Questions and Answers
A population consists of all subjects being studied.
True
A sample is the complete set of persons or objects that are being studied.
False
The target population is also known as the accessible population.
False
Sampling can be more cost-effective than studying an entire population.
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A census involves studying only a sample of the population.
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Probability sampling involves the use of a random selection process to select a sample from members of a population.
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Cluster random sampling is most suitable when the population is concentrated in a single location.
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Stratified random sampling divides the population into strata and ensures each stratum is represented in the sample.
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Multistage random sampling involves several stages of selection to create a final sample.
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Systematic random sampling requires a population list and does not use a random starting point.
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Study Notes
Key Definitions
- Population: Complete set of subjects (people or objects) that share common characteristics being studied.
- Sample: Subgroup of the population selected for study to infer findings about the entire group.
Types of Population
- Target Population: Entire group the researcher wishes to generalize findings to; can include specific criteria (e.g., all institutionalized individuals with psychiatric issues).
- Accessible Population: Group actually available for study; may differ from the target population (e.g., pregnant women in a specific clinic setting).
Importance of Sampling
- Essential to choose a representative sample to answer research questions accurately.
- Sample size must be appropriate to ensure research validity.
Advantages of Sampling
- Cost-effective approach.
- Requires less manpower than studying entire populations.
- Information gathered more quickly.
- Ability to obtain comprehensive data.
- Can yield greater accuracy, sometimes providing better estimates than complete surveys.
Types of Sampling Procedures
- Probability Sampling: Random selection process ensuring every population member has an equal chance of being selected.
- Non-Probability Sampling: Selection based on non-random criteria.
Probability Sampling Methods
- Simple Random Sampling (SRS): Every member has an equal chance; achieved via random selection methods such as shuffling cards or using random number generators.
- Systematic Random Sampling: Selection process involves determining a sampling proportion (sample size/population size) and then selecting members at regular intervals.
- Stratified Random Sampling: Population divided into homogeneous strata; samples are drawn from each stratum ensuring equal representation.
- Multistage Random Sampling: Involves selecting samples at various stages (e.g., regions to villages to schools).
- Cluster Random Sampling: Useful when populations are geographically spread; samples drawn from randomly selected clusters (e.g., schools in a district).
Random Sampling Techniques
- For Systematic Random Sampling, after preparing the sampling frame, determine the starting point randomly, then sample at calculated intervals.
- Stratified Sampling ensures all key groups (strata) within the population are represented proportionally.
Conclusion
- Selecting appropriate sampling methods is crucial for the representativeness and validity of research findings.
- Understanding the structure of the population aids in effective sampling strategies.
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Description
This quiz covers essential concepts related to sampling methods in statistics. Students will learn to define key terms, identify types of populations, and explain various sampling types. Additionally, the quiz will focus on the importance of selecting a representative sample and determining an appropriate sample size for research.
