PCA NLB
Maj Batagelj
2025-02-04
library(readxl)
mydata <- read.csv("~/anketa_final_6..csv")
head(mydata)## ID Q47a Q47b Q47c Q47d Q21 Q50a Q50b Q50c Q50d Q51 Q52a Q52b Q52c Q52d Q52e
## 1 1 1 1 0 0 4 2 2 6 6 4 2 4 2 1 1
## 2 2 1 0 0 0 7 2 6 6 6 5 1 1 1 1 1
## 3 3 1 1 0 0 7 1 2 6 6 4 1 1 1 1 1
## 4 4 1 1 1 0 7 1 2 4 6 1 1 3 1 1 1
## 5 5 1 0 1 0 7 1 6 2 6 5 1 3 1 1 1
## 6 6 1 1 0 1 6 2 1 6 4 3 2 4 2 2 2
## Q52f Q52g Q53a_1 Q53b_1 Q53c_1 Q53d_1 Q54a Q54b Q54c Q54d Q55a Q55b Q55c Q55d
## 1 2 2 4 4 5 3 1 1 0 0 1 0 1 0
## 2 1 1 1 7 7 7 0 0 0 1 1 1 1 1
## 3 1 1 1 7 7 7 0 1 0 0 1 1 1 0
## 4 1 1 3 6 7 7 0 1 0 0 1 1 0 0
## 5 1 1 1 7 7 7 1 1 0 0 1 0 0 0
## 6 1 2 6 5 2 2 1 1 0 0 0 1 1 0
## Q55e Q55f Q55g Q56 Q57 Q58 Q59 Q60a Q60b Q60c Q60d Q60e Q60f Q60g Q61a Q61b
## 1 0 0 0 5 6 3 5 -2 -2 -2 -2 -2 -2 -2 1 0
## 2 1 1 0 7 1 1 1 0 0 0 1 1 -2 0 0 0
## 3 1 1 0 6 3 5 1 1 1 1 1 0 -2 0 1 0
## 4 1 0 0 5 6 6 4 0 1 1 0 0 -2 0 1 0
## 5 0 0 0 5 1 5 6 -2 -2 -2 -2 -2 -2 -2 0 0
## 6 0 0 0 2 5 6 6 -2 -2 -2 -2 -2 -2 -2 1 0
## Q61c Q61d Q61e Q61f Q62 Q63a_1 Q63b_1 Q63c_1 Q63d_1 Q63e_1 Q63f_1 Q64 Q65 Q46
## 1 0 0 0 0 2 -2 -2 -2 -2 -2 -2 3 1 66
## 2 0 0 1 0 2 -2 -2 -2 -2 -2 -2 1 1 0
## 3 0 0 0 0 2 -2 -2 -2 -2 -2 -2 5 3 16
## 4 0 0 0 0 1 6 4 6 7 6 7 5 4 70
## 5 1 0 0 0 2 -2 -2 -2 -2 -2 -2 1 1 49
## 6 1 0 0 0 2 -2 -2 -2 -2 -2 -2 6 6 85
## Q1a_1 Q1b_1 Q1c_1 Q1d_1 Q1e_1 Q1f_1 Q2a_1 Q2b_1 Q2c_1 Q3a_1 Q3b_1 Q3c_1 Q4a_1
## 1 4 6 6 6 5 6 6 6 6 5 6 4 5
## 2 7 7 7 7 7 7 7 1 1 7 1 1 7
## 3 7 7 7 7 7 7 7 5 3 7 7 5 6
## 4 5 7 7 7 3 3 7 6 6 6 7 7 7
## 5 7 7 7 7 7 7 7 1 7 7 1 7 7
## 6 6 6 6 5 5 6 7 6 5 7 6 6 7
## Q4b_1 Q4c_1 Q5a_1 Q5b_1 Q5c_1 Q6a_1 Q6b_1 Q6c_1 Q7a_1 Q7b_1 Q7c_1 Q39 Q40
## 1 5 4 5 5 4 7 5 5 6 4 4 1 1958
## 2 1 1 7 1 1 7 1 1 7 1 1 2 1942
## 3 7 1 7 7 1 7 5 3 7 3 3 2 1953
## 4 6 5 7 6 5 6 6 6 3 5 6 1 1948
## 5 1 7 7 1 7 7 1 7 7 1 7 1 1953
## 6 6 6 7 6 6 7 6 6 7 6 6 1 1955
## Q37 Q38 Q41 Q42 Q43 Q44 Q45
## 1 1 3 4 3 -2 2 6
## 2 1 1 2 3 -2 3 7
## 3 2 -2 4 3 -2 3 1
## 4 5 -2 3 3 -2 3 1
## 5 5 -2 5 3 -2 3 4
## 6 1 4 5 3 -2 3 1mydata_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")]
head(mydata_PCA)## 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
## 1 6 6 6 5 6 4 5 5 4 5 5 4 7
## 2 7 1 1 7 1 1 7 1 1 7 1 1 7
## 3 7 5 3 7 7 5 6 7 1 7 7 1 7
## 4 7 6 6 6 7 7 7 6 5 7 6 5 6
## 5 7 1 7 7 1 7 7 1 7 7 1 7 7
## 6 7 6 5 7 6 6 7 6 6 7 6 6 7
## Q6b_1 Q6c_1 Q7a_1 Q7b_1 Q7c_1
## 1 5 5 6 4 4
## 2 1 1 7 1 1
## 3 5 3 7 3 3
## 4 6 6 3 5 6
## 5 1 7 7 1 7
## 6 6 6 7 6 6colnames(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")
head(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
## Mobile Payment_Speed Cash_Ease Card_Ease Mobile Payment_Ease Cash_Convenience
## 1 4 5 5 4 5
## 2 1 7 1 1 7
## 3 5 6 7 1 7
## 4 7 7 6 5 7
## 5 7 7 1 7 7
## 6 6 7 6 6 7
## Card_Convenience Mobile Payment_Convenience Cash_Privacy Card_Privacy
## 1 5 4 7 5
## 2 1 1 7 1
## 3 7 1 7 5
## 4 6 5 6 6
## 5 1 7 7 1
## 6 6 6 7 6
## Mobile Payment_Privacy Cash_Tracking Card_Tracking Mobile Payment_Tracking
## 1 5 6 4 4
## 2 1 7 1 1
## 3 3 7 3 3
## 4 6 3 5 6
## 5 7 7 1 7
## 6 6 7 6 6library(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/batag/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\batag\AppData\Local\Temp\RtmpyiM3au\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 0.84library(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
## 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
The Principal Component Analysis was conducted to explore consumer perceptions of various payment methods, analyzing factors such as security, speed, ease of use, convenience, privacy, and tracking across cash, card, and mobile payments. The goal was to identify underlying dimensions that explain differences in consumer attitudes toward these payment options.
The Bartlett’s test of sphericity yielded a non-significant result, suggesting that the correlation matrix may not be ideal for factor analysis. However, the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was 0.79, indicating that the data is still reasonably suited for PCA. The determinant of the correlation matrix was very low (3.52e-06), suggesting high multicollinearity among variables, which could impact the stability of the components.
The scree plot and eigenvalues suggested that two principal components should be retained, together explaining a cumulative 91.3% of the total variance.
The first component (PC1: Payment Perceived Efficiency) explained 59.7% of the variance and captured distinctions in transaction speed, tracking, and convenience. Variables related to digital payment methods (card and mobile payments) had high positive loadings on this component, indicating that consumers perceive these methods as more efficient and trackable compared to cash. Conversely, cash had negative loadings, suggesting it is associated with lower perceived efficiency.
The second component (PC2: Perceived Security & Privacy Trade-Off) accounted for 31.6% of the variance and highlighted differences in privacy and security perceptions. Cash had strong positive loadings, reinforcing its association with privacy and control over transactions. In contrast, mobile payments had high negative loadings, indicating that respondents may perceive them as less secure or private due to concerns over data tracking and fraud risks. Card payments were positioned between the two, balancing convenience with moderate privacy and security concerns.
Variable contributions to the components indicate that tracking and convenience were dominant factors in shaping PC1, while privacy and security concerns were key in defining PC2. The findings suggest that consumer adoption of digital payments may hinge on trust and perceived risks, with some respondents prioritizing efficiency and usability while others focus on privacy and security concerns.
These insights provide a clearer segmentation of consumer attitudes, revealing potential barriers to digital payment adoption and opportunities for financial service providers to tailor their offerings to different user groups.