Types of probability sampling

Simple random samplingThis is the easiest form of random sampling. Here everyone in the sampling frame has an equal chance of being in the final sample. This is applicable when population is small, homogeneous & readily available.

One way is to put all the names from your population onto pieces of paper, put them in a hat, and select a subset (e.g., pull out 100 names from the hat). 

Advantages

to reduce the potential for human bias in the selection of cases to be included in the sample

With an appropriate sample size, simple random sampling creates a representative view of the entire population

Easiest method and commonly used

Disadvantages

Time consuming and tedious

Stratified random sampling.

This is used when the population is large and is divided into stratum. Samples per stratum are then randomly selected but considerations must be given to the size of the random samples to be drawn from the subgroups. The goal is to guarantee that all groups in the population are adequately represented.

Advantages of stratified sampling:

Can lead to higher precision because there is less variability within the Groups given that similar characteristics are grouped together.

The necessary sample size can be reduced due to lower variability within groups, therefore saving time and money.

Allows companies to draw insights into the source and level of emissions among different groups.

Reduces the potential for human bias in the selection of cases to be included in the sample. As a result, the stratified random sample provides us with a sample that is highly representative of the population being studied, assuming that there is limited missing data.

Disadvantages of stratified sampling

More complex and requires greater effort than simple random strata must be carefully defined

Systematic random sampling

This is a type of probability sampling which selects samples by following some rules set by the researcher which involves selecting the nth member where the random start is determined. To achieve systematic random sampling the following steps have to be achieved:

Number the units in the population from 1 to N. Decide on the n (sample size) that you want or need. Calculate k = N/n = the interval size. Randomly select an integer between 1 and k. Ta k e every kth unit.

Advantages of systematic sampling include:

•Simple to implement.•The population is guaranteed to be evenly sampled without risk that the sample points are clustered together.

Disadvantages of systematic sampling include:•If there is a periodic pattern in the population to be sampled, it could lead to biased sampling•As with simple random sampling, it may not be possible to obtain a complete list of all activities in the population

Cluster sampling

Cluster sampling is a sampling technique where the entire population is divided into groups, or clusters, and a random sample of these clusters are selected. All observations in the selected clusters are included in the sample. Cluster sampling is typically used when the researcher cannot get a complete list of the members of a population they wish to study but can get a complete list of groups or 'clusters' of the population

The difference between cluster sampling and stratified sampling is that , with cluster sampling, we sample all of the units in a subset of subgroups while With stratication, we sample from each of the subgroups.

Advantages of cluster sampling

It ensures that selected population units will be closer together, thus enumeration costs for personal interviews will be reduced, and field work will be simplified.

Disadvantage of cluster samplingCluster sampling is not accurate because the sample obtained does not cover the population as evenly.

Multi stage sampling

Instead of using all the elements contained in the selected clusters, the researcher randomly selects elements from each cluster either through simple random sampling or systematic random sampling. The first stage is to sample the clusters and the second stage is to sample the respondents from each cluster.

Advantages

Multi-stage sampling can help reduce costs of large-scale research and limit the aspects of a population which needs to be included within the frame for sampling.

Disadvantage of multi stage sampling

This approach is overly-expensive or time consuming for the researcher.

Probability proportional size

A sampling procedure under which the probability of a unit being selected is proportional to the size of the unit. Probability proportional to size (PPS) is a sampling technique in which the probability of selecting a sampling unit (e.g., village, zone, district, health center) is proportional to the size of its population. It gives a probability (i.e., random, representative) sample.

Non probability sampling

This involves the selection of elements from a population using non random sampling procedures. Types of non probability sampling include:

Accidental or convenience sampling

This involves the non random selection of subjects based on their availability or convenient accessibility.

Advantage of Accidental or convenience sampling

Inexpensive way of ensuring sufficient numbers of a studyDisadvantage of accidental or convenience sampling

Can be highly unrepresentative

Quota sampling.

Involves the non random selection of subjects based on identification of specific characteristics. In this method , the research determines what the specific characteristics of the population, creates quotas based on these characteristics and then selects people from each quota.

Advantage of quota sampling

Ensures selection of adequate numbers of subjects with appropriate characteristics

Disadvantage of quota sampling

Not possible to prove that the sample is representative of designated population.

Snow ball sampling

Snowball sampling is particularly appropriate when the population you are interested in is hidden and/or hard-to-reach.

Here the researcher finds a few people that are related to his / her topic and also asks them to refer him/ her to more of them.

Disadvantage of snow ball sampling

No way of knowing whether the sample is representative of the population.

Purposive sampling

This involves the non random selection of elements based on a researcher’s judgment and knowledge about the population.

Disadvantage of purposive sampling

Samples are not easily defensible as being representative of populations due to potential subjectivity of researcher

References:

http://www.ghgprotocol.org/files/ghgp/AppendixA.pdf http://sociology.about.com/od/Types-of-Samples/a/Systematic-Sample.htm http://dissertation.laerd.com/simple-random-sampling.php http://www.ehow.com/info_8723950_advantages-disadvantages-simple-

random-sampling.html http://srmo.sagepub.com/view/the-sage-dictionary-of-social-research-

methods/n7.xml https://stats.oecd.org/glossary/detail.asp?ID=3839 Source: Black, T. R. (1999). Doing quantitative research in the social

sciences: An integrated approach to research design, measurement, and statistics. Thousand Oaks, CA: SAGE Publications, Inc.