Essentials of Marketing Research. 3rd Edition. Chapter 6

Sampling
involves selecting a relatively small number of elements from a larger defined group of elements and expecting that the information gathered from the small group will enable accurate judgement about the larger group.
Census
a research study that includes data about every member of the defined target population.

The best example of a census is the U.S census which is done every ten years.

When is sampling often used?
When it is impossible or unreasonable to conduct a census.
sampling is less time-consuming and less costly than conducting a census.
Population
the identifiable set of elements (i.e. people, products, organizations) of interest to the researcher and pertinent to the information problem.
Defined target population
the complete set of elements identified for investigation.
Sampling units
the target population elements available for selection during the sampling process
sampling frame
after defining the target population, the researcher develops a list of all eligible sampling units. Some common sources of sampling frames are links of registered voters and customer lists from magazine publishers or credit card companies.

It is often difficult and expensive to obtain accurate, representative and current sampling frames.

Lack of knowledge about defined target population
If decision makers had complete knowledge about their defined target populations, they would have perfect information about the realities of those populations, thus eliminating the need to conduct primary research. More than 95 percent of today’s marketing problems exist primarily because decision makers lack information about their problem situations and who their customers are, as well as customers’ attitudes, preferences, and marketplace behaviors.
Center Limit Theorem (CLT)
describes the theoretical characteristics of a sample population. CLT is the backbone of survey research and is important to understanding the concepts of sampling error, statistical significance, and sample sizes.

With the basic understanding of CLT, the researcher can do the following:
1. draw representative samples from any target population
2. Obtain sample stats from a random sample that serve as accurate estimates of the target population’s parameters.
3. Draw one random sample, instead of many, reducing the costs of data collection.
4. More accurately assess the reliability and validity of constructs and scale measurements.
5. Statistically analyze data and transform it into meaningful information about the target population.

Two difficulties associated with detecting sampling error:
1. a census is very seldom conducted in survey research
2. sampling error can be determined only after the sample is drawn and data collection is completed.
sampling error
Any type of bias that is attributable to mistakes in either drawing a sample or determining the sample size. sampling error can be reduced by increasing the sample size.
nonsampling error
A bias that occurs in a research study regardless of whether a sample or census is used. For example, the target population may be inaccurately defined causing population frame error; inappropriate question/scale measurements can result in measurement error.

Unlike sampling error, there are no statistical procedures to asses the impact of nonsampling errors on the quality of the data collected.

Nonsampling errors usually are related to the accuracy of the data, whereas sampling errors relate to the representativeness of the sample to the defined target population.

Probability Sampling
Each sampling unit in the defined target population has a known probability of being selected for the sample
Non-probability sampling
Sampling designs in which the probability of selection of each sampling unit is not known.

The selection of sampling units is based on the judgment of the researcher and may or may not be representative of the target population.

Examples of probability sampling methods
simple random sampling, systematic random sampling, stratified random sampling and cluster sampling.
Examples of non probability sampling
convenience sampling, judgement sampling, quota sampling, and snowball sampling.
simple random sampling
A probability sampling procedure in which every sampling unit has a known and equal chance of being selected
systematic random sampling
Similar to simple random sampling but the defined target population is ordered in some way
– Usually in the form of a customer list, taxpayer roll, or membership roster, and selected systematically.
stratified random sampling
Separation of the target population into different groups, called strata, and the selection of samples from each stratum.
two common methods are used to derive samples from the strata:
1. proportionately stratified sampling- each stratum is dependent on its size relative to the population.
2. disproportionately stratified sampling- the size of each stratum is independent of its relative size in the population.
Advantages: 1. the assurance of representatives in the sample; 2. the opportunity to study each stratum and make comparisons between strata; and 3. the ability to make estimates for the target population with the expectation of greater precision and less error.

Disadvantages: the primary difficulty is determining the basis for stratifying. Stratification is based on the target population’s characteristics of interest. Secondary information relevant to the required stratification factors might not be readily available, therefore forcing the researcher to use less desirable criteria as the factors for stratifying the target population.

cluster sampling
A probability sampling method in which the sampling units are divided into mutually exclusive and collectively exhaustive subpopulations, called clusters. Similar to stratified random sampling, but is different in that the sampling units are divded into mutually exclusive and collectively exhaustive subpopulations.

examples of possible divisions for cluster sampling include customers who patronize a store on a given day, the audience for a movie shown at a particular time, or the invoices processed during a specific week.

area sampling
A form of cluster sampling in which the clusters are formed by geographic designations. Examples include metropolitan statistical, cities, subdivisions, and blocks. Any geographical unit with identifiable boundaries can be used.
advantages: cluster sampling is widely used because of its cost- effectiveness and ease of implementation. In many cases, the only representative sampling frame available to researchers is one based on clusters.

disadvantages: a primary disadvantage of cluster sampling is that the clusters often are homogeneous. the more homogeneous the cluster, the less precise the sample estimates. another concern is the appropriateness of the designated cluster factor used to identify the sampling unites within clusters.

Convenience sampling
A nonprobability sampling method in which samples are drawn at the convenience of the researcher. For example, interviewing individuals at shopping malls or other high-traffic areas is common method of generating a convenience sampling.