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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
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.
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.
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