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    Area of Science:

    • Statistics
    • Psychometrics
    • Data Analysis

    Background:

    • Missing data is a common challenge in statistical analyses, potentially biasing results.
    • Latent class analysis (LCA) is a statistical method used to identify subgroups within a population.

    Purpose of the Study:

    • To evaluate the performance of the Expectation-Maximization (EM) algorithm for correcting missing data in latent class analysis.
    • To assess the bias in parameter estimates under different missing data mechanisms, including non-random missingness.

    Main Methods:

    • Monte Carlo simulation methods were employed to assess the EM algorithm.
    • Bias in parameter estimates was analyzed across various missingness scenarios.

    Main Results:

    • The EM algorithm's utility is constrained by practical limits related to sample size.
    • Higher nonresponse rates significantly impact the accuracy of parameter estimates.
    • The algorithm's performance degrades under non-random missingness assumptions.

    Conclusions:

    • The EM algorithm offers a method for addressing missing data in LCA but has practical limitations.
    • Researchers should carefully consider sample size and expected nonresponse rates when employing the EM algorithm for LCA.
    • Further research may be needed to develop more robust methods for handling missing data in LCA, especially under non-random missingness.