Considering data requirements goes hand in hand with analysis requirements. In many evaluations, the most important, or only, form of analysis may be a simple aggregation of quantitative data or a comparison of categorical variables. When the subject matter is textual and the evaluation questions lend themselves to numerical descriptions or comparisons, content analysis is usually a good choice. For example, in the Stars and Stripes study, a key question pertained to whether the European and Pacific editions differed in the types of stories they covered. Therefore, the evaluators classified textual data into story topic categories and displayed most of the results in simple tables that compared frequency counts for the two editions.
Had the Stars and Stripes report required a comparison of subtleties in the language of the news stories, then content analysis would probably not have been the best methodology to use. Kather than transform the text into categories, a better approach might have been to retrieve and display comparable passages side by side. Evaluators could then systematically form conclusions about the apparent differences. (See appendix I.)
Source: GAO (2013), Content Analysis: A Methodology for Structuring and Analyzing Written Material: PEMD-10.3.1, BiblioGov.