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SAMPLING METHODS

SAMPLING METHODS. TWO-STAGE CLUSTER SAMPLING (WITH QUOTA SAMPLING AT SECOND STAGE). STATISTICAL TABLES: Table A Random Digits. SIMPLE RANDOM SAMPLING. STRATIFIED RANDOM SAMPLING Grouped by characteristic . SYSTEMATIC SAMPLING. CLUSTER SAMPLING.

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SAMPLING METHODS

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  1. SAMPLING METHODS

  2. TWO-STAGE CLUSTER SAMPLING (WITH QUOTA SAMPLING AT SECOND STAGE) Sampling

  3. STATISTICAL TABLES: Table A Random Digits Sampling

  4. SIMPLE RANDOM SAMPLING Sampling

  5. STRATIFIED RANDOM SAMPLINGGrouped by characteristic Sampling

  6. SYSTEMATIC SAMPLING Sampling

  7. CLUSTER SAMPLING Sampling

  8. TWO STAGE CLUSTER SAMPLING (WITH RANDOM SAMPLING AT SECOND STAGE) Sampling

  9. FLOWCHART Sampling

  10. TABLE 1 Sampling

  11. TABLE 2 Sampling

  12. POPULATION • Population units e.g.; children or adults • Population observations, characteristics or attributes e.g.; immunization history • Time and resources are limited so that only sample units and sample observations can be selected from the population. Sampling

  13. Total Count versus sampling: National census is conducted every 10-15 years: • Less accurate over time. • Less accurate in dynamic (shifting) populations. • Very expensive. Sampling

  14. Sample surveys allows obtaining more extensive information (smaller number of persons) • Need to train a limited number of interviewer • More in-depth questions or detailed data • Can quickly provide useful information • Relatively low cost Sampling

  15. "Less is more"Mies Van der Rohe A sample should be representative to the population of interest. Sampling

  16. Simple Random Sampling: • Need a list of all eligible persons in the population • Every person has equal chance (equal probability) to be selected in the sample • Basic method, important for comparison with other sampling methods • Provides an unbiased estimate of a variable in a population Sampling

  17. Simple Random Sampling: (continued) • Permits quantitative assessment of sampling error • Rarely used in actual surveys • Difficult • Expensive • Excessive travel time (different location of subjects) • Excessive local introduction and organization time Sampling

  18. Sampling with replacement: • Individuals from a population of observations may appear more than once in a sample of population Sampling

  19. Sampling without replacement: • Individuals from a population of observations can appear only once in a sample of population. • This is the usual case. • Number of possible samples = N!/n!(N-n)! (if order is not important): • Equal probability selection Method (EPSEM): • Use of random tables, or computers Sampling

  20. Systematic Sampling: Similar Procedure: • List all persons in the population • Define selection interval: = (Sampled population)/(Sample size) = N/n = An integer for ease of field use Sampling

  21. Systematic Sampling:(continued) • Select a random starting point (first person in the sample) • Next selection = the random start + the random interval • And so on and so forth… • Data should not be ordered in a special way. Sampling

  22. Stratified random sample: The population is divided into multiple strata based on common characteristics e.g.; • Residence (Urban or rural) • Tribe, ethnicity or race • Family income (poor, moderate, or wealthy) Sampling

  23. Stratified random sample:(continued) A random sample is selected from each stratum The samples from each stratum are combined for a single estimate of the population mean and variance. Sampling

  24. One-Stage Cluster Sampling: • The population is listed as groups (termed clusters), not individuals e.g.; • Area of residence (village, town, .. etc.) • School or classroom within a school • All clusters are listed and a sample of clusters is selected. • All persons in the selected clusters are examined. • The samples from each of the clusters are combined into a single estimate of the population mean and variance. Sampling

  25. Two-Stage Cluster Sampling with Simple Random Sampling at the Second Stage: Stage I:A random sample of clusters Stage II:A sample from selected clusters The samples from each of the selected clusters are combined into a single estimate of the population mean and variance. Sampling

  26. Two-stage Cluster Sampling with Quota Sampling in the Second Stage: • The population is divided into multiple clusters. Stage I: A random sample of clusters Stage II: A random start Interviewer continues within a cluster until the quota (constant number) is filled. • The samples from each cluster are combined into a single estimate of the population mean and variance. Sampling

  27. Two-stage Proportionate to size (PPS) Cluster Sample with Quota Sampling in the Second Stage: • The population is divided into multiple clusters. Stage I: A random sample of clusters with probability proportionate to their size (PPS) "Size" means the number of eligible persons residing in the cluster. Stage II: A random start Interviewer continues within a cluster until the quota (constant number) is filled. Sampling

  28. Two-stage Proportionate to size (PPS) Cluster Sample with Quota Sampling in the Second Stage: (continued) • The samples from each cluster are combined into a single estimate of the population mean and variance. • This method is favored by Expanded program on Immunization (EPI). Note: No random selection in the second stage. Sampling

  29. Probability sample versus Non-probability sample: • Every person has equal chance (equal probability) to be selected in the sample. • No bias • Generalization of the results • On average, the characteristics of people in probability samples are similar to those of the population from which they were selected, particularly if a larger number are chosen. Sampling

  30. Probability sample versus Non-probability sample: Sampling in clinical trials are usually highly selected and biased samples of all patients with the condition of interest. (Internal validity) [1] Use of inclusion/ exclusion criteria: • Restricts the heterogeneity of patients • Excludes atypical forms of the disease • Improves chances of patients completing the assigned treatment used in the study • Excludes presence of other diseases • Excludes an unusually poor prognosis • Excludes patients with contra-indication for the treatment Sampling

  31. Probability sample versus Non-probability sample (continued) [2] Refusal of patients to participate in the study: Tend to be systematically different from those who agree to enter in the trial: • Socio-economic class • Severity of disease [3] Patients who are thought to be unreliable (would not follow the groundrules of the trial are usually not enrolled. Sampling

  32. PRECISION Determine the desired level of precision i.e. amount of error in parameter estimates that can be tolerated by the decision-maker. Definitions: • Precisionis the size of deviations from the average value of some parameters of interest obtained by repeated application of sampling procedures. • Accuracyis the size of deviations from the true mean of some parameter in a population. In surveys, we cannot measure accuracy but can measure precision Sampling

  33. MATCHING Is stratified sampling in which numbers selected in each stratum are determined by the numbers in that stratum in some other sample. • Main stay in epidemiology. • 1:1 is the best. • Can have up to 5:1. Sampling

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