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From |
"Newson, Roger B" <r.newson@imperial.ac.uk> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: RE: Stat advice for McNemar's test |

Date |
Tue, 21 Oct 2008 15:47:12 +0100 |

I don't know Walt's client, and have never spoken to him/her. However, the word "interaction" usually means "difference between differences" or "ratio between ratios". In this case, I would guess that it means the difference between the before-after difference in the probability of liking Product A and the before-after difference in the probability of liking Product B. If this is the case, then the -glm- command can compute a confidence interval for this difference between differences. However, I would expect this confidence interval to be wide, unless the sample size is very large. Suppose that the data are reformatted into a form where there is one observation per test per product, and therefore 4 observations per subject (corresponding to the tests "Product A before", "Product A after", "Product B before", and Product B after"). Suppose that there are 4 variables, as follows: variable_name variable_label subjid Subject ID product Product (0=B, 1=A) testseq Test sequence (0=Before, 1=After) result Test result (0=Does not like, 1=Likes) The program to use on this dataset might use the -prodvars- package (downloadable from SSC using the -ssc- command). It might be be as follows: xi i.product, noomit prodvars _I*, rv(testseq) sepa(_) glm result _I*, link(iden) family(binomial) noconst robust cluster(subjid) lincom _Iproduct_1_testseq-_Iproduct_0_testseq The -xi- commmand generates 2 identifier variables _Iproduct_0 and _Iproduct_1, identifying Product B and Product A, respectively. The -prodvars- command generates 2 product variables _Iproduct_0_testseq and _Iproduct_1_testseq, which are also identifiers, identifying "After" tests for Product B and Product A, respectively. The -glm- command fits a generalized linear model, with identity link and binomial family. This model has 2 intercepts, corresponding to _Iproduct_0 and _Iproduct_1, and equal to the probabilities of liking Product B and Product A in the "Before" test. It also has 2 slopes, corresponding to _Iproduct_0_testseq and _Iproduct_1_testseq, and equal to the after-before differences in probability of liking Product B and Product A, respectively. All standard errors are adjusted for clustering by subject (ie for repeated measurements). Finally, the -lincom- command computes a confidence interval for the Product A-Product B difference between the 2 after-before differences, which measures how much easier it is to acquire a taste for Product A than to acquire a taste for Product B. I hope this helps. Let me know if you have any queries. Best wishes Roger Roger B Newson BSc MSc DPhil Lecturer in Medical Statistics Respiratory Epidemiology and Public Health Group National Heart and Lung Institute Imperial College London Royal Brompton Campus Room 33, Emmanuel Kaye Building 1B Manresa Road London SW3 6LR UNITED KINGDOM Tel: +44 (0)20 7352 8121 ext 3381 Fax: +44 (0)20 7351 8322 Email: r.newson@imperial.ac.uk Web page: www.imperial.ac.uk/nhli/r.newson/ Departmental Web page: http://www1.imperial.ac.uk/medicine/about/divisions/nhli/respiration/pop genetics/reph/ Opinions expressed are those of the author, not of the institution. -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Data Analytics Corp. Sent: 21 October 2008 14:06 To: Stata Listserve Subject: st: Stat advice for McNemar's test Hi, I have a client who did a basic before-after taste test for two products. For each, the consumers were asked if the like the product on an initial taste and then again after a period of time. Each consumer tested each product. For one product, a McNemar's Test can be used to see if the marginals are the same - to see if there's a difference in liking from the initial to later taste. But what about both products? The client wants to know about interactions between the products and time? Any advice? Thanks, Walt -- ________________________ Walter R. Paczkowski, Ph.D. Data Analytics Corp. 44 Hamilton Lane Plainsboro, NJ 08536 ________________________ (V) 609-936-8999 (F) 609-936-3733 dataanalytics@earthlink.net www.dataanalyticscorp.com * * 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/ * * 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/

**References**:**st: Stat advice for McNemar's test***From:*"Data Analytics Corp." <dataanalytics@earthlink.net>

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