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Date:   Thu, 30 Jun 2005 09:35:28 -0500
Reply-To:   "Swank, Paul R" <Paul.R.Swank@uth.tmc.edu>
Sender:   "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:   "Swank, Paul R" <Paul.R.Swank@uth.tmc.edu>
Subject:   Re: McNemar Test and Study Design
Comments:   To: Philip_Moore@CARMAX.COM
Content-Type:   text/plain; charset="us-ascii"

You may also use the Bowker test (Marascuilo & McSweeney, 1977), which is an extension of the McNemar test to a square table, to test for symmetry of responses using all 5 categories.

Marascuilo, L. & McSweeney, M. (1977). Nonparametric and distribution-free methods for the social sciences. Monterey, CA: Brooks/Cole.

Paul R. Swank, Ph.D. Professor, Developmental Pediatrics Medical School UT Health Science Center at Houston

-----Original Message----- From: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU] On Behalf Of Philip_Moore@CARMAX.COM Sent: Thursday, June 30, 2005 6:46 AM To: SPSSX-L@LISTSERV.UGA.EDU Subject: Re: McNemar Test and Study Design

This sounds like a bad way to measure the impact of your intervention because you are throwing out a bunch of information. If 19% of your respondents gave you a top-box response to the Likert scale, then I would guess your measure of central tendency is well behaved. If strongly agree = 5 and strongly disagree = 1 and your pre treatment mean is 3 and your post-treatment mean is 3.7, then I think something happened regardless of which 19% answered strongly agree.

Regard,

Philip Moore Market Research Manager (804) 747-0422 x4831 (804) 935-4549 FAX

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John Norton <jnorton@lumc.edu > To Sent by: SPSSX-L@LISTSERV.UGA.EDU "SPSSX(r) cc Discussion" <SPSSX-L@LISTSERV Subject .UGA.EDU> McNemar Test and Study Design

06/29/2005 04:54 PM

Please respond to John Norton <jnorton@lumc.edu >

Hi List,

We've been asked by a reviewer to provide further explanations about why we chose to use a McNemar test to determine whether the marginal proportions in a 2X2 table were statistically different, and I am hoping some on this list may be able to provide some guidance.

The study employed a pre/post design where patients were surveyed at intake and then again after their consultations. So, the same people were surveyed twice, once at intake and again after the consultation (which is in effect the "treatment" we're studying). As I understand it, the McNemar test is appropriate for this design.

The response alternatives to the survey questions used a 5 point Likert format, with responses ranging from Strongly Disagree to Strongly Agree. So, to use the McNemar, each response alternative to each question was transformed into a binary variable, such that a "Strongly Agree" answer to Q4 on the pretest meant that the variable Q4pre_e = 1, while Q4pre_a through Q4pre_d all were assigned a zero. Likewise, on the post test, if the respondent answered "Neither" then Q4post_c = 1, while the remaining 4 "post" variables were assigned a value of 0. McNemar tests were executed on all 5 pairs of pre/post variables; none of them were significant.

The reviewer raised the question, "although roughly 1 in 5 patients answered "Strongly Agree" both before and after the consultation, were they the same individuals?" The immediate answer is, well, yes, as is defined in the design of the study. But if the reviewer is actually asking whether the 19% answering "Strongly Agree" at intake are the same individuals composing the 21% answering "Strongly Agree" on the post-test, I am left wondering whether this is a moot concern, or whether this may confound the results of the McNemar. Can anyone help clarify?

Many thanks,

John Norton Biostatistician Oncology Institute Loyola University Medical Center

(708) 327-3095 jnorton@lumc.edu

"Everything that can be counted isn't worth counting, and everything that is worth counting isn't always countable." - Einstein


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