I agree. ANCOVA is often said to be used when the groups are not experimental. It is a linear model of the same nature as ACOVA but technically ANCOVA refers to the experimental situation. The general linear model would allow comparing the groups while controlling for X but as William says, you should check for the interaction first to make sure the simpler model is really appropriate.
At 04:16 PM 4/20/01 -0400, William B. Ware wrote:
>On Fri, 20 Apr 2001, William Levine wrote:
>
>> A study was conducted to assess whether there were age differences in memory
>> for order independent of memory for items. Two preexisting groups (younger
>> and older adults - let's call this variable A) were tested for memory for
>> order information (Y). These groups were also tested for item memory (X).
>>
>> Two ways of analyzing these data came to mind. One was to perform an ANCOVA
>> treating X as a covariate. But the two groups differ with respect to X,
>> which would make interpretation of the ANCOVA difficult. Thus, an ANCOVA did
>> not seem like the correct analysis.
>
>Here's my take on it... The ANCOVA model can be implemented with
>sequential/hierarchical regression as you note below... however, ANCOVA
>has at least two assumptions that your situation does not meet. First, it
>assumes that assignment to treatment condition is random. Second, it
>assumes that the measurement on the covariate is independent of
>treatment. That is, the covariate should be measured before the treatment
>is implemented. Thus, I believe that you should implement the
>hierarchical regression... but I'm not certain what question you are
>really answering...
>
>I guess it is whether there is variabilty in memory for order that is
>related to age, that is independent of variability in memory for
>items... So, I would not call it an ANCOVA... You might also consider the
>possibiltiy of interaction... That is, is the relationship between memory
>for order and memory for items the same for younger and older
>participants...
>
>WBW
>
>__________________________________________________________________________
>William B. Ware, Professor and Chair Educational Psychology,
>CB# 3500 Measurement, and Evaluation
>University of North Carolina PHONE (919)-962-7848
>Chapel Hill, NC 27599-3500 FAX: (919)-962-1533
>http://www.unc.edu/~wbware/ EMAIL: [EMAIL PROTECTED]
>__________________________________________________________________________
>
>>
>> A second analysis option (suggested by a friend) is to perform a sequential
>> regression, entering X first and A second to
>> test if there is significant leftover variance explained by A.
>>
>> This second option sounds to me like the same thing as the first option. In
>> an ANCOVA, variance in Y that is predictable by X is removed from the total
>> variance, and then variance due to A (adjusted) is tested against variance
>> due to S/A (adjusted). In
>> the sequential regression, variance in the Y that is predictable by X is
>> removed from the total variance, and then the leftover variance in Y is
>> regressed on A. Aren't these two analyses identical? If not, what is it that
>> differs? Finally, does anyone have any suggestions?
>>
>> Many thanks!
>> --
>> William Levine
>> Department of Psychology
>> University of North Carolina-Chapel Hill
>> Chapel Hill, NC 27599-3270
>> [EMAIL PROTECTED]
>> http://www.unc.edu/~whlevine
>>
>>
>>
>>
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>
>
>
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------------------------------------
Paul R. Swank, PhD.
Professor & Advanced Quantitative Methodologist
UT-Houston School of Nursing
Center for Nursing Research
Phone (713)500-2031
Fax (713) 500-2033
soon to be moving to the Department of Pediatrics
UT Houston School of Medicine
================================================================= Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =================================================================
- Re: ANCOVA vs. sequential regression Paul Swank
- Re: ANCOVA vs. sequential regression Paul Swank
- Re: ANCOVA vs. sequential regression Paul Swank
