In This Article Expand or collapse the "in this article" section Qualitative Data Analysis Techniques

  • Introduction
  • General Overviews
  • Word Count
  • Semiotic Analysis
  • Text Mining
  • Discourse Analysis
  • Classical Content Analysis
  • Schema Analysis
  • Latent Content Analysis
  • Manifest Content Analysis
  • Keywords-in-context
  • Constant Comparison Analysis
  • Membership Categorization Analysis
  • Narrative Analysis
  • Conversation Analysis
  • Ethnographic Decision Model Analysis
  • Critical Discourse Analysis
  • Frame or Framing Analysis
  • Social Semiotic Analysis
  • Domain Analysis
  • Taxonomic Analysis
  • Componential Analysis
  • Theme Analysis
  • Dialogical Narrative Analysis
  • Qualitative Comparative Analysis
  • Multimodal Discourse Analysis (MDA)
  • Dimensional Analysis
  • Framework Analysis
  • Secondary Data Analysis
  • Interpretative Phenomenological Analysis (IPA)
  • Consensual Qualitative Research
  • Situational Analysis
  • Micro-Interlocutor Analysis
  • Rhetorical Analysis
  • Systematic Data Integration
  • Nonverbal Communication Analysis

Education Qualitative Data Analysis Techniques
Anthony J. Onwuegbuzie, Magdalena Denham
  • LAST REVIEWED: 30 June 2014
  • LAST MODIFIED: 30 June 2014
  • DOI: 10.1093/obo/9780199756810-0078


Qualitative research can be traced back to ancient times; however, the use of qualitative methods began to be formalized in certain disciplines (e.g., sociology, anthropology) only in the 19th century. Broadly speaking, qualitative research involves an in-depth examination of human experiences and human behavior, with the goal of obtaining insights into everyday experiences and meaning attached to these experiences of individuals (via qualitative methodologies such as biography, autobiography, life history, oral history, autoethnography, case study) and groups (via qualitative methodologies such as phenomenology, ethnography, grounded theory), which, optimally, can lead to understanding the meaning of behaviors from the study participant’s/group’s perspective. Qualitative researchers tend to investigate not just what, where, and when, but more importantly the why and how of events, experiences, and behaviors. Thus, qualitative researchers are much more likely to study smaller but focused samples than large samples. In general, qualitative research studies primarily involve the collection, analysis, and interpretation of data (i.e., information) that naturally occur. Of these steps, the analysis of data arguably represents one of the most difficult steps—if not the most difficult step—of the qualitative research process because it involves a systematic exploration of meaning and the achievement of verstehen (i.e., understanding). More specifically, qualitative data analysis is a process that comprises multiple phases, and from which findings are extracted or emerge. These phases include examining, cleaning, organizing, reducing, exploring, describing, explaining, displaying, interrogating, categorizing, pattern finding, transforming, correlating, consolidating, comparing, integrating, synthesizing, and interpreting data, in ways that allow researchers to see patterns, to identify categories and themes, to develop typologies, to discover relationships, to cultivate explanations, to extract interpretations, to develop critiques, to generate or to advance theories, and/or the like, with the goal of meaning making. A criticism of qualitative data analysis is that because it typically involves examination of data extracted from small, nonrandom samples, findings stemming from any qualitative analysis usually are not generalizable beyond the local research participants. However, what is a limitation for one purpose (i.e., generalization of findings to the population the sample was drawn from), is a strength for another purpose. Specifically, the examination of relatively small samples allows qualitative researchers to collect (maximally) rich data (e.g., via in-depth interviews, focus groups, observations, images, nonverbal communication). This, in turn, makes it more likely that as a result of the qualitative data analysis, verstehen will be achieved.

General Overviews

The analysis of data represents the most important and difficult step in the qualitative research process. Therefore, the purpose of this entry is to document the history and development of qualitative analytical approaches. In particular, described here are thirty-four formal qualitative data-analysis approaches that were identified from an exhaustive search of the literature. This OBO entry not only extends the work of Onwuegbuzie, et al. 2011—which identified twenty-three analysis approaches—but by adding numerous other qualitative data analysis approaches, it also extends these works by documenting the origin of each analysis approach, mapping it onto the nine moments described in Denzin and Lincoln 2011 and outlining the sources of qualitative data that it can analyze. With respect to the latter, see Leech and Onwuegbuzie 2008 with typology wherein the following four major sources of qualitative data prevail: talk, observations, images, and documents. Specifically, talk represents data that are extracted directly from the voices of the participants using data collection techniques such as individual interviews and focus groups. Observations involve the collection of data by systematically watching or perceiving one or more events, interactions, or nonverbal communication to address or to inform the research question(s). Images represent still (e.g., drawings, photographs) or moving (e.g., videos) visual data that are observed or perceived. Documents represent the collection of text that exists either in printed or digital form. As Miles and Huberman 1994 declared: “The strengths of qualitative data rest on the competence with which their analysis is carried out” (p. 10). By only being aware of a few qualitative data-analysis approaches, a qualitative researcher might make the data fit the analysis rather than select the most appropriate data-analysis approach given the underlying research elements such as the research question, researcher’s lens, and sampling and design characteristics. In contrast, by being aware of the array of qualitative data-analysis approaches, as well as how and when to conduct them, a qualitative researcher is in a better position not only to conduct analyses that have integrity but also to conduct analyses that emerge as findings emerge. Thus, qualitative researchers likely would put themselves in a better position for making meaning if they adopt a constructivist approach to qualitative data analysis. However, this can only occur if they have an awareness of multiple ways of analyzing qualitative data. This goal helps to establish the significance of the current work in this field.

  • Denzin, Norman K., and Yvonna S. Lincoln. 2011. Introduction: The discipline and practice of qualitative research. In Sage handbook of qualitative research. 4th ed. Edited by Norman K. Denzin and Yvonna S. Lincoln, 1–25. Thousand Oaks, CA: SAGE.

    The authors document the history of qualitative research. This history spans nine moments, starting with they call the “traditional” moment and continuing through to the ninth moment, which they call the “fractured future,” which is the present moment.

  • Leech, Nancy L., and Anthony J. Onwuegbuzie. 2008. Qualitative data analysis: A compendium of techniques and a framework for selection for school psychology research and beyond. School Psychology Quarterly 23:587–604.

    DOI: 10.1037/1045-3830.23.4.587

    The authors describe the following eighteen qualitative analysis techniques: method of constant comparison analysis, keywords-in-context, word count, classical content analysis, domain analysis, taxonomic analysis, componential analysis, conversation analysis, discourse analysis, secondary analysis, membership categorization analysis, narrative analysis, qualitative comparative analysis, semiotics, manifest content analysis, latent content analysis, text mining, and micro-interlocutor analysis.

  • Miles, Matthew B., and A. Michael Huberman. 1994. Qualitative data analysis: An expanded sourcebook. 2d ed. Thousand Oaks, CA: SAGE.

    In this groundbreaking book, the authors conceptualize and describe nineteen within-case analyses (i.e., partially ordered display, time-ordered display, role-ordered display, and conceptually ordered display) and eighteen cross-case analyses (i.e., partially ordered display, case-ordered display, time-ordered display, and conceptually ordered display. Thus, this work is the most comprehensive guidebook to qualitative analysis to date.

  • Onwuegbuzie, Anthony J., Nancy L. Leech, and Kathleen M. T. Collins. 2011. Toward a new era for conducting mixed analyses: The role of quantitative dominant and qualitative dominant crossover mixed analyses. In The Sage handbook of innovation in social research methods. Edited by Malcolm Williams and Paul W. Vogt, 353–384. Thousand Oaks, CA: SAGE.

    In this book chapter, the authors introduce a unified framework for combining qualitative analysis and quantitative analysis—which they call a mixed analysis—regardless of whether the researcher is oriented toward quantitative research or mixed research.

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