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ChaPter 5 • InformatIon GatherInG: unobtrusIve methods 131

Figure 5.1
Not Based on Probability Based on Probability
Four main types of samples a
Sample elements are systems analyst has available.
selected directly Convenience Simple random
without restrictions
Sample elements are Complex random
selected according Purposive (systematic, stratified,
to specific criteria and cluster)

The systems
analyst should
use a complex
random sample
if possible.

data from the last two months are sufficient, or if an entire year’s worth of reports are needed for
analysis.
Similarly, when deciding whom to interview, the systems analyst has to determine whether
the population should include only one level in the organization or all the levels. Or maybe the
analyst should even go outside the system and include the reactions of customers, vendors, sup-
pliers, or competitors. These decisions are explored in more detail in upcoming sections.
CHOOSING THE TYPE OF SAMPLE. A systems analyst can use one of four main types of samples,
as pictured in Figure 5.1. They are convenience, purposive, simple random, and complex random.
Convenience samples are unrestricted, nonprobability samples. A sample could be called a
convenience sample if, for example, the systems analyst posts a notice on the company’s intranet
asking for everyone interested in working with the new sales performance reports to come to a
meeting at 1 p.m. on Tuesday the 12th. Obviously, this sample is the easiest to arrange, but it is
also the most unreliable. A purposive sample is based on judgment.
A systems analyst can choose a group of individuals who appear knowledgeable and who
are interested in the new information system. Here the systems analyst bases the sample on cri-
teria (knowledge about and interest in the new system), but it is still a nonprobability sample.
Thus, purposive sampling is only moderately reliable. If you choose to perform a simple random
sample, you need to obtain a numbered list of the population to ensure that each document or
person in the population has an equal chance of being selected. This step often is not practical,
especially when sampling involves documents and reports. The complex random samples that
are most appropriate for a systems analyst are (1) systematic sampling, (2) stratified sampling,
and (3) cluster sampling.
In the simplest method of probability sampling, systematic sampling, the systems analyst
would, for example, choose to interview every kth person on a list of company employees. This
method has certain disadvantages, however. You would not want to use it to select every kth day
for a sample because of the potential periodicity problem. Furthermore, a systems analyst would
not use this approach if the list were ordered (for example, a list of banks, from the smallest to
the largest), because bias would be introduced.
Stratified samples are perhaps the most important to a systems analyst. Stratification is the
process of identifying subpopulations, or strata, and then selecting objects or people for sampling
in these subpopulations. Stratification is often essential if the systems analyst is to gather data
efficiently. For example, if you want to seek opinions from a wide range of employees on different
levels of the organization, systematic sampling would select a disproportionate number of employ-
ees from the operational control level. A stratified sample would compensate for this. Stratification
is also called for when a systems analyst wants to use different methods to collect data from differ-
ent subgroups. For example, you may want to use a survey to gather data from middle managers,
but you might prefer to use personal interviews to gather similar data from executives.
Sometimes a systems analyst must select a group of people or documents to study. This
process is referred to as cluster sampling. Suppose an organization has 20 help desks scattered