Data Analysis

The process of data analysis involves making sense out of text and image data, it involves preparing the data for analysis, conducting different analyses, moving deeper and deeper into understanding the data, representing the data, and making an interpretation of the larger meaning of the data (Creswell, 2007). Qualitative Data Analysis (QDA) is the range of processes and procedures whereby we move from the qualitative data that have been collected into some form of explanation, understanding or interpretation of the people and situations we are investigating. It is usually based on an interpretative philosophy.

Types of Qualitative Analysis:

• Content analysis

• Narrative analysis

• Discourse analysis

• Framework analysis

• Grounded theory

Points of Focus in Analyzing Text Data:

• The primary message content

• The evaluative attitude of the speaker toward the message

• Whether the content of the message is meant to represent individual or group-shared ideas

• The degree to which the speaker is representing actual vs hypothetical experience.

There are Six Steps to Analyze Data in Qualitative Research According to Creswell (2007): 

1. Organize and prepare the data for analysis 

This involves transcribing interviews, optically scanning material, typing up field notes, or sorting and arranging the data into different types depending on the sources of information.

2. Read through all the data

A first step is to obtain a general sense of the information and to reflect on its overall meaning.

3. Begin detailed analysis with a coding process

It involves taking text data or pictures gathered during data collection, segmenting sentences (or paragraphs) or images into categories, and labeling those categories with a term, often a term based on the actual language of the participant (called an in vivo term).

4. Generate codes that describe the setting, people, and events in detail

These descriptions are essential for case studies, ethnographies, and narrative research. By coding the data, researchers can identify key themes or categories, usually around five to seven, which represent the major findings. These themes should reflect multiple perspectives, backed by diverse quotes and specific evidence. These themes often become the headings in the findings sections of studies.

5. Advance how the description and themes will be represented in the qualitative narrative

The most popular approach is to use a narrative passage to convey the findings of the analysis. This might be a discussion that mentions a chronology of events, a detailed discussion of several themes (complete with subthemes, specific illustrations, multiple perspectives from individuals, and quotations) or a discussion with interconnecting themes.

6. Making an interpretation or meaning of the data

Asking, “What were the lessons learned?” captures the essence of this idea (Lincoln & Guba, 1985). These lessons could be the researcher’s interpretation, couched in the understanding that the inquirer brings to the study from her or his own culture, history, and experiences.

Tools for Helping The Analytical Process:

• Summaries

• Self Memos

• Researcher Diary

Principles of Qualitative Data Analysis:

1. People differ in their experience and understanding of reality.

2. A social phenomenon can't be understood outside its own context.

3. Qualitative research can be used to describe phenomenon or generate theory grounded on data.

4. Understanding human behavior emerges slowly and non-linearly.

5. Exceptional cases may yield insights in to a problem or new idea for further inquiry.

            References

            Creswell, J. W. (2014). Research Design. SAGE. http://books.google.ie/booksid=4uB76IC_pOQC&printsec=frontcover&dq=creswell+research+design+third+edition&hl=&cd=1&source=gbs_api