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    Area of Science:

    • Statistics
    • Biostatistics
    • Quantitative Psychology

    Background:

    • Structural equation models (SEMs) are widely used for analyzing complex relationships between variables.
    • Path analysis, a subset of SEMs, is commonly employed but has limitations with nondirected paths.
    • Existing methods for handling nondirected paths in SEMs without latent variables can be problematic.

    Purpose of the Study:

    • To examine and compare alternative assumptions for nondirected paths in structural equation models without latent variables.
    • To identify and address limitations in conventional path analysis procedures.
    • To propose novel methodologies for analyzing nondirected paths in statistical models.

    Main Methods:

    • Utilized a dataset focused on lung ventilation.
    • Applied structural equation modeling techniques.
    • Compared various assumptions governing nondirected paths within the models.

    Main Results:

    • Identified specific problems associated with conventional path analysis techniques.
    • Demonstrated the utility of alternative assumptions for modeling nondirected paths.
    • Provided a comparative analysis of different methodological approaches.

    Conclusions:

    • The study suggests that alternative assumptions offer viable solutions to limitations in current path analysis.
    • The findings provide guidance for researchers using structural equation models, particularly those without latent variables.
    • Recommendations are made for refining statistical methodologies in path analysis.