We discussed data analysis for most of the day, and worked on coding and classifying our data from the field observation we did on July 2.  I conducted mine at the library, and since it was quite long, it was rather tedious to code.  I found myself wishing I wasn’t quite so long-winded!  All in all, it was a great experience to learn this skill and I am sure in doing my M.Ed. thesis, it will be something I will develop a proficiency in by the end of it.  Below are some notes I took on the slideshow we took in on reflection and data analysis.

Reflection and Data analysis (found in Creswell pages 183-193)

  • An opportunity to make sense of the data we’ve been collecting, identifying units of meaning
  • It is an ongoing process that involves continual reflection
  • Involves asking analytic questions and writing short memos (about your decisions or how you are defining a term in your study)
  • Data analysis involves collecting open-ended data which must be interrogated and developed from the participants’ perspectives and the answers they’ve given
  • The researcher collects the qualitative data and analyzes it for themes and perspective’s; should report 4-5 themes

Interpretation of the data- two major processes provide the means to distill the data that emerge from the ongoing processes of investigation: categorization and coding/ and key experiences

Analysis and interpretation

  • Ask analytical questions
  • Organize and prepare the data for analysis
  • Read through all the data to get a general sense of the data
  • Reflect on the meanings

Categorization and Coding

  • The major task of this procedure is to identify the significant features and elements that make up the experiences and perceptions of the people involved in the study
  • This is the process one undertakes to organize material into chunks of meaningful text
  • Coding is just making categories and themes from your data


Steps for general analysis- guidance for coding

Get a sense of the whole

Pick one document and then go through it.

Cluster your topics.

Abbreviate texts as codes.

Use descriptive wording.

Make a coding book so that you don’t get mixed up about what you’ve coded.

Assemble all your material into categories.

If necessary, recode other data.


Develop how description and themes will be represented in a qualitative narrative

Making interpretation of the data means asking what were the lessons learned? What were my personal interpretations? What meaning was derived from comparison of the findings with info gleaned from literature or theorists


This afternoon, Leslie introduced us to a texting option for teachers and students called Remind (found online).  It is a very useful way of keeping in touch with students using their current technological fad: cellphones.  Unfortunately, none of my students have one, so I will not be using this option.  Unless I use this as a way to connect to their parents.

In the afternoon, Martha and I had the opportunity to talk about my thesis in terms of the theoretical orientation. We discussed the pros and cons of autoethnography, these being: finding voice, the transparency aspect of writing in a very personal way as well as the need to ground oneself on the theoretical literature that is out there.  We talked about my desire to do this in spite of the cautionary concerns Martha offered.  We also discussed the need for me to read up on actual autoethnographies that have been already done, those written by Bochner and Ellis being the most highly recommended.

As a way to end off the day, we shared technologies with one another that we have found useful in our classroom.  Chapter 9 in Stringer had a number of cites that were useful for Action Research.  I will be conducting a search for cites that connect back to autoethnography and hope to compile this in my digital journal.