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Agricultural Census Sampling Frames and Sampling

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20 diapositives · 367 Views · Jul 28, 2014

This guide offers an overview of sampling methods and frames for agricultural censuses. It defines sampling, the purpose of sampling frames, and when to utilize or avoid sampling techniques. Key aspects of a good sampling plan are discussed, highlighting systematic, stratified, and cluster sampling methods. The guide also elaborates on area sampling, describing single-stage and multi-stage processes. Finally, it emphasizes the importance of careful sample design to achieve accuracy while balancing cost and efficiency in agricultural statistics.

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Agricultural Census Sampling Frames and Sampling
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  1. Agricultural Census Sampling Frames and Sampling Section A

  2. What is Sampling? Selecting a representative group of units from a population in order to make estimates about the population from the sample.

  3. What is a Sampling Frame? A list of all the units from which the sample will be selected. It can take various forms, depending upon the unit of analysis.

  4. Why Use Sampling? • Saves time and money in comparison to complete enumeration • Can concentrate on a particular sub-group of interest (e.g. livestock) • Where nonsampling errors are large, sampling allows for better control

  5. When to Not Use Sampling • If data are needed down to the lowest administrative level in a country • Measuring small changes over time • High survey costs related to sampling, field control, etc.

  6. Characteristics of a Good Sampling Plan • Known chance of selection for each unit • Measurable reliability • Feasibility • Economical and efficient

  7. What Size Sample do I Need? The most practical sample size that will accurately estimate a population parameter with a specified precision using the most efficient design.

  8. Systematic Sampling • Basically as accurate as simple random sampling • How to do it: • Determine your sampling rate • Use random number table to find a starting number equal or less than the sampling interval • From starting number take every nth unit

  9. Sampling Frame Consideration • When a sampling frame contains units that are out of scope do not substitute: • Results in higher sampling rate • May introduce bias in the selection process

  10. Sample Stratification • When characteristics of the population are known they can be used to improve sampling • Similar sampling units are grouped together into strata • A simple random sample selects at least one unit from each group or strata • Must know size of strata and have reliable frame for selection of units from each strata

  11. Probability Proportional to Size • Possible when a size measure is available that is correlated to the variable of interest • Can improve accuracy by ensuring that large units which have the greatest impact on population estimates have the same probability of selection • Used when sampling units vary in size

  12. Cluster Sampling Sampling units are grouped into clusters instead of a list and a sample of clusters is chosen rather than individual units. • Good for when no adequate frame exists of the units in the population • Or for saving field costs by cluster sampling on some geographic basis

  13. Single-Stage Cluster Sample • A cluster of units is selected and all units in the selected cluster is sampled • Good for when there is no list of units or when sampling at the unit level is inefficient • Can also use area samples, where a geographic area is considered a cluster

  14. Multi-Stage Cluster Sample • A subsample of units in the selected cluster is selected for the sample • A sample of clusters is selected • A list of all units in the selected clusters is constructed • A sample of units in the selected clusters is selected • Reduces the listing required and

  15. Area Sampling Area sampling is a frequently used method of cluster sampling, used when: • Complete household lists are unavailable but reasonably good maps exist • Travel costs for covering randomly selected households is too great

  16. Single-Stage Area Sampling • Obtain a detailed map and update with local authorities, noting changes on map • Number blocks serially in a serpentine manner to avoid omissions • Select a simple or systematic 1% sample of blocks • Interview all households in the selected sample blocks

  17. Two-Stage Area Sampling • Take one block in every 25 as a sample block instead of 1 in a 100 used in procedure A. • Divide each sample block into 4 parts with approximately equal numbers of households • Number each block segment from 1 to 4 • Randomly select one of the four segments in each sample block and interview all households in the selected segment

  18. Area Sampling with Listing When there are no maps then: • Select 1 in 25 blocks • List and number all households in the sample blocks • Select ¼ of all households in each sample block by simple random or systematic sample • Interview all selected sample households

  19. Choice of Sample Design • Sampling specialists must choose an efficient design to provide the desired precision balancing costs and standard error • When cost is not important, single-stage sampling provides more accurate results • When cost and administrative convenience are important a cluster sample is often used

  20. Section A Quiz • What are the characteristics of a good sampling plan? • What is area sampling and when is it used? • When should you not use sampling? • What is stratification?