Sampling Methods - Research Survey - Lecture Slides, Slides of Research Methodology

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Sampling Methods

Defining the Target Population

• It is critical to the success of the research

project to clearly define the target population.

• Rely on logic and judgment.

• The population should be defined in

connection with the objectives of the study.

Technical Terms

• A sampling frame is a list of sampling units.
• A sample is a collection of sampling units drawn from a

sampling frame.

• Parameter: numerical characteristic of a population
• Statistic: numerical characteristic of a sample

Errors of nonobservation

• The deviation between an estimate from an ideal

sample and the true population value is the sampling

error.

• Almost always, the sampling frame does not match up

perfectly with the target population, leading to errors

of coverage.

Errors of observation

• These errors can be classified as due to the

interviewer, respondent, instrument, or

method of data collection.

Interviewers

• Interviewers have a direct and dramatic effect on the way a person responds to a question. - Most people tend to side with the view apparently favored by the interviewer, especially if they are neutral. - Friendly interviewers are more successful. - In general, interviewers of the same gender, racial, and ethnic groups as those being interviewed are slightly more successful.

Census Sample

• A census study occurs if the entire population

is very small or it is reasonable to include the

entire population (for other reasons).

• It is called a census sample because data is

gathered on every member of the population.

Why sample?

• The population of interest is usually too

large to attempt to survey all of its

members.

• A carefully chosen sample can be used

to represent the population.

• The sample reflects the characteristics of the population from which it is drawn.

Random Sampling

Random sampling is the purest form of probability sampling.

• Each member of the population has an equal and known chance of being selected.
• When there are very large populations, it is often ‘difficult’ to identify every member of the population, so the pool of available subjects becomes biased. - You can use software, such as minitab to generate random numbers or to draw directly from the columns

Systematic Sampling

• Systematic sampling is often used instead of random sampling. It is also called an Nth name selection technique.
• After the required sample size has been calculated, every Nth record is selected from a list of population members.
• As long as the list does not contain any hidden order, this sampling method is as good as the random sampling method.
• Its only advantage over the random sampling technique is simplicity (and possibly cost effectiveness).

Cluster Sampling

• Cluster Sample: a probability sample in which each sampling unit is a collection of elements.
• Effective under the following conditions:
  • A good sampling frame is not available or costly, while a frame listing clusters is easily obtained
  • The cost of obtaining observations increases as the distance separating the elements increases
• Examples of clusters:
  • City blocks – political or geographical
  • Housing units – college students
  • Hospitals – illnesses
  • Automobile – set of four tires

Convenience Sampling

• Convenience sampling is used in exploratory research where the researcher is interested in getting an inexpensive approximation.
• The sample is selected because they are convenient.
• It is a nonprobability method.
  • Often used during preliminary research efforts to get an estimate without incurring the cost or time required to select a random sample

Quota Sampling

• Quota sampling is the nonprobability equivalent of stratified

sampling.

• First identify the stratums and their proportions as they are represented in the population
• Then convenience or judgment sampling is used to select the required number of subjects from each stratum.

Snowball Sampling

• Snowball sampling is a special nonprobability method used when the desired sample characteristic is rare.
• It may be extremely difficult or cost prohibitive to locate respondents in these situations.
• This technique relies on referrals from initial subjects to generate additional subjects.
• It lowers search costs; however, it introduces bias because the technique itself reduces the likelihood that the sample will represent a good cross section from the population.