Sample Survey Design
Survey research is a commonly used technique to collect the data that organizational decision makers need to help design and market products and services. In this approach, a questionnaire is administered to members of a representative sample of potential customers or other persons of interest with the assumption that the responses of the sample will represent the hypothetical responses of the underlying population. Samples can be chosen in a number of ways including random sampling, systematic sampling, convenience sampling, stratified random sampling, and cluster sampling. In any of these sampling techniques, however, it is important not to introduce bias into the sample. There are a number of ways in which survey information can be collected: In-person interviews, mail surveys, telephone surveys, and Internet surveys. No matter the way in which the data are collected, however, the design of a meaningful survey requires attention to a number of items.
Keywords Bias; Data; Distribution; Hypothesis; Normal Distribution; Population; Probability; Reliability; Sample; Validity; Variable
To be successful, businesses cannot operate in a vacuum. On the one end of the chain, businesses need to work with suppliers, partners, and government agencies to make sure that they are able to offer their widgets to the marketplace in a timely manner and at a reasonable quality and cost. On the other end of the chain, businesses also need to work with the consumer. No matter how well a widget works or how inexpensive it is, unless it meets the needs of some segment of the market, it is unlikely that it will be a success. Engineers, designers, and marketers all need to know what potential consumers want as well as what they don't want so that they can design, produce and market widgets that will sell. As the failure of Ford's Edsel in the mid-20th century illustrates, no matter how innovative or well-designed a product is, if it is not well-received by the public, it will not be a commercial success. This is more so true than ever in today's global economy. For example, a marketing campaign that plays well in one culture can not only be ineffective in another, but actually be offensive and hurt the company's reputation and sales in general.
There are many ways to collect data that can be used in decision making about how to design and market a product or service so that it will be well-received in the marketplace. These include laboratory research, field studies, and pilot introductions of the product or service in small, representative marketplaces. Another commonly used technique is survey research in which a questionnaire or interview is administered to members of a representative sample of potential customers with the assumption that the responses of the sample will represent the hypothetical responses of the underlying population.
Defining a Target
One of the first things that needs to be done when considering survey research is to operationally define the target population. Although in some cases it is of value to just randomly interview every 15th person who walks into a shopping mall, in most cases the target population needs to be better defined. For example, if the widget is a product that is aimed at preteen girls who live in the city, one will not gain good data from interviewing people in a retirement community in the suburbs.
Drawing a Sample
Once the population is operationally defined, a sample needs to be drawn from the population. A representative sample can be drawn in a number of different ways.
The simplest approach is to merely randomly select people from the population (e.g., by having a computer pick names at random from a list or by selecting names from a hat) and assigning them to the sample. This approach has the advantage that it will more than likely (based on the laws of probability) be representative of the underlying population. On the other hand, achieving a truly random sample can be more difficult than it sounds. Surveys tend to have notoriously low return rates. This means that many of the people from whom one would like to collect data are taking themselves out of the sample. This self-selection means that the sample is not truly random. For example, suppose one wanted to know the reactions of teenage girls to a new widget. As an incentive, the analyst could send a dollar along with the survey as thanks for completing the questionnaire. However, if the widget is a high priced item that can only be easily purchased by the affluent, it is unlikely that this approach to data gathering would work. The dollar might be a good incentive for teenage girls from lower income families to fill out the survey, but they are unlikely to be able to afford the widget even with the extra dollar. Those who can afford to buy the widget, on the other hand, are unlikely to find an extra dollar in their pockets to be much of an incentive to complete the questionnaire. Even if one is conducting in-person interviews, samples can easily self-select. Certainly, people can decline to participate. However, depending on where the data are being collected, extraneous variables such as time of day can affect the composition of the sample. For example, if one what's to know consumer's opinions about a new gizmo, one could pass out samples and collect feedback at the local mall. However, if the data were collected during the day during the work week, the probability of getting working adults as part of the sample (or even school aged children if it were during the school year) would be greatly diminished. As a result, even if the participants in the study were randomly chosen, the sample (e.g., people in the mall at 2:00 p.m. on Tuesday) would not necessarily represent the population of all shoppers who go to that mall (let alone other malls). Further, it would be difficult to randomly pick who would participate in the study.
Another way to select samples is through systematic sampling. In this approach, the researcher could select every nth person who walks in the door of the mall to participate in the survey. It is easier to select the participants in this scenario, but it still may not be a truly random sample depending on self-selection, what door one chose, the time of day, and so forth.
One could choose a convenience sample instead by asking whomever looks approachable, appears to be interested in the survey or the product, or otherwise is convenient to survey if s/he is willing to participate in the survey. Although this approach has the advantage of making the sample easy to choose, it is also very unlikely that a convenience sample will be truly representative of the underlying population. For example, all the participants from whom it is convenient to collect data may share one or more characteristics such as attractiveness to the person who is collecting the data, extroversion, or not being employed full time.
Stratified Random Sampling
To help ensure that the correct proportions of different demographics are included in the sample, one could use a stratified random sample. In this approach to sample selection, one a priori determines what general characteristics one wants to include in the sample (e.g., an equal number of women and men; equal numbers of children, young adults, and adults). Within each of these subgroups (i.e., strata), a sample is randomly chosen in proportion to the proportion of that strata within the population of interest. This approach helps one to gather information about specific subgroups in the population. In addition, stratified random sampling is more likely to yield an accurate representation of each group than are some other sampling techniques. However, this approach has the potential drawback of introducing bias in some instances.
Another approach to sampling for survey research is cluster sampling. In this approach, the population is divided into non-overlapping areas (i.e., clusters) and participants are randomly selected from...
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