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Postmodeling Sensitivity Analysis to Detect the Effect of Missing Data Mechanisms.

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This study introduces a new method for sensitivity analysis in structural equation models (SEM) to detect issues with missing data. The approach helps researchers identify when missing data assumptions may lead to unreliable research findings.

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Area of Science:

  • Statistics
  • Empirical Research Methodology

Background:

  • Incomplete data is prevalent in empirical research, and common methods like complete case analysis or mean imputation can yield biased results.
  • Maximum likelihood methods can also lead to incorrect inference if data are not missing completely at random (MCAR) or missing at random (MAR).
  • Existing statistical tests for MCAR are limited in scope, and sensitivity analysis methods often model the missing data mechanism, which can be complex.

Purpose of the Study:

  • To develop a novel postmodeling sensitivity analysis method for structural equation models (SEM) to assess the impact of missing data mechanisms.
  • To provide researchers with a tool to detect when MCAR or MAR assumptions may not hold, thus flagging potential issues in their analyses.
  • To introduce a specific statistical test and graphical approach for performing this sensitivity analysis within SEM.

Main Methods:

  • The proposed methodology examines the sensitivity of a given statistical model to the missing data mechanism without explicitly modeling the mechanism itself.
  • A statistical test and graphical representations are employed for postmodeling sensitivity analysis.
  • A simulation study was conducted to evaluate the methodology's performance in the context of structural equation models.

Main Results:

  • The sensitivity analysis method demonstrated success, particularly with sample sizes of 300 or more and missing data percentages of 20% or higher.
  • The method was successfully applied to real-world data concerning physical and social self-concepts in Nigerian adolescents using factor analysis.
  • The simulation study confirmed the utility of the proposed method in identifying potential biases arising from missing data.

Conclusions:

  • The developed method offers a practical approach for researchers using structural equation models to assess the robustness of their findings to missing data.
  • This sensitivity analysis technique serves as a crucial diagnostic tool, alerting researchers to potential problems when standard missing data assumptions are violated.
  • The study highlights the importance of considering missing data mechanisms and provides a valuable method for enhancing the reliability of empirical research findings.