PWB CFA TR Youth
Levi Brackman
28 July 2015
- I live one day at a time and don’t really think about the future. (rs)
- I tend to focus on the present, because the future always brings me problems. (rs)
- My daily activities often seem trivial and unimportant to me. (rs)
- I don’t have a good sense of what it is that I am trying to accomplish in my life. (rs)
- I used to set goals for myself, but that now seems a waste of time. (rs)
- I enjoy making plans for the future and working to make them a reality.
- I am an active person in carrying out the plans I set for myself.
- Some people wander aimlessly through life, but I am not one of them.
- I sometimes feel as if I’ve done all there is to do in life. (rs)
library(lavaan)## This is lavaan 0.5-18
## lavaan is BETA software! Please report any bugs.library(semPlot)
library(dplyr)##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(GPArotation)
library(psych)
library(car)##
## Attaching package: 'car'
##
## The following object is masked from 'package:psych':
##
## logitlibrary(ggplot2)##
## Attaching package: 'ggplot2'
##
## The following object is masked from 'package:psych':
##
## %+%library(GGally)##
## Attaching package: 'GGally'
##
## The following object is masked from 'package:dplyr':
##
## nasaloadthedata
data <- read.csv("~/Psychometric_study_data/allsurveysYT1.csv")
data<-tbl_df(data)
PWB<-select(data, PWB_1, PWB_2, PWB_3, PWB_4, PWB_5, PWB_6,PWB_7, PWB_8, PWB_9)
PWB$PWB_1 <- 7- PWB$PWB_1
PWB$PWB_2 <- 7- PWB$PWB_2
PWB$PWB_3 <- 7- PWB$PWB_3
PWB$PWB_4 <- 7- PWB$PWB_4
PWB$PWB_9 <- 7- PWB$PWB_9
PWB<-tbl_df(PWB)
PWB## Source: local data frame [1,160 x 9]
##
## PWB_1 PWB_2 PWB_3 PWB_4 PWB_5 PWB_6 PWB_7 PWB_8 PWB_9
## 1 4 3 5 2 4 5 4 3 6
## 2 4 5 5 2 2 5 3 2 5
## 3 5 6 5 6 1 4 6 3 6
## 4 2 2 4 4 3 4 5 4 4
## 5 2 2 3 3 4 3 2 3 4
## 6 5 4 6 5 3 4 3 4 6
## 7 2 2 5 2 1 4 3 3 3
## 8 6 6 5 1 2 4 4 4 6
## 9 5 5 5 5 1 5 5 5 6
## 10 6 6 3 3 2 6 6 3 6
## .. ... ... ... ... ... ... ... ... ...create plots
#ggpairs(PWB, columns = 1:15, title="Big 5 Marsh" )create the models
two.model= ' Factor1 =~ PWB_1 + PWB_3 + PWB_4 + PWB_5 + PWB_6 + PWB_9
Factor2 =~ PWB_2+ PWB_7 + PWB_8
' #Models two factors:Positive and Negative
one.model= 'PWB =~ PWB_1 + PWB_2 + PWB_3 + PWB_4 + PWB_5 + PWB_6 + PWB_7 + PWB_8 + PWB_9' #Models as a single purpose factorSecond order models
second.model = ' F1 =~ PWB_1 + PWB_3 + PWB_5 + PWB_6
F2 =~ PWB_4 + PWB_7 + PWB_8
F3 =~ PWB_2 + PWB_9
' #Second order models as Purpose being the higher factor made up of Purpose and PositiveBifactor (like model 7 in Marsh, Scalas & Nagengast, 2010)
bifactor.negative.model = 'Negative =~ PWB_1 + PWB_2 + PWB_3 + PWB_4 + PWB_5 + PWB_9
PWB =~ PWB_1 + PWB_2 + PWB_3 + PWB_4 + PWB_5 + PWB_6 + PWB_7 + PWB_8 + PWB_9
'#Models bifactor as the negatively worded item as a factor uncorolated with the main factor
bifactor.model1 = 'PWB =~ PWB_1 + PWB_2 + PWB_3 + PWB_4 + PWB_5 + PWB_6 + PWB_7 + PWB_8 + PWB_9
Negative =~ PWB_1 + PWB_2 + PWB_3 + PWB_4 + PWB_5 + PWB_9
Positive =~ PWB_6 + PWB_7 + PWB_8
PWB ~~ 0*Negative
PWB ~~ 0*Positive
Negative~~0*Positive
'#Models bifactor with Positive and Purpose as factors uncorolated with the main factor
bifactor.model2 = 'PWB =~ PWB_1 + PWB_2 + PWB_3 + PWB_4 + PWB_5 + PWB_6 + PWB_7 + PWB_8 + PWB_9
F1 =~ PWB_1 + PWB_3 + PWB_5 + PWB_6
F2 =~ PWB_4 + PWB_7 + PWB_8
F3 =~ PWB_2 + PWB_9
PWB ~~ 0*F1
PWB ~~ 0*F2
PWB ~~ 0*F3
F1~~0*F2
F1~~0*F3
F2~~0*F3
'#Models bifactor with Positive and Purpose as factors uncorolated with the main factorrun the models
two.fit=cfa(two.model, data=PWB, missing = "fiml", std.lv = T)## Warning in lav_data_full(data = data, group = group, group.label = group.label, : lavaan WARNING: some cases are empty and will be removed:
## 17 22 23 24 28 29 43 45 78 79 80 81 85 93 94 95 110 111 112 116 121 122 123 124 125 128 129 130 131 133 135 137 138 140 147 151 152 155 156 162 166 169 170 171 172 173 174 176 177 179 180 183 184 186 187 188 189 192 194 195 197 200 202 203 204 207 208 210 212 214 215 217 220 222 223 224 226 227 228 229 230 234 238 240 243 245 246 247 249 252 255 256 265 266 267 268 270 271 274 275 280 281 282 284 286 287 289 291 292 298 300 304 309 310 311 312 315 316 317 320 322 325 327 330 333 334 336 339 340 344 348 350 351 352 354 355 357 360 361 362 364 365 366 367 368 369 370 371 372 373 374 375 376 377 379 380 381 384 385 386 389 390 397 398 399 400 401 402 403 404 405 406 407 408 410 416 417 418 419 420 421 422 423 424 425 427 428 429 430 431 432 434 436 444 445 446 447 448 452 453 454 455 456 457 459 460 462 463 464 465 467 468 470 472 473 474 475 476 478 481 482 485 486 490 491 493 495 539 540 541 542 543 544 545 546 548 549 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 662 679 687 782 783 784 785 809 810 829 903 906 907 909 911 1110 1112 1113 1114 1116 1117 1120 1121 1122 1125 1126 1128 1129 1130 1131 1132 1134 1136 1137 1138 1139 1140 1143 1145 1146 1147 1150 1151 1152 1154 1155 1159 1160one.fit=cfa(one.model, data=PWB, missing = "fiml", std.lv = T)## Warning in lav_data_full(data = data, group = group, group.label = group.label, : lavaan WARNING: some cases are empty and will be removed:
## 17 22 23 24 28 29 43 45 78 79 80 81 85 93 94 95 110 111 112 116 121 122 123 124 125 128 129 130 131 133 135 137 138 140 147 151 152 155 156 162 166 169 170 171 172 173 174 176 177 179 180 183 184 186 187 188 189 192 194 195 197 200 202 203 204 207 208 210 212 214 215 217 220 222 223 224 226 227 228 229 230 234 238 240 243 245 246 247 249 252 255 256 265 266 267 268 270 271 274 275 280 281 282 284 286 287 289 291 292 298 300 304 309 310 311 312 315 316 317 320 322 325 327 330 333 334 336 339 340 344 348 350 351 352 354 355 357 360 361 362 364 365 366 367 368 369 370 371 372 373 374 375 376 377 379 380 381 384 385 386 389 390 397 398 399 400 401 402 403 404 405 406 407 408 410 416 417 418 419 420 421 422 423 424 425 427 428 429 430 431 432 434 436 444 445 446 447 448 452 453 454 455 456 457 459 460 462 463 464 465 467 468 470 472 473 474 475 476 478 481 482 485 486 490 491 493 495 539 540 541 542 543 544 545 546 548 549 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 662 679 687 782 783 784 785 809 810 829 903 906 907 909 911 1110 1112 1113 1114 1116 1117 1120 1121 1122 1125 1126 1128 1129 1130 1131 1132 1134 1136 1137 1138 1139 1140 1143 1145 1146 1147 1150 1151 1152 1154 1155 1159 1160second.fit=cfa(second.model, data=PWB, missing = "fiml", std.lv = T)## Warning in lav_data_full(data = data, group = group, group.label = group.label, : lavaan WARNING: some cases are empty and will be removed:
## 17 22 23 24 28 29 43 45 78 79 80 81 85 93 94 95 110 111 112 116 121 122 123 124 125 128 129 130 131 133 135 137 138 140 147 151 152 155 156 162 166 169 170 171 172 173 174 176 177 179 180 183 184 186 187 188 189 192 194 195 197 200 202 203 204 207 208 210 212 214 215 217 220 222 223 224 226 227 228 229 230 234 238 240 243 245 246 247 249 252 255 256 265 266 267 268 270 271 274 275 280 281 282 284 286 287 289 291 292 298 300 304 309 310 311 312 315 316 317 320 322 325 327 330 333 334 336 339 340 344 348 350 351 352 354 355 357 360 361 362 364 365 366 367 368 369 370 371 372 373 374 375 376 377 379 380 381 384 385 386 389 390 397 398 399 400 401 402 403 404 405 406 407 408 410 416 417 418 419 420 421 422 423 424 425 427 428 429 430 431 432 434 436 444 445 446 447 448 452 453 454 455 456 457 459 460 462 463 464 465 467 468 470 472 473 474 475 476 478 481 482 485 486 490 491 493 495 539 540 541 542 543 544 545 546 548 549 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 662 679 687 782 783 784 785 809 810 829 903 906 907 909 911 1110 1112 1113 1114 1116 1117 1120 1121 1122 1125 1126 1128 1129 1130 1131 1132 1134 1136 1137 1138 1139 1140 1143 1145 1146 1147 1150 1151 1152 1154 1155 1159 1160bifactor1.fit=cfa(bifactor.model1, data=PWB, missing = "fiml", std.lv = T)## Warning in lav_data_full(data = data, group = group, group.label = group.label, : lavaan WARNING: some cases are empty and will be removed:
## 17 22 23 24 28 29 43 45 78 79 80 81 85 93 94 95 110 111 112 116 121 122 123 124 125 128 129 130 131 133 135 137 138 140 147 151 152 155 156 162 166 169 170 171 172 173 174 176 177 179 180 183 184 186 187 188 189 192 194 195 197 200 202 203 204 207 208 210 212 214 215 217 220 222 223 224 226 227 228 229 230 234 238 240 243 245 246 247 249 252 255 256 265 266 267 268 270 271 274 275 280 281 282 284 286 287 289 291 292 298 300 304 309 310 311 312 315 316 317 320 322 325 327 330 333 334 336 339 340 344 348 350 351 352 354 355 357 360 361 362 364 365 366 367 368 369 370 371 372 373 374 375 376 377 379 380 381 384 385 386 389 390 397 398 399 400 401 402 403 404 405 406 407 408 410 416 417 418 419 420 421 422 423 424 425 427 428 429 430 431 432 434 436 444 445 446 447 448 452 453 454 455 456 457 459 460 462 463 464 465 467 468 470 472 473 474 475 476 478 481 482 485 486 490 491 493 495 539 540 541 542 543 544 545 546 548 549 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 662 679 687 782 783 784 785 809 810 829 903 906 907 909 911 1110 1112 1113 1114 1116 1117 1120 1121 1122 1125 1126 1128 1129 1130 1131 1132 1134 1136 1137 1138 1139 1140 1143 1145 1146 1147 1150 1151 1152 1154 1155 1159 1160bifactor2.fit=cfa(bifactor.model2, data=PWB, missing = "fiml", std.lv = T)## Warning in lav_data_full(data = data, group = group, group.label = group.label, : lavaan WARNING: some cases are empty and will be removed:
## 17 22 23 24 28 29 43 45 78 79 80 81 85 93 94 95 110 111 112 116 121 122 123 124 125 128 129 130 131 133 135 137 138 140 147 151 152 155 156 162 166 169 170 171 172 173 174 176 177 179 180 183 184 186 187 188 189 192 194 195 197 200 202 203 204 207 208 210 212 214 215 217 220 222 223 224 226 227 228 229 230 234 238 240 243 245 246 247 249 252 255 256 265 266 267 268 270 271 274 275 280 281 282 284 286 287 289 291 292 298 300 304 309 310 311 312 315 316 317 320 322 325 327 330 333 334 336 339 340 344 348 350 351 352 354 355 357 360 361 362 364 365 366 367 368 369 370 371 372 373 374 375 376 377 379 380 381 384 385 386 389 390 397 398 399 400 401 402 403 404 405 406 407 408 410 416 417 418 419 420 421 422 423 424 425 427 428 429 430 431 432 434 436 444 445 446 447 448 452 453 454 455 456 457 459 460 462 463 464 465 467 468 470 472 473 474 475 476 478 481 482 485 486 490 491 493 495 539 540 541 542 543 544 545 546 548 549 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 662 679 687 782 783 784 785 809 810 829 903 906 907 909 911 1110 1112 1113 1114 1116 1117 1120 1121 1122 1125 1126 1128 1129 1130 1131 1132 1134 1136 1137 1138 1139 1140 1143 1145 1146 1147 1150 1151 1152 1154 1155 1159 1160bifactor.negative.fit=cfa(bifactor.negative.model, data=PWB, missing = "fiml", std.lv = T)## Warning in lav_data_full(data = data, group = group, group.label = group.label, : lavaan WARNING: some cases are empty and will be removed:
## 17 22 23 24 28 29 43 45 78 79 80 81 85 93 94 95 110 111 112 116 121 122 123 124 125 128 129 130 131 133 135 137 138 140 147 151 152 155 156 162 166 169 170 171 172 173 174 176 177 179 180 183 184 186 187 188 189 192 194 195 197 200 202 203 204 207 208 210 212 214 215 217 220 222 223 224 226 227 228 229 230 234 238 240 243 245 246 247 249 252 255 256 265 266 267 268 270 271 274 275 280 281 282 284 286 287 289 291 292 298 300 304 309 310 311 312 315 316 317 320 322 325 327 330 333 334 336 339 340 344 348 350 351 352 354 355 357 360 361 362 364 365 366 367 368 369 370 371 372 373 374 375 376 377 379 380 381 384 385 386 389 390 397 398 399 400 401 402 403 404 405 406 407 408 410 416 417 418 419 420 421 422 423 424 425 427 428 429 430 431 432 434 436 444 445 446 447 448 452 453 454 455 456 457 459 460 462 463 464 465 467 468 470 472 473 474 475 476 478 481 482 485 486 490 491 493 495 539 540 541 542 543 544 545 546 548 549 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 662 679 687 782 783 784 785 809 810 829 903 906 907 909 911 1110 1112 1113 1114 1116 1117 1120 1121 1122 1125 1126 1128 1129 1130 1131 1132 1134 1136 1137 1138 1139 1140 1143 1145 1146 1147 1150 1151 1152 1154 1155 1159 1160create pictures
semPaths(two.fit, whatLabels = "std", layout = "tree")
semPaths(one.fit, whatLabels = "std", layout = "tree")
semPaths(second.fit, whatLabels = "std", layout = "tree")
semPaths(bifactor1.fit, whatLabels = "std", layout = "tree")
semPaths(bifactor2.fit, whatLabels = "std", layout = "tree")
semPaths(bifactor.negative.fit, whatLabels = "std", layout = "tree")
#summaries
summary(two.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 32 iterations
##
## Used Total
## Number of observations 816 1160
##
## Number of missing patterns 1
##
## Estimator ML
## Minimum Function Test Statistic 518.107
## Degrees of freedom 26
## P-value (Chi-square) 0.000
##
## Parameter estimates:
##
## Information Observed
## Standard Errors Standard
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## Factor1 =~
## PWB_1 0.987 0.056 17.715 0.000 0.987 0.612
## PWB_3 1.231 0.051 23.962 0.000 1.231 0.777
## PWB_4 0.805 0.054 14.855 0.000 0.805 0.531
## PWB_5 -1.288 0.052 -24.761 0.000 -1.288 -0.797
## PWB_6 0.698 0.046 15.124 0.000 0.698 0.537
## PWB_9 0.585 0.053 11.144 0.000 0.585 0.410
## Factor2 =~
## PWB_2 0.327 0.063 5.151 0.000 0.327 0.227
## PWB_7 0.891 0.065 13.766 0.000 0.891 0.694
## PWB_8 1.020 0.073 13.929 0.000 1.020 0.732
##
## Covariances:
## Factor1 ~~
## Factor2 0.269 0.047 5.746 0.000 0.269 0.269
##
## Intercepts:
## PWB_1 3.896 0.056 69.041 0.000 3.896 2.417
## PWB_3 4.152 0.055 74.917 0.000 4.152 2.623
## PWB_4 4.023 0.053 75.786 0.000 4.023 2.653
## PWB_5 2.877 0.057 50.878 0.000 2.877 1.781
## PWB_6 4.499 0.045 98.963 0.000 4.499 3.464
## PWB_9 4.798 0.050 95.927 0.000 4.798 3.358
## PWB_2 3.870 0.050 76.678 0.000 3.870 2.684
## PWB_7 4.545 0.045 101.159 0.000 4.545 3.541
## PWB_8 4.362 0.049 89.357 0.000 4.362 3.128
## Factor1 0.000 0.000 0.000
## Factor2 0.000 0.000 0.000
##
## Variances:
## PWB_1 1.625 0.092 1.625 0.625
## PWB_3 0.992 0.074 0.992 0.396
## PWB_4 1.652 0.090 1.652 0.719
## PWB_5 0.952 0.075 0.952 0.365
## PWB_6 1.200 0.065 1.200 0.711
## PWB_9 1.699 0.088 1.699 0.832
## PWB_2 1.972 0.101 1.972 0.949
## PWB_7 0.854 0.101 0.854 0.518
## PWB_8 0.903 0.131 0.903 0.465
## Factor1 1.000 1.000 1.000
## Factor2 1.000 1.000 1.000
##
## R-Square:
##
## PWB_1 0.375
## PWB_3 0.604
## PWB_4 0.281
## PWB_5 0.635
## PWB_6 0.289
## PWB_9 0.168
## PWB_2 0.051
## PWB_7 0.482
## PWB_8 0.535summary(one.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 25 iterations
##
## Used Total
## Number of observations 816 1160
##
## Number of missing patterns 1
##
## Estimator ML
## Minimum Function Test Statistic 584.980
## Degrees of freedom 27
## P-value (Chi-square) 0.000
##
## Parameter estimates:
##
## Information Observed
## Standard Errors Standard
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## PWB =~
## PWB_1 1.017 0.055 18.385 0.000 1.017 0.631
## PWB_2 0.752 0.052 14.502 0.000 0.752 0.522
## PWB_3 1.202 0.051 23.458 0.000 1.202 0.759
## PWB_4 0.841 0.054 15.654 0.000 0.841 0.554
## PWB_5 -1.258 0.052 -24.240 0.000 -1.258 -0.779
## PWB_6 0.679 0.046 14.690 0.000 0.679 0.523
## PWB_7 0.208 0.050 4.176 0.000 0.208 0.162
## PWB_8 0.280 0.054 5.218 0.000 0.280 0.201
## PWB_9 0.629 0.052 12.048 0.000 0.629 0.440
##
## Intercepts:
## PWB_1 3.896 0.056 69.041 0.000 3.896 2.417
## PWB_2 3.870 0.050 76.678 0.000 3.870 2.684
## PWB_3 4.152 0.055 74.917 0.000 4.152 2.623
## PWB_4 4.023 0.053 75.786 0.000 4.023 2.653
## PWB_5 2.877 0.057 50.879 0.000 2.877 1.781
## PWB_6 4.499 0.045 98.964 0.000 4.499 3.464
## PWB_7 4.545 0.045 101.159 0.000 4.545 3.541
## PWB_8 4.362 0.049 89.357 0.000 4.362 3.128
## PWB_9 4.798 0.050 95.927 0.000 4.798 3.358
## PWB 0.000 0.000 0.000
##
## Variances:
## PWB_1 1.564 0.090 1.564 0.602
## PWB_2 1.513 0.082 1.513 0.728
## PWB_3 1.061 0.073 1.061 0.424
## PWB_4 1.593 0.087 1.593 0.693
## PWB_5 1.027 0.074 1.027 0.394
## PWB_6 1.225 0.066 1.225 0.726
## PWB_7 1.604 0.080 1.604 0.974
## PWB_8 1.866 0.093 1.866 0.960
## PWB_9 1.646 0.086 1.646 0.806
## PWB 1.000 1.000 1.000
##
## R-Square:
##
## PWB_1 0.398
## PWB_2 0.272
## PWB_3 0.576
## PWB_4 0.307
## PWB_5 0.606
## PWB_6 0.274
## PWB_7 0.026
## PWB_8 0.040
## PWB_9 0.194summary(second.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 29 iterations
##
## Used Total
## Number of observations 816 1160
##
## Number of missing patterns 1
##
## Estimator ML
## Minimum Function Test Statistic 411.420
## Degrees of freedom 24
## P-value (Chi-square) 0.000
##
## Parameter estimates:
##
## Information Observed
## Standard Errors Standard
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## F1 =~
## PWB_1 1.040 0.056 18.635 0.000 1.040 0.645
## PWB_3 1.206 0.052 23.235 0.000 1.206 0.762
## PWB_5 -1.299 0.052 -24.823 0.000 -1.299 -0.804
## PWB_6 0.693 0.046 14.932 0.000 0.693 0.534
## F2 =~
## PWB_4 0.695 0.069 10.033 0.000 0.695 0.458
## PWB_7 0.900 0.055 16.400 0.000 0.900 0.701
## PWB_8 0.953 0.058 16.405 0.000 0.953 0.684
## F3 =~
## PWB_2 0.986 0.064 15.454 0.000 0.986 0.684
## PWB_9 0.801 0.059 13.521 0.000 0.801 0.561
##
## Covariances:
## F1 ~~
## F2 0.283 0.051 5.553 0.000 0.283 0.283
## F3 0.692 0.041 16.985 0.000 0.692 0.692
## F2 ~~
## F3 0.367 0.058 6.332 0.000 0.367 0.367
##
## Intercepts:
## PWB_1 3.896 0.056 69.041 0.000 3.896 2.417
## PWB_3 4.152 0.055 74.917 0.000 4.152 2.623
## PWB_5 2.877 0.057 50.879 0.000 2.877 1.781
## PWB_6 4.499 0.045 98.964 0.000 4.499 3.464
## PWB_4 4.023 0.053 75.786 0.000 4.023 2.653
## PWB_7 4.545 0.045 101.159 0.000 4.545 3.541
## PWB_8 4.362 0.049 89.357 0.000 4.362 3.128
## PWB_2 3.870 0.050 76.678 0.000 3.870 2.684
## PWB_9 4.798 0.050 95.927 0.000 4.798 3.358
## F1 0.000 0.000 0.000
## F2 0.000 0.000 0.000
## F3 0.000 0.000 0.000
##
## Variances:
## PWB_1 1.517 0.091 1.517 0.584
## PWB_3 1.051 0.076 1.051 0.419
## PWB_5 0.922 0.077 0.922 0.353
## PWB_6 1.206 0.066 1.206 0.715
## PWB_4 1.816 0.112 1.816 0.790
## PWB_7 0.837 0.081 0.837 0.508
## PWB_8 1.035 0.091 1.035 0.532
## PWB_2 1.106 0.106 1.106 0.532
## PWB_9 1.400 0.092 1.400 0.686
## F1 1.000 1.000 1.000
## F2 1.000 1.000 1.000
## F3 1.000 1.000 1.000
##
## R-Square:
##
## PWB_1 0.416
## PWB_3 0.581
## PWB_5 0.647
## PWB_6 0.285
## PWB_4 0.210
## PWB_7 0.492
## PWB_8 0.468
## PWB_2 0.468
## PWB_9 0.314summary(bifactor1.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 51 iterations
##
## Used Total
## Number of observations 816 1160
##
## Number of missing patterns 1
##
## Estimator ML
## Minimum Function Test Statistic 168.910
## Degrees of freedom 18
## P-value (Chi-square) 0.000
##
## Parameter estimates:
##
## Information Observed
## Standard Errors Standard
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## PWB =~
## PWB_1 0.918 0.071 12.862 0.000 0.918 0.569
## PWB_2 0.381 0.095 4.006 0.000 0.381 0.264
## PWB_3 1.216 0.059 20.607 0.000 1.216 0.768
## PWB_4 0.685 0.074 9.203 0.000 0.685 0.452
## PWB_5 -1.266 0.059 -21.479 0.000 -1.266 -0.784
## PWB_6 0.712 0.050 14.343 0.000 0.712 0.548
## PWB_7 0.083 0.053 1.574 0.116 0.083 0.065
## PWB_8 0.180 0.056 3.205 0.001 0.180 0.129
## PWB_9 0.470 0.080 5.885 0.000 0.470 0.329
## Negative =~
## PWB_1 0.507 0.109 4.657 0.000 0.507 0.315
## PWB_2 1.302 0.205 6.334 0.000 1.302 0.903
## PWB_3 0.242 0.111 2.185 0.029 0.242 0.153
## PWB_4 0.431 0.110 3.925 0.000 0.431 0.284
## PWB_5 -0.268 0.110 -2.438 0.015 -0.268 -0.166
## PWB_9 0.475 0.123 3.848 0.000 0.475 0.333
## Positive =~
## PWB_6 0.387 0.048 8.042 0.000 0.387 0.298
## PWB_7 0.999 0.079 12.582 0.000 0.999 0.779
## PWB_8 0.916 0.077 11.894 0.000 0.916 0.657
##
## Covariances:
## PWB ~~
## Negative 0.000 0.000 0.000
## Positive 0.000 0.000 0.000
## Negative ~~
## Positive 0.000 0.000 0.000
##
## Intercepts:
## PWB_1 3.896 0.056 69.041 0.000 3.896 2.417
## PWB_2 3.870 0.050 76.678 0.000 3.870 2.684
## PWB_3 4.152 0.055 74.917 0.000 4.152 2.623
## PWB_4 4.023 0.053 75.786 0.000 4.023 2.653
## PWB_5 2.877 0.057 50.879 0.000 2.877 1.781
## PWB_6 4.499 0.045 98.964 0.000 4.499 3.464
## PWB_7 4.545 0.045 101.159 0.000 4.545 3.541
## PWB_8 4.362 0.049 89.357 0.000 4.362 3.128
## PWB_9 4.798 0.050 95.927 0.000 4.798 3.358
## PWB 0.000 0.000 0.000
## Negative 0.000 0.000 0.000
## Positive 0.000 0.000 0.000
##
## Variances:
## PWB_1 1.498 0.087 1.498 0.577
## PWB_2 0.240 0.541 0.240 0.115
## PWB_3 0.968 0.079 0.968 0.386
## PWB_4 1.644 0.089 1.644 0.715
## PWB_5 0.936 0.080 0.936 0.359
## PWB_6 1.029 0.066 1.029 0.611
## PWB_7 0.642 0.143 0.642 0.390
## PWB_8 1.073 0.127 1.073 0.552
## PWB_9 1.595 0.099 1.595 0.781
## PWB 1.000 1.000 1.000
## Negative 1.000 1.000 1.000
## Positive 1.000 1.000 1.000
##
## R-Square:
##
## PWB_1 0.423
## PWB_2 0.885
## PWB_3 0.614
## PWB_4 0.285
## PWB_5 0.641
## PWB_6 0.389
## PWB_7 0.610
## PWB_8 0.448
## PWB_9 0.219summary(bifactor2.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 45 iterations
##
## Used Total
## Number of observations 816 1160
##
## Number of missing patterns 1
##
## Estimator ML
## Minimum Function Test Statistic 189.914
## Degrees of freedom 18
## P-value (Chi-square) 0.000
##
## Parameter estimates:
##
## Information Observed
## Standard Errors Standard
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## PWB =~
## PWB_1 0.929 0.067 13.921 0.000 0.929 0.576
## PWB_2 0.870 0.061 14.364 0.000 0.870 0.604
## PWB_3 1.016 0.067 15.135 0.000 1.016 0.642
## PWB_4 0.917 0.060 15.348 0.000 0.917 0.605
## PWB_5 -1.015 0.068 -14.877 0.000 -1.015 -0.629
## PWB_6 0.395 0.061 6.520 0.000 0.395 0.304
## PWB_7 0.098 0.058 1.679 0.093 0.098 0.076
## PWB_8 0.185 0.062 2.987 0.003 0.185 0.133
## PWB_9 0.714 0.059 12.050 0.000 0.714 0.499
## F1 =~
## PWB_1 0.470 0.081 5.783 0.000 0.470 0.291
## PWB_3 0.670 0.080 8.341 0.000 0.670 0.423
## PWB_5 -0.805 0.085 -9.447 0.000 -0.805 -0.498
## PWB_6 0.669 0.080 8.393 0.000 0.669 0.515
## F2 =~
## PWB_4 0.435 0.060 7.209 0.000 0.435 0.287
## PWB_7 1.090 0.091 11.914 0.000 1.090 0.849
## PWB_8 0.837 0.079 10.619 0.000 0.837 0.600
## F3 =~
## PWB_2 0.312 NA 0.312 0.216
## PWB_9 0.541 NA 0.541 0.379
##
## Covariances:
## PWB ~~
## F1 0.000 0.000 0.000
## F2 0.000 0.000 0.000
## F3 0.000 0.000 0.000
## F1 ~~
## F2 0.000 0.000 0.000
## F3 0.000 0.000 0.000
## F2 ~~
## F3 0.000 0.000 0.000
##
## Intercepts:
## PWB_1 3.896 0.056 69.041 0.000 3.896 2.417
## PWB_2 3.870 0.050 76.678 0.000 3.870 2.684
## PWB_3 4.152 0.055 74.917 0.000 4.152 2.623
## PWB_4 4.023 0.053 75.786 0.000 4.023 2.653
## PWB_5 2.877 0.057 50.878 0.000 2.877 1.781
## PWB_6 4.499 0.045 98.964 0.000 4.499 3.464
## PWB_7 4.545 0.045 101.159 0.000 4.545 3.541
## PWB_8 4.362 0.049 89.357 0.000 4.362 3.128
## PWB_9 4.798 0.050 95.927 0.000 4.798 3.358
## PWB 0.000 0.000 0.000
## F1 0.000 0.000 0.000
## F2 0.000 0.000 0.000
## F3 0.000 0.000 0.000
##
## Variances:
## PWB_1 1.514 0.088 1.514 0.583
## PWB_2 1.224 NA 1.224 0.589
## PWB_3 1.026 0.073 1.026 0.409
## PWB_4 1.270 0.088 1.270 0.552
## PWB_5 0.931 0.085 0.931 0.357
## PWB_6 1.083 0.090 1.083 0.642
## PWB_7 0.450 0.184 0.450 0.273
## PWB_8 1.209 0.121 1.209 0.622
## PWB_9 1.239 NA 1.239 0.607
## PWB 1.000 1.000 1.000
## F1 1.000 1.000 1.000
## F2 1.000 1.000 1.000
## F3 1.000 1.000 1.000
##
## R-Square:
##
## PWB_1 0.417
## PWB_2 0.411
## PWB_3 0.591
## PWB_4 0.448
## PWB_5 0.643
## PWB_6 0.358
## PWB_7 0.727
## PWB_8 0.378
## PWB_9 0.393summary(bifactor.negative.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 33 iterations
##
## Used Total
## Number of observations 816 1160
##
## Number of missing patterns 1
##
## Estimator ML
## Minimum Function Test Statistic 357.479
## Degrees of freedom 20
## P-value (Chi-square) 0.000
##
## Parameter estimates:
##
## Information Observed
## Standard Errors Standard
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## Negative =~
## PWB_1 1.427 78.748 0.018 0.986 1.427 0.885
## PWB_2 0.972 53.658 0.018 0.986 0.972 0.675
## PWB_3 1.543 85.122 0.018 0.986 1.543 0.974
## PWB_4 0.936 51.666 0.018 0.986 0.936 0.617
## PWB_5 -1.573 86.803 -0.018 0.986 -1.573 -0.974
## PWB_9 0.828 45.708 0.018 0.986 0.828 0.580
## PWB =~
## PWB_1 0.929 121.082 0.008 0.994 0.929 0.577
## PWB_2 0.916 82.505 0.011 0.991 0.916 0.635
## PWB_3 1.253 130.883 0.010 0.992 1.253 0.792
## PWB_4 1.222 79.442 0.015 0.988 1.222 0.806
## PWB_5 -1.335 133.467 -0.010 0.992 -1.335 -0.826
## PWB_6 0.480 0.055 8.778 0.000 0.480 0.370
## PWB_7 0.970 0.056 17.221 0.000 0.970 0.756
## PWB_8 0.953 0.058 16.452 0.000 0.953 0.683
## PWB_9 0.734 70.280 0.010 0.992 0.734 0.513
##
## Covariances:
## Negative ~~
## PWB -0.650 48.954 -0.013 0.989 -0.650 -0.650
##
## Intercepts:
## PWB_1 3.896 0.056 69.041 0.000 3.896 2.417
## PWB_2 3.870 0.050 76.678 0.000 3.870 2.684
## PWB_3 4.152 0.055 74.917 0.000 4.152 2.623
## PWB_4 4.023 0.053 75.786 0.000 4.023 2.653
## PWB_5 2.877 0.057 50.879 0.000 2.877 1.781
## PWB_9 4.798 0.050 95.927 0.000 4.798 3.358
## PWB_6 4.499 0.045 98.964 0.000 4.499 3.464
## PWB_7 4.545 0.045 101.159 0.000 4.545 3.541
## PWB_8 4.362 0.049 89.357 0.000 4.362 3.128
## Negative 0.000 0.000 0.000
## PWB 0.000 0.000 0.000
##
## Variances:
## PWB_1 1.423 0.091 1.423 0.548
## PWB_2 1.453 0.080 1.453 0.699
## PWB_3 1.071 0.077 1.071 0.427
## PWB_4 1.417 0.081 1.417 0.616
## PWB_5 1.085 0.078 1.085 0.416
## PWB_9 1.607 0.085 1.607 0.787
## PWB_6 1.455 0.079 1.455 0.863
## PWB_7 0.706 0.088 0.706 0.428
## PWB_8 1.036 0.090 1.036 0.533
## Negative 1.000 1.000 1.000
## PWB 1.000 1.000 1.000
##
## R-Square:
##
## PWB_1 0.452
## PWB_2 0.301
## PWB_3 0.573
## PWB_4 0.384
## PWB_5 0.584
## PWB_9 0.213
## PWB_6 0.137
## PWB_7 0.572
## PWB_8 0.467Residual correlations
correl = residuals(two.fit, type="cor")
correl## $type
## [1] "cor.bollen"
##
## $cor
## PWB_1 PWB_3 PWB_4 PWB_5 PWB_6 PWB_9 PWB_2 PWB_7 PWB_8
## PWB_1 0.000
## PWB_3 -0.001 0.000
## PWB_4 -0.051 0.024 0.000
## PWB_5 -0.013 -0.004 0.028 0.000
## PWB_6 0.031 -0.010 -0.035 -0.011 0.000
## PWB_9 0.039 -0.006 0.080 0.004 -0.112 0.000
## PWB_2 0.401 0.291 0.344 -0.308 0.131 0.359 0.000
## PWB_7 -0.187 -0.105 0.190 0.068 0.167 -0.018 -0.022 0.000
## PWB_8 -0.132 -0.062 0.148 0.039 0.161 0.029 -0.049 0.012 0.000
##
## $mean
## PWB_1 PWB_3 PWB_4 PWB_5 PWB_6 PWB_9 PWB_2 PWB_7 PWB_8
## 0 0 0 0 0 0 0 0 0View(correl$cor)
correl1 = residuals(one.fit, type="cor")
correl1## $type
## [1] "cor.bollen"
##
## $cor
## PWB_1 PWB_2 PWB_3 PWB_4 PWB_5 PWB_6 PWB_7 PWB_8 PWB_9
## PWB_1 0.000
## PWB_2 0.110 0.000
## PWB_3 -0.004 -0.057 0.000
## PWB_4 -0.076 0.087 0.015 0.000
## PWB_5 -0.010 0.050 -0.033 0.037 0.000
## PWB_6 0.030 -0.109 0.011 -0.040 -0.032 0.000
## PWB_7 -0.176 0.051 -0.083 0.199 0.046 0.183 0.000
## PWB_8 -0.138 0.012 -0.061 0.141 0.038 0.161 0.487 0.000
## PWB_9 0.012 0.154 -0.022 0.053 0.020 -0.122 -0.013 0.021 0.000
##
## $mean
## PWB_1 PWB_2 PWB_3 PWB_4 PWB_5 PWB_6 PWB_7 PWB_8 PWB_9
## 0 0 0 0 0 0 0 0 0View(correl1$cor)
correl0 = residuals(second.fit, type="cor")
correl0## $type
## [1] "cor.bollen"
##
## $cor
## PWB_1 PWB_3 PWB_5 PWB_6 PWB_4 PWB_7 PWB_8 PWB_2 PWB_9
## PWB_1 0.000
## PWB_3 -0.016 0.000
## PWB_5 0.017 -0.011 0.000
## PWB_6 0.015 0.001 -0.010 0.000
## PWB_4 0.191 0.337 -0.291 0.181 0.000
## PWB_7 -0.201 -0.111 0.079 0.162 -0.032 0.000
## PWB_8 -0.136 -0.056 0.037 0.163 -0.061 0.040 0.000
## PWB_2 0.133 -0.022 0.025 -0.089 0.261 -0.040 -0.054 0.000
## PWB_9 0.039 0.017 -0.010 -0.099 0.203 -0.085 -0.031 0.000 0.000
##
## $mean
## PWB_1 PWB_3 PWB_5 PWB_6 PWB_4 PWB_7 PWB_8 PWB_2 PWB_9
## 0 0 0 0 0 0 0 0 0View(correl0$cor)
correl4 = residuals(bifactor1.fit, type="cor")
correl4## $type
## [1] "cor.bollen"
##
## $cor
## PWB_1 PWB_2 PWB_3 PWB_4 PWB_5 PWB_6 PWB_7 PWB_8 PWB_9
## PWB_1 0.000
## PWB_2 0.004 0.000
## PWB_3 -0.011 -0.002 0.000
## PWB_4 -0.073 0.000 0.045 0.000
## PWB_5 -0.003 0.000 0.004 0.006 0.000
## PWB_6 0.048 0.019 -0.013 0.002 -0.010 0.000
## PWB_7 -0.110 0.119 -0.010 0.260 -0.030 0.000 0.000
## PWB_8 -0.085 0.083 -0.008 0.194 -0.017 0.000 0.000 0.000
## PWB_9 -0.002 -0.004 0.009 0.054 -0.010 -0.072 0.037 0.067 0.000
##
## $mean
## PWB_1 PWB_2 PWB_3 PWB_4 PWB_5 PWB_6 PWB_7 PWB_8 PWB_9
## 0 0 0 0 0 0 0 0 0View(correl4$cor)
correl5 = residuals(bifactor2.fit, type="cor")
correl5## $type
## [1] "cor.bollen"
##
## $cor
## PWB_1 PWB_2 PWB_3 PWB_4 PWB_5 PWB_6 PWB_7 PWB_8 PWB_9
## PWB_1 0.000
## PWB_2 0.091 0.000
## PWB_3 -0.018 -0.048 0.000
## PWB_4 -0.074 0.011 0.048 0.000
## PWB_5 0.006 0.023 -0.010 -0.015 0.000
## PWB_6 0.034 -0.020 -0.005 0.066 0.008 0.000
## PWB_7 -0.117 0.090 -0.009 0.000 -0.033 0.245 0.000
## PWB_8 -0.087 0.037 0.006 0.000 -0.035 0.226 0.000 0.000
## PWB_9 0.001 0.000 -0.008 -0.005 -0.008 -0.044 0.021 0.043 0.000
##
## $mean
## PWB_1 PWB_2 PWB_3 PWB_4 PWB_5 PWB_6 PWB_7 PWB_8 PWB_9
## 0 0 0 0 0 0 0 0 0correl3 = residuals(bifactor.negative.fit, type="cor")
correl3## $type
## [1] "cor.bollen"
##
## $cor
## PWB_1 PWB_2 PWB_3 PWB_4 PWB_5 PWB_9 PWB_6 PWB_7 PWB_8
## PWB_1 0.000
## PWB_2 0.094 0.000
## PWB_3 -0.023 -0.071 0.000
## PWB_4 -0.042 0.057 0.025 0.000
## PWB_5 -0.004 0.061 -0.046 0.030 0.000
## PWB_9 -0.007 0.131 -0.035 0.035 0.030 0.000
## PWB_6 0.359 0.091 0.349 0.100 -0.368 0.057 0.000
## PWB_7 -0.074 -0.013 -0.079 -0.016 0.065 -0.044 -0.012 0.000
## PWB_8 -0.012 -0.017 -0.017 -0.024 0.014 0.016 0.014 0.003 0.000
##
## $mean
## PWB_1 PWB_2 PWB_3 PWB_4 PWB_5 PWB_9 PWB_6 PWB_7 PWB_8
## 0 0 0 0 0 0 0 0 0View(correl3$cor)Modification indicies
#modindices(two.fit, sort. = TRUE, minimum.value = 3.84)
modindices(one.fit, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 1 PWB_7 ~~ PWB_8 210.223 0.884 0.884 0.494 0.494
## 2 PWB_4 ~~ PWB_7 53.165 0.429 0.429 0.220 0.220
## 3 PWB_1 ~~ PWB_7 49.767 -0.421 -0.421 -0.204 -0.204
## 4 PWB_6 ~~ PWB_7 41.966 0.332 0.332 0.199 0.199
## 5 PWB_2 ~~ PWB_9 37.937 0.365 0.365 0.177 0.177
## 6 PWB_6 ~~ PWB_8 33.344 0.319 0.319 0.176 0.176
## 7 PWB_1 ~~ PWB_8 31.265 -0.361 -0.361 -0.161 -0.161
## 8 PWB_1 ~~ PWB_2 29.135 0.332 0.332 0.143 0.143
## 9 PWB_4 ~~ PWB_8 26.989 0.330 0.330 0.156 0.156
## 10 PWB_6 ~~ PWB_9 24.008 -0.261 -0.261 -0.141 -0.141
## 11 PWB_2 ~~ PWB_6 22.048 -0.245 -0.245 -0.131 -0.131
## 12 PWB_3 ~~ PWB_7 18.915 -0.234 -0.234 -0.115 -0.115
## 13 PWB_3 ~~ PWB_5 18.006 -0.289 -0.289 -0.113 -0.113
## 14 PWB_2 ~~ PWB_4 14.999 0.232 0.232 0.106 0.106
## 15 PWB_1 ~~ PWB_4 14.942 -0.247 -0.247 -0.101 -0.101
## 16 PWB_2 ~~ PWB_3 14.347 -0.217 -0.217 -0.095 -0.095
## 17 PWB_2 ~~ PWB_5 12.593 0.206 0.206 0.089 0.089
## 18 PWB_3 ~~ PWB_8 10.553 -0.189 -0.189 -0.086 -0.086
## 19 PWB_4 ~~ PWB_5 7.497 0.167 0.167 0.068 0.068
## 20 PWB_5 ~~ PWB_7 6.451 0.138 0.138 0.066 0.066
## 21 PWB_5 ~~ PWB_6 5.171 -0.119 -0.119 -0.057 -0.057
## 22 PWB_4 ~~ PWB_9 4.797 0.134 0.134 0.062 0.062
## 23 PWB_5 ~~ PWB_8 4.690 0.127 0.127 0.056 0.056modindices(bifactor1.fit, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 1 Positive =~ PWB_4 61.215 0.432 0.432 0.285 0.285
## 2 PWB_4 ~~ PWB_7 36.901 0.304 0.304 0.156 0.156
## 3 Positive =~ PWB_1 30.775 -0.302 -0.302 -0.188 -0.188
## 4 PWB_1 ~~ PWB_7 23.753 -0.240 -0.240 -0.116 -0.116
## 5 PWB_1 ~~ PWB_2 22.098 1.002 1.002 0.431 0.431
## 6 PWB_1 ~~ PWB_6 20.609 0.242 0.242 0.116 0.116
## 7 PWB_2 ~~ PWB_9 17.699 -1.127 -1.127 -0.547 -0.547
## 8 PWB_1 ~~ PWB_4 15.434 -0.263 -0.263 -0.107 -0.107
## 9 PWB_6 ~~ PWB_9 14.956 -0.207 -0.207 -0.111 -0.111
## 10 Negative ~~ Positive 13.868 0.175 0.175 0.175 0.175
## 11 PWB_3 ~~ PWB_4 11.412 0.204 0.204 0.085 0.085
## 12 Positive =~ PWB_2 8.807 0.155 0.155 0.108 0.108
## 13 PWB_4 ~~ PWB_6 8.763 -0.157 -0.157 -0.080 -0.080
## 14 Negative =~ PWB_7 7.492 0.115 0.115 0.089 0.089
## 15 Negative =~ PWB_6 6.754 -0.528 -0.528 -0.406 -0.406
## 16 PWB_4 ~~ PWB_8 6.185 0.135 0.135 0.064 0.064
## 17 PWB_4 ~~ PWB_9 5.983 0.165 0.165 0.076 0.076
## 18 PWB_2 ~~ PWB_7 5.699 0.111 0.111 0.060 0.060
## 19 PWB_1 ~~ PWB_8 4.454 -0.113 -0.113 -0.050 -0.050#modindices(bifactor.negative.fit, sort. = TRUE, minimum.value = 3.84)Fit Measures
#fitmeasures(two.fit)#Models two factors:Positive and Negative for Purpose
fitmeasures(one.fit) #Models as a single purpose factor## npar fmin chisq
## 27.000 0.358 584.980
## df pvalue baseline.chisq
## 27.000 0.000 2040.755
## baseline.df baseline.pvalue cfi
## 36.000 0.000 0.722
## tli nnfi rfi
## 0.629 0.629 0.618
## nfi pnfi ifi
## 0.713 0.535 0.723
## rni logl unrestricted.logl
## 0.722 -12466.214 -12173.724
## aic bic ntotal
## 24986.429 25113.448 816.000
## bic2 rmsea rmsea.ci.lower
## 25027.707 0.159 0.148
## rmsea.ci.upper rmsea.pvalue rmr
## 0.170 0.000 0.187
## rmr_nomean srmr srmr_bentler
## 0.205 0.098 0.098
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.107 0.098 0.107
## srmr_mplus srmr_mplus_nomean cn_05
## 0.098 0.107 56.955
## cn_01 gfi agfi
## 66.510 0.991 0.982
## pgfi mfi ecvi
## 0.496 0.710 NAfitmeasures(second.fit)#Second order models as Purpose being the higher factor made up of Purpose and Positive## npar fmin chisq
## 30.000 0.252 411.420
## df pvalue baseline.chisq
## 24.000 0.000 2040.755
## baseline.df baseline.pvalue cfi
## 36.000 0.000 0.807
## tli nnfi rfi
## 0.710 0.710 0.698
## nfi pnfi ifi
## 0.798 0.532 0.808
## rni logl unrestricted.logl
## 0.807 -12379.435 -12173.724
## aic bic ntotal
## 24818.869 24960.001 816.000
## bic2 rmsea rmsea.ci.lower
## 24864.733 0.141 0.129
## rmsea.ci.upper rmsea.pvalue rmr
## 0.153 0.000 0.228
## rmr_nomean srmr srmr_bentler
## 0.250 0.103 0.103
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.113 0.103 0.113
## srmr_mplus srmr_mplus_nomean cn_05
## 0.103 0.113 73.225
## cn_01 gfi agfi
## 86.245 0.994 0.985
## pgfi mfi ecvi
## 0.442 0.789 NAfitmeasures(bifactor1.fit)#Models bifactor with Positive and Purpose as factors uncorolated with the main factor## npar fmin chisq
## 36.000 0.103 168.910
## df pvalue baseline.chisq
## 18.000 0.000 2040.755
## baseline.df baseline.pvalue cfi
## 36.000 0.000 0.925
## tli nnfi rfi
## 0.849 0.849 0.834
## nfi pnfi ifi
## 0.917 0.459 0.925
## rni logl unrestricted.logl
## 0.925 -12258.179 -12173.724
## aic bic ntotal
## 24588.359 24757.717 816.000
## bic2 rmsea rmsea.ci.lower
## 24643.396 0.101 0.088
## rmsea.ci.upper rmsea.pvalue rmr
## 0.116 0.000 0.114
## rmr_nomean srmr srmr_bentler
## 0.125 0.056 0.056
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.062 0.056 0.062
## srmr_mplus srmr_mplus_nomean cn_05
## 0.056 0.062 140.467
## cn_01 gfi agfi
## 169.144 0.997 0.992
## pgfi mfi ecvi
## 0.332 0.912 NAfitmeasures(bifactor2.fit)#Models bifactor with Positive and Purpose as factors uncorolated with the main factor## npar fmin chisq
## 36.000 0.116 189.914
## df pvalue baseline.chisq
## 18.000 0.000 2040.755
## baseline.df baseline.pvalue cfi
## 36.000 0.000 0.914
## tli nnfi rfi
## 0.828 0.828 0.814
## nfi pnfi ifi
## 0.907 0.453 0.915
## rni logl unrestricted.logl
## 0.914 -12268.681 -12173.724
## aic bic ntotal
## 24609.362 24778.721 816.000
## bic2 rmsea rmsea.ci.lower
## 24664.399 0.108 0.095
## rmsea.ci.upper rmsea.pvalue rmr
## 0.122 0.000 0.108
## rmr_nomean srmr srmr_bentler
## 0.118 0.057 0.057
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.062 0.057 0.062
## srmr_mplus srmr_mplus_nomean cn_05
## 0.057 0.062 125.042
## cn_01 gfi agfi
## 150.548 0.997 0.992
## pgfi mfi ecvi
## 0.332 0.900 NAfitmeasures(bifactor.negative.fit)#Models bifactor as the negatively worded item as a factor uncorolated with the main factor## npar fmin chisq
## 34.000 0.219 357.479
## df pvalue baseline.chisq
## 20.000 0.000 2040.755
## baseline.df baseline.pvalue cfi
## 36.000 0.000 0.832
## tli nnfi rfi
## 0.697 0.697 0.685
## nfi pnfi ifi
## 0.825 0.458 0.833
## rni logl unrestricted.logl
## 0.832 -12352.464 -12173.724
## aic bic ntotal
## 24772.927 24932.877 816.000
## bic2 rmsea rmsea.ci.lower
## 24824.907 0.144 0.131
## rmsea.ci.upper rmsea.pvalue rmr
## 0.157 0.000 0.196
## rmr_nomean srmr srmr_bentler
## 0.215 0.094 0.094
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.103 0.094 0.103
## srmr_mplus srmr_mplus_nomean cn_05
## 0.094 0.103 72.699
## cn_01 gfi agfi
## 86.751 0.994 0.984
## pgfi mfi ecvi
## 0.368 0.813 NACreate dataset for Target rotation
all_surveys<-read.csv("allsurveysYT1.csv")
PWBTR<-select(all_surveys, PWB_1, PWB_2, PWB_3,PWB_4, PWB_5,PWB_6,PWB_9, PWB_8,PWB_7)
PWB$PWB_1 <- 7- PWB$PWB_1
PWB$PWB_2 <- 7- PWB$PWB_2
PWB$PWB_3 <- 7- PWB$PWB_3
PWB$PWB_4 <- 7- PWB$PWB_4
PWB$PWB_9 <- 7- PWB$PWB_9
PWBTR<- data.frame(apply(PWBTR,2, as.numeric))
library(GPArotation)
library(psych)
library(dplyr)
PWBTR<-tbl_df(PWBTR)
PWBTR## Source: local data frame [1,160 x 9]
##
## PWB_1 PWB_2 PWB_3 PWB_4 PWB_5 PWB_6 PWB_9 PWB_8 PWB_7
## 1 3 4 2 5 4 5 1 3 4
## 2 3 2 2 5 2 5 2 2 3
## 3 2 1 2 1 1 4 1 3 6
## 4 5 5 3 3 3 4 3 4 5
## 5 5 5 4 4 4 3 3 3 2
## 6 2 3 1 2 3 4 1 4 3
## 7 5 5 2 5 1 4 4 3 3
## 8 1 1 2 6 2 4 1 4 4
## 9 2 2 2 2 1 5 1 5 5
## 10 1 1 4 4 2 6 1 3 6
## .. ... ... ... ... ... ... ... ... ...str(PWBTR)## Classes 'tbl_df', 'tbl' and 'data.frame': 1160 obs. of 9 variables:
## $ PWB_1: num 3 3 2 5 5 2 5 1 2 1 ...
## $ PWB_2: num 4 2 1 5 5 3 5 1 2 1 ...
## $ PWB_3: num 2 2 2 3 4 1 2 2 2 4 ...
## $ PWB_4: num 5 5 1 3 4 2 5 6 2 4 ...
## $ PWB_5: num 4 2 1 3 4 3 1 2 1 2 ...
## $ PWB_6: num 5 5 4 4 3 4 4 4 5 6 ...
## $ PWB_9: num 1 2 1 3 3 1 4 1 1 1 ...
## $ PWB_8: num 3 2 3 4 3 4 3 4 5 3 ...
## $ PWB_7: num 4 3 6 5 2 3 3 4 5 6 ...colnames(PWBTR) <- c("1","2", "3", "4", "5", "6", "7", "8", "9")
#Target rotation: choose "simple structure" a priori and can be applied to oblique and orthogonal rotation based on
#what paper says facotrs should be PWB
Targ_key <- make.keys(9,list(f1=1:6,f2=7:9))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
PWBTR_cor <- corFiml(PWBTR) # convert the raw data to correlation matrix uisng FIML
out_targetQ <- fa(PWBTR_cor,2,rotate="TargetQ",n.obs = 816,Target=Targ_key) #TargetT for orthogonal rotation
out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR1 MR2
## 1 0.690 0.200
## 2 0.511
## 3 0.777
## 4 0.500 -0.254
## 5 0.786
## 6 -0.478 0.222
## 7 0.437
## 8 0.616
## 9 0.843
##
## MR1 MR2
## SS loadings 2.633 1.253
## Proportion Var 0.293 0.139
## Cumulative Var 0.293 0.432
##
## $score.cor
## [,1] [,2]
## [1,] 1.0000000 -0.2125741
## [2,] -0.2125741 1.0000000
##
## $TLI
## [1] 0.8443827
##
## $RMSEA
## RMSEA lower upper confidence
## 0.10308452 0.08936113 0.11650367 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = PWBTR_cor, nfactors = 2, n.obs = 816, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 h2 u2 com
## 1 0.69 0.20 0.46 0.54 1.2
## 2 0.51 -0.06 0.27 0.73 1.0
## 3 0.78 0.07 0.59 0.41 1.0
## 4 0.50 -0.25 0.36 0.64 1.5
## 5 0.79 0.04 0.61 0.39 1.0
## 6 -0.48 0.22 0.32 0.68 1.4
## 7 0.44 -0.01 0.19 0.81 1.0
## 8 -0.07 0.62 0.40 0.60 1.0
## 9 0.02 0.84 0.71 0.29 1.0
##
## MR1 MR2
## SS loadings 2.65 1.27
## Proportion Var 0.29 0.14
## Cumulative Var 0.29 0.43
## Proportion Explained 0.68 0.32
## Cumulative Proportion 0.68 1.00
##
## With factor correlations of
## MR1 MR2
## MR1 1.00 -0.19
## MR2 -0.19 1.00
##
## Mean item complexity = 1.1
## Test of the hypothesis that 2 factors are sufficient.
##
## The degrees of freedom for the null model are 36 and the objective function was 2.5 with Chi Square of 2028.67
## The degrees of freedom for the model are 19 and the objective function was 0.23
##
## The root mean square of the residuals (RMSR) is 0.05
## The df corrected root mean square of the residuals is 0.07
##
## The harmonic number of observations is 816 with the empirical chi square 168.14 with prob < 6.6e-26
## The total number of observations was 816 with MLE Chi Square = 182.39 with prob < 1e-28
##
## Tucker Lewis Index of factoring reliability = 0.844
## RMSEA index = 0.103 and the 90 % confidence intervals are 0.089 0.117
## BIC = 55
## Fit based upon off diagonal values = 0.97
## Measures of factor score adequacy
## MR1 MR2
## Correlation of scores with factors 0.92 0.88
## Multiple R square of scores with factors 0.84 0.77
## Minimum correlation of possible factor scores 0.68 0.54CFI
1-((out_targetQ$STATISTIC - out_targetQ$dof)/(out_targetQ$null.chisq- out_targetQ$null.dof))## [1] 0.9180061Target toration as three factors - works well as three factors but with cross loadings
all_surveys<-read.csv("allsurveysYT1.csv")
PWBTR<-select(all_surveys, PWB_1, PWB_3, PWB_5,PWB_6, PWB_4,PWB_7,PWB_8, PWB_2,PWB_9)
PWB$PWB_1 <- 7- PWB$PWB_1
PWB$PWB_2 <- 7- PWB$PWB_2
PWB$PWB_3 <- 7- PWB$PWB_3
PWB$PWB_4 <- 7- PWB$PWB_4
PWB$PWB_9 <- 7- PWB$PWB_9
PWBTR<- data.frame(apply(PWBTR,2, as.numeric))
library(GPArotation)
library(psych)
library(dplyr)
PWBTR<-tbl_df(PWBTR)
PWBTR## Source: local data frame [1,160 x 9]
##
## PWB_1 PWB_3 PWB_5 PWB_6 PWB_4 PWB_7 PWB_8 PWB_2 PWB_9
## 1 3 2 4 5 5 4 3 4 1
## 2 3 2 2 5 5 3 2 2 2
## 3 2 2 1 4 1 6 3 1 1
## 4 5 3 3 4 3 5 4 5 3
## 5 5 4 4 3 4 2 3 5 3
## 6 2 1 3 4 2 3 4 3 1
## 7 5 2 1 4 5 3 3 5 4
## 8 1 2 2 4 6 4 4 1 1
## 9 2 2 1 5 2 5 5 2 1
## 10 1 4 2 6 4 6 3 1 1
## .. ... ... ... ... ... ... ... ... ...str(PWBTR)## Classes 'tbl_df', 'tbl' and 'data.frame': 1160 obs. of 9 variables:
## $ PWB_1: num 3 3 2 5 5 2 5 1 2 1 ...
## $ PWB_3: num 2 2 2 3 4 1 2 2 2 4 ...
## $ PWB_5: num 4 2 1 3 4 3 1 2 1 2 ...
## $ PWB_6: num 5 5 4 4 3 4 4 4 5 6 ...
## $ PWB_4: num 5 5 1 3 4 2 5 6 2 4 ...
## $ PWB_7: num 4 3 6 5 2 3 3 4 5 6 ...
## $ PWB_8: num 3 2 3 4 3 4 3 4 5 3 ...
## $ PWB_2: num 4 2 1 5 5 3 5 1 2 1 ...
## $ PWB_9: num 1 2 1 3 3 1 4 1 1 1 ...colnames(PWBTR) <- c("1","2", "3", "4", "5", "6", "7", "8", "9")
#Target rotation: choose "simple structure" a priori and can be applied to oblique and orthogonal rotation based on
#what paper says facotrs should be PWB
Targ_key <- make.keys(9,list(f1=1:4,f2=5:7, f3=8:9))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
PWBTR_cor <- corFiml(PWBTR) # convert the raw data to correlation matrix uisng FIML
out_targetQ <- fa(PWBTR_cor,3,rotate="TargetQ",n.obs = 816,Target=Targ_key) #TargetT for orthogonal rotation
out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR1 MR2 MR3
## 1 0.538 0.166 0.238
## 2 0.764
## 3 0.789
## 4 -0.684 0.228 0.274
## 5 0.258 -0.300 0.300
## 6 0.833
## 7 0.642
## 8 -0.130 0.703
## 9 0.471
##
## MR1 MR2 MR3
## SS loadings 2.048 1.299 0.941
## Proportion Var 0.228 0.144 0.105
## Cumulative Var 0.228 0.372 0.477
##
## $score.cor
## [,1] [,2] [,3]
## [1,] 1.0000000 -0.2076056 0.4944621
## [2,] -0.2076056 1.0000000 -0.1451347
## [3,] 0.4944621 -0.1451347 1.0000000
##
## $TLI
## [1] 0.9231504
##
## $RMSEA
## RMSEA lower upper confidence
## 0.07247499 0.05512534 0.09016088 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = PWBTR_cor, nfactors = 3, n.obs = 816, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 MR3 h2 u2 com
## 1 0.54 0.17 0.24 0.48 0.52 1.6
## 2 0.76 0.05 0.03 0.60 0.40 1.0
## 3 0.79 0.02 0.01 0.63 0.37 1.0
## 4 -0.68 0.23 0.27 0.44 0.56 1.6
## 5 0.26 -0.30 0.30 0.38 0.62 2.9
## 6 0.09 0.83 -0.03 0.67 0.33 1.0
## 7 -0.02 0.64 0.00 0.42 0.58 1.0
## 8 0.02 -0.13 0.70 0.54 0.46 1.1
## 9 0.09 -0.06 0.47 0.29 0.71 1.1
##
## MR1 MR2 MR3
## SS loadings 2.12 1.32 1.01
## Proportion Var 0.24 0.15 0.11
## Cumulative Var 0.24 0.38 0.49
## Proportion Explained 0.48 0.30 0.23
## Cumulative Proportion 0.48 0.77 1.00
##
## With factor correlations of
## MR1 MR2 MR3
## MR1 1.00 -0.23 0.58
## MR2 -0.23 1.00 -0.06
## MR3 0.58 -0.06 1.00
##
## Mean item complexity = 1.4
## Test of the hypothesis that 3 factors are sufficient.
##
## The degrees of freedom for the null model are 36 and the objective function was 2.5 with Chi Square of 2028.67
## The degrees of freedom for the model are 12 and the objective function was 0.08
##
## The root mean square of the residuals (RMSR) is 0.03
## The df corrected root mean square of the residuals is 0.04
##
## The harmonic number of observations is 816 with the empirical chi square 37.79 with prob < 0.00017
## The total number of observations was 816 with MLE Chi Square = 62.92 with prob < 6.6e-09
##
## Tucker Lewis Index of factoring reliability = 0.923
## RMSEA index = 0.072 and the 90 % confidence intervals are 0.055 0.09
## BIC = -17.54
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## MR1 MR2 MR3
## Correlation of scores with factors 0.91 0.87 0.83
## Multiple R square of scores with factors 0.84 0.76 0.69
## Minimum correlation of possible factor scores 0.67 0.52 0.37CFI
1-((out_targetQ$STATISTIC - out_targetQ$dof)/(out_targetQ$null.chisq- out_targetQ$null.dof))## [1] 0.9744478