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Date:         Mon, 12 Dec 2011 18:40:21 -0500
Reply-To:     ryan.andrew.black@gmail.com
Sender:       "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:         Ryan Black <ryan.andrew.black@gmail.com>
Subject:      Re: pretest analysis
In-Reply-To:  <139EBE2EF1603B48A3BC8F89B34434D4021252F3@chimbx3.ad.uillinois.edu>
Content-Type: multipart/alternative;

Frankly, this study design leaves much to be desired. Many questions remain unanswered. Was there some sort of intervention? If one were interested in testing for pre-test sensitivity in a 2x2 study, for example, a design such as the Solomon 4-Group would be a viable option. I'm not suggesting that the Solomon 4-Group is THE answer to testing for sensitivity to baseline measures, but that WHEN FEASIBLE, in general, study design should be planned well before any data are collected and the research questions have been well thought out. This allows one to take into consideration dealing with extraneous variables and reducing error variance, along with other pragmatic issues such as budgetary constraints and resourcing. Without taking this step, one could end up with a fatally flawed study.

Ryan

On Dec 12, 2011, at 3:44 PM, "Poes, Matthew Joseph" <mpoes@UILLINOIS.EDU> wrote:

> I would think that giving the same test at multiple time points to the same population would create the strong possibility of a testing effect, and that this could lead to potentially “better” scores than for those who did not have the opportunity for a pre-test. Since this does not appear to be a pre-post design of any sort, the difference in growth would be a non-issue, instead you would just end up with a potential group of 15 who have scores which systematically differ due to prior exposure to the test/survey/stimuli. For me, this would make an indicator variable mandatory even for fairly casual reporting and comparison, especially if there was evidence that the pre-test group was systematically different to begin with (i.e. the people who received the pre-test were a different “kind” of person, say the first that showed up). > > > > Matthew J Poes > > Research Data Specialist > > Center for Prevention Research and Development > > University of Illinois > > > > From: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU] On Behalf Of Rich Ulrich > Sent: Monday, December 12, 2011 2:30 PM > To: SPSSX-L@LISTSERV.UGA.EDU > Subject: Re: pretest analysis > > > > Being in the pretest group might *make* a difference, as Paul suggests - > You can check that further by looking at pre-post scores for the 15 (assuming > that the same test was given later). A paired t-test will have more power

> than a student's t-test when there is a high correlation between scores. > > Or being in the pretest group might reflect some existing differences between > the subjects on demographic or other factors, which may be correlated with

> who received a pre-test... . > > > Date: Mon, 12 Dec 2011 10:47:52 -0600 > > From: Paul.R.Swank@uth.tmc.edu > > Subject: Re: pretest analysis > > To: SPSSX-L@LISTSERV.UGA.EDU > > > > If you categorize the sample into two groups, those with the pretest and those without, then you can compare the groups to see if having a pretest made a difference. However, with 15 subjects in the pretest group, you will likely not have enough power to adequately test that hypothesis. You can't use the pretest score itself in a model since the rest of the sample didn't have it. > > > [snip] > > -- > Rich Ulrich >


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