9.6.7 Analysing Data: Content Analysis and Constant Comparison

Describes Methods of Analysing Data: Content Analysis and Constant Comparison

Content analysis and constant comparison are two common methods of analyzing qualitative data in research studies.

Content Analysis:

Content analysis is a systematic approach to identifying, categorising, and quantifying patterns and themes within a set of qualitative data. It involves identifying units of meaning in the data and systematically coding and categorizing these units into themes or concepts. This method is particularly useful when researchers want to identify common themes across a large dataset or when the research question is focused on identifying patterns or trends in the data.

For example, a researcher may use content analysis to analyze the transcripts of interviews with patients about their experiences of living with a chronic illness. The researcher might code the data for themes related to the challenges of managing symptoms, the impact of the illness on daily life, and the support networks available to the patient.

Constant Comparison:

Constant comparison, on the other hand, involves analyzing data by comparing each piece of data with other data in the dataset, constantly refining categories and concepts as new data is analyzed. This method is particularly useful when the research question is focused on understanding the meanings and perspectives of the participants and the relationships between different concepts or themes in the data.

For example, a researcher might use constant comparison to analyze the data from a series of focus groups exploring the experiences of patients with a specific health condition. The researcher would continually compare the data from each focus group to identify similarities and differences in participants’ experiences and to refine the categories and concepts used to analyze the data.

Both content analysis and constant comparison have their advantages and disadvantages. Content analysis can be a straightforward and efficient way to analyze large datasets, but it may oversimplify complex data and miss important nuances in participants’ experiences. Constant comparison can provide a more in-depth and nuanced understanding of the data, but it can be time-consuming and may require a high level of expertise in qualitative analysis.

Comparison of content analysis and constant comparison:

Approach	Advantages	Disadvantages
Content Analysis	Systematic and objective approach Easy to apply Enables the identification of patterns in data Useful in analysing large data sets	Limited to surface-level data Can be overly descriptive and lacks depth May overlook important contextual factors Can be subjective
Constant Comparison	Emphasises the importance of context and complexity Provides rich and detailed data Facilitates the development of grounded theory Enables comparison and integration of data across different cases	Time-consuming Can be difficult to apply for those without experience in qualitative research Requires constant review and analysis of data Risk of researcher bias Can be subjective

Overall, the choice of data analysis method should depend on the research question, the nature of the data, and the resources and expertise available to the researcher.

References:

1. Adapted from Tracy, S. J. (2010). Qualitative quality: Eight “big-tent” criteria for excellent qualitative research. Qualitative Inquiry, 16(10), 837-851. doi:10.1177/1077800410383121
2. Hsieh, H.-F., & Shannon, S. E. (2005). Three Approaches to Qualitative Content Analysis. Qualitative Health Research, 15(9), 1277–1288. https://doi.org/10.1177/1049732305276687