## The following objects are masked from dataset:
##
## Q10, Q11, Q12, Q13, Q14, Q15, Q16, Q17, Q18, Q19, Q20, Q21,
## Q22, Q23, Q24, Q25, Q26, Q27, Q28, Q29, Q30, Q31, Q32, Q33,
## Q5, Q6, Q7, Q8, Q9
## Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 Q19 Q20 Q21 Q22 Q23
## 1 3 5 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## 2 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3
## 3 5 5 5 5 5 5 5 5 5 5 5 5 4 5 5 5 5 5 4
## 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3
## 5 3 5 5 5 5 5 4 5 5 5 5 4 4 5 5 4 4 5 5
## 6 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## Q24 Q25 Q26 Q27 Q28 Q29 Q30 Q31 Q32 Q33
## 1 5 5 5 5 5 5 5 5 5 5
## 2 5 5 3 3 5 5 5 5 5 3
## 3 5 5 4 4 5 5 5 5 5 5
## 4 5 5 3 3 5 3 5 5 5 3
## 5 4 4 3 5 5 5 4 4 5 4
## 6 5 5 5 5 5 5 5 5 5 5
## Q5 Q6 Q7 Q8
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :5.000 Median :5.000 Median :5.000 Median :5.000
## Mean :4.236 Mean :4.237 Mean :4.218 Mean :4.246
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## Q9 Q10 Q11 Q12
## Min. :2.000 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.00
## Median :5.000 Median :5.000 Median :4.000 Median :5.00
## Mean :4.328 Mean :4.344 Mean :3.938 Mean :4.14
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.00
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.00
## Q13 Q14 Q15 Q16
## Min. :2.000 Min. :1.000 Min. :2.000 Min. :2.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :5.000 Median :5.000 Median :4.000 Median :5.000
## Mean :4.148 Mean :4.113 Mean :3.976 Mean :4.215
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## Q17 Q18 Q19 Q20
## Min. :2.000 Min. :1.000 Min. :2.000 Min. :2.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :5.000 Median :4.000 Median :4.000
## Mean :3.932 Mean :4.122 Mean :3.911 Mean :3.928
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## Q21 Q22 Q23 Q24
## Min. :2.000 Min. :2.000 Min. :1.00 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.00 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.00 Median :4.000
## Mean :3.898 Mean :3.974 Mean :3.62 Mean :3.823
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:4.00 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.00 Max. :5.000
## Q25 Q26 Q27 Q28
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :3.000 Median :3.000 Median :4.000
## Mean :3.789 Mean :3.288 Mean :3.514 Mean :3.733
## 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## Q29 Q30 Q31 Q32
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :3.677 Mean :3.754 Mean :3.739 Mean :3.759
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## Q33
## Min. :1.000
## 1st Qu.:3.000
## Median :3.000
## Mean :3.517
## 3rd Qu.:4.000
## Max. :5.000
## 'data.frame': 1454 obs. of 29 variables:
## $ Q5 : int 3 3 5 5 3 5 5 3 5 5 ...
## $ Q6 : int 5 5 5 5 5 5 5 5 5 5 ...
## $ Q7 : int 2 5 5 5 5 5 5 5 5 5 ...
## $ Q8 : int 5 5 5 5 5 5 5 5 5 5 ...
## $ Q9 : int 5 5 5 5 5 5 5 5 5 5 ...
## $ Q10: int 5 5 5 5 5 5 5 5 5 5 ...
## $ Q11: int 5 5 5 5 4 5 5 5 5 5 ...
## $ Q12: int 5 5 5 5 5 5 5 5 5 5 ...
## $ Q13: int 5 5 5 5 5 5 5 5 5 4 ...
## $ Q14: int 5 5 5 5 5 5 5 5 5 5 ...
## $ Q15: int 5 5 5 5 5 5 5 5 5 5 ...
## $ Q16: int 5 5 5 5 4 5 5 5 5 5 ...
## $ Q17: int 5 5 4 5 4 5 5 5 5 5 ...
## $ Q18: int 5 5 5 5 5 5 5 5 5 5 ...
## $ Q19: int 5 5 5 5 5 5 5 5 5 5 ...
## $ Q20: int 5 5 5 5 4 5 4 5 5 5 ...
## $ Q21: int 5 5 5 5 4 5 5 5 4 5 ...
## $ Q22: int 5 5 5 5 5 5 5 5 4 5 ...
## $ Q23: int 5 3 4 3 5 5 4 3 5 5 ...
## $ Q24: int 5 5 5 5 4 5 5 5 4 5 ...
## $ Q25: int 5 5 5 5 4 5 5 5 4 5 ...
## $ Q26: int 5 3 4 3 3 5 3 3 4 4 ...
## $ Q27: int 5 3 4 3 5 5 3 3 4 5 ...
## $ Q28: int 5 5 5 5 5 5 3 3 4 5 ...
## $ Q29: int 5 5 5 3 5 5 3 3 5 5 ...
## $ Q30: int 5 5 5 5 4 5 5 5 4 5 ...
## $ Q31: int 5 5 5 5 4 5 5 5 4 1 ...
## $ Q32: int 5 5 5 5 5 5 3 3 4 5 ...
## $ Q33: int 5 3 5 3 4 5 3 3 4 3 ...
## - attr(*, "na.action")=Class 'omit' Named int 1377
## .. ..- attr(*, "names")= chr "1377"
## 'data.frame': 1454 obs. of 29 variables:
## $ Q5 : int 3 3 5 5 3 5 5 3 5 5 ...
## $ Q6 : int 5 5 5 5 5 5 5 5 5 5 ...
## $ Q7 : int 2 5 5 5 5 5 5 5 5 5 ...
## $ Q8 : int 5 5 5 5 5 5 5 5 5 5 ...
## $ Q9 : int 5 5 5 5 5 5 5 5 5 5 ...
## $ Q10: int 5 5 5 5 5 5 5 5 5 5 ...
## $ Q11: int 5 5 5 5 4 5 5 5 5 5 ...
## $ Q12: int 5 5 5 5 5 5 5 5 5 5 ...
## $ Q13: int 5 5 5 5 5 5 5 5 5 4 ...
## $ Q14: int 5 5 5 5 5 5 5 5 5 5 ...
## $ Q15: int 5 5 5 5 5 5 5 5 5 5 ...
## $ Q16: int 5 5 5 5 4 5 5 5 5 5 ...
## $ Q17: int 5 5 4 5 4 5 5 5 5 5 ...
## $ Q18: int 5 5 5 5 5 5 5 5 5 5 ...
## $ Q19: int 5 5 5 5 5 5 5 5 5 5 ...
## $ Q20: int 5 5 5 5 4 5 4 5 5 5 ...
## $ Q21: int 5 5 5 5 4 5 5 5 4 5 ...
## $ Q22: int 5 5 5 5 5 5 5 5 4 5 ...
## $ Q23: int 5 3 4 3 5 5 4 3 5 5 ...
## $ Q24: int 5 5 5 5 4 5 5 5 4 5 ...
## $ Q25: int 5 5 5 5 4 5 5 5 4 5 ...
## $ Q26: int 5 3 4 3 3 5 3 3 4 4 ...
## $ Q27: int 5 3 4 3 5 5 3 3 4 5 ...
## $ Q28: int 5 5 5 5 5 5 3 3 4 5 ...
## $ Q29: int 5 5 5 3 5 5 3 3 5 5 ...
## $ Q30: int 5 5 5 5 4 5 5 5 4 5 ...
## $ Q31: int 5 5 5 5 4 5 5 5 4 1 ...
## $ Q32: int 5 5 5 5 5 5 3 3 4 5 ...
## $ Q33: int 5 3 5 3 4 5 3 3 4 3 ...
## [1] 1454 29
## Q5 Q6 Q7 Q8 Q9 Q10
## Q5 1.00000000 0.2372145 0.1185372 0.1335195 0.1555082 0.1401451
## Q6 0.23721450 1.0000000 0.4011913 0.6756959 0.5782384 0.6380026
## Q7 0.11853724 0.4011913 1.0000000 0.3997314 0.4194060 0.4006871
## Q8 0.13351952 0.6756959 0.3997314 1.0000000 0.5799476 0.7015375
## Q9 0.15550816 0.5782384 0.4194060 0.5799476 1.0000000 0.6762254
## Q10 0.14014512 0.6380026 0.4006871 0.7015375 0.6762254 1.0000000
## Q11 0.06489835 0.5593903 0.3296165 0.6419553 0.5035427 0.5945476
## Q12 0.14673893 0.5606091 0.3336536 0.5696523 0.4467718 0.5132309
## Q13 0.11295146 0.6955997 0.3880042 0.6910489 0.5385216 0.6273495
## Q14 0.10177441 0.6315045 0.3684470 0.6891730 0.5325838 0.5873333
## Q15 0.08883684 0.5022496 0.2604240 0.5400353 0.4789239 0.4857500
## Q16 0.12771985 0.4861224 0.2693873 0.4304120 0.4462412 0.4460018
## Q17 0.08051897 0.4747323 0.2359237 0.5319974 0.4393233 0.4643173
## Q18 0.04197983 0.6134254 0.3927946 0.7244529 0.5499383 0.6768935
## Q19 0.08282951 0.4601756 0.2324462 0.5060740 0.4236325 0.4540870
## Q20 0.08569386 0.5791078 0.3466596 0.6722912 0.5226805 0.5855417
## Q21 0.06605603 0.5503985 0.3327086 0.6034323 0.5123665 0.5278772
## Q22 0.05911687 0.5528742 0.3025632 0.6346401 0.4793173 0.5522890
## Q23 0.03367712 0.4628093 0.1918247 0.5396978 0.3679753 0.4481438
## Q24 0.05364076 0.5257266 0.1922896 0.5724451 0.3887424 0.4670784
## Q25 0.06464856 0.5198847 0.1775456 0.5562024 0.3837273 0.4642299
## Q26 0.03525508 0.3601468 0.2133498 0.4027489 0.3468370 0.3743522
## Q27 0.04698169 0.4244744 0.1739755 0.5078527 0.3567207 0.4020329
## Q28 0.06736189 0.5158639 0.1637591 0.5397830 0.3841062 0.4566809
## Q29 0.05094925 0.4919422 0.1768997 0.5549848 0.3998579 0.4565184
## Q11 Q12 Q13 Q14 Q15 Q16
## Q5 0.06489835 0.1467389 0.1129515 0.1017744 0.08883684 0.1277199
## Q6 0.55939027 0.5606091 0.6955997 0.6315045 0.50224965 0.4861224
## Q7 0.32961651 0.3336536 0.3880042 0.3684470 0.26042402 0.2693873
## Q8 0.64195530 0.5696523 0.6910489 0.6891730 0.54003533 0.4304120
## Q9 0.50354273 0.4467718 0.5385216 0.5325838 0.47892389 0.4462412
## Q10 0.59454759 0.5132309 0.6273495 0.5873333 0.48575003 0.4460018
## Q11 1.00000000 0.5249238 0.6089044 0.5832113 0.70493667 0.4287979
## Q12 0.52492381 1.0000000 0.6391546 0.6185922 0.44031191 0.5150299
## Q13 0.60890440 0.6391546 1.0000000 0.7003097 0.51107757 0.4705856
## Q14 0.58321130 0.6185922 0.7003097 1.0000000 0.53142977 0.4728193
## Q15 0.70493667 0.4403119 0.5110776 0.5314298 1.00000000 0.3958636
## Q16 0.42879794 0.5150299 0.4705856 0.4728193 0.39586363 1.0000000
## Q17 0.68894973 0.4362286 0.5080817 0.5116123 0.75529791 0.4096840
## Q18 0.67282212 0.5229473 0.6534021 0.6425437 0.59164824 0.4475570
## Q19 0.65973082 0.4319856 0.4824728 0.5086043 0.73393288 0.3815062
## Q20 0.74520151 0.5566041 0.6139253 0.6412310 0.66427074 0.4556368
## Q21 0.64762842 0.4586035 0.5640448 0.5817672 0.64595192 0.4124647
## Q22 0.77915988 0.4956509 0.5698582 0.6142080 0.73054338 0.3905932
## Q23 0.64625995 0.4538555 0.5018410 0.5142545 0.62180238 0.3412348
## Q24 0.67774613 0.4750010 0.5676444 0.5818099 0.67630543 0.3965920
## Q25 0.67060371 0.4795185 0.5548781 0.5846055 0.67656272 0.4140455
## Q26 0.45089161 0.3302441 0.3956638 0.3882257 0.42725438 0.2773313
## Q27 0.61319160 0.4156485 0.4679413 0.4924595 0.56971688 0.3392118
## Q28 0.64337686 0.5050405 0.5614292 0.5716395 0.64158380 0.4087096
## Q29 0.65833139 0.4374817 0.5152203 0.5426969 0.62554202 0.3519695
## Q17 Q18 Q19 Q20 Q21 Q22
## Q5 0.08051897 0.04197983 0.08282951 0.08569386 0.06605603 0.05911687
## Q6 0.47473226 0.61342541 0.46017563 0.57910779 0.55039849 0.55287422
## Q7 0.23592372 0.39279462 0.23244621 0.34665959 0.33270861 0.30256317
## Q8 0.53199742 0.72445293 0.50607399 0.67229122 0.60343228 0.63464008
## Q9 0.43932327 0.54993834 0.42363254 0.52268050 0.51236653 0.47931733
## Q10 0.46431726 0.67689354 0.45408699 0.58554169 0.52787720 0.55228897
## Q11 0.68894973 0.67282212 0.65973082 0.74520151 0.64762842 0.77915988
## Q12 0.43622862 0.52294734 0.43198556 0.55660413 0.45860349 0.49565093
## Q13 0.50808167 0.65340208 0.48247277 0.61392534 0.56404476 0.56985824
## Q14 0.51161234 0.64254365 0.50860432 0.64123098 0.58176723 0.61420802
## Q15 0.75529791 0.59164824 0.73393288 0.66427074 0.64595192 0.73054338
## Q16 0.40968403 0.44755696 0.38150621 0.45563681 0.41246469 0.39059321
## Q17 1.00000000 0.57490069 0.74994356 0.64263593 0.60919325 0.69915147
## Q18 0.57490069 1.00000000 0.55098079 0.72136743 0.68790831 0.68494391
## Q19 0.74994356 0.55098079 1.00000000 0.65430547 0.64272228 0.66468855
## Q20 0.64263593 0.72136743 0.65430547 1.00000000 0.74091067 0.76130358
## Q21 0.60919325 0.68790831 0.64272228 0.74091067 1.00000000 0.65597550
## Q22 0.69915147 0.68494391 0.66468855 0.76130358 0.65597550 1.00000000
## Q23 0.64148274 0.56334214 0.60904676 0.66458555 0.55015498 0.70429020
## Q24 0.71464743 0.59160321 0.66542655 0.66008810 0.57273647 0.75997647
## Q25 0.69561048 0.56958416 0.66431247 0.66337162 0.57306548 0.75460511
## Q26 0.41122438 0.41595844 0.43853048 0.48000925 0.40199503 0.45121509
## Q27 0.58308465 0.52782082 0.55825686 0.62226994 0.53253755 0.64530358
## Q28 0.66272719 0.56025888 0.62321096 0.64295716 0.53930773 0.72592980
## Q29 0.65403863 0.56011203 0.61132224 0.65331465 0.55303877 0.72306112
## Q23 Q24 Q25 Q26 Q27 Q28
## Q5 0.03367712 0.05364076 0.06464856 0.03525508 0.04698169 0.06736189
## Q6 0.46280930 0.52572660 0.51988466 0.36014677 0.42447443 0.51586391
## Q7 0.19182466 0.19228960 0.17754558 0.21334977 0.17397548 0.16375912
## Q8 0.53969781 0.57244511 0.55620243 0.40274891 0.50785275 0.53978296
## Q9 0.36797531 0.38874243 0.38372730 0.34683704 0.35672070 0.38410622
## Q10 0.44814382 0.46707843 0.46422990 0.37435222 0.40203289 0.45668087
## Q11 0.64625995 0.67774613 0.67060371 0.45089161 0.61319160 0.64337686
## Q12 0.45385548 0.47500096 0.47951851 0.33024411 0.41564847 0.50504046
## Q13 0.50184101 0.56764439 0.55487806 0.39566379 0.46794130 0.56142916
## Q14 0.51425450 0.58180987 0.58460552 0.38822566 0.49245949 0.57163951
## Q15 0.62180238 0.67630543 0.67656272 0.42725438 0.56971688 0.64158380
## Q16 0.34123481 0.39659195 0.41404545 0.27733131 0.33921180 0.40870958
## Q17 0.64148274 0.71464743 0.69561048 0.41122438 0.58308465 0.66272719
## Q18 0.56334214 0.59160321 0.56958416 0.41595844 0.52782082 0.56025888
## Q19 0.60904676 0.66542655 0.66431247 0.43853048 0.55825686 0.62321096
## Q20 0.66458555 0.66008810 0.66337162 0.48000925 0.62226994 0.64295716
## Q21 0.55015498 0.57273647 0.57306548 0.40199503 0.53253755 0.53930773
## Q22 0.70429020 0.75997647 0.75460511 0.45121509 0.64530358 0.72592980
## Q23 1.00000000 0.71871444 0.69365959 0.50831434 0.74578514 0.68982661
## Q24 0.71871444 1.00000000 0.87947534 0.43560879 0.66315093 0.83583721
## Q25 0.69365959 0.87947534 1.00000000 0.42834512 0.65091374 0.84404489
## Q26 0.50831434 0.43560879 0.42834512 1.00000000 0.58261209 0.44851848
## Q27 0.74578514 0.66315093 0.65091374 0.58261209 1.00000000 0.66772785
## Q28 0.68982661 0.83583721 0.84404489 0.44851848 0.66772785 1.00000000
## Q29 0.74708604 0.75142579 0.73828807 0.49064424 0.73714498 0.77203170
## Q29
## Q5 0.05094925
## Q6 0.49194224
## Q7 0.17689969
## Q8 0.55498482
## Q9 0.39985791
## Q10 0.45651839
## Q11 0.65833139
## Q12 0.43748174
## Q13 0.51522029
## Q14 0.54269688
## Q15 0.62554202
## Q16 0.35196945
## Q17 0.65403863
## Q18 0.56011203
## Q19 0.61132224
## Q20 0.65331465
## Q21 0.55303877
## Q22 0.72306112
## Q23 0.74708604
## Q24 0.75142579
## Q25 0.73828807
## Q26 0.49064424
## Q27 0.73714498
## Q28 0.77203170
## Q29 1.00000000
##
## Bartlett's test of sphericity
##
## data: df[1:25]
## Khi-squared = 30872, df = 300, p-value < 2.2e-16
## $KMO
## [1] 0.9729371
##
## $MSA
## Q5 Q6 Q7 Q8 Q9 Q10 Q11
## 0.7862892 0.9749883 0.9736180 0.9784919 0.9629376 0.9614068 0.9823599
## Q12 Q13 Q14 Q15 Q16 Q17 Q18
## 0.9701043 0.9731130 0.9810303 0.9767443 0.9735646 0.9745194 0.9767601
## Q19 Q20 Q21 Q22 Q23 Q24 Q25
## 0.9738770 0.9790391 0.9742349 0.9795862 0.9770538 0.9645243 0.9611039
## Q26 Q27 Q28 Q29
## 0.9712665 0.9636872 0.9703131 0.9770195
## Principal Components Analysis
## Call: principal(r = df[1:25], nfactors = 5, rotate = "varimax", scores = T)
## Standardized loadings (pattern matrix) based upon correlation matrix
## RC1 RC4 RC2 RC5 RC3 h2 u2 com
## Q5 0.01 0.11 0.08 0.01 0.97 0.96 0.038 1.0
## Q6 0.29 0.63 0.41 0.14 0.18 0.70 0.299 2.5
## Q7 0.02 0.17 0.72 0.08 0.07 0.56 0.442 1.2
## Q8 0.36 0.56 0.48 0.21 0.00 0.72 0.278 3.0
## Q9 0.24 0.39 0.63 0.10 0.10 0.63 0.373 2.1
## Q10 0.27 0.51 0.58 0.15 0.03 0.69 0.312 2.6
## Q11 0.68 0.32 0.38 0.19 -0.03 0.74 0.262 2.3
## Q12 0.25 0.74 0.19 0.14 0.05 0.66 0.335 1.5
## Q13 0.31 0.70 0.35 0.18 -0.01 0.74 0.263 2.1
## Q14 0.36 0.66 0.32 0.18 -0.03 0.70 0.298 2.3
## Q15 0.80 0.17 0.32 0.07 0.06 0.78 0.222 1.5
## Q16 0.25 0.64 0.17 -0.02 0.09 0.50 0.495 1.5
## Q17 0.82 0.19 0.24 0.08 0.06 0.77 0.230 1.3
## Q18 0.47 0.44 0.52 0.17 -0.12 0.73 0.269 3.3
## Q19 0.79 0.14 0.28 0.08 0.06 0.74 0.257 1.4
## Q20 0.62 0.36 0.41 0.24 -0.03 0.75 0.251 2.8
## Q21 0.61 0.25 0.50 0.09 -0.05 0.69 0.308 2.4
## Q22 0.75 0.31 0.28 0.22 -0.03 0.79 0.215 1.9
## Q23 0.66 0.27 0.09 0.48 -0.03 0.75 0.250 2.3
## Q24 0.78 0.41 0.00 0.25 0.00 0.83 0.169 1.7
## Q25 0.78 0.42 -0.02 0.23 0.01 0.83 0.170 1.7
## Q26 0.26 0.10 0.28 0.80 0.02 0.79 0.214 1.5
## Q27 0.57 0.23 0.08 0.63 0.00 0.79 0.213 2.3
## Q28 0.72 0.45 -0.05 0.30 0.02 0.82 0.182 2.1
## Q29 0.69 0.32 0.05 0.44 0.00 0.77 0.227 2.2
##
## RC1 RC4 RC2 RC5 RC3
## SS loadings 7.65 4.50 3.19 2.05 1.03
## Proportion Var 0.31 0.18 0.13 0.08 0.04
## Cumulative Var 0.31 0.49 0.61 0.70 0.74
## Proportion Explained 0.42 0.24 0.17 0.11 0.06
## Cumulative Proportion 0.42 0.66 0.83 0.94 1.00
##
## Mean item complexity = 2
## Test of the hypothesis that 5 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.03
## with the empirical chi square 1052 with prob < 3.2e-121
##
## Fit based upon off diagonal values = 1
## ICLUST (Item Cluster Analysis)Call: iclust(r.mat = df[1:25])
## ICLUST
##
## Purified Alpha:
## [1] 0.96
##
## Guttman Lambda6*
## [1] 0.97
##
## Original Beta:
## [1] 0.17
##
## Cluster size:
## [1] 25
##
## Purified scale intercorrelations
## reliabilities on diagonal
## correlations corrected for attenuation above diagonal:
## [,1]
## [1,] 0.96