I am seeking recommendations and/or best practices for analyzing non-independent data. In particular, I am curious about non-independent data that does not reflect typical repeated-measures time-based data in which data for the same question(s) or stimuli are collected at different time points. Rather the data collected is elicited from similar (but not identical) questions or stimuli that are known to be related. A specific example follows.
I have pain perception data for two groups (group A, group B) that can be further divided by gender (i.e., A-women; A-men; B-women; B-men). The pain perception dependent variables include 3 thermal threshold tests (i.e., detection, pain, tolerance) and pain magnitude estimates for a range of specific temperatures (temp X1, X2, X3, X4, X5). These pain perception variables are collected for both heat and cold stimuli (e.g., heat pain tolerance; cold pain tolerance; heat temp X1; cold temp X1). It should be expected (and indeed it is observed) that the various pain perception variables for a given individual are not independent.
The purpose of the analysis is to look for between group differences based on group membership (group A vs. group B) and sex (women vs men). It is desirable to find interactions between group membership and sex. It is also desirable to find interactions between specific pain perception variables (or types of variables; i.e., "cold stimuli") and group membership and/or sex. I have tried running a series of repeated-measures ANOVAs separately for each of the different groupings of pain perception variables (i.e., heat stimuli threshold tests; cold stimuli threshold tests; heat pain magnitude estimates; cold pain magnitude estimates); however this solution does not feel optimal or adequate. My specific questions are:
1) is it appropriate to analyze data elicited from related (but not identical) questions/stimuli using repeated measures?
2) Is it appropriate to analyze chunks of the data (e.g., cold pain magnitude estimates; cold stimuli threshold tests) separately?
2) Would a different strategy (such as multilevel analysis / profile analysis / or some type of multivariate repeated measures ANOVA) be more appropriate?
3) General recommendations and/or best practices for analyzing data such as this.
Thank you for your feedback and input
note: I already searched the site for related questions (i.e., http://stats.stackexchange.com/questions/859/anova-with-non-independent-observations ; http://stats.stackexchange.com/questions/1324/parametric-techniques-for-n-related-samples) but believe the current question to be different enough to warrant unique consideration.