Posted by
Hector Maletta on
May 05, 2012; 5:53pm
URL: http://spssx-discussion.165.s1.nabble.com/analyzing-change-for-one-dichotomous-variable-assessed-at-pre-and-post-tp5688136p5688262.html
On top of the very pertinent advice by Bruce, it should be useful to know
whether there was a 'treatment' or independent variable involved, and a
corresponding control group not subject to the treatment. What should be
significant is not the amount of change, but the difference in the
percentages changing among those undertaking the treatment and those in the
control group. In the absence of a control group, the percentage quitting
smoking may be (partly) an effect of some identifiable independent variable
(e.g. some exposure to advertising against smoking), and partly due to
random quitting that normally occurs among smokers. Since in the long term
people are quitting smoking (consistently reducing the percentage of people
that smoke), the percentage quitting might be statistically significant and
yet not be due to any particular intervention or treatment.
Also, the same 2x2 table tells not only about quitters, but also about the
number of people that did not smoke at the first occasion but became smokers
by the second time of observation. The flows in both directions may be
interesting (they may or may not offset each other). In this case, it would
be worthwhile assessing whether the difference between the two opposite
flows is or is not statistically significant.
As Bruce says, one should ask "significantly different from what".
Hector
-----Mensaje original-----
De: SPSSX(r) Discussion [mailto:
[hidden email]] En nombre de Bruce
Weaver
Enviado el: Saturday, May 05, 2012 14:38
Para:
[hidden email]
Asunto: Re: analyzing change for one dichotomous variable, assessed at pre
and post
"goal is to determine if the percentage of participants who changed from Y
smoking at pre to N smoking at post is significant."
Significantly different from what?
The usual analysis for the situation you describe would be the McNemar
chi-square test, aka the McNemar Change Test. The data are in a 2x2 table
with Yes & No for Pre in the rows, and Yes and No for post in the columns.
McNemar's chi-square is equivalent to a chi-square goodness of fit test on
the two discordant cells,* with a null hypothesis specifying the same number
of changes of each type (Y to N and N to Y). (If the cell counts are too
low, you can use a binomial test instead--SPSS computes one in that case,
IIRC.) Does that address the question you have in mind?
* McNemar's test does not use the data in the concordant cells on the main
diagonal.
pji wrote
>
> I have a situation where I have one variable, smoking yes/no, taken at
> pre then taken at post, few years later. n ~70. same participants at
> both pre and post. the possible combinations are
>
> smoking pre post
> y y
> n n
> y n
> n y
>
> goal is to determine if the percentage of participants who changed
> from Y smoking at pre to N smoking at post is significant.
>
> I don't think any sophisticated analysis has to be conducted. I would
> simply assign the four possible outcomes to four categories (y - y, n
> - n, y - n, n - y) and then run a chi-square to determine if the
> observed frequency counts of those four categories are different from
> the expected frequency counts. there is no post-hoc test, so I would
> have to heuristically examine which categories have the most deviance.
>
> are there any analyses that could be conducted?
> thank you in advance.
> pj
>
-----
--
Bruce Weaver
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