Focus groups have an intuitive association with qualitative research. However, researchers can measure any data quantitatively or qualitatively, depending on the research question you want to answer. ATLAS.ti can help you analyze focus group data regardless of your chosen analysis strategy.
One of the most common goals of analyzing focus group interviews is to identify patterns in the perspectives of focus group members to guide decision-making and theoretical development. Typically, researchers use transcripts of recordings of focus groups as qualitative data, which they can code in ATLAS.ti for essential or interesting themes.
For example, suppose you have conducted a focus group to gather opinions about a new product. In that case, you can apply codes such as "positive brand image" and "doubts about effectiveness" to what focus group members say. Analysis of these codes is a matter of which codes appear more often than others to generate a sense of a focus group's overall impression of that product.
That said, there are quantitative methods for analyzing focus group data. Content analysis, for example, relies on determining the frequency of words or phrases in a text. ATLAS.ti has specific analytic tools that can contribute to a quantitative analysis:
- Text Search
- Word Cloud
- Word List
- Code Co-Occurrence
- Code-Document Table
Tools such as Word List and Code-Document Table can create tables and export them to Microsoft Excel for further statistical analysis.
Researchers can also conduct a quantitative analysis of codes through tools such as Code Co-Occurrence. Using this tool, researchers can consider the relationship between themes and sentiments established by the frequency they appear in focus group members' opinions.
For example, a researcher may code the focus group data for positive sentiments about a product, discussion of a product's effectiveness, and discussion of a product's design. That coding strategy combined with the Code Co-Occurrence tool can determine whether a product's effectiveness or design has more positive sentiments through a comparison of both frequencies.