Sampling:Medical Statistics Part III


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Sampling:Medical Statistics Part III

  1. 1. Sampling Medical Statistics Part-III
  2. 2. Sample is soul of statistics
  3. 3. Distinguish Statistic Response of a sample with respect to a particular variable is called statistic and it is expressed as mean, median, mode, and so on Statistics Statistics is the study of the collection, organization, analysis, interpretation and presentation of data
  4. 4. Keywords
  5. 5. Key words
  6. 6. External validity: Sample touches pupulation
  7. 7. The concept of sampling: External validity Sampling is the process of selecting units (e.g., people, organizations) from a population of interest so that by studying the sample we may fairly generalize our results back to the population from which they were chosen.
  8. 8. Two ways to get sample • Sampling Model • Proximal Similarity Model
  9. 9. Sampling Model • Start by identifying the population you would like to generalize to • Draw a fair sample from that population • Conduct your research with that sample • Generalize your results back to the population •
  10. 10. Limitation of Sampling Model • 1st :Don't know at the time of your study who you might ultimately like to generalize to • 2nd :May not be easily able to draw a fair or representative sample • 3rd : it's impossible to sample across all times that you might like to generalize to (like next year)
  11. 11. Sampling Model
  12. 12. Proximal Similarity Model • • • • • 'Proximal' means 'nearby‘ 'similarity' means... well, it means 'similarity‘ Proximal similarity was suggested by Donald T. Campbell as an appropriate relabeling of the term external validity Begin by thinking about different generalizability contexts and developing a theory about which contexts are more like our study and which are less so For instance, we might imagine several settings that have people who are more similar to the people in our study or people who are less similar. This also holds for times and places. When we place different contexts in terms of their gradient of similarity relative similarities, we can call this implicit theoretical a . Once we have developed this proximal similarity framework, we are able to generalize. How? We conclude that we can generalize the results of our study to other persons, places or times that are more like (that is, more proximally similar) to our study. Notice that here, we can never generalize with certainty -- it is always a question of more or less similar.
  13. 13. Proximal Similarity Model
  14. 14. Threats to sampling • The results of sample analysis may not exactly fits people when all the three parameters of epidemiology(time,place and person ) related in different setting
  15. 15. Improving quality of sample • Accept random sample • Assure that the respondents participate • Keep your dropout rates low • If there is variation in sampling, define at starting only to have proper external validity
  16. 16. Behaviour of a sample across a bell curve
  17. 17. standard error • • • • • The standard deviation of the sampling distribution tells us something about how different samples would be distributed. In statistics it is referred to as the standard error In sampling contexts, the standard error is called sampling error Sampling error gives us some idea of the precision of our statistical estimate A low sampling error means that we had relatively less variability or range in the sampling distribution The greater your sample size, the smaller the standard error
  18. 18. Methods of sampling • Probability • Non Probability
  19. 19. Probability vs. Non Probability Probability  Random selection during sampling  Depends upon the rationale of probability theory  Odds or probability that we have represented the population well  Estimate confidence intervals for the statistic  Best preferred  More accurate and rigorous Non Probability  Sampling does not involve random selection  Cannot depend upon the rationale of probability theory  May or not represent the population well  It is preferred over probability in situation where randomization is not possible
  20. 20. Probability • A probability sampling method is any method of sampling that utilizes some form of random selection. In order to have a random selection method, you must set up some process or procedure that assures that the different units in your population have equal probabilities of being chosen
  21. 21. Keywords • N = the number of cases in the sampling frame • n = the number of cases in the sample • NCn = the number of combinations (subsets) of n from N • f = n/N = the sampling fraction
  22. 22. Probability: Random Sampling Single stage=Simple Random Simple Stratified Systematic Cluster (Area) Multi-Stage
  23. 23. Simple • • Objective: To select n units out of N such that each NCn has an equal chance of being selected. • • Simple random sampling is simple to accomplish and is easy to explain to others • • Procedure: Use a table of random numbers, a computer random number generator, or a mechanical device to select the sample A fair way to select a sample, it is reasonable to generalize the results from the sample back to the population Simple random sampling is not the most statistically efficient method of sampling just because of the luck of the draw, not get good representation of subgroups in a population To deal with these issues, we have to turn to other sampling methods
  24. 24. Stratified :Proportional or quota Divide population into homogeneous subgroups and then taking a simple random sample in each subgroup: and it should be o non-overlapping groups (i.e., strata) N1, N2, N3, ... Ni, such that N1 + N2 + N3 + ... + Ni = N. Then do a simple random sample of f = n/N in each strata.
  25. 25. Systematic
  26. 26. Cluster (Area) :Wider geographical territory • Divide population into clusters (usually along geographic boundaries) • randomly sample clusters • measure all units within sampled clusters
  27. 27. Multi-Stage In most real applied social research, we would use sampling methods that are considerably more complex than these simple variations. The most important principle here is that we can combine the simple methods described earlier in a variety of useful ways that help us address our sampling needs in the most efficient and effective manner possible. When we combine sampling methods, we call this multi-stage sampling
  28. 28. Non Probability • Purposive[more commonly used]:planned • Accidental, Haphazard or Convenience Sampling
  29. 29. Purposive[more commonly used]:planned • Modal Instance Sampling • Expert Sampling • Quota Sampling • Heterogeneity Sampling • Snowball Sampling
  30. 30. Modal Instance Sampling • In statistics, the mode is the most frequently occurring value in a distribution. In sampling, when we do a modal instance sample, we are sampling the most frequent case, or the "typical" case
  31. 31. Expert Sampling Expert sampling involves the assembling of a sample of persons with known or demonstrable experience and expertise in some area
  32. 32. Quota Sampling Select people nonrandomly according to some fixed quota. There are two types of quota sampling: proportional and non proportional. In proportional quota sampling you want to represent the major characteristics of the population by sampling a proportional amount of each.
  33. 33. Heterogeneity Sampling In order to get all of the ideas, and especially the "outlier" or unusual ones, we have to include a broad and diverse range of participants. Heterogeneity sampling is, in this sense, almost the opposite of modal instance sampling
  34. 34. Snowball • Begin by identifying someone who meets the criteria for inclusion in your study. You then ask them to recommend others who they may know who also meet the criteria. Although this method would hardly lead to representative samples, there are times when it may be the best method available. Snowball sampling is especially useful when you are trying to reach populations that are inaccessible or hard to find
  35. 35. Accidental, Haphazard or Convenience Sampling In clinical practice, we might use clients who are available to us as our sample. In many research contexts, we sample simply by asking for volunteers. Clearly, the problem with all of these types of samples is that we have no evidence that they are representative of the populations we're interested in generalizing to and in many cases we would clearly suspect that they are not
  36. 36. Morning is the sample of the day