Three functions are available for sampling.
The first function
allows for simple random sampling. Each case is selected with a probability equal to prop.
For example, for a random sample of one tenth of a data set, use:
Random Samples of Fixed Size
The second function
allows a random sample of fixed size to be drawn. When using this function, the first case is drawn with a probability of sample_size/total_observations, and the succeeding i'th case is drawn with a probability of (sample_size - hits) / (total_observations - i).
For example, if you had a data set of 1000 cases and wished for a random sample of 25 cases, you would specify:
Systematic Random Samples
Finally, a third function
performs a systematic sample of every n'th case after a random start. For instance, to take every 6'th case, use:
Sampling Subsets of the Input Data
Expressions are evaluated from left to right. You can thus sample from a subset of your cases by subsetting them first and then sampling.
For example, to take a random half of high school graduates, use:
where schooling >= 12 & samp_rand(.5)
Sampling Seed and Reproducible Samples
The random number generator that provides the basis of these sampling routines is 'rand_port()' in Jerry Dwyer, "Quick and Portable Random Number Generators." C Users Journal, June, 1995, pp. 33-44. By default, it is seeded using a permutation of the time of day, and will yield a different sample on each run.
If you need a reproducible sample, you can generate it by using the same seed each time. The seed is entered in the General Options section of the Options dialog box and should be a positive integer in the range of one through 2,147,483,646.