Missing Values, Nonnormality, Messy Data

Many modeling techniques that appear to be stable with realistic sample sizes in psychology often times make unrealistic assumptions such as multivariate normality or an otherwise correctly specified model. Missing responses are also a common problem in real datasets. Conversely, sometimes researchers employ planned missing data designs to reduce respondent burden, and preemptively reduce the number of careless or random responses. Our research in this area tests and develops methods for analyzing data under such suboptimal conditions. Much of this research is in the realm of multiple regression or structural equation modeling and/or overlaps with that on mediation analysis. Some research deals with small samples and model misspecification in general.

Representative Publications

  • Chen, L., Miočević, M., & Falk, C. F. (2026). Evaluating approaches for handling sign reflection in Bayesian latent variable models. Structural Equation Modeling: A Multidisciplinary Journal, 33, 223-234. doi: 10.1080/10705511.2025.2588572
    [Author copy]   [OSF]

  • Falk, C. F., & Starr, J. (2025). Regularized cross-sectional network modeling with missing data: A comparison of methods. Multivariate Behavioral Research, 60, 1274-1292. doi: 10.1080/00273171.2025.2551373
    [Preprint] [OSF]

  • Shen, Z., Chen, L., Somer, E., Miočević, M., & Falk, C. F. (2025). Latent variable interactions with categorical indicators: Continuous and categorical latent moderated structural equation approaches. Structural Equation Modeling: A Multidisciplinary Journal, 33, 460-474. doi: 10.1080/10705511.2024.2443943
    [Preprint]

  • Chen, L., Miočević, M., & Falk, C. F. (2024). Tackling challenges in data pooling: missing data handling in latent variable models with continuous and categorical indicators. Structural Equation Modeling: A Multidisciplinary Journal, 31, 651-666. doi: 10.1080/10705511.2023.2300079
    [Preprint]   [Shiny App]

  • Somer, E., Falk, C. F., & Miočević, M. (2024). Comparing factor score approaches to SEM in multigroup models with small samples. Structural Equation Modeling: A Multidisciplinary Journal, 31, 310-328. doi: doi.org/10.1080/10705511.2023.2243387
    [OSF]

  • Falk, C.F. (2018). Are robust standard errors the best approach for interval estimation with non-normal data in structural equation modeling? Structural Equation Modeling: A Multidisciplinary Journal, 25, 244-266. doi: 10.1080/10705511.2017.1367254
    [Supplementary Materials]

    • Won Research Award from the Quantitative Methods Section of the Canadian Psychological Association (2019)