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Latent Dirichlet Analysis of Categorical Survey Responses

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  • Evan Munro
  • Serena Ng

Abstract

Beliefs are important determinants of an individual’s choices and economic outcomes, so understanding how they comove and differ across individuals is of considerable interest. Researchers often rely on surveys that report individual beliefs as qualitative data. We propose using a Bayesian hierarchical latent class model to analyze the comovements and observed heterogeneity in categorical survey responses. We show that the statistical model corresponds to an economic structural model of information acquisition, which guides interpretation and estimation of the model parameters. An algorithm based on stochastic optimization is proposed to estimate a model for repeated surveys when responses follow a dynamic structure and conjugate priors are not appropriate. Guidance on selecting the number of belief types is also provided. Two examples are considered. The first shows that there is information in the Michigan survey responses beyond the consumer sentiment index that is officially published. The second shows that belief types constructed from survey responses can be used in a subsequent analysis to estimate heterogeneous returns to education.

Suggested Citation

  • Evan Munro & Serena Ng, 2022. "Latent Dirichlet Analysis of Categorical Survey Responses," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 256-271, January.
    • Handle: RePEc:taf:jnlbes:v:40:y:2022:i:1:p:256-271
    DOI: 10.1080/07350015.2020.1802285
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    Cited by:

    1. Laura Battaglia & Timothy Christensen & Stephen Hansen & Szymon Sacher, 2024. "Inference for Regression with Variables Generated from Unstructured Data," CESifo Working Paper Series 11119, CESifo.
    2. Battaglia, Laura & Christensen, Tim & Hansen, Stephen & Sacher, Szymon, 2024. "Inference for Regression with Variables Generated by AI or Machine Learning," CEPR Discussion Papers 19115, Centre for Economic Policy Research.
    3. Szymon Sacher & Laura Battaglia & Stephen Hansen, 2021. "Hamiltonian Monte Carlo for Regression with High-Dimensional Categorical Data," Papers 2107.08112, arXiv.org, revised Feb 2024.
    4. Ariel Neufeld & Matthew Ng Cheng En & Ying Zhang, 2024. "Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems," Papers 2403.09532, arXiv.org, revised May 2026.
    5. Igor Custodio João, 2024. "Testing for Clustering Under Switching," Tinbergen Institute Discussion Papers 24-052/III, Tinbergen Institute.
    6. Bernd Prostmaier & Jan V'avra & Bettina Grun & Paul Hofmarcher, 2025. "Seeded Poisson Factorization: leveraging domain knowledge to fit topic models," Papers 2503.02741, arXiv.org, revised Oct 2025.
    7. Custodio João, Igor & Lucas, André & Schaumburg, Julia & Schwaab, Bernd, 2023. "Dynamic clustering of multivariate panel data," Journal of Econometrics, Elsevier, vol. 237(2).
    8. Shikun Ke & José Luis Montiel Olea & James Nesbit, 2024. "Robust machine learning algorithms for text analysis," Quantitative Economics, Econometric Society, vol. 15(4), pages 939-970, November.

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