CFA & Trarget Rotation MLQ (Youth Only)
Levi Brackman
23 July 2015
- I understand my life’s meaning.
- I am looking for something that makes my life feel meaningful.
- I am always looking to find my life’s purpose.
- My life has a clear sense of purpose.
- I have a good sense of what makes my life meaningful.
- I have discovered a satisfying life purpose.
- I am always searching for something that makes my life feel significant.
- I am seeking a purpose or mission for my life.
- My life has no clear purpose. (reverse coded)
- I am searching for meaning in my life.
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)
MLQ<-select(data, MLQ_1, MLQ_2, MLQ_3, MLQ_4, MLQ_5, MLQ_6,MLQ_7, MLQ_8, MLQ_9, MLQ_10)
MLQ$MLQ_9 <- 8- MLQ$MLQ_9
MLQ<-tbl_df(MLQ)
MLQ## Source: local data frame [1,160 x 10]
##
## MLQ_1 MLQ_2 MLQ_3 MLQ_4 MLQ_5 MLQ_6 MLQ_7 MLQ_8 MLQ_9 MLQ_10
## 1 4 7 7 5 6 4 5 7 3 7
## 2 3 5 5 5 4 3 5 4 3 5
## 3 4 7 5 4 4 4 4 5 4 4
## 4 5 6 7 3 5 5 5 5 3 6
## 5 4 6 5 4 4 4 5 5 5 5
## 6 5 5 3 4 5 5 3 4 7 3
## 7 6 2 2 3 6 3 5 4 5 4
## 8 3 7 7 5 5 4 5 7 4 5
## 9 6 5 2 7 6 6 5 7 7 2
## 10 1 7 1 3 5 1 5 5 6 1
## .. ... ... ... ... ... ... ... ... ... ...create the models
two.model= ' Purpose =~ MLQ_1 + MLQ_4 + MLQ_5 + MLQ_6 + MLQ_9
Searching =~ MLQ_2 + MLQ_3 + MLQ_7 + MLQ_8 +MLQ_10' #Models two factors:Purpose and Seraching for Purpose
one.model= 'MLQ =~ MLQ_1 + MLQ_2 + MLQ_3 + MLQ_4 + MLQ_5 + MLQ_6 + MLQ_7 + MLQ_8 + MLQ_9 + MLQ_10' #Models as a single purpose factorSecond order models
second.model = ' Purpose =~ MLQ_1 + MLQ_4 + MLQ_5 + MLQ_6 + MLQ_9
Searching =~ MLQ_2 + MLQ_3 + MLQ_7 + MLQ_8 +MLQ_10
MLQ =~ p1*Purpose + p1*Searching
# MLQ ~~ 1*MLQ
' #Second order models as Purpose being the higher factor made up of Purpose and SearchingBifactor Models (similar to Models 6, 7 & 8 in Marsh, Scalas & Nagengast, 2010)
bifactor.negative.model = 'Negative =~ MLQ_9
MLQ =~ MLQ_1 + MLQ_2 + MLQ_3 + MLQ_4 + MLQ_5 + MLQ_6 + MLQ_7 + MLQ_8 + MLQ_9 + MLQ_10
MLQ ~~ 0*Negative
'#Models bifactor as the negatively worded item as a factor uncorolated with the main factor
bifactor.model1 = 'MLQ =~ MLQ_1 + MLQ_2 + MLQ_3 + MLQ_4 + MLQ_5 + MLQ_6 + MLQ_7 + MLQ_8 + MLQ_9 + MLQ_10
Purpose =~ MLQ_1 + MLQ_4 + MLQ_5 + MLQ_6 + MLQ_9
Searching =~ MLQ_2 + MLQ_3 + MLQ_7 + MLQ_8 +MLQ_10
MLQ ~~ 0*Purpose
MLQ ~~ 0*Searching
Purpose~~0*Searching
'#Models bifactor with Searching and Purpose as factors uncorolated with the main factor
bifactor.model1WO9 = 'MLQ =~ MLQ_1 + MLQ_2 + MLQ_3 + MLQ_4 + MLQ_5 + MLQ_6 + MLQ_7 + MLQ_8 + MLQ_10
Purpose =~ MLQ_1 + MLQ_4 + MLQ_5 + MLQ_6
Searching =~ MLQ_2 + MLQ_3 + MLQ_7 + MLQ_8 +MLQ_10
MLQ ~~ 0*Purpose
MLQ ~~ 0*Searching
Purpose~~0*Searching
'
#Models bifactor with Searching and Purpose as factors uncorolated with the main factor leaving negatively worded questions outrun the models
two.fit=cfa(two.model, data=MLQ, missing = "fiml")## 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 94 110 111 112 116 121 122 123 124 125 128 129 130 131 133 135 137 138 140 147 151 152 155 156 162 166 169 170 171 172 173 174 176 177 179 180 183 184 186 187 188 189 192 194 195 197 200 202 203 204 207 208 210 212 214 215 217 220 222 223 224 226 227 228 229 230 234 238 240 243 245 246 247 249 252 255 256 265 266 267 268 270 271 274 275 280 281 282 284 286 287 289 291 292 298 300 304 309 310 311 312 315 316 317 320 322 325 327 330 333 334 336 339 340 344 348 350 351 352 354 355 357 360 361 362 364 365 366 367 368 369 370 371 372 373 374 375 376 377 379 380 381 384 385 386 389 390 397 398 399 400 401 402 403 404 405 406 407 408 410 416 417 418 419 420 421 422 423 424 425 427 428 429 430 431 432 434 436 444 445 446 447 448 452 453 454 455 456 457 459 460 462 463 464 465 467 468 470 472 473 474 475 476 478 481 482 485 486 490 491 493 495 539 540 541 542 543 544 545 546 548 549 552 553 555 557 559 560 561 562 563 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 581 582 584 585 586 587 588 589 590 591 592 593 594 596 597 598 599 600 601 602 603 604 605 606 609 610 662 679 687 782 783 784 785 809 810 829 903 906 907 909 911 1110 1113 1114 1116 1117 1120 1125 1128 1129 1130 1139 1140 1146 1150 1151 1154 1159 1160one.fit=cfa(one.model, data=MLQ, missing = "fiml")## 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 94 110 111 112 116 121 122 123 124 125 128 129 130 131 133 135 137 138 140 147 151 152 155 156 162 166 169 170 171 172 173 174 176 177 179 180 183 184 186 187 188 189 192 194 195 197 200 202 203 204 207 208 210 212 214 215 217 220 222 223 224 226 227 228 229 230 234 238 240 243 245 246 247 249 252 255 256 265 266 267 268 270 271 274 275 280 281 282 284 286 287 289 291 292 298 300 304 309 310 311 312 315 316 317 320 322 325 327 330 333 334 336 339 340 344 348 350 351 352 354 355 357 360 361 362 364 365 366 367 368 369 370 371 372 373 374 375 376 377 379 380 381 384 385 386 389 390 397 398 399 400 401 402 403 404 405 406 407 408 410 416 417 418 419 420 421 422 423 424 425 427 428 429 430 431 432 434 436 444 445 446 447 448 452 453 454 455 456 457 459 460 462 463 464 465 467 468 470 472 473 474 475 476 478 481 482 485 486 490 491 493 495 539 540 541 542 543 544 545 546 548 549 552 553 555 557 559 560 561 562 563 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 581 582 584 585 586 587 588 589 590 591 592 593 594 596 597 598 599 600 601 602 603 604 605 606 609 610 662 679 687 782 783 784 785 809 810 829 903 906 907 909 911 1110 1113 1114 1116 1117 1120 1125 1128 1129 1130 1139 1140 1146 1150 1151 1154 1159 1160second.fit=cfa(second.model, data=MLQ, missing = "fiml")## 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 94 110 111 112 116 121 122 123 124 125 128 129 130 131 133 135 137 138 140 147 151 152 155 156 162 166 169 170 171 172 173 174 176 177 179 180 183 184 186 187 188 189 192 194 195 197 200 202 203 204 207 208 210 212 214 215 217 220 222 223 224 226 227 228 229 230 234 238 240 243 245 246 247 249 252 255 256 265 266 267 268 270 271 274 275 280 281 282 284 286 287 289 291 292 298 300 304 309 310 311 312 315 316 317 320 322 325 327 330 333 334 336 339 340 344 348 350 351 352 354 355 357 360 361 362 364 365 366 367 368 369 370 371 372 373 374 375 376 377 379 380 381 384 385 386 389 390 397 398 399 400 401 402 403 404 405 406 407 408 410 416 417 418 419 420 421 422 423 424 425 427 428 429 430 431 432 434 436 444 445 446 447 448 452 453 454 455 456 457 459 460 462 463 464 465 467 468 470 472 473 474 475 476 478 481 482 485 486 490 491 493 495 539 540 541 542 543 544 545 546 548 549 552 553 555 557 559 560 561 562 563 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 581 582 584 585 586 587 588 589 590 591 592 593 594 596 597 598 599 600 601 602 603 604 605 606 609 610 662 679 687 782 783 784 785 809 810 829 903 906 907 909 911 1110 1113 1114 1116 1117 1120 1125 1128 1129 1130 1139 1140 1146 1150 1151 1154 1159 1160bifactor1.fit=cfa(bifactor.model1, data=MLQ, missing = "fiml")## 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 94 110 111 112 116 121 122 123 124 125 128 129 130 131 133 135 137 138 140 147 151 152 155 156 162 166 169 170 171 172 173 174 176 177 179 180 183 184 186 187 188 189 192 194 195 197 200 202 203 204 207 208 210 212 214 215 217 220 222 223 224 226 227 228 229 230 234 238 240 243 245 246 247 249 252 255 256 265 266 267 268 270 271 274 275 280 281 282 284 286 287 289 291 292 298 300 304 309 310 311 312 315 316 317 320 322 325 327 330 333 334 336 339 340 344 348 350 351 352 354 355 357 360 361 362 364 365 366 367 368 369 370 371 372 373 374 375 376 377 379 380 381 384 385 386 389 390 397 398 399 400 401 402 403 404 405 406 407 408 410 416 417 418 419 420 421 422 423 424 425 427 428 429 430 431 432 434 436 444 445 446 447 448 452 453 454 455 456 457 459 460 462 463 464 465 467 468 470 472 473 474 475 476 478 481 482 485 486 490 491 493 495 539 540 541 542 543 544 545 546 548 549 552 553 555 557 559 560 561 562 563 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 581 582 584 585 586 587 588 589 590 591 592 593 594 596 597 598 599 600 601 602 603 604 605 606 609 610 662 679 687 782 783 784 785 809 810 829 903 906 907 909 911 1110 1113 1114 1116 1117 1120 1125 1128 1129 1130 1139 1140 1146 1150 1151 1154 1159 1160bifactor1WO9.fit=cfa(bifactor.model1WO9, data=MLQ, missing = "fiml")## 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 94 110 111 112 116 121 122 123 124 125 128 129 130 131 133 135 137 138 140 147 151 152 155 156 162 166 169 170 171 172 173 174 176 177 179 180 183 184 186 187 188 189 192 194 195 197 200 202 203 204 207 208 210 212 214 215 217 220 222 223 224 226 227 228 229 230 234 238 240 243 245 246 247 249 252 255 256 265 266 267 268 270 271 274 275 280 281 282 284 286 287 289 291 292 298 300 304 309 310 311 312 315 316 317 320 322 325 327 330 333 334 336 339 340 344 348 350 351 352 354 355 357 360 361 362 364 365 366 367 368 369 370 371 372 373 374 375 376 377 379 380 381 384 385 386 389 390 397 398 399 400 401 402 403 404 405 406 407 408 410 416 417 418 419 420 421 422 423 424 425 427 428 429 430 431 432 434 436 444 445 446 447 448 452 453 454 455 456 457 459 460 462 463 464 465 467 468 470 472 473 474 475 476 478 481 482 485 486 490 491 493 495 539 540 541 542 543 544 545 546 548 549 552 553 555 557 559 560 561 562 563 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 581 582 584 585 586 587 588 589 590 591 592 593 594 596 597 598 599 600 601 602 603 604 605 606 609 610 662 679 687 782 783 784 785 809 810 829 903 906 907 909 911 1110 1113 1114 1116 1117 1120 1125 1128 1129 1130 1139 1140 1146 1150 1151 1154 1159 1160bifactor.negative.fit=cfa(bifactor.negative.model, data=MLQ, missing = "fiml")## 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 94 110 111 112 116 121 122 123 124 125 128 129 130 131 133 135 137 138 140 147 151 152 155 156 162 166 169 170 171 172 173 174 176 177 179 180 183 184 186 187 188 189 192 194 195 197 200 202 203 204 207 208 210 212 214 215 217 220 222 223 224 226 227 228 229 230 234 238 240 243 245 246 247 249 252 255 256 265 266 267 268 270 271 274 275 280 281 282 284 286 287 289 291 292 298 300 304 309 310 311 312 315 316 317 320 322 325 327 330 333 334 336 339 340 344 348 350 351 352 354 355 357 360 361 362 364 365 366 367 368 369 370 371 372 373 374 375 376 377 379 380 381 384 385 386 389 390 397 398 399 400 401 402 403 404 405 406 407 408 410 416 417 418 419 420 421 422 423 424 425 427 428 429 430 431 432 434 436 444 445 446 447 448 452 453 454 455 456 457 459 460 462 463 464 465 467 468 470 472 473 474 475 476 478 481 482 485 486 490 491 493 495 539 540 541 542 543 544 545 546 548 549 552 553 555 557 559 560 561 562 563 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 581 582 584 585 586 587 588 589 590 591 592 593 594 596 597 598 599 600 601 602 603 604 605 606 609 610 662 679 687 782 783 784 785 809 810 829 903 906 907 909 911 1110 1113 1114 1116 1117 1120 1125 1128 1129 1130 1139 1140 1146 1150 1151 1154 1159 1160## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING: could not compute standard errors!
## lavaan NOTE: this may be a symptom that the model is not identified.create 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(bifactor1WO9.fit, whatLabels = "std", layout = "tree")
semPaths(bifactor.negative.fit, whatLabels = "std", layout = "tree")## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING: could not compute standard errors!
## lavaan NOTE: this may be a symptom that the model is not identified.
#summaries
summary(two.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 33 iterations
##
## Used Total
## Number of observations 842 1160
##
## Number of missing patterns 1
##
## Estimator ML
## Minimum Function Test Statistic 219.172
## Degrees of freedom 34
## 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:
## Purpose =~
## MLQ_1 1.000 1.370 0.811
## MLQ_4 0.886 0.040 22.396 0.000 1.213 0.766
## MLQ_5 0.829 0.035 23.656 0.000 1.135 0.778
## MLQ_6 0.947 0.041 22.863 0.000 1.297 0.772
## MLQ_9 0.593 0.052 11.416 0.000 0.811 0.409
## Searching =~
## MLQ_2 1.000 1.298 0.807
## MLQ_3 0.897 0.041 22.121 0.000 1.165 0.744
## MLQ_7 0.874 0.040 21.698 0.000 1.135 0.721
## MLQ_8 0.896 0.040 22.336 0.000 1.163 0.738
## MLQ_10 1.046 0.043 24.307 0.000 1.358 0.795
##
## Covariances:
## Purpose ~~
## Searching 0.152 0.071 2.138 0.033 0.085 0.085
##
## Intercepts:
## MLQ_1 4.700 0.058 80.722 0.000 4.700 2.782
## MLQ_4 4.985 0.055 91.351 0.000 4.985 3.148
## MLQ_5 5.242 0.050 104.254 0.000 5.242 3.593
## MLQ_6 4.786 0.058 82.650 0.000 4.786 2.848
## MLQ_9 4.844 0.068 70.828 0.000 4.844 2.441
## MLQ_2 5.368 0.055 96.855 0.000 5.368 3.338
## MLQ_3 5.249 0.054 97.252 0.000 5.249 3.352
## MLQ_7 5.183 0.054 95.553 0.000 5.183 3.293
## MLQ_8 5.316 0.054 97.955 0.000 5.316 3.376
## MLQ_10 5.058 0.059 85.972 0.000 5.058 2.963
## Purpose 0.000 0.000 0.000
## Searching 0.000 0.000 0.000
##
## Variances:
## MLQ_1 0.978 0.069 0.978 0.343
## MLQ_4 1.036 0.066 1.036 0.413
## MLQ_5 0.841 0.055 0.841 0.395
## MLQ_6 1.140 0.073 1.140 0.404
## MLQ_9 3.281 0.165 3.281 0.833
## MLQ_2 0.901 0.060 0.901 0.348
## MLQ_3 1.096 0.065 1.096 0.447
## MLQ_7 1.188 0.069 1.188 0.480
## MLQ_8 1.128 0.067 1.128 0.455
## MLQ_10 1.071 0.070 1.071 0.368
## Purpose 1.876 0.140 1.000 1.000
## Searching 1.686 0.125 1.000 1.000
##
## R-Square:
##
## MLQ_1 0.657
## MLQ_4 0.587
## MLQ_5 0.605
## MLQ_6 0.596
## MLQ_9 0.167
## MLQ_2 0.652
## MLQ_3 0.553
## MLQ_7 0.520
## MLQ_8 0.545
## MLQ_10 0.632summary(one.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 28 iterations
##
## Used Total
## Number of observations 842 1160
##
## Number of missing patterns 1
##
## Estimator ML
## Minimum Function Test Statistic 2092.057
## Degrees of freedom 35
## 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:
## MLQ =~
## MLQ_1 1.000 1.358 0.804
## MLQ_2 0.062 0.045 1.383 0.167 0.085 0.053
## MLQ_3 0.214 0.044 4.874 0.000 0.290 0.185
## MLQ_4 0.898 0.040 22.420 0.000 1.220 0.771
## MLQ_5 0.834 0.035 23.528 0.000 1.134 0.777
## MLQ_6 0.955 0.042 22.811 0.000 1.298 0.772
## MLQ_7 0.222 0.044 5.080 0.000 0.302 0.192
## MLQ_8 0.207 0.044 4.705 0.000 0.281 0.178
## MLQ_9 0.575 0.053 10.955 0.000 0.782 0.394
## MLQ_10 0.015 0.048 0.313 0.754 0.020 0.012
##
## Intercepts:
## MLQ_1 4.700 0.058 80.722 0.000 4.700 2.782
## MLQ_2 5.368 0.055 96.855 0.000 5.368 3.338
## MLQ_3 5.249 0.054 97.252 0.000 5.249 3.352
## MLQ_4 4.985 0.055 91.351 0.000 4.985 3.148
## MLQ_5 5.242 0.050 104.254 0.000 5.242 3.593
## MLQ_6 4.786 0.058 82.650 0.000 4.786 2.848
## MLQ_7 5.183 0.054 95.553 0.000 5.183 3.293
## MLQ_8 5.316 0.054 97.955 0.000 5.316 3.376
## MLQ_9 4.844 0.068 70.828 0.000 4.844 2.441
## MLQ_10 5.058 0.059 85.972 0.000 5.058 2.963
## MLQ 0.000 0.000 0.000
##
## Variances:
## MLQ_1 1.008 0.070 1.008 0.353
## MLQ_2 2.579 0.126 2.579 0.997
## MLQ_3 2.369 0.116 2.369 0.966
## MLQ_4 1.018 0.065 1.018 0.406
## MLQ_5 0.844 0.055 0.844 0.396
## MLQ_6 1.140 0.073 1.140 0.404
## MLQ_7 2.386 0.117 2.386 0.963
## MLQ_8 2.401 0.118 2.401 0.968
## MLQ_9 3.328 0.167 3.328 0.845
## MLQ_10 2.914 0.142 2.914 1.000
## MLQ 1.845 0.139 1.000 1.000
##
## R-Square:
##
## MLQ_1 0.647
## MLQ_2 0.003
## MLQ_3 0.034
## MLQ_4 0.594
## MLQ_5 0.604
## MLQ_6 0.596
## MLQ_7 0.037
## MLQ_8 0.032
## MLQ_9 0.155
## MLQ_10 0.000summary(second.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 30 iterations
##
## Used Total
## Number of observations 842 1160
##
## Number of missing patterns 1
##
## Estimator ML
## Minimum Function Test Statistic 219.172
## Degrees of freedom 34
## 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:
## Purpose =~
## MLQ_1 1.000 1.370 0.811
## MLQ_4 0.886 0.040 22.396 0.000 1.213 0.766
## MLQ_5 0.829 0.035 23.656 0.000 1.135 0.778
## MLQ_6 0.947 0.041 22.863 0.000 1.297 0.772
## MLQ_9 0.593 0.052 11.416 0.000 0.811 0.409
## Searching =~
## MLQ_2 1.000 1.298 0.807
## MLQ_3 0.897 0.041 22.121 0.000 1.165 0.744
## MLQ_7 0.874 0.040 21.698 0.000 1.135 0.721
## MLQ_8 0.896 0.040 22.336 0.000 1.163 0.738
## MLQ_10 1.046 0.043 24.307 0.000 1.358 0.795
## MLQ =~
## Purpose (p1) 1.000 0.285 0.285
## Searchng (p1) 1.000 0.300 0.300
##
## Intercepts:
## MLQ_1 4.700 0.058 80.722 0.000 4.700 2.782
## MLQ_4 4.985 0.055 91.351 0.000 4.985 3.148
## MLQ_5 5.242 0.050 104.254 0.000 5.242 3.593
## MLQ_6 4.786 0.058 82.650 0.000 4.786 2.848
## MLQ_9 4.844 0.068 70.828 0.000 4.844 2.441
## MLQ_2 5.368 0.055 96.855 0.000 5.368 3.338
## MLQ_3 5.249 0.054 97.252 0.000 5.249 3.352
## MLQ_7 5.183 0.054 95.553 0.000 5.183 3.293
## MLQ_8 5.316 0.054 97.955 0.000 5.316 3.376
## MLQ_10 5.058 0.059 85.972 0.000 5.058 2.963
## Purpose 0.000 0.000 0.000
## Searching 0.000 0.000 0.000
## MLQ 0.000 0.000 0.000
##
## Variances:
## MLQ_1 0.978 0.069 0.978 0.343
## MLQ_4 1.036 0.066 1.036 0.413
## MLQ_5 0.841 0.055 0.841 0.395
## MLQ_6 1.140 0.073 1.140 0.404
## MLQ_9 3.281 0.165 3.281 0.833
## MLQ_2 0.901 0.060 0.901 0.348
## MLQ_3 1.096 0.065 1.096 0.447
## MLQ_7 1.188 0.069 1.188 0.480
## MLQ_8 1.128 0.067 1.128 0.455
## MLQ_10 1.071 0.070 1.071 0.368
## Purpose 1.724 0.150 0.919 0.919
## Searching 1.534 0.138 0.910 0.910
## MLQ 0.152 0.071 1.000 1.000
##
## R-Square:
##
## MLQ_1 0.657
## MLQ_4 0.587
## MLQ_5 0.605
## MLQ_6 0.596
## MLQ_9 0.167
## MLQ_2 0.652
## MLQ_3 0.553
## MLQ_7 0.520
## MLQ_8 0.545
## MLQ_10 0.632
## Purpose 0.081
## Searching 0.090summary(bifactor1.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 69 iterations
##
## Used Total
## Number of observations 842 1160
##
## Number of missing patterns 1
##
## Estimator ML
## Minimum Function Test Statistic 109.158
## Degrees of freedom 25
## 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:
## MLQ =~
## MLQ_1 1.000 1.201 0.711
## MLQ_2 0.025 0.058 0.427 0.669 0.030 0.019
## MLQ_3 0.232 0.078 2.953 0.003 0.278 0.178
## MLQ_4 0.976 0.096 10.192 0.000 1.172 0.740
## MLQ_5 0.804 0.047 16.934 0.000 0.966 0.662
## MLQ_6 1.199 0.120 9.970 0.000 1.439 0.857
## MLQ_7 0.210 0.067 3.130 0.002 0.252 0.160
## MLQ_8 0.194 0.066 2.944 0.003 0.233 0.148
## MLQ_9 0.493 0.099 5.006 0.000 0.592 0.299
## MLQ_10 -0.061 0.055 -1.101 0.271 -0.073 -0.043
## Purpose =~
## MLQ_1 1.000 0.653 0.386
## MLQ_4 0.477 0.176 2.714 0.007 0.311 0.197
## MLQ_5 1.034 0.200 5.159 0.000 0.675 0.463
## MLQ_6 0.060 0.590 0.102 0.919 0.039 0.023
## MLQ_9 1.117 0.322 3.471 0.001 0.729 0.367
## Searching =~
## MLQ_2 1.000 1.302 0.810
## MLQ_3 0.880 0.040 22.065 0.000 1.146 0.731
## MLQ_7 0.854 0.040 21.538 0.000 1.112 0.707
## MLQ_8 0.877 0.039 22.203 0.000 1.141 0.725
## MLQ_10 1.065 0.043 24.513 0.000 1.387 0.813
##
## Covariances:
## MLQ ~~
## Purpose 0.000 0.000 0.000
## Searching 0.000 0.000 0.000
## Purpose ~~
## Searching 0.000 0.000 0.000
##
## Intercepts:
## MLQ_1 4.700 0.058 80.722 0.000 4.700 2.782
## MLQ_2 5.368 0.055 96.855 0.000 5.368 3.338
## MLQ_3 5.249 0.054 97.252 0.000 5.249 3.352
## MLQ_4 4.985 0.055 91.351 0.000 4.985 3.148
## MLQ_5 5.242 0.050 104.254 0.000 5.242 3.593
## MLQ_6 4.786 0.058 82.650 0.000 4.786 2.848
## MLQ_7 5.183 0.054 95.553 0.000 5.183 3.293
## MLQ_8 5.316 0.054 97.955 0.000 5.316 3.376
## MLQ_9 4.844 0.068 70.828 0.000 4.844 2.441
## MLQ_10 5.058 0.059 85.972 0.000 5.058 2.963
## MLQ 0.000 0.000 0.000
## Purpose 0.000 0.000 0.000
## Searching 0.000 0.000 0.000
##
## Variances:
## MLQ_1 0.985 0.079 0.985 0.345
## MLQ_2 0.891 0.060 0.891 0.344
## MLQ_3 1.063 0.064 1.063 0.433
## MLQ_4 1.036 0.087 1.036 0.413
## MLQ_5 0.741 0.078 0.741 0.348
## MLQ_6 0.750 0.251 0.750 0.266
## MLQ_7 1.177 0.068 1.177 0.475
## MLQ_8 1.123 0.066 1.123 0.453
## MLQ_9 3.056 0.185 3.056 0.776
## MLQ_10 0.985 0.069 0.985 0.338
## MLQ 1.442 0.353 1.000 1.000
## Purpose 0.426 0.350 1.000 1.000
## Searching 1.695 0.126 1.000 1.000
##
## R-Square:
##
## MLQ_1 0.655
## MLQ_2 0.656
## MLQ_3 0.567
## MLQ_4 0.587
## MLQ_5 0.652
## MLQ_6 0.734
## MLQ_7 0.525
## MLQ_8 0.547
## MLQ_9 0.224
## MLQ_10 0.662summary(bifactor1WO9.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 72 iterations
##
## Used Total
## Number of observations 842 1160
##
## Number of missing patterns 1
##
## Estimator ML
## Minimum Function Test Statistic 39.702
## Degrees of freedom 18
## P-value (Chi-square) 0.002
##
## Parameter estimates:
##
## Information Observed
## Standard Errors Standard
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## MLQ =~
## MLQ_1 1.000 0.995 0.589
## MLQ_2 0.058 0.068 0.850 0.395 0.058 0.036
## MLQ_3 0.316 0.096 3.292 0.001 0.315 0.201
## MLQ_4 1.252 0.172 7.295 0.000 1.246 0.787
## MLQ_5 0.944 0.121 7.807 0.000 0.940 0.644
## MLQ_6 1.271 0.168 7.555 0.000 1.266 0.753
## MLQ_7 0.275 0.080 3.420 0.001 0.274 0.174
## MLQ_8 0.271 0.085 3.202 0.001 0.270 0.171
## MLQ_10 -0.061 0.070 -0.871 0.384 -0.061 -0.036
## Purpose =~
## MLQ_1 1.000 1.218 0.721
## MLQ_4 0.243 0.206 1.178 0.239 0.295 0.187
## MLQ_5 0.451 0.337 1.337 0.181 0.550 0.377
## MLQ_6 0.340 0.272 1.253 0.210 0.415 0.247
## Searching =~
## MLQ_2 1.000 1.300 0.808
## MLQ_3 0.876 0.040 22.034 0.000 1.139 0.727
## MLQ_7 0.851 0.040 21.485 0.000 1.106 0.703
## MLQ_8 0.873 0.039 22.151 0.000 1.135 0.720
## MLQ_10 1.072 0.044 24.420 0.000 1.393 0.816
##
## Covariances:
## MLQ ~~
## Purpose 0.000 0.000 0.000
## Searching 0.000 0.000 0.000
## Purpose ~~
## Searching 0.000 0.000 0.000
##
## Intercepts:
## MLQ_1 4.700 0.058 80.722 0.000 4.700 2.782
## MLQ_2 5.368 0.055 96.855 0.000 5.368 3.338
## MLQ_3 5.249 0.054 97.252 0.000 5.249 3.352
## MLQ_4 4.985 0.055 91.351 0.000 4.985 3.148
## MLQ_5 5.242 0.050 104.254 0.000 5.242 3.593
## MLQ_6 4.786 0.058 82.650 0.000 4.786 2.848
## MLQ_7 5.183 0.054 95.553 0.000 5.183 3.293
## MLQ_8 5.316 0.054 97.955 0.000 5.316 3.376
## MLQ_10 5.058 0.059 85.972 0.000 5.058 2.963
## MLQ 0.000 0.000 0.000
## Purpose 0.000 0.000 0.000
## Searching 0.000 0.000 0.000
##
## Variances:
## MLQ_1 0.379 0.856 0.379 0.133
## MLQ_2 0.894 0.060 0.894 0.346
## MLQ_3 1.057 0.064 1.057 0.431
## MLQ_4 0.867 0.102 0.867 0.346
## MLQ_5 0.943 0.100 0.943 0.443
## MLQ_6 1.050 0.094 1.050 0.372
## MLQ_7 1.179 0.068 1.179 0.476
## MLQ_8 1.120 0.066 1.120 0.452
## MLQ_10 0.971 0.070 0.971 0.333
## MLQ 0.991 0.331 1.000 1.000
## Purpose 1.484 0.863 1.000 1.000
## Searching 1.690 0.126 1.000 1.000
##
## R-Square:
##
## MLQ_1 0.867
## MLQ_2 0.654
## MLQ_3 0.569
## MLQ_4 0.654
## MLQ_5 0.557
## MLQ_6 0.628
## MLQ_7 0.524
## MLQ_8 0.548
## MLQ_10 0.667summary(bifactor.negative.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 28 iterations
##
## Used Total
## Number of observations 842 1160
##
## Number of missing patterns 1
##
## Estimator ML
## Minimum Function Test Statistic 2092.057
## Degrees of freedom 34
## 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 =~
## MLQ_9 1.000 0.564 0.284
## MLQ =~
## MLQ_1 1.000 1.358 0.804
## MLQ_2 0.062 0.085 0.053
## MLQ_3 0.214 0.290 0.185
## MLQ_4 0.898 1.220 0.771
## MLQ_5 0.834 1.134 0.777
## MLQ_6 0.955 1.298 0.772
## MLQ_7 0.222 0.302 0.192
## MLQ_8 0.207 0.281 0.178
## MLQ_9 0.575 0.782 0.394
## MLQ_10 0.015 0.020 0.012
##
## Covariances:
## Negative ~~
## MLQ 0.000 0.000 0.000
##
## Intercepts:
## MLQ_9 4.844 4.844 2.441
## MLQ_1 4.700 4.700 2.782
## MLQ_2 5.368 5.368 3.338
## MLQ_3 5.249 5.249 3.352
## MLQ_4 4.985 4.985 3.148
## MLQ_5 5.242 5.242 3.593
## MLQ_6 4.786 4.786 2.848
## MLQ_7 5.183 5.183 3.293
## MLQ_8 5.316 5.316 3.376
## MLQ_10 5.058 5.058 2.963
## Negative 0.000 0.000 0.000
## MLQ 0.000 0.000 0.000
##
## Variances:
## MLQ_9 3.010 3.010 0.764
## MLQ_1 1.008 1.008 0.353
## MLQ_2 2.579 2.579 0.997
## MLQ_3 2.369 2.369 0.966
## MLQ_4 1.018 1.018 0.406
## MLQ_5 0.844 0.844 0.396
## MLQ_6 1.140 1.140 0.404
## MLQ_7 2.386 2.386 0.963
## MLQ_8 2.401 2.401 0.968
## MLQ_10 2.914 2.914 1.000
## Negative 0.318 1.000 1.000
## MLQ 1.845 1.000 1.000
##
## R-Square:
##
## MLQ_9 0.236
## MLQ_1 0.647
## MLQ_2 0.003
## MLQ_3 0.034
## MLQ_4 0.594
## MLQ_5 0.604
## MLQ_6 0.596
## MLQ_7 0.037
## MLQ_8 0.032
## MLQ_10 0.000Residual correlations
correl = residuals(two.fit, type="cor")
correl## $type
## [1] "cor.bollen"
##
## $cor
## MLQ_1 MLQ_4 MLQ_5 MLQ_6 MLQ_9 MLQ_2 MLQ_3 MLQ_7 MLQ_8
## MLQ_1 0.000
## MLQ_4 -0.023 0.000
## MLQ_5 0.020 -0.016 0.000
## MLQ_6 -0.004 0.045 -0.023 0.000
## MLQ_9 0.021 -0.007 0.046 -0.058 0.000
## MLQ_2 -0.070 -0.011 -0.034 -0.048 -0.233 0.000
## MLQ_3 0.033 0.123 0.065 0.100 -0.151 -0.025 0.000
## MLQ_7 0.081 0.096 0.092 0.064 -0.113 0.009 -0.001 0.000
## MLQ_8 0.043 0.095 0.087 0.057 -0.103 0.010 -0.009 0.014 0.000
## MLQ_10 -0.077 -0.070 -0.044 -0.112 -0.262 0.007 0.031 -0.022 -0.016
## MLQ_10
## MLQ_1
## MLQ_4
## MLQ_5
## MLQ_6
## MLQ_9
## MLQ_2
## MLQ_3
## MLQ_7
## MLQ_8
## MLQ_10 0.000
##
## $mean
## MLQ_1 MLQ_4 MLQ_5 MLQ_6 MLQ_9 MLQ_2 MLQ_3 MLQ_7 MLQ_8 MLQ_10
## 0 0 0 0 0 0 0 0 0 0View(correl$cor)
correl1 = residuals(one.fit, type="cor")
correl1## $type
## [1] "cor.bollen"
##
## $cor
## MLQ_1 MLQ_2 MLQ_3 MLQ_4 MLQ_5 MLQ_6 MLQ_7 MLQ_8 MLQ_9
## MLQ_1 0.000
## MLQ_2 -0.056 0.000
## MLQ_3 -0.065 0.566 0.000
## MLQ_4 -0.022 0.001 0.029 0.000
## MLQ_5 0.026 -0.021 -0.029 -0.019 0.000
## MLQ_6 0.001 -0.036 0.006 0.041 -0.022 0.000
## MLQ_7 -0.023 0.581 0.500 -0.005 -0.010 -0.037 0.000
## MLQ_8 -0.050 0.597 0.507 0.006 -0.003 -0.032 0.512 0.000
## MLQ_9 0.036 -0.225 -0.198 0.003 0.058 -0.047 -0.163 -0.148 0.000
## MLQ_10 -0.032 0.649 0.620 -0.027 -0.001 -0.069 0.549 0.569 -0.239
## MLQ_10
## MLQ_1
## MLQ_2
## MLQ_3
## MLQ_4
## MLQ_5
## MLQ_6
## MLQ_7
## MLQ_8
## MLQ_9
## MLQ_10 0.000
##
## $mean
## MLQ_1 MLQ_2 MLQ_3 MLQ_4 MLQ_5 MLQ_6 MLQ_7 MLQ_8 MLQ_9 MLQ_10
## 0 0 0 0 0 0 0 0 0 0View(correl1$cor)
correl0 = residuals(second.fit, type="cor")
correl0## $type
## [1] "cor.bollen"
##
## $cor
## MLQ_1 MLQ_4 MLQ_5 MLQ_6 MLQ_9 MLQ_2 MLQ_3 MLQ_7 MLQ_8
## MLQ_1 0.000
## MLQ_4 -0.023 0.000
## MLQ_5 0.020 -0.016 0.000
## MLQ_6 -0.004 0.045 -0.023 0.000
## MLQ_9 0.021 -0.007 0.046 -0.058 0.000
## MLQ_2 -0.070 -0.011 -0.034 -0.048 -0.233 0.000
## MLQ_3 0.033 0.123 0.065 0.100 -0.151 -0.025 0.000
## MLQ_7 0.081 0.096 0.092 0.064 -0.113 0.009 -0.001 0.000
## MLQ_8 0.043 0.095 0.087 0.057 -0.103 0.010 -0.009 0.014 0.000
## MLQ_10 -0.077 -0.070 -0.044 -0.112 -0.262 0.007 0.031 -0.022 -0.016
## MLQ_10
## MLQ_1
## MLQ_4
## MLQ_5
## MLQ_6
## MLQ_9
## MLQ_2
## MLQ_3
## MLQ_7
## MLQ_8
## MLQ_10 0.000
##
## $mean
## MLQ_1 MLQ_4 MLQ_5 MLQ_6 MLQ_9 MLQ_2 MLQ_3 MLQ_7 MLQ_8 MLQ_10
## 0 0 0 0 0 0 0 0 0 0View(correl0$cor)
correl4 = residuals(bifactor1.fit, type="cor")
correl4## $type
## [1] "cor.bollen"
##
## $cor
## MLQ_1 MLQ_2 MLQ_3 MLQ_4 MLQ_5 MLQ_6 MLQ_7 MLQ_8 MLQ_9
## MLQ_1 0.000
## MLQ_2 -0.027 0.000
## MLQ_3 -0.042 -0.019 0.000
## MLQ_4 -0.004 0.028 0.041 0.000
## MLQ_5 0.002 0.007 -0.003 -0.001 0.000
## MLQ_6 0.004 -0.011 -0.003 -0.003 0.000 0.000
## MLQ_7 0.017 0.016 -0.010 0.024 0.033 -0.026 0.000
## MLQ_8 -0.011 0.017 -0.016 0.034 0.038 -0.021 0.011 0.000
## MLQ_9 -0.001 -0.210 -0.178 0.013 -0.003 -0.007 -0.135 -0.122 0.000
## MLQ_10 0.009 -0.008 0.036 0.014 0.037 -0.023 -0.016 -0.012 -0.222
## MLQ_10
## MLQ_1
## MLQ_2
## MLQ_3
## MLQ_4
## MLQ_5
## MLQ_6
## MLQ_7
## MLQ_8
## MLQ_9
## MLQ_10 0.000
##
## $mean
## MLQ_1 MLQ_2 MLQ_3 MLQ_4 MLQ_5 MLQ_6 MLQ_7 MLQ_8 MLQ_9 MLQ_10
## 0 0 0 0 0 0 0 0 0 0View(correl4$cor)
correl6 = residuals(bifactor1WO9.fit, type="cor")
correl6## $type
## [1] "cor.bollen"
##
## $cor
## MLQ_1 MLQ_2 MLQ_3 MLQ_4 MLQ_5 MLQ_6 MLQ_7 MLQ_8 MLQ_10
## MLQ_1 0.000
## MLQ_2 -0.035 0.000
## MLQ_3 -0.034 -0.019 0.000
## MLQ_4 0.000 0.013 0.014 0.000
## MLQ_5 0.000 -0.004 -0.015 0.003 0.000
## MLQ_6 0.000 -0.022 -0.003 -0.003 -0.001 0.000
## MLQ_7 0.028 0.017 -0.010 0.006 0.027 -0.020 0.000
## MLQ_8 -0.007 0.018 -0.018 0.009 0.025 -0.023 0.010 0.000
## MLQ_10 -0.001 -0.009 0.036 0.010 0.032 -0.033 -0.016 -0.011 0.000
##
## $mean
## MLQ_1 MLQ_2 MLQ_3 MLQ_4 MLQ_5 MLQ_6 MLQ_7 MLQ_8 MLQ_10
## 0 0 0 0 0 0 0 0 0View(correl6$cor)
correl3 = residuals(bifactor.negative.fit, type="cor")
correl3## $type
## [1] "cor.bollen"
##
## $cor
## MLQ_9 MLQ_1 MLQ_2 MLQ_3 MLQ_4 MLQ_5 MLQ_6 MLQ_7 MLQ_8
## MLQ_9 0.000
## MLQ_1 0.036 0.000
## MLQ_2 -0.225 -0.056 0.000
## MLQ_3 -0.198 -0.065 0.566 0.000
## MLQ_4 0.003 -0.022 0.001 0.029 0.000
## MLQ_5 0.058 0.026 -0.021 -0.029 -0.019 0.000
## MLQ_6 -0.047 0.001 -0.036 0.006 0.041 -0.022 0.000
## MLQ_7 -0.163 -0.023 0.581 0.500 -0.005 -0.010 -0.037 0.000
## MLQ_8 -0.148 -0.050 0.597 0.507 0.006 -0.003 -0.032 0.512 0.000
## MLQ_10 -0.239 -0.032 0.649 0.620 -0.027 -0.001 -0.069 0.549 0.569
## MLQ_10
## MLQ_9
## MLQ_1
## MLQ_2
## MLQ_3
## MLQ_4
## MLQ_5
## MLQ_6
## MLQ_7
## MLQ_8
## MLQ_10 0.000
##
## $mean
## MLQ_9 MLQ_1 MLQ_2 MLQ_3 MLQ_4 MLQ_5 MLQ_6 MLQ_7 MLQ_8 MLQ_10
## 0 0 0 0 0 0 0 0 0 0View(correl3$cor)zscore correlation anything over 1.96 is going to be statistically significant at the .05 level
zcorrels = residuals(two.fit, type = "standardized")
View(zcorrels$cov)
zcorrels1 = residuals(one.fit, type = "standardized")
View(zcorrels1$cov)
zcorrel0 = residuals(second.fit, type="cor")
zcorrel0## $type
## [1] "cor.bollen"
##
## $cor
## MLQ_1 MLQ_4 MLQ_5 MLQ_6 MLQ_9 MLQ_2 MLQ_3 MLQ_7 MLQ_8
## MLQ_1 0.000
## MLQ_4 -0.023 0.000
## MLQ_5 0.020 -0.016 0.000
## MLQ_6 -0.004 0.045 -0.023 0.000
## MLQ_9 0.021 -0.007 0.046 -0.058 0.000
## MLQ_2 -0.070 -0.011 -0.034 -0.048 -0.233 0.000
## MLQ_3 0.033 0.123 0.065 0.100 -0.151 -0.025 0.000
## MLQ_7 0.081 0.096 0.092 0.064 -0.113 0.009 -0.001 0.000
## MLQ_8 0.043 0.095 0.087 0.057 -0.103 0.010 -0.009 0.014 0.000
## MLQ_10 -0.077 -0.070 -0.044 -0.112 -0.262 0.007 0.031 -0.022 -0.016
## MLQ_10
## MLQ_1
## MLQ_4
## MLQ_5
## MLQ_6
## MLQ_9
## MLQ_2
## MLQ_3
## MLQ_7
## MLQ_8
## MLQ_10 0.000
##
## $mean
## MLQ_1 MLQ_4 MLQ_5 MLQ_6 MLQ_9 MLQ_2 MLQ_3 MLQ_7 MLQ_8 MLQ_10
## 0 0 0 0 0 0 0 0 0 0View(zcorrel0$cor)
correl6 = residuals(bifactor1WO9.fit, type="cor")
correl6## $type
## [1] "cor.bollen"
##
## $cor
## MLQ_1 MLQ_2 MLQ_3 MLQ_4 MLQ_5 MLQ_6 MLQ_7 MLQ_8 MLQ_10
## MLQ_1 0.000
## MLQ_2 -0.035 0.000
## MLQ_3 -0.034 -0.019 0.000
## MLQ_4 0.000 0.013 0.014 0.000
## MLQ_5 0.000 -0.004 -0.015 0.003 0.000
## MLQ_6 0.000 -0.022 -0.003 -0.003 -0.001 0.000
## MLQ_7 0.028 0.017 -0.010 0.006 0.027 -0.020 0.000
## MLQ_8 -0.007 0.018 -0.018 0.009 0.025 -0.023 0.010 0.000
## MLQ_10 -0.001 -0.009 0.036 0.010 0.032 -0.033 -0.016 -0.011 0.000
##
## $mean
## MLQ_1 MLQ_2 MLQ_3 MLQ_4 MLQ_5 MLQ_6 MLQ_7 MLQ_8 MLQ_10
## 0 0 0 0 0 0 0 0 0View(correl6$cor)
zcorrel3 = residuals(bifactor.negative.fit, type="cor")
zcorrel3## $type
## [1] "cor.bollen"
##
## $cor
## MLQ_9 MLQ_1 MLQ_2 MLQ_3 MLQ_4 MLQ_5 MLQ_6 MLQ_7 MLQ_8
## MLQ_9 0.000
## MLQ_1 0.036 0.000
## MLQ_2 -0.225 -0.056 0.000
## MLQ_3 -0.198 -0.065 0.566 0.000
## MLQ_4 0.003 -0.022 0.001 0.029 0.000
## MLQ_5 0.058 0.026 -0.021 -0.029 -0.019 0.000
## MLQ_6 -0.047 0.001 -0.036 0.006 0.041 -0.022 0.000
## MLQ_7 -0.163 -0.023 0.581 0.500 -0.005 -0.010 -0.037 0.000
## MLQ_8 -0.148 -0.050 0.597 0.507 0.006 -0.003 -0.032 0.512 0.000
## MLQ_10 -0.239 -0.032 0.649 0.620 -0.027 -0.001 -0.069 0.549 0.569
## MLQ_10
## MLQ_9
## MLQ_1
## MLQ_2
## MLQ_3
## MLQ_4
## MLQ_5
## MLQ_6
## MLQ_7
## MLQ_8
## MLQ_10 0.000
##
## $mean
## MLQ_9 MLQ_1 MLQ_2 MLQ_3 MLQ_4 MLQ_5 MLQ_6 MLQ_7 MLQ_8 MLQ_10
## 0 0 0 0 0 0 0 0 0 0View(zcorrel3$cor)
zcorrel4 = residuals(bifactor1.fit, type="cor")
zcorrel4## $type
## [1] "cor.bollen"
##
## $cor
## MLQ_1 MLQ_2 MLQ_3 MLQ_4 MLQ_5 MLQ_6 MLQ_7 MLQ_8 MLQ_9
## MLQ_1 0.000
## MLQ_2 -0.027 0.000
## MLQ_3 -0.042 -0.019 0.000
## MLQ_4 -0.004 0.028 0.041 0.000
## MLQ_5 0.002 0.007 -0.003 -0.001 0.000
## MLQ_6 0.004 -0.011 -0.003 -0.003 0.000 0.000
## MLQ_7 0.017 0.016 -0.010 0.024 0.033 -0.026 0.000
## MLQ_8 -0.011 0.017 -0.016 0.034 0.038 -0.021 0.011 0.000
## MLQ_9 -0.001 -0.210 -0.178 0.013 -0.003 -0.007 -0.135 -0.122 0.000
## MLQ_10 0.009 -0.008 0.036 0.014 0.037 -0.023 -0.016 -0.012 -0.222
## MLQ_10
## MLQ_1
## MLQ_2
## MLQ_3
## MLQ_4
## MLQ_5
## MLQ_6
## MLQ_7
## MLQ_8
## MLQ_9
## MLQ_10 0.000
##
## $mean
## MLQ_1 MLQ_2 MLQ_3 MLQ_4 MLQ_5 MLQ_6 MLQ_7 MLQ_8 MLQ_9 MLQ_10
## 0 0 0 0 0 0 0 0 0 0View(zcorrel4$cor)Modification indicies
modindices(two.fit, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 1 Searching =~ MLQ_9 52.330 -0.379 -0.492 -0.248 -0.248
## 2 Purpose =~ MLQ_10 33.919 -0.188 -0.257 -0.151 -0.151
## 3 MLQ_4 ~~ MLQ_6 24.055 0.284 0.284 0.107 0.107
## 4 Purpose =~ MLQ_7 16.061 0.129 0.176 0.112 0.112
## 5 Purpose =~ MLQ_3 14.209 0.118 0.161 0.103 0.103
## 6 Purpose =~ MLQ_2 14.134 -0.113 -0.155 -0.096 -0.096
## 7 MLQ_6 ~~ MLQ_3 12.821 0.167 0.167 0.063 0.063
## 8 MLQ_6 ~~ MLQ_9 11.725 -0.268 -0.268 -0.080 -0.080
## 9 MLQ_6 ~~ MLQ_10 11.702 -0.164 -0.164 -0.057 -0.057
## 10 Purpose =~ MLQ_8 11.654 0.108 0.148 0.094 0.094
## 11 MLQ_9 ~~ MLQ_10 11.333 -0.248 -0.248 -0.073 -0.073
## 12 MLQ_3 ~~ MLQ_10 10.366 0.174 0.174 0.065 0.065
## 13 MLQ_1 ~~ MLQ_4 9.361 -0.184 -0.184 -0.069 -0.069
## 14 MLQ_4 ~~ MLQ_3 9.331 0.135 0.135 0.055 0.055
## 15 MLQ_1 ~~ MLQ_5 8.390 0.162 0.162 0.066 0.066
## 16 MLQ_5 ~~ MLQ_9 7.733 0.188 0.188 0.065 0.065
## 17 MLQ_2 ~~ MLQ_3 7.235 -0.137 -0.137 -0.055 -0.055
## 18 MLQ_5 ~~ MLQ_6 7.054 -0.143 -0.143 -0.058 -0.058
## 19 MLQ_4 ~~ MLQ_10 6.255 -0.114 -0.114 -0.042 -0.042
## 20 MLQ_9 ~~ MLQ_2 5.997 -0.168 -0.168 -0.053 -0.053
## 21 Searching =~ MLQ_4 4.836 0.072 0.094 0.059 0.059
## 22 MLQ_7 ~~ MLQ_10 4.725 -0.118 -0.118 -0.044 -0.044
## 23 MLQ_1 ~~ MLQ_7 3.910 0.091 0.091 0.034 0.034modindices(one.fit, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 1 MLQ_2 ~~ MLQ_10 355.421 1.782 1.782 0.649 0.649
## 2 MLQ_3 ~~ MLQ_10 337.480 1.668 1.668 0.624 0.624
## 3 MLQ_2 ~~ MLQ_8 312.484 1.520 1.520 0.600 0.600
## 4 MLQ_2 ~~ MLQ_7 297.968 1.480 1.480 0.585 0.585
## 5 MLQ_8 ~~ MLQ_10 282.892 1.537 1.537 0.572 0.572
## 6 MLQ_2 ~~ MLQ_3 281.972 1.435 1.435 0.570 0.570
## 7 MLQ_7 ~~ MLQ_10 264.960 1.484 1.484 0.552 0.552
## 8 MLQ_7 ~~ MLQ_8 239.477 1.283 1.283 0.518 0.518
## 9 MLQ_3 ~~ MLQ_8 233.973 1.264 1.264 0.512 0.512
## 10 MLQ_3 ~~ MLQ_7 228.860 1.247 1.247 0.506 0.506
## 11 MLQ_9 ~~ MLQ_10 58.730 -0.834 -0.834 -0.246 -0.246
## 12 MLQ_2 ~~ MLQ_9 52.235 -0.740 -0.740 -0.232 -0.232
## 13 MLQ_3 ~~ MLQ_9 41.914 -0.637 -0.637 -0.205 -0.205
## 14 MLQ_7 ~~ MLQ_9 28.488 -0.527 -0.527 -0.169 -0.169
## 15 MLQ_8 ~~ MLQ_9 23.220 -0.477 -0.477 -0.153 -0.153
## 16 MLQ_4 ~~ MLQ_6 20.798 0.262 0.262 0.099 0.099
## 17 MLQ_1 ~~ MLQ_3 14.489 -0.240 -0.240 -0.091 -0.091
## 18 MLQ_6 ~~ MLQ_10 12.673 -0.254 -0.254 -0.088 -0.088
## 19 MLQ_1 ~~ MLQ_5 12.426 0.193 0.193 0.078 0.078
## 20 MLQ_5 ~~ MLQ_9 11.902 0.234 0.234 0.081 0.081
## 21 MLQ_1 ~~ MLQ_2 10.528 -0.212 -0.212 -0.078 -0.078
## 22 MLQ_1 ~~ MLQ_8 8.554 -0.186 -0.186 -0.070 -0.070
## 23 MLQ_1 ~~ MLQ_4 7.786 -0.165 -0.165 -0.062 -0.062
## 24 MLQ_6 ~~ MLQ_9 7.397 -0.214 -0.214 -0.064 -0.064
## 25 MLQ_5 ~~ MLQ_6 6.473 -0.135 -0.135 -0.055 -0.055
## 26 MLQ_1 ~~ MLQ_9 5.415 0.179 0.179 0.053 0.053
## 27 MLQ_4 ~~ MLQ_5 4.468 -0.106 -0.106 -0.046 -0.046
## 28 MLQ_6 ~~ MLQ_7 3.852 -0.127 -0.127 -0.048 -0.048#modindices(second.fit, sort. = TRUE, minimum.value = 3.84)
#modindices(bifactor.fit, sort. = TRUE, minimum.value = 3.84)
modindices(bifactor1WO9.fit, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 1 MLQ_3 ~~ MLQ_10 18.099 0.227 0.227 0.085 0.085
## 2 Purpose =~ MLQ_7 6.616 0.123 0.149 0.095 0.095
## 3 MLQ_1 ~~ MLQ_7 5.708 0.121 0.121 0.045 0.045
## 4 MLQ_3 ~~ MLQ_6 4.635 0.108 0.108 0.041 0.041
## 5 MLQ_2 ~~ MLQ_3 4.536 -0.106 -0.106 -0.042 -0.042
## 6 Purpose =~ MLQ_2 4.108 -0.084 -0.103 -0.064 -0.064#modindices(bifactor.fitWO9, sort. = TRUE, minimum.value = 3.84)
modindices(bifactor1.fit, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 1 Searching =~ MLQ_9 52.713 -0.376 -0.489 -0.246 -0.246
## 2 MLQ_3 ~~ MLQ_10 16.784 0.217 0.217 0.081 0.081
## 3 MLQ_9 ~~ MLQ_10 15.067 -0.289 -0.289 -0.085 -0.085
## 4 Purpose =~ MLQ_3 7.424 -0.314 -0.205 -0.131 -0.131
## 5 Searching =~ MLQ_5 5.946 0.071 0.093 0.064 0.064
## 6 Purpose =~ MLQ_2 5.177 -0.221 -0.144 -0.090 -0.090
## 7 MLQ_2 ~~ MLQ_9 5.018 -0.152 -0.152 -0.048 -0.048
## 8 MLQ_2 ~~ MLQ_3 4.700 -0.108 -0.108 -0.043 -0.043
## 9 MLQ_3 ~~ MLQ_4 4.328 0.097 0.097 0.039 0.039
## 10 MLQ_1 ~~ MLQ_3 4.152 -0.092 -0.092 -0.035 -0.035
## 11 Searching =~ MLQ_4 3.949 0.065 0.084 0.053 0.053
## 12 MLQ_1 ~~ MLQ_10 3.940 0.092 0.092 0.032 0.032#modindices(bifactor.negative.fit, sort. = TRUE, minimum.value = 3.84)Fit Measures
fitmeasures(two.fit)#Models two factors:Purpose and Seraching for Purpose ## npar fmin chisq
## 31.000 0.130 219.172
## df pvalue baseline.chisq
## 34.000 0.000 3796.222
## baseline.df baseline.pvalue cfi
## 45.000 0.000 0.951
## tli nnfi rfi
## 0.935 0.935 0.924
## nfi pnfi ifi
## 0.942 0.712 0.951
## rni logl unrestricted.logl
## 0.951 -14312.146 -14202.560
## aic bic ntotal
## 28686.292 28833.101 842.000
## bic2 rmsea rmsea.ci.lower
## 28734.655 0.080 0.070
## rmsea.ci.upper rmsea.pvalue rmr
## 0.091 0.000 0.202
## rmr_nomean srmr srmr_bentler
## 0.220 0.067 0.067
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.073 0.067 0.073
## srmr_mplus srmr_mplus_nomean cn_05
## 0.067 0.073 187.717
## cn_01 gfi agfi
## 216.371 0.993 0.986
## pgfi mfi ecvi
## 0.519 0.896 NAfitmeasures(one.fit) #Models as a single purpose factor## npar fmin chisq
## 30.000 1.242 2092.057
## df pvalue baseline.chisq
## 35.000 0.000 3796.222
## baseline.df baseline.pvalue cfi
## 45.000 0.000 0.452
## tli nnfi rfi
## 0.295 0.295 0.291
## nfi pnfi ifi
## 0.449 0.349 0.453
## rni logl unrestricted.logl
## 0.452 -15248.589 -14202.560
## aic bic ntotal
## 30557.178 30699.251 842.000
## bic2 rmsea rmsea.ci.lower
## 30603.981 0.264 0.255
## rmsea.ci.upper rmsea.pvalue rmr
## 0.274 0.000 0.606
## rmr_nomean srmr srmr_bentler
## 0.659 0.230 0.230
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.250 0.230 0.250
## srmr_mplus srmr_mplus_nomean cn_05
## 0.230 0.250 21.044
## cn_01 gfi agfi
## 24.079 0.944 0.895
## pgfi mfi ecvi
## 0.508 0.295 NAfitmeasures(second.fit)#Second order models as Purpose being the higher factor made up of Purpose and Searching## npar fmin chisq
## 31.000 0.130 219.172
## df pvalue baseline.chisq
## 34.000 0.000 3796.222
## baseline.df baseline.pvalue cfi
## 45.000 0.000 0.951
## tli nnfi rfi
## 0.935 0.935 0.924
## nfi pnfi ifi
## 0.942 0.712 0.951
## rni logl unrestricted.logl
## 0.951 -14312.146 -14202.560
## aic bic ntotal
## 28686.292 28833.101 842.000
## bic2 rmsea rmsea.ci.lower
## 28734.655 0.080 0.070
## rmsea.ci.upper rmsea.pvalue rmr
## 0.091 0.000 0.202
## rmr_nomean srmr srmr_bentler
## 0.220 0.067 0.067
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.073 0.067 0.073
## srmr_mplus srmr_mplus_nomean cn_05
## 0.067 0.073 187.717
## cn_01 gfi agfi
## 216.371 0.993 0.986
## pgfi mfi ecvi
## 0.519 0.896 NAfitmeasures(bifactor1.fit)#Models bifactor with Searching and Purpose as factors uncorolated with the main factor## npar fmin chisq
## 40.000 0.065 109.158
## df pvalue baseline.chisq
## 25.000 0.000 3796.222
## baseline.df baseline.pvalue cfi
## 45.000 0.000 0.978
## tli nnfi rfi
## 0.960 0.960 0.948
## nfi pnfi ifi
## 0.971 0.540 0.978
## rni logl unrestricted.logl
## 0.978 -14257.139 -14202.560
## aic bic ntotal
## 28594.278 28783.710 842.000
## bic2 rmsea rmsea.ci.lower
## 28656.682 0.063 0.051
## rmsea.ci.upper rmsea.pvalue rmr
## 0.076 0.034 0.164
## rmr_nomean srmr srmr_bentler
## 0.178 0.052 0.052
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.056 0.052 0.056
## srmr_mplus srmr_mplus_nomean cn_05
## 0.052 0.056 291.436
## cn_01 gfi agfi
## 342.821 0.996 0.991
## pgfi mfi ecvi
## 0.383 0.951 NAfitmeasures(bifactor1WO9.fit)#Models bifactor with Searching and Purpose as factors uncorolated with the main factor leaving negatively worded questions out## npar fmin chisq
## 36.000 0.024 39.702
## df pvalue baseline.chisq
## 18.000 0.002 3583.218
## baseline.df baseline.pvalue cfi
## 36.000 0.000 0.994
## tli nnfi rfi
## 0.988 0.988 0.978
## nfi pnfi ifi
## 0.989 0.494 0.994
## rni logl unrestricted.logl
## 0.994 -12557.009 -12537.158
## aic bic ntotal
## 25186.018 25356.506 842.000
## bic2 rmsea rmsea.ci.lower
## 25242.182 0.038 0.022
## rmsea.ci.upper rmsea.pvalue rmr
## 0.054 0.889 0.039
## rmr_nomean srmr srmr_bentler
## 0.043 0.015 0.015
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.016 0.015 0.016
## srmr_mplus srmr_mplus_nomean cn_05
## 0.015 0.016 613.257
## cn_01 gfi agfi
## 739.147 0.999 0.996
## pgfi mfi ecvi
## 0.333 0.987 NAfitmeasures(bifactor.negative.fit)#Models bifactor as the negatively worded item as a factor uncorolated with the main factor## npar fmin chisq
## 31.000 1.242 2092.057
## df pvalue baseline.chisq
## 34.000 0.000 3796.222
## baseline.df baseline.pvalue cfi
## 45.000 0.000 0.451
## tli nnfi rfi
## 0.274 0.274 0.271
## nfi pnfi ifi
## 0.449 0.339 0.453
## rni logl unrestricted.logl
## 0.451 -15248.589 -14202.560
## aic bic ntotal
## 30559.178 30705.987 842.000
## bic2 rmsea rmsea.ci.lower
## 30607.541 0.268 0.258
## rmsea.ci.upper rmsea.pvalue rmr
## 0.278 0.000 0.606
## rmr_nomean srmr srmr_bentler
## 0.659 0.230 0.230
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.250 0.230 0.250
## srmr_mplus srmr_mplus_nomean cn_05
## 0.230 0.250 20.561
## cn_01 gfi agfi
## 23.563 0.944 0.892
## pgfi mfi ecvi
## 0.494 0.295 NACreate dataset for Target rotation
all_surveys<-read.csv("allsurveysYT1.csv")
MLQ<-select(all_surveys, MLQ_1, MLQ_4,MLQ_5,MLQ_6,MLQ_9,MLQ_2,MLQ_3,MLQ_7,MLQ_8,MLQ_10)
MLQ<- data.frame(apply(MLQ,2, as.numeric))library(GPArotation)
library(psych)
library(dplyr)
MLQ<-tbl_df(MLQ)
MLQ$MLQ_9 <- 8- MLQ$MLQ_9
MLQ## Source: local data frame [1,160 x 10]
##
## MLQ_1 MLQ_4 MLQ_5 MLQ_6 MLQ_9 MLQ_2 MLQ_3 MLQ_7 MLQ_8 MLQ_10
## 1 4 5 6 4 3 7 7 5 7 7
## 2 3 5 4 3 3 5 5 5 4 5
## 3 4 4 4 4 4 7 5 4 5 4
## 4 5 3 5 5 3 6 7 5 5 6
## 5 4 4 4 4 5 6 5 5 5 5
## 6 5 4 5 5 7 5 3 3 4 3
## 7 6 3 6 3 5 2 2 5 4 4
## 8 3 5 5 4 4 7 7 5 7 5
## 9 6 7 6 6 7 5 2 5 7 2
## 10 1 3 5 1 6 7 1 5 5 1
## .. ... ... ... ... ... ... ... ... ... ...str(MLQ)## Classes 'tbl_df', 'tbl' and 'data.frame': 1160 obs. of 10 variables:
## $ MLQ_1 : num 4 3 4 5 4 5 6 3 6 1 ...
## $ MLQ_4 : num 5 5 4 3 4 4 3 5 7 3 ...
## $ MLQ_5 : num 6 4 4 5 4 5 6 5 6 5 ...
## $ MLQ_6 : num 4 3 4 5 4 5 3 4 6 1 ...
## $ MLQ_9 : num 3 3 4 3 5 7 5 4 7 6 ...
## $ MLQ_2 : num 7 5 7 6 6 5 2 7 5 7 ...
## $ MLQ_3 : num 7 5 5 7 5 3 2 7 2 1 ...
## $ MLQ_7 : num 5 5 4 5 5 3 5 5 5 5 ...
## $ MLQ_8 : num 7 4 5 5 5 4 4 7 7 5 ...
## $ MLQ_10: num 7 5 4 6 5 3 4 5 2 1 ...colnames(MLQ) <- c("1","2", "3", "4", "5", "6", "7", "8", "9", "10")
#Target rotation: choose "simple structure" a priori and can be applied to oblique and orthogonal rotation based on
#what paper says facotrs should be MLQ
Targ_key <- make.keys(10,list(f1=1:5,f2=6:10))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
MLQ_cor <- corFiml(MLQ)
MLQ_cor## 1 2 3 4 5 6
## 1 1.00000000 0.59813088 0.651078193 0.62208391 0.35265392 -0.01390057
## 2 0.59813088 1.00000000 0.579962389 0.63620295 0.30648208 0.04143454
## 3 0.65107819 0.57996239 1.000000000 0.57769010 0.36446540 0.01971311
## 4 0.62208391 0.63620295 0.577690100 1.00000000 0.25745157 0.00497097
## 5 0.35265392 0.30648208 0.364465398 0.25745157 1.00000000 -0.20454251
## 6 -0.01390057 0.04143454 0.019713111 0.00497097 -0.20454251 1.00000000
## 7 0.08444384 0.17205932 0.114931062 0.14887244 -0.12504985 0.57600524
## 8 0.13100874 0.14316875 0.139486113 0.11133895 -0.08745785 0.59132713
## 9 0.09373058 0.14344022 0.135724287 0.10586742 -0.07737298 0.60638994
## 10 -0.02191184 -0.01809295 0.008674444 -0.05939681 -0.23460477 0.64924594
## 7 8 9 10
## 1 0.08444384 0.13100874 0.09373058 -0.021911839
## 2 0.17205932 0.14316875 0.14344022 -0.018092948
## 3 0.11493106 0.13948611 0.13572429 0.008674444
## 4 0.14887244 0.11133895 0.10586742 -0.059396808
## 5 -0.12504985 -0.08745785 -0.07737298 -0.234604768
## 6 0.57600524 0.59132713 0.60638994 0.649245938
## 7 1.00000000 0.53553886 0.54011339 0.622603370
## 8 0.53553886 1.00000000 0.54644712 0.551190296
## 9 0.54011339 0.54644712 1.00000000 0.571000563
## 10 0.62260337 0.55119030 0.57100056 1.000000000out_targetQ <- fa(MLQ_cor,2,rotate="TargetQ",n.obs = 840,Target=Targ_key) #TargetT for orthogonal rotation
out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR2 MR1
## 1 0.806
## 2 0.763
## 3 0.771
## 4 0.772
## 5 -0.231 0.441
## 6 0.814
## 7 0.741
## 8 0.713
## 9 0.730
## 10 0.812 -0.124
##
## MR2 MR1
## SS loadings 2.971 2.658
## Proportion Var 0.297 0.266
## Cumulative Var 0.297 0.563
##
## $score.cor
## [,1] [,2]
## [1,] 1.00000000 0.04814573
## [2,] 0.04814573 1.00000000
##
## $TLI
## [1] 0.9713478
##
## $RMSEA
## RMSEA lower upper confidence
## 0.05333101 0.04104451 0.06551473 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = MLQ_cor, nfactors = 2, n.obs = 840, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR1 h2 u2 com
## 1 0.01 0.81 0.65 0.35 1.0
## 2 0.06 0.76 0.59 0.41 1.0
## 3 0.05 0.77 0.60 0.40 1.0
## 4 0.02 0.77 0.60 0.40 1.0
## 5 -0.23 0.44 0.23 0.77 1.5
## 6 0.81 -0.08 0.66 0.34 1.0
## 7 0.74 0.08 0.56 0.44 1.0
## 8 0.71 0.09 0.53 0.47 1.0
## 9 0.73 0.07 0.55 0.45 1.0
## 10 0.81 -0.12 0.66 0.34 1.0
##
## MR2 MR1
## SS loadings 2.97 2.66
## Proportion Var 0.30 0.27
## Cumulative Var 0.30 0.56
## Proportion Explained 0.53 0.47
## Cumulative Proportion 0.53 1.00
##
## With factor correlations of
## MR2 MR1
## MR2 1.00 0.07
## MR1 0.07 1.00
##
## Mean item complexity = 1.1
## Test of the hypothesis that 2 factors are sufficient.
##
## The degrees of freedom for the null model are 45 and the objective function was 4.51 with Chi Square of 3763.95
## The degrees of freedom for the model are 26 and the objective function was 0.1
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.03
##
## The harmonic number of observations is 840 with the empirical chi square 35.61 with prob < 0.099
## The total number of observations was 840 with MLE Chi Square = 87.47 with prob < 1.4e-08
##
## Tucker Lewis Index of factoring reliability = 0.971
## RMSEA index = 0.053 and the 90 % confidence intervals are 0.041 0.066
## BIC = -87.6
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR2 MR1
## Correlation of scores with factors 0.94 0.93
## Multiple R square of scores with factors 0.88 0.87
## Minimum correlation of possible factor scores 0.76 0.74CFI
1-((out_targetQ$STATISTIC - out_targetQ$dof)/(out_targetQ$null.chisq- out_targetQ$null.dof))## [1] 0.9834722Droping MLQ_9 which is a reversed scoded question
Create dataset for Target rotation
all_surveys<-read.csv("allsurveysYT1.csv")
MLQ<-select(all_surveys, MLQ_1, MLQ_4,MLQ_5,MLQ_6,MLQ_2,MLQ_3,MLQ_7,MLQ_8,MLQ_10)
MLQ<- data.frame(apply(MLQ,2, as.numeric))library(GPArotation)
library(psych)
library(dplyr)
MLQ<-tbl_df(MLQ)
MLQ## Source: local data frame [1,160 x 9]
##
## MLQ_1 MLQ_4 MLQ_5 MLQ_6 MLQ_2 MLQ_3 MLQ_7 MLQ_8 MLQ_10
## 1 4 5 6 4 7 7 5 7 7
## 2 3 5 4 3 5 5 5 4 5
## 3 4 4 4 4 7 5 4 5 4
## 4 5 3 5 5 6 7 5 5 6
## 5 4 4 4 4 6 5 5 5 5
## 6 5 4 5 5 5 3 3 4 3
## 7 6 3 6 3 2 2 5 4 4
## 8 3 5 5 4 7 7 5 7 5
## 9 6 7 6 6 5 2 5 7 2
## 10 1 3 5 1 7 1 5 5 1
## .. ... ... ... ... ... ... ... ... ...str(MLQ)## Classes 'tbl_df', 'tbl' and 'data.frame': 1160 obs. of 9 variables:
## $ MLQ_1 : num 4 3 4 5 4 5 6 3 6 1 ...
## $ MLQ_4 : num 5 5 4 3 4 4 3 5 7 3 ...
## $ MLQ_5 : num 6 4 4 5 4 5 6 5 6 5 ...
## $ MLQ_6 : num 4 3 4 5 4 5 3 4 6 1 ...
## $ MLQ_2 : num 7 5 7 6 6 5 2 7 5 7 ...
## $ MLQ_3 : num 7 5 5 7 5 3 2 7 2 1 ...
## $ MLQ_7 : num 5 5 4 5 5 3 5 5 5 5 ...
## $ MLQ_8 : num 7 4 5 5 5 4 4 7 7 5 ...
## $ MLQ_10: num 7 5 4 6 5 3 4 5 2 1 ...colnames(MLQ) <- 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 MLQ
Targ_key <- make.keys(9,list(f1=1:4,f2=6:9))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
MLQ_cor <- corFiml(MLQ)
MLQ_cor## 1 2 3 4 5 6
## 1 1.00000000 0.59813149 0.651077932 0.622081664 -0.013934847 0.08441444
## 2 0.59813149 1.00000000 0.579962541 0.636199850 0.041409887 0.17204423
## 3 0.65107793 0.57996254 1.000000000 0.577687891 0.019677170 0.11490522
## 4 0.62208166 0.63619985 0.577687891 1.000000000 0.004946828 0.14885881
## 5 -0.01393485 0.04140989 0.019677170 0.004946828 1.000000000 0.57599748
## 6 0.08441444 0.17204423 0.114905217 0.148858805 0.575997484 1.00000000
## 7 0.13098426 0.14315510 0.139467672 0.111326784 0.591320574 0.53552880
## 8 0.09370972 0.14343059 0.135705484 0.105859470 0.606381378 0.54009983
## 9 -0.02194978 -0.01812080 0.008637832 -0.059422796 0.649239072 0.62259338
## 7 8 9
## 1 0.1309843 0.09370972 -0.021949785
## 2 0.1431551 0.14343059 -0.018120805
## 3 0.1394677 0.13570548 0.008637832
## 4 0.1113268 0.10585947 -0.059422796
## 5 0.5913206 0.60638138 0.649239072
## 6 0.5355288 0.54009983 0.622593378
## 7 1.0000000 0.54643821 0.551178983
## 8 0.5464382 1.00000000 0.570988969
## 9 0.5511790 0.57098897 1.000000000out_targetQ <- fa(MLQ_cor,2,rotate="TargetQ",n.obs = 840,Target=Targ_key) #TargetT for orthogonal rotation
out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR2 MR1
## 1 0.803
## 2 0.768
## 3 0.763
## 4 0.787
## 5 0.814
## 6 0.737
## 7 0.712
## 8 0.730
## 9 0.811 -0.110
##
## MR2 MR1
## SS loadings 2.907 2.476
## Proportion Var 0.323 0.275
## Cumulative Var 0.323 0.598
##
## $score.cor
## [,1] [,2]
## [1,] 1.0000000 0.1083554
## [2,] 0.1083554 1.0000000
##
## $TLI
## [1] 0.9770053
##
## $RMSEA
## RMSEA lower upper confidence
## 0.05194268 0.03761114 0.06636138 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = MLQ_cor, nfactors = 2, n.obs = 840, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR1 h2 u2 com
## 1 -0.01 0.80 0.64 0.36 1
## 2 0.05 0.77 0.60 0.40 1
## 3 0.03 0.76 0.59 0.41 1
## 4 0.00 0.79 0.62 0.38 1
## 5 0.81 -0.06 0.66 0.34 1
## 6 0.74 0.09 0.56 0.44 1
## 7 0.71 0.10 0.53 0.47 1
## 8 0.73 0.08 0.55 0.45 1
## 9 0.81 -0.11 0.66 0.34 1
##
## MR2 MR1
## SS loadings 2.91 2.48
## Proportion Var 0.32 0.28
## Cumulative Var 0.32 0.60
## Proportion Explained 0.54 0.46
## Cumulative Proportion 0.54 1.00
##
## With factor correlations of
## MR2 MR1
## MR2 1.00 0.08
## MR1 0.08 1.00
##
## Mean item complexity = 1
## Test of the hypothesis that 2 factors are sufficient.
##
## The degrees of freedom for the null model are 36 and the objective function was 4.26 with Chi Square of 3554.14
## The degrees of freedom for the model are 19 and the objective function was 0.07
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.02
##
## The harmonic number of observations is 840 with the empirical chi square 19.67 with prob < 0.41
## The total number of observations was 840 with MLE Chi Square = 61.63 with prob < 2.1e-06
##
## Tucker Lewis Index of factoring reliability = 0.977
## RMSEA index = 0.052 and the 90 % confidence intervals are 0.038 0.066
## BIC = -66.31
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR2 MR1
## Correlation of scores with factors 0.94 0.93
## Multiple R square of scores with factors 0.88 0.87
## Minimum correlation of possible factor scores 0.76 0.73CFI
1-((out_targetQ$STATISTIC - out_targetQ$dof)/(out_targetQ$null.chisq- out_targetQ$null.dof))## [1] 0.9878835