Not all subjects undergo conjunction of two treatments, two-way within-subject (repeated-measures) ANOVA correct? I am wondering about the correct statistical analysis for an experiment involving 2 independent variables (treatment A, 4 levels and treatment B, 2 levels) and one dependent variable (C, score). Because subjects undergo all levels of both treatments, I am assuming a two-way within-subject (repeated-measures) ANOVA (4x2) should be used.
However, there is a complicating factor: we used a setup in which we measured C for 2 subjects together, according to the following scheme:
\begin{array}{| l | c | r |} \hline
                &  \text{B level 1} &  \text{B level 2}\\ \hline
    \text{A level 1}   &  \text{Subject 1} & \text{Subject 2}\\
    \text{A level 2}   &  \text{Subject 2} & \text{Subject 1}\\
    \text{A level 3}   &  \text{Subject 1} & \text{Subject 2}\\
    \text{A level 4}   &  \text{Subject 2} & \text{Subject 1}\\ \hline
\end{array}
When looking at treatment A, all subjects undergo levels 1 to 4, so within-subject design. Also, when looking at treatment B, all subjects undergo levels 1 and 2, so again within-subject design.
However, it is not the case that all subjects undergo all levels of A in conjunction with all levels of B, e.g. subject 1 has undergone A level 1 in conjunction with B level1, but not with B level 2. And vice versa for subject 2.
Hence, I am inclined to think that regarding interaction between treatment A and B, a within-subject design might not be correct. Any suggestions would be appreciated.
 A: Within and between ANOVA's are an interesting subject.  
Consider the assumption of independence.  Let's imagine you run a full repeated measures design but the data came out so that each condition really had a correlation of 0.  So, the experiment isn't designed for full independence but it appears in the data.  The resulting repeated measures ANOVA SS would be the same as for an independent measures ANOVA SS of the same data.  The reverse is not true though.  If you have a full independent measures design and there are some random amounts of correlation (very likely), then the repeated measures ANOVA will be more sensitive than the between.
I'd say that you can just analyze these data as if they were independent samples.  You'll probably lose power but you'll still be able to analyze the data.  You're simply not accounting for the (probable) within S correlation.  In fact, you're breaking the relationship all together and pairing of data isn't occurring.  While independent group designs cannot be analyzed with a repeated measures ANOVA because they will account for spurious correlations you could get away with the reverse and just admit your power is low because of it.
You might also want to look into multi-level modelling here.  I know it can handle repeated measures with empty cells but I'm not recommending it because I'm not sure what it would do with this kind of a design.  Perhaps someone more expert on them could speak to that.  I think you definitely could not get away with just modeling A, B, and A:B as fixed effects with subjects as a random effect.  You'd need random A and B effects at least.
A: Since subjects were run in pairs, I would use an ordinary repeated measures analysis with Subjects replaced by Pairs: Pairs (n) x A (4) x B (2), with A and B fixed and Pairs random. The error terms for the A, B, and AB effects would be the corresponding interactions with Pairs. 
(With regard to the table layout, I'm in the same state as you. It would be nice to have all the formatting rules and procedures given somewhere, with examples. The only information on formatting that I could find (in a jargon-filled file whose name I don't remember) was that a single carriage return would not be recognized unless it was preceded by two spaces.)
