Random Sampling



SAMPLING

Sampling Terminology

Population

Population is defined as set of all objects that possess some common set of characteristics

Sample Population

It is almost impossible to guarantee that every element meeting your definition of “the population” has a chance to be selected into the sample. Thus the “study population” will be somewhat smaller than “the population”.

Sample

A sample is a subset of a larger population of objects individuals, households, businesses, organizations and so forth. Sampling enables researchers to make estimates of some unknown characteristics of the population in question

Element

Element is the unit about which information is collected. Typically the elements are people but schools, universities, corporations, etc. can also be the element.

Sampling Unit

Typically the sampling units are the same as the elements.

Sampling Frame

It is the actual list of sampling units (or elements). For example, if you want to conduct a study on the students at the University of Gujrat, there may be a list available at SSC of such sampling units.

Sampling Strategies

Basically two sampling strategies available:

← Probability sampling

← Non-probability Sampling

1. Probability sampling

In this kind of sampling each member of the population has a certain probability to be selected into the sample

Types of Probability Sampling

• Random

• Stratified Random

• Systematic

• Cluster

a. Random Sampling

The first statistical sampling method is simple random sampling. In this method, each item in the population has the same probability of being selected as part of the sample as any other item. This method is used when the population from which the sample is to be chosen is homogeneous.

Simple random sampling is the most intuitive sampling approach. If every household in the population has some unique identifier, such as a number or the name of the head of the household, and you know how many households you want to include in the survey sample, then you could simply write this identifier for each household on a separate piece of paper, put all the pieces of paper in a bag, shake well, and draw as many from the bag as you need to achieve your intended sample size. This is simple random sampling.

b. Systematic Sampling

Systematic sampling is another statistical sampling method. In this method, every nth element from the list is selected as the sample, starting with a sample element n randomly selected from the first k elements. For example, if the population has 1000 elements and a sample size of 100 is needed, then k would be 1000/100 = 10. If number 7 is randomly selected from the first ten elements on the list, the sample would continue down the list selecting the 7th element from each group of ten elements. Care must be taken when using systematic sampling to ensure that the original population list has not been ordered in a way that introduces any non-random factors into the sampling.

c. Stratified Sampling

The statistical sampling method called stratified sampling is used when representatives from each subgroup within the population need to be represented in the sample. The first step in stratified sampling is to divide the population into subgroups (strata) based on mutually exclusive criteria.

Random or systematic samples are then taken from each subgroup. The sampling fraction for each subgroup may be taken in the same proportion as the subgroup has in the population. For example, if the person conducting a customer satisfaction survey selected random customers from each customer type in proportion to the number of customers of that type in the population.

For example, if 40 samples are to be selected, and 10% of the customers are managers, 60% are users, 25% are operators and 5% are database administrators then 4 managers, 24 uses, 10 operators and 2 administrators would be randomly selected. Stratified sampling can also sample an equal number of items from each subgroup. For example, a development lead randomly selected three modules out of each programming language used to examine against the coding standard.

d. Cluster Sampling

The fourth statistical sampling method is called cluster sampling, also called block sampling. In cluster sampling, the population that is being sampled is divided into groups called clusters. Instead of these subgroups being homogeneous based on a selected criterion as in stratified sampling, a cluster is as heterogeneous as possible to matching the population. A random sample is then taken from within one or more selected clusters. Cluster sampling can tell us a lot about that particular cluster, but unless the clusters are selected randomly and a lot of clusters are sampled, generalizations cannot always be made about the entire population.

e. Haphazard Sampling

There are also other types of sampling that, while non-statistical (information about the entire population cannot be extrapolated from the sample), may still provide useful information. In haphazard sampling, samples are selected based on convenience but preferably should still be chosen as randomly as possible. The haphazard sampling is usually typically, quicker, and uses smaller sample sizes than other sampling techniques. The main disadvantage of haphazard sampling is that since it is not statistically based, generalizations about the total population should be made with extreme caution.

2. Types of Non-Probability Sampling

▪ convenience sampling

▪ judgement sampling

▪ snowball sampling

▪ quota sampling

a. Convenience sampling

A convenience sample is a sample where the elements are selected, in part or in whole, at the convenience of the researcher. The researcher makes no attempt, or only a limited attempt, to insure that this sample is an accurate representation of some larger group or population. The classic example of a convenience sample is standing at a shopping mall and selecting shoppers as they walk by to fill out a survey.

b. Judgmental Sampling

Another non-statistical sampling method is judgmental sampling. In judgmental sampling, the person doing the sample uses his/her knowledge or experience to select the items to be sampled.

c. Snowball sample. 

When interviewing members of a population, you can ask the interviewed persons to nominate other individuals who could be asked to give information or opinion on the topic. You then interview these new individuals and continue in the same way until the material gets saturated, i.e. you get no new viewpoints from the new persons.

Snowball sampling is a good method for such populations that are not well delimited nor well enumerated, for example the homeless. The drawback is that you get no exact idea of the factual distribution of the opinions in the target population. Besides, people usually propose people that they know well and with whom they share their own views, which mean that small groups of interest often are passed by unnoticed. One method for compensating this could be asking people to nominate both such persons who share the same views and such persons who are of the opposite opinion. Another method is to start the snowball chain from not one but several different people, perhaps from different social groups.

d. Quota sampling

A quota sample is a convenience sample with an effort made to insure a certain distribution of demographic variables. Subjects are recruited as they arrive and the researcher will assign them to demographic groups based on variables like age and sex. When the quota for a given demographic group is filled, the researcher will stop recruiting subjects from that particular group.

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