Three experiences with content analysis may shed light on various ways to optimize the coding of linguistic interview data into quantifiable terms.

First, defining recording or coding units. This is not too problematic in this context but has unusual implications for question-centered statistics.

Second, the choice of a code. The standard conception of a code is a many-to-one mapping from transcripts of verbal exchanges into quantifiable terms. Such codes are typical for much of content analysis and built into structured interview situations in which answers are selected from a list. I suppose their inadequacy is the primary reason for asking questions with open ended answers. Conceiving codes as many-to-one-set mappings proves somewhat closer to respondents' conceptions, but conceiving them as many-to-many-interpretative-schemes might be a semantically more valid approach but makes the analyzability of data more difficult.

Third, optimizing a code. I want to discuss five criteria:

1. The reliability or reproducibility of the coding process -- not an issue in computer applications;
2. The relevance of the quantifiable terms to the research question;
3. The semantic validity of the code, the degree to which quantifiable terms represent what respondents had in mind saying; and
4. The generalizability of the code over diverse interviewing situations, an opportunity that most content analyses have not taken up
5. The efficiency, the costs and time required to develop a code and generate relevant data,

Optimizing one criterion often fails the others and I may offer some qualified generalizations.