One of the key elements in qualitative data analysis is the systematic coding of text (Strauss and Corbin 1990:57�60; Miles and Huberman 1994:56). Codes are the building blocks for theory or model building and the foundation on which the analyst’s arguments rest. Implicitly or explicitly, they embody the assumptions underlying the analysis. Given the context of the interdisciplinary nature of research at the Centers for Disease Control and Prevention (CDC), we have sought to develop explicit guidelines for all aspects of qualitative data analysis, including codebook development. On the one hand, we must often explain basic methods such as this in clear terms to a wide range of scientists who have little or no experience with qualitative research and who may express a deep skepticism of the validity of our results. On the other, our codebook development strategy must be responsive to the teamwork approach that typifies the projects we undertake at CDC, where coding is generally done by two or more persons who may be located at widely dispersed sites. We generally use multiple coders so that we can assess the reliability and validity of the coded data through intercoder agreement measures (e.g., Carey et al. 1996) and, for some projects, as the only reasonable way to handle the sheer volume of data generated. The standardized structure and dynamic process used in our codebook development strategy reflects these concerns. This paper describes (1) how a structured codebook provides a stable frame for the dynamic analysis of textual data; (2) how specific codebook features can improve intercoder agreement among multiple researchers; and (3) the value
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