Using Software in Qualitative Research: A Step-by-Step Guide

This chapter introduces the eclectic field of Computer Assisted Qualitative Data AnalysiS (CAQDAS) in the context of qualitative research methodology and the techniques of analysis generally. We discuss the practicalities of research in the software context, outline some basic principles and distinctions which resonate throughout the book; discuss software developments, debates and functionality; and discuss selected qualitative approaches. The remaining chapters build from here, describing some core tasks you might undertake using CAQDAS packages, illustrated via three case-study examples (Chapter 2). Our overall emphasis is on the inherent fluidity between the processes involved in analysis and how customised CAQDAS packages reflect and reinforce them. We discuss analysis in the context of technological possibilities. Table 1.1 lists common analytic tasks enabled by CAQDAS, but software itself does not dictate their sequencing, or whether certain tasks are undertaken or tools are used. These decisions rest entirely with you, informed by the interplay between methodology, analytic strategy, technology and practicality. Table 1.1 Common tasks of analysis supported by CAQDAS packages Task Analytic rationale Planning and managing your project Keep together the different aspects of your work. Aid continuity, and build an audit trail. Later, illustrate your process and your rigour through transparent writing. Writing analytic memos Manage your developing interpretations by keeping track of ideas as they occur, and building on them as you progress. Discover and mark interesting aspects in the data as you see them. Note insights as they strike you, linked to the data that prompted them – enabling retrieval of thoughts together with data. Explore data according to their content. Discover how content differs across data and considering how familiarising with content helps you understand what is 'going on'. Developing a coding schema Manage your ideas about your data by creating and applying codes (that represent themes, concepts, categories etc.). The structure and function of a coding scheme depends on methodology, analytic strategy and style of working. Coding Capture what is going on in your data. Bring together similar data according to themes, concepts etc. Generate codes from the data level (inductively) or according to existing ideas (deductively) as necessary; define the meaning and application of codes. Retrieval of coded segments Revisit coded data to assess similarity and difference, to consider how coding is helping your analysis, and prioritising 'where to go next'. Recoding Recode into broader or narrower themes or categories if appropriate and necessary. Perhaps bring data …