Clint
It's a little hard to be sure exactly how to approach this most efficiently as
your data all *appear* as strings, but of course may just be labels hiding
numbers. In what follows I will assume that the outcome variables (q1_1 and
q1_2) are in fact numeric - otherwise you won't be able to do a signed rank
test - but if they are strings you had better first change them to numeric. I
will however assume that the variable "eye" holding values "OD" and "OS" is
string, only because this leads to a demonstration of a required extra option
in the -reshape- command. (may one guess that "OD" signifies the right -
dextra- eye, and "OS" the left - sinistra - eye??).
Anyway, assuming that "None" for the outcome variables might really be a score
of 0 in some, at least ordinal, scoring system:
. list , nolabel
+------------------------------------------+
| jmec_no eye iol q1_1 q1_2 sex |
|------------------------------------------|
1. | 30332 OD SA60 0 0 F |
2. | 30332 OS AR40 0 0 F |
3. | 46199 OD SA60 0 0 F |
4. | 46199 OS AR40 0 0 F |
+------------------------------------------+
then
. reshape wide iol q1*, i(jmec_no) j(eye) string
(note: j = OD OS)
Data long -> wide
-----------------------------------------------------------------------------
Number of obs. 4 -> 2
Number of variables 6 -> 8
j variable (2 values) eye -> (dropped)
xij variables:
iol -> iolOD iolOS
q1_1 -> q1_1OD q1_1OS
q1_2 -> q1_2OD q1_2OS
-----------------------------------------------------------------------------
Note that:
1. I had to specify the -string- option, because the j index is ordinarily
expected to be numeric.
2. I had to either drop the iol variable or (as I ended up choosing) include
it in the list of variables I wanted reshaped, since iol is not constant within
each case identifier.
. list, nolabel
+-------------------------------------------------------------------+
| jmec_no iolOD q1_1OD q1_2OD iolOS q1_1OS q1_2OS sex |
|-------------------------------------------------------------------|
1. | 30332 SA60 0 0 AR40 0 0 F |
2. | 46199 SA60 0 0 AR40 0 0 F |
+-------------------------------------------------------------------+
and, given some more data to make it sensible, one could:
. signrank q1_1OD = q1_1OS
to do a paired test on outcome q1_1 between eyes, and
. signrank q1_2OD = q1_2OS
to do the same on outcome q1_2
Phil
Quoting [email protected]:
> Greetings STATA-listers,
> I have a dataset examining eye surgeries and I want to examine
> how responses to certain variables (e.g. q1_1, q1_2, etc.)
> between each eye vary. The problem is, my dataset is
> structured as pasted below:
>
> +------------------------------------------+
> | jmec_no eye iol sex q1_1 q1_2 |
> |------------------------------------------|
> 1. | 30332 OD SA60 F None None |
> 2. | 30332 OS AR40 F None None |
> 3. | 46199 OD SA60 F None None |
> 4. | 46199 OS AR40 F None None |
> +------------------------------------------+
>
> How do I restructure the dataset so that the Wilcoxon sign rank
> test can be performed?? That is, I recognize that I need to
> reshape it or create an array or do some manipulation, but I am
> at a loss as how to proceed (even after a perusal of the
> reference manual and STATA help feature) - any suggestions?
>
> Many thanks,
> Clint Thompson
> *
> * For searches and help try:
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> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
>
--
Philip Ryan
Associate Professor
Department of Public Health
University of Adelaide
5005 South Australia
AUSTRALIA
CRICOS Provider Number 00123M
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