Introduction to Qualitative Data Analysis

Introduction to Qualitative Data Analysis

DOI: 10.4018/978-1-7998-8549-8.ch007
OnDemand PDF Download:
Available
$37.50
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

This chapter introduces readers to the basics of data analysis and the practical handling of open, axial, and selective coding within and outside the grounded theory. Readers are introduced to segmentation/reassembling, constant comparative, and analytic induction concepts in qualitative data analysis in the first section of this chapter. They should be able to trace the origin of coding of qualitative data in qualitative research. The stages of qualitative data analysis are discussed in the second section. The third section takes readers through the practical steps of open, axial, and selective coding, and detailed examples are given.
Chapter Preview
Top

1 Key Concepts

  • Segmentation – the breaking down of qualitative data into pieces/segments for the purpose of analysis.

  • Reassembling – restructuring/ordering of pieces/segments into categories or blocks for the purpose of analysis.

  • Qualitative Data Analysis – the segmentation and reassembling of qualitative data.

  • Transcription – the writing down of recorded data from the research sites, can be done manually or via technology (computer).

  • Transcriber – a person transcribing recorded data.

  • Recorder – a person recording data from the research sites.

    • Coding – the process of breaking up data into pieces/segments and rearranging pieces into blocks/categories/themes.

    • Code – a label that represents a category/theme

    • Apriori Codes – codes developed before data gathering and analysis.

    • Inductive Code – code generated during data collection and analysis.

    • Coder – a person doing coding.

    • Codebook – booklet gathering and containing codes.

Top

2 Learning Outcomes

By the end of the chapter, readers should be able to;

  • ❖ Appreciate the definition of qualitative data analysis from a practical perspective.

  • ❖ Appreciate the rationale behind analysis of qualitative data.

  • ❖ Understand steps of constant comparative in qualitative projects.

  • ❖ Understand steps of analytic induction.

  • ❖ Undertake the task of recording data in research sites.

  • ❖ Transcribe data recorded from the research site

  • ❖ Appreciate the rationale for qualitative data coding.

  • ❖ Understand strategies for qualitative data coding.

  • ❖ Understand the basics of open, axial and selective coding within & outside the grounded theory.

  • ❖ Perform open, axial and selective coding practically.

Top

3 Case Study

Mr. Kosmas was a team leader for a project related HIV & Aids in Africa. The project team collected data from the research sites. Part of the recorded data got lost when their computer was stolen, the team became confused because the data was not backed. The data available was transcribed by three persons who were hired by the team. After three months, the data related to the HIV patients (including the actual names of respondents) who were part of the study leaked. It was difficult for the team leaders to verify whether the leakage was caused by the research team or the hired transcribers. The project team leader was dragged to the courts of law by the respondents to answer charges related to privacy and confidentiality. By the end of this chapter, readers should be able to acquire skills on how to handle Mr. Kosmas’s dilemma.

Critical Thinking Challenge

The challenge of the loss of data and leakage of private information for respondents could have been avoided during the period of data analysis. How?

Complete Chapter List

Search this Book:
Reset