Sampling Techniques Notes: Probability Sampling

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@CharitableZombie

Study Notes

Simple Random Sampling: Ensures every individual has an equal chance of selection, promoting fairness and reducing bias.
Stratified Sampling: Divides the population into subgroups (strata) based on specific characteristics; random samples are drawn from each subgroup to ensure all are represented.
Systematic Sampling: Selects individuals at regular intervals from a numbered list, which can help in creating a structured sample method.
Cluster Sampling: Involves dividing the population into clusters; entire clusters are randomly selected, and all individuals within those clusters are included in the sample.

Convenience Sampling: Selects individuals based on their availability, which can lead to issues in representativeness due to reliance on access.
Quota Sampling: Involves selecting a fixed number of individuals from specific subgroups, irrespective of their chances of being chosen; this can lead to bias favoring certain groups.
Purposive Sampling: Focuses on selecting individuals based on specific criteria deemed important for the study, which can enhance relevance but may introduce selection biases.
Snowball Sampling: Begins with initial participants who refer others meeting study criteria, creating networks of participants, but can lead to sampling bias.

Random Sampling: Applicable for both small and large populations; key feature is the equal chance of selection for every individual, safeguarding against selection bias.
Non-Random Sampling: Varies in bias levels; potential for bias affects sample representativeness and generalizability:
- Convenience Sampling: High bias risk; representation issues likely as it relies on easy-to-reach individuals.
- Quota Sampling: Moderate bias risk; targeted samples can favor particular subgroups, affecting overall representativeness.
- Purposive Sampling: Low bias potential if criteria are well-defined; can maintain a level of representativeness.
- Snowball Sampling: Moderate to high bias; tendency to focus on individuals connected to others in the sample group which skews diversity.

Simple Random Sampling: Each individual has an equal chance of selection, enhancing fairness and minimizing bias.
Stratified Sampling: Population is divided into specific subgroups (strata); random samples are drawn from each to ensure representation across all strata.
Systematic Sampling: Individuals are selected at regular intervals from a numbered list, providing a systematic approach to sampling.
Cluster Sampling: Population is divided into clusters; entire clusters are randomly chosen, including all individuals within selected clusters.

Convenience Sampling: Involves selecting individuals based on their availability leading to possible representativeness issues.
Quota Sampling: Fixed number of individuals are selected from specific subgroups, which can introduce bias by favoring certain groups.
Purposive Sampling: Participants are chosen based on specific criteria important for the study, increasing relevance but risking selection biases.
Snowball Sampling: Starts with initial participants who then refer others; this method builds networks but may cause sampling bias.

Random Sampling: Suitable for both small and large populations, ensuring every individual has an equal chance of selection to guard against selection bias.
Non-Random Sampling: Varies in bias levels, affecting the sample's representativeness and generalizability:
- Convenience Sampling: High risk of bias; likely misrepresentation due to reliance on easily accessible individuals.
- Quota Sampling: Moderate bias risk; targeted choices can favor specific subgroups, impacting overall sample representativeness.
- Purposive Sampling: Low bias potential if selection criteria are clearly defined; can retain representativeness.
- Snowball Sampling: Moderate to high bias; often skews diversity by focusing on interconnected individuals within the sample.

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