R Notebook

pacman::p_load(tidyverse, lavaan, psych, emmeans, arsenal)

Load

load("C:/Users/luisf/Dropbox/Puc-Rio/Projeto - Ivan IGE competencias emocionais/R base - Ivan IGE gestao emocional.RData")

Participantes

dados %>%
  select(sexo_fct, idade,  nivel_escolaridade, estado_fct, regiao) %>% 
  tableby(~., ., 
          test = TRUE,
          control = tableby.control(
            test = T,
            numeric.test = "kwt", 
            cat.test = "chisq",
            cat.stats = c("countpct", "Nmiss2"))) %>% 
  summary()

	Overall (N=787)
sexo_fct
Masc	467 (59.3%)
Fem	320 (40.7%)
N-Miss	0
idade
N-Miss	2
Mean (SD)	24.806 (11.532)
Range	14.000 - 68.000
nivel_escolaridade
Fundamental	183 (23.6%)
Médio	382 (49.3%)
Superior	210 (27.1%)
N-Miss	12
estado_fct
africa	1 (0.1%)
al	75 (9.5%)
am	2 (0.3%)
angola	1 (0.1%)
ba	17 (2.2%)
ce	7 (0.9%)
df	9 (1.1%)
es	1 (0.1%)
go	8 (1.0%)
japao	1 (0.1%)
ma	1 (0.1%)
mg	165 (21.0%)
michoacan	1 (0.1%)
ms	3 (0.4%)
mt	5 (0.6%)
pa	1 (0.1%)
paris	1 (0.1%)
pb	1 (0.1%)
pe	19 (2.4%)
pr	13 (1.7%)
re	1 (0.1%)
rj	93 (11.8%)
rn	5 (0.6%)
ro	1 (0.1%)
rs	5 (0.6%)
sc	5 (0.6%)
se	2 (0.3%)
sp	343 (43.6%)
N-Miss	0
regiao
centro oeste	25 (3.2%)
nordeste	124 (15.8%)
outros	13 (1.7%)
sudeste	602 (76.5%)
sul	23 (2.9%)
N-Miss	0

NA

Confirmatory factor analysis

mod_cfa <- 'f1 =~ ige_1 + ige_11 + ige_13 + ige_22 + ige_30 + ige_33 + ige_34 + ige_36 + ige_43 + ige_47 + ige_48 + ige_53
            f2 =~ ige_5 + ige_8 + ige_9 + ige_10 + ige_15 + ige_16 + ige_23 + ige_25 + ige_27 + ige_29 + ige_50
            f3 =~ ige_4 + ige_37 + ige_38 + ige_39 + ige_41 + ige_44 + ige_45 + ige_51 + ige_54 + ige_55
            f4 =~ ige_17 + ige_18 + ige_19 + ige_24 + ige_26 + ige_31 + ige_40 + ige_46 + ige_52
            f5 =~ ige_2 + ige_3 + ige_6 + ige_12 + ige_20 + ige_32 + ige_57'

cfa_1 <- cfa(model = mod_cfa, 
             data = ds_psych,
             estimator = 'WLSM', 
             ordered=colnames(ds_psych))

#fitMeasures(cfa_1, c("chisq.scaled", "df.scaled", "pvalue.scaled", "cfi.scaled","tli.scaled","rmsea.scaled","srmr"))
summary(cfa_1, standardized=TRUE, fit.measures = TRUE)

lavaan 0.6-9 ended normally after 108 iterations

  Estimator                                       DWLS
  Optimization method                           NLMINB
  Number of model parameters                       304
                                                      
                                                  Used       Total
  Number of observations                           699         787
                                                                  
Model Test User Model:
                                              Standard      Robust
  Test Statistic                              3866.805    5568.700
  Degrees of freedom                              1117        1117
  P-value (Chi-square)                           0.000       0.000
  Scaling correction factor                                  0.694
       Satorra-Bentler correction                                 

Model Test Baseline Model:

  Test statistic                             69244.201   69244.201
  Degrees of freedom                              1176        1176
  P-value                                        0.000       0.000
  Scaling correction factor                                  1.000

User Model versus Baseline Model:

  Comparative Fit Index (CFI)                    0.960       0.935
  Tucker-Lewis Index (TLI)                       0.957       0.931
                                                                  
  Robust Comparative Fit Index (CFI)                         0.955
  Robust Tucker-Lewis Index (TLI)                            0.952

Root Mean Square Error of Approximation:

  RMSEA                                          0.059       0.076
  90 Percent confidence interval - lower         0.057       0.073
  90 Percent confidence interval - upper         0.061       0.078
  P-value RMSEA <= 0.05                          0.000       0.000
                                                                  
  Robust RMSEA                                               0.063
  90 Percent confidence interval - lower                     0.061
  90 Percent confidence interval - upper                     0.065

Standardized Root Mean Square Residual:

  SRMR                                           0.069       0.069

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
  f1 =~                                                                 
    ige_1             1.000                               0.639    0.639
    ige_11            1.074    0.062   17.454    0.000    0.686    0.686
    ige_13            0.865    0.059   14.734    0.000    0.553    0.553
    ige_22            0.976    0.061   16.075    0.000    0.624    0.624
    ige_30            0.782    0.068   11.537    0.000    0.500    0.500
    ige_33            1.068    0.056   19.072    0.000    0.683    0.683
    ige_34            1.102    0.060   18.439    0.000    0.704    0.704
    ige_36            1.115    0.068   16.328    0.000    0.712    0.712
    ige_43            1.061    0.075   14.189    0.000    0.678    0.678
    ige_47            1.015    0.056   18.149    0.000    0.649    0.649
    ige_48            1.010    0.054   18.622    0.000    0.646    0.646
    ige_53            1.059    0.061   17.330    0.000    0.677    0.677
  f2 =~                                                                 
    ige_5             1.000                               0.658    0.658
    ige_8             1.146    0.051   22.633    0.000    0.754    0.754
    ige_9             0.335    0.059    5.687    0.000    0.220    0.220
    ige_10            0.820    0.053   15.354    0.000    0.540    0.540
    ige_15            0.834    0.055   15.112    0.000    0.549    0.549
    ige_16            0.727    0.058   12.578    0.000    0.478    0.478
    ige_23            0.784    0.058   13.567    0.000    0.516    0.516
    ige_25            0.424    0.057    7.401    0.000    0.279    0.279
    ige_27            1.120    0.049   23.076    0.000    0.737    0.737
    ige_29            1.256    0.060   20.918    0.000    0.826    0.826
    ige_50            0.902    0.057   15.878    0.000    0.594    0.594
  f3 =~                                                                 
    ige_4             1.000                               0.440    0.440
    ige_37            1.546    0.130   11.868    0.000    0.681    0.681
    ige_38            1.506    0.124   12.190    0.000    0.663    0.663
    ige_39            1.704    0.143   11.940    0.000    0.750    0.750
    ige_41            0.110    0.088    1.244    0.213    0.048    0.048
    ige_44            1.736    0.137   12.652    0.000    0.764    0.764
    ige_45            1.350    0.107   12.603    0.000    0.594    0.594
    ige_51            1.251    0.108   11.566    0.000    0.551    0.551
    ige_54            1.740    0.143   12.198    0.000    0.766    0.766
    ige_55            1.651    0.130   12.659    0.000    0.727    0.727
  f4 =~                                                                 
    ige_17            1.000                               0.588    0.588
    ige_18            1.025    0.070   14.726    0.000    0.603    0.603
    ige_19            1.054    0.069   15.227    0.000    0.620    0.620
    ige_24            1.207    0.076   15.899    0.000    0.711    0.711
    ige_26            1.118    0.071   15.656    0.000    0.658    0.658
    ige_31            1.055    0.071   14.859    0.000    0.621    0.621
    ige_40            0.983    0.075   13.106    0.000    0.579    0.579
    ige_46            1.147    0.084   13.699    0.000    0.675    0.675
    ige_52            1.290    0.072   17.909    0.000    0.759    0.759
  f5 =~                                                                 
    ige_2             1.000                               0.267    0.267
    ige_3             1.300    0.380    3.419    0.001    0.347    0.347
    ige_6             2.628    0.659    3.990    0.000    0.701    0.701
    ige_12            0.941    0.343    2.745    0.006    0.251    0.251
    ige_20            0.226    0.234    0.968    0.333    0.060    0.060
    ige_32            0.510    0.266    1.917    0.055    0.136    0.136
    ige_57            2.583    0.675    3.828    0.000    0.689    0.689

Covariances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  f1 ~~                                                                 
    f2                0.350    0.023   15.047    0.000    0.833    0.833
    f3                0.211    0.021   10.044    0.000    0.749    0.749
    f4                0.269    0.020   13.239    0.000    0.715    0.715
    f5               -0.034    0.013   -2.713    0.007   -0.201   -0.201
  f2 ~~                                                                 
    f3                0.222    0.021   10.505    0.000    0.767    0.767
    f4                0.246    0.020   12.309    0.000    0.635    0.635
    f5               -0.044    0.014   -3.187    0.001   -0.252   -0.252
  f3 ~~                                                                 
    f4                0.202    0.021    9.592    0.000    0.780    0.780
    f5               -0.033    0.010   -3.394    0.001   -0.277   -0.277
  f4 ~~                                                                 
    f5               -0.019    0.009   -2.183    0.029   -0.119   -0.119

Intercepts:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
   .ige_1             0.000                               0.000    0.000
   .ige_11            0.000                               0.000    0.000
   .ige_13            0.000                               0.000    0.000
   .ige_22            0.000                               0.000    0.000
   .ige_30            0.000                               0.000    0.000
   .ige_33            0.000                               0.000    0.000
   .ige_34            0.000                               0.000    0.000
   .ige_36            0.000                               0.000    0.000
   .ige_43            0.000                               0.000    0.000
   .ige_47            0.000                               0.000    0.000
   .ige_48            0.000                               0.000    0.000
   .ige_53            0.000                               0.000    0.000
   .ige_5             0.000                               0.000    0.000
   .ige_8             0.000                               0.000    0.000
   .ige_9             0.000                               0.000    0.000
   .ige_10            0.000                               0.000    0.000
   .ige_15            0.000                               0.000    0.000
   .ige_16            0.000                               0.000    0.000
   .ige_23            0.000                               0.000    0.000
   .ige_25            0.000                               0.000    0.000
   .ige_27            0.000                               0.000    0.000
   .ige_29            0.000                               0.000    0.000
   .ige_50            0.000                               0.000    0.000
   .ige_4             0.000                               0.000    0.000
   .ige_37            0.000                               0.000    0.000
   .ige_38            0.000                               0.000    0.000
   .ige_39            0.000                               0.000    0.000
   .ige_41            0.000                               0.000    0.000
   .ige_44            0.000                               0.000    0.000
   .ige_45            0.000                               0.000    0.000
   .ige_51            0.000                               0.000    0.000
   .ige_54            0.000                               0.000    0.000
   .ige_55            0.000                               0.000    0.000
   .ige_17            0.000                               0.000    0.000
   .ige_18            0.000                               0.000    0.000
   .ige_19            0.000                               0.000    0.000
   .ige_24            0.000                               0.000    0.000
   .ige_26            0.000                               0.000    0.000
   .ige_31            0.000                               0.000    0.000
   .ige_40            0.000                               0.000    0.000
   .ige_46            0.000                               0.000    0.000
   .ige_52            0.000                               0.000    0.000
   .ige_2             0.000                               0.000    0.000
   .ige_3             0.000                               0.000    0.000
   .ige_6             0.000                               0.000    0.000
   .ige_12            0.000                               0.000    0.000
   .ige_20            0.000                               0.000    0.000
   .ige_32            0.000                               0.000    0.000
   .ige_57            0.000                               0.000    0.000
    f1                0.000                               0.000    0.000
    f2                0.000                               0.000    0.000
    f3                0.000                               0.000    0.000
    f4                0.000                               0.000    0.000
    f5                0.000                               0.000    0.000

Thresholds:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
    ige_1|t1         -0.215    0.048   -4.494    0.000   -0.215   -0.215
    ige_1|t2          0.237    0.048    4.947    0.000    0.237    0.237
    ige_1|t3          0.548    0.050   10.934    0.000    0.548    0.548
    ige_1|t4          0.970    0.057   17.168    0.000    0.970    0.970
    ige_1|t5          1.444    0.071   20.450    0.000    1.444    1.444
    ige_11|t1         0.523    0.050   10.490    0.000    0.523    0.523
    ige_11|t2         1.153    0.061   18.938    0.000    1.153    1.153
    ige_11|t3         1.497    0.073   20.552    0.000    1.497    1.497
    ige_11|t4         1.644    0.080   20.565    0.000    1.644    1.644
    ige_11|t5         1.860    0.093   19.922    0.000    1.860    1.860
    ige_13|t1         0.319    0.048    6.603    0.000    0.319    0.319
    ige_13|t2         0.898    0.055   16.303    0.000    0.898    0.898
    ige_13|t3         1.196    0.062   19.263    0.000    1.196    1.196
    ige_13|t4         1.530    0.074   20.590    0.000    1.530    1.530
    ige_13|t5         1.784    0.088   20.231    0.000    1.784    1.784
    ige_22|t1        -0.099    0.048   -2.078    0.038   -0.099   -0.099
    ige_22|t2         0.274    0.048    5.700    0.000    0.274    0.274
    ige_22|t3         0.507    0.050   10.193    0.000    0.507    0.507
    ige_22|t4         0.820    0.054   15.270    0.000    0.820    0.820
    ige_22|t5         1.099    0.059   18.479    0.000    1.099    1.099
    ige_30|t1         0.495    0.050    9.970    0.000    0.495    0.495
    ige_30|t2         0.965    0.056   17.103    0.000    0.965    0.965
    ige_30|t3         1.249    0.064   19.616    0.000    1.249    1.249
    ige_30|t4         1.554    0.075   20.604    0.000    1.554    1.554
    ige_30|t5         1.687    0.082   20.497    0.000    1.687    1.687
    ige_33|t1         0.193    0.048    4.042    0.000    0.193    0.193
    ige_33|t2         0.805    0.053   15.060    0.000    0.805    0.805
    ige_33|t3         1.241    0.063   19.567    0.000    1.241    1.241
    ige_33|t4         1.673    0.081   20.524    0.000    1.673    1.673
    ige_33|t5         2.084    0.112   18.547    0.000    2.084    2.084
    ige_34|t1         0.491    0.050    9.896    0.000    0.491    0.491
    ige_34|t2         0.976    0.057   17.233    0.000    0.976    0.976
    ige_34|t3         1.167    0.061   19.048    0.000    1.167    1.167
    ige_34|t4         1.414    0.069   20.370    0.000    1.414    1.414
    ige_34|t5         1.718    0.084   20.430    0.000    1.718    1.718
    ige_36|t1         0.898    0.055   16.303    0.000    0.898    0.898
    ige_36|t2         1.454    0.071   20.474    0.000    1.454    1.454
    ige_36|t3         1.784    0.088   20.231    0.000    1.784    1.784
    ige_36|t4         1.947    0.100   19.460    0.000    1.947    1.947
    ige_36|t5         2.084    0.112   18.547    0.000    2.084    2.084
    ige_43|t1         0.999    0.057   17.491    0.000    0.999    0.999
    ige_43|t2         1.444    0.071   20.450    0.000    1.444    1.444
    ige_43|t3         1.702    0.083   20.466    0.000    1.702    1.702
    ige_43|t4         1.947    0.100   19.460    0.000    1.947    1.947
    ige_43|t5         2.084    0.112   18.547    0.000    2.084    2.084
    ige_47|t1         0.178    0.048    3.740    0.000    0.178    0.178
    ige_47|t2         0.719    0.052   13.781    0.000    0.719    0.719
    ige_47|t3         1.126    0.060   18.712    0.000    1.126    1.126
    ige_47|t4         1.519    0.074   20.579    0.000    1.519    1.519
    ige_47|t5         1.840    0.092   20.011    0.000    1.840    1.840
    ige_48|t1        -0.020    0.047   -0.416    0.678   -0.020   -0.020
    ige_48|t2         0.552    0.050   11.008    0.000    0.552    0.552
    ige_48|t3         0.931    0.056   16.706    0.000    0.931    0.931
    ige_48|t4         1.289    0.065   19.847    0.000    1.289    1.289
    ige_48|t5         1.750    0.086   20.342    0.000    1.750    1.750
    ige_53|t1         0.556    0.050   11.082    0.000    0.556    0.556
    ige_53|t2         1.011    0.057   17.619    0.000    1.011    1.011
    ige_53|t3         1.424    0.070   20.398    0.000    1.424    1.424
    ige_53|t4         1.734    0.085   20.388    0.000    1.734    1.734
    ige_53|t5         2.084    0.112   18.547    0.000    2.084    2.084
    ige_5|t1         -0.647    0.051  -12.623    0.000   -0.647   -0.647
    ige_5|t2         -0.052    0.047   -1.096    0.273   -0.052   -0.052
    ige_5|t3          0.392    0.049    8.029    0.000    0.392    0.392
    ige_5|t4          0.795    0.053   14.920    0.000    0.795    0.795
    ige_5|t5          1.249    0.064   19.616    0.000    1.249    1.249
    ige_8|t1         -0.113    0.048   -2.381    0.017   -0.113   -0.113
    ige_8|t2          0.403    0.049    8.254    0.000    0.403    0.403
    ige_8|t3          0.877    0.055   16.031    0.000    0.877    0.877
    ige_8|t4          1.249    0.064   19.616    0.000    1.249    1.249
    ige_8|t5          1.579    0.077   20.608    0.000    1.579    1.579
    ige_9|t1         -0.856    0.054  -15.756    0.000   -0.856   -0.856
    ige_9|t2         -0.419    0.049   -8.553    0.000   -0.419   -0.419
    ige_9|t3         -0.005    0.047   -0.113    0.910   -0.005   -0.005
    ige_9|t4          0.487    0.050    9.821    0.000    0.487    0.487
    ige_9|t5          0.953    0.056   16.971    0.000    0.953    0.953
    ige_10|t1        -0.724    0.052  -13.853    0.000   -0.724   -0.724
    ige_10|t2        -0.304    0.048   -6.302    0.000   -0.304   -0.304
    ige_10|t3        -0.009    0.047   -0.189    0.850   -0.009   -0.009
    ige_10|t4         0.419    0.049    8.553    0.000    0.419    0.419
    ige_10|t5         0.846    0.054   15.618    0.000    0.846    0.846
    ige_15|t1        -0.660    0.051  -12.841    0.000   -0.660   -0.660
    ige_15|t2        -0.208    0.048   -4.344    0.000   -0.208   -0.208
    ige_15|t3         0.193    0.048    4.042    0.000    0.193    0.193
    ige_15|t4         0.673    0.052   13.059    0.000    0.673    0.673
    ige_15|t5         1.126    0.060   18.712    0.000    1.126    1.126
    ige_16|t1        -0.204    0.048   -4.268    0.000   -0.204   -0.204
    ige_16|t2         0.353    0.049    7.279    0.000    0.353    0.353
    ige_16|t3         0.733    0.052   13.997    0.000    0.733    0.733
    ige_16|t4         1.099    0.059   18.479    0.000    1.099    1.099
    ige_16|t5         1.404    0.069   20.340    0.000    1.404    1.404
    ige_23|t1        -0.419    0.049   -8.553    0.000   -0.419   -0.419
    ige_23|t2         0.066    0.047    1.398    0.162    0.066    0.066
    ige_23|t3         0.527    0.050   10.564    0.000    0.527    0.527
    ige_23|t4         1.092    0.059   18.419    0.000    1.092    1.092
    ige_23|t5         1.644    0.080   20.565    0.000    1.644    1.644
    ige_25|t1        -1.289    0.065  -19.847    0.000   -1.289   -1.289
    ige_25|t2        -0.701    0.052  -13.493    0.000   -0.701   -0.701
    ige_25|t3        -0.237    0.048   -4.947    0.000   -0.237   -0.237
    ige_25|t4         0.189    0.048    3.966    0.000    0.189    0.189
    ige_25|t5         0.612    0.051   12.038    0.000    0.612    0.612
    ige_27|t1        -0.491    0.050   -9.896    0.000   -0.491   -0.491
    ige_27|t2         0.120    0.048    2.532    0.011    0.120    0.120
    ige_27|t3         0.552    0.050   11.008    0.000    0.552    0.552
    ige_27|t4         0.959    0.056   17.037    0.000    0.959    0.959
    ige_27|t5         1.340    0.067   20.097    0.000    1.340    1.340
    ige_29|t1         0.146    0.048    3.061    0.002    0.146    0.146
    ige_29|t2         0.719    0.052   13.781    0.000    0.719    0.719
    ige_29|t3         1.054    0.058   18.056    0.000    1.054    1.054
    ige_29|t4         1.542    0.075   20.598    0.000    1.542    1.542
    ige_29|t5         1.821    0.091   20.092    0.000    1.821    1.821
    ige_50|t1        -0.052    0.047   -1.096    0.273   -0.052   -0.052
    ige_50|t2         0.447    0.049    9.076    0.000    0.447    0.447
    ige_50|t3         0.898    0.055   16.303    0.000    0.898    0.898
    ige_50|t4         1.297    0.065   19.891    0.000    1.297    1.297
    ige_50|t5         1.718    0.084   20.430    0.000    1.718    1.718
    ige_4|t1         -0.805    0.053  -15.060    0.000   -0.805   -0.805
    ige_4|t2         -0.153    0.048   -3.212    0.001   -0.153   -0.153
    ige_4|t3          0.319    0.048    6.603    0.000    0.319    0.319
    ige_4|t4          0.841    0.054   15.549    0.000    0.841    0.841
    ige_4|t5          1.233    0.063   19.518    0.000    1.233    1.233
    ige_37|t1         0.153    0.048    3.212    0.001    0.153    0.153
    ige_37|t2         0.678    0.052   13.132    0.000    0.678    0.678
    ige_37|t3         1.036    0.058   17.871    0.000    1.036    1.036
    ige_37|t4         1.367    0.068   20.208    0.000    1.367    1.367
    ige_37|t5         1.702    0.083   20.466    0.000    1.702    1.702
    ige_38|t1        -0.757    0.053  -14.354    0.000   -0.757   -0.757
    ige_38|t2        -0.282    0.048   -5.851    0.000   -0.282   -0.282
    ige_38|t3         0.157    0.048    3.287    0.001    0.157    0.157
    ige_38|t4         0.660    0.051   12.841    0.000    0.660    0.660
    ige_38|t5         1.086    0.059   18.360    0.000    1.086    1.086
    ige_39|t1         0.056    0.047    1.172    0.241    0.056    0.056
    ige_39|t2         0.590    0.051   11.671    0.000    0.590    0.590
    ige_39|t3         0.866    0.055   15.894    0.000    0.866    0.866
    ige_39|t4         1.233    0.063   19.518    0.000    1.233    1.233
    ige_39|t5         1.644    0.080   20.565    0.000    1.644    1.644
    ige_41|t1        -0.781    0.053  -14.708    0.000   -0.781   -0.781
    ige_41|t2        -0.304    0.048   -6.302    0.000   -0.304   -0.304
    ige_41|t3         0.230    0.048    4.796    0.000    0.230    0.230
    ige_41|t4         0.742    0.053   14.140    0.000    0.742    0.742
    ige_41|t5         1.146    0.061   18.882    0.000    1.146    1.146
    ige_44|t1        -0.346    0.048   -7.129    0.000   -0.346   -0.346
    ige_44|t2         0.252    0.048    5.248    0.000    0.252    0.252
 [ reached getOption("max.print") -- omitted 103 rows ]

Variances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
   .ige_1             0.591                               0.591    0.591
   .ige_11            0.529                               0.529    0.529
   .ige_13            0.694                               0.694    0.694
   .ige_22            0.611                               0.611    0.611
   .ige_30            0.750                               0.750    0.750
   .ige_33            0.534                               0.534    0.534
   .ige_34            0.504                               0.504    0.504
   .ige_36            0.492                               0.492    0.492
   .ige_43            0.540                               0.540    0.540
   .ige_47            0.579                               0.579    0.579
   .ige_48            0.583                               0.583    0.583
   .ige_53            0.542                               0.542    0.542
   .ige_5             0.567                               0.567    0.567
   .ige_8             0.431                               0.431    0.431
   .ige_9             0.952                               0.952    0.952
   .ige_10            0.709                               0.709    0.709
   .ige_15            0.699                               0.699    0.699
   .ige_16            0.771                               0.771    0.771
   .ige_23            0.734                               0.734    0.734
   .ige_25            0.922                               0.922    0.922
   .ige_27            0.457                               0.457    0.457
   .ige_29            0.317                               0.317    0.317
   .ige_50            0.648                               0.648    0.648
   .ige_4             0.806                               0.806    0.806
   .ige_37            0.537                               0.537    0.537
   .ige_38            0.560                               0.560    0.560
   .ige_39            0.438                               0.438    0.438
   .ige_41            0.998                               0.998    0.998
   .ige_44            0.416                               0.416    0.416
   .ige_45            0.647                               0.647    0.647
   .ige_51            0.697                               0.697    0.697
   .ige_54            0.413                               0.413    0.413
   .ige_55            0.472                               0.472    0.472
   .ige_17            0.654                               0.654    0.654
   .ige_18            0.636                               0.636    0.636
   .ige_19            0.615                               0.615    0.615
   .ige_24            0.495                               0.495    0.495
   .ige_26            0.568                               0.568    0.568
   .ige_31            0.615                               0.615    0.615
   .ige_40            0.665                               0.665    0.665
   .ige_46            0.545                               0.545    0.545
   .ige_52            0.424                               0.424    0.424
   .ige_2             0.929                               0.929    0.929
   .ige_3             0.880                               0.880    0.880
   .ige_6             0.508                               0.508    0.508
   .ige_12            0.937                               0.937    0.937
   .ige_20            0.996                               0.996    0.996
   .ige_32            0.981                               0.981    0.981
   .ige_57            0.525                               0.525    0.525
    f1                0.409    0.033   12.384    0.000    1.000    1.000
    f2                0.433    0.033   13.001    0.000    1.000    1.000
    f3                0.194    0.029    6.575    0.000    1.000    1.000
    f4                0.346    0.036    9.726    0.000    1.000    1.000
    f5                0.071    0.032    2.213    0.027    1.000    1.000

Scales y*:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
    ige_1             1.000                               1.000    1.000
    ige_11            1.000                               1.000    1.000
    ige_13            1.000                               1.000    1.000
    ige_22            1.000                               1.000    1.000
    ige_30            1.000                               1.000    1.000
    ige_33            1.000                               1.000    1.000
    ige_34            1.000                               1.000    1.000
    ige_36            1.000                               1.000    1.000
    ige_43            1.000                               1.000    1.000
    ige_47            1.000                               1.000    1.000
    ige_48            1.000                               1.000    1.000
    ige_53            1.000                               1.000    1.000
    ige_5             1.000                               1.000    1.000
    ige_8             1.000                               1.000    1.000
    ige_9             1.000                               1.000    1.000
    ige_10            1.000                               1.000    1.000
    ige_15            1.000                               1.000    1.000
    ige_16            1.000                               1.000    1.000
    ige_23            1.000                               1.000    1.000
    ige_25            1.000                               1.000    1.000
    ige_27            1.000                               1.000    1.000
    ige_29            1.000                               1.000    1.000
    ige_50            1.000                               1.000    1.000
    ige_4             1.000                               1.000    1.000
    ige_37            1.000                               1.000    1.000
    ige_38            1.000                               1.000    1.000
    ige_39            1.000                               1.000    1.000
    ige_41            1.000                               1.000    1.000
    ige_44            1.000                               1.000    1.000
    ige_45            1.000                               1.000    1.000
    ige_51            1.000                               1.000    1.000
    ige_54            1.000                               1.000    1.000
    ige_55            1.000                               1.000    1.000
    ige_17            1.000                               1.000    1.000
    ige_18            1.000                               1.000    1.000
    ige_19            1.000                               1.000    1.000
    ige_24            1.000                               1.000    1.000
    ige_26            1.000                               1.000    1.000
    ige_31            1.000                               1.000    1.000
    ige_40            1.000                               1.000    1.000
    ige_46            1.000                               1.000    1.000
    ige_52            1.000                               1.000    1.000
    ige_2             1.000                               1.000    1.000
    ige_3             1.000                               1.000    1.000
    ige_6             1.000                               1.000    1.000
    ige_12            1.000                               1.000    1.000
    ige_20            1.000                               1.000    1.000
    ige_32            1.000                               1.000    1.000
    ige_57            1.000                               1.000    1.000

Plot CFA

library(semPlot)

Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio
Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     

semPaths(cfa_1, 
         residuals=F,
         sizeMan=5,
         sizeLat = 8,
         "std",
         intercepts = F,
         posCol=c("skyblue4", "red"),
         #edge.color="skyblue4",
         edge.label.cex=1.2,
         layout="circle2")

Reliability

alpha(ds_psych, check.keys=TRUE)

Some items were negatively correlated with total scale and were automatically reversed.
 This is indicated by a negative sign for the variable name.

Reliability analysis   
Call: alpha(x = ds_psych, check.keys = TRUE)

 

 lower alpha upper     95% confidence boundaries
0.9 0.91 0.92 

 Reliability if an item is dropped:

 Item statistics 

Non missing response frequency for each item
          1    2    3    4    5    6 miss
ige_1  0.39 0.19 0.13 0.13 0.09 0.07 0.02
ige_2  0.14 0.16 0.21 0.18 0.13 0.18 0.01
ige_3  0.20 0.10 0.08 0.10 0.15 0.37 0.01
ige_4  0.22 0.24 0.19 0.17 0.09 0.10 0.02
ige_5  0.26 0.23 0.18 0.13 0.10 0.10 0.02
ige_6  0.12 0.10 0.16 0.17 0.16 0.29 0.01
ige_8  0.45 0.22 0.16 0.08 0.04 0.05 0.03
ige_9  0.20 0.15 0.17 0.19 0.14 0.15 0.02
ige_10 0.24 0.16 0.13 0.16 0.14 0.18 0.01
ige_11 0.70 0.18 0.06 0.02 0.02 0.03 0.07
ige_12 0.07 0.06 0.08 0.13 0.19 0.48 0.01
ige_13 0.62 0.20 0.07 0.05 0.03 0.03 0.04
ige_15 0.25 0.17 0.17 0.17 0.12 0.12 0.01
ige_16 0.41 0.22 0.14 0.10 0.05 0.07 0.04
ige_17 0.26 0.30 0.22 0.11 0.05 0.06 0.02
ige_18 0.26 0.24 0.16 0.16 0.11 0.07 0.01
ige_19 0.33 0.21 0.17 0.13 0.09 0.07 0.02
ige_20 0.11 0.08 0.13 0.17 0.21 0.30 0.00
ige_22 0.46 0.15 0.09 0.10 0.07 0.13 0.06
ige_23 0.33 0.20 0.19 0.16 0.08 0.04 0.01
ige_24 0.41 0.21 0.16 0.09 0.06 0.07 0.05
ige_25 0.09 0.14 0.17 0.19 0.17 0.24 0.01
ige_26 0.21 0.30 0.17 0.15 0.08 0.08 0.01
ige_27 0.31 0.24 0.16 0.12 0.08 0.08 0.02
ige_29 0.56 0.22 0.09 0.08 0.03 0.03 0.04
ige_30 0.68 0.14 0.07 0.05 0.01 0.04 0.06
ige_31 0.19 0.18 0.17 0.14 0.14 0.18 0.02
ige_32 0.17 0.09 0.11 0.14 0.17 0.33 0.01
ige_33 0.58 0.22 0.10 0.06 0.03 0.02 0.04
ige_34 0.68 0.15 0.05 0.05 0.03 0.04 0.08
ige_36 0.80 0.12 0.04 0.01 0.01 0.02 0.07
ige_37 0.55 0.20 0.10 0.06 0.04 0.04 0.04
ige_38 0.23 0.18 0.18 0.17 0.11 0.13 0.02
ige_39 0.52 0.22 0.08 0.08 0.06 0.05 0.04
ige_40 0.52 0.16 0.11 0.09 0.06 0.07 0.05
ige_41 0.22 0.18 0.22 0.17 0.10 0.11 0.01
ige_43 0.82 0.10 0.03 0.02 0.01 0.02 0.06
ige_44 0.37 0.24 0.14 0.11 0.06 0.08 0.04
ige_45 0.24 0.24 0.19 0.14 0.10 0.09 0.02
ige_46 0.74 0.09 0.03 0.05 0.03 0.05 0.10
ige_47 0.55 0.21 0.12 0.06 0.03 0.03 0.03
ige_48 0.49 0.23 0.12 0.08 0.06 0.04 0.03
ige_50 0.46 0.21 0.16 0.08 0.05 0.04 0.03
ige_51 0.12 0.14 0.20 0.21 0.18 0.15 0.01
ige_52 0.39 0.23 0.15 0.11 0.06 0.06 0.04
ige_53 0.70 0.14 0.09 0.03 0.02 0.02 0.05
ige_54 0.50 0.19 0.12 0.09 0.05 0.06 0.05
ige_55 0.30 0.20 0.17 0.13 0.10 0.10 0.02
ige_57 0.13 0.12 0.15 0.17 0.17 0.24 0.01

psych::splitHalf(ds_psych)

Some items were negatively correlated with total scale and were automatically reversed.

Split half reliabilities  
Call: psych::splitHalf(r = ds_psych)

Maximum split half reliability (lambda 4) =  0.94
Guttman lambda 6                          =  0.93
Average split half reliability            =  0.91
Guttman lambda 3 (alpha)                  =  0.91
Guttman lambda 2                          =  0.92
Minimum split half reliability  (beta)    =  0.85
Average interitem r =  0.18  with median =  0.19

library(semTools)

reliability(cfa_1)

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.

                 f1        f2        f3        f4        f5
alpha     0.8182011 0.7766164 0.8020904 0.8178008 0.5713324
alpha.ord 0.8906992 0.8198210 0.8349484 0.8583297 0.6295103
omega     0.8374093 0.8002272 0.8254517 0.8333609 0.4633902
omega2    0.8374093 0.8002272 0.8254517 0.8333609 0.4633902
omega3    0.8693290 0.8253321 0.8515023 0.8508998 0.3670119
avevar    0.4209350 0.3449216 0.4016277 0.4203952 0.1776979

Test retest

I’ll compute test retest for each item via correlation coef. and ICC

First block: compute the correlation coefficient

correlations <- dados %>% 
  select(ige_1:ige_57,contains("_reteste_"), -contains("copy")) %>% 
  mutate_all(., as.numeric) %>% 
  select(-c(ige_7, ige_14, ige_21, ige_28, ige_35, ige_42, ige_49, ige_56)) %>% #get rid of validity
  select(-c(ige_reteste_7, ige_reteste_14, ige_reteste_21, ige_reteste_28, ige_reteste_35, ige_reteste_42, ige_reteste_49, ige_reteste_56)) %>% #get rid of validity
  {Hmisc::rcorr(as.matrix(.), type = "spearman")}

Second block: report the table

correlations <- left_join(
  #correlation coefficiente
correlations$r %>% 
  data.frame() %>% #transform into dataframe
  rownames_to_column("item") %>%  #get item numbers
  select(item,contains("_reteste_")) %>%  #select everything
  pivot_longer(-item, values_to = "r") %>%  #pivot longer
  mutate(item_fim=stringr::str_sub(item,start=-2)) %>% 
  mutate(reteste_fim=stringr::str_sub(name,start=-2)) %>% 
  filter(item_fim == reteste_fim),

correlations$P %>% 
  data.frame() %>% #transform into dataframe
  rownames_to_column("item") %>%  #get item numbers
  select(item,contains("_reteste_")) %>%  #select everything
  pivot_longer(-item, values_to = "p") %>%  #pivot longer
  mutate(item_fim=stringr::str_sub(item,start=-2)) %>% 
  mutate(reteste_fim=stringr::str_sub(name,start=-2)) %>% 
  filter(item_fim == reteste_fim)) %>% 
  select(item, name, r, p)

Joining, by = c("item", "name", "item_fim", "reteste_fim")

And remove duplicate rows

correlations <- correlations %>% 
  filter(!str_detect(item, 'reteste')) 

Now I’ll compute the ICC

First: create a dataset in which I’ll have all items

ds <- dados %>% 
  select(ige_1:ige_57,contains("_reteste_"), -contains("copy")) %>%
  select(-c(ige_7, ige_14, ige_21, ige_28, ige_35, ige_42, ige_49, ige_56)) %>% #get rid validity
  select(-c(ige_reteste_7, ige_reteste_14, ige_reteste_21, ige_reteste_28, ige_reteste_35, ige_reteste_42, ige_reteste_49, ige_reteste_56)) %>% 
  na.omit()

Then, compute ICC for every T1 T2

purrr::map2(ds %>% select(starts_with("ige_"), -contains("reteste")), ds %>% select(contains("_reteste_")), 
     function (x, y) psych::ICC(cbind(x, y))$results) %>% 
  do.call(rbind, .) %>% 
  data.frame() %>% 
  rownames_to_column("info") %>% 
  filter(type == "ICC3k") -> icc_results

boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular

Minor checks

ds %>% select(ige_20,ige_reteste_20) %>% 
  psych::ICC()

Call: psych::ICC(x = .)

Intraclass correlation coefficients 

 Number of subjects = 44     Number of Judges =  2

Third, add this result to correlation dataset. I double checked the order of variables!

correlations <- bind_cols(
  correlations,
  icc_results %>% select(ICC,p, info))

Error in select(., ICC, p, info) : object 'icc_results' not found

If numeric, round

correlations <- correlations %>% 
  mutate_if(is.numeric, round,2)

Remove unecessary data frames

rm(ds, icc_results)

correlations

rm(correlations)

Now ill check the summative scores

correlations2 <- dados %>% 
  select(ige_agressividade,ige_intolerancia,ige_impulsividade,ige_emocoesnegativas,ige_resilienciaemocio,ige_total,ige_total_ajustado,
         ends_with("_t2"), -contains("copy")) %>% 
  {Hmisc::rcorr(as.matrix(.), type = "spearman")}

correlations2 <- bind_cols( #overall results
  #correlation coefficients
  bind_cols(
    correlations2$r %>% 
      data.frame() %>% #transform into dataframe
      rownames_to_column("item") %>%  #get item numbers
      select(item,ends_with("_t2")) %>% #report all correlations 
      filter(!str_detect(item, '_t2')) %>%  #report only t1 and t2
      select(-item) %>%  #get rid of first column to get diagonals later
      as.matrix(.[,-1]) %>%  #get diagonals
      diag() %>% # diagonals
      data.frame() %>% 
      setNames(., c("correlacao")), #transform into dataframe
    correlations2$r %>% colnames() %>% data.frame() %>% setNames(., c("fator")) %>% filter(!str_detect(fator,"_t2"))),

  # p value
  bind_cols(
    correlations2$P %>% 
      data.frame() %>% #transform into dataframe
      rownames_to_column("item") %>%  #get item numbers
      select(item,ends_with("_t2")) %>% #report all correlations 
      filter(!str_detect(item, '_t2')) %>%  #report only t1 and t2
      select(-item) %>%  #get rid of first column to get diagonals later
      as.matrix(.[,-1]) %>%  #get diagonals
      diag() %>% # diagonals
      data.frame() %>% 
      setNames(., c("pvalor")), #transform into dataframe
    correlations2$P %>% colnames() %>% data.frame() %>% setNames(., c("fator2")) %>% filter(!str_detect(fator2,"_t2")))
) %>% 
  select(fator, correlacao, pvalor)

the condition has length > 1 and only the first element will be usedError: Can't recycle `..1` (size 6) to match `..2` (size 7).
Run `rlang::last_error()` to see where the error occurred.

correlations2 

minor checks

cor.test(dados$ige_total_ajustado, dados$ige_total_ajustado_t2, method = "spearman", use="complete.obs")

Cannot compute exact p-value with ties

    Spearman's rank correlation rho

data:  dados$ige_total_ajustado and dados$ige_total_ajustado_t2
S = 6997.2, p-value = 0.0004459
alternative hypothesis: true rho is not equal to 0
sample estimates:
      rho 
0.5068933 

Now I’ll get the ICC for these same factors

purrr::map2(dados %>% select(ige_agressividade,ige_intolerancia,ige_impulsividade,ige_emocoesnegativas,ige_resilienciaemocio,ige_total, ige_total_ajustado), dados %>% select(ends_with("t2")), 
     function (x, y) psych::ICC(cbind(x, y))$results) %>% 
  do.call(rbind, .) %>% 
  data.frame() %>% 
  rownames_to_column("info") %>% 
  filter(type == "ICC3k") -> icc_results

boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular

And I’ll add to the correlations 2 dataframe (this dataframe reports the correlation at the factor level)

correlations2 <- cbind(correlations2, icc_results %>% select(ICC, p, info))

correlations2

minor check

ICC(cbind(dados$ige_total_ajustado, dados$ige_total_ajustado_t2))

boundary (singular) fit: see ?isSingular

Call: ICC(x = cbind(dados$ige_total_ajustado, dados$ige_total_ajustado_t2))

Intraclass correlation coefficients 

 Number of subjects = 787     Number of Judges =  2

Remove unnecessary vectors

rm(correlations2, icc_results)

After these procedures, a graph would make all interpretation easier.

dados %>% 
  select(ige_agressividade,ige_intol_ans,ige_impulsividade,ige_emocoesnegativas,ige_resilienciaemocio,ends_with("t2"), -ige_total_t2) %>% 
  na.omit() %>% 
  pivot_longer(everything()) %>% 
  mutate(name = str_remove_all(name, pattern = "ige_")) %>% 
  mutate(name = str_replace_all(name, "_t2","-T2")) %>% 
  ggplot(., aes(x=name, y=value)) + geom_boxplot() +
  #scale_x_discrete(limits = rev) +
  labs(x="Fator", y = "Valor obtido") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=0.5))

dados %>% 
  select(ige_total, ige_total_t2) %>% 
  rename("Total T1" = ige_total,
         "Total T2" = ige_total_t2) %>%  
  na.omit() %>% 
  pivot_longer(everything()) %>% 
  ggplot(., aes(name, y = value)) +
  geom_boxplot() +
  labs(x = "Tempo", y = "Resultados médios") +
  ggpubr::stat_compare_means( aes(label = ..p.signif..), 
                        label.x = 1.5, label.y = 300) +
  theme_bw()

NA

dados %>% 
  select(ige_total, ige_total_t2) %>% 
  na.omit() %>% 
  {t.test(.$ige_total, .$ige_total_t2, paired=T,var.equal=T)}

    Paired t-test

data:  .$ige_total and .$ige_total_t2
t = 0.84927, df = 43, p-value = 0.4004
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -8.216487 20.171033
sample estimates:
mean of the differences 
               5.977273 

Correlational evidence

Correlational analyzes

tabela_correlacao <- function(...) {

  #r
  x<-dados %>% 
    select(ige_agressividade,ige_intolerancia,ige_impulsividade,ige_emocoesnegativas,ige_resilienciaemocio,ige_total_ajustado,...) %>% 
    {Hmisc::rcorr(as.matrix(.), type = "spearman")$r} %>% #get correlation between all variables
    data.frame()  %>%  #create a dataframe with the results
    select(-c(contains("ige"))) %>% #remove all columns of IGE
    rownames_to_column("item") %>%  #create a first row 
    filter(str_detect(item, 'ige')) #preserve only IGE items
  #p values
  y<-dados %>% 
    select(ige_agressividade,ige_intolerancia,ige_impulsividade,ige_emocoesnegativas,ige_resilienciaemocio,ige_total_ajustado,...) %>% 
    {Hmisc::rcorr(as.matrix(.), type = "spearman")$P} %>% #get correlation between all variables
    data.frame()  %>%  #create a dataframe with the results
    select(-c(contains("ige"))) %>% #remove all columns of IGE
    rownames_to_column("item") %>%  #create a first row 
    filter(str_detect(item, 'ige')) %>% #preserve just the items
    rename_with(., ~ paste0(.x, "_p"), .cols=-"item") #rename variables to a further matching
  
  z <- left_join(x, y, by = "item") %>% #create an unique dataset with correlations and p values
    mutate_if(is.numeric, round, 2) %>%  #round all results
    select(item,sort(names(.)))   #arrange in the alphabetic order
  
  return(z) 

}

STAXI

tabela_correlacao(staxi_estado_raiva,   staxi_traco_raiva,  staxi_temperamento, staxi_reacao,   staxi_raiva_dentro, staxi_raiva_fora,   staxi_controle, staxi_expressao)

IFP

item	ifp_afago	ifp_afago_p	ifp_afilia	ifp_afilia_p	ifp_agressao	ifp_agressao_p	ifp_assisten	ifp_assisten_p	ifp_autonom	ifp_autonom_p	ifp_deferen	ifp_deferen_p	ifp_deneg	ifp_deneg_p	ifp_desempen	ifp_desempen_p	ifp_dominan	ifp_dominan_p	ifp_exibi	ifp_exibi_p	ifp_heterossex	ifp_heterossex_p	ifp_intracep	ifp_intracep_p	ifp_mudanca	ifp_mudanca_p	ifp_ordem	ifp_ordem_p	ifp_persist	ifp_persist_p
ige_agressividade	0.06	0.59	-0.17	0.11	0.58	0.00	-0.23	0.03	0.34	0.00	0.01	0.95	0.18	0.09	0.26	0.01	0.30	0.00	0.37	0.00	0.31	0.00	0.07	0.50	0.11	0.29	0.00	0.98	0.16	0.13
ige_intolerancia	0.12	0.27	-0.10	0.36	0.55	0.00	-0.21	0.05	0.27	0.01	-0.02	0.84	0.09	0.42	0.28	0.01	0.27	0.01	0.36	0.00	0.33	0.00	0.02	0.87	0.05	0.62	0.03	0.80	0.20	0.06
ige_impulsividade	0.18	0.08	-0.10	0.34	0.57	0.00	-0.04	0.71	0.38	0.00	-0.11	0.29	0.24	0.02	0.20	0.05	0.27	0.01	0.33	0.00	0.35	0.00	0.11	0.30	0.13	0.21	0.06	0.55	0.05	0.62
ige_emocoesnegativas	0.14	0.18	-0.16	0.13	0.50	0.00	-0.17	0.12	0.27	0.01	-0.06	0.58	0.43	0.00	0.17	0.10	0.17	0.11	0.21	0.04	0.20	0.06	-0.01	0.89	0.12	0.26	0.12	0.27	0.09	0.40
ige_resilienciaemocio	0.09	0.40	-0.10	0.33	0.24	0.02	-0.33	0.00	-0.02	0.85	-0.24	0.02	-0.35	0.00	-0.08	0.43	0.04	0.69	0.13	0.24	0.25	0.02	-0.15	0.15	-0.06	0.55	-0.34	0.00	-0.13	0.23
ige_total_ajustado	-0.15	0.16	0.17	0.11	-0.65	0.00	0.23	0.03	-0.35	0.00	0.09	0.39	-0.22	0.04	-0.23	0.03	-0.29	0.00	-0.39	0.00	-0.37	0.00	-0.03	0.79	-0.11	0.29	0.02	0.88	-0.10	0.33

minor check

cor.test(dados$ige_agressividade,dados$ifp_afago, method = "spearman")

Cannot compute exact p-value with ties

    Spearman's rank correlation rho

data:  dados$ige_agressividade and dados$ifp_afago
S = 118374, p-value = 0.589
alternative hypothesis: true rho is not equal to 0
sample estimates:
       rho 
0.05738313 

BFP

tabela_correlacao(bfp_abertura, bfp_extroversao, bfp_neuroticismo, bfp_realizacao, bfp_socializacao) 

Minor check

cor.test(dados$ige_total_ajustado, dados$bfp_abertura, method = "spearman", use = "complete.obs")

Cannot compute exact p-value with ties

    Spearman's rank correlation rho

data:  dados$ige_total_ajustado and dados$bfp_abertura
S = 161641, p-value = 0.9971
alternative hypothesis: true rho is not equal to 0
sample estimates:
         rho 
0.0003649477 

IHS

tabela_correlacao(ihs_f1, ihs_f2, ihs_f3, ihs_f4, ihs_f5)

AC

tabela_correlacao(ac_total)

Dor

tabela_correlacao(dor_total)

COP

tabela_correlacao(cop, cog, cat, avd, bod,tcr) 

IHSA

tabela_correlacao(ihsa_dificult_total)

Palografico

Resultados aproximados por conta do tamanho amostral

dados %>% select(ige_agressividade,ige_intolerancia, ige_impulsividade, ige_emocoesnegativas, ige_resilienciaemocio, ige_total_ajustado,contains("palo")) %>% 
  filter_at(vars(contains("palo")), any_vars(!is.na(.))) %>% #remove if paolo is na
  mutate_at(vars(contains("palo")), as.factor) %>% 
  compareGroups(palo_margens_superior ~ ige_agressividade + 
                  ige_intolerancia +
                  ige_impulsividade + ige_emocoesnegativas + 
                  ige_resilienciaemocio + ige_total_ajustado, 
                data = .,
                max.ylev = 17) %>% 
  createTable()

--------Summary descriptives table by 'palo_margens_superior'---------

___________________________________________________________________ 
                       aumentada   diminuída    normal    p.overall 
                          N=8         N=6        N=81               
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
ige_agressividade     15.8 (5.44) 15.5 (3.94) 17.2 (6.57)   0.692   
ige_intolerancia      24.9 (9.11) 34.3 (8.76) 27.1 (7.78)   0.070   
ige_impulsividade     20.2 (9.08) 26.3 (7.53) 22.4 (7.88)   0.365   
ige_emocoesnegativas  12.5 (4.14) 16.5 (9.97) 16.4 (6.06)   0.247   
ige_resilienciaemocio 21.5 (4.50) 20.3 (6.92) 18.2 (6.64)   0.319   
ige_total_ajustado    248 (22.8)  230 (19.8)  242 (24.1)    0.367   
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

emmeans(lm(dados$ige_resilienciaemocio ~ dados$palo_ritmo), pairwise ~ palo_ritmo, adj = "bonferroni")

$emmeans
 palo_ritmo  emmean    SE df lower.CL upper.CL
 alto          23.1 1.795 90    19.52     26.6
 baixo         19.9 1.269 90    17.40     22.4
 médio         17.1 0.846 90    15.41     18.8
 muito alto    24.0 4.397 90    15.27     32.7
 muito baixo   14.0 3.590 90     6.87     21.1

Confidence level used: 0.95 

$contrasts
 contrast                 estimate   SE df t.ratio p.value
 alto - baixo                3.167 2.20 90   1.441  1.0000
 alto - médio                5.991 1.98 90   3.019  0.0330
 alto - muito alto          -0.917 4.75 90  -0.193  1.0000
 alto - muito baixo          9.083 4.01 90   2.263  0.2603
 baixo - médio               2.824 1.53 90   1.851  0.6739
 baixo - muito alto         -4.083 4.58 90  -0.892  1.0000
 baixo - muito baixo         5.917 3.81 90   1.554  1.0000
 médio - muito alto         -6.907 4.48 90  -1.543  1.0000
 médio - muito baixo         3.093 3.69 90   0.839  1.0000
 muito alto - muito baixo   10.000 5.68 90   1.762  0.8149

P value adjustment: bonferroni method for 10 tests 

Distribuitions

Agressividade

tableby(~ige_agressividade, data = dados) %>% summary

	Overall (N=787)
ige_agressividade
N-Miss	86
Mean (SD)	21.944 (9.151)
Range	12.000 - 72.000

NA

ggstatsplot::gghistostats(
  data =dados, # dataframe from which variable is to be taken
  x = ige_agressividade, # numeric variable whose distribution is of interest
  binwidth = 1, # binwidth value (experiment)
  normal.curve = TRUE, # superimpose a normal distribution curve
  normal.curve.args = list(color = "red", size = 1),
  results.subtitle = F,
  xlab = "Agressividade"
)

Intolerência

ggstatsplot::gghistostats(
  data =dados, # dataframe from which variable is to be taken
  x = ige_intolerancia, # numeric variable whose distribution is of interest
  binwidth = 1, # binwidth value (experiment)
  normal.curve = TRUE, # superimpose a normal distribution curve
  normal.curve.args = list(color = "red", size = 1),
  results.subtitle = F,
  xlab = "Intolerância"
)

Impulsividade

ggstatsplot::gghistostats(
  data = dados, # dataframe from which variable is to be taken
  x = ige_impulsividade, # numeric variable whose distribution is of interest
  binwidth = 1, # binwidth value (experiment)
  normal.curve = TRUE, # superimpose a normal distribution curve
  normal.curve.args = list(color = "red", size = 1),
  results.subtitle = F,
  xlab = "Impulsividade"
)

Emoções negativas

ggstatsplot::gghistostats(
  data = dados, # dataframe from which variable is to be taken
  x = ige_emocoesnegativas, # numeric variable whose distribution is of interest
  binwidth = 1, # binwidth value (experiment)
  normal.curve = TRUE, # superimpose a normal distribution curve
  normal.curve.args = list(color = "red", size = 1),
  results.subtitle = F,
  xlab = "Emoções negativas"
)

Resiliência emocional

ggstatsplot::gghistostats(
  data = dados, # dataframe from which variable is to be taken
  x = ige_resilienciaemocio, # numeric variable whose distribution is of interest
  binwidth = 1, # binwidth value (experiment)
  normal.curve = TRUE, # superimpose a normal distribution curve
  normal.curve.args = list(color = "red", size = 1),
  results.subtitle = F,
  xlab = "Resiliência emocional"
)

IGE total

ggstatsplot::gghistostats(
  data = dados, # dataframe from which variable is to be taken
  x = ige_total_ajustado, # numeric variable whose distribution is of interest
  binwidth = 1, # binwidth value (experiment)
  normal.curve = TRUE, # superimpose a normal distribution curve
  normal.curve.args = list(color = "red", size = 1),
  results.subtitle = F,
  xlab = "IGE Total"
)

Response processes

MIMIC

mimic <- 'f1 =~ ige_1 + ige_11 + ige_13 + ige_22 + ige_30 + ige_33 + ige_34 + ige_36 + ige_43 + ige_47 + ige_48 + ige_53
            f2 =~ ige_5 + ige_8 + ige_9 + ige_10 + ige_15 + ige_16 + ige_23 + ige_25 + ige_27 + ige_29 + ige_50
            f3 =~ ige_4 + ige_37 + ige_38 + ige_39 + ige_41 + ige_44 + ige_45 + ige_51 + ige_54 + ige_55
            f4 =~ ige_17 + ige_18 + ige_19 + ige_24 + ige_26 + ige_31 + ige_40 + ige_46 + ige_52
            f5 =~ ige_2 + ige_3 + ige_6 + ige_12 + ige_20 + ige_32 + ige_57
            
            ige =~ f1 + f2 + f3 + f4 + f5
            
            

          #regression
          ige ~ sexo
'
fit <- sem(mimic, data = cbind(ds_psych, dados %>% select(sexo)))

lavaan::standardizedSolution(fit) %>% dplyr::filter(!is.na(pvalue)) %>% arrange(desc(pvalue)) %>% mutate_if("is.numeric","round",3) %>% select(-ci.lower,-ci.upper,-z)
pvalue_cutoff <- 0.05
obj <- semPlot:::semPlotModel(fit)
# save a copy of the original, so we can compare it later and be sure we removed only what we intended to remove
original_Pars <- obj@Pars
check_Pars <- obj@Pars %>% dplyr::filter(!(edge %in% c("int","<->") | lhs == rhs)) # this is the list of paramater to sift thru
keep_Pars <- obj@Pars %>% dplyr::filter(edge %in% c("int","<->") | lhs == rhs) # this is the list of paramater to keep asis
test_against <- lavaan::standardizedSolution(fit) %>% dplyr::filter(pvalue < pvalue_cutoff, rhs != lhs)
test_against_rev <- test_against %>% rename(rhs2 = lhs,   # for some reason, the rhs and lhs are reversed in the standardizedSolution() output, for some of the values
                                            lhs = rhs) %>% # I'll have to reverse it myself, and test against both orders
    rename(rhs = rhs2)
checked_Pars <-
    check_Pars %>% semi_join(test_against, by = c("lhs", "rhs")) %>% bind_rows(
        check_Pars %>% semi_join(test_against_rev, by = c("lhs", "rhs"))
    )

obj@Pars <- keep_Pars %>% bind_rows(checked_Pars)

#let's verify by looking at the list of the edges we removed from the object
anti_join(original_Pars,obj@Pars)
semPlot::semPaths(fit, "std",fade = F, residuals = F)

semPaths(obj, 
         layout = 'tree3', 
         what = 'std', 
         intercepts = FALSE, 
         residuals = FALSE,
         sizeLat = 7, 
         sizeMan = 6, 
         theme = 'gray', 
         edge.label.cex = 0.8,
         label.font = 2, #bold in all text 
         label.cex = 1, #font size 
         label.prop = 0.9, #font size
         nCharNodes = 5, 
         fade = FALSE, 
         rotation = 2)

Total

apaTables::apa.aov.table(mod_sexo_idade_escolaridade)

ANOVA results using ige_total_ajustado as the dependent variable
 

Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared 

olsrr::ols_regress(mod_sexo_idade_escolaridade)

                         Model Summary                          
---------------------------------------------------------------
R                       0.367       RMSE                30.624 
R-Squared               0.135       Coef. Var           14.017 
Adj. R-Squared          0.125       MSE                937.842 
Pred R-Squared          0.111       MAE                 24.143 
---------------------------------------------------------------
 RMSE: Root Mean Square Error 
 MSE: Mean Square Error 
 MAE: Mean Absolute Error 

                                 ANOVA                                  
-----------------------------------------------------------------------
                  Sum of                                               
                 Squares         DF    Mean Square      F         Sig. 
-----------------------------------------------------------------------
Regression     98843.744          8      12355.468    13.174    0.0000 
Residual      633981.240        676        937.842                     
Total         732824.984        684                                    
-----------------------------------------------------------------------

                                           Parameter Estimates                                             
----------------------------------------------------------------------------------------------------------
                     model       Beta    Std. Error    Std. Beta      t        Sig       lower      upper 
----------------------------------------------------------------------------------------------------------
               (Intercept)    199.113         4.145                 48.032    0.000    190.974    207.253 
               sexo_fctFem     -3.757         2.474       -0.057    -1.519    0.129     -8.614      1.100 
       faixa_etaria21 a 30     13.683         3.147        0.179     4.348    0.000      7.504     19.862 
       faixa_etaria31 a 40     25.834         3.679        0.282     7.023    0.000     18.611     33.058 
       faixa_etaria41 a 50     28.571         4.571        0.243     6.250    0.000     19.595     37.547 
       faixa_etaria51 a 60     13.859         6.260        0.082     2.214    0.027      1.568     26.149 
       faixa_etaria61 a 70     17.407        11.005        0.057     1.582    0.114     -4.200     39.014 
   nivel_escolaridadeMédio      9.509         3.830        0.144     2.483    0.013      1.989     17.028 
nivel_escolaridadeSuperior     17.773         3.936        0.251     4.516    0.000     10.045     25.501 
----------------------------------------------------------------------------------------------------------

Post hoc

emmeans(mod_sexo_idade_escolaridade, pairwise ~ nivel_escolaridade)

$emmeans
 nivel_escolaridade emmean   SE  df lower.CL upper.CL
 Fundamental           210 3.31 680      203      216
 Médio                 216 1.70 680      213      219
 Superior              226 2.20 680      222      231

Results are averaged over the levels of: sexo_fct 
Confidence level used: 0.95 

$contrasts
 contrast               estimate   SE  df t.ratio p.value
 Fundamental - Médio       -6.23 3.82 680  -1.631  0.2334
 Fundamental - Superior   -16.39 3.90 680  -4.203  0.0001
 Médio - Superior         -10.15 2.87 680  -3.532  0.0013

Results are averaged over the levels of: sexo_fct 
P value adjustment: tukey method for comparing a family of 3 estimates 

tableby(nivel_escolaridade ~ ige_total_ajustado, dados) %>% summary

	Fundamental (N=183)	Médio (N=382)	Superior (N=210)	Total (N=775)	p value
ige_total_ajustado					< 0.001
N-Miss	87	1	0	88
Mean (SD)	216.010 (34.729)	214.202 (33.032)	227.519 (29.302)	218.525 (32.697)
Range	89.000 - 268.000	117.000 - 277.000	142.000 - 283.000	89.000 - 283.000

NA

gridExtra::grid.arrange(
  gridExtra::arrangeGrob(
  ggplot(dados, aes(x= idade, y = ige_total_ajustado)) +
  geom_jitter(alpha=.3, aes(color = ige_total_ajustado)) +
  geom_smooth(method = "lm") +
  labs(x= "Idade", y = "") +
  theme_bw() +
  theme(legend.position = "hide"),

  dados %>% filter(!is.na(nivel_escolaridade)) %>% ggplot(., aes(x= nivel_escolaridade, y = ige_total_ajustado)) +
  geom_boxplot() + 
  geom_jitter(alpha=.3, aes(color = nivel_escolaridade)) +
  labs(x= "Escolaridade", y = "") +
  theme_bw() +
  theme(legend.position = "hide")),
  left = grid::textGrob("IGE Total", rot = 90, vjust = 1))

Ige agressividade

mod_sexo_idade_escolaridade_agressividade <- lm(ige_agressividade ~ sexo_fct + idade + nivel_escolaridade, dados) 
apaTables::apa.aov.table(mod_sexo_idade_escolaridade_agressividade)

ANOVA results using ige_agressividade as the dependent variable
 

Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared 

olsrr::ols_regress(mod_sexo_idade_escolaridade_agressividade)

                        Model Summary                          
--------------------------------------------------------------
R                       0.235       RMSE                8.977 
R-Squared               0.055       Coef. Var          40.833 
Adj. R-Squared          0.050       MSE                80.593 
Pred R-Squared          0.039       MAE                 6.618 
--------------------------------------------------------------
 RMSE: Root Mean Square Error 
 MSE: Mean Square Error 
 MAE: Mean Absolute Error 

                                ANOVA                                 
---------------------------------------------------------------------
                 Sum of                                              
                Squares         DF    Mean Square      F        Sig. 
---------------------------------------------------------------------
Regression     3203.383          4        800.846    9.937    0.0000 
Residual      54964.472        682         80.593                    
Total         58167.854        686                                   
---------------------------------------------------------------------

                                          Parameter Estimates                                           
-------------------------------------------------------------------------------------------------------
                     model      Beta    Std. Error    Std. Beta      t        Sig      lower     upper 
-------------------------------------------------------------------------------------------------------
               (Intercept)    28.017         1.497                 18.711    0.000    25.077    30.957 
               sexo_fctFem    -0.978         0.723       -0.053    -1.352    0.177    -2.398     0.442 
                     idade    -0.160         0.032       -0.203    -5.053    0.000    -0.223    -0.098 
   nivel_escolaridadeMédio    -1.063         1.097       -0.057    -0.969    0.333    -3.216     1.091 
nivel_escolaridadeSuperior    -2.714         1.121       -0.136    -2.420    0.016    -4.915    -0.512 
-------------------------------------------------------------------------------------------------------

Ige Impulsividade

mod_sexo_idade_escolaridade_impulsividade <- lm(ige_impulsividade ~ sexo_fct + idade + nivel_escolaridade, dados) 
apaTables::apa.aov.table(mod_sexo_idade_escolaridade_impulsividade)
olsrr::ols_regress(mod_sexo_idade_escolaridade_impulsividade)

Ige Intolerancia

mod_sexo_idade_escolaridade_intolerancia <- lm(ige_intolerancia ~ sexo_fct + idade + nivel_escolaridade, dados) 
apaTables::apa.aov.table(mod_sexo_idade_escolaridade_intolerancia)
olsrr::ols_regress(mod_sexo_idade_escolaridade_intolerancia)

Ige Emoções negativas

mod_sexo_idade_escolaridade_ige_emocoesnegativas <- lm(ige_emocoesnegativas ~ sexo_fct + idade + nivel_escolaridade, dados) 
apaTables::apa.aov.table(mod_sexo_idade_escolaridade_ige_emocoesnegativas)
olsrr::ols_regress(mod_sexo_idade_escolaridade_ige_emocoesnegativas)

Ige Resiliência emocional

mod_sexo_idade_escolaridade_ige_resilienciaemocio <- lm(ige_resilienciaemocio ~ sexo_fct + idade + nivel_escolaridade, dados) 
apaTables::apa.aov.table(mod_sexo_idade_escolaridade_ige_resilienciaemocio)
olsrr::ols_regress(mod_sexo_idade_escolaridade_ige_resilienciaemocio)

Norms

Function

norms_group <- function(grupo, factor) {
  grupo <- enquo(grupo)
  factor <- enquo(factor)

  
  #condicional para ver se o grupo foi definido
    if(rlang::quo_is_missing(grupo) == 0) { #se o grupo TIVER sido definido
  x <- dados %>%   
    group_by(!!grupo) %>% 
    summarise_at(vars(!!factor), 
               ~quantile(!!factor, 
                         probs = c(seq(from = 0.05, to = 0.95, by = 0.05)), na.rm=T)) %>% #get percentiles
    mutate(percentil = seq(0.05, 0.95, by = 0.05)) %>% 
    mutate(classificacao = case_when(
      percentil < 0.1  ~ "inferior",
      percentil < 0.3  ~ "medio inferior",
      percentil < 0.71  ~ "medio",
      percentil < 0.91  ~ "medio superior",
      TRUE ~ "superior"
    )) %>% 
    pivot_wider(names_from=!!grupo, values_from=!!factor) %>% 
    select(classificacao, percentil, everything())
  
  #return
  list(x,
    dados %>% group_by(!!grupo) %>% summarise_at(vars(!!factor), lst(n=~n(), mean, sd), na.rm=T))

    }
  #se nao houver grupo definido
  else { 
    
    x1 <- dados %>%   
    group_by(!!grupo) %>% 
    summarise_at(vars(!!factor), 
               ~quantile(!!factor, 
                         probs = c(seq(from = 0.05, to = 0.95, by = 0.05)), na.rm=T)) %>% #get percentiles
    mutate(percentil = seq(0.05, 0.95, by = 0.05)) %>% 
    mutate(classificacao = case_when(
      percentil < 0.1  ~ "inferior",
      percentil < 0.3  ~ "medio inferior",
      percentil < 0.71  ~ "medio",
      percentil < 0.91  ~ "medio superior",
      TRUE ~ "superior"
    )) %>% 
    #pivot_wider(names_from=!!x, values_from=!!factor) %>% 
    select(classificacao, percentil, everything())

    #return
    list(x1,
    dados %>% summarise_at(vars(!!factor), lst(n=~n(), mean, sd), na.rm=T))
  }
}

Geral

norms_group(factor = ige_total_ajustado) 

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#%>% as.data.frame() %>%   writexl::write_xlsx("data.xlsx")

norms_group(factor = ige_agressividade) %>% map_df(., ~as.data.frame(.)) %>% gt::gt() %>%  writexl::write_xlsx("data.xlsx")

Error in writexl::write_xlsx(., "data.xlsx") : 
  Argument x must be a data frame or list of data frames

Escolaridade

norms_group(factor = ige_total_ajustado, grupo = nivel_escolaridade)

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[[2]]

NA

Faixa etaria

norms_group(factor = ige_total_ajustado, grupo = faixa_etaria)

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#[1] %>%   writexl::write_xlsx("data.xlsx")