Sampling 111121003751-phpapp01


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Sampling 111121003751-phpapp01

  1. 1. Noel Jr. P. Rea VIII-MAILER 1
  2. 2. Introduction   When data is to be collected from each member of the population, it is known as Census Survey When data is to be collected only from some members of the population, it is known as Sample Survey 2
  3. 3. Population Population is defined as The Entire Group under study. Sometimes it is also called as the “Universe.”
  4. 4. Sampling Design    Sampling is defined as the practice of taking a small part of a large bulk to represent the whole. Its main objective is to secure a sample which, subject to limitations of size will reproduce the characteristics of the entire population as closely as possible. Sampling procedures are therefore compared to a mirror which gives a reflection true to the original. 4
  5. 5. Definition of Sampling  According to Non Lin, “sampling design is a subset of cases from the population chosen to represent it. By using the subset, we can infer the characteristics of the population.” 5
  6. 6. Census Vs Sampling     Size of population Amount of Funds for the study Facilities Time 6
  7. 7. Characteristics of a Good Sample     Representativeness Accurate (Unbiased) Precision Adequate in Size 7
  8. 8. Advantages of Sampling      Reduces time and cost Saves labour Quality of Study is Better Provides quicker results Effective if population is infinite 8
  9. 9. Limitations of Sampling     Results obtained may be incorrect or misleading A large sample has all drawbacks of Census Survey Complicated sampling may require more labour Representativeness may not be possible in certain cases 9
  10. 10. METHODS OF SAMPLING    The methods of sampling can be divided on the basis of the element of probability associated with the sampling technique. Probability means chances available to members of the population for getting selected in the sample. Accordingly, the methods of sampling are classified into two broad types: Probability Sampling Non Probability Sampling 10
  11. 11. METHODS OF SAMPLING PROBABILITY METHOD NON PROBABILITY METHOD Simple Random Sampling Systematic Sampling Stratified Sampling Cluster Sampling Area Sampling Multistage and Multiphase Sampling Accidental Sampling Convenience Sampling Judgment Sampling Purposive Sampling Quota Sampling 11
  12. 12. Probability Sampling Method     Probability Sampling is also known as Random Sampling Probability means chance Therefore element of the population has known chance or opportunity of being selected in the sample Eg. If a sample of 100 students is to be selected from a population of 1000 students, then it is known to every one that each student has 1000 / 100 i.e. 1 chance in 10 being selected 12
  13. 13. Features of Probability Sampling     It is the only systematic and objective method of sampling that provides equal chance to every element of the population in getting selected in the sample The results of probability sampling more accurate and reliable It helps in the formulation of a true representative sample by eliminating human biases Under probability method each element of population known in advance about the possibility of being included in the sample 13
  14. 14. Types of Probability Sampling       Simple Random Sampling Systematic Sampling Stratified Random Sampling Cluster Sampling Area Sampling Multistage and Multi Phase Sampling 14
  15. 15. Simple Random Sampling    It is the basic probability sampling technique and all other methods are variations of simple random method. It can be defined as the method of sampling which provides every element in the population an equal and known chance of being selected in the sample. Simple random can be done by A) Lottery Method B) Random Tables 15
  16. 16. Lottery Method    In this method every unit of the population is identified with a number or slip This small number chits are placed in a box, well mixed and then a person is asked to take out a lucky slip This process is continued until the required size of the sample achieved 16
  17. 17. Random Method   It provides use of random numbers specially designed for sampling purposes Such type of random table are mostly found at the end of statistical textbooks 17
  18. 18. Systematic Sampling    It is modification of simple random sampling. It is called as quasi-random sampling. It is called quasi because it is in between probability and non-probability sampling. The procedure of quasi sampling begins with finding out the sample interval. This can be found out by the ratio of the population to the sample. Afterwards a random number is selected from the sample interval. 18
  19. 19. Illustration of Systematic Sampling       Selecting a sample of 100 students out of 1000, the sample interval will be 1000 divided by 100 i.e.10. Then make small chits bearing numbers 1to 10 and put them into a box Then by using lottery method withdraw one slip and suppose we get number 5 then proceed to select numbers starting with 5 with a regular interval of 10. The selected sample consists of elements bearing nos. 5,15,25,..........105,115 and so on . It should be noted that up to selecting no.5,Systematic sampling can be treated as probability sampling and afterwards it is nonprobability because the chances of other elements are certainly affected In this example numbers other than 5 have no chance of being selected 19
  20. 20. Stratified Random Sampling     In this method, the population is divided and subdivided with homogeneous or similar characteristics For example, a group of 200 college teachers can be first divided into teachers in Arts faculty, Commerce Faculty and Science Faculty. After dividing the entire population of teachers into such classes called strata, a sample is selected from each stratum of teachers at random. These samples are put together to form a single sample. Stratified random sampling is more accurate and representative as compared to simple random sampling because under this the population is divided into homogeneous groups. 20
  21. 21. Cluster Sampling    Cluster means group, therefore, sampling units are selected in groups. Cluster sampling is an improvement over stratified sampling. Both simple random and stratified random sampling are not suitable while dealing with large and geographically scattered populations. Therefore, large-scale sample surveys are conducted on cluster sampling basis. The working of cluster sampling is based on the principle that it is beneficial to use a large sample of units closer to each other than to select a small group of sample scattered over a wider area. 21
  22. 22. Illustration of Cluster Sampling  Suppose researcher wants to study the learning habits of the college students from Mumbai. He may select the sample as under 1)First prepare a list of all colleges in Mumbai city 2)Then, select a sample of colleges on random basis. Suppose there are 200 colleges in Mumbai, then he may select 20 colleges by random method. 22
  23. 23. 3)From the 20 sampled colleges, prepare a list of all students. From these lists select the required number of say 1000 students on random basis] In this example the researcher gets a sample 1000 students from 20 colleges only otherwise if researcher decides to select 1000 students on random basis, then he would have to select them out of 200 colleges which would have been expensive and time consuming 23
  24. 24. Area Sampling     It is a method of cluster sampling and used in connection with selection of samples area with help of maps. The procedure is to divide the large areas into several small areas. For example, The city of Mumbai can be divided on the basis of municipal wards of zone A random selection of this is made within each of the areas selected; a sub sample of locality or sample of residence is taken and then investigated. 24
  25. 25. Multistage and Multi Phase Sampling   As the name suggests, multistage sampling is carried out in steps. This method is regularly used in conducting national surveys on large scale. It is an economical and time saving method of selecting a sample out of widely spread population. In this method first the population will be divided on state basis, then districts, then cities, then locality, wards, individuals who are sampled at different stages until a final sample unit. 25
  26. 26. Multiphase sampling is slightly different from multi-stage sampling. With multi-phase sampling, the sampling unit at each phase is the same, but some of them are interviewed in detail or asked more questions than others ask. In other words, all the members of the sample provide basic information and some of them provide more and detailed information. 26
  27. 27. Non-Probability Sampling     Non-probability sampling is also called as judgment sampling. In case of non-probability sampling, units in the population do not have an equal chance or opportunity of being selected in the sample. The non-probability method believes in selecting the sample by choice and not by chance. Non-probability sampling suffers defects like personal bias and sampling error cannot be estimated. This is an unscientific and less accurate method of sampling, hence it is only occasionally used in research activities. 27
  28. 28. Types of Non-Probability Sampling      Accidental Sampling Convenience Sampling Judgment Sampling Purposive Sampling Quota Sampling 28
  29. 29. Accidental Sampling    Under this method, researcher does not take special efforts to select the sample, but simply selects those who are immediately available. Suppose, the researcher wants to survey 200 people, and then he may consider the first 200 persons he comes across for collecting information. This method is less expensive, time saving and generally used by journalists to know public views on a particular issue. The method useful where too much accuracy is not required. 29
  30. 30. Convenience Sampling    In convenience sampling, the sample is selected as per the convenience of the researcher. For example, the producer may add a reply coupon along with product to collect responses from consumers. The duly returned coupons are conveniently available to the researcher for the survey purpose. Manufacturers of consumer goods like Titan watches and Philips provide a questionnaire along with the product purchased and collect information relating to name of retail store, income group etc., similarly sample selected from the telephone directory, pay-roll register, register of members is a type of convenience sampling. 30
  31. 31. Judgment Sampling     The sample selected on someone’s knowledge, experience and judgment is called judgment sampling. For selecting a sample of residents from a locality, the researcher may ask and take help from the senior investigators or those who are well acquainted with the locality. The researcher relies on the experience of seniors because they have better knowledge and idea about the locality than the researcher. This method is certainly better than arbitrary sampling as it makes sampling more representatives. 31
  32. 32. Purposive Sampling   Purposive sampling means deliberate selection of sample units confirm to some predetermined criteria. This is also known as judgment sampling It involves selection of cases when we judge as most appropriate ones for a given study. It is based on the judgment of a researcher. It does not aim at securing a cross section of a population. The selection of samples depends upon the subjective judgment of researcher. 32
  33. 33. Quota Sampling    Quota sampling is the frequently used method of sampling in marketing research. The basic objective of quota sampling is to control biases arising out of nonprobability method by stratification and the setting of quotas for each stratum. For instance, a sample of 40 students can be selected from a group of 200 students comprising of 120 boys and 80 girls. To make the sample representative, the group of 40 should include 24 boys and 16 girls (i.e. 120: 80 = 3: 2). Quota sampling offer benefits of speed, economy and simplicity. It is widely used in market surveys and public opinion polls. 33
  34. 34. To download this file, go to:  34
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