{'fraction_non_alphanumeric': 0.030563514804202482, 'fraction_numerical': 0.0038204393505253103, 'mean_word_length': 4.790055248618785, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Strategies for the Effective Use of NVivo in a Large-Scale Study: Qualitative Analysis and the Repeal of Don’t Ask, Don’t Tell

The use of qualitative data analysis software (QDAS) has expanded over the last two decades, with new technology allowing researchers to analyze more data, faster, and in more complex ways. However, a review of research articles that mentioned the use of QDAS found that authors often omitted critical details about the value-added of the software. This article seeks to fill that gap. We describe our use of NVivo to analyze qualitative data collected for the comprehensive review preceding the repeal of Don’t Ask, Don’t Tell, the 1994 law banning openly gay individuals from serving in the U.S. military. We describe why we chose this software product, detail the staffing strategies we used for the team-based analysis, and provide examples of the kinds of queries we ran to answer the study questions. We conclude with a brief discussion of “lessons learned” from this quick-tempo, large-scale qualitative project.