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

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

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  • 1. Sampling Meaning, Types, Procedure Dr Mira K Desai University Department of Extension Education S.N.D.T. Women’s University
  • 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. 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. 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. 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. 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. 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. 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. Types of Sampling Probability sampling Non-probability sampling Mixed methods or Multi-stage sampling
  • 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. 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. Simple Random Method:  Chits  Random number tables  Blind folded pointers Limitations:  Time consuming  Impractical and deviant  Expensive
  • 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. 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. 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. 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. 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. 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. 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. 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. 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