Distinguish between Cluster sampling and Multi-stage sampling. In order to find out the incidence of Malnutrition among rural households in a given distinct, how would you collect the data by multi-stagesampling? Illustrate

Distinguish between Cluster sampling and Multi-stage sampling. In order to find out the incidence of Malnutrition among rural households in a given distinct, how would you collect the data by multi-stagesampling? Illustrate

Distinguishing Between Cluster Sampling and Multi-Stage Sampling

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Cluster Sampling and Multi-Stage Sampling are both probability sampling methods used in research, but they have distinct differences:

1. Cluster Sampling:

• Definition: In cluster sampling, the population is divided into clusters (usually based on geographical or organizational units), and a random sample of these clusters is selected. All individuals within the chosen clusters are then included in the sample.
• Procedure:
  • Identify clusters (e.g., villages, schools, households).
  • Randomly select a sample of clusters.
  • Include all units within these selected clusters in the sample.
• Advantages: Cost-effective and practical for large populations spread over wide areas.
• Disadvantages: Less precise if the clusters are heterogeneous.

1. Multi-Stage Sampling:

• Definition: Multi-stage sampling involves a series of stages of sampling, where the population is first divided into clusters, and then further sampling is conducted within these clusters in subsequent stages. It combines elements of both cluster sampling and stratified sampling.
• Procedure:
  • Stage 1: Divide the population into clusters and randomly select a sample of clusters.
  • Stage 2: Within the selected clusters, further divide into sub-clusters or strata and randomly sample from these.
  • Stage 3: Continue this process as needed, depending on the research requirements.
• Advantages: Provides more flexibility and precision, especially in large and diverse populations.
• Disadvantages: More complex and may require more resources to implement.

Data Collection Using Multi-Stage Sampling for Malnutrition Among Rural Households

To study the incidence of malnutrition among rural households using multi-stage sampling, follow these steps:

Stage 1: Selection of Primary Sampling Units (PSUs)

1. Define Primary Clusters: Divide the district into primary sampling units, such as villages or towns.
2. Random Selection: Randomly select a sample of villages from the list of all villages in the district.

Stage 2: Selection of Secondary Sampling Units (SSUs)

1. Define Secondary Clusters: Within each selected village, identify secondary sampling units, such as households or groups of households.
2. Random Selection: Randomly select a sample of households within each chosen village.

Stage 3: Data Collection

1. Conduct Surveys: Visit the selected households and collect data on malnutrition indicators, such as height, weight, and dietary intake of children and adults.
2. Collect Additional Data: Gather supplementary information that may affect malnutrition, such as household income, access to healthcare, and education levels.

Illustrative Example

Suppose you are conducting a study on malnutrition in a rural district with 100 villages.

1. Stage 1:

• Primary Sampling Units: Randomly select 10 villages from the 100 available.

1. Stage 2:

• Secondary Sampling Units: Within each of the 10 selected villages, list all households. Randomly select 20 households from each village.

1. Stage 3:

• Data Collection: In each of the 200 selected households (10 villages × 20 households), measure malnutrition indicators and collect relevant data.

By using multi-stage sampling, you effectively manage the complexity and resources required for large-scale surveys, ensuring a representative sample of the rural households in the district.