Class 6 Sampling 2022 Sp

Generalizability

Generalizability

The ability to draw inferences and conclusions from data.

Population

The group of potential participants to whom you want to generalize the results of a study.

Sample

A subset of the population.

Strata

Subgroups of a population based on characteristics like age, race, gender, etc.

Probability sampling

Sampling where the odds of selecting any member of the population are known.

Nonprobability sampling

Sampling where the odds of selecting any member of the population are not known.

Bias

Introduced when there are issues like incomplete identification of population members or convenience sampling.

Sampling error

Introduced due to inadequate sample size, leading to lack of precision.

Inclusion criteria

Characteristics that subjects must possess to participate in a study.

Exclusion criteria

Characteristics that disqualify subjects from participating in a study.

Stratified Head Start Sample

Children enrolled in Head Start sites in the southwestern states, selected based on state and rural/non-rural stratification, representing 5% of sites and 3% of children.

Cluster Sampling

Selection of sub-groups (clusters) instead of individuals randomly, suitable for populations with clusters or units, requiring a large number of clusters due to homogeneity within.

Cluster Random Sampling

Involves dividing clusters and randomly selecting clusters for sampling, as seen in the example of candies divided into clusters and colors.

Multistage Sampling

Utilized in large surveys, involves drawing stratified random samples, then random samples within strata, and finally random selection of participants.

Purposive Sampling

Non-random sampling method where subjects are purposefully selected based on the belief they will provide the best information.

Snowball Sampling

Sampling technique where participants are located through referrals from existing participants, based on trust and confidentiality.