Sampling methods
Probability and non-probability sampling methods
The fundamental difference between sampling methodologies is the use of random selection. Probability sampling or random selection of participants from the population of interest is used in experimental designs. Non-probability sampling is used in observational studies where study participants are not chosen at random but outcomes are available for retrospective or prospective analysis.
Probability sampling allows for researchers to assume that any differences at baseline between randomly assigned groups is due to chance. Probability sampling further helps with the effects of confounding for both measured and unmeasured variables. Probability sampling is necessary in experimental designs that want to make causal inferences regarding treatment effects. With random assignment, groups are thought to possess a state of equipoise or equal levels of prognostic, confounding, and demographic characteristics at baseline between groups.
Non-probability sampling allows for researchers to study rare outcomes, generate hypotheses, establish prevalence, and create measures of odds and risk in patient populations. Causal effects cannot be inferred from non-probability sampling methods because of selection and observation biases associated with convenience and purposive sampling. Quasi-experimental and randomized designs can yield stronger evidence.
Probability sampling allows for researchers to assume that any differences at baseline between randomly assigned groups is due to chance. Probability sampling further helps with the effects of confounding for both measured and unmeasured variables. Probability sampling is necessary in experimental designs that want to make causal inferences regarding treatment effects. With random assignment, groups are thought to possess a state of equipoise or equal levels of prognostic, confounding, and demographic characteristics at baseline between groups.
Non-probability sampling allows for researchers to study rare outcomes, generate hypotheses, establish prevalence, and create measures of odds and risk in patient populations. Causal effects cannot be inferred from non-probability sampling methods because of selection and observation biases associated with convenience and purposive sampling. Quasi-experimental and randomized designs can yield stronger evidence.
Probability or non-probability sampling
Probability sampling methods include simple random sampling, stratified random sampling, and clustered random sampling.
Non-probability sampling methods include convenience sampling and purposive sampling.
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