<p>I suggest "A Better Lemon Squeezer? Maximum Likelihood Regression with Beta distributed Dependent Variables"  by Smithson and Verkuillen</p>

<p>available at <a href="http://psychology3.anu.edu.au/people/smithson/details/betareg/Smithson_Verkuilen06.pdf" rel="nofollow">http://psychology3.anu.edu.au/people/smithson/details/betareg/Smithson_Verkuilen06.pdf</a></p>

<p>Abstract</p>

<blockquote>
  <p>Uncorrectable skew and
  heteroscedasticity are among the
  “lemons” of psychological data, yet
  many important variables naturally
  exhibit these properties. For scales
  with a lower and upper bound, a
  suitable candidate for models is the
  beta distribution, which is very
  flexible and models skew quite well.
  The authors present maximum-likelihood
  regression models assuming that the
  dependent variable is conditionally
  beta distributed rather than Gaussian.
  The approach models both means
  (location) and variances (dispersion)
  with their own distinct sets of
  predictors (continuous and/or
  categorical), thereby modeling
  heteroscedasticity. The location
  submodel link function is the logit
  and thereby analogous to logistic
  regression, whereas the dispersion
  submodel is log linear. Real examples
  show that these models handle the
  independent observations case readily.
  The article discusses comparisons
  between beta regression and
  alternative techniques, model
  selection and interpretation,
  practical estimation, and software.</p>
</blockquote>
