Sampling Design
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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 which results in a small sampling error.
Sample design must be viable in the context of funds available for the research study.
Sample design must be such so that systematic bias can be controlled in a better way.
Sample should be such that the results of the sample study can be applied, in general, for the universe with a reasonable level of confidence.
Different types of sample designs
There are different type of sample designs based on two factors, the representation basis and the element selection technique. On the representation basis the sample may be probability sampling or it may be non probability sampling. Probability is based on random selection, whereas non probability sampling is Ðnon-random sampling. On element selection basis it can be unrestricted or restricted sample. Thus The following chart exhibits the sample designs:
CHART SHOWING BASIC SAMPLING DESIGNS
ELEMENT SELECTION TECHNIQUE
UNRESTRICTED SAMPLING
REPRESENTATION BASIS
PROBABILITY SAMPLING NON-PROBABILITY
SAMPLING
SIMPLE RANDOM SAMPLING
HAPHAZARD SAMPLING OR CONVENIENCE SAMPLING
RESTRICTED SAMPLING
COMPLEX RANDOM SAMPLING (Such as cluster sampling, systematic sampling, stratified sampling etc.)
PURPOSIVE SASMPLING (such as quota sampling, judgement sampling)
probability sampling
PROBABILITY SAMPLES
The idea behind this type is random selection. More specifically, each sample from the population of interest has a known probability of selection under a given sampling scheme. There are four categories of probability samples described below.
SIMPLE RANDOM SAMPLING
The most widely known type of a random sample is the simple random sample (SRS). This is characterized by the fact that the probability of selection is the same for every case in the population. Simple random sampling is a method of selecting n units from a population of size N such that every possible sample of size n has equal chance of being drawn.
An example may make this easier to understand. Imagine you want to carry out a survey of 100 voters in a small town with a population of 1,000 eligible voters. With a town this size, there are “old-fashioned” ways to draw a sample. For example, we could write the names of all voters on a piece of paper, put all pieces of paper into a box and draw 100 tickets at random. You shake the box, draw a piece of paper and set it aside, shake again, draw another, set it aside, etc. until we had 100 slips of paper. These 100 form our sample. And this sample would be drawn through a simple random sampling procedure – at each draw, every name in the box had the same probability of being chosen.
In real-world social research, designs that employ simple random sampling are difficult to come by. We can imagine some situations where it might be possible – you want to interview a sample of doctors in a hospital about work conditions. So you get a list of all the physicians that work in the hospital, write their names on a piece of paper, put those pieces of paper in the box, shake and draw. But in most real-world instances it is impossible to list everything on a piece of paper and put it in a box, then randomly draw numbers until desired sample size is reached.
There are many reasons why one would choose a different type of probability sample in practice.
Example 1
Lets suppose your sampling frame is a large citys 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