Methods of Qualitative Data Analysis
The simplest analysis of qualitative data is observer impression: Expert or bystander observers examine the data, interpret it via forming an impression and report their impression in a structured and sometimes (quasi-)quantitative form. This attempt to give structure to mere observation is referred to as “coding” and forms an important step beyond the mere observation.
Coding is an interpretive technique that seeks to both organize the data and provide a means to introduce the interpretations of it into certain quantitative methods.
Most coding requires the analyst to read the data and demarcate segments within it. Each segment is labeled with a “code” – usually a word or short phrase that suggests how the associated data segments inform the research objectives. When coding is complete, the analyst prepares reports via a mix of: summarizing the prevalence of codes, discussing similarities and differences in related codes across distinct original sources/contexts, or comparing the relationship between one or more codes.
Some qualitative data that is highly structured (e.g., open-end responses from surveys or tightly defined interview questions) is typically coded without additional segmenting of the content. In these cases, codes are often applied as a layer on top of the data. Quantitative analysis of these codes is typically the capstone analytical step for this type of qualitative data.
Contemporary qualitative data analyses are sometimes supported by computer programs. These programs do not supplant the interpretive nature of coding but rather are aimed at enhancing the analyst’s efficiency at data storage/retrieval and at applying the codes to the data. Many programs offer efficiencies in editing and revising coding, which allow for work sharing, peer review, and recursive examination of data.
A frequent criticism of coding method is that it seeks to transform qualitative data into “quasi-quantitative” data, thereby draining the data of its variety, richness, and individual character. Analysts respond to this criticism by thoroughly expositing their definitions of codes and linking those codes soundly to the underlying data, therein bringing back some of the richness that might be absent from a mere list of codes.
Methods of Data Analysis in Qualitative Research
Below is a brief overview of the most common methods of data analysis as used in qualitative research. ATLAS.ti is not limited towards only one specific method. Rather, with its powerful and flexible tools, it supports all the approaches to data listed below in highly efficient ways.
Creation of a system of classification, list of (mutually exclusive) categories.
Essentially a typology with multiple levels of concepts.
Grounded Theory (Constant Comparison)
Coding of documents, categories saturate when no new codes (quotes?!) are added to them; core/axial categories emerge.
Form hypothesis about event, then compare to similar event to verify/falsify/modify hypothesis. Eventually central/general hypothesis will emerge.
Predominantly Use flow charts, diagrams.
Count numbers of events/mentionings, mainly used to support categories.
Event (Frame) Analysis
Identify specific boundaries (start,end) of events, then event phases.
Develop specific metaphors for event, also by asking participants for spontaneous metaphors/comparisons
Focus on cultural context, dscribe social situation and cultural patterns within it, semantic relationships
Meaning of event/text in context (historical, social, cultural etc.)
Ongoing flow of communication between several individuals; identify patterns (incl. temporal, interaction)
Meaning exists in context alone; identify specific meaning in connection with concrete context
Identify themes/topics, find latent themes/emphases. Generally rule-driven (e.g. size of data chunks).
Idiosyncratic meaning to individual, potentially focused mainly on the reseracher’s own experience/reception of the event
Study of the intrinsic structures of how a story is told/text is written.