Wednesday, May 30, 2007

sampling: design and procedures

Chapter 11

Information about the characteristics of a population may be obtained by conducting either a sample or a census. Budget and time limits, large population size, and small variants and a characteristic of interest favor the use of a sample. Sampling is also preferred when the cost of sampling error is low, the cost of non-sampling error is high, the nature of measurement is destructive, and attention must be focused on the individual cases. The opposite set of conditions favor the use of a census.

Sampling begins by defining the target population in terms of:
  • elements
  • sampling units
  • extent
  • time
Then a sampling frame should be determined. A sampling frame is a representation of the elements of the target population. It consists of a list of directions for identifying the target population. At this stage, it is important to recognize any sampling frame errors that may exist. The next steps involve selecting a sampling technique and determining the sample size. In addition to quantitative analysis, several qualitative considerations should be taken into account in determining the sample size. Finally, execution of the sampling process requires detailed specifications for each step in the sampling process.

Sampling techniques may be classified as non-probability and probability techniques. Nonprobability sampling techniques rely on the researcher's judgment. Consequently, they do not permit an objective evaluation of the precision of the sample results, and the estimates obtained are not statistically projectable to the population.

The commonly used the non-probability sampling techniques include:
  • convenience sampling
  • judgmental sampling
  • quota sampling
  • snowball sampling
In probability sampling techniques, sampling units are selected by chance. Each sampling unit has any nonzero chance of being selected in the researcher can pre-specify every potential sample of a given size that could be drawn from the population, as well as the probability of selecting each sample. It is also possible to determine the precision of the sample estimates and inferences and make projections to the target population.

Probability sampling techniques include:
  • simple random sampling
  • systematic sampling
  • stratified sampling
  • cluster sampling
  • sequential sampling
  • double sampling
The choice between probability and non-probability sampling should be based on the nature of the research, the degree of error tolerance, the relative magnitude of sampling and non-sampling errors, the variability in the population, and statistical and operational considerations.

When conducting international marketing research, it is desirable to achieve comparability and sample composition and representativeness even though this may require the use of different sampling techniques in different countries. It is unethical and misleading to treat nonprobability samples as probability samples and project the results to the target population. The Internet and computers can be used to make the sampling design process more effective and efficient.

Population -- the aggregate of all the elements, sharing some common set of characteristics, that comprises the universe for the purpose of the marketing research problem
Census -- a complete enumeration of the elements of the population or study objects
sample -- a subgroup of the elements of the population selected for participation in the study
target population -- the collection of elements or objects that possesses the information sought by the researcher and about which inferences are to be made
element -- objects that possess the information sought by the researcher and about which inferences are to be made
sampling unit -- the basic unit containing the elements of the population to be sampled
sampling frame -- a representation of the elements of the target population. It consists of a list or set of directions for identifying the target population
Bayesian approach -- a selection method with the elements are selected sequentially. This approach explicitly incorporates prior information about population parameters as well as costs and probabilities associated with making wrong decisions
sampling with replacement -- a sampling technique in which an element can be included in the sample more than once
sampling without replacement -- a sampling technique in which an element cannot be included in the sample more than once
sample size -- the number of elements to be included in the study
nonprobability sampling -- sampling techniques that do not use chance selection procedures. Rather, they rely on a personal judgment of the researcher
probability sampling -- a sampling procedure in which each element of the population has a fixed probabilistic chance of being selected for the sample
convenience sampling -- a nonprobability sampling technique that attempts to obtain a sample of convenient elements. The selection of sampling units is left primarily to the interviewer
judgmental sampling -- a form of convenience sampling in which the population elements are purposively based on the judgment of the researcher
quota sampling -- and nonprobability sampling technique that is a two-stage restricted judgmental sampling. The first stage consists of developing control categories or quotas of population elements. In the second stage, sample elements are selected based on convenience or judgment
Snowball sampling -- and nonprobability sampling technique in which an initial group of respondents is selected randomly. Subsequent respondents are selected based on the referrals or information provided by the initial respondents. This process may be carried out in ways by obtaining referrals from referrals
simple random sampling (SRS) -- a probability sampling technique in which each element in the population has a known and equal probability of selection. Every element is selected independently of every other element and the sample is drawn by a random procedure from a sampling frame
systematic sampling -- a probability sampling technique in which the sample is chosen by selecting a random starting point and then picking every ith element in succession from the sampling frame
stratified sampling -- a probability sampling technique that uses a two step process to partition the population into subpopulations, or strata. Elements are selected from each stratum by a random procedure
cluster sampling -- first, the target population is divided into mutually exclusive and collectively exhaustive subpopulations called clusters. Then, a random sample of clusters is selected based on a probability sampling technique such as simple random sampling. For each selected cluster, either all the elements are included in the sample or a sample of elements is drawn probabilistically
area sampling -- a common form of cluster sampling in which the clusters consist of geographic areas such as countries, housing tracts, block, or other area descriptions
probability proportionate to size sampling -- a selection method with the clusters are selected with probability proportional to size and a probability of selecting a sampling unit and a selected cluster varies inversely with the size of the cluster
sequential sampling -- a probability sampling technique in which the population elements are sampled sequentially, data collection and analysis are done at each stage, and a decision is made as to whether additional population elements should be sampled
double sampling -- a sampling technique in which certain population elements are sampled twice