Brian S. Everitt A Handbook of Statistical Analyses using SPSS

(2) Odds ratio
The odds of Variable 2 falling into category “1” when Variable
a
1 takes category “1” is estimated by o1 =
b
Similarly, the odds of Variable 2 falling into category “1” when
c
Variable 1 takes category “2” is estimated by o2 =
d
The odds ratio (OR) of Variable 2 falling into category “1”
comparing Variable 1 category “1” with category “2” is estimated
ab
by OR =
cd
The odds ratio of a disease comparing individuals exposed to
a risk factor with nonexposed individuals can be estimated
from both prospective and retrospective studies. (Again, see
Dunn and Everitt, 1993.)
When the disease is rare (say, less than 5% of the population
are affected), the population RR can be approximated by the
OR (Dunn and Everitt, 1993).
3.3 Analysis Using SPSS
It is common in many areas of research for continuous variables to be
converted to categorical variables by grouping them, prior to analysis,
into two or more categories, although it is well known that this is not
always a wise procedure (see Altman, 1998). In the next two sections we
use continuous variables from the data sets introduced in the previous
chapter to illustrate how continuous variables can be categorized in SPSS
and how, once categorical outcomes are available, these can be easily
summarized using cross-tabulations.
3.3.1 Husbands and Wives Revisited
A fairly broad categorization of the ages at marriage (Table 2.2) is given
by the three categories “Early marriage” (before the age of 30 years),
“Marriage in middle age” (at an age between 30 and 49 years inclusive),
and “Late marriage” (at the age of 50 or later). The Recode command in
SPSS is useful for creating such a grouping. To categorize both age
variables, we can use the following steps:
© 2004 by Chapman & Hall/CRC Press LLC