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This summary is machine-generated.

Multilevel structural equation modeling (MSEM) effectively detects intervention effects in cluster-randomized trials, outperforming traditional multilevel modeling (MLM) by correcting for measurement error in binary outcomes.

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Area of Science:

  • Statistics
  • Psychometrics
  • Educational Research

Background:

  • Multilevel modeling (MLM) is common for analyzing group differences in intervention studies, often using total scores for pre-test and post-test measures.
  • Concerns exist regarding measurement error when using total scores in MLM, potentially biasing results.
  • Multilevel structural equation modeling (MSEM) offers a theoretical framework to address measurement error in covariates and outcomes.

Purpose of the Study:

  • To demonstrate the application of MSEM for detecting intervention effects in studies with binary outcomes.
  • To evaluate the performance of MSEM through a simulation study.
  • To compare the effectiveness of MSEM against MLM in identifying group differences.

Main Methods:

  • Utilized a simulation study to assess MSEM performance under varying conditions (number of clusters, cluster size, intraclass correlation).
  • Applied MSEM to analyze intervention effects in cluster-randomized designs with binary response variables.
  • Compared MSEM results with traditional MLM analyses.

Main Results:

  • MSEM demonstrated adequate performance, improving with increased clusters, cluster size, and intraclass correlation.
  • MSEM outperformed MLM in detecting group differences, particularly in the presence of measurement error.
  • Simulation results highlighted the consequences of using MLM instead of MSEM.

Conclusions:

  • MSEM is a robust method for analyzing intervention effects with binary outcomes in clustered data.
  • MSEM provides a more accurate detection of group differences compared to MLM when measurement error is present.
  • The findings support the broader adoption of MSEM in intervention research to mitigate biases from measurement error.