In light of the research in other chapters in this volume, this chapter considers some of the important and as-yet-unresolved methodological issues in automated content analysis. The chapter focuses on DICTION in particular, but the concerns raised here also apply to automated content analytic techniques more generally. Those concerns are twofold. First, the chapter considers the importance of aggregation for the reliability of content analyses, both human- and computer-coded. Second, the chapter reviews some of the difficulties associated with testing the validity of the kinds of complex (latent) variables on which DICTION is focused. On the whole, the chapter argues that this (and its companion) volume reflect just some of the many possibilities for DICTION-based analyses, but researchers must proceed with a certain amount of caution as well.
An Automated Content Analysis of Forestry Research: Are Socioecological Challenges Being Addressed?
