Data Processing And Analysis
Case Studies are Rich in Qualitative Data
Because a case study involves collection of data from multiple sources, different data analysis methods may be needed for diverse components of the study. However, because an important goal of a case study is to generate a realistic and in-depth description of a case, qualitative data typically figure prominently in the data collection methods used.
A process for organizing and analyzing the large amounts of qualitative data (i.e., words) generated for a case study is needed. Many approaches to qualitative data analysis exist, and often are grounded in different theoretical perspectives to social science.
Content Analysis and Coding
Content analysis and some method for “coding” or categorizing data to identify key ideas and themes is needed to make sense out large volumes of qualitative data generated for a case study. What is described here may be applied to most qualitative data generated from any method (surveys, interviews, observations, etc.).
Regardless of the theoretical perspective, qualitative data analysis includes:
- Content analysis: A process for reducing a large amount of qualitative (textural) data down to a smaller dataset that is relevant to the research questions. For more on Content Analysis .
- Thematic Coding: A process used in content analysis to identify occurrences of key ideas and themes among the data and to enable sorting and retrieval to support qualitative analysis. For more on Thematic Coding .
In the next section, one possible approach to generating understanding out of qualitative data, called Grounded Theory, is outlined.