My first thread on the board... I posted this to the SPSS-X list , but thought this forum may be more appropriate.

I was wondering if you could help me with a data analysis problem. Here is a description of the data and of the issue -- sorry about the length of the post but I wanted to be as clear as possible;

Two age groups:

1. Young

2. Adults

Two conditions (each participant was assigned to one of the two groups):

3. Prime A

4. Prime B

Two sets of responses for each condition (each participant gave answered both sets):

5. Set A

6. Set B

Responses to sets A and B are ordinal and range from zero to seven (integers only). Now, the problem is that most of these eight (2x2x2) distributions are non-normal -- in particular, they are mostly strongly J-shaped, which is to be expected given the experimental question. Here are the frequencies:

[1,3,5]

0 1

1 0

2 0

3 2

4 1

5 7

6 12

7 5

[2,3,5]

0 0

1 0

2 1

3 0

4 2

5 2

6 3

7 15

[1,3,6]

0 1

1 1

2 4

3 7

4 1

5 3

6 6

7 5

[2,3,6]

0 0

1 1

2 7

3 3

4 3

5 3

6 2

7 4

[1,4,5]

0 0

1 0

2 0

3 0

4 0

5 2

6 4

7 22

[2,4,5]

0 0

1 0

2 0

3 0

4 1

5 0

6 7

7 15

[1,4,6]

0 0

1 0

2 1

3 1

4 0

5 6

6 4

7 16

[2,4,6]

0 1

1 2

2 3

3 1

4 4

5 1

6 1

7 10

I want to test differences in the shape of the distribution; more precisely, I am interested in developmental effects, to be assessed by the following tests:

A. [1,3,5] vs [2,3,5]

B. [1,3,6] vs [2,3,6]

C. [1,4,5] vs [2,4,5]

D. [1,4,6] vs [2,4,6]

I believe such distributions call for nonparametric testing; in particular, I would like a test that is sensitive to changes in the lengthening of the tail of the distribution, which is the clearest observable effect. The Moses test is supposed to be able to do just that. I've run it on the data, and the results are as follows (all with outliers automatically trimmed as recommended by SPSS):

Test A: (N = 39, p <.009)

Test B: (N = 46, n.s.)

Test C: (N = 32, p <.001)

Test D: (N = 31, p < .001)

The Mann-Whitney U and Kolmogorov-Smirnov results are similar, except that test A comes out non-significant.

And in priming effects, to be assessed by the following tests:

E. [1,3,5] vs [1,4,5]

F. [1,3,6] vs [1,4,6]

G. [2,3,5] vs [2,4,5]

H. [2,3,6] vs [2,4,6]

Here are the (automatically trimmed) Moses results for the above:

Test E: (N = 35, p <.001)

Test F: (N = 42, p <.001)

Test G: (N = 30, p <.001)

Test H: (N = 40, n.s.)

Again, the M-W and K-S tests are similar, except that for both test G comes out non-significant.

I am not wedded to the Moses test but it seemed the most appropriate for my purposes. I am also not too worried about the difference in results, given the small sample size. I just want to give a truthful statistical picture of the data that does not involve looking at histograms and guessing whether the distributions are different.

Now, my questions are:

-- Is the Moses test adequate for these data, or is there another test that is both powerful and more appropriate?

-- If I end up using Moses, should I change the trimming technique (for example, should I only trim at one end given the shape of the distribution), or not trim at all, given the small sample size? Are these variations justifiable?

-- Whatever test I end up using, should I worry about ties in the data, since SPSS does not adjust for them?

Many thanks for your help, it is very much appreciated.

Nicola Knight