Why do you lose data in logistic regression - that is why do you only have to include the winners? Include the winners coded as 1 and the losers as 0.

I don't understand why the individuals in the trial are not independent? What makes them that way? Obviously they vary on some dimension but that does not mean they are not independent of each other statistically.

I attach some example of the data, maybe it will make our problem clearer or help find where are we wrong:

the following data (also appears in the attachement) comes from this experiment: we take subjects (=subject id) from two schools (=group) and make a spelling contest between them

and the outcome is sin or loose, i.e. 1 or 0 (=result), and we also record each subject previous english grade (=grade), and we might add some more columns in the future

of grades in other related subjects (e.g., literature, 2nd language grades etc.).

thanks.

**line no. trial No. Subject id group grade result (win=1; loose=0) **
1 1 15 1 2 0

2 1 21 2 2.15 1

3 2 8 1 2.15 0

4 2 27 2 1.9 1

1 3 18 1 2 0

2 3 9 2 2.25 1

3 4 3 1 2.05 1

4 4 5 2 2.1 0

In the example we have 4 trials, and 2 data lines for each

trial: one for each of the 2 subjects. Note that the resuls are complementary for each trial, result=1 in one of the

subjects mean that the other has result=o. So this is one problem of redundency of part of the data - regarding the effect of group on the result - sisnce one line determines the other (note

that with grades parameter this is not so, since it is a parametric variable, not a dichotomous one). So even if we choose nested models, still in requires independence of the data points in each lvel - which we don;t have here in the level of each trial , or can the points be dependent?

Of course the win or loose is a result of the individual parametrs of the winner, but also

of the loose, i mean of who was the competitor (although we do not intent to refress also on the Subject id, naturally as it is meanningless) and what were his grades. So how can we find the effects

on the result of the group (should be random effect, since it has many diverse effects, probably also on the grade parameter, at least to some extent) as well as of the grades ?

When we though of mixed and nested effects logistic regression model, we found the possible problem of redindant data in the goup->result effect.

So any suggestions? is there a suitable regression model? should we use other statistical approach?

thanks!