Sampling Design in Social Science Research

• Research objective (e.g., generalization vs. in-depth understanding)

• Population accessibility

• Time and resources

• Desired level of precision

• Ethical considerations

Probability sampling is a sampling technique where every individual in the population has a known and equal chance of being selected. This method reduces bias and allows researchers to generalize results to the larger population with greater confidence. Examples include simple random sampling, stratified sampling, cluster sampling, and systematic sampling.

• Simple Random Sampling: Every individual in the population has an equal chance of selection. It is straightforward but requires a complete list of the population.

• Stratified Sampling: The population is divided into subgroups (strata) based on specific characteristics (e.g., gender, education level), and random samples are drawn from each group. This ensures diversity and representation.

• Cluster Sampling: Groups or clusters (e.g., schools, neighborhoods) are randomly selected, and all individuals within selected clusters are included. This is useful for large or geographically spread populations.

• Systematic Sampling: Researchers select every k-th person from a list (e.g., every 5th student on a roster). It’s simpler than random sampling but requires careful ordering.

Non-probability sampling is a sampling technique where not all individuals in the population have a known or equal chance of being selected. It is often used when random selection is not possible or practical. This method may introduce bias but is useful for exploratory research or when studying specific groups. Examples include convenience sampling, purposive sampling, quota sampling, and snowball sampling.

• Quota Sampling: Similar to stratified sampling but without random selection; researchers select a set number of participants from certain groups.

  Example: Selecting a sample of 100 students while maintaining the same grade-level ratio as the overall population. First year and second students: 80% of 100 = 80 students, third year students: 15% out of 100 = 15 students, fourth year students: 5% of 100 = 5 students.

• Snowball sampling: Used for hard-to-reach populations, where existing participants refer others who meet the criteria.

  Example: Asking a participant to recommend another participant with similar criteria

• Convenience Sampling: Participants are selected based on availability and ease of access. While practical, this method may introduce bias.

  Example: Asking anyone on Facebook to answer questionnaire about study habits

• Purposive Sampling: Researchers deliberately select individuals based on specific traits or experiences relevant to the study.

  Example: Selecting Dean's list students to explore their study habits

• Using too small or unrepresentative samples for quantitative research

• Ignoring potential biases in non-probability sampling

• Overgeneralizing results from convenience samples