<for the server>
Perhaps using the percentage of good habit already known as a lower
bound p(known), test a hypothesis Ha: p(new)>p(known) vs H0:
p(new)=p(known), using the at risk of improving.
p(known) is the best (highest) you have just before your new hypothesis
testing.
Regards,
José Maria
Jose Maria Pacheco de Souza, Professor Titular (aposentado)
Departamento de Epidemiologia/Faculdade de Saude Publica, USP
Av. Dr. Arnaldo, 715
01246-904 - S. Paulo/SP - Brasil
fones (11)3061-7747; (11)3768-8612;(11)3714-2403
www.fsp.usp.br/~jmpsouza
----- Original Message -----
From: "Michael I. Lichter" <[email protected]>
To: <[email protected]>
Sent: Thursday, June 11, 2009 2:10 PM
Subject: Re: st: Re: single sample pre/post comparison of proportions
Svend: The researcher who asked me about this likes the idea of reporting
CIs and forgoing the explicit hypothesis test.
José: Unfortunately, it looks like the server ate the first line of your
post so I'm not certain what you're suggesting. Is p(known) the
pre-intervention proportion of adopters, is it an external estimate, or is
it something else? If it's the first, I'm not sure I could justify that as
a benchmark.
Thanks to both of you for your suggestions.
Michael
José Maria Pacheco de Souza wrote:
bound p(known), test a hypothesis Ha: p(new)>p(known) vs H0:
p(new)=p(known), using the at risk of improving.
Or as Svend presented, just estimate the proportion of new, among
those at risk. In this case, aftward it will difficult to resist the
temptation to compare this result with the p(known).
Cheers,
José Maria
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
