Which of the following are common pitfalls in qualitative data analysis that researchers should be aware of? (Select all that apply)

Common pitfalls in qualitative data analysis include letting researcher bias shape coding, relying on a single coder without triangulation, and overgeneralizing findings from a small sample. Researchers often fall into confirmation bias, selectively highlighting data that confirms their expectations while ignoring contradictory evidence. Poor data management—such as disorganized transcripts or inconsistent coding—can lead to misinterpretation and loss of nuance. Reflexivity, or reflecting on one’s own influence on the data, is frequently neglected, which weakens the credibility of the analysis. For example, a researcher studying community resilience might focus only on success stories, overlooking the stories of struggle, thereby producing an incomplete picture.