Non-Probability sampling methods When you want to do more than simply make inferences about a population!

Probability Sampling What you actually observe in the data What you want to talk about Population Random Sampling Process Sample Sampling Frame Using data to say something (make an inference) with confidence, about a whole (population) based on the study of a only a few (sample).

Probability Sampling Probability sampling uses random selection to allow extrapolation from a small, highly representative sample, to a larger population. This statistical inference allows us to describe a population. Used when you want to answer the “where” and “how many” questions.

NON-Probability Sampling Used when you want to say something about a discrete phenomena, a few select cases (people, places, objects, etc.) or when you want to answer the “how” and “why” questions.

SELECTION OF PARTICIPANTS TYPE OF SAMPLING SELECTION STRATEGY PURPOSE Convenience Select cases based on their availability for the study. Saves time, money and effort; but at the expense of information and credibility.

SELECTION OF PARTICIPANTS TYPE OF SAMPLING SELECTION STRATEGY PURPOSE Critical case Select cases that are key or essential for overall acceptance or assessment. Permits logical generation and maximum application of information to other cases.

SELECTION OF PARTICIPANTS TYPE OF SAMPLING SELECTION STRATEGY PURPOSE Snowball or chain referral Group members identify additional members to be included in the sample. Identifies cases of interest to people who know people, who know what cases are information-rich.

SELECTION OF PARTICIPANTS TYPE OF SAMPLING SELECTION STRATEGY PURPOSE Politically important cases Include or exclude informants that are connected with politically sensitive issues. Attracts desired attention or avoids attracting undesired attention.

SELECTION OF PARTICIPANTS TYPE OF SAMPLING SELECTION STRATEGY PURPOSE Typical case Select cases that are known before-hand to be useful and not too extreme. Highlights what is known to be normal or average.

SELECTION OF PARTICIPANTS TYPE OF SAMPLING SELECTION STRATEGY PURPOSE Maximum variation Select outlier cases to see if main patterns still hold (negative instances, variations). Captures wide variation, identifies key common factors, examines a range of perspectives.

SELECTION OF PARTICIPANTS TYPE OF SAMPLING SELECTION STRATEGY PURPOSE Intensity Select informants with in-depth understanding of the phenomenon under study Information-rich cases that manifest the phenomenon intensively, but not EXTREMELY.

SELECTION OF PARTICIPANTS TYPE OF SAMPLING SELECTION STRATEGY PURPOSE Quota Select a sample that yields the same proportions as the population proportions on easily identified variables. Taking a set number of cases from each subgroup to raise analytic confidence and representativeness.

SELECTION OF PARTICIPANTS TYPE OF SAMPLING SELECTION STRATEGY PURPOSE Theoretical saturation (sequential) Finds as many relevant cases as possible. Reach saturation point; when no new information emerges.

SELECTION OF PARTICIPANTS TYPE OF SAMPLING SELECTION STRATEGY PURPOSE Most similar/ dissimilar cases Select cases that are judged to represent either similar or very different conditions. Elaborating initial analysis, seeking exceptions, looking for variation.

SELECTION OF PARTICIPANTS TYPE OF SAMPLING SELECTION STRATEGY PURPOSE Comparable case Select multiple individuals, sites, and groups on the same relevant characteristics over time. Be able to compare across cases, or over time (pseudo replication).

SELECTION OF PARTICIPANTS TYPE OF SAMPLING SELECTION STRATEGY PURPOSE Criterion All cases that meet some criteria. Useful for quality assurance.