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Using Model-Based Clustering to Improve Qualitative Inquiry: Computer-Aided Qualitative Data Analysis, Latent Class Analysis, and Interpretive Transparency

Abstract

A combination of computer-aided qualitative data analysis (CAQDAS) and latent class analysis (LCA) can substantially augment the qualitative analysis of textual data sources used in third-sector studies. This article explains how to employ both techniques iteratively to capture often implicit ideas and meaning-making by third-sector leaders, donors, and other stakeholders. CAQDAS facilitates the coding, organization, and quantification of qualitative data, effectively creating parallel qualitative and quantitative data structures. LCA facilities the discovery of latent concepts, document classification, and the identification of exemplary qualitative evidence to aid interpretation. For third-sector research, CAQDAS and LCA are particularly promising because diverse stakeholders usually do not share homogenous views about core issues such as organizational effectiveness, collaboration, impact measurement, or philanthropic approaches, for example. The procedure explained here provides a rigorous method for discovering and understanding diversity in perspectives and is especially useful in medium-n research settings common to third-sector scholarship.

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Notes

  1. 1.

    LCA identifies and summarizes patterns by positing a latent variable that explains the statistical associations among the underlying codes. In other words, conditional on the latent variable, the residual statistical association among the codes is essentially minimized. This condition is known as local independence. LCA thus generates a categorical latent variable that explains the information (coding patterns) found in the documents.

  2. 2.

    Intractability is an exponential function of the number of codes involved (two to the power of the number of codes). For example, while ten codes yields 1024 possible patterns, 20 codes yields 1,048,576 possible patterns, 30 codes yields 1,073,741,824 possible patterns, and so on.

References

  1. Ahlquist, J. S., & Breunig, C. (2012). Model-based clustering and typologies in the social sciences. Political Analysis, 20(1), 92–112.

    Article  Google Scholar 

  2. Baumer, E. P. S., Mimno, D., Guha, S., Quan, E., & Gay, G. K. (2017). Comparing grounded theory and topic modeling: Extreme divergence or unlikely convergence? Journal of the Association for Information Science and Technology, 68(6), 1397–1410.

    Article  Google Scholar 

  3. Bazeley, P., & Jackson, K. (2013). Qualitative data analysis with NVivo (2nd ed.). Sage.

    Google Scholar 

  4. Berlan, D. (2018). Understanding nonprofit missions as dynamic and interpretative conceptions. Nonprofit Management and Leadership, 28(3), 413–422.

    Article  Google Scholar 

  5. Blaikie, N., & Priest, J. (2017). Social research: Paradigms in action. Wiley.

    Google Scholar 

  6. Bolck, A., Croon, M., & Hagenaars, J. (2004). Estimating latent structure models with categorical variables: One-step versus three-step estimators. Political Analysis, 12(1), 3–27.

    Article  Google Scholar 

  7. Chaney, P. (2015). Exploring the Pathologies of One-Party-Dominance on Third Sector Public Policy Engagement in Liberal Democracies: Evidence from Meso-Government in the UK. Voluntas: International Journal of Voluntary and Nonprofit Organizations, 26(4), 1460–1484.

    Article  Google Scholar 

  8. Dillman, L. M., & Christie, C. A. (2016). Evaluation policy in a nonprofit foundation: A case study exploration of the Robert Wood Johnson Foundation. American Journal of Evaluation, 38(1), 60–79.

    Article  Google Scholar 

  9. Elliott, V. F. (2018). Thinking about the coding process in qualitative data analysis. The Qualitative Report, 23(11), 2850–2861. https://doi.org/10.46743/2160-3715/2018.3560

    Article  Google Scholar 

  10. Gugerty, M. K., Mitchell, G. E., & Santamarina, F. (2021). Discourses of evaluation: Institutional logics and organizational practices among international development agencies. World Development. https://doi.org/10.1016/j.worlddev.2021.105596

    Article  Google Scholar 

  11. Hodges, J., & Howieson, B. (2017). The challenges of leadership in the third sector. European Management Journal, 35(1), 69–77.

    Article  Google Scholar 

  12. Isoaho, K., Gritsenko, D., & Mäkelä, E. (2021). Topic modeling and text analysis for qualitative policy research. Policy Studies Journal, 49(1), 300–324.

    Article  Google Scholar 

  13. Kingston, L. N., & Stam, K. R. (2013). Online advocacy: Analysis of human rights NGO websites. Journal of Human Rights Practice, 5(1), 75–95.

    Article  Google Scholar 

  14. Li, Y., Chandra, Y., Nie, L., & Fan, Y. (2020). From women for women: The role of social media in online nonprofit activities during Wuhan lockdown. Public Administration and Development, 40(5), 267–272.

    Article  Google Scholar 

  15. Magidson, J., & Vermunt, J. K. (2002). Latent class models for clustering: A comparison with K-means. Canadian Journal of Marketing Research, 20, 37–44.

    Google Scholar 

  16. Mannheimer, S., Pienta, A., Kirilova, D., Elman, C., & Wutich, A. (2018). Qualitative data sharing: Data repositories and academic libraries as key partners in addressing challenges. American Behavioral Scientist, 63(5), 643–664.

    Article  Google Scholar 

  17. Marberg, A., Korzilius, H., & van Kranenburg, H. (2017). What is in a theme? Professionalization in nonprofit and nongovernmental organizations research. Nonprofit Management & Leadership, 30(1), 113–131.

    Article  Google Scholar 

  18. McMullin, C. (2019). Coproduction and the third sector in France: Governmental traditions and the French conceptualization of participation. Social Policy & Administration, 53(2), 295–310.

    Article  Google Scholar 

  19. Mitchell, G. E. (2013a). Collaborative propensities among transnational NGOs registered in the United States. The American Review of Public Administration, 44(5), 575–599.

    Article  Google Scholar 

  20. Mitchell, G. E. (2013b). The construct of organizational effectiveness: Perspectives from leaders of international nonprofits in the United States. Nonprofit and Voluntary Sector Quarterly, 42(2), 324–345.

    Article  Google Scholar 

  21. Mitchell, G. E. (2014a). Latent class analysis: Discovering and interpreting response patterns in coded interview data. SAGE Research Methods Cases.

  22. Mitchell, G. E. (2014b). The strategic orientations of US-based NGOs. Voluntas: International Journal of Voluntary and Nonprofit Organizations, 26(5), 1874–1893.

    Article  Google Scholar 

  23. Mitchell, G. E. (2014c). Strategic responses to resource dependence among transnational NGOs registered in the United States. Voluntas: International Journal of Voluntary and Nonprofit Organizations, 25(1), 67–91.

    Article  Google Scholar 

  24. Santos, M. R. C., Laureano, R. M. S., & Moro, S. (2020). Unveiling research trends for organizational reputation in the nonprofit sector. Voluntas: International Journal of Voluntary and Nonprofit Organizations, 31, 56–70.

    Article  Google Scholar 

  25. Schmitz, H. P., Mitchell, G. E., & McCollim, E. (2021). How billionaires explain their philanthropy: A mixed-method analysis of the giving pledge letters. Voluntas: International Journal of Voluntary and Nonprofit Organizations, 32, 512–523.

    Article  Google Scholar 

  26. Schmitz, H. P., Raggo, P., & Bruno-van Vijfeijken, T. (2012). Accountability of transnational NGOs: Aspirations vs. practice. Nonprofit and Voluntary Sector Quarterly, 41(6), 1175–1194.

    Article  Google Scholar 

  27. Sinkovics, R. R., & Alfoldi, E. A. (2012). Progressive focusing and trustworthiness in qualitative research. Management International Review, 52(6), 817–845.

    Article  Google Scholar 

  28. Souder, L. (2016). A review of research on nonprofit communications from mission statements to annual reports. Voluntas: International Journal of Voluntary and Nonprofit Organizations, 27, 2709–2733.

    Article  Google Scholar 

  29. Valdez, D., Pickett, A. C., & Goodson, P. (2018). Topic modeling: Latent semantic analysis for the social sciences. Social Science Quarterly, 99(5), 1665–1679.

    Article  Google Scholar 

  30. Vermunt, J. K. (2010). Latent class modeling with covariates: Two improved three-step approaches. Political Analysis, 18(4), 450–469.

    Article  Google Scholar 

  31. Walker, R. M., Chandra, Y., Zhang, J., & van Witteloostuijn, A. (2019). Topic modeling the research-practice gap in public administration. Public Administration Review, 79(6), 931–937.

    Article  Google Scholar 

  32. Woolf, N. H., & Silver, C. (2018). Qualitative analysis using NVivo: The five-level QDA® method. Routledge.

    Google Scholar 

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Acknowledgements

For valuable feedback, we would like to thank the editors of this special issue as well as the anonymous reviewers.

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Correspondence to Hans Peter Schmitz.

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Mitchell, G.E., Schmitz, H.P. Using Model-Based Clustering to Improve Qualitative Inquiry: Computer-Aided Qualitative Data Analysis, Latent Class Analysis, and Interpretive Transparency. Voluntas (2021). https://doi.org/10.1007/s11266-021-00409-8

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Keywords

  • Computer-aided qualitative data analysis software
  • Latent class analysis
  • Mixed-methods
  • Nonprofits
  • NGOs