Importar banco
library(readxl)
library(lavaan)
## This is lavaan 0.6-9
## lavaan is FREE software! Please report any bugs.
library(semTools)
##
## ###############################################################################
## This is semTools 0.5-5
## All users of R (or SEM) are invited to submit functions or ideas for functions.
## ###############################################################################
library(semPlot)
library(stringr)
set.seed(1234)
options(width = 132)
banco_geral <- read_excel("responses.xlsx")
Passo 1 - Repetir análises fatoriais confirmatórias por instrumento
Mindset
model<-'
FIXO =~ DMI1 + DMI2 + DMI4 + DMI6
CRESCIMENTO =~ DMI3 + DMI5 + DMI7 + DMI8
DMI1 ~~ DMI2
#DMI4 ~~ DMI6
'
fit<-cfa(model=model,data=banco_geral,ordered=T,estimator="ULSMV")
summary(fit,standardized=T,rsquare=T,fit.measures=T)
## lavaan 0.6-9 ended normally after 23 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 58
##
## Number of observations 327
##
## Model Test User Model:
## Standard Robust
## Test Statistic 13.776 67.330
## Degrees of freedom 18 18
## P-value (Unknown) NA 0.000
## Scaling correction factor 0.216
## Shift parameter 3.684
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 2838.446 1767.510
## Degrees of freedom 28 28
## P-value NA 0.000
## Scaling correction factor 1.622
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 0.972
## Tucker-Lewis Index (TLI) 1.002 0.956
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.092
## 90 Percent confidence interval - lower 0.000 0.069
## 90 Percent confidence interval - upper 0.036 0.116
## P-value RMSEA <= 0.05 0.991 0.002
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.034 0.034
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## FIXO =~
## DMI1 1.000 0.580 0.580
## DMI2 1.170 0.079 14.885 0.000 0.679 0.679
## DMI4 1.366 0.119 11.511 0.000 0.793 0.793
## DMI6 1.461 0.131 11.178 0.000 0.848 0.848
## CRESCIMENTO =~
## DMI3 1.000 0.837 0.837
## DMI5 1.015 0.031 32.639 0.000 0.850 0.850
## DMI7 1.016 0.034 30.121 0.000 0.850 0.850
## DMI8 0.960 0.041 23.696 0.000 0.804 0.804
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DMI1 ~~
## .DMI2 0.296 0.043 6.903 0.000 0.296 0.496
## FIXO ~~
## CRESCIMENTO -0.384 0.044 -8.801 0.000 -0.790 -0.790
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DMI1 0.000 0.000 0.000
## .DMI2 0.000 0.000 0.000
## .DMI4 0.000 0.000 0.000
## .DMI6 0.000 0.000 0.000
## .DMI3 0.000 0.000 0.000
## .DMI5 0.000 0.000 0.000
## .DMI7 0.000 0.000 0.000
## .DMI8 0.000 0.000 0.000
## FIXO 0.000 0.000 0.000
## CRESCIMENTO 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DMI1|t1 0.324 0.071 4.575 0.000 0.324 0.324
## DMI1|t2 0.653 0.075 8.698 0.000 0.653 0.653
## DMI1|t3 0.857 0.080 10.777 0.000 0.857 0.857
## DMI1|t4 0.986 0.083 11.853 0.000 0.986 0.986
## DMI1|t5 1.276 0.094 13.516 0.000 1.276 1.276
## DMI1|t6 1.473 0.105 14.019 0.000 1.473 1.473
## DMI2|t1 0.096 0.070 1.380 0.168 0.096 0.096
## DMI2|t2 0.625 0.075 8.379 0.000 0.625 0.625
## DMI2|t3 0.879 0.080 10.977 0.000 0.879 0.879
## DMI2|t4 1.119 0.088 12.752 0.000 1.119 1.119
## DMI2|t5 1.473 0.105 14.019 0.000 1.473 1.473
## DMI2|t6 1.754 0.126 13.895 0.000 1.754 1.754
## DMI4|t1 0.065 0.069 0.939 0.348 0.065 0.065
## DMI4|t2 0.490 0.073 6.761 0.000 0.490 0.490
## DMI4|t3 0.751 0.077 9.752 0.000 0.751 0.751
## DMI4|t4 1.134 0.088 12.836 0.000 1.134 1.134
## DMI4|t5 1.408 0.101 13.909 0.000 1.408 1.408
## DMI4|t6 1.686 0.120 14.009 0.000 1.686 1.686
## DMI6|t1 0.073 0.069 1.049 0.294 0.073 0.073
## DMI6|t2 0.499 0.073 6.869 0.000 0.499 0.499
## DMI6|t3 0.846 0.079 10.676 0.000 0.846 0.846
## DMI6|t4 1.024 0.084 12.132 0.000 1.024 1.024
## DMI6|t5 1.330 0.097 13.704 0.000 1.330 1.330
## DMI6|t6 1.754 0.126 13.895 0.000 1.754 1.754
## DMI3|t1 -1.872 0.138 -13.573 0.000 -1.872 -1.872
## DMI3|t2 -1.598 0.113 -14.080 0.000 -1.598 -1.598
## DMI3|t3 -1.330 0.097 -13.704 0.000 -1.330 -1.330
## DMI3|t4 -1.050 0.085 -12.314 0.000 -1.050 -1.050
## DMI3|t5 -0.663 0.075 -8.805 0.000 -0.663 -0.663
## DMI3|t6 -0.220 0.070 -3.145 0.002 -0.220 -0.220
## DMI5|t1 -1.754 0.126 -13.895 0.000 -1.754 -1.754
## DMI5|t2 -1.626 0.116 -14.068 0.000 -1.626 -1.626
## DMI5|t3 -1.349 0.098 -13.760 0.000 -1.349 -1.349
## DMI5|t4 -0.974 0.083 -11.758 0.000 -0.974 -0.974
## DMI5|t5 -0.644 0.075 -8.592 0.000 -0.644 -0.644
## DMI5|t6 -0.158 0.070 -2.263 0.024 -0.158 -0.158
## DMI7|t1 -2.026 0.156 -12.959 0.000 -2.026 -2.026
## DMI7|t2 -1.719 0.123 -13.960 0.000 -1.719 -1.719
## DMI7|t3 -1.368 0.099 -13.814 0.000 -1.368 -1.368
## DMI7|t4 -1.091 0.087 -12.580 0.000 -1.091 -1.091
## DMI7|t5 -0.663 0.075 -8.805 0.000 -0.663 -0.663
## DMI7|t6 -0.197 0.070 -2.814 0.005 -0.197 -0.197
## DMI8|t1 -2.089 0.165 -12.647 0.000 -2.089 -2.089
## DMI8|t2 -1.872 0.138 -13.573 0.000 -1.872 -1.872
## DMI8|t3 -1.520 0.108 -14.065 0.000 -1.520 -1.520
## DMI8|t4 -1.134 0.088 -12.836 0.000 -1.134 -1.134
## DMI8|t5 -0.606 0.074 -8.164 0.000 -0.606 -0.606
## DMI8|t6 -0.027 0.069 -0.387 0.699 -0.027 -0.027
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DMI1 0.663 0.663 0.663
## .DMI2 0.539 0.539 0.539
## .DMI4 0.371 0.371 0.371
## .DMI6 0.281 0.281 0.281
## .DMI3 0.299 0.299 0.299
## .DMI5 0.277 0.277 0.277
## .DMI7 0.277 0.277 0.277
## .DMI8 0.354 0.354 0.354
## FIXO 0.337 0.055 6.115 0.000 1.000 1.000
## CRESCIMENTO 0.701 0.041 16.989 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DMI1 1.000 1.000 1.000
## DMI2 1.000 1.000 1.000
## DMI4 1.000 1.000 1.000
## DMI6 1.000 1.000 1.000
## DMI3 1.000 1.000 1.000
## DMI5 1.000 1.000 1.000
## DMI7 1.000 1.000 1.000
## DMI8 1.000 1.000 1.000
##
## R-Square:
## Estimate
## DMI1 0.337
## DMI2 0.461
## DMI4 0.629
## DMI6 0.719
## DMI3 0.701
## DMI5 0.723
## DMI7 0.723
## DMI8 0.646
semTools::reliability(fit)
## For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord" and the response by Zumbo & Kroc (2019). Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.
## FIXO CRESCIMENTO
## alpha 0.7570879 0.8392356
## alpha.ord 0.8435195 0.9021933
## omega 0.7117085 0.8642375
## omega2 0.7117085 0.8642375
## omega3 0.7102847 0.8637027
## avevar 0.5363352 0.6983619
semTools::discriminantValidity(fit)
## Some of the latent variable variances are estimated instead of fixed to 1. The model is re-estimated by scaling the latent variables by fixing their variances and freeing all factor loadings.
## lhs op rhs est ci.lower ci.upper Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## 1 FIXO ~~ CRESCIMENTO -0.7896155 -0.8586785 -0.7205526 19 NA NA 24.46295 10.17262 1 0.001425414
Flexibilidade Psicológica
model<-'
FPT=~FPT1 + FPT2 + FPT3 + FPT4 + FPT5 + FPT6 + FPT7'
fit<-cfa(model=model,data=banco_geral,estimator="ULSMV",ordered=T)
summary(fit,standardized=T,fit=T,rsquare=T)
## lavaan 0.6-9 ended normally after 14 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 49
##
## Number of observations 327
##
## Model Test User Model:
## Standard Robust
## Test Statistic 13.908 42.303
## Degrees of freedom 14 14
## P-value (Unknown) NA 0.000
## Scaling correction factor 0.345
## Shift parameter 1.996
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 1404.410 1532.333
## Degrees of freedom 21 21
## P-value NA 0.000
## Scaling correction factor 0.921
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 0.981
## Tucker-Lewis Index (TLI) 1.000 0.972
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.079
## 90 Percent confidence interval - lower 0.000 0.052
## 90 Percent confidence interval - upper 0.053 0.107
## P-value RMSEA <= 0.05 0.930 0.038
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.039 0.039
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## FPT =~
## FPT1 1.000 0.752 0.752
## FPT2 0.556 0.065 8.609 0.000 0.418 0.418
## FPT3 0.968 0.049 19.817 0.000 0.728 0.728
## FPT4 0.903 0.047 19.153 0.000 0.678 0.678
## FPT5 1.068 0.045 23.773 0.000 0.802 0.802
## FPT6 0.839 0.054 15.659 0.000 0.630 0.630
## FPT7 0.851 0.053 16.068 0.000 0.640 0.640
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .FPT1 0.000 0.000 0.000
## .FPT2 0.000 0.000 0.000
## .FPT3 0.000 0.000 0.000
## .FPT4 0.000 0.000 0.000
## .FPT5 0.000 0.000 0.000
## .FPT6 0.000 0.000 0.000
## .FPT7 0.000 0.000 0.000
## FPT 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## FPT1|t1 -2.026 0.156 -12.959 0.000 -2.026 -2.026
## FPT1|t2 -1.545 0.110 -14.078 0.000 -1.545 -1.545
## FPT1|t3 -1.294 0.095 -13.582 0.000 -1.294 -1.294
## FPT1|t4 -0.846 0.079 -10.676 0.000 -0.846 -0.846
## FPT1|t5 -0.212 0.070 -3.035 0.002 -0.212 -0.212
## FPT1|t6 0.543 0.073 7.411 0.000 0.543 0.543
## FPT2|t1 -2.741 0.329 -8.345 0.000 -2.741 -2.741
## FPT2|t2 -2.359 0.214 -11.040 0.000 -2.359 -2.359
## FPT2|t3 -2.089 0.165 -12.647 0.000 -2.089 -2.089
## FPT2|t4 -1.496 0.107 -14.045 0.000 -1.496 -1.496
## FPT2|t5 -0.782 0.078 -10.063 0.000 -0.782 -0.782
## FPT2|t6 -0.065 0.069 -0.939 0.348 -0.065 -0.065
## FPT3|t1 -2.089 0.165 -12.647 0.000 -2.089 -2.089
## FPT3|t2 -1.655 0.118 -14.044 0.000 -1.655 -1.655
## FPT3|t3 -1.077 0.086 -12.493 0.000 -1.077 -1.077
## FPT3|t4 -0.606 0.074 -8.164 0.000 -0.606 -0.606
## FPT3|t5 0.324 0.071 4.575 0.000 0.324 0.324
## FPT3|t6 1.011 0.084 12.040 0.000 1.011 1.011
## FPT4|t1 -1.473 0.105 -14.019 0.000 -1.473 -1.473
## FPT4|t2 -1.037 0.085 -12.224 0.000 -1.037 -1.037
## FPT4|t3 -0.499 0.073 -6.869 0.000 -0.499 -0.499
## FPT4|t4 0.111 0.070 1.601 0.109 0.111 0.111
## FPT4|t5 0.663 0.075 8.805 0.000 0.663 0.663
## FPT4|t6 1.368 0.099 13.814 0.000 1.368 1.368
## FPT5|t1 -1.969 0.149 -13.209 0.000 -1.969 -1.969
## FPT5|t2 -1.545 0.110 -14.078 0.000 -1.545 -1.545
## FPT5|t3 -0.925 0.081 -11.372 0.000 -0.925 -0.925
## FPT5|t4 -0.422 0.072 -5.889 0.000 -0.422 -0.422
## FPT5|t5 0.348 0.071 4.904 0.000 0.348 0.348
## FPT5|t6 1.024 0.084 12.132 0.000 1.024 1.024
## FPT6|t1 -1.242 0.093 -13.379 0.000 -1.242 -1.242
## FPT6|t2 -1.037 0.085 -12.224 0.000 -1.037 -1.037
## FPT6|t3 -0.543 0.073 -7.411 0.000 -0.543 -0.543
## FPT6|t4 -0.150 0.070 -2.153 0.031 -0.150 -0.150
## FPT6|t5 0.340 0.071 4.794 0.000 0.340 0.340
## FPT6|t6 0.891 0.080 11.077 0.000 0.891 0.891
## FPT7|t1 -1.210 0.091 -13.232 0.000 -1.210 -1.210
## FPT7|t2 -0.803 0.078 -10.269 0.000 -0.803 -0.803
## FPT7|t3 -0.268 0.070 -3.805 0.000 -0.268 -0.268
## FPT7|t4 0.212 0.070 3.035 0.002 0.212 0.212
## FPT7|t5 0.741 0.077 9.647 0.000 0.741 0.741
## FPT7|t6 1.312 0.096 13.644 0.000 1.312 1.312
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .FPT1 0.435 0.435 0.435
## .FPT2 0.825 0.825 0.825
## .FPT3 0.471 0.471 0.471
## .FPT4 0.540 0.540 0.540
## .FPT5 0.356 0.356 0.356
## .FPT6 0.602 0.602 0.602
## .FPT7 0.591 0.591 0.591
## FPT 0.565 0.043 13.170 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## FPT1 1.000 1.000 1.000
## FPT2 1.000 1.000 1.000
## FPT3 1.000 1.000 1.000
## FPT4 1.000 1.000 1.000
## FPT5 1.000 1.000 1.000
## FPT6 1.000 1.000 1.000
## FPT7 1.000 1.000 1.000
##
## R-Square:
## Estimate
## FPT1 0.565
## FPT2 0.175
## FPT3 0.529
## FPT4 0.460
## FPT5 0.644
## FPT6 0.398
## FPT7 0.409
semTools::reliability(fit)
## For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord" and the response by Zumbo & Kroc (2019). Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.
## FPT
## alpha 0.8184692
## alpha.ord 0.8453989
## omega 0.8324085
## omega2 0.8324085
## omega3 0.8312708
## avevar 0.4542928
Metas de Aproximação
modelo<-
'
MAP =~ MRT7 + MRT8 + MRT9 + MRT10
DAP =~ MRT11 + MRT12 + MRT13 + MRT14
'
fit<-cfa(model=modelo,data=banco_geral,estimator="ULSMV",ordered=T,orthogonal=T)
summary(fit,standardized=T,fit=T,rsquare=T)
## lavaan 0.6-9 ended normally after 16 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 56
##
## Number of observations 327
##
## Model Test User Model:
## Standard Robust
## Test Statistic 67.484 48.326
## Degrees of freedom 20 20
## P-value (Unknown) NA 0.000
## Scaling correction factor 1.749
## Shift parameter 9.734
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 1603.098 1057.251
## Degrees of freedom 28 28
## P-value NA 0.000
## Scaling correction factor 1.536
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.970 0.972
## Tucker-Lewis Index (TLI) 0.958 0.961
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.085 0.066
## 90 Percent confidence interval - lower 0.063 0.042
## 90 Percent confidence interval - upper 0.108 0.090
## P-value RMSEA <= 0.05 0.005 0.124
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.076 0.076
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## MAP =~
## MRT7 1.000 0.809 0.809
## MRT8 0.728 0.046 15.816 0.000 0.589 0.589
## MRT9 0.977 0.045 21.648 0.000 0.790 0.790
## MRT10 1.047 0.049 21.323 0.000 0.847 0.847
## DAP =~
## MRT11 1.000 0.735 0.735
## MRT12 1.270 0.051 24.959 0.000 0.934 0.934
## MRT13 1.189 0.045 26.161 0.000 0.874 0.874
## MRT14 0.986 0.044 22.192 0.000 0.725 0.725
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## MAP ~~
## DAP 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .MRT7 0.000 0.000 0.000
## .MRT8 0.000 0.000 0.000
## .MRT9 0.000 0.000 0.000
## .MRT10 0.000 0.000 0.000
## .MRT11 0.000 0.000 0.000
## .MRT12 0.000 0.000 0.000
## .MRT13 0.000 0.000 0.000
## .MRT14 0.000 0.000 0.000
## MAP 0.000 0.000 0.000
## DAP 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## MRT7|t1 -2.505 0.250 -10.034 0.000 -2.505 -2.505
## MRT7|t2 -2.162 0.177 -12.251 0.000 -2.162 -2.162
## MRT7|t3 -1.719 0.123 -13.960 0.000 -1.719 -1.719
## MRT7|t4 -1.276 0.094 -13.516 0.000 -1.276 -1.276
## MRT7|t5 -0.701 0.076 -9.228 0.000 -0.701 -0.701
## MRT7|t6 -0.104 0.070 -1.491 0.136 -0.104 -0.104
## MRT8|t1 -1.686 0.120 -14.009 0.000 -1.686 -1.686
## MRT8|t2 -1.626 0.116 -14.068 0.000 -1.626 -1.626
## MRT8|t3 -1.276 0.094 -13.516 0.000 -1.276 -1.276
## MRT8|t4 -0.653 0.075 -8.698 0.000 -0.653 -0.653
## MRT8|t5 0.035 0.069 0.497 0.619 0.035 0.035
## MRT8|t6 0.663 0.075 8.805 0.000 0.663 0.663
## MRT9|t1 -2.505 0.250 -10.034 0.000 -2.505 -2.505
## MRT9|t2 -2.250 0.192 -11.736 0.000 -2.250 -2.250
## MRT9|t3 -1.754 0.126 -13.895 0.000 -1.754 -1.754
## MRT9|t4 -1.226 0.092 -13.306 0.000 -1.226 -1.226
## MRT9|t5 -0.597 0.074 -8.057 0.000 -0.597 -0.597
## MRT9|t6 -0.073 0.069 -1.049 0.294 -0.073 -0.073
## MRT10|t1 -2.250 0.192 -11.736 0.000 -2.250 -2.250
## MRT10|t2 -1.919 0.143 -13.410 0.000 -1.919 -1.919
## MRT10|t3 -1.598 0.113 -14.080 0.000 -1.598 -1.598
## MRT10|t4 -1.050 0.085 -12.314 0.000 -1.050 -1.050
## MRT10|t5 -0.389 0.071 -5.452 0.000 -0.389 -0.389
## MRT10|t6 0.181 0.070 2.594 0.009 0.181 0.181
## MRT11|t1 -0.615 0.074 -8.272 0.000 -0.615 -0.615
## MRT11|t2 -0.356 0.071 -5.014 0.000 -0.356 -0.356
## MRT11|t3 -0.158 0.070 -2.263 0.024 -0.158 -0.158
## MRT11|t4 0.348 0.071 4.904 0.000 0.348 0.348
## MRT11|t5 0.731 0.077 9.543 0.000 0.731 0.731
## MRT11|t6 1.105 0.087 12.667 0.000 1.105 1.105
## MRT12|t1 -0.741 0.077 -9.647 0.000 -0.741 -0.741
## MRT12|t2 -0.482 0.072 -6.652 0.000 -0.482 -0.482
## MRT12|t3 -0.228 0.070 -3.255 0.001 -0.228 -0.228
## MRT12|t4 0.348 0.071 4.904 0.000 0.348 0.348
## MRT12|t5 0.751 0.077 9.752 0.000 0.751 0.751
## MRT12|t6 1.077 0.086 12.493 0.000 1.077 1.077
## MRT13|t1 -0.490 0.073 -6.761 0.000 -0.490 -0.490
## MRT13|t2 -0.260 0.070 -3.695 0.000 -0.260 -0.260
## MRT13|t3 0.050 0.069 0.718 0.473 0.050 0.050
## MRT13|t4 0.588 0.074 7.950 0.000 0.588 0.588
## MRT13|t5 0.925 0.081 11.372 0.000 0.925 0.925
## MRT13|t6 1.179 0.090 13.078 0.000 1.179 1.179
## MRT14|t1 -1.194 0.091 -13.156 0.000 -1.194 -1.194
## MRT14|t2 -0.792 0.078 -10.166 0.000 -0.792 -0.792
## MRT14|t3 -0.543 0.073 -7.411 0.000 -0.543 -0.543
## MRT14|t4 -0.081 0.069 -1.159 0.246 -0.081 -0.081
## MRT14|t5 0.332 0.071 4.685 0.000 0.332 0.332
## MRT14|t6 0.634 0.075 8.485 0.000 0.634 0.634
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .MRT7 0.346 0.346 0.346
## .MRT8 0.653 0.653 0.653
## .MRT9 0.376 0.376 0.376
## .MRT10 0.283 0.283 0.283
## .MRT11 0.460 0.460 0.460
## .MRT12 0.129 0.129 0.129
## .MRT13 0.236 0.236 0.236
## .MRT14 0.474 0.474 0.474
## MAP 0.654 0.044 14.901 0.000 1.000 1.000
## DAP 0.540 0.040 13.591 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## MRT7 1.000 1.000 1.000
## MRT8 1.000 1.000 1.000
## MRT9 1.000 1.000 1.000
## MRT10 1.000 1.000 1.000
## MRT11 1.000 1.000 1.000
## MRT12 1.000 1.000 1.000
## MRT13 1.000 1.000 1.000
## MRT14 1.000 1.000 1.000
##
## R-Square:
## Estimate
## MRT7 0.654
## MRT8 0.347
## MRT9 0.624
## MRT10 0.717
## MRT11 0.540
## MRT12 0.871
## MRT13 0.764
## MRT14 0.526
semTools::reliability(fit)
## For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord" and the response by Zumbo & Kroc (2019). Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.
## MAP DAP
## alpha 0.7581073 0.8504509
## alpha.ord 0.8425892 0.8879304
## omega 0.7981565 0.8685976
## omega2 0.7981565 0.8685976
## omega3 0.7982473 0.8687667
## avevar 0.5856229 0.6753288
semTools::discriminantValidity(fit)
## Some of the latent variable variances are estimated instead of fixed to 1. The model is re-estimated by scaling the latent variables by fixing their variances and freeing all factor loadings.
## Warning in lavaan::lavTestLRT(object, constrained): lavaan WARNING: some models have the same degrees of freedom
## lhs op rhs est ci.lower ci.upper Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## 1 MAP ~~ DAP 0 0 0 20 NA NA 560.5338 493.0501 0 NA
Modelagem por tipo de Mindset
modelo<-
'
MAP =~ MRT7 + MRT8 + MRT9 + MRT10
DAP =~ MRT11 + MRT12 + MRT13 + MRT14
FIXO =~ DMI1 + DMI2 + DMI4 + DMI6
CRESCIMENTO =~ DMI3 + DMI5 + DMI7 + DMI8
FPT=~FPT1 + FPT2 + FPT3 + FPT4 + FPT5 + FPT6 + FPT7
DMI1 ~~ DMI2
MAP~~0*DAP
'
fit<-cfa(model=modelo,data=banco_geral,estimator="WLSMV",ordered=T)
summary(fit,standardized=T,fit=T,rsquare=T)
## lavaan 0.6-9 ended normally after 56 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 171
##
## Number of observations 327
##
## Model Test User Model:
## Standard Robust
## Test Statistic 396.435 427.305
## Degrees of freedom 220 220
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.219
## Shift parameter 102.063
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 16523.497 7030.461
## Degrees of freedom 253 253
## P-value 0.000 0.000
## Scaling correction factor 2.401
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.989 0.969
## Tucker-Lewis Index (TLI) 0.988 0.965
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.050 0.054
## 90 Percent confidence interval - lower 0.042 0.046
## 90 Percent confidence interval - upper 0.057 0.061
## P-value RMSEA <= 0.05 0.523 0.203
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.067 0.067
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## MAP =~
## MRT7 1.000 0.809 0.809
## MRT8 0.743 0.052 14.368 0.000 0.601 0.601
## MRT9 0.996 0.041 24.296 0.000 0.806 0.806
## MRT10 1.027 0.050 20.684 0.000 0.831 0.831
## DAP =~
## MRT11 1.000 0.744 0.744
## MRT12 1.250 0.046 27.226 0.000 0.929 0.929
## MRT13 1.165 0.039 30.029 0.000 0.866 0.866
## MRT14 0.986 0.043 22.841 0.000 0.733 0.733
## FIXO =~
## DMI1 1.000 0.573 0.573
## DMI2 1.174 0.081 14.539 0.000 0.673 0.673
## DMI4 1.374 0.120 11.496 0.000 0.787 0.787
## DMI6 1.490 0.131 11.385 0.000 0.854 0.854
## CRESCIMENTO =~
## DMI3 1.000 0.835 0.835
## DMI5 0.990 0.033 29.559 0.000 0.826 0.826
## DMI7 1.042 0.033 31.537 0.000 0.870 0.870
## DMI8 0.980 0.034 28.687 0.000 0.818 0.818
## FPT =~
## FPT1 1.000 0.768 0.768
## FPT2 0.616 0.065 9.456 0.000 0.473 0.473
## FPT3 0.950 0.046 20.583 0.000 0.729 0.729
## FPT4 0.894 0.047 19.126 0.000 0.687 0.687
## FPT5 1.025 0.042 24.629 0.000 0.787 0.787
## FPT6 0.786 0.054 14.545 0.000 0.603 0.603
## FPT7 0.854 0.049 17.445 0.000 0.655 0.655
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DMI1 ~~
## .DMI2 0.305 0.043 7.177 0.000 0.305 0.503
## MAP ~~
## DAP 0.000 0.000 0.000
## FIXO -0.168 0.030 -5.678 0.000 -0.362 -0.362
## CRESCIMENTO 0.300 0.038 7.919 0.000 0.444 0.444
## FPT 0.258 0.034 7.560 0.000 0.416 0.416
## DAP ~~
## FIXO 0.042 0.026 1.624 0.104 0.099 0.099
## CRESCIMENTO 0.051 0.037 1.367 0.172 0.082 0.082
## FPT 0.049 0.033 1.498 0.134 0.086 0.086
## FIXO ~~
## CRESCIMENTO -0.383 0.042 -9.054 0.000 -0.802 -0.802
## FPT -0.013 0.027 -0.463 0.644 -0.029 -0.029
## CRESCIMENTO ~~
## FPT 0.152 0.037 4.109 0.000 0.236 0.236
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .MRT7 0.000 0.000 0.000
## .MRT8 0.000 0.000 0.000
## .MRT9 0.000 0.000 0.000
## .MRT10 0.000 0.000 0.000
## .MRT11 0.000 0.000 0.000
## .MRT12 0.000 0.000 0.000
## .MRT13 0.000 0.000 0.000
## .MRT14 0.000 0.000 0.000
## .DMI1 0.000 0.000 0.000
## .DMI2 0.000 0.000 0.000
## .DMI4 0.000 0.000 0.000
## .DMI6 0.000 0.000 0.000
## .DMI3 0.000 0.000 0.000
## .DMI5 0.000 0.000 0.000
## .DMI7 0.000 0.000 0.000
## .DMI8 0.000 0.000 0.000
## .FPT1 0.000 0.000 0.000
## .FPT2 0.000 0.000 0.000
## .FPT3 0.000 0.000 0.000
## .FPT4 0.000 0.000 0.000
## .FPT5 0.000 0.000 0.000
## .FPT6 0.000 0.000 0.000
## .FPT7 0.000 0.000 0.000
## MAP 0.000 0.000 0.000
## DAP 0.000 0.000 0.000
## FIXO 0.000 0.000 0.000
## CRESCIMENTO 0.000 0.000 0.000
## FPT 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## MRT7|t1 -2.505 0.250 -10.034 0.000 -2.505 -2.505
## MRT7|t2 -2.162 0.177 -12.251 0.000 -2.162 -2.162
## MRT7|t3 -1.719 0.123 -13.960 0.000 -1.719 -1.719
## MRT7|t4 -1.276 0.094 -13.516 0.000 -1.276 -1.276
## MRT7|t5 -0.701 0.076 -9.228 0.000 -0.701 -0.701
## MRT7|t6 -0.104 0.070 -1.491 0.136 -0.104 -0.104
## MRT8|t1 -1.686 0.120 -14.009 0.000 -1.686 -1.686
## MRT8|t2 -1.626 0.116 -14.068 0.000 -1.626 -1.626
## MRT8|t3 -1.276 0.094 -13.516 0.000 -1.276 -1.276
## MRT8|t4 -0.653 0.075 -8.698 0.000 -0.653 -0.653
## MRT8|t5 0.035 0.069 0.497 0.619 0.035 0.035
## MRT8|t6 0.663 0.075 8.805 0.000 0.663 0.663
## MRT9|t1 -2.505 0.250 -10.034 0.000 -2.505 -2.505
## MRT9|t2 -2.250 0.192 -11.736 0.000 -2.250 -2.250
## MRT9|t3 -1.754 0.126 -13.895 0.000 -1.754 -1.754
## MRT9|t4 -1.226 0.092 -13.306 0.000 -1.226 -1.226
## MRT9|t5 -0.597 0.074 -8.057 0.000 -0.597 -0.597
## MRT9|t6 -0.073 0.069 -1.049 0.294 -0.073 -0.073
## MRT10|t1 -2.250 0.192 -11.736 0.000 -2.250 -2.250
## MRT10|t2 -1.919 0.143 -13.410 0.000 -1.919 -1.919
## MRT10|t3 -1.598 0.113 -14.080 0.000 -1.598 -1.598
## MRT10|t4 -1.050 0.085 -12.314 0.000 -1.050 -1.050
## MRT10|t5 -0.389 0.071 -5.452 0.000 -0.389 -0.389
## MRT10|t6 0.181 0.070 2.594 0.009 0.181 0.181
## MRT11|t1 -0.615 0.074 -8.272 0.000 -0.615 -0.615
## MRT11|t2 -0.356 0.071 -5.014 0.000 -0.356 -0.356
## MRT11|t3 -0.158 0.070 -2.263 0.024 -0.158 -0.158
## MRT11|t4 0.348 0.071 4.904 0.000 0.348 0.348
## MRT11|t5 0.731 0.077 9.543 0.000 0.731 0.731
## MRT11|t6 1.105 0.087 12.667 0.000 1.105 1.105
## MRT12|t1 -0.741 0.077 -9.647 0.000 -0.741 -0.741
## MRT12|t2 -0.482 0.072 -6.652 0.000 -0.482 -0.482
## MRT12|t3 -0.228 0.070 -3.255 0.001 -0.228 -0.228
## MRT12|t4 0.348 0.071 4.904 0.000 0.348 0.348
## MRT12|t5 0.751 0.077 9.752 0.000 0.751 0.751
## MRT12|t6 1.077 0.086 12.493 0.000 1.077 1.077
## MRT13|t1 -0.490 0.073 -6.761 0.000 -0.490 -0.490
## MRT13|t2 -0.260 0.070 -3.695 0.000 -0.260 -0.260
## MRT13|t3 0.050 0.069 0.718 0.473 0.050 0.050
## MRT13|t4 0.588 0.074 7.950 0.000 0.588 0.588
## MRT13|t5 0.925 0.081 11.372 0.000 0.925 0.925
## MRT13|t6 1.179 0.090 13.078 0.000 1.179 1.179
## MRT14|t1 -1.194 0.091 -13.156 0.000 -1.194 -1.194
## MRT14|t2 -0.792 0.078 -10.166 0.000 -0.792 -0.792
## MRT14|t3 -0.543 0.073 -7.411 0.000 -0.543 -0.543
## MRT14|t4 -0.081 0.069 -1.159 0.246 -0.081 -0.081
## MRT14|t5 0.332 0.071 4.685 0.000 0.332 0.332
## MRT14|t6 0.634 0.075 8.485 0.000 0.634 0.634
## DMI1|t1 0.324 0.071 4.575 0.000 0.324 0.324
## DMI1|t2 0.653 0.075 8.698 0.000 0.653 0.653
## DMI1|t3 0.857 0.080 10.777 0.000 0.857 0.857
## DMI1|t4 0.986 0.083 11.853 0.000 0.986 0.986
## DMI1|t5 1.276 0.094 13.516 0.000 1.276 1.276
## DMI1|t6 1.473 0.105 14.019 0.000 1.473 1.473
## DMI2|t1 0.096 0.070 1.380 0.168 0.096 0.096
## DMI2|t2 0.625 0.075 8.379 0.000 0.625 0.625
## DMI2|t3 0.879 0.080 10.977 0.000 0.879 0.879
## DMI2|t4 1.119 0.088 12.752 0.000 1.119 1.119
## DMI2|t5 1.473 0.105 14.019 0.000 1.473 1.473
## DMI2|t6 1.754 0.126 13.895 0.000 1.754 1.754
## DMI4|t1 0.065 0.069 0.939 0.348 0.065 0.065
## DMI4|t2 0.490 0.073 6.761 0.000 0.490 0.490
## DMI4|t3 0.751 0.077 9.752 0.000 0.751 0.751
## DMI4|t4 1.134 0.088 12.836 0.000 1.134 1.134
## DMI4|t5 1.408 0.101 13.909 0.000 1.408 1.408
## DMI4|t6 1.686 0.120 14.009 0.000 1.686 1.686
## DMI6|t1 0.073 0.069 1.049 0.294 0.073 0.073
## DMI6|t2 0.499 0.073 6.869 0.000 0.499 0.499
## DMI6|t3 0.846 0.079 10.676 0.000 0.846 0.846
## DMI6|t4 1.024 0.084 12.132 0.000 1.024 1.024
## DMI6|t5 1.330 0.097 13.704 0.000 1.330 1.330
## DMI6|t6 1.754 0.126 13.895 0.000 1.754 1.754
## DMI3|t1 -1.872 0.138 -13.573 0.000 -1.872 -1.872
## DMI3|t2 -1.598 0.113 -14.080 0.000 -1.598 -1.598
## DMI3|t3 -1.330 0.097 -13.704 0.000 -1.330 -1.330
## DMI3|t4 -1.050 0.085 -12.314 0.000 -1.050 -1.050
## DMI3|t5 -0.663 0.075 -8.805 0.000 -0.663 -0.663
## DMI3|t6 -0.220 0.070 -3.145 0.002 -0.220 -0.220
## DMI5|t1 -1.754 0.126 -13.895 0.000 -1.754 -1.754
## DMI5|t2 -1.626 0.116 -14.068 0.000 -1.626 -1.626
## DMI5|t3 -1.349 0.098 -13.760 0.000 -1.349 -1.349
## DMI5|t4 -0.974 0.083 -11.758 0.000 -0.974 -0.974
## DMI5|t5 -0.644 0.075 -8.592 0.000 -0.644 -0.644
## DMI5|t6 -0.158 0.070 -2.263 0.024 -0.158 -0.158
## DMI7|t1 -2.026 0.156 -12.959 0.000 -2.026 -2.026
## DMI7|t2 -1.719 0.123 -13.960 0.000 -1.719 -1.719
## DMI7|t3 -1.368 0.099 -13.814 0.000 -1.368 -1.368
## DMI7|t4 -1.091 0.087 -12.580 0.000 -1.091 -1.091
## DMI7|t5 -0.663 0.075 -8.805 0.000 -0.663 -0.663
## DMI7|t6 -0.197 0.070 -2.814 0.005 -0.197 -0.197
## DMI8|t1 -2.089 0.165 -12.647 0.000 -2.089 -2.089
## DMI8|t2 -1.872 0.138 -13.573 0.000 -1.872 -1.872
## DMI8|t3 -1.520 0.108 -14.065 0.000 -1.520 -1.520
## DMI8|t4 -1.134 0.088 -12.836 0.000 -1.134 -1.134
## DMI8|t5 -0.606 0.074 -8.164 0.000 -0.606 -0.606
## DMI8|t6 -0.027 0.069 -0.387 0.699 -0.027 -0.027
## FPT1|t1 -2.026 0.156 -12.959 0.000 -2.026 -2.026
## FPT1|t2 -1.545 0.110 -14.078 0.000 -1.545 -1.545
## FPT1|t3 -1.294 0.095 -13.582 0.000 -1.294 -1.294
## FPT1|t4 -0.846 0.079 -10.676 0.000 -0.846 -0.846
## FPT1|t5 -0.212 0.070 -3.035 0.002 -0.212 -0.212
## FPT1|t6 0.543 0.073 7.411 0.000 0.543 0.543
## FPT2|t1 -2.741 0.329 -8.345 0.000 -2.741 -2.741
## FPT2|t2 -2.359 0.214 -11.040 0.000 -2.359 -2.359
## FPT2|t3 -2.089 0.165 -12.647 0.000 -2.089 -2.089
## FPT2|t4 -1.496 0.107 -14.045 0.000 -1.496 -1.496
## FPT2|t5 -0.782 0.078 -10.063 0.000 -0.782 -0.782
## FPT2|t6 -0.065 0.069 -0.939 0.348 -0.065 -0.065
## FPT3|t1 -2.089 0.165 -12.647 0.000 -2.089 -2.089
## FPT3|t2 -1.655 0.118 -14.044 0.000 -1.655 -1.655
## FPT3|t3 -1.077 0.086 -12.493 0.000 -1.077 -1.077
## FPT3|t4 -0.606 0.074 -8.164 0.000 -0.606 -0.606
## FPT3|t5 0.324 0.071 4.575 0.000 0.324 0.324
## FPT3|t6 1.011 0.084 12.040 0.000 1.011 1.011
## FPT4|t1 -1.473 0.105 -14.019 0.000 -1.473 -1.473
## FPT4|t2 -1.037 0.085 -12.224 0.000 -1.037 -1.037
## FPT4|t3 -0.499 0.073 -6.869 0.000 -0.499 -0.499
## FPT4|t4 0.111 0.070 1.601 0.109 0.111 0.111
## FPT4|t5 0.663 0.075 8.805 0.000 0.663 0.663
## FPT4|t6 1.368 0.099 13.814 0.000 1.368 1.368
## FPT5|t1 -1.969 0.149 -13.209 0.000 -1.969 -1.969
## FPT5|t2 -1.545 0.110 -14.078 0.000 -1.545 -1.545
## FPT5|t3 -0.925 0.081 -11.372 0.000 -0.925 -0.925
## FPT5|t4 -0.422 0.072 -5.889 0.000 -0.422 -0.422
## FPT5|t5 0.348 0.071 4.904 0.000 0.348 0.348
## FPT5|t6 1.024 0.084 12.132 0.000 1.024 1.024
## FPT6|t1 -1.242 0.093 -13.379 0.000 -1.242 -1.242
## FPT6|t2 -1.037 0.085 -12.224 0.000 -1.037 -1.037
## FPT6|t3 -0.543 0.073 -7.411 0.000 -0.543 -0.543
## FPT6|t4 -0.150 0.070 -2.153 0.031 -0.150 -0.150
## FPT6|t5 0.340 0.071 4.794 0.000 0.340 0.340
## FPT6|t6 0.891 0.080 11.077 0.000 0.891 0.891
## FPT7|t1 -1.210 0.091 -13.232 0.000 -1.210 -1.210
## FPT7|t2 -0.803 0.078 -10.269 0.000 -0.803 -0.803
## FPT7|t3 -0.268 0.070 -3.805 0.000 -0.268 -0.268
## FPT7|t4 0.212 0.070 3.035 0.002 0.212 0.212
## FPT7|t5 0.741 0.077 9.647 0.000 0.741 0.741
## FPT7|t6 1.312 0.096 13.644 0.000 1.312 1.312
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .MRT7 0.346 0.346 0.346
## .MRT8 0.639 0.639 0.639
## .MRT9 0.351 0.351 0.351
## .MRT10 0.310 0.310 0.310
## .MRT11 0.447 0.447 0.447
## .MRT12 0.137 0.137 0.137
## .MRT13 0.250 0.250 0.250
## .MRT14 0.462 0.462 0.462
## .DMI1 0.672 0.672 0.672
## .DMI2 0.548 0.548 0.548
## .DMI4 0.380 0.380 0.380
## .DMI6 0.271 0.271 0.271
## .DMI3 0.303 0.303 0.303
## .DMI5 0.317 0.317 0.317
## .DMI7 0.244 0.244 0.244
## .DMI8 0.331 0.331 0.331
## .FPT1 0.410 0.410 0.410
## .FPT2 0.777 0.777 0.777
## .FPT3 0.468 0.468 0.468
## .FPT4 0.529 0.529 0.529
## .FPT5 0.381 0.381 0.381
## .FPT6 0.636 0.636 0.636
## .FPT7 0.570 0.570 0.570
## MAP 0.654 0.043 15.084 0.000 1.000 1.000
## DAP 0.553 0.038 14.491 0.000 1.000 1.000
## FIXO 0.328 0.054 6.035 0.000 1.000 1.000
## CRESCIMENTO 0.697 0.040 17.582 0.000 1.000 1.000
## FPT 0.590 0.041 14.258 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## MRT7 1.000 1.000 1.000
## MRT8 1.000 1.000 1.000
## MRT9 1.000 1.000 1.000
## MRT10 1.000 1.000 1.000
## MRT11 1.000 1.000 1.000
## MRT12 1.000 1.000 1.000
## MRT13 1.000 1.000 1.000
## MRT14 1.000 1.000 1.000
## DMI1 1.000 1.000 1.000
## DMI2 1.000 1.000 1.000
## DMI4 1.000 1.000 1.000
## DMI6 1.000 1.000 1.000
## DMI3 1.000 1.000 1.000
## DMI5 1.000 1.000 1.000
## DMI7 1.000 1.000 1.000
## DMI8 1.000 1.000 1.000
## FPT1 1.000 1.000 1.000
## FPT2 1.000 1.000 1.000
## FPT3 1.000 1.000 1.000
## FPT4 1.000 1.000 1.000
## FPT5 1.000 1.000 1.000
## FPT6 1.000 1.000 1.000
## FPT7 1.000 1.000 1.000
##
## R-Square:
## Estimate
## MRT7 0.654
## MRT8 0.361
## MRT9 0.649
## MRT10 0.690
## MRT11 0.553
## MRT12 0.863
## MRT13 0.750
## MRT14 0.538
## DMI1 0.328
## DMI2 0.452
## DMI4 0.620
## DMI6 0.729
## DMI3 0.697
## DMI5 0.683
## DMI7 0.756
## DMI8 0.669
## FPT1 0.590
## FPT2 0.223
## FPT3 0.532
## FPT4 0.471
## FPT5 0.619
## FPT6 0.364
## FPT7 0.430
semTools::reliability(fit)
## For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord" and the response by Zumbo & Kroc (2019). Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.
## MAP DAP FIXO CRESCIMENTO FPT
## alpha 0.7581073 0.8504509 0.7570879 0.8392356 0.8184692
## alpha.ord 0.8425892 0.8879304 0.8435195 0.9021933 0.8453989
## omega 0.8003491 0.8689805 0.7074952 0.8647073 0.8345881
## omega2 0.8003491 0.8689805 0.7074952 0.8647073 0.8345881
## omega3 0.8048260 0.8710353 0.7029781 0.8660632 0.8421209
## avevar 0.5887517 0.6760014 0.5322392 0.7011918 0.4613295
semTools::discriminantValidity(fit)
## Some of the latent variable variances are estimated instead of fixed to 1. The model is re-estimated by scaling the latent variables by fixing their variances and freeing all factor loadings.
## Warning in lav_object_post_check(object): lavaan WARNING: covariance matrix of latent variables
## is not positive definite;
## use lavInspect(fit, "cov.lv") to investigate.
## Warning in lav_object_post_check(object): lavaan WARNING: covariance matrix of latent variables
## is not positive definite;
## use lavInspect(fit, "cov.lv") to investigate.
## Warning in lav_object_post_check(object): lavaan WARNING: covariance matrix of latent variables
## is not positive definite;
## use lavInspect(fit, "cov.lv") to investigate.
## Warning in lav_object_post_check(object): lavaan WARNING: covariance matrix of latent variables
## is not positive definite;
## use lavInspect(fit, "cov.lv") to investigate.
## Warning in lav_object_post_check(object): lavaan WARNING: covariance matrix of latent variables
## is not positive definite;
## use lavInspect(fit, "cov.lv") to investigate.
## Warning in lav_object_post_check(object): lavaan WARNING: covariance matrix of latent variables
## is not positive definite;
## use lavInspect(fit, "cov.lv") to investigate.
## Warning in lav_object_post_check(object): lavaan WARNING: covariance matrix of latent variables
## is not positive definite;
## use lavInspect(fit, "cov.lv") to investigate.
## Warning in lav_object_post_check(object): lavaan WARNING: covariance matrix of latent variables
## is not positive definite;
## use lavInspect(fit, "cov.lv") to investigate.
## Warning in lavaan::lavTestLRT(object, constrained): lavaan WARNING: some models have the same degrees of freedom
## lhs op rhs est ci.lower ci.upper Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## 1 MAP ~~ DAP 0.00000000 0.00000000 0.00000000 220 NA NA 1175.0309 778.595429 0 NA
## 2 MAP ~~ FIXO -0.36230776 -0.46651581 -0.25809971 221 NA NA 679.7601 95.170604 1 1.746648e-22
## 3 MAP ~~ CRESCIMENTO 0.44398001 0.34817592 0.53978409 221 NA NA 720.2077 90.621900 1 1.739246e-21
## 4 MAP ~~ FPT 0.41615465 0.32446982 0.50783948 221 NA NA 829.4929 110.184325 1 8.928985e-26
## 5 DAP ~~ FIXO 0.09932215 -0.01915672 0.21780102 221 NA NA 1042.0643 132.962440 1 9.213857e-31
## 6 DAP ~~ CRESCIMENTO 0.08180594 -0.03463651 0.19824840 221 NA NA 1416.3590 176.175962 1 3.314450e-40
## 7 DAP ~~ FPT 0.08610217 -0.02515776 0.19736210 221 NA NA 1794.5270 178.359273 1 1.105774e-40
## 8 FIXO ~~ CRESCIMENTO -0.80170045 -0.86767208 -0.73572882 221 NA NA 411.5994 8.895148 1 2.859295e-03
## 9 FIXO ~~ FPT -0.02879729 -0.15138262 0.09378803 221 NA NA 1218.6212 136.612089 1 1.466001e-31
## 10 CRESCIMENTO ~~ FPT 0.23641259 0.12655566 0.34626953 221 NA NA 1251.5642 132.919814 1 9.413827e-31
Correlações entre as variáveis
round(lavInspect(fit,"cor.lv"),2)
## MAP DAP FIXO CRESCI FPT
## MAP 1.00
## DAP 0.00 1.00
## FIXO -0.36 0.10 1.00
## CRESCIMENTO 0.44 0.08 -0.80 1.00
## FPT 0.42 0.09 -0.03 0.24 1.00
Significância das correlações
pvalue<-parameterestimates(fit,standardized = T)
pvalue<-pvalue[(pvalue$op=="~~"&(pvalue$lhs!=pvalue$rhs)),c(1:3,7)]
pvalue[c(-1:-2),]
## lhs op rhs pvalue
## 192 MAP ~~ FIXO 0.000
## 193 MAP ~~ CRESCIMENTO 0.000
## 194 MAP ~~ FPT 0.000
## 195 DAP ~~ FIXO 0.104
## 196 DAP ~~ CRESCIMENTO 0.172
## 197 DAP ~~ FPT 0.134
## 198 FIXO ~~ CRESCIMENTO 0.000
## 199 FIXO ~~ FPT 0.644
## 200 CRESCIMENTO ~~ FPT 0.000
banco_moderacao<-lavaan::predict(fit)
write.csv(banco_moderacao,"professores.csv")
Modelagem por tipo de Mindset
modelo<-
'
MAP =~ MRT7 + MRT8 + MRT9 + MRT10
DAP =~ MRT11 + MRT12 + MRT13 + MRT14
FIXO =~ DMI1 + DMI2 + DMI4 + DMI6
CRESCIMENTO =~ DMI3 + DMI5 + DMI7 + DMI8
FPT=~FPT1 + FPT2 + FPT3 + FPT4 + FPT5 + FPT6 + FPT7
DMI1 ~~ DMI2
MAP~~DAP
'
fit<-cfa(model=modelo,data=banco_geral,estimator="WLSMV",ordered=T)
summary(fit,standardized=T,fit=T,rsquare=T)
## lavaan 0.6-9 ended normally after 51 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 172
##
## Number of observations 327
##
## Model Test User Model:
## Standard Robust
## Test Statistic 356.237 412.380
## Degrees of freedom 219 219
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.131
## Shift parameter 97.516
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 16523.497 7030.461
## Degrees of freedom 253 253
## P-value 0.000 0.000
## Scaling correction factor 2.401
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.992 0.971
## Tucker-Lewis Index (TLI) 0.990 0.967
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.044 0.052
## 90 Percent confidence interval - lower 0.035 0.044
## 90 Percent confidence interval - upper 0.052 0.060
## P-value RMSEA <= 0.05 0.891 0.323
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.064 0.064
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## MAP =~
## MRT7 1.000 0.804 0.804
## MRT8 0.761 0.052 14.631 0.000 0.612 0.612
## MRT9 0.996 0.042 23.903 0.000 0.801 0.801
## MRT10 1.037 0.050 20.855 0.000 0.834 0.834
## DAP =~
## MRT11 1.000 0.745 0.745
## MRT12 1.247 0.045 27.556 0.000 0.929 0.929
## MRT13 1.160 0.038 30.217 0.000 0.864 0.864
## MRT14 0.987 0.043 22.794 0.000 0.736 0.736
## FIXO =~
## DMI1 1.000 0.573 0.573
## DMI2 1.174 0.081 14.538 0.000 0.673 0.673
## DMI4 1.374 0.120 11.495 0.000 0.787 0.787
## DMI6 1.490 0.131 11.384 0.000 0.854 0.854
## CRESCIMENTO =~
## DMI3 1.000 0.835 0.835
## DMI5 0.990 0.033 29.565 0.000 0.826 0.826
## DMI7 1.042 0.033 31.543 0.000 0.870 0.870
## DMI8 0.980 0.034 28.694 0.000 0.818 0.818
## FPT =~
## FPT1 1.000 0.768 0.768
## FPT2 0.616 0.065 9.455 0.000 0.473 0.473
## FPT3 0.950 0.046 20.587 0.000 0.729 0.729
## FPT4 0.894 0.047 19.124 0.000 0.686 0.686
## FPT5 1.025 0.042 24.629 0.000 0.787 0.787
## FPT6 0.786 0.054 14.545 0.000 0.603 0.603
## FPT7 0.854 0.049 17.445 0.000 0.656 0.656
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DMI1 ~~
## .DMI2 0.305 0.043 7.178 0.000 0.305 0.503
## MAP ~~
## DAP 0.087 0.035 2.504 0.012 0.146 0.146
## FIXO -0.167 0.029 -5.679 0.000 -0.362 -0.362
## CRESCIMENTO 0.298 0.038 7.929 0.000 0.444 0.444
## FPT 0.257 0.034 7.543 0.000 0.416 0.416
## DAP ~~
## FIXO 0.042 0.026 1.623 0.105 0.099 0.099
## CRESCIMENTO 0.051 0.037 1.368 0.171 0.082 0.082
## FPT 0.049 0.033 1.500 0.134 0.086 0.086
## FIXO ~~
## CRESCIMENTO -0.383 0.042 -9.053 0.000 -0.802 -0.802
## FPT -0.013 0.027 -0.463 0.644 -0.029 -0.029
## CRESCIMENTO ~~
## FPT 0.152 0.037 4.109 0.000 0.236 0.236
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .MRT7 0.000 0.000 0.000
## .MRT8 0.000 0.000 0.000
## .MRT9 0.000 0.000 0.000
## .MRT10 0.000 0.000 0.000
## .MRT11 0.000 0.000 0.000
## .MRT12 0.000 0.000 0.000
## .MRT13 0.000 0.000 0.000
## .MRT14 0.000 0.000 0.000
## .DMI1 0.000 0.000 0.000
## .DMI2 0.000 0.000 0.000
## .DMI4 0.000 0.000 0.000
## .DMI6 0.000 0.000 0.000
## .DMI3 0.000 0.000 0.000
## .DMI5 0.000 0.000 0.000
## .DMI7 0.000 0.000 0.000
## .DMI8 0.000 0.000 0.000
## .FPT1 0.000 0.000 0.000
## .FPT2 0.000 0.000 0.000
## .FPT3 0.000 0.000 0.000
## .FPT4 0.000 0.000 0.000
## .FPT5 0.000 0.000 0.000
## .FPT6 0.000 0.000 0.000
## .FPT7 0.000 0.000 0.000
## MAP 0.000 0.000 0.000
## DAP 0.000 0.000 0.000
## FIXO 0.000 0.000 0.000
## CRESCIMENTO 0.000 0.000 0.000
## FPT 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## MRT7|t1 -2.505 0.250 -10.034 0.000 -2.505 -2.505
## MRT7|t2 -2.162 0.177 -12.251 0.000 -2.162 -2.162
## MRT7|t3 -1.719 0.123 -13.960 0.000 -1.719 -1.719
## MRT7|t4 -1.276 0.094 -13.516 0.000 -1.276 -1.276
## MRT7|t5 -0.701 0.076 -9.228 0.000 -0.701 -0.701
## MRT7|t6 -0.104 0.070 -1.491 0.136 -0.104 -0.104
## MRT8|t1 -1.686 0.120 -14.009 0.000 -1.686 -1.686
## MRT8|t2 -1.626 0.116 -14.068 0.000 -1.626 -1.626
## MRT8|t3 -1.276 0.094 -13.516 0.000 -1.276 -1.276
## MRT8|t4 -0.653 0.075 -8.698 0.000 -0.653 -0.653
## MRT8|t5 0.035 0.069 0.497 0.619 0.035 0.035
## MRT8|t6 0.663 0.075 8.805 0.000 0.663 0.663
## MRT9|t1 -2.505 0.250 -10.034 0.000 -2.505 -2.505
## MRT9|t2 -2.250 0.192 -11.736 0.000 -2.250 -2.250
## MRT9|t3 -1.754 0.126 -13.895 0.000 -1.754 -1.754
## MRT9|t4 -1.226 0.092 -13.306 0.000 -1.226 -1.226
## MRT9|t5 -0.597 0.074 -8.057 0.000 -0.597 -0.597
## MRT9|t6 -0.073 0.069 -1.049 0.294 -0.073 -0.073
## MRT10|t1 -2.250 0.192 -11.736 0.000 -2.250 -2.250
## MRT10|t2 -1.919 0.143 -13.410 0.000 -1.919 -1.919
## MRT10|t3 -1.598 0.113 -14.080 0.000 -1.598 -1.598
## MRT10|t4 -1.050 0.085 -12.314 0.000 -1.050 -1.050
## MRT10|t5 -0.389 0.071 -5.452 0.000 -0.389 -0.389
## MRT10|t6 0.181 0.070 2.594 0.009 0.181 0.181
## MRT11|t1 -0.615 0.074 -8.272 0.000 -0.615 -0.615
## MRT11|t2 -0.356 0.071 -5.014 0.000 -0.356 -0.356
## MRT11|t3 -0.158 0.070 -2.263 0.024 -0.158 -0.158
## MRT11|t4 0.348 0.071 4.904 0.000 0.348 0.348
## MRT11|t5 0.731 0.077 9.543 0.000 0.731 0.731
## MRT11|t6 1.105 0.087 12.667 0.000 1.105 1.105
## MRT12|t1 -0.741 0.077 -9.647 0.000 -0.741 -0.741
## MRT12|t2 -0.482 0.072 -6.652 0.000 -0.482 -0.482
## MRT12|t3 -0.228 0.070 -3.255 0.001 -0.228 -0.228
## MRT12|t4 0.348 0.071 4.904 0.000 0.348 0.348
## MRT12|t5 0.751 0.077 9.752 0.000 0.751 0.751
## MRT12|t6 1.077 0.086 12.493 0.000 1.077 1.077
## MRT13|t1 -0.490 0.073 -6.761 0.000 -0.490 -0.490
## MRT13|t2 -0.260 0.070 -3.695 0.000 -0.260 -0.260
## MRT13|t3 0.050 0.069 0.718 0.473 0.050 0.050
## MRT13|t4 0.588 0.074 7.950 0.000 0.588 0.588
## MRT13|t5 0.925 0.081 11.372 0.000 0.925 0.925
## MRT13|t6 1.179 0.090 13.078 0.000 1.179 1.179
## MRT14|t1 -1.194 0.091 -13.156 0.000 -1.194 -1.194
## MRT14|t2 -0.792 0.078 -10.166 0.000 -0.792 -0.792
## MRT14|t3 -0.543 0.073 -7.411 0.000 -0.543 -0.543
## MRT14|t4 -0.081 0.069 -1.159 0.246 -0.081 -0.081
## MRT14|t5 0.332 0.071 4.685 0.000 0.332 0.332
## MRT14|t6 0.634 0.075 8.485 0.000 0.634 0.634
## DMI1|t1 0.324 0.071 4.575 0.000 0.324 0.324
## DMI1|t2 0.653 0.075 8.698 0.000 0.653 0.653
## DMI1|t3 0.857 0.080 10.777 0.000 0.857 0.857
## DMI1|t4 0.986 0.083 11.853 0.000 0.986 0.986
## DMI1|t5 1.276 0.094 13.516 0.000 1.276 1.276
## DMI1|t6 1.473 0.105 14.019 0.000 1.473 1.473
## DMI2|t1 0.096 0.070 1.380 0.168 0.096 0.096
## DMI2|t2 0.625 0.075 8.379 0.000 0.625 0.625
## DMI2|t3 0.879 0.080 10.977 0.000 0.879 0.879
## DMI2|t4 1.119 0.088 12.752 0.000 1.119 1.119
## DMI2|t5 1.473 0.105 14.019 0.000 1.473 1.473
## DMI2|t6 1.754 0.126 13.895 0.000 1.754 1.754
## DMI4|t1 0.065 0.069 0.939 0.348 0.065 0.065
## DMI4|t2 0.490 0.073 6.761 0.000 0.490 0.490
## DMI4|t3 0.751 0.077 9.752 0.000 0.751 0.751
## DMI4|t4 1.134 0.088 12.836 0.000 1.134 1.134
## DMI4|t5 1.408 0.101 13.909 0.000 1.408 1.408
## DMI4|t6 1.686 0.120 14.009 0.000 1.686 1.686
## DMI6|t1 0.073 0.069 1.049 0.294 0.073 0.073
## DMI6|t2 0.499 0.073 6.869 0.000 0.499 0.499
## DMI6|t3 0.846 0.079 10.676 0.000 0.846 0.846
## DMI6|t4 1.024 0.084 12.132 0.000 1.024 1.024
## DMI6|t5 1.330 0.097 13.704 0.000 1.330 1.330
## DMI6|t6 1.754 0.126 13.895 0.000 1.754 1.754
## DMI3|t1 -1.872 0.138 -13.573 0.000 -1.872 -1.872
## DMI3|t2 -1.598 0.113 -14.080 0.000 -1.598 -1.598
## DMI3|t3 -1.330 0.097 -13.704 0.000 -1.330 -1.330
## DMI3|t4 -1.050 0.085 -12.314 0.000 -1.050 -1.050
## DMI3|t5 -0.663 0.075 -8.805 0.000 -0.663 -0.663
## DMI3|t6 -0.220 0.070 -3.145 0.002 -0.220 -0.220
## DMI5|t1 -1.754 0.126 -13.895 0.000 -1.754 -1.754
## DMI5|t2 -1.626 0.116 -14.068 0.000 -1.626 -1.626
## DMI5|t3 -1.349 0.098 -13.760 0.000 -1.349 -1.349
## DMI5|t4 -0.974 0.083 -11.758 0.000 -0.974 -0.974
## DMI5|t5 -0.644 0.075 -8.592 0.000 -0.644 -0.644
## DMI5|t6 -0.158 0.070 -2.263 0.024 -0.158 -0.158
## DMI7|t1 -2.026 0.156 -12.959 0.000 -2.026 -2.026
## DMI7|t2 -1.719 0.123 -13.960 0.000 -1.719 -1.719
## DMI7|t3 -1.368 0.099 -13.814 0.000 -1.368 -1.368
## DMI7|t4 -1.091 0.087 -12.580 0.000 -1.091 -1.091
## DMI7|t5 -0.663 0.075 -8.805 0.000 -0.663 -0.663
## DMI7|t6 -0.197 0.070 -2.814 0.005 -0.197 -0.197
## DMI8|t1 -2.089 0.165 -12.647 0.000 -2.089 -2.089
## DMI8|t2 -1.872 0.138 -13.573 0.000 -1.872 -1.872
## DMI8|t3 -1.520 0.108 -14.065 0.000 -1.520 -1.520
## DMI8|t4 -1.134 0.088 -12.836 0.000 -1.134 -1.134
## DMI8|t5 -0.606 0.074 -8.164 0.000 -0.606 -0.606
## DMI8|t6 -0.027 0.069 -0.387 0.699 -0.027 -0.027
## FPT1|t1 -2.026 0.156 -12.959 0.000 -2.026 -2.026
## FPT1|t2 -1.545 0.110 -14.078 0.000 -1.545 -1.545
## FPT1|t3 -1.294 0.095 -13.582 0.000 -1.294 -1.294
## FPT1|t4 -0.846 0.079 -10.676 0.000 -0.846 -0.846
## FPT1|t5 -0.212 0.070 -3.035 0.002 -0.212 -0.212
## FPT1|t6 0.543 0.073 7.411 0.000 0.543 0.543
## FPT2|t1 -2.741 0.329 -8.345 0.000 -2.741 -2.741
## FPT2|t2 -2.359 0.214 -11.040 0.000 -2.359 -2.359
## FPT2|t3 -2.089 0.165 -12.647 0.000 -2.089 -2.089
## FPT2|t4 -1.496 0.107 -14.045 0.000 -1.496 -1.496
## FPT2|t5 -0.782 0.078 -10.063 0.000 -0.782 -0.782
## FPT2|t6 -0.065 0.069 -0.939 0.348 -0.065 -0.065
## FPT3|t1 -2.089 0.165 -12.647 0.000 -2.089 -2.089
## FPT3|t2 -1.655 0.118 -14.044 0.000 -1.655 -1.655
## FPT3|t3 -1.077 0.086 -12.493 0.000 -1.077 -1.077
## FPT3|t4 -0.606 0.074 -8.164 0.000 -0.606 -0.606
## FPT3|t5 0.324 0.071 4.575 0.000 0.324 0.324
## FPT3|t6 1.011 0.084 12.040 0.000 1.011 1.011
## FPT4|t1 -1.473 0.105 -14.019 0.000 -1.473 -1.473
## FPT4|t2 -1.037 0.085 -12.224 0.000 -1.037 -1.037
## FPT4|t3 -0.499 0.073 -6.869 0.000 -0.499 -0.499
## FPT4|t4 0.111 0.070 1.601 0.109 0.111 0.111
## FPT4|t5 0.663 0.075 8.805 0.000 0.663 0.663
## FPT4|t6 1.368 0.099 13.814 0.000 1.368 1.368
## FPT5|t1 -1.969 0.149 -13.209 0.000 -1.969 -1.969
## FPT5|t2 -1.545 0.110 -14.078 0.000 -1.545 -1.545
## FPT5|t3 -0.925 0.081 -11.372 0.000 -0.925 -0.925
## FPT5|t4 -0.422 0.072 -5.889 0.000 -0.422 -0.422
## FPT5|t5 0.348 0.071 4.904 0.000 0.348 0.348
## FPT5|t6 1.024 0.084 12.132 0.000 1.024 1.024
## FPT6|t1 -1.242 0.093 -13.379 0.000 -1.242 -1.242
## FPT6|t2 -1.037 0.085 -12.224 0.000 -1.037 -1.037
## FPT6|t3 -0.543 0.073 -7.411 0.000 -0.543 -0.543
## FPT6|t4 -0.150 0.070 -2.153 0.031 -0.150 -0.150
## FPT6|t5 0.340 0.071 4.794 0.000 0.340 0.340
## FPT6|t6 0.891 0.080 11.077 0.000 0.891 0.891
## FPT7|t1 -1.210 0.091 -13.232 0.000 -1.210 -1.210
## FPT7|t2 -0.803 0.078 -10.269 0.000 -0.803 -0.803
## FPT7|t3 -0.268 0.070 -3.805 0.000 -0.268 -0.268
## FPT7|t4 0.212 0.070 3.035 0.002 0.212 0.212
## FPT7|t5 0.741 0.077 9.647 0.000 0.741 0.741
## FPT7|t6 1.312 0.096 13.644 0.000 1.312 1.312
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .MRT7 0.353 0.353 0.353
## .MRT8 0.626 0.626 0.626
## .MRT9 0.359 0.359 0.359
## .MRT10 0.305 0.305 0.305
## .MRT11 0.445 0.445 0.445
## .MRT12 0.137 0.137 0.137
## .MRT13 0.254 0.254 0.254
## .MRT14 0.459 0.459 0.459
## .DMI1 0.672 0.672 0.672
## .DMI2 0.548 0.548 0.548
## .DMI4 0.380 0.380 0.380
## .DMI6 0.271 0.271 0.271
## .DMI3 0.303 0.303 0.303
## .DMI5 0.317 0.317 0.317
## .DMI7 0.244 0.244 0.244
## .DMI8 0.331 0.331 0.331
## .FPT1 0.410 0.410 0.410
## .FPT2 0.776 0.776 0.776
## .FPT3 0.468 0.468 0.468
## .FPT4 0.529 0.529 0.529
## .FPT5 0.381 0.381 0.381
## .FPT6 0.636 0.636 0.636
## .FPT7 0.570 0.570 0.570
## MAP 0.647 0.043 15.011 0.000 1.000 1.000
## DAP 0.555 0.038 14.624 0.000 1.000 1.000
## FIXO 0.328 0.054 6.034 0.000 1.000 1.000
## CRESCIMENTO 0.697 0.040 17.587 0.000 1.000 1.000
## FPT 0.590 0.041 14.257 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## MRT7 1.000 1.000 1.000
## MRT8 1.000 1.000 1.000
## MRT9 1.000 1.000 1.000
## MRT10 1.000 1.000 1.000
## MRT11 1.000 1.000 1.000
## MRT12 1.000 1.000 1.000
## MRT13 1.000 1.000 1.000
## MRT14 1.000 1.000 1.000
## DMI1 1.000 1.000 1.000
## DMI2 1.000 1.000 1.000
## DMI4 1.000 1.000 1.000
## DMI6 1.000 1.000 1.000
## DMI3 1.000 1.000 1.000
## DMI5 1.000 1.000 1.000
## DMI7 1.000 1.000 1.000
## DMI8 1.000 1.000 1.000
## FPT1 1.000 1.000 1.000
## FPT2 1.000 1.000 1.000
## FPT3 1.000 1.000 1.000
## FPT4 1.000 1.000 1.000
## FPT5 1.000 1.000 1.000
## FPT6 1.000 1.000 1.000
## FPT7 1.000 1.000 1.000
##
## R-Square:
## Estimate
## MRT7 0.647
## MRT8 0.374
## MRT9 0.641
## MRT10 0.695
## MRT11 0.555
## MRT12 0.863
## MRT13 0.746
## MRT14 0.541
## DMI1 0.328
## DMI2 0.452
## DMI4 0.620
## DMI6 0.729
## DMI3 0.697
## DMI5 0.683
## DMI7 0.756
## DMI8 0.669
## FPT1 0.590
## FPT2 0.224
## FPT3 0.532
## FPT4 0.471
## FPT5 0.619
## FPT6 0.364
## FPT7 0.430
semTools::reliability(fit)
## For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord" and the response by Zumbo & Kroc (2019). Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.
## MAP DAP FIXO CRESCIMENTO FPT
## alpha 0.7581073 0.8504509 0.7570879 0.8392356 0.8184692
## alpha.ord 0.8425892 0.8879304 0.8435195 0.9021933 0.8453989
## omega 0.8016734 0.8692052 0.7074828 0.8647086 0.8345871
## omega2 0.8016734 0.8692052 0.7074828 0.8647086 0.8345871
## omega3 0.8083108 0.8718638 0.7029603 0.8660657 0.8421198
## avevar 0.5893862 0.6764462 0.5322326 0.7011917 0.4613299
semTools::discriminantValidity(fit)
## Some of the latent variable variances are estimated instead of fixed to 1. The model is re-estimated by scaling the latent variables by fixing their variances and freeing all factor loadings.
## Warning in lav_object_post_check(object): lavaan WARNING: covariance matrix of latent variables
## is not positive definite;
## use lavInspect(fit, "cov.lv") to investigate.
## Warning in lav_object_post_check(object): lavaan WARNING: covariance matrix of latent variables
## is not positive definite;
## use lavInspect(fit, "cov.lv") to investigate.
## Warning in lav_object_post_check(object): lavaan WARNING: covariance matrix of latent variables
## is not positive definite;
## use lavInspect(fit, "cov.lv") to investigate.
## Warning in lav_object_post_check(object): lavaan WARNING: covariance matrix of latent variables
## is not positive definite;
## use lavInspect(fit, "cov.lv") to investigate.
## Warning in lav_object_post_check(object): lavaan WARNING: covariance matrix of latent variables
## is not positive definite;
## use lavInspect(fit, "cov.lv") to investigate.
## Warning in lav_object_post_check(object): lavaan WARNING: covariance matrix of latent variables
## is not positive definite;
## use lavInspect(fit, "cov.lv") to investigate.
## Warning in lav_object_post_check(object): lavaan WARNING: covariance matrix of latent variables
## is not positive definite;
## use lavInspect(fit, "cov.lv") to investigate.
## lhs op rhs est ci.lower ci.upper Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## 1 MAP ~~ DAP 0.14593377 0.03493199 0.25693554 220 NA NA 1175.0309 174.67626 1 7.045364e-40
## 2 MAP ~~ FIXO -0.36197800 -0.46605290 -0.25790310 220 NA NA 640.1636 95.56837 1 1.428708e-22
## 3 MAP ~~ CRESCIMENTO 0.44393295 0.34825346 0.53961244 220 NA NA 680.4071 90.83328 1 1.563022e-21
## 4 MAP ~~ FPT 0.41565907 0.32405352 0.50726462 220 NA NA 790.6365 110.69806 1 6.890536e-26
## 5 DAP ~~ FIXO 0.09921869 -0.01921834 0.21765571 220 NA NA 1002.2764 133.05007 1 8.816006e-31
## 6 DAP ~~ CRESCIMENTO 0.08186309 -0.03452932 0.19825550 220 NA NA 1376.6949 176.28673 1 3.134896e-40
## 7 DAP ~~ FPT 0.08615814 -0.02507110 0.19738737 220 NA NA 1754.8554 178.40073 1 1.082962e-40
## 8 FIXO ~~ CRESCIMENTO -0.80169941 -0.86767176 -0.73572707 220 NA NA 371.4012 8.89524 1 2.859150e-03
## 9 FIXO ~~ FPT -0.02880366 -0.15138879 0.09378147 220 NA NA 1178.4054 136.60914 1 1.468182e-31
## 10 CRESCIMENTO ~~ FPT 0.23641266 0.12655595 0.34626937 220 NA NA 1211.3634 132.92039 1 9.411095e-31
Correlações entre as variáveis
round(lavInspect(fit,"cor.lv"),2)
## MAP DAP FIXO CRESCI FPT
## MAP 1.00
## DAP 0.15 1.00
## FIXO -0.36 0.10 1.00
## CRESCIMENTO 0.44 0.08 -0.80 1.00
## FPT 0.42 0.09 -0.03 0.24 1.00
Significância das correlações
pvalue<-parameterestimates(fit,standardized = T)
pvalue<-pvalue[(pvalue$op=="~~"&(pvalue$lhs!=pvalue$rhs)),c(1:3,7)]
pvalue[c(-1:-2),]
## lhs op rhs pvalue
## 192 MAP ~~ FIXO 0.000
## 193 MAP ~~ CRESCIMENTO 0.000
## 194 MAP ~~ FPT 0.000
## 195 DAP ~~ FIXO 0.105
## 196 DAP ~~ CRESCIMENTO 0.171
## 197 DAP ~~ FPT 0.134
## 198 FIXO ~~ CRESCIMENTO 0.000
## 199 FIXO ~~ FPT 0.644
## 200 CRESCIMENTO ~~ FPT 0.000
banco_moderacao<-predict(fit)
write.csv(banco_moderacao,"professores.csv")
Modelo Mediacional Mindset FIXO
modelo<-
'
# Medida
MAP =~ MRT7 + MRT8 + MRT9 + MRT10
DAP =~ MRT11 + MRT12 + MRT13 + MRT14
FIXO =~ DMI1 + DMI2 + DMI4 + DMI6
DMI1 ~~ DMI2
FPT=~FPT1 + FPT2 + FPT3 + FPT4 + FPT5 + FPT6 + FPT7
# Estrutural
MAP ~ c1.1*FIXO + b1.1*FPT
DAP ~ c1.2*FIXO + b1.2*FPT
FPT ~ a1.1*FIXO
#Correlated Outcome
MAP~~DAP
#Constrains
# A trajectory
FIXO_FPT := a1.1
# B trajectory
FPT_MAP := b1.1
# AB Indirect effect
# Mediação FIXO -> FPT -> MAP
FIXO_FPT_MAP := a1.1*b1.1
# C reduced on M
FIXO_MAP_R := c1.1
# C total effect
FIXO_MAP := (c1.1) + (FIXO_FPT_MAP)
# B trajectory
FPT_DAP := b1.2
# AB Indirect effect
# Mediação FIXO -> FPT -> DAP
FIXO_FPT_DAP := a1.1*b1.2
# C reduced on M
FIXO_DAP_R := c1.2
# C total effect
FIXO_DAP := (c1.2) + (FIXO_FPT_DAP)
'
#fit<-lavaan::cfa(model=modelo,data=banco_geral,estimator="DWLS",ordered=T,orthogonal=T,se="bootstrap", test="scaled.shifted",verbose=TRUE)
fit<-lavaan::cfa(model=modelo,data=banco_geral,estimator = "ML", test = "yuan.bentler.mplus", se = "bootstrap",verbose=F)
summary(fit,standardized=T,fit=T,rsquare=T)
## lavaan 0.6-9 ended normally after 51 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 45
##
## Number of observations 327
##
## Model Test User Model:
## Standard Robust
## Test Statistic 249.510 218.462
## Degrees of freedom 145 145
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.142
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 2224.853 1776.799
## Degrees of freedom 171 171
## P-value 0.000 0.000
## Scaling correction factor 1.252
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.949 0.954
## Tucker-Lewis Index (TLI) 0.940 0.946
##
## Robust Comparative Fit Index (CFI) 0.958
## Robust Tucker-Lewis Index (TLI) 0.951
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -10809.711 -10809.711
## Scaling correction factor 1.573
## for the MLR correction
## Loglikelihood unrestricted model (H1) -10684.955 -10684.955
## Scaling correction factor 1.244
## for the MLR correction
##
## Akaike (AIC) 21709.421 21709.421
## Bayesian (BIC) 21879.970 21879.970
## Sample-size adjusted Bayesian (BIC) 21737.232 21737.232
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.047 0.039
## 90 Percent confidence interval - lower 0.037 0.029
## 90 Percent confidence interval - upper 0.057 0.049
## P-value RMSEA <= 0.05 0.686 0.965
##
## Robust RMSEA 0.042
## 90 Percent confidence interval - lower 0.030
## 90 Percent confidence interval - upper 0.053
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.054 0.054
##
## Parameter Estimates:
##
## Standard errors Bootstrap
## Number of requested bootstrap draws 1000
## Number of successful bootstrap draws 1000
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## MAP =~
## MRT7 1.000 0.878 0.730
## MRT8 0.863 0.126 6.833 0.000 0.757 0.499
## MRT9 0.980 0.119 8.215 0.000 0.860 0.710
## MRT10 1.167 0.191 6.113 0.000 1.025 0.770
## DAP =~
## MRT11 1.000 1.459 0.689
## MRT12 1.258 0.099 12.649 0.000 1.837 0.896
## MRT13 1.159 0.100 11.578 0.000 1.691 0.820
## MRT14 0.944 0.098 9.673 0.000 1.377 0.674
## FIXO =~
## DMI1 1.000 0.961 0.507
## DMI2 1.148 0.134 8.589 0.000 1.104 0.653
## DMI4 1.204 0.223 5.394 0.000 1.158 0.662
## DMI6 1.322 0.284 4.655 0.000 1.271 0.719
## FPT =~
## FPT1 1.000 1.059 0.720
## FPT2 0.341 0.074 4.589 0.000 0.361 0.347
## FPT3 0.908 0.079 11.532 0.000 0.961 0.696
## FPT4 1.019 0.102 9.957 0.000 1.078 0.658
## FPT5 1.083 0.084 12.834 0.000 1.146 0.780
## FPT6 1.040 0.135 7.715 0.000 1.100 0.584
## FPT7 1.039 0.113 9.209 0.000 1.100 0.618
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## MAP ~
## FIXO (c1.1) -0.240 0.089 -2.679 0.007 -0.263 -0.263
## FPT (b1.1) 0.308 0.062 4.951 0.000 0.371 0.371
## DAP ~
## FIXO (c1.2) 0.121 0.110 1.091 0.275 0.079 0.079
## FPT (b1.2) 0.103 0.100 1.033 0.302 0.075 0.075
## FPT ~
## FIXO (a1.1) 0.042 0.096 0.439 0.660 0.038 0.038
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DMI1 ~~
## .DMI2 0.748 0.244 3.063 0.002 0.748 0.358
## .MAP ~~
## .DAP 0.142 0.079 1.782 0.075 0.124 0.124
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .MRT7 0.673 0.105 6.440 0.000 0.673 0.466
## .MRT8 1.734 0.222 7.800 0.000 1.734 0.751
## .MRT9 0.727 0.114 6.402 0.000 0.727 0.496
## .MRT10 0.719 0.221 3.249 0.001 0.719 0.407
## .MRT11 2.356 0.286 8.250 0.000 2.356 0.525
## .MRT12 0.825 0.206 3.996 0.000 0.825 0.197
## .MRT13 1.390 0.220 6.306 0.000 1.390 0.327
## .MRT14 2.284 0.230 9.947 0.000 2.284 0.546
## .DMI1 2.671 0.396 6.751 0.000 2.671 0.743
## .DMI2 1.637 0.301 5.436 0.000 1.637 0.573
## .DMI4 1.717 0.304 5.651 0.000 1.717 0.562
## .DMI6 1.514 0.348 4.352 0.000 1.514 0.484
## .FPT1 1.044 0.112 9.299 0.000 1.044 0.482
## .FPT2 0.953 0.117 8.162 0.000 0.953 0.880
## .FPT3 0.982 0.112 8.775 0.000 0.982 0.515
## .FPT4 1.522 0.176 8.624 0.000 1.522 0.567
## .FPT5 0.847 0.110 7.679 0.000 0.847 0.392
## .FPT6 2.344 0.258 9.093 0.000 2.344 0.659
## .FPT7 1.961 0.215 9.108 0.000 1.961 0.618
## .MAP 0.617 0.138 4.459 0.000 0.801 0.801
## .DAP 2.104 0.304 6.915 0.000 0.988 0.988
## FIXO 0.924 0.298 3.102 0.002 1.000 1.000
## .FPT 1.119 0.178 6.297 0.000 0.999 0.999
##
## R-Square:
## Estimate
## MRT7 0.534
## MRT8 0.249
## MRT9 0.504
## MRT10 0.593
## MRT11 0.475
## MRT12 0.803
## MRT13 0.673
## MRT14 0.454
## DMI1 0.257
## DMI2 0.427
## DMI4 0.438
## DMI6 0.516
## FPT1 0.518
## FPT2 0.120
## FPT3 0.485
## FPT4 0.433
## FPT5 0.608
## FPT6 0.341
## FPT7 0.382
## MAP 0.199
## DAP 0.012
## FPT 0.001
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## FIXO_FPT 0.042 0.096 0.439 0.661 0.038 0.038
## FPT_MAP 0.308 0.062 4.949 0.000 0.371 0.371
## FIXO_FPT_MAP 0.013 0.029 0.450 0.653 0.014 0.014
## FIXO_MAP_R -0.240 0.090 -2.678 0.007 -0.263 -0.263
## FIXO_MAP -0.227 0.102 -2.223 0.026 -0.248 -0.248
## FPT_DAP 0.103 0.100 1.033 0.302 0.075 0.075
## FIXO_FPT_DAP 0.004 0.013 0.325 0.745 0.003 0.003
## FIXO_DAP_R 0.121 0.111 1.091 0.275 0.079 0.079
## FIXO_DAP 0.125 0.110 1.135 0.256 0.082 0.082
pvalue<-parameterEstimates(fit, boot.ci.type = "perc",standardized = T)
pvalue<-pvalue[(pvalue$op==":="&(pvalue$lhs!=pvalue$rhs)),c(1,5,8,9,10,11)]
pvalue
## lhs est pvalue ci.lower ci.upper std.lv
## 50 FIXO_FPT 0.042 0.661 -0.192 0.192 0.038
## 51 FPT_MAP 0.308 0.000 0.183 0.429 0.371
## 52 FIXO_FPT_MAP 0.013 0.653 -0.057 0.057 0.014
## 53 FIXO_MAP_R -0.240 0.007 -0.468 -0.107 -0.263
## 54 FIXO_MAP -0.227 0.026 -0.485 -0.082 -0.248
## 55 FPT_DAP 0.103 0.302 -0.083 0.316 0.075
## 56 FIXO_FPT_DAP 0.004 0.745 -0.028 0.028 0.003
## 57 FIXO_DAP_R 0.121 0.275 -0.095 0.337 0.079
## 58 FIXO_DAP 0.125 0.256 -0.102 0.333 0.082
semPlot::semPaths(fit,what = "path",exoVar = FALSE,residuals = F,intercepts = F, thresholds = F, exoCov = F,structural = T,whatLabels = "std",layout = "tree3",curvature = 0,reorder=T,rotation=2)
Modelo Mediacional Mindset CRESCIMENTO
modelo<-
'
# Medida
MAP =~ MRT7 + MRT8 + MRT9 + MRT10
DAP =~ MRT11 + MRT12 + MRT13 + MRT14
CRESCIMENTO =~ DMI3 + DMI5 + DMI7 + DMI8
FPT=~FPT1 + FPT2 + FPT3 + FPT4 + FPT5 + FPT6 + FPT7
# Estrutural
MAP ~ c1.1*CRESCIMENTO + b1.1*FPT
DAP ~ c1.2*CRESCIMENTO + b1.2*FPT
FPT ~ a1.1*CRESCIMENTO
#Correlated Outcome
MAP~~DAP
#Constrains
# A trajectory
CRES_FPT := a1.1
# B trajectory
FPT_MAP := b1.1
# AB Indirect effect
# Mediação CRES -> FPT -> MAP
CRES_FPT_MAP := a1.1*b1.1
# C reduced on M
CRES_MAP_R := c1.1
# C total effect
CRES_MAP := (c1.1) + (CRES_FPT_MAP)
# B trajectory
FPT_DAP := b1.2
# AB Indirect effect
# Mediação CRES -> FPT -> DAP
CRES_FPT_DAP := a1.1*b1.2
# C reduced on M
CRES_DAP_R := c1.2
# C total effect
CRES_DAP := (c1.2) + (CRES_FPT_DAP)
'
#fit<-lavaan::cfa(model=modelo,data=banco_geral,estimator="DWLS",ordered=T,orthogonal=T,se="bootstrap", test="scaled.shifted",verbose=F)
fit<-lavaan::sem(model=modelo,data=banco_geral,estimator="ML",ordered=F,orthogonal=T,se="bootstrap", test="yuan.bentler.mplus",verbose=F)
summary(fit,standardized=T,fit=T,rsquare=T)
## lavaan 0.6-9 ended normally after 41 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 44
##
## Number of observations 327
##
## Model Test User Model:
## Standard Robust
## Test Statistic 231.448 200.962
## Degrees of freedom 146 146
## P-value (Chi-square) 0.000 0.002
## Scaling correction factor 1.152
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 2398.966 1870.834
## Degrees of freedom 171 171
## P-value 0.000 0.000
## Scaling correction factor 1.282
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.962 0.968
## Tucker-Lewis Index (TLI) 0.955 0.962
##
## Robust Comparative Fit Index (CFI) 0.971
## Robust Tucker-Lewis Index (TLI) 0.966
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -10483.744 -10483.744
## Scaling correction factor 1.732
## for the MLR correction
## Loglikelihood unrestricted model (H1) -10368.020 -10368.020
## Scaling correction factor 1.286
## for the MLR correction
##
## Akaike (AIC) 21055.488 21055.488
## Bayesian (BIC) 21222.246 21222.246
## Sample-size adjusted Bayesian (BIC) 21082.680 21082.680
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.042 0.034
## 90 Percent confidence interval - lower 0.032 0.022
## 90 Percent confidence interval - upper 0.052 0.044
## P-value RMSEA <= 0.05 0.893 0.996
##
## Robust RMSEA 0.036
## 90 Percent confidence interval - lower 0.023
## 90 Percent confidence interval - upper 0.048
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.047 0.047
##
## Parameter Estimates:
##
## Standard errors Bootstrap
## Number of requested bootstrap draws 1000
## Number of successful bootstrap draws 1000
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## MAP =~
## MRT7 1.000 0.868 0.722
## MRT8 0.876 0.127 6.916 0.000 0.761 0.501
## MRT9 0.991 0.114 8.689 0.000 0.860 0.710
## MRT10 1.190 0.180 6.603 0.000 1.033 0.777
## DAP =~
## MRT11 1.000 1.459 0.689
## MRT12 1.262 0.098 12.916 0.000 1.842 0.899
## MRT13 1.156 0.095 12.224 0.000 1.687 0.818
## MRT14 0.943 0.094 9.987 0.000 1.376 0.673
## CRESCIMENTO =~
## DMI3 1.000 1.163 0.751
## DMI5 0.988 0.099 10.031 0.000 1.149 0.730
## DMI7 1.048 0.086 12.129 0.000 1.219 0.829
## DMI8 0.828 0.101 8.160 0.000 0.963 0.705
## FPT =~
## FPT1 1.000 1.061 0.721
## FPT2 0.343 0.078 4.427 0.000 0.364 0.350
## FPT3 0.909 0.079 11.454 0.000 0.964 0.699
## FPT4 1.019 0.096 10.579 0.000 1.081 0.660
## FPT5 1.077 0.084 12.816 0.000 1.143 0.777
## FPT6 1.033 0.128 8.053 0.000 1.096 0.581
## FPT7 1.034 0.114 9.096 0.000 1.097 0.616
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## MAP ~
## CRESCIM (c1.1) 0.202 0.061 3.298 0.001 0.270 0.270
## FPT (b1.1) 0.258 0.060 4.283 0.000 0.316 0.316
## DAP ~
## CRESCIM (c1.2) 0.119 0.081 1.478 0.139 0.095 0.095
## FPT (b1.2) 0.086 0.093 0.922 0.357 0.062 0.062
## FPT ~
## CRESCIM (a1.1) 0.152 0.067 2.274 0.023 0.166 0.166
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .MAP ~~
## .DAP 0.084 0.079 1.058 0.290 0.075 0.075
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .MRT7 0.690 0.100 6.909 0.000 0.690 0.478
## .MRT8 1.729 0.230 7.527 0.000 1.729 0.749
## .MRT9 0.727 0.118 6.153 0.000 0.727 0.496
## .MRT10 0.702 0.219 3.196 0.001 0.702 0.397
## .MRT11 2.357 0.277 8.523 0.000 2.357 0.525
## .MRT12 0.807 0.196 4.122 0.000 0.807 0.192
## .MRT13 1.406 0.233 6.027 0.000 1.406 0.331
## .MRT14 2.288 0.230 9.944 0.000 2.288 0.547
## .DMI3 1.048 0.227 4.612 0.000 1.048 0.436
## .DMI5 1.160 0.252 4.603 0.000 1.160 0.468
## .DMI7 0.678 0.155 4.370 0.000 0.678 0.313
## .DMI8 0.941 0.167 5.640 0.000 0.941 0.504
## .FPT1 1.039 0.112 9.274 0.000 1.039 0.480
## .FPT2 0.951 0.122 7.785 0.000 0.951 0.878
## .FPT3 0.976 0.113 8.657 0.000 0.976 0.512
## .FPT4 1.516 0.173 8.758 0.000 1.516 0.565
## .FPT5 0.856 0.110 7.815 0.000 0.856 0.396
## .FPT6 2.354 0.256 9.210 0.000 2.354 0.662
## .FPT7 1.968 0.210 9.390 0.000 1.968 0.621
## .MAP 0.602 0.143 4.214 0.000 0.799 0.799
## .DAP 2.098 0.300 6.990 0.000 0.985 0.985
## CRESCIMENTO 1.353 0.254 5.327 0.000 1.000 1.000
## .FPT 1.094 0.176 6.204 0.000 0.972 0.972
##
## R-Square:
## Estimate
## MRT7 0.522
## MRT8 0.251
## MRT9 0.504
## MRT10 0.603
## MRT11 0.475
## MRT12 0.808
## MRT13 0.669
## MRT14 0.453
## DMI3 0.564
## DMI5 0.532
## DMI7 0.687
## DMI8 0.496
## FPT1 0.520
## FPT2 0.122
## FPT3 0.488
## FPT4 0.435
## FPT5 0.604
## FPT6 0.338
## FPT7 0.379
## MAP 0.201
## DAP 0.015
## FPT 0.028
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## CRES_FPT 0.152 0.067 2.273 0.023 0.166 0.166
## FPT_MAP 0.258 0.060 4.281 0.000 0.316 0.316
## CRES_FPT_MAP 0.039 0.020 1.957 0.050 0.052 0.052
## CRES_MAP_R 0.202 0.061 3.297 0.001 0.270 0.270
## CRES_MAP 0.241 0.069 3.507 0.000 0.323 0.323
## FPT_DAP 0.086 0.093 0.921 0.357 0.062 0.062
## CRES_FPT_DAP 0.013 0.018 0.732 0.464 0.010 0.010
## CRES_DAP_R 0.119 0.081 1.478 0.140 0.095 0.095
## CRES_DAP 0.132 0.079 1.681 0.093 0.105 0.105
pvalue<-parameterEstimates(fit, boot.ci.type = "perc",standardized = T)
pvalue<-pvalue[(pvalue$op==":="&(pvalue$lhs!=pvalue$rhs)),c(1,5,8,9,10,11)]
pvalue
## lhs est pvalue ci.lower ci.upper std.lv
## 49 CRES_FPT 0.152 0.023 0.034 0.298 0.166
## 50 FPT_MAP 0.258 0.000 0.156 0.390 0.316
## 51 CRES_FPT_MAP 0.039 0.050 0.009 0.090 0.052
## 52 CRES_MAP_R 0.202 0.001 0.097 0.339 0.270
## 53 CRES_MAP 0.241 0.000 0.124 0.395 0.323
## 54 FPT_DAP 0.086 0.357 -0.096 0.267 0.062
## 55 CRES_FPT_DAP 0.013 0.464 -0.015 0.056 0.010
## 56 CRES_DAP_R 0.119 0.140 -0.043 0.279 0.095
## 57 CRES_DAP 0.132 0.093 -0.023 0.291 0.105
semPlot::semPaths(fit,what = "path",exoVar = FALSE,residuals = F,intercepts = F, thresholds = F, exoCov = F,structural = T,whatLabels = "std",layout = "tree3",curvature = 0,reorder=T,rotation=2)
Terceiro passo criar um modelo estrutal - geral
modelo<-
'
# Medida
MAP =~ MRT7 + MRT8 + MRT9 + MRT10
DAP =~ MRT11 + MRT12 + MRT13 + MRT14
FIXO =~ DMI1 + DMI2 + DMI4 + DMI6
CRESCIMENTO =~ DMI3 + DMI5 + DMI7 + DMI8
FPT=~FPT1 + FPT2 + FPT3 + FPT4 + FPT5 + FPT6 + FPT7
DMI1 ~~ DMI2
#DMI4 ~~ DMI6
# Estrutural
MAP ~ c1.1*FIXO + c2.1*CRESCIMENTO + b1.1*FPT
DAP ~ c1.2*FIXO + c2.2*CRESCIMENTO + b1.2*FPT
FPT ~ a1.1*FIXO + a2.1*CRESCIMENTO
#Correlated Outcome
MAP~~DAP
#Correlated Predictors
FIXO ~~ CRESCIMENTO
#Constrains CRES
# A trajectory
CRES_FPT := a2.1
# B trajectory
FPT_MAP := b1.1
# AB Indirect effect
# Mediação CRES -> FPT -> MAP
CRES_FPT_MAP := a2.1*b1.1
# C reduced on M
CRES_MAP_R := c2.1
# C total effect
CRES_MAP := (c2.1) + (CRES_FPT_MAP)
# B trajectory
FPT_DAP := b1.2
# AB Indirect effect
# Mediação CRES -> FPT -> DAP
CRES_FPT_DAP := a2.1*b1.2
# C reduced on M
CRES_DAP_R := c2.2
# C total effect
CRES_DAP := (c2.2) + (CRES_FPT_DAP)
#Constrains FIXO
# A trajectory
FIXO_FPT := a1.1
# B trajectory
FPT_MAP := b1.1
# AB Indirect effect
# Mediação FIXO -> FPT -> MAP
FIXO_FPT_MAP := a1.1*b1.1
# C reduced on M
FIXO_MAP_R := c1.1
# C total effect
FIXO_MAP := (c1.1) + (FIXO_FPT_MAP)
# B trajectory
FPT_DAP := b1.2
# AB Indirect effect
# Mediação FIXO -> FPT -> DAP
FIXO_FPT_DAP := a1.1*b1.2
# C reduced on M
FIXO_DAP_R := c1.2
# C total effect
FIXO_DAP := (c1.2) + (FIXO_FPT_DAP)
'
fit<-lavaan::sem(model=modelo,data=banco_geral,estimator="ML",ordered=F,orthogonal=T,se="bootstrap", test="yuan.bentler.mplus",verbose=F)
summary(fit,standardized=T,fit=T,rsquare=T)
## lavaan 0.6-9 ended normally after 61 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 57
##
## Number of observations 327
##
## Model Test User Model:
## Standard Robust
## Test Statistic 367.901 331.301
## Degrees of freedom 219 219
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.110
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 2980.477 2415.358
## Degrees of freedom 253 253
## P-value 0.000 0.000
## Scaling correction factor 1.234
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.945 0.948
## Tucker-Lewis Index (TLI) 0.937 0.940
##
## Robust Comparative Fit Index (CFI) 0.953
## Robust Tucker-Lewis Index (TLI) 0.946
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -12867.238 -12867.238
## Scaling correction factor 1.751
## for the MLR correction
## Loglikelihood unrestricted model (H1) -12683.287 -12683.287
## Scaling correction factor 1.243
## for the MLR correction
##
## Akaike (AIC) 25848.476 25848.476
## Bayesian (BIC) 26064.504 26064.504
## Sample-size adjusted Bayesian (BIC) 25883.702 25883.702
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.046 0.040
## 90 Percent confidence interval - lower 0.037 0.031
## 90 Percent confidence interval - upper 0.054 0.048
## P-value RMSEA <= 0.05 0.811 0.985
##
## Robust RMSEA 0.042
## 90 Percent confidence interval - lower 0.032
## 90 Percent confidence interval - upper 0.051
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.053 0.053
##
## Parameter Estimates:
##
## Standard errors Bootstrap
## Number of requested bootstrap draws 1000
## Number of successful bootstrap draws 1000
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## MAP =~
## MRT7 1.000 0.873 0.727
## MRT8 0.873 0.127 6.895 0.000 0.762 0.502
## MRT9 0.985 0.116 8.486 0.000 0.860 0.710
## MRT10 1.177 0.178 6.616 0.000 1.027 0.773
## DAP =~
## MRT11 1.000 1.457 0.688
## MRT12 1.263 0.100 12.682 0.000 1.840 0.898
## MRT13 1.159 0.096 12.103 0.000 1.689 0.819
## MRT14 0.945 0.095 9.956 0.000 1.377 0.674
## FIXO =~
## DMI1 1.000 0.923 0.487
## DMI2 1.129 0.123 9.149 0.000 1.042 0.617
## DMI4 1.268 0.211 5.996 0.000 1.170 0.669
## DMI6 1.421 0.236 6.017 0.000 1.312 0.741
## CRESCIMENTO =~
## DMI3 1.000 1.152 0.743
## DMI5 1.006 0.092 10.920 0.000 1.159 0.735
## DMI7 1.041 0.075 13.795 0.000 1.199 0.815
## DMI8 0.859 0.102 8.424 0.000 0.989 0.724
## FPT =~
## FPT1 1.000 1.058 0.719
## FPT2 0.341 0.073 4.655 0.000 0.361 0.347
## FPT3 0.910 0.076 12.004 0.000 0.963 0.698
## FPT4 1.017 0.098 10.347 0.000 1.076 0.657
## FPT5 1.082 0.090 12.022 0.000 1.145 0.779
## FPT6 1.041 0.135 7.731 0.000 1.101 0.584
## FPT7 1.041 0.121 8.615 0.000 1.102 0.619
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## MAP ~
## FIXO (c1.1) -0.151 0.121 -1.245 0.213 -0.159 -0.159
## CRESCIM (c2.1) 0.120 0.092 1.300 0.194 0.158 0.158
## FPT (b1.1) 0.280 0.064 4.385 0.000 0.339 0.339
## DAP ~
## FIXO (c1.2) 0.411 0.206 1.993 0.046 0.260 0.260
## CRESCIM (c2.2) 0.346 0.151 2.296 0.022 0.274 0.274
## FPT (b1.2) 0.034 0.105 0.322 0.747 0.025 0.025
## FPT ~
## FIXO (a1.1) 0.301 0.144 2.096 0.036 0.262 0.262
## CRESCIM (a2.1) 0.317 0.110 2.881 0.004 0.345 0.345
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DMI1 ~~
## .DMI2 0.847 0.235 3.614 0.000 0.847 0.385
## .MAP ~~
## .DAP 0.112 0.072 1.568 0.117 0.102 0.102
## FIXO ~~
## CRESCIMENTO -0.721 0.146 -4.948 0.000 -0.678 -0.678
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .MRT7 0.682 0.099 6.883 0.000 0.682 0.472
## .MRT8 1.727 0.225 7.686 0.000 1.727 0.748
## .MRT9 0.728 0.122 5.975 0.000 0.728 0.496
## .MRT10 0.713 0.217 3.279 0.001 0.713 0.403
## .MRT11 2.363 0.277 8.515 0.000 2.363 0.527
## .MRT12 0.812 0.204 3.988 0.000 0.812 0.193
## .MRT13 1.399 0.219 6.404 0.000 1.399 0.329
## .MRT14 2.284 0.230 9.941 0.000 2.284 0.546
## .DMI1 2.743 0.366 7.497 0.000 2.743 0.763
## .DMI2 1.769 0.291 6.071 0.000 1.769 0.620
## .DMI4 1.689 0.277 6.085 0.000 1.689 0.552
## .DMI6 1.409 0.309 4.566 0.000 1.409 0.450
## .DMI3 1.074 0.224 4.792 0.000 1.074 0.448
## .DMI5 1.139 0.247 4.612 0.000 1.139 0.459
## .DMI7 0.726 0.164 4.423 0.000 0.726 0.335
## .DMI8 0.890 0.172 5.167 0.000 0.890 0.476
## .FPT1 1.044 0.116 8.982 0.000 1.044 0.482
## .FPT2 0.954 0.114 8.333 0.000 0.954 0.880
## .FPT3 0.978 0.111 8.776 0.000 0.978 0.513
## .FPT4 1.526 0.176 8.686 0.000 1.526 0.569
## .FPT5 0.850 0.105 8.102 0.000 0.850 0.393
## .FPT6 2.342 0.258 9.084 0.000 2.342 0.659
## .FPT7 1.957 0.220 8.913 0.000 1.957 0.617
## .MAP 0.599 0.131 4.562 0.000 0.786 0.786
## .DAP 2.019 0.300 6.724 0.000 0.951 0.951
## FIXO 0.852 0.249 3.416 0.001 1.000 1.000
## CRESCIMENTO 1.326 0.229 5.787 0.000 1.000 1.000
## .FPT 1.047 0.176 5.945 0.000 0.935 0.935
##
## R-Square:
## Estimate
## MRT7 0.528
## MRT8 0.252
## MRT9 0.504
## MRT10 0.597
## MRT11 0.473
## MRT12 0.807
## MRT13 0.671
## MRT14 0.454
## DMI1 0.237
## DMI2 0.380
## DMI4 0.448
## DMI6 0.550
## DMI3 0.552
## DMI5 0.541
## DMI7 0.665
## DMI8 0.524
## FPT1 0.518
## FPT2 0.120
## FPT3 0.487
## FPT4 0.431
## FPT5 0.607
## FPT6 0.341
## FPT7 0.383
## MAP 0.214
## DAP 0.049
## FPT 0.065
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## CRES_FPT 0.317 0.110 2.879 0.004 0.345 0.345
## FPT_MAP 0.280 0.064 4.383 0.000 0.339 0.339
## CRES_FPT_MAP 0.089 0.038 2.339 0.019 0.117 0.117
## CRES_MAP_R 0.120 0.092 1.299 0.194 0.158 0.158
## CRES_MAP 0.208 0.094 2.215 0.027 0.275 0.275
## FPT_DAP 0.034 0.106 0.322 0.748 0.025 0.025
## CRES_FPT_DAP 0.011 0.039 0.279 0.780 0.009 0.009
## CRES_DAP_R 0.346 0.151 2.295 0.022 0.274 0.274
## CRES_DAP 0.357 0.140 2.543 0.011 0.282 0.282
## FIXO_FPT 0.301 0.144 2.095 0.036 0.262 0.262
## FPT_MAP 0.280 0.064 4.383 0.000 0.339 0.339
## FIXO_FPT_MAP 0.084 0.046 1.818 0.069 0.089 0.089
## FIXO_MAP_R -0.151 0.121 -1.245 0.213 -0.159 -0.159
## FIXO_MAP -0.067 0.120 -0.557 0.578 -0.070 -0.070
## FPT_DAP 0.034 0.106 0.322 0.748 0.025 0.025
## FIXO_FPT_DAP 0.010 0.038 0.267 0.789 0.006 0.006
## FIXO_DAP_R 0.411 0.206 1.992 0.046 0.260 0.260
## FIXO_DAP 0.421 0.195 2.161 0.031 0.267 0.267
pvalue<-parameterEstimates(fit, boot.ci.type = "perc",standardized = T)
pvalue<-pvalue[(pvalue$op==":="&(pvalue$lhs!=pvalue$rhs)),c(1,5,8,9,10,11)]
pvalue
## lhs est pvalue ci.lower ci.upper std.lv
## 63 CRES_FPT 0.317 0.004 0.132 0.573 0.345
## 64 FPT_MAP 0.280 0.000 0.171 0.423 0.339
## 65 CRES_FPT_MAP 0.089 0.019 0.034 0.174 0.117
## 66 CRES_MAP_R 0.120 0.194 -0.058 0.294 0.158
## 67 CRES_MAP 0.208 0.027 0.032 0.404 0.275
## 68 FPT_DAP 0.034 0.748 -0.178 0.223 0.025
## 69 CRES_FPT_DAP 0.011 0.780 -0.066 0.091 0.009
## 70 CRES_DAP_R 0.346 0.022 0.058 0.667 0.274
## 71 CRES_DAP 0.357 0.011 0.077 0.648 0.282
## 72 FIXO_FPT 0.301 0.036 0.054 0.664 0.262
## 73 FPT_MAP 0.280 0.000 0.171 0.423 0.339
## 74 FIXO_FPT_MAP 0.084 0.069 0.015 0.195 0.089
## 75 FIXO_MAP_R -0.151 0.213 -0.438 0.033 -0.159
## 76 FIXO_MAP -0.067 0.578 -0.335 0.137 -0.070
## 77 FPT_DAP 0.034 0.748 -0.178 0.223 0.025
## 78 FIXO_FPT_DAP 0.010 0.789 -0.069 0.089 0.006
## 79 FIXO_DAP_R 0.411 0.046 0.036 0.865 0.260
## 80 FIXO_DAP 0.421 0.031 0.059 0.865 0.267
semPlot::semPaths(fit,what = "path",exoVar = FALSE,residuals = F,intercepts = F, thresholds = F, exoCov = F,structural = T,whatLabels = "std",layout = "tree3",curvature = 0,reorder=T,rotation=2)
