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There are basically two ways to choose a sample from a sampling frame: There are benefits to both. Basically, if your sampling frame is approximately the same demographic makeup as your population, you probably want to randomly select your sample, perhaps by flipping a coin or drawing names out of a hat. But what if your sampling frame does not really represent your population? For example, what if the school where Brooke works has a lot more men than women and a lot more whites than minority races?

In the population of every college student in the world, there might be more of a balance, but Brooke's sampling frame her school doesn't really represent that well.

In that case, she might want to non-randomly select her sample in order to get a demographic makeup that is closer to that of her population. Determine the sample size. In general, larger samples are better, but they also require more time and effort to manage. If Brooke ends up having to go through 1, surveys, it will take her more time than if she only has to go through 10 surveys. But the results of her study will be stronger with 1, surveys, so she like all researchers has to make choices and find a balance between what will give her good data and what is practical.

Once you know your population, sampling frame, sampling method, and sample size, you can use all that information to choose your sample. As you can see, choosing a sample is a complicated process.

You might be wondering why it has to be that complicated. Why bother going through all those steps? Why not just go to a class and pull some students out and have them fill out the survey? Why is sampling so important to research?

Get access risk-free for 30 days, just create an account. To answer those questions, let's look at an example of an actual study that was done in the mids. A researcher mailed out surveys to a bunch of married women and asked them questions about their marriage. As you can imagine, this study sent shockwaves through America as husbands looked at their wives and calculated the probability of dissatisfaction or affairs.

Those who got the survey, filled it out, and returned it were much more likely to be dissatisfied than those who didn't return it. Maybe those who were happy in their marriage were too busy having fun with their spouse to cheat. That's why sampling is so important to research. If a sample isn't chosen carefully and systematically, it might not represent the population. And if it doesn't represent the population, then the study can't be generalized to the world beyond the study.

Let's go back to Brooke for a moment. She wants to know, in general, how much stress college students experience during finals. Let's say that she decides to save some time and bypass the normal sampling method. Instead, she just sets up a table outside the mental health office on campus where students go to see counselors. As students go in or out of the office, she gives them the survey. But in this example, Brooke's sample might end up being only college students who are seeing counselors.

They might be more anxious or depressed or high-strung in general, so the stress of finals might hit them particularly hard. As a result, Brooke's sample doesn't represent the population, and she might end up thinking that college students experience more stress than they actually do.

The sample of a study is the group of subjects in the study. Sampling is the process whereby a researcher chooses his or her sample. The five steps to sampling are:. It is important for researchers to follow these steps so that their sample adequately represents their population. If not, the results of the study could be misleading or skewed.

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By creating an account, you agree to Study. Explore over 4, video courses. Find a degree that fits your goals. What is Sampling in Research? In this lesson, we'll look at the procedure for drawing a sample and why it is so important to draw a sample that represents the population. Try it risk-free for 30 days. An error occurred trying to load this video. Try refreshing the page, or contact customer support.

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There is a price, of course, in the potential limited validity of results. When doing field-based observations, it is often impossible to intrude into the lives of people you are studying. Samples must thus be surreptitious and may be based more on who is available and willing to participate in any interviews or studies.

Please help and share: Method Best when Simple random sampling Whole population is available. Stratified sampling random within target groups There are specific sub-groups to investigate eg. Systematic sampling every nth person When a stream of representative people are available eg. Cluster sampling all in limited groups When population groups are separated and access to all is difficult, eg. Method Best when Quota sampling get only as many as you need You have access to a wide population, including sub-groups Proportionate quota sampling in proportion to population sub-groups You know the population distribution across groups, and when normal sampling may not give enough in minority groups Non-proportionate quota sampling minimum number from each sub-group There is likely to a wide variation in the studied characteristic within minority groups.

Method Best when Purposive sampling based on intent You are studying particular groups Expert sampling seeking 'experts' You want expert opinion Snowball sampling ask for recommendations You seek similar subjects eg.

Method Best when Snowball sampling ask for recommendations You are ethically and socially able to ask and seek similar subjects. Convenience sampling use who's available You cannot proactively seek out subjects. A small, but carefully chosen sample can be used to represent the population. The sample reflects the characteristics of the population from which it is drawn.

Sampling methods are classified as either probability or nonprobability. 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 nonprobability sampling, members are selected from the population in some nonrandom 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 nonprobability sampling, the degree to which the sample differs from the population remains unknown.

Random sampling 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.

Systematic sampling is often used instead of random sampling. It is also called an Nth name selection technique.

Techniques > Research > Sampling > Choosing a sampling method Probability | Quota | Selective | Convenience | Ethnographic | See also There are many methods of sampling when doing research.

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.

Feb 19, · So, in this weeks blog I am going to be discussing the different sampling techniques and methods, and considering the issue of sampling bias and the problems associated in research. There are a variety of different sampling methods available to researchers to select individuals for a . 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.

RESEARCH METHOD - SAMPLING 1. Sampling Techniques & Samples Types 2. Outlines Sample definition Purpose of sampling Stages in the selection of a sample Types of sampling in quantitative researches Types of sampling in qualitative researches Ethical Considerations in Data Collection. This type of research is called a census study because data is gathered on every member of the population. Usually, the population is too large for the researcher to attempt to survey all of its members. A small, but carefully chosen sample can be used to represent the population. Sampling methods are classified as either probability or.