A comparison of subset selection and analysis of covariance for the adjustment of confounders

Roderick J. Little, Hyonggin An, Jewel Johanns, Bruno Giordani

Research output: Contribution to journalReview articlepeer-review

21 Citations (Scopus)


Two common methods for adjusting group comparisons for differences in the distribution of confounders, namely analysis of covariance (ANCOVA) and subset selection, are compared using real examples from neuropsychology, theory, and simulations. ANCOVA has potential pitfalls, but the blanket rejection of the method in some areas of empirical psychology is not justified. Assumptions of the methods are reviewed, with issues of selection bias, nonlinearity, and interaction emphasized. Advantages of ANCOVA include better power, improved ability to detect and estimate interactions, and the availability of extensions to deal with measurement error in the covariates. Forms of ANCOVA are advocated that relax the standard assumption of linearity between the outcome and covariates. Specifically, a version of ANCOVA that models the relationship between the covariate and the outcome through cubic spline with fixed knots outperforms other methods in simulations.

Original languageEnglish
Pages (from-to)459-476
Number of pages18
JournalPsychological Methods
Issue number4
Publication statusPublished - 2000 Dec
Externally publishedYes

ASJC Scopus subject areas

  • Psychology (miscellaneous)


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