October 21, 2010

Does N Equal One? Random and Nonrandom Sampling

new janis By Janis Prince Inniss

In a recent blog post, Karen Sternheimer refers to sociological data collection as “systematic and grounded in theory.” Therefore, reflecting on your own thoughts and opinions does not qualify as “systematic” data collection. First you are—to use sociological lingo—an N of one! In other words, your entire study consists of just one person, total! You. Your experience is not a random sample of experiences; it is not a sample by any stretch of the imagination.

Do you know what a sample is? Before we get to samples, consider that the term population refers to all of the cases that a researcher is interested in; this is true regardless of whether the researcher is interested in individuals, groups, or things. For example, if you decided to do some research on the students in your class—let’s say you wanted to learn about their career plans—then the population of interest to you is your class. What about if you were interested in the same subject, but wanted to know the career plans for all students at your school? In this second case, your population of interest is the whole school.

clip_image002 How many students are there in your class? So collecting data—let’s say you decide on a survey as your method of data collecting—from the entire class might be feasible. Now what about from your entire school? Is it too big for you to be able to collect data from all the students? Maybe you could collect data from everyone if you had help, but usually no one wants to help you unless you pay them. While that is reasonable, what would you do if you still wanted to answer to your research question, but could not afford to get data from the entire population? If you face the problem of not being able to get information from the entire population you’re interested in, you’re sharing a predicament faced by many other researchers.

What do we do? We study a sample—a subset of the total cases in which we are interested. We collect data on a sample of the population, knowing that if every member of the population has an equal chance of being in the sample, we can generalize the information from, for example, your random sample to the entire school (the population).

How will you define your sample? In order to decide, you should know that there are two broad categories of sampling in quantitative research: random and nonrandom. Let’s look at three types of nonrandom sampling. Accidental or convenience samples are those that researchers select based on what or who is convenient. If you are using an accidental or convenience sample, you might hand your survey to whoever you encounter around your school until you get the number of surveys you want completed.

A quota sample means that you would select your sample in proportion to some aspect of the population. For example, if your school is 60 percent female and 40 percent male, you might want that proportion of females to males reflected in your sample. If your school has 600 females and 400 males, and you decide that your sample will be made up of 100 students, your quota sample would include 60 females and 40 males.

clip_image006The final nonrandom type of sample is a judgment or purposive sample. In this case, as the researcher you decide what characteristics are of interest to you and your sample consists of people (in this case) who meet that criteria. If you are interested only in learning about the career plans of students who intend to pursue graduate degrees, for example, these are the only people who would be in your sample.

clip_image004 I’ll discuss three types of random samples here. Simple random sampling is akin to putting all the names of the students in your school into an enormous hat and then drawing names; those people will make up your random sample. Unless you have a tiny school such a process is unwieldy; a good alternative is to use a table or a website of random numbers to select those who will be in the sample. Systemic sampling means that you would select every nth person from a list of every student at your school.

Usually, we divide the number in the population by the sample size to find n: From a school of 1,000 students for which we want a sample size of 100, we would choose every 10th student. (You could use a randomly selected number as your starting point.) If you chose to use a stratified sample, you would divide the student population into subgroups based on one or more variables of interest. You might want to examine career plans by student major; in that case you could group students by major and then take a simple random sample of each major.

Next time you hear someone representing their views as shared by “most people,” consider their sample size. Does N equal 1?

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Comments

This post reminds me of my sociolgy class because we are doing survey's in there to learn aboout students.

This was a very interesting article and this has should me that random sampling is way better then just plain sampling because this way you can't judge or be predigeste

Thank you for sharing this information. The information was very helpful and saved a lot of my time.

Appreciate all the feedback

Graduate Dissertation: You're quite welcome!

This article helped me to understand more about how to do a survey in the correct way. I now know that it greatly depends on who you chose for your survey, which can make it either accurate or inaccurate. I can now also see why surveys with the same topic can have totally different results.

I liked the way that his article was broken down. In my sociology class, we are learning about research and how to make a good, non-biased experiment. Your way of describing how if a sample size is large enough then it can represent the entire population, makes sense to me. We are also learning about the importance of random sampling, thanks!

I didn't know there were so many types of sampling. And I also didn't know that it could be so difficult to surveyed a population. It takes money and sometimes a good number of people to be able to get a good result. I don't think that a lot of people that use statistics understand sometimes how difficult it is to find these percentages. Thank you. :)

This was an insightful article; i didnt know there was so many different types of sampling.It also showed me that nonrandom sampling can be easily judged as fixed. I now know that random sampling is the best way to go.

There are some circumstances that the type of sample a researcher gets is appropriate. For example, if a researcher is dealing with a population that consists of warehouse workers using a certain system that he or she has developed. Wouldn't it be appropriate if the samples were nonrandom? In this case, only those who have used the system can answer.

In the case of random samples, I think Starbucks has done this. There are times that a customer gets a long receipt from the cashier. At the end of the receipt is an empty box for an identification number that comes out after you've answered an online survey. That receipt with that ID number will grant you a free drink. Imagine if it was nonrandom: the staff can pick who can get a free drink.

Anyway, just my two cents. =D

This article was very helpful and informational, I enjoyed it! This article made me more aware of the truth behind statics and that the type of sampling used in a survey should most definitely be used in determining whether its valid or not. Thank you for posting!

I liked the way that his article was broken down. In my sociology class, we are learning about research and how to make a good, non-biased experiment. Your way of describing how if a sample size is large enough then it can represent the entire population, makes sense to me. We are also learning about the importance of random sampling, thanks!

Thank you very useful information admin, and pardon me permission to share articles here may help

That is an interesting post. I will return to your blog.

, I have a lot of knowledge from what you share, to say thank you, the information and knowledge here helps me a lot

Very interesting! Thanks you

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