# Generalized Linear Models EM Means

This tab allows you to display the estimated marginal means for levels of factors and factor interactions. You can also request that the overall estimated mean be displayed. Estimated marginal means are not available for ordinal multinomial models.

Factors and Interactions. This list contains factors specified on the Predictors tab and factor interactions specified on the Model tab. Covariates are excluded from this list. Terms can be selected directly from this list or combined into an interaction term using the By * button.

Display Means For. Estimated means are computed for the selected factors and factor interactions. The contrast determines how hypothesis tests are set up to compare the estimated means. The simple contrast requires a reference category or factor level against which the others are compared.

• Pairwise. Pairwise comparisons are computed for all-level combinations of the specified or implied factors. This is the only available contrast for factor interactions.
• Simple. Compares the mean of each level to the mean of a specified level. This type of contrast is useful when there is a control group.
• Deviation. Each level of the factor is compared to the grand mean. Deviation contrasts are not orthogonal.
• Difference. Compares the mean of each level (except the first) to the mean of previous levels. They are sometimes called reverse Helmert contrasts.
• Helmert. Compares the mean of each level of the factor (except the last) to the mean of subsequent levels.
• Repeated. Compares the mean of each level (except the last) to the mean of the subsequent level.
• Polynomial. Compares the linear effect, quadratic effect, cubic effect, and so on. The first degree of freedom contains the linear effect across all categories; the second degree of freedom, the quadratic effect; and so on. These contrasts are often used to estimate polynomial trends.

Scale. Estimated marginal means can be computed for the response, based on the original scale of the dependent variable, or for the linear predictor, based on the dependent variable as transformed by the link function.

Adjustment for Multiple Comparisons. When performing hypothesis tests with multiple contrasts, the overall significance level can be adjusted from the significance levels for the included contrasts. This group allows you to choose the adjustment method.

• Least significant difference. This method does not control the overall probability of rejecting the hypotheses that some linear contrasts are different from the null hypothesis values.
• Bonferroni. This method adjusts the observed significance level for the fact that multiple contrasts are being tested.
• Sequential Bonferroni. This is a sequentially step-down rejective Bonferroni procedure that is much less conservative in terms of rejecting individual hypotheses but maintains the same overall significance level.
• Sidak. This method provides tighter bounds than the Bonferroni approach.
• Sequential Sidak. This is a sequentially step-down rejective Sidak procedure that is much less conservative in terms of rejecting individual hypotheses but maintains the same overall significance level.

How To Specify EM Means for Generalized Linear Models

This feature requires Custom Tables and Advanced Statistics.