Sampling

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Its academic presentation about Sampling. What-Why-How of sampling in research projects.

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Sampling

  1. 1. Sampling Meaning, Types, Procedure Dr Mira K Desai University Department of Extension Education S.N.D.T. Women’s University
  2. 2. When do we do sampling?  Covering entire population is practically impossible and the population is infinite.  When the results are required in a short time.  When the area of study is wide.  When resources are limited particularly in respect of money and trained persons.  When the item or unit is destroyed under investigation.
  3. 3. Why Sampling?  Scientific approach - Inductive reasoning  Economy - time, money, resources  Quick- procedure is faster  Accurate- results can be accurate  Quality- can be improved  Estimation- adequate and tentative measure  Reliable- error and accuracy  Absence of researcher bias
  4. 4. Steps in Sampling  Deciding universe/population  Is population under study finite or infinite?  Decision about sample Size, Frame  Deciding sampling design (Type & Procedure)  Calculating sampling error  Statistical generalization…replication
  5. 5. What is Universe/Population-Sample? UNIVERSE: All the individuals/things/events/ documents etc. having designated set of specifications which a study intends to cover. POPULATION: All the individuals/things/events/ documents etc. confirming to the designated set of specifications which the study in particular covers. SAMPLE: In relation to population, representative population, miniature or aggregate of population.
  6. 6. Here is the example….  UNIVERSE: Children in Mumbai.  POPULATION: Children in the age group of 5 to 10 years, from GMUA, who stay with their families, and who attend private schools.  SAMPLE: Children residing in the Suburban areas of Mumbai and attending to Podar, Jamanabai, Manekji Kooper and Uttpal Sanghavi Schools.
  7. 7. Population versus Sample  Population = Parameter (N-size, μ-mean, s-SD)  Sample = Statistics (n-size, x- Mean, SD-SD)  Statistics gives estimates about parameter.  A finite subset of statistical individuals defined in a population is called a sample.  The number of units in a sample is called the sample size.  The list of the units of sample is sample frame.
  8. 8. Model for Sampling Objectives RESOURCES Cost Time Human Technical Target Group Sampling Size Frame Techniques Procedure TYPE OF STUDY Survey Historical Experiment Ethnographic Case study
  9. 9. Types of Sampling Probability sampling Non-probability sampling Mixed methods or Multi-stage sampling
  10. 10. Types of Sampling PROBABILITY [Equal chance, Estimation of chance]      Simple Random Systematic Random Stratified Random PPS Area/Cluster NON-PROBABILITY [All do not have chance, No way to estimate/specify chance] Accidental/Incidental/ Convenience/Available  Purposive/ Expert choice/ Judgmental  Quota  Sequential Snow ball
  11. 11. Pre-conditions for Probability Sampling  Population is finite  Listing of all the units of the population  Possibility of selection of units at random  Each unit having equal chance of getting selected  Estimation of chance of selection  Estimation of error in case of non-selection
  12. 12. Simple Random Method:  Chits  Random number tables  Blind folded pointers Limitations:  Time consuming  Impractical and deviant  Expensive
  13. 13. Systematic Random Method:  Size = Total Number/Required Number  Random beginning at a particular interval  Limitations:  Time consuming  Difficult if high variance in population  At times the cost of data collection is high
  14. 14. Stratified Random Sampling Method:  Formation of strata  Variance among stratum not within stratum  Random subgroups/strata/correlated categories Limitations:  Base is the strata, need to know the units  Bigger strata may lead to over representation
  15. 15. PPS- Proportionate to Population Sampling/ Probability Proportional to Size Method:  Simple random in stratum  Proportionate to the population in the stratum Limitations:  Time consuming and expensive  Needs estimates of exact population to decide proportions
  16. 16. Area/Cluster Sampling Method:  Assumption of homogeneity in the cluster  Usually part of multi-stage design Limitations:  Deviance or variance within the cluster  Cluster need to be carefully defined
  17. 17. Multi-stage Sampling Example 1st: Administrative Ward (Lottery Method) 2nd:Election Ward (Lottery Method) 3rd: Geographic Location for first unit (Purposively) 4th: Identifying Housing society/ Chawl /Flats/Slums (Random) 5th: Locating household having sample characteristics (Purposive) 6th: Male and female equal ratio through quota (Snow Ball)
  18. 18. Keep in mind….  Higher population variance = Higher S. error  Higher Sampling error = Lower sample reliability  Higher sample size = Lower Sampling error  Higher sample size = Lesser sample reliability
  19. 19. Decision about Sample size  Degree of accuracy  Extent of variation in population with reference to key characteristics  Size of the population  Tolerable limits of sampling error  Degree of stratification
  20. 20. Calculation of sample size For a survey design based on a simple random sample, Formula: n= t² x p(1-p) m² Where, n = required sample size t = confidence level at 95% (standard value of 1.96) p = estimated prevalence of measure m = margin of error at 5% (standard value of 0.05)
  21. 21. Good sampling design  Adequate (larger the size better it is)  Accurate & Reliable (least sampling errors)  Representative (contains all the properties of the population)  Maximum information about population at minimum cost, time and human power

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