Summarize Statistics

You can choose one or more of the following subgroup statistics for the variables within each category of each grouping variable: 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, harmonic mean. The order in which the statistics appear in the Cell Statistics list is the order in which they will be displayed in the output. Summary statistics are also displayed for each variable across all categories.

First. Displays the first data value encountered in the data file.

Geometric Mean. The nth root of the product of the data values, where n represents the number of cases.

Grouped Median. Median that is calculated for data that is coded into groups. For example, with age data, if each value in the 30s is coded 35, each value in the 40s is coded 45, and so on, the grouped median is the median calculated from the coded data.

Harmonic Mean. Used to estimate an average group size when the sample sizes in the groups are not equal. The harmonic mean is the total number of samples divided by the sum of the reciprocals of the sample sizes.

Kurtosis. A measure of the extent to which observations cluster around a central point. For a normal distribution, the value of the kurtosis statistic is zero. Positive kurtosis indicates that, relative to a normal distribution, the observations are more clustered about the center of the distribution and have thinner tails until the extreme values of the distribution, at which point the tails of the leptokurtic distribution are thicker relative to a normal distribution. Negative kurtosis indicates that, relative to a normal distribution, the observations cluster less and have thicker tails until the extreme values of the distribution, at which point the tails of the platykurtic distribution are thinner relative to a normal distribution.

Last. Displays the last data value encountered in the data file.

Maximum. The largest value of a numeric variable.

Mean. A measure of central tendency. The arithmetic average, the sum divided by the number of cases.

Median. The value above and below which half of the cases fall, the 50th percentile. If there is an even number of cases, the median is the average of the two middle cases when they are sorted in ascending or descending order. The median is a measure of central tendency not sensitive to outlying values (unlike the mean, which can be affected by a few extremely high or low values).

Minimum. The smallest value of a numeric variable.

N. The number of cases (observations or records).

Percent of Total N. Percentage of the total number of cases in each category.

Percent of Total Sum. Percentage of the total sum in each category.

Range. The difference between the largest and smallest values of a numeric variable, the maximum minus the minimum.

Skewness. A measure of the asymmetry of a distribution. The normal distribution is symmetric and has a skewness value of 0. A distribution with a significant positive skewness has a long right tail. A distribution with a significant negative skewness has a long left tail. As a guideline, a skewness value more than twice its standard error is taken to indicate a departure from symmetry.

Standard Deviation. A measure of dispersion around the mean. In a normal distribution, 68% of cases fall within one standard deviation of the mean and 95% of cases fall within two standard deviations. For example, if the mean age is 45, with a standard deviation of 10, 95% of the cases would be between 25 and 65 in a normal distribution.

Standard Error of Kurtosis. The ratio of kurtosis to its standard error can be used as a test of normality (that is, you can reject normality if the ratio is less than -2 or greater than +2). A large positive value for kurtosis indicates that the tails of the distribution are longer than those of a normal distribution; a negative value for kurtosis indicates shorter tails (becoming like those of a box-shaped uniform distribution).

Standard Error of Mean. A measure of how much the value of the mean may vary from sample to sample taken from the same distribution. It can be used to roughly compare the observed mean to a hypothesized value (that is, you can conclude the two values are different if the ratio of the difference to the standard error is less than -2 or greater than +2).

Standard Error of Skewness. The ratio of skewness to its standard error can be used as a test of normality (that is, you can reject normality if the ratio is less than -2 or greater than +2). A large positive value for skewness indicates a long right tail; an extreme negative value indicates a long left tail.

Sum. The sum or total of the values, across all cases with nonmissing values.

Variance. A measure of dispersion around the mean, equal to the sum of squared deviations from the mean divided by one less than the number of cases. The variance is measured in units that are the square of those of the variable itself.

Specifying Statistics for Summarize

This feature requires the Statistics Base option.

1. From the menus choose:

   Analyze > Reports > Case Summaries...

2. In the Summarize Cases dialog box, click Statistics.