Thank you for your response. My data is in -wide- format. In order
to use -ologit- or -gllamm-, does my data necessarily need to be in
-long- format? I am guessing that either I need to or I have set up
-ologit- and -gologit- incorrectly since the coefficient and p-value I
get are quite different. Looking at the table it appears there isn't
a difference between the two time points and the results I get from
your programming supports this.
--- In [email protected], Joseph Coveney <jcoveney@b...> wrote:
> Joseph Wagner wrote
>
> I need to do a comparison between two ordinal measures, one at baseline
> (hlths) and the other, repeated at followup(f6hlths). I have done
> something similar in SAS using CATMOD. I wish to know if there has been
> a change between the two time points and in which direction.
>
>
> The data takes this form:
>
>
> Self Rated | 6M Self Rated Health
> Health | 1 2 3 4 5 | Total
> -----------+--------------------------+-----
> 1 | 28 18 6 0 0 | 52
> 2 | 21 78 44 1 0 | 144
> 3 | 7 34 96 5 1 | 143
> 4 | 0 3 18 16 0 | 37
> -----------+--------------------------+-----
> Total | 56 133 164 22 1 | 376
>
>
> Is the command -mvrepeat- that Philip Ender wrote, appropriate?
>
>
----------------------------------------------------------------------------
>
> In this case, -mvrepeat- would give the same answer as -ttest- using the
> paired t-test syntax. I vaguely recall reading that under these
> circumstances Student's t-test does surprisingly well with ordinal
data with
> as few as three categories, but consider using an alternative, such as a
> nonparametric test or a modeling command intended for ordered
categorical
> data. There are several of each from which to choose. In addition
> to -ologit- (illustrated below), Stata has user-written commands
that don't
> rely upon the proportional odds assumption, at least one of which
> (-gologit-) allows the -cluster()- option.
>
> To observe the direction of change and its magnitude, you can either
> use -predict- after one of the modeling commands or plot the data
using a
> graphing command specifically for ordered categorical data. (I've
> illustrated using -ordplot-, but be aware that its author, Nick Cox, has
> enhanced it and updated it for Stata Release 8 under the name
> of -distplot-.)
>
> Joseph Coveney
>
> clear
> set more off
> input byte sco0 byte cou1 byte cou2 byte cou3 byte cou4 byte cou5
> 1 28 18 6 0 0
> 2 21 78 44 1 0
> 3 7 34 96 5 1
> 4 0 3 18 16 0
> end
> reshape long cou, i(sco0) j(sco1)
> drop if cou == 0
> expand cou
> drop cou
> signtest sco0 = sco1
> signrank sco0 = sco1
> generate int pid = _n
> reshape long sco, i(pid) j(tim)
> somersd tim sco, cluster(pid)
> ologit sco tim, cluster(pid)
> npt_s sco, by(tim) strata(pid) nodetail
> version 7: ordplot sco, by(tim)
> gllamm sco tim, i(pid) family(binomial) link(ologit)
> estimates store A
> gllamm sco, i(pid) family(binomial) link(ologit)
> estimates store B
> lrtest A B, stats
> exit
>
>
> *
> * For searches and help try:
> * http://www.stata.com/support/faqs/res/findit.html
> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
*
* For searches and help try:
* http://www.stata.com/support/faqs/res/findit.html
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
