LET CFA Account for Pos and Neg Models
Levi Brackman
25 July 2015
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)
LET<-select(data, LET_1, LET_2, LET_3, LET_4, LET_5, LET_6)
LET$LET_1 <- 6- LET$LET_1
LET$LET_3 <- 6- LET$LET_3
LET$LET_5 <- 6- LET$LET_5
LET<-tbl_df(LET)
LET## Source: local data frame [757 x 6]
##
## LET_1 LET_2 LET_3 LET_4 LET_5 LET_6
## 1 4 4 4 5 5 5
## 2 3 3 4 4 4 5
## 3 3 4 3 4 4 5
## 4 1 4 4 4 4 4
## 5 3 2 3 4 2 4
## 6 5 5 5 5 5 4
## 7 3 4 2 3 3 5
## 8 3 4 3 4 4 5
## 9 5 4 5 5 5 5
## 10 3 3 5 5 5 5
## .. ... ... ... ... ... ...Create the Models
As only one factor
one.factor = 'LET =~ LET_1 + LET_2 + LET_3 + LET_4 + LET_5 + LET_6
' Two Factor, Positive and Nagative Model
first.model= ' Negative =~ LET_1 + LET_3 + LET_5
Positive =~ LET_2 + LET_4 + LET_6
' Second order models
second.model = ' Negative =~ LET_1 + LET_3 + LET_5
Positive =~ LET_2 + LET_4 + LET_6
LET =~ NA*Positive + Negative
'
alternative.model = 'Negative =~ LET_1 + LET_3 + LET_5
Positive =~ LET_2 + LET_4 + LET_6
LET =~ NA*Positive + Negative
LET~~1*LET
'Bifactor Models (similar to Models 6, 7 & 8 in Marsh, Scalas & Nagengast, 2010)
bifactor.model = 'Negative =~ LET_1 + LET_3 + LET_5
Positive =~ LET_2 + LET_4 + LET_6
Life Satisfaction =~ LET_1 + LET_2 + LET_3 + LET_4 + LET_5 + LET_6
'Bifactor only with Negative questions protioned out (like model 7 in Marsh, Scalas & Nagengast, 2010)
bifactor.negative.model = 'Negative =~ LET_1 + LET_3 + LET_5
LET =~ LET_1 + LET_2 + LET_3 + LET_4 + LET_5 + LET_6
'Bifactor only with Positive questions protioned out (like model 8 in Marsh, Scalas & Nagengast, 2010)
bifactor.positive.model = 'Positive =~ LET_2 + LET_4 + LET_6
LET =~ LET_1 + LET_2 + LET_3 + LET_4 + LET_5 + LET_6
'Alternative method of writing bifactor model
bifactor.model1 = 'LET =~ LET_1 + LET_2 + LET_3 + LET_4 + LET_5 + LET_6
Negative =~ LET_1 + LET_3 + LET_5
Positive =~ LET_2 + LET_4 + LET_6
LET ~~ 0*Negative
LET ~~ 0*Positive
Negative~~0*Positive
'Running the models
one.fit=cfa(one.factor, data=LET, std.lv = T)
two.fit=cfa(first.model, data=LET, std.lv = T)
second.fit=cfa(second.model, data=LET, std.lv = T)## 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.alt.fit=cfa(alternative.model, data=LET, std.lv = T)## 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.bifactor.fit=cfa(bifactor.model, data=LET, orthogonal = TRUE, std.lv = T)## 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.bifactor.fit1=cfa(bifactor.model1, data=LET, orthogonal = TRUE, std.lv = T)## 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.bifactor.negative.fit=cfa(bifactor.negative.model, data=LET, orthogonal = TRUE, std.lv = T)
bifactor.positive.fit=cfa(bifactor.positive.model, data=LET, orthogonal = TRUE, std.lv = T)Create pictures of models
#One Factor
semPaths(one.fit, whatLabels = "std", layout = "tree", title=T)
#Two Factors Positive and Nagative
semPaths(two.fit, whatLabels = "std", layout = "tree")
#Second order factors
semPaths(second.fit, whatLabels = "std", layout = "tree")
semPaths(alt.fit, whatLabels = "std", layout = "tree")
#Bifactor Models (similar to Models 6, 7 & 8 in Marsh, Scalas & Nagengast, 2010)
semPaths(bifactor.fit, whatLabels = "std", layout = "tree")
semPaths(bifactor.fit1, whatLabels = "std", layout = "tree")
#Bifactor only with Negative questions protioned out (like model 7 in Marsh, Scalas & Nagengast, 2010)
semPaths(bifactor.negative.fit, whatLabels = "std", layout = "tree")
#Bifactor only with Negative questions protioned out (like model 7 in Marsh, Scalas & Nagengast, 2010)
semPaths(bifactor.positive.fit, whatLabels = "std", layout = "tree")
Summaries
summary(one.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 23 iterations
##
## Used Total
## Number of observations 474 757
##
## Estimator ML
## Minimum Function Test Statistic 294.824
## Degrees of freedom 9
## 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:
## LET =~
## LET_1 0.985 0.067 14.669 0.000 0.985 0.638
## LET_2 -0.095 0.063 -1.513 0.130 -0.095 -0.073
## LET_3 1.397 0.060 23.205 0.000 1.397 0.927
## LET_4 -0.112 0.056 -2.002 0.045 -0.112 -0.097
## LET_5 1.233 0.063 19.461 0.000 1.233 0.808
## LET_6 0.410 0.051 7.995 0.000 0.410 0.374
##
## Variances:
## LET_1 1.410 0.102 1.410 0.593
## LET_2 1.669 0.109 1.669 0.995
## LET_3 0.321 0.086 0.321 0.141
## LET_4 1.323 0.086 1.323 0.991
## LET_5 0.808 0.084 0.808 0.347
## LET_6 1.037 0.069 1.037 0.860
## LET 1.000 1.000 1.000
##
## R-Square:
##
## LET_1 0.407
## LET_2 0.005
## LET_3 0.859
## LET_4 0.009
## LET_5 0.653
## LET_6 0.140summary(two.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 24 iterations
##
## Used Total
## Number of observations 474 757
##
## Estimator ML
## Minimum Function Test Statistic 163.816
## Degrees of freedom 8
## 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 =~
## LET_1 0.987 0.068 14.609 0.000 0.987 0.639
## LET_3 1.396 0.062 22.456 0.000 1.396 0.926
## LET_5 1.231 0.065 19.006 0.000 1.231 0.806
## Positive =~
## LET_2 0.795 0.083 9.533 0.000 0.795 0.614
## LET_4 0.967 0.089 10.903 0.000 0.967 0.837
## LET_6 0.424 0.059 7.140 0.000 0.424 0.386
##
## Covariances:
## Negative ~~
## Positive -0.069 0.056 -1.245 0.213 -0.069 -0.069
##
## Variances:
## LET_1 1.407 0.103 1.407 0.591
## LET_3 0.322 0.096 0.322 0.142
## LET_5 0.814 0.090 0.814 0.350
## LET_2 1.046 0.122 1.046 0.623
## LET_4 0.400 0.153 0.400 0.300
## LET_6 1.025 0.073 1.025 0.851
## Negative 1.000 1.000 1.000
## Positive 1.000 1.000 1.000
##
## R-Square:
##
## LET_1 0.409
## LET_3 0.858
## LET_5 0.650
## LET_2 0.377
## LET_4 0.700
## LET_6 0.149summary(second.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 25 iterations
##
## Used Total
## Number of observations 474 757
##
## Estimator ML
## Minimum Function Test Statistic 163.816
## Degrees of freedom 7
## 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 =~
## LET_1 0.786 0.987 0.639
## LET_3 1.112 1.396 0.926
## LET_5 0.980 1.231 0.806
## Positive =~
## LET_2 0.790 0.795 0.614
## LET_4 0.961 0.967 0.837
## LET_6 0.421 0.424 0.386
## LET =~
## Positive -0.115 -0.115 -0.115
## Negative 0.759 0.605 0.605
##
## Variances:
## LET_1 1.407 1.407 0.591
## LET_3 0.322 0.322 0.142
## LET_5 0.814 0.814 0.350
## LET_2 1.046 1.046 0.623
## LET_4 0.400 0.400 0.300
## LET_6 1.025 1.025 0.851
## Negative 1.000 0.634 0.634
## Positive 1.000 0.987 0.987
## LET 1.000 1.000 1.000
##
## R-Square:
##
## LET_1 0.409
## LET_3 0.858
## LET_5 0.650
## LET_2 0.377
## LET_4 0.700
## LET_6 0.149
## Negative 0.366
## Positive 0.013summary(alt.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 25 iterations
##
## Used Total
## Number of observations 474 757
##
## Estimator ML
## Minimum Function Test Statistic 163.816
## Degrees of freedom 7
## 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 =~
## LET_1 0.786 0.987 0.639
## LET_3 1.112 1.396 0.926
## LET_5 0.980 1.231 0.806
## Positive =~
## LET_2 0.790 0.795 0.614
## LET_4 0.961 0.967 0.837
## LET_6 0.421 0.424 0.386
## LET =~
## Positive -0.115 -0.115 -0.115
## Negative 0.759 0.605 0.605
##
## Variances:
## LET 1.000 1.000 1.000
## LET_1 1.407 1.407 0.591
## LET_3 0.322 0.322 0.142
## LET_5 0.814 0.814 0.350
## LET_2 1.046 1.046 0.623
## LET_4 0.400 0.400 0.300
## LET_6 1.025 1.025 0.851
## Negative 1.000 0.634 0.634
## Positive 1.000 0.987 0.987
##
## R-Square:
##
## LET_1 0.409
## LET_3 0.858
## LET_5 0.650
## LET_2 0.377
## LET_4 0.700
## LET_6 0.149
## Negative 0.366
## Positive 0.013summary(bifactor.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 32 iterations
##
## Used Total
## Number of observations 474 757
##
## Estimator ML
## Minimum Function Test Statistic 46.231
## Degrees of freedom 3
## 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 =~
## LET_1 1.123 1.123 0.728
## LET_3 0.974 0.974 0.646
## LET_5 0.819 0.819 0.537
## Positive =~
## LET_2 0.807 0.807 0.623
## LET_4 0.796 0.796 0.689
## LET_6 0.689 0.689 0.627
## LifeSatisfaction =~
## LET_1 0.305 0.305 0.198
## LET_2 -0.411 -0.411 -0.317
## LET_3 0.938 0.938 0.622
## LET_4 -0.294 -0.294 -0.255
## LET_5 0.979 0.979 0.642
## LET_6 0.460 0.460 0.419
##
## Covariances:
## Negative ~~
## Positive 0.000 0.000 0.000
## LifeSatisfctn 0.000 0.000 0.000
## Positive ~~
## LifeSatisfctn 0.000 0.000 0.000
##
## Variances:
## LET_1 1.027 1.027 0.431
## LET_3 0.443 0.443 0.195
## LET_5 0.698 0.698 0.300
## LET_2 0.859 0.859 0.512
## LET_4 0.616 0.616 0.461
## LET_6 0.519 0.519 0.431
## Negative 1.000 1.000 1.000
## Positive 1.000 1.000 1.000
## LifeSatisfctn 1.000 1.000 1.000
##
## R-Square:
##
## LET_1 0.569
## LET_3 0.805
## LET_5 0.700
## LET_2 0.488
## LET_4 0.539
## LET_6 0.569summary(bifactor.fit1, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 32 iterations
##
## Used Total
## Number of observations 474 757
##
## Estimator ML
## Minimum Function Test Statistic 46.231
## Degrees of freedom 3
## 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:
## LET =~
## LET_1 0.305 0.305 0.198
## LET_2 -0.411 -0.411 -0.317
## LET_3 0.938 0.938 0.622
## LET_4 -0.294 -0.294 -0.255
## LET_5 0.979 0.979 0.642
## LET_6 0.460 0.460 0.419
## Negative =~
## LET_1 1.123 1.123 0.728
## LET_3 0.974 0.974 0.646
## LET_5 0.819 0.819 0.537
## Positive =~
## LET_2 0.807 0.807 0.623
## LET_4 0.796 0.796 0.689
## LET_6 0.689 0.689 0.627
##
## Covariances:
## LET ~~
## Negative 0.000 0.000 0.000
## Positive 0.000 0.000 0.000
## Negative ~~
## Positive 0.000 0.000 0.000
##
## Variances:
## LET_1 1.027 1.027 0.431
## LET_2 0.859 0.859 0.512
## LET_3 0.443 0.443 0.195
## LET_4 0.616 0.616 0.461
## LET_5 0.698 0.698 0.300
## LET_6 0.519 0.519 0.431
## LET 1.000 1.000 1.000
## Negative 1.000 1.000 1.000
## Positive 1.000 1.000 1.000
##
## R-Square:
##
## LET_1 0.569
## LET_2 0.488
## LET_3 0.805
## LET_4 0.539
## LET_5 0.700
## LET_6 0.569summary(bifactor.negative.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 27 iterations
##
## Used Total
## Number of observations 474 757
##
## Estimator ML
## Minimum Function Test Statistic 124.100
## Degrees of freedom 6
## 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 =~
## LET_1 1.036 0.066 15.812 0.000 1.036 0.671
## LET_3 1.360 0.061 22.310 0.000 1.360 0.903
## LET_5 1.247 0.063 19.830 0.000 1.247 0.817
## LET =~
## LET_1 0.328 0.083 3.964 0.000 0.328 0.212
## LET_2 1.030 0.080 12.950 0.000 1.030 0.795
## LET_3 -0.090 0.081 -1.102 0.270 -0.090 -0.060
## LET_4 0.756 0.066 11.449 0.000 0.756 0.654
## LET_5 -0.225 0.082 -2.737 0.006 -0.225 -0.147
## LET_6 0.384 0.058 6.637 0.000 0.384 0.350
##
## Covariances:
## Negative ~~
## LET 0.000 0.000 0.000
##
## Variances:
## LET_1 1.200 0.096 1.200 0.504
## LET_3 0.413 0.086 0.413 0.182
## LET_5 0.723 0.085 0.723 0.311
## LET_2 0.618 0.135 0.618 0.368
## LET_4 0.764 0.086 0.764 0.572
## LET_6 1.058 0.073 1.058 0.878
## Negative 1.000 1.000 1.000
## LET 1.000 1.000 1.000
##
## R-Square:
##
## LET_1 0.496
## LET_3 0.818
## LET_5 0.689
## LET_2 0.632
## LET_4 0.428
## LET_6 0.122summary(bifactor.positive.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 25 iterations
##
## Used Total
## Number of observations 474 757
##
## Estimator ML
## Minimum Function Test Statistic 64.749
## Degrees of freedom 6
## 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:
## Positive =~
## LET_2 0.813 0.073 11.111 0.000 0.813 0.628
## LET_4 0.921 0.072 12.818 0.000 0.921 0.797
## LET_6 0.506 0.054 9.327 0.000 0.506 0.461
## LET =~
## LET_1 0.979 0.067 14.590 0.000 0.979 0.634
## LET_2 -0.107 0.063 -1.711 0.087 -0.107 -0.083
## LET_3 1.393 0.060 23.386 0.000 1.393 0.924
## LET_4 -0.128 0.056 -2.291 0.022 -0.128 -0.111
## LET_5 1.238 0.063 19.703 0.000 1.238 0.811
## LET_6 0.418 0.051 8.152 0.000 0.418 0.381
##
## Covariances:
## Positive ~~
## LET 0.000 0.000 0.000
##
## Variances:
## LET_2 1.006 0.105 1.006 0.599
## LET_4 0.470 0.110 0.470 0.352
## LET_6 0.774 0.061 0.774 0.642
## LET_1 1.422 0.102 1.422 0.597
## LET_3 0.331 0.082 0.331 0.146
## LET_5 0.796 0.082 0.796 0.342
## Positive 1.000 1.000 1.000
## LET 1.000 1.000 1.000
##
## R-Square:
##
## LET_2 0.401
## LET_4 0.648
## LET_6 0.358
## LET_1 0.403
## LET_3 0.854
## LET_5 0.658Residual correlations
correl.1 = residuals(one.fit, type="cor")
correl.1## $type
## [1] "cor.bollen"
##
## $cor
## LET_1 LET_2 LET_3 LET_4 LET_5 LET_6
## LET_1 0.000
## LET_2 0.213 0.000
## LET_3 0.002 -0.007 0.000
## LET_4 0.138 0.502 -0.026 0.000
## LET_5 0.002 -0.112 -0.003 -0.041 0.000
## LET_6 0.030 0.285 0.002 0.361 0.005 0.000
##
## $mean
## LET_1 LET_2 LET_3 LET_4 LET_5 LET_6
## 0 0 0 0 0 0#View(correl.1$cor)
correl = residuals(two.fit, type="cor")
correl## $type
## [1] "cor.bollen"
##
## $cor
## LET_1 LET_3 LET_5 LET_2 LET_4 LET_6
## LET_1 0.000
## LET_3 0.001 0.000
## LET_5 0.002 -0.001 0.000
## LET_2 0.194 -0.035 -0.137 0.000
## LET_4 0.113 -0.062 -0.073 -0.004 0.000
## LET_6 0.286 0.373 0.329 0.020 0.002 0.000
##
## $mean
## LET_1 LET_3 LET_5 LET_2 LET_4 LET_6
## 0 0 0 0 0 0#View(correl$cor)
correl1 = residuals(second.fit, type="cor")
correl1## $type
## [1] "cor.bollen"
##
## $cor
## LET_1 LET_3 LET_5 LET_2 LET_4 LET_6
## LET_1 0.000
## LET_3 0.001 0.000
## LET_5 0.002 -0.001 0.000
## LET_2 0.194 -0.035 -0.137 0.000
## LET_4 0.113 -0.062 -0.073 -0.004 0.000
## LET_6 0.286 0.373 0.329 0.020 0.002 0.000
##
## $mean
## LET_1 LET_3 LET_5 LET_2 LET_4 LET_6
## 0 0 0 0 0 0#View(correl1$cor)
correl2 = residuals(alt.fit, type="cor")
correl2## $type
## [1] "cor.bollen"
##
## $cor
## LET_1 LET_3 LET_5 LET_2 LET_4 LET_6
## LET_1 0.000
## LET_3 0.001 0.000
## LET_5 0.002 -0.001 0.000
## LET_2 0.194 -0.035 -0.137 0.000
## LET_4 0.113 -0.062 -0.073 -0.004 0.000
## LET_6 0.286 0.373 0.329 0.020 0.002 0.000
##
## $mean
## LET_1 LET_3 LET_5 LET_2 LET_4 LET_6
## 0 0 0 0 0 0#View(correl2$cor)
correl3 = residuals(bifactor.fit, type="cor")
correl3## $type
## [1] "cor.bollen"
##
## $cor
## LET_1 LET_3 LET_5 LET_2 LET_4 LET_6
## LET_1 0.000
## LET_3 0.000 0.000
## LET_5 0.000 0.000 0.000
## LET_2 0.229 0.123 0.032 0.000
## LET_4 0.126 0.042 0.044 0.000 0.000
## LET_6 0.186 0.088 0.039 0.000 0.000 0.000
##
## $mean
## LET_1 LET_3 LET_5 LET_2 LET_4 LET_6
## 0 0 0 0 0 0#View(correl3$cor)
correl4 = residuals(bifactor.fit1, type="cor")
correl4## $type
## [1] "cor.bollen"
##
## $cor
## LET_1 LET_2 LET_3 LET_4 LET_5 LET_6
## LET_1 0.000
## LET_2 0.229 0.000
## LET_3 0.000 0.123 0.000
## LET_4 0.126 0.000 0.042 0.000
## LET_5 0.000 0.032 0.000 0.044 0.000
## LET_6 0.186 0.000 0.088 0.000 0.039 0.000
##
## $mean
## LET_1 LET_2 LET_3 LET_4 LET_5 LET_6
## 0 0 0 0 0 0#View(correl4$cor)
correl5 = residuals(bifactor.negative.fit, type="cor")
correl5## $type
## [1] "cor.bollen"
##
## $cor
## LET_1 LET_3 LET_5 LET_2 LET_4 LET_6
## LET_1 0.000
## LET_3 0.000 0.000
## LET_5 0.000 0.000 0.000
## LET_2 -0.002 -0.027 -0.054 0.000
## LET_4 -0.063 -0.077 -0.023 -0.011 0.000
## LET_6 0.194 0.369 0.359 -0.020 0.097 0.000
##
## $mean
## LET_1 LET_3 LET_5 LET_2 LET_4 LET_6
## 0 0 0 0 0 0#View(correl5$cor)
correl6 = residuals(bifactor.positive.fit, type="cor")
correl6## $type
## [1] "cor.bollen"
##
## $cor
## LET_2 LET_4 LET_6 LET_1 LET_3 LET_5
## LET_2 0.000
## LET_4 0.000 0.000
## LET_6 0.000 0.000 0.000
## LET_1 0.219 0.146 0.027 0.000
## LET_3 0.002 -0.013 -0.004 0.007 0.000
## LET_5 -0.104 -0.029 -0.002 0.003 -0.004 0.000
##
## $mean
## LET_2 LET_4 LET_6 LET_1 LET_3 LET_5
## 0 0 0 0 0 0#View(correl6$cor)zscore correlation anything over 1.96 is going to be statistically significant at the .05 level
zcorrels10 = residuals(one.fit, type = "standardized")
zcorrels10$cov## LET_1 LET_2 LET_3 LET_4 LET_5 LET_6
## LET_1 NA
## LET_2 6.156 0.000
## LET_3 0.503 -0.691 0.000
## LET_4 4.071 9.811 -2.606 NA
## LET_5 0.182 -4.479 NA -1.704 0.000
## LET_6 0.954 6.525 0.221 8.118 0.265 NAzcorrels = residuals(two.fit, type = "standardized")
zcorrels$cov## LET_1 LET_3 LET_5 LET_2 LET_4 LET_6
## LET_1 0.000
## LET_3 0.618 NA
## LET_5 0.705 NA NA
## LET_2 4.735 -1.066 -3.677 NA
## LET_4 3.232 -3.882 -2.723 NA NA
## LET_6 6.287 8.447 7.372 2.973 0.822 NA#zcorrels1 = residuals(second.fit, type = "standardized")
#zcorrels1$cov
#zcorrels2 = residuals(alt.fit, type = "standardized")
#zcorrels2$cov
#zcorrels3 = residuals(bifactor.fit, type = "standardized")
#zcorrels3$cov
#zcorrels4 = residuals(bifactor1.fit, type = "standardized")
#zcorrels4$cov
zcorrels5 = residuals(bifactor.negative.fit, type = "standardized")
zcorrels5$cov## LET_1 LET_3 LET_5 LET_2 LET_4 LET_6
## LET_1 0.000
## LET_3 0.000 0.000
## LET_5 0.000 0.000 0.000
## LET_2 -0.164 -1.664 -3.272 NA
## LET_4 -2.429 -2.609 -0.815 NA NA
## LET_6 4.617 8.257 8.265 -2.000 4.104 0.000zcorrels6 = residuals(bifactor.positive.fit, type = "standardized")
zcorrels6$cov## LET_2 LET_4 LET_6 LET_1 LET_3 LET_5
## LET_2 NA
## LET_4 NA NA
## LET_6 NA NA NA
## LET_1 6.316 4.323 0.856 NA
## LET_3 0.225 -1.361 -0.508 1.239 NA
## LET_5 -4.230 -1.244 -0.075 0.255 NA 0.000Modification indicies
modindices(one.fit, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 1 LET_2 ~~ LET_4 121.652 0.754 0.754 0.503 0.503
## 2 LET_4 ~~ LET_6 74.152 0.468 0.468 0.369 0.369
## 3 LET_2 ~~ LET_6 45.901 0.413 0.413 0.291 0.291
## 4 LET_1 ~~ LET_2 39.681 0.463 0.463 0.232 0.232
## 5 LET_2 ~~ LET_5 21.979 -0.283 -0.283 -0.143 -0.143
## 6 LET_1 ~~ LET_4 16.635 0.267 0.267 0.150 0.150
## 7 LET_3 ~~ LET_4 7.648 -0.149 -0.149 -0.086 -0.086
## 8 LET_3 ~~ LET_5 6.441 -0.611 -0.611 -0.266 -0.266modindices(two.fit, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 1 LET_2 ~~ LET_4 88.629 -12.462 -12.462 -8.323 -8.323
## 2 Negative =~ LET_6 88.623 0.479 0.479 0.436 0.436
## 3 Positive =~ LET_1 27.610 0.344 0.344 0.223 0.223
## 4 LET_3 ~~ LET_5 27.610 -8.613 -8.613 -3.745 -3.745
## 5 LET_1 ~~ LET_2 23.908 0.306 0.306 0.153 0.153
## 6 LET_5 ~~ LET_2 20.623 -0.232 -0.232 -0.118 -0.118
## 7 LET_3 ~~ LET_6 16.732 0.182 0.182 0.110 0.110
## 8 LET_2 ~~ LET_6 16.118 1.255 1.255 0.882 0.882
## 9 Negative =~ LET_4 16.118 -0.250 -0.250 -0.217 -0.217
## 10 LET_3 ~~ LET_4 8.872 -0.129 -0.129 -0.074 -0.074
## 11 LET_5 ~~ LET_6 5.708 0.114 0.114 0.068 0.068
## 12 Positive =~ LET_5 4.799 -0.118 -0.118 -0.078 -0.078
## 13 LET_1 ~~ LET_3 4.799 1.905 1.905 0.819 0.819#modindices(second.fit, sort. = TRUE, minimum.value = 3.84)
#modindices(alt.fit, sort. = TRUE, minimum.value = 3.84)
#modindices(bifactor.fit, sort. = TRUE, minimum.value = 3.84)
#modindices(bifactor.fit1, sort. = TRUE, minimum.value = 3.84)
modindices(bifactor.negative.fit, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 1 Negative =~ LET_6 82.067 0.469 0.469 0.427 0.427
## 2 LET_4 ~~ LET_6 21.157 0.294 0.294 0.232 0.232
## 3 LET_2 ~~ LET_4 17.479 -1.080 -1.080 -0.721 -0.721
## 4 LET_3 ~~ LET_6 15.070 0.176 0.176 0.106 0.106
## 5 LET_5 ~~ LET_2 14.469 -0.254 -0.254 -0.129 -0.129
## 6 LET_5 ~~ LET_6 11.719 0.166 0.166 0.099 0.099
## 7 LET_3 ~~ LET_4 6.160 -0.111 -0.111 -0.064 -0.064
## 8 LET_1 ~~ LET_2 5.834 0.231 0.231 0.116 0.116
## 9 Negative =~ LET_4 5.074 -0.114 -0.114 -0.099 -0.099
## 10 Negative =~ LET_2 4.670 -0.143 -0.143 -0.110 -0.110
## 11 LET_5 ~~ LET_4 4.501 0.108 0.108 0.061 0.061modindices(bifactor.positive.fit, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 1 Positive =~ LET_1 27.331 0.348 0.348 0.225 0.225
## 2 LET_2 ~~ LET_1 26.451 0.324 0.324 0.162 0.162
## 3 LET_2 ~~ LET_5 20.366 -0.233 -0.233 -0.118 -0.118
## 4 LET_3 ~~ LET_5 8.792 -0.595 -0.595 -0.259 -0.259
## 5 Positive =~ LET_5 5.264 -0.125 -0.125 -0.082 -0.082Fit Measures
fitmeasures(one.fit)## npar fmin chisq
## 12.000 0.311 294.824
## df pvalue baseline.chisq
## 9.000 0.000 967.563
## baseline.df baseline.pvalue cfi
## 15.000 0.000 0.700
## tli nnfi rfi
## 0.500 0.500 0.492
## nfi pnfi ifi
## 0.695 0.417 0.702
## rni logl unrestricted.logl
## 0.700 -4534.928 -4387.516
## aic bic ntotal
## 9093.856 9143.791 474.000
## bic2 rmsea rmsea.ci.lower
## 9105.705 0.259 0.234
## rmsea.ci.upper rmsea.pvalue rmr
## 0.285 0.000 0.243
## rmr_nomean srmr srmr_bentler
## 0.243 0.161 0.161
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.161 0.161 0.161
## srmr_mplus srmr_mplus_nomean cn_05
## 0.161 0.161 28.201
## cn_01 gfi agfi
## 35.833 0.819 0.578
## pgfi mfi ecvi
## 0.351 0.740 0.673fitmeasures(two.fit)## npar fmin chisq
## 13.000 0.173 163.816
## df pvalue baseline.chisq
## 8.000 0.000 967.563
## baseline.df baseline.pvalue cfi
## 15.000 0.000 0.836
## tli nnfi rfi
## 0.693 0.693 0.683
## nfi pnfi ifi
## 0.831 0.443 0.838
## rni logl unrestricted.logl
## 0.836 -4469.424 -4387.516
## aic bic ntotal
## 8964.848 9018.944 474.000
## bic2 rmsea rmsea.ci.lower
## 8977.684 0.203 0.176
## rmsea.ci.upper rmsea.pvalue rmr
## 0.230 0.000 0.241
## rmr_nomean srmr srmr_bentler
## 0.241 0.140 0.140
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.140 0.140 0.140
## srmr_mplus srmr_mplus_nomean cn_05
## 0.140 0.140 45.870
## cn_01 gfi agfi
## 59.131 0.906 0.754
## pgfi mfi ecvi
## 0.345 0.848 0.400fitmeasures(second.fit)## npar fmin chisq
## 14.000 0.173 163.816
## df pvalue baseline.chisq
## 7.000 0.000 967.563
## baseline.df baseline.pvalue cfi
## 15.000 0.000 0.835
## tli nnfi rfi
## 0.647 0.647 0.637
## nfi pnfi ifi
## 0.831 0.388 0.837
## rni logl unrestricted.logl
## 0.835 -4469.424 -4387.516
## aic bic ntotal
## 8966.848 9025.105 474.000
## bic2 rmsea rmsea.ci.lower
## 8980.671 0.217 0.189
## rmsea.ci.upper rmsea.pvalue rmr
## 0.247 0.000 0.241
## rmr_nomean srmr srmr_bentler
## 0.241 0.140 0.140
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.140 0.140 0.140
## srmr_mplus srmr_mplus_nomean cn_05
## 0.140 0.140 41.703
## cn_01 gfi agfi
## 54.458 0.906 0.719
## pgfi mfi ecvi
## 0.302 0.848 0.405fitmeasures(alt.fit)## npar fmin chisq
## 14.000 0.173 163.816
## df pvalue baseline.chisq
## 7.000 0.000 967.563
## baseline.df baseline.pvalue cfi
## 15.000 0.000 0.835
## tli nnfi rfi
## 0.647 0.647 0.637
## nfi pnfi ifi
## 0.831 0.388 0.837
## rni logl unrestricted.logl
## 0.835 -4469.424 -4387.516
## aic bic ntotal
## 8966.848 9025.105 474.000
## bic2 rmsea rmsea.ci.lower
## 8980.671 0.217 0.189
## rmsea.ci.upper rmsea.pvalue rmr
## 0.247 0.000 0.241
## rmr_nomean srmr srmr_bentler
## 0.241 0.140 0.140
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.140 0.140 0.140
## srmr_mplus srmr_mplus_nomean cn_05
## 0.140 0.140 41.703
## cn_01 gfi agfi
## 54.458 0.906 0.719
## pgfi mfi ecvi
## 0.302 0.848 0.405fitmeasures(bifactor.fit)## npar fmin chisq
## 18.000 0.049 46.231
## df pvalue baseline.chisq
## 3.000 0.000 967.563
## baseline.df baseline.pvalue cfi
## 15.000 0.000 0.955
## tli nnfi rfi
## 0.773 0.773 0.761
## nfi pnfi ifi
## 0.952 0.190 0.955
## rni logl unrestricted.logl
## 0.955 -4410.632 -4387.516
## aic bic ntotal
## 8857.264 8932.166 474.000
## bic2 rmsea rmsea.ci.lower
## 8875.036 0.174 0.132
## rmsea.ci.upper rmsea.pvalue rmr
## 0.220 0.000 0.148
## rmr_nomean srmr srmr_bentler
## 0.148 0.079 0.079
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.079 0.079 0.079
## srmr_mplus srmr_mplus_nomean cn_05
## 0.079 0.079 81.123
## cn_01 gfi agfi
## 117.317 0.970 0.788
## pgfi mfi ecvi
## 0.139 0.955 0.173fitmeasures(bifactor.fit1)## npar fmin chisq
## 18.000 0.049 46.231
## df pvalue baseline.chisq
## 3.000 0.000 967.563
## baseline.df baseline.pvalue cfi
## 15.000 0.000 0.955
## tli nnfi rfi
## 0.773 0.773 0.761
## nfi pnfi ifi
## 0.952 0.190 0.955
## rni logl unrestricted.logl
## 0.955 -4410.632 -4387.516
## aic bic ntotal
## 8857.264 8932.166 474.000
## bic2 rmsea rmsea.ci.lower
## 8875.036 0.174 0.132
## rmsea.ci.upper rmsea.pvalue rmr
## 0.220 0.000 0.148
## rmr_nomean srmr srmr_bentler
## 0.148 0.079 0.079
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.079 0.079 0.079
## srmr_mplus srmr_mplus_nomean cn_05
## 0.079 0.079 81.123
## cn_01 gfi agfi
## 117.317 0.970 0.788
## pgfi mfi ecvi
## 0.139 0.955 0.173fitmeasures(bifactor.negative.fit)## npar fmin chisq
## 15.000 0.131 124.100
## df pvalue baseline.chisq
## 6.000 0.000 967.563
## baseline.df baseline.pvalue cfi
## 15.000 0.000 0.876
## tli nnfi rfi
## 0.690 0.690 0.679
## nfi pnfi ifi
## 0.872 0.349 0.877
## rni logl unrestricted.logl
## 0.876 -4449.566 -4387.516
## aic bic ntotal
## 8929.133 8991.551 474.000
## bic2 rmsea rmsea.ci.lower
## 8943.943 0.204 0.173
## rmsea.ci.upper rmsea.pvalue rmr
## 0.236 0.000 0.208
## rmr_nomean srmr srmr_bentler
## 0.208 0.125 0.125
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.125 0.125 0.125
## srmr_mplus srmr_mplus_nomean cn_05
## 0.125 0.125 49.094
## cn_01 gfi agfi
## 65.213 0.929 0.752
## pgfi mfi ecvi
## 0.265 0.883 0.325fitmeasures(bifactor.positive.fit)## npar fmin chisq
## 15.000 0.068 64.749
## df pvalue baseline.chisq
## 6.000 0.000 967.563
## baseline.df baseline.pvalue cfi
## 15.000 0.000 0.938
## tli nnfi rfi
## 0.846 0.846 0.833
## nfi pnfi ifi
## 0.933 0.373 0.939
## rni logl unrestricted.logl
## 0.938 -4419.891 -4387.516
## aic bic ntotal
## 8869.781 8932.199 474.000
## bic2 rmsea rmsea.ci.lower
## 8884.592 0.144 0.113
## rmsea.ci.upper rmsea.pvalue rmr
## 0.176 0.000 0.121
## rmr_nomean srmr srmr_bentler
## 0.121 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 93.178
## cn_01 gfi agfi
## 124.073 0.959 0.857
## pgfi mfi ecvi
## 0.274 0.940 0.200