Skip Nav

# 3.5 Simple Random Sampling and Other Sampling Methods

❶As described above, systematic sampling is an EPS method, because all elements have the same probability of selection in the example given, one in ten.

## You must create an account to continue watching

Telephone surveys get around this problem by random-digit dialing -- but that assumes that everyone in the population has a telephone. The key to random selection is that there is no bias involved in the selection of the sample. Any variation between the sample characteristics and the population characteristics is only a matter of chance. 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, 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. A note on sample size - Once a sampling method has been determined, the researcher must consider the sample size. In qualitative studies, sampling typically continues until information redundancy or saturation occurs.

This is the point at which no new information is emerging in the data. Therefore, in qualitative studies is it critical that data collection and analysis are occurring simultaneously so that the researcher will know when the saturation point is reached.

It is important to understand that the saturation point may occur prematurely if the researcher has a narrow sampling frame, a skewed analysis of the data, or poor methodology.

Because of this, the researcher must carefully create the research question, select an appropriate target group, eliminate his or her own biases and analyze data continuously and thoroughly throughout the process to bring validity to the data collected. The following slideshare presentation, Collecting Qualitative Data , and the Resource Links on this page provide additional insight into qualitative sampling.

Qualitative Research Methods - A Data Collectors Field Guide - This comprehensive, detailed guide describes various types of sampling techniques and provides examples of each, as well as pros and cons. Qualitative Research Overview - The following link provides a full overview of qualitative research, but also contains sections discussing types of sampling methods and methods of participant recruitment.

Sampling - This resource provides a brief overview of sampling and sample size with links to descriptions of purposeful sampling strategies. A Guide to Using Qualitative Research Methodology - The file linked below contains a full description of how to conduct qualitative sampling, including a chart that lists the types of sampling techniques and includes examples.

Sampling Designs in Qualitative Research - The following article discusses sampling designs and ways to make the sampling process more public. This pin will expire , on Change. This pin never expires. Select an expiration date. About Us Contact Us. Search Community Search Community. Qualitative Sampling Methods The following module describes common methods for collecting qualitative data.

Describe common types of qualitative sampling methodology. Explain the methods typically used in qualitative data collection. Describe how sample size is determined. Purposeful Sampling is the most common sampling strategy. In this type of sampling, participants are selected or sought after based on pre-selected criteria based on the research question. For example, the study may be attempting to collect data from lymphoma patients in a particular city or county.

The sample size may be predetermined or based on theoretical saturation, which is the point at which the newly collected no longer provides additional insights. Click on the following link for a desciption of types of purposeful sampling:

## Main Topics

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.

### Privacy FAQs

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.