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Difference Between Probability & Non-

Probability Sample Designs

Note: Please use readings and other sources for a more complete discussion.

Probability Sampling: subjects are selected in such a way that every member ofthe population actually has a possible chance of being selected.

Type of Probability Sample Designs:

Simple Random: each member of the study population has an equal chance of being

selected.

Systematic sampling: each member of the study population is either assembled or

listed, a random start is designated, then members of the population are selected at

equal intervals.

Stratified: each member of the study population is assigned to a group or stratum,

then a simple random sample is selected from each stratum.

Cluster: each member of the study population is assigned to a group or cluster, then

clusters are selected at random and all members of a selected cluster are included in

the sample.

Multi-stage: clusters are selected as in the cluster sample, then sample members are

selected from the cluster members by simple random sampling. Clustering may be

done at more than one stage.

Non-probability samples: subjects are selected based on the judgment of the

researchers to achieve particular objectives of the researcher.

Types of Non-probability Sample Designs:

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Convenience: select cases based on their availability for the study.

Example: Psychologists interested in the relationship between violence in movies and

aggressive behavior by the American public may use student volunteers to participate

in an experiment. One group is shown a movie without violence, the other group is

shown a movie with graphic violence. Then both groups are observed andinterviewed.

Age and level of stress of college student's may affect the findings.

Most Similar/Dissimilar: Select cases that are judged to represent similar

conditions, or alternatively, very different conditions.

Example: Case studies of nations such as U.S. and France, to contrast the worst and

best case of a policy issue.

Typical case sampling: Select cases that are known beforehand to be useful and not

be extreme

Example: Select a few cases that are said to be normal or usual. For example,

selecting Chicago and New York to study their recycle program to represent major

cities in general. Need to scrutinize researchers selection of cases.

Critical: Select cases that are key or essential for overall acceptance or assessment.

Example: predicting election results - "As Maine goes, so goes the nation"

Snowball: Group members identify additional members to be included in sample.

Example: illegal drug users, illegal aliens

Quota: Interviewers select sample that yields the same proportions as the populationproportions on easily identified variables.

Example: The researcher divides population group being studied into subgroups

: male, female , black , white. In quota sampling, the interviewer is left with the

discretion of selection. Ex. Interviews conducted in shopping malls.

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Utility of Non-probability samples: studies of special populations; exploratory

research in attempting to see if a problem exists; a small pilot study may help to know

whether you should pursue research further.