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. Instead, we decide to do a cluster sampling of five counties marked in red in the figure. Once these are selected, we go to every town government in the five areas. Clearly this strategy will help us to economize on our mileage.
Cluster or area sampling, then, is useful in situations like this, and is done primarily for efficiency of administration. Note also, that we probably don't have to worry about using this approach if we are conducting a mail or telephone survey because it doesn't matter as much or cost more or raise inefficiency where we call or send letters to.
The four methods we've covered so far -- simple, stratified, systematic and cluster -- are the simplest random sampling strategies. In most real applied social research, we would use sampling methods that are considerably more complex than these simple variations. The most important principle here is that we can combine the simple methods described earlier in a variety of useful ways that help us address our sampling needs in the most efficient and effective manner possible.
When we combine sampling methods, we call this multi-stage sampling. For example, consider the idea of sampling New York State residents for face-to-face interviews. Clearly we would want to do some type of cluster sampling as the first stage of the process. 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.
In yet another approach, cluster sampling , a researcher selects the sample in stages, first selecting groups of elements, or clusters e. Suppose some researchers want to find out which of two mayoral candidates is favored by voters. Obtaining a probability sample would involve defining the target population in this case, all eligible voters in the city and using one of many available procedures for selecting a relatively small number probably fewer than 1, of those people for interviewing.
For example, the researchers might create a systematic sample by obtaining the voter registration roster, starting at a randomly selected name, and contacting every th person thereafter. Or, in a more sophisticated procedure, the researchers might use a computer to randomly select telephone numbers from all of those in use in the city, and then interview a registered voter at each telephone number.
This procedure would yield a sample that represents only those people who have a telephone. Several procedures would also be available for recruiting a convenience sample, but none of them would include the entire population as potential respondents. For example, the researchers might ascertain the voting preferences of their own friends and acquaintances. Or they might interview shoppers at a local mall. Or they might publish two telephone numbers in the local newspaper and ask readers to call either number in order to "vote" for one of the candidates.
The important feature of these methods is that they would systematically exclude some members of the population respectively, eligible voters who do not know the researchers, do not go to the shopping mall, and do not read the newspaper.
Consequently, their findings could not be generalized to the population of city voters. Samples are evaluated primarily according to the procedures by which they were selected rather than by their final composition or size.
In the example above, it would be impossible to know if the convenience sample consisting of the researchers' friends or mall shoppers is representative, even if its demographic characteristics closely resembled those of the city electorate e.
And even if several thousand people called the published telephone numbers, the sample would be seriously biased. Of course, results from a probability sample might not be accurate for many reasons. Using probability sampling procedures is necessary but not sufficient for obtaining results that can be generalized with confidence to the entire population.
One of the major concerns about a probability sample is that its response rate is sufficiently high. Once a sample is selected, an attempt is made to collect data e. Some sample members inevitably are traveling, hospitalized, incarcerated, away at school, or in the military. Others cannot be contacted because of their work schedule, community involvement, or social life. Others simply refuse to participate in the study, even after the best efforts of the researcher to persuade them otherwise.
Each type of nonparticipation biases the final sample, usually in unknown ways. In the General Social Survey GSS , for example, those who refused to be interviewed were later found to be more likely than others to be married, middle-income, and over 30 years of age, whereas those who were excluded from the survey because they were never at home were less likely to be married and more likely to live alone Smith, The importance of intensive efforts at recontacting sample members who are difficult to reach e.
The response rate describes the extent to which the final data set includes all sample members. It is calculated as the number of people with whom interviews are completed "completes" divided by the total number of people or households in the entire sample, including those who refused to participate and those who were not at home. Whether data are collected through face-to-face interviews, telephone interviews, or mail-in surveys, a high response rate is extremely important when results will be generalized to a larger population.
The lower the response rate, the greater the sample bias. Fowler , for example, warned that data from mail-in surveys with return rates of "20 or 30 percent, which are not uncommon for mail surveys that are not followed up effectively, usually look nothing at all like the sampled populations" Fowler, , p. This is because "people who have a particular interest in the subject matter or the research itself are more likely to return mail questionnaires than those who are less interested" p.
Fowler warned that: In such instances, the final sample has little relationship to the original sampling process.
Video: What is Sampling in Research? - Definition, Methods & Importance - Definition, Methods & Importance The sample of a study can have a profound impact on the outcome of a study.
SAMPLING IN RESEARCH Sampling In Research Mugo Fridah W. INTRODUCTION This tutorial is a discussion on sampling in research it is mainly designed to eqiup beginners with knowledge on the general issues on sampling that is the purpose of sampling in research, dangers of.
What is Sampling? Imagine, for example, an experiment to test the effects of a new education technique on schoolchildren. It would be impossible to select the entire school age population of a country, divide them into groups and perform research.. A research group sampling the diversity of flowers in the African savannah could not count every single flower, because it would take many years. Sampling Let's begin by covering some of the key terms in sampling like "population" and "sampling frame." Then, because some types of sampling rely upon quantitative models, we'll talk about some of the statistical terms used in sampling.
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. This was a presentation that was carried out in our research method class by our group. It will be useful for PHD and master students quantitative and qualitat.