The incremental progression from fixed to random factors in the analysis of variance: a new synthesis

Abstract

Classically, the distinction between a fixed versus a random factor in analysis of variance has been considered a binary choice. Here we consider that any given factor can also occur along an incremental series of steps between these two extremes, depending on the sampling fraction of its levels from the wider population. Fixed factors occur where all possible levels are drawn, and random factors occur in the limit as the population of possible levels approaches infinity. When some identifiable fraction of a finite population of possible levels is drawn, the factor can be thought of as something in between fixed and random, and can be analysed explicitly as finite directly within the analysis of variance (ANOVA) framework. Requiring explicit specification of the population size from which observed levels are drawn for each factor, we provide a unified approach to derive expectations of mean squares (EMS) in ANOVA for any types of factors along the entire graded progression from fixed to random, inclusive, that may be nested within or crossed with one another, from balanced, asymmetrical or unbalanced designs, including multi-level hierarchical sampling designs, mixed models and interactions. Implications for estimation of variance components, tailored bootstrap methods and tests of hypotheses under minimal assumptions of exchangeability are described and further extended to multivariate dissimilarity-based settings.