Skip Nav
#
Choosing a sampling method

##
Additional Resources

## Resource Links

❶Many psychology studies have a biased sample because they have used an opportunity sample that comprises university students as their participants e.
## The Purpose of Sampling

## Main Topics

### Privacy Policy

### Privacy FAQs

### About Our Ads

### Cookie Info

Before sampling, the population is divided into characteristics of importance for the research. For example, by gender, social class, education level, religion, etc. Then the population is randomly sampled within each category or stratum. How to Construct a probability representative sample. As they are not truly representative, non-probability samples are less desirable than probability samples.

However, a researcher may not be able to obtain a random or stratified sample, or it may be too expensive. A researcher may not care about generalizing to a larger population. The validity of non-probability samples can be increased by trying to approximate random selection, and by eliminating as many sources of bias as possible.

A researcher is interested in the attitudes of members of different religions towards the death penalty. In Iowa a random sample might miss Muslims because there are not many in that state.

However, the sample will no longer be representative of the actual proportions in the population. This may limit generalizing to the state population. But the quota will guarantee that the views of Muslims are represented in the survey. A subset of a purposive sample is a snowball sample -- so named because one picks up the sample along the way, analogous to a snowball accumulating snow. A snowball sample is achieved by asking a participant to suggest someone else who might be willing or appropriate for the study.

Snowball samples are particularly useful in hard-to-track populations, such as truants, drug users, etc. Non-probability samples are limited with regard to generalization.

Because they do not truly represent a population, we cannot make valid inferences about the larger group from which they are drawn.

Validity can be increased by approximating random selection as much as possible, and making every attempt to avoid introducing bias into sample selection. Examples of nonprobability samples.

Using the random numbers table. Two of each species. Random sample The term random has a very precise meaning. The defining characteristic of a quota sample is that the researcher deliberately sets the proportions of levels or strata within the sample.

Such results only provide a snapshot at that moment under certain conditions. The concept of repeating procedures over different conditions and times leads to more valuable and durable results. Within this section of the Gallup article, there is also an error: In 5 of those surveys, the confidence interval would not contain the population percent.

Eberly College of Science. Printer-friendly version Sampling Methods can be classified into one of two categories: Sample has a known probability of being selected Non-probability Sampling: Sample does not have known probability of being selected as in convenience or voluntary response surveys Probability Sampling In probability sampling it is possible to both determine which sampling units belong to which sample and the probability that each sample will be selected.

Simple Random Sampling SRS Stratified Sampling Cluster Sampling Systematic Sampling Multistage Sampling in which some of the methods above are combined in stages Of the five methods listed above, students have the most trouble distinguishing between stratified sampling and cluster sampling. With stratified sampling one should: With cluster sampling one should divide the population into groups clusters. Stratified sampling would be preferred over cluster sampling, particularly if the questions of interest are affected by time zone.

For example the percentage of people watching a live sporting event on television might be highly affected by the time zone they are in. Cluster sampling really works best when there are a reasonable number of clusters relative to the entire population. In this case, selecting 2 clusters from 4 possible clusters really does not provide much advantage over simple random sampling.

Either stratified sampling or cluster sampling could be used. It would depend on what questions are being asked. For instance, consider the question "Do you agree or disagree that you receive adequate attention from the team of doctors at the Sports Medicine Clinic when injured? In contrast, if the question of interest is "Do you agree or disagree that weather affects your performance during an athletic event? Consequently, stratified sampling would be preferred. Cluster sampling would probably be better than stratified sampling if each individual elementary school appropriately represents the entire population as in aschool district where students from throughout the district can attend any school.

Stratified sampling could be used if the elementary schools had very different locations and served only their local neighborhood i. Again, the questions of interest would affect which sampling method should be used.

Non-probability Sampling The following sampling methods that are listed in your text are types of non-probability sampling that should be avoided: Welcome to STAT ! Benefits, Risks, and Measurements Lesson 3: Lesson 3 - Have Fun With It! Getting the Big Picture and Summaries Lesson 5: Relationships Between Measurement Variables Lesson 7: Relationships Between Categorical Variables Lesson 8: Understanding Uncertainty Lesson 9: Confidence Intervals Lesson Hypothesis Testing Lesson

Sampling Methods. Sampling and types of sampling methods commonly used in quantitative research are discussed in the following module. Learning Objectives: Define sampling and randomization. Explain probability and non-probability sampling and describes the different types of each.

Probability sampling is a technique wherein the samples are gathered in a process that gives all the individuals in the population equal chance of being selected. Many consider this to be the more methodologically rigorous approach to sampling because it eliminates social biases that could shape the research sample.

Multistage Sampling (in which some of the methods above are combined in stages) Of the five methods listed above, students have the most trouble distinguishing between stratified sampling and cluster sampling. The best sampling is probability sampling, because it increases the likelihood of obtaining samples that are representative of the population. Probability sampling (Representative samples) Probability samples are selected in such a way as to be representative of the population.

By knowing and understanding some basic information about the different types of sampling methods and designs, you can be aware of their advantages and disadvantages. The two main sampling methods (probability sampling and non-probability sampling) has their specific place in the research . There are many methods of sampling when doing research. This guide can help you choose which method to use. Simple random sampling is the ideal, but researchers seldom have the luxury of time or money to access the whole population, so many compromises often have to be made.