The following module discusses the process of analyzing qualitative data.
Describe the key principles and features of qualitative analysis
List and explain the 5 steps to analyzing qualitative data
Explain the common types of qualitative analysis
Qualitative data consist of words, observations, pictures, and symbols. Qualitative Data Analysis (QDA) refers to the processes and procedures that are used to analyze the data and provide some level or explanation, understanding, or interpretation. Qualitative data analysis typically occurs simultaneously with the data collection. Therefore, meaning and understanding often develop slowly over time in a non-linear fashion as the project progresses. Following are five key steps that are commonly followed in qualitative data analysis:
Become familiar with the data. Researchers need to read and re-read the data, writing down impressions, looking for meaning and determining which pieces of data have value.
Focus the analysis. In this step, researchers identify key questions that they want to answer through the analysis. One approach would be to focus the analysis on the answers to a particular question or topic, by time period, or by event. Another way to focus the analysis would be to examine the data as it relates to a case, an individual, or a particular group.
Categorize the data and create a framework. This is often referred to as coding or indexing the data. The researcher starts by identifying themes or patterns that may consist of ideas, concepts, behaviors, interactions, phrases and so forth. A "code" is then assigned to those pieces of data to label the data and make it easier to organize and retrieve. A coding plan helps to provide a framework that will structure, label and define the data. The framework may be explanatory and is guided by the research question. The framework may also be exploratory in which the analysis is guided by the data that was collected. Examples of coding schemes are included in several of the Resources Links to the right on this page.
Identify patterns and make connections. The researcher must know now identify the themes, look for relative importance of responses received, identify relationships between themes or data sets, and attempt to find explanations from the data. QDA software may be helpful in organizing the data, assigning the coding and looking for connections. See the Qualitative Software module included in Qualitative Research Ready for more information.
Interpret the data and explain findings. After themes, patterns, connections and relationships are identified, the researcher must attach meaning and significance to the data. It can be helpful in this process to develop lists of key ideas, create diagrams, or use models to explain the findings. It is important to remember that qualitative data does not lend itself to generalizations across a population.
There are a variety of approaches to this process of analysis and interpretation. Some of the most used approaches include:
Content Analysis - used to analyze and interpret verbal data, or behavioral data. Content can be analyzed for descriptively or interpretatively.
Narrative Analysis - used to analyze text that may come from variety of sources including transcripts from interviews, diaries, field notes, surveys, and other written forms. Narrative analysis often involves reformulating stories presented by people in different context and based on their different experiences.
Discourse Analysis - a method of analyzing naturally occurring spoken interactions and written text and is concerned with the social context in which the communication occurred. It focuses on how language is used in everyday life and looks at how people express themselves.
Grounded Theory - also called analytic induction. This is a method that attempts to develop causal explanations of a phenomenon from one or more cases being studied. Explanations are altered as additional cases are studied until the researcher arrives at a statement that fits all cases.
Conversation Analysis - examines the use of language by people as a type of action or skilled accomplishment. A key concept in this analysis is the principle of people taking turns in conversation. Meanings are usually shaped in the context of the exchange itself.
The above approaches to qualitative analysis are just a few of the most common types. For more details and information regarding additional approaches, explore the Resource Links on this page and follow the link below:
Berg, B. L., & Lune, H. (2004). Qualitative research methods for the social sciences (Vol. 5). Boston: Pearson. Bryman, A., & Burgess, B. (Eds.). (2002). Analyzing qualitative data. Routledge. Coffey, A., Holbrook, B., & Atkinson, P. (1996). Qualitative data analysis: Technologies and representations. Dey, I. (2003). Qualitative data analysis: A user friendly guide for social scientists. Routledge. Leech, N. L., & Onwuegbuzie, A. J. (2007). An array of qualitative data analysis tools: A call for data analysis triangulation.School Psychology Quarterly, 22(4), 557. Miles, M. B., & Huberman, A. M. (1994).Qualitative data analysis: An expanded sourcebook. Sage. Thomas, D. R. (2006). A general inductive approach for analyzing qualitative evaluation data. American journal of evaluation, 27(2), 237-246.