Routinizing Frame Analysis through the Use of CAQDAS

Abstract

Even though frame analysis has become a popular analytical framework in media studies and social movement research, the methodological underpinnings of the empirical identification of frames lack systematization and have consequently remained underdeveloped. This paper consolidates recent advances in the empirical measurement of frames and explores, in how far computer-assisted qualitative data analysis software (CAQDAS) can extend on these methodologies. Because framing has become a fairly widely used but ill-defined concept, the paper will start with a brief delineation of framing theory as it is understood for present purposes. Next, current attempts to measure frames empirically in a systematic fashion will be discussed and a methodology, which synthesizes some of these approaches will be proposed. This methodology attempts in a first step to draw on existing knowledge on metanarratives to avoid a purely inductive identification of frames. Additionally, automatic word mapping tools such as Leximancer, Sphinx Survey Lexica are suggested as interpretative aids. In a second step, the analyst identifies a set of keywords, key phrases, and possibly audial or visual symbols that indicate frames in his data. These indicators are then used in a third step to semiautomatically identify frames in the remainder of the data. Keywords that might acquire different meanings in different contexts are inspected in their contexts by the analyst, who decide on their coding. This method avoids both the rigidities that come with fully automatic keyword clustering, which may lead to the inclusion non-interpretable keywords as well as the exclusion of so-called stop words such as prepositions and articles, which under certain circumstances might indeed be the strongest indicators for certain frames. At the same time it allows for a degree of routinization and systematization in frame analysis, whose quality has notoriously depended on the creativity of the framing researchers. Five CAQDAS – ATLAS.ti, Kwalitan, MAXqda, NVivo, and Qualrus – are examined with respect to their usability in this type of framing research.

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