Morris-Hansen Sampling
The Morris-Hansen sampling technique, often referred to in the context of survey sampling, is a method developed by Morris H. Hansen and William G. Madow in the mid-20th century. This method is particularly noted for its application in designing efficient sampling strategies for large-scale surveys.
History and Development
The Morris-Hansen method was developed during a time when survey methods were evolving to meet the demands for more accurate and efficient data collection. Here are some key points in its history:
- 1940s-1950s: The development of this method was part of a broader effort to improve sampling techniques, especially in the U.S. Census Bureau. Morris Hansen and William Madow, both statisticians at the Bureau, contributed significantly to this field.
- 1953: Hansen and Madow, along with Joseph Steinberg, published their influential work titled "Sample Survey Methods and Theory," which detailed various sampling techniques, including the Morris-Hansen method.
Methodology
The Morris-Hansen method involves:
- Stratified Sampling: Dividing the population into homogeneous subgroups (strata) before sampling to ensure representativeness.
- Optimal Allocation: Determining the number of samples to take from each stratum to minimize variance for a given cost or to achieve a target precision within budget constraints.
- Sample Size Determination: Using mathematical models to calculate the necessary sample size to achieve desired levels of accuracy and reliability in survey results.
- Use of Auxiliary Information: Incorporating known data about the population to enhance the efficiency of the sampling process.
Applications
This sampling technique has been widely applied in:
- Economic surveys for estimating population parameters.
- Health surveys to understand disease prevalence and health behaviors.
- Election polls for predicting voter turnout and preferences.
Significance
The Morris-Hansen method has been pivotal in:
- Improving the accuracy of national statistics.
- Reducing costs associated with large-scale data collection.
- Providing a framework for complex survey designs that could adapt to different population distributions and survey objectives.
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