Sampling Design


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Sampling Design

  1. 1. Sampling Designs
  2. 2. Sampling  concerned with selection of a subset of individuals from within a statistical population to estimate characteristic of the whole population. Sample  a small amount or part of something that shows you what the rest is or it should be
  3. 3. Terminologies Population- a group of experimental data, persons, etc. Population Total- the sum of all the elements in the sample frame. Population Mean- the average of all elements in a sample frame or population Sampling Fraction- the fraction of the population or data selected in a sample
  4. 4.  Random sample- every unit has the same probability of selection  Simple random sample 1. Selected without replacement -no repetitions are allowed 2. Selected with replacement -repetitions are permitted
  5. 5. 4 Principles of Sampling Design Standardize samples Replicate (for each combination of time, location, and any controlled factor) Establish equal number of suitable Controls Locate all samples Randomly
  6. 6. Advantages of Sampling Very accurate Economical in nature. Very reliable.  High suitability ratio towards the different surveys. Takes less time  In cases, when the universe is very large, then the sampling method is the only practical method for collecting the data.
  7. 7. Disadvantages of Sampling X Inadequacy of the samples. X X X X Chances for bias. Problems of accuracy. Difficulty of getting the representative sample. Untrained manpower.
  8. 8. Planning a Sample Survey 1. Objectives of the survey. 2. Population to be sampled. 3. Data to be collected. 4. Degree of precision to be desired. 5. The questionnaire and the choice of data collectors. 6. Selection of the sample design.
  9. 9. 7. Sampling units. 8. The pre-test. 9. Organization of the field work. 10. Summary and analysis of the data.
  10. 10. Determination of Sample Size  tables, and power function charts are well known approaches to determine sample size.
  11. 11. Sampling Design  specifies for every sample, there is a probability of being drawn Types of Sampling Design 1. Scientific Sampling 2. Non- Scientific Sampling
  12. 12. Scientific Sampling 1. Restricted Random Sampling A method of sampling is described which is a compromise between systematic sampling and stratified random sampling. It has less potential for bias than systematic sampling and also avoids the practical problems associated with stratified random sampling.
  13. 13. 2. Unrestricted Random Sampling This method assumes that each site has an equal chance of being part of the sample selected. Make a list of all project sites, perhaps by alphabetical order. Every project site is given a number. Random sampling isn’t always the most convenient method of choosing a sample.
  14. 14. Difference between restricted and unrestricted sampling Unrestricted sampling occurs when elements are selected individually and directly from the population, whereas, restricted sampling occurs when elements are chosen using a specific methodology as in probability sampling or complex probability sampling.
  15. 15. 3. Stratified random sampling This method of sampling is sometimes used if there are wide variations in site performance within a certain geographic location or type of distribution site (i. e., health centers or schools). All the sites are grouped into segments, each having some uniform, easily identifiable characteristics. Each segment is sampled separately using unrestricted random sampling methods.
  16. 16. 4. Systematic Sampling A statistical method involving the selection of elements from an ordered sampling frame. The most common form of systematic sampling is an equal-probability method. In this approach, progression through the list is treated circularly, with a return to the top once the end of the list is passed.
  17. 17. The sampling starts by selecting an element from the list at random and then every kth element in the frame is selected, where k, the sampling interval (sometimes known as the skip): this is calculated as: where n is the sample size, and N is the population size.
  18. 18. 5. Multistage Sampling A complex form of cluster sampling. Cluster sampling is a type of sampling which involves dividing the population into groups (or clusters). Then, one or more clusters are chosen at random and everyone within the chosen cluster is sampled.
  19. 19. Advantages and Disadvantages cost and speed that the survey can be done in convenience of finding the survey sample normally more accurate than cluster sampling for the same size sample X not as accurate as SRS if the sample is the same size X more testing is difficult to do
  20. 20. 5. Cluster Sampling • It is a sampling technique used when “natural” but relatively homogeneous groupings are evident in statistical population.
  21. 21. Nonscientific Sampling • Here, not all of the individuals in a population are given equal chance of being included as sample , hence, subjectivity occurs. • Three types of nonscientific sampling: 1. Purposive Sampling 2. Convenience Sampling 3. Quota Sampling
  22. 22. PURPOSIVE SAMPLING • This type of nonscientific sampling is based on selecting the individuals as samples according to the purposes of the researcher as his controls.
  23. 23. CONVENIENCE SAMPLING • Also referred to as haphazard or accidental sampling. The process of selecting some people to be part of a sample because they are readily available, not because they are most representative of the population being studied.
  24. 24. Examples of Convenience Sampling • Female moviegoers sitting in the first row of a movie theater • The first 100 customers to enter a department store • The first three callers in a radio contest
  25. 25. QUOTA SAMPLING • This is one of the most common forms of nonprobability sampling. Sampling is done until specific number of units (quotas) for various subpopulations have been selected.
  26. 26. To choose a Quota Sample: 1. Divide the population into strata or groups of individuals that are similar in someway that is important to the response. 2. Choose a separate sample from each stratum. This does not have to be a random sample. 3. Combine these samples to form a quota sample.
  27. 27. GROUP 5 The End