Phenomenology Data Analysis


Phenomenological Data Analysis



This module provides a general overview of the steps used to interpret and analyze phenomenological data.

Learning Objectives

  • Describe how phenomenological data is analyzed in general.
  • Explain the steps involved in analyzing phenomenological data
  • Discuss the interpretation of phenomenological data.

 

Phenomenological research tends to generate a significant quantity of data that may include interview notes, transcripts, recordings, and other types of records that must be analyzed. Pure phenomenology attempt to simply describe the data, but does not attempt to explain or interpret.  However, most phenomenological research adds an interpretive element that allows the data to be used as a basis for theory and allows it to be used to challenge structural or normative assumptions.  It may even to be used to challenge or support policies or actions related to a particular experience or phenomenon.

To begin, the researcher should read through the volume of material to begin to get a feeling for what has been said about the phenomenon being studied.  The researcher can then begin a deeper analysis of the data in an effort to organize the information, focus on a deeper understanding and pull out emerging themes.  The approach should be a fluid one that follows the nature of the data and allows the direction to change through the course of analysis.

The researcher should look at all aspects of the experience as described by the participants.  This includes physical surroundings, objects, other people present, type of activity, outcome, social and personal interactions, time frame, emotions, belief or value systems, attitudes.  The researcher can then look to identify commonalities among these aspects between the accounts from different participants.  The goal is to use these observations to identify themes. This can be difficult, but it may be helpful to keep in mind that themes are elements of the described experience that cannot be changed without losing meaning. For example, if the researcher was reviewing narratives collected from victims of sexual assault, the researcher should consider which aspects of the narrative could not be changed without losing the meaning or understanding of how that phenomenon was experienced by the victim.  If an aspect can be changed and the meaning is not lost, then that aspect is not essential and not part of the theme. 

If a significant amount of data has been collected, it is important that the researcher organize the data.  Breaking the data down and categorizing it by using codes can help to identify those essential aspects and develop themes.  As discussed in previous models, it is critical the researcher remove assumptions, pre-conceived ideas and biases out of the analysis process.  Organizing the data and using codes to assist in developing themes may make the process more objective. The following video offers a step-by-step guide to coding data and developing themes.




There may be both collective and individual themes that emerge from analysis.  Individual themes would be those that are unique to one or few participants that may have some aspect of the experience that varies from others.  Collective themes are those occur across a group of participants who experienced the phenomenon.  Exploring the deeper meaning of these themes may allow for interpretive analysis and some generalization of how the phenomenon is experienced.  For more information on the details regarding interpretive analysis, please see the interpretive analysis resource links in the menu to the right.


Suggested Readings:

  • Giorgi, A. (2012). The descriptive phenomenological psychological method. Journal of Phenomenological psychology, 43(1), 3-12.
  • Giorgi, A. (1997). The theory, practice, and evaluation of the phenomenological method as a qualitative research procedure. Journal of phenomenological psychology, 28(2), 235-260.
  • Hycner, R. H. (1985). Some guidelines for the phenomenological analysis of interview data. Human studies, 8(3), 279-303.
  • Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. sage.
  • Measor, L. (1985). "Interviewing: a Strategy in Qualitative Research" in R Burgess (ed) Strategies of Educational Research: Qualitative Methods. Lewes, Falmer Press.
  • Moustakas, C. (1994). Phenomenological research methods. London, Sage.
  • Polkinghorne, D. E. (1989). Phenomenological research methods. Existential-phenomenological perspectives in psychology: Exploring the breadth of human experience, 41-60.
  • Priest, H. (2002). An approach to the phenomenological analysis of data. Nurse Researcher, 10(2), 50-63.
  • Starks, H., & Brown Trinidad, S. (2007). Choose your method: A comparison of phenomenology, discourse analysis, and grounded theory. Qualitative health research, 17(10), 1372-1380.

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