Questions and Answers
A population consists of all subjects being studied, whether human or otherwise.
True
The accessible population is the entire group of people or objects that a researcher wishes to generalize findings to.
False
Probability sampling involves a random selection process to choose members from a population.
True
The target population is also known as the universe.
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Stratified random sampling is ineffective when there is high variability among population members.
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Choosing a sample that is representative of the population is unimportant for research.
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In systematic random sampling, the first sample member is chosen randomly from the entire population list.
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A census includes a sample rather than the entire population.
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Cluster random sampling is typically used when the population is densely packed in one area.
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Samples in probability sampling guarantee that every member of the population will be selected.
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Study Notes
Key Terms
- Population: A complete set of subjects being studied, may include humans, hospitals, schools, or villages.
- Sample: A subgroup selected from the population for research purposes.
- Target Population: The entire group researchers want to generalize findings to; meets specific criteria.
- Accessible Population: The group available for study; may differ from the target population.
Types of Population
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Target Population examples:
- All institutionalized individuals for psychiatric issues.
- Health records from well-child clinics for a specific year.
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Accessible Population example:
- Pregnant women in a particular clinic setting, typically first-time mothers aged 25-35 years.
Sampling
- Purpose: Choose a sample that represents the population for generalization of study findings.
- Importance of sample size: An appropriate number of subjects is vital to effectively answer research questions.
- Census: A method that includes every member of the population in the study.
Advantages of Sampling
- Cost-effectiveness.
- Reduced manpower requirements.
- Faster data collection.
- More comprehensive data acquisition.
- Higher accuracy; sometimes sampling yields better estimates than full surveys.
Sampling Methods
- Two main procedures: Probability Sampling and Non-probability Sampling.
Probability Sampling
- Involves random selection; each population member has a known probability of being included.
- Goal: To ensure a representative sample of the population.
Types of Random Sampling Procedures
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Simple Random Sampling (SRS):
- All members have an equal chance of selection.
- Requires preparation of a sampling frame, and samples can be selected through various methods (e.g., random number tables or computer generation).
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Systematic Random Sampling:
- Prepare a sampling frame and calculate the sampling proportion (sample size/population size).
- The first sample member is randomly selected, followed by systematic selection based on the calculated interval.
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Stratified Random Sampling:
- Divides the population into homogeneous strata.
- Ensures representation by taking simple random samples from each stratum proportionally.
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Multistage Random Sampling:
- Involves multiple selection stages, starting from regions down to specific households or individuals.
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Cluster Random Sampling:
- Used for geographically dispersed populations, allowing focused sampling within clusters (e.g., specific states or districts).
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Description
This quiz covers key concepts related to population and sampling, including definitions of target and accessible populations. It emphasizes the importance of selecting an appropriate sample size for effective research outcomes. Test your knowledge on how these terms are applied in the research process.
