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

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A cautionary note on modeling growth trends in longitudinal data.

Goran Kuljanin¹, Michael T Braun, Richard P Deshon

¹Department of Psychology, Michigan State University, East Lansing, MI 48824, USA. kuljanin@msu.edu

Psychological Methods

|April 27, 2011

Summary

Dominant longitudinal data models in psychology may yield spurious results if the underlying process has a stochastic trend. Researchers should use a proposed strategy to avoid inaccurate inferences from stochastic processes.

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Area of Science:

Psychology
Statistics
Quantitative Psychology

Background:

Random coefficient and latent growth curve modeling are standard for analyzing psychological longitudinal data.
These models assume deterministic trends, potentially misinterpreting stochastic processes.

Purpose of the Study:

To highlight the issue of spurious findings when analyzing longitudinal data with stochastic trends.
To propose a data analytic strategy for accurate inference in psychological research.

Main Methods:

Demonstration using a data example.
Review of previous research on simple regression models.
Monte Carlo simulations to illustrate the problem.

Main Results:

Random coefficient regression results can be spurious when stochastic trends are present.
The assumption of deterministic trends can lead to inaccurate conclusions.

Conclusions:

Standard longitudinal models may be inappropriate for processes with stochastic trends.
A new data analytic strategy is needed to ensure accurate inferences in psychology.