CFA & Trarget Rotation MLQ
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/allsurveysT1.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 [757 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 plots
#ggpairs(MLQ, columns = 1:15, title="Big 5 Marsh" )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 =~ NA*Purpose + Searching' #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.model = 'Purpose =~ MLQ_1 + MLQ_4 + MLQ_5 + MLQ_6 + MLQ_9
Searching =~ MLQ_2 + MLQ_3 + MLQ_7 + MLQ_8 +MLQ_10
MLQ =~ MLQ_1 + MLQ_2 + MLQ_3 + MLQ_4 + MLQ_5 + MLQ_6 + MLQ_7 + MLQ_8 + MLQ_9 + MLQ_10
'#Models bifactor with Searching and Purpose as factors corolated with the main factor
bifactor.modelWO9 = 'Purpose =~ MLQ_1 + MLQ_4 + MLQ_5 + MLQ_6
Searching =~ MLQ_2 + MLQ_3 + MLQ_7 + MLQ_8 +MLQ_10
MLQ =~ MLQ_1 + MLQ_2 + MLQ_3 + MLQ_4 + MLQ_5 + MLQ_6 + MLQ_7 + MLQ_8 + MLQ_10
'#Models bifactor with Searching and Purpose as factors corolated with the main factor leaving negatively worded questions outBifactor (like model 7 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
'#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)
one.fit=cfa(one.model, data=MLQ)
second.fit=cfa(second.model, data=MLQ)## 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.## Warning in lavaan::lavaan(model = second.model, data = MLQ, model.type =
## "cfa", : lavaan WARNING: some estimated variances are negative## Warning in lavaan::lavaan(model = second.model, data = MLQ, model.type
## = "cfa", : lavaan WARNING: covariance matrix of latent variables is not
## positive definite; use inspect(fit,"cov.lv") to investigate.bifactor.fit=cfa(bifactor.model, data=MLQ)## 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.bifactor1.fit=cfa(bifactor.model1, data=MLQ)
bifactorWO9.fit=cfa(bifactor.modelWO9, data=MLQ)## 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.## Warning in lavaan::lavaan(model = bifactor.modelWO9, data = MLQ, model.type
## = "cfa", : lavaan WARNING: covariance matrix of latent variables is not
## positive definite; use inspect(fit,"cov.lv") to investigate.bifactor1WO9.fit=cfa(bifactor.model1WO9, data=MLQ)
bifactor.negative.fit=cfa(bifactor.negative.model, data=MLQ)## 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")## Warning in sqrt(ETA2): NaNs produced## Warning in sqrt(ETA2): NaNs produced## Warning in sqrt(ETA2): NaNs produced
semPaths(bifactor.fit, whatLabels = "std", layout = "tree")
semPaths(bifactor1.fit, whatLabels = "std", layout = "tree")
semPaths(bifactorWO9.fit, whatLabels = "std", layout = "tree")
semPaths(bifactor1WO9.fit, whatLabels = "std", layout = "tree")
semPaths(bifactor.negative.fit, whatLabels = "std", layout = "tree")
#summaries
summary(two.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 35 iterations
##
## Used Total
## Number of observations 480 757
##
## Estimator ML
## Minimum Function Test Statistic 210.725
## Degrees of freedom 34
## P-value (Chi-square) 0.000
##
## Parameter estimates:
##
## Information Expected
## Standard Errors Standard
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## Purpose =~
## MLQ_1 1.000 1.344 0.766
## MLQ_4 1.002 0.057 17.445 0.000 1.346 0.812
## MLQ_5 0.815 0.052 15.539 0.000 1.095 0.725
## MLQ_6 1.046 0.060 17.514 0.000 1.405 0.816
## MLQ_9 0.698 0.074 9.420 0.000 0.938 0.452
## Searching =~
## MLQ_2 1.000 1.339 0.803
## MLQ_3 0.933 0.055 16.993 0.000 1.250 0.743
## MLQ_7 0.893 0.054 16.651 0.000 1.196 0.730
## MLQ_8 0.954 0.054 17.795 0.000 1.277 0.772
## MLQ_10 1.099 0.059 18.705 0.000 1.472 0.806
##
## Covariances:
## Purpose ~~
## Searching -0.167 0.094 -1.774 0.076 -0.093 -0.093
##
## Variances:
## MLQ_1 1.270 0.105 1.270 0.413
## MLQ_4 0.934 0.087 0.934 0.340
## MLQ_5 1.084 0.084 1.084 0.475
## MLQ_6 0.991 0.093 0.991 0.334
## MLQ_9 3.432 0.230 3.432 0.796
## MLQ_2 0.986 0.085 0.986 0.355
## MLQ_3 1.270 0.098 1.270 0.448
## MLQ_7 1.252 0.096 1.252 0.467
## MLQ_8 1.105 0.089 1.105 0.404
## MLQ_10 1.171 0.101 1.171 0.351
## Purpose 1.806 0.192 1.000 1.000
## Searching 1.794 0.177 1.000 1.000
##
## R-Square:
##
## MLQ_1 0.587
## MLQ_4 0.660
## MLQ_5 0.525
## MLQ_6 0.666
## MLQ_9 0.204
## MLQ_2 0.645
## MLQ_3 0.552
## MLQ_7 0.533
## MLQ_8 0.596
## MLQ_10 0.649summary(one.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 30 iterations
##
## Used Total
## Number of observations 480 757
##
## Estimator ML
## Minimum Function Test Statistic 1343.770
## Degrees of freedom 35
## P-value (Chi-square) 0.000
##
## Parameter estimates:
##
## Information Expected
## Standard Errors Standard
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## MLQ =~
## MLQ_1 1.000 1.345 0.767
## MLQ_2 -0.202 0.060 -3.336 0.001 -0.271 -0.163
## MLQ_3 -0.016 0.061 -0.258 0.796 -0.021 -0.013
## MLQ_4 0.994 0.057 17.339 0.000 1.337 0.807
## MLQ_5 0.811 0.052 15.490 0.000 1.091 0.722
## MLQ_6 1.041 0.060 17.464 0.000 1.401 0.813
## MLQ_7 0.006 0.060 0.103 0.918 0.008 0.005
## MLQ_8 -0.104 0.060 -1.728 0.084 -0.140 -0.085
## MLQ_9 0.721 0.074 9.753 0.000 0.970 0.467
## MLQ_10 -0.298 0.066 -4.499 0.000 -0.400 -0.219
##
## Variances:
## MLQ_1 1.266 0.105 1.266 0.412
## MLQ_2 2.706 0.175 2.706 0.973
## MLQ_3 2.833 0.183 2.833 1.000
## MLQ_4 0.959 0.087 0.959 0.349
## MLQ_5 1.091 0.085 1.091 0.478
## MLQ_6 1.004 0.093 1.004 0.339
## MLQ_7 2.683 0.173 2.683 1.000
## MLQ_8 2.717 0.176 2.717 0.993
## MLQ_9 3.372 0.227 3.372 0.782
## MLQ_10 3.178 0.207 3.178 0.952
## MLQ 1.810 0.193 1.000 1.000
##
## R-Square:
##
## MLQ_1 0.588
## MLQ_2 0.027
## MLQ_3 0.000
## MLQ_4 0.651
## MLQ_5 0.522
## MLQ_6 0.661
## MLQ_7 0.000
## MLQ_8 0.007
## MLQ_9 0.218
## MLQ_10 0.048summary(second.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 33 iterations
##
## Used Total
## Number of observations 480 757
##
## Estimator ML
## Minimum Function Test Statistic 210.725
## Degrees of freedom 32
## P-value (Chi-square) 0.000
##
## Parameter estimates:
##
## Information Expected
## Standard Errors Standard## Warning in sqrt(ETA2): NaNs produced## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## Purpose =~
## MLQ_1 1.000 1.344 0.766
## MLQ_4 1.002 1.346 0.812
## MLQ_5 0.815 1.095 0.725
## MLQ_6 1.046 1.405 0.816
## MLQ_9 0.698 0.938 0.452
## Searching =~
## MLQ_2 1.000 1.339 0.803
## MLQ_3 0.933 1.250 0.743
## MLQ_7 0.893 1.196 0.730
## MLQ_8 0.954 1.277 0.772
## MLQ_10 1.099 1.472 0.806
## MLQ =~
## Purpose 0.522 NaN NaN
## Searching 0.508 NaN NaN
##
## Variances:
## MLQ_1 1.270 1.270 0.413
## MLQ_4 0.934 0.934 0.340
## MLQ_5 1.084 1.084 0.475
## MLQ_6 0.991 0.991 0.334
## MLQ_9 3.432 3.432 0.796
## MLQ_2 0.986 0.986 0.355
## MLQ_3 1.270 1.270 0.448
## MLQ_7 1.252 1.252 0.467
## MLQ_8 1.105 1.105 0.404
## MLQ_10 1.171 1.171 0.351
## Purpose 1.978 1.095 1.095
## Searching 1.957 1.091 1.091
## MLQ -0.631 NaN NaN
##
## R-Square:
##
## MLQ_1 0.587
## MLQ_4 0.660
## MLQ_5 0.525
## MLQ_6 0.666
## MLQ_9 0.204
## MLQ_2 0.645
## MLQ_3 0.552
## MLQ_7 0.533
## MLQ_8 0.596
## MLQ_10 0.649
## Purpose -0.095
## Searching -0.091summary(bifactor.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 251 iterations
##
## Used Total
## Number of observations 480 757
##
## Estimator ML
## Minimum Function Test Statistic 87.956
## Degrees of freedom 22
## P-value (Chi-square) 0.000
##
## Parameter estimates:
##
## Information Expected
## Standard Errors Standard
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## Purpose =~
## MLQ_1 1.000 0.056 0.032
## MLQ_4 7.156 0.403 0.243
## MLQ_5 -0.256 -0.014 -0.010
## MLQ_6 7.596 0.427 0.248
## MLQ_9 -33.215 -1.868 -0.900
## Searching =~
## MLQ_2 1.000 1.476 0.885
## MLQ_3 0.969 1.430 0.849
## MLQ_7 0.923 1.363 0.832
## MLQ_8 0.962 1.420 0.859
## MLQ_10 1.095 1.616 0.884
## MLQ =~
## MLQ_1 1.000 1.345 0.767
## MLQ_2 -0.697 -0.937 -0.562
## MLQ_3 -0.480 -0.646 -0.384
## MLQ_4 1.159 1.559 0.941
## MLQ_5 0.797 1.073 0.710
## MLQ_6 1.216 1.636 0.950
## MLQ_7 -0.431 -0.580 -0.354
## MLQ_8 -0.559 -0.752 -0.455
## MLQ_9 0.129 0.173 0.084
## MLQ_10 -0.848 -1.141 -0.625
##
## Covariances:
## Purpose ~~
## Searching -0.000 -0.001 -0.001
## MLQ -0.041 -0.545 -0.545
## Searching ~~
## MLQ 0.902 0.454 0.454
##
## Variances:
## MLQ_1 1.346 1.346 0.438
## MLQ_4 0.838 0.838 0.305
## MLQ_5 1.115 1.115 0.488
## MLQ_6 0.870 0.870 0.293
## MLQ_9 0.438 0.438 0.101
## MLQ_2 0.980 0.980 0.352
## MLQ_3 1.211 1.211 0.428
## MLQ_7 1.207 1.207 0.450
## MLQ_8 1.124 1.124 0.411
## MLQ_10 1.101 1.101 0.330
## Purpose 0.003 1.000 1.000
## Searching 2.179 1.000 1.000
## MLQ 1.809 1.000 1.000
##
## R-Square:
##
## MLQ_1 0.562
## MLQ_4 0.695
## MLQ_5 0.512
## MLQ_6 0.707
## MLQ_9 0.899
## MLQ_2 0.648
## MLQ_3 0.572
## MLQ_7 0.550
## MLQ_8 0.589
## MLQ_10 0.670summary(bifactor1.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 59 iterations
##
## Used Total
## Number of observations 480 757
##
## Estimator ML
## Minimum Function Test Statistic 111.380
## Degrees of freedom 25
## P-value (Chi-square) 0.000
##
## Parameter estimates:
##
## Information Expected
## Standard Errors Standard
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## MLQ =~
## MLQ_1 1.000 0.946 0.539
## MLQ_2 -0.598 0.148 -4.036 0.000 -0.566 -0.339
## MLQ_3 -0.380 0.122 -3.118 0.002 -0.360 -0.214
## MLQ_4 0.670 0.119 5.606 0.000 0.634 0.382
## MLQ_5 0.860 0.088 9.788 0.000 0.813 0.538
## MLQ_6 0.715 0.121 5.902 0.000 0.676 0.393
## MLQ_7 -0.171 0.102 -1.683 0.092 -0.162 -0.099
## MLQ_8 -0.334 0.116 -2.878 0.004 -0.316 -0.191
## MLQ_9 1.692 0.277 6.112 0.000 1.601 0.771
## MLQ_10 -0.684 0.166 -4.108 0.000 -0.647 -0.354
## Purpose =~
## MLQ_1 1.000 0.942 0.537
## MLQ_4 1.315 0.133 9.918 0.000 1.240 0.748
## MLQ_5 0.783 0.080 9.822 0.000 0.737 0.488
## MLQ_6 1.359 0.135 10.052 0.000 1.281 0.744
## MLQ_9 0.025 0.276 0.090 0.929 0.023 0.011
## Searching =~
## MLQ_2 1.000 1.224 0.734
## MLQ_3 0.980 0.063 15.527 0.000 1.199 0.712
## MLQ_7 0.999 0.066 15.187 0.000 1.222 0.746
## MLQ_8 1.016 0.063 16.198 0.000 1.243 0.752
## MLQ_10 1.089 0.064 17.011 0.000 1.332 0.729
##
## Covariances:
## MLQ ~~
## Purpose 0.000 0.000 0.000
## Searching 0.000 0.000 0.000
## Purpose ~~
## Searching 0.000 0.000 0.000
##
## Variances:
## MLQ_1 1.293 0.104 1.293 0.420
## MLQ_2 0.963 0.082 0.963 0.346
## MLQ_3 1.267 0.098 1.267 0.447
## MLQ_4 0.809 0.097 0.809 0.295
## MLQ_5 1.077 0.084 1.077 0.472
## MLQ_6 0.868 0.103 0.868 0.293
## MLQ_7 1.163 0.096 1.163 0.434
## MLQ_8 1.091 0.090 1.091 0.399
## MLQ_9 1.750 0.505 1.750 0.406
## MLQ_10 1.146 0.098 1.146 0.343
## MLQ 0.895 0.264 1.000 1.000
## Purpose 0.888 0.252 1.000 1.000
## Searching 1.497 0.169 1.000 1.000
##
## R-Square:
##
## MLQ_1 0.580
## MLQ_2 0.654
## MLQ_3 0.553
## MLQ_4 0.705
## MLQ_5 0.528
## MLQ_6 0.707
## MLQ_7 0.566
## MLQ_8 0.601
## MLQ_9 0.594
## MLQ_10 0.657summary(bifactorWO9.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 1796 iterations
##
## Used Total
## Number of observations 480 757
##
## Estimator ML
## Minimum Function Test Statistic 54.474
## Degrees of freedom 15
## P-value (Chi-square) 0.000
##
## Parameter estimates:
##
## Information Expected
## Standard Errors Standard
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## Purpose =~
## MLQ_1 1.000 0.606 0.346
## MLQ_4 154.649 93.779 56.582
## MLQ_5 1125.291 682.375 451.719
## MLQ_6 217.850 132.104 76.710
## Searching =~
## MLQ_2 1.000 26.464 15.873
## MLQ_3 0.956 25.296 15.028
## MLQ_7 0.914 24.189 14.767
## MLQ_8 0.961 25.441 15.380
## MLQ_10 1.101 29.144 15.950
## MLQ =~
## MLQ_1 1.000 1.830 1.043
## MLQ_2 -14.546 -26.619 -15.966
## MLQ_3 -13.772 -25.203 -14.973
## MLQ_4 52.008 95.178 57.426
## MLQ_5 373.595 683.701 452.597
## MLQ_6 73.021 133.632 77.597
## MLQ_7 -13.163 -24.088 -14.706
## MLQ_8 -13.921 -25.476 -15.401
## MLQ_10 -16.076 -29.421 -16.102
##
## Covariances:
## Purpose ~~
## Searching -16.028 -0.999 -0.999
## MLQ -1.110 -1.000 -1.000
## Searching ~~
## MLQ 48.370 0.999 0.999
##
## Variances:
## MLQ_1 1.579 1.579 0.513
## MLQ_4 0.850 0.850 0.310
## MLQ_5 3.659 3.659 1.604
## MLQ_6 0.749 0.749 0.253
## MLQ_2 0.990 0.990 0.356
## MLQ_3 1.227 1.227 0.433
## MLQ_7 1.213 1.213 0.452
## MLQ_8 1.111 1.111 0.406
## MLQ_10 1.114 1.114 0.334
## Purpose 0.368 1.000 1.000
## Searching 700.347 1.000 1.000
## MLQ 3.349 1.000 1.000
##
## R-Square:
##
## MLQ_1 0.487
## MLQ_4 0.690
## MLQ_5 -0.604
## MLQ_6 0.747
## MLQ_2 0.644
## MLQ_3 0.567
## MLQ_7 0.548
## MLQ_8 0.594
## MLQ_10 0.666summary(bifactor1WO9.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 71 iterations
##
## Used Total
## Number of observations 480 757
##
## Estimator ML
## Minimum Function Test Statistic 51.412
## Degrees of freedom 18
## P-value (Chi-square) 0.000
##
## Parameter estimates:
##
## Information Expected
## Standard Errors Standard
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## MLQ =~
## MLQ_1 1.000 1.171 0.667
## MLQ_2 -0.144 0.072 -2.006 0.045 -0.169 -0.101
## MLQ_3 0.094 0.071 1.322 0.186 0.110 0.065
## MLQ_4 1.124 0.106 10.557 0.000 1.315 0.794
## MLQ_5 0.766 0.104 7.358 0.000 0.897 0.594
## MLQ_6 1.333 0.171 7.816 0.000 1.561 0.906
## MLQ_7 0.066 0.069 0.965 0.335 0.078 0.047
## MLQ_8 -0.044 0.069 -0.630 0.529 -0.051 -0.031
## MLQ_10 -0.289 0.084 -3.444 0.001 -0.339 -0.185
## Purpose =~
## MLQ_1 1.000 0.617 0.352
## MLQ_4 0.409 0.263 1.555 0.120 0.252 0.152
## MLQ_5 1.558 1.146 1.360 0.174 0.961 0.636
## MLQ_6 0.026 0.537 0.049 0.961 0.016 0.009
## Searching =~
## MLQ_2 1.000 1.325 0.794
## MLQ_3 0.960 0.056 17.292 0.000 1.272 0.756
## MLQ_7 0.912 0.054 16.786 0.000 1.208 0.737
## MLQ_8 0.960 0.054 17.669 0.000 1.272 0.769
## MLQ_10 1.104 0.058 18.883 0.000 1.463 0.801
##
## Covariances:
## MLQ ~~
## Purpose 0.000 0.000 0.000
## Searching 0.000 0.000 0.000
## Purpose ~~
## Searching 0.000 0.000 0.000
##
## Variances:
## MLQ_1 1.325 0.235 1.325 0.431
## MLQ_2 0.997 0.084 0.997 0.359
## MLQ_3 1.203 0.095 1.203 0.425
## MLQ_4 0.954 0.138 0.954 0.347
## MLQ_5 0.555 0.644 0.555 0.243
## MLQ_6 0.530 0.312 0.530 0.179
## MLQ_7 1.219 0.094 1.219 0.454
## MLQ_8 1.116 0.089 1.116 0.408
## MLQ_10 1.084 0.096 1.084 0.325
## MLQ 1.370 0.309 1.000 1.000
## Purpose 0.380 0.361 1.000 1.000
## Searching 1.754 0.174 1.000 1.000
##
## R-Square:
##
## MLQ_1 0.569
## MLQ_2 0.641
## MLQ_3 0.575
## MLQ_4 0.653
## MLQ_5 0.757
## MLQ_6 0.821
## MLQ_7 0.546
## MLQ_8 0.592
## MLQ_10 0.675summary(bifactor.negative.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 29 iterations
##
## Used Total
## Number of observations 480 757
##
## Estimator ML
## Minimum Function Test Statistic 1343.770
## Degrees of freedom 33
## P-value (Chi-square) 0.000
##
## Parameter estimates:
##
## Information Expected
## Standard Errors Standard
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## Negative =~
## MLQ_9 1.000 0.521 0.251
## MLQ =~
## MLQ_1 1.000 1.345 0.767
## MLQ_2 -0.202 -0.271 -0.163
## MLQ_3 -0.016 -0.021 -0.013
## MLQ_4 0.994 1.337 0.807
## MLQ_5 0.811 1.091 0.722
## MLQ_6 1.041 1.401 0.813
## MLQ_7 0.006 0.008 0.005
## MLQ_8 -0.104 -0.140 -0.085
## MLQ_9 0.941 1.266 0.609
## MLQ_10 -0.298 -0.400 -0.219
##
## Covariances:
## Negative ~~
## MLQ -0.398 -0.567 -0.567
##
## Variances:
## MLQ_9 3.187 3.187 0.739
## MLQ_1 1.266 1.266 0.412
## MLQ_2 2.706 2.706 0.973
## MLQ_3 2.833 2.833 1.000
## MLQ_4 0.959 0.959 0.349
## MLQ_5 1.091 1.091 0.478
## MLQ_6 1.004 1.004 0.339
## MLQ_7 2.683 2.683 1.000
## MLQ_8 2.717 2.717 0.993
## MLQ_10 3.178 3.178 0.952
## Negative 0.272 1.000 1.000
## MLQ 1.810 1.000 1.000
##
## R-Square:
##
## MLQ_9 0.261
## MLQ_1 0.588
## MLQ_2 0.027
## MLQ_3 0.000
## MLQ_4 0.651
## MLQ_5 0.522
## MLQ_6 0.661
## MLQ_7 0.000
## MLQ_8 0.007
## MLQ_10 0.048Residual 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 -0.040 0.000
## MLQ_5 0.065 -0.021 0.000
## MLQ_6 -0.016 0.057 -0.048 0.000
## MLQ_9 0.061 -0.055 0.089 -0.057 0.000
## MLQ_2 -0.066 0.004 -0.025 -0.027 -0.277 0.000
## MLQ_3 0.027 0.140 0.075 0.119 -0.194 -0.040 0.000
## MLQ_7 0.109 0.090 0.145 0.089 -0.127 0.004 0.023 0.000
## MLQ_8 0.006 0.038 0.052 0.032 -0.161 0.018 -0.017 0.026 0.000
## MLQ_10 -0.069 -0.082 -0.025 -0.111 -0.289 0.010 0.042 -0.038 -0.022
## 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 0.002 0.000
## MLQ_3 -0.016 0.554 0.000
## MLQ_4 -0.036 0.074 0.094 0.000
## MLQ_5 0.066 0.039 0.034 -0.015 0.000
## MLQ_6 -0.015 0.044 0.073 0.064 -0.044 0.000
## MLQ_7 0.053 0.592 0.565 0.030 0.093 0.029 0.000
## MLQ_8 0.016 0.624 0.555 0.048 0.061 0.042 0.590 0.000
## MLQ_9 0.049 -0.235 -0.219 -0.065 0.079 -0.069 -0.160 -0.154 0.000
## MLQ_10 0.042 0.621 0.638 0.034 0.079 0.006 0.551 0.582 -0.220
## 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 -0.040 0.000
## MLQ_5 0.065 -0.021 0.000
## MLQ_6 -0.016 0.057 -0.048 0.000
## MLQ_9 0.061 -0.055 0.089 -0.057 0.000
## MLQ_2 -0.066 0.004 -0.025 -0.027 -0.277 0.000
## MLQ_3 0.027 0.140 0.075 0.119 -0.194 -0.040 0.000
## MLQ_7 0.109 0.090 0.145 0.089 -0.127 0.004 0.023 0.000
## MLQ_8 0.006 0.038 0.052 0.032 -0.161 0.018 -0.017 0.026 0.000
## MLQ_10 -0.069 -0.082 -0.025 -0.111 -0.289 0.010 0.042 -0.038 -0.022
## 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)
correl2 = residuals(bifactor.fit, type="cor")
correl2## $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 -0.028 0.000
## MLQ_5 0.084 -0.009 0.000
## MLQ_6 -0.007 0.019 -0.037 0.000
## MLQ_9 -0.003 0.001 0.000 0.000 0.000
## MLQ_2 -0.010 0.019 0.038 -0.012 -0.022 0.000
## MLQ_3 -0.034 0.031 0.026 0.009 -0.038 -0.040 0.000
## MLQ_7 0.032 -0.035 0.081 -0.037 0.014 0.010 0.004 0.000
## MLQ_8 -0.007 -0.020 0.048 -0.027 0.034 0.024 -0.023 0.024 0.000
## MLQ_10 0.033 -0.015 0.082 -0.045 0.002 0.000 0.045 -0.028 -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
##
## $mean
## MLQ_1 MLQ_4 MLQ_5 MLQ_6 MLQ_9 MLQ_2 MLQ_3 MLQ_7 MLQ_8 MLQ_10
## 0 0 0 0 0 0 0 0 0 0View(correl2$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 0.060 0.000
## MLQ_3 0.090 -0.039 0.000
## MLQ_4 -0.025 0.073 0.166 0.000
## MLQ_5 0.067 0.104 0.140 -0.003 0.000
## MLQ_6 -0.002 0.045 0.147 0.014 -0.031 0.000
## MLQ_7 0.110 0.010 0.013 0.072 0.149 0.072 0.000
## MLQ_8 0.054 0.021 -0.020 0.053 0.103 0.048 0.010 0.000
## MLQ_9 -0.015 -0.049 -0.061 0.009 -0.004 0.000 -0.081 -0.047 0.000
## MLQ_10 0.064 0.002 0.046 -0.007 0.111 -0.033 -0.029 -0.015 -0.050
## 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)
correl5 = residuals(bifactorWO9.fit, type="cor")
correl5## $type
## [1] "cor.bollen"
##
## $cor
## MLQ_1 MLQ_4 MLQ_5 MLQ_6 MLQ_2 MLQ_3 MLQ_7 MLQ_8 MLQ_10
## MLQ_1 0.000
## MLQ_4 -0.005 0.000
## MLQ_5 0.010 0.000 0.000
## MLQ_6 -0.009 0.001 -0.001 0.000
## MLQ_2 -0.044 0.033 -0.024 0.004 0.000
## MLQ_3 -0.051 0.048 -0.049 0.023 -0.037 0.000
## MLQ_7 0.027 -0.007 0.017 -0.012 0.009 0.008 0.000
## MLQ_8 -0.020 0.008 -0.008 0.002 0.022 -0.021 0.023 0.000
## MLQ_10 -0.007 -0.004 0.026 -0.028 0.004 0.047 -0.032 -0.021 0.000
##
## $mean
## MLQ_1 MLQ_4 MLQ_5 MLQ_6 MLQ_2 MLQ_3 MLQ_7 MLQ_8 MLQ_10
## 0 0 0 0 0 0 0 0 0View(correl5$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 -0.055 0.000
## MLQ_3 -0.069 -0.037 0.000
## MLQ_4 0.000 0.023 0.032 0.000
## MLQ_5 0.000 -0.019 -0.014 0.000 0.000
## MLQ_6 0.001 0.003 0.003 -0.001 0.000 0.000
## MLQ_7 0.025 0.010 0.005 -0.003 0.068 -0.010 0.000
## MLQ_8 -0.028 0.024 -0.023 0.004 0.018 0.001 0.024 0.000
## MLQ_10 -0.003 0.002 0.048 0.005 0.031 -0.004 -0.031 -0.021 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 0.049 0.000
## MLQ_2 -0.235 0.002 0.000
## MLQ_3 -0.219 -0.016 0.554 0.000
## MLQ_4 -0.065 -0.036 0.074 0.094 0.000
## MLQ_5 0.079 0.066 0.039 0.034 -0.015 0.000
## MLQ_6 -0.069 -0.015 0.044 0.073 0.064 -0.044 0.000
## MLQ_7 -0.160 0.053 0.592 0.565 0.030 0.093 0.029 0.000
## MLQ_8 -0.154 0.016 0.624 0.555 0.048 0.061 0.042 0.590 0.000
## MLQ_10 -0.220 0.042 0.621 0.638 0.034 0.079 0.006 0.551 0.582
## 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 -0.040 0.000
## MLQ_5 0.065 -0.021 0.000
## MLQ_6 -0.016 0.057 -0.048 0.000
## MLQ_9 0.061 -0.055 0.089 -0.057 0.000
## MLQ_2 -0.066 0.004 -0.025 -0.027 -0.277 0.000
## MLQ_3 0.027 0.140 0.075 0.119 -0.194 -0.040 0.000
## MLQ_7 0.109 0.090 0.145 0.089 -0.127 0.004 0.023 0.000
## MLQ_8 0.006 0.038 0.052 0.032 -0.161 0.018 -0.017 0.026 0.000
## MLQ_10 -0.069 -0.082 -0.025 -0.111 -0.289 0.010 0.042 -0.038 -0.022
## 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)
zcorrel2 = residuals(bifactor.fit, type="cor")
zcorrel2## $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 -0.028 0.000
## MLQ_5 0.084 -0.009 0.000
## MLQ_6 -0.007 0.019 -0.037 0.000
## MLQ_9 -0.003 0.001 0.000 0.000 0.000
## MLQ_2 -0.010 0.019 0.038 -0.012 -0.022 0.000
## MLQ_3 -0.034 0.031 0.026 0.009 -0.038 -0.040 0.000
## MLQ_7 0.032 -0.035 0.081 -0.037 0.014 0.010 0.004 0.000
## MLQ_8 -0.007 -0.020 0.048 -0.027 0.034 0.024 -0.023 0.024 0.000
## MLQ_10 0.033 -0.015 0.082 -0.045 0.002 0.000 0.045 -0.028 -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
##
## $mean
## MLQ_1 MLQ_4 MLQ_5 MLQ_6 MLQ_9 MLQ_2 MLQ_3 MLQ_7 MLQ_8 MLQ_10
## 0 0 0 0 0 0 0 0 0 0View(zcorrel2$cor)
zcorrel5 = residuals(bifactorWO9.fit, type="cor")
zcorrel5## $type
## [1] "cor.bollen"
##
## $cor
## MLQ_1 MLQ_4 MLQ_5 MLQ_6 MLQ_2 MLQ_3 MLQ_7 MLQ_8 MLQ_10
## MLQ_1 0.000
## MLQ_4 -0.005 0.000
## MLQ_5 0.010 0.000 0.000
## MLQ_6 -0.009 0.001 -0.001 0.000
## MLQ_2 -0.044 0.033 -0.024 0.004 0.000
## MLQ_3 -0.051 0.048 -0.049 0.023 -0.037 0.000
## MLQ_7 0.027 -0.007 0.017 -0.012 0.009 0.008 0.000
## MLQ_8 -0.020 0.008 -0.008 0.002 0.022 -0.021 0.023 0.000
## MLQ_10 -0.007 -0.004 0.026 -0.028 0.004 0.047 -0.032 -0.021 0.000
##
## $mean
## MLQ_1 MLQ_4 MLQ_5 MLQ_6 MLQ_2 MLQ_3 MLQ_7 MLQ_8 MLQ_10
## 0 0 0 0 0 0 0 0 0View(zcorrel5$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 -0.055 0.000
## MLQ_3 -0.069 -0.037 0.000
## MLQ_4 0.000 0.023 0.032 0.000
## MLQ_5 0.000 -0.019 -0.014 0.000 0.000
## MLQ_6 0.001 0.003 0.003 -0.001 0.000 0.000
## MLQ_7 0.025 0.010 0.005 -0.003 0.068 -0.010 0.000
## MLQ_8 -0.028 0.024 -0.023 0.004 0.018 0.001 0.024 0.000
## MLQ_10 -0.003 0.002 0.048 0.005 0.031 -0.004 -0.031 -0.021 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 0.049 0.000
## MLQ_2 -0.235 0.002 0.000
## MLQ_3 -0.219 -0.016 0.554 0.000
## MLQ_4 -0.065 -0.036 0.074 0.094 0.000
## MLQ_5 0.079 0.066 0.039 0.034 -0.015 0.000
## MLQ_6 -0.069 -0.015 0.044 0.073 0.064 -0.044 0.000
## MLQ_7 -0.160 0.053 0.592 0.565 0.030 0.093 0.029 0.000
## MLQ_8 -0.154 0.016 0.624 0.555 0.048 0.061 0.042 0.590 0.000
## MLQ_10 -0.220 0.042 0.621 0.638 0.034 0.079 0.006 0.551 0.582
## 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 0.060 0.000
## MLQ_3 0.090 -0.039 0.000
## MLQ_4 -0.025 0.073 0.166 0.000
## MLQ_5 0.067 0.104 0.140 -0.003 0.000
## MLQ_6 -0.002 0.045 0.147 0.014 -0.031 0.000
## MLQ_7 0.110 0.010 0.013 0.072 0.149 0.072 0.000
## MLQ_8 0.054 0.021 -0.020 0.053 0.103 0.048 0.010 0.000
## MLQ_9 -0.015 -0.049 -0.061 0.009 -0.004 0.000 -0.081 -0.047 0.000
## MLQ_10 0.064 0.002 0.046 -0.007 0.111 -0.033 -0.029 -0.015 -0.050
## 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 MLQ_4 ~~ MLQ_6 57.467 0.680 0.680 0.238 0.238
## 2 Searching =~ MLQ_9 44.312 -0.459 -0.614 -0.296 -0.296
## 3 Purpose =~ MLQ_10 22.408 -0.216 -0.290 -0.159 -0.159
## 4 MLQ_1 ~~ MLQ_5 19.694 0.330 0.330 0.125 0.125
## 5 MLQ_5 ~~ MLQ_6 17.138 -0.309 -0.309 -0.119 -0.119
## 6 MLQ_1 ~~ MLQ_4 15.970 -0.344 -0.344 -0.118 -0.118
## 7 Purpose =~ MLQ_7 14.467 0.169 0.227 0.139 0.139
## 8 MLQ_5 ~~ MLQ_9 12.859 0.358 0.358 0.114 0.114
## 9 Purpose =~ MLQ_3 11.559 0.153 0.206 0.122 0.122
## 10 MLQ_3 ~~ MLQ_10 11.189 0.266 0.266 0.087 0.087
## 11 MLQ_4 ~~ MLQ_3 10.140 0.198 0.198 0.071 0.071
## 12 MLQ_2 ~~ MLQ_3 9.962 -0.229 -0.229 -0.082 -0.082
## 13 MLQ_6 ~~ MLQ_9 9.413 -0.325 -0.325 -0.091 -0.091
## 14 MLQ_7 ~~ MLQ_10 8.535 -0.227 -0.227 -0.076 -0.076
## 15 MLQ_4 ~~ MLQ_9 8.374 -0.296 -0.296 -0.086 -0.086
## 16 MLQ_6 ~~ MLQ_10 8.353 -0.187 -0.187 -0.059 -0.059
## 17 MLQ_6 ~~ MLQ_3 8.137 0.183 0.183 0.063 0.063
## 18 MLQ_1 ~~ MLQ_9 7.422 0.305 0.305 0.084 0.084
## 19 MLQ_9 ~~ MLQ_2 6.877 -0.252 -0.252 -0.073 -0.073
## 20 MLQ_5 ~~ MLQ_7 5.403 0.144 0.144 0.058 0.058
## 21 MLQ_1 ~~ MLQ_7 5.121 0.155 0.155 0.054 0.054
## 22 Purpose =~ MLQ_2 4.867 -0.092 -0.124 -0.074 -0.074
## 23 MLQ_1 ~~ MLQ_3 4.152 -0.141 -0.141 -0.048 -0.048
## 24 MLQ_9 ~~ MLQ_10 4.010 -0.211 -0.211 -0.056 -0.056
## 25 MLQ_9 ~~ MLQ_3 3.980 -0.209 -0.209 -0.060 -0.060modindices(one.fit, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 1 MLQ_3 ~~ MLQ_10 206.875 1.977 1.977 0.643 0.643
## 2 MLQ_2 ~~ MLQ_10 202.261 1.915 1.915 0.629 0.629
## 3 MLQ_2 ~~ MLQ_8 194.372 1.730 1.730 0.627 0.627
## 4 MLQ_8 ~~ MLQ_10 173.514 1.774 1.774 0.587 0.587
## 5 MLQ_2 ~~ MLQ_7 173.432 1.623 1.623 0.594 0.594
## 6 MLQ_7 ~~ MLQ_8 168.697 1.601 1.601 0.591 0.591
## 7 MLQ_7 ~~ MLQ_10 154.355 1.662 1.662 0.555 0.555
## 8 MLQ_3 ~~ MLQ_7 153.437 1.559 1.559 0.565 0.565
## 9 MLQ_2 ~~ MLQ_3 152.145 1.562 1.562 0.557 0.557
## 10 MLQ_3 ~~ MLQ_8 149.116 1.547 1.547 0.556 0.556
## 11 MLQ_4 ~~ MLQ_6 63.350 0.697 0.697 0.244 0.244
## 12 MLQ_2 ~~ MLQ_9 36.386 -0.852 -0.852 -0.246 -0.246
## 13 MLQ_9 ~~ MLQ_10 32.929 -0.880 -0.880 -0.232 -0.232
## 14 MLQ_3 ~~ MLQ_9 30.864 -0.801 -0.801 -0.229 -0.229
## 15 MLQ_1 ~~ MLQ_5 20.283 0.335 0.335 0.126 0.126
## 16 MLQ_3 ~~ MLQ_4 16.906 0.364 0.364 0.131 0.131
## 17 MLQ_7 ~~ MLQ_9 16.347 -0.567 -0.567 -0.167 -0.167
## 18 MLQ_8 ~~ MLQ_9 15.353 -0.553 -0.553 -0.161 -0.161
## 19 MLQ_5 ~~ MLQ_6 13.709 -0.274 -0.274 -0.105 -0.105
## 20 MLQ_6 ~~ MLQ_9 13.518 -0.389 -0.389 -0.109 -0.109
## 21 MLQ_1 ~~ MLQ_4 12.190 -0.297 -0.297 -0.102 -0.102
## 22 MLQ_4 ~~ MLQ_9 11.440 -0.345 -0.345 -0.100 -0.100
## 23 MLQ_2 ~~ MLQ_4 10.951 0.288 0.288 0.104 0.104
## 24 MLQ_3 ~~ MLQ_6 10.666 0.299 0.299 0.103 0.103
## 25 MLQ_5 ~~ MLQ_9 10.269 0.319 0.319 0.102 0.102
## 26 MLQ_5 ~~ MLQ_7 10.265 0.273 0.273 0.111 0.111
## 27 MLQ_5 ~~ MLQ_10 7.912 0.263 0.263 0.095 0.095
## 28 MLQ_1 ~~ MLQ_9 4.911 0.247 0.247 0.068 0.068
## 29 MLQ_5 ~~ MLQ_8 4.533 0.183 0.183 0.073 0.073
## 30 MLQ_4 ~~ MLQ_8 4.387 0.182 0.182 0.066 0.066
## 31 MLQ_1 ~~ MLQ_7 4.159 0.193 0.193 0.067 0.067
## 32 MLQ_2 ~~ MLQ_6 4.052 0.181 0.181 0.063 0.063#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 17.547 0.323 0.323 0.105 0.105
## 2 Purpose =~ MLQ_7 9.046 0.389 0.240 0.147 0.147
## 3 MLQ_2 ~~ MLQ_3 8.944 -0.213 -0.213 -0.076 -0.076
## 4 MLQ_7 ~~ MLQ_10 6.597 -0.193 -0.193 -0.065 -0.065
## 5 MLQ_1 ~~ MLQ_3 4.625 -0.149 -0.149 -0.050 -0.050
## 6 MLQ_3 ~~ MLQ_4 4.432 0.143 0.143 0.051 0.051
## 7 MLQ_5 ~~ MLQ_7 4.261 0.136 0.136 0.055 0.055
## 8 Purpose =~ MLQ_2 3.953 -0.227 -0.140 -0.084 -0.084
## 9 MLQ_2 ~~ MLQ_8 3.905 0.138 0.138 0.050 0.050#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 34.800 -0.730 -0.893 -0.430 -0.430
## 2 MLQ_4 ~~ MLQ_6 22.375 0.838 0.838 0.294 0.294
## 3 MLQ_1 ~~ MLQ_5 22.331 0.364 0.364 0.137 0.137
## 4 Purpose =~ MLQ_3 19.161 0.309 0.291 0.173 0.173
## 5 Purpose ~~ Searching 14.881 0.350 0.304 0.304 0.304
## 6 Searching =~ MLQ_5 14.303 0.188 0.230 0.152 0.152
## 7 MLQ_3 ~~ MLQ_10 13.022 0.278 0.278 0.090 0.090
## 8 Purpose =~ MLQ_10 12.617 -0.298 -0.281 -0.154 -0.154
## 9 MLQ_7 ~~ MLQ_9 12.242 -0.487 -0.487 -0.143 -0.143
## 10 MLQ_6 ~~ MLQ_10 10.335 -0.207 -0.207 -0.066 -0.066
## 11 MLQ_1 ~~ MLQ_4 9.960 -0.299 -0.299 -0.103 -0.103
## 12 MLQ_5 ~~ MLQ_6 9.904 -0.241 -0.241 -0.092 -0.092
## 13 MLQ_2 ~~ MLQ_3 9.135 -0.213 -0.213 -0.076 -0.076
## 14 MLQ_1 ~~ MLQ_9 6.876 -0.600 -0.600 -0.165 -0.165
## 15 MLQ_3 ~~ MLQ_4 6.550 0.155 0.155 0.055 0.055
## 16 MLQ_5 ~~ MLQ_10 6.346 0.165 0.165 0.060 0.060
## 17 MLQ_1 ~~ MLQ_10 5.685 0.173 0.173 0.054 0.054
## 18 MLQ_7 ~~ MLQ_10 5.650 -0.182 -0.182 -0.061 -0.061
## 19 MLQ_3 ~~ MLQ_6 4.698 0.136 0.136 0.047 0.047
## 20 MLQ_4 ~~ MLQ_9 4.535 0.431 0.431 0.125 0.125
## 21 MLQ_4 ~~ MLQ_10 4.190 -0.128 -0.128 -0.042 -0.042#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
## 21.000 0.220 210.725
## df pvalue baseline.chisq
## 34.000 0.000 2327.115
## baseline.df baseline.pvalue cfi
## 45.000 0.000 0.923
## tli nnfi rfi
## 0.898 0.898 0.880
## nfi pnfi ifi
## 0.909 0.687 0.923
## rni logl unrestricted.logl
## 0.923 -8337.651 -8232.289
## aic bic ntotal
## 16717.302 16804.952 480.000
## bic2 rmsea rmsea.ci.lower
## 16738.300 0.104 0.091
## rmsea.ci.upper rmsea.pvalue rmr
## 0.118 0.000 0.283
## rmr_nomean srmr srmr_bentler
## 0.283 0.085 0.085
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.085 0.085 0.085
## srmr_mplus srmr_mplus_nomean cn_05
## 0.085 0.085 111.709
## cn_01 gfi agfi
## 128.698 0.918 0.868
## pgfi mfi ecvi
## 0.568 0.832 0.527fitmeasures(one.fit) #Models as a single purpose factor## npar fmin chisq
## 20.000 1.400 1343.770
## df pvalue baseline.chisq
## 35.000 0.000 2327.115
## baseline.df baseline.pvalue cfi
## 45.000 0.000 0.427
## tli nnfi rfi
## 0.263 0.263 0.258
## nfi pnfi ifi
## 0.423 0.329 0.429
## rni logl unrestricted.logl
## 0.427 -8904.173 -8232.289
## aic bic ntotal
## 17848.347 17931.823 480.000
## bic2 rmsea rmsea.ci.lower
## 17868.345 0.279 0.266
## rmsea.ci.upper rmsea.pvalue rmr
## 0.292 0.000 0.761
## rmr_nomean srmr srmr_bentler
## 0.761 0.261 0.261
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.261 0.261 0.261
## srmr_mplus srmr_mplus_nomean cn_05
## 0.261 0.261 18.789
## cn_01 gfi agfi
## 21.483 0.548 0.290
## pgfi mfi ecvi
## 0.349 0.256 2.883fitmeasures(second.fit)#Second order models as Purpose being the higher factor made up of Purpose and Searching## npar fmin chisq
## 23.000 0.220 210.725
## df pvalue baseline.chisq
## 32.000 0.000 2327.115
## baseline.df baseline.pvalue cfi
## 45.000 0.000 0.922
## tli nnfi rfi
## 0.890 0.890 0.873
## nfi pnfi ifi
## 0.909 0.647 0.922
## rni logl unrestricted.logl
## 0.922 -8337.651 -8232.289
## aic bic ntotal
## 16721.302 16817.299 480.000
## bic2 rmsea rmsea.ci.lower
## 16744.300 0.108 0.094
## rmsea.ci.upper rmsea.pvalue rmr
## 0.122 0.000 0.283
## rmr_nomean srmr srmr_bentler
## 0.283 0.085 0.085
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.085 0.085 0.085
## srmr_mplus srmr_mplus_nomean cn_05
## 0.085 0.085 106.224
## cn_01 gfi agfi
## 122.832 0.918 0.859
## pgfi mfi ecvi
## 0.534 0.830 0.535fitmeasures(bifactor.fit)#Models bifactor with Searching and Purpose as factors corolated with the main factor## npar fmin chisq
## 33.000 0.092 87.956
## df pvalue baseline.chisq
## 22.000 0.000 2327.115
## baseline.df baseline.pvalue cfi
## 45.000 0.000 0.971
## tli nnfi rfi
## 0.941 0.941 0.923
## nfi pnfi ifi
## 0.962 0.470 0.971
## rni logl unrestricted.logl
## 0.971 -8276.267 -8232.289
## aic bic ntotal
## 16618.533 16756.268 480.000
## bic2 rmsea rmsea.ci.lower
## 16651.530 0.079 0.062
## rmsea.ci.upper rmsea.pvalue rmr
## 0.097 0.003 0.083
## rmr_nomean srmr srmr_bentler
## 0.083 0.030 0.030
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.030 0.030 0.030
## srmr_mplus srmr_mplus_nomean cn_05
## 0.030 0.030 186.135
## cn_01 gfi agfi
## 220.870 0.965 0.911
## pgfi mfi ecvi
## 0.386 0.934 0.321fitmeasures(bifactor1.fit)#Models bifactor with Searching and Purpose as factors uncorolated with the main factor## npar fmin chisq
## 30.000 0.116 111.380
## df pvalue baseline.chisq
## 25.000 0.000 2327.115
## baseline.df baseline.pvalue cfi
## 45.000 0.000 0.962
## tli nnfi rfi
## 0.932 0.932 0.914
## nfi pnfi ifi
## 0.952 0.529 0.962
## rni logl unrestricted.logl
## 0.962 -8287.979 -8232.289
## aic bic ntotal
## 16635.957 16761.171 480.000
## bic2 rmsea rmsea.ci.lower
## 16665.954 0.085 0.069
## rmsea.ci.upper rmsea.pvalue rmr
## 0.101 0.000 0.173
## rmr_nomean srmr srmr_bentler
## 0.173 0.062 0.062
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.062 0.062 0.062
## srmr_mplus srmr_mplus_nomean cn_05
## 0.062 0.062 163.266
## cn_01 gfi agfi
## 191.974 0.957 0.906
## pgfi mfi ecvi
## 0.435 0.914 0.357fitmeasures(bifactorWO9.fit)#Models bifactor with Searching and Purpose as factors corolated with the main factor leaving negatively worded questions out## npar fmin chisq
## 30.000 0.057 54.474
## df pvalue baseline.chisq
## 15.000 0.000 2156.393
## baseline.df baseline.pvalue cfi
## 36.000 0.000 0.981
## tli nnfi rfi
## 0.955 0.955 0.939
## nfi pnfi ifi
## 0.975 0.406 0.982
## rni logl unrestricted.logl
## 0.981 -7313.047 -7285.810
## aic bic ntotal
## 14686.095 14811.308 480.000
## bic2 rmsea rmsea.ci.lower
## 14716.091 0.074 0.053
## rmsea.ci.upper rmsea.pvalue rmr
## 0.096 0.028 0.062
## rmr_nomean srmr srmr_bentler
## 0.062 0.022 0.022
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.022 0.022 0.022
## srmr_mplus srmr_mplus_nomean cn_05
## 0.022 0.022 221.252
## cn_01 gfi agfi
## 270.439 0.978 0.933
## pgfi mfi ecvi
## 0.326 0.960 0.238fitmeasures(bifactor1WO9.fit)#Models bifactor with Searching and Purpose as factors uncorolated with the main factor leaving negatively worded questions out## npar fmin chisq
## 27.000 0.054 51.412
## df pvalue baseline.chisq
## 18.000 0.000 2156.393
## baseline.df baseline.pvalue cfi
## 36.000 0.000 0.984
## tli nnfi rfi
## 0.968 0.968 0.952
## nfi pnfi ifi
## 0.976 0.488 0.984
## rni logl unrestricted.logl
## 0.984 -7311.516 -7285.810
## aic bic ntotal
## 14677.033 14789.725 480.000
## bic2 rmsea rmsea.ci.lower
## 14704.030 0.062 0.043
## rmsea.ci.upper rmsea.pvalue rmr
## 0.082 0.143 0.066
## rmr_nomean srmr srmr_bentler
## 0.066 0.023 0.023
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.023 0.023 0.023
## srmr_mplus srmr_mplus_nomean cn_05
## 0.023 0.023 270.533
## cn_01 gfi agfi
## 325.953 0.979 0.947
## pgfi mfi ecvi
## 0.391 0.966 0.220fitmeasures(bifactor.negative.fit)#Models bifactor as the negatively worded item as a factor uncorolated with the main factor## npar fmin chisq
## 22.000 1.400 1343.770
## df pvalue baseline.chisq
## 33.000 0.000 2327.115
## baseline.df baseline.pvalue cfi
## 45.000 0.000 0.426
## tli nnfi rfi
## 0.217 0.217 0.213
## nfi pnfi ifi
## 0.423 0.310 0.429
## rni logl unrestricted.logl
## 0.426 -8904.173 -8232.289
## aic bic ntotal
## 17852.347 17944.170 480.000
## bic2 rmsea rmsea.ci.lower
## 17874.345 0.288 0.275
## rmsea.ci.upper rmsea.pvalue rmr
## 0.301 0.000 0.761
## rmr_nomean srmr srmr_bentler
## 0.761 0.261 0.261
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.261 0.261 0.261
## srmr_mplus srmr_mplus_nomean cn_05
## 0.261 0.261 17.931
## cn_01 gfi agfi
## 20.566 0.548 0.247
## pgfi mfi ecvi
## 0.329 0.255 2.891Create dataset for Target rotation
all_surveys<-read.csv("allsurveys.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 [757 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': 757 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)
out_targetQ <- fa(MLQ,2,rotate="TargetQ",Target=Targ_key) #TargetT for orthogonal rotation
out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR2 MR1
## 1 0.763
## 2 0.815
## 3 0.729
## 4 0.818
## 5 -0.274 0.438
## 6 0.793
## 7 0.761
## 8 0.743 0.106
## 9 0.767
## 10 0.794 -0.133
##
## MR2 MR1
## SS loadings 3.060 2.683
## Proportion Var 0.306 0.268
## Cumulative Var 0.306 0.574
##
## $score.cor
## [,1] [,2]
## [1,] 1.0000000 -0.1127662
## [2,] -0.1127662 1.0000000
##
## $TLI
## [1] 0.9234499
##
## $RMSEA
## RMSEA lower upper confidence
## 0.09030479 0.07791644 0.10227615 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = MLQ, nfactors = 2, rotate = "TargetQ", Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR1 h2 u2 com
## 1 0.00 0.76 0.58 0.42 1.0
## 2 0.06 0.82 0.66 0.34 1.0
## 3 0.06 0.73 0.53 0.47 1.0
## 4 0.03 0.82 0.67 0.33 1.0
## 5 -0.27 0.44 0.29 0.71 1.7
## 6 0.79 -0.07 0.65 0.35 1.0
## 7 0.76 0.09 0.57 0.43 1.0
## 8 0.74 0.11 0.55 0.45 1.0
## 9 0.77 0.01 0.59 0.41 1.0
## 10 0.79 -0.13 0.67 0.33 1.1
##
## MR2 MR1
## SS loadings 3.06 2.68
## Proportion Var 0.31 0.27
## Cumulative Var 0.31 0.57
## Proportion Explained 0.53 0.47
## Cumulative Proportion 0.53 1.00
##
## With factor correlations of
## MR2 MR1
## MR2 1.0 -0.1
## MR1 -0.1 1.0
##
## Mean item complexity = 1.1
## Test of the hypothesis that 2 factors are sufficient.
##
## The degrees of freedom for the null model are 45 and the objective function was 4.85 with Chi Square of 3645.01
## The degrees of freedom for the model are 26 and the objective function was 0.25
##
## The root mean square of the residuals (RMSR) is 0.03
## The df corrected root mean square of the residuals is 0.04
##
## The harmonic number of observations is 480 with the empirical chi square 49.41 with prob < 0.0037
## The total number of observations was 757 with MLE Chi Square = 184.94 with prob < 6.5e-26
##
## Tucker Lewis Index of factoring reliability = 0.923
## RMSEA index = 0.09 and the 90 % confidence intervals are 0.078 0.102
## BIC = 12.58
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## MR2 MR1
## Correlation of scores with factors 0.94 0.93
## Multiple R square of scores with factors 0.88 0.87
## Minimum correlation of possible factor scores 0.77 0.74CFI
1-((out_targetQ$STATISTIC - out_targetQ$dof)/(out_targetQ$null.chisq- out_targetQ$null.dof))## [1] 0.9558505Droping MLQ_9 which is a reversed scoded question
Create dataset for Target rotation
all_surveys<-read.csv("allsurveys.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 [757 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': 757 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)
out_targetQ <- fa(MLQ,2,rotate="TargetQ",Target=Targ_key) #TargetT for orthogonal rotation
out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR2 MR1
## 1 0.744
## 2 0.830
## 3 0.704
## 4 0.836
## 5 0.794
## 6 0.756
## 7 0.742
## 8 0.770
## 9 0.797 -0.143
##
## MR2 MR1
## SS loadings 2.985 2.481
## Proportion Var 0.332 0.276
## Cumulative Var 0.332 0.607
##
## $score.cor
## [,1] [,2]
## [1,] 1.00000000 -0.04316592
## [2,] -0.04316592 1.00000000
##
## $TLI
## [1] 0.9345899
##
## $RMSEA
## RMSEA lower upper confidence
## 0.08991438 0.07557203 0.10406370 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = MLQ, nfactors = 2, rotate = "TargetQ", Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR1 h2 u2 com
## 1 -0.01 0.74 0.56 0.44 1.0
## 2 0.04 0.83 0.69 0.31 1.0
## 3 0.04 0.70 0.49 0.51 1.0
## 4 0.01 0.84 0.70 0.30 1.0
## 5 0.79 -0.08 0.64 0.36 1.0
## 6 0.76 0.09 0.57 0.43 1.0
## 7 0.74 0.09 0.55 0.45 1.0
## 8 0.77 0.00 0.59 0.41 1.0
## 9 0.80 -0.14 0.67 0.33 1.1
##
## MR2 MR1
## SS loadings 2.98 2.48
## Proportion Var 0.33 0.28
## Cumulative Var 0.33 0.61
## Proportion Explained 0.55 0.45
## Cumulative Proportion 0.55 1.00
##
## With factor correlations of
## MR2 MR1
## MR2 1.00 -0.06
## MR1 -0.06 1.00
##
## Mean item complexity = 1
## Test of the hypothesis that 2 factors are sufficient.
##
## The degrees of freedom for the null model are 36 and the objective function was 4.49 with Chi Square of 3379.1
## The degrees of freedom for the model are 19 and the objective function was 0.18
##
## The root mean square of the residuals (RMSR) is 0.03
## The df corrected root mean square of the residuals is 0.04
##
## The harmonic number of observations is 480 with the empirical chi square 29.72 with prob < 0.055
## The total number of observations was 757 with MLE Chi Square = 134.2 with prob < 2.3e-19
##
## Tucker Lewis Index of factoring reliability = 0.935
## RMSEA index = 0.09 and the 90 % confidence intervals are 0.076 0.104
## BIC = 8.25
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## MR2 MR1
## Correlation of scores with factors 0.94 0.93
## Multiple R square of scores with factors 0.89 0.87
## Minimum correlation of possible factor scores 0.77 0.75CFI
1-((out_targetQ$STATISTIC - out_targetQ$dof)/(out_targetQ$null.chisq- out_targetQ$null.dof))## [1] 0.9655399