Parent Report ADHD - Project
Luis Anunciacao
16 December, 2020
If you want to reproduce, please download the excel file and run all chunks with eval=false. I don’t recommend going this way. If you want to use the processed data, get the R file, and then scrool down to line 310. Any comment, please e-mail me at luisfca@puc-rio.br
1 Get R file
2 Get excel file
Done in June, 3, 2020
3 Data handling
3.1 Clean dataset
3.2 Get target variables
Use Target variables only. In the excel file, these variables are highlighted.
3.3 Adjust and check variables
Adjust variables that exceed the upper allowed value
use only new (English) variables
Insert a unique identification per participant
Check missing
Export this dataset as a csv file for reproduction.
We have two equal datasets: ds is formed of the portuguese names with the original values. ds_selected is formed of variables written in English, with the same values to one will find in ds.
!done with dataset
4 Research questions
The prevalence of ADHD-report was 7.1%,, being more prevalent in boys than girls (9.1% vs. 5.1%, relative risk [RR] = 1.8, 95% CI = 1.5-2.1), in children coming for the upper classes (A or B) compared to poor or near-poor families (7.7% vs. 5.5%, in Class D or E, RR = 1.4, 95% CI = 1.04-1.9), and in children living in the South region of the country than those in the Northeast (9.3% vs. 5.2%, RR = 1.8, 95% CI = 1.2-2.4).
## psychostimulant n prop
## no 5823 0.98063321
## yes 115 0.01936679
## Total 5938 1.00000000The chi-square test was carried out to check the relationship between ADHD and Socioeconomic status. We could conclude that there’s no association between the two variables (X2(2) = 4.56, p = 0.1).
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## |-------------------------|
##
## ==========================================================
## ds_selected$economic_status
## ds_selected$adhd_parent AB C DE Total
## ----------------------------------------------------------
## no 2432 3264 913 6609
## 2447.9 3263.6 897.4
## 0.104 0.000 0.270
## 0.368 0.494 0.138 0.929
## ----------------------------------------------------------
## yes 203 249 53 505
## 187.1 249.4 68.6
## 1.360 0.001 3.537
## 0.402 0.493 0.105 0.071
## ==========================================================
##
## Statistics for All Table Factors
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 5.271565 d.f. = 2 p = 0.07174.1 Table 2
4.1.1 ADHD report
## # A tibble: 2 x 3
## adhd_parent n prop
## <fct> <int> <dbl>
## 1 no 6609 92.9
## 2 yes 505 7.10## sex_male no yes
## female 0.9486244 0.05137563
## male 0.9093468 0.09065315## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
## =====================================
## .$adhd_parent
## .$sex_male no yes Total
## -------------------------------------
## female 3379 183 3562
## 3309.1 252.9
## 1.475 19.298
## 0.949 0.051 0.501
## 0.511 0.362
## 0.475 0.026
## -------------------------------------
## male 3230 322 3552
## 3299.9 252.1
## 1.479 19.353
## 0.909 0.091 0.499
## 0.489 0.638
## 0.454 0.045
## -------------------------------------
## Total 6609 505 7114
## 0.929 0.071
## =====================================
##
## Statistics for All Table Factors
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 41.60464 d.f. = 1 p = 1.12e-10
##
## Pearson's Chi-squared test with Yates' continuity correction
## ------------------------------------------------------------
## Chi^2 = 41.01118 d.f. = 1 p = 1.51e-10## $data
## Outcome
## Predictor no yes Total
## female 3379 183 3562
## male 3230 322 3552
## Total 6609 505 7114
##
## $measure
## risk ratio with 95% C.I.
## Predictor estimate lower upper
## female 1.000000 NA NA
## male 1.764517 1.480658 2.102793
##
## $p.value
## two-sided
## Predictor midp.exact fisher.exact chi.square
## female NA NA NA
## male 9.128209e-11 1.08562e-10 1.117279e-10
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "Unconditional MLE & normal approximation (Wald) CI"## $data
## Outcome
## Predictor no yes Total
## 3 544 32 576
## 2 2123 176 2299
## 1 3942 297 4239
## Total 6609 505 7114
##
## $measure
## risk ratio with 95% C.I.
## Predictor estimate lower upper
## 3 1.000000 NA NA
## 2 1.377990 0.9561945 1.985849
## 1 1.261146 0.8850567 1.797049
##
## $p.value
## two-sided
## Predictor midp.exact fisher.exact chi.square
## 3 NA NA NA
## 2 0.07712139 0.08750415 0.08191846
## 1 0.19208807 0.21788824 0.19536606
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "Unconditional MLE & normal approximation (Wald) CI"## $data
## Outcome
## Predictor no yes Total
## DE 913 53 966
## C 3264 249 3513
## AB 2432 203 2635
## Total 6609 505 7114
##
## $measure
## risk ratio with 95% C.I.
## Predictor estimate lower upper
## DE 1.000000 NA NA
## C 1.291881 0.9687814 1.722738
## AB 1.404160 1.0473197 1.882583
##
## $p.value
## two-sided
## Predictor midp.exact fisher.exact chi.square
## DE NA NA NA
## C 0.07497211 0.08217484 0.07876962
## AB 0.01953964 0.02308901 0.02178100
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "Unconditional MLE & normal approximation (Wald) CI"## $data
## Outcome
## Predictor no yes Total
## white 4287 322 4609
## other 2060 167 2227
## Total 6347 489 6836
##
## $measure
## risk ratio with 95% C.I.
## Predictor estimate lower upper
## white 1.000000 NA NA
## other 1.073364 0.8966131 1.284959
##
## $p.value
## two-sided
## Predictor midp.exact fisher.exact chi.square
## white NA NA NA
## other 0.440305 0.4526988 0.4409126
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "Unconditional MLE & normal approximation (Wald) CI"## $data
## Outcome
## Predictor 0 1 Total
## 1 4287 322 4609
## 2 2060 167 2227
## 3 262 16 278
## Total 6609 505 7114
##
## $measure
## risk ratio with 95% C.I.
## Predictor estimate lower upper
## 1 1.000000 NA NA
## 2 1.073364 0.8966131 1.284959
## 3 0.823808 0.5060987 1.340963
##
## $p.value
## two-sided
## Predictor midp.exact fisher.exact chi.square
## 1 NA NA NA
## 2 0.4403050 0.4526988 0.4409126
## 3 0.4424939 0.5418869 0.4321396
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "Unconditional MLE & normal approximation (Wald) CI"## $data
## Outcome
## Predictor no yes Total
## public 5576 417 5993
## private 1033 88 1121
## Total 6609 505 7114
##
## $measure
## risk ratio with 95% C.I.
## Predictor estimate lower upper
## public 1.000000 NA NA
## private 1.128198 0.9045846 1.407088
##
## $p.value
## two-sided
## Predictor midp.exact fisher.exact chi.square
## public NA NA NA
## private 0.2873481 0.2817659 0.285776
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "Unconditional MLE & normal approximation (Wald) CI"## $data
## Outcome
## Predictor no yes Total
## NE 827 45 872
## CO 593 35 628
## NO 234 16 250
## SE 2153 192 2345
## SU 2802 217 3019
## Total 6609 505 7114
##
## $measure
## risk ratio with 95% C.I.
## Predictor estimate lower upper
## NE 1.000000 NA NA
## CO 1.079972 0.7027771 1.659614
## NO 1.240178 0.7134565 2.155760
## SE 1.586581 1.1576768 2.174390
## SU 1.392838 1.0194556 1.902974
##
## $p.value
## two-sided
## Predictor midp.exact fisher.exact chi.square
## NE NA NA NA
## CO 0.723960605 0.728260479 0.725637019
## NO 0.445173904 0.431718370 0.446085193
## SE 0.002685312 0.003020571 0.003484157
## SU 0.031930689 0.038077263 0.035361539
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "Unconditional MLE & normal approximation (Wald) CI"## $data
## Outcome
## Predictor no yes Total
## big 1193 79 1272
## medium 2999 243 3242
## small 2417 183 2600
## Total 6609 505 7114
##
## $measure
## risk ratio with 95% C.I.
## Predictor estimate lower upper
## big 1.000000 NA NA
## medium 1.206850 0.9442129 1.542541
## small 1.133281 0.8780347 1.462729
##
## $p.value
## two-sided
## Predictor midp.exact fisher.exact chi.square
## big NA NA NA
## medium 0.1293696 0.1394447 0.1313890
## small 0.3368253 0.3758584 0.3354619
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "Unconditional MLE & normal approximation (Wald) CI"4.1.2 ADHD Probable
## $data
## Outcome
## Predictor no yes Total
## 3 564 12 576
## 2 2210 89 2299
## 1 4063 176 4239
## Total 6837 277 7114
##
## $measure
## risk ratio with 95% C.I.
## Predictor estimate lower upper
## 3 1.000000 NA NA
## 2 1.858199 1.024123 3.371573
## 1 1.992923 1.117789 3.553212
##
## $p.value
## two-sided
## Predictor midp.exact fisher.exact chi.square
## 3 NA NA NA
## 2 0.03049368 0.04193336 0.03714494
## 1 0.01076177 0.01543821 0.01617942
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "Unconditional MLE & normal approximation (Wald) CI"## $data
## Outcome
## Predictor no yes Total
## female 3492 70 3562
## male 3345 207 3552
## Total 6837 277 7114
##
## $measure
## risk ratio with 95% C.I.
## Predictor estimate lower upper
## female 1.000000 NA NA
## male 2.965468 2.270635 3.872926
##
## $p.value
## two-sided
## Predictor midp.exact fisher.exact chi.square
## female NA NA NA
## male 0 1.054224e-17 3.748842e-17
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "Unconditional MLE & normal approximation (Wald) CI"## $data
## Outcome
## Predictor no yes Total
## 1 4446 163 4609
## 2 2123 104 2227
## 3 268 10 278
## Total 6837 277 7114
##
## $measure
## risk ratio with 95% C.I.
## Predictor estimate lower upper
## 1 1.000000 NA NA
## 2 1.320481 1.0379801 1.679869
## 3 1.017125 0.5433693 1.903941
##
## $p.value
## two-sided
## Predictor midp.exact fisher.exact chi.square
## 1 NA NA NA
## 2 0.02531767 0.02774041 0.02339304
## 3 0.92300151 0.86789609 0.95767709
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "Unconditional MLE & normal approximation (Wald) CI"## $data
## Outcome
## Predictor no yes Total
## private 1098 23 1121
## public 5739 254 5993
## Total 6837 277 7114
##
## $measure
## risk ratio with 95% C.I.
## Predictor estimate lower upper
## private 1.0000 NA NA
## public 2.0657 1.354563 3.15018
##
## $p.value
## two-sided
## Predictor midp.exact fisher.exact chi.square
## private NA NA NA
## public 0.0002052312 0.0002737903 0.0005137324
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "Unconditional MLE & normal approximation (Wald) CI"## $data
## Outcome
## Predictor no yes Total
## NO 247 3 250
## CO 619 9 628
## NE 847 25 872
## SE 2222 123 2345
## SU 2902 117 3019
## Total 6837 277 7114
##
## $measure
## risk ratio with 95% C.I.
## Predictor estimate lower upper
## NO 1.000000 NA NA
## CO 1.194268 0.3259983 4.375099
## NE 2.389144 0.7273559 7.847613
## SE 4.371002 1.4009316 13.637825
## SU 3.229546 1.0341942 10.085116
##
## $p.value
## two-sided
## Predictor midp.exact fisher.exact chi.square
## NO NA NA NA
## CO 0.829154795 1.000000000 0.788321775
## NE 0.130927744 0.169800862 0.136333264
## SE 0.001408455 0.002750785 0.004672068
## SU 0.018147383 0.033181748 0.030628115
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "Unconditional MLE & normal approximation (Wald) CI"## $data
## Outcome
## Predictor no yes Total
## big 1228 44 1272
## medium 3138 104 3242
## small 2471 129 2600
## Total 6837 277 7114
##
## $measure
## risk ratio with 95% C.I.
## Predictor estimate lower upper
## big 1.0000000 NA NA
## medium 0.9273737 0.6558234 1.311362
## small 1.4343357 1.0254810 2.006199
##
## $p.value
## two-sided
## Predictor midp.exact fisher.exact chi.square
## big NA NA NA
## medium 0.6638413 0.71020436 0.66981601
## small 0.0312949 0.03809428 0.03356016
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "Unconditional MLE & normal approximation (Wald) CI"4.1.3 Psychostimulant use
## $data
## Outcome
## Predictor no yes Total
## 3 566 9 575
## 2 2239 52 2291
## 1 4157 74 4231
## Total 6962 135 7097
##
## $measure
## risk ratio with 95% C.I.
## Predictor estimate lower upper
## 3 1.000000 NA NA
## 2 1.450119 0.7189038 2.925071
## 1 1.117414 0.5624866 2.219810
##
## $p.value
## two-sided
## Predictor midp.exact fisher.exact chi.square
## 3 NA NA NA
## 2 0.2998557 0.3361292 0.2953127
## 1 0.7828656 0.8654783 0.7509478
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "Unconditional MLE & normal approximation (Wald) CI"## $data
## Outcome
## Predictor no yes Total
## 1 4500 94 4594
## 2 2189 36 2225
## 3 273 5 278
## Total 6962 135 7097
##
## $measure
## risk ratio with 95% C.I.
## Predictor estimate lower upper
## 1 1.0000000 NA NA
## 2 0.7907435 0.5403248 1.157221
## 3 0.8789989 0.3604792 2.143367
##
## $p.value
## two-sided
## Predictor midp.exact fisher.exact chi.square
## 1 NA NA NA
## 2 0.2253946 0.2571116 0.2254250
## 3 0.8243120 1.0000000 0.7763272
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "Unconditional MLE & normal approximation (Wald) CI"## $data
## Outcome
## Predictor no yes Total
## DE 955 11 966
## C 3436 65 3501
## AB 2571 59 2630
## Total 6962 135 7097
##
## $measure
## risk ratio with 95% C.I.
## Predictor estimate lower upper
## DE 1.000000 NA NA
## C 1.630443 0.8640134 3.076741
## AB 1.970066 1.0393727 3.734136
##
## $p.value
## two-sided
## Predictor midp.exact fisher.exact chi.square
## DE NA NA NA
## C 0.12053424 0.15884492 0.12665034
## AB 0.02820677 0.04000511 0.03356871
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "Unconditional MLE & normal approximation (Wald) CI"## $data
## Outcome
## Predictor no yes Total
## public 5862 114 5976
## private 1100 21 1121
## Total 6962 135 7097
##
## $measure
## risk ratio with 95% C.I.
## Predictor estimate lower upper
## public 1.0000000 NA NA
## private 0.9820179 0.6192901 1.557201
##
## $p.value
## two-sided
## Predictor midp.exact fisher.exact chi.square
## public NA NA NA
## private 0.9593947 1 0.9385029
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "Unconditional MLE & normal approximation (Wald) CI"## $data
## Outcome
## Predictor no yes Total
## NE 869 3 872
## CO 619 9 628
## NO 248 2 250
## SE 2290 45 2335
## SU 2936 76 3012
## Total 6962 135 7097
##
## $measure
## risk ratio with 95% C.I.
## Predictor estimate lower upper
## NE 1.000000 NA NA
## CO 4.165605 1.1323007 15.32479
## NO 2.325333 0.3907071 13.83946
## SE 5.601713 1.7453711 17.97852
## SU 7.334219 2.3194080 23.19159
##
## $p.value
## two-sided
## Predictor midp.exact fisher.exact chi.square
## NE NA NA NA
## CO 2.494347e-02 3.494130e-02 1.949677e-02
## NO 3.864003e-01 3.099012e-01 3.399785e-01
## SE 2.801815e-04 4.408481e-04 1.018602e-03
## SU 3.980458e-06 7.180548e-06 5.957003e-05
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "Unconditional MLE & normal approximation (Wald) CI"## $data
## Outcome
## Predictor no yes Total
## big 1257 15 1272
## medium 3167 75 3242
## small 2538 45 2583
## Total 6962 135 7097
##
## $measure
## risk ratio with 95% C.I.
## Predictor estimate lower upper
## big 1.000000 NA NA
## medium 1.961752 1.1312034 3.402103
## small 1.477352 0.8267709 2.639871
##
## $p.value
## two-sided
## Predictor midp.exact fisher.exact chi.square
## big NA NA NA
## medium 0.01108165 0.01280013 0.01419559
## small 0.18407298 0.21371351 0.18429815
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "Unconditional MLE & normal approximation (Wald) CI"4.2 Table 4
Report results
##
## Logistic regression predicting adhd_parent : yes vs no
##
## crude OR(95%CI)
## public_School: private vs public 1.14 (0.88,1.47)
##
## city_size: ref.=small
## medium 1.04 (0.84,1.29)
## big 0.83 (0.61,1.12)
##
## region: ref.=CO
## NE 0.85 (0.52,1.39)
## NO 1.08 (0.55,2.1)
## SE 1.36 (0.92,2.03)
## SU 1.32 (0.89,1.95)
##
## age (cont. var.) 0.9919 (0.9542,1.031)
##
## sex_male: male vs female 1.9 (1.54,2.33)
##
## race_white: white vs other 0.9 (0.73,1.11)
##
## married: divorced vs married 1.6 (1.3,1.97)
##
## schooling: ref.=illiteracy
## primary 1.18 (0.65,2.14)
## high_or_above 1.2 (0.66,2.19)
##
## economic_status: ref.=AB
## C 1.02 (0.82,1.25)
## DE 0.75 (0.53,1.08)
##
## smoking: yes vs no 1.66 (1.28,2.16)
##
## alcohol: yes vs no 1.64 (1.17,2.29)
##
## scholar_achievement: ref.=average
## above 0.51 (0.37,0.7)
## below 2.76 (2.21,3.44)
##
## snap_parents_only: yes vs no 3.61 (2.84,4.6)
##
## snap_teachers_only: yes vs no 1.29 (0.97,1.71)
##
## adj. OR(95%CI) P(Wald's test)
## public_School: private vs public 1.73 (1.24,2.39) 0.001
##
## city_size: ref.=small
## medium 1.16 (0.9,1.49) 0.258
## big 0.78 (0.56,1.08) 0.138
##
## region: ref.=CO
## NE 0.94 (0.55,1.62) 0.821
## NO 0.83 (0.41,1.68) 0.611
## SE 1.54 (0.96,2.47) 0.072
## SU 1.53 (0.97,2.43) 0.069
##
## age (cont. var.) 0.9948 (0.9532,1.0382) 0.812
##
## sex_male: male vs female 1.71 (1.38,2.11) < 0.001
##
## race_white: white vs other 0.88 (0.7,1.11) 0.275
##
## married: divorced vs married 1.47 (1.18,1.84) < 0.001
##
## schooling: ref.=illiteracy
## primary 1.43 (0.76,2.68) 0.27
## high_or_above 1.55 (0.8,3) 0.192
##
## economic_status: ref.=AB
## C 0.88 (0.69,1.13) 0.326
## DE 0.57 (0.37,0.88) 0.012
##
## smoking: yes vs no 1.29 (0.95,1.74) 0.103
##
## alcohol: yes vs no 1.24 (0.85,1.82) 0.267
##
## scholar_achievement: ref.=average
## above 0.55 (0.4,0.77) < 0.001
## below 3.1 (2.44,3.94) < 0.001
##
## snap_parents_only: yes vs no 3.45 (2.65,4.48) < 0.001
##
## snap_teachers_only: yes vs no 0.77 (0.56,1.05) 0.104
##
## P(LR-test)
## public_School: private vs public 0.001
##
## city_size: ref.=small 0.059
## medium
## big
##
## region: ref.=CO 0.041
## NE
## NO
## SE
## SU
##
## age (cont. var.) 0.811
##
## sex_male: male vs female < 0.001
##
## race_white: white vs other 0.277
##
## married: divorced vs married < 0.001
##
## schooling: ref.=illiteracy 0.389
## primary
## high_or_above
##
## economic_status: ref.=AB 0.034
## C
## DE
##
## smoking: yes vs no 0.108
##
## alcohol: yes vs no 0.274
##
## scholar_achievement: ref.=average < 0.001
## above
## below
##
## snap_parents_only: yes vs no < 0.001
##
## snap_teachers_only: yes vs no 0.098
##
## Log-likelihood = -1361.0522
## No. of observations = 5954
## AIC value = 2766.1044Second output
##
## Logistic regression predicting adhd_risk : yes vs no
##
## crude OR(95%CI) adj. OR(95%CI)
## public_School: private vs public 0.51 (0.32,0.8) 1.05 (0.62,1.78)
##
## city_size: ref.=small
## medium 0.64 (0.48,0.86) 0.75 (0.53,1.05)
## big 0.75 (0.51,1.09) 0.74 (0.49,1.13)
##
## region: ref.=CO
## NE 2.05 (0.91,4.65) 1.41 (0.59,3.41)
## NO 0.96 (0.25,3.64) 0.76 (0.19,3.02)
## SE 3.39 (1.64,7.01) 2.02 (0.9,4.55)
## SU 2.66 (1.29,5.5) 2.18 (0.98,4.85)
##
## age (cont. var.) 0.96 (0.91,1.01) 0.94 (0.89,1)
##
## sex_male: male vs female 2.84 (2.11,3.82) 2.32 (1.7,3.17)
##
## race_white: white vs other 0.75 (0.57,0.98) 0.89 (0.66,1.21)
##
## married: divorced vs married 2.07 (1.58,2.7) 1.65 (1.23,2.21)
##
## schooling: ref.=illiteracy
## primary 0.5 (0.29,0.86) 0.83 (0.46,1.49)
## high_or_above 0.36 (0.2,0.62) 1.06 (0.56,2.03)
##
## economic_status: ref.=AB
## C 1.83 (1.34,2.52) 1.38 (0.95,2)
## DE 2.6 (1.74,3.91) 1.53 (0.91,2.59)
##
## smoking: yes vs no 1.98 (1.42,2.75) 1.1 (0.75,1.63)
##
## alcohol: yes vs no 2.25 (1.51,3.35) 1.62 (1.01,2.58)
##
## scholar_achievement: ref.=average
## above 0.85 (0.43,1.67) 0.88 (0.45,1.75)
## below 15.28 (9.69,24.08) 13.74 (8.67,21.78)
##
## P(Wald's test) P(LR-test)
## public_School: private vs public 0.861 0.862
##
## city_size: ref.=small 0.168
## medium 0.095
## big 0.163
##
## region: ref.=CO 0.099
## NE 0.44
## NO 0.701
## SE 0.088
## SU 0.057
##
## age (cont. var.) 0.046 0.043
##
## sex_male: male vs female < 0.001 < 0.001
##
## race_white: white vs other 0.467 0.468
##
## married: divorced vs married < 0.001 0.001
##
## schooling: ref.=illiteracy 0.339
## primary 0.531
## high_or_above 0.854
##
## economic_status: ref.=AB 0.183
## C 0.091
## DE 0.109
##
## smoking: yes vs no 0.62 0.622
##
## alcohol: yes vs no 0.045 0.052
##
## scholar_achievement: ref.=average < 0.001
## above 0.722
## below < 0.001
##
## Log-likelihood = -758.8407
## No. of observations = 5954
## AIC value = 1557.6813##
## Logistic regression predicting psychostimulant : yes vs no
##
## crude OR(95%CI)
## public_School: private vs public 1.02 (0.62,1.67)
##
## city_size: ref.=small
## medium 1.48 (0.98,2.23)
## big 0.81 (0.44,1.51)
##
## region: ref.=CO
## NE 0.27 (0.07,1.03)
## NO 0.63 (0.13,3.01)
## SE 1.18 (0.54,2.55)
## SU 1.8 (0.86,3.77)
##
## age (cont. var.) 1 (0.94,1.08)
##
## sex_male: male vs female 2.21 (1.49,3.3)
##
## race_white: white vs other 1.35 (0.89,2.05)
##
## married: divorced vs married 1.5 (1.02,2.2)
##
## schooling: ref.=illiteracy
## primary 0.99 (0.36,2.75)
## high_or_above 0.89 (0.32,2.5)
##
## economic_status: ref.=AB
## C 0.76 (0.52,1.12)
## DE 0.55 (0.27,1.11)
##
## smoking: yes vs no 1.0295 (0.5854,1.8104)
##
## alcohol: yes vs no 0.51 (0.19,1.4)
##
## scholar_achievement: ref.=average
## above 0.63 (0.34,1.18)
## below 3.56 (2.31,5.48)
##
## snap_parents_only: yes vs no 2.1 (1.29,3.43)
##
## age_group: ref.=1
## 2 1.24 (0.84,1.83)
## 3 0.81 (0.37,1.77)
##
## snap_teachers_only: yes vs no 1.34 (0.79,2.25)
##
## adj. OR(95%CI) P(Wald's test)
## public_School: private vs public 1.94 (1.03,3.64) 0.04
##
## city_size: ref.=small
## medium 1.65 (1.03,2.64) 0.036
## big 0.94 (0.49,1.8) 0.841
##
## region: ref.=CO
## NE 0.39 (0.1,1.54) 0.179
## NO 0.53 (0.11,2.57) 0.429
## SE 1.73 (0.7,4.26) 0.231
## SU 2.56 (1.09,6.03) 0.031
##
## age (cont. var.) 1.02 (0.86,1.21) 0.85
##
## sex_male: male vs female 1.95 (1.3,2.93) 0.001
##
## race_white: white vs other 1.18 (0.75,1.85) 0.465
##
## married: divorced vs married 1.68 (1.12,2.53) 0.013
##
## schooling: ref.=illiteracy
## primary 0.93 (0.32,2.69) 0.898
## high_or_above 0.74 (0.24,2.26) 0.594
##
## economic_status: ref.=AB
## C 0.62 (0.39,0.97) 0.036
## DE 0.46 (0.2,1.04) 0.061
##
## smoking: yes vs no 0.9971 (0.5448,1.8247) 0.992
##
## alcohol: yes vs no 0.42 (0.15,1.2) 0.106
##
## scholar_achievement: ref.=average
## above 0.63 (0.33,1.18) 0.146
## below 3.96 (2.51,6.26) < 0.001
##
## snap_parents_only: yes vs no 1.86 (1.11,3.13) 0.019
##
## age_group: ref.=1
## 2 1.27 (0.62,2.62) 0.515
## 3 0.85 (0.19,3.82) 0.833
##
## snap_teachers_only: yes vs no 0.66 (0.38,1.16) 0.153
##
## P(LR-test)
## public_School: private vs public 0.046
##
## city_size: ref.=small 0.063
## medium
## big
##
## region: ref.=CO < 0.001
## NE
## NO
## SE
## SU
##
## age (cont. var.) 0.85
##
## sex_male: male vs female < 0.001
##
## race_white: white vs other 0.46
##
## married: divorced vs married 0.015
##
## schooling: ref.=illiteracy 0.585
## primary
## high_or_above
##
## economic_status: ref.=AB 0.063
## C
## DE
##
## smoking: yes vs no 0.992
##
## alcohol: yes vs no 0.072
##
## scholar_achievement: ref.=average < 0.001
## above
## below
##
## snap_parents_only: yes vs no 0.026
##
## age_group: ref.=1 0.433
## 2
## 3
##
## snap_teachers_only: yes vs no 0.14
##
## Log-likelihood = -502.9575
## No. of observations = 5938
## AIC value = 1053.9149!done If you use this material, please cite. Thanks, Luis Anunciação, 2020