Broadly speaking, data transformation refers to the conversion of the value of a given data point, using some kind of consistent mathematical transformation. There are an almost limitless number of ways in which one can transform data, depending on the needs of the research project or problems at hand. The current entry discusses some of the data transformations more commonly seen in communication research, the instances in which they would be used, and their practical utility to the communication scholar. These include transformation into standard scores, inverse scoring, dichotomizing, and log transformations. Where applicable, some of the shortcomings and tradeoffs associated with these transformations are also addressed.