Thanks to Joseph Coveney for an interesting illustration of Stata categorical
commands and data reshaping.
Joseph W. may want to consider dropping column 5 which has only one, unique
observation, which will allow the use of Stata's -symmetry- command.
Using the data prior to the -reshape long- command,
symmetry sco0 sco1, contrib
---------------------------------------------
| sco1
sco0 | 1 2 3 4 Total
----------+----------------------------------
1 | 28 18 6 0 52
2 | 21 78 44 1 144
3 | 7 34 96 5 142
4 | 0 3 18 16 37
|
Total | 56 133 164 22 375
---------------------------------------------
Contribution
to symmetry
Cells chi-squared
-------------- --------------
n1_2 & n2_1 0.2308
n1_3 & n3_1 0.0769
n1_4 & n4_1 0.0000
n2_3 & n3_2 1.2821
n2_4 & n4_2 1.0000
n3_4 & n4_3 7.3478
chi2 df Prob>chi2
------------------------------------------------------------------------
Symmetry (asymptotic) | 9.94 5 0.0770
Marginal homogeneity (Stuart-Maxwell) | 9.79 3 0.0205
------------------------------------------------------------------------
The symmetry model fits, albeit poorly; the marginals are different. The main source
of difference for the symmetry chi sq is respondents (n=5) who reported 3 at time0
and 4 at time1, versus those (n=18) who responded 4 at time0 and 3 at time1.
Michael Wood
<[email protected]>
> --- 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
> >
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