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How Prism computes two-way ANOVA

How Prism computes two-way ANOVA

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How Prism computes two-way ANOVA

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Two-way ANOVA calculations are quite standard, and these comments only discuss some of the ambiguities.

Model I (fixed effects) vs. Model II (random effects) ANOVA

To understand the difference between fixed and random factors, consider an example of comparing responses in three species at three times. If you were interested in those three particular species, then species is considered to be a fixed factor. It would be a random factor if you were interested in differences between species in general, and you randomly selected those three species. Time is considered to be a fixed factor if you chose time points to span the interval you are interested in. Time would be a random factor if you picked those three time points at random. Since this is not likely, time is almost always considered to be a fixed factor.

When both row and column variables are fixed factors, the analysis is called Model I ANOVA. When both row and column variables are random factors, the analysis is called Model II ANOVA. When one is random and one is fixed, it is termed mixed effects (Model III) ANOVA. With no repeated measures, Prism calculates only Model I two-way ANOVA. Since most experiments deal with fixed-factor variables, this is rarely a limitation.

Missing values

If some values are missing, two-way ANOVA calculations are challenging. David Lane gives a very clear explanation of the challenge of missing values in two-way ANOVA in his online text.

Prism uses the method detailed by Glantz and Slinker (1). This method converts the ANOVA problem to a multiple regression problem and then displays the results as ANOVA. Prism performs multiple regression three times — each time presenting columns, rows, and interaction to the multiple regression procedure in a different order. Although it calculates each sum-of-squares three times, Prism only displays the sum-of-squares for the factor entered last into the multiple regression equation. These are called Type III sum-of-squares. This article explains the difference between Type I, II and III sum-of-square. Type II sum-of-squares assumes no interaction. Type I and III differ only when there are missing values.

Prism cannot perform repeated-measures two-way ANOVA if any values are missing. It is OK to have different numbers of numbers of subjects in each group, so long as you have complete data (at each time point or dose) for each subject.

Data entered as mean, n and SD (or SEM)

If your data are balanced (same sample size for each condition), you'll get the same results if you enter raw data, or if you enter mean, SD (or SEM), and n. If your data are unbalanced, it is impossible to calculate precise results from data entered as mean, SD (or SEM), and n. Instead, Prism uses a simpler method called analysis of unweighted means. This method is detailed in LD Fisher and G vanBelle, Biostatistics, John Wiley, 1993. If sample size is the same in all groups, and in some other special cases, this simpler method gives exactly the same results as obtained by analysis of the raw data.

If you sample sizes are not all the same, these results will only be approximately correct. If your data are almost balanced (just one or a few missing values), the approximation is a good one. When data are unbalanced, you should enter individual replicates whenever possible and avoid entering mean, n and SD or SEM.

David Lane also discusses the method of unweighted means in his online text.

Single values without replicates

Prism can perform two-way ANOVA even if you have entered only a single replicate for each column/row pair. This kind of data does not let you test for interaction between rows and columns (random variability and interaction can't be distinguished unless you measure replicates). Instead, Prism assumes that there is no interaction and only tests for row and column effects. If this assumption is not valid, then the P values for row and column effects won't be meaningful.

Reference

SA Glantz and BK Slinker, Primer of Applied Regression and Analysis of Variance, McGraw-Hill, 1990.