Advanced Qualitative Data Analysis (using NVivo)

Social Research MA module

Module outline

This module is intended to further develop students’ and researchers’ skills in qualitative data analysis. We will focus on the use of computer-assisted methods in handling and managing textual data. We will cover the principles of qualitative data analysis, the challenges of managing qualitative data, moving beyond description and linking and integrating multiple data sets in different forms. Participants will explore the epistemological bases of analytical approaches in qualitative research, reflect on how to achieve rigour in the analytical process and understand how good analysis can underpin credible findings.

Learning  Outcomes

By the end of this course, the participants will be able to:

  • have a clear understanding and appreciation of the principles which underpin high quality qualitative data analysis in a range of contexts;
  • make informed decisions about the management of qualitative data in NVivo;
  • plan and implement qualitative data analysis in NVivo which facilitates explanation and theory building;
  • demonstrate trustworthiness of findings in NVivo
  • report findings using NVivo products

Course Assessment

Course participants will select a topic of their interest and analyse a chosen set of qualitative data (either from their existing projects or collected for the purposes of this assignment), using relevant NVivo tools of coding, memoing, annotating, see also links, modelling, etc. They will document their analysis, reflections and all coding decisions in an audit trail in an NVivo research journal and produce a formal report of findings in a research report of 2500-3000 words. 

The final assignment will consist of:

1) A 2500-3000 word research report (following a standard structure, including a brief theoretical background, research methodology with a clear statement of purpose(s)/research question(s), discussion of results, conclusion)

NVivo project (with all relevant elements, including data sources, journal, coding trees, memos, etc.) as a WebCT attachment.