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Sample size planning for longitudinal models: accuracy in parameter estimation for polynomial change parameters.

Ken Kelley1, Joseph R Rausch

  • 1Department of Management, Mendoza College of Business, University of Notre Dame, IN 46556, USA. kkelley@nd.edu

Psychological Methods
|July 13, 2011
PubMed
Summary
This summary is machine-generated.

Researchers can now determine sample sizes for longitudinal studies to ensure precise estimates of group differences in change. This method focuses on confidence interval width for accurate longitudinal data analysis.

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Area of Science:

  • Psychology
  • Biostatistics
  • Longitudinal Research Methods

Background:

  • Longitudinal studies track individual changes over time, with group differences being key explanatory variables.
  • Traditional sample size planning for these studies prioritizes statistical power over confidence interval (CI) precision.
  • Accurate estimation of group differences in change is crucial for robust longitudinal findings.

Purpose of the Study:

  • To develop methods for sample size planning in longitudinal studies that target desired confidence interval width for group differences in change.
  • To provide researchers with tools to ensure sufficiently narrow CIs for detecting meaningful effects.
  • To enhance the accuracy of effect size estimation in longitudinal research.

Main Methods:

  • Derived mathematical expressions for calculating sample size based on desired CI width for orthogonal polynomial change parameters.
  • Developed an extension to guarantee a specified level of assurance (e.g., 99%) for CI narrowness.
  • Created freely available computer routines to implement the proposed sample size planning methods.

Main Results:

  • The derived expressions enable researchers to plan sample sizes that yield sufficiently narrow confidence intervals for group differences in change.
  • The methods provide a quantifiable approach to ensuring the precision of effect size estimates in longitudinal studies.
  • The extension offers a probabilistic guarantee for achieving the desired CI width.

Conclusions:

  • The developed methods offer a practical solution for sample size determination in longitudinal studies, focusing on CI precision.
  • Researchers can now plan studies to achieve accurate estimates of group differences in change, improving the reliability of findings.
  • Freely available routines facilitate immediate adoption and application of these advanced sample size planning techniques.