ArticleBiometrical journal. Biometrische Zeitschrift2023
The marginality principle revisited: Should "higher-order" terms always be accompanied by "lower-order" terms in regression analyses?
Article in Biometrical journal. Biometrische Zeitschrift, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Analytical approaches to account for muscle size when evaluating strength.European journal of applied physiology · 2026Review
- Heterogeneity in the Analysis of the ALSFRS-R in ALS Clinical Trials and its Effect on the Validity and Precision of Trial Conclusions.Neurology · 2026Article
- Mechanism-constrained D-optimal design of a flexible claw for peanut shelling under biological variability.Frontiers in plant science · 2026Article
- The marginality principle revisited: Should "higher-order" terms always be accompanied by "lower-order" terms in regression analyses?Biometrical journal. Biometrische Zeitschrift · 2023Article
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3 authors.
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Abstract
The marginality principle guides analysts to avoid omitting lower-order terms from models in which higher-order terms are included as covariates. Lower-order terms are viewed as "marginal" to higher-order terms. We consider how this principle applies to three cases: regression models that may include the ratio of two measured variables; polynomial transformations of a measured variable; and factorial arrangements of defined interventions. For each case, we show that which terms or transformations are considered to be lower-order, and therefore marginal, depends on the scale of measurement, which is frequently arbitrary. Understanding the implications of this point leads to an intuitive understanding of the curse of dimensionality. We conclude that the marginality principle may be useful to analysts in some specific cases but caution against invoking it as a context-free recipe.
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