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Planned missing (PM) designs in developmental research offer efficiency similar to complete data designs. This study introduces an effective error metric for comparing PM designs, providing a computationally efficient understanding of design efficiency factors.

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Area of Science:

  • Developmental Research
  • Quantitative Psychology
  • Statistical Modeling

Background:

  • Longitudinal data collection is resource-intensive.
  • Planned missing (PM) designs offer efficiency gains but require further investigation.
  • Previous work identified optimal PM designs via simulation for growth curve models.

Purpose of the Study:

  • To propose an approximate solution for optimizing planned missing designs.
  • To extend the effective error metric to planned and unplanned missing data.
  • To provide a computationally efficient method for understanding PM design efficiency.

Main Methods:

  • Comparing asymptotic effective errors of planned missing designs.
  • Extending the effective error metric from complete data to planned missing data.
  • Analyzing the interaction of design factors (measurement occasions, timing, attrition, PM patterns) on efficiency.

Main Results:

  • Effective error serves as a metric for comparing study designs with planned and unplanned missing data.
  • Simulation-based results for PM designs are explained by an asymptotic solution.
  • The proposed approach is computationally more efficient than prior simulation methods.

Conclusions:

  • The effective error metric provides a better understanding of planned missing design efficiency.
  • Design factors interact to determine overall study efficiency in longitudinal research.
  • This approach facilitates the optimization of longitudinal study designs with missing data.