Stratified Sampling is possible when it makes sense to partition the population into groups based on a factor that may influence the variable that is being measured. These groups are then called strata. An individual group is called a stratum. With stratified sampling one should:. Stratified sampling works best when a heterogeneous population is split into fairly homogeneous groups.
Under these conditions, stratification generally produces more precise estimates of the population percents than estimates that would be found from a simple random sample.
Cluster Sampling is very different from Stratified Sampling. With cluster sampling one should. It is important to note that, unlike with the strata in stratified sampling, the clusters should be microcosms, rather than subsections, of the population. Each cluster should be heterogeneous. Additionally, the statistical analysis used with cluster sampling is not only different, but also more complicated than that used with stratified sampling. Each of the three examples that are found in Tables 3.
However, there are obviously times when one sampling method is preferred over the other. The following explanations add some clarification about when to use which method.
The most common method of carrying out a poll today is using Random Digit Dialing in which a machine random dials phone numbers. Some polls go even farther and have a machine conduct the interview itself rather than just dialing the number!
Such " robo call polls " can be very biased because they have extremely low response rates most people don't like speaking to a machine and because federal law prevents such calls to cell phones. Since the people who have landline phone service tend to be older than people who have cell phone service only, another potential source of bias is introduced. National polling organizations that use random digit dialing in conducting interviewer based polls are very careful to match the number of landline versus cell phones to the population they are trying to survey.
The following sampling methods that are listed in your text are types of non-probability sampling that should be avoided:. Since such non-probability sampling methods are based on human choice rather than random selection, statistical theory cannot explain how they might behave and potential sources of bias are rampant. In your textbook, the two types of non-probability samples listed above are called "sampling disasters.
The article provides great insight into how major polls are conducted. When you are finished reading this article you may want to go to the Gallup Poll Web site, https: It is important to be mindful of margin or error as discussed in this article.
We all need to remember that public opinion on a given topic cannot be appropriately measured with one question that is only asked on one poll. 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: 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. This is generally done to insure the inclusion of a particular segment of the population. The proportions may or may not differ dramatically from the actual proportion in the population.
The researcher sets a quota , independent of population characteristics.
A stratified sample is a mini-reproduction of the population. Before sampling, the population is divided into characteristics of importance for the research. For example, .
Ultimately, though, the sampling technique you choose should be the one that best allows you to respond to your particular research question. Let's review four kinds of probability sampling techniques.
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. 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. The following sampling methods are examples of probability sampling: Of the five methods listed above, students have the most trouble.
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. Types of Probability Sampling Methods. Simple Random Sampling. This is the purest and the clearest probability sampling design and strategy. It is also the most popular way of a selecting a sample because it creates samples that are very highly representative of the population.. Simple random is a fully random technique of selecting subjects.