Background Sampling Notes



The term sampling refers to the selection of items from a larger, specific group of items.

Figure 1. illustrates the three essential elements of the sampling process


The term sample is used in quantitative methods to denote the subset of elements, observations, or measurements being studied (Sanders, 1990, Freund and Simon, 1991; Triola, 1992).


The term population is used in Quantitative Methods to denote the set of all the elements, observations or measurements being studied (Sanders, 1990, Freund and Simon, 1991; Triola, 1992).


The term inference refers to a deduction or a conclusion and is used in Quantitative Methods to describe the process of relating information, derived from a sample, onto a population.

The following table lists the various types of sampling strategies used in research processes.

Sampling Strategies
Probability SamplingNon-Probability Sampling
simple random samplingconsecutive sampling
systematic samplingconvenience sampling
stratified random judgmental sampling
cluster sampling

Probability Sampling

Refers to sampling procedures that are based random selection.

The component of randomness ensures that each unit within the larger group (the population) has an equal chance of being selected.

Randomness in sampling (or unit selection) reduces the effect of bias in the research process.

Non-Probability Sampling

Refers to sampling strategies in which randomness of selection is less important than meeting characteristics related to the research question.

In studies of humans, non-probability samples will include individuals who are determined "to suit" the research question.

In non-probability sampling...

Individuals who "suit" a research project may be more likely to:

  • be accessible for the study;
  • comply with a specific regimen;
  • demonstrate an expected outcome;
  • infer to a specific target "group" but not the general population.

Types of Non-probability Samples

Convenience samples are used:

  • when other procedures are not practical;
  • in research which is restricted by costs (e.g. student research);
  • availability of subjects;
  • geographic proximity of samples;
  • controversial nature of the study and the likelihood that subjects may refrain from participation;
  • in health research where informed consent is required and may inhibit participants;
  • when the intrinsic bias of sampling is less important than observing an outcome.

Consecutive samples:

  • are conducted by selecting every individual who meets the study criteria, and who is available during the duration of the study;
  • refer to an accessible group of individuals but may not be inferential to a larger population. ;
  • are problematic when the duration of the selection process is inappropriate;
    for example measuring an outcome over a month when a year's worth of data is suggested.

Judgemental samples:

  • are conducted by "handpicking" individuals for the study;
  • resembles convenience sampling in its disregard for the effects of bias;
  • produce results which are related to an accessible group of individuals but may not be inferential to a larger population;
  • may be a first step in developing a larger "probability-based" research study.

Types of Probability Samples

Random and independent sampling.:

  • the term random implies that each member of the population has the same chance of being selected for the sample;
  • the term independent implies that the selection of any one member in no way affects the selection of any other member ;
  • random sampling can generalize results to a larger population.

Systematic Sampling:

  • is effective when a complete list of the members of the population is available for selecting the sample;
  • the researcher could develop a systematic method of selecting every other name or every "nth" name, or some division of the total population.

Stratified random sample (common in public opinion polls):

  • the population is divided into subgroups or strata;
  • each strata contains a particular characteristic of interest but only one characteristic is contained in any one strata;
  • is most effective when the variables of interest are closely related to the variables upon which the strata are based;
  • each stratum is sampled randomly and the various sub-samples collected from the strata are combined to form the sample;
  • proportioning the strata is important if the researcher wishes to preserve the natural concentrations of sections within the population.

Cluster Sampling:

  • is used to reduce the large numbers of individuals needed for stratified and random methods;
  • works best when we are studying intact groups (ie. students in classes of a school);
  • is used when it is either impossible or impractical to obtain a list of all members of a defined population.

Considerations when sampling

Consideration #1:

Ideal accuracy in research is achieved only when all members of a population are tested under controlled conditions.

Obviously, because of costs, inaccessibility and limited time, all members of a population could almost never be tested (except when a census is taken, then there will be an effort to collect measures on the entire population).

Consideration #2:

Always use the largest sample possible.

Since the researcher is always attempting to make inferences to a larger population, the larger the sample the more likley it is to represent the population.

Consideration #3:

Use a stratified random sample when attempting to gain information about samples of subgroups.

Consideration #4:

When using volunteers, always determine how these individuals differ from non-volunteers.
Ask yourself, "Have you simply developed a study on a bias sample?"

Consideration #5:

When developing a sampling strategy, and computing sample size, be sure to consider:
  • that some subjects may drop out;
  • that the sample must be specific to the research project;
  • that the control and experimental groups come from the same strata.

The concept of "Validity"

The term validity refers to the extent to which we are measuring what we think we are measuring.

Internal validity inference:

refers to the accuracy of the selected sample as predictors of a larger population.

Two types of External Validity Inference:

  1. the generalization from the intended sample to the accessible population (where the accessible population is defined as the subset of the target population that is accessible to the researchers);
  2. the generalizations that are made from the intended sample to the target populations, (where the term target population refers to the largest population upon which inferences will be made).

When developing a sampling strategy:

Inclusion Criteria:
refer to the strategy for selecting subjects that will be eligible to participate in the study.

Exclusion Criteria:
refer to the strategy for selecting subjects that will not be eligible after they have been selected to participate in your study.

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For more information, please contact:

Professor William J. Montelpare, Ph.D.,
Margaret and Wallace McCain Chair in Human Development and Health,
Department of Applied Human Sciences, Faculty of Science,
Health Sciences Building, University of Prince Edward Island,
550 Charlottetown, PE, Canada, C1A 4P3
(o) 902 620 5186

Visiting Professor, School of Healthcare, University of Leeds,
Leeds, UK, LS2 9JT
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