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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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Statistical power analysis for growth curve models using SAS.

Zhiyong Zhang¹, Lijuan Wang

¹Department of Psychology, University of Notre Dame, 118Haggar Hall, Notre Dame, IN 46556, USA. zhiyongzhang@nd.edu

Behavior Research Methods

|November 10, 2009

Summary

Estimating research power is vital. This study presents simulation methods and SAS macros for growth curve analysis power estimation, showing sample size, effect size, and measurement occasions increase power, while missing data reduces it.

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Area of Science:

Biostatistics
Quantitative Psychology
Longitudinal Data Analysis

Background:

Power analysis is essential for robust research design.
Growth curve analysis models developmental trajectories.
Estimating statistical power aids in determining adequate sample sizes and study feasibility.

Purpose of the Study:

To introduce a simulation-based approach for estimating the statistical power of growth curve analysis.
To develop and demonstrate a set of SAS macros for implementing this power estimation technique.
To evaluate the influence of various factors on power in growth curve models.

Main Methods:

A simulation-based approach using the likelihood ratio test was employed.
SAS macros were developed to automate the power estimation process.
The methodology was applied to scenarios with complete data, missing data, and nonlinear growth trajectories.

Main Results:

Statistical power for growth curve analysis increases with larger sample sizes, greater effect sizes, and more measurement occasions.
The presence of missing data was found to decrease statistical power.
The developed SAS macros effectively estimated power across different growth curve model complexities.

Conclusions:

The simulation-based approach provides a flexible tool for power analysis in growth curve modeling.
The SAS macros offer a practical solution for researchers conducting power estimations.
Understanding factors influencing power is crucial for optimizing research design in longitudinal studies.