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"While these are important options for analysts, they do fundamentally transform the nature of the variable, making the interpretation of the results somewhat more complex."
"The very act of altering the relative distances between data points, which is how these transformations improve normality, raises issues in the interpretation of the data."
"However, this might be undesirable if the original variables were meant to be substantively interpretable (e.g., annual income, years of age, grade, GPA), as the variables become more complex to interpret due to the curvilinear nature of the transformations. Researchers must therefore be careful when interpreting results based on transformed data."
"Data transformations can alter the fundamental nature of the data, such as changing the measurement scale from interval or ratio to ordinal, and creating curvilinear relationships, complicating interpretation."(Osborne, 2002)

Robert B. O’Hara, D. Johan Kotze. (2010) Do not log-transform count data. Methods in Ecology and Evolution 1:2, 118-122.
​http://www3.interscience.wiley.com/cgi-bin/fulltext/123328987/PDFSTART

Osborne, Jason (2002). Notes on the use of data transformations. Practical Assessment, Research & Evaluation, 8(6). Retrieved July 9, 2010.

Zubin J.(1935) Note on a transformation function for proportions and percentages. Journal of Applied Psychology Volume 19, Issue 2, April 1935, Pages 213-220. ​http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WY3-4NP54MD-B&_user=5403746&_coverDate=04%2F30%2F1935&_rdoc=1&_fmt=high&_orig=search&_sort=d&_docanchor=&view=c&_searchStrId=1395618465&_rerunOrigin=google&_acct=C000037979&_version=1&_urlVersion=0&_userid=5403746&md5=e93e38378fab717b4f69f46517fcf0c6