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IPUC, Neuchˆ
atel, February 23-24, 2007
Innovative Data Mining based approaches for
life course analysis
Gilbert Ritschard
Alexis Gabadinho, Nicolas M¨
uller, Matthias Studer
University of Geneva, Switzerland
Outline
1
Aim of the research project
2
Our first results
2.1
Mobility trees
2.2
Survival trees
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1
Aim of the research project
Just started February 1, 2007 FNS project on
“
Mining event histories: Towards new insight on personal Swiss life courses
”
Methodological concern Explore and develop
data mining
approaches for
individual
longitudinal data
•
Methods for time to event analysis
•
Methods for sequence data analysis
Socio-demographic concern Using mainly SHP data, but also other sources,
gain
original insight
on
•
How familial, professional and other socio-demographic events are
entwined,
•
Typical characteristics of Swiss life trajectories,
•
Changes in these characteristics over time.
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What is data mining?
“
Data Mining is the process of finding new and potentially useful
knowledge from data
”
Gregory Piatetsky-Shapiro editor of
http://www.kdnuggets.com
“
Data mining is the analysis of (often large) observational data sets
to find unsuspected relationships and to summarize the data in novel
ways that are both understandable and useful to the data owner
”
(
Hand et al.
,
2001
)
Also called
Knowledge Discovery in Databases
, KDD.
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What is data mining? (2)
Concerned with characterization of interesting patterns
•
per se
(unsupervised learning)
–
Clustering
–
Frequent itemsets
–
Association rules
•
for
classification or prediction purposes
(supervised learning)
–
Decision trees
–
Bayesian networks
–
SVM and Kernel Methods
–
CBR (case based reasoning), K-NN (
k
nearest neighbors)
Proceeds mainly heuristically .
Unlike statistical modeling, makes
no assumptions
about process
generating the data.
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Typology of methods for individual longitudinal data
nature of data
questions
time stamped event
state/event sequences
descriptive
- Survival curves:
- Optimal matching clustering
Parametric (Weibull, Gompertz)
- Frequencies of typical
and non parametric
patterns
(Kaplan-Meier, Nelson-Aalen)
-
Discovering typical patterns
estimators
causality
- Hazard regression models
- Markov models,
Mobility trees
-
Survival trees
-
Association rules
between
subsequences
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2
Our first results
•
Mobility trees
•
Survival trees
•
Characteristic sequences
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2.1
Mobility trees
•
(SHP Data, Waves 1 to 6 (1999-2004), aged between 20 and 64 in 2004.)
•
How does
working status
(occupied active, unemployed, inactive) in 2004
depend on
–
working status in previous year (1999 to 2003)
–
other factors (attained education level, partner working status,
partner education level, ...)
and what are
main interaction effects
?
•
Mobility trees are alternative to Markovian transition models.
•
Growing separate classification trees for
women
and
men
highlights
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Mobility tree, Men
Category % n active occupied 93.06 1194 unemployed 1.56 20 not in labor force 5.38 69 Total (100.00) 1283
Node 0
Category % n active occupied 82.48 113 unemployed 5.84 8 not in labor force 11.68 16 Total (10.68) 137
Node 3
Category % n active occupied 70.13 54 unemployed 10.39 8 not in labor f orce 19.48 15 Total (6.00) 77
Node 7 Category % n
active occupied 98.33 59 unemployed 0.00 0 not in labor force 1.67 1 Total (4.68) 60
Node 6 Category % n active occupied 29.51 18 unemployed 4.92 3 not in labor force 65.57 40 Total (4.75) 61
Node 2 Category % n
active occupied 97.97 1063 unemployed 0.83 9 not in labor f orce 1.20 13 Total (84.57) 1085
Node 1
Category % n active occupied 95.19 356 unemployed 1.87 7 not in labor force 2.94 11 Total (29.15) 374
Node 5 Category % n
active occupied 99.44 707 unemployed 0.28 2 not in labor force 0.28 2 Total (55.42) 711
Node 4
Working status 04
Working status B, 03
Adj. P-value=0.0000, Chi-square=240.3194, df =2
unemployed,<missing>
Partner actual occupation 04, into 6 Adj. P-value=0.0002, Chi-square=20.7799, df=1
education,<missing> at home;part-time paid w ork;full time paid w ork + f amily company;retired or invalid
not in labour force active, f ull time (>= 80%);active, long part time (50%-80%);active, short part time (< 50%)
Partner highest level of education achieved 04 (both grid and individual quest.) Adj. P-value=0.0001, Chi-square=20.7372, df=1
>vocational high school,<missing> <=vocational high school
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Mobility tree, Women
Category % n active occupied 77.78 1281 unemployed 2.31 38 not in labor f orce19.91328 Total (100.00) 1647
Node 0
Category % n active occupied 73.33143 unemployed 7.69 15 not in labor f orce18.97 37 Total (11.84) 195
Node 4
Category % n active occupied 87.50 77 unemployed 5.68 5 not in labor force 6.82 6 Total (5.34) 88
Node 12 Category % n
active occupied 61.68 66 unemployed 9.35 10 not in labor f orce28.97 31 Total (6.50) 107
Node 11 Category % n
active occupied 91.78346 unemployed 0.80 3 not in labor f orce 7.43 28 Total (22.89) 377
Node 3
Category % n active occupied 94.98303 unemployed 0.31 1 not in labor f orce 4.70 15 Total (19.37) 319
Node 10 Category % n
active occupied 74.14 43 unemployed 3.45 2 not in labor f orce22.41 13 Total (3.52) 58
Node 9 Category % n
active occupied 95.69 733 unemployed 1.17 9 not in labor force 3.13 24 Total (46.51) 766
Node 2
Category % n active occupied 89.26 133 unemployed 2.68 4 not in labor force 8.05 12 Total (9.05) 149
Node 8 Category % n
active occupied 97.24600 unemployed 0.81 5 not in labor f orce 1.94 12 Total (37.46) 617
Node 7
Category % n active occupied 99.25265 unemployed 0.37 1 not in labor f orce 0.37 1 Total (16.21) 267
Node 14 Category % n
active occupied 95.71335 unemployed 1.14 4 not in labor f orce 3.14 11 Total (21.25) 350
Node 13 Category % n
active occupied 19.09 59 unemployed 3.56 11 not in labor f orce77.35239 Total (18.76) 309
Node 1
Category % n active occupied 39.73 29 unemployed 9.59 7 not in labor f orce50.68 37 Total (4.43) 73
Node 6 Category % n
active occupied 12.71 30 unemployed 1.69 4 not in labor force85.59 202 Total (14.33) 236
Node 5
Working status 04
Working status B, 03 Adj. P-value=0.0000, Chi-square=750.9194, df=3
unemployed,<missing>
Working status B, 00 Adj. P-value=0.0004, Chi-square=19.1782, df =1
active, full time (>= 80%);active, short part time (< 50%) not in labour f orce;unemployed;active, long part time (50%-80%),<missing>
active, short part time (< 50%)
Working status B, 02 Adj. P-value=0.0003, Chi-square=19.3525, df =1
active, short part time (< 50%);unemployed;active, long part time (50%-80%),<missing> not in labour f orce;active, f ull time (>= 80%)
active, f ull time (>= 80%);active, long part time (50%-80%)
Working status B, 99 Adj. P-value=0.0047, Chi-square=14.3681, df =1
not in labour force;unemployed,<missing> active, full time (>= 80%);active, long part time (50%-80%);active, short part time (< 50%)
Highest level of education achieved 04 (both grid and individual quest.) Adj. P-value=0.0292, Chi-square=8.6618, df =1
>full-time vocational school <=full-time vocational school
not in labour f orce
Working status B, 02 Adj. P-value=0.0000, Chi-square=30.5767, df=1
active, full time (>= 80%);active, short part time (< 50%);unemployed;active, long part time (50%-80%),<missing> not in labour force
Working status B (full time, long part time, short part time, unemployed,
inactive) in 2003 used for first split
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Mobility tree, Women: Details for women inactive in 2003
Category
%
n
active occupied
77.78 1281
unemployed
2.31
38
not in labor f orce
19.91
328
Total
(100.00) 1647
Node 0
Category
%
n
active occupied
73.33
143
unemployed
7.69
15
not in labor f orce
18.97
37
Total
(11.84) 195
Node 4
Category
%
n
active occupied
87.50
77
unemployed
5.68
5
not in labor force
6.82
6
Total
(5.34)
88
Node 12
Category
%
n
active occupied
61.68
66
unemployed
9.35
10
not in labor f orce
28.97
31
Total
(6.50) 107
Node 11
Category
%
n
active occupied
91.78
346
unemployed
0.80
3
not in labor f orce
7.43
28
Total
(22.89) 377
Node 3
Category
%
n
active occupied
94.98
303
unemployed
0.31
1
not in labor f orce
4.70
15
Total
(19.37) 319
Node 10
Category
%
n
active occupied
74.14
43
unemployed
3.45
2
not in labor f orce
22.41
13
Total
(3.52)
58
Node 9
Category
%
n
active occupied
95.69
733
unemployed
1.17
9
not in labor force
3.13
24
Total
(46.51) 766
Node 2
Category
%
n
active occupied
89.26
133
unemployed
2.68
4
not in labor force
8.05
12
Total
(9.05) 149
Node 8
Category
%
n
active occupied
97.24
600
unemployed
0.81
5
not in labor f orce
1.94
12
Total
(37.46) 617
Node 7
Category
%
n
active occupied
99.25
265
unemployed
0.37
1
not in labor f orce
0.37
1
Total
(16.21) 267
Node 14
Category
%
n
active occupied
95.71
335
unemployed
1.14
4
not in labor f orce
3.14
11
Total
(21.25) 350
Node 13
Category
%
n
active occupied
19.09
59
unemployed
3.56
11
not in labor f orce
77.35
239
Total
(18.76) 309
Node 1
Category
%
n
active occupied
39.73
29
unemployed
9.59
7
not in labor f orce
50.68
37
Total
(4.43)
73
Node 6
Category
%
n
active occupied
12.71
30
unemployed
1.69
4
not in labor force
85.59
202
Total
(14.33) 236
Node 5
Working status 04
Working status B, 03
Adj. P-value=0.0000, Chi-square=750.9194, df=3
unemployed,<missing>
Working status B, 00
Adj. P-value=0.0004, Chi-square=19.1782, df =1
active, full time (>= 80%);active, short part time (< 50%)
not in labour f orce;unemployed;active, long part time (50%-80%),<missing>
active, short part time (< 50%)
Working status B, 02
Adj. P-value=0.0003, Chi-square=19.3525, df =1
active, short part time (< 50%);unemployed;active, long part time (50%-80%),<missing>
not in labour f orce;active, f ull time (>= 80%)
active, f ull time (>= 80%);active, long part time (50%-80%)
Working status B, 99
Adj. P-value=0.0047, Chi-square=14.3681, df =1
not in labour force;unemployed,<missing>
active, full time (>= 80%);active, long part time (50%-80%);active, short part time (< 50%)
Highest level of education achieved 04 (both grid and individual quest.)
Adj. P-value=0.0292, Chi-square=8.6618, df =1
>full-time vocational school
<=full-time vocational school
not in labour f orce
Working status B, 02
Adj. P-value=0.0000, Chi-square=30.5767, df=1
active, full time (>= 80%);active, short part time (< 50%);unemployed;active, long part time (50%-80%),<missing>
not in labour force
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2.2
Survival trees
•
(SHP 2002 biographical data, 2002 Wave data for some potential explanatory factors)
•
Which are the most discriminating factors for
marriage duration until
divorce/separation
?
Used same variables as for discrete time logistic model in
Ritschard and
Sauvain-Dugerdil
(
2007
)
•
Tried two methods
–
Maximize differences in KM survival curves using Tarone-Ware (T-W)
p
-value
(
Segal
,
1988
)
.
–
Cox regression tree: maximize differences in proportionality factors
among groups
(
Leblanc and Crowley
,
1992
;
Therneau and Atkinson
,
1997
)
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T-W Survival Tree: Marriage until Divorce/Separation
Population
n
= 3619,
e
= 622
S
< 90% at
11
S
at 30 =
0.77
TW
χ
2(1) = 54.81, p<0.0001
<=1940
n
= 841,
e
= 123
S
< 90% at
21
S
at 30 =
0.86
TW
χ
2(1) = 22.48, p<0.0001
> 1940
n
=2778,
e
= 499
S
<90% at
9
S
at 30 =
0.73
TW
χ
2(1) = 37.44, p<0.0001
<=1940 & French L.
n
= 174,
e
= 44
S
< 90% at
11
S
at 30 =
0.74
<=1940 & Non French L.
n
= 667,
e
= 79
S
< 90% at
26
S
at 30 =
0.89
TW
χ
2(1) = 8.08, p<0.0001
> 1940 & No Child
n
= 603,
e
= 138
S
< 90% at
5
S
at 30 =
0.64
TW
χ
2(1) = 4.45, p=0.0349
> 1940 & Child
n
= 2175,
e
= 361
S
< 90% at
11
S
at 30 =
0.75
TW
χ
2(1) = 9.77, p=0.0018
> 1940 & Child
& German or Italian L.
n
= 1444,
e
= 217
S
< 90% at
13
S
at 30 =
0.77
> 1940 & Child
& French or unknown L.
n
=731,
e
= 144
S
< 90% at
8
S
at 30 =
0.70
<=1940 & Non French L.
& University
n
= 51,
e
= 12
S
< 90% at
10
S
at 30 =
0.76
<=1940 & Non French L.
& Not University
n
= 667,
e
= 79
S
< 90% at
29
S
at 30 =
0.895
> 1940 & No Child
& University
n
= 86,
e
= 23
S
< 90% at
3
S
at 30 =
0.59
> 1940 & No Child
& Not University
n
= 517,
e
= 138
S
< 90% at
6
S
at 30 =
0.65
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$
0 .6 0. 7 0 .8 0. 9 1 .0Noeud finaux
Cohorte <=1940 et Allemand, Italien ou inconnu et Université
Cohorte <=1940 et Langue Allemand, Italien ou inconnu et Non Université Cohorte <=1940 et Langue Français
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Marriage survival probabilities until Divorce/Separation, by leaves
Marriage survival probability until divorce/separation, by leaves
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
<=1940 &
non French L.
& University
<=1940 &
non French L.
& non
University
<=1940 &
French L.
> 1940 & no
Child &
University
> 1940 & no
Child & non
University
> 1940 &
Child &
German or
Italian L.
> 1940 &
Child &
French or
unknown L.
Survival probability
5 years
10 years
20 years
30 years
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Cox Survival Tree: Marriage until Divorce/Separation
Population
n
= 3619,
e
= 622
Prop. fact. =
1.0
-LL improv.= 55.87
<=1940
n
= 841,
e
= 123
Prop. fact. =
0.60
-LL improv.= 18.44
> 1940
n
= 2778,
e
= 499
Prop. fact. =
1.20
-LL improv.= 30.91
<=1940 & French
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%
0
10
20
30
40
0.
5
0
.6
0.
7
0
.8
0
.9
1
.0
Noeud finaux
Cohorte <=1940 et Langue Allemand, Italien ou inconnu
Cohorte <=1940 et Langue Français
Cohorte > 1940 et Avec Enfant
Cohorte > 1940 et Sans Enfant
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2.3
Characteristic sequences
•
(SHP 2002 biographical data)
•
Selection of
pairs of events
, e.g. marriage and first job.
•
For each pair,
order of sequence
:
<
,
=
,
>
, missing
•
Which are the most typical sequences?
•
Most discriminating sequences
between
–
sex
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%
Frequencies of characteristic 2-event sequences
0%
5%
10%
15%
20%
25%
30%
Ch
ild < M
ar
riage
Ma
rriage
<
Child
Ch
ild = M
ar
riage
Ch
ild < Job
Job
<
Child
Ch
ild = Job
Ch
ild < Edu
c end
Edu
c end < Child
Ch
ild = Edu
c end
Ma
rriage
<
Job
Job
<
Mar
riage
Ma
rriage
=
Job
Ma
rriage
<
Educ e
nd
Edu
c end < Marria
ge
Ma
rriage
=
Educ e
nd
Job
<
Educ
en
d
Edu
c end < J
ob
Job
=
Educ
en
d
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$
Cohort discriminating 2-event sequences
Category % n apres 41 79.98 3803 avant 41 20.02 952 Total (100.00) 4755 Node 0 Category % n apres 41 84.13 1124 avant 41 15.87 212 Total (28.10) 1336 Node 4 Category % n apres 41 90.48 979 avant 41 9.52 103 Total (22.75) 1082 Node 11 Category % n apres 41 92.32 902 avant 41 7.68 75 Total (20.55) 977 Node 23 Category % n apres 41 73.33 77 avant 41 26.67 28 Total (2.21) 105 Node 22 Category % n apres 41 78.57 44 avant 41 21.43 12 Total (1.18) 56 Node 10 Category % n apres 41 51.01 101 avant 41 48.99 97 Total (4.16) 198 Node 9 Category % n apres 41 42.36 61 avant 41 57.64 83 Total (3.03) 144 Node 21 Category % n apres 41 74.07 40 avant 41 25.93 14 Total (1.14) 54 Node 20 Category % n apres 41 62.48 726 avant 41 37.52 436 Total (24.44) 1162 Node 3 Category % n apres 41 59.22 562 avant 41 40.78 387 Total (19.96) 949 Node 8 Category % n apres 41 54.43 172 avant 41 45.57 144 Total (6.65) 316 Node 19 Category % n apres 41 61.61 390 avant 41 38.39 243 Total (13.31) 633 Node 18 Category % n apres 41 76.00 164 avant 41 23.00 49 Total (4.48) 213 Node 7 Category % n apres 41 88.17 82 avant 41 11.83 11 Total (1.96) 93 Node 17 Category % n apres 41 68.33 82 avant 41 31.67 38 Total (2.52) 120 Node 16 Category % n apres 41 69.44 50 avant 41 30.56 22 Total (1.51) 72 Node 2 Category % n apres 41 87.09 1903 avant 41 12.91 282 Total (45.95) 2185 Node 1 Category % n apres 41 88.01 1688 avant 41 11.99 230 Total (40.34) 1918 Node 6 Category % n apres 41 84.82 486 avant 41 15.18 87 Total (12.05) 573 Node 15 Category % n apres 41 89.37 1202 avant 41 10.63 143 Total (28.29) 1345 Node 14 Category % n apres 41 80.52 215 avant 41 19.48 52 Total (5.62) 267 Node 5 Category % n apres 41 85.41 158 avant 41 14.59 27 Total (3.89) 185 Node 13 Category % n apres 41 69.51 57 avant 41 30.49 25 Total (1.72) 82 Node 12 Naissance Départ et mariage Adj. P-value=0.0000, Chi-square=310.7048, df=3
<missing>
Mariage et f in des études Adj. P-value=0.0000, Chi-square=196.6698, df=2
<missing>
Enf ant et emploi Adj. P-value=0.0000, Chi-square=39.6959, df =1
=,<missing> >;<
< >;=
Enf ant et emploi Adj. P-value=0.0007, Chi-square=15.8053, df =1
<missing> >;<;=
=
Départ et emploi Adj. P-value=0.0000, Chi-square=23.4451, df =1
>,<missing>
Enf ant et emploi Adj. P-value=0.0339, Chi-square=4.5007, df =1
<missing> >
<;=
Départ et fin des études Adj. P-value=0.0064, Chi-square=11.6421, df =1
<;= >,<missing>
> <
Mariage et emploi Adj. P-value=0.0063, Chi-square=11.6786, df =1
>;<
Départ et f in des études Adj. P-value=0.0498, Chi-square=7.8866, df =1
=,<missing> >;<
=,<missing>
Départ et f in des études Adj. P-value=0.0249, Chi-square=9.1512, df =1
<;=,<missing> >
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%
Cohort: details for Leaving Home before Marriage
Category % n apres 41 79.98 3803 avant 41 20.02 952 Total (100.00) 4755 Node 0 Category % n apres 41 84.13 1124 avant 41 15.87 212 Total (28.10) 1336 Node 4 Category % n apres 41 90.48 979 avant 41 9.52 103 Total (22.75) 1082 Node 11 Category % n apres 41 92.32 902 avant 41 7.68 75 Total (20.55) 977 Node 23 Category % n apres 41 73.33 77 avant 41 26.67 28 Total (2.21) 105 Node 22 Category % n apres 41 78.57 44 avant 41 21.43 12 Total (1.18) 56 Node 10 Category % n apres 41 51.01 101 avant 41 48.99 97 Total (4.16) 198 Node 9 Category % n apres 41 42.36 61 avant 41 57.64 83 Total (3.03) 144 Node 21 Category % n apres 41 74.07 40 avant 41 25.93 14 Total (1.14) 54 Node 20 Category % n apres 41 62.48 726 avant 41 37.52 436 Total (24.44) 1162 Node 3 Category % n apres 41 59.22 562 avant 41 40.78 387 Total (19.96) 949 Node 8 Category % n apres 41 54.43 172 avant 41 45.57 144 Total (6.65) 316 Node 19 Category % n apres 41 61.61 390 avant 41 38.39 243 Total (13.31) 633 Node 18 Category % n apres 41 76.00 164 avant 41 23.00 49 Total (4.48) 213 Node 7 Category % n apres 41 88.17 82 avant 41 11.83 11 Total (1.96) 93 Node 17 Category % n apres 41 68.33 82 avant 41 31.67 38 Total (2.52) 120 Node 16 Category % n apres 41 69.44 50 avant 41 30.56 22 Total (1.51) 72 Node 2 Category % n apres 41 87.09 1903 avant 41 12.91 282 Total (45.95) 2185 Node 1 Category % n apres 41 88.01 1688 avant 41 11.99 230 Total (40.34) 1918 Node 6 Category % n apres 41 84.82 486 avant 41 15.18 87 Total (12.05) 573 Node 15 Category % n apres 41 89.37 1202 avant 41 10.63 143 Total (28.29) 1345 Node 14 Category % n apres 41 80.52 215 avant 41 19.48 52 Total (5.62) 267 Node 5 Category % n apres 41 85.41 158 avant 41 14.59 27 Total (3.89) 185 Node 13 Category % n apres 41 69.51 57 avant 41 30.49 25 Total (1.72) 82 Node 12 Naissance Départ et mariage
Adj. P-value=0.0000, Chi-square=310.7048, df=3
<missing>
Mariage et f in des études
Adj. P-value=0.0000, Chi-square=196.6698, df=2
<missing>
Enf ant et emploi
Adj. P-value=0.0000, Chi-square=39.6959, df =1
=,<missing> >;<
< >;=
Enf ant et emploi
Adj. P-value=0.0007, Chi-square=15.8053, df =1
<missing> >;<;=
=
Départ et emploi
Adj. P-value=0.0000, Chi-square=23.4451, df =1
>,<missing>
Enf ant et emploi
Adj. P-value=0.0339, Chi-square=4.5007, df =1
<missing> >
<;=
Départ et fin des études
Adj. P-value=0.0064, Chi-square=11.6421, df =1
<;= >,<missing>
> <
Mariage et emploi
Adj. P-value=0.0063, Chi-square=11.6786, df =1
>;<
Départ et f in des études
Adj. P-value=0.0498, Chi-square=7.8866, df =1
=,<missing> >;<
=,<missing>
Départ et f in des études
Adj. P-value=0.0249, Chi-square=9.1512, df =1
<;=,<missing> >
'
$
Sex discriminating 2-event sequences
Category % n masculin 46.25 2199 f éminin 53.75 2556 Total (100.00) 4755 Node 0 Category % n masculin 43.60 613 féminin 56.40 793 Total (29.57) 1406 Node 4 Category % n masculin 54.38 205 féminin 45.62 172 Total (7.93) 377 Node 10 Category % n masculin 39.65 408 f éminin 60.35 621 Total (21.64) 1029 Node 9 Category % n masculin 63.16 36 Node 16 Category % n masculin 38.27 372 Node 15 Category % n masculin 41.32 402 féminin 58.68 571 Total (20.46) 973 Node 3 Category % n masculin 58.51 832 f éminin 41.49 590 Total (29.91) 1422 Node 2 Category % n masculin 64.69 480 féminin 35.31 262 Total (15.60) 742 Node 8 Category % n masculin 76.26 196 Node 14 Category % n masculin 58.56 284 Node 13 Category % n masculin 51.76 352 f éminin 48.24 328 Total (14.30) 680 Node 7 Category % n masculin 36.90 352 f éminin 63.10 602 Total (20.06) 954 Node 1 Category % n masculin 21.10 23 féminin 78.90 86 Total (2.29) 109 Node 6 Category % n masculin 38.93 329 f éminin 61.07 516 Total (17.77) 845 Node 5 Category % n masculin 23.81 20 Node 12 Category % n masculin 40.60 309 Node 11 sexe
Emploi et fin des études Adj. P-value=0.0000, Chi-square=133.0423, df=3
<missing>
Départ et emploi
Adj. P-value=0.0000, Chi-square=24.3337, df =1 > <;=,<missing>
Mariage et fin des études Adj. P-value=0.0019, Chi-square=13.9356, df =1
< >;=,<missing>
= <
Départ et emploi
Adj. P-value=0.0000, Chi-square=24.4185, df =1 >
Mariage et fin des études Adj. P-value=0.0000, Chi-square=23.0606, df =1
<;= >,<missing>
<;=,<missing> >
Enfant et emploi
Adj. P-value=0.0028, Chi-square=13.1883, df=1 < >;=,<missing>
Départ et f in des études Adj. P-value=0.0274, Chi-square=8.9750, df=1
= >;<,<missing>
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&
$
%
Sex: details for Job after Education end
Category % n masculin 46.25 2199 f éminin 53.75 2556 Total (100.00) 4755 Node 0 Category % n masculin 43.60 613 féminin 56.40 793 Total (29.57) 1406 Node 4 Category % n masculin 54.38 205 féminin 45.62 172 Total (7.93) 377 Node 10 Category % n masculin 39.65 408 f éminin 60.35 621 Total (21.64) 1029 Node 9 Category % n masculin 63.16 36 f éminin 36.84 21 Total (1.20) 57 Node 16 Category % n masculin 38.27 372 f éminin 61.73 600 Total (20.44) 972 Node 15 Category % n masculin 41.32 402 féminin 58.68 571 Total (20.46) 973 Node 3 Category % n masculin 58.51 832 f éminin 41.49 590 Total (29.91) 1422 Node 2 Category % n masculin 64.69 480 féminin 35.31 262 Total (15.60) 742 Node 8 Category % n masculin 76.26 196 f éminin 23.74 61 Total (5.40) 257 Node 14 Category % n masculin 58.56 284 féminin 41.44 201 Total (10.20) 485 Node 13 Category % n masculin 51.76 352 f éminin 48.24 328 Total (14.30) 680 Node 7 Category % n masculin 36.90 352 f éminin 63.10 602 Total (20.06) 954 Node 1 Category % n masculin 21.10 23 féminin 78.90 86 Total (2.29) 109 Node 6 Category % n masculin 38.93 329 f éminin 61.07 516 Total (17.77) 845 Node 5 Category % n masculin 23.81 20 f éminin 76.19 64 Total (1.77) 84 Node 12 Category % n masculin 40.60 309 f éminin 59.40 452 Total (16.00) 761 Node 11 sexe
Emploi et fin des études
Adj. P-value=0.0000, Chi-square=133.0423, df=3
<missing>
Départ et emploi
Adj. P-value=0.0000, Chi-square=24.3337, df =1
> <;=,<missing>
Mariage et fin des études
Adj. P-value=0.0019, Chi-square=13.9356, df =1
< >;=,<missing>
= <
Départ et emploi
Adj. P-value=0.0000, Chi-square=24.4185, df =1
>
Mariage et fin des études
Adj. P-value=0.0000, Chi-square=23.0606, df =1
<;= >,<missing>
<;=,<missing> >
Enfant et emploi
Adj. P-value=0.0028, Chi-square=13.1883, df=1
< >;=,<missing>
Départ et f in des études
Adj. P-value=0.0274, Chi-square=8.9750, df=1
= >;<,<missing>
