
## SS loadings 1.028 0.800 0.645 0.254 0.181
## Proportion Var 0.051 0.040 0.032 0.013 0.009
## Cumulative Var 0.754 0.794 0.827 0.839 0.848
##
## Test of the hypothesis that 12 factors are sufficient.
## The chi square statistic is 11.7 on 16 degrees of freedom.
## The p-value is 0.764
KMO(validity)
## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = validity)
## Overall MSA = 0.89
## MSA for each item =
## [1] 0.90 0.91 0.84 0.92 0.93 0.88 0.90 0.94 0.78 0.79 0.92 0.86 0.90 0.93
## [15] 0.93 0.94 0.90 0.89 0.82 0.91
summary(prcomp(validity)) ## 8 components explain more than 80% of the variance
## Importance of components%s:
## PC1 PC2 PC3 PC4 PC5 PC6
## Standard deviation 3.6849 1.27217 1.16650 1.06988 0.99443 0.96218
## Proportion of Variance 0.5562 0.06629 0.05574 0.04689 0.04051 0.03792
## Cumulative Proportion 0.5562 0.62247 0.67821 0.72509 0.76560 0.80352
## PC7 PC8 PC9 PC10 PC11 PC12
## Standard deviation 0.90274 0.83894 0.76029 0.7143 0.59944 0.5762
## Proportion of Variance 0.03338 0.02883 0.02368 0.0209 0.01472 0.0136
## Cumulative Proportion 0.83690 0.86573 0.88941 0.9103 0.92502 0.9386
## PC13 PC14 PC15 PC16 PC17 PC18
## Standard deviation 0.55163 0.5298 0.49237 0.45503 0.38997 0.36872
## Proportion of Variance 0.01246 0.0115 0.00993 0.00848 0.00623 0.00557
## Cumulative Proportion 0.95109 0.9626 0.97252 0.98100 0.98723 0.99279
## PC19 PC20
## Standard deviation 0.32705 0.26260
## Proportion of Variance 0.00438 0.00282
## Cumulative Proportion 0.99718 1.00000
screeplot(prcomp(validity),type="lines")
biplot(prcomp(validity,scale.=T),cex=0.5,xlabs=rep(".",nrow(validity)))
rcorr(as.matrix(validity))
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] 1.00 0.59 0.53 0.65 0.59 0.60 0.63 0.58 0.55 0.58 0.50 0.33 0.43
## [2,] 0.59 1.00 0.64 0.58 0.48 0.48 0.33 0.45 0.50 0.50 0.42 0.49 0.53
## [3,] 0.53 0.64 1.00 0.45 0.55 0.37 0.40 0.39 0.47 0.40 0.46 0.30 0.28
## [4,] 0.65 0.58 0.45 1.00 0.47 0.60 0.54 0.58 0.44 0.57 0.47 0.45 0.49
## [5,] 0.59 0.48 0.55 0.47 1.00 0.47 0.51 0.54 0.46 0.49 0.43 0.42 0.43
## [6,] 0.60 0.48 0.37 0.60 0.47 1.00 0.80 0.74 0.49 0.41 0.41 0.42 0.45
## [7,] 0.63 0.33 0.40 0.54 0.51 0.80 1.00 0.70 0.46 0.40 0.52 0.43 0.39
## [8,] 0.58 0.45 0.39 0.58 0.54 0.74 0.70 1.00 0.52 0.54 0.45 0.49 0.50
## [9,] 0.55 0.50 0.47 0.44 0.46 0.49 0.46 0.52 1.00 0.78 0.33 0.27 0.33
## [10,] 0.58 0.50 0.40 0.57 0.49 0.41 0.40 0.54 0.78 1.00 0.40 0.29 0.49
## [11,] 0.50 0.42 0.46 0.47 0.43 0.41 0.52 0.45 0.33 0.40 1.00 0.51 0.44
## [12,] 0.33 0.49 0.30 0.45 0.42 0.42 0.43 0.49 0.27 0.29 0.51 1.00 0.62
## [13,] 0.43 0.53 0.28 0.49 0.43 0.45 0.39 0.50 0.33 0.49 0.44 0.62 1.00
## [14,] 0.52 0.60 0.48 0.60 0.51 0.48 0.48 0.52 0.39 0.52 0.56 0.74 0.72
## [15,] 0.44 0.52 0.38 0.51 0.58 0.39 0.41 0.46 0.38 0.50 0.52 0.56 0.58
## [16,] 0.57 0.53 0.56 0.60 0.71 0.49 0.56 0.58 0.39 0.48 0.60 0.56 0.44
## [17,] 0.52 0.57 0.26 0.47 0.50 0.45 0.39 0.42 0.32 0.45 0.44 0.62 0.59
## [18,] 0.52 0.61 0.54 0.56 0.54 0.58 0.52 0.61 0.52 0.49 0.37 0.65 0.52
## [19,] 0.48 0.61 0.53 0.55 0.61 0.56 0.50 0.65 0.46 0.60 0.48 0.61 0.53
## [20,] 0.66 0.57 0.46 0.58 0.55 0.63 0.63 0.65 0.52 0.62 0.57 0.45 0.56
## [,14] [,15] [,16] [,17] [,18] [,19] [,20]
## [1,] 0.52 0.44 0.57 0.52 0.52 0.48 0.66
## [2,] 0.60 0.52 0.53 0.57 0.61 0.61 0.57
## [3,] 0.48 0.38 0.56 0.26 0.54 0.53 0.46
## [4,] 0.60 0.51 0.60 0.47 0.56 0.55 0.58
## [5,] 0.51 0.58 0.71 0.50 0.54 0.61 0.55
## [6,] 0.48 0.39 0.49 0.45 0.58 0.56 0.63
## [7,] 0.48 0.41 0.56 0.39 0.52 0.50 0.63
## [8,] 0.52 0.46 0.58 0.42 0.61 0.65 0.65
## [9,] 0.39 0.38 0.39 0.32 0.52 0.46 0.52
## [10,] 0.52 0.50 0.48 0.45 0.49 0.60 0.62
## [11,] 0.56 0.52 0.60 0.44 0.37 0.48 0.57
## [12,] 0.74 0.56 0.56 0.62 0.65 0.61 0.45
## [13,] 0.72 0.58 0.44 0.59 0.52 0.53 0.56
## [14,] 1.00 0.70 0.60 0.57 0.58 0.65 0.62
## [15,] 0.70 1.00 0.62 0.50 0.47 0.65 0.59
## [16,] 0.60 0.62 1.00 0.55 0.58 0.71 0.64
## [17,] 0.57 0.50 0.55 1.00 0.59 0.63 0.59
## [18,] 0.58 0.47 0.58 0.59 1.00 0.79 0.62
## [19,] 0.65 0.65 0.71 0.63 0.79 1.00 0.79
## [20,] 0.62 0.59 0.64 0.59 0.62 0.79 1.00
##
## n= 89
##
##
## P
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## [1,] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## [2,] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0016 0.0000 0.0000
## [3,] 0.0000 0.0000 0.0000 0.0000 0.0003 0.0001 0.0002 0.0000
## [4,] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## [5,] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## [6,] 0.0000 0.0000 0.0003 0.0000 0.0000 0.0000 0.0000 0.0000
## [7,] 0.0000 0.0016 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000
## [8,] 0.0000 0.0000 0.0002 0.0000 0.0000 0.0000 0.0000 0.0000
## [9,] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## [10,] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## [11,] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0014
## [12,] 0.0015 0.0000 0.0040 0.0000 0.0000 0.0000 0.0000 0.0000 0.0103
## [13,] 0.0000 0.0000 0.0071 0.0000 0.0000 0.0000 0.0001 0.0000 0.0015
## [14,] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0002
## [15,] 0.0000 0.0000 0.0003 0.0000 0.0000 0.0001 0.0000 0.0000 0.0003
## [16,] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001
## [17,] 0.0000 0.0000 0.0143 0.0000 0.0000 0.0000 0.0002 0.0000 0.0025
## [18,] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## [19,] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## [20,] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## [,10] [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18]
## [1,] 0.0000 0.0000 0.0015 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## [2,] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## [3,] 0.0000 0.0000 0.0040 0.0071 0.0000 0.0003 0.0000 0.0143 0.0000
## [4,] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## [5,] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## [6,] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000 0.0000
## [7,] 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000 0.0000 0.0002 0.0000
## [8,] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## [9,] 0.0000 0.0014 0.0103 0.0015 0.0002 0.0003 0.0001 0.0025 0.0000
## [10,] 0.0001 0.0055 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## [11,] 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0004
## [12,] 0.0055 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## [13,] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## [14,] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## [15,] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## [16,] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## [17,] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## [18,] 0.0000 0.0004 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## [19,] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## [20,] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## [,19] [,20]
## [1,] 0.0000 0.0000
## [2,] 0.0000 0.0000
## [3,] 0.0000 0.0000
## [4,] 0.0000 0.0000
## [5,] 0.0000 0.0000
## [6,] 0.0000 0.0000
## [7,] 0.0000 0.0000
## [8,] 0.0000 0.0000
## [9,] 0.0000 0.0000
## [10,] 0.0000 0.0000
## [11,] 0.0000 0.0000
## [12,] 0.0000 0.0000
## [13,] 0.0000 0.0000
## [14,] 0.0000 0.0000
## [15,] 0.0000 0.0000
## [16,] 0.0000 0.0000
## [17,] 0.0000 0.0000
## [18,] 0.0000 0.0000
## [19,] 0.0000
## [20,] 0.0000