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Data Analysis Strategies - Peer Review - Alex Bratty, I/O Psychology

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This posting will discuss the key elements of data analysis for a phenomenological investigation, the specific data analysis strategy that would be used with this methodology, and an evaluation of its effectiveness.  A brief overview of the research topic, question, and sampling is provided for context.

Overview of Research Topic, Question, & Sampling

The proposed research topic is to explore the experience of flourishing in the workplace for full-time corporate managers.  As such, a question derived from the research topic that would be suitable for a phenomenological investigation could be, what is the essence of flourishing at work for corporate managers?  The primary form of data collection would be open-ended, in-depth interviews, and it is proposed that at least n = 10 corporate managers would be interviewed, consistent with the range of 3 to 25 participants recommended for phenomenology (Creswell & Poth, 2018). 

Data Analysis for Phenomenology

Although a phenomenological study includes more detailed steps, it still follows the general qualitative data analysis approach.  This consists of five broad phases, including (1) preparing and organizing the data, (2) reviewing the data and creating notes, (3) identifying/coding themes, (4) interpretation, and (5) generating written and visual representation of the findings (Creswell & Poth, 2018).  Thus, for the proposed research study, this would involve transcribing the interviews, reading these transcriptions and making notes in the margins and elsewhere related to emergent ideas, identifying themes, interpreting what these imply, and synthesizing these findings into an in-depth understanding of the essence of flourishing at work among corporate managers.  However, this is just a general overview of the phenomenological data analysis process, and because numerous types of phenomenology exist, different and specific procedures are used for data analysis depending on the model that is followed.  For the purpose of this discussion and the proposed study, the model of empirical phenomenology (Giorgi, 1997) would be used, and this is consistent with permitted data analysis methods at Capella University (Percy, Kostere, & Kostere, 2015).

Data Analysis Strategy

The data analysis strategy recommended for the proposed study is that of empirical phenomenology developed by Giorgi (1997).  This method is thought of as two levels of description, including (a) the raw data collected from interviews and (b) the researcher’s account of the essence of the phenomenon based on analysis and interpretation (Percy et al., 2015).  Or, it can be characterized as three broad phases: “(1) the phenomenological reduction, (2) description, and (3) search for essences” (Giorgi, 1997, p. 239).  However, to address these two levels (Percy et al., 2015) or three phases (Giorgi, 1997), seven detailed steps are involved.  First, the data must be transcribed and organized, and second, the researcher must engage in phenomenological reduction (Percy et al., 2015).  This means that the researcher must reflect on and minimize any bias or preconceptions, and bracket pre-existing knowledge of the phenomenon to allow for inductive analysis (i.e., the findings emerge from the data) (Giorgi, 1997).  Once these initial preparatory steps have been taken, the researcher moves through the remaining five stages of data analysis. 

The first of these five stages is to read through all the interviews in their entirety to achieve a holistic impression of the data.  Next, the researcher reads through the interviews again and identifies psychological “meaning units.”  Essentially, in this stage, the researcher is looking for moments where transitions in meaning occur during the participants’ responses.  The meaning units are then examined in relation to one another and the holistic sense of the data.  Additionally, the units are clarified, and any redundancies are removed.  The third stage involves some interpretation from the researcher by reflecting on the meaning units to generate the essence of the participants’ experience and converting these units into psychological language.  The fourth stage is to synthesize the converted meaning units into a summary statement that captures each participant’s experience of the phenomenon.  In the fifth and final stage, all the statements about each participant’s experience are synthesized to produce a detailed statement that describes the essential lived experience being investigated (Percy et al., 2015).  Thus, there are five crucial stages of data analysis and two preparatory steps that comprise Giorgi’s (1997) data analysis strategy of empirical phenomenology.

Evaluation of Data Analysis Strategy

An evaluation of Giorgi’s (1997) empirical data analysis strategy for phenomenology reveals both strengths and limitations.  Perceived strengths include the foundational step of phenomenological reduction and the systematic way in which the data are analyzed.  Indeed, the use of phenomenological reduction helps to ensure that the data are analyzed inductively.  Otherwise, if preconceptions and prior knowledge were included in this process, the analysis could become more deductive in nature (i.e., seeking to confirm or refute what is already known).  Additionally, the use of a systematic step-by-step procedure provides a uniform framework that can help researchers develop an audit trail of their analysis (Patton, 2015).  Consequently, the inclusion of these aspects—phenomenological reduction and systematic analysis—can contribute to the reliability of a phenomenological study (Leedy & Ormrod, 2019).  However, there are also limitations to this data analysis strategy.  Unlike the data analysis methods used by Moustakas or Stevick-Colaizzi-Keen (as cited in Percy et al., 2015), Giorgi’s (1997) model does not differentiate between textural and structural descriptions of the experience being studied, nor does it use imaginative variation, which invites the researcher to experiment with meaning units/themes and examine them from multiple angles.  Finally, consistent with most qualitative studies, Giorgi’s (1997) data analysis method is subjective.  Even when using the standards of phenomenological reduction and a systematic approach, ultimately, each researcher makes his/her own judgments and interpretation of the data (Patton, 2015).  Moreover, phenomenological reduction is unlikely to eliminate or decrease a researcher’s implicit bias because, by its very definition, implicit bias is unconscious to an individual (Kassin, Fein, & Markus, 2017).  Thus, the data analysis approach used in empirical phenomenology has both strengths and limitations.

In conclusion, data analysis for phenomenology fits with the general approach to analyzing data in any qualitative study (Creswell & Poth, 2018).  However, it also has specific and detailed procedures that must be followed, and there are several different types of phenomenology that have their own method of data analysis (Patton, 2015).  The proposed study would use empirical phenomenology (Giorgi, 1997), which means that two preliminary steps (i.e., data preparation and phenomenological reduction) along with five stages of analysis must be closely followed (Percy et al., 2015).  This systematic approach contributes to the reliability of the study (Leedy & Ormrod, 2019), but it also has some limitations that are consistent with the subjective nature of qualitative research (Patton, 2015), and in comparison to other phenomenological approaches (Percy et al., 2015).

References

Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). Thousand Oaks, CA: Sage.

Giorgi, A. (1997). The theory, practice, and evaluation of the phenomenological method as a qualitative research procedure. Journal of Phenomenological Psychology, 28(2), 235-281.

Kassin, S., Fein, S., & Markus, H. R. (2017). Social psychology (10th ed.). Boston, MA: Cengage.

Leedy, P. D., & Ormrod, J. E. (2019). Practical research: Planning and design (12th ed.). New York, NY: Pearson.

Patton, M. Q. (2015). Qualitative research & evaluation methods (4th ed.). Thousand Oaks, CA: Sage.

Percy, W.H., Kostere, K., & Kostere, S. (2015). Qualitative research approaches in psychology. Retrieved from http://assets.capella.edu/campus/doctoral-programs/PsychologyQualitativeResearchApproaches.pdf

Professor feedback

This is a great response to the questions posed within this discussion, Alex. This method is used to analyze the data for the structure and texture of experience. This isn't the same as identifying patterns and themes. Two key elements of Giorgi's method include phenomenological reduction and imaginative variation. These methods are sometimes confused with the identification of patterns and themes, but these steps are not a part of this type of analysis. Within this method the data is broken down into meaning units and examined in an attempt to make sense of what was experienced. It is then put back together in a way that 'gets at' the essence of what was experienced. These steps are often misunderstood and sometimes misrepresented in reprinted texts. This is why it is always a good idea to locate and review the primary source that is being referenced so that you can verify what is stated and acquire an in-depth understanding of what is communicated within that source. From what I can tell, you are off to a great start. To advance your knowledge even further and help you build your library, attached is another article that you might find interesting. Thank you for your contribution to this discussion.

Dr. Roberts

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