SPSSX Discussion

pretest analysis

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hailushepi@gmail.com

pretest analysis

Dear all,I made a pretest in 15 individuals in my study area.Is it possible to included in the analysis meerging with my total sample size?If u know reason why please? 10q

David Marso

Re: pretest analysis

Administrator

Please clarify your question.
I have absolutely *NO IDEA* What you are asking.
--

hailushepi@gmail.com wrote

Dear all,I made a pretest in 15 individuals in my study area.Is it possible to included in the analysis meerging with my total sample size?If u know reason why please? 10q

Please reply to the list and not to my personal email.
Those desiring my consulting or training services please feel free to email me.
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Swank, Paul R

Re: pretest analysis

In reply to this post by hailushepi@gmail.com

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.

Dr. Paul R. Swank,
Children's Learning Institute
Professor, Department of Pediatrics, Medical School
Adjunct Professor, School of Public Health
University of Texas Health Science Center-Houston

-----Original Message-----
From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of [hidden email]
Sent: Monday, December 12, 2011 3:46 AM
To: [hidden email]
Subject: pretest analysis

Dear all,I made a pretest in 15 individuals in my study area.Is it possible
to included in the analysis meerging with my total sample size?If u know
reason why please? 10q

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Rich Ulrich

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: [hidden email]
> Subject: Re: pretest analysis
> To: [hidden email]
>
> 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

Poes, Matthew Joseph

Re: pretest analysis

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:[hidden email]] On Behalf Of Rich Ulrich
Sent: Monday, December 12, 2011 2:30 PM
To: [hidden email]
Subject: Re: pretest analysis

Ryan

Re: pretest analysis

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" <[hidden email]> 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:[hidden email]] On Behalf Of Rich Ulrich
Sent: Monday, December 12, 2011 2:30 PM
To: [hidden email]
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: [hidden email]
> Subject: Re: pretest analysis
> To: [hidden email]
>
> 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