Questions and Answers
Probability sampling involves a random selection process to select a sample from a population.
True
In systematic random sampling, the first sample member is not chosen by simple random sample.
False
Stratified random sampling divides the population into strata and takes a simple random sample from each stratum.
True
Cluster random sampling is used when the population is distributed across a wide geographical area.
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Simple random sampling does not require a sampling frame of the population.
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A sample is a complete set of persons or objects that possess some common characteristic.
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The target population is the group the researcher wishes to generalize findings to.
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An accessible population is the entire group that the researcher is interested in studying.
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Sampling is more cost-effective than conducting a census.
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Census refers to studying every member of the target population.
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Study Notes
Key Terms and Definitions
- Population: Complete set of subjects (humans or objects) with common characteristics of interest to the researcher.
- Sample: Subgroup drawn from the population for study.
- Target Population: Entire group to which findings are intended to be generalized.
- Accessible Population: Subset of the target population available for study.
Types of Population
- Populations can be categorized into:
- Target Population: Includes all individuals or items meeting specific criteria (e.g., all institutionalized psychiatric patients).
- Accessible Population: Those available for study (e.g., pregnant women at a specific clinic).
Sampling Importance
- Choosing a representative sample is crucial for accurate research findings.
- Sample Size: The appropriate number of subjects is needed to address the research question effectively.
Advantages of Sampling
- Cost-effective approach.
- Reduces manpower needs.
- Accelerates information gathering.
- Allows for more comprehensive data collection.
- Can yield more accurate estimates than complete surveys.
Types of Sampling Methods
Probability Sampling
- Elements are chosen through random selection, ensuring each has a known chance of being selected.
- Aims for representative samples from the population.
- Types include:
- Simple Random Sampling (SRS): Every member has an equal chance; utilizes a sampling frame with options for random selection (cards, random numbers, computer generation).
- Systematic Random Sampling: Selection starts from a random point using a calculated sampling proportion.
- Stratified Random Sampling: Population divided into homogeneous strata; simple random samples taken from each stratum.
- Cluster Random Sampling: Involves selecting entire clusters (e.g., geographic areas), useful for large aggregates of data.
- Multistage Sampling: Utilizes multiple stages for selecting samples from larger groups (e.g., regions to villages to schools).
Non-Probability Sampling
- Samples are not selected based on randomization, often based on convenience or subjective judgment.
Summary of Sampling Procedures
- Simple Random Sample: Ensures equal chance for all members.
- Systematic Random Sample: Involves selecting every nth member after a random start.
- Stratified Sample: Targets variability within subgroups for more reliable results.
- Cluster Sample: Focuses on entire groups within the population, useful for geographic distributions.
- Multistage Sample: Combines multiple sampling methods for extensive coverage.
Conclusion
- Proper sampling methods are essential for accuracy in research, leading to valid generalizations about a broader population.
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Description
Explore key concepts related to sampling methods and population types in this quiz. You will define key terms, identify different types of populations, and differentiate between various sampling techniques. Understanding these elements is essential for effective research design.
