Note This is an R Markdown document detailing the data analysis of the psychological tests Brief-Spec A (RABEsp-A) and Brief-Spec F (RABEsp-F). Data and code are available at https://osf.io/gqmkd/. For questions, contact: luisfca@puc-rio.br
Last updated: r format(Sys.time(), ‘%d %B, %Y’)
pacman::p_load(tidyverse, janitor, stringr, psych, corrr, ggcorrplot, gmodels, arsenal,rsvd, lavaan)load("C:/Users/luisf/Dropbox/Puc-Rio/Consultoria - Lucas Fortaleza/Producao de manuscritos/Artigo 2 - CVC e Analise Fatorial/R base - Lucas Fortaleza EFA e Criterio.RData")items_tead5 <- c(
"tead_8", "tead_35", "tead_36", "tead_38", "tead_33", # soc_com
"tead_9", "tead_3", "tead_5", "tead_28", "tead_29", # soc_inter
"tead_70", "tead_69", "tead_4", "tead_73", "tead_66", # sensorial
"tead_52", "tead_59", "tead_60", "tead_63", "tead_56" # rep_beh
)items_team4 <- c(
"team_6", "team_11", "team_1", "team_9", # cam
"team_5", "team_3", "team_4", "team_25", # gender
"team_14", "team_22", "team_7", "team_21", # cam2
"team_36", "team_37", "team_32", "team_35" # sensory
)set.seed(123)
n_total <- 700
# Eligible males and others
males <- df_tead %>%
mutate(rowid = row_number()) %>%
filter(complete.cases(across(all_of(items_tead5))), genero == "Masculino", aq_total <= 8)
others <- df_tead %>%
mutate(rowid = row_number()) %>%
filter(complete.cases(across(all_of(items_tead5))), genero != "Masculino", aq_total <= 8)
n_male <- min(n_total %/% 2, nrow(males))
n_other <- n_total - n_male
idx_tead <- bind_rows(
males %>% slice_sample(n = n_male),
others %>% slice_sample(n = n_other)
) %>%
sample_frac(1) %>%
pull(rowid)
summary(tableby(
~ idade + genero + escolaridade + aq_total + aq_score,
data = df_tead[idx_tead , ]
), text = TRUE) %>%
data.frame() %>%
print.data.frame()## Var.1 Overall..N.700.
## 1 idade
## 2 - Mean (SD) 37.556 (11.710)
## 3 - Range 18.000 - 78.000
## 4 genero
## 5 - Feminino 343 (49.0%)
## 6 - Masculino 350 (50.0%)
## 7 - Outros 7 (1.0%)
## 8 escolaridade
## 9 - Ensino Fundamental 19 (2.7%)
## 10 - Ensino Médio 87 (12.4%)
## 11 - Ensino Superior 306 (43.7%)
## 12 - Outros 26 (3.7%)
## 13 - Pós graduação 262 (37.4%)
## 14 aq_total
## 15 - Mean (SD) 5.240 (2.157)
## 16 - Range 0.000 - 8.000
## 17 aq_score
## 18 - 0 - 6 points 351 (50.1%)
## 19 - 6 or + points 349 (49.9%)set.seed(123)
idx_team <- df_team %>%
mutate(rowid = row_number()) %>%
filter(complete.cases(across(all_of(items_team4))), aq_total <= 6) %>%
slice_sample(n = 700) %>%
pull(rowid)
summary(tableby(
~ idade + genero + escolaridade + aq_total + aq_score,
data = df_team[idx_team, ]
), text = TRUE) %>%
data.frame() %>%
print.data.frame()## Var.1 Overall..N.700.
## 1 idade
## 2 - Mean (SD) 40.286 (10.277)
## 3 - Range 18.000 - 73.000
## 4 genero
## 5 - Feminino 695 (99.3%)
## 6 - Outros 5 (0.7%)
## 7 escolaridade
## 8 - Ensino Fundamental 7 (1.0%)
## 9 - Ensino Médio 69 (9.9%)
## 10 - Ensino Superior 271 (38.7%)
## 11 - Outros 19 (2.7%)
## 12 - Pós graduação 334 (47.7%)
## 13 aq_total
## 14 - Mean (SD) 3.736 (1.699)
## 15 - Range 0.000 - 6.000
## 16 aq_score
## 17 - 0 - 6 points 576 (82.3%)
## 18 - 6 or + points 124 (17.7%)df_tead[idx_tead, ] %>%
select(all_of(items_tead5)) %>%
tableby(~., data = .,
control=tableby.control(total=TRUE, numeric.simplify=TRUE,
numeric.stats=c("meansd", "median", "iqr"),digits=1)
) %>%
summary(text = TRUE) %>%
as.data.frame(.) %>%
print.data.frame(.) ## Overall (N=700)
## 1 tead_8
## 2 - Mean (SD) 2.6 (1.2)
## 3 - Median 2.0
## 4 - IQR 2.0
## 5 tead_35
## 6 - Mean (SD) 2.8 (1.3)
## 7 - Median 3.0
## 8 - IQR 2.0
## 9 tead_36
## 10 - Mean (SD) 3.4 (1.2)
## 11 - Median 4.0
## 12 - IQR 2.0
## 13 tead_38
## 14 - Mean (SD) 3.8 (1.2)
## 15 - Median 4.0
## 16 - IQR 2.0
## 17 tead_33
## 18 - Mean (SD) 2.7 (1.3)
## 19 - Median 2.0
## 20 - IQR 2.0
## 21 tead_9
## 22 - Mean (SD) 4.2 (1.1)
## 23 - Median 4.0
## 24 - IQR 1.0
## 25 tead_3
## 26 - Mean (SD) 4.1 (1.1)
## 27 - Median 4.0
## 28 - IQR 1.0
## 29 tead_5
## 30 - Mean (SD) 3.6 (1.3)
## 31 - Median 4.0
## 32 - IQR 3.0
## 33 tead_28
## 34 - Mean (SD) 3.9 (1.2)
## 35 - Median 4.0
## 36 - IQR 1.0
## 37 tead_29
## 38 - Mean (SD) 3.7 (1.3)
## 39 - Median 4.0
## 40 - IQR 2.0
## 41 tead_70
## 42 - Mean (SD) 3.9 (1.2)
## 43 - Median 4.0
## 44 - IQR 2.0
## 45 tead_69
## 46 - Mean (SD) 3.8 (1.2)
## 47 - Median 4.0
## 48 - IQR 2.0
## 49 tead_4
## 50 - Mean (SD) 3.2 (1.4)
## 51 - Median 4.0
## 52 - IQR 2.0
## 53 tead_73
## 54 - Mean (SD) 3.7 (1.3)
## 55 - Median 4.0
## 56 - IQR 2.0
## 57 tead_66
## 58 - Mean (SD) 3.9 (1.2)
## 59 - Median 4.0
## 60 - IQR 2.0
## 61 tead_52
## 62 - Mean (SD) 3.8 (1.2)
## 63 - Median 4.0
## 64 - IQR 2.0
## 65 tead_59
## 66 - Mean (SD) 3.6 (1.3)
## 67 - Median 4.0
## 68 - IQR 2.0
## 69 tead_60
## 70 - Mean (SD) 4.1 (1.1)
## 71 - Median 4.0
## 72 - IQR 1.0
## 73 tead_63
## 74 - Mean (SD) 3.6 (1.2)
## 75 - Median 4.0
## 76 - IQR 2.0
## 77 tead_56
## 78 - Mean (SD) 3.7 (1.2)
## 79 - Median 4.0
## 80 - IQR 2.0model_tead5 <- '
# First-order factors
soc_com =~ tead_8 + tead_35 + tead_36 + tead_38 + tead_33
soc_inter =~ tead_9 + tead_3 + tead_5 + tead_28 + tead_29
sensorial =~ tead_70 + tead_69 + tead_4 + tead_73 + tead_66
rep_beh =~ tead_52 + tead_59 + tead_60 + tead_63 + tead_56
# Second-order factor
general =~ soc_com + soc_inter + sensorial + rep_beh
'
fit_tead5 <- cfa(
model_tead5,
data = df_tead[idx_tead , items_tead5],
estimator = "WLSMV",
ordered = items_tead5
)
summary(fit_tead5, fit.measures = TRUE, standardized = TRUE)## lavaan 0.6-18 ended normally after 37 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 104
##
## Number of observations 700
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 582.101 913.189
## Degrees of freedom 166 166
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.675
## Shift parameter 50.579
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 59065.078 16804.697
## Degrees of freedom 190 190
## P-value 0.000 0.000
## Scaling correction factor 3.544
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.993 0.955
## Tucker-Lewis Index (TLI) 0.992 0.949
##
## Robust Comparative Fit Index (CFI) 0.915
## Robust Tucker-Lewis Index (TLI) 0.903
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.060 0.080
## 90 Percent confidence interval - lower 0.055 0.075
## 90 Percent confidence interval - upper 0.065 0.085
## P-value H_0: RMSEA <= 0.050 0.001 0.000
## P-value H_0: RMSEA >= 0.080 0.000 0.538
##
## Robust RMSEA 0.085
## 90 Percent confidence interval - lower 0.079
## 90 Percent confidence interval - upper 0.092
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.911
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.048 0.048
##
## Parameter Estimates:
##
## Parameterization Delta
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## soc_com =~
## tead_8 1.000 0.888 0.888
## tead_35 0.960 0.022 43.227 0.000 0.852 0.852
## tead_36 0.951 0.022 43.265 0.000 0.844 0.844
## tead_38 0.906 0.029 31.072 0.000 0.804 0.804
## tead_33 0.823 0.025 32.773 0.000 0.731 0.731
## soc_inter =~
## tead_9 1.000 0.823 0.823
## tead_3 0.960 0.033 28.886 0.000 0.790 0.790
## tead_5 0.932 0.032 29.574 0.000 0.767 0.767
## tead_28 0.956 0.033 29.343 0.000 0.787 0.787
## tead_29 0.973 0.030 31.943 0.000 0.801 0.801
## sensorial =~
## tead_70 1.000 0.820 0.820
## tead_69 0.995 0.029 34.263 0.000 0.816 0.816
## tead_4 0.880 0.032 27.763 0.000 0.722 0.722
## tead_73 0.871 0.033 26.271 0.000 0.715 0.715
## tead_66 0.952 0.030 31.818 0.000 0.781 0.781
## rep_beh =~
## tead_52 1.000 0.845 0.845
## tead_59 0.945 0.027 35.561 0.000 0.798 0.798
## tead_60 0.982 0.026 37.959 0.000 0.830 0.830
## tead_63 0.878 0.030 29.455 0.000 0.742 0.742
## tead_56 0.919 0.028 32.324 0.000 0.777 0.777
## general =~
## soc_com 1.000 0.776 0.776
## soc_inter 1.063 0.045 23.651 0.000 0.889 0.889
## sensorial 1.089 0.043 25.414 0.000 0.914 0.914
## rep_beh 1.030 0.041 24.923 0.000 0.840 0.840
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## tead_8|t1 -1.012 0.057 -17.641 0.000 -1.012 -1.012
## tead_8|t2 0.140 0.048 2.945 0.003 0.140 0.140
## tead_8|t3 0.604 0.051 11.920 0.000 0.604 0.604
## tead_8|t4 1.435 0.070 20.441 0.000 1.435 1.435
## tead_35|t1 -1.061 0.059 -18.140 0.000 -1.061 -1.061
## tead_35|t2 -0.007 0.047 -0.151 0.880 -0.007 -0.007
## tead_35|t3 0.397 0.049 8.136 0.000 0.397 0.397
## tead_35|t4 1.197 0.062 19.283 0.000 1.197 1.197
## tead_36|t1 -1.396 0.069 -20.326 0.000 -1.396 -1.396
## tead_36|t2 -0.635 0.051 -12.432 0.000 -0.635 -0.635
## tead_36|t3 -0.075 0.047 -1.586 0.113 -0.075 -0.075
## tead_36|t4 0.857 0.054 15.781 0.000 0.857 0.857
## tead_38|t1 -1.543 0.075 -20.613 0.000 -1.543 -1.543
## tead_38|t2 -0.787 0.053 -14.805 0.000 -0.787 -0.787
## tead_38|t3 -0.508 0.050 -10.223 0.000 -0.508 -0.508
## tead_38|t4 0.424 0.049 8.659 0.000 0.424 0.424
## tead_33|t1 -0.816 0.054 -15.226 0.000 -0.816 -0.816
## tead_33|t2 0.065 0.047 1.360 0.174 0.065 0.065
## tead_33|t3 0.355 0.049 7.312 0.000 0.355 0.355
## tead_33|t4 1.189 0.062 19.230 0.000 1.189 1.189
## tead_9|t1 -1.821 0.091 -20.104 0.000 -1.821 -1.821
## tead_9|t2 -1.211 0.062 -19.387 0.000 -1.211 -1.211
## tead_9|t3 -0.977 0.057 -17.256 0.000 -0.977 -0.977
## tead_9|t4 0.079 0.047 1.662 0.097 0.079 0.079
## tead_3|t1 -1.688 0.082 -20.510 0.000 -1.688 -1.688
## tead_3|t2 -1.154 0.061 -18.959 0.000 -1.154 -1.154
## tead_3|t3 -0.926 0.056 -16.663 0.000 -0.926 -0.926
## tead_3|t4 0.039 0.047 0.831 0.406 0.039 0.039
## tead_5|t1 -1.396 0.069 -20.326 0.000 -1.396 -1.396
## tead_5|t2 -0.666 0.051 -12.942 0.000 -0.666 -0.666
## tead_5|t3 -0.347 0.048 -7.162 0.000 -0.347 -0.347
## tead_5|t4 0.626 0.051 12.286 0.000 0.626 0.626
## tead_28|t1 -1.543 0.075 -20.613 0.000 -1.543 -1.543
## tead_28|t2 -0.977 0.057 -17.256 0.000 -0.977 -0.977
## tead_28|t3 -0.711 0.052 -13.664 0.000 -0.711 -0.711
## tead_28|t4 0.302 0.048 6.260 0.000 0.302 0.302
## tead_29|t1 -1.368 0.068 -20.225 0.000 -1.368 -1.368
## tead_29|t2 -0.763 0.053 -14.451 0.000 -0.763 -0.763
## tead_29|t3 -0.488 0.050 -9.851 0.000 -0.488 -0.488
## tead_29|t4 0.444 0.049 9.032 0.000 0.444 0.444
## tead_70|t1 -1.605 0.078 -20.615 0.000 -1.605 -1.605
## tead_70|t2 -0.932 0.056 -16.730 0.000 -0.932 -0.932
## tead_70|t3 -0.608 0.051 -11.993 0.000 -0.608 -0.608
## tead_70|t4 0.283 0.048 5.884 0.000 0.283 0.283
## tead_69|t1 -1.567 0.076 -20.622 0.000 -1.567 -1.567
## tead_69|t2 -0.826 0.054 -15.365 0.000 -0.826 -0.826
## tead_69|t3 -0.488 0.050 -9.851 0.000 -0.488 -0.488
## tead_69|t4 0.436 0.049 8.883 0.000 0.436 0.436
## tead_4|t1 -1.000 0.057 -17.514 0.000 -1.000 -1.000
## tead_4|t2 -0.351 0.048 -7.237 0.000 -0.351 -0.351
## tead_4|t3 -0.011 0.047 -0.227 0.821 -0.011 -0.011
## tead_4|t4 0.720 0.052 13.808 0.000 0.720 0.720
## tead_73|t1 -1.435 0.070 -20.441 0.000 -1.435 -1.435
## tead_73|t2 -0.782 0.053 -14.734 0.000 -0.782 -0.782
## tead_73|t3 -0.436 0.049 -8.883 0.000 -0.436 -0.436
## tead_73|t4 0.420 0.049 8.584 0.000 0.420 0.420
## tead_66|t1 -1.618 0.079 -20.606 0.000 -1.618 -1.618
## tead_66|t2 -0.983 0.057 -17.321 0.000 -0.983 -0.983
## tead_66|t3 -0.670 0.051 -13.014 0.000 -0.670 -0.670
## tead_66|t4 0.339 0.048 7.011 0.000 0.339 0.339
## tead_52|t1 -1.631 0.079 -20.594 0.000 -1.631 -1.631
## tead_52|t2 -0.873 0.055 -15.987 0.000 -0.873 -0.873
## tead_52|t3 -0.596 0.051 -11.773 0.000 -0.596 -0.596
## tead_52|t4 0.496 0.050 10.000 0.000 0.496 0.496
## tead_59|t1 -1.435 0.070 -20.441 0.000 -1.435 -1.435
## tead_59|t2 -0.697 0.052 -13.448 0.000 -0.697 -0.697
## tead_59|t3 -0.401 0.049 -8.211 0.000 -0.401 -0.401
## tead_59|t4 0.608 0.051 11.993 0.000 0.608 0.608
## tead_60|t1 -1.881 0.095 -19.834 0.000 -1.881 -1.881
## tead_60|t2 -1.182 0.062 -19.177 0.000 -1.182 -1.182
## tead_60|t3 -0.915 0.055 -16.529 0.000 -0.915 -0.915
## tead_60|t4 0.162 0.048 3.398 0.001 0.162 0.162
## tead_63|t1 -1.425 0.070 -20.415 0.000 -1.425 -1.425
## tead_63|t2 -0.706 0.052 -13.593 0.000 -0.706 -0.706
## tead_63|t3 -0.351 0.048 -7.237 0.000 -0.351 -0.351
## tead_63|t4 0.630 0.051 12.359 0.000 0.630 0.630
## tead_56|t1 -1.520 0.074 -20.595 0.000 -1.520 -1.520
## tead_56|t2 -0.787 0.053 -14.805 0.000 -0.787 -0.787
## tead_56|t3 -0.476 0.049 -9.628 0.000 -0.476 -0.476
## tead_56|t4 0.574 0.050 11.406 0.000 0.574 0.574
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .tead_8 0.212 0.212 0.212
## .tead_35 0.275 0.275 0.275
## .tead_36 0.288 0.288 0.288
## .tead_38 0.354 0.354 0.354
## .tead_33 0.466 0.466 0.466
## .tead_9 0.323 0.323 0.323
## .tead_3 0.376 0.376 0.376
## .tead_5 0.411 0.411 0.411
## .tead_28 0.380 0.380 0.380
## .tead_29 0.359 0.359 0.359
## .tead_70 0.328 0.328 0.328
## .tead_69 0.334 0.334 0.334
## .tead_4 0.479 0.479 0.479
## .tead_73 0.489 0.489 0.489
## .tead_66 0.390 0.390 0.390
## .tead_52 0.286 0.286 0.286
## .tead_59 0.363 0.363 0.363
## .tead_60 0.311 0.311 0.311
## .tead_63 0.449 0.449 0.449
## .tead_56 0.396 0.396 0.396
## .soc_com 0.313 0.023 13.336 0.000 0.398 0.398
## .soc_inter 0.142 0.019 7.300 0.000 0.209 0.209
## .sensorial 0.110 0.017 6.488 0.000 0.164 0.164
## .rep_beh 0.210 0.021 10.118 0.000 0.295 0.295
## general 0.474 0.029 16.138 0.000 1.000 1.000df_tead[idx_tead, ] %>%
select(all_of(items_tead5)) %>% psych::alpha()##
## Reliability analysis
## Call: psych::alpha(x = .)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.94 0.94 0.95 0.43 15 0.0035 3.6 0.83 0.42
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.93 0.94 0.94
## Duhachek 0.93 0.94 0.94
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## tead_8 0.93 0.94 0.94 0.43 14 0.0036 0.0062 0.42
## tead_35 0.93 0.94 0.94 0.43 15 0.0036 0.0059 0.42
## tead_36 0.93 0.93 0.94 0.43 14 0.0037 0.0069 0.42
## tead_38 0.93 0.93 0.95 0.43 14 0.0036 0.0071 0.42
## tead_33 0.94 0.94 0.95 0.44 15 0.0035 0.0065 0.43
## tead_9 0.93 0.93 0.95 0.43 14 0.0037 0.0071 0.41
## tead_3 0.93 0.94 0.95 0.43 14 0.0036 0.0068 0.42
## tead_5 0.93 0.94 0.95 0.43 14 0.0036 0.0070 0.42
## tead_28 0.93 0.94 0.94 0.43 14 0.0036 0.0067 0.42
## tead_29 0.93 0.93 0.95 0.43 14 0.0037 0.0071 0.42
## tead_70 0.93 0.93 0.95 0.43 14 0.0037 0.0072 0.41
## tead_69 0.93 0.93 0.94 0.43 14 0.0037 0.0069 0.42
## tead_4 0.94 0.94 0.95 0.43 15 0.0036 0.0073 0.42
## tead_73 0.93 0.94 0.95 0.43 15 0.0036 0.0071 0.42
## tead_66 0.93 0.94 0.95 0.43 14 0.0036 0.0069 0.42
## tead_52 0.93 0.93 0.94 0.43 14 0.0037 0.0067 0.42
## tead_59 0.93 0.94 0.95 0.43 14 0.0036 0.0069 0.42
## tead_60 0.93 0.94 0.95 0.43 14 0.0036 0.0066 0.42
## tead_63 0.93 0.94 0.95 0.43 15 0.0036 0.0070 0.42
## tead_56 0.93 0.94 0.95 0.43 14 0.0036 0.0073 0.42
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## tead_8 700 0.67 0.66 0.65 0.62 2.6 1.2
## tead_35 700 0.64 0.64 0.63 0.60 2.8 1.3
## tead_36 700 0.73 0.72 0.71 0.69 3.4 1.2
## tead_38 700 0.69 0.69 0.67 0.64 3.8 1.2
## tead_33 700 0.61 0.61 0.58 0.56 2.7 1.3
## tead_9 700 0.71 0.72 0.70 0.68 4.2 1.1
## tead_3 700 0.67 0.67 0.65 0.63 4.1 1.1
## tead_5 700 0.68 0.68 0.66 0.64 3.6 1.3
## tead_28 700 0.67 0.68 0.66 0.63 3.9 1.2
## tead_29 700 0.72 0.72 0.70 0.68 3.7 1.3
## tead_70 700 0.72 0.72 0.70 0.68 3.9 1.2
## tead_69 700 0.70 0.70 0.69 0.66 3.8 1.2
## tead_4 700 0.65 0.64 0.61 0.59 3.2 1.4
## tead_73 700 0.65 0.65 0.63 0.61 3.7 1.3
## tead_66 700 0.67 0.67 0.65 0.63 3.9 1.2
## tead_52 700 0.72 0.72 0.71 0.68 3.8 1.2
## tead_59 700 0.67 0.68 0.66 0.63 3.6 1.3
## tead_60 700 0.67 0.67 0.66 0.63 4.1 1.1
## tead_63 700 0.64 0.64 0.62 0.60 3.6 1.2
## tead_56 700 0.67 0.68 0.65 0.63 3.7 1.2
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## tead_8 0.16 0.40 0.17 0.20 0.08 0
## tead_35 0.14 0.35 0.16 0.23 0.12 0
## tead_36 0.08 0.18 0.21 0.33 0.20 0
## tead_38 0.06 0.15 0.09 0.36 0.34 0
## tead_33 0.21 0.32 0.11 0.24 0.12 0
## tead_9 0.03 0.08 0.05 0.37 0.47 0
## tead_3 0.05 0.08 0.05 0.34 0.48 0
## tead_5 0.08 0.17 0.11 0.37 0.27 0
## tead_28 0.06 0.10 0.07 0.38 0.38 0
## tead_29 0.09 0.14 0.09 0.36 0.33 0
## tead_70 0.05 0.12 0.10 0.34 0.39 0
## tead_69 0.06 0.15 0.11 0.36 0.33 0
## tead_4 0.16 0.20 0.13 0.27 0.24 0
## tead_73 0.08 0.14 0.11 0.33 0.34 0
## tead_66 0.05 0.11 0.09 0.38 0.37 0
## tead_52 0.05 0.14 0.08 0.41 0.31 0
## tead_59 0.08 0.17 0.10 0.38 0.27 0
## tead_60 0.03 0.09 0.06 0.38 0.44 0
## tead_63 0.08 0.16 0.12 0.37 0.26 0
## tead_56 0.06 0.15 0.10 0.40 0.28 0df_team[idx_team, ] %>%
select(all_of(items_team4)) %>%
tableby(~., data = .,
control=tableby.control(total=TRUE, numeric.simplify=TRUE,
numeric.stats=c("meansd", "median", "iqr"),digits=1)
) %>%
summary(text = TRUE) %>%
as.data.frame(.) %>%
print.data.frame(.) ## Overall (N=700)
## 1 team_6
## 2 - Mean (SD) 2.7 (1.3)
## 3 - Median 2.0
## 4 - IQR 2.0
## 5 team_11
## 6 - Mean (SD) 3.0 (1.3)
## 7 - Median 3.0
## 8 - IQR 2.0
## 9 team_1
## 10 - Mean (SD) 2.7 (1.3)
## 11 - Median 3.0
## 12 - IQR 2.0
## 13 team_9
## 14 - Mean (SD) 2.3 (1.2)
## 15 - Median 2.0
## 16 - IQR 2.0
## 17 team_5
## 18 - Mean (SD) 3.3 (1.3)
## 19 - Median 4.0
## 20 - IQR 2.0
## 21 team_3
## 22 - Mean (SD) 2.6 (1.4)
## 23 - Median 2.0
## 24 - IQR 3.0
## 25 team_4
## 26 - Mean (SD) 2.5 (1.4)
## 27 - Median 2.0
## 28 - IQR 3.0
## 29 team_25
## 30 - Mean (SD) 1.8 (1.1)
## 31 - Median 1.0
## 32 - IQR 1.0
## 33 team_14
## 34 - Mean (SD) 3.3 (1.3)
## 35 - Median 4.0
## 36 - IQR 2.0
## 37 team_22
## 38 - Mean (SD) 3.3 (1.3)
## 39 - Median 4.0
## 40 - IQR 2.0
## 41 team_7
## 42 - Mean (SD) 3.5 (1.3)
## 43 - Median 4.0
## 44 - IQR 2.0
## 45 team_21
## 46 - Mean (SD) 3.4 (1.3)
## 47 - Median 4.0
## 48 - IQR 2.0
## 49 team_36
## 50 - Mean (SD) 2.8 (1.4)
## 51 - Median 3.0
## 52 - IQR 2.0
## 53 team_37
## 54 - Mean (SD) 2.8 (1.4)
## 55 - Median 3.0
## 56 - IQR 2.0
## 57 team_32
## 58 - Mean (SD) 3.8 (1.3)
## 59 - Median 4.0
## 60 - IQR 2.0
## 61 team_35
## 62 - Mean (SD) 3.5 (1.3)
## 63 - Median 4.0
## 64 - IQR 3.0model_team4 <- '
cam =~ team_6 + team_11 + team_1 + team_9
gender =~ team_5 + team_3 + team_4 + team_25
cam2 =~ team_14 + team_22 + team_7 + team_21
sensory =~ team_36 + team_37 + team_32 + team_35
general =~ cam + gender + cam2 + sensory
'
fit_team4 <- cfa(
model_team4,
data = df_team[idx_team, items_team4],
estimator = "WLSMV",
ordered = items_team4
)
summary(fit_team4, fit.measures = TRUE, standardized = TRUE)## lavaan 0.6-18 ended normally after 45 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 84
##
## Number of observations 700
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 608.270 887.953
## Degrees of freedom 100 100
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.713
## Shift parameter 34.814
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 59050.149 19378.404
## Degrees of freedom 120 120
## P-value 0.000 0.000
## Scaling correction factor 3.060
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.991 0.959
## Tucker-Lewis Index (TLI) 0.990 0.951
##
## Robust Comparative Fit Index (CFI) 0.922
## Robust Tucker-Lewis Index (TLI) 0.907
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.085 0.106
## 90 Percent confidence interval - lower 0.079 0.100
## 90 Percent confidence interval - upper 0.092 0.113
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 0.911 1.000
##
## Robust RMSEA 0.098
## 90 Percent confidence interval - lower 0.089
## 90 Percent confidence interval - upper 0.106
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.067 0.067
##
## Parameter Estimates:
##
## Parameterization Delta
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cam =~
## team_6 1.000 0.895 0.895
## team_11 0.968 0.016 58.956 0.000 0.866 0.866
## team_1 0.968 0.017 57.451 0.000 0.866 0.866
## team_9 0.933 0.020 47.324 0.000 0.835 0.835
## gender =~
## team_5 1.000 0.807 0.807
## team_3 1.139 0.036 31.872 0.000 0.919 0.919
## team_4 1.065 0.035 30.165 0.000 0.859 0.859
## team_25 0.823 0.047 17.657 0.000 0.664 0.664
## cam2 =~
## team_14 1.000 0.903 0.903
## team_22 1.000 0.014 72.096 0.000 0.904 0.904
## team_7 0.919 0.016 55.800 0.000 0.830 0.830
## team_21 0.935 0.016 58.335 0.000 0.844 0.844
## sensory =~
## team_36 1.000 0.836 0.836
## team_37 1.008 0.033 30.405 0.000 0.843 0.843
## team_32 0.856 0.037 22.864 0.000 0.716 0.716
## team_35 0.837 0.038 22.127 0.000 0.699 0.699
## general =~
## cam 1.000 0.868 0.868
## gender 0.642 0.034 18.668 0.000 0.618 0.618
## cam2 1.107 0.036 30.588 0.000 0.952 0.952
## sensory 0.847 0.034 24.842 0.000 0.787 0.787
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## team_6|t1 -0.688 0.052 -13.304 0.000 -0.688 -0.688
## team_6|t2 0.090 0.047 1.888 0.059 0.090 0.090
## team_6|t3 0.424 0.049 8.659 0.000 0.424 0.424
## team_6|t4 1.258 0.064 19.683 0.000 1.258 1.258
## team_11|t1 -0.977 0.057 -17.256 0.000 -0.977 -0.977
## team_11|t2 -0.291 0.048 -6.035 0.000 -0.291 -0.291
## team_11|t3 0.155 0.048 3.247 0.001 0.155 0.155
## team_11|t4 1.204 0.062 19.335 0.000 1.204 1.204
## team_1|t1 -0.842 0.054 -15.574 0.000 -0.842 -0.842
## team_1|t2 -0.011 0.047 -0.227 0.821 -0.011 -0.011
## team_1|t3 0.389 0.049 7.986 0.000 0.389 0.389
## team_1|t4 1.359 0.067 20.189 0.000 1.359 1.359
## team_9|t1 -0.529 0.050 -10.593 0.000 -0.529 -0.529
## team_9|t2 0.339 0.048 7.011 0.000 0.339 0.339
## team_9|t3 0.826 0.054 15.365 0.000 0.826 0.826
## team_9|t4 1.734 0.085 20.401 0.000 1.734 1.734
## team_5|t1 -1.197 0.062 -19.283 0.000 -1.197 -1.197
## team_5|t2 -0.460 0.049 -9.331 0.000 -0.460 -0.460
## team_5|t3 -0.082 0.047 -1.737 0.082 -0.082 -0.082
## team_5|t4 0.782 0.053 14.734 0.000 0.782 0.782
## team_3|t1 -0.652 0.051 -12.724 0.000 -0.652 -0.652
## team_3|t2 0.151 0.048 3.172 0.002 0.151 0.151
## team_3|t3 0.472 0.049 9.554 0.000 0.472 0.472
## team_3|t4 1.161 0.061 19.014 0.000 1.161 1.161
## team_4|t1 -0.579 0.050 -11.479 0.000 -0.579 -0.579
## team_4|t2 0.257 0.048 5.358 0.000 0.257 0.257
## team_4|t3 0.524 0.050 10.519 0.000 0.524 0.524
## team_4|t4 1.234 0.063 19.538 0.000 1.234 1.234
## team_25|t1 0.206 0.048 4.303 0.000 0.206 0.206
## team_25|t2 0.831 0.054 15.435 0.000 0.831 0.831
## team_25|t3 1.126 0.060 18.733 0.000 1.126 1.126
## team_25|t4 1.785 0.088 20.243 0.000 1.785 1.785
## team_14|t1 -1.161 0.061 -19.014 0.000 -1.161 -1.161
## team_14|t2 -0.460 0.049 -9.331 0.000 -0.460 -0.460
## team_14|t3 -0.184 0.048 -3.850 0.000 -0.184 -0.184
## team_14|t4 0.797 0.053 14.946 0.000 0.797 0.797
## team_22|t1 -1.126 0.060 -18.733 0.000 -1.126 -1.126
## team_22|t2 -0.428 0.049 -8.734 0.000 -0.428 -0.428
## team_22|t3 -0.129 0.048 -2.719 0.007 -0.129 -0.129
## team_22|t4 0.847 0.054 15.643 0.000 0.847 0.847
## team_7|t1 -1.258 0.064 -19.683 0.000 -1.258 -1.258
## team_7|t2 -0.630 0.051 -12.359 0.000 -0.630 -0.630
## team_7|t3 -0.355 0.049 -7.312 0.000 -0.355 -0.355
## team_7|t4 0.787 0.053 14.805 0.000 0.787 0.787
## team_21|t1 -1.211 0.062 -19.387 0.000 -1.211 -1.211
## team_21|t2 -0.579 0.050 -11.479 0.000 -0.579 -0.579
## team_21|t3 -0.287 0.048 -5.960 0.000 -0.287 -0.287
## team_21|t4 0.763 0.053 14.451 0.000 0.763 0.763
## team_36|t1 -0.716 0.052 -13.736 0.000 -0.716 -0.716
## team_36|t2 -0.032 0.047 -0.680 0.497 -0.032 -0.032
## team_36|t3 0.228 0.048 4.755 0.000 0.228 0.228
## team_36|t4 1.175 0.061 19.123 0.000 1.175 1.175
## team_37|t1 -0.758 0.053 -14.380 0.000 -0.758 -0.758
## team_37|t2 -0.025 0.047 -0.529 0.597 -0.025 -0.025
## team_37|t3 0.202 0.048 4.227 0.000 0.202 0.202
## team_37|t4 1.080 0.059 18.321 0.000 1.080 1.080
## team_32|t1 -1.405 0.069 -20.357 0.000 -1.405 -1.405
## team_32|t2 -0.831 0.054 -15.435 0.000 -0.831 -0.831
## team_32|t3 -0.591 0.051 -11.700 0.000 -0.591 -0.591
## team_32|t4 0.420 0.049 8.584 0.000 0.420 0.420
## team_35|t1 -1.227 0.063 -19.488 0.000 -1.227 -1.227
## team_35|t2 -0.608 0.051 -11.993 0.000 -0.608 -0.608
## team_35|t3 -0.217 0.048 -4.529 0.000 -0.217 -0.217
## team_35|t4 0.596 0.051 11.773 0.000 0.596 0.596
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .team_6 0.199 0.199 0.199
## .team_11 0.250 0.250 0.250
## .team_1 0.250 0.250 0.250
## .team_9 0.304 0.304 0.304
## .team_5 0.349 0.349 0.349
## .team_3 0.156 0.156 0.156
## .team_4 0.261 0.261 0.261
## .team_25 0.559 0.559 0.559
## .team_14 0.184 0.184 0.184
## .team_22 0.183 0.183 0.183
## .team_7 0.311 0.311 0.311
## .team_21 0.287 0.287 0.287
## .team_36 0.302 0.302 0.302
## .team_37 0.290 0.290 0.290
## .team_32 0.488 0.488 0.488
## .team_35 0.511 0.511 0.511
## .cam 0.197 0.024 8.263 0.000 0.247 0.247
## .gender 0.402 0.031 13.121 0.000 0.618 0.618
## .cam2 0.077 0.021 3.673 0.000 0.095 0.095
## .sensory 0.266 0.021 12.755 0.000 0.381 0.381
## general 0.603 0.030 19.827 0.000 1.000 1.000df_team[idx_team, ] %>%
select(all_of(items_team4)) %>% psych::alpha()##
## Reliability analysis
## Call: psych::alpha(x = .)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.92 0.92 0.94 0.42 12 0.0044 3 0.89 0.39
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.91 0.92 0.93
## Duhachek 0.91 0.92 0.93
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## team_6 0.91 0.91 0.93 0.41 11 0.0048 0.019 0.39
## team_11 0.91 0.91 0.93 0.41 11 0.0048 0.019 0.39
## team_1 0.91 0.91 0.93 0.42 11 0.0047 0.018 0.39
## team_9 0.91 0.91 0.93 0.41 11 0.0047 0.020 0.38
## team_5 0.92 0.92 0.94 0.42 11 0.0046 0.022 0.40
## team_3 0.92 0.92 0.93 0.43 11 0.0045 0.019 0.40
## team_4 0.92 0.92 0.93 0.44 12 0.0044 0.017 0.40
## team_25 0.92 0.92 0.94 0.44 12 0.0044 0.019 0.41
## team_14 0.91 0.91 0.93 0.41 10 0.0049 0.018 0.38
## team_22 0.91 0.91 0.93 0.41 10 0.0049 0.018 0.38
## team_7 0.91 0.91 0.93 0.42 11 0.0048 0.019 0.38
## team_21 0.91 0.91 0.93 0.41 11 0.0048 0.019 0.38
## team_36 0.92 0.92 0.93 0.42 11 0.0046 0.021 0.39
## team_37 0.92 0.92 0.93 0.42 11 0.0046 0.021 0.39
## team_32 0.92 0.92 0.94 0.43 11 0.0045 0.021 0.39
## team_35 0.92 0.92 0.94 0.43 11 0.0046 0.021 0.39
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## team_6 700 0.76 0.76 0.75 0.71 2.7 1.3
## team_11 700 0.73 0.73 0.72 0.68 3.0 1.3
## team_1 700 0.72 0.72 0.71 0.67 2.7 1.3
## team_9 700 0.72 0.73 0.71 0.68 2.3 1.2
## team_5 700 0.64 0.64 0.60 0.58 3.3 1.3
## team_3 700 0.60 0.60 0.58 0.53 2.6 1.4
## team_4 700 0.53 0.53 0.51 0.46 2.5 1.4
## team_25 700 0.48 0.49 0.43 0.42 1.8 1.1
## team_14 700 0.80 0.80 0.80 0.76 3.3 1.3
## team_22 700 0.79 0.79 0.79 0.75 3.3 1.3
## team_7 700 0.72 0.72 0.71 0.67 3.5 1.3
## team_21 700 0.76 0.76 0.74 0.71 3.4 1.3
## team_36 700 0.67 0.66 0.64 0.61 2.8 1.4
## team_37 700 0.68 0.67 0.65 0.62 2.8 1.4
## team_32 700 0.60 0.60 0.56 0.54 3.8 1.3
## team_35 700 0.61 0.61 0.57 0.55 3.5 1.3
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## team_6 0.25 0.29 0.13 0.23 0.10 0
## team_11 0.16 0.22 0.18 0.32 0.11 0
## team_1 0.20 0.30 0.16 0.26 0.09 0
## team_9 0.30 0.33 0.16 0.16 0.04 0
## team_5 0.12 0.21 0.14 0.32 0.22 0
## team_3 0.26 0.30 0.12 0.20 0.12 0
## team_4 0.28 0.32 0.10 0.19 0.11 0
## team_25 0.58 0.22 0.07 0.09 0.04 0
## team_14 0.12 0.20 0.10 0.36 0.21 0
## team_22 0.13 0.20 0.11 0.35 0.20 0
## team_7 0.10 0.16 0.10 0.42 0.22 0
## team_21 0.11 0.17 0.11 0.39 0.22 0
## team_36 0.24 0.25 0.10 0.29 0.12 0
## team_37 0.22 0.27 0.09 0.28 0.14 0
## team_32 0.08 0.12 0.07 0.39 0.34 0
## team_35 0.11 0.16 0.14 0.31 0.28 0# For Brief-Spec-A (fit_tead5)
list("team 20",semTools::reliability(fit_tead5),
"tead 16",semTools::reliability(fit_team4))## 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.
##
## 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.## [[1]]
## [1] "team 20"
##
## [[2]]
## soc_com soc_inter sensorial rep_beh
## alpha 0.8679975 0.8605879 0.8307374 0.8568766
## alpha.ord 0.8995426 0.8926398 0.8685952 0.8922951
## omega 0.8857457 0.8564335 0.8444639 0.8641581
## omega2 0.8857457 0.8564335 0.8444639 0.8641581
## omega3 0.9180544 0.8610669 0.8711119 0.8792135
## avevar 0.6809998 0.6301366 0.5959756 0.6389393
##
## [[3]]
## [1] "tead 16"
##
## [[4]]
## cam gender cam2 sensory
## alpha 0.8915353 0.7903861 0.8976807 0.7825448
## alpha.ord 0.9199460 0.8362149 0.9257882 0.8174303
## omega 0.8987770 0.8600699 0.8993120 0.8231042
## omega2 0.8987770 0.8600699 0.8993120 0.8231042
## omega3 0.9037434 0.9411507 0.8992852 0.8812163
## avevar 0.7491958 0.6686843 0.7584719 0.6024564df_tead <- df_tead %>%
mutate(
brief_soc_com = tead_8 + tead_35 + tead_36 + tead_38 + tead_33,
brief_soc_inter = tead_9 + tead_3 + tead_5 + tead_28 + tead_29,
brief_sensorial = tead_70 + tead_69 + tead_4 + tead_73 + tead_66,
brief_rep_beh = tead_52 + tead_59 + tead_60 + tead_63 + tead_56,
brief_total = brief_soc_com + brief_soc_inter + brief_sensorial + brief_rep_beh
)summary(tableby(
~ idade + escolaridade + genero + brief_soc_com + brief_soc_inter + brief_sensorial + brief_rep_beh + brief_total,
data = df_tead[idx_tead, ]
), text = TRUE) %>%
data.frame() %>%
print.data.frame()## Var.1 Overall..N.700.
## 1 idade
## 2 - Mean (SD) 37.556 (11.710)
## 3 - Range 18.000 - 78.000
## 4 escolaridade
## 5 - Ensino Fundamental 19 (2.7%)
## 6 - Ensino Médio 87 (12.4%)
## 7 - Ensino Superior 306 (43.7%)
## 8 - Outros 26 (3.7%)
## 9 - Pós graduação 262 (37.4%)
## 10 genero
## 11 - Feminino 343 (49.0%)
## 12 - Masculino 350 (50.0%)
## 13 - Outros 7 (1.0%)
## 14 brief_soc_com
## 15 - Mean (SD) 15.337 (5.053)
## 16 - Range 5.000 - 25.000
## 17 brief_soc_inter
## 18 - Mean (SD) 19.486 (4.756)
## 19 - Range 5.000 - 25.000
## 20 brief_sensorial
## 21 - Mean (SD) 18.474 (4.862)
## 22 - Range 5.000 - 25.000
## 23 brief_rep_beh
## 24 - Mean (SD) 18.777 (4.749)
## 25 - Range 5.000 - 25.000
## 26 brief_total
## 27 - Mean (SD) 72.074 (16.512)
## 28 - Range 20.000 - 100.000df_tead[idx_tead, ] %>%
summarise(
brief_soc_com_p60 = quantile(brief_soc_com, 0.60, na.rm = TRUE),
brief_soc_inter_p60 = quantile(brief_soc_inter, 0.60, na.rm = TRUE),
brief_sensorial_p60 = quantile(brief_sensorial, 0.60, na.rm = TRUE),
brief_rep_beh_p60 = quantile(brief_rep_beh, 0.60, na.rm = TRUE),
brief_total_p60 = quantile(brief_total, 0.60, na.rm = TRUE)
) %>% t()## [,1]
## brief_soc_com_p60 17
## brief_soc_inter_p60 22
## brief_sensorial_p60 21
## brief_rep_beh_p60 21
## brief_total_p60 79df_tead[idx_tead, ] %>%
select(brief_soc_com, brief_soc_inter, brief_sensorial, brief_rep_beh, brief_total, aq_total) %>%
correlation::correlation() %>%
as.data.frame() %>%
filter(Parameter2 %in% ("aq_total"))## Parameter1 Parameter2 r CI CI_low CI_high t
## 1 brief_soc_com aq_total 0.6855058 0.95 0.6441231 0.7228875 24.87525
## 2 brief_soc_inter aq_total 0.5608157 0.95 0.5078167 0.6095853 17.89570
## 3 brief_sensorial aq_total 0.5984113 0.95 0.5486373 0.6439585 19.73297
## 4 brief_rep_beh aq_total 0.5406884 0.95 0.4860603 0.5911076 16.98102
## 5 brief_total aq_total 0.7030005 0.95 0.6634601 0.7386251 26.11548
## df_error p Method n_Obs
## 1 698 2.746686e-97 Pearson correlation 700
## 2 698 9.338547e-59 Pearson correlation 700
## 3 698 1.613433e-68 Pearson correlation 700
## 4 698 2.154638e-54 Pearson correlation 700
## 5 698 2.287577e-104 Pearson correlation 700df_team <- df_team %>%
mutate(
brief_cam = team_6 + team_11 + team_1 + team_9,
brief_gender = team_5 + team_3 + team_4 + team_25,
brief_cam2 = team_14 + team_22 + team_7 + team_21,
brief_sensory = team_36 + team_37 + team_32 + team_35,
brief_total = brief_cam + brief_gender + brief_cam2 + brief_sensory
)summary(tableby(
~ idade + escolaridade + genero + brief_cam + brief_gender + brief_cam2 + brief_sensory + brief_total,
data = df_team[idx_team, ]
), text = TRUE) %>%
data.frame() %>%
print.data.frame()## Var.1 Overall..N.700.
## 1 idade
## 2 - Mean (SD) 40.286 (10.277)
## 3 - Range 18.000 - 73.000
## 4 escolaridade
## 5 - Ensino Fundamental 7 (1.0%)
## 6 - Ensino Médio 69 (9.9%)
## 7 - Ensino Superior 271 (38.7%)
## 8 - Outros 19 (2.7%)
## 9 - Pós graduação 334 (47.7%)
## 10 genero
## 11 - Feminino 695 (99.3%)
## 12 - Outros 5 (0.7%)
## 13 brief_cam
## 14 - Mean (SD) 10.716 (4.429)
## 15 - Range 4.000 - 20.000
## 16 brief_gender
## 17 - Mean (SD) 10.250 (4.088)
## 18 - Range 4.000 - 20.000
## 19 brief_cam2
## 20 - Mean (SD) 13.553 (4.602)
## 21 - Range 4.000 - 20.000
## 22 brief_sensory
## 23 - Mean (SD) 12.909 (4.197)
## 24 - Range 4.000 - 20.000
## 25 brief_total
## 26 - Mean (SD) 47.427 (14.174)
## 27 - Range 16.000 - 80.000df_team[idx_team, ] %>%
summarise(
brief_cam_p60 = quantile(brief_cam, 0.60, na.rm = TRUE),
brief_gender_p60 = quantile(brief_gender, 0.60, na.rm = TRUE),
brief_cam2_p60 = quantile(brief_cam2, 0.60, na.rm = TRUE),
brief_sensory_p60 = quantile(brief_sensory, 0.60, na.rm = TRUE),
brief_total_p60 = quantile(brief_total, 0.60, na.rm = TRUE)
) %>% t()## [,1]
## brief_cam_p60 12
## brief_gender_p60 11
## brief_cam2_p60 16
## brief_sensory_p60 14
## brief_total_p60 52df_team[idx_team, ] %>%
select(brief_cam, brief_gender, brief_cam2, brief_sensory, brief_total, aq_total) %>%
correlation::correlation() %>%
as.data.frame() %>%
filter(Parameter2 %in% ("aq_total"))## Parameter1 Parameter2 r CI CI_low CI_high t
## 1 brief_cam aq_total 0.4432212 0.95 0.3816534 0.5008734 13.062925
## 2 brief_gender aq_total 0.2984622 0.95 0.2294337 0.3645034 8.261842
## 3 brief_cam2 aq_total 0.4828456 0.95 0.4239104 0.5377091 14.567261
## 4 brief_sensory aq_total 0.4700173 0.95 0.4102017 0.5258065 14.068556
## 5 brief_total aq_total 0.5205238 0.95 0.4643312 0.5725423 16.106015
## df_error p Method n_Obs
## 1 698 1.439785e-34 Pearson correlation 700
## 2 698 7.202651e-16 Pearson correlation 700
## 3 698 2.178330e-41 Pearson correlation 700
## 4 698 4.649548e-39 Pearson correlation 700
## 5 698 5.864073e-49 Pearson correlation 700! Done (July 21, 2025 - Goducks!)