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Why the use of segmented regression analysis to explore change in relations between variables is problematic: A
Moritz Breit1, Julian Preuß1, Vsevolod Scherrer1
1Department of Psychology, University of Trier.
Psychological Methods
|March 27, 2023
Summary
Segmented regression analysis (SRA) frequently misidentifies breakpoints in nonlinear data, making it unreliable for exploratory research. Alternative methods are recommended for analyzing complex variable relationships.
Area of Science:
- Social Sciences
- Statistics
- Data Analysis
Background:
- Variable relationships can be linear, piecewise linear, or nonlinear.
- Segmented regression analysis (SRA) is a statistical method used to detect relationship breakpoints.
- SRA is often employed for exploratory analyses in social sciences.
Purpose of the Study:
- To investigate the performance of SRA, specifically the Davies test, when applied to nonlinear data.
- To evaluate the suitability of SRA for exploratory analyses in the presence of nonlinearity.
Main Methods:
- A simulation study was conducted.
- The Davies test within SRA was applied to datasets exhibiting various forms of nonlinearity.
Main Results:
- Moderate to strong nonlinearities frequently led to the identification of statistically significant breakpoints.
- Identified breakpoints were widely distributed across the data, indicating spurious findings.
- SRA demonstrated a high rate of false positives for breakpoints in nonlinear relationships.
Conclusions:
- Segmented regression analysis is not appropriate for exploratory data analysis when nonlinearity is present.
- The study recommends alternative statistical methods for exploratory analyses.
- Conditions for the appropriate and legitimate use of SRA in social sciences are outlined.
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