Motor Performance Ability: CFA
Ivan Ropovik
Tue Jul 25 07:35:58 2017
Data preparation
Required R libraries
library(lavaan, quietly = TRUE, warn.conflicts = FALSE)
library(semPlot, quietly = TRUE, warn.conflicts = FALSE)
library(dplyr, quietly = TRUE, warn.conflicts = FALSE)
library(psych, quietly = TRUE, warn.conflicts = FALSE)
library(ICC, quietly = TRUE, warn.conflicts = FALSE)
library(Amelia, quietly = TRUE, warn.conflicts = FALSE)
library(BaylorEdPsych, quietly = TRUE, warn.conflicts = FALSE)
library(ggplot2)
library(reshape2)Read in the datafile
#rm(list = ls())
setwd(dir = "~/OneDrive/MANUSCRIPTS/2017 MP CFA")
data <- read.csv(file = "data.csv", header = TRUE, sep = ";")
#View(data)Percentage of missing data
paste(round(sum(is.na(data))/prod(dim(data))*100, 5), "%", sep = "")## [1] "0%"Demographics
Sample size
nrow(data)## [1] 436Gender
1 = female; 2 = male
table(data$Gender)##
## 1 2
## 222 214Age
table(data$Age)##
## 7 8 9 10
## 104 113 109 110Mean age??
Descriptive statistics
describe(data[,8:21], na.rm = TRUE, skew = TRUE, ranges = TRUE, type = 2, trim = 0)## vars n mean sd median trimmed mad min max range skew
## FBT 1 436 8.19 4.37 7.50 8.19 5.19 1.00 24.00 23.00 0.75
## PT 2 436 19.64 4.00 19.12 19.64 3.83 12.30 32.65 20.35 0.79
## SAR 3 436 21.44 5.85 21.00 21.44 5.93 3.00 35.00 32.00 0.12
## SBJ 4 436 123.86 22.59 123.50 123.86 21.50 63.00 186.00 123.00 0.15
## HT 5 436 16.62 4.84 16.00 16.62 4.45 7.00 35.00 28.00 0.44
## SU 6 436 16.67 5.31 17.00 16.67 4.45 0.00 34.00 34.00 -0.09
## BAH 7 436 15.95 11.80 12.85 15.95 10.07 0.00 69.18 69.18 1.37
## SR 8 436 24.28 2.60 24.18 24.28 2.52 18.23 33.90 15.67 0.44
## ESR 9 436 22.24 9.13 22.00 22.24 10.38 5.00 50.00 45.00 0.37
## BB 10 436 44.61 14.76 44.00 44.61 17.79 5.00 72.00 67.00 -0.09
## OLH 11 436 49.89 17.09 51.00 49.89 20.02 8.00 78.00 70.00 -0.22
## JS 12 436 49.53 15.30 50.50 49.53 17.05 17.00 89.00 72.00 -0.01
## MS 13 436 39.79 9.28 39.00 39.79 8.90 20.00 76.00 56.00 0.53
## MQ 14 436 96.38 14.48 96.00 96.38 14.83 54.00 144.00 90.00 0.11
## kurtosis se
## FBT 0.57 0.21
## PT 0.42 0.19
## SAR -0.22 0.28
## SBJ -0.05 1.08
## HT 0.44 0.23
## SU -0.07 0.25
## BAH 2.13 0.57
## SR 0.47 0.12
## ESR -0.22 0.44
## BB -0.75 0.71
## OLH -0.81 0.82
## JS -0.72 0.73
## MS 0.34 0.44
## MQ 0.30 0.69Matrix of zero-order Pearsons’ \(r\) correlations
cor.mat <- cor(data[,8:21], method = "pearson")
round(cor.mat, 2)## FBT PT SAR SBJ HT SU BAH SR ESR BB OLH
## FBT 1.00 0.14 0.01 -0.16 -0.13 -0.17 -0.12 0.17 -0.18 -0.17 -0.08
## PT 0.14 1.00 -0.01 -0.21 -0.31 -0.15 -0.04 0.32 -0.21 -0.27 -0.32
## SAR 0.01 -0.01 1.00 0.04 -0.13 0.02 0.08 -0.06 -0.07 -0.09 -0.07
## SBJ -0.16 -0.21 0.04 1.00 0.46 0.50 0.24 -0.35 0.44 0.27 0.39
## HT -0.13 -0.31 -0.13 0.46 1.00 0.37 0.02 -0.19 0.33 0.26 0.40
## SU -0.17 -0.15 0.02 0.50 0.37 1.00 0.22 -0.30 0.28 0.26 0.30
## BAH -0.12 -0.04 0.08 0.24 0.02 0.22 1.00 -0.13 0.20 0.05 0.06
## SR 0.17 0.32 -0.06 -0.35 -0.19 -0.30 -0.13 1.00 -0.42 -0.30 -0.29
## ESR -0.18 -0.21 -0.07 0.44 0.33 0.28 0.20 -0.42 1.00 0.25 0.31
## BB -0.17 -0.27 -0.09 0.27 0.26 0.26 0.05 -0.30 0.25 1.00 0.61
## OLH -0.08 -0.32 -0.07 0.39 0.40 0.30 0.06 -0.29 0.31 0.61 1.00
## JS -0.13 -0.41 -0.05 0.37 0.41 0.32 -0.02 -0.37 0.28 0.56 0.70
## MS -0.13 -0.30 0.01 0.34 0.36 0.23 0.00 -0.27 0.22 0.45 0.60
## MQ -0.14 -0.16 -0.05 0.27 0.17 0.18 0.11 -0.24 0.23 0.69 0.73
## JS MS MQ
## FBT -0.13 -0.13 -0.14
## PT -0.41 -0.30 -0.16
## SAR -0.05 0.01 -0.05
## SBJ 0.37 0.34 0.27
## HT 0.41 0.36 0.17
## SU 0.32 0.23 0.18
## BAH -0.02 0.00 0.11
## SR -0.37 -0.27 -0.24
## ESR 0.28 0.22 0.23
## BB 0.56 0.45 0.69
## OLH 0.70 0.60 0.73
## JS 1.00 0.68 0.65
## MS 0.68 1.00 0.73
## MQ 0.65 0.73 1.00Mean correlation
cor.mat.low <- cor.mat[lower.tri(cor.mat)]
mean(abs(cor.mat.low))## [1] 0.2577828Distribution plots
df_melt <- melt(data[,8:21])## No id variables; using all as measure variableshist(data[,8:21], rugs = TRUE)
plot(df_melt)
Confirmatory factor analysis
Model specification
model <- '
Endurance =~ ESR
Strength =~ SU + BAH + SBJ + HT
Speed =~ PT + SR + JS
Coordination =~ FBT + BB + OLH + MS
Flexibility =~ SAR
SEX =~ Gender
BMI =~ BMI_obs
AGE =~ Age
Endurance ~ BMI + SEX + AGE
Strength ~ BMI + SEX + AGE
Speed ~ BMI + SEX + 1*AGE
Coordination ~ BMI + SEX + AGE
Flexibility ~ BMI + SEX + AGE
AGE ~~ 0*SEX
'
#model <- '
#Endurance =~ ESR
#Strength =~ SU + BAH + SBJ + HT
#Speed =~ PT + SR + JS
#Coordination =~ FBT + BB + OLH + MS
#Flexibility =~ SAR
#MPA =~ Endurance + Strength + Speed + Coordination + FlexibilityModel fitting
According to Muthén, 1984
fitted.model <- cfa(model = model, data = data, std.lv = TRUE, mimic = "Mplus", std.ov = FALSE, estimator = "MLR", orthogonal = FALSE, bootstrap = 5000)## Warning in lav_data_full(data = data, group = group, cluster = cluster, :
## lavaan WARNING: some observed variances are (at least) a factor 1000 times
## larger than others; use varTable(fit) to investigatePower analysis
Power analysis for the exact fit test (based on the RMSEA distribution). Statistical power for the detection of a misspecified model (RMSEA > .08).
df <- fitted.model@test[[1]]$df
alfa <- .05
n <- nrow(data)
rmsea0 <- .05 # RMSEA under H0
rmseaa <- .08 # RMSEA under H1
ncp0 <- (n-1)*df*rmsea0**2 ;
ncpa <-(n-1)*df*rmseaa**2 ;
if(rmsea0 < rmseaa) {
cval <- qchisq(1-alfa,df=df,ncp=ncp0)
sila.rmsea <- 1 - pchisq(cval,df=df,ncp=ncpa)
} else {
cval <- qchisq(alfa,df=df,ncp=ncp0)
sila.rmsea <- pchisq(cval,df=df,ncp=ncpa)
}
rm(ncp0, ncpa, cval)
print(round(sila.rmsea,10))## [1] 0.9996337Model test, parameter estimates
Test of the model, estimates of free parameters (factor loadings)
summary(fitted.model, standardized = TRUE, rsquare = TRUE)## lavaan (0.5-23.1097) converged normally after 148 iterations
##
## Number of observations 436
##
## Number of missing patterns 1
##
## Estimator ML Robust
## Minimum Function Test Statistic 494.699 494.350
## Degrees of freedom 83 83
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.001
## for the Yuan-Bentler correction (Mplus variant)
##
## Parameter Estimates:
##
## Information Observed
## Standard Errors Robust.huber.white
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Endurance =~
## ESR 8.256 0.281 29.348 0.000 9.035 1.000
## Strength =~
## SU 1.778 0.300 5.918 0.000 2.834 0.541
## BAH 1.128 0.624 1.809 0.070 1.799 0.153
## SBJ 9.426 1.434 6.573 0.000 15.025 0.678
## HT 2.148 0.168 12.751 0.000 3.424 0.722
## Speed =~
## PT -1.175 0.127 -9.222 0.000 -1.643 -0.417
## SR -0.673 0.083 -8.112 0.000 -0.942 -0.367
## JS 9.754 0.371 26.262 0.000 13.645 0.959
## Coordination =~
## FBT -0.417 0.143 -2.905 0.004 -0.652 -0.150
## BB 5.816 0.531 10.958 0.000 9.104 0.639
## OLH 8.279 0.625 13.248 0.000 12.961 0.800
## MS 4.114 0.333 12.346 0.000 6.441 0.725
## Flexibility =~
## SAR 5.765 0.181 31.817 0.000 5.843 1.000
## SEX =~
## Gender 0.500 0.000 1137.800 0.000 0.500 1.000
## BMI =~
## BMI_obs 2.852 0.117 24.357 0.000 2.852 1.000
## AGE =~
## Age 1.026 0.019 53.351 0.000 1.026 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Endurance ~
## BMI -0.290 0.051 -5.623 0.000 -0.265 -0.265
## SEX 0.175 0.047 3.691 0.000 0.160 0.160
## AGE 0.351 0.050 7.005 0.000 0.321 0.321
## Strength ~
## BMI -0.117 0.112 -1.046 0.295 -0.074 -0.074
## SEX 0.607 0.094 6.456 0.000 0.381 0.381
## AGE 1.099 0.147 7.473 0.000 0.690 0.690
## Speed ~
## BMI -0.187 0.049 -3.853 0.000 -0.134 -0.134
## SEX -0.002 0.046 -0.049 0.961 -0.002 -0.002
## AGE 1.000 0.715 0.715
## Coordination ~
## BMI -0.471 0.073 -6.417 0.000 -0.301 -0.301
## SEX 0.208 0.065 3.212 0.001 0.133 0.133
## AGE 1.189 0.119 10.017 0.000 0.760 0.760
## Flexibility ~
## BMI 0.048 0.052 0.917 0.359 0.047 0.047
## SEX -0.150 0.048 -3.133 0.002 -0.148 -0.148
## AGE -0.056 0.047 -1.176 0.240 -0.055 -0.055
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SEX ~~
## AGE 0.000 0.000 0.000
## BMI -0.023 0.047 -0.491 0.624 -0.023 -0.023
## BMI ~~
## AGE 0.208 0.041 5.016 0.000 0.208 0.208
## .Endurance ~~
## .Strength 0.458 0.068 6.699 0.000 0.458 0.458
## .Speed 0.057 0.057 0.993 0.321 0.057 0.057
## .Coordination 0.074 0.058 1.288 0.198 0.074 0.074
## .Flexibility -0.020 0.051 -0.390 0.697 -0.020 -0.020
## .Strength ~~
## .Speed -0.022 0.091 -0.239 0.811 -0.022 -0.022
## .Coordination 0.084 0.103 0.815 0.415 0.084 0.084
## .Flexibility 0.066 0.076 0.867 0.386 0.066 0.066
## .Speed ~~
## .Coordination 0.775 0.072 10.827 0.000 0.775 0.775
## .Flexibility 0.005 0.063 0.076 0.940 0.005 0.005
## .Coordination ~~
## .Flexibility 0.013 0.065 0.192 0.847 0.013 0.013
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ESR 22.243 0.437 50.953 0.000 22.243 2.462
## .SU 16.674 0.254 65.691 0.000 16.674 3.182
## .BAH 15.946 0.564 28.249 0.000 15.946 1.354
## .SBJ 123.865 1.081 114.606 0.000 123.865 5.586
## .HT 16.619 0.232 71.724 0.000 16.619 3.504
## .PT 19.638 0.191 102.660 0.000 19.638 4.988
## .SR 24.276 0.124 195.507 0.000 24.276 9.468
## .JS 49.532 0.732 67.671 0.000 49.532 3.481
## .FBT 8.186 0.209 39.151 0.000 8.186 1.879
## .BB 44.608 0.706 63.192 0.000 44.608 3.130
## .OLH 49.892 0.817 61.044 0.000 49.892 3.078
## .MS 39.791 0.444 89.650 0.000 39.791 4.481
## .SAR 21.435 0.280 76.593 0.000 21.435 3.669
## .Gender 1.491 0.024 62.269 0.000 1.491 2.982
## .BMI_obs 16.779 0.137 122.415 0.000 16.779 5.884
## .Age 8.516 0.053 160.245 0.000 8.516 8.301
## .Endurance 0.000 0.000 0.000
## .Strength 0.000 0.000 0.000
## .Speed 0.000 0.000 0.000
## .Coordination 0.000 0.000 0.000
## .Flexibility 0.000 0.000 0.000
## SEX 0.000 0.000 0.000
## BMI 0.000 0.000 0.000
## AGE 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ESR 0.000 0.000 0.000
## .SU 19.436 2.135 9.105 0.000 19.436 0.708
## .BAH 135.449 12.899 10.501 0.000 135.449 0.977
## .SBJ 266.001 31.960 8.323 0.000 266.001 0.541
## .HT 10.771 1.588 6.784 0.000 10.771 0.479
## .PT 12.802 1.083 11.824 0.000 12.802 0.826
## .SR 5.688 0.463 12.291 0.000 5.688 0.865
## .JS 16.305 9.672 1.686 0.092 16.305 0.081
## .FBT 18.562 1.384 13.408 0.000 18.562 0.978
## .BB 120.280 9.488 12.678 0.000 120.280 0.592
## .OLH 94.685 10.293 9.199 0.000 94.685 0.360
## .MS 37.353 3.397 10.995 0.000 37.353 0.474
## .SAR 0.000 0.000 0.000
## .Gender 0.000 0.000 0.000
## .BMI_obs 0.000 0.000 0.000
## .Age 0.000 0.000 0.000
## .Endurance 1.000 0.835 0.835
## .Strength 1.000 0.394 0.394
## .Speed 1.000 0.511 0.511
## .Coordination 1.000 0.408 0.408
## .Flexibility 1.000 0.973 0.973
## SEX 1.000 1.000 1.000
## BMI 1.000 1.000 1.000
## AGE 1.000 1.000 1.000
##
## R-Square:
## Estimate
## ESR 1.000
## SU 0.292
## BAH 0.023
## SBJ 0.459
## HT 0.521
## PT 0.174
## SR 0.135
## JS 0.919
## FBT 0.022
## BB 0.408
## OLH 0.640
## MS 0.526
## SAR 1.000
## Gender 1.000
## BMI_obs 1.000
## Age 1.000
## Endurance 0.165
## Strength 0.606
## Speed 0.489
## Coordination 0.592
## Flexibility 0.027Mean factor loading
mean(inspect(fitted.model,what="std")$lambda[inspect(fitted.model,what="std")$lambda > .0])## [1] 0.7858236Approximate fit indices
Note the .robust indices
fitMeasures(fitted.model)## npar fmin
## 69.000 0.567
## chisq df
## 494.699 83.000
## pvalue chisq.scaled
## 0.000 494.350
## df.scaled pvalue.scaled
## 83.000 0.000
## chisq.scaling.factor baseline.chisq
## 1.001 2609.243
## baseline.df baseline.pvalue
## 120.000 0.000
## baseline.chisq.scaled baseline.df.scaled
## 2494.934 120.000
## baseline.pvalue.scaled baseline.chisq.scaling.factor
## 0.000 1.046
## cfi tli
## 0.835 0.761
## nnfi rfi
## 0.761 0.726
## nfi pnfi
## 0.810 0.561
## ifi rni
## 0.837 0.835
## cfi.scaled tli.scaled
## 0.827 0.750
## cfi.robust tli.robust
## 0.834 0.760
## nnfi.scaled nnfi.robust
## 0.750 0.760
## rfi.scaled nfi.scaled
## 0.714 0.802
## ifi.scaled rni.scaled
## 0.802 0.835
## rni.robust logl
## 0.834 -20856.397
## unrestricted.logl aic
## -20609.047 41850.793
## bic ntotal
## 42132.150 436.000
## bic2 scaling.factor.h1
## 41913.181 1.033
## scaling.factor.h0 rmsea
## 1.071 0.107
## rmsea.ci.lower rmsea.ci.upper
## 0.098 0.116
## rmsea.pvalue rmsea.scaled
## 0.000 0.107
## rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.098 0.116
## rmsea.pvalue.scaled rmsea.robust
## 0.000 0.107
## rmsea.ci.lower.robust rmsea.ci.upper.robust
## 0.098 0.116
## rmsea.pvalue.robust rmr
## NA 9.411
## rmr_nomean srmr
## 9.411 0.103
## srmr_bentler srmr_bentler_nomean
## 0.091 0.096
## srmr_bollen srmr_bollen_nomean
## 0.101 0.086
## srmr_mplus srmr_mplus_nomean
## 0.103 0.088
## cn_05 cn_01
## 93.777 103.127
## gfi agfi
## 0.996 0.993
## pgfi mfi
## 0.544 0.624
## ecvi
## NADiagram
Zatial iba narychlo vygenerovany, neskor upravim.
semPaths(fitted.model, style = "mx", pastel = TRUE,
edge.label.cex = 0.5, sizeLat = 5, nCharNodes = 0,
nDigits = 2, "Standardized", intercepts = FALSE,
residuals = TRUE, exoVar = FALSE, fade = TRUE,
groups = "latents", what = "path")
Exact test of the model indicates the presence of a model misspecification. Apart from approximate fit indices, it was also necessary to inspect local sources of model misfit based on the matrix of residual correlations.
Local fit
Matrix of residual correlations
residuals <- residuals(fitted.model, type = "normalized")$cov
residuals## ESR SU BAH SBJ HT PT SR JS FBT
## ESR 0.275
## SU 0.031 0.335
## BAH 2.193 2.765 0.019
## SBJ 1.905 2.883 2.603 0.517
## HT -0.780 -0.152 -1.658 -0.181 0.522
## PT -2.104 -1.106 -0.191 -1.683 -3.789 0.381
## SR -7.398 -4.454 -1.885 -4.420 -1.569 3.404 0.294
## JS 1.265 2.138 -1.743 1.970 2.507 -0.906 -0.865 2.467
## FBT -2.793 -2.497 -2.425 -1.939 -1.367 1.702 2.390 -0.348 0.050
## BB 0.986 1.502 -0.063 0.576 0.110 -1.003 -2.210 1.819 -1.757
## OLH 1.438 1.486 -0.181 2.231 1.988 -1.008 -1.022 2.364 0.790
## MS -0.027 0.229 -1.264 1.459 1.447 -1.232 -0.997 2.732 -0.551
## SAR -0.053 1.102 2.103 1.534 -2.122 -0.409 -1.465 -0.326 0.006
## Gender 0.039 -0.797 2.863 0.033 0.244 2.176 -2.029 0.112 1.852
## BMI_obs 0.196 -1.936 -5.541 -2.731 4.033 -1.136 2.981 0.533 2.328
## Age 1.091 0.818 -3.884 0.289 3.542 -5.274 -3.101 4.897 0.351
## BB OLH MS SAR Gender BMI_bs Age
## ESR
## SU
## BAH
## SBJ
## HT
## PT
## SR
## JS
## FBT
## BB 1.217
## OLH 2.907 1.880
## MS 0.431 1.599 1.126
## SAR -1.083 -0.577 1.177 0.006
## Gender -2.404 1.373 0.108 -0.007 0.000
## BMI_obs -1.611 1.105 1.023 -0.035 0.030 0.088
## Age 2.005 3.669 3.284 -0.150 0.135 0.682 3.736Mean residual correlation
mean(abs(residuals))## [1] 1.580138Number of observed variables
p = 14Given \((p(p+1)/2 - p) = 300\) elements in the lower diagonal,
(p*(p+1)/2 - p)*.05## [1] 4.55of them can be significant at the \(\alpha\) = .05 level.
Significance of the residuals
Diag = diagonal, * = residual significant at the \(\alpha\) = .05 level.
ifelse(round(residuals, 3) == 0, "Dg", ifelse(residuals > 1.96, "*", "-"))## ESR SU BAH SBJ HT PT SR JS FBT BB OLH MS SAR Gender BMI_obs
## ESR "-" "-" "*" "-" "-" "-" "-" "-" "-" "-" "-" "-" "-" "-" "-"
## SU "-" "-" "*" "*" "-" "-" "-" "*" "-" "-" "-" "-" "-" "-" "-"
## BAH "*" "*" "-" "*" "-" "-" "-" "-" "-" "-" "-" "-" "*" "*" "-"
## SBJ "-" "*" "*" "-" "-" "-" "-" "*" "-" "-" "*" "-" "-" "-" "-"
## HT "-" "-" "-" "-" "-" "-" "-" "*" "-" "-" "*" "-" "-" "-" "*"
## PT "-" "-" "-" "-" "-" "-" "*" "-" "-" "-" "-" "-" "-" "*" "-"
## SR "-" "-" "-" "-" "-" "*" "-" "-" "*" "-" "-" "-" "-" "-" "*"
## JS "-" "*" "-" "*" "*" "-" "-" "*" "-" "-" "*" "*" "-" "-" "-"
## FBT "-" "-" "-" "-" "-" "-" "*" "-" "-" "-" "-" "-" "-" "-" "*"
## BB "-" "-" "-" "-" "-" "-" "-" "-" "-" "-" "*" "-" "-" "-" "-"
## OLH "-" "-" "-" "*" "*" "-" "-" "*" "-" "*" "-" "-" "-" "-" "-"
## MS "-" "-" "-" "-" "-" "-" "-" "*" "-" "-" "-" "-" "-" "-" "-"
## SAR "-" "-" "*" "-" "-" "-" "-" "-" "-" "-" "-" "-" "-" "-" "-"
## Gender "-" "-" "*" "-" "-" "*" "-" "-" "-" "-" "-" "-" "-" "Dg" "-"
## BMI_obs "-" "-" "-" "-" "*" "-" "*" "-" "*" "-" "-" "-" "-" "-" "-"
## Age "-" "-" "-" "-" "*" "-" "-" "*" "-" "*" "*" "*" "-" "-" "-"
## Age
## ESR "-"
## SU "-"
## BAH "-"
## SBJ "-"
## HT "*"
## PT "-"
## SR "-"
## JS "*"
## FBT "-"
## BB "*"
## OLH "*"
## MS "*"
## SAR "-"
## Gender "-"
## BMI_obs "-"
## Age "*"Modification indices
Minimum value of modification index (currently set at 10).
min.value <- 10
mi <- modindices(fitted.model, standardized = TRUE,
free.remove = TRUE, minimum.value = min.value)
mi[mi$op == "=~",]## lhs op rhs mi mi.scaled epc sepc.lv sepc.all
## 95 Endurance =~ SBJ 11.333 11.325 3.776 4.133 0.186
## 96 Endurance =~ HT 11.318 11.310 -0.830 -0.908 -0.191
## 98 Endurance =~ SR 53.377 53.340 -0.798 -0.873 -0.341
## 99 Endurance =~ JS 14.414 14.404 -3.892 -4.259 -0.299
## 106 Endurance =~ BMI_obs 46.971 46.938 42.432 46.439 16.285
## 110 Strength =~ SR 28.703 28.683 -0.514 -0.820 -0.320
## 118 Strength =~ BMI_obs 46.951 46.918 8.418 13.418 4.705
## 131 Speed =~ BMI_obs 46.948 46.915 -8.303 -11.616 -4.073
## 132 Speed =~ Age 46.953 46.920 0.324 0.453 0.442
## 139 Coordination =~ SR 14.582 14.571 -0.859 -1.345 -0.525
## 140 Coordination =~ JS 17.387 17.375 10.716 16.776 1.179
## 143 Coordination =~ BMI_obs 46.943 46.910 -19.795 -30.988 -10.867
## 144 Coordination =~ Age 43.973 43.942 0.407 0.637 0.621
## 149 Flexibility =~ HT 15.696 15.685 -0.836 -0.848 -0.179
## 158 Flexibility =~ BMI_obs 46.979 46.946 -145.970 -147.944 -51.880
## 169 SEX =~ BB 13.956 13.946 -2.208 -2.208 -0.155
## 170 SEX =~ OLH 10.132 10.125 2.109 2.109 0.130
## 177 BMI =~ BAH 37.276 37.250 -3.428 -3.428 -0.291
## 178 BMI =~ SBJ 25.249 25.231 -4.909 -4.909 -0.221
## 179 BMI =~ HT 69.809 69.760 1.789 1.789 0.377
## 181 BMI =~ SR 13.697 13.688 0.426 0.426 0.166
## 184 BMI =~ BB 10.124 10.117 -1.882 -1.882 -0.132
## 189 BMI =~ Age 44.482 44.451 2.974 2.974 2.899
## 192 AGE =~ BAH 43.059 43.029 -5.508 -5.508 -0.468
## 193 AGE =~ SBJ 13.308 13.299 -5.715 -5.715 -0.258
## 194 AGE =~ HT 32.834 32.811 1.985 1.985 0.418
## 195 AGE =~ PT 31.299 31.277 -1.463 -1.463 -0.371
## 197 AGE =~ JS 17.947 17.934 4.317 4.317 0.303
## 204 AGE =~ BMI_obs 46.948 46.915 9.651 9.651 3.384
## sepc.nox
## 95 0.186
## 96 -0.191
## 98 -0.341
## 99 -0.299
## 106 16.285
## 110 -0.320
## 118 4.705
## 131 -4.073
## 132 0.442
## 139 -0.525
## 140 1.179
## 143 -10.867
## 144 0.621
## 149 -0.179
## 158 -51.880
## 169 -0.155
## 170 0.130
## 177 -0.291
## 178 -0.221
## 179 0.377
## 181 0.166
## 184 -0.132
## 189 2.899
## 192 -0.468
## 193 -0.258
## 194 0.418
## 195 -0.371
## 197 0.303
## 204 3.384