Do profiles have incremental validity in predicting grades ?.
- Rio data: senna v1. Six Factors. 24k students
Open data
setwd("~/Dropbox/R Stat")
load("cluster_rio_som.RData")
library(ggplot2)
library(sjPlot)
library(kohonen)
## Loading required package: class
## Loading required package: MASS
library(forcats)
Cluster solutions (ward and self organizing maps som)
table(grp9)
## grp9
## 1 2 3 4 5 6 7 8 9
## 4187 1503 4168 2144 813 4343 3384 1259 2786
table(cluster_som$unit.classif)
##
## 1 2 3 4 5 6 7 8 9
## 2781 3624 1454 2622 2365 2619 2000 4343 2779
table(cluster_som$unit.classif, grp9)
## grp9
## 1 2 3 4 5 6 7 8 9
## 1 3 712 693 2 14 48 924 89 296
## 2 90 1 429 254 35 2345 161 0 309
## 3 0 361 0 0 29 0 50 881 133
## 4 2 143 231 0 17 153 1553 138 385
## 5 342 1 77 1460 62 354 0 0 69
## 6 204 249 350 89 556 653 410 57 51
## 7 0 16 92 11 100 103 53 94 1531
## 8 1248 20 2172 37 0 621 233 0 12
## 9 2298 0 124 291 0 66 0 0 0
library(gmodels)
CrossTable(cluster_som$unit.classif, grp9,
expected = FALSE, prop.r = TRUE, prop.c = FALSE, prop.t = FALSE)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## |-------------------------|
##
##
## Total Observations in Table: 24587
##
##
## | grp9
## cluster_som$unit.classif | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | Row Total |
## -------------------------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|
## 1 | 3 | 712 | 693 | 2 | 14 | 48 | 924 | 89 | 296 | 2781 |
## | 467.605 | 1727.988 | 104.129 | 238.521 | 66.089 | 399.921 | 765.341 | 20.027 | 1.160 | |
## | 0.001 | 0.256 | 0.249 | 0.001 | 0.005 | 0.017 | 0.332 | 0.032 | 0.106 | 0.113 |
## -------------------------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|
## 2 | 90 | 1 | 429 | 254 | 35 | 2345 | 161 | 0 | 309 | 3624 |
## | 450.268 | 219.539 | 55.916 | 12.170 | 60.055 | 4540.533 | 228.753 | 185.570 | 25.159 | |
## | 0.025 | 0.000 | 0.118 | 0.070 | 0.010 | 0.647 | 0.044 | 0.000 | 0.085 | 0.147 |
## -------------------------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|
## 3 | 0 | 361 | 0 | 0 | 29 | 0 | 50 | 881 | 133 | 1454 |
## | 247.606 | 833.094 | 246.483 | 126.790 | 7.571 | 256.832 | 112.612 | 8737.241 | 6.121 | |
## | 0.000 | 0.248 | 0.000 | 0.000 | 0.020 | 0.000 | 0.034 | 0.606 | 0.091 | 0.059 |
## -------------------------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|
## 4 | 2 | 143 | 231 | 0 | 17 | 153 | 1553 | 138 | 385 | 2622 |
## | 442.518 | 1.863 | 102.535 | 228.640 | 56.033 | 207.689 | 3938.090 | 0.104 | 26.003 | |
## | 0.001 | 0.055 | 0.088 | 0.000 | 0.006 | 0.058 | 0.592 | 0.053 | 0.147 | 0.107 |
## -------------------------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|
## 5 | 342 | 1 | 77 | 1460 | 62 | 354 | 0 | 0 | 69 | 2365 |
## | 9.162 | 142.579 | 261.705 | 7622.297 | 3.357 | 9.728 | 325.504 | 121.102 | 147.749 | |
## | 0.145 | 0.000 | 0.033 | 0.617 | 0.026 | 0.150 | 0.000 | 0.000 | 0.029 | 0.096 |
## -------------------------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|
## 6 | 204 | 249 | 350 | 89 | 556 | 653 | 410 | 57 | 51 | 2619 |
## | 131.308 | 49.365 | 19.891 | 85.062 | 2544.279 | 78.351 | 6.808 | 44.335 | 203.528 | |
## | 0.078 | 0.095 | 0.134 | 0.034 | 0.212 | 0.249 | 0.157 | 0.022 | 0.019 | 0.107 |
## -------------------------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|
## 7 | 0 | 16 | 92 | 11 | 100 | 103 | 53 | 94 | 1531 | 2000 |
## | 340.586 | 92.354 | 180.005 | 153.095 | 17.344 | 177.306 | 179.472 | 0.691 | 7507.583 | |
## | 0.000 | 0.008 | 0.046 | 0.005 | 0.050 | 0.051 | 0.026 | 0.047 | 0.765 | 0.081 |
## -------------------------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|
## 8 | 1248 | 20 | 2172 | 37 | 0 | 621 | 233 | 0 | 12 | 4343 |
## | 349.504 | 226.994 | 2800.008 | 308.327 | 143.607 | 27.839 | 222.566 | 222.387 | 468.406 | |
## | 0.287 | 0.005 | 0.500 | 0.009 | 0.000 | 0.143 | 0.054 | 0.000 | 0.003 | 0.177 |
## -------------------------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|
## 9 | 2298 | 0 | 124 | 291 | 0 | 66 | 0 | 0 | 0 | 2779 |
## | 7035.957 | 169.880 | 255.736 | 9.775 | 91.891 | 367.751 | 382.484 | 142.301 | 314.894 | |
## | 0.827 | 0.000 | 0.045 | 0.105 | 0.000 | 0.024 | 0.000 | 0.000 | 0.000 | 0.113 |
## -------------------------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|
## Column Total | 4187 | 1503 | 4168 | 2144 | 813 | 4343 | 3384 | 1259 | 2786 | 24587 |
## -------------------------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|
##
##
subs <- round(cluster$height - c(0, cluster$height[-length(cluster$height)]), 3)
plot(cluster$height)
plot(cluster, hang = -1)
rect.hclust(cluster, k=9, border="red")
source("cluster_fig_senna1.R")
What profiles characterizes each cluster? Results from ward method
cluster_fig(bd = sv1_rio[ , 178:183], grp = grp9, min=-3, max=3, interv = .5, interc = 0)
- What profiles characterizes each cluster? Results from self organizing maps (som)
cluster_fig(bd = sv1_rio[ , 178:183], grp = cluster_som$unit.classif, min=-3, max=3, interv = .5, interc = 0)
What is the incremental validity of clusters in predicting standardized achievement ?
- VD: language, ward clusters
fit <- lm(formula = cog_portug~grade0+age2+sex+correct_grade2+
F1.Cons.th+F2.Extr.th+F3.Nint.th+F4.Nloc.th+F5.Agre.th+
F6.Opns.th+ cls_w+cls_w*F1.Cons.th+cls_w*F6.Opns.th,
contrasts = list( cls_w = contr.sum), data=sv1_rio )
|
|
|
cog_portug
|
|
|
|
B
|
CI
|
std. Beta
|
CI
|
p
|
|
(Intercept)
|
|
6.24
|
5.74 – 6.74
|
|
|
<.001
|
|
grade0
|
|
0.43
|
0.39 – 0.48
|
0.15
|
0.13 – 0.16
|
<.001
|
|
age2
|
|
-0.02
|
-0.04 – 0.00
|
-0.01
|
-0.03 – 0.00
|
.094
|
|
sex
|
|
1.05
|
0.92 – 1.18
|
0.11
|
0.09 – 0.12
|
<.001
|
|
correct_grade2
|
|
1.92
|
1.78 – 2.06
|
0.19
|
0.17 – 0.20
|
<.001
|
|
F1.Cons.th
|
|
0.23
|
0.14 – 0.32
|
0.05
|
0.03 – 0.07
|
<.001
|
|
F2.Extr.th
|
|
-0.47
|
-0.55 – -0.38
|
-0.10
|
-0.11 – -0.08
|
<.001
|
|
F3.Nint.th
|
|
0.05
|
-0.03 – 0.13
|
0.01
|
-0.01 – 0.03
|
.215
|
|
F4.Nloc.th
|
|
-0.50
|
-0.58 – -0.42
|
-0.10
|
-0.12 – -0.08
|
<.001
|
|
F5.Agre.th
|
|
0.08
|
-0.01 – 0.17
|
0.02
|
-0.00 – 0.03
|
.091
|
|
F6.Opns.th
|
|
0.74
|
0.65 – 0.82
|
0.15
|
0.13 – 0.17
|
<.001
|
|
cls_w
|
|
cls_w1
|
|
-0.26
|
-0.54 – 0.01
|
-0.03
|
-0.06 – 0.00
|
.059
|
|
cls_w2
|
|
-0.39
|
-1.00 – 0.23
|
-0.03
|
-0.09 – 0.02
|
.217
|
|
cls_w3
|
|
-0.07
|
-0.28 – 0.15
|
-0.01
|
-0.03 – 0.02
|
.541
|
|
cls_w4
|
|
0.07
|
-0.27 – 0.41
|
0.01
|
-0.03 – 0.04
|
.702
|
|
cls_w5
|
|
0.83
|
0.25 – 1.42
|
0.06
|
0.02 – 0.11
|
.005
|
|
cls_w6
|
|
-0.13
|
-0.34 – 0.08
|
-0.01
|
-0.04 – 0.01
|
.209
|
|
cls_w7
|
|
-0.08
|
-0.32 – 0.15
|
-0.01
|
-0.03 – 0.02
|
.472
|
|
cls_w8
|
|
-0.09
|
-0.58 – 0.39
|
-0.01
|
-0.05 – 0.03
|
.709
|
|
F1.Cons.th:cls_w1
|
|
-0.03
|
-0.24 – 0.17
|
-0.00
|
-0.02 – 0.02
|
.767
|
|
F1.Cons.th:cls_w2
|
|
0.16
|
-0.15 – 0.48
|
0.02
|
-0.02 – 0.05
|
.303
|
|
F1.Cons.th:cls_w3
|
|
0.14
|
-0.08 – 0.36
|
0.01
|
-0.01 – 0.03
|
.207
|
|
F1.Cons.th:cls_w4
|
|
-0.13
|
-0.39 – 0.14
|
-0.01
|
-0.03 – 0.01
|
.352
|
|
F1.Cons.th:cls_w5
|
|
0.38
|
0.05 – 0.71
|
0.02
|
0.00 – 0.05
|
.022
|
|
F1.Cons.th:cls_w6
|
|
-0.01
|
-0.23 – 0.21
|
-0.00
|
-0.02 – 0.02
|
.909
|
|
F1.Cons.th:cls_w7
|
|
-0.15
|
-0.40 – 0.10
|
-0.01
|
-0.03 – 0.01
|
.241
|
|
F1.Cons.th:cls_w8
|
|
-0.26
|
-0.47 – -0.06
|
-0.03
|
-0.05 – -0.01
|
.012
|
|
F6.Opns.th:cls_w1
|
|
0.09
|
-0.09 – 0.27
|
0.01
|
-0.01 – 0.03
|
.305
|
|
F6.Opns.th:cls_w2
|
|
-0.02
|
-0.34 – 0.29
|
-0.00
|
-0.03 – 0.02
|
.891
|
|
F6.Opns.th:cls_w3
|
|
0.26
|
0.07 – 0.45
|
0.02
|
0.01 – 0.04
|
.008
|
|
F6.Opns.th:cls_w4
|
|
0.12
|
-0.12 – 0.37
|
0.01
|
-0.01 – 0.03
|
.325
|
|
F6.Opns.th:cls_w5
|
|
-0.53
|
-0.85 – -0.21
|
-0.05
|
-0.08 – -0.02
|
.001
|
|
F6.Opns.th:cls_w6
|
|
0.08
|
-0.15 – 0.30
|
0.01
|
-0.01 – 0.03
|
.489
|
|
F6.Opns.th:cls_w7
|
|
0.04
|
-0.17 – 0.24
|
0.00
|
-0.02 – 0.02
|
.737
|
|
F6.Opns.th:cls_w8
|
|
-0.10
|
-0.36 – 0.16
|
-0.01
|
-0.04 – 0.02
|
.456
|
|
Observations
|
|
21944
|
|
R2 / adj. R2
|
|
.145 / .144
|
fit <- lm(formula = cog_math~grade0+age2+sex+correct_grade2+
F1.Cons.th+F2.Extr.th+F3.Nint.th+F4.Nloc.th+F5.Agre.th+
F6.Opns.th+ cls_w + cls_w*F1.Cons.th + cls_w*F6.Opns.th,
contrasts = list( cls_w = contr.sum), data=sv1_rio )
|
|
|
cog_math
|
|
|
|
B
|
CI
|
std. Beta
|
CI
|
p
|
|
(Intercept)
|
|
14.38
|
13.93 – 14.82
|
|
|
<.001
|
|
grade0
|
|
-0.59
|
-0.63 – -0.55
|
-0.23
|
-0.25 – -0.22
|
<.001
|
|
age2
|
|
-0.01
|
-0.02 – 0.01
|
-0.00
|
-0.02 – 0.01
|
.553
|
|
sex
|
|
-0.31
|
-0.43 – -0.19
|
-0.04
|
-0.05 – -0.02
|
<.001
|
|
correct_grade2
|
|
1.26
|
1.13 – 1.38
|
0.14
|
0.13 – 0.16
|
<.001
|
|
F1.Cons.th
|
|
0.38
|
0.29 – 0.46
|
0.09
|
0.07 – 0.11
|
<.001
|
|
F2.Extr.th
|
|
-0.24
|
-0.31 – -0.16
|
-0.06
|
-0.07 – -0.04
|
<.001
|
|
F3.Nint.th
|
|
0.05
|
-0.02 – 0.12
|
0.01
|
-0.01 – 0.03
|
.178
|
|
F4.Nloc.th
|
|
-0.11
|
-0.18 – -0.03
|
-0.03
|
-0.04 – -0.01
|
.004
|
|
F5.Agre.th
|
|
0.02
|
-0.06 – 0.10
|
0.01
|
-0.01 – 0.02
|
.561
|
|
F6.Opns.th
|
|
0.28
|
0.20 – 0.36
|
0.07
|
0.05 – 0.09
|
<.001
|
|
cls_w
|
|
cls_w1
|
|
-0.01
|
-0.25 – 0.24
|
-0.00
|
-0.03 – 0.03
|
.948
|
|
cls_w2
|
|
-0.40
|
-0.94 – 0.15
|
-0.04
|
-0.09 – 0.01
|
.152
|
|
cls_w3
|
|
0.07
|
-0.12 – 0.26
|
0.01
|
-0.02 – 0.03
|
.481
|
|
cls_w4
|
|
-0.06
|
-0.36 – 0.24
|
-0.01
|
-0.04 – 0.03
|
.692
|
|
cls_w5
|
|
0.34
|
-0.18 – 0.85
|
0.03
|
-0.02 – 0.08
|
.204
|
|
cls_w6
|
|
-0.06
|
-0.24 – 0.13
|
-0.01
|
-0.03 – 0.02
|
.554
|
|
cls_w7
|
|
0.19
|
-0.02 – 0.39
|
0.02
|
-0.00 – 0.05
|
.070
|
|
cls_w8
|
|
-0.22
|
-0.65 – 0.21
|
-0.02
|
-0.06 – 0.02
|
.319
|
|
F1.Cons.th:cls_w1
|
|
-0.07
|
-0.26 – 0.11
|
-0.01
|
-0.03 – 0.01
|
.440
|
|
F1.Cons.th:cls_w2
|
|
0.36
|
0.08 – 0.63
|
0.04
|
0.01 – 0.08
|
.012
|
|
F1.Cons.th:cls_w3
|
|
0.12
|
-0.07 – 0.32
|
0.01
|
-0.01 – 0.03
|
.221
|
|
F1.Cons.th:cls_w4
|
|
-0.06
|
-0.30 – 0.18
|
-0.01
|
-0.03 – 0.02
|
.632
|
|
F1.Cons.th:cls_w5
|
|
0.15
|
-0.14 – 0.44
|
0.01
|
-0.01 – 0.03
|
.305
|
|
F1.Cons.th:cls_w6
|
|
-0.34
|
-0.53 – -0.14
|
-0.03
|
-0.05 – -0.01
|
.001
|
|
F1.Cons.th:cls_w7
|
|
0.02
|
-0.21 – 0.24
|
0.00
|
-0.02 – 0.02
|
.889
|
|
F1.Cons.th:cls_w8
|
|
-0.02
|
-0.20 – 0.16
|
-0.00
|
-0.02 – 0.02
|
.826
|
|
F6.Opns.th:cls_w1
|
|
0.05
|
-0.11 – 0.21
|
0.01
|
-0.02 – 0.03
|
.578
|
|
F6.Opns.th:cls_w2
|
|
0.23
|
-0.05 – 0.50
|
0.02
|
-0.00 – 0.05
|
.110
|
|
F6.Opns.th:cls_w3
|
|
0.12
|
-0.05 – 0.29
|
0.01
|
-0.01 – 0.03
|
.163
|
|
F6.Opns.th:cls_w4
|
|
-0.19
|
-0.41 – 0.03
|
-0.02
|
-0.04 – 0.00
|
.086
|
|
F6.Opns.th:cls_w5
|
|
-0.07
|
-0.36 – 0.21
|
-0.01
|
-0.04 – 0.02
|
.606
|
|
F6.Opns.th:cls_w6
|
|
-0.18
|
-0.38 – 0.02
|
-0.02
|
-0.04 – 0.00
|
.077
|
|
F6.Opns.th:cls_w7
|
|
-0.08
|
-0.27 – 0.10
|
-0.01
|
-0.03 – 0.01
|
.382
|
|
F6.Opns.th:cls_w8
|
|
0.11
|
-0.13 – 0.34
|
0.01
|
-0.01 – 0.04
|
.373
|
|
Observations
|
|
21944
|
|
R2 / adj. R2
|
|
.090 / .089
|
- VD: language, som clusters
fit <- lm(formula = cog_portug~grade0+age2+sex+correct_grade2+
F1.Cons.th+F2.Extr.th+F3.Nint.th+F4.Nloc.th+F5.Agre.th+
F6.Opns.th+ cls_som+cls_som*F1.Cons.th+cls_som*F6.Opns.th,
contrasts = list( cls_som = contr.sum), data=sv1_rio )
|
|
|
cog_portug
|
|
|
|
B
|
CI
|
std. Beta
|
CI
|
p
|
|
(Intercept)
|
|
6.19
|
5.69 – 6.69
|
|
|
<.001
|
|
grade0
|
|
0.44
|
0.39 – 0.48
|
0.15
|
0.14 – 0.16
|
<.001
|
|
age2
|
|
-0.02
|
-0.04 – 0.00
|
-0.01
|
-0.03 – 0.00
|
.101
|
|
sex
|
|
1.04
|
0.91 – 1.17
|
0.11
|
0.09 – 0.12
|
<.001
|
|
correct_grade2
|
|
1.92
|
1.78 – 2.06
|
0.19
|
0.17 – 0.20
|
<.001
|
|
F1.Cons.th
|
|
0.11
|
0.02 – 0.20
|
0.02
|
0.00 – 0.04
|
.015
|
|
F2.Extr.th
|
|
-0.46
|
-0.55 – -0.37
|
-0.09
|
-0.11 – -0.08
|
<.001
|
|
F3.Nint.th
|
|
0.15
|
0.06 – 0.24
|
0.03
|
0.01 – 0.05
|
.001
|
|
F4.Nloc.th
|
|
-0.38
|
-0.47 – -0.30
|
-0.08
|
-0.10 – -0.06
|
<.001
|
|
F5.Agre.th
|
|
-0.02
|
-0.11 – 0.07
|
-0.00
|
-0.02 – 0.01
|
.709
|
|
F6.Opns.th
|
|
0.75
|
0.66 – 0.84
|
0.15
|
0.14 – 0.17
|
<.001
|
|
cls_som
|
|
cls_som1
|
|
0.24
|
-0.07 – 0.56
|
0.02
|
-0.01 – 0.05
|
.127
|
|
cls_som2
|
|
-0.09
|
-0.32 – 0.14
|
-0.01
|
-0.03 – 0.01
|
.444
|
|
cls_som3
|
|
1.01
|
0.44 – 1.58
|
0.09
|
0.04 – 0.13
|
.001
|
|
cls_som4
|
|
0.11
|
-0.13 – 0.36
|
0.01
|
-0.01 – 0.03
|
.362
|
|
cls_som5
|
|
-0.50
|
-0.82 – -0.18
|
-0.05
|
-0.08 – -0.02
|
.002
|
|
cls_som6
|
|
-0.33
|
-0.62 – -0.05
|
-0.03
|
-0.06 – -0.01
|
.020
|
|
cls_som7
|
|
0.44
|
0.11 – 0.78
|
0.04
|
0.01 – 0.07
|
.010
|
|
cls_som8
|
|
-0.02
|
-0.27 – 0.23
|
-0.00
|
-0.03 – 0.02
|
.863
|
|
F1.Cons.th:cls_som1
|
|
0.12
|
-0.11 – 0.36
|
0.01
|
-0.01 – 0.04
|
.298
|
|
F1.Cons.th:cls_som2
|
|
0.10
|
-0.15 – 0.35
|
0.01
|
-0.01 – 0.03
|
.437
|
|
F1.Cons.th:cls_som3
|
|
-0.25
|
-0.46 – -0.04
|
-0.03
|
-0.06 – -0.00
|
.023
|
|
F1.Cons.th:cls_som4
|
|
-0.09
|
-0.37 – 0.19
|
-0.01
|
-0.03 – 0.02
|
.531
|
|
F1.Cons.th:cls_som5
|
|
-0.16
|
-0.42 – 0.10
|
-0.02
|
-0.04 – 0.01
|
.217
|
|
F1.Cons.th:cls_som6
|
|
0.15
|
-0.10 – 0.40
|
0.01
|
-0.01 – 0.03
|
.239
|
|
F1.Cons.th:cls_som7
|
|
0.12
|
-0.15 – 0.39
|
0.01
|
-0.01 – 0.03
|
.385
|
|
F1.Cons.th:cls_som8
|
|
0.04
|
-0.19 – 0.26
|
0.00
|
-0.02 – 0.02
|
.740
|
|
F6.Opns.th:cls_som1
|
|
-0.19
|
-0.44 – 0.06
|
-0.02
|
-0.04 – 0.01
|
.135
|
|
F6.Opns.th:cls_som2
|
|
0.00
|
-0.26 – 0.26
|
0.00
|
-0.03 – 0.03
|
.978
|
|
F6.Opns.th:cls_som3
|
|
-0.21
|
-0.45 – 0.04
|
-0.02
|
-0.05 – 0.00
|
.094
|
|
F6.Opns.th:cls_som4
|
|
0.29
|
0.03 – 0.54
|
0.03
|
0.00 – 0.05
|
.026
|
|
F6.Opns.th:cls_som5
|
|
0.14
|
-0.12 – 0.39
|
0.01
|
-0.01 – 0.04
|
.289
|
|
F6.Opns.th:cls_som6
|
|
0.17
|
-0.06 – 0.40
|
0.02
|
-0.01 – 0.05
|
.152
|
|
F6.Opns.th:cls_som7
|
|
-0.26
|
-0.51 – -0.01
|
-0.03
|
-0.06 – -0.00
|
.041
|
|
F6.Opns.th:cls_som8
|
|
0.14
|
-0.08 – 0.37
|
0.02
|
-0.01 – 0.05
|
.215
|
|
Observations
|
|
21944
|
|
R2 / adj. R2
|
|
.146 / .144
|
fit <- lm(formula = cog_math~grade0+age2+sex+correct_grade2+
F1.Cons.th+F2.Extr.th+F3.Nint.th+F4.Nloc.th+F5.Agre.th+
F6.Opns.th+ cls_som+cls_som*F1.Cons.th+cls_som*F6.Opns.th,
contrasts = list( cls_som = contr.sum), data=sv1_rio )
|
|
|
cog_math
|
|
|
|
B
|
CI
|
std. Beta
|
CI
|
p
|
|
(Intercept)
|
|
14.34
|
13.89 – 14.78
|
|
|
<.001
|
|
grade0
|
|
-0.59
|
-0.62 – -0.55
|
-0.23
|
-0.25 – -0.22
|
<.001
|
|
age2
|
|
-0.01
|
-0.02 – 0.01
|
-0.00
|
-0.02 – 0.01
|
.557
|
|
sex
|
|
-0.31
|
-0.43 – -0.20
|
-0.04
|
-0.05 – -0.02
|
<.001
|
|
correct_grade2
|
|
1.26
|
1.13 – 1.38
|
0.14
|
0.13 – 0.16
|
<.001
|
|
F1.Cons.th
|
|
0.32
|
0.24 – 0.40
|
0.08
|
0.06 – 0.10
|
<.001
|
|
F2.Extr.th
|
|
-0.23
|
-0.31 – -0.15
|
-0.06
|
-0.07 – -0.04
|
<.001
|
|
F3.Nint.th
|
|
0.09
|
0.01 – 0.17
|
0.02
|
0.00 – 0.04
|
.029
|
|
F4.Nloc.th
|
|
-0.06
|
-0.13 – 0.02
|
-0.01
|
-0.03 – 0.00
|
.123
|
|
F5.Agre.th
|
|
0.00
|
-0.08 – 0.08
|
0.00
|
-0.02 – 0.02
|
.986
|
|
F6.Opns.th
|
|
0.29
|
0.21 – 0.37
|
0.07
|
0.05 – 0.09
|
<.001
|
|
cls_som
|
|
cls_som1
|
|
0.03
|
-0.25 – 0.31
|
0.00
|
-0.03 – 0.04
|
.823
|
|
cls_som2
|
|
0.07
|
-0.14 – 0.27
|
0.01
|
-0.02 – 0.03
|
.528
|
|
cls_som3
|
|
0.01
|
-0.50 – 0.52
|
0.00
|
-0.05 – 0.05
|
.964
|
|
cls_som4
|
|
0.19
|
-0.03 – 0.41
|
0.02
|
-0.00 – 0.05
|
.085
|
|
cls_som5
|
|
-0.20
|
-0.49 – 0.08
|
-0.02
|
-0.05 – 0.01
|
.159
|
|
cls_som6
|
|
-0.13
|
-0.38 – 0.12
|
-0.01
|
-0.04 – 0.01
|
.305
|
|
cls_som7
|
|
0.28
|
-0.02 – 0.57
|
0.03
|
-0.00 – 0.06
|
.070
|
|
cls_som8
|
|
0.14
|
-0.08 – 0.36
|
0.02
|
-0.01 – 0.05
|
.216
|
|
F1.Cons.th:cls_som1
|
|
0.33
|
0.12 – 0.54
|
0.04
|
0.02 – 0.07
|
.002
|
|
F1.Cons.th:cls_som2
|
|
-0.20
|
-0.42 – 0.02
|
-0.02
|
-0.05 – 0.00
|
.081
|
|
F1.Cons.th:cls_som3
|
|
0.10
|
-0.09 – 0.29
|
0.01
|
-0.01 – 0.04
|
.283
|
|
F1.Cons.th:cls_som4
|
|
0.05
|
-0.20 – 0.30
|
0.00
|
-0.02 – 0.03
|
.683
|
|
F1.Cons.th:cls_som5
|
|
-0.08
|
-0.31 – 0.15
|
-0.01
|
-0.04 – 0.02
|
.493
|
|
F1.Cons.th:cls_som6
|
|
0.24
|
0.02 – 0.46
|
0.02
|
0.00 – 0.05
|
.032
|
|
F1.Cons.th:cls_som7
|
|
-0.23
|
-0.47 – 0.02
|
-0.02
|
-0.05 – 0.00
|
.066
|
|
F1.Cons.th:cls_som8
|
|
-0.14
|
-0.34 – 0.06
|
-0.01
|
-0.04 – 0.01
|
.176
|
|
F6.Opns.th:cls_som1
|
|
-0.03
|
-0.25 – 0.19
|
-0.00
|
-0.03 – 0.02
|
.793
|
|
F6.Opns.th:cls_som2
|
|
-0.11
|
-0.34 – 0.12
|
-0.01
|
-0.04 – 0.01
|
.350
|
|
F6.Opns.th:cls_som3
|
|
0.11
|
-0.11 – 0.32
|
0.01
|
-0.02 – 0.04
|
.340
|
|
F6.Opns.th:cls_som4
|
|
0.15
|
-0.07 – 0.38
|
0.02
|
-0.01 – 0.04
|
.177
|
|
F6.Opns.th:cls_som5
|
|
-0.04
|
-0.27 – 0.18
|
-0.01
|
-0.03 – 0.02
|
.701
|
|
F6.Opns.th:cls_som6
|
|
0.18
|
-0.03 – 0.39
|
0.02
|
-0.00 – 0.05
|
.085
|
|
F6.Opns.th:cls_som7
|
|
-0.18
|
-0.40 – 0.05
|
-0.02
|
-0.05 – 0.01
|
.120
|
|
F6.Opns.th:cls_som8
|
|
0.04
|
-0.17 – 0.24
|
0.01
|
-0.02 – 0.03
|
.729
|
|
Observations
|
|
21944
|
|
R2 / adj. R2
|
|
.090 / .089
|