PWB CFA TR Youth
Levi Brackman
26 January 2016
- I live one day at a time and don’t really think about the future. (rs)
- I tend to focus on the present, because the future always brings me problems. (rs)
- My daily activities often seem trivial and unimportant to me. (rs)
- I don’t have a good sense of what it is that I am trying to accomplish in my life. (rs)
- I used to set goals for myself, but that now seems a waste of time. (rs)
- I enjoy making plans for the future and working to make them a reality.
- I am an active person in carrying out the plans I set for myself.
- Some people wander aimlessly through life, but I am not one of them.
- I sometimes feel as if I’ve done all there is to do in life. (rs)
library(lavaan)## This is lavaan 0.5-20## 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)## Warning: package 'car' was built under R version 3.2.3##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logitlibrary(ggplot2)## Warning: package 'ggplot2' was built under R version 3.2.3##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
##
## %+%, alphalibrary(GGally)## Warning: package 'GGally' was built under R version 3.2.3##
## Attaching package: 'GGally'## The following object is masked from 'package:dplyr':
##
## nasalibrary(xtable) data preparation
data <- read.csv("~/Dropbox/Git/stats/allsurveysYT1_Jan2016.csv", header=T)
PWB<-select(data, PWB_1, PWB_2, PWB_3, PWB_4, PWB_5, PWB_6, PWB_7, PWB_8, PWB_9)
PWB$PWB_1 <- 7- PWB$PWB_1
PWB$PWB_2 <- 7- PWB$PWB_2
PWB$PWB_3 <- 7- PWB$PWB_3
PWB$PWB_4 <- 7- PWB$PWB_4
PWB$PWB_9 <- 7- PWB$PWB_9
PWB<- data.frame(apply(PWB,2, as.numeric))
str(PWB)## 'data.frame': 1288 obs. of 9 variables:
## $ PWB_1: num 4 2 2 5 3 1 3 1 5 2 ...
## $ PWB_2: num 6 2 3 2 3 2 4 2 5 2 ...
## $ PWB_3: num 5 4 3 5 3 2 3 1 5 2 ...
## $ PWB_4: num 3 4 2 5 3 2 4 1 5 2 ...
## $ PWB_5: num 5 6 5 3 4 4 3 6 1 5 ...
## $ PWB_6: num 4 1 4 5 4 4 3 6 5 5 ...
## $ PWB_7: num 3 2 6 4 4 4 4 2 5 5 ...
## $ PWB_8: num 5 4 6 4 4 4 3 6 5 5 ...
## $ PWB_9: num 1 3 2 6 3 2 4 1 5 2 ...colnames(PWB) <- c("1","2", "3", "4", "5", "6", "7", "8", "9")
PWB<- PWB[complete.cases(PWB[,]),]EFA
number of factors
parallal analysis and scree plot
parallel<-fa.parallel(PWB, fm="ml",fa="fa")
## Parallel analysis suggests that the number of factors = 4 and the number of components = NA#two factors are greater than one Eigenvalue scree plot says there are two factors. Paralel analysis suggests 4 factorseigenvalues (kaiser)
parallel$fa.values## [1] 2.73344015 0.71182568 0.28802420 0.06142515 -0.02194723 -0.10593730
## [7] -0.18210012 -0.29494755 -0.47494233#over 1=2, over .7=2doign aprincipal components analysis to see how many factors there might be using that method
Deal with NA doing principle componant analysis.
princomp(na.omit(PWB), cor = TRUE)## Call:
## princomp(x = na.omit(PWB), cor = TRUE)
##
## Standard deviations:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7
## 1.8271781 1.2556471 0.9950615 0.8300724 0.8048028 0.7172643 0.6810303
## Comp.8 Comp.9
## 0.6407490 0.6075076
##
## 9 variables and 944 observations.parallel2<-princomp(na.omit(PWB), cor = TRUE)
summary(parallel2)## Importance of components:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
## Standard deviation 1.8271781 1.2556471 0.9950615 0.83007244 0.8048028
## Proportion of Variance 0.3709533 0.1751833 0.1100164 0.07655781 0.0719675
## Cumulative Proportion 0.3709533 0.5461366 0.6561530 0.73271081 0.8046783
## Comp.6 Comp.7 Comp.8 Comp.9
## Standard deviation 0.71726434 0.68103033 0.6407490 0.60750759
## Proportion of Variance 0.05716313 0.05153359 0.0456177 0.04100727
## Cumulative Proportion 0.86184143 0.91337502 0.9589927 1.00000000plot(parallel2)##results show at least two factors
#simple structure
twofactor<-fa(PWB, nfactors=2, rotate="oblimin", fm="ml")
twofactor## Factor Analysis using method = ml
## Call: fa(r = PWB, nfactors = 2, rotate = "oblimin", fm = "ml")
## Standardized loadings (pattern matrix) based upon correlation matrix
## ML1 ML2 h2 u2 com
## 1 0.67 -0.15 0.44 0.56 1.1
## 2 0.51 0.06 0.27 0.73 1.0
## 3 0.77 -0.02 0.59 0.41 1.0
## 4 0.51 0.26 0.38 0.62 1.5
## 5 -0.78 0.00 0.60 0.40 1.0
## 6 0.43 0.30 0.32 0.68 1.8
## 7 -0.03 0.87 0.75 0.25 1.0
## 8 0.04 0.59 0.36 0.64 1.0
## 9 0.46 0.02 0.21 0.79 1.0
##
## ML1 ML2
## SS loadings 2.60 1.32
## Proportion Var 0.29 0.15
## Cumulative Var 0.29 0.44
## Proportion Explained 0.66 0.34
## Cumulative Proportion 0.66 1.00
##
## With factor correlations of
## ML1 ML2
## ML1 1.00 0.19
## ML2 0.19 1.00
##
## Mean item complexity = 1.2
## Test of the hypothesis that 2 factors are sufficient.
##
## The degrees of freedom for the null model are 36 and the objective function was 2.48 with Chi Square of 2325.18
## The degrees of freedom for the model are 19 and the objective function was 0.21
##
## The root mean square of the residuals (RMSR) is 0.05
## The df corrected root mean square of the residuals is 0.07
##
## The harmonic number of observations is 944 with the empirical chi square 176.93 with prob < 1.2e-27
## The total number of observations was 944 with MLE Chi Square = 195.13 with prob < 3.2e-31
##
## Tucker Lewis Index of factoring reliability = 0.854
## RMSEA index = 0.099 and the 90 % confidence intervals are 0.087 0.112
## BIC = 64.98
## Fit based upon off diagonal values = 0.97
## Measures of factor score adequacy
## ML1 ML2
## Correlation of scores with factors 0.91 0.89
## Multiple R square of scores with factors 0.84 0.79
## Minimum correlation of possible factor scores 0.67 0.591-((twofactor$STATISTIC - twofactor$dof)/(twofactor$null.chisq- twofactor$null.dof))## [1] 0.9230604fa2latex(fa(PWB,2,rotate="oblimin", fm="ml"), heading="Table 1. Factor Loadings for Exploratory Factor Analysis PWB")## % Called in the psych package fa2latex % Called in the psych package fa(PWB, 2, rotate = "oblimin", fm = "ml") % Called in the psych package Table 1. Factor Loadings for Exploratory Factor Analysis PWB
## \begin{table}[htpb]\caption{fa2latex}
## \begin{center}
## \begin{scriptsize}
## \begin{tabular} {l r r r r r }
## \multicolumn{ 5 }{l}{ Table 1. Factor Loadings for Exploratory Factor Analysis PWB } \cr
## \hline Variable & ML1 & ML2 & h2 & u2 & com \cr
## \hline
## 1 & \bf{ 0.67} & -0.15 & 0.44 & 0.56 & 1.10 \cr
## 2 & \bf{ 0.51} & 0.06 & 0.27 & 0.73 & 1.03 \cr
## 3 & \bf{ 0.77} & -0.02 & 0.59 & 0.41 & 1.00 \cr
## 4 & \bf{ 0.51} & 0.26 & 0.38 & 0.62 & 1.49 \cr
## 5 & \bf{-0.78} & 0.00 & 0.60 & 0.40 & 1.00 \cr
## 6 & \bf{ 0.43} & 0.30 & 0.32 & 0.68 & 1.79 \cr
## 7 & -0.03 & \bf{ 0.87} & 0.75 & 0.25 & 1.00 \cr
## 8 & 0.04 & \bf{ 0.59} & 0.36 & 0.64 & 1.01 \cr
## 9 & \bf{ 0.46} & 0.02 & 0.21 & 0.79 & 1.00 \cr
## \hline \cr SS loadings & 2.6 & 1.32 & \cr
## \cr
## \hline \cr
## ML1 & 1.00 & 0.19 \cr
## ML2 & 0.19 & 1.00 \cr
## \hline
## \end{tabular}
## \end{scriptsize}
## \end{center}
## \label{default}
## \end{table}threefactor<-fa(PWB, nfactors=3, rotate="oblimin", fm="ml")
threefactor## Factor Analysis using method = ml
## Call: fa(r = PWB, nfactors = 3, rotate = "oblimin", fm = "ml")
## Standardized loadings (pattern matrix) based upon correlation matrix
## ML1 ML2 ML3 h2 u2 com
## 1 0.54 -0.17 0.23 0.44 0.56 1.6
## 2 0.13 0.12 0.58 0.44 0.56 1.2
## 3 0.70 -0.07 0.15 0.59 0.41 1.1
## 4 0.27 0.29 0.38 0.42 0.58 2.7
## 5 -0.72 0.05 -0.15 0.61 0.39 1.1
## 6 0.69 0.23 -0.28 0.52 0.48 1.6
## 7 -0.04 0.84 0.05 0.70 0.30 1.0
## 8 0.03 0.61 0.02 0.38 0.62 1.0
## 9 0.13 0.07 0.48 0.32 0.68 1.2
##
## ML1 ML2 ML3
## SS loadings 2.07 1.29 1.06
## Proportion Var 0.23 0.14 0.12
## Cumulative Var 0.23 0.37 0.49
## Proportion Explained 0.47 0.29 0.24
## Cumulative Proportion 0.47 0.76 1.00
##
## With factor correlations of
## ML1 ML2 ML3
## ML1 1.00 0.25 0.44
## ML2 0.25 1.00 -0.02
## ML3 0.44 -0.02 1.00
##
## Mean item complexity = 1.4
## Test of the hypothesis that 3 factors are sufficient.
##
## The degrees of freedom for the null model are 36 and the objective function was 2.48 with Chi Square of 2325.18
## The degrees of freedom for the model are 12 and the objective function was 0.07
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.04
##
## The harmonic number of observations is 944 with the empirical chi square 40.04 with prob < 7.1e-05
## The total number of observations was 944 with MLE Chi Square = 69.45 with prob < 4.1e-10
##
## Tucker Lewis Index of factoring reliability = 0.925
## RMSEA index = 0.072 and the 90 % confidence intervals are 0.056 0.088
## BIC = -12.76
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## ML1 ML2 ML3
## Correlation of scores with factors 0.91 0.88 0.80
## Multiple R square of scores with factors 0.82 0.77 0.64
## Minimum correlation of possible factor scores 0.64 0.53 0.281-((threefactor$STATISTIC - threefactor$dof)/(threefactor$null.chisq- threefactor$null.dof))## [1] 0.9749055fa2latex(fa(PWB,3,rotate="oblimin", fm="ml"), heading="Table 2. Factor Loadings for Exploratory Factor Analysis PWB")## % Called in the psych package fa2latex % Called in the psych package fa(PWB, 3, rotate = "oblimin", fm = "ml") % Called in the psych package Table 2. Factor Loadings for Exploratory Factor Analysis PWB
## \begin{table}[htpb]\caption{fa2latex}
## \begin{center}
## \begin{scriptsize}
## \begin{tabular} {l r r r r r r }
## \multicolumn{ 6 }{l}{ Table 2. Factor Loadings for Exploratory Factor Analysis PWB } \cr
## \hline Variable & ML1 & ML2 & ML3 & h2 & u2 & com \cr
## \hline
## 1 & \bf{ 0.54} & -0.17 & 0.23 & 0.44 & 0.56 & 1.58 \cr
## 2 & 0.13 & 0.12 & \bf{ 0.58} & 0.44 & 0.56 & 1.18 \cr
## 3 & \bf{ 0.70} & -0.07 & 0.15 & 0.59 & 0.41 & 1.11 \cr
## 4 & 0.27 & 0.29 & \bf{ 0.38} & 0.42 & 0.58 & 2.72 \cr
## 5 & \bf{-0.72} & 0.05 & -0.15 & 0.61 & 0.39 & 1.09 \cr
## 6 & \bf{ 0.69} & 0.23 & -0.28 & 0.52 & 0.48 & 1.57 \cr
## 7 & -0.04 & \bf{ 0.84} & 0.05 & 0.70 & 0.30 & 1.01 \cr
## 8 & 0.03 & \bf{ 0.61} & 0.02 & 0.38 & 0.62 & 1.01 \cr
## 9 & 0.13 & 0.07 & \bf{ 0.48} & 0.32 & 0.68 & 1.19 \cr
## \hline \cr SS loadings & 2.07 & 1.29 & 1.06 & \cr
## \cr
## \hline \cr
## ML1 & 1.00 & 0.25 & 0.44 \cr
## ML2 & 0.25 & 1.00 & -0.02 \cr
## ML3 & 0.44 & -0.02 & 1.00 \cr
## \hline
## \end{tabular}
## \end{scriptsize}
## \end{center}
## \label{default}
## \end{table}fourfactor<-fa(PWB, nfactors=4, rotate="oblimin", fm="ml")
fourfactor## Factor Analysis using method = ml
## Call: fa(r = PWB, nfactors = 4, rotate = "oblimin", fm = "ml")
## Standardized loadings (pattern matrix) based upon correlation matrix
## ML1 ML2 ML4 ML3 h2 u2 com
## 1 0.13 -0.23 0.52 0.30 0.54 0.46 2.2
## 2 -0.01 0.09 0.75 -0.06 0.56 0.44 1.0
## 3 0.84 -0.05 -0.05 0.04 0.68 0.32 1.0
## 4 0.50 0.31 0.15 -0.15 0.45 0.55 2.1
## 5 -0.60 0.04 -0.13 -0.16 0.58 0.42 1.2
## 6 0.11 0.19 0.00 0.70 0.66 0.34 1.2
## 7 -0.02 0.79 0.01 0.08 0.65 0.35 1.0
## 8 0.01 0.60 0.01 0.09 0.40 0.60 1.0
## 9 0.31 0.07 0.32 -0.17 0.29 0.71 2.6
##
## ML1 ML2 ML4 ML3
## SS loadings 1.68 1.22 1.15 0.75
## Proportion Var 0.19 0.14 0.13 0.08
## Cumulative Var 0.19 0.32 0.45 0.53
## Proportion Explained 0.35 0.25 0.24 0.16
## Cumulative Proportion 0.35 0.60 0.84 1.00
##
## With factor correlations of
## ML1 ML2 ML4 ML3
## ML1 1.00 0.14 0.63 0.48
## ML2 0.14 1.00 0.12 0.20
## ML4 0.63 0.12 1.00 0.19
## ML3 0.48 0.20 0.19 1.00
##
## Mean item complexity = 1.5
## Test of the hypothesis that 4 factors are sufficient.
##
## The degrees of freedom for the null model are 36 and the objective function was 2.48 with Chi Square of 2325.18
## The degrees of freedom for the model are 6 and the objective function was 0.01
##
## 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 944 with the empirical chi square 2.69 with prob < 0.85
## The total number of observations was 944 with MLE Chi Square = 4.93 with prob < 0.55
##
## Tucker Lewis Index of factoring reliability = 1.003
## RMSEA index = 0 and the 90 % confidence intervals are NA 0.038
## BIC = -36.17
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## ML1 ML2 ML4 ML3
## Correlation of scores with factors 0.91 0.86 0.85 0.82
## Multiple R square of scores with factors 0.83 0.74 0.73 0.68
## Minimum correlation of possible factor scores 0.65 0.48 0.45 0.351-((fourfactor$STATISTIC - fourfactor$dof)/(fourfactor$null.chisq- fourfactor$null.dof))## [1] 1.000468fa2latex(fa(PWB,4,rotate="oblimin", fm="ml"), heading="Table 3. Factor Loadings for Exploratory Factor Analysis PWB")## % Called in the psych package fa2latex % Called in the psych package fa(PWB, 4, rotate = "oblimin", fm = "ml") % Called in the psych package Table 3. Factor Loadings for Exploratory Factor Analysis PWB
## \begin{table}[htpb]\caption{fa2latex}
## \begin{center}
## \begin{scriptsize}
## \begin{tabular} {l r r r r r r r }
## \multicolumn{ 7 }{l}{ Table 3. Factor Loadings for Exploratory Factor Analysis PWB } \cr
## \hline Variable & ML1 & ML2 & ML4 & ML3 & h2 & u2 & com \cr
## \hline
## 1 & 0.13 & -0.23 & \bf{ 0.52} & 0.30 & 0.54 & 0.46 & 2.19 \cr
## 2 & -0.01 & 0.09 & \bf{ 0.75} & -0.06 & 0.56 & 0.44 & 1.04 \cr
## 3 & \bf{ 0.84} & -0.05 & -0.05 & 0.04 & 0.68 & 0.32 & 1.02 \cr
## 4 & \bf{ 0.50} & \bf{ 0.31} & 0.15 & -0.15 & 0.45 & 0.55 & 2.08 \cr
## 5 & \bf{-0.60} & 0.04 & -0.13 & -0.16 & 0.58 & 0.42 & 1.24 \cr
## 6 & 0.11 & 0.19 & 0.00 & \bf{ 0.70} & 0.66 & 0.34 & 1.20 \cr
## 7 & -0.02 & \bf{ 0.79} & 0.01 & 0.08 & 0.65 & 0.35 & 1.02 \cr
## 8 & 0.01 & \bf{ 0.60} & 0.01 & 0.09 & 0.40 & 0.60 & 1.05 \cr
## 9 & \bf{ 0.31} & 0.07 & \bf{ 0.32} & -0.17 & 0.29 & 0.71 & 2.60 \cr
## \hline \cr SS loadings & 1.68 & 1.22 & 1.15 & 0.75 & \cr
## \cr
## \hline \cr
## ML1 & 1.00 & 0.14 & 0.63 & 0.48 \cr
## ML2 & 0.14 & 1.00 & 0.12 & 0.20 \cr
## ML4 & 0.63 & 0.12 & 1.00 & 0.19 \cr
## ML3 & 0.48 & 0.20 & 0.19 & 1.00 \cr
## \hline
## \end{tabular}
## \end{scriptsize}
## \end{center}
## \label{default}
## \end{table}question 1,3,5,6,9 seems to be one factor and all talk about plans or lack of plans
PWBWO15<-select(PWB, 1,3,5,6,9)
PWBWO15<-tbl_df(PWBWO15)
PWBWO15## Source: local data frame [944 x 5]
##
## 1 3 5 6 9
## (dbl) (dbl) (dbl) (dbl) (dbl)
## 1 4 5 5 4 1
## 2 2 4 6 1 3
## 3 2 3 5 4 2
## 4 5 5 3 5 6
## 5 3 3 4 4 3
## 6 1 2 4 4 2
## 7 3 3 3 3 4
## 8 1 1 6 6 1
## 9 5 5 1 5 5
## 10 2 2 5 5 2
## .. ... ... ... ... ...twofactorWO15<-fa(PWBWO15, nfactors=1, rotate="oblimin", fm="ml")
twofactorWO15## Factor Analysis using method = ml
## Call: fa(r = PWBWO15, nfactors = 1, rotate = "oblimin", fm = "ml")
## Standardized loadings (pattern matrix) based upon correlation matrix
## ML1 h2 u2 com
## 1 0.61 0.37 0.63 1
## 3 0.77 0.59 0.41 1
## 5 -0.81 0.65 0.35 1
## 6 0.51 0.26 0.74 1
## 9 0.42 0.18 0.82 1
##
## ML1
## SS loadings 2.05
## Proportion Var 0.41
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## The degrees of freedom for the null model are 10 and the objective function was 1.23 with Chi Square of 1158.85
## The degrees of freedom for the model are 5 and the objective function was 0.03
##
## The root mean square of the residuals (RMSR) is 0.04
## The df corrected root mean square of the residuals is 0.05
##
## The harmonic number of observations is 944 with the empirical chi square 26.66 with prob < 6.6e-05
## The total number of observations was 944 with MLE Chi Square = 23.97 with prob < 0.00022
##
## Tucker Lewis Index of factoring reliability = 0.967
## RMSEA index = 0.064 and the 90 % confidence intervals are 0.039 0.09
## BIC = -10.28
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## ML1
## Correlation of scores with factors 0.90
## Multiple R square of scores with factors 0.82
## Minimum correlation of possible factor scores 0.63fa2latex(fa(PWBWO15,3,rotate="oblimin", fm="ml"), heading="Table 4. Factor Loadings for Exploratory Factor Analysis PWB")## % Called in the psych package fa2latex % Called in the psych package fa(PWBWO15, 3, rotate = "oblimin", fm = "ml") % Called in the psych package Table 4. Factor Loadings for Exploratory Factor Analysis PWB
## \begin{table}[htpb]\caption{fa2latex}
## \begin{center}
## \begin{scriptsize}
## \begin{tabular} {l r r r r r r }
## \multicolumn{ 6 }{l}{ Table 4. Factor Loadings for Exploratory Factor Analysis PWB } \cr
## \hline Variable & ML1 & ML2 & ML3 & h2 & u2 & com \cr
## \hline
## 1 & -0.03 & \bf{ 0.36} & \bf{ 0.37} & 0.40 & 0.60 & 2.01 \cr
## 3 & -0.28 & \bf{ 0.31} & 0.27 & 0.56 & 0.44 & 2.97 \cr
## 5 & \bf{ 0.88} & 0.00 & 0.00 & 0.76 & 0.24 & 1.00 \cr
## 6 & -0.04 & \bf{ 0.64} & -0.06 & 0.42 & 0.58 & 1.03 \cr
## 9 & -0.06 & -0.08 & \bf{ 0.57} & 0.34 & 0.66 & 1.06 \cr
## \hline \cr SS loadings & 1.03 & 0.78 & 0.67 & \cr
## \cr
## \hline \cr
## ML1 & 1.00 & -0.75 & -0.72 \cr
## ML2 & -0.75 & 1.00 & 0.40 \cr
## ML3 & -0.72 & 0.40 & 1.00 \cr
## \hline
## \end{tabular}
## \end{scriptsize}
## \end{center}
## \label{default}
## \end{table}CFI, should be slightly higher than the TLI
1-((twofactorWO15$STATISTIC - twofactorWO15$dof)/(twofactorWO15$null.chisq- twofactorWO15$null.dof))## [1] 0.9834837question 7,8 seems to be one factor and all talk about being active or wondering aimlessly
PWB78<-select(PWB, 7,8)
PWB78<-tbl_df(PWB78)
PWB78## Source: local data frame [944 x 2]
##
## 7 8
## (dbl) (dbl)
## 1 3 5
## 2 2 4
## 3 6 6
## 4 4 4
## 5 4 4
## 6 4 4
## 7 4 3
## 8 2 6
## 9 5 5
## 10 5 5
## .. ... ...twofactor78<-fa(PWB78, nfactors=1, rotate="oblimin", fm="ml")
twofactor78## Factor Analysis using method = ml
## Call: fa(r = PWB78, nfactors = 1, rotate = "oblimin", fm = "ml")
## Standardized loadings (pattern matrix) based upon correlation matrix
## ML1 h2 u2 com
## 7 0.71 0.5 0.5 1
## 8 0.71 0.5 0.5 1
##
## ML1
## SS loadings 1.01
## Proportion Var 0.50
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## The degrees of freedom for the null model are 1 and the objective function was 0.29 with Chi Square of 277.04
## The degrees of freedom for the model are -1 and the objective function was 0
##
## The root mean square of the residuals (RMSR) is 0
## The df corrected root mean square of the residuals is NA
##
## The harmonic number of observations is 944 with the empirical chi square 0 with prob < NA
## The total number of observations was 944 with MLE Chi Square = 0 with prob < NA
##
## Tucker Lewis Index of factoring reliability = 1.004
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## ML1
## Correlation of scores with factors 0.82
## Multiple R square of scores with factors 0.67
## Minimum correlation of possible factor scores 0.34fa2latex(fa(PWB78,1,rotate="oblimin", fm="ml"), heading="Table 5. Factor Loadings for Exploratory Factor Analysis PWB")## % Called in the psych package fa2latex % Called in the psych package fa(PWB78, 1, rotate = "oblimin", fm = "ml") % Called in the psych package Table 5. Factor Loadings for Exploratory Factor Analysis PWB
## \begin{table}[htpb]\caption{fa2latex}
## \begin{center}
## \begin{scriptsize}
## \begin{tabular} {l r r r r }
## \multicolumn{ 4 }{l}{ Table 5. Factor Loadings for Exploratory Factor Analysis PWB } \cr
## \hline Variable & ML1 & ML1.1 & ML1.2 & com \cr
## \hline
## 7 & \bf{0.71} & 0.5 & 0.5 & 1 \cr
## 8 & \bf{0.71} & 0.5 & 0.5 & 1 \cr
## \hline \cr SS loadings & 1.01 & \cr
## \hline
## \end{tabular}
## \end{scriptsize}
## \end{center}
## \label{default}
## \end{table}CFI, should be slightly higher than the TLI
1-((twofactor78$STATISTIC - twofactor78$dof)/(twofactor78$null.chisq- twofactor78$null.dof))## [1] 0.9963773question 2,8 seems to be one factor
PWB29<-select(PWB, 2,8)
PWB29<-tbl_df(PWB29)
PWB29## Source: local data frame [944 x 2]
##
## 2 8
## (dbl) (dbl)
## 1 6 5
## 2 2 4
## 3 3 6
## 4 2 4
## 5 3 4
## 6 2 4
## 7 4 3
## 8 2 6
## 9 5 5
## 10 2 5
## .. ... ...twofactor29<-fa(PWB29, nfactors=1, rotate="oblimin", fm="ml")
twofactor29## Factor Analysis using method = ml
## Call: fa(r = PWB29, nfactors = 1, rotate = "oblimin", fm = "ml")
## Standardized loadings (pattern matrix) based upon correlation matrix
## ML1 h2 u2 com
## 2 0.34 0.12 0.88 1
## 8 0.34 0.12 0.88 1
##
## ML1
## SS loadings 0.24
## Proportion Var 0.12
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## The degrees of freedom for the null model are 1 and the objective function was 0.01 with Chi Square of 13.32
## The degrees of freedom for the model are -1 and the objective function was 0
##
## The root mean square of the residuals (RMSR) is 0
## The df corrected root mean square of the residuals is NA
##
## The harmonic number of observations is 944 with the empirical chi square 0 with prob < NA
## The total number of observations was 944 with MLE Chi Square = 0 with prob < NA
##
## Tucker Lewis Index of factoring reliability = 1.081
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## ML1
## Correlation of scores with factors 0.46
## Multiple R square of scores with factors 0.21
## Minimum correlation of possible factor scores -0.58fa2latex(fa(PWB29,1,rotate="oblimin", fm="ml"), heading="Table 6. Factor Loadings for Exploratory Factor Analysis PWB")## % Called in the psych package fa2latex % Called in the psych package fa(PWB29, 1, rotate = "oblimin", fm = "ml") % Called in the psych package Table 6. Factor Loadings for Exploratory Factor Analysis PWB
## \begin{table}[htpb]\caption{fa2latex}
## \begin{center}
## \begin{scriptsize}
## \begin{tabular} {l r r r r }
## \multicolumn{ 4 }{l}{ Table 6. Factor Loadings for Exploratory Factor Analysis PWB } \cr
## \hline Variable & ML1 & ML1.1 & ML1.2 & com \cr
## \hline
## 2 & \bf{0.34} & 0.12 & 0.88 & 1 \cr
## 8 & \bf{0.34} & 0.12 & 0.88 & 1 \cr
## \hline \cr SS loadings & 0.24 & \cr
## \hline
## \end{tabular}
## \end{scriptsize}
## \end{center}
## \label{default}
## \end{table}CFI, should be slightly higher than the TLI
1-((twofactor29$STATISTIC - twofactor29$dof)/(twofactor29$null.chisq- twofactor29$null.dof))## [1] 0.9187993Finding Alpha
#alpha(PWB, na.rm = TRUE, check.keys=TRUE)CFA in Lavaan
data <- read.csv("~/Dropbox/Git/stats/allsurveysYT1_Jan2016.csv", header=T)
data<-tbl_df(data)
PWB<-select(data, PWB_1, PWB_2, PWB_3, PWB_4, PWB_5, PWB_6,PWB_7, PWB_8, PWB_9)
PWB$PWB_1 <- 7- PWB$PWB_1
PWB$PWB_2 <- 7- PWB$PWB_2
PWB$PWB_3 <- 7- PWB$PWB_3
PWB$PWB_4 <- 7- PWB$PWB_4
PWB$PWB_9 <- 7- PWB$PWB_9
PWB<-tbl_df(PWB)
PWB## Source: local data frame [1,288 x 9]
##
## PWB_1 PWB_2 PWB_3 PWB_4 PWB_5 PWB_6 PWB_7 PWB_8 PWB_9
## (dbl) (dbl) (dbl) (dbl) (int) (int) (int) (int) (dbl)
## 1 4 6 5 3 5 4 3 5 1
## 2 2 2 4 4 6 1 2 4 3
## 3 2 3 3 2 5 4 6 6 2
## 4 5 2 5 5 3 5 4 4 6
## 5 3 3 3 3 4 4 4 4 3
## 6 1 2 2 2 4 4 4 4 2
## 7 3 4 3 4 3 3 4 3 4
## 8 1 2 1 1 6 6 2 6 1
## 9 5 5 5 5 1 5 5 5 5
## 10 2 2 2 2 5 5 5 5 2
## .. ... ... ... ... ... ... ... ... ...create plots
#ggpairs(PWB, columns = 1:15, title="Big 5 Marsh" )create the models
two.model= ' Factor1 =~ PWB_1 + PWB_3 + PWB_4 + PWB_5 + PWB_6 + PWB_9
Factor2 =~ PWB_2+ PWB_7 + PWB_8
' #Models two factors:Positive and Negative
one.model= 'PWB =~ PWB_1 + PWB_2 + PWB_3 + PWB_4 + PWB_5 + PWB_6 + PWB_7 + PWB_8 + PWB_9' #Models as a single purpose factorSecond order models
second.model = ' Negative =~ PWB_1 + PWB_2 + PWB_3 + PWB_4 + PWB_5 + PWB_9
Positive =~ PWB_6 + PWB_7 + PWB_8
Purpose =~ Negative + Positive
' #Second order models as Purpose being the higher factor made up of Purpose and PositiveBifactor (like model 7 in Marsh, Scalas & Nagengast, 2010)
bifactor.negative.model = 'Negative =~ PWB_1 + PWB_2 + PWB_3 + PWB_4 + PWB_5 + PWB_9
PWB =~ PWB_1 + PWB_2 + PWB_3 + PWB_4 + PWB_5 + PWB_6 + PWB_7 + PWB_8 + PWB_9
'
#Models bifactor as the negatively worded item as a factor uncorolated with the main factor
bifactor.model1 = 'PWB =~ PWB_1 + PWB_2 + PWB_3 + PWB_4 + PWB_5 + PWB_6 + PWB_7 + PWB_8 + PWB_9
Negative =~ PWB_1 + PWB_2 + PWB_3 + PWB_4 + PWB_5 + PWB_9
Positive =~ PWB_6 + PWB_7 + PWB_8
PWB ~~ 0*Negative
PWB ~~ 0*Positive
Negative~~0*Positive
'#Models bifactor with Positive and Purpose as factors uncorolated with the main factor
bifactor.model2 = 'PWB =~ PWB_1 + PWB_2 + PWB_3 + PWB_4 + PWB_5 + PWB_6 + PWB_7 + PWB_8 + PWB_9
F1 =~ PWB_1 + PWB_3 + PWB_5 + PWB_6
F2 =~ PWB_4 + PWB_7 + PWB_8
F3 =~ PWB_2 + PWB_9
PWB ~~ 0*F1
PWB ~~ 0*F2
PWB ~~ 0*F3
F1~~0*F2
F1~~0*F3
F2~~0*F3
'#Models bifactor with Positive and Purpose as factors uncorolated with the main factorrun the models
two.fit=cfa(two.model, data=PWB, missing = "fiml", std.lv = T)## Warning in lav_data_full(data = data, group = group, group.label = group.label, : lavaan WARNING: some cases are empty and will be removed:
## 145 150 151 152 156 157 171 173 206 207 208 209 213 221 222 223 238 239 240 244 249 250 251 252 253 256 257 258 259 261 263 265 266 268 275 279 280 283 284 290 294 297 298 299 300 301 302 304 305 307 308 311 312 314 315 316 317 320 322 323 325 328 330 331 332 335 336 338 340 342 343 345 348 350 351 352 354 355 356 357 358 362 366 368 371 373 374 375 377 380 383 384 393 394 395 396 398 399 402 403 408 409 410 412 414 415 417 419 420 426 428 432 437 438 439 440 443 444 445 448 450 453 455 458 461 462 464 467 468 472 476 478 479 480 482 483 485 488 489 490 492 493 494 495 496 497 498 499 500 501 502 503 504 505 507 508 509 512 513 514 517 518 525 526 527 528 529 530 531 532 533 534 535 536 538 544 545 546 547 548 549 550 551 552 553 555 556 557 558 559 560 562 564 572 573 574 575 576 580 581 582 583 584 585 587 588 590 591 592 593 595 596 598 600 601 602 603 604 606 609 610 613 614 618 619 621 623 667 668 669 670 671 672 673 674 676 677 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 790 807 815 910 911 912 913 937 938 957 1031 1034 1035 1037 1039 1238 1240 1241 1242 1244 1245 1248 1249 1250 1253 1254 1256 1257 1258 1259 1260 1262 1264 1265 1266 1267 1268 1271 1273 1274 1275 1278 1279 1280 1282 1283 1287 1288## Found more than one class "Model" in cache; using the first, from namespace 'lavaan'one.fit=cfa(one.model, data=PWB, missing = "fiml", std.lv = T)## Warning in lav_data_full(data = data, group = group, group.label = group.label, : lavaan WARNING: some cases are empty and will be removed:
## 145 150 151 152 156 157 171 173 206 207 208 209 213 221 222 223 238 239 240 244 249 250 251 252 253 256 257 258 259 261 263 265 266 268 275 279 280 283 284 290 294 297 298 299 300 301 302 304 305 307 308 311 312 314 315 316 317 320 322 323 325 328 330 331 332 335 336 338 340 342 343 345 348 350 351 352 354 355 356 357 358 362 366 368 371 373 374 375 377 380 383 384 393 394 395 396 398 399 402 403 408 409 410 412 414 415 417 419 420 426 428 432 437 438 439 440 443 444 445 448 450 453 455 458 461 462 464 467 468 472 476 478 479 480 482 483 485 488 489 490 492 493 494 495 496 497 498 499 500 501 502 503 504 505 507 508 509 512 513 514 517 518 525 526 527 528 529 530 531 532 533 534 535 536 538 544 545 546 547 548 549 550 551 552 553 555 556 557 558 559 560 562 564 572 573 574 575 576 580 581 582 583 584 585 587 588 590 591 592 593 595 596 598 600 601 602 603 604 606 609 610 613 614 618 619 621 623 667 668 669 670 671 672 673 674 676 677 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 790 807 815 910 911 912 913 937 938 957 1031 1034 1035 1037 1039 1238 1240 1241 1242 1244 1245 1248 1249 1250 1253 1254 1256 1257 1258 1259 1260 1262 1264 1265 1266 1267 1268 1271 1273 1274 1275 1278 1279 1280 1282 1283 1287 1288second.fit=cfa(second.model, data=PWB, missing = "fiml", std.lv = T)## Warning in lav_data_full(data = data, group = group, group.label = group.label, : lavaan WARNING: some cases are empty and will be removed:
## 145 150 151 152 156 157 171 173 206 207 208 209 213 221 222 223 238 239 240 244 249 250 251 252 253 256 257 258 259 261 263 265 266 268 275 279 280 283 284 290 294 297 298 299 300 301 302 304 305 307 308 311 312 314 315 316 317 320 322 323 325 328 330 331 332 335 336 338 340 342 343 345 348 350 351 352 354 355 356 357 358 362 366 368 371 373 374 375 377 380 383 384 393 394 395 396 398 399 402 403 408 409 410 412 414 415 417 419 420 426 428 432 437 438 439 440 443 444 445 448 450 453 455 458 461 462 464 467 468 472 476 478 479 480 482 483 485 488 489 490 492 493 494 495 496 497 498 499 500 501 502 503 504 505 507 508 509 512 513 514 517 518 525 526 527 528 529 530 531 532 533 534 535 536 538 544 545 546 547 548 549 550 551 552 553 555 556 557 558 559 560 562 564 572 573 574 575 576 580 581 582 583 584 585 587 588 590 591 592 593 595 596 598 600 601 602 603 604 606 609 610 613 614 618 619 621 623 667 668 669 670 671 672 673 674 676 677 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 790 807 815 910 911 912 913 937 938 957 1031 1034 1035 1037 1039 1238 1240 1241 1242 1244 1245 1248 1249 1250 1253 1254 1256 1257 1258 1259 1260 1262 1264 1265 1266 1267 1268 1271 1273 1274 1275 1278 1279 1280 1282 1283 1287 1288bifactor1.fit=cfa(bifactor.model1, data=PWB, missing = "fiml", std.lv = T)## Warning in lav_data_full(data = data, group = group, group.label = group.label, : lavaan WARNING: some cases are empty and will be removed:
## 145 150 151 152 156 157 171 173 206 207 208 209 213 221 222 223 238 239 240 244 249 250 251 252 253 256 257 258 259 261 263 265 266 268 275 279 280 283 284 290 294 297 298 299 300 301 302 304 305 307 308 311 312 314 315 316 317 320 322 323 325 328 330 331 332 335 336 338 340 342 343 345 348 350 351 352 354 355 356 357 358 362 366 368 371 373 374 375 377 380 383 384 393 394 395 396 398 399 402 403 408 409 410 412 414 415 417 419 420 426 428 432 437 438 439 440 443 444 445 448 450 453 455 458 461 462 464 467 468 472 476 478 479 480 482 483 485 488 489 490 492 493 494 495 496 497 498 499 500 501 502 503 504 505 507 508 509 512 513 514 517 518 525 526 527 528 529 530 531 532 533 534 535 536 538 544 545 546 547 548 549 550 551 552 553 555 556 557 558 559 560 562 564 572 573 574 575 576 580 581 582 583 584 585 587 588 590 591 592 593 595 596 598 600 601 602 603 604 606 609 610 613 614 618 619 621 623 667 668 669 670 671 672 673 674 676 677 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 790 807 815 910 911 912 913 937 938 957 1031 1034 1035 1037 1039 1238 1240 1241 1242 1244 1245 1248 1249 1250 1253 1254 1256 1257 1258 1259 1260 1262 1264 1265 1266 1267 1268 1271 1273 1274 1275 1278 1279 1280 1282 1283 1287 1288bifactor2.fit=cfa(bifactor.model2, data=PWB, missing = "fiml", std.lv = T)## Warning in lav_data_full(data = data, group = group, group.label = group.label, : lavaan WARNING: some cases are empty and will be removed:
## 145 150 151 152 156 157 171 173 206 207 208 209 213 221 222 223 238 239 240 244 249 250 251 252 253 256 257 258 259 261 263 265 266 268 275 279 280 283 284 290 294 297 298 299 300 301 302 304 305 307 308 311 312 314 315 316 317 320 322 323 325 328 330 331 332 335 336 338 340 342 343 345 348 350 351 352 354 355 356 357 358 362 366 368 371 373 374 375 377 380 383 384 393 394 395 396 398 399 402 403 408 409 410 412 414 415 417 419 420 426 428 432 437 438 439 440 443 444 445 448 450 453 455 458 461 462 464 467 468 472 476 478 479 480 482 483 485 488 489 490 492 493 494 495 496 497 498 499 500 501 502 503 504 505 507 508 509 512 513 514 517 518 525 526 527 528 529 530 531 532 533 534 535 536 538 544 545 546 547 548 549 550 551 552 553 555 556 557 558 559 560 562 564 572 573 574 575 576 580 581 582 583 584 585 587 588 590 591 592 593 595 596 598 600 601 602 603 604 606 609 610 613 614 618 619 621 623 667 668 669 670 671 672 673 674 676 677 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 790 807 815 910 911 912 913 937 938 957 1031 1034 1035 1037 1039 1238 1240 1241 1242 1244 1245 1248 1249 1250 1253 1254 1256 1257 1258 1259 1260 1262 1264 1265 1266 1267 1268 1271 1273 1274 1275 1278 1279 1280 1282 1283 1287 1288bifactorneg.fit = cfa(bifactor.negative.model, data=PWB, missing = "fiml", std.lv = T)## Warning in lav_data_full(data = data, group = group, group.label = group.label, : lavaan WARNING: some cases are empty and will be removed:
## 145 150 151 152 156 157 171 173 206 207 208 209 213 221 222 223 238 239 240 244 249 250 251 252 253 256 257 258 259 261 263 265 266 268 275 279 280 283 284 290 294 297 298 299 300 301 302 304 305 307 308 311 312 314 315 316 317 320 322 323 325 328 330 331 332 335 336 338 340 342 343 345 348 350 351 352 354 355 356 357 358 362 366 368 371 373 374 375 377 380 383 384 393 394 395 396 398 399 402 403 408 409 410 412 414 415 417 419 420 426 428 432 437 438 439 440 443 444 445 448 450 453 455 458 461 462 464 467 468 472 476 478 479 480 482 483 485 488 489 490 492 493 494 495 496 497 498 499 500 501 502 503 504 505 507 508 509 512 513 514 517 518 525 526 527 528 529 530 531 532 533 534 535 536 538 544 545 546 547 548 549 550 551 552 553 555 556 557 558 559 560 562 564 572 573 574 575 576 580 581 582 583 584 585 587 588 590 591 592 593 595 596 598 600 601 602 603 604 606 609 610 613 614 618 619 621 623 667 668 669 670 671 672 673 674 676 677 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 790 807 815 910 911 912 913 937 938 957 1031 1034 1035 1037 1039 1238 1240 1241 1242 1244 1245 1248 1249 1250 1253 1254 1256 1257 1258 1259 1260 1262 1264 1265 1266 1267 1268 1271 1273 1274 1275 1278 1279 1280 1282 1283 1287 1288bifactor.negative.fit=cfa(bifactor.negative.model, data=PWB, missing = "fiml", std.lv = T)## Warning in lav_data_full(data = data, group = group, group.label = group.label, : lavaan WARNING: some cases are empty and will be removed:
## 145 150 151 152 156 157 171 173 206 207 208 209 213 221 222 223 238 239 240 244 249 250 251 252 253 256 257 258 259 261 263 265 266 268 275 279 280 283 284 290 294 297 298 299 300 301 302 304 305 307 308 311 312 314 315 316 317 320 322 323 325 328 330 331 332 335 336 338 340 342 343 345 348 350 351 352 354 355 356 357 358 362 366 368 371 373 374 375 377 380 383 384 393 394 395 396 398 399 402 403 408 409 410 412 414 415 417 419 420 426 428 432 437 438 439 440 443 444 445 448 450 453 455 458 461 462 464 467 468 472 476 478 479 480 482 483 485 488 489 490 492 493 494 495 496 497 498 499 500 501 502 503 504 505 507 508 509 512 513 514 517 518 525 526 527 528 529 530 531 532 533 534 535 536 538 544 545 546 547 548 549 550 551 552 553 555 556 557 558 559 560 562 564 572 573 574 575 576 580 581 582 583 584 585 587 588 590 591 592 593 595 596 598 600 601 602 603 604 606 609 610 613 614 618 619 621 623 667 668 669 670 671 672 673 674 676 677 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 790 807 815 910 911 912 913 937 938 957 1031 1034 1035 1037 1039 1238 1240 1241 1242 1244 1245 1248 1249 1250 1253 1254 1256 1257 1258 1259 1260 1262 1264 1265 1266 1267 1268 1271 1273 1274 1275 1278 1279 1280 1282 1283 1287 1288create pictures
semPaths(two.fit, whatLabels = "std", layout = "tree")
semPaths(two.fit, intercepts = FALSE, residual = FALSE, layout = "tree2", sizeMan = 4, font = 3,
sizeLat=4.3, bifactor = "Purpose",edge.color="black", nCharNodes = 6, mar = c(3, 1,3, 1))
semPaths(one.fit, whatLabels = "std", layout = "tree")
semPaths(one.fit, intercepts = FALSE, residual = FALSE, layout = "tree2", sizeMan = 4, font = 3,
sizeLat=4.3, bifactor = "Purpose",edge.color="black", nCharNodes = 6, mar = c(3, 1,3, 1))
semPaths(second.fit, whatLabels = "std", layout = "tree")
semPaths(second.fit, intercepts = FALSE, residual = FALSE, layout = "tree2", sizeMan = 4, font = 3,
sizeLat=4.3, bifactor = "Purpose",edge.color="black", nCharNodes = 6, mar = c(3, 1,3, 1))
semPaths(bifactor1.fit, whatLabels = "std", layout = "tree")
semPaths(bifactor1.fit, intercepts = FALSE, residual = FALSE, layout = "tree2", sizeMan = 5, font = 3,
sizeLat=4.3, bifactor = "Purpose",edge.color="black", nCharNodes = 6, mar = c(3, 1,3, 1))
semPaths(bifactor2.fit, whatLabels = "std", layout = "tree")
semPaths(bifactor2.fit, intercepts = FALSE, residual = FALSE, layout = "tree2", sizeMan = 4, font = 3,
sizeLat=4.3, bifactor = "Purpose",edge.color="black", nCharNodes = 6, mar = c(3, 1,3, 1))
semPaths(bifactorneg.fit, whatLabels = "std", layout = "tree")
semPaths(bifactorneg.fit, intercepts = FALSE, residual = FALSE, layout = "tree2", sizeMan = 4, font = 3,
sizeLat=4.3, bifactor = "Purpose",edge.color="black", nCharNodes = 6, mar = c(3, 1,3, 1))
#summaries
summary(two.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-20) converged normally after 32 iterations
##
## Used Total
## Number of observations 944 1288
##
## Number of missing patterns 1
##
## Estimator ML
## Minimum Function Test Statistic 587.190
## Degrees of freedom 26
## 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
## Factor1 =~
## PWB_1 0.947 0.052 18.368 0.000 0.947 0.595
## PWB_3 1.212 0.047 25.767 0.000 1.212 0.778
## PWB_4 0.852 0.050 17.174 0.000 0.852 0.565
## PWB_5 -1.254 0.048 -26.153 0.000 -1.254 -0.787
## PWB_6 0.673 0.043 15.563 0.000 0.673 0.518
## PWB_9 0.638 0.050 12.873 0.000 0.638 0.438
## Factor2 =~
## PWB_2 0.353 0.060 5.882 0.000 0.353 0.244
## PWB_7 0.920 0.059 15.513 0.000 0.920 0.706
## PWB_8 0.966 0.063 15.422 0.000 0.966 0.691
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## Factor1 ~~
## Factor2 0.315 0.044 7.113 0.000 0.315 0.315
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## PWB_1 3.860 0.052 74.452 0.000 3.860 2.423
## PWB_3 4.138 0.051 81.586 0.000 4.138 2.655
## PWB_4 3.969 0.049 80.845 0.000 3.969 2.631
## PWB_5 2.895 0.052 55.850 0.000 2.895 1.818
## PWB_6 4.469 0.042 105.703 0.000 4.469 3.440
## PWB_9 4.761 0.047 100.241 0.000 4.761 3.263
## PWB_2 3.822 0.047 81.370 0.000 3.822 2.648
## PWB_7 4.483 0.042 105.743 0.000 4.483 3.442
## PWB_8 4.315 0.046 94.758 0.000 4.315 3.084
## Factor1 0.000 0.000 0.000
## Factor2 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## PWB_1 1.641 0.085 19.200 0.000 1.641 0.647
## PWB_3 0.960 0.066 14.472 0.000 0.960 0.395
## PWB_4 1.549 0.080 19.434 0.000 1.549 0.681
## PWB_5 0.965 0.069 14.000 0.000 0.965 0.381
## PWB_6 1.234 0.062 19.988 0.000 1.234 0.732
## PWB_9 1.721 0.083 20.628 0.000 1.721 0.809
## PWB_2 1.958 0.094 20.816 0.000 1.958 0.940
## PWB_7 0.851 0.094 9.034 0.000 0.851 0.501
## PWB_8 1.023 0.105 9.761 0.000 1.023 0.523
## Factor1 1.000 1.000 1.000
## Factor2 1.000 1.000 1.000
##
## R-Square:
## Estimate
## PWB_1 0.353
## PWB_3 0.605
## PWB_4 0.319
## PWB_5 0.619
## PWB_6 0.268
## PWB_9 0.191
## PWB_2 0.060
## PWB_7 0.499
## PWB_8 0.477(xtable(parameterEstimates(two.fit, ci = F, standardized = T, fmi = F, remove.eq = F,
remove.ineq = F, remove.def = T)))## % latex table generated in R 3.2.2 by xtable 1.8-0 package
## % Mon Jan 25 12:41:13 2016
## \begin{table}[ht]
## \centering
## \begin{tabular}{rlllrrrrrrr}
## \hline
## & lhs & op & rhs & est & se & z & pvalue & std.lv & std.all & std.nox \\
## \hline
## 1 & Factor1 & =\~{} & PWB\_1 & 0.95 & 0.05 & 18.37 & 0.00 & 0.95 & 0.59 & 0.59 \\
## 2 & Factor1 & =\~{} & PWB\_3 & 1.21 & 0.05 & 25.77 & 0.00 & 1.21 & 0.78 & 0.78 \\
## 3 & Factor1 & =\~{} & PWB\_4 & 0.85 & 0.05 & 17.17 & 0.00 & 0.85 & 0.56 & 0.56 \\
## 4 & Factor1 & =\~{} & PWB\_5 & -1.25 & 0.05 & -26.15 & 0.00 & -1.25 & -0.79 & -0.79 \\
## 5 & Factor1 & =\~{} & PWB\_6 & 0.67 & 0.04 & 15.56 & 0.00 & 0.67 & 0.52 & 0.52 \\
## 6 & Factor1 & =\~{} & PWB\_9 & 0.64 & 0.05 & 12.87 & 0.00 & 0.64 & 0.44 & 0.44 \\
## 7 & Factor2 & =\~{} & PWB\_2 & 0.35 & 0.06 & 5.88 & 0.00 & 0.35 & 0.24 & 0.24 \\
## 8 & Factor2 & =\~{} & PWB\_7 & 0.92 & 0.06 & 15.51 & 0.00 & 0.92 & 0.71 & 0.71 \\
## 9 & Factor2 & =\~{} & PWB\_8 & 0.97 & 0.06 & 15.42 & 0.00 & 0.97 & 0.69 & 0.69 \\
## 10 & PWB\_1 & \~{}\~{} & PWB\_1 & 1.64 & 0.09 & 19.20 & 0.00 & 1.64 & 0.65 & 0.65 \\
## 11 & PWB\_3 & \~{}\~{} & PWB\_3 & 0.96 & 0.07 & 14.47 & 0.00 & 0.96 & 0.40 & 0.40 \\
## 12 & PWB\_4 & \~{}\~{} & PWB\_4 & 1.55 & 0.08 & 19.43 & 0.00 & 1.55 & 0.68 & 0.68 \\
## 13 & PWB\_5 & \~{}\~{} & PWB\_5 & 0.97 & 0.07 & 14.00 & 0.00 & 0.97 & 0.38 & 0.38 \\
## 14 & PWB\_6 & \~{}\~{} & PWB\_6 & 1.23 & 0.06 & 19.99 & 0.00 & 1.23 & 0.73 & 0.73 \\
## 15 & PWB\_9 & \~{}\~{} & PWB\_9 & 1.72 & 0.08 & 20.63 & 0.00 & 1.72 & 0.81 & 0.81 \\
## 16 & PWB\_2 & \~{}\~{} & PWB\_2 & 1.96 & 0.09 & 20.82 & 0.00 & 1.96 & 0.94 & 0.94 \\
## 17 & PWB\_7 & \~{}\~{} & PWB\_7 & 0.85 & 0.09 & 9.03 & 0.00 & 0.85 & 0.50 & 0.50 \\
## 18 & PWB\_8 & \~{}\~{} & PWB\_8 & 1.02 & 0.10 & 9.76 & 0.00 & 1.02 & 0.52 & 0.52 \\
## 19 & Factor1 & \~{}\~{} & Factor1 & 1.00 & 0.00 & & & 1.00 & 1.00 & 1.00 \\
## 20 & Factor2 & \~{}\~{} & Factor2 & 1.00 & 0.00 & & & 1.00 & 1.00 & 1.00 \\
## 21 & Factor1 & \~{}\~{} & Factor2 & 0.31 & 0.04 & 7.11 & 0.00 & 0.31 & 0.31 & 0.31 \\
## 22 & PWB\_1 & \~{}1 & & 3.86 & 0.05 & 74.45 & 0.00 & 3.86 & 2.42 & 2.42 \\
## 23 & PWB\_3 & \~{}1 & & 4.14 & 0.05 & 81.59 & 0.00 & 4.14 & 2.66 & 2.66 \\
## 24 & PWB\_4 & \~{}1 & & 3.97 & 0.05 & 80.85 & 0.00 & 3.97 & 2.63 & 2.63 \\
## 25 & PWB\_5 & \~{}1 & & 2.90 & 0.05 & 55.85 & 0.00 & 2.90 & 1.82 & 1.82 \\
## 26 & PWB\_6 & \~{}1 & & 4.47 & 0.04 & 105.70 & 0.00 & 4.47 & 3.44 & 3.44 \\
## 27 & PWB\_9 & \~{}1 & & 4.76 & 0.05 & 100.24 & 0.00 & 4.76 & 3.26 & 3.26 \\
## 28 & PWB\_2 & \~{}1 & & 3.82 & 0.05 & 81.37 & 0.00 & 3.82 & 2.65 & 2.65 \\
## 29 & PWB\_7 & \~{}1 & & 4.48 & 0.04 & 105.74 & 0.00 & 4.48 & 3.44 & 3.44 \\
## 30 & PWB\_8 & \~{}1 & & 4.31 & 0.05 & 94.76 & 0.00 & 4.31 & 3.08 & 3.08 \\
## 31 & Factor1 & \~{}1 & & 0.00 & 0.00 & & & 0.00 & 0.00 & 0.00 \\
## 32 & Factor2 & \~{}1 & & 0.00 & 0.00 & & & 0.00 & 0.00 & 0.00 \\
## \hline
## \end{tabular}
## \end{table}summary(one.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-20) converged normally after 24 iterations
##
## Used Total
## Number of observations 944 1288
##
## Number of missing patterns 1
##
## Estimator ML
## Minimum Function Test Statistic 643.119
## Degrees of freedom 27
## 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
## PWB =~
## PWB_1 0.975 0.051 19.048 0.000 0.975 0.612
## PWB_2 0.752 0.048 15.616 0.000 0.752 0.521
## PWB_3 1.181 0.047 25.135 0.000 1.181 0.758
## PWB_4 0.885 0.049 18.010 0.000 0.885 0.587
## PWB_5 -1.229 0.048 -25.725 0.000 -1.229 -0.772
## PWB_6 0.655 0.043 15.100 0.000 0.655 0.504
## PWB_7 0.272 0.047 5.796 0.000 0.272 0.209
## PWB_8 0.288 0.050 5.746 0.000 0.288 0.206
## PWB_9 0.675 0.049 13.718 0.000 0.675 0.463
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## PWB_1 3.860 0.052 74.452 0.000 3.860 2.423
## PWB_2 3.822 0.047 81.370 0.000 3.822 2.648
## PWB_3 4.138 0.051 81.586 0.000 4.138 2.655
## PWB_4 3.969 0.049 80.845 0.000 3.969 2.631
## PWB_5 2.895 0.052 55.850 0.000 2.895 1.818
## PWB_6 4.469 0.042 105.703 0.000 4.469 3.440
## PWB_7 4.483 0.042 105.743 0.000 4.483 3.442
## PWB_8 4.315 0.046 94.758 0.000 4.315 3.084
## PWB_9 4.761 0.047 100.241 0.000 4.761 3.263
## PWB 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## PWB_1 1.586 0.084 18.961 0.000 1.586 0.625
## PWB_2 1.517 0.076 19.889 0.000 1.517 0.728
## PWB_3 1.034 0.066 15.688 0.000 1.034 0.426
## PWB_4 1.492 0.078 19.205 0.000 1.492 0.656
## PWB_5 1.026 0.068 15.098 0.000 1.026 0.405
## PWB_6 1.259 0.062 20.143 0.000 1.259 0.746
## PWB_7 1.623 0.076 21.477 0.000 1.623 0.956
## PWB_8 1.874 0.087 21.496 0.000 1.874 0.958
## PWB_9 1.673 0.082 20.479 0.000 1.673 0.786
## PWB 1.000 1.000 1.000
##
## R-Square:
## Estimate
## PWB_1 0.375
## PWB_2 0.272
## PWB_3 0.574
## PWB_4 0.344
## PWB_5 0.595
## PWB_6 0.254
## PWB_7 0.044
## PWB_8 0.042
## PWB_9 0.214(xtable(parameterEstimates(one.fit, ci = F, standardized = T, fmi = F, remove.eq = F,
remove.ineq = F, remove.def = T)))## % latex table generated in R 3.2.2 by xtable 1.8-0 package
## % Mon Jan 25 12:41:13 2016
## \begin{table}[ht]
## \centering
## \begin{tabular}{rlllrrrrrrr}
## \hline
## & lhs & op & rhs & est & se & z & pvalue & std.lv & std.all & std.nox \\
## \hline
## 1 & PWB & =\~{} & PWB\_1 & 0.98 & 0.05 & 19.05 & 0.00 & 0.98 & 0.61 & 0.61 \\
## 2 & PWB & =\~{} & PWB\_2 & 0.75 & 0.05 & 15.62 & 0.00 & 0.75 & 0.52 & 0.52 \\
## 3 & PWB & =\~{} & PWB\_3 & 1.18 & 0.05 & 25.13 & 0.00 & 1.18 & 0.76 & 0.76 \\
## 4 & PWB & =\~{} & PWB\_4 & 0.89 & 0.05 & 18.01 & 0.00 & 0.89 & 0.59 & 0.59 \\
## 5 & PWB & =\~{} & PWB\_5 & -1.23 & 0.05 & -25.73 & 0.00 & -1.23 & -0.77 & -0.77 \\
## 6 & PWB & =\~{} & PWB\_6 & 0.65 & 0.04 & 15.10 & 0.00 & 0.65 & 0.50 & 0.50 \\
## 7 & PWB & =\~{} & PWB\_7 & 0.27 & 0.05 & 5.80 & 0.00 & 0.27 & 0.21 & 0.21 \\
## 8 & PWB & =\~{} & PWB\_8 & 0.29 & 0.05 & 5.75 & 0.00 & 0.29 & 0.21 & 0.21 \\
## 9 & PWB & =\~{} & PWB\_9 & 0.68 & 0.05 & 13.72 & 0.00 & 0.68 & 0.46 & 0.46 \\
## 10 & PWB\_1 & \~{}\~{} & PWB\_1 & 1.59 & 0.08 & 18.96 & 0.00 & 1.59 & 0.63 & 0.63 \\
## 11 & PWB\_2 & \~{}\~{} & PWB\_2 & 1.52 & 0.08 & 19.89 & 0.00 & 1.52 & 0.73 & 0.73 \\
## 12 & PWB\_3 & \~{}\~{} & PWB\_3 & 1.03 & 0.07 & 15.69 & 0.00 & 1.03 & 0.43 & 0.43 \\
## 13 & PWB\_4 & \~{}\~{} & PWB\_4 & 1.49 & 0.08 & 19.21 & 0.00 & 1.49 & 0.66 & 0.66 \\
## 14 & PWB\_5 & \~{}\~{} & PWB\_5 & 1.03 & 0.07 & 15.10 & 0.00 & 1.03 & 0.40 & 0.40 \\
## 15 & PWB\_6 & \~{}\~{} & PWB\_6 & 1.26 & 0.06 & 20.14 & 0.00 & 1.26 & 0.75 & 0.75 \\
## 16 & PWB\_7 & \~{}\~{} & PWB\_7 & 1.62 & 0.08 & 21.48 & 0.00 & 1.62 & 0.96 & 0.96 \\
## 17 & PWB\_8 & \~{}\~{} & PWB\_8 & 1.87 & 0.09 & 21.50 & 0.00 & 1.87 & 0.96 & 0.96 \\
## 18 & PWB\_9 & \~{}\~{} & PWB\_9 & 1.67 & 0.08 & 20.48 & 0.00 & 1.67 & 0.79 & 0.79 \\
## 19 & PWB & \~{}\~{} & PWB & 1.00 & 0.00 & & & 1.00 & 1.00 & 1.00 \\
## 20 & PWB\_1 & \~{}1 & & 3.86 & 0.05 & 74.45 & 0.00 & 3.86 & 2.42 & 2.42 \\
## 21 & PWB\_2 & \~{}1 & & 3.82 & 0.05 & 81.37 & 0.00 & 3.82 & 2.65 & 2.65 \\
## 22 & PWB\_3 & \~{}1 & & 4.14 & 0.05 & 81.59 & 0.00 & 4.14 & 2.66 & 2.66 \\
## 23 & PWB\_4 & \~{}1 & & 3.97 & 0.05 & 80.85 & 0.00 & 3.97 & 2.63 & 2.63 \\
## 24 & PWB\_5 & \~{}1 & & 2.90 & 0.05 & 55.85 & 0.00 & 2.90 & 1.82 & 1.82 \\
## 25 & PWB\_6 & \~{}1 & & 4.47 & 0.04 & 105.70 & 0.00 & 4.47 & 3.44 & 3.44 \\
## 26 & PWB\_7 & \~{}1 & & 4.48 & 0.04 & 105.74 & 0.00 & 4.48 & 3.44 & 3.44 \\
## 27 & PWB\_8 & \~{}1 & & 4.31 & 0.05 & 94.76 & 0.00 & 4.31 & 3.08 & 3.08 \\
## 28 & PWB\_9 & \~{}1 & & 4.76 & 0.05 & 100.24 & 0.00 & 4.76 & 3.26 & 3.26 \\
## 29 & PWB & \~{}1 & & 0.00 & 0.00 & & & 0.00 & 0.00 & 0.00 \\
## \hline
## \end{tabular}
## \end{table}summary(second.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-20) converged normally after 30 iterations
##
## Used Total
## Number of observations 944 1288
##
## Number of missing patterns 1
##
## Estimator ML
## Minimum Function Test Statistic 442.762
## Degrees of freedom 25
## 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
## Negative =~
## PWB_1 0.829 NA 0.981 0.616
## PWB_2 0.661 NA 0.782 0.542
## PWB_3 0.994 NA 1.176 0.755
## PWB_4 0.752 NA 0.890 0.590
## PWB_5 -1.029 NA -1.218 -0.765
## PWB_9 0.597 NA 0.706 0.484
## Positive =~
## PWB_6 0.499 NA 0.655 0.504
## PWB_7 0.695 NA 0.913 0.701
## PWB_8 0.707 NA 0.928 0.663
## Purpose =~
## Negative 0.633 NA 0.535 0.535
## Positive 0.851 NA 0.648 0.648
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## PWB_1 3.860 0.052 74.452 0.000 3.860 2.423
## PWB_2 3.822 0.047 81.370 0.000 3.822 2.648
## PWB_3 4.138 0.051 81.586 0.000 4.138 2.655
## PWB_4 3.969 0.049 80.845 0.000 3.969 2.631
## PWB_5 2.895 0.052 55.850 0.000 2.895 1.818
## PWB_9 4.761 0.047 100.241 0.000 4.761 3.263
## PWB_6 4.469 0.042 105.703 0.000 4.469 3.440
## PWB_7 4.483 0.042 105.743 0.000 4.483 3.442
## PWB_8 4.315 0.046 94.758 0.000 4.315 3.084
## Negative 0.000 0.000 0.000
## Positive 0.000 0.000 0.000
## Purpose 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## PWB_1 1.576 0.084 18.739 0.000 1.576 0.621
## PWB_2 1.471 0.076 19.471 0.000 1.471 0.706
## PWB_3 1.045 0.068 15.323 0.000 1.045 0.430
## PWB_4 1.484 0.078 19.005 0.000 1.484 0.652
## PWB_5 1.054 0.071 14.917 0.000 1.054 0.416
## PWB_9 1.631 0.081 20.217 0.000 1.631 0.766
## PWB_6 1.259 0.075 16.800 0.000 1.259 0.746
## PWB_7 0.863 0.077 11.207 0.000 0.863 0.508
## PWB_8 1.096 0.083 13.253 0.000 1.096 0.560
## Negative 1.000 0.714 0.714
## Positive 1.000 0.580 0.580
## Purpose 1.000 1.000 1.000
##
## R-Square:
## Estimate
## PWB_1 0.379
## PWB_2 0.294
## PWB_3 0.570
## PWB_4 0.348
## PWB_5 0.584
## PWB_9 0.234
## PWB_6 0.254
## PWB_7 0.492
## PWB_8 0.440
## Negative 0.286
## Positive 0.420(xtable(parameterEstimates(second.fit, ci = F, standardized = T, fmi = F, remove.eq = F,
remove.ineq = F, remove.def = T)))## % latex table generated in R 3.2.2 by xtable 1.8-0 package
## % Mon Jan 25 12:41:13 2016
## \begin{table}[ht]
## \centering
## \begin{tabular}{rlllrrrrrrr}
## \hline
## & lhs & op & rhs & est & se & z & pvalue & std.lv & std.all & std.nox \\
## \hline
## 1 & Negative & =\~{} & PWB\_1 & 0.83 & & & & 0.98 & 0.62 & 0.62 \\
## 2 & Negative & =\~{} & PWB\_2 & 0.66 & & & & 0.78 & 0.54 & 0.54 \\
## 3 & Negative & =\~{} & PWB\_3 & 0.99 & & & & 1.18 & 0.75 & 0.75 \\
## 4 & Negative & =\~{} & PWB\_4 & 0.75 & & & & 0.89 & 0.59 & 0.59 \\
## 5 & Negative & =\~{} & PWB\_5 & -1.03 & & & & -1.22 & -0.76 & -0.76 \\
## 6 & Negative & =\~{} & PWB\_9 & 0.60 & & & & 0.71 & 0.48 & 0.48 \\
## 7 & Positive & =\~{} & PWB\_6 & 0.50 & & & & 0.65 & 0.50 & 0.50 \\
## 8 & Positive & =\~{} & PWB\_7 & 0.70 & & & & 0.91 & 0.70 & 0.70 \\
## 9 & Positive & =\~{} & PWB\_8 & 0.71 & & & & 0.93 & 0.66 & 0.66 \\
## 10 & Purpose & =\~{} & Negative & 0.63 & & & & 0.53 & 0.53 & 0.53 \\
## 11 & Purpose & =\~{} & Positive & 0.85 & & & & 0.65 & 0.65 & 0.65 \\
## 12 & PWB\_1 & \~{}\~{} & PWB\_1 & 1.58 & 0.08 & 18.74 & 0.00 & 1.58 & 0.62 & 0.62 \\
## 13 & PWB\_2 & \~{}\~{} & PWB\_2 & 1.47 & 0.08 & 19.47 & 0.00 & 1.47 & 0.71 & 0.71 \\
## 14 & PWB\_3 & \~{}\~{} & PWB\_3 & 1.04 & 0.07 & 15.32 & 0.00 & 1.04 & 0.43 & 0.43 \\
## 15 & PWB\_4 & \~{}\~{} & PWB\_4 & 1.48 & 0.08 & 19.01 & 0.00 & 1.48 & 0.65 & 0.65 \\
## 16 & PWB\_5 & \~{}\~{} & PWB\_5 & 1.05 & 0.07 & 14.92 & 0.00 & 1.05 & 0.42 & 0.42 \\
## 17 & PWB\_9 & \~{}\~{} & PWB\_9 & 1.63 & 0.08 & 20.22 & 0.00 & 1.63 & 0.77 & 0.77 \\
## 18 & PWB\_6 & \~{}\~{} & PWB\_6 & 1.26 & 0.07 & 16.80 & 0.00 & 1.26 & 0.75 & 0.75 \\
## 19 & PWB\_7 & \~{}\~{} & PWB\_7 & 0.86 & 0.08 & 11.21 & 0.00 & 0.86 & 0.51 & 0.51 \\
## 20 & PWB\_8 & \~{}\~{} & PWB\_8 & 1.10 & 0.08 & 13.25 & 0.00 & 1.10 & 0.56 & 0.56 \\
## 21 & Negative & \~{}\~{} & Negative & 1.00 & 0.00 & & & 0.71 & 0.71 & 0.71 \\
## 22 & Positive & \~{}\~{} & Positive & 1.00 & 0.00 & & & 0.58 & 0.58 & 0.58 \\
## 23 & Purpose & \~{}\~{} & Purpose & 1.00 & 0.00 & & & 1.00 & 1.00 & 1.00 \\
## 24 & PWB\_1 & \~{}1 & & 3.86 & 0.05 & 74.45 & 0.00 & 3.86 & 2.42 & 2.42 \\
## 25 & PWB\_2 & \~{}1 & & 3.82 & 0.05 & 81.37 & 0.00 & 3.82 & 2.65 & 2.65 \\
## 26 & PWB\_3 & \~{}1 & & 4.14 & 0.05 & 81.59 & 0.00 & 4.14 & 2.66 & 2.66 \\
## 27 & PWB\_4 & \~{}1 & & 3.97 & 0.05 & 80.85 & 0.00 & 3.97 & 2.63 & 2.63 \\
## 28 & PWB\_5 & \~{}1 & & 2.90 & 0.05 & 55.85 & 0.00 & 2.90 & 1.82 & 1.82 \\
## 29 & PWB\_9 & \~{}1 & & 4.76 & 0.05 & 100.24 & 0.00 & 4.76 & 3.26 & 3.26 \\
## 30 & PWB\_6 & \~{}1 & & 4.47 & 0.04 & 105.70 & 0.00 & 4.47 & 3.44 & 3.44 \\
## 31 & PWB\_7 & \~{}1 & & 4.48 & 0.04 & 105.74 & 0.00 & 4.48 & 3.44 & 3.44 \\
## 32 & PWB\_8 & \~{}1 & & 4.31 & 0.05 & 94.76 & 0.00 & 4.31 & 3.08 & 3.08 \\
## 33 & Negative & \~{}1 & & 0.00 & 0.00 & & & 0.00 & 0.00 & 0.00 \\
## 34 & Positive & \~{}1 & & 0.00 & 0.00 & & & 0.00 & 0.00 & 0.00 \\
## 35 & Purpose & \~{}1 & & 0.00 & 0.00 & & & 0.00 & 0.00 & 0.00 \\
## \hline
## \end{tabular}
## \end{table}summary(bifactor1.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-20) converged normally after 53 iterations
##
## Used Total
## Number of observations 944 1288
##
## Number of missing patterns 1
##
## Estimator ML
## Minimum Function Test Statistic 183.769
## Degrees of freedom 18
## 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
## PWB =~
## PWB_1 0.854 0.074 11.612 0.000 0.854 0.536
## PWB_2 0.353 0.094 3.765 0.000 0.353 0.245
## PWB_3 1.195 0.058 20.615 0.000 1.195 0.767
## PWB_4 0.717 0.076 9.433 0.000 0.717 0.475
## PWB_5 -1.218 0.060 -20.420 0.000 -1.218 -0.765
## PWB_6 0.692 0.049 14.145 0.000 0.692 0.532
## PWB_7 0.148 0.050 2.950 0.003 0.148 0.113
## PWB_8 0.176 0.053 3.331 0.001 0.176 0.126
## PWB_9 0.503 0.086 5.880 0.000 0.503 0.345
## Negative =~
## PWB_1 0.543 0.108 5.029 0.000 0.543 0.341
## PWB_2 1.240 0.185 6.697 0.000 1.240 0.859
## PWB_3 0.276 0.117 2.368 0.018 0.276 0.177
## PWB_4 0.457 0.113 4.055 0.000 0.457 0.303
## PWB_5 -0.327 0.118 -2.767 0.006 -0.327 -0.206
## PWB_9 0.493 0.128 3.846 0.000 0.493 0.338
## Positive =~
## PWB_6 0.446 0.045 9.826 0.000 0.446 0.344
## PWB_7 1.001 0.069 14.561 0.000 1.001 0.768
## PWB_8 0.893 0.066 13.444 0.000 0.893 0.639
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## PWB ~~
## Negative 0.000 0.000 0.000
## Positive 0.000 0.000 0.000
## Negative ~~
## Positive 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## PWB_1 3.860 0.052 74.452 0.000 3.860 2.423
## PWB_2 3.822 0.047 81.370 0.000 3.822 2.648
## PWB_3 4.138 0.051 81.586 0.000 4.138 2.655
## PWB_4 3.969 0.049 80.845 0.000 3.969 2.631
## PWB_5 2.895 0.052 55.850 0.000 2.895 1.818
## PWB_6 4.469 0.042 105.703 0.000 4.469 3.440
## PWB_7 4.483 0.042 105.743 0.000 4.483 3.442
## PWB_8 4.315 0.046 94.758 0.000 4.315 3.084
## PWB_9 4.761 0.047 100.241 0.000 4.761 3.263
## PWB 0.000 0.000 0.000
## Negative 0.000 0.000 0.000
## Positive 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## PWB_1 1.513 0.081 18.655 0.000 1.513 0.596
## PWB_2 0.420 0.466 0.900 0.368 0.420 0.202
## PWB_3 0.923 0.073 12.670 0.000 0.923 0.380
## PWB_4 1.553 0.079 19.584 0.000 1.553 0.682
## PWB_5 0.947 0.073 12.918 0.000 0.947 0.373
## PWB_6 1.010 0.064 15.769 0.000 1.010 0.598
## PWB_7 0.674 0.121 5.573 0.000 0.674 0.397
## PWB_8 1.128 0.106 10.633 0.000 1.128 0.576
## PWB_9 1.633 0.094 17.402 0.000 1.633 0.767
## PWB 1.000 1.000 1.000
## Negative 1.000 1.000 1.000
## Positive 1.000 1.000 1.000
##
## R-Square:
## Estimate
## PWB_1 0.404
## PWB_2 0.798
## PWB_3 0.620
## PWB_4 0.318
## PWB_5 0.627
## PWB_6 0.402
## PWB_7 0.603
## PWB_8 0.424
## PWB_9 0.233(xtable(parameterEstimates(bifactor1.fit, ci = F, standardized = T, fmi = F, remove.eq = F,
remove.ineq = F, remove.def = T)))## % latex table generated in R 3.2.2 by xtable 1.8-0 package
## % Mon Jan 25 12:41:13 2016
## \begin{table}[ht]
## \centering
## \begin{tabular}{rlllrrrrrrr}
## \hline
## & lhs & op & rhs & est & se & z & pvalue & std.lv & std.all & std.nox \\
## \hline
## 1 & PWB & =\~{} & PWB\_1 & 0.85 & 0.07 & 11.61 & 0.00 & 0.85 & 0.54 & 0.54 \\
## 2 & PWB & =\~{} & PWB\_2 & 0.35 & 0.09 & 3.77 & 0.00 & 0.35 & 0.24 & 0.24 \\
## 3 & PWB & =\~{} & PWB\_3 & 1.20 & 0.06 & 20.62 & 0.00 & 1.20 & 0.77 & 0.77 \\
## 4 & PWB & =\~{} & PWB\_4 & 0.72 & 0.08 & 9.43 & 0.00 & 0.72 & 0.48 & 0.48 \\
## 5 & PWB & =\~{} & PWB\_5 & -1.22 & 0.06 & -20.42 & 0.00 & -1.22 & -0.76 & -0.76 \\
## 6 & PWB & =\~{} & PWB\_6 & 0.69 & 0.05 & 14.15 & 0.00 & 0.69 & 0.53 & 0.53 \\
## 7 & PWB & =\~{} & PWB\_7 & 0.15 & 0.05 & 2.95 & 0.00 & 0.15 & 0.11 & 0.11 \\
## 8 & PWB & =\~{} & PWB\_8 & 0.18 & 0.05 & 3.33 & 0.00 & 0.18 & 0.13 & 0.13 \\
## 9 & PWB & =\~{} & PWB\_9 & 0.50 & 0.09 & 5.88 & 0.00 & 0.50 & 0.34 & 0.34 \\
## 10 & Negative & =\~{} & PWB\_1 & 0.54 & 0.11 & 5.03 & 0.00 & 0.54 & 0.34 & 0.34 \\
## 11 & Negative & =\~{} & PWB\_2 & 1.24 & 0.19 & 6.70 & 0.00 & 1.24 & 0.86 & 0.86 \\
## 12 & Negative & =\~{} & PWB\_3 & 0.28 & 0.12 & 2.37 & 0.02 & 0.28 & 0.18 & 0.18 \\
## 13 & Negative & =\~{} & PWB\_4 & 0.46 & 0.11 & 4.05 & 0.00 & 0.46 & 0.30 & 0.30 \\
## 14 & Negative & =\~{} & PWB\_5 & -0.33 & 0.12 & -2.77 & 0.01 & -0.33 & -0.21 & -0.21 \\
## 15 & Negative & =\~{} & PWB\_9 & 0.49 & 0.13 & 3.85 & 0.00 & 0.49 & 0.34 & 0.34 \\
## 16 & Positive & =\~{} & PWB\_6 & 0.45 & 0.05 & 9.83 & 0.00 & 0.45 & 0.34 & 0.34 \\
## 17 & Positive & =\~{} & PWB\_7 & 1.00 & 0.07 & 14.56 & 0.00 & 1.00 & 0.77 & 0.77 \\
## 18 & Positive & =\~{} & PWB\_8 & 0.89 & 0.07 & 13.44 & 0.00 & 0.89 & 0.64 & 0.64 \\
## 19 & PWB & \~{}\~{} & Negative & 0.00 & 0.00 & & & 0.00 & 0.00 & 0.00 \\
## 20 & PWB & \~{}\~{} & Positive & 0.00 & 0.00 & & & 0.00 & 0.00 & 0.00 \\
## 21 & Negative & \~{}\~{} & Positive & 0.00 & 0.00 & & & 0.00 & 0.00 & 0.00 \\
## 22 & PWB\_1 & \~{}\~{} & PWB\_1 & 1.51 & 0.08 & 18.65 & 0.00 & 1.51 & 0.60 & 0.60 \\
## 23 & PWB\_2 & \~{}\~{} & PWB\_2 & 0.42 & 0.47 & 0.90 & 0.37 & 0.42 & 0.20 & 0.20 \\
## 24 & PWB\_3 & \~{}\~{} & PWB\_3 & 0.92 & 0.07 & 12.67 & 0.00 & 0.92 & 0.38 & 0.38 \\
## 25 & PWB\_4 & \~{}\~{} & PWB\_4 & 1.55 & 0.08 & 19.58 & 0.00 & 1.55 & 0.68 & 0.68 \\
## 26 & PWB\_5 & \~{}\~{} & PWB\_5 & 0.95 & 0.07 & 12.92 & 0.00 & 0.95 & 0.37 & 0.37 \\
## 27 & PWB\_6 & \~{}\~{} & PWB\_6 & 1.01 & 0.06 & 15.77 & 0.00 & 1.01 & 0.60 & 0.60 \\
## 28 & PWB\_7 & \~{}\~{} & PWB\_7 & 0.67 & 0.12 & 5.57 & 0.00 & 0.67 & 0.40 & 0.40 \\
## 29 & PWB\_8 & \~{}\~{} & PWB\_8 & 1.13 & 0.11 & 10.63 & 0.00 & 1.13 & 0.58 & 0.58 \\
## 30 & PWB\_9 & \~{}\~{} & PWB\_9 & 1.63 & 0.09 & 17.40 & 0.00 & 1.63 & 0.77 & 0.77 \\
## 31 & PWB & \~{}\~{} & PWB & 1.00 & 0.00 & & & 1.00 & 1.00 & 1.00 \\
## 32 & Negative & \~{}\~{} & Negative & 1.00 & 0.00 & & & 1.00 & 1.00 & 1.00 \\
## 33 & Positive & \~{}\~{} & Positive & 1.00 & 0.00 & & & 1.00 & 1.00 & 1.00 \\
## 34 & PWB\_1 & \~{}1 & & 3.86 & 0.05 & 74.45 & 0.00 & 3.86 & 2.42 & 2.42 \\
## 35 & PWB\_2 & \~{}1 & & 3.82 & 0.05 & 81.37 & 0.00 & 3.82 & 2.65 & 2.65 \\
## 36 & PWB\_3 & \~{}1 & & 4.14 & 0.05 & 81.59 & 0.00 & 4.14 & 2.66 & 2.66 \\
## 37 & PWB\_4 & \~{}1 & & 3.97 & 0.05 & 80.85 & 0.00 & 3.97 & 2.63 & 2.63 \\
## 38 & PWB\_5 & \~{}1 & & 2.90 & 0.05 & 55.85 & 0.00 & 2.90 & 1.82 & 1.82 \\
## 39 & PWB\_6 & \~{}1 & & 4.47 & 0.04 & 105.70 & 0.00 & 4.47 & 3.44 & 3.44 \\
## 40 & PWB\_7 & \~{}1 & & 4.48 & 0.04 & 105.74 & 0.00 & 4.48 & 3.44 & 3.44 \\
## 41 & PWB\_8 & \~{}1 & & 4.31 & 0.05 & 94.76 & 0.00 & 4.31 & 3.08 & 3.08 \\
## 42 & PWB\_9 & \~{}1 & & 4.76 & 0.05 & 100.24 & 0.00 & 4.76 & 3.26 & 3.26 \\
## 43 & PWB & \~{}1 & & 0.00 & 0.00 & & & 0.00 & 0.00 & 0.00 \\
## 44 & Negative & \~{}1 & & 0.00 & 0.00 & & & 0.00 & 0.00 & 0.00 \\
## 45 & Positive & \~{}1 & & 0.00 & 0.00 & & & 0.00 & 0.00 & 0.00 \\
## \hline
## \end{tabular}
## \end{table}summary(bifactor2.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-20) converged normally after 46 iterations
##
## Used Total
## Number of observations 944 1288
##
## Number of missing patterns 1
##
## Estimator ML
## Minimum Function Test Statistic 236.692
## Degrees of freedom 18
## 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
## PWB =~
## PWB_1 0.913 0.060 15.187 0.000 0.913 0.573
## PWB_2 0.857 0.055 15.647 0.000 0.857 0.594
## PWB_3 1.018 0.060 17.031 0.000 1.018 0.653
## PWB_4 0.939 0.054 17.383 0.000 0.939 0.623
## PWB_5 -1.031 0.061 -16.954 0.000 -1.031 -0.648
## PWB_6 0.379 0.056 6.731 0.000 0.379 0.292
## PWB_7 0.139 0.054 2.555 0.011 0.139 0.107
## PWB_8 0.174 0.057 3.041 0.002 0.174 0.125
## PWB_9 0.757 0.055 13.795 0.000 0.757 0.519
## F1 =~
## PWB_1 0.415 0.076 5.455 0.000 0.415 0.260
## PWB_3 0.634 0.075 8.471 0.000 0.634 0.407
## PWB_5 -0.733 0.080 -9.188 0.000 -0.733 -0.460
## PWB_6 0.669 0.080 8.376 0.000 0.669 0.515
## F2 =~
## PWB_4 0.422 0.056 7.513 0.000 0.422 0.280
## PWB_7 1.096 0.089 12.326 0.000 1.096 0.842
## PWB_8 0.817 0.075 10.907 0.000 0.817 0.584
## F3 =~
## PWB_2 0.261 NA 0.261 0.181
## PWB_9 0.481 NA 0.481 0.329
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## PWB ~~
## F1 0.000 0.000 0.000
## F2 0.000 0.000 0.000
## F3 0.000 0.000 0.000
## F1 ~~
## F2 0.000 0.000 0.000
## F3 0.000 0.000 0.000
## F2 ~~
## F3 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## PWB_1 3.860 0.052 74.452 0.000 3.860 2.423
## PWB_2 3.822 0.047 81.370 0.000 3.822 2.648
## PWB_3 4.138 0.051 81.586 0.000 4.138 2.655
## PWB_4 3.969 0.049 80.845 0.000 3.969 2.631
## PWB_5 2.895 0.052 55.850 0.000 2.895 1.818
## PWB_6 4.469 0.042 105.703 0.000 4.469 3.440
## PWB_7 4.483 0.042 105.743 0.000 4.483 3.442
## PWB_8 4.315 0.046 94.758 0.000 4.315 3.084
## PWB_9 4.761 0.047 100.241 0.000 4.761 3.263
## PWB 0.000 0.000 0.000
## F1 0.000 0.000 0.000
## F2 0.000 0.000 0.000
## F3 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## PWB_1 1.533 0.082 18.721 0.000 1.533 0.604
## PWB_2 1.280 NA 1.280 0.614
## PWB_3 0.990 0.066 14.912 0.000 0.990 0.408
## PWB_4 1.215 0.079 15.406 0.000 1.215 0.534
## PWB_5 0.936 0.075 12.422 0.000 0.936 0.369
## PWB_6 1.097 0.092 11.910 0.000 1.097 0.650
## PWB_7 0.476 0.181 2.629 0.009 0.476 0.280
## PWB_8 1.259 0.114 11.058 0.000 1.259 0.643
## PWB_9 1.326 NA 1.326 0.623
## PWB 1.000 1.000 1.000
## F1 1.000 1.000 1.000
## F2 1.000 1.000 1.000
## F3 1.000 1.000 1.000
##
## R-Square:
## Estimate
## PWB_1 0.396
## PWB_2 0.386
## PWB_3 0.592
## PWB_4 0.466
## PWB_5 0.631
## PWB_6 0.350
## PWB_7 0.720
## PWB_8 0.357
## PWB_9 0.377(xtable(parameterEstimates(bifactor2.fit, ci = F, standardized = T, fmi = F, remove.eq = F,
remove.ineq = F, remove.def = T)))## % latex table generated in R 3.2.2 by xtable 1.8-0 package
## % Mon Jan 25 12:41:13 2016
## \begin{table}[ht]
## \centering
## \begin{tabular}{rlllrrrrrrr}
## \hline
## & lhs & op & rhs & est & se & z & pvalue & std.lv & std.all & std.nox \\
## \hline
## 1 & PWB & =\~{} & PWB\_1 & 0.91 & 0.06 & 15.19 & 0.00 & 0.91 & 0.57 & 0.57 \\
## 2 & PWB & =\~{} & PWB\_2 & 0.86 & 0.05 & 15.65 & 0.00 & 0.86 & 0.59 & 0.59 \\
## 3 & PWB & =\~{} & PWB\_3 & 1.02 & 0.06 & 17.03 & 0.00 & 1.02 & 0.65 & 0.65 \\
## 4 & PWB & =\~{} & PWB\_4 & 0.94 & 0.05 & 17.38 & 0.00 & 0.94 & 0.62 & 0.62 \\
## 5 & PWB & =\~{} & PWB\_5 & -1.03 & 0.06 & -16.95 & 0.00 & -1.03 & -0.65 & -0.65 \\
## 6 & PWB & =\~{} & PWB\_6 & 0.38 & 0.06 & 6.73 & 0.00 & 0.38 & 0.29 & 0.29 \\
## 7 & PWB & =\~{} & PWB\_7 & 0.14 & 0.05 & 2.56 & 0.01 & 0.14 & 0.11 & 0.11 \\
## 8 & PWB & =\~{} & PWB\_8 & 0.17 & 0.06 & 3.04 & 0.00 & 0.17 & 0.12 & 0.12 \\
## 9 & PWB & =\~{} & PWB\_9 & 0.76 & 0.05 & 13.79 & 0.00 & 0.76 & 0.52 & 0.52 \\
## 10 & F1 & =\~{} & PWB\_1 & 0.41 & 0.08 & 5.45 & 0.00 & 0.41 & 0.26 & 0.26 \\
## 11 & F1 & =\~{} & PWB\_3 & 0.63 & 0.07 & 8.47 & 0.00 & 0.63 & 0.41 & 0.41 \\
## 12 & F1 & =\~{} & PWB\_5 & -0.73 & 0.08 & -9.19 & 0.00 & -0.73 & -0.46 & -0.46 \\
## 13 & F1 & =\~{} & PWB\_6 & 0.67 & 0.08 & 8.38 & 0.00 & 0.67 & 0.51 & 0.51 \\
## 14 & F2 & =\~{} & PWB\_4 & 0.42 & 0.06 & 7.51 & 0.00 & 0.42 & 0.28 & 0.28 \\
## 15 & F2 & =\~{} & PWB\_7 & 1.10 & 0.09 & 12.33 & 0.00 & 1.10 & 0.84 & 0.84 \\
## 16 & F2 & =\~{} & PWB\_8 & 0.82 & 0.07 & 10.91 & 0.00 & 0.82 & 0.58 & 0.58 \\
## 17 & F3 & =\~{} & PWB\_2 & 0.26 & & & & 0.26 & 0.18 & 0.18 \\
## 18 & F3 & =\~{} & PWB\_9 & 0.48 & & & & 0.48 & 0.33 & 0.33 \\
## 19 & PWB & \~{}\~{} & F1 & 0.00 & 0.00 & & & 0.00 & 0.00 & 0.00 \\
## 20 & PWB & \~{}\~{} & F2 & 0.00 & 0.00 & & & 0.00 & 0.00 & 0.00 \\
## 21 & PWB & \~{}\~{} & F3 & 0.00 & 0.00 & & & 0.00 & 0.00 & 0.00 \\
## 22 & F1 & \~{}\~{} & F2 & 0.00 & 0.00 & & & 0.00 & 0.00 & 0.00 \\
## 23 & F1 & \~{}\~{} & F3 & 0.00 & 0.00 & & & 0.00 & 0.00 & 0.00 \\
## 24 & F2 & \~{}\~{} & F3 & 0.00 & 0.00 & & & 0.00 & 0.00 & 0.00 \\
## 25 & PWB\_1 & \~{}\~{} & PWB\_1 & 1.53 & 0.08 & 18.72 & 0.00 & 1.53 & 0.60 & 0.60 \\
## 26 & PWB\_2 & \~{}\~{} & PWB\_2 & 1.28 & & & & 1.28 & 0.61 & 0.61 \\
## 27 & PWB\_3 & \~{}\~{} & PWB\_3 & 0.99 & 0.07 & 14.91 & 0.00 & 0.99 & 0.41 & 0.41 \\
## 28 & PWB\_4 & \~{}\~{} & PWB\_4 & 1.22 & 0.08 & 15.41 & 0.00 & 1.22 & 0.53 & 0.53 \\
## 29 & PWB\_5 & \~{}\~{} & PWB\_5 & 0.94 & 0.08 & 12.42 & 0.00 & 0.94 & 0.37 & 0.37 \\
## 30 & PWB\_6 & \~{}\~{} & PWB\_6 & 1.10 & 0.09 & 11.91 & 0.00 & 1.10 & 0.65 & 0.65 \\
## 31 & PWB\_7 & \~{}\~{} & PWB\_7 & 0.48 & 0.18 & 2.63 & 0.01 & 0.48 & 0.28 & 0.28 \\
## 32 & PWB\_8 & \~{}\~{} & PWB\_8 & 1.26 & 0.11 & 11.06 & 0.00 & 1.26 & 0.64 & 0.64 \\
## 33 & PWB\_9 & \~{}\~{} & PWB\_9 & 1.33 & & & & 1.33 & 0.62 & 0.62 \\
## 34 & PWB & \~{}\~{} & PWB & 1.00 & 0.00 & & & 1.00 & 1.00 & 1.00 \\
## 35 & F1 & \~{}\~{} & F1 & 1.00 & 0.00 & & & 1.00 & 1.00 & 1.00 \\
## 36 & F2 & \~{}\~{} & F2 & 1.00 & 0.00 & & & 1.00 & 1.00 & 1.00 \\
## 37 & F3 & \~{}\~{} & F3 & 1.00 & 0.00 & & & 1.00 & 1.00 & 1.00 \\
## 38 & PWB\_1 & \~{}1 & & 3.86 & 0.05 & 74.45 & 0.00 & 3.86 & 2.42 & 2.42 \\
## 39 & PWB\_2 & \~{}1 & & 3.82 & 0.05 & 81.37 & 0.00 & 3.82 & 2.65 & 2.65 \\
## 40 & PWB\_3 & \~{}1 & & 4.14 & 0.05 & 81.59 & 0.00 & 4.14 & 2.66 & 2.66 \\
## 41 & PWB\_4 & \~{}1 & & 3.97 & 0.05 & 80.85 & 0.00 & 3.97 & 2.63 & 2.63 \\
## 42 & PWB\_5 & \~{}1 & & 2.90 & 0.05 & 55.85 & 0.00 & 2.90 & 1.82 & 1.82 \\
## 43 & PWB\_6 & \~{}1 & & 4.47 & 0.04 & 105.70 & 0.00 & 4.47 & 3.44 & 3.44 \\
## 44 & PWB\_7 & \~{}1 & & 4.48 & 0.04 & 105.74 & 0.00 & 4.48 & 3.44 & 3.44 \\
## 45 & PWB\_8 & \~{}1 & & 4.31 & 0.05 & 94.76 & 0.00 & 4.31 & 3.08 & 3.08 \\
## 46 & PWB\_9 & \~{}1 & & 4.76 & 0.05 & 100.24 & 0.00 & 4.76 & 3.26 & 3.26 \\
## 47 & PWB & \~{}1 & & 0.00 & 0.00 & & & 0.00 & 0.00 & 0.00 \\
## 48 & F1 & \~{}1 & & 0.00 & 0.00 & & & 0.00 & 0.00 & 0.00 \\
## 49 & F2 & \~{}1 & & 0.00 & 0.00 & & & 0.00 & 0.00 & 0.00 \\
## 50 & F3 & \~{}1 & & 0.00 & 0.00 & & & 0.00 & 0.00 & 0.00 \\
## \hline
## \end{tabular}
## \end{table}summary(bifactor.negative.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-20) converged normally after 32 iterations
##
## Used Total
## Number of observations 944 1288
##
## Number of missing patterns 1
##
## Estimator ML
## Minimum Function Test Statistic 357.805
## Degrees of freedom 20
## 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
## Negative =~
## PWB_1 1.373 25.379 0.054 0.957 1.373 0.862
## PWB_2 0.969 17.923 0.054 0.957 0.969 0.672
## PWB_3 1.493 27.601 0.054 0.957 1.493 0.958
## PWB_4 0.962 17.783 0.054 0.957 0.962 0.638
## PWB_5 -1.535 28.388 -0.054 0.957 -1.535 -0.964
## PWB_9 0.886 16.380 0.054 0.957 0.886 0.607
## PWB =~
## PWB_1 0.949 38.938 0.024 0.981 0.949 0.596
## PWB_2 0.910 27.499 0.033 0.974 0.910 0.631
## PWB_3 1.286 42.347 0.030 0.976 1.286 0.825
## PWB_4 1.250 27.283 0.046 0.963 1.250 0.828
## PWB_5 -1.348 43.554 -0.031 0.975 -1.348 -0.846
## PWB_6 0.569 0.051 11.216 0.000 0.569 0.438
## PWB_7 0.994 0.052 19.087 0.000 0.994 0.763
## PWB_8 0.913 0.052 17.517 0.000 0.913 0.653
## PWB_9 0.789 25.132 0.031 0.975 0.789 0.541
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## Negative ~~
## PWB -0.652 16.316 -0.040 0.968 -0.652 -0.652
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## PWB_1 3.860 0.052 74.452 0.000 3.860 2.423
## PWB_2 3.822 0.047 81.370 0.000 3.822 2.648
## PWB_3 4.138 0.051 81.586 0.000 4.138 2.655
## PWB_4 3.969 0.049 80.845 0.000 3.969 2.631
## PWB_5 2.895 0.052 55.850 0.000 2.895 1.818
## PWB_9 4.761 0.047 100.241 0.000 4.761 3.263
## PWB_6 4.469 0.042 105.703 0.000 4.469 3.440
## PWB_7 4.483 0.042 105.743 0.000 4.483 3.442
## PWB_8 4.315 0.046 94.758 0.000 4.315 3.084
## Negative 0.000 0.000 0.000
## PWB 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## PWB_1 1.451 0.084 17.188 0.000 1.451 0.572
## PWB_2 1.465 0.075 19.498 0.000 1.465 0.703
## PWB_3 1.048 0.069 15.134 0.000 1.048 0.432
## PWB_4 1.356 0.073 18.604 0.000 1.356 0.596
## PWB_5 1.060 0.071 14.943 0.000 1.060 0.418
## PWB_9 1.633 0.081 20.236 0.000 1.633 0.767
## PWB_6 1.364 0.072 18.957 0.000 1.364 0.808
## PWB_7 0.708 0.082 8.607 0.000 0.708 0.417
## PWB_8 1.123 0.079 14.159 0.000 1.123 0.574
## Negative 1.000 1.000 1.000
## PWB 1.000 1.000 1.000
##
## R-Square:
## Estimate
## PWB_1 0.428
## PWB_2 0.297
## PWB_3 0.568
## PWB_4 0.404
## PWB_5 0.582
## PWB_9 0.233
## PWB_6 0.192
## PWB_7 0.583
## PWB_8 0.426(xtable(parameterEstimates(bifactor.negative.fit, ci = F, standardized = T, fmi = F, remove.eq = F,
remove.ineq = F, remove.def = T)))## % latex table generated in R 3.2.2 by xtable 1.8-0 package
## % Mon Jan 25 12:41:13 2016
## \begin{table}[ht]
## \centering
## \begin{tabular}{rlllrrrrrrr}
## \hline
## & lhs & op & rhs & est & se & z & pvalue & std.lv & std.all & std.nox \\
## \hline
## 1 & Negative & =\~{} & PWB\_1 & 1.37 & 25.38 & 0.05 & 0.96 & 1.37 & 0.86 & 0.86 \\
## 2 & Negative & =\~{} & PWB\_2 & 0.97 & 17.92 & 0.05 & 0.96 & 0.97 & 0.67 & 0.67 \\
## 3 & Negative & =\~{} & PWB\_3 & 1.49 & 27.60 & 0.05 & 0.96 & 1.49 & 0.96 & 0.96 \\
## 4 & Negative & =\~{} & PWB\_4 & 0.96 & 17.78 & 0.05 & 0.96 & 0.96 & 0.64 & 0.64 \\
## 5 & Negative & =\~{} & PWB\_5 & -1.54 & 28.39 & -0.05 & 0.96 & -1.54 & -0.96 & -0.96 \\
## 6 & Negative & =\~{} & PWB\_9 & 0.89 & 16.38 & 0.05 & 0.96 & 0.89 & 0.61 & 0.61 \\
## 7 & PWB & =\~{} & PWB\_1 & 0.95 & 38.94 & 0.02 & 0.98 & 0.95 & 0.60 & 0.60 \\
## 8 & PWB & =\~{} & PWB\_2 & 0.91 & 27.50 & 0.03 & 0.97 & 0.91 & 0.63 & 0.63 \\
## 9 & PWB & =\~{} & PWB\_3 & 1.29 & 42.35 & 0.03 & 0.98 & 1.29 & 0.83 & 0.83 \\
## 10 & PWB & =\~{} & PWB\_4 & 1.25 & 27.28 & 0.05 & 0.96 & 1.25 & 0.83 & 0.83 \\
## 11 & PWB & =\~{} & PWB\_5 & -1.35 & 43.55 & -0.03 & 0.98 & -1.35 & -0.85 & -0.85 \\
## 12 & PWB & =\~{} & PWB\_6 & 0.57 & 0.05 & 11.22 & 0.00 & 0.57 & 0.44 & 0.44 \\
## 13 & PWB & =\~{} & PWB\_7 & 0.99 & 0.05 & 19.09 & 0.00 & 0.99 & 0.76 & 0.76 \\
## 14 & PWB & =\~{} & PWB\_8 & 0.91 & 0.05 & 17.52 & 0.00 & 0.91 & 0.65 & 0.65 \\
## 15 & PWB & =\~{} & PWB\_9 & 0.79 & 25.13 & 0.03 & 0.97 & 0.79 & 0.54 & 0.54 \\
## 16 & PWB\_1 & \~{}\~{} & PWB\_1 & 1.45 & 0.08 & 17.19 & 0.00 & 1.45 & 0.57 & 0.57 \\
## 17 & PWB\_2 & \~{}\~{} & PWB\_2 & 1.46 & 0.08 & 19.50 & 0.00 & 1.46 & 0.70 & 0.70 \\
## 18 & PWB\_3 & \~{}\~{} & PWB\_3 & 1.05 & 0.07 & 15.13 & 0.00 & 1.05 & 0.43 & 0.43 \\
## 19 & PWB\_4 & \~{}\~{} & PWB\_4 & 1.36 & 0.07 & 18.60 & 0.00 & 1.36 & 0.60 & 0.60 \\
## 20 & PWB\_5 & \~{}\~{} & PWB\_5 & 1.06 & 0.07 & 14.94 & 0.00 & 1.06 & 0.42 & 0.42 \\
## 21 & PWB\_9 & \~{}\~{} & PWB\_9 & 1.63 & 0.08 & 20.24 & 0.00 & 1.63 & 0.77 & 0.77 \\
## 22 & PWB\_6 & \~{}\~{} & PWB\_6 & 1.36 & 0.07 & 18.96 & 0.00 & 1.36 & 0.81 & 0.81 \\
## 23 & PWB\_7 & \~{}\~{} & PWB\_7 & 0.71 & 0.08 & 8.61 & 0.00 & 0.71 & 0.42 & 0.42 \\
## 24 & PWB\_8 & \~{}\~{} & PWB\_8 & 1.12 & 0.08 & 14.16 & 0.00 & 1.12 & 0.57 & 0.57 \\
## 25 & Negative & \~{}\~{} & Negative & 1.00 & 0.00 & & & 1.00 & 1.00 & 1.00 \\
## 26 & PWB & \~{}\~{} & PWB & 1.00 & 0.00 & & & 1.00 & 1.00 & 1.00 \\
## 27 & Negative & \~{}\~{} & PWB & -0.65 & 16.32 & -0.04 & 0.97 & -0.65 & -0.65 & -0.65 \\
## 28 & PWB\_1 & \~{}1 & & 3.86 & 0.05 & 74.45 & 0.00 & 3.86 & 2.42 & 2.42 \\
## 29 & PWB\_2 & \~{}1 & & 3.82 & 0.05 & 81.37 & 0.00 & 3.82 & 2.65 & 2.65 \\
## 30 & PWB\_3 & \~{}1 & & 4.14 & 0.05 & 81.59 & 0.00 & 4.14 & 2.66 & 2.66 \\
## 31 & PWB\_4 & \~{}1 & & 3.97 & 0.05 & 80.85 & 0.00 & 3.97 & 2.63 & 2.63 \\
## 32 & PWB\_5 & \~{}1 & & 2.90 & 0.05 & 55.85 & 0.00 & 2.90 & 1.82 & 1.82 \\
## 33 & PWB\_9 & \~{}1 & & 4.76 & 0.05 & 100.24 & 0.00 & 4.76 & 3.26 & 3.26 \\
## 34 & PWB\_6 & \~{}1 & & 4.47 & 0.04 & 105.70 & 0.00 & 4.47 & 3.44 & 3.44 \\
## 35 & PWB\_7 & \~{}1 & & 4.48 & 0.04 & 105.74 & 0.00 & 4.48 & 3.44 & 3.44 \\
## 36 & PWB\_8 & \~{}1 & & 4.31 & 0.05 & 94.76 & 0.00 & 4.31 & 3.08 & 3.08 \\
## 37 & Negative & \~{}1 & & 0.00 & 0.00 & & & 0.00 & 0.00 & 0.00 \\
## 38 & PWB & \~{}1 & & 0.00 & 0.00 & & & 0.00 & 0.00 & 0.00 \\
## \hline
## \end{tabular}
## \end{table}?parameterEstimatesResidual correlations
correl = residuals(two.fit, type="cor")
correl## $type
## [1] "cor.bollen"
##
## $cor
## PWB_1 PWB_3 PWB_4 PWB_5 PWB_6 PWB_9 PWB_2 PWB_7 PWB_8
## PWB_1 0.000
## PWB_3 0.001 0.000
## PWB_4 -0.041 0.018 0.000
## PWB_5 -0.018 -0.006 0.033 0.000
## PWB_6 0.021 -0.006 -0.031 -0.011 0.000
## PWB_9 0.030 -0.011 0.071 -0.007 -0.124 0.000
## PWB_2 0.386 0.277 0.334 -0.302 0.106 0.334 0.000
## PWB_7 -0.175 -0.096 0.176 0.070 0.209 -0.014 -0.040 0.000
## PWB_8 -0.138 -0.071 0.118 0.068 0.174 0.001 -0.050 0.017 0.000
##
## $mean
## PWB_1 PWB_3 PWB_4 PWB_5 PWB_6 PWB_9 PWB_2 PWB_7 PWB_8
## 0 0 0 0 0 0 0 0 0View(correl$cor)
correl1 = residuals(one.fit, type="cor")
correl1## $type
## [1] "cor.bollen"
##
## $cor
## PWB_1 PWB_2 PWB_3 PWB_4 PWB_5 PWB_6 PWB_7 PWB_8 PWB_9
## PWB_1 0.000
## PWB_2 0.113 0.000
## PWB_3 0.000 -0.058 0.000
## PWB_4 -0.065 0.072 0.012 0.000
## PWB_5 -0.014 0.039 -0.033 0.041 0.000
## PWB_6 0.020 -0.117 0.015 -0.034 -0.030 0.000
## PWB_7 -0.170 0.024 -0.081 0.179 0.056 0.219 0.000
## PWB_8 -0.134 0.011 -0.058 0.120 0.055 0.183 0.462 0.000
## PWB_9 0.007 0.126 -0.021 0.047 0.005 -0.131 -0.013 0.001 0.000
##
## $mean
## PWB_1 PWB_2 PWB_3 PWB_4 PWB_5 PWB_6 PWB_7 PWB_8 PWB_9
## 0 0 0 0 0 0 0 0 0View(correl1$cor)
correl0 = residuals(second.fit, type="cor")
correl0## $type
## [1] "cor.bollen"
##
## $cor
## PWB_1 PWB_2 PWB_3 PWB_4 PWB_5 PWB_9 PWB_6 PWB_7 PWB_8
## PWB_1 0.000
## PWB_2 0.098 0.000
## PWB_3 -0.001 -0.072 0.000
## PWB_4 -0.069 0.058 0.012 0.000
## PWB_5 -0.016 0.051 -0.041 0.040 0.000
## PWB_9 -0.008 0.105 -0.036 0.033 0.018 0.000
## PWB_6 0.222 0.052 0.265 0.159 -0.285 0.018 0.000
## PWB_7 -0.192 0.001 -0.107 0.159 0.080 -0.034 -0.029 0.000
## PWB_8 -0.150 -0.006 -0.076 0.105 0.072 -0.015 -0.048 0.040 0.000
##
## $mean
## PWB_1 PWB_2 PWB_3 PWB_4 PWB_5 PWB_9 PWB_6 PWB_7 PWB_8
## 0 0 0 0 0 0 0 0 0View(correl0$cor)
correl4 = residuals(bifactor1.fit, type="cor")
correl4## $type
## [1] "cor.bollen"
##
## $cor
## PWB_1 PWB_2 PWB_3 PWB_4 PWB_5 PWB_6 PWB_7 PWB_8 PWB_9
## PWB_1 0.000
## PWB_2 0.008 0.000
## PWB_3 -0.008 -0.003 0.000
## PWB_4 -0.064 0.001 0.039 0.000
## PWB_5 -0.006 0.001 0.005 0.014 0.000
## PWB_6 0.044 0.016 -0.011 0.009 -0.011 0.000
## PWB_7 -0.103 0.105 -0.010 0.248 -0.019 0.000 0.000
## PWB_8 -0.076 0.088 0.001 0.181 -0.007 0.000 0.000 0.000
## PWB_9 -0.010 -0.007 0.005 0.052 -0.019 -0.081 0.044 0.053 0.000
##
## $mean
## PWB_1 PWB_2 PWB_3 PWB_4 PWB_5 PWB_6 PWB_7 PWB_8 PWB_9
## 0 0 0 0 0 0 0 0 0View(correl4$cor)
correl5 = residuals(bifactor2.fit, type="cor")
correl5## $type
## [1] "cor.bollen"
##
## $cor
## PWB_1 PWB_2 PWB_3 PWB_4 PWB_5 PWB_6 PWB_7 PWB_8 PWB_9
## PWB_1 0.000
## PWB_2 0.092 0.000
## PWB_3 -0.017 -0.051 0.000
## PWB_4 -0.062 0.008 0.050 0.000
## PWB_5 0.004 0.022 -0.008 -0.008 0.000
## PWB_6 0.028 -0.027 -0.003 0.080 0.007 0.000
## PWB_7 -0.104 0.070 0.007 0.000 -0.036 0.293 0.000
## PWB_8 -0.080 0.044 0.016 0.000 -0.023 0.250 0.000 0.000
## PWB_9 -0.007 0.000 -0.009 -0.005 -0.016 -0.049 0.028 0.032 0.000
##
## $mean
## PWB_1 PWB_2 PWB_3 PWB_4 PWB_5 PWB_6 PWB_7 PWB_8 PWB_9
## 0 0 0 0 0 0 0 0 0correl3 = residuals(bifactor.negative.fit, type="cor")
correl3## $type
## [1] "cor.bollen"
##
## $cor
## PWB_1 PWB_2 PWB_3 PWB_4 PWB_5 PWB_9 PWB_6 PWB_7 PWB_8
## PWB_1 0.000
## PWB_2 0.092 0.000
## PWB_3 -0.018 -0.072 0.000
## PWB_4 -0.036 0.052 0.023 0.000
## PWB_5 -0.001 0.052 -0.043 0.032 0.000
## PWB_9 -0.016 0.105 -0.034 0.036 0.017 0.000
## PWB_6 0.314 0.062 0.309 0.081 -0.323 0.039 0.000
## PWB_7 -0.068 -0.014 -0.077 -0.013 0.061 -0.028 -0.010 0.000
## PWB_8 -0.031 -0.007 -0.033 -0.028 0.039 0.002 0.001 0.007 0.000
##
## $mean
## PWB_1 PWB_2 PWB_3 PWB_4 PWB_5 PWB_9 PWB_6 PWB_7 PWB_8
## 0 0 0 0 0 0 0 0 0View(correl3$cor)Modification indicies
#modindices(two.fit, sort. = TRUE, minimum.value = 3.84)
#modindices(one.fit, sort. = TRUE, minimum.value = 3.84)
#modindices(bifactor1.fit, sort. = TRUE, minimum.value = 3.84)
#modindices(bifactor.negative.fit, sort. = TRUE, minimum.value = 3.84)Fit Measures
fitmeasures(two.fit)#Models two factors:Positive and Negative for Purpose ## npar fmin chisq
## 28.000 0.311 587.190
## df pvalue baseline.chisq
## 26.000 0.000 2337.145
## baseline.df baseline.pvalue cfi
## 36.000 0.000 0.756
## tli nnfi rfi
## 0.662 0.662 0.652
## nfi pnfi ifi
## 0.749 0.541 0.757
## rni logl unrestricted.logl
## 0.756 -14382.524 -14088.929
## aic bic ntotal
## 28821.048 28956.852 944.000
## bic2 rmsea rmsea.ci.lower
## 28867.925 0.151 0.141
## rmsea.ci.upper rmsea.pvalue rmr
## 0.162 0.000 0.258
## rmr_nomean srmr srmr_bentler
## 0.283 0.120 0.120
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.131 0.120 0.131
## srmr_mplus srmr_mplus_nomean cn_05
## 0.120 0.131 63.514
## cn_01 gfi agfi
## 74.376 0.993 0.985
## pgfi mfi ecvi
## 0.478 0.743 NAfitmeasures(one.fit) #Models as a single purpose factor## npar fmin chisq
## 27.000 0.341 643.119
## df pvalue baseline.chisq
## 27.000 0.000 2337.145
## baseline.df baseline.pvalue cfi
## 36.000 0.000 0.732
## tli nnfi rfi
## 0.643 0.643 0.633
## nfi pnfi ifi
## 0.725 0.544 0.733
## rni logl unrestricted.logl
## 0.732 -14410.488 -14088.929
## aic bic ntotal
## 28874.977 29005.930 944.000
## bic2 rmsea rmsea.ci.lower
## 28920.180 0.155 0.145
## rmsea.ci.upper rmsea.pvalue rmr
## 0.166 0.000 0.182
## rmr_nomean srmr srmr_bentler
## 0.200 0.095 0.095
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.104 0.095 0.104
## srmr_mplus srmr_mplus_nomean cn_05
## 0.095 0.104 59.880
## cn_01 gfi agfi
## 69.934 0.991 0.982
## pgfi mfi ecvi
## 0.496 0.722 NAfitmeasures(second.fit)#Second order models as Purpose being the higher factor made up of Purpose and Positive## npar fmin chisq
## 29.000 0.235 442.762
## df pvalue baseline.chisq
## 25.000 0.000 2337.145
## baseline.df baseline.pvalue cfi
## 36.000 0.000 0.818
## tli nnfi rfi
## 0.739 0.739 0.727
## nfi pnfi ifi
## 0.811 0.563 0.819
## rni logl unrestricted.logl
## 0.818 -14310.310 -14088.929
## aic bic ntotal
## 28678.620 28819.273 944.000
## bic2 rmsea rmsea.ci.lower
## 28727.171 0.133 0.122
## rmsea.ci.upper rmsea.pvalue rmr
## 0.144 0.000 0.179
## rmr_nomean srmr srmr_bentler
## 0.197 0.086 0.086
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.095 0.086 0.095
## srmr_mplus srmr_mplus_nomean cn_05
## 0.086 0.095 81.278
## cn_01 gfi agfi
## 95.481 0.993 0.985
## pgfi mfi ecvi
## 0.460 0.801 NAfitmeasures(bifactor1.fit)#Models bifactor with Positive and Purpose as factors uncorolated with the main factor## npar fmin chisq
## 36.000 0.097 183.769
## df pvalue baseline.chisq
## 18.000 0.000 2337.145
## baseline.df baseline.pvalue cfi
## 36.000 0.000 0.928
## tli nnfi rfi
## 0.856 0.856 0.843
## nfi pnfi ifi
## 0.921 0.461 0.929
## rni logl unrestricted.logl
## 0.928 -14180.814 -14088.929
## aic bic ntotal
## 28433.627 28608.232 944.000
## bic2 rmsea rmsea.ci.lower
## 28493.898 0.099 0.086
## rmsea.ci.upper rmsea.pvalue rmr
## 0.112 0.000 0.108
## rmr_nomean srmr srmr_bentler
## 0.119 0.053 0.053
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.058 0.053 0.058
## srmr_mplus srmr_mplus_nomean cn_05
## 0.053 0.058 149.298
## cn_01 gfi agfi
## 179.790 0.997 0.992
## pgfi mfi ecvi
## 0.332 0.916 NAfitmeasures(bifactor2.fit)#Models bifactor with Positive and Purpose as factors uncorolated with the main factor## npar fmin chisq
## 36.000 0.125 236.692
## df pvalue baseline.chisq
## 18.000 0.000 2337.145
## baseline.df baseline.pvalue cfi
## 36.000 0.000 0.905
## tli nnfi rfi
## 0.810 0.810 0.797
## nfi pnfi ifi
## 0.899 0.449 0.906
## rni logl unrestricted.logl
## 0.905 -14207.275 -14088.929
## aic bic ntotal
## 28486.550 28661.154 944.000
## bic2 rmsea rmsea.ci.lower
## 28546.820 0.113 0.101
## rmsea.ci.upper rmsea.pvalue rmr
## 0.127 0.000 0.115
## rmr_nomean srmr srmr_bentler
## 0.126 0.062 0.062
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.068 0.062 0.068
## srmr_mplus srmr_mplus_nomean cn_05
## 0.062 0.068 116.140
## cn_01 gfi agfi
## 139.814 0.997 0.991
## pgfi mfi ecvi
## 0.332 0.891 NAfitmeasures(bifactor.negative.fit)#Models bifactor as the negatively worded item as a factor uncorolated with the main factor## npar fmin chisq
## 34.000 0.190 357.805
## df pvalue baseline.chisq
## 20.000 0.000 2337.145
## baseline.df baseline.pvalue cfi
## 36.000 0.000 0.853
## tli nnfi rfi
## 0.736 0.736 0.724
## nfi pnfi ifi
## 0.847 0.471 0.854
## rni logl unrestricted.logl
## 0.853 -14267.831 -14088.929
## aic bic ntotal
## 28603.663 28768.567 944.000
## bic2 rmsea rmsea.ci.lower
## 28660.585 0.134 0.122
## rmsea.ci.upper rmsea.pvalue rmr
## 0.146 0.000 0.171
## rmr_nomean srmr srmr_bentler
## 0.188 0.083 0.083
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.091 0.083 0.091
## srmr_mplus srmr_mplus_nomean cn_05
## 0.083 0.091 83.870
## cn_01 gfi agfi
## 100.111 0.995 0.986
## pgfi mfi ecvi
## 0.368 0.836 NACreate dataset for Target rotation
all_surveys <- read.csv("~/Dropbox/Git/stats/allsurveysYT1_Jan2016.csv", header=T)
PWBTR<-select(all_surveys, PWB_1, PWB_2, PWB_3,PWB_4, PWB_5,PWB_6,PWB_9, PWB_8,PWB_7)
PWB$PWB_1 <- 7- PWB$PWB_1
PWB$PWB_2 <- 7- PWB$PWB_2
PWB$PWB_3 <- 7- PWB$PWB_3
PWB$PWB_4 <- 7- PWB$PWB_4
PWB$PWB_9 <- 7- PWB$PWB_9
PWBTR<- data.frame(apply(PWBTR,2, as.numeric))
library(GPArotation)
library(psych)
library(dplyr)
PWBTR<-tbl_df(PWBTR)
PWBTR## Source: local data frame [1,288 x 9]
##
## PWB_1 PWB_2 PWB_3 PWB_4 PWB_5 PWB_6 PWB_9 PWB_8 PWB_7
## (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl)
## 1 3 1 2 4 5 4 6 5 3
## 2 5 5 3 3 6 1 4 4 2
## 3 5 4 4 5 5 4 5 6 6
## 4 2 5 2 2 3 5 1 4 4
## 5 4 4 4 4 4 4 4 4 4
## 6 6 5 5 5 4 4 5 4 4
## 7 4 3 4 3 3 3 3 3 4
## 8 6 5 6 6 6 6 6 6 2
## 9 2 2 2 2 1 5 2 5 5
## 10 5 5 5 5 5 5 5 5 5
## .. ... ... ... ... ... ... ... ... ...str(PWBTR)## Classes 'tbl_df', 'tbl' and 'data.frame': 1288 obs. of 9 variables:
## $ PWB_1: num 3 5 5 2 4 6 4 6 2 5 ...
## $ PWB_2: num 1 5 4 5 4 5 3 5 2 5 ...
## $ PWB_3: num 2 3 4 2 4 5 4 6 2 5 ...
## $ PWB_4: num 4 3 5 2 4 5 3 6 2 5 ...
## $ PWB_5: num 5 6 5 3 4 4 3 6 1 5 ...
## $ PWB_6: num 4 1 4 5 4 4 3 6 5 5 ...
## $ PWB_9: num 6 4 5 1 4 5 3 6 2 5 ...
## $ PWB_8: num 5 4 6 4 4 4 3 6 5 5 ...
## $ PWB_7: num 3 2 6 4 4 4 4 2 5 5 ...colnames(PWBTR) <- c("1","2", "3", "4", "5", "6", "7", "8", "9")
#Target rotation: choose "simple structure" a priori and can be applied to oblique and orthogonal rotation based on
#what paper says facotrs should be PWB
Targ_key <- make.keys(9,list(f1=1:6,f2=7:9))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
PWBTR_cor <- corFiml(PWBTR) # convert the raw data to correlation matrix uisng FIML
out_targetQ <- fa(PWBTR_cor,2,rotate="TargetQ",n.obs = 816,Target=Targ_key) #TargetT for orthogonal rotation
fa2latex(fa(PWBTR_cor,2,rotate="TargetQ",n.obs = 816,Target=Targ_key), heading="Table 7. Factor Loadings for Exploratory Factor Analysis PWB")## % Called in the psych package fa2latex % Called in the psych package fa(PWBTR_cor, 2, rotate = "TargetQ", n.obs = 816, Target = Targ_key) % Called in the psych package Table 7. Factor Loadings for Exploratory Factor Analysis PWB
## \begin{table}[htpb]\caption{fa2latex}
## \begin{center}
## \begin{scriptsize}
## \begin{tabular} {l r r r r r }
## \multicolumn{ 5 }{l}{ Table 7. Factor Loadings for Exploratory Factor Analysis PWB } \cr
## \hline Variable & MR1 & MR2 & h2 & u2 & com \cr
## \hline
## 1 & \bf{ 0.68} & 0.19 & 0.44 & 0.56 & 1.16 \cr
## 2 & \bf{ 0.51} & -0.03 & 0.27 & 0.73 & 1.01 \cr
## 3 & \bf{ 0.78} & 0.06 & 0.59 & 0.41 & 1.01 \cr
## 4 & \bf{ 0.53} & -0.24 & 0.39 & 0.61 & 1.39 \cr
## 5 & \bf{ 0.79} & 0.05 & 0.60 & 0.40 & 1.01 \cr
## 6 & \bf{-0.44} & 0.28 & 0.32 & 0.68 & 1.69 \cr
## 7 & \bf{ 0.46} & 0.00 & 0.21 & 0.79 & 1.00 \cr
## 8 & -0.05 & \bf{ 0.60} & 0.38 & 0.62 & 1.02 \cr
## 9 & -0.01 & \bf{ 0.83} & 0.70 & 0.30 & 1.00 \cr
## \hline \cr SS loadings & 2.64 & 1.25 & \cr
## \cr
## \hline \cr
## MR1 & 1.00 & -0.21 \cr
## MR2 & -0.21 & 1.00 \cr
## \hline
## \end{tabular}
## \end{scriptsize}
## \end{center}
## \label{default}
## \end{table}out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR1 MR2
## 1 0.675 0.192
## 2 0.514
## 3 0.777
## 4 0.527 -0.236
## 5 0.785
## 6 -0.440 0.277
## 7 0.461
## 8 0.603
## 9 0.833
##
## MR1 MR2
## SS loadings 2.628 1.234
## Proportion Var 0.292 0.137
## Cumulative Var 0.292 0.429
##
## $score.cor
## [,1] [,2]
## [1,] 1.0000000 -0.2350767
## [2,] -0.2350767 1.0000000
##
## $TLI
## [1] 0.8557789
##
## $RMSEA
## RMSEA lower upper confidence
## 0.09873280 0.08500035 0.11220761 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = PWBTR_cor, nfactors = 2, n.obs = 816, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 h2 u2 com
## 1 0.68 0.19 0.44 0.56 1.2
## 2 0.51 -0.03 0.27 0.73 1.0
## 3 0.78 0.06 0.59 0.41 1.0
## 4 0.53 -0.24 0.39 0.61 1.4
## 5 0.79 0.05 0.60 0.40 1.0
## 6 -0.44 0.28 0.32 0.68 1.7
## 7 0.46 0.00 0.21 0.79 1.0
## 8 -0.05 0.60 0.38 0.62 1.0
## 9 -0.01 0.83 0.70 0.30 1.0
##
## MR1 MR2
## SS loadings 2.64 1.25
## Proportion Var 0.29 0.14
## Cumulative Var 0.29 0.43
## Proportion Explained 0.68 0.32
## Cumulative Proportion 0.68 1.00
##
## With factor correlations of
## MR1 MR2
## MR1 1.00 -0.21
## MR2 -0.21 1.00
##
## Mean item complexity = 1.1
## Test of the hypothesis that 2 factors are sufficient.
##
## The degrees of freedom for the null model are 36 and the objective function was 2.48 with Chi Square of 2008.29
## The degrees of freedom for the model are 19 and the objective function was 0.21
##
## The root mean square of the residuals (RMSR) is 0.05
## The df corrected root mean square of the residuals is 0.07
##
## The harmonic number of observations is 816 with the empirical chi square 152.45 with prob < 7.4e-23
## The total number of observations was 816 with MLE Chi Square = 168.87 with prob < 4.7e-26
##
## Tucker Lewis Index of factoring reliability = 0.856
## RMSEA index = 0.099 and the 90 % confidence intervals are 0.085 0.112
## BIC = 41.49
## Fit based upon off diagonal values = 0.97
## Measures of factor score adequacy
## MR1 MR2
## Correlation of scores with factors 0.92 0.87
## Multiple R square of scores with factors 0.84 0.76
## Minimum correlation of possible factor scores 0.68 0.52CFI
1-((out_targetQ$STATISTIC - out_targetQ$dof)/(out_targetQ$null.chisq- out_targetQ$null.dof))## [1] 0.9240107Target toration as three factors based on EFA - works well as three factors but with cross loadings
all_surveys <- read.csv("~/Dropbox/Git/stats/allsurveysYT1_Jan2016.csv", header=T)
PWBTR<-select(all_surveys, PWB_1, PWB_3, PWB_5,PWB_6, PWB_7, PWB_4,PWB_8, PWB_2,PWB_9)
PWB$PWB_1 <- 7- PWB$PWB_1
PWB$PWB_2 <- 7- PWB$PWB_2
PWB$PWB_3 <- 7- PWB$PWB_3
PWB$PWB_4 <- 7- PWB$PWB_4
PWB$PWB_9 <- 7- PWB$PWB_9
PWBTR<- data.frame(apply(PWBTR,2, as.numeric))
library(GPArotation)
library(psych)
library(dplyr)
PWBTR<-tbl_df(PWBTR)
PWBTR## Source: local data frame [1,288 x 9]
##
## PWB_1 PWB_3 PWB_5 PWB_6 PWB_7 PWB_4 PWB_8 PWB_2 PWB_9
## (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl)
## 1 3 2 5 4 3 4 5 1 6
## 2 5 3 6 1 2 3 4 5 4
## 3 5 4 5 4 6 5 6 4 5
## 4 2 2 3 5 4 2 4 5 1
## 5 4 4 4 4 4 4 4 4 4
## 6 6 5 4 4 4 5 4 5 5
## 7 4 4 3 3 4 3 3 3 3
## 8 6 6 6 6 2 6 6 5 6
## 9 2 2 1 5 5 2 5 2 2
## 10 5 5 5 5 5 5 5 5 5
## .. ... ... ... ... ... ... ... ... ...str(PWBTR)## Classes 'tbl_df', 'tbl' and 'data.frame': 1288 obs. of 9 variables:
## $ PWB_1: num 3 5 5 2 4 6 4 6 2 5 ...
## $ PWB_3: num 2 3 4 2 4 5 4 6 2 5 ...
## $ PWB_5: num 5 6 5 3 4 4 3 6 1 5 ...
## $ PWB_6: num 4 1 4 5 4 4 3 6 5 5 ...
## $ PWB_7: num 3 2 6 4 4 4 4 2 5 5 ...
## $ PWB_4: num 4 3 5 2 4 5 3 6 2 5 ...
## $ PWB_8: num 5 4 6 4 4 4 3 6 5 5 ...
## $ PWB_2: num 1 5 4 5 4 5 3 5 2 5 ...
## $ PWB_9: num 6 4 5 1 4 5 3 6 2 5 ...colnames(PWBTR) <- c("1","2", "3", "4", "5", "6", "7", "8", "9")
#Target rotation: choose "simple structure" a priori and can be applied to oblique and orthogonal rotation based on
#what paper says facotrs should be PWB
Targ_key <- make.keys(9,list(f1=1:4,f2=5:6, f3=7:9))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
PWBTR_cor <- corFiml(PWBTR) # convert the raw data to correlation matrix uisng FIML
out_targetQ <- fa(PWBTR_cor,3,rotate="TargetQ",n.obs = 816,Target=Targ_key) #TargetT for orthogonal rotation
fa2latex(fa(PWBTR_cor,3,rotate="TargetQ",n.obs = 816,Target=Targ_key), heading="Table 8. Factor Loadings for Exploratory Factor Analysis PWB")## % Called in the psych package fa2latex % Called in the psych package fa(PWBTR_cor, 3, rotate = "TargetQ", n.obs = 816, Target = Targ_key) % Called in the psych package Table 8. Factor Loadings for Exploratory Factor Analysis PWB
## \begin{table}[htpb]\caption{fa2latex}
## \begin{center}
## \begin{scriptsize}
## \begin{tabular} {l r r r r r r }
## \multicolumn{ 6 }{l}{ Table 8. Factor Loadings for Exploratory Factor Analysis PWB } \cr
## \hline Variable & MR1 & MR2 & MR3 & h2 & u2 & com \cr
## \hline
## 1 & \bf{ 0.54} & 0.17 & 0.20 & 0.44 & 0.56 & 1.48 \cr
## 2 & \bf{ 0.72} & 0.07 & 0.09 & 0.59 & 0.41 & 1.05 \cr
## 3 & \bf{ 0.74} & 0.06 & 0.09 & 0.61 & 0.39 & 1.04 \cr
## 4 & \bf{-0.77} & 0.20 & \bf{ 0.38} & 0.52 & 0.48 & 1.62 \cr
## 5 & 0.01 & \bf{ 0.83} & -0.01 & 0.69 & 0.31 & 1.00 \cr
## 6 & 0.25 & -0.30 & \bf{ 0.36} & 0.42 & 0.58 & 2.77 \cr
## 7 & -0.05 & \bf{ 0.60} & 0.01 & 0.38 & 0.62 & 1.02 \cr
## 8 & 0.08 & -0.14 & \bf{ 0.59} & 0.44 & 0.56 & 1.15 \cr
## 9 & 0.09 & -0.08 & \bf{ 0.49} & 0.32 & 0.68 & 1.13 \cr
## \hline \cr SS loadings & 2.13 & 1.27 & 1 & \cr
## \cr
## \hline \cr
## MR1 & 1.00 & -0.25 & 0.56 \cr
## MR2 & -0.25 & 1.00 & 0.01 \cr
## MR3 & 0.56 & 0.01 & 1.00 \cr
## \hline
## \end{tabular}
## \end{scriptsize}
## \end{center}
## \label{default}
## \end{table}out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR1 MR2 MR3
## 1 0.544 0.171 0.202
## 2 0.722
## 3 0.739
## 4 -0.765 0.203 0.376
## 5 0.833
## 6 0.254 -0.299 0.361
## 7 0.603
## 8 -0.138 0.594
## 9 0.494
##
## MR1 MR2 MR3
## SS loadings 2.031 1.252 0.926
## Proportion Var 0.226 0.139 0.103
## Cumulative Var 0.226 0.365 0.468
##
## $score.cor
## [,1] [,2] [,3]
## [1,] 1.0000000 -0.2351284 0.5058006
## [2,] -0.2351284 1.0000000 -0.1502985
## [3,] 0.5058006 -0.1502985 1.0000000
##
## $TLI
## [1] 0.9268462
##
## $RMSEA
## RMSEA lower upper confidence
## 0.07035195 0.05295846 0.08810460 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = PWBTR_cor, nfactors = 3, n.obs = 816, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 MR3 h2 u2 com
## 1 0.54 0.17 0.20 0.44 0.56 1.5
## 2 0.72 0.07 0.09 0.59 0.41 1.1
## 3 0.74 0.06 0.09 0.61 0.39 1.0
## 4 -0.77 0.20 0.38 0.52 0.48 1.6
## 5 0.01 0.83 -0.01 0.69 0.31 1.0
## 6 0.25 -0.30 0.36 0.42 0.58 2.8
## 7 -0.05 0.60 0.01 0.38 0.62 1.0
## 8 0.08 -0.14 0.59 0.44 0.56 1.1
## 9 0.09 -0.08 0.49 0.32 0.68 1.1
##
## MR1 MR2 MR3
## SS loadings 2.13 1.27 1.00
## Proportion Var 0.24 0.14 0.11
## Cumulative Var 0.24 0.38 0.49
## Proportion Explained 0.48 0.29 0.23
## Cumulative Proportion 0.48 0.77 1.00
##
## With factor correlations of
## MR1 MR2 MR3
## MR1 1.00 -0.25 0.56
## MR2 -0.25 1.00 0.01
## MR3 0.56 0.01 1.00
##
## Mean item complexity = 1.4
## Test of the hypothesis that 3 factors are sufficient.
##
## The degrees of freedom for the null model are 36 and the objective function was 2.48 with Chi Square of 2008.29
## The degrees of freedom for the model are 12 and the objective function was 0.07
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.04
##
## The harmonic number of observations is 816 with the empirical chi square 34.55 with prob < 0.00055
## The total number of observations was 816 with MLE Chi Square = 59.97 with prob < 2.3e-08
##
## Tucker Lewis Index of factoring reliability = 0.927
## RMSEA index = 0.07 and the 90 % confidence intervals are 0.053 0.088
## BIC = -20.48
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## MR1 MR2 MR3
## Correlation of scores with factors 0.91 0.87 0.82
## Multiple R square of scores with factors 0.83 0.76 0.67
## Minimum correlation of possible factor scores 0.67 0.53 0.34CFI
1-((out_targetQ$STATISTIC - out_targetQ$dof)/(out_targetQ$null.chisq- out_targetQ$null.dof))## [1] 0.9756766Create dataset for Target rotation
all_surveys <- read.csv("~/Dropbox/Git/stats/allsurveysYT1_Jan2016.csv", header=T)
PWB<-select(all_surveys, PWB_1, PWB_3, PWB_5,PWB_6, PWB_7, PWB_8, PWB_2, PWB_4, PWB_9)
PWB<- data.frame(apply(PWB,2, as.numeric))
library(GPArotation)
library(psych)
library(dplyr)
PWB<-tbl_df(PWB)
PWB## Source: local data frame [1,288 x 9]
##
## PWB_1 PWB_3 PWB_5 PWB_6 PWB_7 PWB_8 PWB_2 PWB_4 PWB_9
## (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl)
## 1 3 2 5 4 3 5 1 4 6
## 2 5 3 6 1 2 4 5 3 4
## 3 5 4 5 4 6 6 4 5 5
## 4 2 2 3 5 4 4 5 2 1
## 5 4 4 4 4 4 4 4 4 4
## 6 6 5 4 4 4 4 5 5 5
## 7 4 4 3 3 4 3 3 3 3
## 8 6 6 6 6 2 6 5 6 6
## 9 2 2 1 5 5 5 2 2 2
## 10 5 5 5 5 5 5 5 5 5
## .. ... ... ... ... ... ... ... ... ...str(PWB)## Classes 'tbl_df', 'tbl' and 'data.frame': 1288 obs. of 9 variables:
## $ PWB_1: num 3 5 5 2 4 6 4 6 2 5 ...
## $ PWB_3: num 2 3 4 2 4 5 4 6 2 5 ...
## $ PWB_5: num 5 6 5 3 4 4 3 6 1 5 ...
## $ PWB_6: num 4 1 4 5 4 4 3 6 5 5 ...
## $ PWB_7: num 3 2 6 4 4 4 4 2 5 5 ...
## $ PWB_8: num 5 4 6 4 4 4 3 6 5 5 ...
## $ PWB_2: num 1 5 4 5 4 5 3 5 2 5 ...
## $ PWB_4: num 4 3 5 2 4 5 3 6 2 5 ...
## $ PWB_9: num 6 4 5 1 4 5 3 6 2 5 ...colnames(PWB) <- c("1","2", "3", "4", "5", "6", "7", "8", "9")
#Target rotation: choose "simple structure" a priori and can be applied to oblique and orthogonal rotation based on
#what paper says facotrs should be PWB
Targ_key <- make.keys(9,list(f1=1:4,f2=5:7, f3=8:9))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
PWB_cor <- corFiml(PWB) # convert the raw data to correlation matrix uisng FIML
out_targetQ <- fa(PWB_cor,3,rotate="TargetQ",n.obs = 816,Target=Targ_key) #TargetT for orthogonal rotation
fa2latex(fa(PWB_cor,3,rotate="TargetQ",n.obs = 816,Target=Targ_key), heading="Table 9. Factor Loadings for Exploratory Factor Analysis PWB")## % Called in the psych package fa2latex % Called in the psych package fa(PWB_cor, 3, rotate = "TargetQ", n.obs = 816, Target = Targ_key) % Called in the psych package Table 9. Factor Loadings for Exploratory Factor Analysis PWB
## \begin{table}[htpb]\caption{fa2latex}
## \begin{center}
## \begin{scriptsize}
## \begin{tabular} {l r r r r r r }
## \multicolumn{ 6 }{l}{ Table 9. Factor Loadings for Exploratory Factor Analysis PWB } \cr
## \hline Variable & MR1 & MR2 & MR3 & h2 & u2 & com \cr
## \hline
## 1 & \bf{ 0.58} & 0.19 & 0.18 & 0.44 & 0.56 & 1.41 \cr
## 2 & \bf{ 0.74} & 0.08 & 0.08 & 0.59 & 0.41 & 1.05 \cr
## 3 & \bf{ 0.75} & 0.06 & 0.08 & 0.61 & 0.39 & 1.04 \cr
## 4 & \bf{-0.71} & 0.23 & \bf{ 0.34} & 0.52 & 0.48 & 1.68 \cr
## 5 & 0.07 & \bf{ 0.85} & -0.06 & 0.69 & 0.31 & 1.02 \cr
## 6 & -0.01 & \bf{ 0.61} & -0.03 & 0.38 & 0.62 & 1.00 \cr
## 7 & 0.14 & -0.10 & \bf{ 0.57} & 0.44 & 0.56 & 1.19 \cr
## 8 & 0.28 & -0.28 & \bf{ 0.36} & 0.42 & 0.58 & 2.83 \cr
## 9 & 0.14 & -0.05 & \bf{ 0.47} & 0.32 & 0.68 & 1.22 \cr
## \hline \cr SS loadings & 2.18 & 1.28 & 0.94 & \cr
## \cr
## \hline \cr
## MR1 & 1.00 & -0.28 & 0.50 \cr
## MR2 & -0.28 & 1.00 & 0.00 \cr
## MR3 & 0.50 & 0.00 & 1.00 \cr
## \hline
## \end{tabular}
## \end{scriptsize}
## \end{center}
## \label{default}
## \end{table}out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR1 MR2 MR3
## 1 0.579 0.187 0.180
## 2 0.737
## 3 0.753
## 4 -0.709 0.229 0.341
## 5 0.846
## 6 0.614
## 7 0.140 -0.103 0.565
## 8 0.275 -0.281 0.356
## 9 0.145 0.468
##
## MR1 MR2 MR3
## SS loadings 2.069 1.282 0.832
## Proportion Var 0.230 0.142 0.092
## Cumulative Var 0.230 0.372 0.465
##
## $score.cor
## [,1] [,2] [,3]
## [1,] 1.0000000 -0.2351284 0.5058006
## [2,] -0.2351284 1.0000000 -0.1502985
## [3,] 0.5058006 -0.1502985 1.0000000
##
## $TLI
## [1] 0.9268462
##
## $RMSEA
## RMSEA lower upper confidence
## 0.07035195 0.05295846 0.08810460 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = PWB_cor, nfactors = 3, n.obs = 816, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 MR3 h2 u2 com
## 1 0.58 0.19 0.18 0.44 0.56 1.4
## 2 0.74 0.08 0.08 0.59 0.41 1.0
## 3 0.75 0.06 0.08 0.61 0.39 1.0
## 4 -0.71 0.23 0.34 0.52 0.48 1.7
## 5 0.07 0.85 -0.06 0.69 0.31 1.0
## 6 -0.01 0.61 -0.03 0.38 0.62 1.0
## 7 0.14 -0.10 0.57 0.44 0.56 1.2
## 8 0.28 -0.28 0.36 0.42 0.58 2.8
## 9 0.14 -0.05 0.47 0.32 0.68 1.2
##
## MR1 MR2 MR3
## SS loadings 2.18 1.28 0.94
## Proportion Var 0.24 0.14 0.10
## Cumulative Var 0.24 0.38 0.49
## Proportion Explained 0.49 0.29 0.21
## Cumulative Proportion 0.49 0.79 1.00
##
## With factor correlations of
## MR1 MR2 MR3
## MR1 1.00 -0.28 0.5
## MR2 -0.28 1.00 0.0
## MR3 0.50 0.00 1.0
##
## Mean item complexity = 1.4
## Test of the hypothesis that 3 factors are sufficient.
##
## The degrees of freedom for the null model are 36 and the objective function was 2.48 with Chi Square of 2008.29
## The degrees of freedom for the model are 12 and the objective function was 0.07
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.04
##
## The harmonic number of observations is 816 with the empirical chi square 34.55 with prob < 0.00055
## The total number of observations was 816 with MLE Chi Square = 59.97 with prob < 2.3e-08
##
## Tucker Lewis Index of factoring reliability = 0.927
## RMSEA index = 0.07 and the 90 % confidence intervals are 0.053 0.088
## BIC = -20.48
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## MR1 MR2 MR3
## Correlation of scores with factors 0.91 0.88 0.80
## Multiple R square of scores with factors 0.83 0.77 0.64
## Minimum correlation of possible factor scores 0.67 0.53 0.27#The best fit to the data seems to be three factors. F1: questions 1,3,5,6. f2: 8,7,4. f3: 2,9CFI
1-((out_targetQ$STATISTIC - out_targetQ$dof)/(out_targetQ$null.chisq- out_targetQ$null.dof))## [1] 0.9756766Based on the above model we try F1: questions 1,3,5,6. f2: 8,7,4. f3: 2,9. this the best fit to the data. (PWB_4 crossloads)
all_surveys <- read.csv("~/Dropbox/Git/stats/allsurveysYT1_Jan2016.csv", header=T)
PWB<-select(all_surveys, PWB_1, PWB_3, PWB_5,PWB_6, PWB_7, PWB_8,PWB_4, PWB_2, PWB_9)
PWB<- data.frame(apply(PWB,2, as.numeric))
PWB<-tbl_df(PWB)
PWB## Source: local data frame [1,288 x 9]
##
## PWB_1 PWB_3 PWB_5 PWB_6 PWB_7 PWB_8 PWB_4 PWB_2 PWB_9
## (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl)
## 1 3 2 5 4 3 5 4 1 6
## 2 5 3 6 1 2 4 3 5 4
## 3 5 4 5 4 6 6 5 4 5
## 4 2 2 3 5 4 4 2 5 1
## 5 4 4 4 4 4 4 4 4 4
## 6 6 5 4 4 4 4 5 5 5
## 7 4 4 3 3 4 3 3 3 3
## 8 6 6 6 6 2 6 6 5 6
## 9 2 2 1 5 5 5 2 2 2
## 10 5 5 5 5 5 5 5 5 5
## .. ... ... ... ... ... ... ... ... ...colnames(PWB) <- c("1","2", "3", "4", "5", "6", "7", "8", "9")
#Target rotation: choose "simple structure" a priori and can be applied to oblique and orthogonal rotation based on
#what paper says facotrs should be PWB
Targ_key <- make.keys(9,list(f1=1:4,f2=5:7, f3=8:9))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
PWB_cor <- corFiml(PWB) # convert the raw data to correlation matrix uisng FIML
out_targetQ <- fa(PWB_cor,3,rotate="TargetQ",n.obs = 816,Target=Targ_key) #TargetT for orthogonal rotation
fa2latex(fa(PWB_cor,3,rotate="TargetQ",n.obs = 816,Target=Targ_key), heading="Table 10. Factor Loadings for Exploratory Factor Analysis PWB")## % Called in the psych package fa2latex % Called in the psych package fa(PWB_cor, 3, rotate = "TargetQ", n.obs = 816, Target = Targ_key) % Called in the psych package Table 10. Factor Loadings for Exploratory Factor Analysis PWB
## \begin{table}[htpb]\caption{fa2latex}
## \begin{center}
## \begin{scriptsize}
## \begin{tabular} {l r r r r r r }
## \multicolumn{ 6 }{l}{ Table 10. Factor Loadings for Exploratory Factor Analysis PWB } \cr
## \hline Variable & MR1 & MR2 & MR3 & h2 & u2 & com \cr
## \hline
## 1 & \bf{ 0.56} & 0.16 & 0.20 & 0.44 & 0.56 & 1.42 \cr
## 2 & \bf{ 0.73} & 0.06 & 0.09 & 0.59 & 0.41 & 1.04 \cr
## 3 & \bf{ 0.74} & 0.04 & 0.08 & 0.61 & 0.39 & 1.03 \cr
## 4 & \bf{-0.74} & 0.22 & \bf{ 0.37} & 0.52 & 0.48 & 1.69 \cr
## 5 & 0.08 & \bf{ 0.85} & -0.02 & 0.69 & 0.31 & 1.02 \cr
## 6 & -0.01 & \bf{ 0.62} & 0.00 & 0.38 & 0.62 & 1.00 \cr
## 7 & 0.24 & \bf{-0.31} & \bf{ 0.36} & 0.42 & 0.58 & 2.73 \cr
## 8 & 0.08 & -0.14 & \bf{ 0.59} & 0.44 & 0.56 & 1.16 \cr
## 9 & 0.10 & -0.09 & \bf{ 0.49} & 0.32 & 0.68 & 1.15 \cr
## \hline \cr SS loadings & 2.1 & 1.32 & 0.99 & \cr
## \cr
## \hline \cr
## MR1 & 1.00 & -0.29 & 0.56 \cr
## MR2 & -0.29 & 1.00 & -0.02 \cr
## MR3 & 0.56 & -0.02 & 1.00 \cr
## \hline
## \end{tabular}
## \end{scriptsize}
## \end{center}
## \label{default}
## \end{table}out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR1 MR2 MR3
## 1 0.559 0.162 0.196
## 2 0.726
## 3 0.742
## 4 -0.739 0.222 0.374
## 5 0.850
## 6 0.616
## 7 0.237 -0.312 0.358
## 8 -0.145 0.588
## 9 0.489
##
## MR1 MR2 MR3
## SS loadings 2.015 1.308 0.906
## Proportion Var 0.224 0.145 0.101
## Cumulative Var 0.224 0.369 0.470
##
## $score.cor
## [,1] [,2] [,3]
## [1,] 1.0000000 -0.2351284 0.5058006
## [2,] -0.2351284 1.0000000 -0.1502985
## [3,] 0.5058006 -0.1502985 1.0000000
##
## $TLI
## [1] 0.9268462
##
## $RMSEA
## RMSEA lower upper confidence
## 0.07035195 0.05295846 0.08810460 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = PWB_cor, nfactors = 3, n.obs = 816, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 MR3 h2 u2 com
## 1 0.56 0.16 0.20 0.44 0.56 1.4
## 2 0.73 0.06 0.09 0.59 0.41 1.0
## 3 0.74 0.04 0.08 0.61 0.39 1.0
## 4 -0.74 0.22 0.37 0.52 0.48 1.7
## 5 0.08 0.85 -0.02 0.69 0.31 1.0
## 6 -0.01 0.62 0.00 0.38 0.62 1.0
## 7 0.24 -0.31 0.36 0.42 0.58 2.7
## 8 0.08 -0.14 0.59 0.44 0.56 1.2
## 9 0.10 -0.09 0.49 0.32 0.68 1.1
##
## MR1 MR2 MR3
## SS loadings 2.10 1.32 0.99
## Proportion Var 0.23 0.15 0.11
## Cumulative Var 0.23 0.38 0.49
## Proportion Explained 0.48 0.30 0.22
## Cumulative Proportion 0.48 0.78 1.00
##
## With factor correlations of
## MR1 MR2 MR3
## MR1 1.00 -0.29 0.56
## MR2 -0.29 1.00 -0.02
## MR3 0.56 -0.02 1.00
##
## Mean item complexity = 1.4
## Test of the hypothesis that 3 factors are sufficient.
##
## The degrees of freedom for the null model are 36 and the objective function was 2.48 with Chi Square of 2008.29
## The degrees of freedom for the model are 12 and the objective function was 0.07
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.04
##
## The harmonic number of observations is 816 with the empirical chi square 34.55 with prob < 0.00055
## The total number of observations was 816 with MLE Chi Square = 59.97 with prob < 2.3e-08
##
## Tucker Lewis Index of factoring reliability = 0.927
## RMSEA index = 0.07 and the 90 % confidence intervals are 0.053 0.088
## BIC = -20.48
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## MR1 MR2 MR3
## Correlation of scores with factors 0.91 0.88 0.81
## Multiple R square of scores with factors 0.83 0.77 0.66
## Minimum correlation of possible factor scores 0.67 0.54 0.33The best fit to the data seems to be three factors. F1: questions 1,3,5,6. f2: 8,7,4. f3: 2,9
CFI
1-((out_targetQ$STATISTIC - out_targetQ$dof)/(out_targetQ$null.chisq- out_targetQ$null.dof))## [1] 0.9756766Droping question 1 as well because it also loads on all of the factors. Much better fit to the data
all_surveys <- read.csv("~/Dropbox/Git/stats/allsurveysYT1_Jan2016.csv", header=T)
PWB<-select(all_surveys, PWB_3, PWB_5,PWB_6, PWB_8,PWB_7, PWB_2, PWB_9)
PWB<- data.frame(apply(PWB,2, as.numeric))
PWB<-tbl_df(PWB)
PWB## Source: local data frame [1,288 x 7]
##
## PWB_3 PWB_5 PWB_6 PWB_8 PWB_7 PWB_2 PWB_9
## (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl)
## 1 2 5 4 5 3 1 6
## 2 3 6 1 4 2 5 4
## 3 4 5 4 6 6 4 5
## 4 2 3 5 4 4 5 1
## 5 4 4 4 4 4 4 4
## 6 5 4 4 4 4 5 5
## 7 4 3 3 3 4 3 3
## 8 6 6 6 6 2 5 6
## 9 2 1 5 5 5 2 2
## 10 5 5 5 5 5 5 5
## .. ... ... ... ... ... ... ...colnames(PWB) <- c("1","2", "3", "4", "5", "6", "7")
#Target rotation: choose "simple structure" a priori and can be applied to oblique and orthogonal rotation based on
#what paper says facotrs should be PWB
Targ_key <- make.keys(7,list(f1=1:3,f2=4:5, f3=6:7))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
PWB_cor <- corFiml(PWB) # convert the raw data to correlation matrix uisng FIML
out_targetQ <- fa(PWB_cor,3,rotate="TargetQ",n.obs = 816,Target=Targ_key) #TargetT for orthogonal rotation
fa2latex(fa(PWB_cor,3,rotate="TargetQ",n.obs = 816,Target=Targ_key), heading="Table 11. Factor Loadings for Exploratory Factor Analysis PWB")## % Called in the psych package fa2latex % Called in the psych package fa(PWB_cor, 3, rotate = "TargetQ", n.obs = 816, Target = Targ_key) % Called in the psych package Table 11. Factor Loadings for Exploratory Factor Analysis PWB
## \begin{table}[htpb]\caption{fa2latex}
## \begin{center}
## \begin{scriptsize}
## \begin{tabular} {l r r r r r r }
## \multicolumn{ 6 }{l}{ Table 11. Factor Loadings for Exploratory Factor Analysis PWB } \cr
## \hline Variable & MR1 & MR2 & MR3 & h2 & u2 & com \cr
## \hline
## 1 & \bf{ 0.72} & 0.12 & 0.12 & 0.59 & 0.41 & 1.11 \cr
## 2 & \bf{ 0.74} & 0.10 & 0.14 & 0.65 & 0.35 & 1.12 \cr
## 3 & \bf{-0.69} & 0.23 & 0.27 & 0.49 & 0.51 & 1.54 \cr
## 4 & -0.02 & \bf{ 0.64} & -0.07 & 0.43 & 0.57 & 1.02 \cr
## 5 & 0.00 & \bf{ 0.77} & -0.06 & 0.60 & 0.40 & 1.01 \cr
## 6 & 0.08 & -0.10 & \bf{ 0.53} & 0.35 & 0.65 & 1.12 \cr
## 7 & 0.03 & -0.06 & \bf{ 0.61} & 0.39 & 0.61 & 1.02 \cr
## \hline \cr SS loadings & 1.59 & 1.11 & 0.8 & \cr
## \cr
## \hline \cr
## MR1 & 1.00 & -0.26 & 0.54 \cr
## MR2 & -0.26 & 1.00 & 0.01 \cr
## MR3 & 0.54 & 0.01 & 1.00 \cr
## \hline
## \end{tabular}
## \end{scriptsize}
## \end{center}
## \label{default}
## \end{table}out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR1 MR2 MR3
## 1 0.720 0.125 0.116
## 2 0.737 0.104 0.142
## 3 -0.691 0.230 0.269
## 4 0.643
## 5 0.772
## 6 -0.102 0.534
## 7 0.606
##
## MR1 MR2 MR3
## SS loadings 1.547 1.102 0.766
## Proportion Var 0.221 0.157 0.109
## Cumulative Var 0.221 0.378 0.488
##
## $score.cor
## [,1] [,2] [,3]
## [1,] 1.0000000 -0.2365795 0.4069225
## [2,] -0.2365795 1.0000000 -0.1502887
## [3,] 0.4069225 -0.1502887 1.0000000
##
## $TLI
## [1] 1.012585
##
## $RMSEA
## RMSEA lower upper confidence
## 0.00000000 NA 0.03151667 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = PWB_cor, nfactors = 3, n.obs = 816, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 MR3 h2 u2 com
## 1 0.72 0.12 0.12 0.59 0.41 1.1
## 2 0.74 0.10 0.14 0.65 0.35 1.1
## 3 -0.69 0.23 0.27 0.49 0.51 1.5
## 4 -0.02 0.64 -0.07 0.43 0.57 1.0
## 5 0.00 0.77 -0.06 0.60 0.40 1.0
## 6 0.08 -0.10 0.53 0.35 0.65 1.1
## 7 0.03 -0.06 0.61 0.39 0.61 1.0
##
## MR1 MR2 MR3
## SS loadings 1.59 1.11 0.80
## Proportion Var 0.23 0.16 0.11
## Cumulative Var 0.23 0.38 0.50
## Proportion Explained 0.45 0.32 0.23
## Cumulative Proportion 0.45 0.77 1.00
##
## With factor correlations of
## MR1 MR2 MR3
## MR1 1.00 -0.26 0.54
## MR2 -0.26 1.00 0.01
## MR3 0.54 0.01 1.00
##
## Mean item complexity = 1.1
## Test of the hypothesis that 3 factors are sufficient.
##
## The degrees of freedom for the null model are 21 and the objective function was 1.56 with Chi Square of 1268.09
## The degrees of freedom for the model are 3 and the objective function was 0
##
## The root mean square of the residuals (RMSR) is 0
## The df corrected root mean square of the residuals is 0.01
##
## The harmonic number of observations is 816 with the empirical chi square 0.38 with prob < 0.94
## The total number of observations was 816 with MLE Chi Square = 0.76 with prob < 0.86
##
## Tucker Lewis Index of factoring reliability = 1.013
## RMSEA index = 0 and the 90 % confidence intervals are NA 0.032
## BIC = -19.35
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1 MR2 MR3
## Correlation of scores with factors 0.90 0.84 0.79
## Multiple R square of scores with factors 0.80 0.71 0.62
## Minimum correlation of possible factor scores 0.61 0.42 0.24#The best fit to the data seems to be three factors. F1: questions 1,3,5,6. f2: 8,7,4. f3: 2,9CFI
1-((out_targetQ$STATISTIC - out_targetQ$dof)/(out_targetQ$null.chisq- out_targetQ$null.dof))## [1] 1.001793Dropping PWB_4 – still not great and PWB_1 crossloads significantly and PWB_9 does not load well on any of the factors.
all_surveys <- read.csv("~/Dropbox/Git/stats/allsurveysYT1_Jan2016.csv", header=T)
PWB<-select(all_surveys, PWB_1, PWB_3, PWB_5,PWB_6, PWB_7, PWB_8, PWB_2, PWB_9)
PWB<- data.frame(apply(PWB,2, as.numeric))
PWB<-tbl_df(PWB)
PWB## Source: local data frame [1,288 x 8]
##
## PWB_1 PWB_3 PWB_5 PWB_6 PWB_7 PWB_8 PWB_2 PWB_9
## (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl)
## 1 3 2 5 4 3 5 1 6
## 2 5 3 6 1 2 4 5 4
## 3 5 4 5 4 6 6 4 5
## 4 2 2 3 5 4 4 5 1
## 5 4 4 4 4 4 4 4 4
## 6 6 5 4 4 4 4 5 5
## 7 4 4 3 3 4 3 3 3
## 8 6 6 6 6 2 6 5 6
## 9 2 2 1 5 5 5 2 2
## 10 5 5 5 5 5 5 5 5
## .. ... ... ... ... ... ... ... ...colnames(PWB) <- c("1","2", "3", "4", "5", "6", "7", "8")
#Target rotation: choose "simple structure" a priori and can be applied to oblique and orthogonal rotation based on
#what paper says facotrs should be PWB
Targ_key <- make.keys(8,list(f1=1:4,f2=5:6, f3=7:8))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
PWB_cor <- corFiml(PWB) # convert the raw data to correlation matrix uisng FIML
out_targetQ <- fa(PWB_cor,3,rotate="TargetQ",n.obs = 816,Target=Targ_key) #TargetT for orthogonal rotation
fa2latex(fa(PWB_cor,3,rotate="TargetQ",n.obs = 816,Target=Targ_key), heading="Table 12. Factor Loadings for Exploratory Factor Analysis PWB")## % Called in the psych package fa2latex % Called in the psych package fa(PWB_cor, 3, rotate = "TargetQ", n.obs = 816, Target = Targ_key) % Called in the psych package Table 12. Factor Loadings for Exploratory Factor Analysis PWB
## \begin{table}[htpb]\caption{fa2latex}
## \begin{center}
## \begin{scriptsize}
## \begin{tabular} {l r r r r r r }
## \multicolumn{ 6 }{l}{ Table 12. Factor Loadings for Exploratory Factor Analysis PWB } \cr
## \hline Variable & MR1 & MR2 & MR3 & h2 & u2 & com \cr
## \hline
## 1 & \bf{ 0.51} & 0.16 & 0.25 & 0.47 & 0.53 & 1.71 \cr
## 2 & \bf{ 0.76} & 0.07 & 0.01 & 0.58 & 0.42 & 1.02 \cr
## 3 & \bf{ 0.78} & 0.05 & 0.04 & 0.63 & 0.37 & 1.01 \cr
## 4 & \bf{-0.66} & 0.27 & 0.23 & 0.46 & 0.54 & 1.60 \cr
## 5 & 0.01 & \bf{ 0.81} & -0.08 & 0.65 & 0.35 & 1.02 \cr
## 6 & -0.04 & \bf{ 0.62} & -0.06 & 0.40 & 0.60 & 1.02 \cr
## 7 & 0.00 & -0.12 & \bf{ 0.76} & 0.59 & 0.41 & 1.05 \cr
## 8 & 0.17 & -0.04 & \bf{ 0.40} & 0.26 & 0.74 & 1.38 \cr
## \hline \cr SS loadings & 1.96 & 1.17 & 0.91 & \cr
## \cr
## \hline \cr
## MR1 & 1.00 & -0.18 & 0.56 \cr
## MR2 & -0.18 & 1.00 & 0.03 \cr
## MR3 & 0.56 & 0.03 & 1.00 \cr
## \hline
## \end{tabular}
## \end{scriptsize}
## \end{center}
## \label{default}
## \end{table}out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR1 MR2 MR3
## 1 0.507 0.163 0.255
## 2 0.764
## 3 0.777
## 4 -0.657 0.270 0.229
## 5 0.810
## 6 0.625
## 7 -0.116 0.761
## 8 0.172 0.396
##
## MR1 MR2 MR3
## SS loadings 1.907 1.168 0.865
## Proportion Var 0.238 0.146 0.108
## Cumulative Var 0.238 0.384 0.493
##
## $score.cor
## [,1] [,2] [,3]
## [1,] 1.0000000 -0.1771134 0.4632567
## [2,] -0.1771134 1.0000000 -0.1502922
## [3,] 0.4632567 -0.1502922 1.0000000
##
## $TLI
## [1] 0.9268019
##
## $RMSEA
## RMSEA lower upper confidence
## 0.07261013 0.05031065 0.09606291 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = PWB_cor, nfactors = 3, n.obs = 816, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 MR3 h2 u2 com
## 1 0.51 0.16 0.25 0.47 0.53 1.7
## 2 0.76 0.07 0.01 0.58 0.42 1.0
## 3 0.78 0.05 0.04 0.63 0.37 1.0
## 4 -0.66 0.27 0.23 0.46 0.54 1.6
## 5 0.01 0.81 -0.08 0.65 0.35 1.0
## 6 -0.04 0.62 -0.06 0.40 0.60 1.0
## 7 0.00 -0.12 0.76 0.59 0.41 1.0
## 8 0.17 -0.04 0.40 0.26 0.74 1.4
##
## MR1 MR2 MR3
## SS loadings 1.96 1.17 0.91
## Proportion Var 0.24 0.15 0.11
## Cumulative Var 0.24 0.39 0.50
## Proportion Explained 0.49 0.29 0.23
## Cumulative Proportion 0.49 0.77 1.00
##
## With factor correlations of
## MR1 MR2 MR3
## MR1 1.00 -0.18 0.56
## MR2 -0.18 1.00 0.03
## MR3 0.56 0.03 1.00
##
## Mean item complexity = 1.2
## Test of the hypothesis that 3 factors are sufficient.
##
## The degrees of freedom for the null model are 28 and the objective function was 2.05 with Chi Square of 1662.08
## The degrees of freedom for the model are 7 and the objective function was 0.05
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.04
##
## The harmonic number of observations is 816 with the empirical chi square 23.1 with prob < 0.0016
## The total number of observations was 816 with MLE Chi Square = 36.83 with prob < 5.1e-06
##
## Tucker Lewis Index of factoring reliability = 0.927
## RMSEA index = 0.073 and the 90 % confidence intervals are 0.05 0.096
## BIC = -10.1
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## MR1 MR2 MR3
## Correlation of scores with factors 0.91 0.86 0.83
## Multiple R square of scores with factors 0.82 0.74 0.69
## Minimum correlation of possible factor scores 0.65 0.47 0.38#The best fit to the data seems to be three factors. F1: questions 1,3,5,6. f2: 8,7,4. f3: 2,9CFI
1-((out_targetQ$STATISTIC - out_targetQ$dof)/(out_targetQ$null.chisq- out_targetQ$null.dof))## [1] 0.9817464