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# Answer to Question #3145 in Statistics and Probability for cynthia Hicks

Question #3145
1
2011-06-20T12:21:49-0400
The basic formula for hypothesis testing statistics:
& differences between groups
& variability within the groups

The bottom part of the equation (variability within groups) is composed of two parts:
1.& due to measurement error
2.& due to individual differences
& differences between groups
& measurement error + individual differences

If individual differences are large, then the overall ration will be low.& This means even for important effects you may not be able to pick them up in your stats because of large individual differences.
&
the goal of within-subject designs
The goal of within-subjects variables is to remove individual differences from the bottom part of the equation.& Consider the example.
within-subject t-test
For each item you compute a difference score.
di = Xi1 - Xi2
This difference score ignores the range of scores for that item -- it equates items which started high and items which started low by looking to see if they changed.
It is then possible to compute a mean of these difference scores:
Md = SUMi (di)

What we then ask with the within-subjects t-test is:& Is the average difference score large compared to the variability in the difference scores?
&
t = Md / SEMd& = Md / (sd / sqrt N)
Example:
Text Complexity
Student simple complex d
1 10.4& & & 14.3 -3.9
2 16.9 & 20.4 -3.5
3 & 6.7 & & 7.5 -0.8
4 25.9 26.8 -0.9
5 12.4 14.6 -2.2
6 18.5 20.3 -1.8
7 13.5 12.3 1.2
8 & & & & & & & 8.2 10.2 -2.0
. . . .
M 14.06 15.80 -1.74
SD & & & 6.24 & 6.31 & 1.62
&
Within-subjects t-test
t = Md / SEMd & = Md / (sd / sqrt N)
& t& =& - 1.74 / (1.62 / sqrt 8)
t (7)& =& - 3.038, p < .02

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