PERMA CFA Youth
Levi Brackman
December 3rd 2015
From Julie Butler and Peggy Kerns PERMA & EPOCH
- 514 of these cases onl answered 17 of the questions done on an online study using Qualtrics participents
- 239 answered all 23 questions from students at tow sydney schools one private and one public
- Below is two analysis one with complete cases and one with all the data and therefore imputed
Items
- In general, to what extent do you lead a purposeful and meaningful life? (only in 239 of the questions)
- How much of the time do you feel you are making progress towards accomplishing your goals? (only in 239 of the questions)
- How often do you become absorbed in what you are doing? (only in 239 of the questions)
- In general, how would you say your health is? (only in 239 of the questions)
- In general, how often do you feel joyful?
- To what extent do you receive help and support from others when you need it?
- In general, how often do you feel anxious
- How often do you achieve the important goals you have set for yourself?
- In general, to what extent do you feel that what you do in your life is valuable and worthwhile?
- In general, how often do you feel positive?
- In general, to what extent do you feel excited and interested in things?
- How lonely do you feel in your daily life?
- How satisfied are you with your current physical health? (only in 239 of the questions)
- In general, how often do you feel angry?
- To what extent have you been feeling loved?
- How often are you able to handle your responsibilities?
- To what extent do you generally feel you have a sense of direction in your life?
- Compared to others of your same age and sex, how is your health? (only in 239 of the questions)
- How satisfied are you with your personal relationships?
- In general, how often do you feel sad?
- How often do you lose track of time while doing something you enjoy?
- In general, to what extent do you feel contented?
- Taking all things together, how happy would you say you are?
library(lavaan)## This is lavaan 0.5-19
## lavaan is BETA software! Please report any bugs.require(semPlot)## Loading required package: semPlotlibrary(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)Loadthedata
data <- read.csv("~/Git/stats/Perma_Study/PERMAfinal.csv")Create Dataset with Coplete Cases
data1 <- na.omit(data)Create the model with 9 factors as the instrument was designed
nine.model= 'Acomplishment =~ PERMA_A1_1 + PERMA_A2_1 + PERMA_A3_1
Engagement =~ PERMA_E1_1 + PERMA_E2_1 + PERMA_E3_1
Positive Emotion =~ PERMA_P1_1 + PERMA_P2 + PERMA_P3_1
Relationship =~ PERMA_R1_1 + PERMA_R2_1 + PERMA_R3_1
Meaning =~ PERMA_M1_1 + PERMA_M2_1 + PERMA_M3_1
Negative Emotion =~ PERMA_N1_1 + PERMA_N2_1 + PERMA_N3_1
Health =~ PERMA_Hea1_1 + PERMA_Hea2_1 + PERMA_Hea3_1
Lonely =~ PERMA_Lone_1
Happy =~ PERMA_HPY_1' Run the model with all data
nine.fit=cfa(nine.model, data=data, 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:
## 14 15 16 20 25 26 27 28 29 32 33 34 35 37 39 41 42 44 51 55 56 59 60 66 70 73 74 75 76 77 78 80 81 83 84 87 88 90 91 92 93 96 98 99 101 104 106 107 108 111 112 114 116 118 119 121 124 126 127 128 130 131 132 133 134 138 142 144 147 149 150 151 153 156 159 160 169 170 171 172 174 175 178 179 184 185 186 188 190 191 193 195 196 202 204 208 213 214 215 216 219 220 221 224 226 229 231 234 237 238 240 243 244 248 252 254 255 256 258 259 261 264 265 266 268 269 270 271 272 273 274 275 276 277 278 279 280 281 283 284 285 288 289 290 293 294 301 302 303 304 305 306 307 308 309 310 311 312 314 320 321 322 323 324 325 326 327 328 329 331 332 333 334 335 336 338 340 348 349 350 351 352 356 357 358 359 360 361 363 364 366 367 368 369 371 372 374 376 377 378 379 380 382 385 386 389 390 394 395 397 399 443 444 445 446 447 448 449 450 452 453 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 485 486 488 489 490 491 492 493 494 495 496 497 498 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514## Found more than one class "Model" in cache; using the first, from namespace 'lavaan'## Warning in lav_object_post_check(lavobject): lavaan WARNING:
## covariance matrix of latent variables is not positive definite; use
## inspect(fit,"cov.lv") to investigate.Run the model with complete cases
nine.fit1=cfa(nine.model, data=data1, missing = "fiml")Create pictures with all data
semPaths(nine.fit, whatLabels = "std", layout = "tree")## Warning in lav_object_post_check(lavobject): lavaan WARNING:
## covariance matrix of latent variables is not positive definite; use
## inspect(fit,"cov.lv") to investigate.
Create pictures with all complet cases
semPaths(nine.fit1, whatLabels = "std", layout = "tree")
Summarie with all data
summary(nine.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-19) converged normally after 258 iterations
##
## Used Total
## Number of observations 476 753
##
## Number of missing patterns 2
##
## Estimator ML
## Minimum Function Test Statistic 448.510
## Degrees of freedom 196
## P-value (Chi-square) 0.000## Warning in lav_object_post_check(lavobject): lavaan WARNING:
## covariance matrix of latent variables is not positive definite; use
## inspect(fit,"cov.lv") to investigate.##
## Parameter Estimates:
##
## Information Observed
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## Acomplishment =~
## PERMA_A1_1 1.000 1.897 0.794
## PERMA_A2_1 0.873 0.055 15.883 0.000 1.655 0.704
## PERMA_A3_1 0.796 0.054 14.751 0.000 1.510 0.663
## Engagement =~
## PERMA_E1_1 1.000 1.561 0.698
## PERMA_E2_1 1.120 0.076 14.700 0.000 1.749 0.781
## PERMA_E3_1 0.827 0.079 10.509 0.000 1.291 0.521
## PositiveEmotion =~
## PERMA_P1_1 1.000 1.901 0.823
## PERMA_P2 1.081 0.048 22.576 0.000 2.054 0.849
## PERMA_P3_1 0.806 0.050 16.068 0.000 1.532 0.679
## Relationship =~
## PERMA_R1_1 1.000 1.650 0.615
## PERMA_R2_1 1.177 0.095 12.353 0.000 1.943 0.728
## PERMA_R3_1 1.092 0.094 11.678 0.000 1.801 0.677
## Meaning =~
## PERMA_M1_1 1.000 2.003 0.853
## PERMA_M2_1 1.051 0.056 18.862 0.000 2.105 0.899
## PERMA_M3_1 0.936 0.057 16.431 0.000 1.874 0.834
## NegativeEmotion =~
## PERMA_N1_1 1.000 1.619 0.568
## PERMA_N2_1 0.880 0.128 6.847 0.000 1.424 0.480
## PERMA_N3_1 1.116 0.162 6.879 0.000 1.807 0.618
## Health =~
## PERMA_Hea1_1 1.000 2.293 0.931
## PERMA_Hea2_1 0.978 0.049 19.864 0.000 2.243 0.857
## PERMA_Hea3_1 1.005 0.038 26.218 0.000 2.304 0.944
## Lonely =~
## PERMA_Lone_1 1.000 3.270 1.000
## Happy =~
## PERMA_HPY_1 1.000 2.363 1.000
## fmi
##
## NA
## 0.103
## 0.104
##
## NA
## 0.067
## -0.012
##
## NA
## 0.037
## 0.073
##
## NA
## 0.047
## 0.053
##
## NA
## 0.509
## 0.509
##
## NA
## -0.006
## 0.174
##
## NA
## 0.535
## 0.526
##
## NA
##
## NA
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## Acomplishment ~~
## Engagement 2.629 0.253 10.383 0.000 0.888 0.888
## PositiveEmotin 3.212 0.277 11.584 0.000 0.891 0.891
## Relationship 2.658 0.285 9.333 0.000 0.849 0.849
## Meaning 3.773 0.333 11.314 0.000 0.993 0.993
## NegativeEmotin -0.014 0.210 -0.069 0.945 -0.005 -0.005
## Health 3.421 0.341 10.045 0.000 0.787 0.787
## Lonely -0.838 0.326 -2.571 0.010 -0.135 -0.135
## Happy 3.049 0.285 10.689 0.000 0.680 0.680
## Engagement ~~
## PositiveEmotin 2.632 0.245 10.732 0.000 0.887 0.887
## Relationship 2.234 0.251 8.892 0.000 0.867 0.867
## Meaning 2.683 0.269 9.985 0.000 0.858 0.858
## NegativeEmotin 0.190 0.182 1.043 0.297 0.075 0.075
## Health 2.422 0.279 8.696 0.000 0.676 0.676
## Lonely -0.133 0.274 -0.487 0.626 -0.026 -0.026
## Happy 2.595 0.253 10.277 0.000 0.703 0.703
## PositiveEmotion ~~
## Relationship 2.788 0.284 9.813 0.000 0.889 0.889
## Meaning 3.571 0.316 11.313 0.000 0.938 0.938
## NegativeEmotin -0.550 0.206 -2.673 0.008 -0.179 -0.179
## Health 3.757 0.349 10.769 0.000 0.862 0.862
## Lonely -1.181 0.316 -3.740 0.000 -0.190 -0.190
## Happy 3.858 0.302 12.762 0.000 0.859 0.859
## Relationship ~~
## Meaning 2.766 0.306 9.033 0.000 0.837 0.837
## NegativeEmotin -0.223 0.190 -1.176 0.240 -0.084 -0.084
## Health 3.044 0.340 8.959 0.000 0.804 0.804
## Lonely -0.883 0.297 -2.972 0.003 -0.164 -0.164
## Happy 2.998 0.304 9.866 0.000 0.769 0.769
## Meaning ~~
## NegativeEmotin -0.597 0.232 -2.573 0.010 -0.184 -0.184
## Health 3.543 0.360 9.850 0.000 0.771 0.771
## Lonely -1.054 0.387 -2.721 0.007 -0.161 -0.161
## Happy 3.756 0.347 10.832 0.000 0.793 0.793
## NegativeEmotion ~~
## Health -0.853 0.281 -3.035 0.002 -0.230 -0.230
## Lonely 2.359 0.387 6.097 0.000 0.446 0.446
## Happy -0.769 0.237 -3.245 0.001 -0.201 -0.201
## Health ~~
## Lonely -1.425 0.491 -2.903 0.004 -0.190 -0.190
## Happy 3.256 0.372 8.765 0.000 0.601 0.601
## Lonely ~~
## Happy -1.430 0.360 -3.969 0.000 -0.185 -0.185
## fmi
##
## 0.011
## 0.002
## 0.038
## 0.180
## 0.006
## 0.231
## 0.002
## 0.016
##
## -0.011
## 0.036
## 0.128
## 0.050
## 0.194
## 0.017
## -0.014
##
## 0.020
## 0.172
## -0.017
## 0.245
## 0.001
## 0.005
##
## 0.152
## -0.007
## 0.183
## -0.007
## 0.016
##
## 0.161
## 0.314
## 0.314
## 0.249
##
## 0.257
## 0.018
## -0.027
##
## 0.462
## 0.348
##
## 0.000
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## PERMA_A1_1 6.826 0.110 62.314 0.000 6.826 2.856
## PERMA_A2_1 7.088 0.108 65.787 0.000 7.088 3.015
## PERMA_A3_1 7.828 0.104 75.034 0.000 7.828 3.439
## PERMA_E1_1 7.721 0.102 75.333 0.000 7.721 3.453
## PERMA_E2_1 7.683 0.103 74.850 0.000 7.683 3.431
## PERMA_E3_1 8.359 0.114 73.615 0.000 8.359 3.374
## PERMA_P1_1 7.298 0.106 68.963 0.000 7.298 3.161
## PERMA_P2 7.330 0.111 66.087 0.000 7.330 3.029
## PERMA_P3_1 6.834 0.103 66.074 0.000 6.834 3.028
## PERMA_R1_1 7.265 0.123 59.125 0.000 7.265 2.710
## PERMA_R2_1 7.513 0.122 61.446 0.000 7.513 2.816
## PERMA_R3_1 7.263 0.122 59.549 0.000 7.263 2.729
## PERMA_M1_1 7.248 0.126 57.577 0.000 7.248 3.088
## PERMA_M2_1 7.308 0.122 59.726 0.000 7.308 3.122
## PERMA_M3_1 7.027 0.122 57.703 0.000 7.027 3.127
## PERMA_N1_1 6.479 0.131 49.634 0.000 6.479 2.275
## PERMA_N2_1 5.859 0.136 43.048 0.000 5.859 1.973
## PERMA_N3_1 5.876 0.134 43.877 0.000 5.876 2.011
## PERMA_Hea1_1 7.675 0.136 56.293 0.000 7.675 3.117
## PERMA_Hea2_1 7.334 0.149 49.226 0.000 7.334 2.801
## PERMA_Hea3_1 7.524 0.134 56.068 0.000 7.524 3.083
## PERMA_Lone_1 5.626 0.150 37.538 0.000 5.626 1.721
## PERMA_HPY_1 7.508 0.108 69.310 0.000 7.508 3.177
## Acomplishment 0.000 0.000 0.000
## Engagement 0.000 0.000 0.000
## PositiveEmotin 0.000 0.000 0.000
## Relationship 0.000 0.000 0.000
## Meaning 0.000 0.000 0.000
## NegativeEmotin 0.000 0.000 0.000
## Health 0.000 0.000 0.000
## Lonely 0.000 0.000 0.000
## Happy 0.000 0.000 0.000
## fmi
## 0.000
## 0.000
## 0.000
## 0.000
## 0.000
## 0.000
## 0.000
## 0.000
## 0.000
## 0.000
## 0.000
## 0.000
## 0.270
## 0.231
## 0.284
## 0.000
## 0.000
## 0.000
## 0.315
## 0.351
## 0.305
## 0.000
## 0.000
## NA
## NA
## NA
## NA
## NA
## NA
## NA
## NA
## NA
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## PERMA_A1_1 2.114 0.186 11.381 0.000 2.114 0.370
## PERMA_A2_1 2.785 0.212 13.169 0.000 2.785 0.504
## PERMA_A3_1 2.901 0.208 13.916 0.000 2.901 0.560
## PERMA_E1_1 2.562 0.202 12.695 0.000 2.562 0.512
## PERMA_E2_1 1.957 0.176 11.116 0.000 1.957 0.390
## PERMA_E3_1 4.472 0.313 14.308 0.000 4.472 0.729
## PERMA_P1_1 1.719 0.143 12.029 0.000 1.719 0.322
## PERMA_P2 1.637 0.144 11.383 0.000 1.637 0.280
## PERMA_P3_1 2.746 0.189 14.524 0.000 2.746 0.539
## PERMA_R1_1 4.464 0.322 13.849 0.000 4.464 0.621
## PERMA_R2_1 3.342 0.290 11.538 0.000 3.342 0.470
## PERMA_R3_1 3.835 0.294 13.022 0.000 3.835 0.542
## PERMA_M1_1 1.496 0.165 9.076 0.000 1.496 0.272
## PERMA_M2_1 1.047 0.133 7.868 0.000 1.047 0.191
## PERMA_M3_1 1.538 0.165 9.343 0.000 1.538 0.304
## PERMA_N1_1 5.490 0.510 10.767 0.000 5.490 0.677
## PERMA_N2_1 6.791 0.540 12.572 0.000 6.791 0.770
## PERMA_N3_1 5.272 0.557 9.465 0.000 5.272 0.618
## PERMA_Hea1_1 0.805 0.121 6.645 0.000 0.805 0.133
## PERMA_Hea2_1 1.821 0.198 9.196 0.000 1.821 0.266
## PERMA_Hea3_1 0.647 0.111 5.824 0.000 0.647 0.109
## PERMA_Lone_1 0.000 0.000 0.000
## PERMA_HPY_1 0.000 0.000 0.000
## Acomplishment 3.598 0.366 9.828 0.000 1.000 1.000
## Engagement 2.437 0.301 8.098 0.000 1.000 1.000
## PositiveEmotin 3.612 0.339 10.651 0.000 1.000 1.000
## Relationship 2.722 0.392 6.949 0.000 1.000 1.000
## Meaning 4.012 0.437 9.173 0.000 1.000 1.000
## NegativeEmotin 2.621 0.532 4.925 0.000 1.000 1.000
## Health 5.259 0.523 10.063 0.000 1.000 1.000
## Lonely 10.692 0.693 15.427 0.000 1.000 1.000
## Happy 5.586 0.362 15.427 0.000 1.000 1.000
## fmi
## 0.174
## 0.128
## 0.074
## 0.046
## -0.031
## 0.035
## 0.185
## 0.187
## 0.037
## 0.010
## 0.114
## 0.028
## 0.520
## 0.531
## 0.519
## 0.097
## 0.053
## 0.122
## 0.532
## 0.514
## 0.525
## NA
## NA
## 0.045
## 0.021
## 0.033
## 0.007
## 0.364
## 0.089
## 0.429
## 0.000
## 0.000
##
## R-Square:
## Estimate
## PERMA_A1_1 0.630
## PERMA_A2_1 0.496
## PERMA_A3_1 0.440
## PERMA_E1_1 0.488
## PERMA_E2_1 0.610
## PERMA_E3_1 0.271
## PERMA_P1_1 0.678
## PERMA_P2 0.720
## PERMA_P3_1 0.461
## PERMA_R1_1 0.379
## PERMA_R2_1 0.530
## PERMA_R3_1 0.458
## PERMA_M1_1 0.728
## PERMA_M2_1 0.809
## PERMA_M3_1 0.696
## PERMA_N1_1 0.323
## PERMA_N2_1 0.230
## PERMA_N3_1 0.382
## PERMA_Hea1_1 0.867
## PERMA_Hea2_1 0.734
## PERMA_Hea3_1 0.891
## PERMA_Lone_1 1.000
## PERMA_HPY_1 1.000Summarie with complete cases
summary(nine.fit1, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-19) converged normally after 164 iterations
##
## Number of observations 239
##
## Number of missing patterns 1
##
## Estimator ML
## Minimum Function Test Statistic 535.393
## Degrees of freedom 196
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Information Observed
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## Acomplishment =~
## PERMA_A1_1 1.000 1.857 0.839
## PERMA_A2_1 0.962 0.061 15.686 0.000 1.787 0.826
## PERMA_A3_1 0.814 0.066 12.393 0.000 1.512 0.719
## Engagement =~
## PERMA_E1_1 1.000 1.565 0.766
## PERMA_E2_1 1.217 0.083 14.680 0.000 1.905 0.897
## PERMA_E3_1 0.996 0.094 10.554 0.000 1.559 0.667
## PositiveEmotion =~
## PERMA_P1_1 1.000 2.013 0.907
## PERMA_P2 1.025 0.042 24.455 0.000 2.063 0.927
## PERMA_P3_1 0.682 0.054 12.536 0.000 1.372 0.682
## Relationship =~
## PERMA_R1_1 1.000 1.879 0.756
## PERMA_R2_1 1.054 0.083 12.665 0.000 1.981 0.809
## PERMA_R3_1 0.978 0.082 11.877 0.000 1.837 0.780
## Meaning =~
## PERMA_M1_1 1.000 1.995 0.853
## PERMA_M2_1 1.051 0.056 18.942 0.000 2.098 0.900
## PERMA_M3_1 0.933 0.057 16.393 0.000 1.861 0.831
## NegativeEmotion =~
## PERMA_N1_1 1.000 1.788 0.735
## PERMA_N2_1 1.006 0.097 10.405 0.000 1.798 0.723
## PERMA_N3_1 1.172 0.101 11.654 0.000 2.095 0.862
## Health =~
## PERMA_Hea1_1 1.000 2.255 0.929
## PERMA_Hea2_1 0.979 0.049 19.877 0.000 2.207 0.854
## PERMA_Hea3_1 1.005 0.038 26.196 0.000 2.265 0.942
## Lonely =~
## PERMA_Lone_1 1.000 2.698 1.000
## Happy =~
## PERMA_HPY_1 1.000 2.117 1.000
## fmi
##
## NA
## 0.006
## 0.059
##
## NA
## 0.029
## 0.003
##
## NA
## -0.010
## 0.062
##
## NA
## 0.012
## 0.059
##
## NA
## 0.013
## 0.012
##
## NA
## 0.006
## 0.041
##
## NA
## 0.039
## 0.016
##
## NA
##
## NA
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## Acomplishment ~~
## Engagement 2.606 0.321 8.119 0.000 0.897 0.897
## PositiveEmotin 3.257 0.368 8.842 0.000 0.871 0.871
## Relationship 2.954 0.379 7.792 0.000 0.847 0.847
## Meaning 3.599 0.400 9.007 0.000 0.971 0.971
## NegativeEmotin 0.254 0.258 0.986 0.324 0.076 0.076
## Health 3.217 0.388 8.298 0.000 0.768 0.768
## Lonely -0.025 0.352 -0.071 0.943 -0.005 -0.005
## Happy 3.235 0.363 8.919 0.000 0.823 0.823
## Engagement ~~
## PositiveEmotin 2.849 0.333 8.568 0.000 0.904 0.904
## Relationship 2.537 0.337 7.535 0.000 0.863 0.863
## Meaning 2.768 0.336 8.232 0.000 0.886 0.886
## NegativeEmotin 0.252 0.221 1.140 0.254 0.090 0.090
## Health 2.488 0.329 7.551 0.000 0.705 0.705
## Lonely -0.117 0.297 -0.393 0.694 -0.028 -0.028
## Happy 2.833 0.327 8.668 0.000 0.855 0.855
## PositiveEmotion ~~
## Relationship 3.438 0.409 8.414 0.000 0.909 0.909
## Meaning 3.686 0.403 9.151 0.000 0.918 0.918
## NegativeEmotin -0.410 0.269 -1.522 0.128 -0.114 -0.114
## Health 3.767 0.420 8.965 0.000 0.830 0.830
## Lonely -0.457 0.368 -1.243 0.214 -0.084 -0.084
## Happy 3.913 0.394 9.929 0.000 0.918 0.918
## Relationship ~~
## Meaning 3.147 0.400 7.866 0.000 0.840 0.840
## NegativeEmotin -0.023 0.263 -0.089 0.929 -0.007 -0.007
## Health 3.415 0.428 7.979 0.000 0.806 0.806
## Lonely -0.560 0.363 -1.542 0.123 -0.110 -0.110
## Happy 3.279 0.393 8.350 0.000 0.824 0.824
## Meaning ~~
## NegativeEmotin -0.321 0.269 -1.196 0.232 -0.090 -0.090
## Health 3.487 0.415 8.396 0.000 0.775 0.775
## Lonely -0.455 0.369 -1.234 0.217 -0.085 -0.085
## Happy 3.679 0.396 9.300 0.000 0.871 0.871
## NegativeEmotion ~~
## Health -0.612 0.298 -2.057 0.040 -0.152 -0.152
## Lonely 3.188 0.444 7.186 0.000 0.661 0.661
## Happy -0.439 0.270 -1.626 0.104 -0.116 -0.116
## Health ~~
## Lonely -0.784 0.409 -1.917 0.055 -0.129 -0.129
## Happy 3.357 0.393 8.544 0.000 0.703 0.703
## Lonely ~~
## Happy -0.678 0.372 -1.823 0.068 -0.119 -0.119
## fmi
##
## 0.014
## -0.010
## 0.016
## 0.004
## 0.013
## -0.007
## 0.003
## 0.000
##
## -0.011
## 0.022
## -0.002
## 0.058
## -0.002
## 0.016
## -0.005
##
## 0.008
## -0.000
## 0.011
## 0.016
## 0.001
## -0.001
##
## 0.014
## 0.015
## 0.012
## -0.002
## 0.008
##
## 0.010
## 0.010
## 0.002
## 0.006
##
## -0.006
## 0.003
## 0.000
##
## 0.002
## 0.005
##
## 0.000
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## PERMA_A1_1 7.163 0.143 50.037 0.000 7.163 3.237
## PERMA_A2_1 7.126 0.140 50.927 0.000 7.126 3.294
## PERMA_A3_1 7.845 0.136 57.664 0.000 7.845 3.730
## PERMA_E1_1 7.816 0.132 59.133 0.000 7.816 3.825
## PERMA_E2_1 7.845 0.137 57.074 0.000 7.845 3.692
## PERMA_E3_1 8.268 0.151 54.702 0.000 8.268 3.538
## PERMA_P1_1 7.695 0.144 53.565 0.000 7.695 3.465
## PERMA_P2 7.552 0.144 52.477 0.000 7.552 3.394
## PERMA_P3_1 7.184 0.130 55.189 0.000 7.184 3.570
## PERMA_R1_1 7.406 0.161 46.046 0.000 7.406 2.978
## PERMA_R2_1 7.527 0.158 47.514 0.000 7.527 3.073
## PERMA_R3_1 7.531 0.152 49.478 0.000 7.531 3.200
## PERMA_M1_1 7.456 0.151 49.286 0.000 7.456 3.188
## PERMA_M2_1 7.527 0.151 49.903 0.000 7.527 3.228
## PERMA_M3_1 7.222 0.145 49.841 0.000 7.222 3.224
## PERMA_N1_1 6.439 0.157 40.915 0.000 6.439 2.647
## PERMA_N2_1 5.958 0.161 37.045 0.000 5.958 2.396
## PERMA_N3_1 5.862 0.157 37.278 0.000 5.862 2.411
## PERMA_Hea1_1 7.929 0.157 50.506 0.000 7.929 3.267
## PERMA_Hea2_1 7.582 0.167 45.326 0.000 7.582 2.932
## PERMA_Hea3_1 7.778 0.156 50.020 0.000 7.778 3.236
## PERMA_Lone_1 5.201 0.175 29.796 0.000 5.201 1.927
## PERMA_HPY_1 7.732 0.137 56.472 0.000 7.732 3.653
## Acomplishment 0.000 0.000 0.000
## Engagement 0.000 0.000 0.000
## PositiveEmotin 0.000 0.000 0.000
## Relationship 0.000 0.000 0.000
## Meaning 0.000 0.000 0.000
## NegativeEmotin 0.000 0.000 0.000
## Health 0.000 0.000 0.000
## Lonely 0.000 0.000 0.000
## Happy 0.000 0.000 0.000
## fmi
## 0.000
## 0.000
## 0.000
## 0.000
## 0.000
## 0.000
## 0.000
## 0.000
## 0.000
## 0.000
## 0.000
## 0.000
## 0.000
## 0.000
## 0.000
## 0.000
## 0.000
## 0.000
## 0.000
## 0.000
## 0.000
## 0.000
## 0.000
## NA
## NA
## NA
## NA
## NA
## NA
## NA
## NA
## NA
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## PERMA_A1_1 1.450 0.176 8.255 0.000 1.450 0.296
## PERMA_A2_1 1.486 0.175 8.517 0.000 1.486 0.318
## PERMA_A3_1 2.139 0.214 9.996 0.000 2.139 0.484
## PERMA_E1_1 1.726 0.184 9.361 0.000 1.726 0.413
## PERMA_E2_1 0.885 0.138 6.417 0.000 0.885 0.196
## PERMA_E3_1 3.030 0.301 10.070 0.000 3.030 0.555
## PERMA_P1_1 0.879 0.110 7.988 0.000 0.879 0.178
## PERMA_P2 0.693 0.099 7.004 0.000 0.693 0.140
## PERMA_P3_1 2.167 0.211 10.279 0.000 2.167 0.535
## PERMA_R1_1 2.654 0.288 9.215 0.000 2.654 0.429
## PERMA_R2_1 2.074 0.265 7.824 0.000 2.074 0.346
## PERMA_R3_1 2.165 0.240 9.013 0.000 2.165 0.391
## PERMA_M1_1 1.489 0.164 9.086 0.000 1.489 0.272
## PERMA_M2_1 1.038 0.132 7.857 0.000 1.038 0.191
## PERMA_M3_1 1.554 0.166 9.355 0.000 1.554 0.310
## PERMA_N1_1 2.724 0.316 8.620 0.000 2.724 0.460
## PERMA_N2_1 2.949 0.334 8.834 0.000 2.949 0.477
## PERMA_N3_1 1.520 0.279 5.455 0.000 1.520 0.257
## PERMA_Hea1_1 0.806 0.121 6.647 0.000 0.806 0.137
## PERMA_Hea2_1 1.815 0.198 9.180 0.000 1.815 0.271
## PERMA_Hea3_1 0.649 0.111 5.838 0.000 0.649 0.112
## PERMA_Lone_1 0.000 0.000 0.000
## PERMA_HPY_1 0.000 0.000 0.000
## Acomplishment 3.448 0.443 7.779 0.000 1.000 1.000
## Engagement 2.450 0.361 6.793 0.000 1.000 1.000
## PositiveEmotin 4.053 0.450 9.002 0.000 1.000 1.000
## Relationship 3.529 0.534 6.611 0.000 1.000 1.000
## Meaning 3.981 0.490 8.124 0.000 1.000 1.000
## NegativeEmotin 3.196 0.519 6.163 0.000 1.000 1.000
## Health 5.084 0.542 9.373 0.000 1.000 1.000
## Lonely 7.282 0.666 10.932 0.000 1.000 1.000
## Happy 4.481 0.410 10.932 0.000 1.000 1.000
## fmi
## 0.111
## 0.107
## 0.018
## 0.052
## 0.013
## 0.027
## 0.154
## 0.181
## 0.053
## 0.023
## 0.162
## 0.001
## 0.042
## 0.064
## 0.046
## 0.031
## 0.018
## 0.081
## 0.064
## 0.031
## 0.047
## NA
## NA
## 0.017
## 0.014
## 0.009
## 0.007
## 0.005
## 0.012
## 0.003
## 0.000
## 0.000
##
## R-Square:
## Estimate
## PERMA_A1_1 0.704
## PERMA_A2_1 0.682
## PERMA_A3_1 0.516
## PERMA_E1_1 0.587
## PERMA_E2_1 0.804
## PERMA_E3_1 0.445
## PERMA_P1_1 0.822
## PERMA_P2 0.860
## PERMA_P3_1 0.465
## PERMA_R1_1 0.571
## PERMA_R2_1 0.654
## PERMA_R3_1 0.609
## PERMA_M1_1 0.728
## PERMA_M2_1 0.809
## PERMA_M3_1 0.690
## PERMA_N1_1 0.540
## PERMA_N2_1 0.523
## PERMA_N3_1 0.743
## PERMA_Hea1_1 0.863
## PERMA_Hea2_1 0.729
## PERMA_Hea3_1 0.888
## PERMA_Lone_1 1.000
## PERMA_HPY_1 1.000Residual correlations with all data
correl = residuals(nine.fit, type="cor")
correl## $type
## [1] "cor.bollen"
##
## $cor
## PERMA_A1 PERMA_A2 PERMA_A3 PERMA_E1 PERMA_E2 PERMA_E3
## PERMA_A1_1 0.000
## PERMA_A2_1 0.016 0.000
## PERMA_A3_1 -0.018 0.000 0.000
## PERMA_E1_1 -0.019 0.011 0.048 0.000
## PERMA_E2_1 0.028 -0.013 0.048 -0.034 0.000
## PERMA_E3_1 -0.108 -0.076 -0.001 0.106 -0.008 0.000
## PERMA_P1_1 -0.051 -0.048 0.027 -0.050 0.026 -0.016
## PERMA_P2 -0.015 -0.010 0.035 -0.044 0.037 -0.008
## PERMA_P3_1 0.079 0.064 0.038 0.028 0.006 -0.019
## PERMA_R1_1 0.029 0.012 0.005 0.026 0.031 0.003
## PERMA_R2_1 -0.019 -0.031 -0.036 -0.019 -0.023 -0.010
## PERMA_R3_1 0.012 0.014 0.038 0.013 -0.007 0.021
## PERMA_M1_1 0.005 -0.084 -0.135 -0.039 0.025 -0.042
## PERMA_M2_1 0.027 -0.019 -0.034 -0.072 0.061 -0.033
## PERMA_M3_1 0.025 0.023 0.058 -0.038 0.024 -0.059
## PERMA_N1_1 0.028 0.046 -0.006 0.071 0.002 0.061
## PERMA_N2_1 -0.039 0.009 -0.035 0.018 -0.012 0.041
## PERMA_N3_1 -0.033 0.062 -0.038 0.049 -0.101 -0.006
## PERMA_Hea1_1 -0.058 0.035 0.009 -0.053 -0.001 0.009
## PERMA_Hea2_1 -0.028 0.067 -0.074 -0.087 0.026 0.095
## PERMA_Hea3_1 -0.071 0.039 0.052 -0.072 -0.003 0.029
## PERMA_Lone_1 -0.008 0.011 0.000 0.086 -0.075 0.048
## PERMA_HPY_1 0.011 -0.050 0.039 -0.039 0.032 -0.016
## PERMA_P1 PERMA_P2 PERMA_P3 PERMA_R1 PERMA_R2 PERMA_R3
## PERMA_A1_1
## PERMA_A2_1
## PERMA_A3_1
## PERMA_E1_1
## PERMA_E2_1
## PERMA_E3_1
## PERMA_P1_1 0.000
## PERMA_P2 0.038 0.000
## PERMA_P3_1 -0.030 -0.052 0.000
## PERMA_R1_1 -0.055 0.029 0.042 0.000
## PERMA_R2_1 0.009 -0.045 0.044 0.011 0.000
## PERMA_R3_1 0.051 -0.049 0.069 -0.058 0.028 0.000
## PERMA_M1_1 -0.020 0.036 -0.017 0.010 0.070 0.001
## PERMA_M2_1 0.002 0.060 0.012 -0.062 -0.051 -0.037
## PERMA_M3_1 -0.129 -0.080 0.025 -0.016 0.035 -0.057
## PERMA_N1_1 0.031 0.035 0.028 0.044 -0.013 0.034
## PERMA_N2_1 -0.036 0.027 0.020 0.003 -0.004 -0.016
## PERMA_N3_1 -0.073 -0.039 0.091 0.069 -0.045 -0.031
## PERMA_Hea1_1 0.071 -0.008 -0.001 -0.006 -0.064 -0.058
## PERMA_Hea2_1 -0.070 -0.068 -0.007 0.009 0.019 0.043
## PERMA_Hea3_1 -0.043 -0.077 -0.005 -0.033 -0.038 0.018
## PERMA_Lone_1 -0.004 0.003 0.000 0.046 -0.030 0.001
## PERMA_HPY_1 0.002 0.001 -0.008 -0.003 0.016 -0.017
## PERMA_M1 PERMA_M2 PERMA_M3 PERMA_N1 PERMA_N2 PERMA_N3
## PERMA_A1_1
## PERMA_A2_1
## PERMA_A3_1
## PERMA_E1_1
## PERMA_E2_1
## PERMA_E3_1
## PERMA_P1_1
## PERMA_P2
## PERMA_P3_1
## PERMA_R1_1
## PERMA_R2_1
## PERMA_R3_1
## PERMA_M1_1 0.000
## PERMA_M2_1 -0.014 0.000
## PERMA_M3_1 -0.005 -0.034 0.000
## PERMA_N1_1 0.009 0.085 0.099 0.000
## PERMA_N2_1 -0.047 -0.005 0.011 0.038 0.000
## PERMA_N3_1 -0.080 -0.066 0.009 -0.008 -0.021 0.000
## PERMA_Hea1_1 -0.021 -0.006 -0.064 0.134 -0.045 0.003
## PERMA_Hea2_1 -0.003 -0.011 -0.022 0.109 -0.108 -0.041
## PERMA_Hea3_1 -0.048 -0.052 -0.031 0.066 -0.076 0.014
## PERMA_Lone_1 0.038 -0.020 0.052 0.000 -0.016 0.010
## PERMA_HPY_1 0.059 -0.057 -0.008 0.045 0.034 -0.059
## PERMA_H1 PERMA_H2 PERMA_H3 PERMA_L PERMA_HP
## PERMA_A1_1
## PERMA_A2_1
## PERMA_A3_1
## PERMA_E1_1
## PERMA_E2_1
## PERMA_E3_1
## PERMA_P1_1
## PERMA_P2
## PERMA_P3_1
## PERMA_R1_1
## PERMA_R2_1
## PERMA_R3_1
## PERMA_M1_1
## PERMA_M2_1
## PERMA_M3_1
## PERMA_N1_1
## PERMA_N2_1
## PERMA_N3_1
## PERMA_Hea1_1 0.000
## PERMA_Hea2_1 -0.054 0.000
## PERMA_Hea3_1 -0.017 0.002 0.000
## PERMA_Lone_1 -0.053 -0.007 0.018 0.000
## PERMA_HPY_1 -0.034 -0.044 -0.047 0.000 0.000
##
## $mean
## PERMA_A1_1 PERMA_A2_1 PERMA_A3_1 PERMA_E1_1 PERMA_E2_1
## 0.000 0.000 0.000 0.000 0.000
## PERMA_E3_1 PERMA_P1_1 PERMA_P2 PERMA_P3_1 PERMA_R1_1
## 0.000 0.000 0.000 0.000 0.000
## PERMA_R2_1 PERMA_R3_1 PERMA_M1_1 PERMA_M2_1 PERMA_M3_1
## 0.000 0.000 -0.060 -0.081 0.002
## PERMA_N1_1 PERMA_N2_1 PERMA_N3_1 PERMA_Hea1_1 PERMA_Hea2_1
## 0.000 0.000 0.000 -0.023 0.078
## PERMA_Hea3_1 PERMA_Lone_1 PERMA_HPY_1
## 0.084 0.000 0.000Residual correlations with complete cases
correl1 = residuals(nine.fit1, type="cor")
correl1## $type
## [1] "cor.bollen"
##
## $cor
## PERMA_A1 PERMA_A2 PERMA_A3 PERMA_E1 PERMA_E2 PERMA_E3
## PERMA_A1_1 0.000
## PERMA_A2_1 0.034 0.000
## PERMA_A3_1 -0.047 -0.014 0.000
## PERMA_E1_1 0.015 0.065 0.081 0.000
## PERMA_E2_1 -0.004 -0.049 0.065 -0.002 0.000
## PERMA_E3_1 -0.090 -0.077 0.071 0.065 -0.023 0.000
## PERMA_P1_1 -0.067 -0.029 0.073 -0.062 -0.002 0.007
## PERMA_P2 -0.051 0.001 0.068 -0.061 0.031 0.010
## PERMA_P3_1 0.105 0.139 0.129 0.057 0.030 0.012
## PERMA_R1_1 0.011 0.078 -0.003 0.031 0.050 0.040
## PERMA_R2_1 -0.057 -0.031 -0.017 -0.024 -0.066 0.070
## PERMA_R3_1 0.000 0.008 0.060 -0.039 0.020 -0.006
## PERMA_M1_1 0.006 -0.041 -0.084 -0.024 -0.002 0.010
## PERMA_M2_1 0.012 -0.021 -0.001 -0.036 0.024 -0.010
## PERMA_M3_1 0.038 0.029 0.068 0.013 -0.008 0.004
## PERMA_N1_1 0.076 0.092 0.055 0.186 0.121 0.054
## PERMA_N2_1 -0.044 -0.050 -0.015 0.077 -0.018 0.020
## PERMA_N3_1 -0.041 0.020 -0.045 0.055 -0.100 -0.118
## PERMA_Hea1_1 -0.048 0.035 0.050 -0.024 0.021 0.032
## PERMA_Hea2_1 -0.015 0.050 -0.022 -0.053 0.019 0.090
## PERMA_Hea3_1 -0.068 0.017 0.070 -0.057 -0.011 0.038
## PERMA_Lone_1 -0.048 0.036 0.028 0.114 -0.064 0.067
## PERMA_HPY_1 -0.008 -0.019 0.049 -0.044 0.003 0.059
## PERMA_P1 PERMA_P2 PERMA_P3 PERMA_R1 PERMA_R2 PERMA_R3
## PERMA_A1_1
## PERMA_A2_1
## PERMA_A3_1
## PERMA_E1_1
## PERMA_E2_1
## PERMA_E3_1
## PERMA_P1_1 0.000
## PERMA_P2 0.022 0.000
## PERMA_P3_1 -0.048 -0.053 0.000
## PERMA_R1_1 -0.016 0.009 0.058 0.000
## PERMA_R2_1 -0.012 -0.029 -0.018 0.047 0.000
## PERMA_R3_1 0.035 -0.004 0.094 -0.092 0.028 0.000
## PERMA_M1_1 -0.008 0.026 0.015 0.036 0.035 0.034
## PERMA_M2_1 0.004 0.037 0.049 -0.017 -0.045 0.011
## PERMA_M3_1 -0.092 -0.059 0.086 0.008 -0.011 -0.018
## PERMA_N1_1 0.081 0.098 0.174 0.074 0.097 0.060
## PERMA_N2_1 0.006 0.014 0.107 0.004 0.006 0.010
## PERMA_N3_1 -0.074 -0.088 0.182 0.025 -0.048 -0.089
## PERMA_Hea1_1 0.065 0.022 0.029 0.026 -0.013 -0.024
## PERMA_Hea2_1 -0.028 -0.012 0.026 0.038 0.045 0.041
## PERMA_Hea3_1 -0.026 -0.038 0.030 -0.018 -0.024 0.009
## PERMA_Lone_1 -0.008 -0.013 0.098 0.073 -0.025 -0.035
## PERMA_HPY_1 -0.005 -0.005 0.045 0.002 -0.035 0.039
## PERMA_M1 PERMA_M2 PERMA_M3 PERMA_N1 PERMA_N2 PERMA_N3
## PERMA_A1_1
## PERMA_A2_1
## PERMA_A3_1
## PERMA_E1_1
## PERMA_E2_1
## PERMA_E3_1
## PERMA_P1_1
## PERMA_P2
## PERMA_P3_1
## PERMA_R1_1
## PERMA_R2_1
## PERMA_R3_1
## PERMA_M1_1 0.000
## PERMA_M2_1 0.003 0.000
## PERMA_M3_1 0.007 -0.009 0.000
## PERMA_N1_1 0.062 0.115 0.169 0.000
## PERMA_N2_1 -0.058 -0.021 0.021 0.002 0.000
## PERMA_N3_1 -0.088 -0.069 0.050 0.000 -0.001 0.000
## PERMA_Hea1_1 0.036 0.006 -0.009 0.125 -0.020 -0.027
## PERMA_Hea2_1 0.046 0.013 0.017 0.093 -0.086 -0.076
## PERMA_Hea3_1 -0.006 -0.036 -0.001 0.092 -0.035 -0.021
## PERMA_Lone_1 -0.019 -0.021 0.058 -0.006 0.002 0.002
## PERMA_HPY_1 0.030 -0.014 -0.011 0.098 -0.002 -0.046
## PERMA_H1 PERMA_H2 PERMA_H3 PERMA_L PERMA_HP
## PERMA_A1_1
## PERMA_A2_1
## PERMA_A3_1
## PERMA_E1_1
## PERMA_E2_1
## PERMA_E3_1
## PERMA_P1_1
## PERMA_P2
## PERMA_P3_1
## PERMA_R1_1
## PERMA_R2_1
## PERMA_R3_1
## PERMA_M1_1
## PERMA_M2_1
## PERMA_M3_1
## PERMA_N1_1
## PERMA_N2_1
## PERMA_N3_1
## PERMA_Hea1_1 0.000
## PERMA_Hea2_1 -0.025 0.000
## PERMA_Hea3_1 0.002 0.016 0.000
## PERMA_Lone_1 -0.008 -0.034 0.019 0.000
## PERMA_HPY_1 0.011 0.013 -0.013 0.000 0.000
##
## $mean
## PERMA_A1_1 PERMA_A2_1 PERMA_A3_1 PERMA_E1_1 PERMA_E2_1
## 0 0 0 0 0
## PERMA_E3_1 PERMA_P1_1 PERMA_P2 PERMA_P3_1 PERMA_R1_1
## 0 0 0 0 0
## PERMA_R2_1 PERMA_R3_1 PERMA_M1_1 PERMA_M2_1 PERMA_M3_1
## 0 0 0 0 0
## PERMA_N1_1 PERMA_N2_1 PERMA_N3_1 PERMA_Hea1_1 PERMA_Hea2_1
## 0 0 0 0 0
## PERMA_Hea3_1 PERMA_Lone_1 PERMA_HPY_1
## 0 0 0Zscore correlation for all data anything over 1.96 is going to be statistically significant at the .05 level
zcorrels = residuals(nine.fit, type = "standardized")Zscore correlation for complete cases anything over 1.96 is going to be statistically significant at the .05 level
zcorrels1 = residuals(nine.fit1, type = "standardized")Modification indicies for all data
modindices(nine.fit, sort. = TRUE, minimum.value = 3.84)## Warning in lav_object_post_check(lavobject): lavaan WARNING:
## covariance matrix of latent variables is not positive definite; use
## inspect(fit,"cov.lv") to investigate.## lhs op rhs mi epc sepc.lv sepc.all
## 208 Meaning =~ PERMA_E2_1 28.654 0.829 1.661 0.742
## 425 PERMA_P1_1 ~~ PERMA_P2 22.759 0.627 0.627 0.112
## 372 PERMA_E1_1 ~~ PERMA_E3_1 19.051 0.783 0.783 0.141
## 168 PositiveEmotion =~ PERMA_E2_1 19.038 0.673 1.279 0.571
## 192 Relationship =~ PERMA_P3_1 18.708 0.851 1.405 0.622
## 436 PERMA_P1_1 ~~ PERMA_Hea1_1 17.809 0.415 0.415 0.073
## 228 NegativeEmotion =~ PERMA_E2_1 15.334 -0.286 -0.463 -0.207
## 268 Lonely =~ PERMA_E2_1 15.075 -0.112 -0.368 -0.164
## 125 Acomplishment =~ PERMA_E2_1 14.955 0.716 1.358 0.606
## 360 PERMA_A3_1 ~~ PERMA_M1_1 14.663 -0.591 -0.591 -0.111
## 167 PositiveEmotion =~ PERMA_E1_1 13.471 -0.527 -1.002 -0.448
## 209 Meaning =~ PERMA_E3_1 13.458 -0.555 -1.112 -0.449
## 312 PERMA_A1_1 ~~ PERMA_E3_1 13.221 -0.583 -0.583 -0.098
## 441 PERMA_P2 ~~ PERMA_P3_1 12.925 -0.426 -0.426 -0.078
## 464 PERMA_P3_1 ~~ PERMA_N3_1 12.793 0.734 0.734 0.111
## 129 Acomplishment =~ PERMA_P3_1 12.613 0.444 0.842 0.373
## 551 PERMA_Hea1_1 ~~ PERMA_Hea2_1 12.211 -0.484 -0.484 -0.075
## 432 PERMA_P1_1 ~~ PERMA_M3_1 12.130 -0.415 -0.415 -0.080
## 227 NegativeEmotion =~ PERMA_E1_1 11.330 0.233 0.377 0.168
## 126 Acomplishment =~ PERMA_E3_1 10.607 -0.601 -1.140 -0.460
## 371 PERMA_E1_1 ~~ PERMA_E2_1 10.195 -0.634 -0.634 -0.127
## 267 Lonely =~ PERMA_E1_1 10.151 0.087 0.286 0.128
## 427 PERMA_P1_1 ~~ PERMA_R1_1 9.746 -0.449 -0.449 -0.073
## 191 Relationship =~ PERMA_P2 9.657 -0.608 -1.003 -0.415
## 446 PERMA_P2 ~~ PERMA_M2_1 9.392 0.330 0.330 0.058
## 429 PERMA_P1_1 ~~ PERMA_R3_1 9.305 0.419 0.419 0.068
## 367 PERMA_A3_1 ~~ PERMA_Hea2_1 9.046 -0.491 -0.491 -0.082
## 210 Meaning =~ PERMA_P1_1 8.925 -0.468 -0.937 -0.406
## 127 Acomplishment =~ PERMA_P1_1 8.922 -0.347 -0.658 -0.285
## 166 PositiveEmotion =~ PERMA_A3_1 8.782 0.401 0.761 0.334
## 555 PERMA_Hea2_1 ~~ PERMA_Hea3_1 8.376 0.404 0.404 0.063
## 315 PERMA_A1_1 ~~ PERMA_P3_1 8.263 0.361 0.361 0.067
## 212 Meaning =~ PERMA_P3_1 7.807 0.470 0.941 0.417
## 205 Meaning =~ PERMA_A2_1 7.790 -0.576 -1.153 -0.490
## 238 NegativeEmotion =~ PERMA_M3_1 7.705 0.200 0.325 0.144
## 444 PERMA_P2 ~~ PERMA_R3_1 7.660 -0.380 -0.380 -0.059
## 368 PERMA_A3_1 ~~ PERMA_Hea3_1 7.497 0.322 0.322 0.058
## 406 PERMA_E2_1 ~~ PERMA_Lone_1 7.453 -0.786 -0.786 -0.107
## 146 Engagement =~ PERMA_A3_1 7.373 0.518 0.808 0.355
## 471 PERMA_R1_1 ~~ PERMA_R3_1 7.221 -0.628 -0.628 -0.088
## 447 PERMA_P2 ~~ PERMA_M3_1 7.216 -0.318 -0.318 -0.058
## 207 Meaning =~ PERMA_E1_1 7.116 -0.381 -0.764 -0.342
## 290 Happy =~ PERMA_E2_1 6.991 0.169 0.398 0.178
## 151 Engagement =~ PERMA_R2_1 6.804 -0.632 -0.986 -0.370
## 287 Happy =~ PERMA_A2_1 6.718 -0.143 -0.338 -0.144
## 345 PERMA_A2_1 ~~ PERMA_N3_1 6.525 0.552 0.552 0.080
## 248 Health =~ PERMA_E2_1 6.183 0.207 0.475 0.212
## 232 NegativeEmotion =~ PERMA_P3_1 6.021 0.160 0.258 0.114
## 388 PERMA_E1_1 ~~ PERMA_Lone_1 5.913 0.662 0.662 0.091
## 313 PERMA_A1_1 ~~ PERMA_P1_1 5.816 -0.259 -0.259 -0.047
## 443 PERMA_P2 ~~ PERMA_R2_1 5.788 -0.322 -0.322 -0.050
## 336 PERMA_A2_1 ~~ PERMA_P3_1 5.293 0.317 0.317 0.060
## 303 Happy =~ PERMA_N3_1 5.250 -0.144 -0.340 -0.116
## 289 Happy =~ PERMA_E1_1 5.224 -0.137 -0.324 -0.145
## 477 PERMA_R1_1 ~~ PERMA_N3_1 5.175 0.612 0.612 0.078
## 437 PERMA_P1_1 ~~ PERMA_Hea2_1 5.092 -0.290 -0.290 -0.048
## 438 PERMA_P1_1 ~~ PERMA_Hea3_1 4.995 -0.211 -0.211 -0.038
## 402 PERMA_E2_1 ~~ PERMA_N3_1 4.978 -0.448 -0.448 -0.068
## 524 PERMA_M2_1 ~~ PERMA_HPY_1 4.910 -0.272 -0.272 -0.049
## 247 Health =~ PERMA_E1_1 4.737 -0.169 -0.389 -0.174
## 233 NegativeEmotion =~ PERMA_R1_1 4.614 0.184 0.299 0.111
## 442 PERMA_P2 ~~ PERMA_R1_1 4.586 0.307 0.307 0.047
## 181 PositiveEmotion =~ PERMA_Hea3_1 4.500 -0.191 -0.362 -0.148
## 149 Engagement =~ PERMA_P3_1 4.420 0.322 0.502 0.223
## 522 PERMA_M2_1 ~~ PERMA_Hea3_1 4.276 -0.162 -0.162 -0.028
## 236 NegativeEmotion =~ PERMA_M1_1 4.260 -0.151 -0.245 -0.104
## 165 PositiveEmotion =~ PERMA_A2_1 4.249 -0.286 -0.543 -0.231
## 150 Engagement =~ PERMA_R1_1 4.209 0.479 0.747 0.279
## 530 PERMA_M3_1 ~~ PERMA_Hea3_1 4.193 0.182 0.182 0.033
## 161 Engagement =~ PERMA_Hea3_1 4.074 -0.164 -0.255 -0.105
## 173 PositiveEmotion =~ PERMA_M1_1 4.031 0.373 0.710 0.302
## 221 Meaning =~ PERMA_Hea3_1 3.991 -0.140 -0.281 -0.115
## 141 Acomplishment =~ PERMA_Hea3_1 3.972 -0.147 -0.279 -0.114
## 193 Relationship =~ PERMA_M1_1 3.947 0.314 0.518 0.221
## 358 PERMA_A3_1 ~~ PERMA_R2_1 3.939 -0.328 -0.328 -0.054
## 225 NegativeEmotion =~ PERMA_A2_1 3.881 0.137 0.223 0.095
## 458 PERMA_P3_1 ~~ PERMA_R3_1 3.868 0.322 0.322 0.054
## 459 PERMA_P3_1 ~~ PERMA_M1_1 3.843 -0.284 -0.284 -0.054
## sepc.nox
## 208 0.742
## 425 0.112
## 372 0.141
## 168 0.571
## 192 0.622
## 436 0.073
## 228 -0.207
## 268 -0.164
## 125 0.606
## 360 -0.111
## 167 -0.448
## 209 -0.449
## 312 -0.098
## 441 -0.078
## 464 0.111
## 129 0.373
## 551 -0.075
## 432 -0.080
## 227 0.168
## 126 -0.460
## 371 -0.127
## 267 0.128
## 427 -0.073
## 191 -0.415
## 446 0.058
## 429 0.068
## 367 -0.082
## 210 -0.406
## 127 -0.285
## 166 0.334
## 555 0.063
## 315 0.067
## 212 0.417
## 205 -0.490
## 238 0.144
## 444 -0.059
## 368 0.058
## 406 -0.107
## 146 0.355
## 471 -0.088
## 447 -0.058
## 207 -0.342
## 290 0.178
## 151 -0.370
## 287 -0.144
## 345 0.080
## 248 0.212
## 232 0.114
## 388 0.091
## 313 -0.047
## 443 -0.050
## 336 0.060
## 303 -0.116
## 289 -0.145
## 477 0.078
## 437 -0.048
## 438 -0.038
## 402 -0.068
## 524 -0.049
## 247 -0.174
## 233 0.111
## 442 0.047
## 181 -0.148
## 149 0.223
## 522 -0.028
## 236 -0.104
## 165 -0.231
## 150 0.279
## 530 0.033
## 161 -0.105
## 173 0.302
## 221 -0.115
## 141 -0.114
## 193 0.221
## 358 -0.054
## 225 0.095
## 458 0.054
## 459 -0.054Modification indicies for complet cases
modindices(nine.fit1, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all
## 129 Acomplishment =~ PERMA_P3_1 33.400 0.806 1.497 0.744
## 425 PERMA_P1_1 ~~ PERMA_P2 30.990 0.712 0.712 0.144
## 436 PERMA_P1_1 ~~ PERMA_Hea1_1 26.892 0.382 0.382 0.071
## 471 PERMA_R1_1 ~~ PERMA_R3_1 21.129 -0.940 -0.940 -0.161
## 146 Engagement =~ PERMA_A3_1 18.040 0.919 1.438 0.684
## 232 NegativeEmotion =~ PERMA_P3_1 17.845 0.253 0.453 0.225
## 166 PositiveEmotion =~ PERMA_A3_1 17.207 0.573 1.154 0.549
## 135 Acomplishment =~ PERMA_M3_1 17.191 1.151 2.137 0.954
## 360 PERMA_A3_1 ~~ PERMA_M1_1 17.112 -0.548 -0.548 -0.111
## 212 Meaning =~ PERMA_P3_1 16.786 0.670 1.336 0.664
## 167 PositiveEmotion =~ PERMA_E1_1 16.023 -0.659 -1.327 -0.649
## 406 PERMA_E2_1 ~~ PERMA_Lone_1 15.460 -0.853 -0.853 -0.149
## 446 PERMA_P2 ~~ PERMA_M2_1 14.405 0.286 0.286 0.055
## 268 Lonely =~ PERMA_E2_1 14.199 -0.143 -0.387 -0.182
## 464 PERMA_P3_1 ~~ PERMA_N3_1 13.865 0.574 0.574 0.117
## 441 PERMA_P2 ~~ PERMA_P3_1 13.508 -0.356 -0.356 -0.080
## 432 PERMA_P1_1 ~~ PERMA_M3_1 13.456 -0.330 -0.330 -0.066
## 551 PERMA_Hea1_1 ~~ PERMA_Hea2_1 12.750 -0.498 -0.498 -0.079
## 175 PositiveEmotion =~ PERMA_M3_1 12.749 -0.564 -1.135 -0.507
## 149 Engagement =~ PERMA_P3_1 12.281 0.691 1.081 0.537
## 227 NegativeEmotion =~ PERMA_E1_1 12.066 0.202 0.362 0.177
## 395 PERMA_E2_1 ~~ PERMA_R2_1 11.997 -0.432 -0.432 -0.083
## 367 PERMA_A3_1 ~~ PERMA_Hea2_1 11.884 -0.489 -0.489 -0.090
## 524 PERMA_M2_1 ~~ PERMA_HPY_1 11.502 -0.280 -0.280 -0.057
## 156 Engagement =~ PERMA_N1_1 11.425 0.274 0.428 0.176
## 244 Health =~ PERMA_A1_1 11.213 -0.270 -0.608 -0.275
## 151 Engagement =~ PERMA_R2_1 10.694 -0.789 -1.235 -0.504
## 164 PositiveEmotion =~ PERMA_A1_1 10.243 -0.446 -0.897 -0.405
## 331 PERMA_A2_1 ~~ PERMA_E1_1 10.240 0.385 0.385 0.087
## 171 PositiveEmotion =~ PERMA_R2_1 10.213 -0.794 -1.600 -0.653
## 423 PERMA_E3_1 ~~ PERMA_Lone_1 10.031 0.805 0.805 0.128
## 267 Lonely =~ PERMA_E1_1 9.762 0.112 0.302 0.148
## 392 PERMA_E2_1 ~~ PERMA_P2 9.729 0.243 0.243 0.051
## 437 PERMA_P1_1 ~~ PERMA_Hea2_1 9.642 -0.301 -0.301 -0.052
## 136 Acomplishment =~ PERMA_N1_1 9.611 0.210 0.389 0.160
## 216 Meaning =~ PERMA_N1_1 9.591 0.194 0.387 0.159
## 210 Meaning =~ PERMA_P1_1 9.521 -0.418 -0.834 -0.375
## 176 PositiveEmotion =~ PERMA_N1_1 9.337 0.189 0.381 0.157
## 368 PERMA_A3_1 ~~ PERMA_Hea3_1 9.312 0.312 0.312 0.062
## 127 Acomplishment =~ PERMA_P1_1 9.254 -0.354 -0.658 -0.296
## 501 PERMA_R3_1 ~~ PERMA_Hea1_1 9.090 -0.341 -0.341 -0.060
## 424 PERMA_E3_1 ~~ PERMA_HPY_1 8.949 0.328 0.328 0.066
## 291 Happy =~ PERMA_E3_1 8.946 0.389 0.823 0.352
## 469 PERMA_P3_1 ~~ PERMA_HPY_1 8.917 0.273 0.273 0.064
## 192 Relationship =~ PERMA_P3_1 8.902 0.554 1.040 0.517
## 158 Engagement =~ PERMA_N3_1 8.730 -0.237 -0.370 -0.152
## 259 Health =~ PERMA_N1_1 8.689 0.165 0.373 0.153
## 447 PERMA_P2 ~~ PERMA_M3_1 8.673 -0.246 -0.246 -0.049
## 332 PERMA_A2_1 ~~ PERMA_E2_1 8.623 -0.305 -0.305 -0.066
## 312 PERMA_A1_1 ~~ PERMA_E3_1 8.480 -0.446 -0.446 -0.086
## 238 NegativeEmotion =~ PERMA_M3_1 8.439 0.163 0.291 0.130
## 412 PERMA_E3_1 ~~ PERMA_R2_1 8.384 0.539 0.539 0.094
## 289 Happy =~ PERMA_E1_1 8.371 -0.314 -0.665 -0.326
## 196 Relationship =~ PERMA_N1_1 8.283 0.196 0.367 0.151
## 555 PERMA_Hea2_1 ~~ PERMA_Hea3_1 8.197 0.402 0.402 0.065
## 181 PositiveEmotion =~ PERMA_Hea3_1 7.938 -0.233 -0.469 -0.195
## 294 Happy =~ PERMA_P3_1 7.923 0.378 0.801 0.398
## 308 PERMA_A1_1 ~~ PERMA_A2_1 7.882 0.433 0.433 0.091
## 426 PERMA_P1_1 ~~ PERMA_P3_1 7.548 -0.283 -0.283 -0.063
## 178 PositiveEmotion =~ PERMA_N3_1 7.155 -0.164 -0.330 -0.136
## 470 PERMA_R1_1 ~~ PERMA_R2_1 7.093 0.570 0.570 0.094
## 419 PERMA_E3_1 ~~ PERMA_N3_1 7.058 -0.495 -0.495 -0.087
## 345 PERMA_A2_1 ~~ PERMA_N3_1 7.006 0.372 0.372 0.071
## 179 PositiveEmotion =~ PERMA_Hea1_1 6.869 0.219 0.441 0.182
## 184 Relationship =~ PERMA_A1_1 6.830 -0.369 -0.693 -0.313
## 301 Happy =~ PERMA_N1_1 6.631 0.149 0.316 0.130
## 400 PERMA_E2_1 ~~ PERMA_N1_1 6.603 0.347 0.347 0.067
## 296 Happy =~ PERMA_R2_1 6.598 -0.315 -0.666 -0.272
## 297 Happy =~ PERMA_R3_1 6.476 0.299 0.632 0.269
## 150 Engagement =~ PERMA_R1_1 6.447 0.614 0.961 0.387
## 214 Meaning =~ PERMA_R2_1 6.351 -0.412 -0.821 -0.335
## 337 PERMA_A2_1 ~~ PERMA_R1_1 6.346 0.381 0.381 0.071
## 461 PERMA_P3_1 ~~ PERMA_M3_1 6.227 0.320 0.320 0.071
## 298 Happy =~ PERMA_M1_1 6.179 0.256 0.542 0.232
## 228 NegativeEmotion =~ PERMA_E2_1 6.153 -0.154 -0.275 -0.129
## 173 PositiveEmotion =~ PERMA_M1_1 6.021 0.397 0.798 0.341
## 198 Relationship =~ PERMA_N3_1 6.012 -0.165 -0.310 -0.128
## 484 PERMA_R2_1 ~~ PERMA_M1_1 5.966 0.336 0.336 0.059
## 131 Acomplishment =~ PERMA_R2_1 5.930 -0.425 -0.789 -0.322
## 309 PERMA_A1_1 ~~ PERMA_A3_1 5.908 -0.347 -0.347 -0.075
## 450 PERMA_P2 ~~ PERMA_N3_1 5.723 -0.246 -0.246 -0.045
## 169 PositiveEmotion =~ PERMA_E3_1 5.674 0.461 0.928 0.397
## 372 PERMA_E1_1 ~~ PERMA_E3_1 5.666 0.397 0.397 0.083
## 336 PERMA_A2_1 ~~ PERMA_P3_1 5.643 0.302 0.302 0.069
## 172 PositiveEmotion =~ PERMA_R3_1 5.467 0.547 1.100 0.468
## 438 PERMA_P1_1 ~~ PERMA_Hea3_1 5.447 -0.165 -0.165 -0.031
## 288 Happy =~ PERMA_A3_1 5.326 0.221 0.467 0.222
## 201 Relationship =~ PERMA_Hea3_1 5.302 -0.208 -0.390 -0.162
## 186 Relationship =~ PERMA_A3_1 5.189 0.323 0.608 0.289
## 133 Acomplishment =~ PERMA_M1_1 4.938 -0.625 -1.160 -0.496
## 333 PERMA_A2_1 ~~ PERMA_E3_1 4.842 -0.338 -0.338 -0.067
## 147 Engagement =~ PERMA_P1_1 4.783 -0.349 -0.546 -0.246
## 272 Lonely =~ PERMA_P3_1 4.700 0.080 0.215 0.107
## 421 PERMA_E3_1 ~~ PERMA_Hea2_1 4.675 0.364 0.364 0.060
## 218 Meaning =~ PERMA_N3_1 4.672 -0.134 -0.268 -0.110
## 303 Happy =~ PERMA_N3_1 4.591 -0.123 -0.260 -0.107
## 221 Meaning =~ PERMA_Hea3_1 4.555 -0.154 -0.308 -0.128
## 530 PERMA_M3_1 ~~ PERMA_Hea3_1 4.431 0.188 0.188 0.035
## 459 PERMA_P3_1 ~~ PERMA_M1_1 4.384 -0.266 -0.266 -0.057
## 314 PERMA_A1_1 ~~ PERMA_P2 4.379 -0.178 -0.178 -0.036
## 344 PERMA_A2_1 ~~ PERMA_N2_1 4.345 -0.333 -0.333 -0.062
## 491 PERMA_R2_1 ~~ PERMA_Hea2_1 4.304 0.310 0.310 0.049
## 161 Engagement =~ PERMA_Hea3_1 4.193 -0.164 -0.257 -0.107
## 273 Lonely =~ PERMA_R1_1 4.190 0.095 0.256 0.103
## 246 Health =~ PERMA_A3_1 4.164 0.167 0.377 0.179
## 554 PERMA_Hea1_1 ~~ PERMA_HPY_1 4.113 -0.134 -0.134 -0.026
## 453 PERMA_P2 ~~ PERMA_Hea3_1 4.108 -0.133 -0.133 -0.025
## 193 Relationship =~ PERMA_M1_1 4.086 0.261 0.489 0.209
## 532 PERMA_M3_1 ~~ PERMA_HPY_1 4.056 0.170 0.170 0.036
## 138 Acomplishment =~ PERMA_N3_1 4.020 -0.134 -0.249 -0.103
## 398 PERMA_E2_1 ~~ PERMA_M2_1 3.928 0.181 0.181 0.037
## 187 Relationship =~ PERMA_E1_1 3.926 -0.314 -0.590 -0.289
## 247 Health =~ PERMA_E1_1 3.898 -0.136 -0.306 -0.150
## 388 PERMA_E1_1 ~~ PERMA_Lone_1 3.866 0.400 0.400 0.073
## sepc.nox
## 129 0.744
## 425 0.144
## 436 0.071
## 471 -0.161
## 146 0.684
## 232 0.225
## 166 0.549
## 135 0.954
## 360 -0.111
## 212 0.664
## 167 -0.649
## 406 -0.149
## 446 0.055
## 268 -0.182
## 464 0.117
## 441 -0.080
## 432 -0.066
## 551 -0.079
## 175 -0.507
## 149 0.537
## 227 0.177
## 395 -0.083
## 367 -0.090
## 524 -0.057
## 156 0.176
## 244 -0.275
## 151 -0.504
## 164 -0.405
## 331 0.087
## 171 -0.653
## 423 0.128
## 267 0.148
## 392 0.051
## 437 -0.052
## 136 0.160
## 216 0.159
## 210 -0.375
## 176 0.157
## 368 0.062
## 127 -0.296
## 501 -0.060
## 424 0.066
## 291 0.352
## 469 0.064
## 192 0.517
## 158 -0.152
## 259 0.153
## 447 -0.049
## 332 -0.066
## 312 -0.086
## 238 0.130
## 412 0.094
## 289 -0.326
## 196 0.151
## 555 0.065
## 181 -0.195
## 294 0.398
## 308 0.091
## 426 -0.063
## 178 -0.136
## 470 0.094
## 419 -0.087
## 345 0.071
## 179 0.182
## 184 -0.313
## 301 0.130
## 400 0.067
## 296 -0.272
## 297 0.269
## 150 0.387
## 214 -0.335
## 337 0.071
## 461 0.071
## 298 0.232
## 228 -0.129
## 173 0.341
## 198 -0.128
## 484 0.059
## 131 -0.322
## 309 -0.075
## 450 -0.045
## 169 0.397
## 372 0.083
## 336 0.069
## 172 0.468
## 438 -0.031
## 288 0.222
## 201 -0.162
## 186 0.289
## 133 -0.496
## 333 -0.067
## 147 -0.246
## 272 0.107
## 421 0.060
## 218 -0.110
## 303 -0.107
## 221 -0.128
## 530 0.035
## 459 -0.057
## 314 -0.036
## 344 -0.062
## 491 0.049
## 161 -0.107
## 273 0.103
## 246 0.179
## 554 -0.026
## 453 -0.025
## 193 0.209
## 532 0.036
## 138 -0.103
## 398 0.037
## 187 -0.289
## 247 -0.150
## 388 0.073Fit Measures for all data
fitmeasures(nine.fit)## npar fmin chisq
## 103.000 0.471 448.510
## df pvalue baseline.chisq
## 196.000 0.000 5451.520
## baseline.df baseline.pvalue cfi
## 253.000 0.000 0.951
## tli nnfi rfi
## 0.937 0.937 0.894
## nfi pnfi ifi
## 0.918 0.711 0.952
## rni logl unrestricted.logl
## 0.951 -19782.321 -19558.066
## aic bic ntotal
## 39770.641 40199.679 476.000
## bic2 rmsea rmsea.ci.lower
## 39872.772 0.052 0.046
## rmsea.ci.upper rmsea.pvalue rmr
## 0.058 0.292 0.262
## rmr_nomean srmr srmr_bentler
## 0.273 0.042 0.042
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.043 0.042 0.043
## srmr_mplus srmr_mplus_nomean cn_05
## 0.042 0.043 244.740
## cn_01 gfi agfi
## 260.992 0.975 0.962
## pgfi mfi ecvi
## 0.639 0.767 NAFit Measures for complate cases
fitmeasures(nine.fit1)## npar fmin chisq
## 103.000 1.120 535.393
## df pvalue baseline.chisq
## 196.000 0.000 5049.316
## baseline.df baseline.pvalue cfi
## 253.000 0.000 0.929
## tli nnfi rfi
## 0.909 0.909 0.863
## nfi pnfi ifi
## 0.894 0.693 0.930
## rni logl unrestricted.logl
## 0.929 -10139.711 -9872.014
## aic bic ntotal
## 20485.421 20843.497 239.000
## bic2 rmsea rmsea.ci.lower
## 20517.016 0.085 0.077
## rmsea.ci.upper rmsea.pvalue rmr
## 0.094 0.000 0.261
## rmr_nomean srmr srmr_bentler
## 0.272 0.050 0.050
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.052 0.050 0.052
## srmr_mplus srmr_mplus_nomean cn_05
## 0.050 0.052 103.522
## cn_01 gfi agfi
## 110.358 0.953 0.928
## pgfi mfi ecvi
## 0.624 0.492 NACreate dataset for Target rotation for all data
PermaTR<-select(data, PERMA_P1_1, PERMA_P2, PERMA_P3_1, PERMA_E1_1, PERMA_E2_1, PERMA_E3_1, PERMA_R1_1, PERMA_R2_1, PERMA_R3_1, PERMA_M1_1, PERMA_M2_1, PERMA_M3_1, PERMA_A1_1, PERMA_A2_1, PERMA_A3_1, PERMA_N1_1, PERMA_N2_1, PERMA_N3_1, PERMA_Hea1_1, PERMA_Hea2_1, PERMA_Hea3_1, PERMA_Lone_1, PERMA_HPY_1)
colnames(PermaTR) <- c("1","2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23")
PermaTR<-tbl_df(PermaTR)
PermaTR## Source: local data frame [753 x 23]
##
## 1 2 3 4 5 6 7 8 9 10 11 12
## (int) (int) (int) (int) (int) (int) (int) (int) (int) (int) (int) (int)
## 1 7 7 8 8 7 7 9 11 8 NA NA NA
## 2 9 9 9 7 11 7 11 11 11 NA NA NA
## 3 2 3 5 3 3 5 2 3 2 NA NA NA
## 4 7 7 7 9 6 11 8 7 7 NA NA NA
## 5 8 4 9 6 7 8 7 11 7 NA NA NA
## 6 7 7 8 6 6 6 6 9 7 NA NA NA
## 7 9 7 9 5 8 9 8 11 11 NA NA NA
## 8 11 9 8 8 9 8 5 9 7 NA NA NA
## 9 5 5 5 5 7 7 5 7 5 NA NA NA
## 10 9 9 8 9 9 9 11 8 9 NA NA NA
## .. ... ... ... ... ... ... ... ... ... ... ... ...
## Variables not shown: 13 (int), 14 (int), 15 (int), 16 (int), 17 (int), 18
## (int), 19 (int), 20 (int), 21 (int), 22 (int), 23 (int)Target Rotation for all 753 cases
Targ_key <- make.keys(23,list(f1=1:3,f2=4:6, f3=7:9, f4=10:12, f5=13:15, f6=16:18, f7=19:21, f8=22, f9=23))fix the 0s, allow the NAs to be estimated
Targ_key <- scrub(Targ_key,isvalue=1)
Targ_key <- list(Targ_key)convert the raw data to correlation matrix uisng FIML
Perma_cor <- corFiml(PermaTR) TargetT for orthogonal rotation
out_targetQ <- fa(Perma_cor,9,rotate="TargetQ", n.obs = 753, Target=Targ_key)
out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA","uniquenesses")]## $loadings
##
## Loadings:
## MR7 MR4 MR6 MR5 MR3 MR1 MR2 MR9 MR8
## 1 0.207 0.209 0.594 0.111
## 2 0.167 0.124 0.114 -0.128 0.387 0.311 0.210 0.169
## 3 0.143 0.148 0.155 0.301 0.138
## 4 0.222 0.264 0.250 0.122 0.144 0.115
## 5 0.202 0.194 0.131 0.182 0.259
## 6 0.176 0.144 0.106 0.336 -0.120
## 7 0.210 0.134 0.126 0.278 0.307
## 8 0.128 0.162 0.445 0.211
## 9 -0.118 0.126 0.644 0.108 0.129
## 10 0.485 0.142 0.169 0.183 0.163
## 11 0.490 0.169 0.326 0.232 -0.173 0.135
## 12 0.836 0.261 -0.181 0.195 -0.113
## 13 0.414 0.207 0.213 -0.128 0.276
## 14 0.268 0.274 0.114 0.241 -0.103 0.123
## 15 0.774 -0.112
## 16 0.130 0.109 0.637 -0.133 -0.139
## 17 -0.188 0.457
## 18 -0.153 0.548 -0.131
## 19 0.879 0.103 0.315 -0.210
## 20 0.816 -0.127 0.154 -0.167 0.273
## 21 0.788 0.182
## 22 -0.136 0.366 -0.111 0.149 -0.110
## 23 0.301 -0.112 0.210 0.276 0.131 0.418
##
## MR7 MR4 MR6 MR5 MR3 MR1 MR2 MR9 MR8
## SS loadings 2.418 1.683 1.199 1.060 1.035 0.996 0.580 0.532 0.384
## Proportion Var 0.105 0.073 0.052 0.046 0.045 0.043 0.025 0.023 0.017
## Cumulative Var 0.105 0.178 0.230 0.277 0.322 0.365 0.390 0.413 0.430
##
## $score.cor
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 1.0000000 0.7058693 -0.204665222 0.52084912 0.6723104 0.7174397
## [2,] 0.7058693 1.0000000 -0.119884210 0.69639056 0.7490928 0.8100801
## [3,] -0.2046652 -0.1198842 1.000000000 0.02567865 -0.1093866 -0.1728783
## [4,] 0.5208491 0.6963906 0.025678651 1.00000000 0.6058066 0.6251798
## [5,] 0.6723104 0.7490928 -0.109386574 0.60580663 1.0000000 0.6828303
## [6,] 0.7174397 0.8100801 -0.172878315 0.62517981 0.6828303 1.0000000
## [7,] 0.3916259 0.3960123 0.071950342 0.45366777 0.3850544 0.4016295
## [8,] 0.4723622 0.5220183 -0.008784135 0.43939959 0.5043578 0.4772856
## [,7] [,8]
## [1,] 0.39162586 0.472362173
## [2,] 0.39601228 0.522018319
## [3,] 0.07195034 -0.008784135
## [4,] 0.45366777 0.439399592
## [5,] 0.38505445 0.504357778
## [6,] 0.40162951 0.477285566
## [7,] 1.00000000 0.281441456
## [8,] 0.28144146 1.000000000
##
## $TLI
## [1] 0.926751
##
## $RMSEA
## RMSEA lower upper confidence
## 0.06685246 0.05894545 0.07315164 0.10000000
##
## $uniquenesses
## 1 2 3 4 5 6
## 0.176143459 0.163453046 0.487072308 0.509555858 0.428079401 0.669009210
## 7 8 9 10 11 12
## 0.520414298 0.449149942 0.405648445 0.226935714 0.078788626 0.049782875
## 13 14 15 16 17 18
## 0.282094929 0.456052517 0.271207305 0.594297660 0.739823083 0.607568317
## 19 20 21 22 23
## 0.004990261 0.085039669 0.137257055 0.774222768 0.190657101out_targetQ## Factor Analysis using method = minres
## Call: fa(r = Perma_cor, nfactors = 9, n.obs = 753, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR7 MR4 MR6 MR5 MR3 MR1 MR2 MR9 MR8 h2 u2 com
## 1 0.21 0.04 -0.01 0.05 0.21 0.59 0.01 0.11 -0.06 0.82 0.176 1.6
## 2 0.17 0.12 -0.06 0.11 -0.13 0.39 0.31 0.21 0.17 0.84 0.163 4.4
## 3 0.14 0.15 0.03 0.15 0.30 0.08 -0.01 0.04 0.14 0.51 0.487 3.4
## 4 -0.05 0.01 0.22 0.26 0.25 0.12 0.14 0.12 0.09 0.49 0.510 4.8
## 5 0.01 0.20 0.04 0.19 0.13 0.18 0.26 0.06 0.05 0.57 0.428 4.6
## 6 0.18 -0.04 0.14 0.05 0.11 0.08 0.34 0.09 -0.12 0.33 0.669 3.0
## 7 0.21 -0.07 0.08 0.08 0.13 -0.07 0.13 0.28 0.31 0.48 0.520 4.2
## 8 0.13 0.16 -0.02 -0.05 0.45 0.02 0.04 0.21 0.02 0.55 0.449 2.0
## 9 0.07 -0.12 -0.05 0.13 0.64 0.11 0.13 -0.04 -0.06 0.59 0.406 1.4
## 10 0.08 0.49 -0.06 -0.09 0.14 0.17 0.08 0.18 0.16 0.77 0.227 2.3
## 11 0.06 0.49 -0.06 0.17 -0.04 0.33 0.23 -0.17 0.14 0.92 0.079 3.2
## 12 0.09 0.84 0.03 0.26 -0.01 -0.18 -0.07 0.19 -0.11 0.95 0.050 1.5
## 13 0.01 0.41 0.03 0.21 0.21 0.04 0.04 -0.13 0.28 0.72 0.282 3.2
## 14 0.27 0.27 0.11 0.24 0.08 -0.05 0.07 -0.10 0.12 0.54 0.456 4.5
## 15 0.03 0.04 -0.08 0.77 -0.02 0.06 0.09 0.06 -0.11 0.73 0.271 1.1
## 16 0.13 0.11 0.64 -0.13 -0.07 0.05 0.06 -0.09 -0.14 0.41 0.594 1.5
## 17 -0.19 -0.02 0.46 0.00 -0.03 0.08 0.08 0.05 -0.01 0.26 0.740 1.5
## 18 -0.05 -0.15 0.55 0.09 0.05 -0.13 0.01 -0.02 0.10 0.39 0.608 1.4
## 19 0.88 0.01 0.10 0.06 -0.09 0.31 -0.21 0.03 0.08 1.00 0.005 1.5
## 20 0.82 0.09 -0.10 -0.13 0.15 -0.17 0.27 -0.08 -0.04 0.91 0.085 1.5
## 21 0.79 -0.02 -0.06 0.18 0.07 0.01 0.01 0.01 -0.03 0.86 0.137 1.1
## 22 -0.14 0.02 0.37 -0.03 -0.02 -0.11 0.15 0.01 -0.11 0.23 0.774 2.1
## 23 -0.07 0.30 -0.11 -0.01 0.21 0.28 0.13 0.42 -0.01 0.81 0.191 3.8
##
## MR7 MR4 MR6 MR5 MR3 MR1 MR2 MR9 MR8
## SS loadings 3.17 2.60 1.23 1.72 1.77 1.62 1.10 0.85 0.62
## Proportion Var 0.14 0.11 0.05 0.07 0.08 0.07 0.05 0.04 0.03
## Cumulative Var 0.14 0.25 0.30 0.38 0.46 0.53 0.57 0.61 0.64
## Proportion Explained 0.22 0.18 0.08 0.12 0.12 0.11 0.08 0.06 0.04
## Cumulative Proportion 0.22 0.39 0.48 0.59 0.71 0.82 0.90 0.96 1.00
##
## With factor correlations of
## MR7 MR4 MR6 MR5 MR3 MR1 MR2 MR9 MR8
## MR7 1.00 0.51 -0.11 0.47 0.54 0.42 0.39 0.28 0.30
## MR4 0.51 1.00 -0.06 0.51 0.53 0.43 0.44 0.27 0.39
## MR6 -0.11 -0.06 1.00 0.13 0.02 -0.13 0.12 0.00 0.02
## MR5 0.47 0.51 0.13 1.00 0.46 0.37 0.31 0.31 0.26
## MR3 0.54 0.53 0.02 0.46 1.00 0.35 0.42 0.35 0.30
## MR1 0.42 0.43 -0.13 0.37 0.35 1.00 0.32 0.26 0.25
## MR2 0.39 0.44 0.12 0.31 0.42 0.32 1.00 0.24 0.24
## MR9 0.28 0.27 0.00 0.31 0.35 0.26 0.24 1.00 0.20
## MR8 0.30 0.39 0.02 0.26 0.30 0.25 0.24 0.20 1.00
##
## Mean item complexity = 2.6
## Test of the hypothesis that 9 factors are sufficient.
##
## The degrees of freedom for the null model are 253 and the objective function was 15.69 with Chi Square of 11665.09
## The degrees of freedom for the model are 82 and the objective function was 0.48
##
## 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 753 with the empirical chi square 126.56 with prob < 0.0012
## The total number of observations was 753 with MLE Chi Square = 350.7 with prob < 6.4e-35
##
## Tucker Lewis Index of factoring reliability = 0.927
## RMSEA index = 0.067 and the 90 % confidence intervals are 0.059 0.073
## BIC = -192.48
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR7 MR4 MR6 MR5 MR3
## Correlation of scores with factors 0.99 0.97 0.83 0.90 0.89
## Multiple R square of scores with factors 0.98 0.94 0.69 0.82 0.79
## Minimum correlation of possible factor scores 0.95 0.87 0.38 0.64 0.58
## MR1 MR2 MR9 MR8
## Correlation of scores with factors 0.94 0.90 0.85 0.77
## Multiple R square of scores with factors 0.88 0.81 0.73 0.59
## Minimum correlation of possible factor scores 0.75 0.61 0.45 0.19CFI
1-((out_targetQ$STATISTIC - out_targetQ$dof)/(out_targetQ$null.chisq- out_targetQ$null.dof))## [1] 0.9764551Complete 239 cases
PermaTR<-select(data1, PERMA_P1_1, PERMA_P2, PERMA_P3_1, PERMA_E1_1, PERMA_E2_1, PERMA_E3_1, PERMA_R1_1, PERMA_R2_1, PERMA_R3_1, PERMA_M1_1, PERMA_M2_1, PERMA_M3_1, PERMA_A1_1, PERMA_A2_1, PERMA_A3_1, PERMA_N1_1, PERMA_N2_1, PERMA_N3_1, PERMA_Hea1_1, PERMA_Hea2_1, PERMA_Hea3_1, PERMA_Lone_1, PERMA_HPY_1)
colnames(PermaTR) <- c("1","2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23")
PermaTR<-tbl_df(PermaTR)
PermaTR## Source: local data frame [239 x 23]
##
## 1 2 3 4 5 6 7 8 9 10 11 12
## (int) (int) (int) (int) (int) (int) (int) (int) (int) (int) (int) (int)
## 1 11 11 11 9 11 11 11 11 11 7 11 11
## 2 11 11 11 11 11 11 11 11 11 11 11 11
## 3 11 11 11 11 11 11 11 11 11 11 11 11
## 4 6 8 10 11 10 11 9 8 9 8 8 9
## 5 11 11 11 11 11 11 11 11 11 11 11 11
## 6 2 6 6 6 6 4 3 1 5 6 6 4
## 7 6 5 6 6 5 6 6 5 6 6 5 6
## 8 7 5 5 10 7 7 4 8 8 6 7 9
## 9 10 9 3 10 9 11 9 11 11 10 10 11
## 10 8 8 8 8 8 8 8 8 8 8 8 8
## .. ... ... ... ... ... ... ... ... ... ... ... ...
## Variables not shown: 13 (int), 14 (int), 15 (int), 16 (int), 17 (int), 18
## (int), 19 (int), 20 (int), 21 (int), 22 (int), 23 (int)Target Roratation for complete cases
Targ_key <- make.keys(23,list(f1=1:3,f2=4:6, f3=7:9, f4=10:12, f5=13:15, f6=16:18, f7=19:21, f8=22, f9=23))fix the 0s, allow the NAs to be estimated
Targ_key <- scrub(Targ_key,isvalue=1)
Targ_key <- list(Targ_key)convert the raw data to correlation matrix uisng FIML
Perma_cor <- corFiml(PermaTR)TargetT for orthogonal rotation
out_targetQ <- fa(Perma_cor,9,rotate="TargetQ", n.obs = 239, Target=Targ_key)## Warning in GPArotation::GPFoblq(L, Tmat = Tmat, normalize = normalize, eps
## = eps, : convergence not obtained in GPFoblq. 1000 iterations used.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate =
## rotate, : A Heywood case was detected. Examine the loadings carefully.out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA","uniquenesses")]## $loadings
##
## Loadings:
## MR7 MR6 MR2 MR3 MR4 MR1 MR5 MR8 MR9
## 1 0.139 0.176 0.648
## 2 0.135 0.134 0.120 0.187 0.515
## 3 0.120 0.269 0.397
## 4 0.111 0.505 0.103 0.247
## 5 0.159 0.669 0.150 -0.424
## 6 0.901 -0.140 0.293 -0.108
## 7 0.167 0.543 0.454
## 8 0.108 0.657 -0.123 0.105
## 9 0.883 -0.146 -0.344
## 10 0.111 0.138 0.522 0.165
## 11 0.120 0.508 0.263 0.148
## 12 0.155 0.190 0.424 0.343
## 13 0.143 0.493 0.306
## 14 0.180 0.191 0.295 0.373 0.115
## 15 0.149 0.288 0.188 0.485 -0.122
## 16 0.805 0.118 0.109 -0.126
## 17 -0.137 0.686 0.110
## 18 0.786 0.163 0.135
## 19 0.841 0.243
## 20 0.801 0.140 0.118 -0.147 -0.128
## 21 1.005
## 22 0.544 -0.137 0.315
## 23 -0.112 0.250 0.212 0.220 0.327 0.146
##
## MR7 MR6 MR2 MR3 MR4 MR1 MR5 MR8 MR9
## SS loadings 2.565 2.113 1.823 1.813 1.209 1.076 0.971 0.452 0.405
## Proportion Var 0.112 0.092 0.079 0.079 0.053 0.047 0.042 0.020 0.018
## Cumulative Var 0.112 0.203 0.283 0.361 0.414 0.461 0.503 0.523 0.540
##
## $score.cor
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 1.0000000 -0.14023208 0.67056875 0.7214719 0.71029665 0.75975437
## [2,] -0.1402321 1.00000000 0.04298947 -0.0172183 -0.04028287 -0.09891012
## [3,] 0.6705688 0.04298947 1.00000000 0.7643231 0.81162708 0.82718769
## [4,] 0.7214719 -0.01721830 0.76432308 1.0000000 0.73222796 0.78332046
## [5,] 0.7102966 -0.04028287 0.81162708 0.7322280 1.00000000 0.79391012
## [6,] 0.7597544 -0.09891012 0.82718769 0.7833205 0.79391012 1.00000000
## [7,] 0.7031188 0.08534509 0.78828997 0.7215718 0.81873658 0.75590101
## [,7]
## [1,] 0.70311876
## [2,] 0.08534509
## [3,] 0.78828997
## [4,] 0.72157182
## [5,] 0.81873658
## [6,] 0.75590101
## [7,] 1.00000000
##
## $TLI
## [1] 0.9656622
##
## $RMSEA
## RMSEA lower upper confidence
## 0.05464769 0.03372761 0.06589380 0.10000000
##
## $uniquenesses
## 1 2 3 4 5 6
## 0.103894397 0.126363645 0.405407550 0.381724879 0.004996017 0.148932906
## 7 8 9 10 11 12
## 0.198180762 0.317123804 0.081367146 0.236746530 0.177439941 0.254782518
## 13 14 15 16 17 18
## 0.248284151 0.268594084 0.331367658 0.379036858 0.449517491 0.211339465
## 19 20 21 22 23
## 0.129008213 0.214805396 0.060272826 0.513616530 0.157691193out_targetQ## Factor Analysis using method = minres
## Call: fa(r = Perma_cor, nfactors = 9, n.obs = 239, rotate = "TargetQ",
## Target = Targ_key)
##
## Warning: A Heywood case was detected.
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR7 MR6 MR2 MR3 MR4 MR1 MR5 MR8 MR9 h2 u2 com
## 1 0.14 -0.05 0.03 0.18 0.07 0.65 0.04 0.04 -0.04 0.90 0.104 1.3
## 2 0.14 -0.04 0.13 0.12 0.19 0.52 0.02 -0.02 0.02 0.87 0.126 1.7
## 3 0.12 0.06 0.06 0.27 0.07 0.03 0.40 0.04 -0.02 0.59 0.405 2.2
## 4 -0.06 0.11 0.50 0.10 0.08 0.02 0.25 0.02 0.06 0.62 0.382 1.8
## 5 0.07 0.16 0.67 0.03 0.02 0.15 0.07 -0.42 0.07 1.00 0.005 2.0
## 6 0.05 -0.08 0.90 0.04 0.03 -0.08 -0.14 0.29 -0.11 0.85 0.149 1.3
## 7 0.07 0.00 0.17 0.54 -0.09 0.08 0.05 0.03 0.45 0.80 0.198 2.3
## 8 0.11 0.02 0.08 0.66 0.05 0.07 -0.12 0.11 0.06 0.68 0.317 1.3
## 9 0.02 0.06 -0.04 0.88 -0.04 0.05 0.06 -0.15 -0.34 0.92 0.081 1.4
## 10 0.11 -0.05 0.09 0.14 0.52 0.16 0.01 0.00 0.03 0.76 0.237 1.5
## 11 0.09 -0.03 0.12 -0.01 0.51 0.26 0.15 -0.05 -0.03 0.82 0.177 2.0
## 12 0.15 -0.02 0.19 0.00 0.42 -0.06 0.34 0.03 0.00 0.75 0.255 2.7
## 13 0.03 0.05 0.07 0.14 0.49 -0.03 0.31 -0.09 0.06 0.75 0.248 2.1
## 14 0.18 0.06 -0.05 0.19 0.29 0.04 0.37 0.06 0.12 0.73 0.269 3.5
## 15 0.15 -0.04 0.29 -0.05 -0.07 0.19 0.48 0.04 -0.12 0.67 0.331 2.5
## 16 0.09 0.81 0.12 -0.05 0.11 -0.04 -0.13 -0.01 -0.03 0.62 0.379 1.2
## 17 -0.14 0.69 0.09 0.09 -0.08 0.06 -0.05 0.11 -0.07 0.55 0.450 1.3
## 18 -0.03 0.79 -0.09 0.05 -0.09 -0.08 0.16 0.14 0.09 0.79 0.211 1.3
## 19 0.84 0.01 -0.08 -0.05 0.00 0.24 0.02 0.03 0.04 0.87 0.129 1.2
## 20 0.80 -0.01 0.07 0.14 0.12 -0.15 -0.13 -0.03 0.00 0.79 0.215 1.3
## 21 1.00 -0.01 0.01 -0.02 -0.10 -0.03 0.10 0.00 -0.06 0.94 0.060 1.0
## 22 -0.01 0.54 -0.03 -0.14 0.00 0.08 0.07 0.32 -0.01 0.49 0.514 1.9
## 23 -0.01 -0.11 0.25 0.21 0.22 0.33 0.15 0.03 -0.04 0.84 0.158 4.4
##
## MR7 MR6 MR2 MR3 MR4 MR1 MR5 MR8 MR9
## SS loadings 3.33 2.20 2.67 2.69 2.15 1.93 1.70 0.51 0.42
## Proportion Var 0.14 0.10 0.12 0.12 0.09 0.08 0.07 0.02 0.02
## Cumulative Var 0.14 0.24 0.36 0.47 0.57 0.65 0.72 0.75 0.77
## Proportion Explained 0.19 0.13 0.15 0.15 0.12 0.11 0.10 0.03 0.02
## Cumulative Proportion 0.19 0.31 0.47 0.62 0.74 0.85 0.95 0.98 1.00
##
## With factor correlations of
## MR7 MR6 MR2 MR3 MR4 MR1 MR5 MR8 MR9
## MR7 1.00 -0.14 0.60 0.71 0.58 0.60 0.47 -0.02 0.08
## MR6 -0.14 1.00 0.05 -0.03 -0.05 -0.05 0.20 0.20 0.06
## MR2 0.60 0.05 1.00 0.66 0.57 0.64 0.48 0.05 0.07
## MR3 0.71 -0.03 0.66 1.00 0.61 0.59 0.48 -0.04 0.07
## MR4 0.58 -0.05 0.57 0.61 1.00 0.46 0.49 -0.10 0.10
## MR1 0.60 -0.05 0.64 0.59 0.46 1.00 0.41 -0.12 0.03
## MR5 0.47 0.20 0.48 0.48 0.49 0.41 1.00 -0.05 0.07
## MR8 -0.02 0.20 0.05 -0.04 -0.10 -0.12 -0.05 1.00 0.02
## MR9 0.08 0.06 0.07 0.07 0.10 0.03 0.07 0.02 1.00
##
## Mean item complexity = 1.9
## Test of the hypothesis that 9 factors are sufficient.
##
## The degrees of freedom for the null model are 253 and the objective function was 21.13 with Chi Square of 4848.66
## The degrees of freedom for the model are 82 and the objective function was 0.59
##
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.02
##
## The harmonic number of observations is 239 with the empirical chi square 17.69 with prob < 1
## The total number of observations was 239 with MLE Chi Square = 131.74 with prob < 0.00041
##
## Tucker Lewis Index of factoring reliability = 0.966
## RMSEA index = 0.055 and the 90 % confidence intervals are 0.034 0.066
## BIC = -317.33
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR7 MR6 MR2 MR3 MR4
## Correlation of scores with factors 0.98 0.94 0.97 0.96 0.92
## Multiple R square of scores with factors 0.96 0.88 0.95 0.93 0.85
## Minimum correlation of possible factor scores 0.93 0.75 0.89 0.86 0.70
## MR1 MR5 MR8 MR9
## Correlation of scores with factors 0.94 0.89 0.93 0.85
## Multiple R square of scores with factors 0.89 0.79 0.86 0.72
## Minimum correlation of possible factor scores 0.78 0.57 0.72 0.45CFI
1-((out_targetQ$STATISTIC - out_targetQ$dof)/(out_targetQ$null.chisq- out_targetQ$null.dof))## [1] 0.9891777