ANOVA on percentage data I am confused about when I can and/or cannot run an ANOVA on percentage data. MY situation is as follows:
I have two separate treatment groups (T1 and T2). Each of these treatment groups receives 1 of 3 drugs (D0, D3, D30) only once. My DV is muscle activity (continuous data). Only one muscle response is recorded. 3 consistent pre-drug baseline measurements and 3 consistent post-drug measurements are taken.
I take the mean of the pre and post to give single values, and then calculate the post-drug as a % of pre drug for each subject, and then compare mean % for each treatment/drug group 
The raw data are generally not normally distributed (though one group out of six comes out as normal but only just)
The problem I have is that pre-drug values are quite variable and so I think it's better to look at the proportion change (if indeed that it the correct thing to do?). Previously I have just expressed the post-drug values as a percentage of the pre-drug (not percentage difference), and then compared the mean percentages of the various different groups with a two-way ANOVA. (nb. the percentage data are normally distributed according to shapiro-wilks test)
So for example, pre-drug value = 500, post drug = 100, thus the % baseline is 0.2 (20%).
Is this a valid comparison?? I am less interested in absolute size of pre or post-drug values. It seems quite logical to me but my stats knowledge is pretty basic.
 A: There is a more and more strongly emerging consensus that you cannot analyze percentage data with ANOVA. Some of the two latest references are Jaeger (2008) and Dixon (2008) the abstract of which I post below.
Jaeger, T. F. (2008). Categorical data analysis: Away from ANOVAs (transformation or not) and towards logit mixed models. Journal of Memory and Language, 59(4), 434–446. http://doi.org/10.1016/j.jml.2007.11.007

This paper identifies several serious problems with the widespread use
  of ANOVAs for the analysis of categorical outcome variables such as
  forced-choice variables, question-answer accuracy, choice in
  production (e.g. in syntactic priming research), et cetera. I show
  that even after applying the arcsine-square-root transformation to
  proportional data, ANOVA can yield spurious results. I discuss
  conceptual issues underlying these problems and alternatives provided
  by modern statistics. Specifically, I introduce ordinary logit models
  (i.e. logistic regression), which are well-suited to analyze
  categorical data and offer many advantages over ANOVA. Unfortunately,
  ordinary logit models do not include random effect modeling. To
  address this issue, I describe mixed logit models (Generalized Linear
  Mixed Models for binomially distributed outcomes, Breslow and Clayton
  [Breslow, N. E. & Clayton, D. G. (1993). Approximate inference in
  generalized linear mixed models. Journal of the American Statistical
  Society 88(421), 9–25]), which combine the advantages of ordinary
  logit models with the ability to account for random subject and item
  effects in one step of analysis. Throughout the paper, I use a
  psycholinguistic data set to compare the different statistical
  methods.

Dixon, P. (2008). Models of accuracy in repeated-measures designs. Journal of Memory and Language, 59(4), 447–456. http://doi.org/10.1016/j.jml.2007.11.004

Accuracy is often analyzed using analysis of variance techniques in
  which the data are assumed to be normally distributed. However,
  accuracy data are discrete rather than continuous, and proportion
  correct are constrained to the range 0–1. Monte Carlo simulations are
  presented illustrating how this can lead to distortions in the pattern
  of means. An alternative is to analyze accuracy using logistic
  regression. In this technique, the log odds (or logit) of proportion
  correct is modeled as a linear function of the factors in the design.
  In effect, accuracy is rescaled in terms of a logit
  “response-strength” measure. Because the logit scale is unbounded, it
  is not susceptible to the same scaling artifacts as proportion
  correct. However, repeated-measures designs are not readily handled in
  standard logistic regression. I consider two approaches to analyzing
  such designs: conditional logistic regression, in which a Rasch model
  is assumed for the data, and generalized linear mixed-effect analysis,
  in which quasi-maximum likelihood techniques are used to estimate
  model parameters. Monte Carlo simulations demonstrate that the latter
  is superior when effect size varies over subjects.

