Multistage Sampling (Multistage Cluster Sampling)

Multistage sampling, also known as multistage cluster sampling, is an extension of cluster sampling designed to be more efficient and cost-effective, especially when dealing with large, geographically dispersed populations.

• Addressing Cluster Sampling Limitations: While cluster sampling selects entire clusters, multistage sampling acknowledges that studying everyone in all selected clusters can still be too resource-intensive or unnecessary for the research objectives.

• Hierarchical Cluster Selection: Multistage sampling involves multiple levels or stages of cluster selection in a hierarchical manner. Instead of using all members of the initially chosen clusters, researchers take it a step further by randomly selecting a sample of individuals within each chosen cluster.

Example: Household Survey in a Metropolitan Region

1. Stage 1: Districts (Primary Clusters): Divide the entire metropolitan region into geographical districts. Randomly select a sample of districts from all districts in the region.

2. Stage 2: Blocks (Secondary Clusters): Within each of the districts selected in Stage 1, further divide them into smaller geographical blocks. Randomly select a sample of blocks from within each selected district.

3. Stage 3: Dwellings (Final Sampling Units): Within each of the blocks selected in Stage 2, create a list of all dwellings (households, apartments). Randomly select a sample of dwellings from within each selected block.

Outcome: In this three-stage example, the final sample consists of individuals residing in the dwellings selected in the final stage.

Key Advantage: Multistage sampling avoids the need to create a complete list of every dwelling (or individual) in the entire metropolitan region at the outset. Listing is only required for the selected blocks at the final stage, significantly reducing the effort and resources needed for large-scale surveys.

Non-Random Sampling Methods

Non-random sampling methods are characterized by the fact that:

• Unequal Selection Chance: Some members of the population have no chance of being selected for the sample.

• Unknown Probability: For those who could be selected, the probability of selection cannot be accurately determined. Selection is not based on chance but on other criteria.

• Qualitative Research Focus: These methods are primarily used in qualitative research, where the goal is often in-depth understanding rather than statistical generalization to a population.

Advantages of Non-Random Sampling Methods:

• Cost-Effective: Generally less expensive to implement than random sampling.

• Simpler Implementation: Often have a less complex approach to sample selection.

• Faster Results: Can yield quicker results compared to probability sampling, as recruitment may be faster.

• No Sample Frame Needed (Often): May not require a complete list of the entire population, making them practical when such lists are unavailable.

Disadvantages of Non-Random Sampling Methods:

• Bias Susceptibility: Prone to human error and bias because selection is not randomized and relies on researcher judgment or convenience.

• Limited Generalizability: Findings are generally not appropriate for generalizing beyond the specific sample studied to the broader population. Conclusions are specific to the sample itself.

• Inappropriate for Statistical Inference: Statistical analysis designed to generalize to a population (e.g., confidence intervals, hypothesis testing) is not valid when using non-random sampling methods because the sample may not be representative.

Types of Non-Random Sampling Methods:

1. Convenience Sampling:

   • Selection Basis: Sample selection is based on the researcher’s convenience and accessibility. Participants are chosen because they are readily available, easy to reach, and willing to participate.

   • Example: Choosing study participants from the two parishes “nearest” to the researcher simply for ease of access.

2. Purposive/Judgmental Sampling:

   • Selection Basis: Sample selection relies entirely on the researcher’s judgment and specific research interests. The researcher deliberately selects participants who are believed to be particularly knowledgeable, experienced, or relevant to the study’s purpose.

   • Example: Choosing to interview only nurses who are currently “on duty” because they are readily available and assumed to have relevant insights.

3. Snowball Sampling Method:

   • Selection Basis: Participants are recruited through referrals. Initial participants are asked to recommend other potential participants who meet the study criteria. This method is useful for reaching populations that are hard to access directly.

   • Process: Each interviewed person suggests further respondents who they know can provide valuable data. The sample “snowballs” as referrals expand the participant pool.

4. Quota Sampling:

   • Analogy to Stratified Sampling: A non-probability counterpart to stratified random sampling.

   • Process: The population is first divided into subgroups (quotas) based on specific characteristics, similar to stratification. However, instead of random selection within each subgroup, researchers use judgment to select participants from each quota to meet a pre-set proportion. Randomness is not used in the final selection.

5. Accidental Sampling:

   • Selection Basis: Participants are included in the sample purely by chance or accident, not through any deliberate selection strategy. The sample is formed incidentally based on who happens to be available at a particular place and time.

   • Example: Interviewing “every student who passes by” a specific location (like a university gate) without any pre-defined selection criteria.

Important Notes on Sampling Errors:

i) Sampling Errors: These errors arise because conclusions are drawn about a whole population based on a smaller sample. Sampling errors are more likely when a sample is small and non-random. Larger, randomly selected samples generally reduce sampling error.

ii) Non-probability Sampling and Error Estimation: Non-probability sampling methods do not allow for the estimation of sampling error. Statistical methods to quantify sampling error (e.g., calculating confidence intervals) are not valid with non-random samples.

Types of Sampling Errors:

1. Random Error: This is a chance error that can lead to inaccurate results simply due to random variation in the sample. Random error can be reduced by increasing the sample size.

2. Systematic Error (Bias): This is a more serious error resulting from a flaw in the sampling method or research design itself, leading to a consistent distortion of results in a particular direction. Systematic error is due to bias, not chance. Increasing sample size alone will not fix systematic error. It requires addressing the source of the bias in the study design or sampling procedure.