SOTL Qualitative Data Analysis and Interpretation Techniques

From Mills, G. E. (2006). Action Research: A guide for the teacher researcher (3rd Ed.). Uper Saddle Rive, NJ: Merrill Prentice Hall. & Stringer, E. T. (1993). Socially responsive educational research: Linking theory and practice. In D. Flinders, & G. E. Mills (Eds.). Theory and concepts in qualitative research: Perspectives from the field (pp. 141-162). New York: Teachers College Press.

Analysis

These techniques are not mutually exclusive, rather, combinations of these techniques will yield a better understanding of your data.

Identifying Themes. A beginning technique is to examine all data inductively, looking for common patterns, repeating events, or key phases used by respondents.

Coding Surveys, Interviews, and Questionnaires. A method of looking for patterns or reducing data. This could be done by recoding units or blocks of data on 3 X 5 index cards and attempting to sort them into categories. This data could be comments made during interviews, for example. Sorting does not have to be done with categories in mind, in fact, it may be preferable to let categories emerge from the data.

Asking Key Questions. These may be the original research questions or questions raised by the data or the participant.

Doing an Organizational Review. Looking at the vision, goals, structure, practices, and issues of the organization/school. This can give insight into the data that you have collected. Having participants do this also allows them to gain a better understanding of operations that are relevant to their concerns.

Concept Mapping. This could consist of having the participants visually represent the major variables or factors operating in the context investigated and the interconnections between them. Having different participants map these relations can give insight into the real and perceived connections between factors, the different perspectives that exist and different analyses of existing problems. Doing this activity yourself with collected data also creates useful visualizations.

Analyzing Antecedents and Consequences. Map the cause and effect relationships revealed in your data and compare these to the relations found in the literature or previous experience.

Displaying Findings. Summarizing data for sharing with others using tables, graphs, or concept maps, or incorporating these with collected video and/or auditory clips in multimedia presentations. These may also reveal new aspects of your data or patters/relations not previously noticed.

State What’s Missing. This could be information not yet collected or questions not answered or newly raised.

Using Computer Software to Assist with Data Analysis. Software exists that can help with the sorting or searching of qualitative data, however, it does not do the analysis. As an example, the software package QRS NS(NUD*IST 5.0) can perform categorical searches (for example, a phrase) and analyze the results across demographical categories. Other software packages include Ethnograph and HyperRESEARCH.

Interpretation

Extend the Analysis. This involves raising questions about the study and noting the implications that could be drawn, without actually making those implications. These could include findings suggested by the data that were not part of the original research plan but may be considerations for future research.

Connect Findings with Personal Experience. Interpret and share your data based on the intimate knowledge and understanding you now have of the contexts you investigated. You have a unique perspective from becoming an expert on your data.

Seek the Advice of “Critical” Friends. These could be colleagues or participants who are asked for their insight; though the more people are asked, the more differing opinions you may generate.

Contextualize Findings in the Literature. The use of external sources can help draw connections or support your findings and highlight your unique contributions to a topic.

Turn to Theory. This helps link your work to larger issues, aids the abstraction and applications of your localized findings, and can add additional meanings to your data.

Know When to Say “When”. It is acceptable to simply summarize data and provide questions or plans for future studies, and this may be preferable to providing weak interpretations .

Share Your Interpretations Wisely. Ensuring that interpretations are closely connected to your data and analyses will help to keep your research from merely confirming and defending prior beliefs and values.

 

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