Centre for Longitudinal Studies



Step 9

Having excluded from the algorithm all cases flagged in Steps 4 and 7, prepare to re-
order all cases flagged in step 5 as having been wrongly bunched together in the same
pregnancy, or wrongly ordered chronologically.

The re-ordering not only has to move each outcome date to a new slot, but also every
one of the 29 variables associated with that particular baby (birth weight, outcome,
etc), as well as the 10 variables associated with that particular pregnancy (number of
babies in pregnancy, whether smoked, whether used contraception, etc). In particular,
the corresponding
pregnum is set to a value of 1, indicating a single, not multiple
birth. As documented above, once a bogus ‘multiple’ pregnancy is separated out into
two or more single-baby pregnancies it is unfortunately not possible to tell which of
these pregnancies these latter variables (smoking/contraception, etc) relate to. A
decision was made to copy the same pregnancy data into each of the new pregnancy
slots; users should therefore be careful in the way these data are interpreted. The
cases affected are all those listed in
Appendix 1.

A dummy set of variables in vector format of length 40 is set up so that all the 29
‘baby’ variables can be copied and indexed by the slot in which they erroneously
appeared.

Having been first copied to the dummy vectors, the original 29 variables are set to the
system-missing value in each of the 40 slots.

Similarly, dummy vectors of length 8 are set up for the ten ‘pregnancy’ variables; the
data are copied from the original variables, which are then set to system-missing.

Step 10

Complete the re-ordering process. Copy the data for the most recent baby into the
‘slot 1’ versions of all 29 variables, applying the ‘
KEY1’ variable from Step 6 as the
index for the vector; then copy the data for the next most recent into the ‘slot 6’
versions by applying ‘
KEY2’, etc., then ‘slot 11’ by applying KEY3’, etc. until all
babies carried have been accounted for.

We know that all such pregnancies are single outcomes, as the multiples and dubious
cases have been excluded from the algorithm. So the pregnancy variables in slots 2-5,
6-10, 12-15, ... etc are bound to remain system-missing. It should be noted that for
these cases the re-ordering addresses both problems outlined in (A)-(C) above (i.e. the
‘bunching’ as well as the ‘out-of-order’ problem).

Similarly, copy the data for eight of the ten ‘pregnancy’ variables into the correct
positions. Of the other two,
pregnum is set to 1 (as multiple pregnancies have been
excluded) and
morepreg is set to 1 or 2 depending on a machine-check as to whether
other earlier pregnancies exist.



More intriguing information

1. The name is absent
2. The name is absent
3. The name is absent
4. Financial Development and Sectoral Output Growth in 19th Century Germany
5. Comparative study of hatching rates of African catfish (Clarias gariepinus Burchell 1822) eggs on different substrates
6. A Pure Test for the Elasticity of Yield Spreads
7. The name is absent
8. The name is absent
9. Estimating the Impact of Medication on Diabetics' Diet and Lifestyle Choices
10. Insecure Property Rights and Growth: The Roles of Appropriation Costs, Wealth Effects, and Heterogeneity
11. Explaining Growth in Dutch Agriculture: Prices, Public R&D, and Technological Change
12. Social Irresponsibility in Management
13. MULTIPLE COMPARISONS WITH THE BEST: BAYESIAN PRECISION MEASURES OF EFFICIENCY RANKINGS
14. The name is absent
15. The name is absent
16. L'organisation en réseau comme forme « indéterminée »
17. PROTECTING CONTRACT GROWERS OF BROILER CHICKEN INDUSTRY
18. The WTO and the Cartagena Protocol: International Policy Coordination or Conflict?
19. The name is absent
20. Konjunkturprognostiker unter Panik: Kommentar