Paul’s data
G. A
2022-11-13
Question 4 (Education)
## H.S. University Private company Government sector
## 5 20 32 162
Question 5 (Using Social Media)
## Twitter Instagram TikToc Snapchat nota
## 105 206 44 153 5
Question 6 (Time in hours spent on messaging app)
## Less1 Between1and2 Between2and4 Between4and6 More6
## Twitter 87 5 14 10 39
## Instagram 19 54 10 5 27
## TikToc 9 57 35 2 12
## Snapchat 4 52 19 66 5## Warning: package 'ca' was built under R version 4.2.2##
## Principal inertias (eigenvalues):
## 1 2 3
## Value 0.420549 0.19471 0.036271
## Percentage 64.55% 29.89% 5.57%
##
##
## Rows:
## Twitter Instagram TikToc Snapchat
## Mass 0.291902 0.216573 0.216573 0.274953
## ChiDist 0.946720 0.484703 0.711338 0.913489
## Inertia 0.261626 0.050881 0.109586 0.229438
## Dim. 1 1.398900 0.043602 -0.415002 -1.192593
## Dim. 2 0.596057 -0.806279 -1.395645 1.101591
##
##
## Columns:
## Less1 Between1and2 Between2and4 Between4and6 More6
## Mass 0.224105 0.316384 0.146893 0.156309 0.156309
## ChiDist 0.989601 0.600048 0.575990 1.169990 0.595571
## Inertia 0.219469 0.113917 0.048734 0.213968 0.055443
## Dim. 1 1.477590 -0.700529 -0.339319 -1.213818 0.832162
## Dim. 2 0.540532 -0.847517 -0.802926 1.961606 -0.266572##
## Principal inertias (eigenvalues):
##
## dim value % cum% scree plot
## 1 0.420549 64.5 64.5 ****************
## 2 0.194710 29.9 94.4 *******
## 3 0.036271 5.6 100.0 *
## -------- -----
## Total: 0.651530 100.0
##
##
## Rows:
## name mass qlt inr k=1 cor ctr k=2 cor ctr
## 1 | Twtt | 292 995 402 | 907 918 571 | 263 77 104 |
## 2 | Inst | 217 542 78 | 28 3 0 | -356 539 141 |
## 3 | TkTc | 217 893 168 | -269 143 37 | -616 750 422 |
## 4 | Snpc | 275 1000 352 | -773 717 391 | 486 283 334 |
##
## Columns:
## name mass qlt inr k=1 cor ctr k=2 cor ctr
## 1 | Lss1 | 224 996 337 | 958 938 489 | 239 58 65 |
## 2 | Bt12 | 316 962 175 | -454 573 155 | -374 388 227 |
## 3 | Bt24 | 147 524 75 | -220 146 17 | -354 378 95 |
## 4 | Bt46 | 156 1000 328 | -787 453 230 | 866 547 601 |
## 5 | Mor6 | 156 860 85 | 540 821 108 | -118 39 11 |
We find that Twitter and Snapchat are at two extremes. Hence they are highly different from others. We notee that the points for Twitter and “Less than 1 hour” are very close, we can say that the users of “Twiteer” are relatively spend less than 1 hour with that app. And users of “Instagram” and “Tiktok” are associated with the spending time “Between 2 and 4 hours”. The “snapchat” users are spending “between 4 and 6 hours”
Question 7 (provides contents that match my interest.)
## Strongly Agree Agree Neutral Disagree Strongly Disagree
## Twitter 22 13 2 24 52
## Instagram 57 67 1 20 53
## TikToc 33 91 15 28 23
## Snapchat 20 24 19 22 10##
## Principal inertias (eigenvalues):
## 1 2 3
## Value 0.141247 0.071956 0.011036
## Percentage 62.99% 32.09% 4.92%
##
##
## Rows:
## Twitter Instagram TikToc Snapchat
## Mass 0.189597 0.332215 0.318792 0.159396
## ChiDist 0.645929 0.314183 0.372423 0.653754
## Inertia 0.079104 0.032793 0.044216 0.068125
## Dim. 1 1.521987 0.529250 -0.838746 -1.235939
## Dim. 2 -1.059818 0.810862 0.621271 -1.671925
##
##
## Columns:
## Strongly Agree Agree Neutral Disagree Strongly Disagree
## Mass 0.221477 0.327181 0.062081 0.157718 0.231544
## ChiDist 0.218793 0.396186 1.089728 0.319469 0.559425
## Inertia 0.010602 0.051356 0.073721 0.016097 0.072463
## Dim. 1 0.226843 -0.692390 -2.336519 -0.100851 1.456551
## Dim. 2 0.281476 1.088931 -2.393561 -1.134446 -0.393454##
## Principal inertias (eigenvalues):
##
## dim value % cum% scree plot
## 1 0.141247 63.0 63.0 ****************
## 2 0.071956 32.1 95.1 ********
## 3 0.011036 4.9 100.0 *
## -------- -----
## Total: 0.224238 100.0
##
##
## Rows:
## name mass qlt inr k=1 cor ctr k=2 cor ctr
## 1 | Twtt | 190 978 353 | 572 784 439 | -284 194 213 |
## 2 | Inst | 332 880 146 | 199 401 93 | 218 479 218 |
## 3 | TkTc | 319 917 197 | -315 716 224 | 167 200 123 |
## 4 | Snpc | 159 975 304 | -465 505 243 | -448 471 446 |
##
## Columns:
## name mass qlt inr k=1 cor ctr k=2 cor ctr
## 1 | StrA | 221 271 47 | 85 152 11 | 76 119 18 |
## 2 | Agre | 327 975 229 | -260 431 157 | 292 544 388 |
## 3 | Ntrl | 62 997 329 | -878 649 339 | -642 347 356 |
## 4 | Dsgr | 158 921 72 | -38 14 2 | -304 907 203 |
## 5 | StrD | 232 993 323 | 547 958 491 | -106 36 36 |
Users of “Twitter” strongly disagree with the concept “contents that match my interest”.
whereas “Instagram” people strongly agree” with.. “Tiktoc” users agree to this. “Snapchat” users are away from these notions. They are between being neutral and disagree categories.
Question 8 (my surrounding friends and family members are using)
## Strongly Agree Agree Neutral Disagree Strongly Disagree
## Twitter 6 21 14 26 67
## Instagram 29 54 5 31 19
## TikToc 48 85 3 30 13
## Snapchat 38 27 15 48 10##
## Principal inertias (eigenvalues):
## 1 2 3
## Value 0.238061 0.067284 0.001154
## Percentage 77.67% 21.95% 0.38%
##
##
## Rows:
## Twitter Instagram TikToc Snapchat
## Mass 0.227504 0.234295 0.303905 0.234295
## ChiDist 0.880939 0.201850 0.464538 0.483698
## Inertia 0.176555 0.009546 0.065581 0.054817
## Dim. 1 1.799484 -0.338857 -0.834060 -0.326607
## Dim. 2 -0.274893 -0.384335 -0.855033 1.760324
##
##
## Columns:
## Strongly Agree Agree Neutral Disagree Strongly Disagree
## Mass 0.205433 0.317487 0.062818 0.229202 0.185059
## ChiDist 0.441295 0.424472 0.656020 0.300272 0.934439
## Inertia 0.040006 0.057204 0.027035 0.020666 0.161589
## Dim. 1 -0.871916 -0.660046 0.891671 -0.067057 1.880654
## Dim. 2 0.415965 -1.065342 1.882736 1.136073 -0.680219##
## Principal inertias (eigenvalues):
##
## dim value % cum% scree plot
## 1 0.238061 77.7 77.7 *******************
## 2 0.067284 22.0 99.6 *****
## 3 0.001154 0.4 100.0
## -------- -----
## Total: 0.306499 100.0
##
##
## Rows:
## name mass qlt inr k=1 cor ctr k=2 cor ctr
## 1 | Twtt | 228 1000 576 | 878 993 737 | -71 7 17 |
## 2 | Inst | 234 915 31 | -165 671 27 | -100 244 35 |
## 3 | TkTc | 304 995 214 | -407 767 211 | -222 228 222 |
## 4 | Snpc | 234 1000 179 | -159 109 25 | 457 891 726 |
##
## Columns:
## name mass qlt inr k=1 cor ctr k=2 cor ctr
## 1 | StrA | 205 989 131 | -425 929 156 | 108 60 36 |
## 2 | Agre | 317 999 187 | -322 576 138 | -276 424 360 |
## 3 | Ntrl | 63 994 88 | 435 440 50 | 488 554 223 |
## 4 | Dsgr | 229 975 67 | -33 12 1 | 295 963 296 |
## 5 | StrD | 185 1000 527 | 918 964 655 | -176 36 86 |
Using amap
library(amap)ca06.am<-afc(q062)
plot(ca06.am)