Sampling and Sampling Techniques

 Sampling is a statistical method of obtaining representative data or information from a population. Sampling is used when a census, collecting data from every unit or person in a population, is cost- prohibitive. As long as a sampling method is used in which each unit or person in the population has a known and positive chance (probability) of being selected, the sample is called "representative" because the characteristics of the population can be inferred from the characteristics of the sample. We sample for the following reasons:

-          Firstly, collecting data for a sample is less expensive than for a census.

-          Secondly, having to collect data from fewer people can be done faster than a census,

-          Thirdly, more attention can be given to each person than would be possible for a census. More attention to each person can result in more accurate data of higher quality and higher response rates.

The sampling process passes through the following Six Stages

-          Defining the population of interest

-          Identifying a sampling frame or list of individuals or households to measure

-          Specifying a sampling method for selecting individuals or households from the frame

-          Determining the sample size

-          Implementing the sampling plan to select the sample

-          Collecting data from each sample member (i.e., conducting the survey)

 Sampling frame

It is the list of all the elements in a population. Examples of sampling frames include phone books, college student directories, directories of members of an association, a list of all the teachers in your county, etc. Note that some sampling frames are better than others; for example, the phone book excludes many people (that’s why a special technique called random digit dialling is used to obtain telephone samples rather than relying on the phone book).

Sampling Methods

-          Sampling methods are classified as either probability or non-probability.

-          In probability samples, each member of the population has a known non-zero probability of being selected. Probability methods include random sampling, systematic sampling, and stratified sampling.

-          In non-probability sampling, members are selected from the population in some non- random manner. These include convenience sampling, judgment sampling, quota sampling, and snowball sampling. The advantage of probability sampling is that sampling error can be calculated.

-          Sampling error is the degree to which a sample might differ from the population. When inferring to the population, results are reported plus or minus the sampling error. In non-probability sampling, the degree to which the sample differs from the population remains unknown.

1.       Probability Sampling Design

This refers to sampling when the chance of any given individual being selected is known and these individuals are sampled independently of each other. This is also known as random sampling.

A researcher can simply use a random number generator to choose participants (known as simple random sampling), or every n^th individual (known as systematic sampling) can be included.

Researchers also may break their target population into strata, and then apply these techniques within each stratum to ensure that they are getting enough participants from each stratum to be able to draw conclusions.

For example, if there are several ethnic communities in one geographical area that a researcher wishes to study, that researcher might aim to have 30 participants from each group, selected randomly from within the groups, in order to have a good representation of all the relevant groups.

Probability Sampling Techniques are as follows:

(a)                         Random sampling

This is the purest form of probability sampling. Each member of the population has an equal and known chance of being selected.

When there are very large populations, it is often difficult or impossible to identify every member of the population, so the pool of available subjects becomes biased.

Suppose there are N=850 students in a school from which a sample of n=10 students is to be taken. The students are numbered from 1 to 850. Since our data runs into three digits we use random numbers that contain three digits.

All numbers exceeding 850 are ignored because they do not correspond to any serial numbers in the data. In case the same number occurs again, the repetition is skipped.

(b)                        Systematic sampling

This is often used instead of random sampling. It is also called an n^th name selection technique.

After the required sample size has been calculated, every n^th record is selected from a list of population members. As long as the list does not contain any hidden order, this sampling method is as good as the random sampling method. Its only advantage over the random sampling technique is simplicity. Systematic sampling is frequently used to select a specified number of records from a computer file.

In this method first, we have to number the data items from 1 to N. Suppose the sample size be n, then we have to calculate the sampling interval by dividing N by n. And generate a number between 1 and N/n and select that data item to be in the sample.

Other items in the sample are obtained by adding the sampling interval N/n successively to the random number.

Advantage of this method is that the sample is evenly distributed over the entire data. For example, the town of Lusaka is divided up into N = 576 blocks which are numbered consecutively.

A 10 percent sample of blocks is to be taken, which gives a sampling interval of k = 10. If the random number between 1 and 10 is 3, the blocks with the numbers 03,13, 23, 33,43... 573 are in the sample.

(c)                      Stratified sampling

This is commonly used probability method that is superior to random sampling because it reduces sampling error.

A stratum is a subset of the population that share at least one common characteristic. Examples of strata might be males and females, or managers and non-managers.

The researcher first identifies the relevant strata and their actual representation in the population.

Random sampling is then used to select a sufficient number of subjects from each stratum. "Sufficient" refers to a sample size large enough for us to be reasonably confident that the stratum represents the population.

Stratified sampling is often used when one or more of the strata in the population have a low incidence relative to the other strata.

2.       Non-Probability Sampling Design

This is when researchers take whatever individuals happen to be easiest to access as participants in a study. This is only done when the processes the researchers are testing are assumed to be so basic and universal that they can be generalized beyond such a narrow sample.

For example, snowball sampling is an approach for locating information-rich key informants. Using this approach, a few potential respondents are contacted and asked whether they know of anybody with the characteristics that you are looking for in your research.

Snowball sampling is not a stand-alone tool; the tool is a way of selecting participants and then using other tools, such as interviews or surveys.

Non-Probability Sampling Techniques are as follows:

(a)  Convenience sampling

This is used in exploratory research where the researcher is interested in getting an inexpensive approximation of the truth. As the name implies, the sample is selected because they are convenient. This nonprobability method is often used during preliminary research efforts to get a gross estimate of the results, without incurring the cost or time required to select a random sample.

(b)      Judgment sampling

-          This is a common nonprobability method.

-          The researcher selects the sample based on judgment. This is usually and extension of convenience sampling.

-          For example, a researcher may decide to draw the entire sample from one "representative" city, even though the population includes all cities.

-          When using this method, the researcher must be confident that the chosen sample is truly representative of the entire population.

(c)    Quota sampling

This is the nonprobability equivalent of stratified sampling. Like stratified sampling, the researcher first identifies the strata and their proportions as they are represented in the population. Then convenience or judgment sampling is used to select the required number of subjects from each stratum. This differs from stratified sampling, where the strata are filled by random sampling.

(d)      Snowball sampling

This is a special nonprobability method used when the desired sample characteristic is rare. It may be extremely difficult or cost-prohibitive to locate respondents in these situations. Snowball sampling relies on referrals from initial subjects to generate additional subjects. While this technique can dramatically lower search costs, it comes at the expense of introducing bias because the technique itself reduces the likelihood that the sample will represent a good cross-section from the population.

Designing a Statistical Study

1.            Identify the variable(s) of interest (the focus) and the population of the study.

2.   Develop a detailed plan for collecting data. If you use a sample, make sure the sample is representative of the population.

3.            Collect the data.

4.            Describe the data, using descriptivestatistics techniques.

5.            Interpret the data and make decisions about the population using inferential statistics.

6.            Identify any possible errors.