Chapter 7 - Sampling: Estimating the Frequency of Behaviors and Beliefs

Population

A population refers to the entire group of individuals or instances about whom we seek to gather information in a study. It includes every member of a defined group that shares specific characteristics.

Sample

A subset of a population selected for analysis to represent the larger group. Samples are used in research to make inferences about the entire population.

Consensus

A consensus is a general agreement among members of a group, often reached through discussion and consideration of different opinions. It reflects a collective opinion or decision.

Probability Sampling

• A method of selecting samples from a population where each member has a known, non-zero chance of being selected. This approach helps ensure that the sample is representative of the larger population.

• To calculate, we must know sample size and have an unbiased method of selection.

• Different types of Probability Sample

• - Simple Random Sampling

• - Systematic Sampling

• - Cluster Sampling

• - Multistage Sampling

• - Stratified Random Sampling

• - Oversampling

Sample Random Sampling

A method of probability sampling where each member of the population has an equal chance of being selected. It is often achieved by randomly selecting individuals from the entire population.

Systematic Sampling

A method of probability sampling where researchers select every nth member from a population list. This technique helps ensure that the sample is spread evenly across the population.

Cluster Sample

A method of probability sampling that involves dividing the population into clusters and randomly selecting entire clusters to include in the sample. This approach is useful when populations are large and geographically dispersed.

Multistage Sampling

A complex form of sampling that involves selecting samples in multiple stages, often combining different sampling methods. This technique is particularly effective for large populations or geographically diverse areas.

Stratified Random Sampling

A method of probability sampling that involves dividing the population into subgroups, or strata, based on shared characteristics, and then randomly selecting samples from each stratum. This approach ensures representation across key segments of the population.

Oversampling

A sampling technique where researchers intentionally sample more individuals from a particular subgroup than their proportion in the population, often to ensure sufficient data from that subgroup for analysis.

Non Probability Sampling

A sampling method where not all individuals have a known or equal chance of being selected. This can lead to biases in the results, as the sample may not accurately represent the larger population.

• Convenience sampling

• Quota Sample

Quota Sample

A non-probability sampling technique where researchers ensure equal representation of specific characteristics by setting quotas for different subgroups within the population. This method allows for targeted sampling based on defined criteria.