Perception map
Importing the data
library(readxl)
mydata <- read.csv("anketa_final_6.csv")mydata_PCA <- mydata[ , c("Q2a_1", "Q2b_1", "Q2c_1", "Q3a_1", "Q3b_1", "Q3c_1", "Q4a_1", "Q4b_1", "Q4c_1", "Q5a_1", "Q5b_1", "Q5c_1", "Q6a_1", "Q6b_1", "Q6c_1","Q7a_1", "Q7b_1", "Q7c_1")]Renaming the variables.
colnames(mydata_PCA) <- c("Cash_Secure", "Cash_Secure", "Mobile Payment_Secure", "Cash_Speed", "Card_Speed", "Mobile Payment_Speed", "Cash_Ease", "Card_Ease", "Mobile Payment_Ease", "Cash_Convenience", "Card_Convenience", "Mobile Payment_Convenience", "Cash_Privacy", "Card_Privacy", "Mobile Payment_Privacy", "Cash_Tracking", "Card_Tracking", "Mobile Payment_Tracking")library(psych)
psych::describe(mydata_PCA[ ,-1])## vars n mean sd median trimmed mad min max range
## Cash_Secure 1 106 5.60 1.20 6.0 5.77 1.48 1 7 6
## Mobile Payment_Secure 2 106 4.71 1.78 5.0 4.84 1.48 1 7 6
## Cash_Speed 3 106 5.13 1.81 6.0 5.33 1.48 1 7 6
## Card_Speed 4 106 5.90 1.34 6.0 6.16 1.48 1 7 6
## Mobile Payment_Speed 5 106 5.36 1.69 6.0 5.63 1.48 1 7 6
## Cash_Ease 6 106 5.88 1.59 6.0 6.21 1.48 1 7 6
## Card_Ease 7 106 6.05 1.43 6.5 6.37 0.74 1 7 6
## Mobile Payment_Ease 8 106 4.92 1.84 5.0 5.13 1.48 1 7 6
## Cash_Convenience 9 106 4.95 1.90 5.0 5.12 2.97 1 7 6
## Card_Convenience 10 106 5.83 1.42 6.0 6.12 1.48 1 7 6
## Mobile Payment_Convenience 11 106 5.05 1.86 6.0 5.27 1.48 1 7 6
## Cash_Privacy 12 106 5.87 1.60 7.0 6.19 0.00 1 7 6
## Card_Privacy 13 106 4.92 1.62 5.0 5.08 1.48 1 7 6
## Mobile Payment_Privacy 14 106 4.42 1.75 5.0 4.51 1.48 1 7 6
## Cash_Tracking 15 106 4.97 2.05 6.0 5.16 1.48 1 7 6
## Card_Tracking 16 106 5.22 1.65 6.0 5.43 1.48 1 7 6
## Mobile Payment_Tracking 17 106 5.08 1.77 6.0 5.29 1.48 1 7 6
## skew kurtosis se
## Cash_Secure -1.63 3.56 0.12
## Mobile Payment_Secure -0.67 -0.79 0.17
## Cash_Speed -0.73 -0.70 0.18
## Card_Speed -1.86 3.73 0.13
## Mobile Payment_Speed -1.23 0.77 0.16
## Cash_Ease -1.66 2.02 0.15
## Card_Ease -2.09 4.25 0.14
## Mobile Payment_Ease -0.71 -0.52 0.18
## Cash_Convenience -0.59 -0.93 0.18
## Card_Convenience -1.82 3.31 0.14
## Mobile Payment_Convenience -0.80 -0.46 0.18
## Cash_Privacy -1.51 1.43 0.16
## Card_Privacy -0.79 -0.11 0.16
## Mobile Payment_Privacy -0.42 -0.78 0.17
## Cash_Tracking -0.52 -1.18 0.20
## Card_Tracking -1.00 0.20 0.16
## Mobile Payment_Tracking -0.76 -0.42 0.17options(repos = c(CRAN = "https://cloud.r-project.org/"))
install.packages("pastecs")## Installing package into 'C:/Users/Miha Fabjan/AppData/Local/R/win-library/4.4'
## (as 'lib' is unspecified)## package 'pastecs' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\Miha Fabjan\AppData\Local\Temp\Rtmp2Zffcx\downloaded_packageslibrary("pastecs")## Warning: package 'pastecs' was built under R version 4.4.2round(stat.desc(mydata_PCA, basic = FALSE), 2)## Cash_Secure Cash_Secure Mobile Payment_Secure Cash_Speed
## median 6.00 6.00 5.00 6.00
## mean 5.58 5.60 4.71 5.13
## SE.mean 0.17 0.12 0.17 0.18
## CI.mean.0.95 0.33 0.23 0.34 0.35
## var 2.91 1.44 3.16 3.28
## std.dev 1.71 1.20 1.78 1.81
## coef.var 0.31 0.21 0.38 0.35
## Card_Speed Mobile Payment_Speed Cash_Ease Card_Ease
## median 6.00 6.00 6.00 6.50
## mean 5.90 5.36 5.88 6.05
## SE.mean 0.13 0.16 0.15 0.14
## CI.mean.0.95 0.26 0.32 0.31 0.28
## var 1.81 2.84 2.53 2.05
## std.dev 1.34 1.69 1.59 1.43
## coef.var 0.23 0.31 0.27 0.24
## Mobile Payment_Ease Cash_Convenience Card_Convenience
## median 5.00 5.00 6.00
## mean 4.92 4.95 5.83
## SE.mean 0.18 0.18 0.14
## CI.mean.0.95 0.35 0.37 0.27
## var 3.38 3.63 2.03
## std.dev 1.84 1.90 1.42
## coef.var 0.37 0.38 0.24
## Mobile Payment_Convenience Cash_Privacy Card_Privacy
## median 6.00 7.00 5.00
## mean 5.05 5.87 4.92
## SE.mean 0.18 0.16 0.16
## CI.mean.0.95 0.36 0.31 0.31
## var 3.45 2.55 2.62
## std.dev 1.86 1.60 1.62
## coef.var 0.37 0.27 0.33
## Mobile Payment_Privacy Cash_Tracking Card_Tracking
## median 5.00 6.00 6.00
## mean 4.42 4.97 5.22
## SE.mean 0.17 0.20 0.16
## CI.mean.0.95 0.34 0.39 0.32
## var 3.05 4.20 2.72
## std.dev 1.75 2.05 1.65
## coef.var 0.40 0.41 0.32
## Mobile Payment_Tracking
## median 6.00
## mean 5.08
## SE.mean 0.17
## CI.mean.0.95 0.34
## var 3.13
## std.dev 1.77
## coef.var 0.35R <- cor(mydata_PCA)
round(R, 3)## Cash_Secure Cash_Secure Mobile Payment_Secure
## Cash_Secure 1.000 -0.109 -0.144
## Cash_Secure -0.109 1.000 0.302
## Mobile Payment_Secure -0.144 0.302 1.000
## Cash_Speed 0.237 -0.239 -0.219
## Card_Speed -0.110 0.393 0.218
## Mobile Payment_Speed -0.031 0.108 0.560
## Cash_Ease 0.550 -0.160 -0.316
## Card_Ease -0.027 0.499 0.223
## Mobile Payment_Ease -0.086 0.223 0.730
## Cash_Convenience 0.565 -0.300 -0.285
## Card_Convenience -0.045 0.484 0.236
## Mobile Payment_Convenience -0.021 0.311 0.655
## Cash_Privacy 0.629 -0.147 -0.265
## Card_Privacy -0.204 0.499 0.257
## Mobile Payment_Privacy -0.159 0.161 0.558
## Cash_Tracking 0.623 -0.252 -0.149
## Card_Tracking -0.032 0.279 0.330
## Mobile Payment_Tracking 0.053 0.047 0.638
## Cash_Speed Card_Speed Mobile Payment_Speed Cash_Ease
## Cash_Secure 0.237 -0.110 -0.031 0.550
## Cash_Secure -0.239 0.393 0.108 -0.160
## Mobile Payment_Secure -0.219 0.218 0.560 -0.316
## Cash_Speed 1.000 -0.006 -0.053 0.476
## Card_Speed -0.006 1.000 0.386 -0.059
## Mobile Payment_Speed -0.053 0.386 1.000 -0.154
## Cash_Ease 0.476 -0.059 -0.154 1.000
## Card_Ease -0.161 0.716 0.293 0.078
## Mobile Payment_Ease -0.177 0.232 0.697 -0.241
## Cash_Convenience 0.532 -0.087 -0.131 0.586
## Card_Convenience -0.076 0.742 0.359 -0.056
## Mobile Payment_Convenience -0.189 0.330 0.618 -0.153
## Cash_Privacy 0.319 -0.006 -0.028 0.665
## Card_Privacy -0.104 0.530 0.303 -0.059
## Mobile Payment_Privacy -0.099 0.169 0.493 -0.184
## Cash_Tracking 0.335 -0.132 -0.102 0.472
## Card_Tracking -0.134 0.487 0.513 -0.073
## Mobile Payment_Tracking -0.126 0.324 0.728 -0.132
## Card_Ease Mobile Payment_Ease Cash_Convenience
## Cash_Secure -0.027 -0.086 0.565
## Cash_Secure 0.499 0.223 -0.300
## Mobile Payment_Secure 0.223 0.730 -0.285
## Cash_Speed -0.161 -0.177 0.532
## Card_Speed 0.716 0.232 -0.087
## Mobile Payment_Speed 0.293 0.697 -0.131
## Cash_Ease 0.078 -0.241 0.586
## Card_Ease 1.000 0.345 -0.174
## Mobile Payment_Ease 0.345 1.000 -0.284
## Cash_Convenience -0.174 -0.284 1.000
## Card_Convenience 0.752 0.297 -0.094
## Mobile Payment_Convenience 0.404 0.784 -0.228
## Cash_Privacy -0.010 -0.152 0.442
## Card_Privacy 0.499 0.346 -0.156
## Mobile Payment_Privacy 0.229 0.668 -0.083
## Cash_Tracking -0.169 -0.142 0.588
## Card_Tracking 0.435 0.432 -0.166
## Mobile Payment_Tracking 0.296 0.670 -0.169
## Card_Convenience Mobile Payment_Convenience
## Cash_Secure -0.045 -0.021
## Cash_Secure 0.484 0.311
## Mobile Payment_Secure 0.236 0.655
## Cash_Speed -0.076 -0.189
## Card_Speed 0.742 0.330
## Mobile Payment_Speed 0.359 0.618
## Cash_Ease -0.056 -0.153
## Card_Ease 0.752 0.404
## Mobile Payment_Ease 0.297 0.784
## Cash_Convenience -0.094 -0.228
## Card_Convenience 1.000 0.435
## Mobile Payment_Convenience 0.435 1.000
## Cash_Privacy 0.040 -0.062
## Card_Privacy 0.502 0.416
## Mobile Payment_Privacy 0.228 0.631
## Cash_Tracking -0.073 -0.130
## Card_Tracking 0.466 0.540
## Mobile Payment_Tracking 0.335 0.712
## Cash_Privacy Card_Privacy Mobile Payment_Privacy
## Cash_Secure 0.629 -0.204 -0.159
## Cash_Secure -0.147 0.499 0.161
## Mobile Payment_Secure -0.265 0.257 0.558
## Cash_Speed 0.319 -0.104 -0.099
## Card_Speed -0.006 0.530 0.169
## Mobile Payment_Speed -0.028 0.303 0.493
## Cash_Ease 0.665 -0.059 -0.184
## Card_Ease -0.010 0.499 0.229
## Mobile Payment_Ease -0.152 0.346 0.668
## Cash_Convenience 0.442 -0.156 -0.083
## Card_Convenience 0.040 0.502 0.228
## Mobile Payment_Convenience -0.062 0.416 0.631
## Cash_Privacy 1.000 -0.184 -0.264
## Card_Privacy -0.184 1.000 0.577
## Mobile Payment_Privacy -0.264 0.577 1.000
## Cash_Tracking 0.447 -0.173 -0.130
## Card_Tracking -0.058 0.469 0.372
## Mobile Payment_Tracking -0.030 0.245 0.501
## Cash_Tracking Card_Tracking Mobile Payment_Tracking
## Cash_Secure 0.623 -0.032 0.053
## Cash_Secure -0.252 0.279 0.047
## Mobile Payment_Secure -0.149 0.330 0.638
## Cash_Speed 0.335 -0.134 -0.126
## Card_Speed -0.132 0.487 0.324
## Mobile Payment_Speed -0.102 0.513 0.728
## Cash_Ease 0.472 -0.073 -0.132
## Card_Ease -0.169 0.435 0.296
## Mobile Payment_Ease -0.142 0.432 0.670
## Cash_Convenience 0.588 -0.166 -0.169
## Card_Convenience -0.073 0.466 0.335
## Mobile Payment_Convenience -0.130 0.540 0.712
## Cash_Privacy 0.447 -0.058 -0.030
## Card_Privacy -0.173 0.469 0.245
## Mobile Payment_Privacy -0.130 0.372 0.501
## Cash_Tracking 1.000 0.002 -0.031
## Card_Tracking 0.002 1.000 0.545
## Mobile Payment_Tracking -0.031 0.545 1.000library(tibble)
library(tidyr)##
## Attaching package: 'tidyr'## The following object is masked from 'package:pastecs':
##
## extractmydataPCA <- mydata_PCA %>%
pivot_longer(everything(), names_to = "name", values_to = "score") %>%
separate(name, into = c("retailer", "dimension"), sep = "_")%>%
pivot_wider(names_from = dimension, values_from = score, values_fn = mean) %>%
column_to_rownames(var = "retailer")
print(mydata_PCA)## Cash_Secure Cash_Secure Mobile Payment_Secure Cash_Speed Card_Speed
## 1 6 6 6 5 6
## 2 7 1 1 7 1
## 3 7 5 3 7 7
## 4 7 6 6 6 7
## 5 7 1 7 7 1
## 6 7 6 5 7 6
## 7 7 5 6 7 6
## 8 3 6 6 3 6
## 9 3 5 5 5 6
## 10 6 6 6 5 7
## 11 4 5 5 4 6
## 12 7 7 6 4 7
## 13 5 6 1 7 7
## 14 5 6 6 5 6
## 15 2 6 7 2 5
## 16 6 7 6 5 7
## 17 7 5 5 7 7
## 18 7 6 7 5 7
## 19 3 5 5 3 6
## 20 5 5 5 7 6
## 21 6 6 6 6 6
## 22 7 6 2 3 7
## 23 7 7 6 7 7
## 24 1 7 3 6 7
## 25 5 4 3 5 6
## 26 6 6 6 4 6
## 27 2 5 5 6 6
## 28 6 6 6 7 6
## 29 7 6 6 3 7
## 30 6 6 6 6 6
## 31 5 6 7 6 7
## 32 7 4 2 7 6
## 33 7 7 2 6 7
## 34 7 5 5 7 7
## 35 6 6 5 6 6
## 36 6 6 6 6 6
## 37 7 6 6 5 5
## 38 5 6 6 6 7
## 39 6 5 6 6 7
## 40 6 6 3 3 5
## 41 5 7 3 6 7
## 42 6 6 6 6 6
## 43 4 5 7 4 6
## 44 4 5 5 4 5
## 45 7 7 4 7 7
## 46 2 6 6 7 6
## 47 7 6 2 7 3
## 48 7 7 5 6 6
## 49 6 6 3 5 7
## 50 4 6 6 2 6
## 51 7 6 5 2 6
## 52 6 6 6 3 6
## 53 7 7 7 7 7
## 54 7 6 6 2 6
## 55 6 6 1 6 7
## 56 6 5 3 3 5
## 57 6 3 2 6 5
## 58 7 5 6 3 6
## 59 7 7 3 3 7
## 60 6 5 3 6 6
## 61 6 5 3 2 3
## 62 3 6 5 6 7
## 63 7 2 1 6 6
## 64 2 6 6 2 7
## 65 3 5 2 7 7
## 66 3 6 6 2 6
## 67 4 4 4 6 5
## 68 7 7 7 6 6
## 69 6 6 6 3 6
## 70 7 5 3 5 5
## 71 6 5 5 4 6
## 72 6 7 5 3 7
## 73 7 6 6 7 7
## 74 1 7 7 1 7
## 75 5 6 5 6 5
## 76 7 6 5 1 3
## 77 7 5 7 7 7
## 78 7 6 1 7 1
## 79 5 6 6 5 7
## 80 6 6 5 6 7
## 81 2 6 1 2 6
## 82 2 6 7 6 6
## 83 7 3 2 7 7
## 84 6 6 6 3 5
## 85 4 4 4 7 4
## 86 7 6 4 7 7
## 87 7 5 6 5 5
## 88 1 7 7 1 7
## 89 7 5 6 3 6
## 90 7 7 3 3 3
## 91 6 6 4 6 6
## 92 7 2 2 7 5
## 93 5 7 6 7 7
## 94 6 7 3 5 5
## 95 7 6 6 1 7
## 96 7 5 6 5 6
## 97 3 6 6 6 2
## 98 6 6 1 5 6
## 99 6 5 2 7 5
## 100 7 5 3 7 5
## 101 6 6 5 7 7
## 102 3 4 3 7 7
## 103 7 7 4 7 4
## 104 7 7 6 6 7
## 105 4 6 6 6 6
## 106 7 4 5 5 5
## Mobile Payment_Speed Cash_Ease Card_Ease Mobile Payment_Ease
## 1 4 5 5 4
## 2 1 7 1 1
## 3 5 6 7 1
## 4 7 7 6 5
## 5 7 7 1 7
## 6 6 7 6 6
## 7 5 7 6 5
## 8 6 5 5 5
## 9 5 5 6 5
## 10 7 7 7 3
## 11 6 5 6 6
## 12 6 7 7 7
## 13 7 7 7 7
## 14 7 6 7 6
## 15 7 3 6 7
## 16 6 5 7 6
## 17 7 7 7 6
## 18 7 6 6 6
## 19 6 5 6 6
## 20 6 7 5 5
## 21 6 5 6 5
## 22 7 6 7 2
## 23 6 7 7 7
## 24 5 7 7 4
## 25 4 6 6 4
## 26 6 7 7 7
## 27 6 2 7 7
## 28 6 7 6 4
## 29 7 6 7 7
## 30 7 6 6 7
## 31 7 7 7 7
## 32 6 7 6 4
## 33 2 7 7 3
## 34 7 7 7 6
## 35 6 7 7 7
## 36 6 7 7 6
## 37 5 5 5 6
## 38 7 2 7 7
## 39 6 6 7 6
## 40 5 5 5 3
## 41 3 6 7 3
## 42 6 7 7 6
## 43 7 4 6 7
## 44 4 4 6 5
## 45 1 7 7 1
## 46 6 6 7 6
## 47 3 7 3 3
## 48 6 7 7 6
## 49 5 5 7 5
## 50 6 2 6 6
## 51 5 6 6 4
## 52 5 6 6 6
## 53 7 7 7 7
## 54 5 7 7 6
## 55 2 6 7 1
## 56 3 6 6 3
## 57 5 7 3 2
## 58 6 5 6 7
## 59 7 7 7 3
## 60 6 7 5 3
## 61 5 7 6 3
## 62 5 7 7 4
## 63 1 7 7 1
## 64 7 1 6 6
## 65 5 5 3 3
## 66 6 5 7 7
## 67 5 6 6 4
## 68 6 6 6 6
## 69 7 3 7 7
## 70 3 7 5 2
## 71 7 1 2 5
## 72 3 5 7 6
## 73 7 7 7 7
## 74 7 1 7 7
## 75 3 6 5 5
## 76 6 3 5 6
## 77 7 7 7 7
## 78 1 7 1 1
## 79 6 6 7 7
## 80 6 6 7 3
## 81 1 6 6 1
## 82 6 6 6 6
## 83 7 7 5 3
## 84 5 6 6 5
## 85 4 7 4 4
## 86 7 7 6 4
## 87 6 6 6 6
## 88 7 1 7 7
## 89 6 7 7 7
## 90 3 7 7 5
## 91 4 6 7 5
## 92 5 7 4 5
## 93 5 3 7 6
## 94 5 7 7 4
## 95 5 7 7 6
## 96 6 7 6 6
## 97 1 3 1 1
## 98 1 7 7 1
## 99 7 7 6 5
## 100 5 7 5 5
## 101 7 7 7 4
## 102 5 6 7 4
## 103 7 7 7 7
## 104 6 7 7 6
## 105 6 6 6 4
## 106 5 7 7 5
## Cash_Convenience Card_Convenience Mobile Payment_Convenience Cash_Privacy
## 1 5 5 4 7
## 2 7 1 1 7
## 3 7 7 1 7
## 4 7 6 5 6
## 5 7 1 7 7
## 6 7 6 6 7
## 7 7 6 6 7
## 8 4 5 5 5
## 9 6 6 5 2
## 10 5 7 7 7
## 11 4 5 5 5
## 12 4 7 7 7
## 13 7 7 7 7
## 14 5 6 6 5
## 15 2 6 7 3
## 16 5 7 6 6
## 17 7 7 7 7
## 18 6 6 6 7
## 19 3 5 6 3
## 20 6 5 5 5
## 21 4 6 5 4
## 22 5 7 6 7
## 23 7 7 7 7
## 24 1 7 3 7
## 25 6 5 4 7
## 26 6 7 7 6
## 27 3 6 1 1
## 28 6 6 6 7
## 29 6 7 7 7
## 30 5 6 7 7
## 31 7 7 7 5
## 32 7 7 4 7
## 33 7 7 6 6
## 34 6 6 6 7
## 35 6 3 6 7
## 36 6 7 7 6
## 37 5 5 5 6
## 38 5 7 7 3
## 39 4 7 6 7
## 40 5 5 3 3
## 41 5 7 3 6
## 42 7 7 6 7
## 43 2 4 6 4
## 44 4 5 5 5
## 45 7 7 1 7
## 46 2 6 6 7
## 47 7 3 3 7
## 48 7 7 7 7
## 49 5 5 5 5
## 50 2 6 6 4
## 51 6 6 5 6
## 52 6 6 6 6
## 53 6 6 7 7
## 54 2 6 6 6
## 55 5 5 5 5
## 56 6 6 5 6
## 57 7 3 2 6
## 58 3 6 6 5
## 59 5 7 3 7
## 60 7 7 2 7
## 61 2 3 2 5
## 62 6 6 5 5
## 63 7 6 1 7
## 64 2 7 7 2
## 65 4 6 3 7
## 66 2 6 7 7
## 67 6 6 4 7
## 68 6 7 7 7
## 69 3 7 7 6
## 70 7 5 2 6
## 71 4 6 6 7
## 72 2 7 5 6
## 73 2 7 7 6
## 74 1 7 7 1
## 75 5 6 5 6
## 76 1 5 6 7
## 77 4 7 7 7
## 78 7 1 1 7
## 79 3 7 7 5
## 80 5 6 3 6
## 81 2 6 1 7
## 82 2 5 7 2
## 83 7 5 3 7
## 84 5 6 5 7
## 85 7 6 4 4
## 86 7 6 4 7
## 87 6 6 7 7
## 88 1 7 7 1
## 89 5 5 6 7
## 90 7 6 5 5
## 91 6 6 4 6
## 92 7 2 2 7
## 93 2 7 6 3
## 94 4 4 3 7
## 95 3 6 6 7
## 96 5 6 6 6
## 97 3 1 1 2
## 98 4 6 4 5
## 99 6 7 5 7
## 100 7 5 3 7
## 101 7 7 3 7
## 102 3 7 7 7
## 103 1 7 7 7
## 104 7 7 6 7
## 105 6 6 5 5
## 106 7 7 5 7
## Card_Privacy Mobile Payment_Privacy Cash_Tracking Card_Tracking
## 1 5 5 6 4
## 2 1 1 7 1
## 3 5 3 7 3
## 4 6 6 3 5
## 5 1 7 7 1
## 6 6 6 7 6
## 7 5 4 7 6
## 8 3 3 3 6
## 9 5 5 5 5
## 10 5 3 3 7
## 11 3 3 3 6
## 12 6 6 7 7
## 13 7 7 1 7
## 14 7 4 5 7
## 15 5 7 2 5
## 16 5 5 5 5
## 17 5 5 7 7
## 18 7 7 5 5
## 19 5 6 3 5
## 20 5 5 6 7
## 21 4 4 4 6
## 22 5 1 6 6
## 23 7 7 7 6
## 24 7 1 1 3
## 25 4 4 6 2
## 26 6 6 5 7
## 27 3 3 3 3
## 28 6 3 7 6
## 29 6 6 4 6
## 30 7 7 6 6
## 31 6 6 6 7
## 32 5 4 7 6
## 33 7 3 7 7
## 34 5 5 7 7
## 35 4 4 7 3
## 36 6 6 6 6
## 37 6 5 5 5
## 38 6 6 2 6
## 39 4 4 4 7
## 40 5 3 3 5
## 41 6 3 3 6
## 42 7 6 7 7
## 43 5 7 2 6
## 44 5 5 5 5
## 45 2 1 7 2
## 46 6 6 1 7
## 47 3 3 7 6
## 48 6 6 7 6
## 49 7 3 5 5
## 50 6 6 3 5
## 51 4 4 6 6
## 52 6 6 7 6
## 53 5 5 7 6
## 54 6 6 6 6
## 55 5 5 5 5
## 56 3 3 6 5
## 57 6 3 7 5
## 58 7 6 7 6
## 59 7 3 7 7
## 60 2 2 7 3
## 61 3 5 2 5
## 62 7 5 5 6
## 63 1 1 7 2
## 64 6 6 2 7
## 65 5 4 7 3
## 66 6 6 2 6
## 67 4 4 4 4
## 68 5 5 6 6
## 69 6 6 6 7
## 70 5 5 7 5
## 71 2 3 6 6
## 72 7 5 3 6
## 73 6 3 5 7
## 74 7 7 1 7
## 75 5 5 6 5
## 76 2 2 3 3
## 77 4 4 7 7
## 78 1 1 7 1
## 79 6 6 5 6
## 80 5 3 6 6
## 81 6 1 2 6
## 82 6 7 3 5
## 83 2 2 7 7
## 84 3 3 7 5
## 85 6 6 7 4
## 86 5 4 4 4
## 87 3 6 2 7
## 88 7 7 1 7
## 89 1 1 7 7
## 90 6 6 7 2
## 91 3 3 7 5
## 92 5 5 7 6
## 93 7 6 3 6
## 94 5 4 3 4
## 95 5 3 3 3
## 96 5 5 7 5
## 97 2 1 2 1
## 98 4 1 3 3
## 99 5 5 5 5
## 100 3 2 3 3
## 101 5 5 7 5
## 102 4 4 1 4
## 103 4 4 7 7
## 104 6 5 1 1
## 105 6 6 3 6
## 106 5 5 7 5
## Mobile Payment_Tracking
## 1 4
## 2 1
## 3 3
## 4 6
## 5 7
## 6 6
## 7 6
## 8 6
## 9 5
## 10 7
## 11 6
## 12 7
## 13 1
## 14 6
## 15 7
## 16 5
## 17 7
## 18 5
## 19 6
## 20 5
## 21 4
## 22 6
## 23 7
## 24 3
## 25 4
## 26 7
## 27 3
## 28 5
## 29 6
## 30 6
## 31 7
## 32 7
## 33 2
## 34 7
## 35 3
## 36 6
## 37 6
## 38 7
## 39 7
## 40 3
## 41 3
## 42 6
## 43 7
## 44 5
## 45 1
## 46 6
## 47 2
## 48 6
## 49 3
## 50 5
## 51 5
## 52 6
## 53 7
## 54 6
## 55 5
## 56 5
## 57 5
## 58 6
## 59 3
## 60 3
## 61 3
## 62 5
## 63 2
## 64 7
## 65 4
## 66 6
## 67 4
## 68 7
## 69 7
## 70 3
## 71 5
## 72 5
## 73 7
## 74 7
## 75 3
## 76 6
## 77 7
## 78 1
## 79 6
## 80 6
## 81 1
## 82 5
## 83 7
## 84 5
## 85 4
## 86 4
## 87 7
## 88 7
## 89 7
## 90 2
## 91 4
## 92 6
## 93 6
## 94 4
## 95 6
## 96 6
## 97 1
## 98 3
## 99 7
## 100 5
## 101 5
## 102 4
## 103 7
## 104 7
## 105 6
## 106 5library(psych)
cortest.bartlett(R, n = nrow(mydataPCA))## $chisq
## [1] -60.68919
##
## $p.value
## [1] 1
##
## $df
## [1] 153det(R)## [1] 3.52234e-06library(psych)
KMO(R)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = R)
## Overall MSA = 0.79
## MSA for each item =
## Cash_Secure Cash_Secure
## 0.74 0.72
## Mobile Payment_Secure Cash_Speed
## 0.84 0.76
## Card_Speed Mobile Payment_Speed
## 0.76 0.87
## Cash_Ease Card_Ease
## 0.67 0.70
## Mobile Payment_Ease Cash_Convenience
## 0.83 0.78
## Card_Convenience Mobile Payment_Convenience
## 0.81 0.90
## Cash_Privacy Card_Privacy
## 0.72 0.75
## Mobile Payment_Privacy Cash_Tracking
## 0.78 0.79
## Card_Tracking Mobile Payment_Tracking
## 0.90 0.84KMO is 0.79 in our case, indicating that the data is suitable.
library(FactoMineR)## Warning: package 'FactoMineR' was built under R version 4.4.2library(factoextra)## Warning: package 'factoextra' was built under R version 4.4.2## Loading required package: ggplot2##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
##
## %+%, alpha## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBacomponents <- PCA(mydataPCA,
scale.unit = TRUE,
graph = FALSE)## Warning in PCA(mydataPCA, scale.unit = TRUE, graph = FALSE): Missing values are
## imputed by the mean of the variable: you should use the imputePCA function of
## the missMDA packagelibrary(factoextra)
fviz_eig(components,
choice = "eigenvalue",
main = "Scree plot",
ylab = "Eigenvalue",
addlabels = TRUE)
library(psych)
fa.parallel(mydataPCA,
sim = FALSE,
fa = "pc")## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(model): Matrix was not positive definite, smoothing was
## done## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully## In factor.scores, the correlation matrix is singular, the pseudo inverse is used## Warning in cor(sampledata, use = use): the standard deviation is zero## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Warning in cor(sampledata, use = use): the standard deviation is zero
## Parallel analysis suggests that the number of factors = NA and the number of components = 0library(FactoMineR)
components <- PCA(mydataPCA,
scale.unit = TRUE,
graph = FALSE,
ncp = 2)## Warning in PCA(mydataPCA, scale.unit = TRUE, graph = FALSE, ncp = 2): Missing
## values are imputed by the mean of the variable: you should use the imputePCA
## function of the missMDA packagecomponents## **Results for the Principal Component Analysis (PCA)**
## The analysis was performed on 3 individuals, described by 6 variables
## *The results are available in the following objects:
##
## name description
## 1 "$eig" "eigenvalues"
## 2 "$var" "results for the variables"
## 3 "$var$coord" "coord. for the variables"
## 4 "$var$cor" "correlations variables - dimensions"
## 5 "$var$cos2" "cos2 for the variables"
## 6 "$var$contrib" "contributions of the variables"
## 7 "$ind" "results for the individuals"
## 8 "$ind$coord" "coord. for the individuals"
## 9 "$ind$cos2" "cos2 for the individuals"
## 10 "$ind$contrib" "contributions of the individuals"
## 11 "$call" "summary statistics"
## 12 "$call$centre" "mean of the variables"
## 13 "$call$ecart.type" "standard error of the variables"
## 14 "$call$row.w" "weights for the individuals"
## 15 "$call$col.w" "weights for the variables"print(components$var$cor)## Dim.1 Dim.2
## Secure -0.6254384 0.7802735
## Speed 0.9275059 0.3738086
## Ease -0.0113184 0.9999359
## Convenience 0.8377770 0.5460125
## Privacy -0.7489452 0.6626320
## Tracking 0.9807604 0.1952152print(components$var$contrib)## Dim.1 Dim.2
## Secure 11.252739350 24.123883
## Speed 24.746996599 5.536712
## Ease 0.003685186 39.618480
## Convenience 20.190453139 11.812956
## Privacy 16.135752069 17.397956
## Tracking 27.670373657 1.510014library(factoextra)
fviz_pca_biplot(components)
## Conclusion
Principal component analysis was performed on 6 standardized variables (n=106). The KMO measure confirms the appropriateness of the variables, KMO=0.79, indicating that the data is still reasonably suited for PCA. It can be observed that Card is positively associated with Convenience, Speed and Tracking expenses. Cash on the other hand, is negatively associated with Convenience, Speed and Tracking expenses, but positively with Privacy and Security. Mobile payment is positioned lower, and it is closer to Convenience, Speed and Tracking expenses, meaning that users associate it with these benefits, but it is further away from Ease of use compared to Card, meaning that Cards may be seen as easier to use than Mobile Payments.