In both your test you would need to "block" the effects of the students.
The way you have the tests being conducted/analyzed, the above average
students may just catch on more quickly than the below average.
Therefore, you have to block this effect so that it is not affect your
analysis. (I'm assuming you are using a factorial experiment)
ReliabilityMan
In article <8ilmf0$hmk$[EMAIL PROTECTED]>,
"Donal" <[EMAIL PROTECTED]> wrote:
> Greetings All,
>
> I'm currently analysing data resulting from a study of children's
reading
> ability. The study involves two treatments and each child's reading
ability
> was measured before and after the application of one of the
treatments.
> Thus, each child received one or the other (but not both) of two
possible
> treatments. The children are divided into two groups:
>
> Weak readers: those whose pre-treatment reading score was less than
the mean
> pre-treatment reading score
> Strong readers: those whose pre-treatment reading score was greater
than the
> mean pre-treatment reading score
>
> Anyhow, I would like to test (for each treatment) whether or not the
change
> in the reading score (Post-treatment score - Pre-treatment score) is
the
> same for weak readers and strong readers. I have atempted to test this
by:
>
> 1. Creating a new variable, "Change"
> Change = Post-treatment score - Pre-treatment score
>
> 2. Using a two-sample t-test to determine whether or not the mean
value of
> "Change" measured over the weak readers is significantly different
from the
> mean value of "Change" measured over the strong readers
>
> However, I am not certain that this is the best way to test my
hypothesis,
> if anyone can suggest a better way, I'd be very grateful for their
> assistance.
>
> Similarly, I'd like to test whether or not the change in the reading
score
> is the same for each treatment. I have attempted to test this by:
>
> 1. Creating a new variable, "Change"
> Change = Post-treatment score - Pre-treatment score
>
> 2. Using a two-sample t-test to determine whether or not the mean
value of
> "Change" measured over treatment A is significantly different from
the mean
> value of "Change" measured over treatment B
>
> Again, if anyone knows of a better way of performing this test, I'd be
very
> appreciative if they would let me know about it.
>
> Thanks in advance,
> D�nal.
>
>
Sent via Deja.com http://www.deja.com/
Before you buy.
===========================================================================
This list is open to everyone. Occasionally, less thoughtful
people send inappropriate messages. Please DO NOT COMPLAIN TO
THE POSTMASTER about these messages because the postmaster has no
way of controlling them, and excessive complaints will result in
termination of the list.
For information about this list, including information about the
problem of inappropriate messages and information about how to
unsubscribe, please see the web page at
http://jse.stat.ncsu.edu/
===========================================================================
