Qualitative method-data analysis tool

• Descriptive phenomenology is concerned with revealing the “essence” or “essential structure” of any phenomenon under investigation – that is, those features that make it what it is, rather than something else.
• Colaizzi’s Phenomenological Method (1978), is not that known in psychology but widely used in other disciplines such as the health sciences despite this, the method has considerable potential for qualitative psychologists, especially those coming fresh to descriptive phenomenology
• Colaizzi’s (1978) distinctive seven step process provides a rigorous analysis, with eachstepstaying close to the data .

  
  

  Familiarisation - The researcher familiarizes him or herself with the data, by reading through all the participant accounts several times
• Identifying significant statements- The researcher identifies all statements in the accounts that are of direct relevance to the phenomenon under investigation
• Formulating meanings- The researcher identifies meanings relevant to the phenomenon that arise from a careful consideration of the significant statements. The researcher must reflexively “bracket” his or her pre-suppositions to stick closely to the phenomenon as experienced.
• Clustering themes- The researcher clusters the identified meanings into themes that are common across all accounts. Again bracketing of pre- suppositions is crucial, especially to avoid any potential influence of existing theory
• Developing an exhaustive description- The researcher writes a full and inclusive description of the phenomenon, incorporating all the themes produced at step 4.
• Producing the fundamental structure- The researcher condenses the exhaustive description down to a short, dense statement that captures just those aspects deemed to be essential to the structure of the phenomenon.
• Seeking verification of the fundamental structure- The researcher returns the fundamental structure statement to all participants to ask whether it captures their experience. He or she may go back and modify earlier steps in the analysis in the light of this feedback.
• Thematic Analysis- is a method for systematically identifying, organizing, and offering insight into, patterns of meaning (themes) across a dataset. Through focusing on meaning across a dataset, this analysis allows the researcher to see and make sense of collective or shared meanings and experiences. Identifying unique and idiosyncratic meanings and experiences found only within a single data item is not the focus of this type of analysis.
• Thematic Analysis. This method, then, is a way of identifying what is common to the way a topic is talked or written about, and of making sense of those commonalities.
• Thematic analysis is a flexible method that allows the researcher to focus on the data in numerous different ways. You can legitimately focus on analysing meaning across the entire data set, or you can examine one particular aspect of a phenomenon in depth. You can report the obvious or semantic meanings in the data, or you can interrogate the latent meanings, the assumptions and ideas that lie behind what is explicitly stated.
• The two main reasons to use TA are because of its accessibility and its flexibility
• An inductive approach to data coding and analysis is a ‘bottom up’ approach, and is driven by what is in the data. What this means is that the codes and themes derive from the content of the data themselves – so that what is ‘mapped’ by the researcher during analysis closely matches the content of the data.
• A deductive approach to data coding and analysis is a ‘top down’ approach, where the researcher brings to the data a series of concepts, ideas, or topics that they use to code and interpret the data. What this means is that the codes and themes derive more from concepts and ideas the researcher brings to the data – here what is ‘mapped’ by the researcher during analysis does not necessarily closely link to the semantic data content.
• A six-phase approach to thematic analysis

  Phase 1: Familiarizing yourself with the data

  Phase 2: Generating initial codes

  Phase 3: Searching for themes

  Phase 4: Reviewing potential themes

  Phase 5: Defining and naming themes

  Phase 6: Producing the report