Patrick Madden

>Sampling is the process of selecting units (these units are often, but not always, individual people) from a population of interest in a way that allows us to study a smaller group, but generalize our findings back to a larger population.

Of course there are times when you don’t care about generalizing back to a larger group. If you are evaluating how satisfied your employees are, there is no need to select a sample of employees (unless you happen to be GE or something). Instead, it is easier to survey all your employees. This is called a census.

However, unless the group of people you are interesting in surveying is relatively small (in the thousands or less), then it makes financial sense to select a sample of your group, conduct a survey with those people, and generalize those results back to the larger group. In most market and social research, we are interested in generalizing to specific groups. The group you wish to generalize to is referred to as the study population and your sample will come from it.

Once you have identified your study population, you have to get a list of all the members that are accessible, and this list becomes your sampling frame. Finally, you actually draw your sample (using one of the many sampling procedures). The sample is the group of people whom you select to be in your study.

Note that the sample is not the group of people who are actually complete the study. You may not be able to find all of the people you actually sample, or some could drop out over the course of the study. Unless you can get every single person in your sample to respond, the group that actually completes your study will be a portion of the sample (the people who do not complete the survey are often referred to as non-respondents or dropouts).

Sampling is a complex, multi-step process, with lots of opportunities to go wrong. There is the possibility of introducing bias when going through the process of identifying a sample. For instance, you may be able to clearly identify the population of interest, but you probably will not have access to all of them. There are opportunities for error when drawing the sample from the sampling frame. And, of those in the sample, some probably will not fully participate (drop-outs and non-responses).

Sometimes, these problems can be corrected by using weighting. Though statistically complicated, weighting allows the researcher to adjust research results based on non – response, sampling, data collection processes and population characteristics.

Types of Sampling Approaches

Broadly speaking, there are two types of sampling: probability sampling and non-probability sampling. A probability sampling method is sampling that uses random selection, a process that assures that the different units in the study population have equal and known probabilities of being chosen for the sample. A probability sample is required when the objective is to generalize results to a population rather than just those who responded to the survey.

There are a number of sampling methods in this category and each is appropriate in different circumstances.

Simple random sampling (like drawing names from a hat) is easy to accomplish and explain to others, “Everyone has the same chance to be selected”. It is a fair way to select a sample so you can generalize the results from the sample back to the population. It is not the most statistically efficient method of sampling because by the luck of the draw, you may not get good representation of population subgroups.

Stratified random sampling remedies this problem by dividing the study population into homogeneous subgroups (strata) and then taking a simple random sample in each subgroup. Stratified sampling has a couple advantages over simple random sampling. First, it assures representation of subgroups of the population, particularly subgroups based on geography (for example, counties or cities). In fact, if you want to be able to talk about subgroups, this may be the only way to go. Second, if the strata are homogeneous, then this method usually is more precise statistically. The reason is that the variability within groups is usually lower than the variability in the whole population.

Probability proportional to size sampling uses a size measurement (such as the number of employees in a company or the number of students in a school) to assist in the sampling process. Using this method, each item (in this case a business or a school) is assigned a sampling probability in proportion to its size. So a company with twice as many employees as another would have twice the probability to be sampled. This method can increase the representativeness of the sample by focusing it on the larger members of your sampling frame, where more business is conducted.

Cluster or area random sampling solves for administrative efficiency problems when sampling a population that is spread out geographically. The steps in cluster sampling are: divide the population into clusters (sometimes along geographic boundaries), randomly sample clusters, then measure all units within sampled clusters.

Multi-stage sampling is when sampling methods are combined in useful ways to address sampling needs in the most efficient and effective manner possible. Most real marketing and social research uses complex sampling strategies that combine aspects of the ones described above.

Non-probability sampling is sampling that does not involve random selection. Non-probability samples may or may not represent the population well, and there is no sure way to tell. In general, researchers prefer probabilistic or random sampling methods and consider them to be more accurate and rigorous. However, in market and social research, there may be circumstances where it is not feasible, practical or theoretically sensible to do random sampling. For example, it may be impossible to obtain or compile a complete list of the group you want to survey, making it impossible to pull a random sample.

There are two broad types of non-probability sampling: accidental and purposive.

Convenience sampling is another name for accidental sampling. This type of sampling uses whoever is readily available or convenient to the researcher.

Purposive sampling is sampling with a purpose in mind and usually targets specific, predefined groups. Intercepts are generally this type of sampling. A researcher goes to a location and then observes the people passing by to see who appears to be in the target category. Once identified, the researcher will stop him/her to request participation. If the person agrees, the first thing the researcher must do is verify that the respondent meets the criteria for being in the sample.

Purposive sampling can be very useful for situations where you need to reach a targeted sample quickly and where sampling for proportionality is not a concern. With a purposive sample, you are likely to get the opinions of your target population, but you are also likely to over represent subgroups in your population that are more readily accessible.

There are a number of purposive sampling techniques, such as: modal instance sampling (targeting the “typical case”); heterogeneity sampling (diversity sampling, aiming to solicit the full range of possibilities); expert sampling (targeting people with known expertise in some area); quota sampling (targeting people according to some fixed quota); and snowball sampling (identifying someone who meets the criteria and then asking him/her to recommend others). Despite their limited ability to generate population generalizations, each of these methods may make sense under certain circumstances.