# Sampling Techniques - PowerPoint PPT Presentation

Sampling Techniques

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Sampling Techniques

## Sampling Techniques

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1. Sampling Techniques Presenter – Anil Koparkar Moderator – Bharambhe sir

2. Framework • Introduction • Need and advantages • Methods of sampling • Probability sampling • Simple Random Sampling – With & Without Replacement • Stratified Random Sampling • Systematic Random Sampling • Cluster Sampling • Non probability sampling • Convenient sampling • Judgment sampling • Quota sampling • Uses of sampling • References

3. A famous sampling mistake That’s Truman They only asked rich, white people with telephones who’d they vote for. Sadly, they published their mistake

4. Few Terms

5. Need of sampling techniques • Census requires enormous time, trained personnel, money etc. and slightest bias can get magnified when no. of observations are increased. • Thus most of the time its not feasible to collect data from whole population. • Thus with sampling techniques data collected and analyzed in much less time and money and also reduces inspection fatigue

6. What is Sampling ? • Procedure by which some members of a population are selected as representative of the entire population. OR • Procedure of selection of part of an aggregate to represent the whole population. • The sub-group thus selected to represent the whole population is known as SAMPLE • Thus sampling method is the scientific & objective procedure of selecting unit from a population and provides a sample that is expected to be representative of the population as a whole.

7. What exactly IS a “sample”?

8. what exactly IS a “sample”? A subset of the population, selected by either “probability” or “non-probability” methods. If you have a “probability sample” you simply know the Probablity of any member of the population being included (not necessarily that it is “random.”)

9. Types of sampling

10. Simple Random Sample • Def:- The technique of selecting sample in which there is equal probability of individual getting selected at each draw. • A sample selected such that each possible sample combination has equal probability of being chosen.  • Random does not mean haphazard. • Two types of Simple Random Sampling •  1 ) Simple random sampling without replacement •  2 ) Simple random sampling with replacement

11. Simple Random Sample • Get a list or “sampling frame”-coding • Let N= no of units in population then • Selection/identification random numbers between 1 to N Methods of selection/identification of a random number: • Lottery Method • Table of Random numbers • Random number selections using computer 3. Identify sample unit according to random number generated.(Decoding)

12. Simple random sampling without replacement • Simplest and basic method of drawing samples. • The probability of including a specified unit in a sample of size n at rthdrawis 1/N-(r-1). (N=total population size, n =is sample size)

13. Simple random sampling with replacement • Definition: It is a method of sampling such that every one of the possible sample of size n from N has the same probability. • The probability of drawing a specified unit at the rth draw is equal to the probability of drawing it at the 1st draw. • The population units may enter the sample more than once •  This method is used when there is follow up in any survey. E.g School health survey • The probability of including a specified unit in a sample of size n is n/N. (N=total population size, n =is sample size) • probability of including a specified unit in a sample of size n at rthdraw = 1/N

14. Lottery method • Ex. Suppose 20 patients from 100 is to be selected by lottery method, then- • All the 100 patients can be given serial number 1 to 100. • 100 pieces of paper with equal size will be marked with 1 t0 100 no they are then folded and shuffled. • Draw out 1 & note the no. • Replace the piece of paper drawn. Again repeat the process. Reject if same no is selected. Repeat till 20 samples are drawn. Now decode those numbers to select proper subject from our population 100.

15. Random number procedures • By computer (Demo) • By random number table (Demo) Example:- nine blocks in a certain administrative zone contain 793, 170, 970, 657, 1721, 864, 383 and 826 households respectively. If we want to select 6 households using a method of SRS Without Replacement. Answer

16. Advantage and disadvantage of SRS Advantages of Simple random sampling • It is simple technique • Gives equal chances of selection for every individual from population. Disadvantages of Simple random sampling • If study is repeated, same samples couldn’t be identified • If population is heterogeneous, then SRS is not good technique to draw samples. • If population is divided in different strata, then SRS may leave representatives from some strata.

17. Stratified Random Sample • Also sometimes called proportional or quota random sampling. • Stratification means division of population into mutually exclusive and exhaustive groups. • This method divide the population into non-overlapping (mutually exclusive)groups (i.e., strata) of size N1, N2, N3, ... Ni, such that N1 + N2 + N3 + ... + Ni = N. • Identify size of sub-samples (n1, n2, n3,…ni; n=n1+n2+n3….ni) • ni=(ni/N) × n. • Then using SRS in each strata to get sample of size ni. • Combined together to form the required sample from the population.

18. Stratified Random Sample

19. Systematic Random Sample • Divide the population size by the sample size, to get sampling fraction • Select a random number between 1 and sampling fraction, which is the first sampling unit • Systematically select the remaining sample units, by adding sampling fraction

20. Systematic Random Sample • Select a random number, which will be known as k • Get a list of people, or observe a flow of people (e.g., pedestrians on a corner) • Select every kthperson.

21. Merits of Systematic random sampling 1. Procedure is simple and convenient to use. 2. Relatively less labor and time is needed. 3. If the population is sufficiently large and homogeneous and if numbering of subjects are available, then this method can provide good results. 4. If start number (1st sample) is known, whole samples can be determined. Thus sampling can be repeated or checked.

22. Demerits of Systematic random sampling • If engulfed in any hidden cyclic trend, then this technique can be dangerous. E.g. studying no of issue of books per day. If sampling interval (k)=7, then it will induce error.

23. Cluster Sample •          Used in large scale investigations in wide geographical area. •          First stage- preparation of large sized sampling units •          Randomly selecting a certain number •          Second stage- Another list prepared from them •          Sub-samples drawn by random sampling

24. Cluster Sample If we have to do a survey of town in India..

25. Multi-stage Cluster Sample • E.g. 30×7 cluster survey… • Arrange clusters in ascending or descending order of population or according to alphabetical order. Calculate cumulative frequency. • Identify sampling interval. Population/30. • Identify random number. Say K. • So 1st cluster will fall in Kth population n later cluster will be calculated by K+1SI, K+2SI,….etc. th cumulative population. • Randomly sample people within those clusters.

26. Multi- Stage Sampling • When sampling procedure is carried out in several stages • This procedure is used in large scale country wise or region wise surveys.   •     First stage- preparation of large sized sampling units •     Randomly selecting a certain number •     Second stage- Another list prepared from them •     Sub-samples drawn by random sampling and so on….

27. Multi –Phase Sampling • Used to obtain supplementary information •         Certain items of information collected from all units of sample •         Other items collected from only some of sampling units • Ex. In health examination survey among school children

28. Non probability sampling • the techniques which do not provide any basis for estimating the probability of item in the population for getting included in the sample • Other characters-– • Representativeness is in question as sampling error cannot be measured • More suitable for small in-depth enquiries than large surveys. • Saves time and money (speed and administration convenience) • More flexible

29. Convenience/ haphazard Sampling • In this technique samples are selected at the convenience of the researcher • useful in formulative or explorative studies, pilot surveys, testing questionnaire, ..etc • Ex. 1. Choosing fruits from basket, pilot testing of thesis questionnaire, The "person on the street" interviews conducted frequently by television news programs. Sampling those most convenient. Gets a quick reading of public opinion. • Sampling those most convenient • Gets a quick reading of public opinion • Also called Haphazard or Convenience Sampling

30. Purposive Sampling • Sampling with a purpose in mind • Handpicking supposedly typical or interesting cases • Reaches a targeted sample quickly • Types • Judgment sampling • Quota sampling

31. Judgment sampling • Researcher Purposively or deliberately draws a sample which he/she thinks is representative. • Thus personnel biases of investigator have great chances. • Ex. selecting talkative children for interviewing to find the cause of tobacco chewing, Selecting mothers who are willing to participate in study of Health Care Seeking for Newborn Danger Signs

32. Quota sampling • selection is based on some basic parameters like age, sex, income etc • field workers are assigned quotas of number of units satisfying the required characteristics for collecting data • Properties • when parameters are large, the number of cells increases. • It misleads if relevant parameter is omitted. • In this method field worker tend to visit respondents who are more likely to be available and accessible.

33. Quota sampling • Ex. Communication behavior of user to be carry out on a quota sample from a population having the following parameters: • %age of grad., post. Grad.& doctorates are 20, 35, 45 resp. • Male: female = 60:40 • If sample size is 200, find the quota sampling as per above 2 parameters.

34. Quota sampling • Ans:- Qualification ratio/ proportion G:P:D = 4:7:9, gender ratio/ proportion M:F = 3:2 • Therefore graduate male = 4/20 × 60 = 12 or 3/5 × 20 = 12. • Likewise all cells can be filled and then samples are collected purposively. • Distribution of particulars of population in %

35. Distribution of particulars of population in % Quota sample in numbers as per parameters