Means

This feature requires the Statistics Base option.

The Means procedure calculates subgroup means and related univariate statistics for dependent variables within categories of one or more independent variables. Optionally, you can obtain a one-way analysis of variance, eta, and tests for linearity.

Example. Measure the average amount of fat absorbed by three different types of cooking oil, and perform a one-way analysis of variance to see whether the means differ.

Statistics. Sum, number of cases, mean, median, grouped median, standard error of the mean, minimum, maximum, range, variable value of the first category of the grouping variable, variable value of the last category of the grouping variable, standard deviation, variance, kurtosis, standard error of kurtosis, skewness, standard error of skewness, percentage of total sum, percentage of total N, percentage of sum in, percentage of N in, geometric mean, and harmonic mean. Options include analysis of variance, eta, eta squared, and tests for linearity R and R ².

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Means Data Considerations

Data. The dependent variables are quantitative, and the independent variables are categorical. The values of categorical variables can be numeric or string.

Assumptions. Some of the optional subgroup statistics, such as the mean and standard deviation, are based on normal theory and are appropriate for quantitative variables with symmetric distributions. Robust statistics, such as the median, are appropriate for quantitative variables that may or may not meet the assumption of normality. Analysis of variance is robust to departures from normality, but the data in each cell should be symmetric. Analysis of variance also assumes that the groups come from populations with equal variances. To test this assumption, use Levene's homogeneity-of-variance test, available in the One-Way ANOVA procedure.

To Obtain Subgroup Means

This feature requires the Statistics Base option.

1. From the menus choose:

   Analyze > Compare Means > Means...

2. Select one or more dependent variables.
3. Use one of the following methods to select categorical independent variables:
   • Select one or more independent variables. Separate results are displayed for each independent variable.
   • Select one or more layers of independent variables. Each layer further subdivides the sample. If you have one independent variable in Layer 1 and one independent variable in Layer 2, the results are displayed in one crossed table, as opposed to separate tables for each independent variable.
4. Optionally, click Options for optional statistics, an analysis of variance table, eta, eta squared, R, and R ².