Sampling Methods - Eppp Knowledge
- What is coverage error in sampling?
- Mismatch between the sampling frame and the target population. This bias arises when the frame excludes or overincludes population segments, leading to non-representative samples.
- What is consecutive sampling in clinical research settings?
- Enroll all eligible cases encountered over a specified time period. This captures a comprehensive sample in sequential order, minimizing selection bias in time-bound clinical or medical contexts.
- What is multistage sampling?
- Probability sampling conducted in stages (for example, clusters then units). This hierarchical approach combines methods like clustering and stratification for complex, large populations to enhance efficiency.
- What is stratified random sampling?
- Randomly sample within strata to ensure subgroup representation. Dividing the population into homogeneous subgroups improves precision and ensures proportional representation of key characteristics.
- Identify the sampling type: randomly select schools, then sample students within selected schools.
- Cluster (multistage probability) sampling. This multistage approach first selects natural groups randomly, then subsamples, reducing costs for widespread populations.
- In systematic sampling, what does represent?
- The sampling interval (step size). It determines the fixed gap between selected units, ensuring systematic coverage of the population list.
- What is convenience sampling?
- Selecting participants who are easiest to access. This method prioritizes practicality but introduces bias by not ensuring equal selection chances for all population elements.
- What is the defining feature of a probability sampling method?
- Each population element has a known, nonzero selection probability. This ensures representativeness and allows for statistical inference by giving every unit a calculable chance of inclusion.
- What is probability proportional to size (PPS) sampling?
- Clusters are selected with probability proportional to their size. This adjusts for varying cluster sizes to maintain equal probability of selection for individual units across the population.
- Identify the sampling type: recruit participants from a psychology subject pool for course credit.
- Convenience (non-probability) sampling. This relies on readily available volunteers, making it efficient but introducing bias from non-random, self-selected participants.
- Which sampling method can ensure subgroup counts but still remains non-probability?
- Quota sampling. It guarantees demographic proportions similar to the population but lacks randomness, preventing probability-based inferences.
- Identify the sampling type: selecting every 10th name from a roster after a random start.
- Systematic probability sampling. This method combines randomness with a fixed interval to approximate simple random sampling for ordered lists efficiently.
- What is quota sampling?
- Nonrandom selection to fill preset subgroup quotas. This mimics stratification but without randomness, aiming for demographic balance while remaining susceptible to selection bias.
- Identify the sampling type: randomly sample 50 men and 50 women from separate lists.
- Stratified random (probability) sampling. This ensures proportional representation by gender through random selection within predefined strata, enhancing sample accuracy.
- What is purposive (judgmental) sampling?
- Selecting cases based on researcher-defined criteria or judgment. It allows researchers to target specific expertise or characteristics, useful for qualitative studies but prone to subjectivity.
- Identify the sampling type: set quotas by race and age, then recruit nonrandomly until filled.
- Quota (non-probability) sampling. This non-random method fills demographic categories to mirror population diversity but cannot support statistical generalizations.
- What is a sampling frame?
- The list or mechanism identifying all population elements for selection. It provides the basis for random selection, ensuring the sample is drawn from a complete and accurate population representation.
- What is cluster sampling?
- Randomly sample clusters, then sample all or some units within them. This method is efficient for geographically dispersed populations by first selecting groups and then subsampling within them.
- What is systematic sampling?
- Select every th unit after a random start. This approach provides a structured way to achieve randomness while being efficient for large, ordered populations.
- Which probability design typically reduces cost by sampling groups rather than individuals?
- Cluster sampling. It lowers logistical costs in large-scale surveys by focusing resources on naturally occurring groups like schools or neighborhoods.
- What is the defining feature of a non-probability sampling method?
- Selection probabilities are unknown; not all elements have a known chance. This limits generalizability since selection is not random and probabilities cannot be estimated for inference.
- Which sampling approach best supports statistical generalization to a population?
- Probability sampling. It allows for unbiased estimates and calculation of sampling error, enabling valid inferences to the broader population.
- What is simple random sampling?
- Every element has an equal, independent chance of selection. This method minimizes bias by ensuring independence and equality in selection probabilities across the population.
- Which sampling method is most appropriate for hard-to-reach populations?
- Snowball (chain-referral) sampling. It effectively builds samples through referrals, ideal for populations like drug users or immigrants who are difficult to locate directly.
- What is snowball (chain-referral) sampling?
- Participants recruit other participants from their networks. This leverages social networks to access hidden or stigmatized groups where traditional methods fail to identify members.