chapter 11 textbook Flashcards
The concept of sampling
Sampling: The process of obtaining infor- mation from a subset of a larger group.
The key to making accurate predictions about the characteristics or behaviour of a large population on the basis of a relatively small sample lies in the way in which individuals are selected for the sample.
It is critical that they be selected in a scientific manner, which ensures that the sample is representative—that it is a true miniature of the population.
population
The population, or popu- lation of interest, is the entire group of elements about whom the researcher needs to obtain information.
he elements can be consumers, companies, stores, houses, cars, trees, dogs, cats, or whatever it is that data will be collected from for the study.
One of the first steps in the sampling process is to define the population of interest. This often involves defining the target market for the product or service in question.
Sample versus census
A sample is a subset of all the members of a population. Information is obtained from or about a sample and used to make estimates about various characteristics of the entire population. Ideally, the sample from or about which information is obtained is a representative cross-section of the total population
In a census, data are obtained from every or almost every member of the population of interest.
steps to a sampling plan
step 1: define the population of interest
step 2: choose a data collection method
3: identify a sampling frame
4: select a sampling method
5: determine the sample soze
6: develop operational producers for selecting sample elements
7: execute the operational sampling plan
step 1: define the population of interest
The basic issue in developing a sampling plan is to specify the characteristics of those individuals or things (for example, customers, companies, stores) from whom or about whom information is needed to meet the research objectives.
The population of inter- est is often specified in terms of geographic area, demographic characteristics, product or service usage characteristics, or awareness measures
In addition to defining who will be included in the population of interest, research- ers should also define the characteristics of individuals who should be excluded
Step 2: choose a data collection method
The selection of a data-collection method has implications for the sampling process.
Step 3: identify a sampling frame
A list of population elements from which units to be sampled can be selected, or a specified procedure for generating such a list.
In the ideal situation, the list of population members is complete and accurate
To get a representauve sample for phones: random digit dialing A method of generating lists of telephone numbers at random.
Step 4: select a sampling method
The fourth step in developing a sampling plan is selection of a sampling method, which will depend on the objectives of the study, the financial resources available, time limita- tions, and the nature of the problem under investigation
The major sampling method alternatives can be divided into two groups: probability sampling methods and non- probability sampling methods
probability samples (step 4)
Samples in which every element of the population has a known, non-zero likelihood of selection.
Simple random sampling is the best known and most widely used probability sampling method
When these procedures are followed strictly, the laws of probability hold, allowing calculation of the extent to which a sam- ple value can be expected to differ from a population value. This difference is referred to as sampling error.
Advantages: The researcher can be sure of obtaining information from a representative cross-sec- tion of the population of interest.
* Sampling error can be computed.
* The survey results can be projected to the total population. For example, if 5 percent of the individuals in a probability sample give a particular response, the researcher can project this percentage, plus or minus the sampling error, to the total population.
non prob samples (step 4)
Samples in which specific ele- ments from the population have been selected in a non-random manner.
Non-randomness results when popu- lation elements are selected on a basis that doesn’t allow them to be chosen randomly
Purposeful non-randomness occurs when a sampling plan systematically excludes or over-represents certain subsets of the population
Disdvanatages: The researcher does not know the degree to which the sample is representative of the population from which it was drawn.
* Sampling error cannot be computed. * The results cannot and should not be projected to the total population, because the
representativeness of the sample is unknown.
Advantages: Non-probability samples cost less than probability samples. Lower costs have consid- erable appeal in situations where accuracy is not of critical importance; for example, in exploratory research.
* Non-probability samples ordinarily can be gathered more quickly than probability samples can.
* Non-probability samples of the population are reasonably representative if collected in a careful, thorough manner.
Step 5: determine the sample size
In the case of non-probability samples, researchers tend to rely on such factors as available budget, rules of thumb, and number of subgroups to be analyzed in their determination of sample size.
However, with probability samples, researchers use formulas to calculate the sample size required, given target levels of acceptable error (the difference between sample result and population value) and levels of confidence (the like- lihood that the confidence interval—sample result plus or minus the acceptable error— will take in the true population value)
Step 6: develop operational procedures for selecting sample elements
The operational procedures to be used in selecting sample elements in the data-col- lection phase of a project should be developed and specified, whether a probability or a non-probability sample is being used.
Failure to develop a proper opera- tional plan for selecting sample elements can jeopardize the entire sampling process
the different methods of probability sampling
simple random
systematic
stratified
cluster
simple random sampling
A probability sample selected by assigning a number to every ele- ment of the population and then using a table of random numbers to select specific elements for inclusion in the sample
Simple random sampling is the purest form of probability sampling.
For a simple ran- dom sample, the known and equal probability is computed as follows: sample size/ pop size
Simple random sampling is appealing because it seems easy and meets all the nec- essary requirements of a probability sample.
t guarantees that every member of the population has a known and equal chance of being selected for the sample
Simple random sampling begins with a current and complete listing of the population. But difficult or impossible to obtain
Systematic sampling
Because of its simplicity, systematic sampling is often used as a substitute for simple random sampling. It produces samples that are almost identical to those generated by simple random sampling
Probability sampling in which the entire population is numbered and elements are selected using a skip interval.
To obtain a systematic sample, the researcher first numbers the entire population, as in simple random sampling. Then the researcher determines a skip interval and selects elements based on this interval
A random starting point should be used in systematic sampling.
The main advantage of systematic sampling over simple random sampling is econ- omy. Systematic sampling is often simpler, less time-consuming, and less expensive to use than simple random sampling.
Stratified sampling
A probability sample that is forced to be more representative through simple random sampling of mutually exclusive and exhaus- tive subset
the original population is divided into two or more mutually exclusive and collectively exhaustive subsets
simple random samples of elements from the twi or more subsets are chosen independently of each other
Stratified samples are statistically morev efficient because one source of variation has been eliminated.
Not used all the time because : First, the information necessary to properly stratify the sample may not be available. Second, even if the necessary information is available, the potential value of the information may not warrant the time and costs associated with stratification
Cluster sampling
A probability sample in which the sampling units are selected from a number of small geographic areas to reduce data-collection costs.
Two steps: 1. The population of interest is divided into mutually exclusive and exhaustive subsets. 2. Arandomsampleofthesubsetsisselected.
If the sample consists of all the elements in the selected subsets, it is called a one- stage cluster sample. However, if the sample of elements is chosen in some probabilistic manner from the selected subsets, the sample is a two-stage cluster sample.
The most popular type of cluster sample is the area sample, in which the clusters are units of geography (for example, city blocks)
non probability sampling methods
convenience
judgement
quota
snowball
Convenience samples
Non-probability samples based on using people who are easily accessible.
Convenience samples are primarily used, as their name implies, for reasons of conve- nience.
Judgmet sampling
Non-probability samples in which the selection criteria are based on the researcher’s personal judgement about the representa- tiveness of the population under study.
Used for malls
where the researcher chooses participants based on their own judgment about who best represents the population
Quota sampling
Non-probability samples in which quotas, based on demographic or classification factors selected by the researcher, are established for population subgroups.
There are, however, two key differences between a quota sample and a stratified sample. First, respondents for a quota sample are not selected randomly, as they must be for a stratified sample. Second, the classification factors used for a stratified sample are selected based on the existence of a correlation between the factor and the behaviour of interest. There is no such requirement in the case of a quota sample. The demographic or classification factors of interest in a quota sample are selected on the basis of researcher judgemen
The researcher divides the population into subgroups based on chosen characteristics (e.g., 50% female, 50% male).
A specific number of participants (quota) is selected for each subgroup.
Participants are NOT chosen randomly—the researcher chooses them based on convenience or judgment.
Snowball sampling
Non-probability samples in which additional respondents are selected based on referrals from initial respondent
This procedure is used to sample from low-incidence or rare populations—that is, populations that make up a very small percentage of the total population
The main advantage of snowball sampling is a dramatic reduction in search costs. However, this advantage comes at the expense of sample quality.
The total sample is likely to be biased because the individuals whose names were obtained from those sampled in the initial phase are likely to be very similar to those initially sampled. As a result, the sample may not be a good cross-section of the total population.
