All Purpose Scales Youth CFA and TA
Levi Brackman
August 11 2015
##load packages
library(psych)
library(GPArotation)
library(plyr)
library(dplyr)##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(lavaan)## This is lavaan 0.5-18
## lavaan is BETA software! Please report any bugs.library(semPlot)
# data preparation
data <- read.csv("~/Psychometric_study_data/allsurveysYT1.csv")
purposescales<-select(data, PWB_1, PWB_2, PWB_3, PWB_4, PWB_5, PWB_6, PWB_7, PWB_8, PWB_9, APSI_1, APSI_2, APSI_3, APSI_4, APSI_5, APSI_6, APSI_7, APSI_8, LET_1, LET_2, LET_3, LET_4, LET_5, LET_6, MLQ_1, MLQ_2, MLQ_3, MLQ_4, MLQ_5, MLQ_6,MLQ_7, MLQ_8, MLQ_9, MLQ_10)
purposescales$PWB_1 <- 7- purposescales$PWB_1
purposescales$PWB_2 <- 7- purposescales$PWB_2
purposescales$PWB_3 <- 7- purposescales$PWB_3
purposescales$PWB_4 <- 7- purposescales$PWB_4
purposescales$PWB_9 <- 7- purposescales$PWB_9
purposescales$MLQ_9 <- 8- purposescales$MLQ_9
purposescales$LET_1 <- 6- purposescales$LET_1
purposescales$LET_3 <- 6- purposescales$LET_3
purposescales$LET_5 <- 6- purposescales$LET_5
purposescales<- data.frame(apply(purposescales,2, as.numeric))
purposescales<-tbl_df(purposescales)
purposescales## Source: local data frame [1,160 x 33]
##
## PWB_1 PWB_2 PWB_3 PWB_4 PWB_5 PWB_6 PWB_7 PWB_8 PWB_9 APSI_1 APSI_2
## 1 4 3 5 2 4 5 4 3 6 2 4
## 2 4 5 5 2 2 5 3 2 5 4 3
## 3 5 6 5 6 1 4 6 3 6 3 4
## 4 2 2 4 4 3 4 5 4 4 4 4
## 5 2 2 3 3 4 3 2 3 4 3 3
## 6 5 4 6 5 3 4 3 4 6 3 4
## 7 2 2 5 2 1 4 3 3 3 2 2
## 8 6 6 5 1 2 4 4 4 6 3 3
## 9 5 5 5 5 1 5 5 5 6 4 5
## 10 6 6 3 3 2 6 6 3 6 2 2
## .. ... ... ... ... ... ... ... ... ... ... ...
## Variables not shown: APSI_3 (dbl), APSI_4 (dbl), APSI_5 (dbl), APSI_6
## (dbl), APSI_7 (dbl), APSI_8 (dbl), LET_1 (dbl), LET_2 (dbl), LET_3
## (dbl), LET_4 (dbl), LET_5 (dbl), LET_6 (dbl), MLQ_1 (dbl), MLQ_2 (dbl),
## MLQ_3 (dbl), MLQ_4 (dbl), MLQ_5 (dbl), MLQ_6 (dbl), MLQ_7 (dbl), MLQ_8
## (dbl), MLQ_9 (dbl), MLQ_10 (dbl)str(purposescales)## Classes 'tbl_df', 'tbl' and 'data.frame': 1160 obs. of 33 variables:
## $ PWB_1 : num 4 4 5 2 2 5 2 6 5 6 ...
## $ PWB_2 : num 3 5 6 2 2 4 2 6 5 6 ...
## $ PWB_3 : num 5 5 5 4 3 6 5 5 5 3 ...
## $ PWB_4 : num 2 2 6 4 3 5 2 1 5 3 ...
## $ PWB_5 : num 4 2 1 3 4 3 1 2 1 2 ...
## $ PWB_6 : num 5 5 4 4 3 4 4 4 5 6 ...
## $ PWB_7 : num 4 3 6 5 2 3 3 4 5 6 ...
## $ PWB_8 : num 3 2 3 4 3 4 3 4 5 3 ...
## $ PWB_9 : num 6 5 6 4 4 6 3 6 6 6 ...
## $ APSI_1: num 2 4 3 4 3 3 2 3 4 2 ...
## $ APSI_2: num 4 3 4 4 3 4 2 3 5 2 ...
## $ APSI_3: num 4 4 4 5 4 4 4 4 5 2 ...
## $ APSI_4: num 4 5 3 4 3 4 3 3 4 3 ...
## $ APSI_5: num 4 4 3 5 4 4 4 5 4 5 ...
## $ APSI_6: num 4 3 3 4 3 2 4 3 2 3 ...
## $ APSI_7: num 4 4 4 4 2 5 2 3 4 3 ...
## $ APSI_8: num 4 4 3 3 3 3 2 1 5 4 ...
## $ LET_1 : num 4 3 3 1 3 5 3 3 5 3 ...
## $ LET_2 : num 4 3 4 4 2 5 4 4 4 3 ...
## $ LET_3 : num 4 4 3 4 3 5 2 3 5 5 ...
## $ LET_4 : num 5 4 4 4 4 5 3 4 5 5 ...
## $ LET_5 : num 5 4 4 4 2 5 3 4 5 5 ...
## $ LET_6 : num 5 5 5 4 4 4 5 5 5 5 ...
## $ MLQ_1 : num 4 3 4 5 4 5 6 3 6 1 ...
## $ MLQ_2 : num 7 5 7 6 6 5 2 7 5 7 ...
## $ MLQ_3 : num 7 5 5 7 5 3 2 7 2 1 ...
## $ MLQ_4 : num 5 5 4 3 4 4 3 5 7 3 ...
## $ MLQ_5 : num 6 4 4 5 4 5 6 5 6 5 ...
## $ MLQ_6 : num 4 3 4 5 4 5 3 4 6 1 ...
## $ MLQ_7 : num 5 5 4 5 5 3 5 5 5 5 ...
## $ MLQ_8 : num 7 4 5 5 5 4 4 7 7 5 ...
## $ MLQ_9 : num 3 3 4 3 5 7 5 4 7 6 ...
## $ MLQ_10: num 7 5 4 6 5 3 4 5 2 1 ...colnames(purposescales) <- c("1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32","33")
allpurpose_cor <- corFiml(purposescales)#uses FIML for missing data
#purposescales<- purposescales[complete.cases(purposescales[,]),]
##EFA
##number of factors
##parallal analysis and scree plot
parallel<-fa.parallel(allpurpose_cor, n.obs=1160, fm="ml")
## Parallel analysis suggests that the number of factors = 6 and the number of components = 4#three factors are greater than one Eigenvalue scree plot says there are three factors.
#Paralel analysis suggests 6 factors
#eigenvalues (kaiser)
parallel$fa.values## [1] 7.730731192 4.950650230 2.243581737 0.755933990 0.401718885
## [6] 0.360340781 0.147044868 0.093252394 0.007217009 -0.060354182
## [11] -0.074183953 -0.093075528 -0.130093202 -0.158699903 -0.178925535
## [16] -0.197950826 -0.208254842 -0.254801526 -0.308713218 -0.330945389
## [21] -0.384128630 -0.403914929 -0.431176794 -0.451596694 -0.492232522
## [26] -0.540555152 -0.558665916 -0.578338836 -0.597344981 -0.652793430
## [31] -0.665428781 -0.691380869 -0.725135629#over 1=3, over .7=4
#doign aprincipal components analysis to see how many factors there might be using that method
#Deal with NA doing principle componant analysis
princomp(na.omit(allpurpose_cor), cor = TRUE)## Call:
## princomp(x = na.omit(allpurpose_cor), cor = TRUE)
##
## Standard deviations:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7
## 3.6767670 3.3625851 1.7596533 1.0250228 0.8797408 0.7610443 0.5956521
## Comp.8 Comp.9 Comp.10 Comp.11 Comp.12 Comp.13 Comp.14
## 0.5490586 0.5037634 0.4666216 0.4294245 0.3722103 0.3570967 0.3376847
## Comp.15 Comp.16 Comp.17 Comp.18 Comp.19 Comp.20 Comp.21
## 0.3320795 0.3212987 0.3098279 0.2904436 0.2677999 0.2578405 0.2427510
## Comp.22 Comp.23 Comp.24 Comp.25 Comp.26 Comp.27 Comp.28
## 0.2324844 0.2244075 0.2166477 0.2090026 0.1868175 0.1851999 0.1803107
## Comp.29 Comp.30 Comp.31 Comp.32 Comp.33
## 0.1674579 0.1581687 0.1503039 0.1362795 0.0000000
##
## 33 variables and 33 observations.parallel2<-princomp(na.omit(allpurpose_cor), cor = TRUE)
summary(parallel2)## Importance of components:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
## Standard deviation 3.676767 3.3625851 1.75965332 1.02502280 0.87974077
## Proportion of Variance 0.409655 0.3426357 0.09382969 0.03183854 0.02345284
## Cumulative Proportion 0.409655 0.7522907 0.84612041 0.87795895 0.90141179
## Comp.6 Comp.7 Comp.8 Comp.9
## Standard deviation 0.76104425 0.59565208 0.549058625 0.503763408
## Proportion of Variance 0.01755116 0.01075156 0.009135314 0.007690229
## Cumulative Proportion 0.91896295 0.92971451 0.938849824 0.946540054
## Comp.10 Comp.11 Comp.12 Comp.13
## Standard deviation 0.466621586 0.429424474 0.372210311 0.357096678
## Proportion of Variance 0.006598052 0.005588042 0.004198197 0.003864183
## Cumulative Proportion 0.953138106 0.958726147 0.962924345 0.966788528
## Comp.14 Comp.15 Comp.16 Comp.17
## Standard deviation 0.337684660 0.332079545 0.321298741 0.309827909
## Proportion of Variance 0.003455483 0.003341722 0.003128269 0.002908889
## Cumulative Proportion 0.970244010 0.973585732 0.976714001 0.979622890
## Comp.18 Comp.19 Comp.20 Comp.21
## Standard deviation 0.290443605 0.267799890 0.257840516 0.242750971
## Proportion of Variance 0.002556288 0.002173236 0.002014598 0.001785698
## Cumulative Proportion 0.982179178 0.984352414 0.986367012 0.988152710
## Comp.22 Comp.23 Comp.24 Comp.25
## Standard deviation 0.232484357 0.224407497 0.216647656 0.2090026
## Proportion of Variance 0.001637848 0.001526022 0.001422309 0.0013237
## Cumulative Proportion 0.989790557 0.991316579 0.992738889 0.9940626
## Comp.26 Comp.27 Comp.28 Comp.29
## Standard deviation 0.1868175 0.185199926 0.1803106683 0.1674578644
## Proportion of Variance 0.0010576 0.001039364 0.0009852102 0.0008497617
## Cumulative Proportion 0.9951202 0.996159552 0.9971447624 0.9979945242
## Comp.30 Comp.31 Comp.32 Comp.33
## Standard deviation 0.1581687261 0.1503038864 0.1362794871 0
## Proportion of Variance 0.0007581014 0.0006845836 0.0005627909 0
## Cumulative Proportion 0.9987526255 0.9994372091 1.0000000000 1plot(parallel2)##results show at least two factors
#simple structure
twofactor<-fa(allpurpose_cor, nfactors=2, rotate="oblimin", fm="ml")
twofactor## Factor Analysis using method = ml
## Call: fa(r = allpurpose_cor, nfactors = 2, rotate = "oblimin", fm = "ml")
## Standardized loadings (pattern matrix) based upon correlation matrix
## ML1 ML2 h2 u2 com
## 1 -0.19 0.58 0.346 0.65 1.2
## 2 0.07 0.41 0.176 0.82 1.1
## 3 -0.03 0.80 0.641 0.36 1.0
## 4 0.34 0.49 0.397 0.60 1.8
## 5 -0.01 -0.72 0.525 0.47 1.0
## 6 0.19 0.53 0.341 0.66 1.2
## 7 0.70 0.04 0.497 0.50 1.0
## 8 0.60 0.10 0.376 0.62 1.1
## 9 0.02 0.33 0.114 0.89 1.0
## 10 0.84 -0.10 0.695 0.30 1.0
## 11 0.77 -0.06 0.581 0.42 1.0
## 12 0.22 0.33 0.174 0.83 1.8
## 13 0.79 -0.08 0.618 0.38 1.0
## 14 0.66 -0.16 0.436 0.56 1.1
## 15 0.05 -0.80 0.635 0.36 1.0
## 16 0.75 -0.10 0.553 0.45 1.0
## 17 0.79 -0.11 0.616 0.38 1.0
## 18 0.12 0.66 0.471 0.53 1.1
## 19 0.62 0.00 0.383 0.62 1.0
## 20 -0.12 0.79 0.623 0.38 1.0
## 21 0.54 0.00 0.297 0.70 1.0
## 22 -0.17 0.77 0.596 0.40 1.1
## 23 0.37 0.42 0.349 0.65 2.0
## 24 0.51 0.37 0.436 0.56 1.8
## 25 0.10 -0.22 0.055 0.95 1.4
## 26 0.24 -0.19 0.081 0.92 1.9
## 27 0.66 0.21 0.513 0.49 1.2
## 28 0.52 0.41 0.480 0.52 1.9
## 29 0.67 0.19 0.517 0.48 1.2
## 30 0.15 -0.11 0.031 0.97 1.8
## 31 0.20 -0.11 0.047 0.95 1.6
## 32 0.14 0.62 0.422 0.58 1.1
## 33 0.01 -0.20 0.041 0.96 1.0
##
## ML1 ML2
## SS loadings 7.12 5.94
## Proportion Var 0.22 0.18
## Cumulative Var 0.22 0.40
## Proportion Explained 0.54 0.46
## Cumulative Proportion 0.54 1.00
##
## With factor correlations of
## ML1 ML2
## ML1 1.00 0.11
## ML2 0.11 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 2 factors are sufficient.
##
## The degrees of freedom for the null model are 528 and the objective function was 18.23
## The degrees of freedom for the model are 463 and the objective function was 5.2
##
## The root mean square of the residuals (RMSR) is 0.09
## The df corrected root mean square of the residuals is 0.1
##
## Fit based upon off diagonal values = 0.9
## Measures of factor score adequacy
## ML1 ML2
## Correlation of scores with factors 0.97 0.96
## Multiple R square of scores with factors 0.94 0.93
## Minimum correlation of possible factor scores 0.88 0.85#CFI
1-((twofactor$STATISTIC - twofactor$dof)/(twofactor$null.chisq- twofactor$null.dof))## numeric(0)fa2latex(fa(allpurpose_cor,2,n.obs=1160, rotate="oblimin", fm="ml"),heading="2f")## % Called in the psych package fa2latex % Called in the psych package fa(allpurpose_cor, 2, n.obs = 1160, rotate = "oblimin", fm = "ml") % Called in the psych package 2f
## \begin{table}[htdp]\caption{fa2latex}
## \begin{center}
## \begin{scriptsize}
## \begin{tabular} {l r r r r r }
## \multicolumn{ 5 }{l}{ 2f } \cr
## \hline Variable & ML1 & ML2 & h2 & u2 & com \cr
## \hline
## 1 & -0.19 & \bf{ 0.58} & 0.35 & 0.65 & 1.21 \cr
## 2 & 0.07 & \bf{ 0.41} & 0.18 & 0.82 & 1.06 \cr
## 3 & -0.03 & \bf{ 0.80} & 0.64 & 0.36 & 1.00 \cr
## 4 & \bf{ 0.34} & \bf{ 0.49} & 0.40 & 0.60 & 1.78 \cr
## 5 & -0.01 & \bf{-0.72} & 0.53 & 0.47 & 1.00 \cr
## 6 & 0.19 & \bf{ 0.53} & 0.34 & 0.66 & 1.24 \cr
## 7 & \bf{ 0.70} & 0.04 & 0.50 & 0.50 & 1.01 \cr
## 8 & \bf{ 0.60} & 0.10 & 0.38 & 0.62 & 1.05 \cr
## 9 & 0.02 & \bf{ 0.33} & 0.11 & 0.89 & 1.01 \cr
## 10 & \bf{ 0.84} & -0.10 & 0.70 & 0.30 & 1.03 \cr
## 11 & \bf{ 0.77} & -0.06 & 0.58 & 0.42 & 1.01 \cr
## 12 & 0.22 & \bf{ 0.33} & 0.17 & 0.83 & 1.76 \cr
## 13 & \bf{ 0.79} & -0.08 & 0.62 & 0.38 & 1.02 \cr
## 14 & \bf{ 0.66} & -0.16 & 0.44 & 0.56 & 1.12 \cr
## 15 & 0.05 & \bf{-0.80} & 0.64 & 0.36 & 1.01 \cr
## 16 & \bf{ 0.75} & -0.10 & 0.55 & 0.45 & 1.03 \cr
## 17 & \bf{ 0.79} & -0.11 & 0.62 & 0.38 & 1.04 \cr
## 18 & 0.12 & \bf{ 0.66} & 0.47 & 0.53 & 1.07 \cr
## 19 & \bf{ 0.62} & 0.00 & 0.38 & 0.62 & 1.00 \cr
## 20 & -0.12 & \bf{ 0.79} & 0.62 & 0.38 & 1.04 \cr
## 21 & \bf{ 0.54} & 0.00 & 0.30 & 0.70 & 1.00 \cr
## 22 & -0.17 & \bf{ 0.77} & 0.60 & 0.40 & 1.09 \cr
## 23 & \bf{ 0.37} & \bf{ 0.42} & 0.35 & 0.65 & 1.98 \cr
## 24 & \bf{ 0.51} & \bf{ 0.37} & 0.44 & 0.56 & 1.81 \cr
## 25 & 0.10 & -0.22 & 0.05 & 0.95 & 1.43 \cr
## 26 & 0.24 & -0.19 & 0.08 & 0.92 & 1.90 \cr
## 27 & \bf{ 0.66} & 0.21 & 0.51 & 0.49 & 1.19 \cr
## 28 & \bf{ 0.52} & \bf{ 0.41} & 0.48 & 0.52 & 1.90 \cr
## 29 & \bf{ 0.67} & 0.19 & 0.52 & 0.48 & 1.16 \cr
## 30 & 0.15 & -0.11 & 0.03 & 0.97 & 1.82 \cr
## 31 & 0.20 & -0.11 & 0.05 & 0.95 & 1.57 \cr
## 32 & 0.14 & \bf{ 0.62} & 0.42 & 0.58 & 1.10 \cr
## 33 & 0.01 & -0.20 & 0.04 & 0.96 & 1.01 \cr
## \hline \cr SS loadings & 7.12 & 5.94 & \cr
## \cr
## \hline \cr
## ML1 & 1.00 & 0.11 \cr
## ML2 & 0.11 & 1.00 \cr
## \hline
## \end{tabular}
## \end{scriptsize}
## \end{center}
## \label{default}
## \end{table}fa2latex(fa(allpurpose_cor,3,n.obs=1160, rotate="oblimin", fm="ml"),heading="3f")## % Called in the psych package fa2latex % Called in the psych package fa(allpurpose_cor, 3, n.obs = 1160, rotate = "oblimin", fm = "ml") % Called in the psych package 3f
## \begin{table}[htdp]\caption{fa2latex}
## \begin{center}
## \begin{scriptsize}
## \begin{tabular} {l r r r r r r }
## \multicolumn{ 6 }{l}{ 3f } \cr
## \hline Variable & ML1 & ML2 & ML3 & h2 & u2 & com \cr
## \hline
## 1 & -0.19 & \bf{ 0.57} & -0.04 & 0.35 & 0.65 & 1.23 \cr
## 2 & 0.08 & \bf{ 0.39} & -0.08 & 0.18 & 0.82 & 1.18 \cr
## 3 & -0.04 & \bf{ 0.80} & 0.00 & 0.64 & 0.36 & 1.01 \cr
## 4 & \bf{ 0.35} & \bf{ 0.47} & -0.10 & 0.40 & 0.60 & 1.96 \cr
## 5 & 0.01 & \bf{-0.73} & -0.01 & 0.53 & 0.47 & 1.00 \cr
## 6 & 0.15 & \bf{ 0.58} & 0.17 & 0.39 & 0.61 & 1.31 \cr
## 7 & \bf{ 0.69} & 0.06 & 0.06 & 0.50 & 0.50 & 1.03 \cr
## 8 & \bf{ 0.60} & 0.09 & -0.03 & 0.38 & 0.62 & 1.05 \cr
## 9 & 0.02 & \bf{ 0.33} & -0.01 & 0.11 & 0.89 & 1.01 \cr
## 10 & \bf{ 0.85} & -0.11 & -0.05 & 0.71 & 0.29 & 1.04 \cr
## 11 & \bf{ 0.78} & -0.07 & -0.07 & 0.60 & 0.40 & 1.03 \cr
## 12 & 0.19 & \bf{ 0.36} & 0.12 & 0.19 & 0.81 & 1.80 \cr
## 13 & \bf{ 0.80} & -0.09 & -0.04 & 0.63 & 0.37 & 1.03 \cr
## 14 & \bf{ 0.65} & -0.15 & 0.06 & 0.44 & 0.56 & 1.12 \cr
## 15 & 0.05 & \bf{-0.79} & 0.05 & 0.64 & 0.36 & 1.02 \cr
## 16 & \bf{ 0.73} & -0.08 & 0.09 & 0.55 & 0.45 & 1.05 \cr
## 17 & \bf{ 0.79} & -0.11 & -0.01 & 0.62 & 0.38 & 1.04 \cr
## 18 & 0.14 & \bf{ 0.63} & -0.17 & 0.49 & 0.51 & 1.24 \cr
## 19 & \bf{ 0.62} & 0.00 & -0.01 & 0.39 & 0.61 & 1.00 \cr
## 20 & -0.12 & \bf{ 0.79} & -0.04 & 0.62 & 0.38 & 1.05 \cr
## 21 & \bf{ 0.54} & 0.00 & 0.02 & 0.29 & 0.71 & 1.00 \cr
## 22 & -0.18 & \bf{ 0.78} & 0.02 & 0.60 & 0.40 & 1.11 \cr
## 23 & \bf{ 0.36} & \bf{ 0.43} & 0.06 & 0.35 & 0.65 & 1.97 \cr
## 24 & \bf{ 0.49} & \bf{ 0.39} & 0.07 & 0.44 & 0.56 & 1.95 \cr
## 25 & -0.03 & -0.07 & \bf{ 0.79} & 0.64 & 0.36 & 1.02 \cr
## 26 & 0.12 & -0.04 & \bf{ 0.73} & 0.57 & 0.43 & 1.06 \cr
## 27 & \bf{ 0.65} & 0.23 & 0.08 & 0.52 & 0.48 & 1.27 \cr
## 28 & \bf{ 0.49} & \bf{ 0.44} & 0.12 & 0.50 & 0.50 & 2.11 \cr
## 29 & \bf{ 0.66} & 0.20 & 0.03 & 0.52 & 0.48 & 1.19 \cr
## 30 & 0.03 & 0.04 & \bf{ 0.73} & 0.53 & 0.47 & 1.01 \cr
## 31 & 0.08 & 0.04 & \bf{ 0.73} & 0.55 & 0.45 & 1.03 \cr
## 32 & 0.16 & \bf{ 0.59} & -0.14 & 0.43 & 0.57 & 1.25 \cr
## 33 & -0.13 & -0.04 & \bf{ 0.82} & 0.68 & 0.32 & 1.06 \cr
## \hline \cr SS loadings & 7 & 5.86 & 3.09 & \cr
## \cr
## \hline \cr
## ML1 & 1.00 & 0.11 & 0.11 \cr
## ML2 & 0.11 & 1.00 & -0.12 \cr
## ML3 & 0.11 & -0.12 & 1.00 \cr
## \hline
## \end{tabular}
## \end{scriptsize}
## \end{center}
## \label{default}
## \end{table}fa2latex(fa(allpurpose_cor,4,n.obs=1160, rotate="oblimin", fm="ml"),heading="4f")## % Called in the psych package fa2latex % Called in the psych package fa(allpurpose_cor, 4, n.obs = 1160, rotate = "oblimin", fm = "ml") % Called in the psych package 4f
## \begin{table}[htdp]\caption{fa2latex}
## \begin{center}
## \begin{scriptsize}
## \begin{tabular} {l r r r r r r r }
## \multicolumn{ 7 }{l}{ 4f } \cr
## \hline Variable & ML1 & ML2 & ML3 & ML4 & h2 & u2 & com \cr
## \hline
## 1 & -0.08 & \bf{ 0.64} & -0.02 & -0.16 & 0.39 & 0.61 & 1.16 \cr
## 2 & 0.21 & \bf{ 0.50} & -0.06 & -0.20 & 0.25 & 0.75 & 1.75 \cr
## 3 & -0.01 & \bf{ 0.82} & 0.01 & -0.01 & 0.66 & 0.34 & 1.00 \cr
## 4 & \bf{ 0.36} & \bf{ 0.50} & -0.09 & 0.03 & 0.42 & 0.58 & 1.91 \cr
## 5 & -0.04 & \bf{-0.76} & -0.02 & 0.03 & 0.56 & 0.44 & 1.01 \cr
## 6 & 0.08 & \bf{ 0.53} & 0.17 & 0.16 & 0.38 & 0.62 & 1.44 \cr
## 7 & \bf{ 0.63} & 0.06 & 0.06 & 0.11 & 0.50 & 0.50 & 1.10 \cr
## 8 & \bf{ 0.58} & 0.12 & -0.03 & 0.04 & 0.39 & 0.61 & 1.10 \cr
## 9 & 0.24 & \bf{ 0.51} & 0.02 & \bf{-0.36} & 0.25 & 0.75 & 2.30 \cr
## 10 & \bf{ 0.78} & -0.11 & -0.05 & 0.14 & 0.71 & 0.29 & 1.11 \cr
## 11 & \bf{ 0.74} & -0.04 & -0.07 & 0.08 & 0.60 & 0.40 & 1.05 \cr
## 12 & 0.15 & \bf{ 0.33} & 0.12 & 0.11 & 0.19 & 0.81 & 1.93 \cr
## 13 & \bf{ 0.75} & -0.06 & -0.04 & 0.09 & 0.63 & 0.37 & 1.05 \cr
## 14 & \bf{ 0.75} & -0.03 & 0.08 & -0.15 & 0.50 & 0.50 & 1.11 \cr
## 15 & 0.18 & \bf{-0.68} & 0.07 & -0.26 & 0.65 & 0.35 & 1.46 \cr
## 16 & \bf{ 0.75} & 0.00 & 0.10 & -0.02 & 0.58 & 0.42 & 1.04 \cr
## 17 & \bf{ 0.79} & -0.05 & 0.00 & 0.02 & 0.64 & 0.36 & 1.01 \cr
## 18 & 0.05 & \bf{ 0.56} & -0.17 & 0.19 & 0.48 & 0.52 & 1.47 \cr
## 19 & \bf{ 0.59} & 0.03 & 0.00 & 0.06 & 0.39 & 0.61 & 1.03 \cr
## 20 & -0.11 & \bf{ 0.77} & -0.04 & 0.03 & 0.62 & 0.38 & 1.05 \cr
## 21 & \bf{ 0.55} & 0.05 & 0.02 & 0.00 & 0.31 & 0.69 & 1.02 \cr
## 22 & -0.19 & \bf{ 0.74} & 0.02 & 0.06 & 0.60 & 0.40 & 1.15 \cr
## 23 & 0.30 & \bf{ 0.41} & 0.06 & 0.13 & 0.35 & 0.65 & 2.10 \cr
## 24 & 0.08 & 0.10 & 0.03 & \bf{ 0.76} & 0.70 & 0.30 & 1.06 \cr
## 25 & 0.01 & -0.02 & \bf{ 0.80} & -0.07 & 0.65 & 0.35 & 1.02 \cr
## 26 & 0.10 & -0.03 & \bf{ 0.72} & 0.04 & 0.56 & 0.44 & 1.05 \cr
## 27 & \bf{ 0.36} & 0.04 & 0.05 & \bf{ 0.53} & 0.60 & 0.40 & 1.81 \cr
## 28 & 0.17 & 0.23 & 0.09 & \bf{ 0.59} & 0.61 & 0.39 & 1.54 \cr
## 29 & \bf{ 0.37} & 0.01 & 0.00 & \bf{ 0.54} & 0.62 & 0.38 & 1.76 \cr
## 30 & -0.05 & 0.00 & \bf{ 0.72} & 0.14 & 0.53 & 0.47 & 1.08 \cr
## 31 & 0.08 & 0.06 & \bf{ 0.73} & 0.01 & 0.55 & 0.45 & 1.04 \cr
## 32 & 0.05 & \bf{ 0.51} & -0.15 & 0.21 & 0.43 & 0.57 & 1.54 \cr
## 33 & -0.12 & -0.03 & \bf{ 0.82} & -0.01 & 0.68 & 0.32 & 1.05 \cr
## \hline \cr SS loadings & 5.98 & 5.44 & 3.08 & 2.48 & \cr
## \cr
## \hline \cr
## ML1 & 1.00 & 0.02 & 0.10 & 0.45 \cr
## ML2 & 0.02 & 1.00 & -0.15 & 0.33 \cr
## ML3 & 0.10 & -0.15 & 1.00 & 0.04 \cr
## ML4 & 0.45 & 0.33 & 0.04 & 1.00 \cr
## \hline
## \end{tabular}
## \end{scriptsize}
## \end{center}
## \label{default}
## \end{table}fa2latex(fa(allpurpose_cor,5,n.obs=1160, rotate="oblimin", fm="ml"),heading="5f")## % Called in the psych package fa2latex % Called in the psych package fa(allpurpose_cor, 5, n.obs = 1160, rotate = "oblimin", fm = "ml") % Called in the psych package 5f
## \begin{table}[htdp]\caption{fa2latex}
## \begin{center}
## \begin{scriptsize}
## \begin{tabular} {l r r r r r r r r }
## \multicolumn{ 8 }{l}{ 5f } \cr
## \hline Variable & ML2 & ML1 & ML3 & ML4 & ML5 & h2 & u2 & com \cr
## \hline
## 1 & \bf{ 0.66} & 0.13 & -0.04 & -0.17 & -0.27 & 0.46 & 0.54 & 1.58 \cr
## 2 & \bf{ 0.50} & 0.22 & -0.07 & -0.18 & 0.01 & 0.25 & 0.75 & 1.70 \cr
## 3 & \bf{ 0.82} & -0.11 & 0.01 & 0.00 & 0.15 & 0.69 & 0.31 & 1.11 \cr
## 4 & \bf{ 0.49} & \bf{ 0.33} & -0.10 & 0.07 & 0.04 & 0.42 & 0.58 & 1.91 \cr
## 5 & \bf{-0.76} & -0.08 & -0.02 & 0.02 & 0.05 & 0.57 & 0.43 & 1.03 \cr
## 6 & \bf{ 0.52} & 0.19 & 0.15 & 0.18 & -0.17 & 0.42 & 0.58 & 1.99 \cr
## 7 & 0.06 & \bf{ 0.43} & 0.06 & 0.16 & 0.28 & 0.51 & 0.49 & 2.12 \cr
## 8 & 0.12 & \bf{ 0.48} & -0.03 & 0.09 & 0.14 & 0.38 & 0.62 & 1.40 \cr
## 9 & \bf{ 0.51} & 0.18 & 0.03 & \bf{-0.34} & 0.12 & 0.25 & 0.75 & 2.15 \cr
## 10 & -0.11 & \bf{ 0.59} & -0.05 & 0.19 & 0.24 & 0.70 & 0.30 & 1.65 \cr
## 11 & -0.05 & \bf{ 0.52} & -0.06 & 0.14 & 0.30 & 0.61 & 0.39 & 1.83 \cr
## 12 & \bf{ 0.33} & 0.20 & 0.11 & 0.13 & -0.09 & 0.21 & 0.79 & 2.49 \cr
## 13 & -0.07 & \bf{ 0.76} & -0.05 & 0.14 & 0.00 & 0.67 & 0.33 & 1.09 \cr
## 14 & -0.03 & \bf{ 0.56} & 0.09 & -0.11 & 0.26 & 0.49 & 0.51 & 1.55 \cr
## 15 & \bf{-0.66} & 0.12 & 0.08 & -0.27 & 0.11 & 0.65 & 0.35 & 1.50 \cr
## 16 & 0.01 & \bf{ 0.82} & 0.09 & 0.01 & -0.06 & 0.65 & 0.35 & 1.03 \cr
## 17 & -0.05 & \bf{ 0.80} & -0.01 & 0.06 & 0.00 & 0.68 & 0.32 & 1.02 \cr
## 18 & \bf{ 0.54} & -0.06 & -0.17 & 0.22 & 0.13 & 0.49 & 0.51 & 1.73 \cr
## 19 & 0.03 & 0.19 & 0.02 & 0.08 & \bf{ 0.61} & 0.57 & 0.43 & 1.23 \cr
## 20 & \bf{ 0.75} & -0.17 & -0.04 & 0.05 & 0.07 & 0.63 & 0.37 & 1.13 \cr
## 21 & 0.06 & 0.25 & 0.04 & 0.02 & \bf{ 0.45} & 0.39 & 0.61 & 1.64 \cr
## 22 & \bf{ 0.73} & -0.18 & 0.02 & 0.08 & -0.03 & 0.60 & 0.40 & 1.15 \cr
## 23 & \bf{ 0.41} & 0.11 & 0.07 & 0.16 & 0.26 & 0.38 & 0.62 & 2.32 \cr
## 24 & 0.07 & 0.04 & 0.02 & \bf{ 0.80} & -0.02 & 0.70 & 0.30 & 1.02 \cr
## 25 & -0.02 & -0.02 & \bf{ 0.80} & -0.07 & 0.05 & 0.65 & 0.35 & 1.02 \cr
## 26 & -0.03 & 0.03 & \bf{ 0.73} & 0.04 & 0.09 & 0.57 & 0.43 & 1.05 \cr
## 27 & 0.02 & 0.17 & 0.05 & \bf{ 0.58} & 0.22 & 0.62 & 0.38 & 1.49 \cr
## 28 & 0.20 & 0.12 & 0.08 & \bf{ 0.63} & 0.01 & 0.62 & 0.38 & 1.33 \cr
## 29 & -0.01 & 0.28 & -0.01 & \bf{ 0.59} & 0.08 & 0.62 & 0.38 & 1.46 \cr
## 30 & -0.01 & 0.02 & \bf{ 0.71} & 0.14 & -0.11 & 0.54 & 0.46 & 1.13 \cr
## 31 & 0.06 & 0.07 & \bf{ 0.73} & 0.02 & 0.01 & 0.55 & 0.45 & 1.03 \cr
## 32 & \bf{ 0.49} & 0.03 & -0.16 & 0.24 & 0.00 & 0.43 & 0.57 & 1.68 \cr
## 33 & -0.02 & -0.10 & \bf{ 0.82} & -0.02 & -0.03 & 0.67 & 0.33 & 1.03 \cr
## \hline \cr SS loadings & 5.35 & 4.57 & 3.09 & 2.89 & 1.73 & \cr
## \cr
## \hline \cr
## ML2 & 1.00 & -0.01 & -0.15 & 0.33 & -0.01 \cr
## ML1 & -0.01 & 1.00 & 0.11 & 0.44 & 0.48 \cr
## ML3 & -0.15 & 0.11 & 1.00 & 0.05 & 0.02 \cr
## ML4 & 0.33 & 0.44 & 0.05 & 1.00 & 0.29 \cr
## ML5 & -0.01 & 0.48 & 0.02 & 0.29 & 1.00 \cr
## \hline
## \end{tabular}
## \end{scriptsize}
## \end{center}
## \label{default}
## \end{table}fa2latex(fa(allpurpose_cor,6,n.obs=1160, rotate="oblimin", fm="ml"),heading="6f")## % Called in the psych package fa2latex % Called in the psych package fa(allpurpose_cor, 6, n.obs = 1160, rotate = "oblimin", fm = "ml") % Called in the psych package 6f
## \begin{table}[htdp]\caption{fa2latex}
## \begin{center}
## \begin{scriptsize}
## \begin{tabular} {l r r r r r r r r r }
## \multicolumn{ 9 }{l}{ 6f } \cr
## \hline Variable & ML2 & ML1 & ML3 & ML4 & ML5 & ML6 & h2 & u2 & com \cr
## \hline
## 1 & \bf{ 0.63} & 0.13 & -0.04 & -0.15 & -0.26 & 0.10 & 0.46 & 0.54 & 1.62 \cr
## 2 & \bf{ 0.59} & 0.18 & -0.01 & -0.11 & -0.05 & -0.23 & 0.33 & 0.67 & 1.61 \cr
## 3 & \bf{ 0.77} & -0.15 & 0.00 & 0.03 & 0.16 & 0.08 & 0.69 & 0.31 & 1.19 \cr
## 4 & \bf{ 0.58} & 0.28 & -0.04 & 0.16 & -0.03 & -0.26 & 0.53 & 0.47 & 2.13 \cr
## 5 & \bf{-0.71} & -0.06 & 0.00 & 0.00 & 0.02 & -0.13 & 0.56 & 0.44 & 1.08 \cr
## 6 & \bf{ 0.40} & 0.22 & 0.09 & 0.13 & -0.11 & \bf{ 0.40} & 0.50 & 0.50 & 3.06 \cr
## 7 & 0.04 & \bf{ 0.41} & 0.05 & 0.16 & 0.30 & 0.04 & 0.51 & 0.49 & 2.29 \cr
## 8 & 0.08 & \bf{ 0.46} & -0.05 & 0.09 & 0.18 & 0.09 & 0.39 & 0.61 & 1.57 \cr
## 9 & \bf{ 0.59} & 0.13 & 0.07 & -0.28 & 0.09 & -0.21 & 0.31 & 0.69 & 1.91 \cr
## 10 & -0.09 & \bf{ 0.56} & -0.04 & 0.22 & 0.25 & -0.10 & 0.71 & 0.29 & 1.90 \cr
## 11 & -0.05 & \bf{ 0.49} & -0.06 & 0.15 & \bf{ 0.33} & -0.02 & 0.61 & 0.39 & 2.05 \cr
## 12 & 0.18 & 0.25 & 0.03 & 0.03 & 0.01 & \bf{ 0.49} & 0.38 & 0.62 & 1.82 \cr
## 13 & -0.07 & \bf{ 0.73} & -0.05 & 0.15 & 0.04 & 0.02 & 0.67 & 0.33 & 1.13 \cr
## 14 & -0.06 & \bf{ 0.56} & 0.05 & -0.14 & \bf{ 0.32} & 0.11 & 0.52 & 0.48 & 1.89 \cr
## 15 & \bf{-0.61} & 0.14 & 0.08 & -0.29 & 0.09 & -0.13 & 0.65 & 0.35 & 1.76 \cr
## 16 & -0.01 & \bf{ 0.79} & 0.08 & 0.02 & -0.01 & 0.06 & 0.65 & 0.35 & 1.03 \cr
## 17 & -0.03 & \bf{ 0.77} & 0.00 & 0.10 & 0.01 & -0.06 & 0.69 & 0.31 & 1.05 \cr
## 18 & \bf{ 0.54} & -0.10 & -0.15 & 0.27 & 0.11 & -0.05 & 0.50 & 0.50 & 1.89 \cr
## 19 & 0.04 & 0.14 & 0.02 & 0.10 & \bf{ 0.62} & -0.09 & 0.56 & 0.44 & 1.21 \cr
## 20 & \bf{ 0.71} & -0.19 & -0.04 & 0.08 & 0.06 & 0.07 & 0.62 & 0.38 & 1.22 \cr
## 21 & 0.03 & 0.22 & 0.02 & 0.00 & \bf{ 0.51} & 0.07 & 0.42 & 0.58 & 1.40 \cr
## 22 & \bf{ 0.67} & -0.19 & 0.00 & 0.08 & -0.01 & 0.16 & 0.60 & 0.40 & 1.33 \cr
## 23 & \bf{ 0.31} & 0.09 & 0.01 & 0.11 & \bf{ 0.36} & 0.28 & 0.46 & 0.54 & 3.24 \cr
## 24 & 0.02 & 0.03 & 0.02 & \bf{ 0.79} & -0.01 & 0.10 & 0.69 & 0.31 & 1.04 \cr
## 25 & 0.01 & -0.03 & \bf{ 0.82} & -0.05 & 0.03 & -0.08 & 0.66 & 0.34 & 1.03 \cr
## 26 & -0.02 & 0.02 & \bf{ 0.74} & 0.06 & 0.08 & -0.05 & 0.57 & 0.43 & 1.05 \cr
## 27 & 0.03 & 0.12 & 0.08 & \bf{ 0.63} & 0.20 & -0.10 & 0.64 & 0.36 & 1.38 \cr
## 28 & 0.15 & 0.11 & 0.07 & \bf{ 0.63} & 0.03 & 0.12 & 0.61 & 0.39 & 1.29 \cr
## 29 & 0.01 & 0.24 & 0.03 & \bf{ 0.65} & 0.04 & -0.11 & 0.64 & 0.36 & 1.35 \cr
## 30 & -0.01 & 0.02 & \bf{ 0.72} & 0.15 & -0.12 & 0.02 & 0.55 & 0.45 & 1.15 \cr
## 31 & 0.06 & 0.06 & \bf{ 0.73} & 0.02 & 0.01 & 0.01 & 0.55 & 0.45 & 1.03 \cr
## 32 & \bf{ 0.48} & 0.00 & -0.14 & 0.28 & -0.01 & -0.01 & 0.44 & 0.56 & 1.81 \cr
## 33 & -0.05 & -0.09 & \bf{ 0.80} & -0.04 & -0.02 & 0.09 & 0.68 & 0.32 & 1.07 \cr
## \hline \cr SS loadings & 5.03 & 4.27 & 3.07 & 3.09 & 1.94 & 0.95 & \cr
## \cr
## \hline \cr
## ML2 & 1.00 & 0.00 & -0.16 & 0.33 & 0.02 & 0.25 \cr
## ML1 & 0.00 & 1.00 & 0.12 & 0.43 & 0.49 & -0.11 \cr
## ML3 & -0.16 & 0.12 & 1.00 & 0.02 & 0.05 & 0.09 \cr
## ML4 & 0.33 & 0.43 & 0.02 & 1.00 & 0.34 & 0.16 \cr
## ML5 & 0.02 & 0.49 & 0.05 & 0.34 & 1.00 & -0.05 \cr
## ML6 & 0.25 & -0.11 & 0.09 & 0.16 & -0.05 & 1.00 \cr
## \hline
## \end{tabular}
## \end{scriptsize}
## \end{center}
## \label{default}
## \end{table}threefactor<-fa(allpurpose_cor, nfactors=3, n.obs=1160, rotate=“oblimin”, fm=“ml”) threefactor #CFI 1-((threefactor\(STATISTIC - threefactor\)dof)/(threefactor\(null.chisq- threefactor\)null.dof))
fourfactor<-fa(allpurpose_cor, nfactors=4, n.obs=1160, rotate=“oblimin”, fm=“ml”) fourfactor #CFI 1-((fourfactor\(STATISTIC - fourfactor\)dof)/(fourfactor\(null.chisq- fourfactor\)null.dof)) fivefactor<-fa(allpurpose_cor, nfactors=5, n.obs=1160, rotate=“oblimin”, fm=“ml”) fivefactor #CFI 1-((fivefactor\(STATISTIC - fivefactor\)dof)/(fivefactor\(null.chisq- fivefactor\)null.dof)) sixfactor<-fa(allpurpose_cor, nfactors=6,n.obs=1160, rotate=“oblimin”, fm=“ml”) sixfactor #CFI 1-((sixfactor\(STATISTIC - sixfactor\)dof)/(sixfactor\(null.chisq- sixfactor\)null.dof))
threefactor2<-fa(allpurpose_cor[,-c(4,9,12,23)], nfactors=3,n.obs=1160, rotate=“oblimin”, fm=“ml”) threefactor2
fourfactor2<-fa(allpurpose_cor[,-c(4,9,12,23)], nfactors=4, n.obs=1160,rotate=“oblimin”, fm=“ml”) fourfactor2 ``` # EFA removing problematic qyestions: PWB_1, PWB_4, PWB_6, PWB_7, APSI_3, LET_4, LET_6, LET_3, MLQ_9, LET_5
data <- read.csv("~/Psychometric_study_data/allsurveysYT1.csv")
purposescales<-select(data, PWB_2, APSI_2, APSI_4, PWB_8, APSI_7, APSI_8, APSI_5, APSI_1,
LET_2,PWB_9, PWB_3, PWB_5,
MLQ_4, MLQ_5, MLQ_6, MLQ_1, APSI_6,LET_1,
MLQ_2, MLQ_3, MLQ_7, MLQ_8, MLQ_10
)
purposescales$PWB_2 <- 7- purposescales$PWB_2
purposescales$PWB_3 <- 7- purposescales$PWB_3
purposescales$PWB_9 <- 7- purposescales$PWB_9
purposescales<- data.frame(apply(purposescales,2, as.numeric))
purposescales<-tbl_df(purposescales)
purposescales## Source: local data frame [1,160 x 23]
##
## PWB_2 APSI_2 APSI_4 PWB_8 APSI_7 APSI_8 APSI_5 APSI_1 LET_2 PWB_9 PWB_3
## 1 3 4 4 3 4 4 4 2 4 6 5
## 2 5 3 5 2 4 4 4 4 3 5 5
## 3 6 4 3 3 4 3 3 3 4 6 5
## 4 2 4 4 4 4 3 5 4 4 4 4
## 5 2 3 3 3 2 3 4 3 2 4 3
## 6 4 4 4 4 5 3 4 3 5 6 6
## 7 2 2 3 3 2 2 4 2 4 3 5
## 8 6 3 3 4 3 1 5 3 4 6 5
## 9 5 5 4 5 4 5 4 4 4 6 5
## 10 6 2 3 3 3 4 5 2 3 6 3
## .. ... ... ... ... ... ... ... ... ... ... ...
## Variables not shown: PWB_5 (dbl), MLQ_4 (dbl), MLQ_5 (dbl), MLQ_6 (dbl),
## MLQ_1 (dbl), APSI_6 (dbl), LET_1 (dbl), MLQ_2 (dbl), MLQ_3 (dbl), MLQ_7
## (dbl), MLQ_8 (dbl), MLQ_10 (dbl)str(purposescales)## Classes 'tbl_df', 'tbl' and 'data.frame': 1160 obs. of 23 variables:
## $ PWB_2 : num 3 5 6 2 2 4 2 6 5 6 ...
## $ APSI_2: num 4 3 4 4 3 4 2 3 5 2 ...
## $ APSI_4: num 4 5 3 4 3 4 3 3 4 3 ...
## $ PWB_8 : num 3 2 3 4 3 4 3 4 5 3 ...
## $ APSI_7: num 4 4 4 4 2 5 2 3 4 3 ...
## $ APSI_8: num 4 4 3 3 3 3 2 1 5 4 ...
## $ APSI_5: num 4 4 3 5 4 4 4 5 4 5 ...
## $ APSI_1: num 2 4 3 4 3 3 2 3 4 2 ...
## $ LET_2 : num 4 3 4 4 2 5 4 4 4 3 ...
## $ PWB_9 : num 6 5 6 4 4 6 3 6 6 6 ...
## $ PWB_3 : num 5 5 5 4 3 6 5 5 5 3 ...
## $ PWB_5 : num 4 2 1 3 4 3 1 2 1 2 ...
## $ MLQ_4 : num 5 5 4 3 4 4 3 5 7 3 ...
## $ MLQ_5 : num 6 4 4 5 4 5 6 5 6 5 ...
## $ MLQ_6 : num 4 3 4 5 4 5 3 4 6 1 ...
## $ MLQ_1 : num 4 3 4 5 4 5 6 3 6 1 ...
## $ APSI_6: num 4 3 3 4 3 2 4 3 2 3 ...
## $ LET_1 : num 2 3 3 5 3 1 3 3 1 3 ...
## $ MLQ_2 : num 7 5 7 6 6 5 2 7 5 7 ...
## $ MLQ_3 : num 7 5 5 7 5 3 2 7 2 1 ...
## $ MLQ_7 : num 5 5 4 5 5 3 5 5 5 5 ...
## $ MLQ_8 : num 7 4 5 5 5 4 4 7 7 5 ...
## $ MLQ_10: num 7 5 4 6 5 3 4 5 2 1 ...colnames(purposescales) <- c("1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23")
allpurpose_cor <- corFiml(purposescales)# uses FIML for missing data
##EFA
##number of factors
##parallal analysis and scree plot
parallel<-fa.parallel(allpurpose_cor, n.obs=1160, fm="ml")
## Parallel analysis suggests that the number of factors = 5 and the number of components = 4#three factors are greater than one Eigenvalue scree plot says there are three factors.
#Paralel analysis suggests 6 factors
#eigenvalues (kaiser)
parallel$fa.values## [1] 5.973003634 2.971378628 1.879465732 0.609861269 0.204705264
## [6] 0.009613400 -0.007236128 -0.032086386 -0.061552741 -0.066591984
## [11] -0.118842752 -0.156361686 -0.187454653 -0.263044136 -0.353009389
## [16] -0.392184183 -0.437919231 -0.521115014 -0.538389770 -0.588426171
## [21] -0.647211831 -0.669589243 -0.693792994#over 1=3, over .7=4
#doign aprincipal components analysis to see how many factors there might be using that method
#Deal with NA doing principle componant analysis
princomp(na.omit(allpurpose_cor), cor = TRUE)## Call:
## princomp(x = na.omit(allpurpose_cor), cor = TRUE)
##
## Standard deviations:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
## 3.458287e+00 2.140434e+00 1.834325e+00 9.742516e-01 5.766034e-01
## Comp.6 Comp.7 Comp.8 Comp.9 Comp.10
## 5.188197e-01 4.796313e-01 4.520150e-01 4.434186e-01 3.810556e-01
## Comp.11 Comp.12 Comp.13 Comp.14 Comp.15
## 3.533112e-01 3.293431e-01 2.911212e-01 2.674597e-01 2.536552e-01
## Comp.16 Comp.17 Comp.18 Comp.19 Comp.20
## 2.419281e-01 2.234571e-01 2.172348e-01 2.122314e-01 2.015208e-01
## Comp.21 Comp.22 Comp.23
## 1.994738e-01 1.789674e-01 8.918170e-09
##
## 23 variables and 23 observations.parallel2<-princomp(na.omit(allpurpose_cor), cor = TRUE)
summary(parallel2)## Importance of components:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
## Standard deviation 3.458287 2.1404338 1.8343255 0.97425156 0.57660345
## Proportion of Variance 0.519989 0.1991938 0.1462935 0.04126809 0.01445528
## Cumulative Proportion 0.519989 0.7191828 0.8654762 0.90674432 0.92119961
## Comp.6 Comp.7 Comp.8 Comp.9
## Standard deviation 0.51881967 0.47963134 0.452015041 0.443418593
## Proportion of Variance 0.01170321 0.01000201 0.008883374 0.008548698
## Cumulative Proportion 0.93290282 0.94290483 0.951788199 0.960336897
## Comp.10 Comp.11 Comp.12 Comp.13
## Standard deviation 0.38105560 0.353311159 0.329343115 0.291121172
## Proportion of Variance 0.00631319 0.005427338 0.004715952 0.003684849
## Cumulative Proportion 0.96665009 0.972077425 0.976793377 0.980478226
## Comp.14 Comp.15 Comp.16 Comp.17
## Standard deviation 0.267459674 0.253655171 0.241928133 0.223457145
## Proportion of Variance 0.003110203 0.002797432 0.002544749 0.002171004
## Cumulative Proportion 0.983588430 0.986385862 0.988930611 0.991101615
## Comp.18 Comp.19 Comp.20 Comp.21
## Standard deviation 0.21723477 0.212231380 0.201520769 0.199473841
## Proportion of Variance 0.00205178 0.001958355 0.001765679 0.001729992
## Cumulative Proportion 0.99315340 0.995111750 0.996877429 0.998607421
## Comp.22 Comp.23
## Standard deviation 0.178967356 8.918170e-09
## Proportion of Variance 0.001392579 3.457989e-18
## Cumulative Proportion 1.000000000 1.000000e+00plot(parallel2)##results show at least two factors
#simple structure
twofactor<-fa(allpurpose_cor, nfactors=2, n.obs=1160,rotate="oblimin", fm="ml")
twofactor## Factor Analysis using method = ml
## Call: fa(r = allpurpose_cor, nfactors = 2, n.obs = 1160, rotate = "oblimin",
## fm = "ml")
## Standardized loadings (pattern matrix) based upon correlation matrix
## ML1 ML2 h2 u2 com
## 1 0.05 -0.39 0.163 0.84 1.0
## 2 0.76 0.03 0.577 0.42 1.0
## 3 0.80 0.06 0.635 0.36 1.0
## 4 0.58 -0.10 0.361 0.64 1.1
## 5 0.76 0.10 0.561 0.44 1.0
## 6 0.80 0.11 0.626 0.37 1.0
## 7 0.66 0.18 0.440 0.56 1.1
## 8 0.84 0.08 0.698 0.30 1.0
## 9 0.59 -0.04 0.354 0.65 1.0
## 10 0.01 -0.30 0.092 0.91 1.0
## 11 -0.07 -0.77 0.579 0.42 1.0
## 12 0.03 0.70 0.489 0.51 1.0
## 13 0.65 -0.26 0.533 0.47 1.3
## 14 0.50 -0.44 0.489 0.51 2.0
## 15 0.66 -0.24 0.541 0.46 1.3
## 16 0.49 -0.42 0.472 0.53 2.0
## 17 0.09 0.81 0.650 0.35 1.0
## 18 -0.09 0.68 0.485 0.52 1.0
## 19 0.12 0.27 0.082 0.92 1.4
## 20 0.25 0.22 0.099 0.90 2.0
## 21 0.17 0.15 0.045 0.95 2.0
## 22 0.21 0.16 0.064 0.94 1.9
## 23 0.03 0.26 0.065 0.93 1.0
##
## ML1 ML2
## SS loadings 5.81 3.29
## Proportion Var 0.25 0.14
## Cumulative Var 0.25 0.40
## Proportion Explained 0.64 0.36
## Cumulative Proportion 0.64 1.00
##
## With factor correlations of
## ML1 ML2
## ML1 1.00 -0.13
## ML2 -0.13 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 2 factors are sufficient.
##
## The degrees of freedom for the null model are 253 and the objective function was 11.97 with Chi Square of 13772.67
## The degrees of freedom for the model are 208 and the objective function was 3.6
##
## The root mean square of the residuals (RMSR) is 0.12
## The df corrected root mean square of the residuals is 0.13
##
## The harmonic number of observations is 1160 with the empirical chi square 8068.22 with prob < 0
## The total number of observations was 1160 with MLE Chi Square = 4134.49 with prob < 0
##
## Tucker Lewis Index of factoring reliability = 0.646
## RMSEA index = 0.128 and the 90 % confidence intervals are 0.124 0.131
## BIC = 2666.81
## Fit based upon off diagonal values = 0.86
## Measures of factor score adequacy
## ML1 ML2
## Correlation of scores with factors 0.96 0.93
## Multiple R square of scores with factors 0.93 0.87
## Minimum correlation of possible factor scores 0.86 0.75threefactor<-fa(allpurpose_cor, nfactors=3, n.obs=1160,rotate="oblimin", fm="ml")
threefactor## Factor Analysis using method = ml
## Call: fa(r = allpurpose_cor, nfactors = 3, n.obs = 1160, rotate = "oblimin",
## fm = "ml")
## Standardized loadings (pattern matrix) based upon correlation matrix
## ML1 ML3 ML2 h2 u2 com
## 1 0.05 0.37 -0.08 0.16 0.84 1.1
## 2 0.78 -0.04 -0.07 0.60 0.40 1.0
## 3 0.82 -0.07 -0.04 0.65 0.35 1.0
## 4 0.58 0.10 -0.04 0.36 0.64 1.1
## 5 0.74 -0.07 0.08 0.56 0.44 1.0
## 6 0.80 -0.10 -0.01 0.63 0.37 1.0
## 7 0.66 -0.16 0.05 0.44 0.56 1.1
## 8 0.86 -0.08 -0.04 0.71 0.29 1.0
## 9 0.59 0.05 0.00 0.36 0.64 1.0
## 10 0.00 0.30 -0.01 0.09 0.91 1.0
## 11 -0.10 0.77 -0.01 0.58 0.42 1.0
## 12 0.07 -0.71 0.00 0.50 0.50 1.0
## 13 0.61 0.31 0.09 0.55 0.45 1.5
## 14 0.44 0.50 0.13 0.52 0.48 2.1
## 15 0.64 0.27 0.04 0.55 0.45 1.4
## 16 0.45 0.47 0.09 0.49 0.51 2.1
## 17 0.12 -0.81 0.06 0.65 0.35 1.1
## 18 -0.10 -0.64 0.17 0.49 0.51 1.2
## 19 -0.04 -0.07 0.79 0.64 0.36 1.0
## 20 0.11 -0.03 0.73 0.57 0.43 1.0
## 21 0.02 0.05 0.73 0.53 0.47 1.0
## 22 0.06 0.04 0.73 0.55 0.45 1.0
## 23 -0.14 -0.05 0.82 0.67 0.33 1.1
##
## ML1 ML3 ML2
## SS loadings 5.66 3.17 3.02
## Proportion Var 0.25 0.14 0.13
## Cumulative Var 0.25 0.38 0.52
## Proportion Explained 0.48 0.27 0.25
## Cumulative Proportion 0.48 0.75 1.00
##
## With factor correlations of
## ML1 ML3 ML2
## ML1 1.00 0.15 0.12
## ML3 0.15 1.00 -0.11
## ML2 0.12 -0.11 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 253 and the objective function was 11.97 with Chi Square of 13772.67
## The degrees of freedom for the model are 187 and the objective function was 1.24
##
## 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 1160 with the empirical chi square 1093.31 with prob < 6.8e-128
## The total number of observations was 1160 with MLE Chi Square = 1419.02 with prob < 3.6e-188
##
## Tucker Lewis Index of factoring reliability = 0.876
## RMSEA index = 0.076 and the 90 % confidence intervals are 0.072 0.079
## BIC = 99.52
## Fit based upon off diagonal values = 0.98
## Measures of factor score adequacy
## ML1 ML3 ML2
## Correlation of scores with factors 0.97 0.93 0.94
## Multiple R square of scores with factors 0.93 0.87 0.88
## Minimum correlation of possible factor scores 0.86 0.75 0.77fourfactor<-fa(allpurpose_cor, nfactors=4, n.obs=1160,rotate="oblimin", fm="ml")
fourfactor## Factor Analysis using method = ml
## Call: fa(r = allpurpose_cor, nfactors = 4, n.obs = 1160, rotate = "oblimin",
## fm = "ml")
## Standardized loadings (pattern matrix) based upon correlation matrix
## ML1 ML2 ML3 ML4 h2 u2 com
## 1 0.17 -0.05 0.50 -0.16 0.24 0.76 1.5
## 2 0.74 -0.07 -0.01 0.09 0.60 0.40 1.0
## 3 0.77 -0.04 -0.04 0.09 0.65 0.35 1.0
## 4 0.57 -0.04 0.13 0.06 0.37 0.63 1.1
## 5 0.76 0.10 0.02 -0.02 0.59 0.41 1.0
## 6 0.79 0.00 -0.03 0.03 0.65 0.35 1.0
## 7 0.75 0.08 0.00 -0.16 0.50 0.50 1.1
## 8 0.78 -0.05 -0.08 0.13 0.71 0.29 1.1
## 9 0.55 0.00 0.07 0.08 0.36 0.64 1.1
## 10 0.22 0.04 0.54 -0.34 0.27 0.73 2.1
## 11 -0.06 0.01 0.80 0.04 0.66 0.34 1.0
## 12 0.00 -0.02 -0.77 0.00 0.58 0.42 1.0
## 13 0.35 0.06 0.06 0.53 0.60 0.40 1.8
## 14 0.15 0.08 0.19 0.62 0.61 0.39 1.4
## 15 0.36 0.00 0.01 0.56 0.62 0.38 1.7
## 16 0.05 0.03 0.07 0.79 0.71 0.29 1.0
## 17 0.21 0.07 -0.65 -0.29 0.64 0.36 1.6
## 18 -0.03 0.17 -0.54 -0.21 0.48 0.52 1.5
## 19 0.01 0.80 -0.02 -0.06 0.65 0.35 1.0
## 20 0.10 0.73 -0.01 0.03 0.57 0.43 1.0
## 21 -0.05 0.72 -0.02 0.14 0.53 0.47 1.1
## 22 0.08 0.74 0.06 0.01 0.55 0.45 1.0
## 23 -0.13 0.82 -0.03 -0.01 0.67 0.33 1.1
##
## ML1 ML2 ML3 ML4
## SS loadings 4.82 3.00 2.69 2.31
## Proportion Var 0.21 0.13 0.12 0.10
## Cumulative Var 0.21 0.34 0.46 0.56
## Proportion Explained 0.38 0.23 0.21 0.18
## Cumulative Proportion 0.38 0.61 0.82 1.00
##
## With factor correlations of
## ML1 ML2 ML3 ML4
## ML1 1.00 0.10 0.01 0.44
## ML2 0.10 1.00 -0.15 0.03
## ML3 0.01 -0.15 1.00 0.34
## ML4 0.44 0.03 0.34 1.00
##
## Mean item complexity = 1.2
## Test of the hypothesis that 4 factors are sufficient.
##
## The degrees of freedom for the null model are 253 and the objective function was 11.97 with Chi Square of 13772.67
## The degrees of freedom for the model are 167 and the objective function was 0.56
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.03
##
## The harmonic number of observations is 1160 with the empirical chi square 306.34 with prob < 2.8e-10
## The total number of observations was 1160 with MLE Chi Square = 639.33 with prob < 2e-56
##
## Tucker Lewis Index of factoring reliability = 0.947
## RMSEA index = 0.05 and the 90 % confidence intervals are 0.045 0.053
## BIC = -539.05
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## ML1 ML2 ML3 ML4
## Correlation of scores with factors 0.96 0.94 0.93 0.92
## Multiple R square of scores with factors 0.92 0.88 0.86 0.85
## Minimum correlation of possible factor scores 0.84 0.77 0.72 0.70fivefactor<-fa(allpurpose_cor, nfactors=5,n.obs=1160, rotate="oblimin", fm="ml")
fivefactor## Factor Analysis using method = ml
## Call: fa(r = allpurpose_cor, nfactors = 5, n.obs = 1160, rotate = "oblimin",
## fm = "ml")
## Standardized loadings (pattern matrix) based upon correlation matrix
## ML3 ML2 ML4 ML1 ML5 h2 u2 com
## 1 -0.05 0.50 -0.13 0.09 0.13 0.24 0.76 1.4
## 2 -0.04 -0.04 0.21 0.27 0.50 0.64 0.36 2.0
## 3 -0.07 -0.05 0.14 0.70 0.10 0.68 0.32 1.1
## 4 -0.03 0.11 0.13 0.34 0.24 0.37 0.63 2.4
## 5 0.06 0.04 -0.03 0.90 -0.07 0.72 0.28 1.0
## 6 -0.02 -0.04 0.08 0.69 0.13 0.67 0.33 1.1
## 7 0.10 -0.01 -0.05 0.33 0.48 0.53 0.47 1.9
## 8 -0.04 -0.10 0.25 0.40 0.39 0.72 0.28 2.8
## 9 0.03 0.05 0.19 0.12 0.46 0.41 0.59 1.5
## 10 0.05 0.54 -0.30 0.06 0.23 0.27 0.73 2.0
## 11 0.01 0.79 0.06 -0.11 0.06 0.66 0.34 1.1
## 12 -0.01 -0.78 0.02 -0.14 0.12 0.61 0.39 1.1
## 13 0.07 0.03 0.63 0.06 0.24 0.62 0.38 1.3
## 14 0.07 0.17 0.66 0.11 -0.05 0.61 0.39 1.2
## 15 0.00 -0.02 0.64 0.16 0.13 0.62 0.38 1.2
## 16 0.02 0.04 0.84 0.00 -0.05 0.71 0.29 1.0
## 17 0.08 -0.64 -0.28 0.08 0.17 0.64 0.36 1.6
## 18 0.16 -0.53 -0.26 0.11 -0.13 0.49 0.51 1.9
## 19 0.81 -0.02 -0.05 -0.04 0.06 0.65 0.35 1.0
## 20 0.74 -0.02 0.06 -0.01 0.12 0.58 0.42 1.1
## 21 0.71 -0.01 0.13 0.06 -0.13 0.54 0.46 1.1
## 22 0.74 0.06 0.02 0.07 0.02 0.55 0.45 1.0
## 23 0.81 -0.03 -0.03 -0.06 -0.07 0.67 0.33 1.0
##
## ML3 ML2 ML4 ML1 ML5
## SS loadings 3.00 2.63 2.85 3.02 1.72
## Proportion Var 0.13 0.11 0.12 0.13 0.07
## Cumulative Var 0.13 0.24 0.37 0.50 0.57
## Proportion Explained 0.23 0.20 0.22 0.23 0.13
## Cumulative Proportion 0.23 0.43 0.64 0.87 1.00
##
## With factor correlations of
## ML3 ML2 ML4 ML1 ML5
## ML3 1.00 -0.16 0.04 0.13 0.01
## ML2 -0.16 1.00 0.32 -0.03 -0.04
## ML4 0.04 0.32 1.00 0.48 0.29
## ML1 0.13 -0.03 0.48 1.00 0.60
## ML5 0.01 -0.04 0.29 0.60 1.00
##
## Mean item complexity = 1.4
## Test of the hypothesis that 5 factors are sufficient.
##
## The degrees of freedom for the null model are 253 and the objective function was 11.97 with Chi Square of 13772.67
## The degrees of freedom for the model are 148 and the objective function was 0.35
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.02
##
## The harmonic number of observations is 1160 with the empirical chi square 193.23 with prob < 0.0074
## The total number of observations was 1160 with MLE Chi Square = 399.82 with prob < 4.9e-25
##
## Tucker Lewis Index of factoring reliability = 0.968
## RMSEA index = 0.039 and the 90 % confidence intervals are 0.034 0.043
## BIC = -644.5
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## ML3 ML2 ML4 ML1 ML5
## Correlation of scores with factors 0.94 0.93 0.94 0.94 0.87
## Multiple R square of scores with factors 0.88 0.86 0.87 0.89 0.76
## Minimum correlation of possible factor scores 0.77 0.72 0.75 0.78 0.53sixfactor<-fa(allpurpose_cor, nfactors=6,n.obs=1160, rotate="oblimin", fm="ml")
sixfactor## Factor Analysis using method = ml
## Call: fa(r = allpurpose_cor, nfactors = 6, n.obs = 1160, rotate = "oblimin",
## fm = "ml")
## Standardized loadings (pattern matrix) based upon correlation matrix
## ML3 ML4 ML1 ML2 ML5 ML6 h2 u2 com
## 1 -0.07 0.09 -0.01 -0.15 -0.08 0.54 0.40 0.60 1.3
## 2 -0.03 0.16 0.27 -0.03 0.55 -0.02 0.67 0.33 1.6
## 3 -0.06 0.10 0.70 0.00 0.13 -0.05 0.69 0.31 1.1
## 4 -0.04 0.15 0.33 -0.07 0.21 0.08 0.37 0.63 2.5
## 5 0.05 -0.03 0.90 -0.02 -0.07 0.02 0.73 0.27 1.0
## 6 -0.02 0.08 0.68 0.05 0.13 0.03 0.67 0.33 1.1
## 7 0.10 -0.05 0.32 0.04 0.47 0.09 0.53 0.47 2.0
## 8 -0.04 0.25 0.39 0.10 0.38 0.03 0.72 0.28 2.9
## 9 0.03 0.20 0.11 -0.02 0.42 0.09 0.40 0.60 1.7
## 10 0.03 -0.12 -0.03 -0.21 0.06 0.54 0.39 0.61 1.5
## 11 0.03 -0.03 -0.06 -0.84 0.12 0.04 0.70 0.30 1.1
## 12 -0.01 0.04 -0.16 0.72 0.13 -0.13 0.60 0.40 1.2
## 13 0.06 0.70 0.01 0.03 0.16 0.09 0.63 0.37 1.2
## 14 0.07 0.67 0.10 -0.18 -0.06 -0.02 0.61 0.39 1.2
## 15 -0.02 0.75 0.10 0.11 0.04 0.11 0.66 0.34 1.1
## 16 0.02 0.79 0.00 -0.14 -0.02 -0.13 0.70 0.30 1.1
## 17 0.08 -0.22 0.05 0.68 0.14 0.01 0.64 0.36 1.3
## 18 0.16 -0.24 0.09 0.52 -0.12 -0.07 0.49 0.51 1.9
## 19 0.80 0.00 -0.06 0.09 0.01 0.10 0.66 0.34 1.1
## 20 0.74 0.05 0.00 0.00 0.12 -0.01 0.58 0.42 1.1
## 21 0.69 0.17 0.05 0.05 -0.16 0.02 0.54 0.46 1.2
## 22 0.73 0.04 0.07 -0.03 0.00 0.05 0.55 0.45 1.0
## 23 0.83 -0.10 -0.02 -0.08 0.00 -0.14 0.70 0.30 1.1
##
## ML3 ML4 ML1 ML2 ML5 ML6
## SS loadings 3.00 2.93 2.90 2.35 1.60 0.84
## Proportion Var 0.13 0.13 0.13 0.10 0.07 0.04
## Cumulative Var 0.13 0.26 0.38 0.49 0.56 0.59
## Proportion Explained 0.22 0.22 0.21 0.17 0.12 0.06
## Cumulative Proportion 0.22 0.44 0.65 0.82 0.94 1.00
##
## With factor correlations of
## ML3 ML4 ML1 ML2 ML5 ML6
## ML3 1.00 0.05 0.13 0.16 0.00 -0.05
## ML4 0.05 1.00 0.54 -0.33 0.36 0.10
## ML1 0.13 0.54 1.00 0.08 0.58 0.20
## ML2 0.16 -0.33 0.08 1.00 0.13 -0.34
## ML5 0.00 0.36 0.58 0.13 1.00 0.16
## ML6 -0.05 0.10 0.20 -0.34 0.16 1.00
##
## Mean item complexity = 1.4
## Test of the hypothesis that 6 factors are sufficient.
##
## The degrees of freedom for the null model are 253 and the objective function was 11.97 with Chi Square of 13772.67
## The degrees of freedom for the model are 130 and the objective function was 0.23
##
## 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 1160 with the empirical chi square 95 with prob < 0.99
## The total number of observations was 1160 with MLE Chi Square = 263.58 with prob < 4.1e-11
##
## Tucker Lewis Index of factoring reliability = 0.981
## RMSEA index = 0.03 and the 90 % confidence intervals are 0.025 0.035
## BIC = -653.72
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## ML3 ML4 ML1 ML2 ML5
## Correlation of scores with factors 0.94 0.94 0.94 0.93 0.87
## Multiple R square of scores with factors 0.89 0.89 0.89 0.87 0.76
## Minimum correlation of possible factor scores 0.78 0.77 0.78 0.73 0.52
## ML6
## Correlation of scores with factors 0.78
## Multiple R square of scores with factors 0.61
## Minimum correlation of possible factor scores 0.21fourfactor2<-fa(purposescales, nfactors=4, n.obs=1160, rotate="oblimin", fm="ml")
fourfactor2## Factor Analysis using method = ml
## Call: fa(r = purposescales, nfactors = 4, n.obs = 1160, rotate = "oblimin",
## fm = "ml")
## Standardized loadings (pattern matrix) based upon correlation matrix
## ML1 ML2 ML3 ML4 h2 u2 com
## 1 0.17 -0.05 0.50 -0.16 0.24 0.76 1.5
## 2 0.74 -0.06 -0.01 0.09 0.60 0.40 1.0
## 3 0.77 -0.04 -0.04 0.09 0.65 0.35 1.0
## 4 0.57 -0.04 0.13 0.05 0.38 0.62 1.1
## 5 0.76 0.10 0.02 -0.01 0.59 0.41 1.0
## 6 0.79 0.00 -0.04 0.03 0.65 0.35 1.0
## 7 0.75 0.08 0.00 -0.16 0.50 0.50 1.1
## 8 0.78 -0.05 -0.08 0.13 0.71 0.29 1.1
## 9 0.56 0.00 0.07 0.07 0.36 0.64 1.1
## 10 0.22 0.04 0.54 -0.34 0.27 0.73 2.1
## 11 -0.06 0.01 0.80 0.04 0.66 0.34 1.0
## 12 0.00 -0.02 -0.77 0.00 0.58 0.42 1.0
## 13 0.35 0.06 0.06 0.53 0.60 0.40 1.8
## 14 0.15 0.09 0.20 0.61 0.61 0.39 1.4
## 15 0.36 0.00 0.01 0.56 0.62 0.38 1.7
## 16 0.05 0.03 0.07 0.79 0.71 0.29 1.0
## 17 0.21 0.07 -0.65 -0.28 0.64 0.36 1.6
## 18 -0.03 0.17 -0.54 -0.21 0.48 0.52 1.5
## 19 0.01 0.80 -0.01 -0.06 0.65 0.35 1.0
## 20 0.10 0.73 -0.01 0.03 0.57 0.43 1.0
## 21 -0.05 0.72 -0.02 0.14 0.53 0.47 1.1
## 22 0.08 0.74 0.06 0.01 0.55 0.45 1.0
## 23 -0.13 0.82 -0.03 -0.01 0.67 0.33 1.1
##
## ML1 ML2 ML3 ML4
## SS loadings 4.84 3.00 2.69 2.30
## Proportion Var 0.21 0.13 0.12 0.10
## Cumulative Var 0.21 0.34 0.46 0.56
## Proportion Explained 0.38 0.23 0.21 0.18
## Cumulative Proportion 0.38 0.61 0.82 1.00
##
## With factor correlations of
## ML1 ML2 ML3 ML4
## ML1 1.00 0.11 0.01 0.44
## ML2 0.11 1.00 -0.16 0.03
## ML3 0.01 -0.16 1.00 0.34
## ML4 0.44 0.03 0.34 1.00
##
## Mean item complexity = 1.2
## Test of the hypothesis that 4 factors are sufficient.
##
## The degrees of freedom for the null model are 253 and the objective function was 12 with Chi Square of 13806.27
## The degrees of freedom for the model are 167 and the objective function was 0.56
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.03
##
## The harmonic number of observations is 816 with the empirical chi square 214.92 with prob < 0.0073
## The total number of observations was 1160 with MLE Chi Square = 641.82 with prob < 7.9e-57
##
## Tucker Lewis Index of factoring reliability = 0.947
## RMSEA index = 0.05 and the 90 % confidence intervals are 0.045 0.054
## BIC = -536.56
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## ML1 ML2 ML3 ML4
## Correlation of scores with factors 0.96 0.94 0.93 0.92
## Multiple R square of scores with factors 0.92 0.88 0.86 0.85
## Minimum correlation of possible factor scores 0.84 0.77 0.72 0.70fa2latex(fa(allpurpose_cor,2,n.obs=1160, rotate="oblimin", fm="ml"),heading="2f")## % Called in the psych package fa2latex % Called in the psych package fa(allpurpose_cor, 2, n.obs = 1160, rotate = "oblimin", fm = "ml") % Called in the psych package 2f
## \begin{table}[htdp]\caption{fa2latex}
## \begin{center}
## \begin{scriptsize}
## \begin{tabular} {l r r r r r }
## \multicolumn{ 5 }{l}{ 2f } \cr
## \hline Variable & ML1 & ML2 & h2 & u2 & com \cr
## \hline
## 1 & 0.05 & \bf{-0.39} & 0.16 & 0.84 & 1.03 \cr
## 2 & \bf{ 0.76} & 0.03 & 0.58 & 0.42 & 1.00 \cr
## 3 & \bf{ 0.80} & 0.06 & 0.64 & 0.36 & 1.01 \cr
## 4 & \bf{ 0.58} & -0.10 & 0.36 & 0.64 & 1.06 \cr
## 5 & \bf{ 0.76} & 0.10 & 0.56 & 0.44 & 1.04 \cr
## 6 & \bf{ 0.80} & 0.11 & 0.63 & 0.37 & 1.04 \cr
## 7 & \bf{ 0.66} & 0.18 & 0.44 & 0.56 & 1.15 \cr
## 8 & \bf{ 0.84} & 0.08 & 0.70 & 0.30 & 1.02 \cr
## 9 & \bf{ 0.59} & -0.04 & 0.35 & 0.65 & 1.01 \cr
## 10 & 0.01 & -0.30 & 0.09 & 0.91 & 1.00 \cr
## 11 & -0.07 & \bf{-0.77} & 0.58 & 0.42 & 1.02 \cr
## 12 & 0.03 & \bf{ 0.70} & 0.49 & 0.51 & 1.00 \cr
## 13 & \bf{ 0.65} & -0.26 & 0.53 & 0.47 & 1.32 \cr
## 14 & \bf{ 0.50} & \bf{-0.44} & 0.49 & 0.51 & 1.97 \cr
## 15 & \bf{ 0.66} & -0.24 & 0.54 & 0.46 & 1.26 \cr
## 16 & \bf{ 0.49} & \bf{-0.42} & 0.47 & 0.53 & 1.95 \cr
## 17 & 0.09 & \bf{ 0.81} & 0.65 & 0.35 & 1.03 \cr
## 18 & -0.09 & \bf{ 0.68} & 0.48 & 0.52 & 1.04 \cr
## 19 & 0.12 & 0.27 & 0.08 & 0.92 & 1.38 \cr
## 20 & 0.25 & 0.22 & 0.10 & 0.90 & 1.97 \cr
## 21 & 0.17 & 0.15 & 0.05 & 0.95 & 1.99 \cr
## 22 & 0.21 & 0.16 & 0.06 & 0.94 & 1.87 \cr
## 23 & 0.03 & 0.26 & 0.07 & 0.93 & 1.02 \cr
## \hline \cr SS loadings & 5.81 & 3.29 & \cr
## \cr
## \hline \cr
## ML1 & 1.00 & -0.13 \cr
## ML2 & -0.13 & 1.00 \cr
## \hline
## \end{tabular}
## \end{scriptsize}
## \end{center}
## \label{default}
## \end{table}fa2latex(fa(allpurpose_cor,3,n.obs=1160, rotate="oblimin", fm="ml"),heading="3f")## % Called in the psych package fa2latex % Called in the psych package fa(allpurpose_cor, 3, n.obs = 1160, rotate = "oblimin", fm = "ml") % Called in the psych package 3f
## \begin{table}[htdp]\caption{fa2latex}
## \begin{center}
## \begin{scriptsize}
## \begin{tabular} {l r r r r r r }
## \multicolumn{ 6 }{l}{ 3f } \cr
## \hline Variable & ML1 & ML3 & ML2 & h2 & u2 & com \cr
## \hline
## 1 & 0.05 & \bf{ 0.37} & -0.08 & 0.16 & 0.84 & 1.14 \cr
## 2 & \bf{ 0.78} & -0.04 & -0.07 & 0.60 & 0.40 & 1.02 \cr
## 3 & \bf{ 0.82} & -0.07 & -0.04 & 0.65 & 0.35 & 1.02 \cr
## 4 & \bf{ 0.58} & 0.10 & -0.04 & 0.36 & 0.64 & 1.07 \cr
## 5 & \bf{ 0.74} & -0.07 & 0.08 & 0.56 & 0.44 & 1.04 \cr
## 6 & \bf{ 0.80} & -0.10 & -0.01 & 0.63 & 0.37 & 1.03 \cr
## 7 & \bf{ 0.66} & -0.16 & 0.05 & 0.44 & 0.56 & 1.14 \cr
## 8 & \bf{ 0.86} & -0.08 & -0.04 & 0.71 & 0.29 & 1.02 \cr
## 9 & \bf{ 0.59} & 0.05 & 0.00 & 0.36 & 0.64 & 1.01 \cr
## 10 & 0.00 & 0.30 & -0.01 & 0.09 & 0.91 & 1.01 \cr
## 11 & -0.10 & \bf{ 0.77} & -0.01 & 0.58 & 0.42 & 1.04 \cr
## 12 & 0.07 & \bf{-0.71} & 0.00 & 0.50 & 0.50 & 1.02 \cr
## 13 & \bf{ 0.61} & \bf{ 0.31} & 0.09 & 0.55 & 0.45 & 1.53 \cr
## 14 & \bf{ 0.44} & \bf{ 0.50} & 0.13 & 0.52 & 0.48 & 2.12 \cr
## 15 & \bf{ 0.64} & 0.27 & 0.04 & 0.55 & 0.45 & 1.37 \cr
## 16 & \bf{ 0.45} & \bf{ 0.47} & 0.09 & 0.49 & 0.51 & 2.07 \cr
## 17 & 0.12 & \bf{-0.81} & 0.06 & 0.65 & 0.35 & 1.05 \cr
## 18 & -0.10 & \bf{-0.64} & 0.17 & 0.49 & 0.51 & 1.18 \cr
## 19 & -0.04 & -0.07 & \bf{ 0.79} & 0.64 & 0.36 & 1.02 \cr
## 20 & 0.11 & -0.03 & \bf{ 0.73} & 0.57 & 0.43 & 1.05 \cr
## 21 & 0.02 & 0.05 & \bf{ 0.73} & 0.53 & 0.47 & 1.01 \cr
## 22 & 0.06 & 0.04 & \bf{ 0.73} & 0.55 & 0.45 & 1.02 \cr
## 23 & -0.14 & -0.05 & \bf{ 0.82} & 0.67 & 0.33 & 1.07 \cr
## \hline \cr SS loadings & 5.66 & 3.17 & 3.02 & \cr
## \cr
## \hline \cr
## ML1 & 1.00 & 0.15 & 0.12 \cr
## ML3 & 0.15 & 1.00 & -0.11 \cr
## ML2 & 0.12 & -0.11 & 1.00 \cr
## \hline
## \end{tabular}
## \end{scriptsize}
## \end{center}
## \label{default}
## \end{table}fa2latex(fa(allpurpose_cor,4,n.obs=1160, rotate="oblimin", fm="ml"),heading="4f")## % Called in the psych package fa2latex % Called in the psych package fa(allpurpose_cor, 4, n.obs = 1160, rotate = "oblimin", fm = "ml") % Called in the psych package 4f
## \begin{table}[htdp]\caption{fa2latex}
## \begin{center}
## \begin{scriptsize}
## \begin{tabular} {l r r r r r r r }
## \multicolumn{ 7 }{l}{ 4f } \cr
## \hline Variable & ML1 & ML2 & ML3 & ML4 & h2 & u2 & com \cr
## \hline
## 1 & 0.17 & -0.05 & \bf{ 0.50} & -0.16 & 0.24 & 0.76 & 1.47 \cr
## 2 & \bf{ 0.74} & -0.07 & -0.01 & 0.09 & 0.60 & 0.40 & 1.04 \cr
## 3 & \bf{ 0.77} & -0.04 & -0.04 & 0.09 & 0.65 & 0.35 & 1.04 \cr
## 4 & \bf{ 0.57} & -0.04 & 0.13 & 0.06 & 0.37 & 0.63 & 1.13 \cr
## 5 & \bf{ 0.76} & 0.10 & 0.02 & -0.02 & 0.59 & 0.41 & 1.04 \cr
## 6 & \bf{ 0.79} & 0.00 & -0.03 & 0.03 & 0.65 & 0.35 & 1.01 \cr
## 7 & \bf{ 0.75} & 0.08 & 0.00 & -0.16 & 0.50 & 0.50 & 1.11 \cr
## 8 & \bf{ 0.78} & -0.05 & -0.08 & 0.13 & 0.71 & 0.29 & 1.09 \cr
## 9 & \bf{ 0.55} & 0.00 & 0.07 & 0.08 & 0.36 & 0.64 & 1.07 \cr
## 10 & 0.22 & 0.04 & \bf{ 0.54} & \bf{-0.34} & 0.27 & 0.73 & 2.06 \cr
## 11 & -0.06 & 0.01 & \bf{ 0.80} & 0.04 & 0.66 & 0.34 & 1.02 \cr
## 12 & 0.00 & -0.02 & \bf{-0.77} & 0.00 & 0.58 & 0.42 & 1.00 \cr
## 13 & \bf{ 0.35} & 0.06 & 0.06 & \bf{ 0.53} & 0.60 & 0.40 & 1.80 \cr
## 14 & 0.15 & 0.08 & 0.19 & \bf{ 0.62} & 0.61 & 0.39 & 1.36 \cr
## 15 & \bf{ 0.36} & 0.00 & 0.01 & \bf{ 0.56} & 0.62 & 0.38 & 1.69 \cr
## 16 & 0.05 & 0.03 & 0.07 & \bf{ 0.79} & 0.71 & 0.29 & 1.03 \cr
## 17 & 0.21 & 0.07 & \bf{-0.65} & -0.29 & 0.64 & 0.36 & 1.65 \cr
## 18 & -0.03 & 0.17 & \bf{-0.54} & -0.21 & 0.48 & 0.52 & 1.51 \cr
## 19 & 0.01 & \bf{ 0.80} & -0.02 & -0.06 & 0.65 & 0.35 & 1.01 \cr
## 20 & 0.10 & \bf{ 0.73} & -0.01 & 0.03 & 0.57 & 0.43 & 1.04 \cr
## 21 & -0.05 & \bf{ 0.72} & -0.02 & 0.14 & 0.53 & 0.47 & 1.09 \cr
## 22 & 0.08 & \bf{ 0.74} & 0.06 & 0.01 & 0.55 & 0.45 & 1.04 \cr
## 23 & -0.13 & \bf{ 0.82} & -0.03 & -0.01 & 0.67 & 0.33 & 1.05 \cr
## \hline \cr SS loadings & 4.82 & 3 & 2.69 & 2.31 & \cr
## \cr
## \hline \cr
## ML1 & 1.00 & 0.10 & 0.01 & 0.44 \cr
## ML2 & 0.10 & 1.00 & -0.15 & 0.03 \cr
## ML3 & 0.01 & -0.15 & 1.00 & 0.34 \cr
## ML4 & 0.44 & 0.03 & 0.34 & 1.00 \cr
## \hline
## \end{tabular}
## \end{scriptsize}
## \end{center}
## \label{default}
## \end{table}fa2latex(fa(allpurpose_cor,5,n.obs=1160, rotate="oblimin", fm="ml"),heading="5f")## % Called in the psych package fa2latex % Called in the psych package fa(allpurpose_cor, 5, n.obs = 1160, rotate = "oblimin", fm = "ml") % Called in the psych package 5f
## \begin{table}[htdp]\caption{fa2latex}
## \begin{center}
## \begin{scriptsize}
## \begin{tabular} {l r r r r r r r r }
## \multicolumn{ 8 }{l}{ 5f } \cr
## \hline Variable & ML3 & ML2 & ML4 & ML1 & ML5 & h2 & u2 & com \cr
## \hline
## 1 & -0.05 & \bf{ 0.50} & -0.13 & 0.09 & 0.13 & 0.24 & 0.76 & 1.35 \cr
## 2 & -0.04 & -0.04 & 0.21 & 0.27 & \bf{ 0.50} & 0.64 & 0.36 & 1.97 \cr
## 3 & -0.07 & -0.05 & 0.14 & \bf{ 0.70} & 0.10 & 0.68 & 0.32 & 1.15 \cr
## 4 & -0.03 & 0.11 & 0.13 & \bf{ 0.34} & 0.24 & 0.37 & 0.63 & 2.44 \cr
## 5 & 0.06 & 0.04 & -0.03 & \bf{ 0.90} & -0.07 & 0.72 & 0.28 & 1.03 \cr
## 6 & -0.02 & -0.04 & 0.08 & \bf{ 0.69} & 0.13 & 0.67 & 0.33 & 1.11 \cr
## 7 & 0.10 & -0.01 & -0.05 & \bf{ 0.33} & \bf{ 0.48} & 0.53 & 0.47 & 1.91 \cr
## 8 & -0.04 & -0.10 & 0.25 & \bf{ 0.40} & \bf{ 0.39} & 0.72 & 0.28 & 2.81 \cr
## 9 & 0.03 & 0.05 & 0.19 & 0.12 & \bf{ 0.46} & 0.41 & 0.59 & 1.53 \cr
## 10 & 0.05 & \bf{ 0.54} & -0.30 & 0.06 & 0.23 & 0.27 & 0.73 & 2.03 \cr
## 11 & 0.01 & \bf{ 0.79} & 0.06 & -0.11 & 0.06 & 0.66 & 0.34 & 1.06 \cr
## 12 & -0.01 & \bf{-0.78} & 0.02 & -0.14 & 0.12 & 0.61 & 0.39 & 1.12 \cr
## 13 & 0.07 & 0.03 & \bf{ 0.63} & 0.06 & 0.24 & 0.62 & 0.38 & 1.32 \cr
## 14 & 0.07 & 0.17 & \bf{ 0.66} & 0.11 & -0.05 & 0.61 & 0.39 & 1.23 \cr
## 15 & 0.00 & -0.02 & \bf{ 0.64} & 0.16 & 0.13 & 0.62 & 0.38 & 1.22 \cr
## 16 & 0.02 & 0.04 & \bf{ 0.84} & 0.00 & -0.05 & 0.71 & 0.29 & 1.01 \cr
## 17 & 0.08 & \bf{-0.64} & -0.28 & 0.08 & 0.17 & 0.64 & 0.36 & 1.60 \cr
## 18 & 0.16 & \bf{-0.53} & -0.26 & 0.11 & -0.13 & 0.49 & 0.51 & 1.92 \cr
## 19 & \bf{ 0.81} & -0.02 & -0.05 & -0.04 & 0.06 & 0.65 & 0.35 & 1.02 \cr
## 20 & \bf{ 0.74} & -0.02 & 0.06 & -0.01 & 0.12 & 0.58 & 0.42 & 1.07 \cr
## 21 & \bf{ 0.71} & -0.01 & 0.13 & 0.06 & -0.13 & 0.54 & 0.46 & 1.15 \cr
## 22 & \bf{ 0.74} & 0.06 & 0.02 & 0.07 & 0.02 & 0.55 & 0.45 & 1.04 \cr
## 23 & \bf{ 0.81} & -0.03 & -0.03 & -0.06 & -0.07 & 0.67 & 0.33 & 1.03 \cr
## \hline \cr SS loadings & 3 & 2.63 & 2.85 & 3.02 & 1.72 & \cr
## \cr
## \hline \cr
## ML3 & 1.00 & -0.16 & 0.04 & 0.13 & 0.01 \cr
## ML2 & -0.16 & 1.00 & 0.32 & -0.03 & -0.04 \cr
## ML4 & 0.04 & 0.32 & 1.00 & 0.48 & 0.29 \cr
## ML1 & 0.13 & -0.03 & 0.48 & 1.00 & 0.60 \cr
## ML5 & 0.01 & -0.04 & 0.29 & 0.60 & 1.00 \cr
## \hline
## \end{tabular}
## \end{scriptsize}
## \end{center}
## \label{default}
## \end{table}fa2latex(fa(allpurpose_cor,6,n.obs=1160, rotate="oblimin", fm="ml"),heading="6f")## % Called in the psych package fa2latex % Called in the psych package fa(allpurpose_cor, 6, n.obs = 1160, rotate = "oblimin", fm = "ml") % Called in the psych package 6f
## \begin{table}[htdp]\caption{fa2latex}
## \begin{center}
## \begin{scriptsize}
## \begin{tabular} {l r r r r r r r r r }
## \multicolumn{ 9 }{l}{ 6f } \cr
## \hline Variable & ML3 & ML4 & ML1 & ML2 & ML5 & ML6 & h2 & u2 & com \cr
## \hline
## 1 & -0.07 & 0.09 & -0.01 & -0.15 & -0.08 & \bf{ 0.54} & 0.40 & 0.60 & 1.29 \cr
## 2 & -0.03 & 0.16 & 0.27 & -0.03 & \bf{ 0.55} & -0.02 & 0.67 & 0.33 & 1.64 \cr
## 3 & -0.06 & 0.10 & \bf{ 0.70} & 0.00 & 0.13 & -0.05 & 0.69 & 0.31 & 1.14 \cr
## 4 & -0.04 & 0.15 & \bf{ 0.33} & -0.07 & 0.21 & 0.08 & 0.37 & 0.63 & 2.52 \cr
## 5 & 0.05 & -0.03 & \bf{ 0.90} & -0.02 & -0.07 & 0.02 & 0.73 & 0.27 & 1.02 \cr
## 6 & -0.02 & 0.08 & \bf{ 0.68} & 0.05 & 0.13 & 0.03 & 0.67 & 0.33 & 1.13 \cr
## 7 & 0.10 & -0.05 & \bf{ 0.32} & 0.04 & \bf{ 0.47} & 0.09 & 0.53 & 0.47 & 2.04 \cr
## 8 & -0.04 & 0.25 & \bf{ 0.39} & 0.10 & \bf{ 0.38} & 0.03 & 0.72 & 0.28 & 2.90 \cr
## 9 & 0.03 & 0.20 & 0.11 & -0.02 & \bf{ 0.42} & 0.09 & 0.40 & 0.60 & 1.72 \cr
## 10 & 0.03 & -0.12 & -0.03 & -0.21 & 0.06 & \bf{ 0.54} & 0.39 & 0.61 & 1.46 \cr
## 11 & 0.03 & -0.03 & -0.06 & \bf{-0.84} & 0.12 & 0.04 & 0.70 & 0.30 & 1.06 \cr
## 12 & -0.01 & 0.04 & -0.16 & \bf{ 0.72} & 0.13 & -0.13 & 0.60 & 0.40 & 1.24 \cr
## 13 & 0.06 & \bf{ 0.70} & 0.01 & 0.03 & 0.16 & 0.09 & 0.63 & 0.37 & 1.16 \cr
## 14 & 0.07 & \bf{ 0.67} & 0.10 & -0.18 & -0.06 & -0.02 & 0.61 & 0.39 & 1.24 \cr
## 15 & -0.02 & \bf{ 0.75} & 0.10 & 0.11 & 0.04 & 0.11 & 0.66 & 0.34 & 1.13 \cr
## 16 & 0.02 & \bf{ 0.79} & 0.00 & -0.14 & -0.02 & -0.13 & 0.70 & 0.30 & 1.12 \cr
## 17 & 0.08 & -0.22 & 0.05 & \bf{ 0.68} & 0.14 & 0.01 & 0.64 & 0.36 & 1.33 \cr
## 18 & 0.16 & -0.24 & 0.09 & \bf{ 0.52} & -0.12 & -0.07 & 0.49 & 0.51 & 1.87 \cr
## 19 & \bf{ 0.80} & 0.00 & -0.06 & 0.09 & 0.01 & 0.10 & 0.66 & 0.34 & 1.07 \cr
## 20 & \bf{ 0.74} & 0.05 & 0.00 & 0.00 & 0.12 & -0.01 & 0.58 & 0.42 & 1.06 \cr
## 21 & \bf{ 0.69} & 0.17 & 0.05 & 0.05 & -0.16 & 0.02 & 0.54 & 0.46 & 1.25 \cr
## 22 & \bf{ 0.73} & 0.04 & 0.07 & -0.03 & 0.00 & 0.05 & 0.55 & 0.45 & 1.03 \cr
## 23 & \bf{ 0.83} & -0.10 & -0.02 & -0.08 & 0.00 & -0.14 & 0.70 & 0.30 & 1.11 \cr
## \hline \cr SS loadings & 3 & 2.93 & 2.9 & 2.35 & 1.6 & 0.84 & \cr
## \cr
## \hline \cr
## ML3 & 1.00 & 0.05 & 0.13 & 0.16 & 0.00 & -0.05 \cr
## ML4 & 0.05 & 1.00 & 0.54 & -0.33 & 0.36 & 0.10 \cr
## ML1 & 0.13 & 0.54 & 1.00 & 0.08 & 0.58 & 0.20 \cr
## ML2 & 0.16 & -0.33 & 0.08 & 1.00 & 0.13 & -0.34 \cr
## ML5 & 0.00 & 0.36 & 0.58 & 0.13 & 1.00 & 0.16 \cr
## ML6 & -0.05 & 0.10 & 0.20 & -0.34 & 0.16 & 1.00 \cr
## \hline
## \end{tabular}
## \end{scriptsize}
## \end{center}
## \label{default}
## \end{table}#1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33Create dataset for Target rotation
all_surveys<-read.csv("allsurveysYT1.csv")
purposescales<-select(all_surveys, PWB_7, PWB_8, APSI_1, APSI_2, APSI_4, APSI_5, APSI_7, APSI_8, LET_2, LET_4, PWB_1, PWB_2, PWB_3, PWB_4, PWB_5, PWB_6, PWB_9, APSI_3, APSI_6, LET_1, LET_3, LET_5, LET_6, MLQ_9, MLQ_2, MLQ_3, MLQ_7, MLQ_8,MLQ_10, MLQ_1, MLQ_4, MLQ_5, MLQ_6)
purposescales$PWB_1 <- 7- purposescales$PWB_1
purposescales$PWB_2 <- 7- purposescales$PWB_2
purposescales$PWB_3 <- 7- purposescales$PWB_3
purposescales$PWB_4 <- 7- purposescales$PWB_4
purposescales$PWB_9 <- 7- purposescales$PWB_9
purposescales$MLQ_9 <- 8- purposescales$MLQ_9
purposescales$LET_1 <- 6- purposescales$LET_1
purposescales$LET_3 <- 6- purposescales$LET_3
purposescales$LET_5 <- 6- purposescales$LET_5
purposescales<- data.frame(apply(purposescales,2, as.numeric))
library(GPArotation)
library(psych)
library(dplyr)
purposescales<-tbl_df(purposescales)
purposescales## Source: local data frame [1,160 x 33]
##
## PWB_7 PWB_8 APSI_1 APSI_2 APSI_4 APSI_5 APSI_7 APSI_8 LET_2 LET_4 PWB_1
## 1 4 3 2 4 4 4 4 4 4 5 4
## 2 3 2 4 3 5 4 4 4 3 4 4
## 3 6 3 3 4 3 3 4 3 4 4 5
## 4 5 4 4 4 4 5 4 3 4 4 2
## 5 2 3 3 3 3 4 2 3 2 4 2
## 6 3 4 3 4 4 4 5 3 5 5 5
## 7 3 3 2 2 3 4 2 2 4 3 2
## 8 4 4 3 3 3 5 3 1 4 4 6
## 9 5 5 4 5 4 4 4 5 4 5 5
## 10 6 3 2 2 3 5 3 4 3 5 6
## .. ... ... ... ... ... ... ... ... ... ... ...
## Variables not shown: PWB_2 (dbl), PWB_3 (dbl), PWB_4 (dbl), PWB_5 (dbl),
## PWB_6 (dbl), PWB_9 (dbl), APSI_3 (dbl), APSI_6 (dbl), LET_1 (dbl), LET_3
## (dbl), LET_5 (dbl), LET_6 (dbl), MLQ_9 (dbl), MLQ_2 (dbl), MLQ_3 (dbl),
## MLQ_7 (dbl), MLQ_8 (dbl), MLQ_10 (dbl), MLQ_1 (dbl), MLQ_4 (dbl), MLQ_5
## (dbl), MLQ_6 (dbl)str(purposescales)## Classes 'tbl_df', 'tbl' and 'data.frame': 1160 obs. of 33 variables:
## $ PWB_7 : num 4 3 6 5 2 3 3 4 5 6 ...
## $ PWB_8 : num 3 2 3 4 3 4 3 4 5 3 ...
## $ APSI_1: num 2 4 3 4 3 3 2 3 4 2 ...
## $ APSI_2: num 4 3 4 4 3 4 2 3 5 2 ...
## $ APSI_4: num 4 5 3 4 3 4 3 3 4 3 ...
## $ APSI_5: num 4 4 3 5 4 4 4 5 4 5 ...
## $ APSI_7: num 4 4 4 4 2 5 2 3 4 3 ...
## $ APSI_8: num 4 4 3 3 3 3 2 1 5 4 ...
## $ LET_2 : num 4 3 4 4 2 5 4 4 4 3 ...
## $ LET_4 : num 5 4 4 4 4 5 3 4 5 5 ...
## $ PWB_1 : num 4 4 5 2 2 5 2 6 5 6 ...
## $ PWB_2 : num 3 5 6 2 2 4 2 6 5 6 ...
## $ PWB_3 : num 5 5 5 4 3 6 5 5 5 3 ...
## $ PWB_4 : num 2 2 6 4 3 5 2 1 5 3 ...
## $ PWB_5 : num 4 2 1 3 4 3 1 2 1 2 ...
## $ PWB_6 : num 5 5 4 4 3 4 4 4 5 6 ...
## $ PWB_9 : num 6 5 6 4 4 6 3 6 6 6 ...
## $ APSI_3: num 4 4 4 5 4 4 4 4 5 2 ...
## $ APSI_6: num 4 3 3 4 3 2 4 3 2 3 ...
## $ LET_1 : num 4 3 3 1 3 5 3 3 5 3 ...
## $ LET_3 : num 4 4 3 4 3 5 2 3 5 5 ...
## $ LET_5 : num 5 4 4 4 2 5 3 4 5 5 ...
## $ LET_6 : num 5 5 5 4 4 4 5 5 5 5 ...
## $ MLQ_9 : num 3 3 4 3 5 7 5 4 7 6 ...
## $ MLQ_2 : num 7 5 7 6 6 5 2 7 5 7 ...
## $ MLQ_3 : num 7 5 5 7 5 3 2 7 2 1 ...
## $ MLQ_7 : num 5 5 4 5 5 3 5 5 5 5 ...
## $ MLQ_8 : num 7 4 5 5 5 4 4 7 7 5 ...
## $ MLQ_10: num 7 5 4 6 5 3 4 5 2 1 ...
## $ MLQ_1 : num 4 3 4 5 4 5 6 3 6 1 ...
## $ MLQ_4 : num 5 5 4 3 4 4 3 5 7 3 ...
## $ MLQ_5 : num 6 4 4 5 4 5 6 5 6 5 ...
## $ MLQ_6 : num 4 3 4 5 4 5 3 4 6 1 ...colnames(purposescales) <- c("1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32","33")
#Target rotation: choose "simple structure" a priori and can be applied to oblique and orthogonal rotation based on
#what paper says facotrs should be purposescales
Targ_key <- make.keys(33,list(f1=1:10,f2=11:24, f3=25:29,f4=30:33))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
purposescales_cor <- corFiml(purposescales)
out_targetQ <- fa(purposescales_cor,4,rotate="TargetQ", n.obs = 1160, Target=Targ_key) #TargetT for orthogonal rotation
out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR2 MR1 MR3 MR4
## 1 0.604 0.140
## 2 0.134 0.560
## 3 0.755 0.179
## 4 0.719 0.120
## 5 0.730 0.132
## 6 0.747 -0.131
## 7 0.739
## 8 0.772
## 9 0.572
## 10 0.534
## 11 0.659 -0.199
## 12 0.519 0.201 -0.226
## 13 0.832
## 14 0.513 0.318
## 15 -0.775
## 16 0.532 0.173 0.151
## 17 0.532 0.244 -0.391
## 18 0.340 0.114 0.126 0.109
## 19 -0.683 0.240 -0.238
## 20 0.564 -0.166 0.185
## 21 0.777 -0.162
## 22 0.750 -0.241
## 23 0.422 0.257 0.131
## 24 0.520 -0.142 0.203
## 25 0.797
## 26 0.723
## 27 0.717 0.141
## 28 0.736
## 29 -0.122 0.815
## 30 0.801
## 31 0.302 0.564
## 32 0.221 0.615
## 33 0.309 0.583
##
## MR2 MR1 MR3 MR4
## SS loadings 5.451 5.275 3.036 2.254
## Proportion Var 0.165 0.160 0.092 0.068
## Cumulative Var 0.165 0.325 0.417 0.485
##
## $score.cor
## [,1] [,2] [,3] [,4]
## [1,] 1.0000000 0.1455312 -0.1295486 0.4274069
## [2,] 0.1455312 1.0000000 0.1120732 0.6172193
## [3,] -0.1295486 0.1120732 1.0000000 0.1083251
## [4,] 0.4274069 0.6172193 0.1083251 1.0000000
##
## $TLI
## [1] 0.8839974
##
## $RMSEA
## RMSEA lower upper confidence
## 0.06256369 0.05953080 0.06461021 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = purposescales_cor, nfactors = 4, n.obs = 1160, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR1 MR3 MR4 h2 u2 com
## 1 0.07 0.60 0.06 0.14 0.50 0.50 1.2
## 2 0.13 0.56 -0.03 0.07 0.39 0.61 1.1
## 3 -0.10 0.75 -0.06 0.18 0.71 0.29 1.2
## 4 -0.03 0.72 -0.07 0.12 0.60 0.40 1.1
## 5 -0.05 0.73 -0.04 0.13 0.63 0.37 1.1
## 6 -0.01 0.75 0.08 -0.13 0.50 0.50 1.1
## 7 0.01 0.74 0.10 0.01 0.58 0.42 1.0
## 8 -0.04 0.77 0.00 0.05 0.64 0.36 1.0
## 9 0.04 0.57 0.00 0.09 0.39 0.61 1.1
## 10 0.06 0.53 0.02 0.02 0.31 0.69 1.0
## 11 0.66 -0.10 -0.01 -0.20 0.39 0.61 1.2
## 12 0.52 0.20 -0.05 -0.23 0.25 0.75 1.7
## 13 0.83 -0.06 0.02 -0.04 0.66 0.34 1.0
## 14 0.51 0.32 -0.09 0.03 0.42 0.58 1.7
## 15 -0.78 0.01 -0.04 0.06 0.56 0.44 1.0
## 16 0.53 0.02 0.17 0.15 0.38 0.62 1.4
## 17 0.53 0.24 0.04 -0.39 0.26 0.74 2.3
## 18 0.34 0.11 0.13 0.11 0.20 0.80 1.8
## 19 -0.68 0.24 0.06 -0.24 0.65 0.35 1.5
## 20 0.56 0.00 -0.17 0.19 0.48 0.52 1.4
## 21 0.78 -0.16 -0.03 0.00 0.62 0.38 1.1
## 22 0.75 -0.24 0.03 0.03 0.60 0.40 1.2
## 23 0.42 0.26 0.06 0.13 0.35 0.65 1.9
## 24 0.52 0.00 -0.14 0.20 0.43 0.57 1.5
## 25 -0.03 0.01 0.80 -0.07 0.64 0.36 1.0
## 26 -0.04 0.09 0.72 0.04 0.56 0.44 1.0
## 27 -0.01 -0.06 0.72 0.14 0.53 0.47 1.1
## 28 0.05 0.07 0.74 0.01 0.55 0.45 1.0
## 29 -0.04 -0.12 0.82 -0.02 0.67 0.33 1.1
## 30 0.08 0.00 0.02 0.80 0.70 0.30 1.0
## 31 0.04 0.30 0.04 0.56 0.60 0.40 1.6
## 32 0.22 0.10 0.09 0.61 0.61 0.39 1.4
## 33 0.01 0.31 -0.01 0.58 0.61 0.39 1.5
##
## MR2 MR1 MR3 MR4
## SS loadings 5.57 5.63 3.06 2.72
## Proportion Var 0.17 0.17 0.09 0.08
## Cumulative Var 0.17 0.34 0.43 0.51
## Proportion Explained 0.33 0.33 0.18 0.16
## Cumulative Proportion 0.33 0.66 0.84 1.00
##
## With factor correlations of
## MR2 MR1 MR3 MR4
## MR2 1.00 0.07 -0.14 0.39
## MR1 0.07 1.00 0.11 0.48
## MR3 -0.14 0.11 1.00 0.06
## MR4 0.39 0.48 0.06 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 4 factors are sufficient.
##
## The degrees of freedom for the null model are 528 and the objective function was 18.23 with Chi Square of 20910.11
## The degrees of freedom for the model are 402 and the objective function was 1.92
##
## The root mean square of the residuals (RMSR) is 0.03
## The df corrected root mean square of the residuals is 0.04
##
## The harmonic number of observations is 1160 with the empirical chi square 1430.22 with prob < 3.6e-115
## The total number of observations was 1160 with MLE Chi Square = 2197.86 with prob < 1.3e-244
##
## Tucker Lewis Index of factoring reliability = 0.884
## RMSEA index = 0.063 and the 90 % confidence intervals are 0.06 0.065
## BIC = -638.72
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## MR2 MR1 MR3 MR4
## Correlation of scores with factors 0.96 0.96 0.94 0.93
## Multiple R square of scores with factors 0.92 0.93 0.88 0.86
## Minimum correlation of possible factor scores 0.85 0.85 0.77 0.73#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$null.dof))## [1] 0.9118905Target rotation Droping 18 (APSI_3)
all_surveys<-read.csv("allsurveysYT1.csv")
purposescales<-select(all_surveys, PWB_7, PWB_8, APSI_1, APSI_2, APSI_4, APSI_5, APSI_7, APSI_8, LET_2, LET_4, PWB_1, PWB_2, PWB_3, PWB_4, PWB_5, PWB_6, PWB_9, APSI_6, LET_1, LET_3, LET_5, LET_6, MLQ_9, MLQ_2, MLQ_3, MLQ_7, MLQ_8,MLQ_10, MLQ_1, MLQ_4, MLQ_5, MLQ_6)
purposescales$PWB_1 <- 7- purposescales$PWB_1
purposescales$PWB_2 <- 7- purposescales$PWB_2
purposescales$PWB_3 <- 7- purposescales$PWB_3
purposescales$PWB_4 <- 7- purposescales$PWB_4
purposescales$PWB_9 <- 7- purposescales$PWB_9
purposescales$MLQ_9 <- 8- purposescales$MLQ_9
purposescales$LET_1 <- 6- purposescales$LET_1
purposescales$LET_3 <- 6- purposescales$LET_3
purposescales$LET_5 <- 6- purposescales$LET_5
purposescales<- data.frame(apply(purposescales,2, as.numeric))
library(GPArotation)
library(psych)
library(dplyr)
purposescales<-tbl_df(purposescales)
purposescales## Source: local data frame [1,160 x 32]
##
## PWB_7 PWB_8 APSI_1 APSI_2 APSI_4 APSI_5 APSI_7 APSI_8 LET_2 LET_4 PWB_1
## 1 4 3 2 4 4 4 4 4 4 5 4
## 2 3 2 4 3 5 4 4 4 3 4 4
## 3 6 3 3 4 3 3 4 3 4 4 5
## 4 5 4 4 4 4 5 4 3 4 4 2
## 5 2 3 3 3 3 4 2 3 2 4 2
## 6 3 4 3 4 4 4 5 3 5 5 5
## 7 3 3 2 2 3 4 2 2 4 3 2
## 8 4 4 3 3 3 5 3 1 4 4 6
## 9 5 5 4 5 4 4 4 5 4 5 5
## 10 6 3 2 2 3 5 3 4 3 5 6
## .. ... ... ... ... ... ... ... ... ... ... ...
## Variables not shown: PWB_2 (dbl), PWB_3 (dbl), PWB_4 (dbl), PWB_5 (dbl),
## PWB_6 (dbl), PWB_9 (dbl), APSI_6 (dbl), LET_1 (dbl), LET_3 (dbl), LET_5
## (dbl), LET_6 (dbl), MLQ_9 (dbl), MLQ_2 (dbl), MLQ_3 (dbl), MLQ_7 (dbl),
## MLQ_8 (dbl), MLQ_10 (dbl), MLQ_1 (dbl), MLQ_4 (dbl), MLQ_5 (dbl), MLQ_6
## (dbl)str(purposescales)## Classes 'tbl_df', 'tbl' and 'data.frame': 1160 obs. of 32 variables:
## $ PWB_7 : num 4 3 6 5 2 3 3 4 5 6 ...
## $ PWB_8 : num 3 2 3 4 3 4 3 4 5 3 ...
## $ APSI_1: num 2 4 3 4 3 3 2 3 4 2 ...
## $ APSI_2: num 4 3 4 4 3 4 2 3 5 2 ...
## $ APSI_4: num 4 5 3 4 3 4 3 3 4 3 ...
## $ APSI_5: num 4 4 3 5 4 4 4 5 4 5 ...
## $ APSI_7: num 4 4 4 4 2 5 2 3 4 3 ...
## $ APSI_8: num 4 4 3 3 3 3 2 1 5 4 ...
## $ LET_2 : num 4 3 4 4 2 5 4 4 4 3 ...
## $ LET_4 : num 5 4 4 4 4 5 3 4 5 5 ...
## $ PWB_1 : num 4 4 5 2 2 5 2 6 5 6 ...
## $ PWB_2 : num 3 5 6 2 2 4 2 6 5 6 ...
## $ PWB_3 : num 5 5 5 4 3 6 5 5 5 3 ...
## $ PWB_4 : num 2 2 6 4 3 5 2 1 5 3 ...
## $ PWB_5 : num 4 2 1 3 4 3 1 2 1 2 ...
## $ PWB_6 : num 5 5 4 4 3 4 4 4 5 6 ...
## $ PWB_9 : num 6 5 6 4 4 6 3 6 6 6 ...
## $ APSI_6: num 4 3 3 4 3 2 4 3 2 3 ...
## $ LET_1 : num 4 3 3 1 3 5 3 3 5 3 ...
## $ LET_3 : num 4 4 3 4 3 5 2 3 5 5 ...
## $ LET_5 : num 5 4 4 4 2 5 3 4 5 5 ...
## $ LET_6 : num 5 5 5 4 4 4 5 5 5 5 ...
## $ MLQ_9 : num 3 3 4 3 5 7 5 4 7 6 ...
## $ MLQ_2 : num 7 5 7 6 6 5 2 7 5 7 ...
## $ MLQ_3 : num 7 5 5 7 5 3 2 7 2 1 ...
## $ MLQ_7 : num 5 5 4 5 5 3 5 5 5 5 ...
## $ MLQ_8 : num 7 4 5 5 5 4 4 7 7 5 ...
## $ MLQ_10: num 7 5 4 6 5 3 4 5 2 1 ...
## $ MLQ_1 : num 4 3 4 5 4 5 6 3 6 1 ...
## $ MLQ_4 : num 5 5 4 3 4 4 3 5 7 3 ...
## $ MLQ_5 : num 6 4 4 5 4 5 6 5 6 5 ...
## $ MLQ_6 : num 4 3 4 5 4 5 3 4 6 1 ...colnames(purposescales) <- c("1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32")
#Target rotation: choose "simple structure" a priori and can be applied to oblique and orthogonal rotation based on
#what paper says facotrs should be purposescales
Targ_key <- make.keys(32,list(f1=1:10,f2=11:23, f3=24:28,f4=29:32))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
purposescales_cor <- corFiml(purposescales)
out_targetQ <- fa(purposescales_cor,4,rotate="TargetQ", n.obs = 1160, Target=Targ_key) #TargetT for orthogonal rotation
out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR2 MR1 MR3 MR4
## 1 0.605 0.142
## 2 0.132 0.560
## 3 0.755 0.179
## 4 0.718 0.121
## 5 0.730 0.132
## 6 0.742 -0.125
## 7 0.738
## 8 0.773
## 9 0.573
## 10 0.534
## 11 0.657 -0.196
## 12 0.524 0.206 -0.226
## 13 0.831
## 14 0.518 0.324
## 15 -0.773
## 16 0.524 0.174 0.158
## 17 0.539 0.248 -0.389
## 18 -0.680 0.236 -0.244
## 19 0.566 -0.159 0.189
## 20 0.775 -0.158
## 21 0.746 -0.238
## 22 0.416 0.258 0.137
## 23 0.519 -0.136 0.208
## 24 0.798
## 25 0.724
## 26 0.718 0.143
## 27 0.736
## 28 -0.121 0.812
## 29 0.797
## 30 0.304 0.565
## 31 0.215 0.102 0.616
## 32 0.312 0.583
##
## MR2 MR1 MR3 MR4
## SS loadings 5.315 5.261 3.015 2.246
## Proportion Var 0.166 0.164 0.094 0.070
## Cumulative Var 0.166 0.330 0.425 0.495
##
## $score.cor
## [,1] [,2] [,3] [,4]
## [1,] 1.0000000 0.1255420 -0.1440666 0.4107911
## [2,] 0.1255420 1.0000000 0.1120651 0.6172085
## [3,] -0.1440666 0.1120651 1.0000000 0.1083005
## [4,] 0.4107911 0.6172085 0.1083005 1.0000000
##
## $TLI
## [1] 0.8988709
##
## $RMSEA
## RMSEA lower upper confidence
## 0.05951876 0.05640655 0.06170032 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = purposescales_cor, nfactors = 4, n.obs = 1160, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR1 MR3 MR4 h2 u2 com
## 1 0.07 0.60 0.06 0.14 0.50 0.50 1.2
## 2 0.13 0.56 -0.03 0.07 0.39 0.61 1.1
## 3 -0.10 0.76 -0.06 0.18 0.71 0.29 1.2
## 4 -0.03 0.72 -0.07 0.12 0.60 0.40 1.1
## 5 -0.05 0.73 -0.04 0.13 0.63 0.37 1.1
## 6 -0.02 0.74 0.07 -0.13 0.49 0.51 1.1
## 7 0.01 0.74 0.10 0.01 0.58 0.42 1.0
## 8 -0.04 0.77 0.00 0.05 0.64 0.36 1.0
## 9 0.04 0.57 0.00 0.09 0.39 0.61 1.1
## 10 0.06 0.53 0.03 0.02 0.31 0.69 1.0
## 11 0.66 -0.10 0.00 -0.20 0.39 0.61 1.2
## 12 0.52 0.21 -0.04 -0.23 0.25 0.75 1.7
## 13 0.83 -0.06 0.03 -0.03 0.66 0.34 1.0
## 14 0.52 0.32 -0.08 0.03 0.43 0.57 1.7
## 15 -0.77 0.00 -0.04 0.06 0.56 0.44 1.0
## 16 0.52 0.03 0.17 0.16 0.38 0.62 1.4
## 17 0.54 0.25 0.04 -0.39 0.26 0.74 2.3
## 18 -0.68 0.24 0.05 -0.24 0.65 0.35 1.5
## 19 0.57 0.00 -0.16 0.19 0.49 0.51 1.4
## 20 0.77 -0.16 -0.02 0.01 0.62 0.38 1.1
## 21 0.75 -0.24 0.04 0.04 0.60 0.40 1.2
## 22 0.42 0.26 0.07 0.14 0.35 0.65 2.0
## 23 0.52 0.01 -0.14 0.21 0.43 0.57 1.5
## 24 -0.03 0.02 0.80 -0.07 0.65 0.35 1.0
## 25 -0.04 0.09 0.72 0.04 0.57 0.43 1.0
## 26 -0.01 -0.06 0.72 0.14 0.54 0.46 1.1
## 27 0.05 0.07 0.74 0.01 0.55 0.45 1.0
## 28 -0.04 -0.12 0.81 -0.02 0.67 0.33 1.1
## 29 0.08 0.00 0.02 0.80 0.69 0.31 1.0
## 30 0.04 0.30 0.05 0.57 0.61 0.39 1.6
## 31 0.22 0.10 0.09 0.62 0.61 0.39 1.3
## 32 0.01 0.31 0.00 0.58 0.62 0.38 1.5
##
## MR2 MR1 MR3 MR4
## SS loadings 5.43 5.62 3.04 2.70
## Proportion Var 0.17 0.18 0.09 0.08
## Cumulative Var 0.17 0.35 0.44 0.52
## Proportion Explained 0.32 0.33 0.18 0.16
## Cumulative Proportion 0.32 0.66 0.84 1.00
##
## With factor correlations of
## MR2 MR1 MR3 MR4
## MR2 1.00 0.06 -0.15 0.38
## MR1 0.06 1.00 0.11 0.48
## MR3 -0.15 0.11 1.00 0.05
## MR4 0.38 0.48 0.05 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 4 factors are sufficient.
##
## The degrees of freedom for the null model are 496 and the objective function was 17.76 with Chi Square of 20374.35
## The degrees of freedom for the model are 374 and the objective function was 1.65
##
## The root mean square of the residuals (RMSR) is 0.03
## The df corrected root mean square of the residuals is 0.04
##
## The harmonic number of observations is 1160 with the empirical chi square 1145.64 with prob < 3.2e-79
## The total number of observations was 1160 with MLE Chi Square = 1886.21 with prob < 7.8e-200
##
## Tucker Lewis Index of factoring reliability = 0.899
## RMSEA index = 0.06 and the 90 % confidence intervals are 0.056 0.062
## BIC = -752.8
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## MR2 MR1 MR3 MR4
## Correlation of scores with factors 0.96 0.96 0.94 0.93
## Multiple R square of scores with factors 0.92 0.93 0.88 0.86
## Minimum correlation of possible factor scores 0.84 0.85 0.76 0.72#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$null.dof))## [1] 0.923927``` # Target rotation Droping 18 (APSI_3), 14(APSI_5), 17(APSI_8), 23(LET_6)
all_surveys<-read.csv("allsurveysYT1.csv")
purposescales<-select(all_surveys, PWB_7, PWB_8, APSI_1, APSI_2, APSI_4, APSI_7, LET_2, LET_4, PWB_1, PWB_2, PWB_3, PWB_4, PWB_5, PWB_6, PWB_9, APSI_6, LET_1, LET_3, LET_5, MLQ_9, MLQ_2, MLQ_3, MLQ_7, MLQ_8,MLQ_10, MLQ_1, MLQ_4, MLQ_5, MLQ_6)
purposescales$PWB_1 <- 7- purposescales$PWB_1
purposescales$PWB_2 <- 7- purposescales$PWB_2
purposescales$PWB_3 <- 7- purposescales$PWB_3
purposescales$PWB_4 <- 7- purposescales$PWB_4
purposescales$PWB_9 <- 7- purposescales$PWB_9
purposescales$MLQ_9 <- 8- purposescales$MLQ_9
purposescales$LET_1 <- 6- purposescales$LET_1
purposescales$LET_3 <- 6- purposescales$LET_3
purposescales$LET_5 <- 6- purposescales$LET_5
purposescales<- data.frame(apply(purposescales,2, as.numeric))
library(GPArotation)
library(psych)
library(dplyr)
purposescales<-tbl_df(purposescales)
purposescales## Source: local data frame [1,160 x 29]
##
## PWB_7 PWB_8 APSI_1 APSI_2 APSI_4 APSI_7 LET_2 LET_4 PWB_1 PWB_2 PWB_3
## 1 4 3 2 4 4 4 4 5 4 3 5
## 2 3 2 4 3 5 4 3 4 4 5 5
## 3 6 3 3 4 3 4 4 4 5 6 5
## 4 5 4 4 4 4 4 4 4 2 2 4
## 5 2 3 3 3 3 2 2 4 2 2 3
## 6 3 4 3 4 4 5 5 5 5 4 6
## 7 3 3 2 2 3 2 4 3 2 2 5
## 8 4 4 3 3 3 3 4 4 6 6 5
## 9 5 5 4 5 4 4 4 5 5 5 5
## 10 6 3 2 2 3 3 3 5 6 6 3
## .. ... ... ... ... ... ... ... ... ... ... ...
## Variables not shown: PWB_4 (dbl), PWB_5 (dbl), PWB_6 (dbl), PWB_9 (dbl),
## APSI_6 (dbl), LET_1 (dbl), LET_3 (dbl), LET_5 (dbl), MLQ_9 (dbl), MLQ_2
## (dbl), MLQ_3 (dbl), MLQ_7 (dbl), MLQ_8 (dbl), MLQ_10 (dbl), MLQ_1 (dbl),
## MLQ_4 (dbl), MLQ_5 (dbl), MLQ_6 (dbl)str(purposescales)## Classes 'tbl_df', 'tbl' and 'data.frame': 1160 obs. of 29 variables:
## $ PWB_7 : num 4 3 6 5 2 3 3 4 5 6 ...
## $ PWB_8 : num 3 2 3 4 3 4 3 4 5 3 ...
## $ APSI_1: num 2 4 3 4 3 3 2 3 4 2 ...
## $ APSI_2: num 4 3 4 4 3 4 2 3 5 2 ...
## $ APSI_4: num 4 5 3 4 3 4 3 3 4 3 ...
## $ APSI_7: num 4 4 4 4 2 5 2 3 4 3 ...
## $ LET_2 : num 4 3 4 4 2 5 4 4 4 3 ...
## $ LET_4 : num 5 4 4 4 4 5 3 4 5 5 ...
## $ PWB_1 : num 4 4 5 2 2 5 2 6 5 6 ...
## $ PWB_2 : num 3 5 6 2 2 4 2 6 5 6 ...
## $ PWB_3 : num 5 5 5 4 3 6 5 5 5 3 ...
## $ PWB_4 : num 2 2 6 4 3 5 2 1 5 3 ...
## $ PWB_5 : num 4 2 1 3 4 3 1 2 1 2 ...
## $ PWB_6 : num 5 5 4 4 3 4 4 4 5 6 ...
## $ PWB_9 : num 6 5 6 4 4 6 3 6 6 6 ...
## $ APSI_6: num 4 3 3 4 3 2 4 3 2 3 ...
## $ LET_1 : num 4 3 3 1 3 5 3 3 5 3 ...
## $ LET_3 : num 4 4 3 4 3 5 2 3 5 5 ...
## $ LET_5 : num 5 4 4 4 2 5 3 4 5 5 ...
## $ MLQ_9 : num 3 3 4 3 5 7 5 4 7 6 ...
## $ MLQ_2 : num 7 5 7 6 6 5 2 7 5 7 ...
## $ MLQ_3 : num 7 5 5 7 5 3 2 7 2 1 ...
## $ MLQ_7 : num 5 5 4 5 5 3 5 5 5 5 ...
## $ MLQ_8 : num 7 4 5 5 5 4 4 7 7 5 ...
## $ MLQ_10: num 7 5 4 6 5 3 4 5 2 1 ...
## $ MLQ_1 : num 4 3 4 5 4 5 6 3 6 1 ...
## $ MLQ_4 : num 5 5 4 3 4 4 3 5 7 3 ...
## $ MLQ_5 : num 6 4 4 5 4 5 6 5 6 5 ...
## $ MLQ_6 : num 4 3 4 5 4 5 3 4 6 1 ...colnames(purposescales) <- c("1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29")
#Target rotation: choose "simple structure" a priori and can be applied to oblique and orthogonal rotation based on
#what paper says facotrs should be purposescales
Targ_key <- make.keys(29,list(f1=1:10,f2=11:20, f3=21:25,f4=26:29))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
purposescales_cor <- corFiml(purposescales)
out_targetQ <- fa(purposescales_cor,4,rotate="TargetQ", n.obs = 1160, Target=Targ_key) #TargetT for orthogonal rotation
out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR2 MR1 MR3 MR4
## 1 0.621 0.128
## 2 0.559
## 3 -0.127 0.753 0.166
## 4 0.702 0.127
## 5 0.705 0.140
## 6 0.717 0.111
## 7 0.614
## 8 0.543
## 9 0.635 -0.171
## 10 0.489 0.258 -0.228
## 11 0.817
## 12 0.503 0.357
## 13 -0.753
## 14 0.524 0.165 0.189
## 15 0.475 0.279 -0.365
## 16 -0.708 0.205 -0.262
## 17 0.582 -0.166 0.193
## 18 0.772 -0.126
## 19 0.752 -0.220
## 20 0.531 -0.145 0.230
## 21 0.800
## 22 0.727
## 23 0.719 0.131
## 24 0.737
## 25 -0.135 0.810
## 26 0.166 0.769
## 27 0.317 0.540
## 28 0.270 0.110 0.606
## 29 0.321 0.557
##
## MR2 MR1 MR3 MR4
## SS loadings 5.072 4.079 3.012 2.093
## Proportion Var 0.175 0.141 0.104 0.072
## Cumulative Var 0.175 0.316 0.419 0.492
##
## $score.cor
## [,1] [,2] [,3] [,4]
## [1,] 1.0000000 0.10648663 -0.15626593 0.3802711
## [2,] 0.1064866 1.00000000 0.09884156 0.6324372
## [3,] -0.1562659 0.09884156 1.00000000 0.1083479
## [4,] 0.3802711 0.63243722 0.10834790 1.0000000
##
## $TLI
## [1] 0.9026726
##
## $RMSEA
## RMSEA lower upper confidence
## 0.06047110 0.05706720 0.06300695 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = purposescales_cor, nfactors = 4, n.obs = 1160, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR1 MR3 MR4 h2 u2 com
## 1 0.05 0.62 0.07 0.13 0.50 0.50 1.1
## 2 0.10 0.56 -0.02 0.08 0.38 0.62 1.1
## 3 -0.13 0.75 -0.04 0.17 0.71 0.29 1.2
## 4 -0.07 0.70 -0.06 0.13 0.59 0.41 1.1
## 5 -0.09 0.70 -0.03 0.14 0.61 0.39 1.1
## 6 -0.04 0.72 0.11 0.02 0.56 0.44 1.1
## 7 0.01 0.61 0.01 0.05 0.42 0.58 1.0
## 8 0.03 0.54 0.03 0.01 0.31 0.69 1.0
## 9 0.64 -0.06 -0.01 -0.17 0.40 0.60 1.2
## 10 0.49 0.26 -0.04 -0.23 0.27 0.73 2.0
## 11 0.82 -0.01 0.01 0.00 0.66 0.34 1.0
## 12 0.50 0.36 -0.08 0.03 0.44 0.56 1.9
## 13 -0.75 -0.03 -0.03 0.03 0.56 0.44 1.0
## 14 0.52 0.03 0.16 0.19 0.37 0.63 1.5
## 15 0.47 0.28 0.04 -0.36 0.26 0.74 2.6
## 16 -0.71 0.21 0.07 -0.26 0.64 0.36 1.5
## 17 0.58 0.04 -0.17 0.19 0.49 0.51 1.4
## 18 0.77 -0.13 -0.04 0.04 0.62 0.38 1.1
## 19 0.75 -0.22 0.02 0.08 0.60 0.40 1.2
## 20 0.53 0.02 -0.14 0.23 0.44 0.56 1.5
## 21 -0.03 0.02 0.80 -0.07 0.65 0.35 1.0
## 22 -0.04 0.09 0.73 0.04 0.57 0.43 1.0
## 23 0.01 -0.05 0.72 0.13 0.54 0.46 1.1
## 24 0.05 0.07 0.74 0.01 0.55 0.45 1.0
## 25 -0.04 -0.14 0.81 0.00 0.67 0.33 1.1
## 26 0.17 0.01 0.02 0.77 0.69 0.31 1.1
## 27 0.08 0.32 0.05 0.54 0.61 0.39 1.7
## 28 0.27 0.11 0.09 0.61 0.61 0.39 1.5
## 29 0.06 0.32 0.00 0.56 0.62 0.38 1.6
##
## MR2 MR1 MR3 MR4
## SS loadings 5.22 4.45 3.05 2.59
## Proportion Var 0.18 0.15 0.11 0.09
## Cumulative Var 0.18 0.33 0.44 0.53
## Proportion Explained 0.34 0.29 0.20 0.17
## Cumulative Proportion 0.34 0.63 0.83 1.00
##
## With factor correlations of
## MR2 MR1 MR3 MR4
## MR2 1.00 0.06 -0.15 0.23
## MR1 0.06 1.00 0.08 0.51
## MR3 -0.15 0.08 1.00 0.07
## MR4 0.23 0.51 0.07 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 4 factors are sufficient.
##
## The degrees of freedom for the null model are 406 and the objective function was 15.57 with Chi Square of 17878.78
## The degrees of freedom for the model are 296 and the objective function was 1.34
##
## The root mean square of the residuals (RMSR) is 0.03
## The df corrected root mean square of the residuals is 0.04
##
## The harmonic number of observations is 1160 with the empirical chi square 891.24 with prob < 6.4e-61
## The total number of observations was 1160 with MLE Chi Square = 1532.89 with prob < 1e-165
##
## Tucker Lewis Index of factoring reliability = 0.903
## RMSEA index = 0.06 and the 90 % confidence intervals are 0.057 0.063
## BIC = -555.74
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## MR2 MR1 MR3 MR4
## Correlation of scores with factors 0.96 0.95 0.94 0.92
## Multiple R square of scores with factors 0.92 0.90 0.88 0.85
## Minimum correlation of possible factor scores 0.84 0.81 0.76 0.69fa2latex(fa(purposescales_cor,4,rotate="TargetQ", n.obs = 1160, Target=Targ_key),heading="Table 7 Factor Based on theory")## % Called in the psych package fa2latex % Called in the psych package fa(purposescales_cor, 4, rotate = "TargetQ", n.obs = 1160, Target = Targ_key) % Called in the psych package Table 7 Factor Based on theory
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## \label{default}
## \end{table}#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$null.dof))## [1] 0.9292107Target rotation based on content anlysis 7 factors
all_surveys<-read.csv("allsurveysYT1.csv")
purposescales<-select(all_surveys,
#Factor 1 Present Focused:
PWB_1, PWB_2,
#Factor 2 Understanding Self and Life:
APSI_2, APSI_4, APSI_6, LET_6, PWB_8, PWB_4,
#Factor 3 Making Plans:
APSI_7, APSI_8, PWB_7, PWB_5, PWB_6, PWB_9,
#Factor 4 Meaningful activities:
LET_4, PWB_3, LET_2, LET_3, LET_5,
#Factor 5 Values and Morals:
APSI_5, APSI_3,
#Factor 6 Have Purpose:
APSI_1, LET_1, MLQ_4, MLQ_5, MLQ_6, MLQ_9, MLQ_1,
#Factor 7 Searching for meaning
MLQ_2, MLQ_3, MLQ_7, MLQ_8, MLQ_10
)
purposescales$PWB_1 <- 7- purposescales$PWB_1
purposescales$PWB_2 <- 7- purposescales$PWB_2
purposescales$PWB_3 <- 7- purposescales$PWB_3
purposescales$PWB_4 <- 7- purposescales$PWB_4
purposescales$PWB_9 <- 7- purposescales$PWB_9
purposescales$MLQ_9 <- 8- purposescales$MLQ_9
purposescales$LET_1 <- 6- purposescales$LET_1
purposescales$LET_3 <- 6- purposescales$LET_3
purposescales$LET_5 <- 6- purposescales$LET_5
purposescales<- data.frame(apply(purposescales,2, as.numeric))
library(GPArotation)
library(psych)
library(dplyr)
purposescales<-tbl_df(purposescales)
purposescales## Source: local data frame [1,160 x 33]
##
## PWB_1 PWB_2 APSI_2 APSI_4 APSI_6 LET_6 PWB_8 PWB_4 APSI_7 APSI_8 PWB_7
## 1 4 3 4 4 4 5 3 2 4 4 4
## 2 4 5 3 5 3 5 2 2 4 4 3
## 3 5 6 4 3 3 5 3 6 4 3 6
## 4 2 2 4 4 4 4 4 4 4 3 5
## 5 2 2 3 3 3 4 3 3 2 3 2
## 6 5 4 4 4 2 4 4 5 5 3 3
## 7 2 2 2 3 4 5 3 2 2 2 3
## 8 6 6 3 3 3 5 4 1 3 1 4
## 9 5 5 5 4 2 5 5 5 4 5 5
## 10 6 6 2 3 3 5 3 3 3 4 6
## .. ... ... ... ... ... ... ... ... ... ... ...
## Variables not shown: PWB_5 (dbl), PWB_6 (dbl), PWB_9 (dbl), LET_4 (dbl),
## PWB_3 (dbl), LET_2 (dbl), LET_3 (dbl), LET_5 (dbl), APSI_5 (dbl), APSI_3
## (dbl), APSI_1 (dbl), LET_1 (dbl), MLQ_4 (dbl), MLQ_5 (dbl), MLQ_6 (dbl),
## MLQ_9 (dbl), MLQ_1 (dbl), MLQ_2 (dbl), MLQ_3 (dbl), MLQ_7 (dbl), MLQ_8
## (dbl), MLQ_10 (dbl)str(purposescales)## Classes 'tbl_df', 'tbl' and 'data.frame': 1160 obs. of 33 variables:
## $ PWB_1 : num 4 4 5 2 2 5 2 6 5 6 ...
## $ PWB_2 : num 3 5 6 2 2 4 2 6 5 6 ...
## $ APSI_2: num 4 3 4 4 3 4 2 3 5 2 ...
## $ APSI_4: num 4 5 3 4 3 4 3 3 4 3 ...
## $ APSI_6: num 4 3 3 4 3 2 4 3 2 3 ...
## $ LET_6 : num 5 5 5 4 4 4 5 5 5 5 ...
## $ PWB_8 : num 3 2 3 4 3 4 3 4 5 3 ...
## $ PWB_4 : num 2 2 6 4 3 5 2 1 5 3 ...
## $ APSI_7: num 4 4 4 4 2 5 2 3 4 3 ...
## $ APSI_8: num 4 4 3 3 3 3 2 1 5 4 ...
## $ PWB_7 : num 4 3 6 5 2 3 3 4 5 6 ...
## $ PWB_5 : num 4 2 1 3 4 3 1 2 1 2 ...
## $ PWB_6 : num 5 5 4 4 3 4 4 4 5 6 ...
## $ PWB_9 : num 6 5 6 4 4 6 3 6 6 6 ...
## $ LET_4 : num 5 4 4 4 4 5 3 4 5 5 ...
## $ PWB_3 : num 5 5 5 4 3 6 5 5 5 3 ...
## $ LET_2 : num 4 3 4 4 2 5 4 4 4 3 ...
## $ LET_3 : num 4 4 3 4 3 5 2 3 5 5 ...
## $ LET_5 : num 5 4 4 4 2 5 3 4 5 5 ...
## $ APSI_5: num 4 4 3 5 4 4 4 5 4 5 ...
## $ APSI_3: num 4 4 4 5 4 4 4 4 5 2 ...
## $ APSI_1: num 2 4 3 4 3 3 2 3 4 2 ...
## $ LET_1 : num 4 3 3 1 3 5 3 3 5 3 ...
## $ MLQ_4 : num 5 5 4 3 4 4 3 5 7 3 ...
## $ MLQ_5 : num 6 4 4 5 4 5 6 5 6 5 ...
## $ MLQ_6 : num 4 3 4 5 4 5 3 4 6 1 ...
## $ MLQ_9 : num 3 3 4 3 5 7 5 4 7 6 ...
## $ MLQ_1 : num 4 3 4 5 4 5 6 3 6 1 ...
## $ MLQ_2 : num 7 5 7 6 6 5 2 7 5 7 ...
## $ MLQ_3 : num 7 5 5 7 5 3 2 7 2 1 ...
## $ MLQ_7 : num 5 5 4 5 5 3 5 5 5 5 ...
## $ MLQ_8 : num 7 4 5 5 5 4 4 7 7 5 ...
## $ MLQ_10: num 7 5 4 6 5 3 4 5 2 1 ...colnames(purposescales) <- c("1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32","33")
#Target rotation: choose "simple structure" a priori and can be applied to oblique and orthogonal rotation based on
#what paper says facotrs should be purposescales
Targ_key <- make.keys(33,list(f1=1:2,f2=3:8, f3=9:14,f4=15:19, f5=20:21,f6=22:28, f7=29:33))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
purposescales_cor <- corFiml(purposescales)
out_targetQ <- fa(purposescales_cor,7,rotate="TargetQ", n.obs = 1160, Target=Targ_key) #TargetT for orthogonal rotation
out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR6 MR7 MR4 MR2 MR1 MR5 MR3
## 1 0.741 0.102
## 2 0.103 0.638 -0.186 0.273
## 3 0.265 0.344 -0.148 0.210 0.237
## 4 0.256 -0.154 0.498 0.243
## 5 -0.358 0.102 -0.407 0.151 -0.239
## 6 0.234 0.105 -0.182 0.417 0.196
## 7 0.186 0.263 0.278 0.125
## 8 0.293 0.246 0.315 0.280 -0.158 0.189
## 9 0.116 0.113 -0.155 0.506 0.303
## 10 0.194 -0.137 0.571 0.187
## 11 0.294 0.186 0.238 0.235
## 12 -0.295 -0.431 -0.172
## 13 0.172 0.101 0.268 0.209 0.411 -0.204
## 14 -0.184 0.151 0.127 0.407 0.354
## 15 0.149 -0.240 -0.105 0.338 0.422
## 16 0.115 0.517 -0.153 0.237 0.122 0.199
## 17 0.272 -0.145 -0.113 0.155 0.548
## 18 0.827
## 19 0.658 0.102 0.108
## 20 0.352 -0.114 0.382 0.236
## 21 0.157 0.527 -0.189
## 22 0.349 -0.118 0.428 -0.133 0.123 0.204
## 23 0.344 -0.154 0.509 0.120
## 24 0.793 0.108
## 25 0.772 -0.101
## 26 0.803 -0.112 0.154 -0.111
## 27 0.339 -0.136 0.461 0.102
## 28 0.960 -0.109 -0.136 -0.194
## 29 0.829
## 30 0.752
## 31 0.145 0.727 -0.107
## 32 0.753 0.118
## 33 -0.102 0.815 -0.114
##
## MR6 MR7 MR4 MR2 MR1 MR5 MR3
## SS loadings 3.971 3.133 2.531 1.676 1.658 1.464 1.229
## Proportion Var 0.120 0.095 0.077 0.051 0.050 0.044 0.037
## Cumulative Var 0.120 0.215 0.292 0.343 0.393 0.437 0.475
##
## $score.cor
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 1.0000000 0.08387500 0.40864514 0.64386635 0.24605268 0.59896458
## [2,] 0.0838750 1.00000000 -0.18928899 0.11661378 -0.12074667 0.07689791
## [3,] 0.4086451 -0.18928899 1.00000000 -0.04207668 0.61971541 0.48927974
## [4,] 0.6438664 0.11661378 -0.04207668 1.00000000 -0.01070144 0.45472856
## [5,] 0.2460527 -0.12074667 0.61971541 -0.01070144 1.00000000 0.36131603
## [6,] 0.5989646 0.07689791 0.48927974 0.45472856 0.36131603 1.00000000
## [7,] 0.5118378 0.07270189 0.05112551 0.59483568 0.03067560 0.36494863
## [,7]
## [1,] 0.51183779
## [2,] 0.07270189
## [3,] 0.05112551
## [4,] 0.59483568
## [5,] 0.03067560
## [6,] 0.36494863
## [7,] 1.00000000
##
## $TLI
## [1] 0.936139
##
## $RMSEA
## RMSEA lower upper confidence
## 0.04648843 0.04304837 0.04899023 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = purposescales_cor, nfactors = 7, n.obs = 1160, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR6 MR7 MR4 MR2 MR1 MR5 MR3 h2 u2 com
## 1 -0.08 -0.07 0.01 -0.06 0.74 0.10 -0.07 0.60 0.40 1.1
## 2 0.04 -0.04 -0.07 0.10 0.64 -0.19 0.27 0.45 0.55 1.7
## 3 0.27 -0.04 -0.07 0.34 -0.15 0.21 0.24 0.61 0.39 4.1
## 4 0.26 -0.02 -0.15 0.50 0.00 0.24 0.03 0.67 0.33 2.3
## 5 -0.36 0.10 -0.41 0.15 -0.24 -0.04 0.09 0.64 0.36 3.3
## 6 0.23 0.00 0.11 -0.18 0.07 0.42 0.20 0.47 0.53 2.8
## 7 0.19 -0.03 -0.02 0.26 0.00 0.28 0.12 0.39 0.61 3.2
## 8 0.29 -0.04 0.25 0.31 0.28 -0.16 0.19 0.52 0.48 5.2
## 9 0.12 0.11 -0.15 0.51 0.09 0.30 0.03 0.64 0.36 2.2
## 10 0.19 0.04 -0.14 0.57 0.03 0.19 0.07 0.69 0.31 1.6
## 11 0.29 0.07 -0.10 0.19 -0.02 0.24 0.24 0.51 0.49 4.1
## 12 -0.10 0.02 -0.30 0.09 -0.43 -0.17 -0.07 0.58 0.42 2.5
## 13 0.17 0.10 0.27 -0.02 0.21 0.41 -0.20 0.50 0.50 3.5
## 14 -0.18 0.05 0.15 0.13 0.41 -0.08 0.35 0.32 0.68 3.1
## 15 0.15 0.00 -0.24 -0.11 0.01 0.34 0.42 0.48 0.52 3.0
## 16 0.11 -0.02 0.52 -0.15 0.24 0.12 0.20 0.68 0.32 2.2
## 17 0.27 0.01 -0.14 -0.05 -0.11 0.16 0.55 0.59 0.41 1.9
## 18 0.06 -0.03 0.83 0.01 -0.02 0.04 0.09 0.72 0.28 1.0
## 19 0.08 0.00 0.66 -0.10 0.10 0.11 -0.02 0.63 0.37 1.2
## 20 -0.06 0.09 -0.05 0.35 -0.11 0.38 0.24 0.52 0.48 3.1
## 21 0.07 0.04 0.16 -0.03 0.08 0.53 -0.19 0.36 0.64 1.6
## 22 0.35 0.00 -0.12 0.43 -0.13 0.12 0.20 0.72 0.28 3.0
## 23 0.34 -0.15 0.51 0.04 0.00 -0.07 0.12 0.53 0.47 2.2
## 24 0.79 0.08 -0.08 0.03 -0.06 -0.08 0.11 0.64 0.36 1.1
## 25 0.77 0.07 0.00 -0.06 0.06 0.07 -0.10 0.62 0.38 1.1
## 26 0.80 0.03 -0.11 0.15 0.00 -0.11 -0.01 0.64 0.36 1.2
## 27 0.34 -0.14 0.46 0.10 0.03 -0.03 0.00 0.46 0.54 2.2
## 28 0.96 0.01 -0.11 -0.14 0.03 -0.01 -0.19 0.71 0.29 1.2
## 29 -0.06 0.83 0.02 -0.03 0.03 -0.08 0.09 0.66 0.34 1.1
## 30 0.07 0.75 0.03 0.01 -0.04 -0.04 0.08 0.57 0.43 1.1
## 31 0.14 0.73 -0.01 -0.03 0.07 -0.04 -0.11 0.54 0.46 1.2
## 32 0.01 0.75 0.12 0.04 -0.02 0.02 0.02 0.55 0.45 1.1
## 33 -0.10 0.81 0.07 -0.11 -0.04 0.05 -0.06 0.68 0.32 1.1
##
## MR6 MR7 MR4 MR2 MR1 MR5 MR3
## SS loadings 4.65 3.17 3.11 2.28 2.10 1.98 1.58
## Proportion Var 0.14 0.10 0.09 0.07 0.06 0.06 0.05
## Cumulative Var 0.14 0.24 0.33 0.40 0.46 0.52 0.57
## Proportion Explained 0.25 0.17 0.16 0.12 0.11 0.11 0.08
## Cumulative Proportion 0.25 0.41 0.58 0.70 0.81 0.92 1.00
##
## With factor correlations of
## MR6 MR7 MR4 MR2 MR1 MR5 MR3
## MR6 1.00 0.04 0.23 0.36 0.22 0.56 0.37
## MR7 0.04 1.00 -0.22 0.08 -0.13 0.13 -0.01
## MR4 0.23 -0.22 1.00 -0.29 0.61 0.05 -0.02
## MR2 0.36 0.08 -0.29 1.00 -0.08 0.30 0.30
## MR1 0.22 -0.13 0.61 -0.08 1.00 0.13 0.03
## MR5 0.56 0.13 0.05 0.30 0.13 1.00 0.31
## MR3 0.37 -0.01 -0.02 0.30 0.03 0.31 1.00
##
## Mean item complexity = 2.2
## Test of the hypothesis that 7 factors are sufficient.
##
## The degrees of freedom for the null model are 528 and the objective function was 18.23 with Chi Square of 20910.11
## The degrees of freedom for the model are 318 and the objective function was 0.96
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.03
##
## The harmonic number of observations is 1160 with the empirical chi square 469.7 with prob < 6.3e-08
## The total number of observations was 1160 with MLE Chi Square = 1098.66 with prob < 1.6e-86
##
## Tucker Lewis Index of factoring reliability = 0.936
## RMSEA index = 0.046 and the 90 % confidence intervals are 0.043 0.049
## BIC = -1145.2
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR6 MR7 MR4 MR2 MR1
## Correlation of scores with factors 0.96 0.94 0.94 0.90 0.90
## Multiple R square of scores with factors 0.92 0.89 0.89 0.81 0.81
## Minimum correlation of possible factor scores 0.84 0.78 0.77 0.63 0.62
## MR5 MR3
## Correlation of scores with factors 0.87 0.86
## Multiple R square of scores with factors 0.77 0.73
## Minimum correlation of possible factor scores 0.53 0.47fa2latex(fa(purposescales_cor,7,rotate="TargetQ", n.obs = 1160, Target=Targ_key),heading="Table 7 Factor Based on theory")## % Called in the psych package fa2latex % Called in the psych package fa(purposescales_cor, 7, rotate = "TargetQ", n.obs = 1160, Target = Targ_key) % Called in the psych package Table 7 Factor Based on theory
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## \end{table}#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$null.dof))## [1] 0.9616988Target rotation Droping based on theory 6 factors
all_surveys<-read.csv("allsurveysYT1.csv")
purposescales<-select(all_surveys,
#Factor 1 Present Focused:
PWB_1, PWB_2,
#Factor 2 Understanding Self and Life, Making Plans::
APSI_1, APSI_2, APSI_4, APSI_6, LET_6, PWB_8, PWB_4, APSI_7, APSI_8, PWB_7, PWB_5, PWB_6, PWB_9,
#Factor 3 Meaningful activities:
LET_4, PWB_3, LET_2, LET_3, LET_5,
#Factor 4 Values and Morals:
APSI_5, APSI_3,
#Factor 5 Have Purpose:
LET_1, MLQ_4, MLQ_5, MLQ_6, MLQ_9, MLQ_1,
#Factor 6 Searching for meaning
MLQ_2, MLQ_3, MLQ_7, MLQ_8, MLQ_10
)
purposescales$PWB_1 <- 7- purposescales$PWB_1
purposescales$PWB_2 <- 7- purposescales$PWB_2
purposescales$PWB_3 <- 7- purposescales$PWB_3
purposescales$PWB_4 <- 7- purposescales$PWB_4
purposescales$PWB_9 <- 7- purposescales$PWB_9
purposescales$MLQ_9 <- 8- purposescales$MLQ_9
purposescales$LET_1 <- 6- purposescales$LET_1
purposescales$LET_3 <- 6- purposescales$LET_3
purposescales$LET_5 <- 6- purposescales$LET_5
purposescales<- data.frame(apply(purposescales,2, as.numeric))
library(GPArotation)
library(psych)
library(dplyr)
purposescales<-tbl_df(purposescales)
purposescales## Source: local data frame [1,160 x 33]
##
## PWB_1 PWB_2 APSI_1 APSI_2 APSI_4 APSI_6 LET_6 PWB_8 PWB_4 APSI_7 APSI_8
## 1 4 3 2 4 4 4 5 3 2 4 4
## 2 4 5 4 3 5 3 5 2 2 4 4
## 3 5 6 3 4 3 3 5 3 6 4 3
## 4 2 2 4 4 4 4 4 4 4 4 3
## 5 2 2 3 3 3 3 4 3 3 2 3
## 6 5 4 3 4 4 2 4 4 5 5 3
## 7 2 2 2 2 3 4 5 3 2 2 2
## 8 6 6 3 3 3 3 5 4 1 3 1
## 9 5 5 4 5 4 2 5 5 5 4 5
## 10 6 6 2 2 3 3 5 3 3 3 4
## .. ... ... ... ... ... ... ... ... ... ... ...
## Variables not shown: PWB_7 (dbl), PWB_5 (dbl), PWB_6 (dbl), PWB_9 (dbl),
## LET_4 (dbl), PWB_3 (dbl), LET_2 (dbl), LET_3 (dbl), LET_5 (dbl), APSI_5
## (dbl), APSI_3 (dbl), LET_1 (dbl), MLQ_4 (dbl), MLQ_5 (dbl), MLQ_6 (dbl),
## MLQ_9 (dbl), MLQ_1 (dbl), MLQ_2 (dbl), MLQ_3 (dbl), MLQ_7 (dbl), MLQ_8
## (dbl), MLQ_10 (dbl)str(purposescales)## Classes 'tbl_df', 'tbl' and 'data.frame': 1160 obs. of 33 variables:
## $ PWB_1 : num 4 4 5 2 2 5 2 6 5 6 ...
## $ PWB_2 : num 3 5 6 2 2 4 2 6 5 6 ...
## $ APSI_1: num 2 4 3 4 3 3 2 3 4 2 ...
## $ APSI_2: num 4 3 4 4 3 4 2 3 5 2 ...
## $ APSI_4: num 4 5 3 4 3 4 3 3 4 3 ...
## $ APSI_6: num 4 3 3 4 3 2 4 3 2 3 ...
## $ LET_6 : num 5 5 5 4 4 4 5 5 5 5 ...
## $ PWB_8 : num 3 2 3 4 3 4 3 4 5 3 ...
## $ PWB_4 : num 2 2 6 4 3 5 2 1 5 3 ...
## $ APSI_7: num 4 4 4 4 2 5 2 3 4 3 ...
## $ APSI_8: num 4 4 3 3 3 3 2 1 5 4 ...
## $ PWB_7 : num 4 3 6 5 2 3 3 4 5 6 ...
## $ PWB_5 : num 4 2 1 3 4 3 1 2 1 2 ...
## $ PWB_6 : num 5 5 4 4 3 4 4 4 5 6 ...
## $ PWB_9 : num 6 5 6 4 4 6 3 6 6 6 ...
## $ LET_4 : num 5 4 4 4 4 5 3 4 5 5 ...
## $ PWB_3 : num 5 5 5 4 3 6 5 5 5 3 ...
## $ LET_2 : num 4 3 4 4 2 5 4 4 4 3 ...
## $ LET_3 : num 4 4 3 4 3 5 2 3 5 5 ...
## $ LET_5 : num 5 4 4 4 2 5 3 4 5 5 ...
## $ APSI_5: num 4 4 3 5 4 4 4 5 4 5 ...
## $ APSI_3: num 4 4 4 5 4 4 4 4 5 2 ...
## $ LET_1 : num 4 3 3 1 3 5 3 3 5 3 ...
## $ MLQ_4 : num 5 5 4 3 4 4 3 5 7 3 ...
## $ MLQ_5 : num 6 4 4 5 4 5 6 5 6 5 ...
## $ MLQ_6 : num 4 3 4 5 4 5 3 4 6 1 ...
## $ MLQ_9 : num 3 3 4 3 5 7 5 4 7 6 ...
## $ MLQ_1 : num 4 3 4 5 4 5 6 3 6 1 ...
## $ MLQ_2 : num 7 5 7 6 6 5 2 7 5 7 ...
## $ MLQ_3 : num 7 5 5 7 5 3 2 7 2 1 ...
## $ MLQ_7 : num 5 5 4 5 5 3 5 5 5 5 ...
## $ MLQ_8 : num 7 4 5 5 5 4 4 7 7 5 ...
## $ MLQ_10: num 7 5 4 6 5 3 4 5 2 1 ...colnames(purposescales) <- c("1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32","33")
#Target rotation: choose "simple structure" a priori and can be applied to oblique and orthogonal rotation based on
#what paper says facotrs should be purposescales
Targ_key <- make.keys(33,list(f1=1:2,f2=3:14, f3=15:19, f4=20:21,f5=22:28, f6=29:33))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
purposescales_cor <- corFiml(purposescales)
out_targetQ <- fa(purposescales_cor,6,rotate="TargetQ", n.obs = 1160, Target=Targ_key) #TargetT for orthogonal rotation
out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR2 MR5 MR6 MR3 MR1 MR4
## 1 0.524 0.261
## 2 0.175 0.270 0.471 -0.115
## 3 0.683 0.205 -0.101 -0.139
## 4 0.607 0.132 0.181 -0.168
## 5 0.809 0.144 -0.104
## 6 0.206 -0.437 0.106 -0.172 -0.253 -0.216
## 7 0.121 0.186 0.401 -0.176 0.287
## 8 0.523 0.105 0.126
## 9 0.303 0.256 0.224 0.431 -0.158
## 10 0.850 0.158
## 11 0.850 0.144
## 12 0.501 0.172 0.207 -0.131
## 13 -0.134 -0.312 -0.349 -0.260
## 14 0.142 0.243 0.178 0.481
## 15 0.130 -0.217 0.419 0.385
## 16 0.328 0.408 -0.300
## 17 -0.187 0.174 0.522 0.218 0.190
## 18 0.289 0.503 -0.344 -0.165
## 19 -0.248 0.219 0.407 0.235 0.175
## 20 -0.271 0.221 0.313 0.234 0.267
## 21 0.647 -0.170 0.212 -0.152 0.108
## 22 0.203 0.549
## 23 0.389 -0.173 0.320 0.145
## 24 0.214 0.670 -0.155 -0.175
## 25 0.135 0.713
## 26 0.321 0.689 -0.161
## 27 0.392 -0.160 0.185 0.200
## 28 0.869 -0.141 -0.124
## 29 0.834
## 30 0.757
## 31 0.159 0.734 -0.100
## 32 0.747
## 33 -0.141 0.820
##
## MR2 MR5 MR6 MR3 MR1 MR4
## SS loadings 4.703 3.251 3.179 1.923 1.695 1.152
## Proportion Var 0.143 0.099 0.096 0.058 0.051 0.035
## Cumulative Var 0.143 0.241 0.337 0.396 0.447 0.482
##
## $score.cor
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 1.0000000 0.495778249 0.112073142 0.1958982 -0.0731306 0.2582537
## [2,] 0.4957782 1.000000000 0.006009564 0.6152412 0.3575539 0.4887588
## [3,] 0.1120731 0.006009564 1.000000000 -0.1347144 -0.1475729 0.1016746
## [4,] 0.1958982 0.615241150 -0.134714443 1.0000000 0.6952982 0.4853942
## [5,] -0.0731306 0.357553887 -0.147572857 0.6952982 1.0000000 0.4068429
## [6,] 0.2582537 0.488758819 0.101674585 0.4853942 0.4068429 1.0000000
##
## $TLI
## [1] 0.9227126
##
## $RMSEA
## RMSEA lower upper confidence
## 0.05111220 0.04782935 0.05344921 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = purposescales_cor, nfactors = 6, n.obs = 1160, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR5 MR6 MR3 MR1 MR4 h2 u2 com
## 1 0.03 -0.04 -0.08 0.09 0.52 0.26 0.47 0.53 1.6
## 2 0.18 -0.03 -0.05 0.27 0.47 -0.11 0.34 0.66 2.1
## 3 0.68 0.21 -0.02 0.08 -0.10 -0.14 0.71 0.29 1.4
## 4 0.61 0.13 -0.05 0.18 -0.17 -0.06 0.61 0.39 1.5
## 5 0.81 0.14 -0.05 -0.10 0.06 0.02 0.67 0.33 1.1
## 6 0.21 -0.44 0.11 -0.17 -0.25 -0.22 0.65 0.35 3.3
## 7 0.12 0.19 0.02 0.40 -0.18 0.29 0.45 0.55 3.0
## 8 0.52 0.11 -0.04 0.13 -0.03 0.09 0.39 0.61 1.3
## 9 0.30 0.26 -0.07 0.22 0.43 -0.16 0.52 0.48 3.6
## 10 0.85 0.01 0.08 -0.10 0.16 0.10 0.65 0.35 1.1
## 11 0.85 0.08 0.01 -0.09 0.14 -0.04 0.69 0.31 1.1
## 12 0.50 0.17 0.07 0.21 -0.13 0.01 0.51 0.49 1.8
## 13 0.01 -0.13 0.03 -0.31 -0.35 -0.26 0.57 0.43 3.2
## 14 0.14 0.24 0.09 0.03 0.18 0.48 0.50 0.50 2.1
## 15 0.13 -0.22 0.03 0.42 0.38 -0.09 0.31 0.69 2.8
## 16 0.33 0.00 0.03 0.41 -0.30 0.02 0.41 0.59 2.8
## 17 -0.19 0.17 -0.02 0.52 0.22 0.19 0.69 0.31 2.2
## 18 0.29 0.10 0.04 0.50 -0.34 -0.17 0.56 0.44 2.8
## 19 -0.25 0.22 -0.07 0.41 0.24 0.18 0.62 0.38 3.6
## 20 -0.27 0.22 -0.03 0.31 0.23 0.27 0.60 0.40 4.7
## 21 0.65 -0.17 0.07 0.21 -0.15 0.11 0.53 0.47 1.6
## 22 0.20 0.10 0.03 0.03 -0.04 0.55 0.39 0.61 1.4
## 23 -0.09 0.39 -0.17 0.32 0.15 -0.01 0.51 0.49 2.8
## 24 0.21 0.67 0.09 0.08 -0.15 -0.17 0.64 0.36 1.6
## 25 0.14 0.71 0.08 -0.02 -0.05 0.09 0.61 0.39 1.1
## 26 0.32 0.69 0.04 -0.08 -0.03 -0.16 0.64 0.36 1.6
## 27 -0.01 0.39 -0.16 0.19 0.20 0.05 0.44 0.56 2.4
## 28 0.07 0.87 0.04 -0.14 -0.12 0.04 0.69 0.31 1.1
## 29 -0.05 -0.06 0.83 0.09 0.06 -0.07 0.66 0.34 1.1
## 30 0.02 0.05 0.76 0.08 -0.01 -0.07 0.57 0.43 1.1
## 31 -0.02 0.16 0.73 -0.10 0.10 0.02 0.55 0.45 1.2
## 32 0.03 0.03 0.75 0.06 0.06 0.03 0.55 0.45 1.0
## 33 -0.14 -0.05 0.82 0.01 -0.01 0.09 0.68 0.32 1.1
##
## MR2 MR5 MR6 MR3 MR1 MR4
## SS loadings 5.16 3.91 3.20 2.53 2.08 1.46
## Proportion Var 0.16 0.12 0.10 0.08 0.06 0.04
## Cumulative Var 0.16 0.27 0.37 0.45 0.51 0.56
## Proportion Explained 0.28 0.21 0.17 0.14 0.11 0.08
## Cumulative Proportion 0.28 0.49 0.67 0.81 0.92 1.00
##
## With factor correlations of
## MR2 MR5 MR6 MR3 MR1 MR4
## MR2 1.00 0.32 0.17 0.21 -0.33 -0.11
## MR5 0.32 1.00 -0.05 0.52 0.22 0.32
## MR6 0.17 -0.05 1.00 -0.15 -0.20 0.00
## MR3 0.21 0.52 -0.15 1.00 0.32 0.28
## MR1 -0.33 0.22 -0.20 0.32 1.00 0.28
## MR4 -0.11 0.32 0.00 0.28 0.28 1.00
##
## Mean item complexity = 2
## Test of the hypothesis that 6 factors are sufficient.
##
## The degrees of freedom for the null model are 528 and the objective function was 18.23 with Chi Square of 20910.11
## The degrees of freedom for the model are 345 and the objective function was 1.2
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.03
##
## The harmonic number of observations is 1160 with the empirical chi square 649.93 with prob < 5.8e-21
## The total number of observations was 1160 with MLE Chi Square = 1370.62 with prob < 4.4e-122
##
## Tucker Lewis Index of factoring reliability = 0.923
## RMSEA index = 0.051 and the 90 % confidence intervals are 0.048 0.053
## BIC = -1063.76
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## MR2 MR5 MR6 MR3 MR1
## Correlation of scores with factors 0.96 0.95 0.94 0.91 0.89
## Multiple R square of scores with factors 0.93 0.90 0.89 0.82 0.79
## Minimum correlation of possible factor scores 0.85 0.80 0.78 0.65 0.57
## MR4
## Correlation of scores with factors 0.84
## Multiple R square of scores with factors 0.71
## Minimum correlation of possible factor scores 0.42fa2latex(fa(purposescales_cor,6,rotate="TargetQ", n.obs = 1160, Target=Targ_key),heading="Table 7 Factor Based on theory")## % Called in the psych package fa2latex % Called in the psych package fa(purposescales_cor, 6, rotate = "TargetQ", n.obs = 1160, Target = Targ_key) % Called in the psych package Table 7 Factor Based on theory
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## \hline \cr SS loadings & 5.16 & 3.91 & 3.2 & 2.53 & 2.08 & 1.46 & \cr
## \cr
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## \label{default}
## \end{table}#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$null.dof))## [1] 0.9496804Target rotation Droping based on theory 5 factors and results of previous analysis of 6 factors
all_surveys<-read.csv("allsurveysYT1.csv")
purposescales<-select(all_surveys,
#Factor 1 Present Focused:
PWB_1, PWB_2, PWB_4, PWB_5,
#Factor 2 Understanding Self and Life, Making Plans:
APSI_2, APSI_4, PWB_8, APSI_7, APSI_8, PWB_7, APSI_5, APSI_1,
#Factor 3 Meaningful activities:
LET_4, PWB_3, LET_2, LET_3, LET_5, LET_6, PWB_9, LET_1,
#Factor 5 Have Purpose:
MLQ_4, MLQ_5, MLQ_6, MLQ_9, MLQ_1, APSI_6,
#Factor 6 Searching for meaning
MLQ_2, MLQ_3, MLQ_7, MLQ_8, MLQ_10)
purposescales$PWB_1 <- 7- purposescales$PWB_1
purposescales$PWB_2 <- 7- purposescales$PWB_2
purposescales$PWB_3 <- 7- purposescales$PWB_3
purposescales$PWB_9 <- 7- purposescales$PWB_9
purposescales$MLQ_9 <- 8- purposescales$MLQ_9
purposescales$LET_1 <- 6- purposescales$LET_1
purposescales$LET_3 <- 6- purposescales$LET_3
purposescales$LET_5 <- 6- purposescales$LET_5
purposescales<- data.frame(apply(purposescales,2, as.numeric))
library(GPArotation)
library(psych)
library(dplyr)
purposescales<-tbl_df(purposescales)
purposescales## Source: local data frame [1,160 x 31]
##
## PWB_1 PWB_2 PWB_4 PWB_5 APSI_2 APSI_4 PWB_8 APSI_7 APSI_8 PWB_7 APSI_5
## 1 4 3 5 4 4 4 3 4 4 4 4
## 2 4 5 5 2 3 5 2 4 4 3 4
## 3 5 6 1 1 4 3 3 4 3 6 3
## 4 2 2 3 3 4 4 4 4 3 5 5
## 5 2 2 4 4 3 3 3 2 3 2 4
## 6 5 4 2 3 4 4 4 5 3 3 4
## 7 2 2 5 1 2 3 3 2 2 3 4
## 8 6 6 6 2 3 3 4 3 1 4 5
## 9 5 5 2 1 5 4 5 4 5 5 4
## 10 6 6 4 2 2 3 3 3 4 6 5
## .. ... ... ... ... ... ... ... ... ... ... ...
## Variables not shown: APSI_1 (dbl), LET_4 (dbl), PWB_3 (dbl), LET_2 (dbl),
## LET_3 (dbl), LET_5 (dbl), LET_6 (dbl), PWB_9 (dbl), LET_1 (dbl), MLQ_4
## (dbl), MLQ_5 (dbl), MLQ_6 (dbl), MLQ_9 (dbl), MLQ_1 (dbl), APSI_6 (dbl),
## MLQ_2 (dbl), MLQ_3 (dbl), MLQ_7 (dbl), MLQ_8 (dbl), MLQ_10 (dbl)str(purposescales)## Classes 'tbl_df', 'tbl' and 'data.frame': 1160 obs. of 31 variables:
## $ PWB_1 : num 4 4 5 2 2 5 2 6 5 6 ...
## $ PWB_2 : num 3 5 6 2 2 4 2 6 5 6 ...
## $ PWB_4 : num 5 5 1 3 4 2 5 6 2 4 ...
## $ PWB_5 : num 4 2 1 3 4 3 1 2 1 2 ...
## $ APSI_2: num 4 3 4 4 3 4 2 3 5 2 ...
## $ APSI_4: num 4 5 3 4 3 4 3 3 4 3 ...
## $ PWB_8 : num 3 2 3 4 3 4 3 4 5 3 ...
## $ APSI_7: num 4 4 4 4 2 5 2 3 4 3 ...
## $ APSI_8: num 4 4 3 3 3 3 2 1 5 4 ...
## $ PWB_7 : num 4 3 6 5 2 3 3 4 5 6 ...
## $ APSI_5: num 4 4 3 5 4 4 4 5 4 5 ...
## $ APSI_1: num 2 4 3 4 3 3 2 3 4 2 ...
## $ LET_4 : num 5 4 4 4 4 5 3 4 5 5 ...
## $ PWB_3 : num 5 5 5 4 3 6 5 5 5 3 ...
## $ LET_2 : num 4 3 4 4 2 5 4 4 4 3 ...
## $ LET_3 : num 4 4 3 4 3 5 2 3 5 5 ...
## $ LET_5 : num 5 4 4 4 2 5 3 4 5 5 ...
## $ LET_6 : num 5 5 5 4 4 4 5 5 5 5 ...
## $ PWB_9 : num 6 5 6 4 4 6 3 6 6 6 ...
## $ LET_1 : num 4 3 3 1 3 5 3 3 5 3 ...
## $ MLQ_4 : num 5 5 4 3 4 4 3 5 7 3 ...
## $ MLQ_5 : num 6 4 4 5 4 5 6 5 6 5 ...
## $ MLQ_6 : num 4 3 4 5 4 5 3 4 6 1 ...
## $ MLQ_9 : num 3 3 4 3 5 7 5 4 7 6 ...
## $ MLQ_1 : num 4 3 4 5 4 5 6 3 6 1 ...
## $ APSI_6: num 4 3 3 4 3 2 4 3 2 3 ...
## $ MLQ_2 : num 7 5 7 6 6 5 2 7 5 7 ...
## $ MLQ_3 : num 7 5 5 7 5 3 2 7 2 1 ...
## $ MLQ_7 : num 5 5 4 5 5 3 5 5 5 5 ...
## $ MLQ_8 : num 7 4 5 5 5 4 4 7 7 5 ...
## $ MLQ_10: num 7 5 4 6 5 3 4 5 2 1 ...colnames(purposescales) <- c("1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31")
#Target rotation: choose "simple structure" a priori and can be applied to oblique and orthogonal rotation based on
#what paper says facotrs should be purposescales
Targ_key <- make.keys(31,list(f1=1:4,f2=5:12, f3=13:20, f4=21:26,f5=27:31))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
purposescales_cor <- corFiml(purposescales)
out_targetQ <- fa(purposescales_cor,5,rotate="TargetQ", n.obs = 1160, Target=Targ_key) #TargetT for orthogonal rotation
out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR2 MR5 MR4 MR3 MR1
## 1 0.136 0.591
## 2 0.224 0.205 0.442
## 3 -0.349 -0.186 -0.175 -0.402
## 4 -0.118 -0.390 -0.432
## 5 0.648 0.102 0.163 -0.132
## 6 0.805 0.175 -0.128 0.127
## 7 0.516 0.100 0.161
## 8 0.830 0.103 -0.120 0.233
## 9 0.862 0.101 -0.138 0.185
## 10 0.518 0.132 0.242 -0.119
## 11 0.664 -0.149 0.190
## 12 0.731 0.176
## 13 0.368 0.453 -0.281
## 14 -0.183 0.104 0.611 0.275
## 15 0.373 0.512 -0.380
## 16 -0.257 0.161 0.495 0.279
## 17 -0.309 0.194 0.425 0.298
## 18 0.165 0.516
## 19 0.200 -0.277 0.343 0.373
## 20 -0.173 0.308 0.374 0.146
## 21 0.252 0.591 0.105 -0.178
## 22 0.697
## 23 0.334 0.637
## 24 -0.149 0.352 0.226 0.239
## 25 0.847 -0.102
## 26 0.253 -0.414 -0.280 -0.312
## 27 0.807
## 28 0.739
## 29 0.727 0.172
## 30 0.742
## 31 -0.134 0.823
##
## MR2 MR5 MR4 MR3 MR1
## SS loadings 4.985 3.076 2.763 2.401 1.884
## Proportion Var 0.161 0.099 0.089 0.077 0.061
## Cumulative Var 0.161 0.260 0.349 0.427 0.487
##
## $score.cor
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.00000000 0.1120504 0.6172364 0.2221590 -0.02122252
## [2,] 0.11205039 1.0000000 0.1082917 -0.1644822 -0.14549440
## [3,] 0.61723637 0.1082917 1.0000000 0.4847094 0.24749080
## [4,] 0.22215902 -0.1644822 0.4847094 1.0000000 0.74769162
## [5,] -0.02122252 -0.1454944 0.2474908 0.7476916 1.00000000
##
## $TLI
## [1] 0.9299961
##
## $RMSEA
## RMSEA lower upper confidence
## 0.05028266 0.04692984 0.05277840 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = purposescales_cor, nfactors = 5, n.obs = 1160, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR5 MR4 MR3 MR1 h2 u2 com
## 1 -0.03 -0.03 -0.01 0.14 0.59 0.45 0.55 1.1
## 2 0.22 -0.06 -0.09 0.20 0.44 0.28 0.72 2.1
## 3 -0.35 0.09 -0.19 -0.18 -0.40 0.46 0.54 2.9
## 4 0.04 -0.02 -0.12 -0.39 -0.43 0.56 0.44 2.2
## 5 0.65 -0.06 0.10 0.16 -0.13 0.61 0.39 1.3
## 6 0.80 -0.03 0.18 -0.13 0.13 0.66 0.34 1.2
## 7 0.52 -0.03 0.10 0.16 0.02 0.38 0.62 1.3
## 8 0.83 0.10 0.07 -0.12 0.23 0.63 0.37 1.2
## 9 0.86 0.01 0.10 -0.14 0.19 0.68 0.32 1.2
## 10 0.52 0.06 0.13 0.24 -0.12 0.51 0.49 1.7
## 11 0.66 0.08 -0.15 0.19 -0.06 0.50 0.50 1.3
## 12 0.73 -0.04 0.18 0.04 -0.09 0.71 0.29 1.2
## 13 0.37 0.04 -0.07 0.45 -0.28 0.42 0.58 2.7
## 14 -0.18 0.00 0.10 0.61 0.28 0.69 0.31 1.7
## 15 0.37 0.01 -0.02 0.51 -0.38 0.55 0.45 2.7
## 16 -0.26 -0.05 0.16 0.50 0.28 0.62 0.38 2.4
## 17 -0.31 0.01 0.19 0.43 0.30 0.59 0.41 3.2
## 18 0.10 0.06 0.16 0.52 -0.07 0.40 0.60 1.4
## 19 0.20 0.02 -0.28 0.34 0.37 0.26 0.74 3.4
## 20 -0.08 -0.17 0.31 0.37 0.15 0.49 0.51 2.9
## 21 0.25 0.07 0.59 0.10 -0.18 0.62 0.38 1.7
## 22 0.10 0.10 0.70 0.07 -0.01 0.61 0.39 1.1
## 23 0.33 0.02 0.64 -0.06 -0.06 0.62 0.38 1.6
## 24 -0.02 -0.15 0.35 0.23 0.24 0.44 0.56 3.0
## 25 0.02 0.04 0.85 -0.04 -0.10 0.68 0.32 1.0
## 26 0.25 0.07 -0.41 -0.28 -0.31 0.64 0.36 3.5
## 27 -0.01 0.81 -0.08 0.04 0.01 0.65 0.35 1.0
## 28 0.06 0.74 0.03 0.05 -0.04 0.57 0.43 1.0
## 29 -0.02 0.73 0.17 -0.10 0.08 0.54 0.46 1.2
## 30 0.06 0.74 0.03 0.05 0.06 0.55 0.45 1.0
## 31 -0.13 0.82 -0.03 0.01 0.00 0.67 0.33 1.1
##
## MR2 MR5 MR4 MR3 MR1
## SS loadings 5.32 3.11 3.34 3.03 2.25
## Proportion Var 0.17 0.10 0.11 0.10 0.07
## Cumulative Var 0.17 0.27 0.38 0.48 0.55
## Proportion Explained 0.31 0.18 0.20 0.18 0.13
## Cumulative Proportion 0.31 0.49 0.69 0.87 1.00
##
## With factor correlations of
## MR2 MR5 MR4 MR3 MR1
## MR2 1.00 0.13 0.34 0.18 -0.35
## MR5 0.13 1.00 -0.03 -0.13 -0.18
## MR4 0.34 -0.03 1.00 0.55 0.20
## MR3 0.18 -0.13 0.55 1.00 0.40
## MR1 -0.35 -0.18 0.20 0.40 1.00
##
## Mean item complexity = 1.8
## Test of the hypothesis that 5 factors are sufficient.
##
## The degrees of freedom for the null model are 465 and the objective function was 17.13 with Chi Square of 19660.29
## The degrees of freedom for the model are 320 and the objective function was 1.09
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.03
##
## The harmonic number of observations is 1160 with the empirical chi square 611.68 with prob < 1.6e-20
## The total number of observations was 1160 with MLE Chi Square = 1241.98 with prob < 1.2e-108
##
## Tucker Lewis Index of factoring reliability = 0.93
## RMSEA index = 0.05 and the 90 % confidence intervals are 0.047 0.053
## BIC = -1016
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## MR2 MR5 MR4 MR3 MR1
## Correlation of scores with factors 0.96 0.94 0.94 0.93 0.90
## Multiple R square of scores with factors 0.93 0.88 0.88 0.86 0.81
## Minimum correlation of possible factor scores 0.86 0.77 0.77 0.71 0.63fa2latex(fa(purposescales_cor,5,rotate="TargetQ", n.obs = 1160, Target=Targ_key),heading="Table 7 Factor Based on theory")## % Called in the psych package fa2latex % Called in the psych package fa(purposescales_cor, 5, rotate = "TargetQ", n.obs = 1160, Target = Targ_key) % Called in the psych package Table 7 Factor Based on theory
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## \end{table}#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$null.dof))## [1] 0.9519686Target rotation based on theory 5 factors and number 19 (PWB_9) was changed to factor 1 where it loaded better in the previous analysis
all_surveys<-read.csv("allsurveysYT1.csv")
purposescales<-select(all_surveys,
#Factor 1 Present Focused:
PWB_1, PWB_2, PWB_4, PWB_5,PWB_9,
#Factor 2 Understanding Self and Life, Making Plans:
APSI_2, APSI_4, PWB_8, APSI_7, APSI_8, PWB_7, APSI_5, APSI_1,
#Factor 3 Meaningful activities:
LET_4, PWB_3, LET_2, LET_3, LET_5, LET_6, LET_1,
#Factor 5 Have Purpose:
MLQ_4, MLQ_5, MLQ_6, MLQ_9, MLQ_1, APSI_6,
#Factor 6 Searching for meaning
MLQ_2, MLQ_3, MLQ_7, MLQ_8, MLQ_10)
purposescales$PWB_1 <- 7- purposescales$PWB_1
purposescales$PWB_2 <- 7- purposescales$PWB_2
purposescales$PWB_3 <- 7- purposescales$PWB_3
purposescales$PWB_9 <- 7- purposescales$PWB_9
purposescales$MLQ_9 <- 8- purposescales$MLQ_9
purposescales$LET_1 <- 6- purposescales$LET_1
purposescales$LET_3 <- 6- purposescales$LET_3
purposescales$LET_5 <- 6- purposescales$LET_5
purposescales<- data.frame(apply(purposescales,2, as.numeric))
library(GPArotation)
library(psych)
library(dplyr)
purposescales<-tbl_df(purposescales)
purposescales## Source: local data frame [1,160 x 31]
##
## PWB_1 PWB_2 PWB_4 PWB_5 PWB_9 APSI_2 APSI_4 PWB_8 APSI_7 APSI_8 PWB_7
## 1 4 3 5 4 6 4 4 3 4 4 4
## 2 4 5 5 2 5 3 5 2 4 4 3
## 3 5 6 1 1 6 4 3 3 4 3 6
## 4 2 2 3 3 4 4 4 4 4 3 5
## 5 2 2 4 4 4 3 3 3 2 3 2
## 6 5 4 2 3 6 4 4 4 5 3 3
## 7 2 2 5 1 3 2 3 3 2 2 3
## 8 6 6 6 2 6 3 3 4 3 1 4
## 9 5 5 2 1 6 5 4 5 4 5 5
## 10 6 6 4 2 6 2 3 3 3 4 6
## .. ... ... ... ... ... ... ... ... ... ... ...
## Variables not shown: APSI_5 (dbl), APSI_1 (dbl), LET_4 (dbl), PWB_3 (dbl),
## LET_2 (dbl), LET_3 (dbl), LET_5 (dbl), LET_6 (dbl), LET_1 (dbl), MLQ_4
## (dbl), MLQ_5 (dbl), MLQ_6 (dbl), MLQ_9 (dbl), MLQ_1 (dbl), APSI_6 (dbl),
## MLQ_2 (dbl), MLQ_3 (dbl), MLQ_7 (dbl), MLQ_8 (dbl), MLQ_10 (dbl)str(purposescales)## Classes 'tbl_df', 'tbl' and 'data.frame': 1160 obs. of 31 variables:
## $ PWB_1 : num 4 4 5 2 2 5 2 6 5 6 ...
## $ PWB_2 : num 3 5 6 2 2 4 2 6 5 6 ...
## $ PWB_4 : num 5 5 1 3 4 2 5 6 2 4 ...
## $ PWB_5 : num 4 2 1 3 4 3 1 2 1 2 ...
## $ PWB_9 : num 6 5 6 4 4 6 3 6 6 6 ...
## $ APSI_2: num 4 3 4 4 3 4 2 3 5 2 ...
## $ APSI_4: num 4 5 3 4 3 4 3 3 4 3 ...
## $ PWB_8 : num 3 2 3 4 3 4 3 4 5 3 ...
## $ APSI_7: num 4 4 4 4 2 5 2 3 4 3 ...
## $ APSI_8: num 4 4 3 3 3 3 2 1 5 4 ...
## $ PWB_7 : num 4 3 6 5 2 3 3 4 5 6 ...
## $ APSI_5: num 4 4 3 5 4 4 4 5 4 5 ...
## $ APSI_1: num 2 4 3 4 3 3 2 3 4 2 ...
## $ LET_4 : num 5 4 4 4 4 5 3 4 5 5 ...
## $ PWB_3 : num 5 5 5 4 3 6 5 5 5 3 ...
## $ LET_2 : num 4 3 4 4 2 5 4 4 4 3 ...
## $ LET_3 : num 4 4 3 4 3 5 2 3 5 5 ...
## $ LET_5 : num 5 4 4 4 2 5 3 4 5 5 ...
## $ LET_6 : num 5 5 5 4 4 4 5 5 5 5 ...
## $ LET_1 : num 4 3 3 1 3 5 3 3 5 3 ...
## $ MLQ_4 : num 5 5 4 3 4 4 3 5 7 3 ...
## $ MLQ_5 : num 6 4 4 5 4 5 6 5 6 5 ...
## $ MLQ_6 : num 4 3 4 5 4 5 3 4 6 1 ...
## $ MLQ_9 : num 3 3 4 3 5 7 5 4 7 6 ...
## $ MLQ_1 : num 4 3 4 5 4 5 6 3 6 1 ...
## $ APSI_6: num 4 3 3 4 3 2 4 3 2 3 ...
## $ MLQ_2 : num 7 5 7 6 6 5 2 7 5 7 ...
## $ MLQ_3 : num 7 5 5 7 5 3 2 7 2 1 ...
## $ MLQ_7 : num 5 5 4 5 5 3 5 5 5 5 ...
## $ MLQ_8 : num 7 4 5 5 5 4 4 7 7 5 ...
## $ MLQ_10: num 7 5 4 6 5 3 4 5 2 1 ...colnames(purposescales) <- c("1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31")
#Target rotation: choose "simple structure" a priori and can be applied to oblique and orthogonal rotation based on
#what paper says facotrs should be purposescales
Targ_key <- make.keys(31,list(f1=1:5,f2=6:13, f3=14:20, f4=21:26,f5=27:31))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
purposescales_cor <- corFiml(purposescales)
out_targetQ <- fa(purposescales_cor,5,rotate="TargetQ", n.obs = 1160, Target=Targ_key) #TargetT for orthogonal rotation
out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR2 MR5 MR4 MR1 MR3
## 1 0.639
## 2 0.228 0.505 0.123
## 3 -0.355 -0.197 -0.441 -0.113
## 4 -0.132 -0.498 -0.304
## 5 0.198 -0.267 0.466 0.257
## 6 0.647 -0.106 0.185
## 7 0.818 0.173 0.124 -0.126
## 8 0.518 0.158
## 9 0.845 0.106 0.246 -0.139
## 10 0.877 0.101 0.191 -0.147
## 11 0.515 0.122 0.257
## 12 0.664 -0.158 0.190
## 13 0.736 0.166
## 14 0.355 -0.215 0.472
## 15 -0.195 0.113 0.362 0.536
## 16 0.357 -0.315 0.546
## 17 -0.266 0.171 0.343 0.427
## 18 -0.317 0.205 0.349 0.359
## 19 0.159 0.505
## 20 -0.174 0.311 0.180 0.342
## 21 0.251 0.581 -0.210 0.151
## 22 0.100 0.694
## 23 0.340 0.631 -0.108
## 24 -0.148 0.359 0.254 0.190
## 25 0.842 -0.176
## 26 0.255 -0.425 -0.328 -0.229
## 27 0.807
## 28 0.738
## 29 0.728 0.175 -0.104
## 30 0.743
## 31 -0.135 0.822
##
## MR2 MR5 MR4 MR1 MR3
## SS loadings 5.065 3.076 2.752 2.284 2.039
## Proportion Var 0.163 0.099 0.089 0.074 0.066
## Cumulative Var 0.163 0.263 0.351 0.425 0.491
##
## $score.cor
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.00000000 0.11205038 0.57935527 -0.03343417 0.2381114
## [2,] 0.11205038 1.00000000 0.01868692 -0.15457446 -0.1227911
## [3,] 0.57935527 0.01868692 1.00000000 0.42752334 0.5894654
## [4,] -0.03343417 -0.15457446 0.42752334 1.00000000 0.7233879
## [5,] 0.23811141 -0.12279114 0.58946544 0.72338791 1.0000000
##
## $TLI
## [1] 0.9299961
##
## $RMSEA
## RMSEA lower upper confidence
## 0.05028265 0.04692984 0.05277840 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = purposescales_cor, nfactors = 5, n.obs = 1160, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR5 MR4 MR1 MR3 h2 u2 com
## 1 -0.02 -0.03 0.01 0.64 0.04 0.45 0.55 1.0
## 2 0.23 -0.06 -0.08 0.51 0.12 0.28 0.72 1.6
## 3 -0.36 0.09 -0.20 -0.44 -0.11 0.46 0.54 2.6
## 4 0.04 -0.02 -0.13 -0.50 -0.30 0.56 0.44 1.8
## 5 0.20 0.02 -0.27 0.47 0.26 0.26 0.74 2.7
## 6 0.65 -0.06 0.09 -0.11 0.19 0.61 0.39 1.3
## 7 0.82 -0.03 0.17 0.12 -0.13 0.66 0.34 1.2
## 8 0.52 -0.03 0.10 0.05 0.16 0.38 0.62 1.3
## 9 0.85 0.11 0.07 0.25 -0.14 0.63 0.37 1.3
## 10 0.88 0.01 0.10 0.19 -0.15 0.68 0.32 1.2
## 11 0.52 0.06 0.12 -0.09 0.26 0.51 0.49 1.7
## 12 0.66 0.08 -0.16 -0.01 0.19 0.50 0.50 1.3
## 13 0.74 -0.04 0.17 -0.08 0.06 0.71 0.29 1.1
## 14 0.35 0.03 -0.08 -0.22 0.47 0.42 0.58 2.4
## 15 -0.19 0.00 0.11 0.36 0.54 0.69 0.31 2.2
## 16 0.36 0.01 -0.03 -0.32 0.55 0.55 0.45 2.4
## 17 -0.27 -0.05 0.17 0.34 0.43 0.62 0.38 3.1
## 18 -0.32 0.01 0.20 0.35 0.36 0.59 0.41 3.6
## 19 0.09 0.06 0.16 -0.01 0.51 0.40 0.60 1.3
## 20 -0.08 -0.17 0.31 0.18 0.34 0.49 0.51 3.2
## 21 0.25 0.07 0.58 -0.21 0.15 0.62 0.38 1.8
## 22 0.10 0.10 0.69 -0.05 0.10 0.61 0.39 1.1
## 23 0.34 0.02 0.63 -0.11 -0.02 0.62 0.38 1.6
## 24 -0.02 -0.15 0.36 0.25 0.19 0.44 0.56 2.8
## 25 0.03 0.04 0.84 -0.18 0.01 0.68 0.32 1.1
## 26 0.26 0.07 -0.43 -0.33 -0.23 0.64 0.36 3.3
## 27 -0.01 0.81 -0.08 0.03 0.03 0.65 0.35 1.0
## 28 0.06 0.74 0.03 -0.03 0.05 0.57 0.43 1.0
## 29 -0.02 0.73 0.18 0.06 -0.10 0.54 0.46 1.2
## 30 0.06 0.74 0.03 0.07 0.04 0.55 0.45 1.0
## 31 -0.14 0.82 -0.03 0.00 0.00 0.67 0.33 1.1
##
## MR2 MR5 MR4 MR1 MR3
## SS loadings 5.34 3.11 3.32 2.59 2.69
## Proportion Var 0.17 0.10 0.11 0.08 0.09
## Cumulative Var 0.17 0.27 0.38 0.46 0.55
## Proportion Explained 0.31 0.18 0.19 0.15 0.16
## Cumulative Proportion 0.31 0.50 0.69 0.84 1.00
##
## With factor correlations of
## MR2 MR5 MR4 MR1 MR3
## MR2 1.00 0.14 0.29 -0.34 0.25
## MR5 0.14 1.00 -0.04 -0.20 -0.10
## MR4 0.29 -0.04 1.00 0.30 0.55
## MR1 -0.34 -0.20 0.30 1.00 0.39
## MR3 0.25 -0.10 0.55 0.39 1.00
##
## Mean item complexity = 1.8
## Test of the hypothesis that 5 factors are sufficient.
##
## The degrees of freedom for the null model are 465 and the objective function was 17.13 with Chi Square of 19660.29
## The degrees of freedom for the model are 320 and the objective function was 1.09
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.03
##
## The harmonic number of observations is 1160 with the empirical chi square 611.68 with prob < 1.6e-20
## The total number of observations was 1160 with MLE Chi Square = 1241.98 with prob < 1.2e-108
##
## Tucker Lewis Index of factoring reliability = 0.93
## RMSEA index = 0.05 and the 90 % confidence intervals are 0.047 0.053
## BIC = -1016
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## MR2 MR5 MR4 MR1 MR3
## Correlation of scores with factors 0.96 0.94 0.94 0.92 0.91
## Multiple R square of scores with factors 0.93 0.88 0.88 0.84 0.83
## Minimum correlation of possible factor scores 0.86 0.77 0.76 0.68 0.67fa2latex(fa(purposescales_cor,5,rotate="TargetQ", n.obs = 1160, Target=Targ_key),heading="Table 7 Factor Based on theory")## % Called in the psych package fa2latex % Called in the psych package fa(purposescales_cor, 5, rotate = "TargetQ", n.obs = 1160, Target = Targ_key) % Called in the psych package Table 7 Factor Based on theory
## \begin{table}[htdp]\caption{fa2latex}
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## \end{table}#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$null.dof))## [1] 0.9519686Target rotation based on theory 5 factors same as previous but droping number 18 (LET_5) because it loaded on two factors (1 and 3)
all_surveys<-read.csv("allsurveysYT1.csv")
purposescales<-select(all_surveys,
#Factor 1 Present Focused:
PWB_1, PWB_2, PWB_4, PWB_5,PWB_9,
#Factor 2 Understanding Self and Life, Making Plans:
APSI_2, APSI_4, PWB_8, APSI_7, APSI_8, PWB_7, APSI_5, APSI_1,
#Factor 3 Meaningful activities:
LET_4, PWB_3, LET_2, LET_3, LET_6, LET_1,
#Factor 4 Have Purpose:
MLQ_4, MLQ_5, MLQ_6, MLQ_9, MLQ_1, APSI_6,
#Factor 5 Searching for meaning
MLQ_2, MLQ_3, MLQ_7, MLQ_8, MLQ_10)
purposescales$PWB_1 <- 7- purposescales$PWB_1
purposescales$PWB_2 <- 7- purposescales$PWB_2
purposescales$PWB_3 <- 7- purposescales$PWB_3
purposescales$PWB_9 <- 7- purposescales$PWB_9
purposescales$MLQ_9 <- 8- purposescales$MLQ_9
purposescales$LET_1 <- 6- purposescales$LET_1
purposescales$LET_3 <- 6- purposescales$LET_3
purposescales<- data.frame(apply(purposescales,2, as.numeric))
library(GPArotation)
library(psych)
library(dplyr)
purposescales<-tbl_df(purposescales)
purposescales## Source: local data frame [1,160 x 30]
##
## PWB_1 PWB_2 PWB_4 PWB_5 PWB_9 APSI_2 APSI_4 PWB_8 APSI_7 APSI_8 PWB_7
## 1 4 3 5 4 6 4 4 3 4 4 4
## 2 4 5 5 2 5 3 5 2 4 4 3
## 3 5 6 1 1 6 4 3 3 4 3 6
## 4 2 2 3 3 4 4 4 4 4 3 5
## 5 2 2 4 4 4 3 3 3 2 3 2
## 6 5 4 2 3 6 4 4 4 5 3 3
## 7 2 2 5 1 3 2 3 3 2 2 3
## 8 6 6 6 2 6 3 3 4 3 1 4
## 9 5 5 2 1 6 5 4 5 4 5 5
## 10 6 6 4 2 6 2 3 3 3 4 6
## .. ... ... ... ... ... ... ... ... ... ... ...
## Variables not shown: APSI_5 (dbl), APSI_1 (dbl), LET_4 (dbl), PWB_3 (dbl),
## LET_2 (dbl), LET_3 (dbl), LET_6 (dbl), LET_1 (dbl), MLQ_4 (dbl), MLQ_5
## (dbl), MLQ_6 (dbl), MLQ_9 (dbl), MLQ_1 (dbl), APSI_6 (dbl), MLQ_2 (dbl),
## MLQ_3 (dbl), MLQ_7 (dbl), MLQ_8 (dbl), MLQ_10 (dbl)str(purposescales)## Classes 'tbl_df', 'tbl' and 'data.frame': 1160 obs. of 30 variables:
## $ PWB_1 : num 4 4 5 2 2 5 2 6 5 6 ...
## $ PWB_2 : num 3 5 6 2 2 4 2 6 5 6 ...
## $ PWB_4 : num 5 5 1 3 4 2 5 6 2 4 ...
## $ PWB_5 : num 4 2 1 3 4 3 1 2 1 2 ...
## $ PWB_9 : num 6 5 6 4 4 6 3 6 6 6 ...
## $ APSI_2: num 4 3 4 4 3 4 2 3 5 2 ...
## $ APSI_4: num 4 5 3 4 3 4 3 3 4 3 ...
## $ PWB_8 : num 3 2 3 4 3 4 3 4 5 3 ...
## $ APSI_7: num 4 4 4 4 2 5 2 3 4 3 ...
## $ APSI_8: num 4 4 3 3 3 3 2 1 5 4 ...
## $ PWB_7 : num 4 3 6 5 2 3 3 4 5 6 ...
## $ APSI_5: num 4 4 3 5 4 4 4 5 4 5 ...
## $ APSI_1: num 2 4 3 4 3 3 2 3 4 2 ...
## $ LET_4 : num 5 4 4 4 4 5 3 4 5 5 ...
## $ PWB_3 : num 5 5 5 4 3 6 5 5 5 3 ...
## $ LET_2 : num 4 3 4 4 2 5 4 4 4 3 ...
## $ LET_3 : num 4 4 3 4 3 5 2 3 5 5 ...
## $ LET_6 : num 5 5 5 4 4 4 5 5 5 5 ...
## $ LET_1 : num 4 3 3 1 3 5 3 3 5 3 ...
## $ MLQ_4 : num 5 5 4 3 4 4 3 5 7 3 ...
## $ MLQ_5 : num 6 4 4 5 4 5 6 5 6 5 ...
## $ MLQ_6 : num 4 3 4 5 4 5 3 4 6 1 ...
## $ MLQ_9 : num 3 3 4 3 5 7 5 4 7 6 ...
## $ MLQ_1 : num 4 3 4 5 4 5 6 3 6 1 ...
## $ APSI_6: num 4 3 3 4 3 2 4 3 2 3 ...
## $ MLQ_2 : num 7 5 7 6 6 5 2 7 5 7 ...
## $ MLQ_3 : num 7 5 5 7 5 3 2 7 2 1 ...
## $ MLQ_7 : num 5 5 4 5 5 3 5 5 5 5 ...
## $ MLQ_8 : num 7 4 5 5 5 4 4 7 7 5 ...
## $ MLQ_10: num 7 5 4 6 5 3 4 5 2 1 ...colnames(purposescales) <- c("1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30")
#Target rotation: choose "simple structure" a priori and can be applied to oblique and orthogonal rotation based on
#what paper says facotrs should be purposescales
Targ_key <- make.keys(30,list(f1=1:5,f2=6:13, f3=14:19, f4=20:25,f5=26:30))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
purposescales_cor <- corFiml(purposescales)
out_targetQ <- fa(purposescales_cor,5,rotate="TargetQ", n.obs = 1160, Target=Targ_key) #TargetT for orthogonal rotation
out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR2 MR5 MR4 MR1 MR3
## 1 0.656
## 2 0.207 0.531
## 3 -0.340 -0.205 -0.462
## 4 -0.154 -0.546 -0.251
## 5 0.169 -0.257 0.504 0.223
## 6 0.629 0.206
## 7 0.823 0.154 0.106 -0.106
## 8 0.504 0.157
## 9 0.849 0.104 0.229 -0.125
## 10 0.886 0.172 -0.133
## 11 0.493 0.121 0.266
## 12 0.644 -0.167 0.208
## 13 0.723 0.152
## 14 0.306 -0.169 0.502
## 15 -0.243 0.146 0.429 0.475
## 16 0.304 -0.264 0.577
## 17 -0.289 0.209 0.388 0.341
## 18 0.177 0.490
## 19 -0.111 -0.171 0.337 0.220 0.295
## 20 0.237 0.583 -0.200 0.163
## 21 0.100 0.703
## 22 0.342 0.629 -0.118
## 23 -0.145 0.379 0.278 0.144
## 24 0.848 -0.181
## 25 0.274 -0.453 -0.359 -0.172
## 26 0.807
## 27 0.739
## 28 0.729 0.175 -0.110
## 29 0.744
## 30 -0.135 0.824
##
## MR2 MR5 MR4 MR1 MR3
## SS loadings 4.830 3.079 2.805 2.380 1.718
## Proportion Var 0.161 0.103 0.093 0.079 0.057
## Cumulative Var 0.161 0.264 0.357 0.436 0.494
##
## $score.cor
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.00000000 0.11343906 0.53616337 -0.00946579 0.60184781
## [2,] 0.11343906 1.00000000 -0.02508599 -0.15405454 0.05528958
## [3,] 0.53616337 -0.02508599 1.00000000 0.50483736 0.52720609
## [4,] -0.00946579 -0.15405454 0.50483736 1.00000000 0.25799457
## [5,] 0.60184781 0.05528958 0.52720609 0.25799457 1.00000000
##
## $TLI
## [1] 0.9325668
##
## $RMSEA
## RMSEA lower upper confidence
## 0.04972455 0.04625895 0.05236026 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = purposescales_cor, nfactors = 5, n.obs = 1160, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR5 MR4 MR1 MR3 h2 u2 com
## 1 -0.03 -0.03 0.02 0.66 -0.02 0.45 0.55 1.0
## 2 0.21 -0.06 -0.07 0.53 0.09 0.29 0.71 1.4
## 3 -0.34 0.08 -0.21 -0.46 -0.08 0.46 0.54 2.4
## 4 0.07 -0.02 -0.15 -0.55 -0.25 0.57 0.43 1.6
## 5 0.17 0.03 -0.26 0.50 0.22 0.27 0.73 2.2
## 6 0.63 -0.06 0.08 -0.09 0.21 0.61 0.39 1.3
## 7 0.82 -0.03 0.15 0.11 -0.11 0.66 0.34 1.1
## 8 0.50 -0.03 0.09 0.06 0.16 0.38 0.62 1.3
## 9 0.85 0.10 0.05 0.23 -0.13 0.63 0.37 1.2
## 10 0.89 0.01 0.08 0.17 -0.13 0.69 0.31 1.1
## 11 0.49 0.06 0.12 -0.07 0.27 0.51 0.49 1.8
## 12 0.64 0.07 -0.17 0.00 0.21 0.50 0.50 1.4
## 13 0.72 -0.04 0.15 -0.08 0.09 0.70 0.30 1.2
## 14 0.31 0.03 -0.08 -0.17 0.50 0.42 0.58 2.0
## 15 -0.24 0.01 0.15 0.43 0.48 0.70 0.30 2.7
## 16 0.30 0.01 -0.03 -0.26 0.58 0.55 0.45 2.0
## 17 -0.29 -0.05 0.21 0.39 0.34 0.59 0.41 3.5
## 18 0.04 0.06 0.18 0.04 0.49 0.40 0.60 1.3
## 19 -0.11 -0.17 0.34 0.22 0.30 0.49 0.51 3.5
## 20 0.24 0.07 0.58 -0.20 0.16 0.62 0.38 1.8
## 21 0.10 0.10 0.70 -0.04 0.08 0.61 0.39 1.1
## 22 0.34 0.02 0.63 -0.12 -0.01 0.62 0.38 1.6
## 23 -0.03 -0.15 0.38 0.28 0.14 0.44 0.56 2.5
## 24 0.03 0.05 0.85 -0.18 0.02 0.68 0.32 1.1
## 25 0.27 0.07 -0.45 -0.36 -0.17 0.65 0.35 3.0
## 26 -0.01 0.81 -0.08 0.03 0.03 0.65 0.35 1.0
## 27 0.05 0.74 0.03 -0.02 0.06 0.57 0.43 1.0
## 28 -0.01 0.73 0.17 0.05 -0.11 0.54 0.46 1.2
## 29 0.05 0.74 0.03 0.08 0.03 0.55 0.45 1.0
## 30 -0.14 0.82 -0.03 0.00 0.00 0.67 0.33 1.1
##
## MR2 MR5 MR4 MR1 MR3
## SS loadings 5.13 3.11 3.32 2.60 2.30
## Proportion Var 0.17 0.10 0.11 0.09 0.08
## Cumulative Var 0.17 0.27 0.39 0.47 0.55
## Proportion Explained 0.31 0.19 0.20 0.16 0.14
## Cumulative Proportion 0.31 0.50 0.70 0.86 1.00
##
## With factor correlations of
## MR2 MR5 MR4 MR1 MR3
## MR2 1.00 0.15 0.30 -0.31 0.36
## MR5 0.15 1.00 -0.05 -0.21 -0.07
## MR4 0.30 -0.05 1.00 0.33 0.54
## MR1 -0.31 -0.21 0.33 1.00 0.32
## MR3 0.36 -0.07 0.54 0.32 1.00
##
## Mean item complexity = 1.7
## Test of the hypothesis that 5 factors are sufficient.
##
## The degrees of freedom for the null model are 435 and the objective function was 16.26 with Chi Square of 18670.29
## The degrees of freedom for the model are 295 and the objective function was 0.98
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.03
##
## The harmonic number of observations is 1160 with the empirical chi square 560.77 with prob < 9.8e-19
## The total number of observations was 1160 with MLE Chi Square = 1126.43 with prob < 2.2e-97
##
## Tucker Lewis Index of factoring reliability = 0.933
## RMSEA index = 0.05 and the 90 % confidence intervals are 0.046 0.052
## BIC = -955.14
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## MR2 MR5 MR4 MR1 MR3
## Correlation of scores with factors 0.96 0.94 0.94 0.92 0.90
## Multiple R square of scores with factors 0.93 0.89 0.88 0.85 0.81
## Minimum correlation of possible factor scores 0.86 0.77 0.77 0.69 0.62fa2latex(fa(purposescales_cor,5,rotate="TargetQ", n.obs = 1160, Target=Targ_key),heading="Table 7 Factor Based on theory")## % Called in the psych package fa2latex % Called in the psych package fa(purposescales_cor, 5, rotate = "TargetQ", n.obs = 1160, Target = Targ_key) % Called in the psych package Table 7 Factor Based on theory
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## \end{table}#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$null.dof))## [1] 0.9544053Target rotation based on theory 5 Factor Based in Theory (results) activities factor seems not to hold up on its own so I am making it into two factors:positive and negative questions
all_surveys<-read.csv("allsurveysYT1.csv")
purposescales<-select(all_surveys,
#Factor 1 Present Focused:
PWB_1, PWB_2, PWB_5, PWB_4,
#Factor 2 Understanding Self and Life, Making Plans:
APSI_2, APSI_4, PWB_8, APSI_7, APSI_8, PWB_7, APSI_5, APSI_1,
#Factor 3 Meaningful activities:
LET_4, LET_2, LET_6,
#Factor 4 Meaningful activities Negative
PWB_9, LET_3, PWB_3,
#Factor 5 Have Purpose:
MLQ_4, MLQ_5, MLQ_6, MLQ_9, MLQ_1, APSI_6,LET_1,
#Factor 6 Searching for meaning
MLQ_2, MLQ_3, MLQ_7, MLQ_8, MLQ_10)
purposescales$PWB_1 <- 7- purposescales$PWB_1
purposescales$PWB_2 <- 7- purposescales$PWB_2
purposescales$PWB_3 <- 7- purposescales$PWB_3
purposescales$PWB_9 <- 7- purposescales$PWB_9
purposescales$MLQ_9 <- 8- purposescales$MLQ_9
purposescales$LET_1 <- 6- purposescales$LET_1
purposescales$LET_3 <- 6- purposescales$LET_3
purposescales<- data.frame(apply(purposescales,2, as.numeric))
library(GPArotation)
library(psych)
library(dplyr)
purposescales<-tbl_df(purposescales)
purposescales## Source: local data frame [1,160 x 30]
##
## PWB_1 PWB_2 PWB_5 PWB_4 APSI_2 APSI_4 PWB_8 APSI_7 APSI_8 PWB_7 APSI_5
## 1 4 3 4 5 4 4 3 4 4 4 4
## 2 4 5 2 5 3 5 2 4 4 3 4
## 3 5 6 1 1 4 3 3 4 3 6 3
## 4 2 2 3 3 4 4 4 4 3 5 5
## 5 2 2 4 4 3 3 3 2 3 2 4
## 6 5 4 3 2 4 4 4 5 3 3 4
## 7 2 2 1 5 2 3 3 2 2 3 4
## 8 6 6 2 6 3 3 4 3 1 4 5
## 9 5 5 1 2 5 4 5 4 5 5 4
## 10 6 6 2 4 2 3 3 3 4 6 5
## .. ... ... ... ... ... ... ... ... ... ... ...
## Variables not shown: APSI_1 (dbl), LET_4 (dbl), LET_2 (dbl), LET_6 (dbl),
## PWB_9 (dbl), LET_3 (dbl), PWB_3 (dbl), MLQ_4 (dbl), MLQ_5 (dbl), MLQ_6
## (dbl), MLQ_9 (dbl), MLQ_1 (dbl), APSI_6 (dbl), LET_1 (dbl), MLQ_2 (dbl),
## MLQ_3 (dbl), MLQ_7 (dbl), MLQ_8 (dbl), MLQ_10 (dbl)str(purposescales)## Classes 'tbl_df', 'tbl' and 'data.frame': 1160 obs. of 30 variables:
## $ PWB_1 : num 4 4 5 2 2 5 2 6 5 6 ...
## $ PWB_2 : num 3 5 6 2 2 4 2 6 5 6 ...
## $ PWB_5 : num 4 2 1 3 4 3 1 2 1 2 ...
## $ PWB_4 : num 5 5 1 3 4 2 5 6 2 4 ...
## $ APSI_2: num 4 3 4 4 3 4 2 3 5 2 ...
## $ APSI_4: num 4 5 3 4 3 4 3 3 4 3 ...
## $ PWB_8 : num 3 2 3 4 3 4 3 4 5 3 ...
## $ APSI_7: num 4 4 4 4 2 5 2 3 4 3 ...
## $ APSI_8: num 4 4 3 3 3 3 2 1 5 4 ...
## $ PWB_7 : num 4 3 6 5 2 3 3 4 5 6 ...
## $ APSI_5: num 4 4 3 5 4 4 4 5 4 5 ...
## $ APSI_1: num 2 4 3 4 3 3 2 3 4 2 ...
## $ LET_4 : num 5 4 4 4 4 5 3 4 5 5 ...
## $ LET_2 : num 4 3 4 4 2 5 4 4 4 3 ...
## $ LET_6 : num 5 5 5 4 4 4 5 5 5 5 ...
## $ PWB_9 : num 6 5 6 4 4 6 3 6 6 6 ...
## $ LET_3 : num 4 4 3 4 3 5 2 3 5 5 ...
## $ PWB_3 : num 5 5 5 4 3 6 5 5 5 3 ...
## $ MLQ_4 : num 5 5 4 3 4 4 3 5 7 3 ...
## $ MLQ_5 : num 6 4 4 5 4 5 6 5 6 5 ...
## $ MLQ_6 : num 4 3 4 5 4 5 3 4 6 1 ...
## $ MLQ_9 : num 3 3 4 3 5 7 5 4 7 6 ...
## $ MLQ_1 : num 4 3 4 5 4 5 6 3 6 1 ...
## $ APSI_6: num 4 3 3 4 3 2 4 3 2 3 ...
## $ LET_1 : num 4 3 3 1 3 5 3 3 5 3 ...
## $ MLQ_2 : num 7 5 7 6 6 5 2 7 5 7 ...
## $ MLQ_3 : num 7 5 5 7 5 3 2 7 2 1 ...
## $ MLQ_7 : num 5 5 4 5 5 3 5 5 5 5 ...
## $ MLQ_8 : num 7 4 5 5 5 4 4 7 7 5 ...
## $ MLQ_10: num 7 5 4 6 5 3 4 5 2 1 ...colnames(purposescales) <- c("1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30")
#Target rotation: choose "simple structure" a priori and can be applied to oblique and orthogonal rotation based on
#what paper says facotrs should be purposescales
Targ_key <- make.keys(30,list(f1=1:6,f2=6:14, f3=14:18, f4=19:25,f5=26:30))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
purposescales_cor <- corFiml(purposescales)
out_targetQ <- fa(purposescales_cor,5,rotate="TargetQ", n.obs = 1160, Target=Targ_key) #TargetT for orthogonal rotation
out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR2 MR4 MR5 MR1 MR3
## 1 -0.119 0.567 0.134
## 2 0.183 0.448 0.181
## 3 -0.185 -0.444 -0.345
## 4 -0.287 -0.228 -0.411 -0.119
## 5 0.707 0.120
## 6 0.742 0.161 0.138 -0.179
## 7 0.544 0.122
## 8 0.760 0.109 0.240 -0.162
## 9 0.795 0.196 -0.191
## 10 0.592 0.162 0.156
## 11 0.741 -0.146 0.121
## 12 0.744 0.181
## 13 0.535 -0.205 0.378
## 14 0.569 -0.293 0.420
## 15 0.214 0.239 0.429
## 16 0.223 -0.246 0.396 0.308
## 17 -0.222 0.252 0.294 0.410
## 18 -0.120 0.200 0.309 0.538
## 19 0.262 0.633 -0.153
## 20 0.746 0.104
## 21 0.279 0.663 -0.119
## 22 0.415 -0.143 0.243 0.166
## 23 0.893 -0.109
## 24 0.299 -0.490 -0.305 -0.235
## 25 0.387 -0.173 0.169 0.299
## 26 0.804
## 27 0.737
## 28 0.156 0.731
## 29 0.742
## 30 -0.114 0.822
##
## MR2 MR4 MR5 MR1 MR3
## SS loadings 5.189 3.251 3.068 1.715 1.600
## Proportion Var 0.173 0.108 0.102 0.057 0.053
## Cumulative Var 0.173 0.281 0.384 0.441 0.494
##
## $score.cor
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.000000000 0.57934558 0.11205829 0.008962812 0.1555875
## [2,] 0.579345584 1.00000000 0.01868717 0.399993384 0.5633471
## [3,] 0.112058290 0.01868717 1.00000000 -0.144712000 -0.1410586
## [4,] 0.008962812 0.39999338 -0.14471200 1.000000000 0.6817215
## [5,] 0.155587461 0.56334712 -0.14105863 0.681721493 1.0000000
##
## $TLI
## [1] 0.9325668
##
## $RMSEA
## RMSEA lower upper confidence
## 0.04972455 0.04625895 0.05236026 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = purposescales_cor, nfactors = 5, n.obs = 1160, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR4 MR5 MR1 MR3 h2 u2 com
## 1 -0.12 0.01 -0.02 0.57 0.13 0.45 0.55 1.2
## 2 0.18 -0.06 -0.05 0.45 0.18 0.29 0.71 1.8
## 3 0.05 -0.18 -0.02 -0.44 -0.34 0.57 0.43 2.3
## 4 -0.29 -0.23 0.08 -0.41 -0.12 0.46 0.54 2.7
## 5 0.71 0.12 -0.06 -0.08 0.08 0.61 0.39 1.1
## 6 0.74 0.16 -0.03 0.14 -0.18 0.66 0.34 1.3
## 7 0.54 0.12 -0.03 0.06 0.09 0.38 0.62 1.2
## 8 0.76 0.05 0.11 0.24 -0.16 0.63 0.37 1.4
## 9 0.80 0.08 0.01 0.20 -0.19 0.69 0.31 1.3
## 10 0.59 0.16 0.06 -0.07 0.16 0.51 0.49 1.3
## 11 0.74 -0.15 0.07 -0.01 0.12 0.50 0.50 1.2
## 12 0.74 0.18 -0.04 -0.05 -0.03 0.70 0.30 1.1
## 13 0.53 -0.03 0.02 -0.21 0.38 0.42 0.58 2.1
## 14 0.57 0.04 -0.01 -0.29 0.42 0.55 0.45 2.4
## 15 0.21 0.24 0.05 -0.02 0.43 0.40 0.60 2.1
## 16 0.22 -0.25 0.02 0.40 0.31 0.27 0.73 3.3
## 17 -0.22 0.25 -0.05 0.29 0.41 0.59 0.41 3.2
## 18 -0.12 0.20 0.00 0.31 0.54 0.70 0.30 2.0
## 19 0.26 0.63 0.07 -0.15 0.04 0.62 0.38 1.5
## 20 0.06 0.75 0.10 0.00 0.01 0.61 0.39 1.1
## 21 0.28 0.66 0.02 -0.05 -0.12 0.62 0.38 1.4
## 22 -0.05 0.41 -0.14 0.24 0.17 0.44 0.56 2.3
## 23 -0.04 0.89 0.05 -0.11 -0.08 0.68 0.32 1.1
## 24 0.30 -0.49 0.07 -0.31 -0.23 0.65 0.35 3.0
## 25 -0.06 0.39 -0.17 0.17 0.30 0.49 0.51 2.8
## 26 0.02 -0.10 0.80 0.01 0.05 0.65 0.35 1.0
## 27 0.09 0.02 0.74 -0.03 0.05 0.57 0.43 1.0
## 28 -0.06 0.16 0.73 0.06 -0.08 0.54 0.46 1.1
## 29 0.07 0.02 0.74 0.06 0.05 0.55 0.45 1.0
## 30 -0.11 -0.05 0.82 -0.01 0.03 0.67 0.33 1.0
##
## MR2 MR4 MR5 MR1 MR3
## SS loadings 5.47 3.78 3.10 2.08 2.04
## Proportion Var 0.18 0.13 0.10 0.07 0.07
## Cumulative Var 0.18 0.31 0.41 0.48 0.55
## Proportion Explained 0.33 0.23 0.19 0.13 0.12
## Cumulative Proportion 0.33 0.56 0.75 0.88 1.00
##
## With factor correlations of
## MR2 MR4 MR5 MR1 MR3
## MR2 1.00 0.42 0.12 -0.17 -0.02
## MR4 0.42 1.00 -0.03 0.28 0.40
## MR5 0.12 -0.03 1.00 -0.18 -0.15
## MR1 -0.17 0.28 -0.18 1.00 0.42
## MR3 -0.02 0.40 -0.15 0.42 1.00
##
## Mean item complexity = 1.7
## Test of the hypothesis that 5 factors are sufficient.
##
## The degrees of freedom for the null model are 435 and the objective function was 16.26 with Chi Square of 18670.29
## The degrees of freedom for the model are 295 and the objective function was 0.98
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.03
##
## The harmonic number of observations is 1160 with the empirical chi square 560.77 with prob < 9.8e-19
## The total number of observations was 1160 with MLE Chi Square = 1126.43 with prob < 2.2e-97
##
## Tucker Lewis Index of factoring reliability = 0.933
## RMSEA index = 0.05 and the 90 % confidence intervals are 0.046 0.052
## BIC = -955.14
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## MR2 MR4 MR5 MR1 MR3
## Correlation of scores with factors 0.96 0.95 0.94 0.89 0.89
## Multiple R square of scores with factors 0.93 0.90 0.88 0.79 0.79
## Minimum correlation of possible factor scores 0.86 0.80 0.77 0.58 0.59fa2latex(fa(purposescales_cor,5,rotate="TargetQ", n.obs = 1160, Target=Targ_key),heading="Table 7 Factor Based on theory")## % Called in the psych package fa2latex % Called in the psych package fa(purposescales_cor, 5, rotate = "TargetQ", n.obs = 1160, Target = Targ_key) % Called in the psych package Table 7 Factor Based on theory
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## \label{default}
## \end{table}#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$null.dof))## [1] 0.9544053Target rotation based on theory 5 Factor Based in Theory (results) but moving 17 (LET_3) to 1 and 19 (LET_1) to 4 where they load better
all_surveys<-read.csv("allsurveysYT1.csv")
purposescales<-select(all_surveys,
#Factor 1 Present Focused:
PWB_1, PWB_2, PWB_4,
#Factor 2 Understanding Self and Life, Making Plans:
APSI_2, APSI_4, PWB_8, APSI_7, APSI_8, PWB_7, APSI_5, APSI_1, APSI_5, APSI_6,
#Factor 3 Meaningful activities:
LET_4, LET_2, LET_6,
#Factor 4 Meaningful activities Negative
PWB_9, LET_3, PWB_3, PWB_5,
#Factor 5 Have Purpose:
MLQ_4, MLQ_5, MLQ_6, MLQ_9, MLQ_1, APSI_6,LET_1,
#Factor 6 Searching for meaning
MLQ_2, MLQ_3, MLQ_7, MLQ_8, MLQ_10)
purposescales$PWB_1 <- 7- purposescales$PWB_1
purposescales$PWB_2 <- 7- purposescales$PWB_2
purposescales$PWB_3 <- 7- purposescales$PWB_3
purposescales$PWB_9 <- 7- purposescales$PWB_9
purposescales$MLQ_9 <- 8- purposescales$MLQ_9
purposescales$LET_1 <- 6- purposescales$LET_1
purposescales$LET_3 <- 6- purposescales$LET_3
purposescales<- data.frame(apply(purposescales,2, as.numeric))
library(GPArotation)
library(psych)
library(dplyr)
purposescales<-tbl_df(purposescales)
purposescales## Source: local data frame [1,160 x 30]
##
## PWB_1 PWB_2 PWB_4 APSI_2 APSI_4 PWB_8 APSI_7 APSI_8 PWB_7 APSI_5 APSI_1
## 1 4 3 5 4 4 3 4 4 4 4 2
## 2 4 5 5 3 5 2 4 4 3 4 4
## 3 5 6 1 4 3 3 4 3 6 3 3
## 4 2 2 3 4 4 4 4 3 5 5 4
## 5 2 2 4 3 3 3 2 3 2 4 3
## 6 5 4 2 4 4 4 5 3 3 4 3
## 7 2 2 5 2 3 3 2 2 3 4 2
## 8 6 6 6 3 3 4 3 1 4 5 3
## 9 5 5 2 5 4 5 4 5 5 4 4
## 10 6 6 4 2 3 3 3 4 6 5 2
## .. ... ... ... ... ... ... ... ... ... ... ...
## Variables not shown: APSI_6 (dbl), LET_4 (dbl), LET_2 (dbl), LET_6 (dbl),
## PWB_9 (dbl), LET_3 (dbl), PWB_3 (dbl), PWB_5 (dbl), MLQ_4 (dbl), MLQ_5
## (dbl), MLQ_6 (dbl), MLQ_9 (dbl), MLQ_1 (dbl), LET_1 (dbl), MLQ_2 (dbl),
## MLQ_3 (dbl), MLQ_7 (dbl), MLQ_8 (dbl), MLQ_10 (dbl)str(purposescales)## Classes 'tbl_df', 'tbl' and 'data.frame': 1160 obs. of 30 variables:
## $ PWB_1 : num 4 4 5 2 2 5 2 6 5 6 ...
## $ PWB_2 : num 3 5 6 2 2 4 2 6 5 6 ...
## $ PWB_4 : num 5 5 1 3 4 2 5 6 2 4 ...
## $ APSI_2: num 4 3 4 4 3 4 2 3 5 2 ...
## $ APSI_4: num 4 5 3 4 3 4 3 3 4 3 ...
## $ PWB_8 : num 3 2 3 4 3 4 3 4 5 3 ...
## $ APSI_7: num 4 4 4 4 2 5 2 3 4 3 ...
## $ APSI_8: num 4 4 3 3 3 3 2 1 5 4 ...
## $ PWB_7 : num 4 3 6 5 2 3 3 4 5 6 ...
## $ APSI_5: num 4 4 3 5 4 4 4 5 4 5 ...
## $ APSI_1: num 2 4 3 4 3 3 2 3 4 2 ...
## $ APSI_6: num 4 3 3 4 3 2 4 3 2 3 ...
## $ LET_4 : num 5 4 4 4 4 5 3 4 5 5 ...
## $ LET_2 : num 4 3 4 4 2 5 4 4 4 3 ...
## $ LET_6 : num 5 5 5 4 4 4 5 5 5 5 ...
## $ PWB_9 : num 6 5 6 4 4 6 3 6 6 6 ...
## $ LET_3 : num 4 4 3 4 3 5 2 3 5 5 ...
## $ PWB_3 : num 5 5 5 4 3 6 5 5 5 3 ...
## $ PWB_5 : num 4 2 1 3 4 3 1 2 1 2 ...
## $ MLQ_4 : num 5 5 4 3 4 4 3 5 7 3 ...
## $ MLQ_5 : num 6 4 4 5 4 5 6 5 6 5 ...
## $ MLQ_6 : num 4 3 4 5 4 5 3 4 6 1 ...
## $ MLQ_9 : num 3 3 4 3 5 7 5 4 7 6 ...
## $ MLQ_1 : num 4 3 4 5 4 5 6 3 6 1 ...
## $ LET_1 : num 4 3 3 1 3 5 3 3 5 3 ...
## $ MLQ_2 : num 7 5 7 6 6 5 2 7 5 7 ...
## $ MLQ_3 : num 7 5 5 7 5 3 2 7 2 1 ...
## $ MLQ_7 : num 5 5 4 5 5 3 5 5 5 5 ...
## $ MLQ_8 : num 7 4 5 5 5 4 4 7 7 5 ...
## $ MLQ_10: num 7 5 4 6 5 3 4 5 2 1 ...colnames(purposescales) <- c("1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30")
#Target rotation: choose "simple structure" a priori and can be applied to oblique and orthogonal rotation based on
#what paper says facotrs should be purposescales
Targ_key <- make.keys(30,list(f1=1:3,f2=4:11, f3=12:14, f4=15:18,f5=19:25, f6=26:30))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
purposescales_cor <- corFiml(purposescales)
out_targetQ <- fa(purposescales_cor,6,rotate="TargetQ", n.obs = 1160, Target=Targ_key) #TargetT for orthogonal rotation
out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR2 MR6 MR5 MR4 MR1 MR3
## 1 0.208 0.460 -0.267
## 2 -0.173 0.903 0.125
## 3 -0.316 -0.182 -0.180 -0.316 0.116
## 4 0.587 0.115 0.211
## 5 0.861 -0.114
## 6 0.478 0.107 0.102
## 7 0.886 -0.144
## 8 0.878
## 9 0.392 0.209 0.276
## 10 0.618 -0.146 0.211
## 11 0.661 0.167 0.144
## 12 0.142 -0.395 -0.421 -0.119 0.238
## 13 0.299 0.284 0.408
## 14 0.185 0.141 0.194 0.584
## 15 0.105 0.226 0.426 0.197
## 16 -0.208 0.229 0.450
## 17 -0.158 0.183 0.555 0.147
## 18 0.127 0.686 0.167
## 19 -0.508 -0.248 0.174
## 20 0.114 0.691 0.173
## 21 0.100 0.725
## 22 0.203 0.704 -0.214
## 23 -0.152 0.326 0.328 -0.172
## 24 0.880
## 25 -0.178 0.345 0.375 0.108
## 26 0.814
## 27 0.737
## 28 0.730 0.154 -0.106
## 29 0.740
## 30 0.826 0.105
##
## MR2 MR6 MR5 MR4 MR1 MR3
## SS loadings 4.232 3.085 3.022 2.033 1.518 1.078
## Proportion Var 0.141 0.103 0.101 0.068 0.051 0.036
## Cumulative Var 0.141 0.244 0.345 0.412 0.463 0.499
##
## $score.cor
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 1.00000000 0.11347753 0.6164897 0.03540792 0.13256973 0.62061798
## [2,] 0.11347753 1.00000000 0.1001286 -0.18655392 -0.13248180 0.07270612
## [3,] 0.61648968 0.10012856 1.0000000 0.44663479 0.27300045 0.49033112
## [4,] 0.03540792 -0.18655392 0.4466348 1.00000000 0.60711152 0.08170085
## [5,] 0.13256973 -0.13248180 0.2730005 0.60711152 1.00000000 0.08881474
## [6,] 0.62061798 0.07270612 0.4903311 0.08170085 0.08881474 1.00000000
##
## $TLI
## [1] 0.945109
##
## $RMSEA
## RMSEA lower upper confidence
## 0.04489006 0.04123371 0.04772054 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = purposescales_cor, nfactors = 6, n.obs = 1160, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR6 MR5 MR4 MR1 MR3 h2 u2 com
## 1 -0.01 -0.01 -0.04 0.21 0.46 -0.27 0.47 0.53 2.1
## 2 -0.07 -0.02 0.05 -0.17 0.90 0.12 0.69 0.31 1.1
## 3 -0.32 0.08 -0.18 -0.18 -0.32 0.12 0.46 0.54 3.6
## 4 0.59 -0.07 0.12 0.03 -0.04 0.21 0.60 0.40 1.4
## 5 0.86 -0.04 0.05 -0.06 -0.04 -0.11 0.69 0.31 1.1
## 6 0.48 -0.04 0.11 0.08 0.04 0.10 0.38 0.62 1.3
## 7 0.89 0.10 -0.06 -0.03 0.04 -0.14 0.65 0.35 1.1
## 8 0.88 0.00 -0.01 -0.09 0.03 -0.10 0.69 0.31 1.0
## 9 0.39 0.05 0.21 0.03 0.08 0.28 0.52 0.48 2.6
## 10 0.62 0.06 -0.15 0.07 0.00 0.21 0.49 0.51 1.4
## 11 0.66 -0.05 0.17 -0.07 -0.03 0.14 0.70 0.30 1.3
## 12 0.14 0.07 -0.39 -0.42 -0.12 0.24 0.66 0.34 3.1
## 13 0.30 0.01 0.01 0.28 -0.09 0.41 0.42 0.58 2.8
## 14 0.18 -0.01 0.14 0.19 0.00 0.58 0.58 0.42 1.6
## 15 0.11 0.04 0.23 0.43 0.01 0.20 0.40 0.60 2.2
## 16 0.10 0.03 -0.21 0.23 0.45 0.07 0.29 0.71 2.2
## 17 -0.16 -0.06 0.18 0.56 0.15 -0.09 0.60 0.40 1.6
## 18 -0.10 0.00 0.13 0.69 0.17 -0.02 0.71 0.29 1.2
## 19 -0.05 -0.02 -0.10 -0.51 -0.25 0.17 0.57 0.43 1.8
## 20 0.11 0.07 0.69 -0.09 0.03 0.17 0.63 0.37 1.2
## 21 0.07 0.10 0.73 0.03 0.02 -0.04 0.61 0.39 1.1
## 22 0.20 0.02 0.70 -0.21 0.07 0.04 0.65 0.35 1.4
## 23 0.07 -0.15 0.33 0.33 0.07 -0.17 0.45 0.55 3.1
## 24 -0.01 0.05 0.88 -0.08 -0.05 -0.05 0.68 0.32 1.0
## 25 -0.05 -0.18 0.35 0.37 0.11 -0.02 0.49 0.51 2.7
## 26 -0.05 0.81 -0.05 0.00 0.08 0.07 0.66 0.34 1.0
## 27 0.04 0.74 0.05 0.03 0.00 0.07 0.57 0.43 1.0
## 28 -0.01 0.73 0.15 -0.04 0.00 -0.11 0.54 0.46 1.1
## 29 0.08 0.74 0.02 0.09 0.01 -0.02 0.55 0.45 1.1
## 30 -0.06 0.83 -0.06 0.10 -0.09 -0.05 0.69 0.31 1.1
##
## MR2 MR6 MR5 MR4 MR1 MR3
## SS loadings 4.74 3.11 3.53 2.48 1.85 1.37
## Proportion Var 0.16 0.10 0.12 0.08 0.06 0.05
## Cumulative Var 0.16 0.26 0.38 0.46 0.52 0.57
## Proportion Explained 0.28 0.18 0.21 0.15 0.11 0.08
## Cumulative Proportion 0.28 0.46 0.67 0.81 0.92 1.00
##
## With factor correlations of
## MR2 MR6 MR5 MR4 MR1 MR3
## MR2 1.00 0.11 0.53 0.02 0.08 0.54
## MR6 0.11 1.00 -0.03 -0.17 -0.17 0.13
## MR5 0.53 -0.03 1.00 0.43 0.26 0.14
## MR4 0.02 -0.17 0.43 1.00 0.49 -0.19
## MR1 0.08 -0.17 0.26 0.49 1.00 -0.14
## MR3 0.54 0.13 0.14 -0.19 -0.14 1.00
##
## Mean item complexity = 1.7
## Test of the hypothesis that 6 factors are sufficient.
##
## The degrees of freedom for the null model are 435 and the objective function was 16.26 with Chi Square of 18670.29
## The degrees of freedom for the model are 270 and the objective function was 0.78
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.02
##
## The harmonic number of observations is 1160 with the empirical chi square 389.45 with prob < 2.6e-06
## The total number of observations was 1160 with MLE Chi Square = 889.06 with prob < 4.1e-67
##
## Tucker Lewis Index of factoring reliability = 0.945
## RMSEA index = 0.045 and the 90 % confidence intervals are 0.041 0.048
## BIC = -1016.1
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR2 MR6 MR5 MR4 MR1
## Correlation of scores with factors 0.96 0.94 0.95 0.92 0.90
## Multiple R square of scores with factors 0.93 0.89 0.90 0.85 0.81
## Minimum correlation of possible factor scores 0.85 0.78 0.80 0.70 0.62
## MR3
## Correlation of scores with factors 0.86
## Multiple R square of scores with factors 0.75
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## \label{default}
## \end{table}#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$null.dof))## [1] 0.9660513Target rotation based taking out all questions that siqnificantly crossload PWB_1, PWB_4, PWB_7, LET_4, LET_6, LET_3, MLQ_9, LET_5
all_surveys<-read.csv("allsurveysYT1.csv")
purposescales<-select(all_surveys,
#Factor 1:
APSI_2, APSI_4, PWB_8, APSI_7, APSI_8, APSI_5, APSI_1, LET_2,
#Factor2:
PWB_2, PWB_9, PWB_3, PWB_5, LET_1, APSI_6,
#Factor3
MLQ_4, MLQ_5, MLQ_6, MLQ_1,
#Factor 4
MLQ_2, MLQ_3, MLQ_7, MLQ_8, MLQ_10)
purposescales$PWB_3 <- 7- purposescales$PWB_3
purposescales$PWB_9 <- 7- purposescales$PWB_9
purposescales$LET_1 <- 6- purposescales$LET_1
purposescales<- data.frame(apply(purposescales,2, as.numeric))
library(GPArotation)
library(psych)
library(dplyr)
purposescales<-tbl_df(purposescales)
purposescales## Source: local data frame [1,160 x 23]
##
## APSI_2 APSI_4 PWB_8 APSI_7 APSI_8 APSI_5 APSI_1 LET_2 PWB_2 PWB_9 PWB_3
## 1 4 4 3 4 4 4 2 4 4 6 5
## 2 3 5 2 4 4 4 4 3 2 5 5
## 3 4 3 3 4 3 3 3 4 1 6 5
## 4 4 4 4 4 3 5 4 4 5 4 4
## 5 3 3 3 2 3 4 3 2 5 4 3
## 6 4 4 4 5 3 4 3 5 3 6 6
## 7 2 3 3 2 2 4 2 4 5 3 5
## 8 3 3 4 3 1 5 3 4 1 6 5
## 9 5 4 5 4 5 4 4 4 2 6 5
## 10 2 3 3 3 4 5 2 3 1 6 3
## .. ... ... ... ... ... ... ... ... ... ... ...
## Variables not shown: PWB_5 (dbl), LET_1 (dbl), APSI_6 (dbl), MLQ_4 (dbl),
## MLQ_5 (dbl), MLQ_6 (dbl), MLQ_1 (dbl), MLQ_2 (dbl), MLQ_3 (dbl), MLQ_7
## (dbl), MLQ_8 (dbl), MLQ_10 (dbl)str(purposescales)## Classes 'tbl_df', 'tbl' and 'data.frame': 1160 obs. of 23 variables:
## $ APSI_2: num 4 3 4 4 3 4 2 3 5 2 ...
## $ APSI_4: num 4 5 3 4 3 4 3 3 4 3 ...
## $ PWB_8 : num 3 2 3 4 3 4 3 4 5 3 ...
## $ APSI_7: num 4 4 4 4 2 5 2 3 4 3 ...
## $ APSI_8: num 4 4 3 3 3 3 2 1 5 4 ...
## $ APSI_5: num 4 4 3 5 4 4 4 5 4 5 ...
## $ APSI_1: num 2 4 3 4 3 3 2 3 4 2 ...
## $ LET_2 : num 4 3 4 4 2 5 4 4 4 3 ...
## $ PWB_2 : num 4 2 1 5 5 3 5 1 2 1 ...
## $ PWB_9 : num 6 5 6 4 4 6 3 6 6 6 ...
## $ PWB_3 : num 5 5 5 4 3 6 5 5 5 3 ...
## $ PWB_5 : num 4 2 1 3 4 3 1 2 1 2 ...
## $ LET_1 : num 4 3 3 1 3 5 3 3 5 3 ...
## $ APSI_6: num 4 3 3 4 3 2 4 3 2 3 ...
## $ MLQ_4 : num 5 5 4 3 4 4 3 5 7 3 ...
## $ MLQ_5 : num 6 4 4 5 4 5 6 5 6 5 ...
## $ MLQ_6 : num 4 3 4 5 4 5 3 4 6 1 ...
## $ MLQ_1 : num 4 3 4 5 4 5 6 3 6 1 ...
## $ MLQ_2 : num 7 5 7 6 6 5 2 7 5 7 ...
## $ MLQ_3 : num 7 5 5 7 5 3 2 7 2 1 ...
## $ MLQ_7 : num 5 5 4 5 5 3 5 5 5 5 ...
## $ MLQ_8 : num 7 4 5 5 5 4 4 7 7 5 ...
## $ MLQ_10: num 7 5 4 6 5 3 4 5 2 1 ...colnames(purposescales) <- c("1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23")
#Target rotation: choose "simple structure" a priori and can be applied to oblique and orthogonal rotation based on
#what paper says facotrs should be purposescales
Targ_key <- make.keys(23,list(f1=1:8,f2=9:14, f3=15:18, f4=19:23))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
purposescales_cor <- corFiml(purposescales)
out_targetQ <- fa(purposescales_cor,4,rotate="TargetQ", n.obs = 1160, Target=Targ_key) #TargetT for orthogonal rotation
out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR1 MR4 MR2 MR3
## 1 0.716 0.120
## 2 0.745 0.126
## 3 0.546 0.127
## 4 0.742
## 5 0.773
## 6 0.748 -0.135
## 7 0.756 0.173
## 8 0.535 0.102
## 9 -0.174 -0.516 0.163
## 10 0.232 0.559 -0.356
## 11 0.804
## 12 -0.774
## 13 -0.152 0.541 0.218
## 14 0.251 -0.644 -0.288
## 15 0.295 0.569
## 16 0.173 0.653
## 17 0.300 0.606
## 18 0.832
## 19 0.802
## 20 0.730
## 21 0.714 0.152
## 22 0.739
## 23 -0.129 0.815
##
## MR1 MR4 MR2 MR3
## SS loadings 4.304 2.966 2.603 2.224
## Proportion Var 0.187 0.129 0.113 0.097
## Cumulative Var 0.187 0.316 0.429 0.526
##
## $score.cor
## [,1] [,2] [,3] [,4]
## [1,] 1.00000000 0.1068236 0.06369279 0.6044766
## [2,] 0.10682363 1.0000000 -0.16689627 0.1083969
## [3,] 0.06369279 -0.1668963 1.00000000 0.3392522
## [4,] 0.60447660 0.1083969 0.33925221 1.0000000
##
## $TLI
## [1] 0.9469382
##
## $RMSEA
## RMSEA lower upper confidence
## 0.04972782 0.04536105 0.05346715 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = purposescales_cor, nfactors = 4, n.obs = 1160, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR4 MR2 MR3 h2 u2 com
## 1 0.72 -0.07 -0.02 0.12 0.60 0.40 1.1
## 2 0.74 -0.04 -0.04 0.13 0.65 0.35 1.1
## 3 0.55 -0.03 0.13 0.08 0.37 0.63 1.2
## 4 0.74 0.10 0.02 0.01 0.59 0.41 1.0
## 5 0.77 0.00 -0.04 0.06 0.65 0.35 1.0
## 6 0.75 0.08 0.00 -0.14 0.50 0.50 1.1
## 7 0.76 -0.05 -0.09 0.17 0.71 0.29 1.1
## 8 0.53 0.01 0.06 0.10 0.36 0.64 1.1
## 9 -0.17 0.04 -0.52 0.16 0.25 0.75 1.5
## 10 0.23 0.05 0.56 -0.36 0.27 0.73 2.1
## 11 -0.09 0.03 0.80 0.03 0.66 0.34 1.0
## 12 0.02 -0.05 -0.77 0.01 0.58 0.42 1.0
## 13 0.00 -0.15 0.54 0.22 0.48 0.52 1.5
## 14 0.25 0.05 -0.64 -0.29 0.64 0.36 1.7
## 15 0.30 0.06 0.04 0.57 0.60 0.40 1.5
## 16 0.08 0.09 0.17 0.65 0.61 0.39 1.2
## 17 0.30 0.00 -0.01 0.61 0.62 0.38 1.5
## 18 -0.02 0.02 0.04 0.83 0.71 0.29 1.0
## 19 0.01 0.80 -0.02 -0.06 0.65 0.35 1.0
## 20 0.09 0.73 -0.02 0.04 0.57 0.43 1.0
## 21 -0.07 0.71 -0.03 0.15 0.53 0.47 1.1
## 22 0.07 0.74 0.06 0.01 0.55 0.45 1.0
## 23 -0.13 0.82 -0.04 -0.01 0.67 0.33 1.1
##
## MR1 MR4 MR2 MR3
## SS loadings 4.57 2.98 2.68 2.57
## Proportion Var 0.20 0.13 0.12 0.11
## Cumulative Var 0.20 0.33 0.45 0.56
## Proportion Explained 0.36 0.23 0.21 0.20
## Cumulative Proportion 0.36 0.59 0.80 1.00
##
## With factor correlations of
## MR1 MR4 MR2 MR3
## MR1 1.00 0.11 0.03 0.47
## MR4 0.11 1.00 -0.18 0.04
## MR2 0.03 -0.18 1.00 0.37
## MR3 0.47 0.04 0.37 1.00
##
## Mean item complexity = 1.2
## Test of the hypothesis that 4 factors are sufficient.
##
## The degrees of freedom for the null model are 253 and the objective function was 11.97 with Chi Square of 13772.67
## The degrees of freedom for the model are 167 and the objective function was 0.56
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.03
##
## The harmonic number of observations is 1160 with the empirical chi square 305.7 with prob < 3.3e-10
## The total number of observations was 1160 with MLE Chi Square = 639.41 with prob < 1.9e-56
##
## Tucker Lewis Index of factoring reliability = 0.947
## RMSEA index = 0.05 and the 90 % confidence intervals are 0.045 0.053
## BIC = -538.97
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## MR1 MR4 MR2 MR3
## Correlation of scores with factors 0.96 0.94 0.93 0.93
## Multiple R square of scores with factors 0.92 0.88 0.86 0.87
## Minimum correlation of possible factor scores 0.83 0.77 0.72 0.73fa2latex(fa(purposescales_cor,4,rotate="TargetQ", n.obs = 1160, Target=Targ_key),heading="Table 7 Factor Based on theory")## % Called in the psych package fa2latex % Called in the psych package fa(purposescales_cor, 4, rotate = "TargetQ", n.obs = 1160, Target = Targ_key) % Called in the psych package Table 7 Factor Based on theory
## \begin{table}[htdp]\caption{fa2latex}
## \begin{center}
## \begin{scriptsize}
## \begin{tabular} {l r r r r r r r }
## \multicolumn{ 7 }{l}{ Table 7 Factor Based on theory } \cr
## \hline Variable & MR1 & MR4 & MR2 & MR3 & h2 & u2 & com \cr
## \hline
## 1 & \bf{ 0.72} & -0.07 & -0.02 & 0.12 & 0.60 & 0.40 & 1.08 \cr
## 2 & \bf{ 0.74} & -0.04 & -0.04 & 0.13 & 0.65 & 0.35 & 1.07 \cr
## 3 & \bf{ 0.55} & -0.03 & 0.13 & 0.08 & 0.37 & 0.63 & 1.16 \cr
## 4 & \bf{ 0.74} & 0.10 & 0.02 & 0.01 & 0.59 & 0.41 & 1.04 \cr
## 5 & \bf{ 0.77} & 0.00 & -0.04 & 0.06 & 0.65 & 0.35 & 1.02 \cr
## 6 & \bf{ 0.75} & 0.08 & 0.00 & -0.14 & 0.50 & 0.50 & 1.09 \cr
## 7 & \bf{ 0.76} & -0.05 & -0.09 & 0.17 & 0.71 & 0.29 & 1.14 \cr
## 8 & \bf{ 0.53} & 0.01 & 0.06 & 0.10 & 0.36 & 0.64 & 1.10 \cr
## 9 & -0.17 & 0.04 & \bf{-0.52} & 0.16 & 0.25 & 0.75 & 1.45 \cr
## 10 & 0.23 & 0.05 & \bf{ 0.56} & \bf{-0.36} & 0.27 & 0.73 & 2.11 \cr
## 11 & -0.09 & 0.03 & \bf{ 0.80} & 0.03 & 0.66 & 0.34 & 1.03 \cr
## 12 & 0.02 & -0.05 & \bf{-0.77} & 0.01 & 0.58 & 0.42 & 1.01 \cr
## 13 & 0.00 & -0.15 & \bf{ 0.54} & 0.22 & 0.48 & 0.52 & 1.49 \cr
## 14 & 0.25 & 0.05 & \bf{-0.64} & -0.29 & 0.64 & 0.36 & 1.73 \cr
## 15 & 0.30 & 0.06 & 0.04 & \bf{ 0.57} & 0.60 & 0.40 & 1.54 \cr
## 16 & 0.08 & 0.09 & 0.17 & \bf{ 0.65} & 0.61 & 0.39 & 1.21 \cr
## 17 & 0.30 & 0.00 & -0.01 & \bf{ 0.61} & 0.62 & 0.38 & 1.46 \cr
## 18 & -0.02 & 0.02 & 0.04 & \bf{ 0.83} & 0.71 & 0.29 & 1.01 \cr
## 19 & 0.01 & \bf{ 0.80} & -0.02 & -0.06 & 0.65 & 0.35 & 1.01 \cr
## 20 & 0.09 & \bf{ 0.73} & -0.02 & 0.04 & 0.57 & 0.43 & 1.04 \cr
## 21 & -0.07 & \bf{ 0.71} & -0.03 & 0.15 & 0.53 & 0.47 & 1.11 \cr
## 22 & 0.07 & \bf{ 0.74} & 0.06 & 0.01 & 0.55 & 0.45 & 1.03 \cr
## 23 & -0.13 & \bf{ 0.82} & -0.04 & -0.01 & 0.67 & 0.33 & 1.06 \cr
## \hline \cr SS loadings & 4.57 & 2.98 & 2.68 & 2.57 & \cr
## \cr
## \hline \cr
## MR1 & 1.00 & 0.11 & 0.03 & 0.47 \cr
## MR4 & 0.11 & 1.00 & -0.18 & 0.04 \cr
## MR2 & 0.03 & -0.18 & 1.00 & 0.37 \cr
## MR3 & 0.47 & 0.04 & 0.37 & 1.00 \cr
## \hline
## \end{tabular}
## \end{scriptsize}
## \end{center}
## \label{default}
## \end{table}#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$null.dof))## [1] 0.9650577Target rotation based on EFA of individual intruments
all_surveys<-read.csv("allsurveysYT1.csv")
purposescales<-select(all_surveys,
#Factor 1 Plans:
PWB_1, PWB_3, PWB_5, PWB_6,
#Factor 2 Understanding Self and Life, Making Plans:
APSI_1, APSI_2, APSI_4, APSI_5, APSI_7, APSI_8,
#Factor 3 Meaningful activities:
LET_1, LET_3, LET_5,
#Factor 5 Have Purpose:
MLQ_4, MLQ_5, MLQ_6, MLQ_9, MLQ_1,
#Factor 6 Searching for meaning
MLQ_2, MLQ_3, MLQ_7, MLQ_8, MLQ_10)
purposescales$PWB_1 <- 7- purposescales$PWB_1
purposescales$PWB_5 <- 7- purposescales$PWB_5
purposescales$PWB_3 <- 7- purposescales$PWB_3
purposescales$MLQ_9 <- 8- purposescales$MLQ_9
purposescales$LET_1 <- 6- purposescales$LET_1
purposescales$LET_3 <- 6- purposescales$LET_3
purposescales$LET_5 <- 6- purposescales$LET_5
purposescales<- data.frame(apply(purposescales,2, as.numeric))
library(GPArotation)
library(psych)
library(dplyr)
purposescales<-tbl_df(purposescales)
purposescales## Source: local data frame [1,160 x 23]
##
## PWB_1 PWB_3 PWB_5 PWB_6 APSI_1 APSI_2 APSI_4 APSI_5 APSI_7 APSI_8 LET_1
## 1 4 5 3 5 2 4 4 4 4 4 4
## 2 4 5 5 5 4 3 5 4 4 4 3
## 3 5 5 6 4 3 4 3 3 4 3 3
## 4 2 4 4 4 4 4 4 5 4 3 1
## 5 2 3 3 3 3 3 3 4 2 3 3
## 6 5 6 4 4 3 4 4 4 5 3 5
## 7 2 5 6 4 2 2 3 4 2 2 3
## 8 6 5 5 4 3 3 3 5 3 1 3
## 9 5 5 6 5 4 5 4 4 4 5 5
## 10 6 3 5 6 2 2 3 5 3 4 3
## .. ... ... ... ... ... ... ... ... ... ... ...
## Variables not shown: LET_3 (dbl), LET_5 (dbl), MLQ_4 (dbl), MLQ_5 (dbl),
## MLQ_6 (dbl), MLQ_9 (dbl), MLQ_1 (dbl), MLQ_2 (dbl), MLQ_3 (dbl), MLQ_7
## (dbl), MLQ_8 (dbl), MLQ_10 (dbl)str(purposescales)## Classes 'tbl_df', 'tbl' and 'data.frame': 1160 obs. of 23 variables:
## $ PWB_1 : num 4 4 5 2 2 5 2 6 5 6 ...
## $ PWB_3 : num 5 5 5 4 3 6 5 5 5 3 ...
## $ PWB_5 : num 3 5 6 4 3 4 6 5 6 5 ...
## $ PWB_6 : num 5 5 4 4 3 4 4 4 5 6 ...
## $ APSI_1: num 2 4 3 4 3 3 2 3 4 2 ...
## $ APSI_2: num 4 3 4 4 3 4 2 3 5 2 ...
## $ APSI_4: num 4 5 3 4 3 4 3 3 4 3 ...
## $ APSI_5: num 4 4 3 5 4 4 4 5 4 5 ...
## $ APSI_7: num 4 4 4 4 2 5 2 3 4 3 ...
## $ APSI_8: num 4 4 3 3 3 3 2 1 5 4 ...
## $ LET_1 : num 4 3 3 1 3 5 3 3 5 3 ...
## $ LET_3 : num 4 4 3 4 3 5 2 3 5 5 ...
## $ LET_5 : num 5 4 4 4 2 5 3 4 5 5 ...
## $ MLQ_4 : num 5 5 4 3 4 4 3 5 7 3 ...
## $ MLQ_5 : num 6 4 4 5 4 5 6 5 6 5 ...
## $ MLQ_6 : num 4 3 4 5 4 5 3 4 6 1 ...
## $ MLQ_9 : num 3 3 4 3 5 7 5 4 7 6 ...
## $ MLQ_1 : num 4 3 4 5 4 5 6 3 6 1 ...
## $ MLQ_2 : num 7 5 7 6 6 5 2 7 5 7 ...
## $ MLQ_3 : num 7 5 5 7 5 3 2 7 2 1 ...
## $ MLQ_7 : num 5 5 4 5 5 3 5 5 5 5 ...
## $ MLQ_8 : num 7 4 5 5 5 4 4 7 7 5 ...
## $ MLQ_10: num 7 5 4 6 5 3 4 5 2 1 ...colnames(purposescales) <- c("1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23")
#Target rotation: choose "simple structure" a priori and can be applied to oblique and orthogonal rotation based on
#what paper says facotrs should be purposescales
Targ_key <- make.keys(23,list(f1=1:4,f2=5:10, f3=11:13, f4=14:18,f5=19:23))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
purposescales_cor <- corFiml(purposescales)
out_targetQ <- fa(purposescales_cor,5,rotate="TargetQ", n.obs = 1160, Target=Targ_key) #TargetT for orthogonal rotation
out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR2 MR5 MR4 MR1 MR3
## 1 0.615
## 2 0.407 0.509
## 3 0.568 0.259
## 4 0.111 0.184 0.563
## 5 0.735 0.160 -0.162
## 6 0.709 -0.180 0.181
## 7 0.787 0.134 -0.145
## 8 0.765 -0.165 -0.118 0.181
## 9 0.826 0.298 -0.218
## 10 0.806 0.129 -0.128
## 11 -0.159 0.251 0.167 0.447
## 12 0.327 0.582
## 13 -0.175 0.103 0.402 0.436
## 14 0.160 0.690 -0.152
## 15 0.742 0.114
## 16 0.181 0.701
## 17 -0.147 0.266 0.191 0.354
## 18 -0.124 0.913
## 19 0.812
## 20 0.742
## 21 0.706 0.162 0.107 -0.121
## 22 0.739
## 23 0.823
##
## MR2 MR5 MR4 MR1 MR3
## SS loadings 3.733 3.026 2.654 1.774 1.376
## Proportion Var 0.162 0.132 0.115 0.077 0.060
## Cumulative Var 0.162 0.294 0.409 0.486 0.546
##
## $score.cor
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.00000000 0.1187769 0.5791661 -0.00269043 -0.0296822
## [2,] 0.11877694 1.0000000 0.1083203 -0.04870210 -0.1858455
## [3,] 0.57916609 0.1083203 1.0000000 0.28115501 0.3595210
## [4,] -0.00269043 -0.0487021 0.2811550 1.00000000 0.6771676
## [5,] -0.02968220 -0.1858455 0.3595210 0.67716764 1.0000000
##
## $TLI
## [1] 0.9700588
##
## $RMSEA
## RMSEA lower upper confidence
## 0.03871062 0.03389355 0.04292026 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = purposescales_cor, nfactors = 5, n.obs = 1160, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR5 MR4 MR1 MR3 h2 u2 com
## 1 -0.03 -0.08 -0.10 0.61 0.07 0.42 0.58 1.1
## 2 0.00 0.00 0.05 0.41 0.51 0.66 0.34 1.9
## 3 0.06 -0.02 0.04 0.57 0.26 0.55 0.45 1.4
## 4 0.09 0.11 0.18 0.56 0.03 0.45 0.55 1.4
## 5 0.74 -0.03 0.16 -0.16 0.08 0.73 0.27 1.2
## 6 0.71 -0.04 0.10 -0.18 0.18 0.63 0.37 1.3
## 7 0.79 -0.06 0.08 0.13 -0.14 0.69 0.31 1.2
## 8 0.77 0.10 -0.17 -0.12 0.18 0.51 0.49 1.3
## 9 0.83 0.06 -0.06 0.30 -0.22 0.68 0.32 1.4
## 10 0.81 -0.02 0.02 0.13 -0.13 0.67 0.33 1.1
## 11 0.02 -0.16 0.25 0.17 0.45 0.52 0.48 2.2
## 12 -0.08 -0.03 0.05 0.33 0.58 0.68 0.32 1.6
## 13 -0.18 0.01 0.10 0.40 0.44 0.62 0.38 2.4
## 14 0.16 0.06 0.69 -0.15 0.08 0.63 0.37 1.2
## 15 0.00 0.06 0.74 0.11 -0.01 0.62 0.38 1.1
## 16 0.18 0.00 0.70 -0.08 -0.03 0.64 0.36 1.2
## 17 0.03 -0.15 0.27 0.19 0.35 0.44 0.56 2.9
## 18 -0.12 0.01 0.91 0.01 -0.07 0.69 0.31 1.1
## 19 0.01 0.81 -0.07 -0.03 0.03 0.65 0.35 1.0
## 20 0.08 0.74 0.04 -0.07 0.06 0.57 0.43 1.1
## 21 -0.08 0.71 0.16 0.11 -0.12 0.55 0.45 1.2
## 22 0.08 0.74 0.01 0.04 0.06 0.55 0.45 1.0
## 23 -0.09 0.82 -0.05 0.01 0.00 0.67 0.33 1.0
##
## MR2 MR5 MR4 MR1 MR3
## SS loadings 3.86 3.05 2.91 2.17 1.84
## Proportion Var 0.17 0.13 0.13 0.09 0.08
## Cumulative Var 0.17 0.30 0.43 0.52 0.60
## Proportion Explained 0.28 0.22 0.21 0.16 0.13
## Cumulative Proportion 0.28 0.50 0.71 0.87 1.00
##
## With factor correlations of
## MR2 MR5 MR4 MR1 MR3
## MR2 1.00 0.12 0.57 -0.13 -0.08
## MR5 0.12 1.00 0.04 -0.08 -0.20
## MR4 0.57 0.04 1.00 0.30 0.31
## MR1 -0.13 -0.08 0.30 1.00 0.49
## MR3 -0.08 -0.20 0.31 0.49 1.00
##
## Mean item complexity = 1.4
## Test of the hypothesis that 5 factors are sufficient.
##
## The degrees of freedom for the null model are 253 and the objective function was 12.81 with Chi Square of 14735.17
## The degrees of freedom for the model are 148 and the objective function was 0.35
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.02
##
## The harmonic number of observations is 1160 with the empirical chi square 135.96 with prob < 0.75
## The total number of observations was 1160 with MLE Chi Square = 400.91 with prob < 3.4e-25
##
## Tucker Lewis Index of factoring reliability = 0.97
## RMSEA index = 0.039 and the 90 % confidence intervals are 0.034 0.043
## BIC = -643.41
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR2 MR5 MR4 MR1 MR3
## Correlation of scores with factors 0.96 0.94 0.95 0.90 0.89
## Multiple R square of scores with factors 0.92 0.89 0.89 0.81 0.79
## Minimum correlation of possible factor scores 0.84 0.77 0.79 0.61 0.58fa2latex(fa(purposescales_cor,5,rotate="TargetQ", n.obs = 1160, Target=Targ_key),heading="Table 7 Factor Based on theory")## % Called in the psych package fa2latex % Called in the psych package fa(purposescales_cor, 5, rotate = "TargetQ", n.obs = 1160, Target = Targ_key) % Called in the psych package Table 7 Factor Based on theory
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## \end{table}#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$null.dof))## [1] 0.9825366``` # Target rotation based on EFA of words using LDA
all_surveys<-read.csv("allsurveysYT1.csv")
purposescales<-select(all_surveys,
#Factor 1 Plans:
PWB_9, APSI_1, APSI_2, APSI_4,APSI_8, LET_1, LET_4, LET_6,
#Factor 2 Understanding Self and Life, Making Plans:
PWB_1, PWB_3, PWB_4, PWB_5, APSI_7, LET_3,
#Factor 3 Meaningful activities:
PWB_2, PWB_6, LET_2, LET_5, PWB_7,
#Factor 4 Meaningful activities:
PWB_8, APSI_3, APSI_5, APSI_6,
#Factor 5 Have Purpose:
MLQ_4, MLQ_5, MLQ_6, MLQ_9, MLQ_1,
#Factor 6 Searching for meaning
MLQ_2, MLQ_3, MLQ_7, MLQ_8, MLQ_10)
purposescales$PWB_1 <- 7- purposescales$PWB_1
purposescales$PWB_5 <- 7- purposescales$PWB_5
purposescales$PWB_3 <- 7- purposescales$PWB_3
purposescales$PWB_9 <- 7- purposescales$PWB_9
purposescales$MLQ_9 <- 8- purposescales$MLQ_9
purposescales$LET_1 <- 6- purposescales$LET_1
purposescales$LET_3 <- 6- purposescales$LET_3
purposescales$LET_5 <- 6- purposescales$LET_5
purposescales<- data.frame(apply(purposescales,2, as.numeric))
library(GPArotation)
library(psych)
library(dplyr)
purposescales<-tbl_df(purposescales)
purposescales## Source: local data frame [1,160 x 33]
##
## PWB_9 APSI_1 APSI_2 APSI_4 APSI_8 LET_1 LET_4 LET_6 PWB_1 PWB_3 PWB_4
## 1 6 2 4 4 4 4 5 5 4 5 5
## 2 5 4 3 5 4 3 4 5 4 5 5
## 3 6 3 4 3 3 3 4 5 5 5 1
## 4 4 4 4 4 3 1 4 4 2 4 3
## 5 4 3 3 3 3 3 4 4 2 3 4
## 6 6 3 4 4 3 5 5 4 5 6 2
## 7 3 2 2 3 2 3 3 5 2 5 5
## 8 6 3 3 3 1 3 4 5 6 5 6
## 9 6 4 5 4 5 5 5 5 5 5 2
## 10 6 2 2 3 4 3 5 5 6 3 4
## .. ... ... ... ... ... ... ... ... ... ... ...
## Variables not shown: PWB_5 (dbl), APSI_7 (dbl), LET_3 (dbl), PWB_2 (dbl),
## PWB_6 (dbl), LET_2 (dbl), LET_5 (dbl), PWB_7 (dbl), PWB_8 (dbl), APSI_3
## (dbl), APSI_5 (dbl), APSI_6 (dbl), MLQ_4 (dbl), MLQ_5 (dbl), MLQ_6
## (dbl), MLQ_9 (dbl), MLQ_1 (dbl), MLQ_2 (dbl), MLQ_3 (dbl), MLQ_7 (dbl),
## MLQ_8 (dbl), MLQ_10 (dbl)str(purposescales)## Classes 'tbl_df', 'tbl' and 'data.frame': 1160 obs. of 33 variables:
## $ PWB_9 : num 6 5 6 4 4 6 3 6 6 6 ...
## $ APSI_1: num 2 4 3 4 3 3 2 3 4 2 ...
## $ APSI_2: num 4 3 4 4 3 4 2 3 5 2 ...
## $ APSI_4: num 4 5 3 4 3 4 3 3 4 3 ...
## $ APSI_8: num 4 4 3 3 3 3 2 1 5 4 ...
## $ LET_1 : num 4 3 3 1 3 5 3 3 5 3 ...
## $ LET_4 : num 5 4 4 4 4 5 3 4 5 5 ...
## $ LET_6 : num 5 5 5 4 4 4 5 5 5 5 ...
## $ PWB_1 : num 4 4 5 2 2 5 2 6 5 6 ...
## $ PWB_3 : num 5 5 5 4 3 6 5 5 5 3 ...
## $ PWB_4 : num 5 5 1 3 4 2 5 6 2 4 ...
## $ PWB_5 : num 3 5 6 4 3 4 6 5 6 5 ...
## $ APSI_7: num 4 4 4 4 2 5 2 3 4 3 ...
## $ LET_3 : num 4 4 3 4 3 5 2 3 5 5 ...
## $ PWB_2 : num 4 2 1 5 5 3 5 1 2 1 ...
## $ PWB_6 : num 5 5 4 4 3 4 4 4 5 6 ...
## $ LET_2 : num 4 3 4 4 2 5 4 4 4 3 ...
## $ LET_5 : num 5 4 4 4 2 5 3 4 5 5 ...
## $ PWB_7 : num 4 3 6 5 2 3 3 4 5 6 ...
## $ PWB_8 : num 3 2 3 4 3 4 3 4 5 3 ...
## $ APSI_3: num 4 4 4 5 4 4 4 4 5 2 ...
## $ APSI_5: num 4 4 3 5 4 4 4 5 4 5 ...
## $ APSI_6: num 4 3 3 4 3 2 4 3 2 3 ...
## $ MLQ_4 : num 5 5 4 3 4 4 3 5 7 3 ...
## $ MLQ_5 : num 6 4 4 5 4 5 6 5 6 5 ...
## $ MLQ_6 : num 4 3 4 5 4 5 3 4 6 1 ...
## $ MLQ_9 : num 3 3 4 3 5 7 5 4 7 6 ...
## $ MLQ_1 : num 4 3 4 5 4 5 6 3 6 1 ...
## $ MLQ_2 : num 7 5 7 6 6 5 2 7 5 7 ...
## $ MLQ_3 : num 7 5 5 7 5 3 2 7 2 1 ...
## $ MLQ_7 : num 5 5 4 5 5 3 5 5 5 5 ...
## $ MLQ_8 : num 7 4 5 5 5 4 4 7 7 5 ...
## $ MLQ_10: num 7 5 4 6 5 3 4 5 2 1 ...colnames(purposescales) <- c("1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33")
#Target rotation: choose "simple structure" a priori and can be applied to oblique and orthogonal rotation based on
#what paper says facotrs should be purposescales
Targ_key <- make.keys(33,list(f1=1:8,f2=9:14, f3=15:18, f4=19:22,f5=23:27, f6=28:33))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
purposescales_cor <- corFiml(purposescales)
out_targetQ <- fa(purposescales_cor,6,rotate="TargetQ", n.obs = 1160, Target=Targ_key) #TargetT for orthogonal rotation
out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR2 MR6 MR5 MR1 MR4 MR3
## 1 0.612 -0.162 0.137 0.139
## 2 0.232 0.507 0.179 0.258
## 3 0.157 0.393 0.275 0.285
## 4 0.149 0.636 0.237
## 5 0.699 0.166
## 6 0.432 -0.172 0.384
## 7 0.364 0.379
## 8 0.219 0.163 -0.110 0.484 0.114
## 9 0.564 -0.331
## 10 0.661 0.166 -0.237 0.163
## 11 -0.537 -0.284 -0.337 0.148
## 12 0.605 0.113 0.168 -0.157
## 13 0.108 0.667 0.271
## 14 0.595 0.202 -0.250
## 15 -0.587 -0.230 0.168
## 16 0.271 0.172 0.409 -0.350
## 17 0.148 0.242 0.547
## 18 0.535 0.186 -0.275 0.140 -0.155
## 19 0.184 0.297 0.293 0.221
## 20 0.108 0.334 0.307
## 21 0.574 -0.282
## 22 -0.146 0.372 0.425 0.214
## 23 -0.456 0.102 -0.389 0.179 0.208
## 24 0.103 0.658 0.149 0.207
## 25 0.658 0.168
## 26 0.670 0.290
## 27 0.378 -0.153 0.375
## 28 -0.116 0.802 0.104
## 29 0.829
## 30 0.754
## 31 0.738 0.134 -0.126
## 32 0.746
## 33 0.813 -0.155
##
## MR2 MR6 MR5 MR1 MR4 MR3
## SS loadings 3.488 3.164 2.913 2.676 1.748 1.239
## Proportion Var 0.106 0.096 0.088 0.081 0.053 0.038
## Cumulative Var 0.106 0.202 0.290 0.371 0.424 0.461
##
## $score.cor
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 1.00000000 -0.17978209 0.3591163 0.01322533 0.3977301 0.07642717
## [2,] -0.17978209 1.00000000 0.1083251 0.11661379 0.1015749 0.06331723
## [3,] 0.35911633 0.10832509 1.0000000 0.57767498 0.6302797 0.43439628
## [4,] 0.01322533 0.11661379 0.5776750 1.00000000 0.6142907 0.55081320
## [5,] 0.39773014 0.10157487 0.6302797 0.61429072 1.0000000 0.49585825
## [6,] 0.07642717 0.06331723 0.4343963 0.55081320 0.4958582 1.00000000
##
## $TLI
## [1] 0.9227126
##
## $RMSEA
## RMSEA lower upper confidence
## 0.05111220 0.04782935 0.05344921 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = purposescales_cor, nfactors = 6, n.obs = 1160, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR6 MR5 MR1 MR4 MR3 h2 u2 com
## 1 0.61 0.03 -0.16 0.14 -0.09 0.14 0.31 0.69 1.4
## 2 -0.07 -0.01 0.23 0.51 0.18 0.26 0.71 0.29 2.3
## 3 -0.04 -0.04 0.16 0.39 0.27 0.28 0.61 0.39 3.1
## 4 -0.06 -0.02 0.15 0.64 0.24 0.02 0.67 0.33 1.4
## 5 0.00 0.03 0.10 0.70 0.17 0.03 0.69 0.31 1.2
## 6 0.43 -0.17 0.38 -0.09 0.01 0.07 0.51 0.49 2.5
## 7 0.04 0.03 0.02 0.08 0.36 0.38 0.41 0.59 2.1
## 8 0.22 0.01 0.16 -0.11 0.48 0.11 0.45 0.55 1.9
## 9 0.56 -0.06 -0.06 0.07 0.04 -0.33 0.47 0.53 1.7
## 10 0.66 -0.03 0.17 -0.24 0.16 0.02 0.69 0.31 1.5
## 11 -0.54 0.05 -0.28 -0.34 0.15 -0.06 0.52 0.48 2.5
## 12 0.61 -0.02 0.11 -0.05 0.17 -0.16 0.57 0.43 1.4
## 13 0.02 0.11 0.02 0.67 0.27 -0.06 0.65 0.35 1.4
## 14 0.59 -0.08 0.20 -0.25 0.09 -0.05 0.62 0.38 1.7
## 15 -0.59 0.04 -0.01 -0.23 0.17 -0.04 0.34 0.66 1.5
## 16 0.27 0.10 0.17 0.01 0.41 -0.35 0.50 0.50 3.3
## 17 0.06 0.03 0.15 0.07 0.24 0.55 0.56 0.44 1.6
## 18 0.54 -0.03 0.19 -0.28 0.14 -0.15 0.60 0.40 2.2
## 19 0.03 0.07 0.18 0.30 0.29 0.22 0.51 0.49 3.7
## 20 0.07 -0.03 0.11 0.33 0.31 0.09 0.39 0.61 2.5
## 21 0.07 0.03 0.03 -0.01 0.57 -0.28 0.39 0.61 1.5
## 22 -0.01 0.07 -0.15 0.37 0.42 0.21 0.53 0.47 2.8
## 23 -0.46 0.10 -0.39 0.18 -0.08 0.21 0.65 0.35 2.9
## 24 -0.04 0.10 0.66 0.15 0.04 0.21 0.64 0.36 1.4
## 25 0.03 0.10 0.66 0.07 0.17 -0.07 0.61 0.39 1.2
## 26 -0.06 0.06 0.67 0.29 -0.01 0.08 0.64 0.36 1.4
## 27 0.38 -0.15 0.37 0.00 0.02 -0.04 0.44 0.56 2.3
## 28 -0.12 0.05 0.80 0.04 0.10 -0.09 0.69 0.31 1.1
## 29 0.08 0.83 -0.05 -0.04 -0.08 0.05 0.66 0.34 1.1
## 30 0.03 0.75 0.06 0.00 -0.02 0.08 0.57 0.43 1.0
## 31 0.01 0.74 0.13 0.01 -0.05 -0.13 0.55 0.45 1.1
## 32 0.09 0.75 0.02 0.00 0.03 -0.01 0.55 0.45 1.0
## 33 -0.01 0.81 -0.07 -0.15 0.04 -0.07 0.68 0.32 1.1
##
## MR2 MR6 MR5 MR1 MR4 MR3
## SS loadings 3.98 3.21 3.68 3.37 2.49 1.62
## Proportion Var 0.12 0.10 0.11 0.10 0.08 0.05
## Cumulative Var 0.12 0.22 0.33 0.43 0.51 0.56
## Proportion Explained 0.22 0.17 0.20 0.18 0.14 0.09
## Cumulative Proportion 0.22 0.39 0.59 0.78 0.91 1.00
##
## With factor correlations of
## MR2 MR6 MR5 MR1 MR4 MR3
## MR2 1.00 -0.18 0.33 -0.11 0.22 -0.15
## MR6 -0.18 1.00 -0.02 0.14 0.13 0.08
## MR5 0.33 -0.02 1.00 0.24 0.49 0.10
## MR1 -0.11 0.14 0.24 1.00 0.33 0.42
## MR4 0.22 0.13 0.49 0.33 1.00 0.22
## MR3 -0.15 0.08 0.10 0.42 0.22 1.00
##
## Mean item complexity = 1.9
## Test of the hypothesis that 6 factors are sufficient.
##
## The degrees of freedom for the null model are 528 and the objective function was 18.23 with Chi Square of 20910.11
## The degrees of freedom for the model are 345 and the objective function was 1.2
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.03
##
## The harmonic number of observations is 1160 with the empirical chi square 649.93 with prob < 5.8e-21
## The total number of observations was 1160 with MLE Chi Square = 1370.62 with prob < 4.4e-122
##
## Tucker Lewis Index of factoring reliability = 0.923
## RMSEA index = 0.051 and the 90 % confidence intervals are 0.048 0.053
## BIC = -1063.76
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## MR2 MR6 MR5 MR1 MR4
## Correlation of scores with factors 0.94 0.94 0.94 0.93 0.88
## Multiple R square of scores with factors 0.88 0.89 0.88 0.87 0.78
## Minimum correlation of possible factor scores 0.76 0.78 0.77 0.73 0.56
## MR3
## Correlation of scores with factors 0.86
## Multiple R square of scores with factors 0.73
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## 32 & 0.09 & \bf{ 0.75} & 0.02 & 0.00 & 0.03 & -0.01 & 0.55 & 0.45 & 1.03 \cr
## 33 & -0.01 & \bf{ 0.81} & -0.07 & -0.15 & 0.04 & -0.07 & 0.68 & 0.32 & 1.11 \cr
## \hline \cr SS loadings & 3.98 & 3.21 & 3.68 & 3.37 & 2.49 & 1.62 & \cr
## \cr
## \hline \cr
## MR2 & 1.00 & -0.18 & 0.33 & -0.11 & 0.22 & -0.15 \cr
## MR6 & -0.18 & 1.00 & -0.02 & 0.14 & 0.13 & 0.08 \cr
## MR5 & 0.33 & -0.02 & 1.00 & 0.24 & 0.49 & 0.10 \cr
## MR1 & -0.11 & 0.14 & 0.24 & 1.00 & 0.33 & 0.42 \cr
## MR4 & 0.22 & 0.13 & 0.49 & 0.33 & 1.00 & 0.22 \cr
## MR3 & -0.15 & 0.08 & 0.10 & 0.42 & 0.22 & 1.00 \cr
## \hline
## \end{tabular}
## \end{scriptsize}
## \end{center}
## \label{default}
## \end{table}#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$null.dof))## [1] 0.9496804``` # Target rotation based on 5 factors 1. Understanding Self, 2. Making Plans, 3. Daily Activities, 4, Sense of Purpose, 5. Serching for Purpose
all_surveys<-read.csv("allsurveysYT1.csv")
purposescales<-select(all_surveys,
#Factor 1 Understanding Self:
APSI_2, APSI_4, APSI_5, APSI_6,
#Factor 2: (making plans)
PWB_8, APSI_7, APSI_8, PWB_5, PWB_7, PWB_6,
#Factor 3: (Daily Activities)
LET_2, PWB_2, PWB_3, PWB_9, LET_4,
#Factor 4
MLQ_4, MLQ_5, MLQ_6, MLQ_1, MLQ_9,
#Factor 5
MLQ_2, MLQ_3, MLQ_7, MLQ_8, MLQ_10)
purposescales$PWB_5 <- 7- purposescales$PWB_5
purposescales$PWB_3 <- 7- purposescales$PWB_3
purposescales$PWB_9 <- 7- purposescales$PWB_9
purposescales$MLQ_9 <- 8- purposescales$MLQ_9
purposescales<- data.frame(apply(purposescales,2, as.numeric))
library(GPArotation)
library(psych)
library(dplyr)
purposescales<-tbl_df(purposescales)
purposescales## Source: local data frame [1,160 x 25]
##
## APSI_2 APSI_4 APSI_5 APSI_6 PWB_8 APSI_7 APSI_8 PWB_5 PWB_7 PWB_6 LET_2
## 1 4 4 4 4 3 4 4 3 4 5 4
## 2 3 5 4 3 2 4 4 5 3 5 3
## 3 4 3 3 3 3 4 3 6 6 4 4
## 4 4 4 5 4 4 4 3 4 5 4 4
## 5 3 3 4 3 3 2 3 3 2 3 2
## 6 4 4 4 2 4 5 3 4 3 4 5
## 7 2 3 4 4 3 2 2 6 3 4 4
## 8 3 3 5 3 4 3 1 5 4 4 4
## 9 5 4 4 2 5 4 5 6 5 5 4
## 10 2 3 5 3 3 3 4 5 6 6 3
## .. ... ... ... ... ... ... ... ... ... ... ...
## Variables not shown: PWB_2 (dbl), PWB_3 (dbl), PWB_9 (dbl), LET_4 (dbl),
## MLQ_4 (dbl), MLQ_5 (dbl), MLQ_6 (dbl), MLQ_1 (dbl), MLQ_9 (dbl), MLQ_2
## (dbl), MLQ_3 (dbl), MLQ_7 (dbl), MLQ_8 (dbl), MLQ_10 (dbl)str(purposescales)## Classes 'tbl_df', 'tbl' and 'data.frame': 1160 obs. of 25 variables:
## $ APSI_2: num 4 3 4 4 3 4 2 3 5 2 ...
## $ APSI_4: num 4 5 3 4 3 4 3 3 4 3 ...
## $ APSI_5: num 4 4 3 5 4 4 4 5 4 5 ...
## $ APSI_6: num 4 3 3 4 3 2 4 3 2 3 ...
## $ PWB_8 : num 3 2 3 4 3 4 3 4 5 3 ...
## $ APSI_7: num 4 4 4 4 2 5 2 3 4 3 ...
## $ APSI_8: num 4 4 3 3 3 3 2 1 5 4 ...
## $ PWB_5 : num 3 5 6 4 3 4 6 5 6 5 ...
## $ PWB_7 : num 4 3 6 5 2 3 3 4 5 6 ...
## $ PWB_6 : num 5 5 4 4 3 4 4 4 5 6 ...
## $ LET_2 : num 4 3 4 4 2 5 4 4 4 3 ...
## $ PWB_2 : num 4 2 1 5 5 3 5 1 2 1 ...
## $ PWB_3 : num 5 5 5 4 3 6 5 5 5 3 ...
## $ PWB_9 : num 6 5 6 4 4 6 3 6 6 6 ...
## $ LET_4 : num 5 4 4 4 4 5 3 4 5 5 ...
## $ MLQ_4 : num 5 5 4 3 4 4 3 5 7 3 ...
## $ MLQ_5 : num 6 4 4 5 4 5 6 5 6 5 ...
## $ MLQ_6 : num 4 3 4 5 4 5 3 4 6 1 ...
## $ MLQ_1 : num 4 3 4 5 4 5 6 3 6 1 ...
## $ MLQ_9 : num 3 3 4 3 5 7 5 4 7 6 ...
## $ MLQ_2 : num 7 5 7 6 6 5 2 7 5 7 ...
## $ MLQ_3 : num 7 5 5 7 5 3 2 7 2 1 ...
## $ MLQ_7 : num 5 5 4 5 5 3 5 5 5 5 ...
## $ MLQ_8 : num 7 4 5 5 5 4 4 7 7 5 ...
## $ MLQ_10: num 7 5 4 6 5 3 4 5 2 1 ...colnames(purposescales) <- c("1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24", "25")
#Target rotation: choose "simple structure" a priori and can be applied to oblique and orthogonal rotation based on
#what paper says facotrs should be purposescales
Targ_key <- make.keys(25,list(f1=1:4,f2=5:10, f3=11:15, f4=16:20,f5=21:25))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
purposescales_cor <- corFiml(purposescales)
out_targetQ <- fa(purposescales_cor,5,rotate="TargetQ", n.obs = 1160, Target=Targ_key) #TargetT for orthogonal rotation
out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR5 MR4 MR1 MR2 MR3
## 1 0.245 0.410 0.344 0.126
## 2 0.188 0.194 0.653
## 3 0.413 0.404 0.179
## 4 0.117 -0.330 0.555 -0.307
## 5 0.162 0.185 0.376 0.161
## 6 0.128 0.745
## 7 0.118 0.235 0.665
## 8 -0.452 0.146 0.504
## 9 0.270 0.336 0.269 0.206
## 10 0.109 0.219 -0.413 0.257 0.178
## 11 0.221 0.580 0.434
## 12 0.120 -0.133 -0.405
## 13 0.105 -0.344 0.632
## 14 -0.277 0.148 0.506
## 15 0.120 0.457 0.397
## 16 0.695 0.149
## 17 0.731 -0.135
## 18 0.706 0.140
## 19 0.907 -0.135
## 20 -0.180 0.310 -0.325 0.241
## 21 0.815
## 22 0.739
## 23 0.720 0.119 -0.139
## 24 0.737
## 25 0.818
##
## MR5 MR4 MR1 MR2 MR3
## SS loadings 3.044 2.987 2.163 2.106 1.766
## Proportion Var 0.122 0.119 0.087 0.084 0.071
## Cumulative Var 0.122 0.241 0.328 0.412 0.483
##
## $score.cor
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.0000000 0.10837734 0.15700546 0.1182883 -0.1048692
## [2,] 0.1083773 1.00000000 0.06481165 0.6013136 0.3651705
## [3,] 0.1570055 0.06481165 1.00000000 0.4479663 -0.3863090
## [4,] 0.1182883 0.60131359 0.44796633 1.0000000 0.1469474
## [5,] -0.1048692 0.36517047 -0.38630896 0.1469474 1.0000000
##
## $TLI
## [1] 0.9404295
##
## $RMSEA
## RMSEA lower upper confidence
## 0.04941118 0.04520159 0.05291182 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = purposescales_cor, nfactors = 5, n.obs = 1160, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR5 MR4 MR1 MR2 MR3 h2 u2 com
## 1 -0.05 0.24 0.41 0.34 0.13 0.59 0.41 2.9
## 2 -0.04 0.19 0.19 0.65 -0.08 0.68 0.32 1.4
## 3 0.09 -0.03 0.41 0.40 0.18 0.48 0.52 2.5
## 4 0.12 -0.33 0.55 0.02 -0.31 0.67 0.33 2.4
## 5 -0.03 0.16 0.18 0.38 0.16 0.39 0.61 2.3
## 6 0.09 0.04 0.13 0.75 -0.05 0.67 0.33 1.1
## 7 0.00 0.12 0.23 0.67 -0.04 0.68 0.32 1.3
## 8 -0.02 0.03 -0.45 0.15 0.50 0.61 0.39 2.2
## 9 0.06 0.27 0.34 0.27 0.21 0.52 0.48 3.7
## 10 0.11 0.22 -0.41 0.26 0.18 0.39 0.61 2.9
## 11 0.03 0.22 0.58 -0.02 0.43 0.57 0.43 2.2
## 12 0.08 0.08 0.12 -0.13 -0.40 0.22 0.78 1.6
## 13 -0.03 0.11 -0.34 -0.07 0.63 0.67 0.33 1.6
## 14 0.01 -0.28 -0.07 0.15 0.51 0.25 0.75 1.8
## 15 0.05 0.12 0.46 0.08 0.40 0.42 0.58 2.2
## 16 0.05 0.70 0.15 0.04 0.06 0.62 0.38 1.1
## 17 0.07 0.73 -0.14 0.06 0.04 0.61 0.39 1.1
## 18 0.00 0.71 0.09 0.14 -0.06 0.62 0.38 1.1
## 19 0.01 0.91 -0.14 -0.04 -0.10 0.71 0.29 1.1
## 20 -0.18 0.31 -0.32 0.08 0.24 0.42 0.58 3.6
## 21 0.81 -0.08 0.03 -0.02 0.05 0.66 0.34 1.0
## 22 0.74 0.05 0.08 0.00 0.06 0.57 0.43 1.0
## 23 0.72 0.12 -0.14 0.05 -0.09 0.55 0.45 1.2
## 24 0.74 0.01 -0.04 0.09 0.05 0.55 0.45 1.0
## 25 0.82 -0.06 -0.06 -0.06 -0.02 0.66 0.34 1.0
##
## MR5 MR4 MR1 MR2 MR3
## SS loadings 3.07 3.45 2.43 2.77 2.05
## Proportion Var 0.12 0.14 0.10 0.11 0.08
## Cumulative Var 0.12 0.26 0.36 0.47 0.55
## Proportion Explained 0.22 0.25 0.18 0.20 0.15
## Cumulative Proportion 0.22 0.47 0.65 0.85 1.00
##
## With factor correlations of
## MR5 MR4 MR1 MR2 MR3
## MR5 1.00 0.06 0.12 0.09 -0.11
## MR4 0.06 1.00 0.10 0.55 0.44
## MR1 0.12 0.10 1.00 0.31 -0.22
## MR2 0.09 0.55 0.31 1.00 0.31
## MR3 -0.11 0.44 -0.22 0.31 1.00
##
## Mean item complexity = 1.8
## Test of the hypothesis that 5 factors are sufficient.
##
## The degrees of freedom for the null model are 300 and the objective function was 12.51 with Chi Square of 14383.98
## The degrees of freedom for the model are 185 and the objective function was 0.61
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.03
##
## The harmonic number of observations is 1160 with the empirical chi square 383.48 with prob < 5.8e-16
## The total number of observations was 1160 with MLE Chi Square = 700.85 with prob < 4.6e-61
##
## Tucker Lewis Index of factoring reliability = 0.94
## RMSEA index = 0.049 and the 90 % confidence intervals are 0.045 0.053
## BIC = -604.55
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## MR5 MR4 MR1 MR2 MR3
## Correlation of scores with factors 0.94 0.95 0.91 0.93 0.90
## Multiple R square of scores with factors 0.89 0.90 0.83 0.86 0.81
## Minimum correlation of possible factor scores 0.77 0.79 0.67 0.71 0.62fa2latex(fa(purposescales_cor,5,rotate="TargetQ", n.obs = 1160, Target=Targ_key),heading="Table 7 Factor Based on theory")## % Called in the psych package fa2latex % Called in the psych package fa(purposescales_cor, 5, rotate = "TargetQ", n.obs = 1160, Target = Targ_key) % Called in the psych package Table 7 Factor Based on theory
## \begin{table}[htdp]\caption{fa2latex}
## \begin{center}
## \begin{scriptsize}
## \begin{tabular} {l r r r r r r r r }
## \multicolumn{ 8 }{l}{ Table 7 Factor Based on theory } \cr
## \hline Variable & MR5 & MR4 & MR1 & MR2 & MR3 & h2 & u2 & com \cr
## \hline
## 1 & -0.05 & 0.24 & \bf{ 0.41} & \bf{ 0.34} & 0.13 & 0.59 & 0.41 & 2.89 \cr
## 2 & -0.04 & 0.19 & 0.19 & \bf{ 0.65} & -0.08 & 0.68 & 0.32 & 1.40 \cr
## 3 & 0.09 & -0.03 & \bf{ 0.41} & \bf{ 0.40} & 0.18 & 0.48 & 0.52 & 2.47 \cr
## 4 & 0.12 & \bf{-0.33} & \bf{ 0.55} & 0.02 & \bf{-0.31} & 0.67 & 0.33 & 2.38 \cr
## 5 & -0.03 & 0.16 & 0.18 & \bf{ 0.38} & 0.16 & 0.39 & 0.61 & 2.32 \cr
## 6 & 0.09 & 0.04 & 0.13 & \bf{ 0.75} & -0.05 & 0.67 & 0.33 & 1.10 \cr
## 7 & 0.00 & 0.12 & 0.23 & \bf{ 0.67} & -0.04 & 0.68 & 0.32 & 1.32 \cr
## 8 & -0.02 & 0.03 & \bf{-0.45} & 0.15 & \bf{ 0.50} & 0.61 & 0.39 & 2.17 \cr
## 9 & 0.06 & 0.27 & \bf{ 0.34} & 0.27 & 0.21 & 0.52 & 0.48 & 3.69 \cr
## 10 & 0.11 & 0.22 & \bf{-0.41} & 0.26 & 0.18 & 0.39 & 0.61 & 2.92 \cr
## 11 & 0.03 & 0.22 & \bf{ 0.58} & -0.02 & \bf{ 0.43} & 0.57 & 0.43 & 2.19 \cr
## 12 & 0.08 & 0.08 & 0.12 & -0.13 & \bf{-0.40} & 0.22 & 0.78 & 1.60 \cr
## 13 & -0.03 & 0.11 & \bf{-0.34} & -0.07 & \bf{ 0.63} & 0.67 & 0.33 & 1.65 \cr
## 14 & 0.01 & -0.28 & -0.07 & 0.15 & \bf{ 0.51} & 0.25 & 0.75 & 1.79 \cr
## 15 & 0.05 & 0.12 & \bf{ 0.46} & 0.08 & \bf{ 0.40} & 0.42 & 0.58 & 2.21 \cr
## 16 & 0.05 & \bf{ 0.70} & 0.15 & 0.04 & 0.06 & 0.62 & 0.38 & 1.13 \cr
## 17 & 0.07 & \bf{ 0.73} & -0.14 & 0.06 & 0.04 & 0.61 & 0.39 & 1.10 \cr
## 18 & 0.00 & \bf{ 0.71} & 0.09 & 0.14 & -0.06 & 0.62 & 0.38 & 1.12 \cr
## 19 & 0.01 & \bf{ 0.91} & -0.14 & -0.04 & -0.10 & 0.71 & 0.29 & 1.07 \cr
## 20 & -0.18 & \bf{ 0.31} & \bf{-0.32} & 0.08 & 0.24 & 0.42 & 0.58 & 3.60 \cr
## 21 & \bf{ 0.81} & -0.08 & 0.03 & -0.02 & 0.05 & 0.66 & 0.34 & 1.03 \cr
## 22 & \bf{ 0.74} & 0.05 & 0.08 & 0.00 & 0.06 & 0.57 & 0.43 & 1.05 \cr
## 23 & \bf{ 0.72} & 0.12 & -0.14 & 0.05 & -0.09 & 0.55 & 0.45 & 1.18 \cr
## 24 & \bf{ 0.74} & 0.01 & -0.04 & 0.09 & 0.05 & 0.55 & 0.45 & 1.04 \cr
## 25 & \bf{ 0.82} & -0.06 & -0.06 & -0.06 & -0.02 & 0.66 & 0.34 & 1.03 \cr
## \hline \cr SS loadings & 3.07 & 3.45 & 2.43 & 2.77 & 2.05 & \cr
## \cr
## \hline \cr
## MR5 & 1.00 & 0.06 & 0.12 & 0.09 & -0.11 \cr
## MR4 & 0.06 & 1.00 & 0.10 & 0.55 & 0.44 \cr
## MR1 & 0.12 & 0.10 & 1.00 & 0.31 & -0.22 \cr
## MR2 & 0.09 & 0.55 & 0.31 & 1.00 & 0.31 \cr
## MR3 & -0.11 & 0.44 & -0.22 & 0.31 & 1.00 \cr
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## \label{default}
## \end{table}#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$null.dof))## [1] 0.9633736Target rotation based on 5 (moving some around based on previos results) factors – take 2. 1. Understanding Self, 2. Making Plans, 3. Daily Activities, 4, Sense of Purpose, 5. Serching for Purpose
all_surveys<-read.csv("allsurveysYT1.csv")
purposescales<-select(all_surveys,
#Factor 1 Understanding Self:
APSI_2, LET_2, APSI_5, LET_4,
#Factor 2: (making plans)
PWB_8, APSI_7, APSI_8, APSI_4,
#Factor 3: (Daily Activities)
PWB_2, PWB_3, PWB_9, PWB_6,
#Factor 4
MLQ_4, MLQ_5, MLQ_6, MLQ_1, MLQ_9,
#Factor 5
MLQ_2, MLQ_3, MLQ_7, MLQ_8, MLQ_10)
purposescales$PWB_3 <- 7- purposescales$PWB_3
purposescales$PWB_9 <- 7- purposescales$PWB_9
purposescales$MLQ_9 <- 8- purposescales$MLQ_9
purposescales<- data.frame(apply(purposescales,2, as.numeric))
library(GPArotation)
library(psych)
library(dplyr)
purposescales<-tbl_df(purposescales)
purposescales## Source: local data frame [1,160 x 22]
##
## APSI_2 LET_2 APSI_5 LET_4 PWB_8 APSI_7 APSI_8 APSI_4 PWB_2 PWB_3 PWB_9
## 1 4 4 4 5 3 4 4 4 4 5 6
## 2 3 3 4 4 2 4 4 5 2 5 5
## 3 4 4 3 4 3 4 3 3 1 5 6
## 4 4 4 5 4 4 4 3 4 5 4 4
## 5 3 2 4 4 3 2 3 3 5 3 4
## 6 4 5 4 5 4 5 3 4 3 6 6
## 7 2 4 4 3 3 2 2 3 5 5 3
## 8 3 4 5 4 4 3 1 3 1 5 6
## 9 5 4 4 5 5 4 5 4 2 5 6
## 10 2 3 5 5 3 3 4 3 1 3 6
## .. ... ... ... ... ... ... ... ... ... ... ...
## Variables not shown: PWB_6 (dbl), MLQ_4 (dbl), MLQ_5 (dbl), MLQ_6 (dbl),
## MLQ_1 (dbl), MLQ_9 (dbl), MLQ_2 (dbl), MLQ_3 (dbl), MLQ_7 (dbl), MLQ_8
## (dbl), MLQ_10 (dbl)str(purposescales)## Classes 'tbl_df', 'tbl' and 'data.frame': 1160 obs. of 22 variables:
## $ APSI_2: num 4 3 4 4 3 4 2 3 5 2 ...
## $ LET_2 : num 4 3 4 4 2 5 4 4 4 3 ...
## $ APSI_5: num 4 4 3 5 4 4 4 5 4 5 ...
## $ LET_4 : num 5 4 4 4 4 5 3 4 5 5 ...
## $ PWB_8 : num 3 2 3 4 3 4 3 4 5 3 ...
## $ APSI_7: num 4 4 4 4 2 5 2 3 4 3 ...
## $ APSI_8: num 4 4 3 3 3 3 2 1 5 4 ...
## $ APSI_4: num 4 5 3 4 3 4 3 3 4 3 ...
## $ PWB_2 : num 4 2 1 5 5 3 5 1 2 1 ...
## $ PWB_3 : num 5 5 5 4 3 6 5 5 5 3 ...
## $ PWB_9 : num 6 5 6 4 4 6 3 6 6 6 ...
## $ PWB_6 : num 5 5 4 4 3 4 4 4 5 6 ...
## $ MLQ_4 : num 5 5 4 3 4 4 3 5 7 3 ...
## $ MLQ_5 : num 6 4 4 5 4 5 6 5 6 5 ...
## $ MLQ_6 : num 4 3 4 5 4 5 3 4 6 1 ...
## $ MLQ_1 : num 4 3 4 5 4 5 6 3 6 1 ...
## $ MLQ_9 : num 3 3 4 3 5 7 5 4 7 6 ...
## $ MLQ_2 : num 7 5 7 6 6 5 2 7 5 7 ...
## $ MLQ_3 : num 7 5 5 7 5 3 2 7 2 1 ...
## $ MLQ_7 : num 5 5 4 5 5 3 5 5 5 5 ...
## $ MLQ_8 : num 7 4 5 5 5 4 4 7 7 5 ...
## $ MLQ_10: num 7 5 4 6 5 3 4 5 2 1 ...colnames(purposescales) <- c("1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22")
#Target rotation: choose "simple structure" a priori and can be applied to oblique and orthogonal rotation based on
#what paper says facotrs should be purposescales
Targ_key <- make.keys(22,list(f1=1:4,f2=5:8, f3=9:12, f4=13:17,f5=18:22))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
purposescales_cor <- corFiml(purposescales)
out_targetQ <- fa(purposescales_cor,5,rotate="TargetQ", n.obs = 1160, Target=Targ_key) #TargetT for orthogonal rotation
out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR5 MR4 MR2 MR1 MR3
## 1 0.164 0.329 0.430
## 2 0.149 0.722
## 3 0.375 0.423
## 4 0.607
## 5 0.123 0.374 0.202 0.119
## 6 0.764
## 7 0.700 0.145
## 8 0.128 0.683 0.125
## 9 -0.489
## 10 0.195 -0.217 0.701
## 11 -0.259 0.146 0.571
## 12 0.122 0.284 0.214 -0.254 0.399
## 13 0.631 0.258
## 14 0.713 0.101
## 15 0.639 0.167 0.128
## 16 0.891
## 17 -0.164 0.346 -0.112 0.413
## 18 0.816
## 19 0.732 0.124
## 20 0.720 0.130 -0.143
## 21 0.740
## 22 0.817
##
## MR5 MR4 MR2 MR1 MR3
## SS loadings 3.012 2.546 2.089 1.563 1.442
## Proportion Var 0.137 0.116 0.095 0.071 0.066
## Cumulative Var 0.137 0.253 0.348 0.419 0.484
##
## $score.cor
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.00000000 0.1083962 0.1067275 0.09807489 -0.1033237
## [2,] 0.10839622 1.0000000 0.5751622 0.52302379 0.3672876
## [3,] 0.10672748 0.5751622 1.0000000 0.71533250 0.1580159
## [4,] 0.09807489 0.5230238 0.7153325 1.00000000 0.1268710
## [5,] -0.10332371 0.3672876 0.1580159 0.12687098 1.0000000
##
## $TLI
## [1] 0.9421466
##
## $RMSEA
## RMSEA lower upper confidence
## 0.05008507 0.04519804 0.05434029 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = purposescales_cor, nfactors = 5, n.obs = 1160, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR5 MR4 MR2 MR1 MR3 h2 u2 com
## 1 -0.06 0.16 0.33 0.43 -0.06 0.59 0.41 2.3
## 2 0.01 0.15 -0.09 0.72 0.04 0.55 0.45 1.1
## 3 0.08 -0.10 0.37 0.42 0.01 0.49 0.51 2.2
## 4 0.03 0.08 -0.01 0.61 0.08 0.42 0.58 1.1
## 5 -0.04 0.12 0.37 0.20 0.12 0.38 0.62 2.1
## 6 0.09 0.01 0.76 0.04 0.02 0.66 0.34 1.0
## 7 0.00 0.06 0.70 0.15 -0.03 0.68 0.32 1.1
## 8 -0.05 0.13 0.68 0.12 -0.05 0.69 0.31 1.2
## 9 0.06 0.06 -0.08 -0.06 -0.49 0.25 0.75 1.1
## 10 -0.02 0.19 -0.22 0.06 0.70 0.63 0.37 1.4
## 11 0.03 -0.26 0.06 0.15 0.57 0.29 0.71 1.6
## 12 0.12 0.28 0.21 -0.25 0.40 0.36 0.64 3.5
## 13 0.04 0.63 0.03 0.26 -0.03 0.61 0.39 1.3
## 14 0.06 0.71 0.04 0.01 0.10 0.61 0.39 1.1
## 15 -0.01 0.64 0.17 0.13 -0.07 0.61 0.39 1.2
## 16 0.01 0.89 -0.04 -0.02 -0.05 0.72 0.28 1.0
## 17 -0.16 0.35 0.03 -0.11 0.41 0.41 0.59 2.5
## 18 0.82 -0.10 -0.03 0.06 0.03 0.66 0.34 1.0
## 19 0.73 0.04 -0.02 0.12 -0.01 0.57 0.43 1.1
## 20 0.72 0.13 0.05 -0.14 -0.01 0.55 0.45 1.2
## 21 0.74 0.00 0.07 0.01 0.08 0.56 0.44 1.0
## 22 0.82 -0.05 -0.09 -0.03 -0.02 0.67 0.33 1.0
##
## MR5 MR4 MR2 MR1 MR3
## SS loadings 3.02 2.87 2.56 1.99 1.50
## Proportion Var 0.14 0.13 0.12 0.09 0.07
## Cumulative Var 0.14 0.27 0.38 0.47 0.54
## Proportion Explained 0.25 0.24 0.21 0.17 0.13
## Cumulative Proportion 0.25 0.49 0.71 0.87 1.00
##
## With factor correlations of
## MR5 MR4 MR2 MR1 MR3
## MR5 1.00 0.07 0.13 0.08 -0.14
## MR4 0.07 1.00 0.47 0.37 0.34
## MR2 0.13 0.47 1.00 0.64 0.02
## MR1 0.08 0.37 0.64 1.00 0.03
## MR3 -0.14 0.34 0.02 0.03 1.00
##
## Mean item complexity = 1.5
## Test of the hypothesis that 5 factors are sufficient.
##
## The degrees of freedom for the null model are 231 and the objective function was 10.18 with Chi Square of 11718.83
## The degrees of freedom for the model are 131 and the objective function was 0.44
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.03
##
## The harmonic number of observations is 1160 with the empirical chi square 277.11 with prob < 1.7e-12
## The total number of observations was 1160 with MLE Chi Square = 506.79 with prob < 1.3e-45
##
## Tucker Lewis Index of factoring reliability = 0.942
## RMSEA index = 0.05 and the 90 % confidence intervals are 0.045 0.054
## BIC = -417.57
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## MR5 MR4 MR2 MR1 MR3
## Correlation of scores with factors 0.94 0.94 0.93 0.89 0.86
## Multiple R square of scores with factors 0.89 0.88 0.87 0.80 0.75
## Minimum correlation of possible factor scores 0.77 0.76 0.74 0.59 0.49fa2latex(fa(purposescales_cor,5,rotate="TargetQ", n.obs = 1160, Target=Targ_key),heading="Table 7 Factor Based on theory")## % Called in the psych package fa2latex % Called in the psych package fa(purposescales_cor, 5, rotate = "TargetQ", n.obs = 1160, Target = Targ_key) % Called in the psych package Table 7 Factor Based on theory
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## \hline
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## 7 & 0.00 & 0.06 & \bf{ 0.70} & 0.15 & -0.03 & 0.68 & 0.32 & 1.10 \cr
## 8 & -0.05 & 0.13 & \bf{ 0.68} & 0.12 & -0.05 & 0.69 & 0.31 & 1.16 \cr
## 9 & 0.06 & 0.06 & -0.08 & -0.06 & \bf{-0.49} & 0.25 & 0.75 & 1.14 \cr
## 10 & -0.02 & 0.19 & -0.22 & 0.06 & \bf{ 0.70} & 0.63 & 0.37 & 1.37 \cr
## 11 & 0.03 & -0.26 & 0.06 & 0.15 & \bf{ 0.57} & 0.29 & 0.71 & 1.58 \cr
## 12 & 0.12 & 0.28 & 0.21 & -0.25 & \bf{ 0.40} & 0.36 & 0.64 & 3.48 \cr
## 13 & 0.04 & \bf{ 0.63} & 0.03 & 0.26 & -0.03 & 0.61 & 0.39 & 1.35 \cr
## 14 & 0.06 & \bf{ 0.71} & 0.04 & 0.01 & 0.10 & 0.61 & 0.39 & 1.07 \cr
## 15 & -0.01 & \bf{ 0.64} & 0.17 & 0.13 & -0.07 & 0.61 & 0.39 & 1.25 \cr
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## 17 & -0.16 & \bf{ 0.35} & 0.03 & -0.11 & \bf{ 0.41} & 0.41 & 0.59 & 2.47 \cr
## 18 & \bf{ 0.82} & -0.10 & -0.03 & 0.06 & 0.03 & 0.66 & 0.34 & 1.04 \cr
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## 22 & \bf{ 0.82} & -0.05 & -0.09 & -0.03 & -0.02 & 0.67 & 0.33 & 1.03 \cr
## \hline \cr SS loadings & 3.02 & 2.87 & 2.56 & 1.99 & 1.5 & \cr
## \cr
## \hline \cr
## MR5 & 1.00 & 0.07 & 0.13 & 0.08 & -0.14 \cr
## MR4 & 0.07 & 1.00 & 0.47 & 0.37 & 0.34 \cr
## MR2 & 0.13 & 0.47 & 1.00 & 0.64 & 0.02 \cr
## MR1 & 0.08 & 0.37 & 0.64 & 1.00 & 0.03 \cr
## MR3 & -0.14 & 0.34 & 0.02 & 0.03 & 1.00 \cr
## \hline
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## \end{scriptsize}
## \end{center}
## \label{default}
## \end{table}#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$null.dof))## [1] 0.9672883Target rotation based on 5 (moving some around based on previos results) factors – take 3. Removing factor 3. 1. Understanding Self, 2. Making Plans, 4, Sense of Purpose, 5. Serching for Purpose. Results: not a good fit.
all_surveys<-read.csv("allsurveysYT1.csv")
purposescales<-select(all_surveys,
#Factor 1 Understanding Self:
APSI_2, LET_2, APSI_5, LET_4,
#Factor 2: (making plans)
PWB_8, APSI_7, APSI_8, APSI_4,
#Factor 4
MLQ_4, MLQ_5, MLQ_6, MLQ_1, MLQ_9,
#Factor 5
MLQ_2, MLQ_3, MLQ_7, MLQ_8, MLQ_10)
purposescales$MLQ_9 <- 8- purposescales$MLQ_9
purposescales<- data.frame(apply(purposescales,2, as.numeric))
library(GPArotation)
library(psych)
library(dplyr)
purposescales<-tbl_df(purposescales)
purposescales## Source: local data frame [1,160 x 18]
##
## APSI_2 LET_2 APSI_5 LET_4 PWB_8 APSI_7 APSI_8 APSI_4 MLQ_4 MLQ_5 MLQ_6
## 1 4 4 4 5 3 4 4 4 5 6 4
## 2 3 3 4 4 2 4 4 5 5 4 3
## 3 4 4 3 4 3 4 3 3 4 4 4
## 4 4 4 5 4 4 4 3 4 3 5 5
## 5 3 2 4 4 3 2 3 3 4 4 4
## 6 4 5 4 5 4 5 3 4 4 5 5
## 7 2 4 4 3 3 2 2 3 3 6 3
## 8 3 4 5 4 4 3 1 3 5 5 4
## 9 5 4 4 5 5 4 5 4 7 6 6
## 10 2 3 5 5 3 3 4 3 3 5 1
## .. ... ... ... ... ... ... ... ... ... ... ...
## Variables not shown: MLQ_1 (dbl), MLQ_9 (dbl), MLQ_2 (dbl), MLQ_3 (dbl),
## MLQ_7 (dbl), MLQ_8 (dbl), MLQ_10 (dbl)str(purposescales)## Classes 'tbl_df', 'tbl' and 'data.frame': 1160 obs. of 18 variables:
## $ APSI_2: num 4 3 4 4 3 4 2 3 5 2 ...
## $ LET_2 : num 4 3 4 4 2 5 4 4 4 3 ...
## $ APSI_5: num 4 4 3 5 4 4 4 5 4 5 ...
## $ LET_4 : num 5 4 4 4 4 5 3 4 5 5 ...
## $ PWB_8 : num 3 2 3 4 3 4 3 4 5 3 ...
## $ APSI_7: num 4 4 4 4 2 5 2 3 4 3 ...
## $ APSI_8: num 4 4 3 3 3 3 2 1 5 4 ...
## $ APSI_4: num 4 5 3 4 3 4 3 3 4 3 ...
## $ MLQ_4 : num 5 5 4 3 4 4 3 5 7 3 ...
## $ MLQ_5 : num 6 4 4 5 4 5 6 5 6 5 ...
## $ MLQ_6 : num 4 3 4 5 4 5 3 4 6 1 ...
## $ MLQ_1 : num 4 3 4 5 4 5 6 3 6 1 ...
## $ MLQ_9 : num 3 3 4 3 5 7 5 4 7 6 ...
## $ MLQ_2 : num 7 5 7 6 6 5 2 7 5 7 ...
## $ MLQ_3 : num 7 5 5 7 5 3 2 7 2 1 ...
## $ MLQ_7 : num 5 5 4 5 5 3 5 5 5 5 ...
## $ MLQ_8 : num 7 4 5 5 5 4 4 7 7 5 ...
## $ MLQ_10: num 7 5 4 6 5 3 4 5 2 1 ...colnames(purposescales) <- c("1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18")
#Target rotation: choose "simple structure" a priori and can be applied to oblique and orthogonal rotation based on
#what paper says facotrs should be purposescales
Targ_key <- make.keys(18,list(f1=1:4,f2=5:8, f3=9:13, f4=14:18))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
purposescales_cor <- corFiml(purposescales)
out_targetQ <- fa(purposescales_cor,4,rotate="TargetQ", n.obs = 1160, Target=Targ_key) #TargetT for orthogonal rotation
out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR4 MR3 MR2 MR1
## 1 0.106 0.378 0.383
## 2 -0.179 0.864
## 3 -0.116 0.405 0.385
## 4 0.703
## 5 0.167 0.321 0.218
## 6 0.739
## 7 0.718 0.111
## 8 0.113 0.705
## 9 0.631 0.211
## 10 0.792
## 11 0.642 0.178
## 12 0.874
## 13 -0.202 0.548 -0.135
## 14 0.809
## 15 0.734
## 16 0.728 0.148 -0.130
## 17 0.728
## 18 0.819
##
## MR4 MR3 MR2 MR1
## SS loadings 2.995 2.611 2.051 1.710
## Proportion Var 0.166 0.145 0.114 0.095
## Cumulative Var 0.166 0.311 0.425 0.520
##
## $score.cor
## [,1] [,2] [,3] [,4]
## [1,] 1.00000000 0.04810883 0.1252037 0.0655529
## [2,] 0.04810883 1.00000000 0.5199448 0.4936196
## [3,] 0.12520366 0.51994479 1.0000000 0.7007947
## [4,] 0.06555290 0.49361955 0.7007947 1.0000000
##
## $TLI
## [1] 0.966681
##
## $RMSEA
## RMSEA lower upper confidence
## 0.04388573 0.03792893 0.04939438 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = purposescales_cor, nfactors = 4, n.obs = 1160, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR4 MR3 MR2 MR1 h2 u2 com
## 1 -0.06 0.11 0.38 0.38 0.58 0.42 2.2
## 2 -0.01 0.06 -0.18 0.86 0.61 0.39 1.1
## 3 0.07 -0.12 0.40 0.38 0.47 0.53 2.2
## 4 0.00 0.01 -0.07 0.70 0.44 0.56 1.0
## 5 -0.05 0.17 0.32 0.22 0.36 0.64 2.4
## 6 0.09 0.03 0.74 0.04 0.65 0.35 1.0
## 7 0.00 0.05 0.72 0.11 0.69 0.31 1.1
## 8 -0.04 0.11 0.71 0.09 0.69 0.31 1.1
## 9 0.06 0.63 0.03 0.21 0.62 0.38 1.2
## 10 0.07 0.79 -0.01 -0.02 0.61 0.39 1.0
## 11 0.02 0.64 0.18 0.06 0.62 0.38 1.2
## 12 0.04 0.87 -0.04 -0.07 0.68 0.32 1.0
## 13 -0.20 0.55 -0.06 -0.13 0.26 0.74 1.4
## 14 0.81 -0.09 -0.04 0.06 0.66 0.34 1.0
## 15 0.73 0.02 0.01 0.10 0.57 0.43 1.0
## 16 0.73 0.15 0.03 -0.13 0.54 0.46 1.2
## 17 0.73 0.05 0.04 0.01 0.55 0.45 1.0
## 18 0.82 -0.06 -0.05 -0.05 0.67 0.33 1.0
##
## MR4 MR3 MR2 MR1
## SS loadings 3.00 2.80 2.44 2.03
## Proportion Var 0.17 0.16 0.14 0.11
## Cumulative Var 0.17 0.32 0.46 0.57
## Proportion Explained 0.29 0.27 0.24 0.20
## Cumulative Proportion 0.29 0.56 0.80 1.00
##
## With factor correlations of
## MR4 MR3 MR2 MR1
## MR4 1.00 0.04 0.12 0.11
## MR3 0.04 1.00 0.49 0.52
## MR2 0.12 0.49 1.00 0.70
## MR1 0.11 0.52 0.70 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 4 factors are sufficient.
##
## The degrees of freedom for the null model are 153 and the objective function was 8.94 with Chi Square of 10305.31
## The degrees of freedom for the model are 87 and the objective function was 0.24
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.02
##
## The harmonic number of observations is 1160 with the empirical chi square 108.8 with prob < 0.057
## The total number of observations was 1160 with MLE Chi Square = 278.9 with prob < 5.9e-22
##
## Tucker Lewis Index of factoring reliability = 0.967
## RMSEA index = 0.044 and the 90 % confidence intervals are 0.038 0.049
## BIC = -334.99
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR4 MR3 MR2 MR1
## Correlation of scores with factors 0.94 0.94 0.93 0.91
## Multiple R square of scores with factors 0.88 0.88 0.87 0.83
## Minimum correlation of possible factor scores 0.77 0.76 0.74 0.66fa2latex(fa(purposescales_cor,4,rotate="TargetQ", n.obs = 1160, Target=Targ_key),heading="Table 7 Factor Based on theory")## % Called in the psych package fa2latex % Called in the psych package fa(purposescales_cor, 4, rotate = "TargetQ", n.obs = 1160, Target = Targ_key) % Called in the psych package Table 7 Factor Based on theory
## \begin{table}[htdp]\caption{fa2latex}
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## \begin{scriptsize}
## \begin{tabular} {l r r r r r r r }
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## \hline Variable & MR4 & MR3 & MR2 & MR1 & h2 & u2 & com \cr
## \hline
## 1 & -0.06 & 0.11 & \bf{ 0.38} & \bf{ 0.38} & 0.58 & 0.42 & 2.20 \cr
## 2 & -0.01 & 0.06 & -0.18 & \bf{ 0.86} & 0.61 & 0.39 & 1.10 \cr
## 3 & 0.07 & -0.12 & \bf{ 0.40} & \bf{ 0.38} & 0.47 & 0.53 & 2.22 \cr
## 4 & 0.00 & 0.01 & -0.07 & \bf{ 0.70} & 0.44 & 0.56 & 1.02 \cr
## 5 & -0.05 & 0.17 & \bf{ 0.32} & 0.22 & 0.36 & 0.64 & 2.41 \cr
## 6 & 0.09 & 0.03 & \bf{ 0.74} & 0.04 & 0.65 & 0.35 & 1.04 \cr
## 7 & 0.00 & 0.05 & \bf{ 0.72} & 0.11 & 0.69 & 0.31 & 1.06 \cr
## 8 & -0.04 & 0.11 & \bf{ 0.71} & 0.09 & 0.69 & 0.31 & 1.09 \cr
## 9 & 0.06 & \bf{ 0.63} & 0.03 & 0.21 & 0.62 & 0.38 & 1.25 \cr
## 10 & 0.07 & \bf{ 0.79} & -0.01 & -0.02 & 0.61 & 0.39 & 1.02 \cr
## 11 & 0.02 & \bf{ 0.64} & 0.18 & 0.06 & 0.62 & 0.38 & 1.17 \cr
## 12 & 0.04 & \bf{ 0.87} & -0.04 & -0.07 & 0.68 & 0.32 & 1.02 \cr
## 13 & -0.20 & \bf{ 0.55} & -0.06 & -0.13 & 0.26 & 0.74 & 1.43 \cr
## 14 & \bf{ 0.81} & -0.09 & -0.04 & 0.06 & 0.66 & 0.34 & 1.04 \cr
## 15 & \bf{ 0.73} & 0.02 & 0.01 & 0.10 & 0.57 & 0.43 & 1.04 \cr
## 16 & \bf{ 0.73} & 0.15 & 0.03 & -0.13 & 0.54 & 0.46 & 1.15 \cr
## 17 & \bf{ 0.73} & 0.05 & 0.04 & 0.01 & 0.55 & 0.45 & 1.02 \cr
## 18 & \bf{ 0.82} & -0.06 & -0.05 & -0.05 & 0.67 & 0.33 & 1.03 \cr
## \hline \cr SS loadings & 3 & 2.8 & 2.44 & 2.03 & \cr
## \cr
## \hline \cr
## MR4 & 1.00 & 0.04 & 0.12 & 0.11 \cr
## MR3 & 0.04 & 1.00 & 0.49 & 0.52 \cr
## MR2 & 0.12 & 0.49 & 1.00 & 0.70 \cr
## MR1 & 0.11 & 0.52 & 0.70 & 1.00 \cr
## \hline
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## \end{center}
## \label{default}
## \end{table}#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$null.dof))## [1] 0.9810984``` # Target rotation based on 5 (moving some around based on previos results) factors – take 4. 1. Understanding Self, 2. Making Plans, 3. Daily Activities, 4, Sense of Purpose, 5. Serching for Purpose. Removing question 4 (PWB_6) from factor 3.
all_surveys<-read.csv("allsurveysYT1.csv")
purposescales<-select(all_surveys,
#Factor 1 Understanding Self:
APSI_2, LET_2, APSI_5, LET_4,
#Factor 2: (making plans)
PWB_8, APSI_7, APSI_8, APSI_4,
#Factor 3: (Daily Activities)
PWB_2, PWB_3, PWB_9,
#Factor 4
MLQ_4, MLQ_5, MLQ_6, MLQ_1, MLQ_9,
#Factor 5
MLQ_2, MLQ_3, MLQ_7, MLQ_8, MLQ_10)
purposescales$PWB_3 <- 7- purposescales$PWB_3
purposescales$PWB_9 <- 7- purposescales$PWB_9
purposescales$MLQ_9 <- 8- purposescales$MLQ_9
purposescales<- data.frame(apply(purposescales,2, as.numeric))
library(GPArotation)
library(psych)
library(dplyr)
purposescales<-tbl_df(purposescales)
purposescales## Source: local data frame [1,160 x 21]
##
## APSI_2 LET_2 APSI_5 LET_4 PWB_8 APSI_7 APSI_8 APSI_4 PWB_2 PWB_3 PWB_9
## 1 4 4 4 5 3 4 4 4 4 5 6
## 2 3 3 4 4 2 4 4 5 2 5 5
## 3 4 4 3 4 3 4 3 3 1 5 6
## 4 4 4 5 4 4 4 3 4 5 4 4
## 5 3 2 4 4 3 2 3 3 5 3 4
## 6 4 5 4 5 4 5 3 4 3 6 6
## 7 2 4 4 3 3 2 2 3 5 5 3
## 8 3 4 5 4 4 3 1 3 1 5 6
## 9 5 4 4 5 5 4 5 4 2 5 6
## 10 2 3 5 5 3 3 4 3 1 3 6
## .. ... ... ... ... ... ... ... ... ... ... ...
## Variables not shown: MLQ_4 (dbl), MLQ_5 (dbl), MLQ_6 (dbl), MLQ_1 (dbl),
## MLQ_9 (dbl), MLQ_2 (dbl), MLQ_3 (dbl), MLQ_7 (dbl), MLQ_8 (dbl), MLQ_10
## (dbl)str(purposescales)## Classes 'tbl_df', 'tbl' and 'data.frame': 1160 obs. of 21 variables:
## $ APSI_2: num 4 3 4 4 3 4 2 3 5 2 ...
## $ LET_2 : num 4 3 4 4 2 5 4 4 4 3 ...
## $ APSI_5: num 4 4 3 5 4 4 4 5 4 5 ...
## $ LET_4 : num 5 4 4 4 4 5 3 4 5 5 ...
## $ PWB_8 : num 3 2 3 4 3 4 3 4 5 3 ...
## $ APSI_7: num 4 4 4 4 2 5 2 3 4 3 ...
## $ APSI_8: num 4 4 3 3 3 3 2 1 5 4 ...
## $ APSI_4: num 4 5 3 4 3 4 3 3 4 3 ...
## $ PWB_2 : num 4 2 1 5 5 3 5 1 2 1 ...
## $ PWB_3 : num 5 5 5 4 3 6 5 5 5 3 ...
## $ PWB_9 : num 6 5 6 4 4 6 3 6 6 6 ...
## $ MLQ_4 : num 5 5 4 3 4 4 3 5 7 3 ...
## $ MLQ_5 : num 6 4 4 5 4 5 6 5 6 5 ...
## $ MLQ_6 : num 4 3 4 5 4 5 3 4 6 1 ...
## $ MLQ_1 : num 4 3 4 5 4 5 6 3 6 1 ...
## $ MLQ_9 : num 3 3 4 3 5 7 5 4 7 6 ...
## $ MLQ_2 : num 7 5 7 6 6 5 2 7 5 7 ...
## $ MLQ_3 : num 7 5 5 7 5 3 2 7 2 1 ...
## $ MLQ_7 : num 5 5 4 5 5 3 5 5 5 5 ...
## $ MLQ_8 : num 7 4 5 5 5 4 4 7 7 5 ...
## $ MLQ_10: num 7 5 4 6 5 3 4 5 2 1 ...colnames(purposescales) <- c("1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21")
#Target rotation: choose "simple structure" a priori and can be applied to oblique and orthogonal rotation based on
#what paper says facotrs should be purposescales
Targ_key <- make.keys(21,list(f1=1:4,f2=5:8, f3=9:11, f4=12:16,f5=17:21))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
purposescales_cor <- corFiml(purposescales)
out_targetQ <- fa(purposescales_cor,5,rotate="TargetQ", n.obs = 1160, Target=Targ_key) #TargetT for orthogonal rotation
out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR5 MR4 MR2 MR1 MR3
## 1 0.133 0.364 0.398
## 2 -0.143 0.842
## 3 -0.112 0.406 0.387
## 4 0.723
## 5 0.146 0.341 0.224
## 6 0.695
## 7 0.679 0.141
## 8 0.169 0.653 0.131
## 9 -0.114 -0.539
## 10 0.306 -0.267 0.561
## 11 -0.189 0.117 0.656
## 12 0.601 0.208
## 13 0.747
## 14 0.633 0.210
## 15 0.896 -0.100
## 16 -0.140 0.454 -0.163 0.372
## 17 0.816
## 18 0.730
## 19 0.724 0.155 -0.131
## 20 0.745
## 21 0.812
##
## MR5 MR4 MR2 MR1 MR3
## SS loadings 2.996 2.594 1.978 1.748 1.221
## Proportion Var 0.143 0.124 0.094 0.083 0.058
## Cumulative Var 0.143 0.266 0.360 0.444 0.502
##
## $score.cor
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.00000000 0.1083901 0.12524624 0.06562208 -0.15440403
## [2,] 0.10839011 1.0000000 0.55983739 0.53480086 0.30152322
## [3,] 0.12524624 0.5598374 1.00000000 0.70099330 0.09285421
## [4,] 0.06562208 0.5348009 0.70099330 1.00000000 0.12609983
## [5,] -0.15440403 0.3015232 0.09285421 0.12609983 1.00000000
##
## $TLI
## [1] 0.9515973
##
## $RMSEA
## RMSEA lower upper confidence
## 0.04715062 0.04193413 0.05178404 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = purposescales_cor, nfactors = 5, n.obs = 1160, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR5 MR4 MR2 MR1 MR3 h2 u2 com
## 1 -0.07 0.13 0.36 0.40 -0.04 0.58 0.42 2.3
## 2 -0.02 0.03 -0.14 0.84 0.03 0.60 0.40 1.1
## 3 0.08 -0.11 0.41 0.39 0.04 0.48 0.52 2.3
## 4 0.00 0.00 -0.08 0.72 0.04 0.46 0.54 1.0
## 5 -0.04 0.15 0.34 0.22 0.09 0.37 0.63 2.4
## 6 0.10 0.08 0.69 0.08 0.00 0.64 0.36 1.1
## 7 0.01 0.10 0.68 0.14 -0.02 0.69 0.31 1.1
## 8 -0.04 0.17 0.65 0.13 -0.06 0.69 0.31 1.2
## 9 0.04 -0.02 -0.11 0.03 -0.54 0.31 0.69 1.1
## 10 -0.02 0.31 -0.27 0.07 0.56 0.52 0.48 2.1
## 11 0.06 -0.19 0.12 0.05 0.66 0.39 0.61 1.3
## 12 0.05 0.60 0.09 0.21 -0.02 0.60 0.40 1.3
## 13 0.08 0.75 0.04 -0.01 0.06 0.61 0.39 1.0
## 14 0.01 0.63 0.21 0.08 -0.06 0.61 0.39 1.3
## 15 0.02 0.90 -0.03 -0.04 -0.10 0.72 0.28 1.0
## 16 -0.14 0.45 0.03 -0.16 0.37 0.42 0.58 2.4
## 17 0.82 -0.10 -0.02 0.04 0.04 0.66 0.34 1.0
## 18 0.73 0.03 0.00 0.10 -0.01 0.57 0.43 1.0
## 19 0.72 0.16 0.03 -0.13 -0.03 0.54 0.46 1.2
## 20 0.75 0.03 0.07 0.00 0.08 0.56 0.44 1.0
## 21 0.81 -0.04 -0.09 -0.04 -0.04 0.67 0.33 1.0
##
## MR5 MR4 MR2 MR1 MR3
## SS loadings 3.00 2.86 2.43 2.13 1.25
## Proportion Var 0.14 0.14 0.12 0.10 0.06
## Cumulative Var 0.14 0.28 0.39 0.50 0.56
## Proportion Explained 0.26 0.24 0.21 0.18 0.11
## Cumulative Proportion 0.26 0.50 0.71 0.89 1.00
##
## With factor correlations of
## MR5 MR4 MR2 MR1 MR3
## MR5 1.00 0.04 0.11 0.12 -0.18
## MR4 0.04 1.00 0.39 0.49 0.25
## MR2 0.11 0.39 1.00 0.66 -0.03
## MR1 0.12 0.49 0.66 1.00 0.04
## MR3 -0.18 0.25 -0.03 0.04 1.00
##
## Mean item complexity = 1.4
## Test of the hypothesis that 5 factors are sufficient.
##
## The degrees of freedom for the null model are 210 and the objective function was 9.79 with Chi Square of 11272.25
## The degrees of freedom for the model are 115 and the objective function was 0.35
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.03
##
## The harmonic number of observations is 1160 with the empirical chi square 178.03 with prob < 0.00015
## The total number of observations was 1160 with MLE Chi Square = 407.35 with prob < 2.6e-34
##
## Tucker Lewis Index of factoring reliability = 0.952
## RMSEA index = 0.047 and the 90 % confidence intervals are 0.042 0.052
## BIC = -404.11
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR5 MR4 MR2 MR1 MR3
## Correlation of scores with factors 0.94 0.94 0.93 0.91 0.83
## Multiple R square of scores with factors 0.89 0.88 0.86 0.83 0.69
## Minimum correlation of possible factor scores 0.77 0.76 0.72 0.66 0.38fa2latex(fa(purposescales_cor,5,rotate="TargetQ", n.obs = 1160, Target=Targ_key),heading="Table 7 Factor Based on theory")## % Called in the psych package fa2latex % Called in the psych package fa(purposescales_cor, 5, rotate = "TargetQ", n.obs = 1160, Target = Targ_key) % Called in the psych package Table 7 Factor Based on theory
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## 12 & 0.05 & \bf{ 0.60} & 0.09 & 0.21 & -0.02 & 0.60 & 0.40 & 1.30 \cr
## 13 & 0.08 & \bf{ 0.75} & 0.04 & -0.01 & 0.06 & 0.61 & 0.39 & 1.04 \cr
## 14 & 0.01 & \bf{ 0.63} & 0.21 & 0.08 & -0.06 & 0.61 & 0.39 & 1.27 \cr
## 15 & 0.02 & \bf{ 0.90} & -0.03 & -0.04 & -0.10 & 0.72 & 0.28 & 1.03 \cr
## 16 & -0.14 & \bf{ 0.45} & 0.03 & -0.16 & \bf{ 0.37} & 0.42 & 0.58 & 2.44 \cr
## 17 & \bf{ 0.82} & -0.10 & -0.02 & 0.04 & 0.04 & 0.66 & 0.34 & 1.04 \cr
## 18 & \bf{ 0.73} & 0.03 & 0.00 & 0.10 & -0.01 & 0.57 & 0.43 & 1.04 \cr
## 19 & \bf{ 0.72} & 0.16 & 0.03 & -0.13 & -0.03 & 0.54 & 0.46 & 1.17 \cr
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## 21 & \bf{ 0.81} & -0.04 & -0.09 & -0.04 & -0.04 & 0.67 & 0.33 & 1.04 \cr
## \hline \cr SS loadings & 3 & 2.86 & 2.43 & 2.13 & 1.25 & \cr
## \cr
## \hline \cr
## MR5 & 1.00 & 0.04 & 0.11 & 0.12 & -0.18 \cr
## MR4 & 0.04 & 1.00 & 0.39 & 0.49 & 0.25 \cr
## MR2 & 0.11 & 0.39 & 1.00 & 0.66 & -0.03 \cr
## MR1 & 0.12 & 0.49 & 0.66 & 1.00 & 0.04 \cr
## MR3 & -0.18 & 0.25 & -0.03 & 0.04 & 1.00 \cr
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## \end{center}
## \label{default}
## \end{table}#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$null.dof))## [1] 0.9735719Target rotation based on 4 factors – based on new EFA
all_surveys<-read.csv("allsurveysYT1.csv")
purposescales<-select(all_surveys,
#Factor 1 Understanding Self:
PWB_1, PWB_3, PWB_5, APSI_6, LET_1, LET_3, LET_5,
#Factor 2: (making plans)
PWB_2, PWB_7, PWB_8, APSI_2, APSI_4, APSI_7, APSI_8,
#Factor 3
MLQ_4, MLQ_5, MLQ_6, MLQ_1, MLQ_9,
#Factor 4
MLQ_2, MLQ_3, MLQ_7, MLQ_8, MLQ_10)
purposescales$PWB_1 <- 7- purposescales$PWB_1
purposescales$PWB_5 <- 7- purposescales$PWB_5
purposescales$PWB_3 <- 7- purposescales$PWB_3
purposescales$MLQ_9 <- 8- purposescales$MLQ_9
purposescales$LET_1 <- 6- purposescales$LET_1
purposescales$LET_3 <- 6- purposescales$LET_3
purposescales$LET_5 <- 6- purposescales$LET_5
purposescales<- data.frame(apply(purposescales,2, as.numeric))
library(GPArotation)
library(psych)
library(dplyr)
purposescales<-tbl_df(purposescales)
purposescales## Source: local data frame [1,160 x 24]
##
## PWB_1 PWB_3 PWB_5 APSI_6 LET_1 LET_3 LET_5 PWB_2 PWB_7 PWB_8 APSI_2
## 1 4 5 3 4 4 4 5 4 4 3 4
## 2 4 5 5 3 3 4 4 2 3 2 3
## 3 5 5 6 3 3 3 4 1 6 3 4
## 4 2 4 4 4 1 4 4 5 5 4 4
## 5 2 3 3 3 3 3 2 5 2 3 3
## 6 5 6 4 2 5 5 5 3 3 4 4
## 7 2 5 6 4 3 2 3 5 3 3 2
## 8 6 5 5 3 3 3 4 1 4 4 3
## 9 5 5 6 2 5 5 5 2 5 5 5
## 10 6 3 5 3 3 5 5 1 6 3 2
## .. ... ... ... ... ... ... ... ... ... ... ...
## Variables not shown: APSI_4 (dbl), APSI_7 (dbl), APSI_8 (dbl), MLQ_4
## (dbl), MLQ_5 (dbl), MLQ_6 (dbl), MLQ_1 (dbl), MLQ_9 (dbl), MLQ_2 (dbl),
## MLQ_3 (dbl), MLQ_7 (dbl), MLQ_8 (dbl), MLQ_10 (dbl)str(purposescales)## Classes 'tbl_df', 'tbl' and 'data.frame': 1160 obs. of 24 variables:
## $ PWB_1 : num 4 4 5 2 2 5 2 6 5 6 ...
## $ PWB_3 : num 5 5 5 4 3 6 5 5 5 3 ...
## $ PWB_5 : num 3 5 6 4 3 4 6 5 6 5 ...
## $ APSI_6: num 4 3 3 4 3 2 4 3 2 3 ...
## $ LET_1 : num 4 3 3 1 3 5 3 3 5 3 ...
## $ LET_3 : num 4 4 3 4 3 5 2 3 5 5 ...
## $ LET_5 : num 5 4 4 4 2 5 3 4 5 5 ...
## $ PWB_2 : num 4 2 1 5 5 3 5 1 2 1 ...
## $ PWB_7 : num 4 3 6 5 2 3 3 4 5 6 ...
## $ PWB_8 : num 3 2 3 4 3 4 3 4 5 3 ...
## $ APSI_2: num 4 3 4 4 3 4 2 3 5 2 ...
## $ APSI_4: num 4 5 3 4 3 4 3 3 4 3 ...
## $ APSI_7: num 4 4 4 4 2 5 2 3 4 3 ...
## $ APSI_8: num 4 4 3 3 3 3 2 1 5 4 ...
## $ MLQ_4 : num 5 5 4 3 4 4 3 5 7 3 ...
## $ MLQ_5 : num 6 4 4 5 4 5 6 5 6 5 ...
## $ MLQ_6 : num 4 3 4 5 4 5 3 4 6 1 ...
## $ MLQ_1 : num 4 3 4 5 4 5 6 3 6 1 ...
## $ MLQ_9 : num 3 3 4 3 5 7 5 4 7 6 ...
## $ MLQ_2 : num 7 5 7 6 6 5 2 7 5 7 ...
## $ MLQ_3 : num 7 5 5 7 5 3 2 7 2 1 ...
## $ MLQ_7 : num 5 5 4 5 5 3 5 5 5 5 ...
## $ MLQ_8 : num 7 4 5 5 5 4 4 7 7 5 ...
## $ MLQ_10: num 7 5 4 6 5 3 4 5 2 1 ...colnames(purposescales) <- c("1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22", "23", "24")
#Target rotation: choose "simple structure" a priori and can be applied to oblique and orthogonal rotation based on
#what paper says facotrs should be purposescales
Targ_key <- make.keys(24,list(f1=1:7,f2=8:14, f3=15:19, f4=20:24))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
purposescales_cor <- corFiml(purposescales)
out_targetQ <- fa(purposescales_cor,4,rotate="TargetQ", n.obs = 1160, Target=Targ_key) #TargetT for orthogonal rotation
out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR1 MR2 MR4 MR3
## 1 0.670 -0.230
## 2 0.815
## 3 0.762 0.131
## 4 -0.661 0.144 -0.277
## 5 0.536 -0.154 0.251
## 6 0.768
## 7 0.745 -0.124
## 8 -0.478 -0.269 0.167
## 9 0.561 0.178
## 10 0.565
## 11 -0.103 0.624 0.170
## 12 0.766
## 13 0.836 0.100
## 14 0.834
## 15 0.175 0.676
## 16 0.160 0.677
## 17 0.221 0.657
## 18 0.830
## 19 0.487 -0.132 0.264
## 20 0.803
## 21 0.731
## 22 0.722 0.122
## 23 0.738
## 24 -0.105 0.820
##
## MR1 MR2 MR4 MR3
## SS loadings 4.095 3.255 2.999 2.440
## Proportion Var 0.171 0.136 0.125 0.102
## Cumulative Var 0.171 0.306 0.431 0.533
##
## $score.cor
## [,1] [,2] [,3] [,4]
## [1,] 1.00000000 0.02984821 -0.1872742 0.3435562
## [2,] 0.02984821 1.00000000 0.1064184 0.6158025
## [3,] -0.18727417 0.10641836 1.0000000 0.1082920
## [4,] 0.34355623 0.61580245 0.1082920 1.0000000
##
## $TLI
## [1] 0.9413912
##
## $RMSEA
## RMSEA lower upper confidence
## 0.05198605 0.04783528 0.05546721 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = purposescales_cor, nfactors = 4, n.obs = 1160, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 MR4 MR3 h2 u2 com
## 1 0.67 0.09 -0.01 -0.23 0.39 0.61 1.3
## 2 0.82 0.04 0.03 0.03 0.67 0.33 1.0
## 3 0.76 0.13 0.04 -0.04 0.56 0.44 1.1
## 4 -0.66 0.14 0.05 -0.28 0.63 0.37 1.5
## 5 0.54 0.03 -0.15 0.25 0.50 0.50 1.6
## 6 0.77 -0.06 -0.02 0.06 0.63 0.37 1.0
## 7 0.75 -0.12 0.04 0.08 0.60 0.40 1.1
## 8 -0.48 -0.27 0.05 0.17 0.22 0.78 1.9
## 9 0.00 0.56 0.05 0.18 0.47 0.53 1.2
## 10 0.08 0.56 -0.04 0.08 0.38 0.62 1.1
## 11 -0.10 0.62 -0.07 0.17 0.54 0.46 1.2
## 12 -0.09 0.77 -0.04 0.07 0.66 0.34 1.0
## 13 0.00 0.84 0.10 -0.09 0.64 0.36 1.1
## 14 -0.07 0.83 0.00 -0.02 0.69 0.31 1.0
## 15 -0.04 0.18 0.05 0.68 0.62 0.38 1.2
## 16 0.16 0.06 0.09 0.68 0.61 0.39 1.2
## 17 -0.06 0.22 0.00 0.66 0.63 0.37 1.2
## 18 0.04 -0.06 0.03 0.83 0.66 0.34 1.0
## 19 0.49 0.04 -0.13 0.26 0.44 0.56 1.7
## 20 0.00 0.00 0.80 -0.06 0.65 0.35 1.0
## 21 -0.02 0.08 0.73 0.05 0.57 0.43 1.0
## 22 0.01 -0.05 0.72 0.12 0.53 0.47 1.1
## 23 0.07 0.08 0.74 0.02 0.55 0.45 1.0
## 24 0.01 -0.10 0.82 -0.04 0.67 0.33 1.0
##
## MR1 MR2 MR4 MR3
## SS loadings 4.22 3.48 3.02 2.78
## Proportion Var 0.18 0.15 0.13 0.12
## Cumulative Var 0.18 0.32 0.45 0.56
## Proportion Explained 0.31 0.26 0.22 0.21
## Cumulative Proportion 0.31 0.57 0.79 1.00
##
## With factor correlations of
## MR1 MR2 MR4 MR3
## MR1 1.00 -0.05 -0.21 0.31
## MR2 -0.05 1.00 0.10 0.60
## MR4 -0.21 0.10 1.00 0.04
## MR3 0.31 0.60 0.04 1.00
##
## Mean item complexity = 1.2
## Test of the hypothesis that 4 factors are sufficient.
##
## The degrees of freedom for the null model are 276 and the objective function was 12.93 with Chi Square of 14867.78
## The degrees of freedom for the model are 186 and the objective function was 0.66
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.03
##
## The harmonic number of observations is 1160 with the empirical chi square 373.02 with prob < 1.3e-14
## The total number of observations was 1160 with MLE Chi Square = 760.97 with prob < 1.5e-70
##
## Tucker Lewis Index of factoring reliability = 0.941
## RMSEA index = 0.052 and the 90 % confidence intervals are 0.048 0.055
## BIC = -551.48
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## MR1 MR2 MR4 MR3
## Correlation of scores with factors 0.95 0.95 0.94 0.94
## Multiple R square of scores with factors 0.91 0.90 0.88 0.88
## Minimum correlation of possible factor scores 0.82 0.80 0.76 0.76fa2latex(fa(purposescales_cor,4,rotate="TargetQ", n.obs = 1160, Target=Targ_key),heading="Table 7 Factor Based on theory")## % Called in the psych package fa2latex % Called in the psych package fa(purposescales_cor, 4, rotate = "TargetQ", n.obs = 1160, Target = Targ_key) % Called in the psych package Table 7 Factor Based on theory
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## \end{table}#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$null.dof))## [1] 0.9605961Target rotation based on 4 factors – based on new EFA – quick tweek based on previous results also droped MLQ_9 and PWB_2 because they were loading on two factors.
all_surveys<-read.csv("allsurveysYT1.csv")
purposescales<-select(all_surveys,
#Factor 1 Understanding Self:
PWB_1, PWB_3, APSI_6, LET_3, LET_5,
#Factor 2: (making plans)
PWB_7, PWB_8, APSI_2, APSI_4, APSI_7, APSI_8,
#Factor 3
MLQ_4, MLQ_5, MLQ_6, MLQ_1,
#Factor 4
MLQ_2, MLQ_3, MLQ_7, MLQ_8, MLQ_10)
purposescales$PWB_1 <- 7- purposescales$PWB_1
purposescales$PWB_3 <- 7- purposescales$PWB_3
purposescales$LET_3 <- 6- purposescales$LET_3
purposescales$LET_5 <- 6- purposescales$LET_5
purposescales<- data.frame(apply(purposescales,2, as.numeric))
library(GPArotation)
library(psych)
library(dplyr)
purposescales<-tbl_df(purposescales)
purposescales## Source: local data frame [1,160 x 20]
##
## PWB_1 PWB_3 APSI_6 LET_3 LET_5 PWB_7 PWB_8 APSI_2 APSI_4 APSI_7 APSI_8
## 1 4 5 4 4 5 4 3 4 4 4 4
## 2 4 5 3 4 4 3 2 3 5 4 4
## 3 5 5 3 3 4 6 3 4 3 4 3
## 4 2 4 4 4 4 5 4 4 4 4 3
## 5 2 3 3 3 2 2 3 3 3 2 3
## 6 5 6 2 5 5 3 4 4 4 5 3
## 7 2 5 4 2 3 3 3 2 3 2 2
## 8 6 5 3 3 4 4 4 3 3 3 1
## 9 5 5 2 5 5 5 5 5 4 4 5
## 10 6 3 3 5 5 6 3 2 3 3 4
## .. ... ... ... ... ... ... ... ... ... ... ...
## Variables not shown: MLQ_4 (dbl), MLQ_5 (dbl), MLQ_6 (dbl), MLQ_1 (dbl),
## MLQ_2 (dbl), MLQ_3 (dbl), MLQ_7 (dbl), MLQ_8 (dbl), MLQ_10 (dbl)str(purposescales)## Classes 'tbl_df', 'tbl' and 'data.frame': 1160 obs. of 20 variables:
## $ PWB_1 : num 4 4 5 2 2 5 2 6 5 6 ...
## $ PWB_3 : num 5 5 5 4 3 6 5 5 5 3 ...
## $ APSI_6: num 4 3 3 4 3 2 4 3 2 3 ...
## $ LET_3 : num 4 4 3 4 3 5 2 3 5 5 ...
## $ LET_5 : num 5 4 4 4 2 5 3 4 5 5 ...
## $ PWB_7 : num 4 3 6 5 2 3 3 4 5 6 ...
## $ PWB_8 : num 3 2 3 4 3 4 3 4 5 3 ...
## $ APSI_2: num 4 3 4 4 3 4 2 3 5 2 ...
## $ APSI_4: num 4 5 3 4 3 4 3 3 4 3 ...
## $ APSI_7: num 4 4 4 4 2 5 2 3 4 3 ...
## $ APSI_8: num 4 4 3 3 3 3 2 1 5 4 ...
## $ MLQ_4 : num 5 5 4 3 4 4 3 5 7 3 ...
## $ MLQ_5 : num 6 4 4 5 4 5 6 5 6 5 ...
## $ MLQ_6 : num 4 3 4 5 4 5 3 4 6 1 ...
## $ MLQ_1 : num 4 3 4 5 4 5 6 3 6 1 ...
## $ MLQ_2 : num 7 5 7 6 6 5 2 7 5 7 ...
## $ MLQ_3 : num 7 5 5 7 5 3 2 7 2 1 ...
## $ MLQ_7 : num 5 5 4 5 5 3 5 5 5 5 ...
## $ MLQ_8 : num 7 4 5 5 5 4 4 7 7 5 ...
## $ MLQ_10: num 7 5 4 6 5 3 4 5 2 1 ...colnames(purposescales) <- c("1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20")
#Target rotation: choose "simple structure" a priori and can be applied to oblique and orthogonal rotation based on
#what paper says facotrs should be purposescales
Targ_key <- make.keys(20,list(f1=1:5,f2=6:11, f3=12:15, f4=16:20))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
purposescales_cor <- corFiml(purposescales)
out_targetQ <- fa(purposescales_cor,4,rotate="TargetQ", n.obs = 1160, Target=Targ_key) #TargetT for orthogonal rotation
out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA")]## $loadings
##
## Loadings:
## MR2 MR4 MR1 MR3
## 1 0.607 -0.167
## 2 0.821
## 3 0.121 -0.655 -0.258
## 4 0.819
## 5 0.802
## 6 0.572 0.158
## 7 0.589 0.110
## 8 0.646 0.136
## 9 0.779
## 10 0.837
## 11 0.867
## 12 0.147 0.690
## 13 0.163 0.693
## 14 0.192 0.675
## 15 -0.112 0.883
## 16 0.808
## 17 0.726
## 18 0.714 0.137
## 19 0.738
## 20 0.818
##
## MR2 MR4 MR1 MR3
## SS loadings 3.277 2.935 2.844 2.377
## Proportion Var 0.164 0.147 0.142 0.119
## Cumulative Var 0.164 0.311 0.453 0.572
##
## $score.cor
## [,1] [,2] [,3] [,4]
## [1,] 1.00000000 0.1068177 -0.06783523 0.6159032
## [2,] 0.10681771 1.0000000 -0.16311633 0.1083810
## [3,] -0.06783523 -0.1631163 1.00000000 0.2634312
## [4,] 0.61590321 0.1083810 0.26343125 1.0000000
##
## $TLI
## [1] 0.9611547
##
## $RMSEA
## RMSEA lower upper confidence
## 0.04636942 0.04119086 0.05102479 0.10000000out_targetQ## Factor Analysis using method = minres
## Call: fa(r = purposescales_cor, nfactors = 4, n.obs = 1160, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR4 MR1 MR3 h2 u2 com
## 1 0.05 -0.02 0.61 -0.17 0.33 0.67 1.2
## 2 0.08 0.02 0.82 -0.01 0.65 0.35 1.0
## 3 0.12 0.07 -0.65 -0.26 0.61 0.39 1.4
## 4 0.03 -0.02 0.82 -0.04 0.65 0.35 1.0
## 5 -0.04 0.04 0.80 -0.01 0.64 0.36 1.0
## 6 0.57 0.05 0.03 0.16 0.47 0.53 1.2
## 7 0.59 -0.04 0.11 0.05 0.38 0.62 1.1
## 8 0.65 -0.07 -0.06 0.14 0.54 0.46 1.1
## 9 0.78 -0.05 -0.05 0.05 0.66 0.34 1.0
## 10 0.84 0.10 0.01 -0.09 0.63 0.37 1.1
## 11 0.87 0.00 -0.02 -0.06 0.70 0.30 1.0
## 12 0.15 0.03 -0.05 0.69 0.61 0.39 1.1
## 13 0.04 0.06 0.16 0.69 0.62 0.38 1.1
## 14 0.19 -0.02 -0.06 0.68 0.63 0.37 1.2
## 15 -0.11 0.00 0.02 0.88 0.68 0.32 1.0
## 16 0.00 0.81 -0.02 -0.06 0.66 0.34 1.0
## 17 0.07 0.73 -0.04 0.04 0.57 0.43 1.0
## 18 -0.07 0.71 -0.02 0.14 0.53 0.47 1.1
## 19 0.09 0.74 0.05 0.00 0.55 0.45 1.0
## 20 -0.09 0.82 0.00 -0.05 0.66 0.34 1.0
##
## MR2 MR4 MR1 MR3
## SS loadings 3.43 2.95 2.87 2.55
## Proportion Var 0.17 0.15 0.14 0.13
## Cumulative Var 0.17 0.32 0.46 0.59
## Proportion Explained 0.29 0.25 0.24 0.22
## Cumulative Proportion 0.29 0.54 0.78 1.00
##
## With factor correlations of
## MR2 MR4 MR1 MR3
## MR2 1.00 0.11 -0.11 0.63
## MR4 0.11 1.00 -0.17 0.09
## MR1 -0.11 -0.17 1.00 0.31
## MR3 0.63 0.09 0.31 1.00
##
## Mean item complexity = 1.1
## Test of the hypothesis that 4 factors are sufficient.
##
## The degrees of freedom for the null model are 190 and the objective function was 10.64 with Chi Square of 12257.4
## The degrees of freedom for the model are 116 and the objective function was 0.35
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.02
##
## The harmonic number of observations is 1160 with the empirical chi square 164.59 with prob < 0.002
## The total number of observations was 1160 with MLE Chi Square = 401.52 with prob < 4e-33
##
## Tucker Lewis Index of factoring reliability = 0.961
## RMSEA index = 0.046 and the 90 % confidence intervals are 0.041 0.051
## BIC = -417
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR2 MR4 MR1 MR3
## Correlation of scores with factors 0.95 0.94 0.94 0.94
## Multiple R square of scores with factors 0.90 0.88 0.89 0.88
## Minimum correlation of possible factor scores 0.81 0.77 0.77 0.77fa2latex(fa(purposescales_cor,4,rotate="TargetQ", n.obs = 1160, Target=Targ_key),heading="Table 7 Factor Based on theory")## % Called in the psych package fa2latex % Called in the psych package fa(purposescales_cor, 4, rotate = "TargetQ", n.obs = 1160, Target = Targ_key) % Called in the psych package Table 7 Factor Based on theory
## \begin{table}[htdp]\caption{fa2latex}
## \begin{center}
## \begin{scriptsize}
## \begin{tabular} {l r r r r r r r }
## \multicolumn{ 7 }{l}{ Table 7 Factor Based on theory } \cr
## \hline Variable & MR2 & MR4 & MR1 & MR3 & h2 & u2 & com \cr
## \hline
## 1 & 0.05 & -0.02 & \bf{ 0.61} & -0.17 & 0.33 & 0.67 & 1.17 \cr
## 2 & 0.08 & 0.02 & \bf{ 0.82} & -0.01 & 0.65 & 0.35 & 1.02 \cr
## 3 & 0.12 & 0.07 & \bf{-0.65} & -0.26 & 0.61 & 0.39 & 1.40 \cr
## 4 & 0.03 & -0.02 & \bf{ 0.82} & -0.04 & 0.65 & 0.35 & 1.01 \cr
## 5 & -0.04 & 0.04 & \bf{ 0.80} & -0.01 & 0.64 & 0.36 & 1.01 \cr
## 6 & \bf{ 0.57} & 0.05 & 0.03 & 0.16 & 0.47 & 0.53 & 1.17 \cr
## 7 & \bf{ 0.59} & -0.04 & 0.11 & 0.05 & 0.38 & 0.62 & 1.09 \cr
## 8 & \bf{ 0.65} & -0.07 & -0.06 & 0.14 & 0.54 & 0.46 & 1.13 \cr
## 9 & \bf{ 0.78} & -0.05 & -0.05 & 0.05 & 0.66 & 0.34 & 1.02 \cr
## 10 & \bf{ 0.84} & 0.10 & 0.01 & -0.09 & 0.63 & 0.37 & 1.05 \cr
## 11 & \bf{ 0.87} & 0.00 & -0.02 & -0.06 & 0.70 & 0.30 & 1.01 \cr
## 12 & 0.15 & 0.03 & -0.05 & \bf{ 0.69} & 0.61 & 0.39 & 1.10 \cr
## 13 & 0.04 & 0.06 & 0.16 & \bf{ 0.69} & 0.62 & 0.38 & 1.13 \cr
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## 17 & 0.07 & \bf{ 0.73} & -0.04 & 0.04 & 0.57 & 0.43 & 1.03 \cr
## 18 & -0.07 & \bf{ 0.71} & -0.02 & 0.14 & 0.53 & 0.47 & 1.09 \cr
## 19 & 0.09 & \bf{ 0.74} & 0.05 & 0.00 & 0.55 & 0.45 & 1.04 \cr
## 20 & -0.09 & \bf{ 0.82} & 0.00 & -0.05 & 0.66 & 0.34 & 1.03 \cr
## \hline \cr SS loadings & 3.43 & 2.95 & 2.87 & 2.55 & \cr
## \cr
## \hline \cr
## MR2 & 1.00 & 0.11 & -0.11 & 0.63 \cr
## MR4 & 0.11 & 1.00 & -0.17 & 0.09 \cr
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## \end{center}
## \label{default}
## \end{table}#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] 0.9763397Now let’s look at this as a bifactor model
bifactor.model = 'Definate =~ APSI_2 + APSI_4 + PWB_8 + APSI_7 + APSI_8 + APSI_5 + APSI_1 + LET_2
Tend =~ PWB_2 + PWB_9 + PWB_3 + PWB_5 + LET_1 + APSI_6
MLQP =~ MLQ_4 + MLQ_5 + MLQ_6 + MLQ_1
MLQS =~ MLQ_2 + MLQ_3 + MLQ_7 + MLQ_8 + MLQ_10
Definate ~~ 0*Tend
Definate ~~ 0*MLQP
Definate~~0*MLQS
MLQP~~0*Tend
MLQP~~0*MLQS
MLQS~~0*Tend'
bifactor.fit=cfa(bifactor.model, data=all_surveys, 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:
## 17 22 23 24 28 29 43 45 78 79 80 81 85 94 110 111 112 116 121 122 123 124 125 128 129 130 131 133 135 137 138 140 147 151 152 155 156 162 166 169 170 171 172 173 174 176 177 179 180 183 184 186 187 188 189 192 194 195 197 200 202 203 204 207 208 210 212 214 215 217 220 222 223 224 226 227 228 229 230 234 238 240 243 245 246 247 249 252 255 256 265 266 267 268 270 271 274 275 280 281 282 284 286 287 289 291 292 298 300 304 309 310 311 312 315 316 317 320 322 325 327 330 333 334 336 339 340 344 348 350 351 352 354 355 357 360 361 362 364 365 366 367 368 369 370 371 372 373 374 375 376 377 379 380 381 384 385 386 389 390 397 398 399 400 401 402 403 404 405 406 407 408 410 416 417 418 419 420 421 422 423 424 425 427 428 429 430 431 432 434 436 444 445 446 447 448 452 453 454 455 456 457 459 460 462 463 464 465 467 468 470 472 473 474 475 476 478 481 482 485 486 490 491 493 495 539 540 541 542 543 544 545 546 548 549 552 553 555 557 559 560 561 562 563 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 581 582 584 585 586 587 588 589 590 591 592 593 594 596 597 598 599 600 601 602 603 604 605 606 609 610 662 679 687 782 783 784 785 809 810 829 903 906 907 909 911 1110 1113 1114 1116 1117 1120 1125 1128 1129 1130 1139 1140 1146 1150 1151 1154 1159 1160semPaths(bifactor.fit, whatLabels = "std", layout = "tree")
summary(bifactor.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 62 iterations
##
## Used Total
## Number of observations 842 1160
##
## Number of missing patterns 4
##
## Estimator ML
## Minimum Function Test Statistic 1620.339
## Degrees of freedom 230
## P-value (Chi-square) 0.000
##
## Parameter estimates:
##
## Information Observed
## Standard Errors Standard
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## Definate =~
## APSI_2 0.852 0.034 25.372 0.000 0.852 0.771
## APSI_4 0.975 0.035 27.507 0.000 0.975 0.814
## PWB_8 0.816 0.046 17.692 0.000 0.816 0.586
## APSI_7 0.863 0.035 25.000 0.000 0.863 0.764
## APSI_8 0.975 0.036 27.206 0.000 0.975 0.808
## APSI_5 0.700 0.033 20.914 0.000 0.700 0.670
## APSI_1 1.013 0.035 28.590 0.000 1.013 0.835
## LET_2 0.641 0.037 17.538 0.000 0.641 0.580
## Tend =~
## PWB_2 0.627 0.052 12.097 0.000 0.627 0.435
## PWB_9 0.542 0.052 10.415 0.000 0.542 0.380
## PWB_3 1.300 0.049 26.669 0.000 1.300 0.822
## PWB_5 1.222 0.051 23.788 0.000 1.222 0.757
## LET_1 0.889 0.045 19.633 0.000 0.889 0.653
## APSI_6 1.087 0.046 23.889 0.000 1.087 0.760
## MLQP =~
## MLQ_4 1.217 0.049 24.724 0.000 1.217 0.768
## MLQ_5 1.123 0.045 24.819 0.000 1.123 0.770
## MLQ_6 1.314 0.052 25.349 0.000 1.314 0.782
## MLQ_1 1.362 0.051 26.452 0.000 1.362 0.806
## MLQS =~
## MLQ_2 1.301 0.048 26.984 0.000 1.301 0.809
## MLQ_3 1.163 0.049 23.887 0.000 1.163 0.743
## MLQ_7 1.133 0.049 22.902 0.000 1.133 0.720
## MLQ_8 1.161 0.049 23.663 0.000 1.161 0.737
## MLQ_10 1.361 0.051 26.436 0.000 1.361 0.797
##
## Covariances:
## Definate ~~
## Tend 0.000 0.000 0.000
## MLQP 0.000 0.000 0.000
## MLQS 0.000 0.000 0.000
## Tend ~~
## MLQP 0.000 0.000 0.000
## MLQP ~~
## MLQS 0.000 0.000 0.000
## Tend ~~
## MLQS 0.000 0.000 0.000
##
## Intercepts:
## APSI_2 3.938 0.039 101.591 0.000 3.938 3.565
## APSI_4 3.868 0.042 92.024 0.000 3.868 3.228
## PWB_8 4.360 0.049 89.437 0.000 4.360 3.128
## APSI_7 3.921 0.040 98.855 0.000 3.921 3.469
## APSI_8 3.871 0.042 91.430 0.000 3.871 3.207
## APSI_5 4.189 0.037 114.115 0.000 4.189 4.007
## APSI_1 3.752 0.043 88.120 0.000 3.752 3.090
## LET_2 3.906 0.038 101.752 0.000 3.906 3.534
## PWB_2 3.130 0.050 62.065 0.000 3.130 2.171
## PWB_9 2.202 0.050 44.059 0.000 2.202 1.542
## PWB_3 2.848 0.055 51.546 0.000 2.848 1.800
## PWB_5 2.877 0.056 51.010 0.000 2.877 1.782
## LET_1 2.479 0.047 52.385 0.000 2.479 1.819
## APSI_6 2.893 0.050 57.730 0.000 2.893 2.023
## MLQ_4 4.985 0.055 91.352 0.000 4.985 3.148
## MLQ_5 5.242 0.050 104.254 0.000 5.242 3.593
## MLQ_6 4.786 0.058 82.650 0.000 4.786 2.848
## MLQ_1 4.700 0.058 80.722 0.000 4.700 2.782
## MLQ_2 5.368 0.055 96.855 0.000 5.368 3.338
## MLQ_3 5.249 0.054 97.252 0.000 5.249 3.352
## MLQ_7 5.183 0.054 95.553 0.000 5.183 3.293
## MLQ_8 5.316 0.054 97.955 0.000 5.316 3.376
## MLQ_10 5.058 0.059 85.972 0.000 5.058 2.963
## Definate 0.000 0.000 0.000
## Tend 0.000 0.000 0.000
## MLQP 0.000 0.000 0.000
## MLQS 0.000 0.000 0.000
##
## Variances:
## APSI_2 0.494 0.029 0.494 0.405
## APSI_4 0.485 0.030 0.485 0.338
## PWB_8 1.276 0.067 1.276 0.657
## APSI_7 0.532 0.031 0.532 0.417
## APSI_8 0.505 0.031 0.505 0.347
## APSI_5 0.603 0.033 0.603 0.552
## APSI_1 0.447 0.029 0.447 0.303
## LET_2 0.810 0.042 0.810 0.663
## PWB_2 1.684 0.087 1.684 0.811
## PWB_9 1.746 0.089 1.746 0.856
## PWB_3 0.811 0.063 0.811 0.324
## PWB_5 1.113 0.072 1.113 0.427
## LET_1 1.066 0.060 1.066 0.574
## APSI_6 0.863 0.057 0.863 0.422
## MLQ_4 1.027 0.066 1.027 0.410
## MLQ_5 0.868 0.056 0.868 0.408
## MLQ_6 1.096 0.073 1.096 0.388
## MLQ_1 0.998 0.071 0.998 0.350
## MLQ_2 0.895 0.060 0.895 0.346
## MLQ_3 1.101 0.065 1.101 0.449
## MLQ_7 1.194 0.069 1.194 0.482
## MLQ_8 1.133 0.067 1.133 0.457
## MLQ_10 1.061 0.070 1.061 0.364
## Definate 1.000 1.000 1.000
## Tend 1.000 1.000 1.000
## MLQP 1.000 1.000 1.000
## MLQS 1.000 1.000 1.000
##
## R-Square:
##
## APSI_2 0.595
## APSI_4 0.662
## PWB_8 0.343
## APSI_7 0.583
## APSI_8 0.653
## APSI_5 0.448
## APSI_1 0.697
## LET_2 0.337
## PWB_2 0.189
## PWB_9 0.144
## PWB_3 0.676
## PWB_5 0.573
## LET_1 0.426
## APSI_6 0.578
## MLQ_4 0.590
## MLQ_5 0.592
## MLQ_6 0.612
## MLQ_1 0.650
## MLQ_2 0.654
## MLQ_3 0.551
## MLQ_7 0.518
## MLQ_8 0.543
## MLQ_10 0.636modindices(bifactor.fit, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 1 Definate ~~ MLQP 292.062 0.672 0.672 0.672 0.672
## 2 Tend ~~ MLQP 104.730 -0.415 -0.415 -0.415 -0.415
## 3 Definate =~ MLQ_6 65.995 0.351 0.351 0.209 0.209
## 4 PWB_2 ~~ PWB_9 60.164 0.483 0.483 0.235 0.235
## 5 Definate =~ MLQ_4 59.175 0.318 0.318 0.201 0.201
## 6 Tend =~ MLQ_5 45.026 -0.263 -0.263 -0.180 -0.180
## 7 MLQP =~ LET_1 42.220 -0.266 -0.266 -0.195 -0.195
## 8 Definate =~ MLQ_10 35.869 -0.255 -0.255 -0.149 -0.149
## 9 APSI_2 ~~ APSI_7 32.777 -0.124 -0.124 -0.099 -0.099
## 10 PWB_9 ~~ MLQ_1 31.013 0.302 0.302 0.125 0.125
## 11 LET_1 ~~ MLQ_10 28.892 0.238 0.238 0.102 0.102
## 12 Definate =~ MLQ_3 28.031 0.220 0.220 0.141 0.141
## 13 APSI_4 ~~ APSI_7 26.941 0.117 0.117 0.086 0.086
## 14 MLQP =~ MLQ_10 26.237 -0.221 -0.221 -0.129 -0.129
## 15 Tend =~ PWB_8 25.923 -0.223 -0.223 -0.160 -0.160
## 16 Tend ~~ MLQS 24.449 0.199 0.199 0.199 0.199
## 17 Tend =~ MLQ_1 23.999 -0.214 -0.214 -0.126 -0.126
## 18 APSI_5 ~~ APSI_6 23.327 0.142 0.142 0.095 0.095
## 19 MLQS =~ LET_1 22.911 0.194 0.194 0.143 0.143
## 20 APSI_7 ~~ APSI_8 21.557 0.106 0.106 0.077 0.077
## 21 Definate =~ LET_1 21.379 -0.185 -0.185 -0.136 -0.136
## 22 MLQP =~ APSI_6 20.554 -0.178 -0.178 -0.125 -0.125
## 23 MLQS =~ APSI_7 20.203 0.131 0.131 0.116 0.116
## 24 APSI_7 ~~ PWB_5 19.468 -0.142 -0.142 -0.078 -0.078
## 25 APSI_6 ~~ MLQ_1 19.067 -0.184 -0.184 -0.076 -0.076
## 26 MLQP =~ APSI_1 18.414 0.121 0.121 0.100 0.100
## 27 MLQP =~ MLQ_7 18.197 0.186 0.186 0.118 0.118
## 28 MLQ_4 ~~ MLQ_6 18.098 0.259 0.259 0.097 0.097
## 29 MLQ_5 ~~ MLQ_1 18.097 0.247 0.247 0.100 0.100
## 30 MLQP =~ MLQ_3 17.778 0.178 0.178 0.114 0.114
## 31 APSI_5 ~~ PWB_9 17.671 -0.159 -0.159 -0.107 -0.107
## 32 PWB_2 ~~ MLQ_10 16.456 0.218 0.218 0.089 0.089
## 33 APSI_2 ~~ APSI_5 16.071 0.088 0.088 0.076 0.076
## 34 LET_2 ~~ PWB_3 15.716 -0.138 -0.138 -0.079 -0.079
## 35 Definate =~ MLQ_8 15.330 0.165 0.165 0.105 0.105
## 36 LET_2 ~~ MLQ_4 14.860 0.140 0.140 0.080 0.080
## 37 APSI_2 ~~ APSI_1 14.855 0.083 0.083 0.062 0.062
## 38 MLQP =~ LET_2 13.708 0.128 0.128 0.116 0.116
## 39 APSI_5 ~~ MLQ_1 13.323 -0.120 -0.120 -0.068 -0.068
## 40 MLQP =~ MLQ_8 13.282 0.156 0.156 0.099 0.099
## 41 MLQ_6 ~~ MLQ_10 12.813 -0.169 -0.169 -0.059 -0.059
## 42 MLQP =~ PWB_8 12.752 0.156 0.156 0.112 0.112
## 43 APSI_2 ~~ LET_1 12.720 -0.103 -0.103 -0.068 -0.068
## 44 PWB_9 ~~ APSI_6 12.666 -0.181 -0.181 -0.089 -0.089
## 45 MLQ_6 ~~ MLQ_3 12.331 0.163 0.163 0.062 0.062
## 46 PWB_2 ~~ MLQ_2 11.591 -0.170 -0.170 -0.073 -0.073
## 47 Definate =~ APSI_6 11.477 0.129 0.129 0.090 0.090
## 48 PWB_2 ~~ MLQ_1 10.432 0.173 0.173 0.071 0.071
## 49 Tend =~ LET_2 10.406 -0.112 -0.112 -0.101 -0.101
## 50 MLQS =~ APSI_5 10.266 0.097 0.097 0.093 0.093
## 51 MLQ_3 ~~ MLQ_10 10.139 0.172 0.172 0.064 0.064
## 52 APSI_1 ~~ LET_2 10.132 0.080 0.080 0.059 0.059
## 53 APSI_7 ~~ MLQ_7 10.032 0.102 0.102 0.057 0.057
## 54 Tend =~ APSI_5 9.949 0.097 0.097 0.092 0.092
## 55 LET_1 ~~ APSI_6 9.701 0.142 0.142 0.073 0.073
## 56 APSI_2 ~~ PWB_5 9.652 0.096 0.096 0.054 0.054
## 57 APSI_2 ~~ LET_2 9.422 0.076 0.076 0.063 0.063
## 58 APSI_6 ~~ MLQ_5 9.338 -0.116 -0.116 -0.056 -0.056
## 59 MLQP =~ MLQ_2 9.281 -0.122 -0.122 -0.076 -0.076
## 60 Tend =~ MLQ_2 9.253 0.124 0.124 0.077 0.077
## 61 LET_2 ~~ APSI_6 8.913 0.100 0.100 0.063 0.063
## 62 MLQ_4 ~~ MLQ_1 8.787 -0.187 -0.187 -0.070 -0.070
## 63 MLQ_5 ~~ MLQ_6 8.787 -0.166 -0.166 -0.068 -0.068
## 64 MLQ_4 ~~ MLQ_3 8.770 0.131 0.131 0.053 0.053
## 65 APSI_2 ~~ MLQ_1 8.712 0.090 0.090 0.048 0.048
## 66 APSI_5 ~~ MLQ_7 8.660 -0.098 -0.098 -0.059 -0.059
## 67 PWB_2 ~~ MLQ_6 8.381 -0.158 -0.158 -0.065 -0.065
## 68 LET_2 ~~ LET_1 8.283 -0.101 -0.101 -0.067 -0.067
## 69 MLQP =~ PWB_9 8.066 0.143 0.143 0.100 0.100
## 70 PWB_5 ~~ MLQ_7 8.061 -0.137 -0.137 -0.054 -0.054
## 71 APSI_6 ~~ MLQ_3 7.847 0.116 0.116 0.052 0.052
## 72 Tend =~ MLQ_10 7.627 0.121 0.121 0.071 0.071
## 73 LET_2 ~~ MLQ_3 7.513 0.100 0.100 0.058 0.058
## 74 PWB_8 ~~ LET_1 7.496 -0.121 -0.121 -0.064 -0.064
## 75 MLQP =~ APSI_2 7.440 0.078 0.078 0.070 0.070
## 76 APSI_7 ~~ LET_1 7.404 0.081 0.081 0.053 0.053
## 77 LET_1 ~~ MLQ_6 7.380 -0.122 -0.122 -0.053 -0.053
## 78 APSI_1 ~~ LET_1 7.306 -0.077 -0.077 -0.047 -0.047
## 79 MLQ_2 ~~ MLQ_3 7.217 -0.137 -0.137 -0.054 -0.054
## 80 PWB_2 ~~ APSI_6 7.205 -0.136 -0.136 -0.066 -0.066
## 81 APSI_4 ~~ APSI_8 7.201 0.062 0.062 0.043 0.043
## 82 APSI_4 ~~ LET_2 7.110 -0.068 -0.068 -0.051 -0.051
## 83 MLQP =~ APSI_5 7.017 -0.081 -0.081 -0.077 -0.077
## 84 APSI_1 ~~ MLQ_6 6.994 0.082 0.082 0.040 0.040
## 85 MLQ_4 ~~ MLQ_10 6.979 -0.120 -0.120 -0.044 -0.044
## 86 Definate =~ PWB_2 6.936 -0.128 -0.128 -0.088 -0.088
## 87 MLQP =~ APSI_4 6.787 0.075 0.075 0.063 0.063
## 88 MLQP ~~ MLQS 6.734 0.103 0.103 0.103 0.103
## 89 APSI_8 ~~ MLQ_6 6.685 0.083 0.083 0.041 0.041
## 90 APSI_8 ~~ LET_2 6.670 -0.067 -0.067 -0.050 -0.050
## 91 APSI_1 ~~ MLQ_10 6.616 -0.078 -0.078 -0.038 -0.038
## 92 PWB_3 ~~ MLQ_7 6.536 0.113 0.113 0.045 0.045
## 93 Definate ~~ MLQS 6.416 0.099 0.099 0.099 0.099
## 94 APSI_8 ~~ APSI_5 6.323 -0.058 -0.058 -0.046 -0.046
## 95 APSI_5 ~~ MLQ_4 6.272 0.080 0.080 0.048 0.048
## 96 PWB_9 ~~ LET_1 6.106 -0.129 -0.129 -0.066 -0.066
## 97 APSI_1 ~~ MLQ_4 6.068 0.073 0.073 0.038 0.038
## 98 APSI_8 ~~ APSI_1 6.067 -0.056 -0.056 -0.039 -0.039
## 99 MLQS =~ APSI_6 5.996 0.095 0.095 0.067 0.067
## 100 PWB_5 ~~ MLQ_2 5.950 0.108 0.108 0.042 0.042
## 101 APSI_7 ~~ APSI_1 5.945 -0.054 -0.054 -0.040 -0.040
## 102 Tend =~ MLQ_6 5.943 0.109 0.109 0.065 0.065
## 103 PWB_3 ~~ MLQ_10 5.860 -0.106 -0.106 -0.039 -0.039
## 104 PWB_9 ~~ MLQ_2 5.566 -0.119 -0.119 -0.052 -0.052
## 105 APSI_2 ~~ APSI_4 5.480 -0.051 -0.051 -0.039 -0.039
## 106 LET_2 ~~ MLQ_10 5.450 -0.087 -0.087 -0.046 -0.046
## 107 APSI_7 ~~ MLQ_8 5.382 0.073 0.073 0.041 0.041
## 108 APSI_6 ~~ MLQ_6 4.914 0.096 0.096 0.040 0.040
## 109 MLQ_7 ~~ MLQ_10 4.873 -0.120 -0.120 -0.045 -0.045
## 110 PWB_5 ~~ LET_1 4.855 -0.113 -0.113 -0.051 -0.051
## 111 APSI_4 ~~ APSI_6 4.806 -0.061 -0.061 -0.036 -0.036
## 112 MLQS =~ APSI_2 4.577 -0.060 -0.060 -0.055 -0.055
## 113 PWB_5 ~~ MLQ_6 4.375 0.102 0.102 0.037 0.037
## 114 MLQ_1 ~~ MLQ_7 4.369 0.098 0.098 0.037 0.037
## 115 PWB_9 ~~ MLQ_8 4.298 -0.112 -0.112 -0.050 -0.050
## 116 PWB_5 ~~ MLQ_5 4.199 -0.088 -0.088 -0.037 -0.037
## 117 PWB_9 ~~ MLQ_4 4.144 -0.108 -0.108 -0.048 -0.048
## 118 APSI_4 ~~ APSI_5 4.075 -0.046 -0.046 -0.036 -0.036
## 119 MLQS =~ MLQ_4 3.997 0.083 0.083 0.053 0.053
## 120 PWB_8 ~~ MLQ_5 3.930 0.084 0.084 0.041 0.041
## 121 LET_1 ~~ MLQ_4 3.852 -0.084 -0.084 -0.039 -0.039
## 122 PWB_3 ~~ MLQ_3 3.846 -0.084 -0.084 -0.034 -0.034fitmeasures(bifactor.fit)## npar fmin chisq
## 69.000 0.962 1620.339
## df pvalue baseline.chisq
## 230.000 0.000 9855.572
## baseline.df baseline.pvalue cfi
## 253.000 0.000 0.855
## tli nnfi rfi
## 0.841 0.841 0.819
## nfi pnfi ifi
## 0.836 0.760 0.856
## rni logl unrestricted.logl
## 0.855 -29305.421 -28495.252
## aic bic ntotal
## 58748.843 59075.612 842.000
## bic2 rmsea rmsea.ci.lower
## 58856.490 0.085 0.081
## rmsea.ci.upper rmsea.pvalue rmr
## 0.089 0.000 0.326
## rmr_nomean srmr srmr_bentler
## 0.340 0.162 0.162
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.168 0.162 0.168
## srmr_mplus srmr_mplus_nomean cn_05
## 0.162 0.168 139.422
## cn_01 gfi agfi
## 147.963 0.975 0.967
## pgfi mfi ecvi
## 0.750 0.438 NAbifactor1.model = 'Definate =~ APSI_2 + APSI_4 + PWB_8 + APSI_7 + APSI_8 + APSI_5 + APSI_1 + LET_2
Tend =~ PWB_2 + PWB_9 + PWB_3 + PWB_5 + LET_1 + APSI_6
MLQP =~ MLQ_4 + MLQ_5 + MLQ_6 + MLQ_1
MLQS =~ MLQ_2 + MLQ_3 + MLQ_7 + MLQ_8 + MLQ_10
Purpose =~ APSI_2 + APSI_4 + PWB_8 + APSI_7 + APSI_8 + APSI_5 + APSI_1 + LET_2 + PWB_2 + PWB_9 + PWB_3 + PWB_5 + LET_1 + APSI_6 + MLQ_4 + MLQ_5 + MLQ_6 + MLQ_1 + MLQ_2 + MLQ_3 + MLQ_7 + MLQ_8 + MLQ_10'
bifactor1.fit=cfa(bifactor1.model, data=all_surveys, 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:
## 17 22 23 24 28 29 43 45 78 79 80 81 85 94 110 111 112 116 121 122 123 124 125 128 129 130 131 133 135 137 138 140 147 151 152 155 156 162 166 169 170 171 172 173 174 176 177 179 180 183 184 186 187 188 189 192 194 195 197 200 202 203 204 207 208 210 212 214 215 217 220 222 223 224 226 227 228 229 230 234 238 240 243 245 246 247 249 252 255 256 265 266 267 268 270 271 274 275 280 281 282 284 286 287 289 291 292 298 300 304 309 310 311 312 315 316 317 320 322 325 327 330 333 334 336 339 340 344 348 350 351 352 354 355 357 360 361 362 364 365 366 367 368 369 370 371 372 373 374 375 376 377 379 380 381 384 385 386 389 390 397 398 399 400 401 402 403 404 405 406 407 408 410 416 417 418 419 420 421 422 423 424 425 427 428 429 430 431 432 434 436 444 445 446 447 448 452 453 454 455 456 457 459 460 462 463 464 465 467 468 470 472 473 474 475 476 478 481 482 485 486 490 491 493 495 539 540 541 542 543 544 545 546 548 549 552 553 555 557 559 560 561 562 563 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 581 582 584 585 586 587 588 589 590 591 592 593 594 596 597 598 599 600 601 602 603 604 605 606 609 610 662 679 687 782 783 784 785 809 810 829 903 906 907 909 911 1110 1113 1114 1116 1117 1120 1125 1128 1129 1130 1139 1140 1146 1150 1151 1154 1159 1160## Warning in lavaan::lavaan(model = bifactor1.model, data = all_surveys,
## std.lv = T, : lavaan WARNING: covariance matrix of latent variables is not
## positive definite; use inspect(fit,"cov.lv") to investigate.semPaths(bifactor1.fit, whatLabels = "std", layout = "tree")## Warning in lavaan(slotOptions = object@Options, slotParTable =
## object@ParTable, : lavaan WARNING: covariance matrix of latent variables is
## not positive definite; use inspect(fit,"cov.lv") to investigate.
summary(bifactor1.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 123 iterations
##
## Used Total
## Number of observations 842 1160
##
## Number of missing patterns 4
##
## Estimator ML
## Minimum Function Test Statistic 711.323
## Degrees of freedom 197
## P-value (Chi-square) 0.000
##
## Parameter estimates:
##
## Information Observed
## Standard Errors Standard
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## Definate =~
## APSI_2 0.138 5.171 0.027 0.979 0.138 0.125
## APSI_4 0.138 5.167 0.027 0.979 0.138 0.115
## PWB_8 0.478 17.905 0.027 0.979 0.478 0.342
## APSI_7 -0.314 11.752 -0.027 0.979 -0.314 -0.277
## APSI_8 -0.195 7.318 -0.027 0.979 -0.195 -0.162
## APSI_5 -0.637 23.877 -0.027 0.979 -0.637 -0.609
## APSI_1 0.020 0.755 0.026 0.979 0.020 0.016
## LET_2 0.100 3.743 0.027 0.979 0.100 0.090
## Tend =~
## PWB_2 0.762 18.786 0.041 0.968 0.762 0.528
## PWB_9 0.625 15.407 0.041 0.968 0.625 0.437
## PWB_3 1.605 39.589 0.041 0.968 1.605 1.015
## PWB_5 1.505 37.124 0.041 0.968 1.505 0.933
## LET_1 1.156 28.509 0.041 0.968 1.156 0.849
## APSI_6 1.433 35.331 0.041 0.968 1.433 1.002
## MLQP =~
## MLQ_4 0.749 2.381 0.315 0.753 0.749 0.473
## MLQ_5 0.948 3.011 0.315 0.753 0.948 0.649
## MLQ_6 0.783 2.489 0.315 0.753 0.783 0.466
## MLQ_1 1.170 3.717 0.315 0.753 1.170 0.693
## MLQS =~
## MLQ_2 1.449 NA 1.449 0.901
## MLQ_3 1.287 NA 1.287 0.822
## MLQ_7 1.254 NA 1.254 0.797
## MLQ_8 1.277 NA 1.277 0.811
## MLQ_10 1.561 NA 1.561 0.914
## Purpose =~
## APSI_2 0.994 5.033 0.197 0.843 0.994 0.898
## APSI_4 1.110 5.029 0.221 0.825 1.110 0.925
## PWB_8 1.306 17.425 0.075 0.940 1.306 0.936
## APSI_7 0.537 11.437 0.047 0.963 0.537 0.475
## APSI_8 0.772 7.122 0.108 0.914 0.772 0.639
## APSI_5 0.045 23.237 0.002 0.998 0.045 0.043
## APSI_1 1.046 0.738 1.417 0.156 1.046 0.860
## LET_2 0.756 3.643 0.207 0.836 0.756 0.683
## PWB_2 0.330 30.811 0.011 0.991 0.330 0.229
## PWB_9 0.315 25.269 0.012 0.990 0.315 0.221
## PWB_3 1.010 64.929 0.016 0.988 1.010 0.638
## PWB_5 0.889 60.886 0.015 0.988 0.889 0.551
## LET_1 0.506 46.757 0.011 0.991 0.506 0.372
## APSI_6 0.941 57.945 0.016 0.987 0.941 0.659
## MLQ_4 1.079 18.591 0.058 0.954 1.079 0.681
## MLQ_5 0.795 23.509 0.034 0.973 0.795 0.545
## MLQ_6 1.173 19.429 0.060 0.952 1.173 0.698
## MLQ_1 0.919 29.028 0.032 0.975 0.919 0.544
## MLQ_2 0.717 NA 0.717 0.446
## MLQ_3 0.858 NA 0.858 0.548
## MLQ_7 0.708 NA 0.708 0.450
## MLQ_8 0.805 NA 0.805 0.511
## MLQ_10 0.597 NA 0.597 0.350
##
## Covariances:
## Definate ~~
## Tend 0.471 30.110 0.016 0.988 0.471 0.471
## MLQP 0.382 26.603 0.014 0.989 0.382 0.382
## MLQS 0.294 NA 0.294 0.294
## Purpose -1.028 2.038 -0.504 0.614 -1.028 -1.028
## Tend ~~
## MLQP -0.369 NA -0.369 -0.369
## MLQS 0.421 12.119 0.035 0.972 0.421 0.421
## Purpose -0.610 25.408 -0.024 0.981 -0.610 -0.610
## MLQP ~~
## MLQS 0.107 7.475 0.014 0.989 0.107 0.107
## Purpose -0.128 24.402 -0.005 0.996 -0.128 -0.128
## MLQS ~~
## Purpose -0.450 NA -0.450 -0.450
##
## Intercepts:
## APSI_2 3.937 0.039 101.864 0.000 3.937 3.558
## APSI_4 3.867 0.042 92.285 0.000 3.867 3.221
## PWB_8 4.359 0.049 89.563 0.000 4.359 3.124
## APSI_7 3.919 0.040 99.081 0.000 3.919 3.464
## APSI_8 3.869 0.042 91.669 0.000 3.869 3.202
## APSI_5 4.187 0.037 114.345 0.000 4.187 4.004
## APSI_1 3.750 0.042 88.403 0.000 3.750 3.084
## LET_2 3.907 0.038 101.891 0.000 3.907 3.534
## PWB_2 3.129 0.050 62.084 0.000 3.129 2.171
## PWB_9 2.201 0.050 44.055 0.000 2.201 1.541
## PWB_3 2.845 0.055 51.584 0.000 2.845 1.799
## PWB_5 2.875 0.056 51.042 0.000 2.875 1.781
## LET_1 2.478 0.047 52.422 0.000 2.478 1.819
## APSI_6 2.891 0.050 57.818 0.000 2.891 2.022
## MLQ_4 4.985 0.055 91.351 0.000 4.985 3.148
## MLQ_5 5.242 0.050 104.254 0.000 5.242 3.593
## MLQ_6 4.786 0.058 82.650 0.000 4.786 2.848
## MLQ_1 4.700 0.058 80.722 0.000 4.700 2.782
## MLQ_2 5.368 0.055 96.855 0.000 5.368 3.338
## MLQ_3 5.249 0.054 97.252 0.000 5.249 3.352
## MLQ_7 5.183 0.054 95.553 0.000 5.183 3.293
## MLQ_8 5.316 0.054 97.955 0.000 5.316 3.376
## MLQ_10 5.058 0.059 85.972 0.000 5.058 2.963
## Definate 0.000 0.000 0.000
## Tend 0.000 0.000 0.000
## MLQP 0.000 0.000 0.000
## MLQS 0.000 0.000 0.000
## Purpose 0.000 0.000 0.000
##
## Variances:
## APSI_2 0.499 0.029 0.499 0.408
## APSI_4 0.504 0.031 0.504 0.349
## PWB_8 1.296 0.071 1.296 0.666
## APSI_7 0.547 0.032 0.547 0.428
## APSI_8 0.516 0.031 0.516 0.353
## APSI_5 0.627 0.045 0.627 0.573
## APSI_1 0.426 0.027 0.426 0.288
## LET_2 0.797 0.041 0.797 0.652
## PWB_2 1.696 0.087 1.696 0.816
## PWB_9 1.791 0.091 1.791 0.878
## PWB_3 0.881 0.063 0.881 0.352
## PWB_5 1.181 0.073 1.181 0.453
## LET_1 0.977 0.056 0.977 0.526
## APSI_6 0.749 0.053 0.749 0.367
## MLQ_4 0.989 0.057 0.989 0.394
## MLQ_5 0.793 0.054 0.793 0.372
## MLQ_6 1.070 0.062 1.070 0.379
## MLQ_1 0.916 0.071 0.916 0.321
## MLQ_2 0.908 0.060 0.908 0.351
## MLQ_3 1.054 0.063 1.054 0.429
## MLQ_7 1.202 0.069 1.202 0.485
## MLQ_8 1.127 0.066 1.127 0.454
## MLQ_10 0.961 0.066 0.961 0.330
## Definate 1.000 1.000 1.000
## Tend 1.000 1.000 1.000
## MLQP 1.000 1.000 1.000
## MLQS 1.000 1.000 1.000
## Purpose 1.000 1.000 1.000
##
## R-Square:
##
## APSI_2 0.592
## APSI_4 0.651
## PWB_8 0.334
## APSI_7 0.572
## APSI_8 0.647
## APSI_5 0.427
## APSI_1 0.712
## LET_2 0.348
## PWB_2 0.184
## PWB_9 0.122
## PWB_3 0.648
## PWB_5 0.547
## LET_1 0.474
## APSI_6 0.633
## MLQ_4 0.606
## MLQ_5 0.628
## MLQ_6 0.621
## MLQ_1 0.679
## MLQ_2 0.649
## MLQ_3 0.571
## MLQ_7 0.515
## MLQ_8 0.546
## MLQ_10 0.670###modindices(bifactor1.fit, sort. = TRUE, minimum.value = 3.84)
fitmeasures(bifactor1.fit)## npar fmin chisq
## 102.000 0.422 711.323
## df pvalue baseline.chisq
## 197.000 0.000 9855.572
## baseline.df baseline.pvalue cfi
## 253.000 0.000 0.946
## tli nnfi rfi
## 0.931 0.931 0.907
## nfi pnfi ifi
## 0.928 0.722 0.947
## rni logl unrestricted.logl
## 0.946 -28850.914 -28495.252
## aic bic ntotal
## 57905.827 58388.877 842.000
## bic2 rmsea rmsea.ci.lower
## 58064.957 0.056 0.051
## rmsea.ci.upper rmsea.pvalue rmr
## 0.060 0.017 0.084
## rmr_nomean srmr srmr_bentler
## 0.087 0.039 0.039
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.041 0.039 0.041
## srmr_mplus srmr_mplus_nomean cn_05
## 0.039 0.041 274.137
## cn_01 gfi agfi
## 292.305 0.986 0.978
## pgfi mfi ecvi
## 0.649 0.737 NAsee whether purpose scales corrolate with ADSQII
Corrolation = 'Definate =~ APSI_2 + APSI_4 + PWB_8 + APSI_7 + APSI_8 + APSI_5 + APSI_1 + LET_2
Tend =~ PWB_2 + PWB_9 + PWB_3 + PWB_5 + LET_1 + APSI_6
MLQP =~ MLQ_4 + MLQ_5 + MLQ_6 + MLQ_1
MLQS =~ MLQ_2 + MLQ_3 + MLQ_7 + MLQ_8 + MLQ_10
English =~ ASDQII_1 + ASDQII_2 + ASDQII_3 + ASDQII_4 + ASDQII_5
Math =~ ASDQII_6 + ASDQII_7 + ASDQII_8 + ASDQII_9 + ASDQII_10
Science =~ ASDQII_11 + ASDQII_12 + ASDQII_13 + ASDQII_14 + ASDQII_15
General =~ ASDQII_16 + ASDQII_17 + ASDQII_18 + ASDQII_19 + ASDQII_20'
corrolation.fit=cfa(Corrolation, data=all_surveys, 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:
## 17 22 23 24 28 29 43 45 78 79 85 94 110 111 112 116 123 128 129 152 170 183 184 220 226 234 240 243 247 271 275 292 304 311 312 327 360 361 364 365 368 445 457 459 460 463 470 478 539 540 541 545 546 548 549 553 555 557 560 563 573 575 577 584 585 587 588 589 591 592 596 598 599 600 602 603 606 610 662 679 687 782 783 784 785 809 810 829 903 1110 1113 1114 1117 1120 1125 1130 1146 1150 1159 1160semPaths(corrolation.fit, whatLabels = "std", layout = "tree")
summary(corrolation.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 125 iterations
##
## Used Total
## Number of observations 1060 1160
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 2494.296
## Degrees of freedom 832
## P-value (Chi-square) 0.000
##
## Parameter estimates:
##
## Information Observed
## Standard Errors Standard
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## Definate =~
## APSI_2 0.856 0.033 25.568 0.000 0.856 0.774
## APSI_4 0.977 0.035 27.578 0.000 0.977 0.814
## PWB_8 0.822 0.046 17.832 0.000 0.822 0.589
## APSI_7 0.857 0.035 24.780 0.000 0.857 0.758
## APSI_8 0.972 0.036 27.086 0.000 0.972 0.804
## APSI_5 0.693 0.034 20.670 0.000 0.693 0.663
## APSI_1 1.023 0.035 29.053 0.000 1.023 0.842
## LET_2 0.650 0.036 17.870 0.000 0.650 0.588
## Tend =~
## PWB_2 0.623 0.052 11.998 0.000 0.623 0.432
## PWB_9 0.513 0.052 9.785 0.000 0.513 0.359
## PWB_3 1.270 0.049 25.866 0.000 1.270 0.801
## PWB_5 1.207 0.052 23.406 0.000 1.207 0.747
## LET_1 0.919 0.045 20.481 0.000 0.919 0.674
## APSI_6 1.127 0.045 25.074 0.000 1.127 0.787
## MLQP =~
## MLQ_4 1.251 0.048 26.079 0.000 1.251 0.789
## MLQ_5 1.121 0.045 24.963 0.000 1.121 0.767
## MLQ_6 1.333 0.051 26.229 0.000 1.333 0.792
## MLQ_1 1.320 0.052 25.613 0.000 1.320 0.780
## MLQS =~
## MLQ_2 1.298 0.048 26.913 0.000 1.298 0.807
## MLQ_3 1.167 0.049 24.003 0.000 1.167 0.745
## MLQ_7 1.136 0.049 22.999 0.000 1.136 0.722
## MLQ_8 1.161 0.049 23.669 0.000 1.161 0.737
## MLQ_10 1.358 0.052 26.343 0.000 1.358 0.795
## English =~
## ASDQII_1 1.030 0.029 35.977 0.000 1.030 0.881
## ASDQII_2 1.027 0.030 34.343 0.000 1.027 0.856
## ASDQII_3 1.038 0.029 35.676 0.000 1.038 0.876
## ASDQII_4 1.019 0.030 33.446 0.000 1.019 0.841
## ASDQII_5 1.033 0.031 33.833 0.000 1.033 0.848
## Math =~
## ASDQII_6 1.315 0.035 37.413 0.000 1.315 0.897
## ASDQII_7 1.363 0.036 38.119 0.000 1.363 0.906
## ASDQII_8 1.380 0.035 39.496 0.000 1.380 0.925
## ASDQII_9 1.332 0.035 38.092 0.000 1.332 0.906
## ASDQII_10 1.313 0.034 38.369 0.000 1.313 0.910
## Science =~
## ASDQII_11 1.183 0.031 38.360 0.000 1.183 0.911
## ASDQII_12 1.180 0.032 37.345 0.000 1.180 0.897
## ASDQII_13 1.236 0.033 37.495 0.000 1.236 0.899
## ASDQII_14 1.190 0.032 37.231 0.000 1.190 0.895
## ASDQII_15 1.196 0.032 36.841 0.000 1.196 0.889
## General =~
## ASDQII_16 0.977 0.030 33.065 0.000 0.977 0.835
## ASDQII_17 0.976 0.029 34.099 0.000 0.976 0.852
## ASDQII_18 0.962 0.028 34.114 0.000 0.962 0.852
## ASDQII_19 0.906 0.028 32.134 0.000 0.906 0.820
## ASDQII_20 0.976 0.029 33.434 0.000 0.976 0.841
##
## Covariances:
## Definate ~~
## Tend -0.017 0.040 -0.425 0.670 -0.017 -0.017
## MLQP 0.687 0.024 29.053 0.000 0.687 0.687
## MLQS 0.102 0.039 2.632 0.008 0.102 0.102
## English 0.201 0.037 5.431 0.000 0.201 0.201
## Math 0.163 0.037 4.456 0.000 0.163 0.163
## Science 0.151 0.037 4.080 0.000 0.151 0.151
## General 0.169 0.038 4.432 0.000 0.169 0.169
## Tend ~~
## MLQP -0.420 0.035 -11.945 0.000 -0.420 -0.420
## MLQS 0.203 0.039 5.178 0.000 0.203 0.203
## English -0.179 0.039 -4.621 0.000 -0.179 -0.179
## Math -0.203 0.038 -5.414 0.000 -0.203 -0.203
## Science -0.132 0.038 -3.445 0.001 -0.132 -0.132
## General -0.252 0.038 -6.561 0.000 -0.252 -0.252
## MLQP ~~
## MLQS 0.106 0.040 2.685 0.007 0.106 0.106
## English 0.294 0.036 8.162 0.000 0.294 0.294
## Math 0.176 0.037 4.723 0.000 0.176 0.176
## Science 0.177 0.037 4.751 0.000 0.177 0.177
## General 0.251 0.038 6.650 0.000 0.251 0.251
## MLQS ~~
## English 0.063 0.039 1.624 0.104 0.063 0.063
## Math -0.012 0.038 -0.309 0.757 -0.012 -0.012
## Science 0.068 0.038 1.776 0.076 0.068 0.068
## General 0.007 0.040 0.175 0.861 0.007 0.007
## English ~~
## Math 0.251 0.031 8.214 0.000 0.251 0.251
## Science 0.403 0.028 14.648 0.000 0.403 0.403
## General 0.696 0.018 38.029 0.000 0.696 0.696
## Math ~~
## Science 0.524 0.024 22.118 0.000 0.524 0.524
## General 0.693 0.018 38.692 0.000 0.693 0.693
## Science ~~
## General 0.698 0.018 39.055 0.000 0.698 0.698
##
## Intercepts:
## APSI_2 3.938 0.039 102.155 0.000 3.938 3.561
## APSI_4 3.869 0.042 92.587 0.000 3.869 3.224
## PWB_8 4.360 0.049 89.693 0.000 4.360 3.126
## APSI_7 3.921 0.039 99.370 0.000 3.921 3.466
## APSI_8 3.871 0.042 91.971 0.000 3.871 3.204
## APSI_5 4.189 0.037 114.566 0.000 4.189 4.004
## APSI_1 3.752 0.042 88.706 0.000 3.752 3.087
## LET_2 3.907 0.038 102.020 0.000 3.907 3.533
## PWB_2 3.131 0.050 62.162 0.000 3.131 2.171
## PWB_9 2.203 0.050 44.114 0.000 2.203 1.542
## PWB_3 2.851 0.055 51.792 0.000 2.851 1.799
## PWB_5 2.880 0.056 51.228 0.000 2.880 1.781
## LET_1 2.482 0.047 52.568 0.000 2.482 1.820
## APSI_6 2.896 0.050 58.022 0.000 2.896 2.022
## MLQ_4 4.986 0.054 91.747 0.000 4.986 3.145
## MLQ_5 5.244 0.050 104.677 0.000 5.244 3.589
## MLQ_6 4.788 0.058 83.014 0.000 4.788 2.845
## MLQ_1 4.701 0.058 81.068 0.000 4.701 2.779
## MLQ_2 5.371 0.055 96.934 0.000 5.371 3.340
## MLQ_3 5.252 0.054 97.322 0.000 5.252 3.353
## MLQ_7 5.186 0.054 95.621 0.000 5.186 3.295
## MLQ_8 5.319 0.054 98.026 0.000 5.319 3.377
## MLQ_10 5.062 0.059 86.047 0.000 5.062 2.965
## ASDQII_1 4.518 0.036 125.824 0.000 4.518 3.865
## ASDQII_2 4.371 0.037 118.628 0.000 4.371 3.644
## ASDQII_3 4.479 0.036 123.146 0.000 4.479 3.782
## ASDQII_4 4.417 0.037 118.705 0.000 4.417 3.646
## ASDQII_5 4.392 0.037 117.387 0.000 4.392 3.606
## ASDQII_6 4.180 0.045 92.785 0.000 4.180 2.850
## ASDQII_7 4.123 0.046 89.276 0.000 4.123 2.742
## ASDQII_8 4.148 0.046 90.516 0.000 4.148 2.780
## ASDQII_9 4.047 0.045 89.655 0.000 4.047 2.754
## ASDQII_10 4.245 0.044 95.805 0.000 4.245 2.943
## ASDQII_11 4.267 0.040 106.922 0.000 4.267 3.284
## ASDQII_12 4.129 0.040 102.170 0.000 4.129 3.138
## ASDQII_13 4.191 0.042 99.239 0.000 4.191 3.048
## ASDQII_14 4.176 0.041 102.264 0.000 4.176 3.141
## ASDQII_15 4.143 0.041 100.333 0.000 4.143 3.082
## ASDQII_16 4.419 0.036 123.007 0.000 4.419 3.778
## ASDQII_17 4.458 0.035 126.708 0.000 4.458 3.892
## ASDQII_18 4.538 0.035 130.840 0.000 4.538 4.019
## ASDQII_19 4.362 0.034 128.599 0.000 4.362 3.950
## ASDQII_20 4.441 0.036 124.593 0.000 4.441 3.827
## Definate 0.000 0.000 0.000
## Tend 0.000 0.000 0.000
## MLQP 0.000 0.000 0.000
## MLQS 0.000 0.000 0.000
## English 0.000 0.000 0.000
## Math 0.000 0.000 0.000
## Science 0.000 0.000 0.000
## General 0.000 0.000 0.000
##
## Variances:
## APSI_2 0.490 0.028 0.490 0.401
## APSI_4 0.486 0.029 0.486 0.337
## PWB_8 1.270 0.066 1.270 0.653
## APSI_7 0.545 0.031 0.545 0.426
## APSI_8 0.515 0.031 0.515 0.353
## APSI_5 0.614 0.033 0.614 0.561
## APSI_1 0.431 0.027 0.431 0.291
## LET_2 0.801 0.041 0.801 0.655
## PWB_2 1.692 0.087 1.692 0.814
## PWB_9 1.779 0.090 1.779 0.871
## PWB_3 0.898 0.063 0.898 0.358
## PWB_5 1.157 0.073 1.157 0.443
## LET_1 1.016 0.058 1.016 0.546
## APSI_6 0.782 0.054 0.782 0.381
## MLQ_4 0.950 0.060 0.950 0.378
## MLQ_5 0.879 0.054 0.879 0.412
## MLQ_6 1.054 0.067 1.054 0.372
## MLQ_1 1.119 0.070 1.119 0.391
## MLQ_2 0.903 0.060 0.903 0.349
## MLQ_3 1.091 0.065 1.091 0.445
## MLQ_7 1.187 0.069 1.187 0.479
## MLQ_8 1.133 0.067 1.133 0.457
## MLQ_10 1.071 0.070 1.071 0.367
## ASDQII_1 0.306 0.017 0.306 0.224
## ASDQII_2 0.385 0.020 0.385 0.267
## ASDQII_3 0.326 0.018 0.326 0.232
## ASDQII_4 0.429 0.022 0.429 0.292
## ASDQII_5 0.417 0.022 0.417 0.281
## ASDQII_6 0.422 0.022 0.422 0.196
## ASDQII_7 0.403 0.022 0.403 0.178
## ASDQII_8 0.322 0.018 0.322 0.144
## ASDQII_9 0.387 0.021 0.387 0.179
## ASDQII_10 0.358 0.019 0.358 0.172
## ASDQII_11 0.288 0.016 0.288 0.170
## ASDQII_12 0.339 0.018 0.339 0.196
## ASDQII_13 0.363 0.020 0.363 0.192
## ASDQII_14 0.352 0.019 0.352 0.199
## ASDQII_15 0.378 0.020 0.378 0.209
## ASDQII_16 0.414 0.021 0.414 0.302
## ASDQII_17 0.360 0.019 0.360 0.275
## ASDQII_18 0.349 0.018 0.349 0.274
## ASDQII_19 0.400 0.020 0.400 0.328
## ASDQII_20 0.394 0.020 0.394 0.292
## Definate 1.000 1.000 1.000
## Tend 1.000 1.000 1.000
## MLQP 1.000 1.000 1.000
## MLQS 1.000 1.000 1.000
## English 1.000 1.000 1.000
## Math 1.000 1.000 1.000
## Science 1.000 1.000 1.000
## General 1.000 1.000 1.000
##
## R-Square:
##
## APSI_2 0.599
## APSI_4 0.663
## PWB_8 0.347
## APSI_7 0.574
## APSI_8 0.647
## APSI_5 0.439
## APSI_1 0.709
## LET_2 0.345
## PWB_2 0.186
## PWB_9 0.129
## PWB_3 0.642
## PWB_5 0.557
## LET_1 0.454
## APSI_6 0.619
## MLQ_4 0.622
## MLQ_5 0.588
## MLQ_6 0.628
## MLQ_1 0.609
## MLQ_2 0.651
## MLQ_3 0.555
## MLQ_7 0.521
## MLQ_8 0.543
## MLQ_10 0.633
## ASDQII_1 0.776
## ASDQII_2 0.733
## ASDQII_3 0.768
## ASDQII_4 0.708
## ASDQII_5 0.719
## ASDQII_6 0.804
## ASDQII_7 0.822
## ASDQII_8 0.856
## ASDQII_9 0.821
## ASDQII_10 0.828
## ASDQII_11 0.830
## ASDQII_12 0.804
## ASDQII_13 0.808
## ASDQII_14 0.801
## ASDQII_15 0.791
## ASDQII_16 0.698
## ASDQII_17 0.725
## ASDQII_18 0.726
## ASDQII_19 0.672
## ASDQII_20 0.708modindices(corrolation.fit, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 1 PWB_2 ~~ PWB_9 64.262 0.503 0.503 0.244 0.244
## 2 ASDQII_1 ~~ ASDQII_18 49.941 0.087 0.087 0.066 0.066
## 3 Definate =~ MLQ_10 47.546 -0.299 -0.299 -0.175 -0.175
## 4 PWB_9 ~~ MLQ_1 43.952 0.365 0.365 0.151 0.151
## 5 Definate =~ MLQ_1 43.140 -0.450 -0.450 -0.266 -0.266
## 6 MLQP =~ MLQ_10 40.369 -0.279 -0.279 -0.163 -0.163
## 7 Definate =~ MLQ_6 37.491 0.414 0.414 0.246 0.246
## 8 ASDQII_14 ~~ ASDQII_17 35.963 0.079 0.079 0.052 0.052
## 9 ASDQII_10 ~~ ASDQII_11 33.993 0.072 0.072 0.038 0.038
## 10 General =~ ASDQII_1 33.741 0.174 0.174 0.149 0.149
## 11 ASDQII_12 ~~ ASDQII_13 33.738 0.086 0.086 0.048 0.048
## 12 ASDQII_11 ~~ ASDQII_18 33.469 0.069 0.069 0.047 0.047
## 13 ASDQII_2 ~~ ASDQII_12 32.794 0.077 0.077 0.049 0.049
## 14 Tend =~ MLQ_5 31.552 -0.254 -0.254 -0.174 -0.174
## 15 ASDQII_12 ~~ ASDQII_19 30.371 0.073 0.073 0.051 0.051
## 16 ASDQII_10 ~~ ASDQII_18 29.923 0.072 0.072 0.044 0.044
## 17 APSI_4 ~~ APSI_7 29.911 0.121 0.121 0.089 0.089
## 18 Tend =~ MLQ_6 29.681 0.277 0.277 0.165 0.165
## 19 MLQ_5 ~~ MLQ_1 28.797 0.254 0.254 0.103 0.103
## 20 ASDQII_10 ~~ ASDQII_19 28.623 -0.074 -0.074 -0.046 -0.046
## 21 ASDQII_2 ~~ ASDQII_3 28.383 0.080 0.080 0.056 0.056
## 22 APSI_2 ~~ APSI_7 28.037 -0.113 -0.113 -0.090 -0.090
## 23 Tend =~ PWB_8 27.649 -0.229 -0.229 -0.164 -0.164
## 24 LET_1 ~~ MLQ_10 27.505 0.228 0.228 0.098 0.098
## 25 ASDQII_13 ~~ ASDQII_18 27.105 -0.069 -0.069 -0.044 -0.044
## 26 MLQP =~ LET_1 26.920 -0.237 -0.237 -0.173 -0.173
## 27 ASDQII_1 ~~ ASDQII_10 26.736 0.066 0.066 0.039 0.039
## 28 Definate =~ MLQ_4 26.365 0.328 0.328 0.207 0.207
## 29 APSI_7 ~~ APSI_8 26.301 0.115 0.115 0.084 0.084
## 30 Definate =~ MLQ_3 25.927 0.214 0.214 0.136 0.136
## 31 Definate =~ LET_1 25.860 -0.202 -0.202 -0.148 -0.148
## 32 ASDQII_1 ~~ ASDQII_11 25.511 0.058 0.058 0.038 0.038
## 33 Definate =~ MLQ_5 25.497 -0.301 -0.301 -0.206 -0.206
## 34 APSI_7 ~~ PWB_5 23.854 -0.159 -0.159 -0.087 -0.087
## 35 ASDQII_17 ~~ ASDQII_18 23.675 0.068 0.068 0.053 0.053
## 36 MLQP =~ APSI_5 23.164 -0.219 -0.219 -0.210 -0.210
## 37 APSI_5 ~~ APSI_6 22.515 0.135 0.135 0.090 0.090
## 38 APSI_6 ~~ MLQ_1 22.181 -0.195 -0.195 -0.081 -0.081
## 39 APSI_5 ~~ MLQ_1 21.980 -0.156 -0.156 -0.088 -0.088
## 40 General =~ MLQ_8 21.908 0.194 0.194 0.123 0.123
## 41 APSI_5 ~~ PWB_9 21.136 -0.176 -0.176 -0.118 -0.118
## 42 ASDQII_1 ~~ ASDQII_3 20.748 -0.065 -0.065 -0.047 -0.047
## 43 ASDQII_5 ~~ ASDQII_16 19.973 0.067 0.067 0.047 0.047
## 44 ASDQII_1 ~~ ASDQII_5 18.109 0.064 0.064 0.045 0.045
## 45 MLQP =~ MLQ_3 17.943 0.180 0.180 0.115 0.115
## 46 APSI_2 ~~ APSI_5 17.839 0.092 0.092 0.079 0.079
## 47 MLQS =~ APSI_7 17.720 0.126 0.126 0.111 0.111
## 48 ASDQII_11 ~~ ASDQII_15 17.702 0.060 0.060 0.034 0.034
## 49 ASDQII_4 ~~ ASDQII_17 17.392 0.059 0.059 0.043 0.043
## 50 ASDQII_12 ~~ ASDQII_18 17.268 -0.053 -0.053 -0.036 -0.036
## 51 ASDQII_2 ~~ ASDQII_18 16.809 -0.055 -0.055 -0.041 -0.041
## 52 PWB_2 ~~ MLQ_1 16.742 0.221 0.221 0.091 0.091
## 53 Science =~ MLQ_8 16.123 0.165 0.165 0.105 0.105
## 54 Math =~ ASDQII_2 16.038 -0.090 -0.090 -0.075 -0.075
## 55 General =~ PWB_2 15.872 -0.199 -0.199 -0.138 -0.138
## 56 Tend =~ MLQ_1 15.808 -0.206 -0.206 -0.121 -0.121
## 57 ASDQII_3 ~~ ASDQII_13 15.730 0.052 0.052 0.032 0.032
## 58 Definate =~ ASDQII_20 15.659 0.102 0.102 0.088 0.088
## 59 APSI_6 ~~ MLQ_5 15.253 -0.142 -0.142 -0.068 -0.068
## 60 MLQS =~ ASDQII_15 15.118 0.097 0.097 0.072 0.072
## 61 MLQS =~ LET_1 15.058 0.161 0.161 0.118 0.118
## 62 LET_2 ~~ PWB_3 15.007 -0.135 -0.135 -0.077 -0.077
## 63 ASDQII_11 ~~ ASDQII_13 14.819 -0.055 -0.055 -0.031 -0.031
## 64 PWB_2 ~~ MLQ_10 14.765 0.207 0.207 0.084 0.084
## 65 General =~ ASDQII_11 14.656 0.110 0.110 0.085 0.085
## 66 ASDQII_17 ~~ ASDQII_20 14.310 -0.055 -0.055 -0.042 -0.042
## 67 MLQP =~ MLQ_8 14.307 0.163 0.163 0.103 0.103
## 68 Tend =~ MLQ_4 14.233 0.181 0.181 0.114 0.114
## 69 Math =~ ASDQII_1 14.179 0.078 0.078 0.067 0.067
## 70 MLQP =~ PWB_8 13.813 0.240 0.240 0.172 0.172
## 71 Definate =~ MLQ_8 13.770 0.158 0.158 0.100 0.100
## 72 Science =~ APSI_5 13.697 0.110 0.110 0.105 0.105
## 73 ASDQII_8 ~~ ASDQII_14 13.620 0.048 0.048 0.024 0.024
## 74 ASDQII_12 ~~ ASDQII_17 13.561 -0.048 -0.048 -0.032 -0.032
## 75 LET_2 ~~ APSI_6 13.453 0.118 0.118 0.075 0.075
## 76 LET_2 ~~ MLQ_4 13.410 0.126 0.126 0.072 0.072
## 77 ASDQII_3 ~~ ASDQII_16 13.269 -0.050 -0.050 -0.036 -0.036
## 78 ASDQII_2 ~~ ASDQII_19 13.265 0.051 0.051 0.039 0.039
## 79 Tend =~ APSI_5 13.155 0.112 0.112 0.107 0.107
## 80 MLQP =~ MLQ_2 13.141 -0.148 -0.148 -0.092 -0.092
## 81 ASDQII_1 ~~ ASDQII_6 13.111 -0.049 -0.049 -0.029 -0.029
## 82 General =~ APSI_5 13.087 0.109 0.109 0.104 0.104
## 83 MLQP =~ MLQ_7 12.901 0.157 0.157 0.100 0.100
## 84 English =~ APSI_5 12.683 0.108 0.108 0.103 0.103
## 85 ASDQII_6 ~~ ASDQII_9 12.665 0.058 0.058 0.027 0.027
## 86 ASDQII_6 ~~ ASDQII_11 12.589 -0.047 -0.047 -0.024 -0.024
## 87 PWB_2 ~~ MLQ_2 12.510 -0.177 -0.177 -0.076 -0.076
## 88 ASDQII_2 ~~ ASDQII_20 12.475 0.050 0.050 0.036 0.036
## 89 General =~ ASDQII_2 12.445 -0.114 -0.114 -0.095 -0.095
## 90 ASDQII_5 ~~ ASDQII_20 12.445 -0.052 -0.052 -0.037 -0.037
## 91 MLQP =~ ASDQII_16 12.388 -0.096 -0.096 -0.082 -0.082
## 92 MLQ_2 ~~ ASDQII_4 12.071 -0.092 -0.092 -0.047 -0.047
## 93 General =~ APSI_1 11.914 -0.095 -0.095 -0.078 -0.078
## 94 MLQP =~ ASDQII_20 11.896 0.092 0.092 0.079 0.079
## 95 MLQP =~ ASDQII_19 11.656 0.090 0.090 0.081 0.081
## 96 ASDQII_19 ~~ ASDQII_20 11.507 0.050 0.050 0.039 0.039
## 97 Science =~ ASDQII_18 11.409 -0.105 -0.105 -0.093 -0.093
## 98 ASDQII_4 ~~ ASDQII_14 11.406 0.048 0.048 0.030 0.030
## 99 ASDQII_13 ~~ ASDQII_19 11.304 0.046 0.046 0.031 0.031
## 100 APSI_5 ~~ ASDQII_11 11.300 0.058 0.058 0.043 0.043
## 101 Definate =~ APSI_6 11.228 0.128 0.128 0.089 0.089
## 102 MLQP =~ LET_2 11.094 0.170 0.170 0.153 0.153
## 103 PWB_2 ~~ APSI_6 11.040 -0.163 -0.163 -0.079 -0.079
## 104 PWB_9 ~~ APSI_6 11.027 -0.164 -0.164 -0.080 -0.080
## 105 General =~ MLQ_6 10.902 -0.147 -0.147 -0.087 -0.087
## 106 Math =~ MLQ_8 10.891 0.135 0.135 0.086 0.086
## 107 English =~ APSI_1 10.844 -0.091 -0.091 -0.075 -0.075
## 108 ASDQII_9 ~~ ASDQII_19 10.767 0.047 0.047 0.029 0.029
## 109 ASDQII_4 ~~ ASDQII_19 10.653 -0.048 -0.048 -0.036 -0.036
## 110 APSI_5 ~~ MLQ_7 10.575 -0.108 -0.108 -0.066 -0.066
## 111 APSI_2 ~~ ASDQII_7 10.547 0.060 0.060 0.036 0.036
## 112 Tend =~ LET_2 10.350 -0.111 -0.111 -0.100 -0.100
## 113 MLQS =~ ASDQII_4 10.217 -0.084 -0.084 -0.070 -0.070
## 114 ASDQII_5 ~~ ASDQII_7 10.199 0.048 0.048 0.026 0.026
## 115 General =~ PWB_3 10.154 0.137 0.137 0.086 0.086
## 116 ASDQII_9 ~~ ASDQII_12 10.064 0.043 0.043 0.022 0.022
## 117 ASDQII_6 ~~ ASDQII_20 9.993 0.047 0.047 0.027 0.027
## 118 English =~ MLQ_8 9.851 0.130 0.130 0.083 0.083
## 119 APSI_2 ~~ APSI_1 9.763 0.065 0.065 0.048 0.048
## 120 APSI_2 ~~ LET_1 9.761 -0.088 -0.088 -0.058 -0.058
## 121 MLQ_3 ~~ MLQ_10 9.622 0.167 0.167 0.062 0.062
## 122 ASDQII_18 ~~ ASDQII_20 9.583 -0.045 -0.045 -0.034 -0.034
## 123 MLQS =~ APSI_5 9.478 0.095 0.095 0.090 0.090
## 124 ASDQII_9 ~~ ASDQII_18 9.300 -0.041 -0.041 -0.025 -0.025
## 125 APSI_1 ~~ ASDQII_10 9.257 -0.052 -0.052 -0.030 -0.030
## 126 Tend =~ MLQ_8 9.209 -0.136 -0.136 -0.086 -0.086
## 127 Math =~ PWB_9 9.187 -0.150 -0.150 -0.105 -0.105
## 128 MLQ_5 ~~ MLQ_6 9.170 -0.142 -0.142 -0.058 -0.058
## 129 PWB_3 ~~ PWB_5 9.157 0.175 0.175 0.068 0.068
## 130 Definate =~ ASDQII_16 9.080 -0.079 -0.079 -0.068 -0.068
## 131 APSI_2 ~~ PWB_5 8.911 0.093 0.093 0.052 0.052
## 132 MLQ_6 ~~ MLQ_10 8.856 -0.137 -0.137 -0.048 -0.048
## 133 English =~ MLQ_2 8.831 -0.117 -0.117 -0.073 -0.073
## 134 MLQ_7 ~~ ASDQII_16 8.787 -0.083 -0.083 -0.045 -0.045
## 135 ASDQII_8 ~~ ASDQII_10 8.742 0.045 0.045 0.021 0.021
## 136 ASDQII_6 ~~ ASDQII_8 8.727 -0.047 -0.047 -0.021 -0.021
## 137 APSI_4 ~~ LET_2 8.700 -0.074 -0.074 -0.056 -0.056
## 138 English =~ MLQ_6 8.693 -0.133 -0.133 -0.079 -0.079
## 139 Tend =~ ASDQII_19 8.677 -0.080 -0.080 -0.072 -0.072
## 140 Math =~ PWB_8 8.630 0.123 0.123 0.088 0.088
## 141 APSI_4 ~~ APSI_8 8.623 0.066 0.066 0.045 0.045
## 142 PWB_5 ~~ MLQ_6 8.539 0.139 0.139 0.051 0.051
## 143 ASDQII_1 ~~ ASDQII_13 8.420 -0.037 -0.037 -0.023 -0.023
## 144 English =~ ASDQII_11 8.383 0.062 0.062 0.048 0.048
## 145 MLQP =~ PWB_3 8.382 0.139 0.139 0.088 0.088
## 146 PWB_9 ~~ PWB_5 8.379 0.166 0.166 0.072 0.072
## 147 ASDQII_14 ~~ ASDQII_19 8.343 -0.039 -0.039 -0.027 -0.027
## 148 PWB_9 ~~ ASDQII_1 8.316 -0.085 -0.085 -0.051 -0.051
## 149 APSI_1 ~~ MLQ_6 8.313 0.085 0.085 0.042 0.042
## 150 ASDQII_3 ~~ ASDQII_11 8.301 -0.034 -0.034 -0.022 -0.022
## 151 LET_2 ~~ ASDQII_20 8.205 0.062 0.062 0.048 0.048
## 152 LET_1 ~~ ASDQII_19 8.172 -0.072 -0.072 -0.048 -0.048
## 153 ASDQII_16 ~~ ASDQII_18 8.153 0.042 0.042 0.032 0.032
## 154 PWB_3 ~~ MLQ_10 8.137 -0.127 -0.127 -0.047 -0.047
## 155 APSI_6 ~~ MLQ_3 8.110 0.114 0.114 0.051 0.051
## 156 ASDQII_13 ~~ ASDQII_15 8.095 -0.044 -0.044 -0.024 -0.024
## 157 Math =~ ASDQII_5 8.057 0.066 0.066 0.054 0.054
## 158 APSI_8 ~~ ASDQII_7 7.909 -0.054 -0.054 -0.030 -0.030
## 159 ASDQII_7 ~~ ASDQII_20 7.876 -0.041 -0.041 -0.023 -0.023
## 160 MLQS =~ ASDQII_17 7.869 -0.070 -0.070 -0.061 -0.061
## 161 ASDQII_1 ~~ ASDQII_2 7.761 -0.041 -0.041 -0.029 -0.029
## 162 ASDQII_13 ~~ ASDQII_20 7.714 0.039 0.039 0.024 0.024
## 163 General =~ MLQ_5 7.690 0.109 0.109 0.075 0.075
## 164 APSI_7 ~~ MLQ_7 7.671 0.089 0.089 0.050 0.050
## 165 Tend =~ ASDQII_7 7.613 0.075 0.075 0.050 0.050
## 166 ASDQII_1 ~~ ASDQII_14 7.583 -0.034 -0.034 -0.022 -0.022
## 167 LET_1 ~~ ASDQII_18 7.553 0.066 0.066 0.043 0.043
## 168 MLQ_2 ~~ MLQ_3 7.525 -0.139 -0.139 -0.055 -0.055
## 169 English =~ PWB_3 7.523 0.115 0.115 0.073 0.073
## 170 General =~ PWB_8 7.495 0.116 0.116 0.083 0.083
## 171 APSI_2 ~~ LET_2 7.442 0.067 0.067 0.054 0.054
## 172 APSI_1 ~~ MLQ_4 7.266 0.075 0.075 0.039 0.039
## 173 Science =~ PWB_9 7.213 -0.131 -0.131 -0.092 -0.092
## 174 PWB_5 ~~ LET_1 7.201 -0.132 -0.132 -0.060 -0.060
## 175 ASDQII_9 ~~ ASDQII_11 7.192 -0.034 -0.034 -0.018 -0.018
## 176 APSI_8 ~~ LET_2 7.167 -0.068 -0.068 -0.051 -0.051
## 177 ASDQII_6 ~~ ASDQII_13 7.149 0.039 0.039 0.019 0.019
## 178 ASDQII_12 ~~ ASDQII_16 7.127 -0.037 -0.037 -0.024 -0.024
## 179 ASDQII_3 ~~ ASDQII_20 7.096 0.035 0.035 0.026 0.026
## 180 ASDQII_2 ~~ ASDQII_5 7.024 -0.043 -0.043 -0.029 -0.029
## 181 APSI_8 ~~ APSI_1 6.959 -0.058 -0.058 -0.039 -0.039
## 182 ASDQII_3 ~~ ASDQII_19 6.911 0.035 0.035 0.027 0.027
## 183 APSI_1 ~~ ASDQII_13 6.905 0.045 0.045 0.027 0.027
## 184 LET_2 ~~ MLQ_3 6.869 0.095 0.095 0.055 0.055
## 185 MLQ_6 ~~ MLQ_3 6.853 0.117 0.117 0.044 0.044
## 186 Science =~ PWB_2 6.828 -0.125 -0.125 -0.087 -0.087
## 187 APSI_7 ~~ LET_1 6.809 0.077 0.077 0.050 0.050
## 188 ASDQII_2 ~~ ASDQII_17 6.757 -0.036 -0.036 -0.026 -0.026
## 189 MLQ_4 ~~ ASDQII_16 6.754 -0.067 -0.067 -0.036 -0.036
## 190 ASDQII_3 ~~ ASDQII_6 6.750 0.036 0.036 0.021 0.021
## 191 APSI_1 ~~ LET_2 6.637 0.062 0.062 0.046 0.046
## 192 English =~ ASDQII_6 6.634 -0.060 -0.060 -0.041 -0.041
## 193 MLQP =~ ASDQII_11 6.625 -0.058 -0.058 -0.045 -0.045
## 194 MLQP =~ ASDQII_12 6.622 0.062 0.062 0.047 0.047
## 195 ASDQII_1 ~~ ASDQII_9 6.587 -0.034 -0.034 -0.020 -0.020
## 196 PWB_3 ~~ MLQ_7 6.585 0.115 0.115 0.046 0.046
## 197 General =~ PWB_9 6.540 -0.130 -0.130 -0.091 -0.091
## 198 PWB_9 ~~ MLQ_2 6.536 -0.130 -0.130 -0.057 -0.057
## 199 LET_1 ~~ MLQ_6 6.520 -0.109 -0.109 -0.048 -0.048
## 200 English =~ PWB_2 6.494 -0.125 -0.125 -0.086 -0.086
## 201 LET_2 ~~ LET_1 6.494 -0.087 -0.087 -0.058 -0.058
## 202 PWB_5 ~~ MLQ_7 6.475 -0.123 -0.123 -0.048 -0.048
## 203 MLQ_10 ~~ ASDQII_19 6.445 -0.069 -0.069 -0.036 -0.036
## 204 ASDQII_10 ~~ ASDQII_13 6.415 -0.034 -0.034 -0.017 -0.017
## 205 APSI_1 ~~ MLQ_10 6.366 -0.075 -0.075 -0.036 -0.036
## 206 ASDQII_1 ~~ ASDQII_19 6.327 -0.033 -0.033 -0.025 -0.025
## 207 ASDQII_3 ~~ ASDQII_7 6.233 -0.034 -0.034 -0.019 -0.019
## 208 ASDQII_11 ~~ ASDQII_12 6.173 -0.034 -0.034 -0.020 -0.020
## 209 ASDQII_2 ~~ ASDQII_11 6.145 -0.031 -0.031 -0.020 -0.020
## 210 APSI_7 ~~ MLQ_4 6.129 -0.074 -0.074 -0.041 -0.041
## 211 General =~ MLQ_10 6.106 -0.104 -0.104 -0.061 -0.061
## 212 Tend =~ MLQ_2 6.101 0.105 0.105 0.065 0.065
## 213 MLQ_4 ~~ MLQ_5 6.091 -0.109 -0.109 -0.047 -0.047
## 214 MLQS =~ APSI_2 6.073 -0.070 -0.070 -0.064 -0.064
## 215 MLQP =~ PWB_9 6.039 0.139 0.139 0.097 0.097
## 216 PWB_9 ~~ ASDQII_2 6.030 0.079 0.079 0.046 0.046
## 217 English =~ ASDQII_10 6.007 0.054 0.054 0.037 0.037
## 218 MLQ_1 ~~ ASDQII_12 5.959 0.063 0.063 0.028 0.028
## 219 ASDQII_11 ~~ ASDQII_19 5.937 -0.031 -0.031 -0.021 -0.021
## 220 APSI_2 ~~ APSI_4 5.927 -0.052 -0.052 -0.039 -0.039
## 221 General =~ ASDQII_6 5.920 -0.080 -0.080 -0.055 -0.055
## 222 General =~ ASDQII_12 5.895 -0.074 -0.074 -0.056 -0.056
## 223 Definate =~ PWB_2 5.866 -0.117 -0.117 -0.081 -0.081
## 224 ASDQII_4 ~~ ASDQII_8 5.825 0.034 0.034 0.019 0.019
## 225 MLQ_1 ~~ MLQ_7 5.788 0.113 0.113 0.043 0.043
## 226 Science =~ ASDQII_20 5.777 0.079 0.079 0.068 0.068
## 227 ASDQII_5 ~~ ASDQII_12 5.768 -0.033 -0.033 -0.021 -0.021
## 228 MLQ_10 ~~ ASDQII_2 5.715 0.065 0.065 0.032 0.032
## 229 ASDQII_4 ~~ ASDQII_10 5.674 -0.034 -0.034 -0.020 -0.020
## 230 APSI_7 ~~ APSI_1 5.631 -0.051 -0.051 -0.037 -0.037
## 231 APSI_5 ~~ ASDQII_3 5.579 -0.043 -0.043 -0.035 -0.035
## 232 MLQS =~ ASDQII_13 5.503 -0.058 -0.058 -0.042 -0.042
## 233 APSI_6 ~~ ASDQII_1 5.484 0.051 0.051 0.031 0.031
## 234 Science =~ PWB_8 5.479 0.098 0.098 0.070 0.070
## 235 Science =~ ASDQII_1 5.413 0.051 0.051 0.044 0.044
## 236 APSI_7 ~~ MLQ_8 5.393 0.073 0.073 0.041 0.041
## 237 General =~ ASDQII_10 5.333 0.072 0.072 0.050 0.050
## 238 Math =~ MLQ_6 5.318 -0.099 -0.099 -0.059 -0.059
## 239 Math =~ PWB_2 5.301 -0.112 -0.112 -0.078 -0.078
## 240 Tend =~ MLQ_7 5.299 -0.105 -0.105 -0.067 -0.067
## 241 English =~ PWB_8 5.268 0.098 0.098 0.070 0.070
## 242 MLQS =~ PWB_5 5.251 -0.107 -0.107 -0.066 -0.066
## 243 PWB_2 ~~ ASDQII_12 5.211 0.068 0.068 0.036 0.036
## 244 APSI_2 ~~ MLQ_7 5.169 -0.070 -0.070 -0.040 -0.040
## 245 PWB_8 ~~ LET_1 5.137 -0.098 -0.098 -0.051 -0.051
## 246 Math =~ APSI_1 5.080 -0.061 -0.061 -0.050 -0.050
## 247 PWB_9 ~~ ASDQII_20 5.067 -0.072 -0.072 -0.043 -0.043
## 248 APSI_7 ~~ ASDQII_18 5.050 0.040 0.040 0.031 0.031
## 249 Tend =~ MLQ_10 5.044 0.103 0.103 0.060 0.060
## 250 English =~ MLQ_5 5.042 0.090 0.090 0.062 0.062
## 251 APSI_8 ~~ MLQ_6 4.934 0.070 0.070 0.034 0.034
## 252 PWB_9 ~~ MLQ_8 4.892 -0.120 -0.120 -0.053 -0.053
## 253 PWB_3 ~~ MLQ_6 4.885 0.097 0.097 0.036 0.036
## 254 MLQ_7 ~~ MLQ_10 4.884 -0.120 -0.120 -0.045 -0.045
## 255 MLQ_1 ~~ ASDQII_11 4.812 -0.053 -0.053 -0.024 -0.024
## 256 ASDQII_8 ~~ ASDQII_15 4.804 -0.029 -0.029 -0.015 -0.015
## 257 MLQ_4 ~~ MLQ_3 4.802 0.093 0.093 0.037 0.037
## 258 MLQP =~ ASDQII_6 4.794 0.058 0.058 0.039 0.039
## 259 PWB_9 ~~ ASDQII_19 4.761 0.069 0.069 0.044 0.044
## 260 Science =~ MLQ_4 4.750 -0.089 -0.089 -0.056 -0.056
## 261 PWB_9 ~~ LET_1 4.732 -0.112 -0.112 -0.057 -0.057
## 262 Math =~ ASDQII_20 4.727 0.070 0.070 0.060 0.060
## 263 ASDQII_7 ~~ ASDQII_9 4.695 -0.035 -0.035 -0.016 -0.016
## 264 Definate =~ ASDQII_11 4.682 -0.048 -0.048 -0.037 -0.037
## 265 General =~ ASDQII_4 4.680 -0.073 -0.073 -0.060 -0.060
## 266 MLQ_10 ~~ ASDQII_16 4.666 0.060 0.060 0.030 0.030
## 267 LET_2 ~~ MLQ_10 4.662 -0.080 -0.080 -0.042 -0.042
## 268 ASDQII_17 ~~ ASDQII_19 4.657 -0.031 -0.031 -0.025 -0.025
## 269 General =~ MLQ_1 4.627 0.097 0.097 0.057 0.057
## 270 ASDQII_5 ~~ ASDQII_9 4.589 0.032 0.032 0.018 0.018
## 271 MLQ_8 ~~ ASDQII_13 4.572 -0.057 -0.057 -0.026 -0.026
## 272 APSI_1 ~~ LET_1 4.568 -0.059 -0.059 -0.035 -0.035
## 273 ASDQII_6 ~~ ASDQII_7 4.561 0.036 0.036 0.016 0.016
## 274 PWB_3 ~~ MLQ_3 4.545 -0.093 -0.093 -0.037 -0.037
## 275 APSI_2 ~~ MLQ_1 4.542 0.065 0.065 0.035 0.035
## 276 ASDQII_7 ~~ ASDQII_15 4.540 0.031 0.031 0.015 0.015
## 277 ASDQII_18 ~~ ASDQII_19 4.509 -0.030 -0.030 -0.024 -0.024
## 278 General =~ PWB_5 4.477 -0.097 -0.097 -0.060 -0.060
## 279 ASDQII_16 ~~ ASDQII_17 4.471 0.032 0.032 0.024 0.024
## 280 ASDQII_6 ~~ ASDQII_12 4.433 0.029 0.029 0.015 0.015
## 281 ASDQII_12 ~~ ASDQII_14 4.423 -0.030 -0.030 -0.017 -0.017
## 282 APSI_5 ~~ MLQ_5 4.419 -0.061 -0.061 -0.040 -0.040
## 283 MLQ_3 ~~ ASDQII_14 4.417 -0.054 -0.054 -0.026 -0.026
## 284 MLQS =~ PWB_3 4.369 -0.091 -0.091 -0.058 -0.058
## 285 Science =~ ASDQII_19 4.337 0.067 0.067 0.061 0.061
## 286 APSI_7 ~~ ASDQII_15 4.332 0.038 0.038 0.025 0.025
## 287 ASDQII_10 ~~ ASDQII_12 4.313 -0.027 -0.027 -0.014 -0.014
## 288 ASDQII_13 ~~ ASDQII_14 4.278 0.031 0.031 0.017 0.017
## 289 MLQ_1 ~~ MLQ_3 4.215 -0.094 -0.094 -0.035 -0.035
## 290 LET_1 ~~ ASDQII_15 4.186 0.051 0.051 0.028 0.028
## 291 APSI_5 ~~ ASDQII_1 4.182 0.036 0.036 0.030 0.030
## 292 MLQ_1 ~~ ASDQII_9 4.169 0.056 0.056 0.023 0.023
## 293 MLQ_8 ~~ ASDQII_7 4.142 -0.057 -0.057 -0.024 -0.024
## 294 Math =~ PWB_3 4.126 0.085 0.085 0.054 0.054
## 295 MLQ_1 ~~ ASDQII_13 4.124 0.055 0.055 0.023 0.023
## 296 Math =~ APSI_5 4.103 0.060 0.060 0.057 0.057
## 297 ASDQII_2 ~~ ASDQII_10 4.096 -0.028 -0.028 -0.016 -0.016
## 298 Definate =~ PWB_3 4.087 0.084 0.084 0.053 0.053
## 299 APSI_7 ~~ MLQ_1 4.005 -0.064 -0.064 -0.034 -0.034
## 300 MLQ_8 ~~ ASDQII_1 3.941 0.049 0.049 0.027 0.027
## 301 ASDQII_7 ~~ ASDQII_17 3.919 0.028 0.028 0.016 0.016
## 302 Science =~ MLQ_3 3.900 -0.080 -0.080 -0.051 -0.051
## 303 MLQ_6 ~~ ASDQII_18 3.886 -0.050 -0.050 -0.026 -0.026
## 304 MLQS =~ ASDQII_5 3.881 0.052 0.052 0.042 0.042
## 305 ASDQII_6 ~~ ASDQII_10 3.874 -0.031 -0.031 -0.015 -0.015
## 306 PWB_5 ~~ ASDQII_1 3.865 -0.051 -0.051 -0.027 -0.027
## 307 Science =~ APSI_4 3.860 -0.055 -0.055 -0.046 -0.046
## 308 APSI_4 ~~ APSI_1 3.860 -0.042 -0.042 -0.029 -0.029fitmeasures(corrolation.fit)## npar fmin chisq
## 157.000 1.177 2494.296
## df pvalue baseline.chisq
## 832.000 0.000 32703.201
## baseline.df baseline.pvalue cfi
## 903.000 0.000 0.948
## tli nnfi rfi
## 0.943 0.943 0.917
## nfi pnfi ifi
## 0.924 0.851 0.948
## rni logl unrestricted.logl
## 0.948 -53632.770 -52385.623
## aic bic ntotal
## 107579.541 108359.207 1060.000
## bic2 rmsea rmsea.ci.lower
## 107860.548 0.043 0.041
## rmsea.ci.upper rmsea.pvalue rmr
## 0.045 1.000 0.096
## rmr_nomean srmr srmr_bentler
## 0.098 0.050 0.050
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.051 0.050 0.051
## srmr_mplus srmr_mplus_nomean cn_05
## 0.050 0.051 383.564
## cn_01 gfi agfi
## 396.148 0.968 0.962
## pgfi mfi ecvi
## 0.814 0.457 NAcorolations<-(corrolation.fit)
fitted<-fitted(corrolation.fit)See is APSI_6 (I don;t know where I fit in the world) is corrolated with APSI_2 and APSI_4 (3. I have a firm sense of who I am. I know what I want out of life.) – it is not.
cor(all_surveys$APSI_4, all_surveys$APSI_2, use = "complete.obs")## [1] 0.6054402?cor## starting httpd help server ... donewith(all_surveys, cor(APSI_6, APSI_4))## [1] NACorrorlations
second.corrolation = ' F1 =~ PWB_1 + PWB_3 + APSI_6 + LET_1 + LET_3 + LET_5
F2 =~ PWB_7 + PWB_8 + APSI_2 + APSI_4 + APSI_7 + APSI_8
MLQP =~ MLQ_4 + MLQ_5 + MLQ_6 + MLQ_1
MLQS =~ MLQ_2 + MLQ_3 + MLQ_7 + MLQ_8 + MLQ_10
English =~ ASDQII_1 + ASDQII_2 + ASDQII_3 + ASDQII_4 + ASDQII_5
Math =~ ASDQII_6 + ASDQII_7 + ASDQII_8 + ASDQII_9 + ASDQII_10
Science =~ ASDQII_11 + ASDQII_12 + ASDQII_13 + ASDQII_14 + ASDQII_15
General =~ ASDQII_16 + ASDQII_17 + ASDQII_18 + ASDQII_19 + ASDQII_20'
second.fit=cfa(second.corrolation, data=all_surveys, 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:
## 17 22 23 24 28 29 43 45 78 79 85 94 110 111 112 116 123 128 129 152 170 183 184 220 226 234 240 243 247 271 275 292 304 311 312 327 360 361 364 365 368 445 457 459 460 463 470 478 539 540 541 545 546 548 549 553 555 557 560 563 573 575 577 584 585 587 588 589 591 592 596 598 599 600 602 603 606 610 662 679 687 782 783 784 785 809 810 829 903 1110 1113 1114 1117 1120 1125 1130 1146 1150 1159 1160semPaths(second.fit, whatLabels = "std", layout = "tree")
summary(second.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 123 iterations
##
## Used Total
## Number of observations 1060 1160
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 2188.724
## Degrees of freedom 751
## P-value (Chi-square) 0.000
##
## Parameter estimates:
##
## Information Observed
## Standard Errors Standard
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## F1 =~
## PWB_1 0.859 0.055 15.615 0.000 0.859 0.533
## PWB_3 1.272 0.048 26.727 0.000 1.272 0.804
## APSI_6 1.105 0.044 25.142 0.000 1.105 0.773
## LET_1 0.914 0.044 20.780 0.000 0.914 0.670
## LET_3 1.080 0.040 27.089 0.000 1.080 0.811
## LET_5 1.043 0.041 25.639 0.000 1.043 0.781
## F2 =~
## PWB_7 0.879 0.041 21.282 0.000 0.879 0.685
## PWB_8 0.847 0.046 18.284 0.000 0.847 0.607
## APSI_2 0.820 0.034 23.783 0.000 0.820 0.742
## APSI_4 0.978 0.036 27.295 0.000 0.978 0.815
## APSI_7 0.876 0.035 25.273 0.000 0.876 0.774
## APSI_8 0.996 0.036 27.816 0.000 0.996 0.824
## MLQP =~
## MLQ_4 1.237 0.048 25.647 0.000 1.237 0.780
## MLQ_5 1.131 0.045 25.264 0.000 1.131 0.774
## MLQ_6 1.332 0.051 26.160 0.000 1.332 0.792
## MLQ_1 1.326 0.051 25.775 0.000 1.326 0.784
## MLQS =~
## MLQ_2 1.298 0.048 26.919 0.000 1.298 0.807
## MLQ_3 1.168 0.049 24.032 0.000 1.168 0.746
## MLQ_7 1.136 0.049 23.001 0.000 1.136 0.722
## MLQ_8 1.161 0.049 23.682 0.000 1.161 0.737
## MLQ_10 1.356 0.052 26.294 0.000 1.356 0.794
## English =~
## ASDQII_1 1.030 0.029 35.979 0.000 1.030 0.881
## ASDQII_2 1.027 0.030 34.342 0.000 1.027 0.856
## ASDQII_3 1.038 0.029 35.676 0.000 1.038 0.876
## ASDQII_4 1.019 0.030 33.447 0.000 1.019 0.841
## ASDQII_5 1.032 0.031 33.831 0.000 1.032 0.848
## Math =~
## ASDQII_6 1.315 0.035 37.415 0.000 1.315 0.897
## ASDQII_7 1.363 0.036 38.121 0.000 1.363 0.906
## ASDQII_8 1.380 0.035 39.492 0.000 1.380 0.925
## ASDQII_9 1.332 0.035 38.089 0.000 1.332 0.906
## ASDQII_10 1.313 0.034 38.371 0.000 1.313 0.910
## Science =~
## ASDQII_11 1.183 0.031 38.359 0.000 1.183 0.911
## ASDQII_12 1.180 0.032 37.347 0.000 1.180 0.897
## ASDQII_13 1.236 0.033 37.493 0.000 1.236 0.899
## ASDQII_14 1.190 0.032 37.232 0.000 1.190 0.895
## ASDQII_15 1.196 0.032 36.842 0.000 1.196 0.889
## General =~
## ASDQII_16 0.977 0.030 33.076 0.000 0.977 0.835
## ASDQII_17 0.976 0.029 34.115 0.000 0.976 0.852
## ASDQII_18 0.962 0.028 34.112 0.000 0.962 0.852
## ASDQII_19 0.905 0.028 32.127 0.000 0.905 0.820
## ASDQII_20 0.976 0.029 33.420 0.000 0.976 0.841
##
## Covariances:
## F1 ~~
## F2 0.057 0.039 1.454 0.146 0.057 0.057
## MLQP -0.355 0.036 -9.868 0.000 -0.355 -0.355
## MLQS 0.204 0.038 5.357 0.000 0.204 0.204
## English -0.154 0.038 -4.059 0.000 -0.154 -0.154
## Math -0.172 0.037 -4.641 0.000 -0.172 -0.172
## Science -0.110 0.038 -2.902 0.004 -0.110 -0.110
## General -0.229 0.038 -6.053 0.000 -0.229 -0.229
## F2 ~~
## MLQP 0.691 0.024 28.609 0.000 0.691 0.691
## MLQS 0.112 0.039 2.851 0.004 0.112 0.112
## English 0.212 0.037 5.647 0.000 0.212 0.212
## Math 0.180 0.037 4.847 0.000 0.180 0.180
## Science 0.150 0.037 4.011 0.000 0.150 0.150
## General 0.188 0.038 4.906 0.000 0.188 0.188
## MLQP ~~
## MLQS 0.106 0.040 2.682 0.007 0.106 0.106
## English 0.295 0.036 8.183 0.000 0.295 0.295
## Math 0.176 0.037 4.747 0.000 0.176 0.176
## Science 0.178 0.037 4.782 0.000 0.178 0.178
## General 0.252 0.038 6.685 0.000 0.252 0.252
## MLQS ~~
## English 0.063 0.039 1.625 0.104 0.063 0.063
## Math -0.012 0.038 -0.307 0.759 -0.012 -0.012
## Science 0.068 0.038 1.778 0.075 0.068 0.068
## General 0.007 0.040 0.177 0.859 0.007 0.007
## English ~~
## Math 0.251 0.031 8.214 0.000 0.251 0.251
## Science 0.403 0.028 14.648 0.000 0.403 0.403
## General 0.696 0.018 38.026 0.000 0.696 0.696
## Math ~~
## Science 0.524 0.024 22.118 0.000 0.524 0.524
## General 0.693 0.018 38.690 0.000 0.693 0.693
## Science ~~
## General 0.698 0.018 39.052 0.000 0.698 0.698
##
## Intercepts:
## PWB_1 3.106 0.056 55.256 0.000 3.106 1.928
## PWB_3 2.851 0.055 51.891 0.000 2.851 1.802
## APSI_6 2.897 0.050 58.171 0.000 2.897 2.027
## LET_1 2.482 0.047 52.537 0.000 2.482 1.820
## LET_3 2.517 0.046 54.622 0.000 2.517 1.890
## LET_5 2.196 0.046 47.518 0.000 2.196 1.644
## PWB_7 4.544 0.045 101.671 0.000 4.544 3.538
## PWB_8 4.360 0.049 89.706 0.000 4.360 3.126
## APSI_2 3.940 0.039 102.095 0.000 3.940 3.562
## APSI_4 3.870 0.042 92.621 0.000 3.870 3.225
## APSI_7 3.923 0.039 99.443 0.000 3.923 3.466
## APSI_8 3.873 0.042 92.057 0.000 3.873 3.204
## MLQ_4 4.986 0.054 91.740 0.000 4.986 3.145
## MLQ_5 5.244 0.050 104.687 0.000 5.244 3.589
## MLQ_6 4.788 0.058 83.015 0.000 4.788 2.845
## MLQ_1 4.701 0.058 81.073 0.000 4.701 2.779
## MLQ_2 5.371 0.055 96.934 0.000 5.371 3.340
## MLQ_3 5.252 0.054 97.323 0.000 5.252 3.353
## MLQ_7 5.186 0.054 95.621 0.000 5.186 3.295
## MLQ_8 5.319 0.054 98.026 0.000 5.319 3.377
## MLQ_10 5.062 0.059 86.047 0.000 5.062 2.965
## ASDQII_1 4.518 0.036 125.823 0.000 4.518 3.865
## ASDQII_2 4.371 0.037 118.628 0.000 4.371 3.644
## ASDQII_3 4.479 0.036 123.146 0.000 4.479 3.782
## ASDQII_4 4.417 0.037 118.705 0.000 4.417 3.646
## ASDQII_5 4.392 0.037 117.387 0.000 4.392 3.606
## ASDQII_6 4.180 0.045 92.785 0.000 4.180 2.850
## ASDQII_7 4.123 0.046 89.275 0.000 4.123 2.742
## ASDQII_8 4.148 0.046 90.516 0.000 4.148 2.780
## ASDQII_9 4.047 0.045 89.655 0.000 4.047 2.754
## ASDQII_10 4.245 0.044 95.805 0.000 4.245 2.943
## ASDQII_11 4.267 0.040 106.922 0.000 4.267 3.284
## ASDQII_12 4.129 0.040 102.170 0.000 4.129 3.138
## ASDQII_13 4.191 0.042 99.239 0.000 4.191 3.048
## ASDQII_14 4.176 0.041 102.264 0.000 4.176 3.141
## ASDQII_15 4.143 0.041 100.332 0.000 4.143 3.082
## ASDQII_16 4.419 0.036 123.007 0.000 4.419 3.778
## ASDQII_17 4.458 0.035 126.707 0.000 4.458 3.892
## ASDQII_18 4.538 0.035 130.839 0.000 4.538 4.019
## ASDQII_19 4.362 0.034 128.599 0.000 4.362 3.950
## ASDQII_20 4.441 0.036 124.593 0.000 4.441 3.827
## F1 0.000 0.000 0.000
## F2 0.000 0.000 0.000
## MLQP 0.000 0.000 0.000
## MLQS 0.000 0.000 0.000
## English 0.000 0.000 0.000
## Math 0.000 0.000 0.000
## Science 0.000 0.000 0.000
## General 0.000 0.000 0.000
##
## Variances:
## PWB_1 1.859 0.097 1.859 0.716
## PWB_3 0.885 0.057 0.885 0.354
## APSI_6 0.821 0.051 0.821 0.402
## LET_1 1.024 0.056 1.024 0.551
## LET_3 0.609 0.039 0.609 0.343
## LET_5 0.696 0.043 0.696 0.390
## PWB_7 0.876 0.049 0.876 0.531
## PWB_8 1.229 0.065 1.229 0.631
## APSI_2 0.551 0.032 0.551 0.450
## APSI_4 0.484 0.031 0.484 0.336
## APSI_7 0.514 0.031 0.514 0.401
## APSI_8 0.468 0.030 0.468 0.320
## MLQ_4 0.984 0.061 0.984 0.392
## MLQ_5 0.856 0.053 0.856 0.401
## MLQ_6 1.057 0.068 1.057 0.373
## MLQ_1 1.104 0.069 1.104 0.386
## MLQ_2 0.902 0.060 0.902 0.349
## MLQ_3 1.089 0.065 1.089 0.444
## MLQ_7 1.187 0.069 1.187 0.479
## MLQ_8 1.131 0.067 1.131 0.456
## MLQ_10 1.076 0.070 1.076 0.369
## ASDQII_1 0.306 0.017 0.306 0.224
## ASDQII_2 0.385 0.021 0.385 0.268
## ASDQII_3 0.326 0.018 0.326 0.232
## ASDQII_4 0.428 0.022 0.428 0.292
## ASDQII_5 0.417 0.022 0.417 0.281
## ASDQII_6 0.422 0.022 0.422 0.196
## ASDQII_7 0.403 0.022 0.403 0.178
## ASDQII_8 0.322 0.018 0.322 0.145
## ASDQII_9 0.387 0.021 0.387 0.179
## ASDQII_10 0.358 0.019 0.358 0.172
## ASDQII_11 0.288 0.016 0.288 0.170
## ASDQII_12 0.339 0.018 0.339 0.196
## ASDQII_13 0.363 0.020 0.363 0.192
## ASDQII_14 0.352 0.019 0.352 0.199
## ASDQII_15 0.378 0.020 0.378 0.209
## ASDQII_16 0.413 0.021 0.413 0.302
## ASDQII_17 0.360 0.019 0.360 0.274
## ASDQII_18 0.350 0.018 0.350 0.274
## ASDQII_19 0.400 0.020 0.400 0.328
## ASDQII_20 0.394 0.020 0.394 0.293
## F1 1.000 1.000 1.000
## F2 1.000 1.000 1.000
## MLQP 1.000 1.000 1.000
## MLQS 1.000 1.000 1.000
## English 1.000 1.000 1.000
## Math 1.000 1.000 1.000
## Science 1.000 1.000 1.000
## General 1.000 1.000 1.000
##
## R-Square:
##
## PWB_1 0.284
## PWB_3 0.646
## APSI_6 0.598
## LET_1 0.449
## LET_3 0.657
## LET_5 0.610
## PWB_7 0.469
## PWB_8 0.369
## APSI_2 0.550
## APSI_4 0.664
## APSI_7 0.599
## APSI_8 0.680
## MLQ_4 0.608
## MLQ_5 0.599
## MLQ_6 0.627
## MLQ_1 0.614
## MLQ_2 0.651
## MLQ_3 0.556
## MLQ_7 0.521
## MLQ_8 0.544
## MLQ_10 0.631
## ASDQII_1 0.776
## ASDQII_2 0.732
## ASDQII_3 0.768
## ASDQII_4 0.708
## ASDQII_5 0.719
## ASDQII_6 0.804
## ASDQII_7 0.822
## ASDQII_8 0.855
## ASDQII_9 0.821
## ASDQII_10 0.828
## ASDQII_11 0.830
## ASDQII_12 0.804
## ASDQII_13 0.808
## ASDQII_14 0.801
## ASDQII_15 0.791
## ASDQII_16 0.698
## ASDQII_17 0.726
## ASDQII_18 0.726
## ASDQII_19 0.672
## ASDQII_20 0.707modindices(second.fit, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 1 ASDQII_1 ~~ ASDQII_18 50.091 0.087 0.087 0.066 0.066
## 2 F2 =~ LET_1 47.025 -0.270 -0.270 -0.198 -0.198
## 3 MLQP =~ LET_1 46.765 -0.293 -0.293 -0.215 -0.215
## 4 F2 =~ MLQ_10 43.468 -0.290 -0.290 -0.170 -0.170
## 5 F2 =~ MLQ_1 38.356 -0.432 -0.432 -0.255 -0.255
## 6 F1 =~ MLQ_5 38.307 -0.262 -0.262 -0.179 -0.179
## 7 MLQP =~ MLQ_10 37.915 -0.270 -0.270 -0.158 -0.158
## 8 LET_1 ~~ MLQ_10 37.620 0.263 0.263 0.113 0.113
## 9 PWB_7 ~~ APSI_4 37.207 -0.175 -0.175 -0.113 -0.113
## 10 F2 =~ MLQ_6 36.409 0.416 0.416 0.247 0.247
## 11 ASDQII_14 ~~ ASDQII_17 36.078 0.079 0.079 0.052 0.052
## 12 ASDQII_10 ~~ ASDQII_11 34.024 0.072 0.072 0.038 0.038
## 13 ASDQII_12 ~~ ASDQII_13 33.745 0.086 0.086 0.048 0.048
## 14 General =~ ASDQII_1 33.572 0.173 0.173 0.148 0.148
## 15 ASDQII_11 ~~ ASDQII_18 33.305 0.069 0.069 0.047 0.047
## 16 ASDQII_2 ~~ ASDQII_12 32.776 0.077 0.077 0.049 0.049
## 17 PWB_7 ~~ PWB_8 32.378 0.228 0.228 0.127 0.127
## 18 ASDQII_12 ~~ ASDQII_19 30.418 0.074 0.074 0.051 0.051
## 19 ASDQII_10 ~~ ASDQII_18 29.785 0.072 0.072 0.044 0.044
## 20 F1 =~ MLQ_6 29.286 0.260 0.260 0.154 0.154
## 21 ASDQII_10 ~~ ASDQII_19 28.512 -0.073 -0.073 -0.046 -0.046
## 22 ASDQII_2 ~~ ASDQII_3 28.398 0.080 0.080 0.056 0.056
## 23 LET_3 ~~ LET_5 28.111 0.173 0.173 0.097 0.097
## 24 ASDQII_13 ~~ ASDQII_18 27.092 -0.069 -0.069 -0.044 -0.044
## 25 ASDQII_1 ~~ ASDQII_10 26.750 0.066 0.066 0.039 0.039
## 26 ASDQII_1 ~~ ASDQII_11 25.463 0.058 0.058 0.038 0.038
## 27 F1 =~ PWB_8 24.979 -0.213 -0.213 -0.153 -0.153
## 28 MLQP =~ PWB_7 24.227 0.277 0.277 0.216 0.216
## 29 MLQP =~ PWB_1 24.210 0.278 0.278 0.172 0.172
## 30 F2 =~ MLQ_3 23.991 0.208 0.208 0.133 0.133
## 31 APSI_4 ~~ APSI_7 23.769 0.116 0.116 0.085 0.085
## 32 ASDQII_17 ~~ ASDQII_18 23.475 0.068 0.068 0.053 0.053
## 33 MLQ_5 ~~ MLQ_1 22.785 0.227 0.227 0.092 0.092
## 34 General =~ MLQ_8 22.020 0.194 0.194 0.123 0.123
## 35 APSI_6 ~~ LET_3 20.991 -0.161 -0.161 -0.085 -0.085
## 36 F2 =~ MLQ_4 20.955 0.300 0.300 0.189 0.189
## 37 F2 =~ MLQ_5 20.864 -0.277 -0.277 -0.190 -0.190
## 38 ASDQII_1 ~~ ASDQII_3 20.815 -0.065 -0.065 -0.047 -0.047
## 39 ASDQII_5 ~~ ASDQII_16 19.960 0.067 0.067 0.047 0.047
## 40 F1 =~ MLQ_4 19.735 0.203 0.203 0.128 0.128
## 41 APSI_2 ~~ APSI_7 18.869 -0.101 -0.101 -0.080 -0.080
## 42 ASDQII_1 ~~ ASDQII_5 18.130 0.064 0.064 0.045 0.045
## 43 ASDQII_11 ~~ ASDQII_15 17.713 0.060 0.060 0.034 0.034
## 44 MLQS =~ APSI_7 17.692 0.126 0.126 0.111 0.111
## 45 APSI_6 ~~ MLQ_1 17.628 -0.170 -0.170 -0.070 -0.070
## 46 ASDQII_4 ~~ ASDQII_17 17.255 0.059 0.059 0.043 0.043
## 47 ASDQII_12 ~~ ASDQII_18 17.200 -0.053 -0.053 -0.036 -0.036
## 48 ASDQII_2 ~~ ASDQII_18 16.845 -0.055 -0.055 -0.041 -0.041
## 49 PWB_1 ~~ MLQ_4 16.837 0.219 0.219 0.086 0.086
## 50 MLQP =~ MLQ_3 16.761 0.174 0.174 0.111 0.111
## 51 Science =~ MLQ_8 16.092 0.165 0.165 0.105 0.105
## 52 MLQP =~ APSI_6 16.079 -0.164 -0.164 -0.115 -0.115
## 53 Math =~ ASDQII_2 16.038 -0.090 -0.090 -0.075 -0.075
## 54 ASDQII_3 ~~ ASDQII_13 15.759 0.052 0.052 0.032 0.032
## 55 F2 =~ LET_5 15.476 0.135 0.135 0.101 0.101
## 56 MLQS =~ ASDQII_15 15.074 0.097 0.097 0.072 0.072
## 57 F1 =~ MLQ_1 14.855 -0.187 -0.187 -0.111 -0.111
## 58 General =~ ASDQII_11 14.739 0.111 0.111 0.085 0.085
## 59 ASDQII_11 ~~ ASDQII_13 14.717 -0.055 -0.055 -0.031 -0.031
## 60 F2 =~ ASDQII_20 14.673 0.100 0.100 0.087 0.087
## 61 LET_1 ~~ APSI_2 14.539 -0.112 -0.112 -0.075 -0.075
## 62 ASDQII_17 ~~ ASDQII_20 14.323 -0.055 -0.055 -0.042 -0.042
## 63 Math =~ ASDQII_1 14.176 0.078 0.078 0.067 0.067
## 64 MLQP =~ MLQ_8 14.077 0.161 0.161 0.103 0.103
## 65 MLQP =~ MLQ_2 13.951 -0.152 -0.152 -0.095 -0.095
## 66 MLQS =~ LET_1 13.899 0.152 0.152 0.111 0.111
## 67 F2 =~ MLQ_8 13.713 0.159 0.159 0.101 0.101
## 68 ASDQII_8 ~~ ASDQII_14 13.539 0.048 0.048 0.024 0.024
## 69 ASDQII_12 ~~ ASDQII_17 13.503 -0.047 -0.047 -0.031 -0.031
## 70 MLQ_5 ~~ MLQ_6 13.388 -0.173 -0.173 -0.070 -0.070
## 71 MLQP =~ MLQ_7 13.298 0.159 0.159 0.101 0.101
## 72 ASDQII_2 ~~ ASDQII_19 13.277 0.051 0.051 0.039 0.039
## 73 ASDQII_3 ~~ ASDQII_16 13.256 -0.049 -0.049 -0.036 -0.036
## 74 MLQP =~ LET_5 13.203 0.136 0.136 0.102 0.102
## 75 ASDQII_1 ~~ ASDQII_6 13.123 -0.049 -0.049 -0.029 -0.029
## 76 General =~ PWB_7 12.938 0.132 0.132 0.102 0.102
## 77 ASDQII_6 ~~ ASDQII_9 12.666 0.058 0.058 0.027 0.027
## 78 ASDQII_6 ~~ ASDQII_11 12.606 -0.047 -0.047 -0.024 -0.024
## 79 MLQP =~ APSI_8 12.565 -0.163 -0.163 -0.135 -0.135
## 80 APSI_2 ~~ ASDQII_7 12.455 0.069 0.069 0.041 0.041
## 81 ASDQII_2 ~~ ASDQII_20 12.452 0.050 0.050 0.036 0.036
## 82 General =~ ASDQII_2 12.422 -0.114 -0.114 -0.095 -0.095
## 83 ASDQII_5 ~~ ASDQII_20 12.412 -0.052 -0.052 -0.037 -0.037
## 84 F1 =~ PWB_7 12.328 -0.129 -0.129 -0.101 -0.101
## 85 MLQ_2 ~~ ASDQII_4 11.890 -0.091 -0.091 -0.047 -0.047
## 86 ASDQII_19 ~~ ASDQII_20 11.726 0.051 0.051 0.040 0.040
## 87 MLQP =~ ASDQII_16 11.615 -0.093 -0.093 -0.079 -0.079
## 88 ASDQII_4 ~~ ASDQII_14 11.422 0.048 0.048 0.030 0.030
## 89 Science =~ ASDQII_18 11.361 -0.105 -0.105 -0.093 -0.093
## 90 LET_1 ~~ LET_5 11.322 -0.121 -0.121 -0.066 -0.066
## 91 ASDQII_13 ~~ ASDQII_19 11.284 0.046 0.046 0.031 0.031
## 92 F2 =~ PWB_1 11.146 0.173 0.173 0.107 0.107
## 93 General =~ MLQ_6 11.102 -0.148 -0.148 -0.088 -0.088
## 94 Math =~ MLQ_8 10.881 0.135 0.135 0.086 0.086
## 95 ASDQII_9 ~~ ASDQII_19 10.845 0.047 0.047 0.029 0.029
## 96 MLQP =~ ASDQII_20 10.820 0.088 0.088 0.076 0.076
## 97 LET_1 ~~ ASDQII_19 10.798 -0.082 -0.082 -0.054 -0.054
## 98 MLQP =~ ASDQII_19 10.759 0.087 0.087 0.078 0.078
## 99 ASDQII_4 ~~ ASDQII_19 10.714 -0.048 -0.048 -0.036 -0.036
## 100 APSI_7 ~~ APSI_8 10.681 0.078 0.078 0.057 0.057
## 101 PWB_1 ~~ LET_1 10.564 -0.171 -0.171 -0.078 -0.078
## 102 MLQ_6 ~~ MLQ_10 10.373 -0.148 -0.148 -0.052 -0.052
## 103 PWB_3 ~~ MLQ_3 10.355 -0.134 -0.134 -0.054 -0.054
## 104 MLQS =~ ASDQII_4 10.328 -0.085 -0.085 -0.070 -0.070
## 105 ASDQII_5 ~~ ASDQII_7 10.249 0.048 0.048 0.026 0.026
## 106 MLQP =~ LET_3 10.239 0.115 0.115 0.087 0.087
## 107 LET_5 ~~ ASDQII_13 10.233 0.068 0.068 0.037 0.037
## 108 ASDQII_9 ~~ ASDQII_12 10.034 0.043 0.043 0.022 0.022
## 109 ASDQII_6 ~~ ASDQII_20 10.015 0.047 0.047 0.027 0.027
## 110 MLQP =~ APSI_7 9.996 -0.144 -0.144 -0.127 -0.127
## 111 MLQP =~ PWB_8 9.893 0.205 0.205 0.147 0.147
## 112 English =~ MLQ_8 9.826 0.130 0.130 0.083 0.083
## 113 MLQ_3 ~~ MLQ_10 9.734 0.168 0.168 0.063 0.063
## 114 PWB_1 ~~ PWB_3 9.607 0.165 0.165 0.065 0.065
## 115 PWB_7 ~~ APSI_2 9.489 0.088 0.088 0.062 0.062
## 116 ASDQII_18 ~~ ASDQII_20 9.357 -0.044 -0.044 -0.034 -0.034
## 117 ASDQII_9 ~~ ASDQII_18 9.329 -0.041 -0.041 -0.025 -0.025
## 118 Math =~ PWB_7 9.081 0.109 0.109 0.085 0.085
## 119 English =~ MLQ_2 8.878 -0.117 -0.117 -0.073 -0.073
## 120 PWB_1 ~~ APSI_2 8.870 0.116 0.116 0.065 0.065
## 121 English =~ MLQ_6 8.771 -0.134 -0.134 -0.080 -0.080
## 122 ASDQII_8 ~~ ASDQII_10 8.770 0.045 0.045 0.021 0.021
## 123 ASDQII_6 ~~ ASDQII_8 8.701 -0.047 -0.047 -0.021 -0.021
## 124 F2 =~ ASDQII_16 8.581 -0.078 -0.078 -0.067 -0.067
## 125 MLQ_7 ~~ ASDQII_16 8.565 -0.082 -0.082 -0.044 -0.044
## 126 ASDQII_1 ~~ ASDQII_13 8.423 -0.037 -0.037 -0.023 -0.023
## 127 English =~ ASDQII_11 8.384 0.062 0.062 0.048 0.048
## 128 F1 =~ APSI_8 8.363 0.086 0.086 0.071 0.071
## 129 ASDQII_14 ~~ ASDQII_19 8.334 -0.039 -0.039 -0.027 -0.027
## 130 ASDQII_3 ~~ ASDQII_11 8.295 -0.034 -0.034 -0.022 -0.022
## 131 MLQS =~ ASDQII_17 8.089 -0.071 -0.071 -0.062 -0.062
## 132 ASDQII_13 ~~ ASDQII_15 8.078 -0.044 -0.044 -0.024 -0.024
## 133 ASDQII_16 ~~ ASDQII_18 8.071 0.042 0.042 0.032 0.032
## 134 LET_1 ~~ MLQ_4 8.068 -0.115 -0.115 -0.053 -0.053
## 135 Math =~ ASDQII_5 8.039 0.066 0.066 0.054 0.054
## 136 LET_1 ~~ MLQ_6 8.028 -0.120 -0.120 -0.052 -0.052
## 137 MLQ_2 ~~ MLQ_3 8.024 -0.143 -0.143 -0.057 -0.057
## 138 LET_5 ~~ ASDQII_16 7.957 -0.062 -0.062 -0.040 -0.040
## 139 LET_1 ~~ ASDQII_18 7.887 0.067 0.067 0.043 0.043
## 140 MLQ_6 ~~ MLQ_3 7.869 0.126 0.126 0.048 0.048
## 141 ASDQII_7 ~~ ASDQII_20 7.866 -0.041 -0.041 -0.023 -0.023
## 142 APSI_6 ~~ MLQ_5 7.790 -0.099 -0.099 -0.047 -0.047
## 143 ASDQII_1 ~~ ASDQII_2 7.773 -0.041 -0.041 -0.029 -0.029
## 144 ASDQII_13 ~~ ASDQII_20 7.705 0.039 0.039 0.024 0.024
## 145 APSI_8 ~~ MLQ_1 7.682 -0.088 -0.088 -0.043 -0.043
## 146 ASDQII_1 ~~ ASDQII_14 7.636 -0.035 -0.035 -0.022 -0.022
## 147 MLQ_4 ~~ ASDQII_16 7.461 -0.071 -0.071 -0.039 -0.039
## 148 Math =~ APSI_8 7.444 -0.079 -0.079 -0.066 -0.066
## 149 PWB_1 ~~ LET_3 7.386 -0.121 -0.121 -0.056 -0.056
## 150 General =~ MLQ_5 7.294 0.106 0.106 0.073 0.073
## 151 LET_5 ~~ ASDQII_19 7.245 0.058 0.058 0.039 0.039
## 152 ASDQII_9 ~~ ASDQII_11 7.216 -0.034 -0.034 -0.018 -0.018
## 153 ASDQII_6 ~~ ASDQII_13 7.193 0.039 0.039 0.019 0.019
## 154 LET_1 ~~ PWB_8 7.129 -0.113 -0.113 -0.059 -0.059
## 155 ASDQII_3 ~~ ASDQII_20 7.128 0.036 0.036 0.026 0.026
## 156 F1 =~ MLQ_8 7.128 -0.117 -0.117 -0.075 -0.075
## 157 LET_5 ~~ MLQ_5 7.118 -0.086 -0.086 -0.044 -0.044
## 158 ASDQII_12 ~~ ASDQII_16 7.080 -0.036 -0.036 -0.024 -0.024
## 159 APSI_7 ~~ MLQ_1 7.017 -0.085 -0.085 -0.044 -0.044
## 160 ASDQII_2 ~~ ASDQII_5 6.980 -0.042 -0.042 -0.029 -0.029
## 161 ASDQII_3 ~~ ASDQII_19 6.963 0.035 0.035 0.027 0.027
## 162 LET_3 ~~ MLQ_1 6.921 0.093 0.093 0.041 0.041
## 163 ASDQII_2 ~~ ASDQII_17 6.791 -0.036 -0.036 -0.026 -0.026
## 164 ASDQII_3 ~~ ASDQII_6 6.748 0.036 0.036 0.021 0.021
## 165 MLQP =~ ASDQII_12 6.724 0.062 0.062 0.047 0.047
## 166 APSI_8 ~~ ASDQII_7 6.657 -0.049 -0.049 -0.027 -0.027
## 167 English =~ ASDQII_6 6.637 -0.060 -0.060 -0.041 -0.041
## 168 LET_3 ~~ PWB_7 6.631 -0.077 -0.077 -0.045 -0.045
## 169 Math =~ PWB_8 6.569 0.107 0.107 0.077 0.077
## 170 ASDQII_1 ~~ ASDQII_9 6.568 -0.034 -0.034 -0.020 -0.020
## 171 LET_1 ~~ ASDQII_20 6.559 -0.064 -0.064 -0.040 -0.040
## 172 MLQ_10 ~~ ASDQII_19 6.534 -0.069 -0.069 -0.037 -0.037
## 173 PWB_8 ~~ APSI_4 6.466 -0.083 -0.083 -0.050 -0.050
## 174 APSI_7 ~~ ASDQII_15 6.460 0.047 0.047 0.031 0.031
## 175 MLQP =~ ASDQII_11 6.396 -0.057 -0.057 -0.044 -0.044
## 176 LET_5 ~~ ASDQII_17 6.365 -0.052 -0.052 -0.034 -0.034
## 177 ASDQII_10 ~~ ASDQII_13 6.348 -0.034 -0.034 -0.017 -0.017
## 178 F1 =~ APSI_7 6.256 0.074 0.074 0.065 0.065
## 179 ASDQII_3 ~~ ASDQII_7 6.236 -0.034 -0.034 -0.019 -0.019
## 180 ASDQII_1 ~~ ASDQII_19 6.223 -0.032 -0.032 -0.025 -0.025
## 181 PWB_7 ~~ MLQ_4 6.192 0.094 0.094 0.046 0.046
## 182 ASDQII_11 ~~ ASDQII_12 6.178 -0.034 -0.034 -0.020 -0.020
## 183 ASDQII_2 ~~ ASDQII_11 6.140 -0.031 -0.031 -0.020 -0.020
## 184 General =~ MLQ_10 6.111 -0.104 -0.104 -0.061 -0.061
## 185 APSI_7 ~~ MLQ_7 6.104 0.079 0.079 0.044 0.044
## 186 English =~ ASDQII_10 6.008 0.054 0.054 0.037 0.037
## 187 APSI_2 ~~ MLQ_7 6.002 -0.080 -0.080 -0.046 -0.046
## 188 ASDQII_11 ~~ ASDQII_19 5.981 -0.031 -0.031 -0.021 -0.021
## 189 General =~ ASDQII_12 5.945 -0.074 -0.074 -0.056 -0.056
## 190 General =~ ASDQII_6 5.926 -0.080 -0.080 -0.055 -0.055
## 191 Science =~ ASDQII_20 5.847 0.079 0.079 0.068 0.068
## 192 MLQ_1 ~~ ASDQII_12 5.788 0.062 0.062 0.028 0.028
## 193 ASDQII_5 ~~ ASDQII_12 5.773 -0.033 -0.033 -0.021 -0.021
## 194 ASDQII_4 ~~ ASDQII_8 5.765 0.034 0.034 0.019 0.019
## 195 MLQ_10 ~~ ASDQII_2 5.727 0.065 0.065 0.032 0.032
## 196 ASDQII_4 ~~ ASDQII_10 5.722 -0.035 -0.035 -0.020 -0.020
## 197 MLQS =~ APSI_2 5.663 -0.072 -0.072 -0.066 -0.066
## 198 APSI_8 ~~ MLQ_6 5.586 0.074 0.074 0.036 0.036
## 199 General =~ PWB_8 5.550 0.100 0.100 0.072 0.072
## 200 F2 =~ LET_3 5.524 0.077 0.077 0.058 0.058
## 201 MLQ_1 ~~ MLQ_7 5.481 0.110 0.110 0.041 0.041
## 202 MLQ_4 ~~ MLQ_3 5.478 0.100 0.100 0.040 0.040
## 203 Science =~ PWB_8 5.445 0.097 0.097 0.070 0.070
## 204 Science =~ ASDQII_1 5.421 0.052 0.052 0.044 0.044
## 205 Math =~ MLQ_6 5.409 -0.100 -0.100 -0.059 -0.059
## 206 MLQ_4 ~~ MLQ_5 5.409 -0.104 -0.104 -0.045 -0.045
## 207 MLQS =~ ASDQII_13 5.394 -0.058 -0.058 -0.042 -0.042
## 208 General =~ ASDQII_10 5.351 0.072 0.072 0.050 0.050
## 209 MLQ_1 ~~ ASDQII_11 5.211 -0.055 -0.055 -0.025 -0.025
## 210 F1 =~ MLQ_2 5.182 0.095 0.095 0.059 0.059
## 211 F1 =~ ASDQII_7 5.156 0.060 0.060 0.040 0.040
## 212 MLQS =~ PWB_3 5.089 -0.093 -0.093 -0.059 -0.059
## 213 LET_1 ~~ ASDQII_15 5.075 0.056 0.056 0.030 0.030
## 214 LET_5 ~~ APSI_4 5.019 0.056 0.056 0.035 0.035
## 215 MLQP =~ ASDQII_6 4.947 0.059 0.059 0.040 0.040
## 216 MLQ_4 ~~ MLQ_10 4.860 -0.097 -0.097 -0.036 -0.036
## 217 MLQ_10 ~~ ASDQII_16 4.836 0.061 0.061 0.031 0.031
## 218 APSI_7 ~~ MLQ_8 4.826 0.069 0.069 0.039 0.039
## 219 ASDQII_8 ~~ ASDQII_15 4.810 -0.029 -0.029 -0.015 -0.015
## 220 PWB_1 ~~ APSI_7 4.788 -0.083 -0.083 -0.046 -0.046
## 221 MLQ_4 ~~ MLQ_6 4.767 0.112 0.112 0.042 0.042
## 222 Math =~ ASDQII_20 4.764 0.070 0.070 0.061 0.061
## 223 F1 =~ ASDQII_19 4.730 -0.057 -0.057 -0.052 -0.052
## 224 PWB_3 ~~ MLQ_7 4.714 0.093 0.093 0.037 0.037
## 225 ASDQII_17 ~~ ASDQII_19 4.707 -0.031 -0.031 -0.025 -0.025
## 226 ASDQII_7 ~~ ASDQII_9 4.692 -0.035 -0.035 -0.016 -0.016
## 227 English =~ MLQ_5 4.654 0.086 0.086 0.059 0.059
## 228 MLQ_8 ~~ ASDQII_13 4.638 -0.057 -0.057 -0.026 -0.026
## 229 ASDQII_5 ~~ ASDQII_9 4.628 0.032 0.032 0.018 0.018
## 230 MLQ_1 ~~ ASDQII_13 4.620 0.058 0.058 0.025 0.025
## 231 General =~ ASDQII_4 4.618 -0.072 -0.072 -0.060 -0.060
## 232 APSI_2 ~~ MLQ_4 4.588 0.066 0.066 0.037 0.037
## 233 MLQS =~ LET_5 4.587 -0.076 -0.076 -0.057 -0.057
## 234 PWB_3 ~~ ASDQII_13 4.564 -0.052 -0.052 -0.024 -0.024
## 235 ASDQII_7 ~~ ASDQII_15 4.557 0.031 0.031 0.015 0.015
## 236 F2 =~ MLQ_2 4.557 -0.087 -0.087 -0.054 -0.054
## 237 MLQ_7 ~~ MLQ_10 4.525 -0.115 -0.115 -0.043 -0.043
## 238 ASDQII_6 ~~ ASDQII_7 4.510 0.035 0.035 0.016 0.016
## 239 LET_5 ~~ ASDQII_11 4.476 -0.041 -0.041 -0.023 -0.023
## 240 MLQS =~ PWB_8 4.473 -0.092 -0.092 -0.066 -0.066
## 241 ASDQII_12 ~~ ASDQII_14 4.460 -0.030 -0.030 -0.017 -0.017
## 242 ASDQII_6 ~~ ASDQII_12 4.430 0.029 0.029 0.015 0.015
## 243 ASDQII_18 ~~ ASDQII_19 4.416 -0.030 -0.030 -0.024 -0.024
## 244 Science =~ ASDQII_19 4.380 0.068 0.068 0.061 0.061
## 245 MLQ_3 ~~ ASDQII_14 4.375 -0.053 -0.053 -0.026 -0.026
## 246 LET_5 ~~ ASDQII_18 4.362 -0.043 -0.043 -0.028 -0.028
## 247 Science =~ MLQ_4 4.360 -0.086 -0.086 -0.054 -0.054
## 248 F2 =~ ASDQII_11 4.322 -0.047 -0.047 -0.036 -0.036
## 249 MLQ_1 ~~ ASDQII_9 4.321 0.057 0.057 0.023 0.023
## 250 ASDQII_10 ~~ ASDQII_12 4.306 -0.027 -0.027 -0.014 -0.014
## 251 ASDQII_13 ~~ ASDQII_14 4.288 0.031 0.031 0.017 0.017
## 252 ASDQII_16 ~~ ASDQII_17 4.281 0.031 0.031 0.023 0.023
## 253 General =~ MLQ_1 4.245 0.093 0.093 0.055 0.055
## 254 APSI_2 ~~ MLQ_5 4.177 -0.058 -0.058 -0.036 -0.036
## 255 MLQ_6 ~~ ASDQII_18 4.174 -0.052 -0.052 -0.027 -0.027
## 256 PWB_3 ~~ LET_5 4.098 -0.079 -0.079 -0.037 -0.037
## 257 ASDQII_2 ~~ ASDQII_10 4.093 -0.028 -0.028 -0.016 -0.016
## 258 English =~ PWB_8 4.084 0.086 0.086 0.062 0.062
## 259 MLQ_8 ~~ ASDQII_7 4.063 -0.056 -0.056 -0.024 -0.024
## 260 APSI_6 ~~ ASDQII_5 4.013 -0.049 -0.049 -0.028 -0.028
## 261 General =~ LET_5 3.959 -0.069 -0.069 -0.051 -0.051
## 262 PWB_1 ~~ APSI_6 3.955 0.100 0.100 0.043 0.043
## 263 Science =~ MLQ_3 3.942 -0.081 -0.081 -0.052 -0.052
## 264 MLQ_8 ~~ ASDQII_1 3.933 0.049 0.049 0.027 0.027
## 265 ASDQII_6 ~~ ASDQII_10 3.930 -0.031 -0.031 -0.015 -0.015
## 266 LET_3 ~~ ASDQII_20 3.914 0.041 0.041 0.026 0.026
## 267 MLQS =~ ASDQII_5 3.912 0.052 0.052 0.043 0.043
## 268 LET_5 ~~ MLQ_3 3.896 0.071 0.071 0.034 0.034
## 269 F1 =~ MLQ_7 3.889 -0.088 -0.088 -0.056 -0.056
## 270 General =~ PWB_3 3.863 0.079 0.079 0.050 0.050
## 271 PWB_1 ~~ ASDQII_5 3.848 0.067 0.067 0.034 0.034
## 272 ASDQII_7 ~~ ASDQII_17 3.845 0.028 0.028 0.016 0.016fitmeasures(second.fit)## npar fmin chisq
## 151.000 1.032 2188.724
## df pvalue baseline.chisq
## 751.000 0.000 32069.444
## baseline.df baseline.pvalue cfi
## 820.000 0.000 0.954
## tli nnfi rfi
## 0.950 0.950 0.925
## nfi pnfi ifi
## 0.932 0.853 0.954
## rni logl unrestricted.logl
## 0.954 -51334.226 -50239.864
## aic bic ntotal
## 102970.453 103720.323 1060.000
## bic2 rmsea rmsea.ci.lower
## 103240.721 0.042 0.040
## rmsea.ci.upper rmsea.pvalue rmr
## 0.045 1.000 0.093
## rmr_nomean srmr srmr_bentler
## 0.095 0.046 0.046
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.047 0.046 0.047
## srmr_mplus srmr_mplus_nomean cn_05
## 0.046 0.047 396.123
## cn_01 gfi agfi
## 409.793 0.969 0.963
## pgfi mfi ecvi
## 0.807 0.508 NAcorolations<-(second.fit)
fitted<-fitted(second.fit)