Sampling Methods in Research: A Comprehensive Guide
UNIT 5: Sampling – Concept and Classification
Population and Sample
Population refers to the entire group you want to gather information about (e.g., individuals, families, households). A sample is a subset of the population selected to represent the whole. Information gathered from the sample is used to make inferences about the population.
Sample selection is done through a process called sampling, which depends on the chosen research technique. A sample must be representative, meaning the characteristics of the population are proportionally reflected in the sample. This representativeness allows for generalization, where conclusions drawn from the sample can be applied to the population. The sampling procedure should ensure that the sample accurately reflects the population.
Sample Size
Several factors influence the determination of sample size:
- Time and resources: Available time and resources significantly impact the final sample size.
- Sampling mode:
- Probability sampling generally requires a larger sample.
- Non-probability sampling typically uses a smaller sample.
- Diversity of data: When applying multivariate statistical techniques, a larger sample size helps reduce sampling errors and provides more robust analysis.
- Population variance: A more heterogeneous population with greater variance necessitates a larger sample size for accurate representation.
- Margin of error: In probability sampling, a larger sample size generally leads to a smaller margin of error.
- Confidence level: This refers to the degree of certainty that the sample accurately reflects the population. Common confidence levels are 68%, 95.5%, and 99.7%, with 95.5% being the most frequently used.
Formula for sample size when the population is greater than 100,000: [Insert formula here]
Formula for sample size when the population is 100,000 or less: [Insert formula here]
Types of Sampling
Sampling methods are broadly categorized into two types: probability sampling and non-probability sampling.
Probability Sampling
In probability sampling, each element of the population has a known probability of being selected for the sample. Selection is random, ensuring every element has an equal chance of inclusion.
Characteristics of probability sampling:
- Random selection
- Equal probability of selection for all elements
- Calculation of sampling error and confidence level
- Generalizability of results to the population
- Ability to assess sample representativeness
- Higher cost and complexity
- Can be time-consuming
Non-Probability Sampling
Non-probability sampling does not rely on random selection. The researcher’s judgment or convenience plays a role in selecting sample elements.
Characteristics of non-probability sampling:
- Unequal probability of selection for population elements
- Difficulty in calculating sampling error and confidence level
- Potential for researcher bias
- Lower cost and easier implementation
Generally, probability sampling offers more advantages in terms of representativeness and generalizability, but it can be more resource-intensive.
Types of Probability Sampling
Simple Random Sampling
In simple random sampling, every element in the population has an equal chance of being selected. This method is straightforward but less common in social research, except for small and homogeneous populations.
Systematic Random Sampling
Systematic random sampling involves randomly selecting the first element and then choosing subsequent elements at a fixed interval. The interval is calculated by dividing the population size by the desired sample size (N/n).
Stratified Random Sampling
Stratified random sampling is used when the population can be divided into distinct groups or strata based on relevant characteristics. This is a widely used technique in social research.
Key aspects of stratified random sampling:
- Stratification: Creating homogeneous strata that are heterogeneous between each other.
- Allocation: Determining the sample size for each stratum. Common allocation methods include:
- Simple allocation: Equal sample size for all strata.
- Proportional allocation: Sample size proportional to the stratum’s size within the population (most commonly used).
- Optimum allocation: Considers both stratum size and variability regarding the study variable.
Cluster Sampling
Cluster sampling is suitable for large, geographically dispersed populations. Instead of selecting individual elements, clusters (groups of elements) are randomly selected. Clusters are internally heterogeneous but homogeneous among themselves.
Multistage or Multi-phase Sampling:
Multistage sampling involves selecting a sample within a previously selected cluster. This allows for studying larger and more diverse populations. National surveys often employ multistage sampling, typically with three or four stages.
Random Route Sampling
In random route sampling, the interviewer follows a randomly selected route on a map to choose sampling units. This method can lead to over-representation of certain types of housing or individuals, so it’s crucial to interview specific individuals within selected units.
Types of Non-Probability Sampling
Quota Sampling
Quota sampling is frequently used in market research and opinion polls. It involves identifying homogeneous groups and determining the sample size for each group. However, unlike stratified sampling, elements within each group are not randomly selected but chosen based on convenience or researcher judgment.
Convenience or Haphazard Sampling
Convenience sampling is often employed in exploratory research due to its low cost and ease of implementation. The researcher selects participants based on their availability and accessibility.
Snowball Sampling
Snowball sampling, also known as chain sampling, is used to study small, hard-to-reach populations (e.g., illegal immigrants, cults). The researcher identifies one member of the sample, who then refers other potential participants. The sample size is determined by the researcher.
The main disadvantage of snowball sampling is the potential for bias, as it tends to reach the most visible or connected individuals within the target population.