Qualitative data analysis

A topic in research methodology

“A quality is an inherent or phenomenal property or essential characteristic of some thing (object or experience).”

Schwandt, 2001: 214

Qualitative data

In research data is normally classed as quantitative (when in the form of numbers) or qualitative (otherwise, usually words)

Qualitative data: “nonnumeric data in the form of words”

Schwandt, 2001: 213

“Words … are by far the most common form of qualitative data”

Robson, 2002: 455

However research also collect other forms of data such as images or artifacts (e.g. students’ models)

Analysis

Once data has been collected, it needs to be analysed. The nature of data analysis is different in confirmatory research working with quantitative data and in interpretivist studies working with qualitative data, where it is acknowledged that analysis depends upon the analyst's idiosyncractic fund of interpretive resources (and theoretical sensitivity)

However, not all research with qualitative data is understood in these ways – sometimes qualitative data leads to numerical or even statistical findings! So not all research working with qualitative data is ‘Qualitative research’ in the way that term is often understood.

The need for careful planning of data analysis

“the analysis of data needs to be planned, just as much as the collection of data. It can also consume a significant proportion of the time available for a project, and the research schedule needs to allow for this in view of when any report needs to be completed”

(Taber, 2013: 283)

Paton warns that “Qualitative analysis transforms data into findings. No formula exist for that transformation”, and suggests that

"The challenge of qualitative analysis lies in making sense of massive amounts of data. This involves reducing the volume of raw information, sifting trivia from significance, identifying significant patterns, and constructing a framework for communicating the essence of what the data reveal.”

Patton, 2002: 432

When analysing data in interpretivist research it is important to immerse oneself in the data – but to avoid ‘drowning’ in it! Robson warns:

“Naïve researchers may be injured by unforeseen problems with qualitative data. This can occur at the collection stage, where overload is a constant danger.”

Robson, 2002: 456

There are various approaches to qualitative data analysis.

Sources cited:

• Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Thousand Oaks, California: Sage.
• Robson, C. (2002). Real World Research (2nd ed.): Blackwell.
• Schwandt, T. A. (2001). Dictionary of Qualitative Inquiry (2nd ed.). Thousand Oaks, California: Sage Publications.
• Taber, K. S. (2013). Classroom-based Research and Evidence-based Practice: An introduction (2nd ed.). London: Sage.