Paragraph-wise Explanation
In research, studying the entire population is often impractical or impossible due to constraints of time, cost, and accessibility. Therefore, researchers select a subset of the population called a sample. A sample design refers to the technique or plan that a researcher adopts to select units from the population. It ensures that the sample is representative, so that conclusions drawn from the sample can be generalized to the whole population.
A good sample design is clear, efficient, unbiased, and feasible. It should accurately reflect the characteristics of the population, minimize sampling errors, and serve the objectives of the research. Factors that influence sample design include the objectives of the study, nature and size of the population, available resources, and level of precision required. Sample design is not merely a technical step; it is a critical research decision that affects the validity and reliability of the findings.
Once the sample design is finalized, the researcher chooses the sample size, which is determined by population variability, desired accuracy, resources, and research design. Sample size and selection method together form the backbone of sampling, ensuring that the research produces scientifically valid results.
Types of Sampling Methods
According to Kothari, sampling methods are broadly classified into two categories: Probability Sampling and Non-Probability Sampling.
A. Probability Sampling
In probability sampling, each unit in the population has a known and non-zero chance of being selected. It allows for statistical estimation of sampling errors and is more scientific and representative.
Types of Probability Sampling:
- Simple Random Sampling (SRS)
- Every unit has an equal chance of being selected.
- Selection can be done using random numbers, lottery, or tables.
- Advantages: Easy to understand, unbiased, good for small populations.
- Disadvantages: Not suitable for large populations without sampling frames.
- Systematic Sampling
- Selects every k-th unit from a list of population after a random start.
- Advantage: Simple and ensures uniform coverage.
- Disadvantage: Can introduce bias if there’s a hidden pattern in the list.
- Stratified Sampling
- Population is divided into strata (homogeneous subgroups), and a sample is drawn from each stratum.
- Ensures representation of all groups (e.g., age, gender, region).
- Advantage: Increases precision and reduces sampling error.
- Disadvantage: Requires knowledge of strata and population characteristics.
- Cluster Sampling
- Population is divided into clusters, usually geographically, and a few clusters are randomly selected.
- All units in selected clusters are studied or a sample is drawn within clusters.
- Advantage: Cost-effective for large, dispersed populations.
- Disadvantage: Higher sampling error if clusters are heterogeneous.
- Multistage Sampling
- Combines two or more probability sampling methods in stages.
- Example: First clusters are selected, then stratified sampling within clusters.
- Useful for national surveys or large-scale research.
B. Non-Probability Sampling
In non-probability sampling, the probability of inclusion of each unit is unknown. It is more convenient but less scientifically rigorous.
Types of Non-Probability Sampling:
- Convenience Sampling
- Samples units that are easily accessible.
- Advantage: Quick, inexpensive.
- Disadvantage: High bias, not representative.
- Judgmental or Purposive Sampling
- Researcher selects units based on judgment of which are most useful or representative.
- Advantage: Useful for specialized studies or expert opinions.
- Disadvantage: Subjective and prone to bias.
- Quota Sampling
- Population is segmented into groups (like stratified), but selection is non-random, based on quotas.
- Advantage: Ensures representation of key groups.
- Disadvantage: Selection is biased, cannot compute sampling error.
- Snowball Sampling
- Existing respondents refer new respondents, used in hidden or hard-to-reach populations.
- Advantage: Useful for specialized or network-based research.
- Disadvantage: Not representative of the population.
Important Considerations in Sample Design
- Representativeness: Sample should reflect population characteristics.
- Sample Size: Larger samples reduce sampling error; smaller samples may suffice for exploratory research.
- Cost and Time: Practical constraints affect design choice.
- Sampling Frame: A complete list of population units is needed for probability sampling.
- Precision & Reliability: Design should minimize errors and bias.
Summary Table: Types of Sampling
| Category | Type | Key Feature | Advantages | Disadvantages |
|---|---|---|---|---|
| Probability | Simple Random | Equal chance for all units | Unbiased, easy | Difficult for large pop. |
| Probability | Systematic | Every k-th unit | Simple, uniform coverage | Can be biased by patterns |
| Probability | Stratified | Population divided into strata | Reduces error, precise | Needs strata info |
| Probability | Cluster | Random clusters selected | Cost-effective | High sampling error |
| Probability | Multistage | Combination of methods | Flexible, practical | Complex |
| Non-Probability | Convenience | Easily accessible | Quick, cheap | High bias |
| Non-Probability | Judgment/Purposive | Researcher’s choice | Useful for experts | Subjective |
| Non-Probability | Quota | Groups represented | Ensures key groups | Biased selection |
| Non-Probability | Snowball | Existing respondents refer others | Hidden populations | Not representative |
