SAMPLING
Sampling is the process of systematically selecting representative elements of a population. When these selected elements are examined closely, it is assumed that the analysis will reveal useful information about the population as a whole. The systems analyst has to make a decision on two key issues. First, there are many reports, forms, output documents, memos, and Web sites that have been generated by people in the organization. Which of these should the systems analyst pay attention to, and which should the systems analyst ignore?Second, a great many employees can be affected by the proposed information system. Whichpeople should the systems analyst interview, seek information from via questionnaires, or observe
in the process of carrying out their decision-making roles?
The Need for Sampling
There are many reasons a systems analyst would want to select either a representative sample of data to examine or representative people to interview, question, or observe. They include:
1. Containing costs.
2. Speeding up the data gathering.
3. Improving effectiveness.
4. Reducing bias.
Examining every scrap of paper, talking with everyone, and reading every Web page from the organization would be far too costly for the systems analyst. Copying reports, asking employees for valuable time, and duplicating unnecessary surveys would result in much needless expense. Sampling helps accelerate the process by gathering selected data rather than all data for the entire population. In addition, the systems analyst is spared the burden of analyzing data from the entire population. Effectiveness in data gathering is an important consideration as well. Sampling can help improve effectiveness if information that is more accurate can be obtained. Such sampling is accomplished, for example, by talking to fewer employees but asking them questions that are more detailed. In addition, if fewer people are interviewed, the systems analyst can afford the time to follow up on missing or incomplete data, thus improving the effectiveness of data gathering. Finally, data gathering bias can be reduced by sampling. When the systems analyst interviews an executive of the corporation, for example, the executive is involved with the project, because this person has already given a certain amount of time to the project and would like it to succeed. When the systems analyst asks for an opinion about a permanent feature of the installed information system, the executive interviewed may provide a biased evaluation, because there is little possibility of changing it.
Sampling Design
A systems analyst must follow four steps to design a good sample:
1. Determine the data to be collected or described.
2. Determine the population to be sampled.
3. Choose the type of sample.
4. Decide on the sample size.
DETERMINING THE DATA TO BE COLLECTED OR DESCRIBED. The systems analyst needs a realistic plan about what will be done with the data once they are collected. If irrelevant data are gathered, then time and money are wasted in the collection, storage, and analysis of useless data. The duties and responsibilities of the systems analyst at this point are to identify the variables, attributes, and associated data items that need to be gathered in the sample. The objectives of the study must be considered as well as the type of data-gathering method (investigation, interviews, questionnaires, observation) to be used.
DETERMINING THE POPULATION TO BE SAMPLED. Next, the systems analyst must determine what
the population is. In the case of hard data, the systems analyst needs to decide, for example, if 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 whethe the population should include only one level in the organization or all the levels, or maybe the analyst should even go outside of the system to include the reactions of customers, vendors, suppliers,
or competitors.
CHOOSING THE TYPE OF SAMPLE. The systems analyst can use one of four main types of samples,
as pictured in Figure 5.1. They are convenience, purposive, simple, and complex. 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
Sampling is the process of systematically selecting representative elements of a population. When these selected elements are examined closely, it is assumed that the analysis will reveal useful information about the population as a whole. The systems analyst has to make a decision on two key issues. First, there are many reports, forms, output documents, memos, and Web sites that have been generated by people in the organization. Which of these should the systems analyst pay attention to, and which should the systems analyst ignore?Second, a great many employees can be affected by the proposed information system. Whichpeople should the systems analyst interview, seek information from via questionnaires, or observe
in the process of carrying out their decision-making roles?
The Need for Sampling
There are many reasons a systems analyst would want to select either a representative sample of data to examine or representative people to interview, question, or observe. They include:
1. Containing costs.
2. Speeding up the data gathering.
3. Improving effectiveness.
4. Reducing bias.
Examining every scrap of paper, talking with everyone, and reading every Web page from the organization would be far too costly for the systems analyst. Copying reports, asking employees for valuable time, and duplicating unnecessary surveys would result in much needless expense. Sampling helps accelerate the process by gathering selected data rather than all data for the entire population. In addition, the systems analyst is spared the burden of analyzing data from the entire population. Effectiveness in data gathering is an important consideration as well. Sampling can help improve effectiveness if information that is more accurate can be obtained. Such sampling is accomplished, for example, by talking to fewer employees but asking them questions that are more detailed. In addition, if fewer people are interviewed, the systems analyst can afford the time to follow up on missing or incomplete data, thus improving the effectiveness of data gathering. Finally, data gathering bias can be reduced by sampling. When the systems analyst interviews an executive of the corporation, for example, the executive is involved with the project, because this person has already given a certain amount of time to the project and would like it to succeed. When the systems analyst asks for an opinion about a permanent feature of the installed information system, the executive interviewed may provide a biased evaluation, because there is little possibility of changing it.
Sampling Design
A systems analyst must follow four steps to design a good sample:
1. Determine the data to be collected or described.
2. Determine the population to be sampled.
3. Choose the type of sample.
4. Decide on the sample size.
DETERMINING THE DATA TO BE COLLECTED OR DESCRIBED. The systems analyst needs a realistic plan about what will be done with the data once they are collected. If irrelevant data are gathered, then time and money are wasted in the collection, storage, and analysis of useless data. The duties and responsibilities of the systems analyst at this point are to identify the variables, attributes, and associated data items that need to be gathered in the sample. The objectives of the study must be considered as well as the type of data-gathering method (investigation, interviews, questionnaires, observation) to be used.
DETERMINING THE POPULATION TO BE SAMPLED. Next, the systems analyst must determine what
the population is. In the case of hard data, the systems analyst needs to decide, for example, if 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 whethe the population should include only one level in the organization or all the levels, or maybe the analyst should even go outside of the system to include the reactions of customers, vendors, suppliers,
or competitors.
CHOOSING THE TYPE OF SAMPLE. The systems analyst can use one of four main types of samples,
as pictured in Figure 5.1. They are convenience, purposive, simple, and complex. 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 criteria (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 the 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 the 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 employees from the operational control level. A stratified sample would compensate for this. Stratification is also called for when the systems analyst wants to use different methods to collect data from different 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 the systems analyst must select a group of people or documents to study. This process is referred to as cluster sampling. Suppose an organization had 20 help desks scattered across the country. You may want to select one or two of these help desks under the assumption
that they are typical of the remaining ones.
DECIDING ON THE SAMPLE SIZE. Obviously, if everyone in the population viewed the world the same way or if each of the documents in a population contained exactly the same information as every other document, a sample size of one would be sufficient. Because that is not the case, it is necessary to set a sample size greater than one but less than the size of the population itself. It is important to remember that the absolute number is more important in sampling than the percentage of the population. We can obtain satisfactory results sampling 20 people in 200 or 20 people in 2,000,000.
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 the 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 employees from the operational control level. A stratified sample would compensate for this. Stratification is also called for when the systems analyst wants to use different methods to collect data from different 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 the systems analyst must select a group of people or documents to study. This process is referred to as cluster sampling. Suppose an organization had 20 help desks scattered across the country. You may want to select one or two of these help desks under the assumption
that they are typical of the remaining ones.
DECIDING ON THE SAMPLE SIZE. Obviously, if everyone in the population viewed the world the same way or if each of the documents in a population contained exactly the same information as every other document, a sample size of one would be sufficient. Because that is not the case, it is necessary to set a sample size greater than one but less than the size of the population itself. It is important to remember that the absolute number is more important in sampling than the percentage of the population. We can obtain satisfactory results sampling 20 people in 200 or 20 people in 2,000,000.
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