Sampling Design

3357 Words Sep 25th, 2007 14 Pages
SAMPLE DESIGN The way of selecting a sample from a population is known as sample design. It describes various sampling techniques and sample size. It refers to the technique or procedure the researcher would adopt in selecting items for the sample.

STEPS IN SAMPLE DESIGN
• Type of universe
• Sampling unit
• Source List
• Size of Sample
• Parameters of Interest
• Budgetary Constraint
• Sampling Procedure

CRITERIA OF SELECTING A SAMPLING PROCEDURE
• Inappropriate sampling frame
• Defective measuring device
• Non-Respondents
• Indeterminancy principle
• Natural bias in the reporting of data

CHARACTERISTICS OF A GOOD SAMPLE DESIGN
• Sample design must result in a truly representative sample.
• Sample design must be such
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There are many reasons why one would choose a different type of probability sample in practice.

Example 1
Let's suppose your sampling frame is a large city's telephone book that has 2,000,000 entries. To take a SRS, you need to associate each entry with a number and choose n= 200 numbers from N= 2,000,000. This could be quite an ordeal. Instead, you decide to take a random start between 1 and N/n= 20,000 and then take every 20,000th name, etc. This is an example of systematic sampling, a technique discussed more fully below.

Example 2
Suppose you wanted to study dance club and bar employees in MUMBAI with a sample of n = 600. Yet there is no list of these employees from which to draw a simple random sample. Suppose you obtained a list of all bars/clubs in MUMBAI. One way to get this would be to randomly sample 300 bars and then randomly sample 2 employees within each bars/club. This is an example of cluster sampling. Here the unit of analysis (employee) is different from the primary sampling unit (the bar/ club).
In each of these two examples, a probability sample is drawn, yet none is an example of simple random sampling. Each of these methods is described in greater detail below.
Although simple random sampling is the ideal for social science and most of the statistics used are based on assumptions of SRS, in practice, SRS are rarely seen. It can be terribly inefficient, and particularly difficult when large samples are needed. Other probability methods
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