First, it assures that you will be able to represent not only the overall population, but also key subgroups of the population, especially small minority groups. If you want to be able to talk about subgroups, this may be the only way to effectively assure you'll be able to. If the subgroup is extremely small, you can use different sampling fractions f within the different strata to randomly over-sample the small group although you'll then have to weight the within-group estimates using the sampling fraction whenever you want overall population estimates.
When we use the same sampling fraction within strata we are conducting proportionate stratified random sampling. When we use different sampling fractions in the strata, we call this disproportionate stratified random sampling.
Second, stratified random sampling will generally have more statistical precision than simple random sampling. This will only be true if the strata or groups are homogeneous. If they are, we expect that the variability within-groups is lower than the variability for the population as a whole. Stratified sampling capitalizes on that fact.
For example, let's say that the population of clients for our agency can be divided into three groups: Caucasian, African-American and Hispanic-American. And, by chance, we could get fewer than that! If we stratify, we can do better. First, let's determine how many people we want to have in each group. Let's say we still want to take a sample of from the population of clients over the past year.
But we think that in order to say anything about subgroups we will need at least 25 cases in each group. Finally, by subtraction we know that there are Caucasian clients. Because the groups are more homogeneous within-group than across the population as a whole, we can expect greater statistical precision less variance.
And, because we stratified, we know we will have enough cases from each group to make meaningful subgroup inferences. Here are the steps you need to follow in order to achieve a systematic random sample:. All of this will be much clearer with an example. To use systematic sampling, the population must be listed in a random order. Now, select a random integer from 1 to 5. In our example, imagine that you chose 4. You would be sampling units 4, 9, 14, 19, and so on to and you would wind up with 20 units in your sample.
For this to work, it is essential that the units in the population are randomly ordered, at least with respect to the characteristics you are measuring. Why would you ever want to use systematic random sampling? For one thing, it is fairly easy to do. You only have to select a single random number to start things off. It may also be more precise than simple random sampling. Finally, in some situations there is simply no easier way to do random sampling.
For instance, I once had to do a study that involved sampling from all the books in a library. Once selected, I would have to go to the shelf, locate the book, and record when it last circulated. I knew that I had a fairly good sampling frame in the form of the shelf list which is a card catalog where the entries are arranged in the order they occur on the shelf.
To do a simple random sample, I could have estimated the total number of books and generated random numbers to draw the sample; but how would I find book 74, easily if that is the number I selected? I couldn't very well count the cards until I came to 74,!
Stratifying wouldn't solve that problem either. For instance, I could have stratified by card catalog drawer and drawn a simple random sample within each drawer. But I'd still be stuck counting cards. Instead, I did a systematic random sample. I estimated the number of books in the entire collection. Let's imagine it was , Then I selected a random integer between 1 and Let's say I got Next I did a little side study to determine how thick a thousand cards are in the card catalog taking into account the varying ages of the cards.
Let's say that on average I found that two cards that were separated by cards were about. That information gave me everything I needed to draw the sample. I counted to the 57th by hand and recorded the book information. Then, I took a compass. Remember those from your high-school math class? They're the funny little metal instruments with a sharp pin on one end and a pencil on the other that you used to draw circles in geometry class. Then I set the compass at.
In this way, I approximated selecting the th, th, th, and so on. I was able to accomplish the entire selection procedure in very little time using this systematic random sampling approach. I'd probably still be there counting cards if I'd tried another random sampling method. Okay, so I have no life. I got compensated nicely, I don't mind saying, for coming up with this scheme. The problem with random sampling methods when we have to sample a population that's disbursed across a wide geographic region is that you will have to cover a lot of ground geographically in order to get to each of the units you sampled.
Imagine taking a simple random sample of all the residents of New York State in order to conduct personal interviews. By the luck of the draw you will wind up with respondents who come from all over the state.
Your interviewers are going to have a lot of traveling to do. It is for precisely this problem that cluster or area random sampling was invented.
For instance, in the figure we see a map of the counties in New York State. Let's say that we have to do a survey of town governments that will require us going to the towns personally.
If we do a simple random sample state-wide we'll have to cover the entire state geographically. It is important to note that all the strata must have no overlaps. Researchers usually use stratified random sampling if they want to study a particular subgroup within the population. It is also preferred over the simple random sampling because it warrants more precise statistical outcomes.
Systematic random sampling can be likened to an arithmetic progression wherein the difference between any two consecutive numbers is the same. Say for example you are in a clinic and you have patients. There is no clear advantage when using this technique. Cluster random sampling is done when simple random sampling is almost impossible because of the size of the population. Just imagine doing a simple random sampling when the population in question is the entire population of Asia.
This probability sampling technique involves a combination of two or more sampling techniques enumerated above. In most of the complex researches done in the field or in the lab, it is not suited to use just a single type of probability sampling. Most of the researches are done in different stages with each stage applying a different random sampling technique. Check out our quiz-page with tests about:.
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A probability sampling method is any method of sampling that utilizes some form of random selection. In order to have a random selection method, you must set up some process or procedure that assures that the different units in your population have equal probabilities of being chosen.
Sampling techniques can be divided into two categories: probability and non-probability. In probability sampling, each population member has a known, non-zero chance of participating in the study. Randomization or chance is the core of probability sampling technique. In non-probability sampling.
Types of Probability Sampling Simple Random Sampling Simple random sampling is the easiest form of probability sampling. All the researcher needs to do is assure that all the members of the population are included in the list and then randomly select the desired number of . Will the research papers with convenient sampling and advanced statistical methods valid? There are several research publications with non probability sampling and advanced statistical methods.
Probability Sampling – Uses randomization and takes steps to ensure all members of a population have a chance of being selected. There are several variations on this type of sampling and following is a list of ways probability sampling may occur: Quantitative Research Design: Sampling and Measurement - The link below defines sampling and. In non-probability sampling (also known as non-random sampling) not all members of the population has a chance of participating in the study. This is contrary to probability sampling, where each member of the population has a known, non-zero chance of being selected to participate in the study.