Lecture – 7 Sample Design and Sampling Procedure

2. Lecture – 7 Sample Design and Sampling Procedure Determination of Sample Size: A review of Statistical theory

3. Sample Design and Sampling Procedure

4. Pragmatic reasonsAccurate and reliable results Destruction of test units Why Sampling Budget and time constraints. Often, Not be possible to contact the whole population Samples are accurate only when researchers have taken care. A sample may be more accurate than a census. In a census there is greater likelihood of non-sampling errors. A small, well-trained, closely supervised group may do a more accurate job At times testing require the destruction If all tested that way, there would be no product left after testing.

5. Stages in Sample Selection Define the target population Select a sample frame Determine if a probability or non probability sample will be chosen Plan procedure for selecting sampling units Determine sample size Select actual sampling units Conduct fieldwork

6. Types of Sampling Non Probability Sampling Probability Sampling

7. Convenience samplingJudgment sampling Quota sampling Snowball sampling Non Probability Sampling

8. Simple random sampling Systematic sampling Stratified sampling Proportional versus disproportional strata Cluster sampling Probability Sampling

9. Internet Sampling Internet surveys allow researchers to rapidly reach a large sample. This is both an advantage and a disadvantage. Sample size requirements can be met overnight or in some cases almost instantaneously. A major disadvantage of Internet surveys is the lack of computer ownership and Internet access among certain segments of the population.

11. Determination of Sample Size: A review of Statistical theory

12. Descriptive StatisticsInferential statisticsSample Statistics Population parameters Basic Terminology Statistics used to describe or summarize information about population or sample Statistics used to make inferences or judgments about a population on the basis of a sample Variables in a sample or measures computed from sample data Variables in a population or measured characteristics of a population

13. Frequency DistributionPercentage distributionCentral TendencyMeasure of DispersionNormal Distribution Making the Data Useable

14. Frequency DistributionPercentage distributionCentral TendencyMeasure of DispersionNormal Distribution Making the Data Useable

15. Frequency DistributionPercentage distributionCentral TendencyMeasure of DispersionNormal Distribution Making the Data Useable Mean Median Mode

16. Frequency DistributionPercentage distributionCentral TendencyMeasure of DispersionNormal Distribution Making the Data Useable Range Deviation Scores Variance Standard Deviation

17. Frequency DistributionPercentage distributionCentral TendencyMeasure of DispersionNormal Distribution Making the Data Useable Z Mean X Value of X – Mean Z = Standard Deviation

18. Some Formula

19. Factors of Sample Size • Variance (standard deviation) • Magnitude of error • Confidence level

20. Sample Size Formula - Example Suppose a survey researcher, studying expenditures on lipstick, wishes to have a 95 percent confident level (Z) and a range of error (E) of less than $2.00. The estimate of the standard deviation is $29.00.

21. Sample Size Formula - Example Suppose a survey researcher, studying expenditures on lipstick, wishes to have a 95 percent confident level (Z) and a range of error (E) of less than $2.00. The estimate of the standard deviation is $29.00.

22. Sample Size Formula - Example Suppose, in the same example as the one before, the range of error (E) is acceptable at $4.00, sample size is reduced.

23. Sample Size Formula - Example Suppose, in the same example as the one before, the range of error (E) is acceptable at $4.00, sample size is reduced.

24. 2 2 é ù é ù ( 2 . 57 )( 29 ) ( 2 . 57 )( 29 ) = = n n ê ú ê ú 4 2 ë û ë û 2 2 é ù é ù 74 . 53 74 . 53 = = ê ú ê ú 4 2 ë û ë û ] [ [ ] 2 2 = = 6325 18 . 37 . 265 = = 347 1389 Calculating Sample Size 99% Confidence

25. Standard Error of the Proportion

26. Confidence Interval for a Proportion Confidence Interval

27. 2 z pq = n 2 E Where: n = Number of items in samples Z2 = The square of the confidence interval in standard error units. p = Estimated proportion of success q = (1-p) or estimated the proportion of failures E2 = The square of the maximum allowance for error between the true proportion and sample proportion or zsp squared.

28. Calculating Sample Size at the 95% Confidence Level = p . 6 2 ( 96 )(. 1. ) (. 6 4 ) = n = ( . 035 ) 2 q . 4 ( 3 . 8416 )(. 24 ) = 001225 . 922 = . 001225 = 753 Suppose a simple random sample shows 60% of the respondents (p) recognize the name. Researcher wishes to estimate with 95% confidence (I.e., Z=1.96) that the allowance for sampling error is not more that 3.5% (E). Solution: As given:

29. ? Any Question?

30. Thanks for your contribution

31. Assignment Gp Assignment Case-23: Business Forum Industry Submission date is 17th Jul Submission time: 0630 p.m. Selected person will present for 10 mins Discussion to focus, how the data were analyzed

32. See You Next Week

33. Stages in Sampling Target population What is the relevant population? In many cases this is not a difficult question, but in other cases, the decision may be a difficult one. Answering questions about the crucial characteristics of the population is the usual technique for defining the target population. The question “Whom do we want to talk to?” must be answered. Sample frame Sampling Method Choice Procedure for sampling units Determine sample size Actual sampling units Conduct fieldwork

34. Stages in Sampling Target population A sampling frame is a list of elements from which the sample may be drawn. The sampling frame is also called the working population, because it provides the list that can be operationally worked with. Sample frame Sampling Method Choice Procedure for sampling units Determine sample size Actual sampling units Conduct fieldwork

35. Stages in Sampling Target population Sample frame Probability or Non probability sample Sampling Method Choice In probability sampling every element in the population has a known nonzero probability of selection; each member of the population has an equal probability of being selected. Procedure for sampling units In nonprobability sampling, the probability of any particular member of the population being chosen is unknown. Nevertheless, there are occasions when the nonprobability samples are best suited for the researcher’s purpose. Determine sample size Actual sampling units Conduct fieldwork

36. Stages in Sampling Target population The sampling unit is a single element or group of elements subject to selection in the sample. If the target population has been divided into stages, the term primary sampling unit (PSU), secondary sampling units, or tertiary sampling units is used. When there is no list of population elements, the sampling unit is generally something other than the population element. For example, in a random digit dialing study the sampling unit will be telephone numbers. Sample frame Sampling Method Choice Procedure for sampling units Determine sample size Actual sampling units Conduct fieldwork

37. Convenience sampling Judgment sampling Quota sampling Snowball sampling Non Probability Sampling Researchers generally use convenience samples to obtain a large number of completed questionnaires quickly and economically Convenience samples are best utilized for exploratory research when additional research will subsequently be conducted with a probability sample

38. Convenience sampling Judgment samplingQuota sampling Snowball sampling Non Probability Sampling Judgment or purposive sampling is a nonprobability technique in which an experienced individual selects the sample upon his or her judgment about some appropriate characteristic required of the sample members

39. Convenience sampling Judgment sampling Quota samplingSnowball sampling Non Probability Sampling The purpose of quota sampling is to ensure that the various subgroups in a population are represented on pertinent sample characteristics to the exact extent that the investigators desire

40. Convenience sampling Judgment sampling Quota sampling Snowball sampling Non Probability Sampling Snowball sampling refers to a variety of procedures in which initial respondents are selected by probability methods, but additional respondents are then obtained from information provided by the initial respondents. This technique is used to locate members of rare populations by referrals.

41. Simple random samplingSystematic sampling Stratified sampling Proportional versus disproportional strata Cluster sampling Probability Sampling A simple random sample is a sampling procedure that assures that each element in the population will have an equal chance of being included in the sample

42. Simple random sampling Systematic samplingStratified sampling Proportional versus disproportional strata Cluster sampling Probability Sampling Systematic sampling is extremely simple: An initial starting point is selected by a random process; then every nth number on the list is selected.

43. Simple random sampling Systematic sampling Stratified samplingProportional versus disproportional strata Cluster sampling Probability Sampling In stratified sampling, a subsample is drawn utilizing a simple random sample within each stratum. The reason for taking a stratified sample is to have a more efficient sample than could be taken on the basis of simple random sampling

44. Simple random sampling Systematic sampling Stratified sampling Proportional versus disproportional strataCluster sampling Probability Sampling If the number of sampling units from each stratum is in proportion to the relative population size of the stratum, the sample is a proportional stratified sample.

45. Simple random sampling Systematic sampling Stratified sampling Proportional versus disproportional strata Cluster sampling Probability Sampling The purpose of cluster sampling is to sample economically while retaining the characteristics of a probability sample. In a cluster sample, the primary sampling unit is no, longer the individual element in the population (for example, grocery stores) but a larger cluster of elements located in proximity to one another (for example, cities). The area sample is the most popular type of cluster sample.