Questions and Answers
What is defined as the complete set of people or objects with a common characteristic of interest?
Population
What is the primary purpose of defining the population in research?
To determine the sampling frame and select an appropriate sample.
Sampling is the act of selecting a group from a larger __________ to conduct measurements.
population
Probability sampling ensures that every member of the population has a known, non-zero chance of being selected.
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Which of the following is NOT a technique of probability sampling?
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In which sampling method are members selected at regular intervals from a list?
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What is a disadvantage of probability sampling compared to non-probability methods?
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Match the following types of sampling with their definitions:
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How does the researcher ensure that all employees have an equal chance of being selected in Simple Random Sampling?
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What is the sampling interval calculated for systematic sampling if the total population is 1000 and the desired sample size is 100?
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In stratified sampling, how does the researcher ensure the sample reflects the demographic distribution?
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What is the purpose of the class exercise mentioned in the context of cluster sampling?
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Study Notes
Population in Research
- A population encompasses all objects, subjects, or members that meet specific criteria.
- The target population is the group of interest that researchers aim to extrapolate findings to.
- Defining the population is crucial for:
- Determining the sampling frame and selecting a suitable sample.
- Ensuring that the sample represents the target population.
- Facilitating proper generalization of research results.
Sampling
- Sampling involves selecting a subset from a larger population for measurement and data collection.
- A representative sample allows researchers to generalize findings without studying the entire population.
- Sampling is often necessary due to constraints, including time, cost, and accessibility.
Types of Sampling: Probability Sampling
- Probability sampling, or random sampling, ensures every population member has a known, non-zero chance of selection.
- Benefits include minimized bias and the ability to make statistical inferences.
- Despite advantages, it may be time-consuming and costly compared to non-probability sampling methods.
Probability Sampling Techniques
- Simple Random Sampling: Every population member has equal selection probability.
- Systematic Sampling: Members selected at fixed intervals from a complete list.
- Stratified Sampling: Population divided into strata based on characteristics, with random samples drawn from each.
- Cluster Sampling: Population divided into clusters, with selected clusters including all their members.
Simple Random Sampling Example
- Target population: 1,000 employees at Raymond Mhlaba Municipality.
- Researcher surveys 100 employees to assess job satisfaction.
- A list of all employees is created, and each assigned a unique number.
- A random number generator selects 100 unique numbers for sampling.
Systematic Sampling Example
- Target population: 1,000 employees in Eastern Cape Department of Education.
- Researcher surveys 100 employees regarding training program perceptions.
- Employees numbered 1 to 1,000; the sampling interval is calculated as 10 (1000/100).
- A random starting point is selected, and every 10th employee is chosen from that point.
Stratified Sampling Example
- Target population: 2,000 second-year students at UFH, stratified by age.
- Age groups: 16-20 years (500), 21-25 years (900), 26-30 years (300), 31 years and older (300).
- Total survey sample: 120 students, with specific distribution based on age strata.
- Random selection occurs within each age group to reflect true distribution.
Cluster Sampling Example
- Research on a new community policing initiative focuses on 20 city neighborhoods.
- A cluster sampling method would randomly select clusters (neighborhoods) to assess the initiative’s effectiveness.
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Description
This quiz focuses on the key concepts of population and sampling in research methods. It covers definitions, the importance of target populations, and the different types of sampling techniques, particularly probability sampling. Understanding these concepts is crucial for effective research design and data analysis.
