The sample selection process in a research study depends heavily on the study's design, objectives, and the population being studied. It can range from probability-based methods ensuring representativeness to non-probability methods selected for specific characteristics or accessibility.
Probability Sampling Methods
Probability sampling methods give every member of the population a known (and often equal) chance of being selected. This helps to ensure the sample is representative of the population, allowing for generalizations.
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Simple Random Sampling: Each member of the population has an equal chance of being selected. This is often done using a random number generator.
- Example: Drawing names from a hat.
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Stratified Sampling: The population is divided into subgroups (strata) based on shared characteristics (e.g., age, gender, income). Then, a random sample is taken from each stratum, proportionally or disproportionately to its size in the population.
- Example: Sampling equal numbers of men and women in a study, even if their proportions are unequal in the overall population.
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Cluster Sampling: The population is divided into clusters (e.g., schools, neighborhoods). A random sample of clusters is selected, and then all individuals within the selected clusters are included in the sample.
- Example: Surveying all students in randomly selected schools within a school district.
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Systematic Sampling: Every nth member of the population is selected, starting with a randomly chosen starting point.
- Example: Selecting every 10th person on a list.
Non-Probability Sampling Methods
Non-probability sampling methods do not give every member of the population a chance of being selected. These methods are often used when probability sampling is not feasible or when the research question does not require a representative sample.
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Convenience Sampling: Participants are selected based on their availability and willingness to participate.
- Example: Surveying students in a researcher's class.
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Purposive Sampling: Participants are selected based on specific characteristics or expertise relevant to the research question.
- Example: Interviewing experts in a particular field.
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Quota Sampling: The researcher sets quotas for certain characteristics (e.g., age, gender) to ensure the sample reflects the population's proportions on those characteristics. However, selection within each quota is non-random.
- Example: Ensuring a sample has 50% men and 50% women, but using convenience sampling to fill each quota.
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Snowball Sampling: Participants are asked to refer other potential participants who meet the study criteria. This is useful for reaching populations that are difficult to access.
- Example: Studying drug users by asking initial participants to refer their friends.
Cohort Studies: Representative vs. Non-Representative Samples
As the reference states, at the beginning (baseline) of a cohort study, the sample can be selected using either a representative (population-based) or a non-representative sample. However, it's crucial that in subsequent follow-ups, the participants who remain in the study are representative of the original baseline cohort. Loss to follow-up that isn't random can introduce bias.
| Sample Type | Description | When Used |
|---|---|---|
| Representative (Probability-Based) | A sample that accurately reflects the characteristics of the larger population. | When the goal is to generalize the findings to the entire population. |
| Non-Representative (Non-Probability-Based) | A sample that does not necessarily reflect the characteristics of the larger population. | When the goal is to explore a specific phenomenon or gain in-depth understanding of a particular group, or when access to a representative sample is limited. However, generalizability is limited. |
The choice of sampling method should be justified based on the research question, study design, and available resources. Ensuring proper sampling techniques is critical for the validity and generalizability of research findings.
