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«Abstract Analysis of variance (ANOVA) is a statistical procedure for summarizing a classical linear model—a decomposition of sum of squares into a ...»

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Analysis of variance∗

Andrew Gelman†

March 22, 2006


Analysis of variance (ANOVA) is a statistical procedure for summarizing a classical linear

model—a decomposition of sum of squares into a component for each source of variation in the

model—along with an associated test (the F-test) of the hypothesis that any given source of

variation in the model is zero. When applied to generalized linear models, multilevel models,

and other extensions of classical regression, ANOVA can be extended in two different directions.

First, the F-test can be used (in an asymptotic or approximate fashion) to compare nested models, to test the hypothesis that the simpler of the models is sufficient to explain the data.

Second, the idea of variance decomposition can be interpreted as inference for the variances of batches of parameters (sources of variation) in multilevel regressions.

1 Introduction Analysis of variance (ANOVA) represents a set of models that can be fit to data, and also a set of methods for summarize an existing fitted model. We first consider ANOVA as it applies to classical linear models (the context for which it was originally devised; Fisher, 1925) and then discuss how ANOVA has been extended to generalized linear models and multilevel models. Analysis of variance is particularly effective for analyzing highly structured experimental data (in agriculture, multiple treatments applied to different batches of animals or crops; in psychology, multi-factorial experiments manipulating several independent experimental conditions and applied to groups of people; industrial experiments in which multiple factors can be altered at different times and in different locations).

At the end of this article, we compare ANOVA to simple linear regression.

2 Analysis of variance for classical linear models

2.1 ANOVA as a family of statistical methods When formulated as a statistical model, analysis of variance refers to an additive decomposition of data into a grand mean, main effects, possible interactions, and an error term. For example, Gawron et al. (2003) describe a flight-simulator experiment that we summarize as a 5 × 8 array of measurements under 5 treatment conditions and 8 different airports. The corresponding two-way ANOVA model is yij = µ + αi + βj + ij. The data as described here have no replication, and so the two-way interaction becomes part of the error term.1 ∗ For the New Palgrave Dictionary of Economics, second edition. We thank Jack Needleman, Matthew Rafferty, David Pattison, Marc Shivers, Gregor Gorjanc, and several anonymous commenters for helpful suggestions and the National Science Foundation for financial support.

† Department of Statistics and Department of Political Science, Columbia University, New York, gelman@stat.columbia.edu, www.stat.columbia.edu/∼gelman 1 If, for example, each treatment × airport condition were replicated three times, then the 120 data points could be modeled as yijk = µ + αi + βj + γij + ijk, with two sets of main effects, a two-way interaction, and an error term.

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Figure 1: Classical two-way analysis of variance for data on 5 treatments and 8 airports with no replication. The treatment-level variation is not statistically distinguishable from noise, but the airport effects are statistically significant. This and the other examples in this article come from Gelman (2005) and Gelman and Hill (2006).

This is a linear model with 1 + 4 + 7 coefficients, which is typically identified by constraining the 5 8

αi = 0 and j=1 βj = 0. The corresponding ANOVA display is shown in Figure 1:


• For each source of variation, the degrees of freedom represent the number of effects at that level, minus the number of constraints (the 5 treatment effects sum to zero, the 8 airport effects sum to zero, and each row and column of the 40 residuals sums to zero).

5 8 ¯2

• The total sum of squares—that is, j=1 (yij − y.. ) —is 0.078 + 3.944 + 1.417, which i=1 can be decomposed into these three terms corresponding to variance described by treatment, variance described by airport, and residuals.

• The mean square for each row is the sum of squares divided by degrees of freedom. Under the null hypothesis of zero row and column effects, their mean squares would, in expectation, simply equal the mean square of the residuals.

• The F -ratio for each row (excluding the residuals) is the mean square, divided by the residual mean square. This ratio should be approximately 1 (in expectation) if the corresponding effects are zero; otherwise we would generally expect the F -ratio to exceed 1. We would expect the F -ratio to be less than 1 only in unusual models with negative within-group correlations (for example, if the data y have been renormalized in some way, and this had not been accounted for in the data analysis.)

• The p-value gives the statistical significance of the F -ratio with reference to the Fν1,ν2, where ν1 and ν2 are the numerator and denominator degrees of freedom, respectively. (Thus, the two F -ratios in Figure 1 are being compared to F4,28 and F7,28 distributions, respectively.) In this example, the treatment mean square is lower than expected (an F -ratio of less than 1), but the difference from 1 is not statistically significant (a p-value of 82%), hence it is reasonable to judge this difference as explainable by chance, and consistent with zero treatment effects. The airport mean square, is much higher than would be expected by chance, with an F -ratio that is highly statistically-significantly larger than 1; hence we can confidently reject the hypothesis of zero airport effects.

More complicated designs have correspondingly complicated ANOVA models, and complexities arise with multiple error terms. We do not intend to explain such hierarchical designs and analyses here, but we wish to alert the reader to such complications. Textbooks such as Snedecor and Cochran (1989) and Kirk (1995) provide examples of analysis of variance for a wide range of designs.

2.2 ANOVA to summarize a model that has already been fitted We have just demonstrated ANOVA as a method of analyzing highly structured data by decomposing variance into different sources, and comparing the explained variance at each level to what would be expected by chance alone. Any classical analysis of variance corresponds to a linear model (that is, a regression model, possibly with multiple error terms); conversely, ANOVA tools can be used to summarize an existing linear model.

2 The key is the idea of “sources of variation,” each of which corresponds to a batch of coefficients in a regression. Thus, with the model y = Xβ +, the columns of X can often be batched in a reasonable way (for example, from the previous section, a constant term, 4 treatment indicators, and 7 airport indicators), and the mean squares and F -tests then provide information about the amount of variance explained by each batch.

Such models could be fit without any reference to ANOVA, but ANOVA tools could then be used to make some sense of the fitted models, and to test hypotheses about batches of coefficients.

2.3 Balanced and unbalanced data In general, the amount of variance explained by a batch of predictors in a regression depends on which other variables have already been included in the model. With balanced data, however, in which all groups have the same number of observations (for example, each treatment applied exactly eight times, and each airport used for exactly five observations), the variance decomposition does not depend on the order in which the variables are entered. ANOVA is thus particularly easy to interpret with balanced data. The analysis of variance can also be applied to unbalanced data, but then the sums of squares, mean squares, and F -ratios will depend on the order in which the sources of variation are considered.

3 ANOVA for more general models Analysis of variance represents a way of summarizing regressions with large numbers of predictors that can be arranged in batches, and a way of testing hypotheses about batches of coefficients. Both these ideas can be applied in settings more general than linear models with balanced data.

3.1 F tests In a classical balanced design (as in the examples of the previous section), each F -ratio compares a particular batch of effects to zero, testing the hypothesis that this particular source of variation is not necessary to fit the data.

More generally, the F test can compare two nested models, testing the hypothesis that the smaller model fits the data adequately and (so that the larger model is unnecessary). In a linear model, the F -ratio is (SS2 −SS11)/(df 2 −df 1 ), where SS1, df 1 and SS2, df 2 are the residual sums of squares and SS /df 1 degrees of freedom from fitting the larger and smaller models, respectively.

For generalized linear models, formulas exist using the deviance (the log-likelihood multiplied by −2) that are asymptotically equivalent to F -ratios. In general, such models are not balanced, and the test for including another batch of coefficients depends on which other sources of variation have already been included in the model.

3.2 Inference for variance parameters A different sort of generalization interprets the ANOVA display as inference about the variance of each batch of coefficients, which we can think of as the relative importance of each source of variation in predicting the data. Even in a classical balanced ANOVA, the sums of squares and mean squares do not exactly do this, but the information contained therein can be used to estimate the variance components (Cornfield and Tukey, 1956, Searle, Casella, and McCulloch, 1992). Bayesian simulation can then be used to obtain confidence intervals for the variance parameters. As illustrated below, we display inferences for standard deviations (rather than variances) because these are more directly interpretable. Compared to the classical ANOVA display, our plots emphasize the estimated variance parameters rather than testing the hypothesis that they are zero.

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Figure 2: ANOVA display for two logistic regression models of the probability that a survey respondent prefers the Republican candidate for the 1988 U.S. Presidential election, based on data from seven CBS News polls. Point estimates and error bars show median estimates, 50% intervals, and 95% intervals of the standard deviation of each batch of coefficients. The large coefficients for ethnicity, region, and state suggest that it might make sense to include interactions, hence the inclusion of ethnicity × region and ethnicity × state interactions in the second model.

3.3 Generalized linear models The idea of estimating variance parameters applies directly to generalized linear models as well as unbalanced datasets. All that is needed is that the parameters of a regression model are batched into “sources of variation.” Figure 2 illustrates with a multilevel logistic regression model, predicting vote preference given a set of demographic and geographic variables.

3.4 Multilevel models and Bayesian inference Analysis of variance is closely tied to multilevel (hierarchical) modeling, with each source of variation in the ANOVA table corresponding to a variance component in a multilevel model (see Gelman, 2005). In practice, this can mean that we perform ANOVA by fitting a multilevel model, or that we use ANOVA ideas to summarize multilevel inferences. Multilevel modeling is inherently Bayesian in that it involves a potentially large number of parameters that are modeled with probability distributions (see, for example, Goldstein, 1995, Kreft and De Leeuw, 1998, Snijders and Bosker, 1999). The differences between Bayesian and non-Bayesian multilevel models are typically minor except in settings with many sources of variation and little information on each, in which case some benefit can be gained from a fully-Bayesian approach which models the variance parameters.

4 Related topics

4.1 Finite-population and superpopulation variances So far in this article we have considered, at each level (that is, each source of variation) of a model, the standard deviation of the corresponding set of coefficients. We call this the finite-population standard deviation. Another quantity of potential interest is the standard deviation of the hypothetical superpopulation from which these particular coefficients were drawn. The point estimates of these two variance parameters are similar—with the classical method of moments, the estimates are identical, because the superpopulation variance is the expected value of the finite-population variance—but they will have different uncertainties. The inferences for the finite-population standard deviations are more precise, as they correspond to effects for which we actually have data.

Figure 3 illustrates the finite-population and superpopulation inferences at each level of the model for the flight-simulator example. We know much more about the 5 treatments and 8 airports in our dataset than for the general populations of treatments and airports. (We similarly know more

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0 0.2 0.4 0.6 0.8 0 0.2 0.4 0.6 0.8 Figure 3: Median estimates, 50% intervals, and 95% intervals for (a) finite-population and (b) superpopulation standard deviations of the treatment-level, airport-level, and data-level errors in the flight-simulator example from Figure 1. The two sorts of standard deviation parameters have essentially the same estimates, but the finite-population quantities are estimated much more precisely.

(We follow the general practice in statistical notation, using Greek and Roman letters for population and sample quantities, respectively.)

–  –  –

Figure 4: ANOVA displays for a 5 × 5 latin square experiment (an example of a crossed three-way structure): (a) with no group-level predictors, (b) contrast analysis including linear trends for rows, columns, and treatments. See also the plots of coefficient estimates and trends in Figure 5.

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