Techniques: Dichotomizing a Network
Publié en ligne: 18 janv. 2019
Pages: 1 - 11
© 2018 Stephen P. Borgatti published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
Figure 1
DGG Women by Women dataset dichotomized above 1.Figure 2
DGG Women by Women dataset dichotomized above 2.Figure 3
DGG Women by Women dataset dichotomized above 3.Figure 4
BKS FRATERNITY dataset dichotomized above 0.Figure 5
BKS FRATERNITY dataset dichotomized above 2.Figure 6
BKS FRATERNITY dataset dichotomized above 4.Figure 7
BKS FRATERNITY dataset dichotomized above 6.Figure 8
DGG Women by Women dataset dichotomized at 4.Figure 9
DGG Women by Women dataset dichotomized at 3. Strong ties in bold.Figure A1
Screenshot of Netdraw.Figure A2
Screenshot of UCINET’s Interactive Dichotomization routine’s results.G-transitivity decomposition command line instruction and output in UCINET.
->dsp gtrans(women)
|
|
|
|
1
|
2
|
3
|
4
|
Level
|
Trans
|
Intrans
|
Possible
|
Prop Trans
|
|
|
|
|
n
|
|
--------
|
--------
|
--------
|
--------
|
7
|
0
|
0
|
0
|
|
6
|
26
|
0
|
26
|
1
|
5
|
30
|
0
|
30
|
1
|
4
|
160
|
0
|
160
|
1
|
3
|
526
|
4
|
530
|
0.992
|
2
|
2,032
|
44
|
2,076
|
0.979
|
1
|
3,786
|
292
|
4,078
|
0.928
|
0
|
4,448
|
448
|
4,896
|
0.908
|
One mode DGG Women by Women network projection.
|
EV |
LA |
TH |
BR |
CH |
FR |
EL |
PE |
RU |
VE |
MY |
KA |
SY |
NO |
HE |
DO |
OL |
FL |
EVELYN |
8 |
6 |
7 |
6 |
3 |
4 |
3 |
3 |
3 |
2 |
2 |
2 |
2 |
2 |
1 |
2 |
1 |
1 |
LAURA |
6 |
7 |
6 |
6 |
3 |
4 |
4 |
2 |
3 |
2 |
1 |
1 |
2 |
2 |
2 |
1 |
0 |
0 |
THERESA |
7 |
6 |
8 |
6 |
4 |
4 |
4 |
3 |
4 |
3 |
2 |
2 |
3 |
3 |
2 |
2 |
1 |
1 |
BRENDA |
6 |
6 |
6 |
7 |
4 |
4 |
4 |
2 |
3 |
2 |
1 |
1 |
2 |
2 |
2 |
1 |
0 |
0 |
CHARLOTTE |
3 |
3 |
4 |
4 |
4 |
2 |
2 |
0 |
2 |
1 |
0 |
0 |
1 |
1 |
1 |
0 |
0 |
0 |
FRANCES |
4 |
4 |
4 |
4 |
2 |
4 |
3 |
2 |
2 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
0 |
0 |
ELEANOR |
3 |
4 |
4 |
4 |
2 |
3 |
4 |
2 |
3 |
2 |
1 |
1 |
2 |
2 |
2 |
1 |
0 |
0 |
PEARL |
3 |
2 |
3 |
2 |
0 |
2 |
2 |
3 |
2 |
2 |
2 |
2 |
2 |
2 |
1 |
2 |
1 |
1 |
RUTH |
3 |
3 |
4 |
3 |
2 |
2 |
3 |
2 |
4 |
3 |
2 |
2 |
3 |
2 |
2 |
2 |
1 |
1 |
VERNE |
2 |
2 |
3 |
2 |
1 |
1 |
2 |
2 |
3 |
4 |
3 |
3 |
4 |
3 |
3 |
2 |
1 |
1 |
MYRNA |
2 |
1 |
2 |
1 |
0 |
1 |
1 |
2 |
2 |
3 |
4 |
4 |
4 |
3 |
3 |
2 |
1 |
1 |
KATHERINE |
2 |
1 |
2 |
1 |
0 |
1 |
1 |
2 |
2 |
3 |
4 |
6 |
6 |
5 |
3 |
2 |
1 |
1 |
SYLVIA |
2 |
2 |
3 |
2 |
1 |
1 |
2 |
2 |
3 |
4 |
4 |
6 |
7 |
6 |
4 |
2 |
1 |
1 |
NORA |
2 |
2 |
3 |
2 |
1 |
1 |
2 |
2 |
2 |
3 |
3 |
5 |
6 |
8 |
4 |
1 |
2 |
2 |
HELEN |
1 |
2 |
2 |
2 |
1 |
1 |
2 |
1 |
2 |
3 |
3 |
3 |
4 |
4 |
5 |
1 |
1 |
1 |
DOROTHY |
2 |
1 |
2 |
1 |
0 |
1 |
1 |
2 |
2 |
2 |
2 |
2 |
2 |
1 |
1 |
2 |
1 |
1 |
OLIVIA |
1 |
0 |
1 |
0 |
0 |
0 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
2 |
1 |
1 |
2 |
2 |
FLORA |
1 |
0 |
1 |
0 |
0 |
0 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
2 |
1 |
1 |
2 |
2 |
R-square of models predicting performance using betweenness centrality at different levels of dichotomization.
Dichot. level |
R2
|
1 |
0.05 |
2 |
0.09 |
3 |
0.12 |
4 |
0.23 |
5 |
0.31 |
6 |
0.27 |
7 |
0.22 |
8 |
0.15 |
9 |
0.07 |
Number of g-transitive and intransitive triples in the DGG dataset at different dichotomization levels.
Value |
Trans |
Intrans |
7 |
0 |
0 |
6 |
26 |
0 |
5 |
30 |
0 |
4 |
160 |
0 |
3 |
526 |
4 |
2 |
2,032 |
44 |
1 |
3,786 |
292 |
0 |
4,448 |
448 |
Z-score, correlation, number of ties and density of the DGG dataset at different dichotomization levels.
Value |
Z-score |
Correlation |
Ties |
Density |
7 |
3.352 |
0.271887 |
2 |
0.006536 |
6 |
2.667 |
0.646625 |
16 |
0.052288 |
5 |
1.983 |
0.666829 |
18 |
0.058824 |
4 |
1.298 |
0.781314 |
48 |
0.156863 |
3 |
0.613 |
0.811928 |
92 |
0.300654 |
2 |
−0.072 |
0.720115 |
190 |
0.620915 |
1 |
−0.756 |
0.457341 |
278 |
0.908497 |
0 |
−1.441 |
|
306 |
1.000000 |