Includes figure. Includes bibliography. Review(s) of: B. G. Glaser and A. L. Strauss, The Discovery of Grounded Theory: Strategies for Qualitative Research, Aldine Publishing Co., Chicago, 1967, ISBN 0202302601, 271 pages, paperback
Qualitative data analysis is an approach to the use of unstructured data that is widely practised (and studied) in the social sciences, history and literary studies. It is used in many areas derivative from social sciences, such as market analysis, legal evidence analysis, health and demographic studies, education, journalism, etc. It is also coming to be recognised as significant in the knowledge acquisition stage of knowledge engineering or systems analysis. The raw material of qualitative analysis is text or other unstructured material, such as audio or video tapes and music scores. The purposes of qualitative analysis are various, e.g. to analyse the material to support a hypothesis, to determine a factual narrative of events from the material, or to find patterns and themes that "make sense" of the material. For most of these purposes, attempts to organise this material into the record-and-field format of a standard database package, or to find specific material in the data by text search, is of little use. Consequently existing software systems for supporting qualitative analysis, e.g. The Ethnograph, have adopted a "cut-and-paste" approach in which text portions, selected visually by the user, are collected into one or more of a possibly large number of categories. Retrieval is simply on the categories or a restricted subset of boolean combinations of them. This paper begins by arguing that such an approach is insufficent for qualitative analysis methodology, and indeed distorts it. In its place it describes an approach using a tree-structured database and operators of several types for database insertion and deletion, structure modification, storage of comments and access histories, and the relational combination of existing database items to make new ones, representing concepts or categories built out of ones already contained in the database. This latter is argued to be central to the type of creative data-driven analysis that is typically wanted in qualitative research, and is opposed to the hypothesis-driven deductive analysis that typifies e.g. the handling of structured questionnaires. The system described is currently in wide use in many places. The method described lends itself readily to extensions that explicitly employ artificial intelligence techniques. The paper concludes with a discussion of planned extensions to frame-based reasoning, incorporation of semantic nets, and search based on patterns of concepts.
Qualitative research techniques are used when there is a need for a new understanding of a situation. To achieve an understanding of complex situations, the challenge faced by the researcher is to manage that complexity. This chapter shows that all qualitative research requires knowledge organisation. Managing the overwhelming detail of data and putting it into context requires sophisticated storage and access methods, which can assist a project of any size to achieve a better, more rigorous outcome. Software designed for that purpose is useful in qualitative research of any scale. And a toolkit for qualitative analysis is also a toolkit for organising many types of knowledge — what is known a priori and what is discovered during the enquiry, as well as the knowledge derived from search and scrutiny. Researcher and manager share the task of bringing these together in order to reach an understanding of a situation, an issue or a problem.KeywordsQualitative ResearchIndex SystemQualitative Data AnalysisQualitative ResearcherFiling CabinetThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Specialized computer programs for Qualitative Research in social sciences have greatly changed ways of doing QR, the reliability and comprehensiveness of results, the ability to inspect and challenge a researcher's working, and the relationship with quantitative methods in social research. This article explores these claims in the context of N6 (NUD*IST) and NVivo, the two programs designed by the authors; and considers possible future developments in the field.
Qualitative computing is often described by enthusiasts as revolutionary, but in stark contrast, its methodological innovations are rarely discussed. Why is the debate missing? The paper charts some of the major developments in support for coding and theory-building, exploring the many reasons why these are either taken for granted or unrecognized and often unused. Whilst these tools had the potential to change methods, their adoption and their impact have been uneven, and they have been subjected to remarkably little sharp and critical debate. The paper concludes by asking what is required if researchers are to benefit from and drive this technological change.
OBJECTIVE:To explore whether qualitative methods are problematic and persuasive in health education research.METHOD:Explored this problem through the 3 goals of rigor, rapidity, and reliability and their special meanings in qualitative analysis.RESULTS:For each, contributions of qualitative computing software are identified and their effects assessed.CONCLUSION:Qualitative researchers are assisted by software tools in pursuit of each of these goals, but in each area there is a need for software design to address the tasks of research where rigor, rapidity, and reliability are paramount requirements.
In our paper in the Denzin & Lincoln volume (Richards & Richards (1994)), we made a distinction between the conceptual and textual levels of work in qualitative data analysis (QDA), and argued that future work in the computerisation of QDA would involve finding ways of crossing the gap from the textual to the conceptual levels. In this paper we will discuss more closely the relation between the two levels; will argue that bridges from textual to conceptual levels are still poorly supported; and will discuss requirements for making the bridge.
This paper exploits the thesis (Popper, Koestler) that one significant locus of creativity lies in the process of making sense of data. Data-driven thinking, to be opposed to hypothesis-driven thinking, concerns approaching more or less unstructured, uninformed, raw data and wondering how it can be explained, or cohered.
This work by mostly British scholars presents 11 essays on qualitative computing, one of them an overview essay by the editors. An initial section surveys a dozen software packages such as Ethno, Ethnograph, and WordCruncher and discusses the application of Nudist and a package created at the University of Edinburgh for conceptual modeling. A middle section, "Implications for Research Practice," contains essays on integrating computing in methods courses; ethical issues and data protection; and methodological pitfalls such as reification. A final section, "Qualitative Knowledge and Computing" contains essays on ethnographic research, event structure analysis (by David Heise), and hypertext. A concluding essay by Michael Agar, "The Right Brain Strikes Back," presents personal experiences and a critical view of computing in qualitative research.
This paper identifies criteria seen as essential to feminist research. In light of these criteria, issues which have arisen during our current research on women and their experiences of midlife and menopause are discussed. Issues considered include the researchers' responsibilities to participants when exploring sensitive and highly personal issues relating to participants' life experiences, and less clear cut issues such as knowledge construction, power and control. In relation to the latter the balance of power in the research-participant relationship, and the role and responsibilities of the researcher in knowledge construction, are explored. Foucault's notions of knowledge construction and power and control and the feminist researcher's position, are considered in terms of rigour in feminist research and dissemination of research reports. Issues which are seen as problematic and worthy of further debate are: the relations between interviewer and interviewee; the intellectual (the researcher) as the bearer of universal values and as truth teller; and the level of critical activism possible in research studies of this nature.
The analysis of unstructured information, particularly in the form of text, has long been a technique in the armory of social scientists, who have to deal with conversational records, historical documents, unstructured interviews, and the like. Unsurprisingly, a considerable amount of methodological literature has developed on the subject. The methods of “qualitative data analysis” have now spread to areas of information analysis as diverse as market research and legal evidence analysis. Related computer techniques, from database management systems and word‐processors to specialized qualitative data analysis software, have been pressed into use. This article discusses the information processing methodology and theory assumed by computer‐based qualitative data analysis software; and, in particular, describes and analyzes the methodology of the NUDIST system developed by the authors.
Most computer approaches to qualitative data analysis have concentrated on coding and retrieval of text. This paper describes a research project which set out to support a range of methods for the analysis of unstructured data, with emphasis on the building and testing of grounded theory. It resulted in software whose innovations include: a) No limit to the number of coding categories and sub-categories, and no limit to the number of times a given text passage can be coded; b) The use of separate document and indexing databases, interrelated and of unlimited flexibility; c) Comprehensive hypermedia-like browsing tools for both document and indexing databases; d) Ability to search for words and lexical patterns occurring in text and to combine this with indexing of the text; e) Ability to handle off-line textual and non-textual data as well as on-line data; f) Ability to record textual comments in indexing categories — a memoing facility for emerging ideas and categories; g) Support and exploitation of hierarchical indexing systems; h) Mechanisms for creating new indexing categories out of existing ones, relating them to the data documents, and using them for further analyses. New goals of the project are to provide a number of artificial intelligence based information structuring and reasoning facilities which can be used to aid the organization and retrieval of qualitative data, and to extend the present capabilities of the software to express and test new ideas, concepts, generalizations and hypotheses about the data.
In all fields where qualitative data are important, and especially in fields where rigorous qualitative analysis is demanded, computers are remaking methodology. Demand for, and faith in, computing for qualitative analysis is now strongly influencing health research. In this article, the authors argue for an evaluation of the impact of computer techniques and an opening of debate among developers and users of programs to address the purposes, power, and potential of computing in qualitative research. As a contribution to such a debate, these authors explore the original goals, design, and implementation of one approach to qualitative computing: NUDIST, a newly developed program for the mainframe and the Macintosh™ is introduced. In light of this, the ways in which old goals were achieved and the means by which the new goals imposed by users and critics may be met are discussed.
