Human Resources/MS – 95 Research Methodology for Management Decisions
Discuss the importance of sampling frame. What are the possible errors resulting from a faulty sampling frame?
2. Discuss the importance of sampling frame. What are the possible errors resulting from a faulty sampling frame?
A sampling frame is a list or other device used to define a researcher's population of interest. The sampling frame defines a set of elements from which a researcher can select a sample of the target population. Because a researcher rarely has direct access to the entire population of interest in social science research, a researcher must rely upon a sampling frame to represent all of the elements of the population of interest. Generally, sampling frames can be divided into two types, list and nonlist. Examples of list frames include a list of registered voters in a town, residents listed in a local telephone directory, or a roster of students enrolled in a course.
The sample of a study can have a profound impact on the outcome of a study. 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.
Brooke is a psychologist who is interested in studying how much stress college students face during finals. She works at a university, so she is planning to send out a survey around finals time and ask some students to rank on a scale of 1 to 5 how stressed out they are.
But which students should she survey? All of the students at the university? Only the students in the psychology department? Only freshmen? There are a lot of possibilities for Brooke's sample. The sample of a study is simply the participants in a study. In Brooke's case, her sample will be the students who fill out her survey.
Sampling is the process whereby a researcher chooses her sample. This might seem pretty straightforward: just get some people together, right? But how does Brooke do that? Should she just stand on a corner and start asking people to take her survey? Should she send out an email to every college student in the world? Where does she even begin?
Because sampling isn't as straightforward as it initially seems, there is a set process to help researchers choose a good sample. Let's look closer at the process and importance of sampling.
So Brooke wants to choose a group of college students to take part in her study. To select her sample, she goes through the basic steps of sampling.
1. Identify the population of interest. A population is the group of people that you want to make assumptions about. For example, Brooke wants to know how much stress college students experience during finals. Her population is every college student in the world because that's who she's interested in. Of course, there's no way that Brooke can feasibly study every college student in the world, so she moves on to the next step.
2. Specify a sampling frame. A sampling frame is the group of people from which you will draw your sample. For example, Brooke might decide that her sampling frame is every student at the university where she works. Notice that a sampling frame is not as large as the population, but it's still a pretty big group of people. Brooke still won't be able to study every single student at her university, but that's a good place from which to draw her sample.
3. Specify a sampling method. There are basically two ways to choose a sample from a sampling frame: randomly or non-randomly. 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.
4. 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 1,000 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 1,000 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.
5. Implement the plan. 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?
To answer those questions, let's look at an example of an actual study that was done in the mid-1970s. A researcher mailed out surveys to a bunch of married women and asked them questions about their marriage. Only 4% of people responded, and of those who did, 98% said they were dissatisfied in their marriage and 75% said they had or were having an extramarital affair.
As you can imagine, this study sent shockwaves through America as husbands looked at their wives and calculated the probability of dissatisfaction or affairs. But the sample (the 4% who responded) didn't reflect the population of married women. 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.
A sampling frame is a list of all eligible members of a population from which samples are drawn. It can be thought of as the pool from which samples are obtained. It is a statistical framework used in surveys, social research, marketing research, and different types of studies. This frame is necessary to arrive at an unbiased, accurate conclusion or finding because it defines the population being studied completely. It's not normally possible or even practical to make direct observations of every element in the population of interest, and a frame restricts the population being studied to a manageable figure, ultimately helping the researchers to draw conclusion ns about the entire population.
For instance, a study aimed at figuring out the time teenagers spend online can't really include every teenager in the world. Certain parameters are introduced to make the population of interest smaller. A sampling frame in this case might specify that the teenager live in and around New York, be between the ages of 13 and 15, has access to a computer at home, and attends a public school. A study conducted this way may hope to bring out insights that apply to teenagers in general in this segment. Establishing a clear frame is critical to the success of any survey or study as a faulty frame leads to inconsistent or inaccurate findings or results.
Though the frame narrows down the pool from which the sample is drawn, it differs from the population of interest to some extent. For instance, using the above example, the sampling frame doesn't include teenagers who access the web from their mobile phone, who aren't at home at the time of the call, or who simply aren't interested in participating in surveys. Even getting into the sampling frame doesn't ensure that the person becomes a part of the final sample group. Samples may be drawn randomly from the frame where every person has a chance of being included or in a more systematic fashion, say, when every tenth person in the list is selected.
There are numerous problems, which those drawing up a sampling frame experience, that may skew the results. Missing members are a very common problem where those who need to be within the frame have been left out by accident. Duplicate members are also a big issue, where a member is listed more than once. Sometimes foreign entries — people who don't represent the population of interest — can be found within the frame. Other times, instead of individuals being listed, the frame may contain groups. When mistakes exist within the sampling frame, the final sample drawn is faulty, either as a sample that is unrepresentative of the group being studied or containing significant bias.
Market research involves the collection of data to obtain insight and knowledge into the needs and wants of customers and the structure and dynamics of a market. In nearly all cases, it would be very costly and time-consuming to collect data from the entire population of a market. Accordingly, in market research, extensive use is made of sampling from which, through careful design and analysis, Marketers can draw information about the market.
Sample design covers the method of selection, the sample structure and plans for analysing and interpreting the results. Sample designs can vary from simple to complex and depend on the type of information required and the way the sample is selected.
Sample design affects the size of the sample and the way in which analysis is carried out. In simple terms the more precision the market researcher requires, the more complex will be the design and the larger the sample size.
The sample design may make use of the characteristics of the overall market population, but it does not have to be proportionally representative. It may be necessary to draw a larger sample than would be expected from some parts of the population; for example, to select more from a minority grouping to ensure that sufficient data is obtained for analysis on such groups.
Many sample designs are built around the concept of random selection. This permits justifiable inference from the sample to the population, at quantified levels of precision. Random selection also helps guard against sample bias in a way that selecting by judgement or convenience cannot.
Defining the Population
The first step in good sample design is to ensure that the specification of the target population is as clear and complete as possible to ensure that all elements within the population are represented. The target population is sampled using a sampling frame. Often the units in the population can be identified by existing information; for example, pay-rolls, company lists, government registers etc. A sampling frame could also be geographical; for example postcodes have become a well-used means of selecting a sample.
For any sample design deciding upon the appropriate sample size will depend on several key factors
(1) No estimate taken from a sample is expected to be exact: Any assumptions about the overall population based on the results of a sample will have an attached margin of error.
(2) To lower the margin of error usually requires a larger sample size. The amount of variability in the population (i.e. the range of values or opinions) will also affect accuracy and therefore the size of sample.
(3) The confidence level is the likelihood that the results obtained from the sample lie within a required precision. The higher the confidence level, that is the more certain you wish to be that the results are not atypical. Statisticians often use a 95 per cent confidence level to provide strong conclusions.
(4) Population size does not normally affect sample size. In fact the larger the population size the lower the proportion of that population that needs to be sampled to be representative. It is only when the proposed sample size is more than 5 per cent of the population that the population size becomes part of the formulae to calculate the sample size.
Types of Sampling
There are many different types of sampling technique. We have summarised the most popular below:
Sampling Method Definition Uses Limitations
Cluster Sampling) Units in the population can often be found in certain geographic groups or "clusters" (e.g. primary school children in Derbyshire. A random sample of clusters is taken, then all units within the cluster are examined Quick & easy; does not require complete population information; good for face-to-face surveys Expensive if the clusters are large; greater risk of sampling error
Convenience Sampling Uses those who are willing to volunteer Readily available; large amount of information can be gathered quickly Cannot extrapolate from sample to infer about the population; prone to volunteer bias
Judgement Sampling A deliberate choice of a sample - the opposite of random Good for providing illustrative examples or case studies Very prone to bias; samples often small; cannot extrapolate from sample
Quota Sampling Aim is to obtain a sample that is "representative" of the overall population; the population is divided ("stratified") by the most important variables (e.g. income,. age, location) and a required quota sample is drawn from each stratum Quick & easy way of obtaining a sample Not random, so still some risk of bias; need to understand the population to be able to identify the basis of stratification
Simply Random Sampling Ensures that every member of the population has an equal chance of selection Simply to design and interpret; can calculate estimate of the population and the sampling error Need a complete and accurate population listing; may not be practical if the sample requires lots of small visits all over the country
Systematic Sampling After randomly selecting a starting point from the population, between 1 and "n", every nth unit is selected, where n equals the population size divided by the sample size Easier to extract the sample than via simple random; ensures sample is spread across the population Can be costly and time-consuming if the sample is not conveniently located