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2. Population and Sampling  Probability Sampling  Non-probability Sampling 2

3.  Definition A group of potential participants to whom you want to generalize the results of a study. 3

4. Generalize : The key to a successful study; because it is only the results that can be generalized from a sample to a population; that research results have meaning beyond the limited setting. 4

5. Not generalize : The sample selected is not an accurate representation of the population. 5

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7.  Population the a group of people or things you are interested in.  Census is a measurement of all the units in the population 7

8.  PP = number that results from measuring all the units in the population.  Statistic = number that results from measuring all the units in the sample; statistics from samples are used to estimate PP. 8

9.  SF = specific data from which sample is drawn, e.g., a phone book.  UA = type of object of interest, e.g., arsons, fire departments, firefighters. 9

10.  Is a list or quasi list of the members of a population.  Resource used in the selection of a sample.  A sample’s representativeness depends directly on the extent to which a sampling frame contains all the members of the total population that the sample is intented to represent. 10

11.  The data for this research were obtained from a random sample of parents of children in the third grade in government primary schools in Selangor. 11

12. Definition : Sample is a subset of the population.  Good sampling : include maximizing the degree to which this selected group represent the population. 12

13. POPULATION Sample 13

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15. Types of sampling 1. Probability sampling 2. Non probability sampling 15

16.  Allows use of statistics, tests hypotheses.  Can estimate population parameter.  Eliminates bias.  Must have random selections of units. 16

17.  Exploratory research, generates hypotheses.  Population parameters not of interests.  Adequacy of sample unknown.  Cheaper, easier, quicker to carry out.  Cant generalized findings.  Non-representative. 17

18.  A type of sampling where the likelihood of any one member of the population being selected is known.  Commonly used because the selection of participants is determined by chance. 18

19.  e.g., if there are 4,500 students in the Faculty of Human Ecology, and if there are 1,000 seniors, the odds of selecting one senior as part of the sample is 1000:4,500 or 0.22. 19

20.  Where the likelihood of selecting any one member from the population or where the probability of selecting a single individual is not known. 20

21.  e.g., if you do not know how many seniors in the Faculty of Human Ecology, the likelihood of anyone being selected cannot be computed. 21

22. 1. Simple Random Sampling 2. Systematic Sampling 3. Stratified Random Sampling 4. Cluster Sampling 22

23. 1. Simple Random Sampling When the population’s members are similar to one another. 23

24. http://www.google.com.my/search?q=cluster+sampling+design+ppt&ie=utf-8&oe=utf- 8&aq=t&rls=org.mozilla:en-US:official&client=firefox-a 24

25. Adv: Ensures a high degree of representativeness Disadv: Time consuming and tedious 25

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27.  Let's assume that we have a population of 185 students and each student has been assigned a number from 1 to 185. Suppose we wish to sample 5 students (although we would normally sample more, we will use 5 for this example).  Since we have a population of 185 and 185 is a three digit number, we need to use the first three digits of the numbers listed on the chart. 27

28.  We close our eyes and randomly point to a spot on the chart. For this example, we will assume that we selected 20631 in the first column.  We interpret that number as 206 (first three digits). Since we don't have a member of our population with that number, we go to the next number 899 (89990). Once again we don't have someone with that number, so we continue at the top of the next column. 28

29.  As we work down the column, we find that the first number to match our population is 100 (actually 10005 on the chart). Student number 100 would be in our sample. Continuing down the chart, we see that the other four subjects in our sample would be students 049, 082, 153, and 005. http://www.google.com/imgres?imgurl=http://www.gifted.uconn.edu/sieg le/research/Samples/RANTBLE.JPG&imgrefurl 29

30. 2. Systematic Sampling When the population’s members are similar to one another. 30

31. http://www.google.com.my/search?q=cluster+sampling+design+ppt&ie=utf-8&oe=utf- 8&aq=t&rls=org.mozilla:en-US:official&client=firefox-a 31

32. Adv : Ensures a high degree of representativeness; no need to use a table of random numbers. Disadv : Less truly random than simple random sampling 32

33. 3. Stratified Random Sampling When the population is heterogeneous in nature and contains several different groups. 33

34. http://www.google.com.my/search?q=cluster+sampling+design+ppt&ie=utf-8&oe=utf- 8&aq=t&rls=org.mozilla:en-US:official&client=firefox-a 34

35. Adv : Ensures a high degree of representativeness of all the strata in the population. Disadv : Time consuming and tedious 35

36.  Proportionate SRM  Non-Proportionate SRM 36

37.  Sampel selected is in proportion to the size of each stratum in the population 37

38.  Population = 100  Layer 1 = 40 males  Layer 2 = 60 females  For a sample size of 10, you will take 4 males + 6 females. 38

39.  Selection of sample is not according to size of stratum in the population 39

40.  Population = 100  Layer 1 = 40 males  Layer 2 = 60 females  For a sample size of 10, you will take 5 males + 5 females. 40

41. 4. Cluster Sampling When the population consist of units rather than individuals. 41

42. http://www.google.com.my/search?q=cluster+sampling+design+ppt&ie=utf-8&oe=utf- 8&aq=t&rls=org.mozilla:en-US:official&client=firefox-a 42

43. http://www.google.com.my/search?q=cluster+sampling+design+ppt&ie=utf-8&oe=utf- 8&aq=t&rls=org.mozilla:en-US:official&client=firefox-a 43

44. Adv : Easy and convenient Disadv : Possibility that members of units are different from one another, decreasing the sampling’s effectiveness 44

45. 1. Convenience Sampling 2. Quota sampling 3. Purposive Sampling 4. Snowball sampling 45

46. 1. Convenience Sampling When the sample is captive.  Adv : convenient and inexpensive  Disadv : results in questionable representativeness. 46

47. 2. Quota sampling When strata are present, and stratified, sampling is not possible  Adv : Ensures some degree of representativeness of all the strata in the population  Disadv : Results in questionable representativeness 47

48. 3. Purposive Sampling Researcher uses own judgment in the selection of sample members Sometimes called a judgmental sample. 48

49. 4. Snowball sampling A technique often used in rare populations; each subject interviewed is asked to identify others. 49

50.  Lack of fit between the sample and the population.  The difference between the characteristics of the sample and the characteristics of the population from which the sample was selected. 50

51.  Reducing sampling error is the major goal of any selection technique.  Larger sample, lower sampling error. 51

52.  How big?  Depends on type of research design.  Desired confidence level of results.  Amount of accuracy wanted.  Characteristics of population of interest. 52

53.  Big enough to answer research question.  But not so big that the process of sampling becomes uneconomical.  Heterogeneous sample = bigger size  Homogeneous sample = smaller size 53

54.  General Rule of Thumb 30 participants/ respondents in each group. 54

55. 1. Larger sample, smaller sampling error, better representativeness. 2. If using several subgroups, starts with large enough subjects to account for the eventual breaking down of subject groups. 55

56. 3. If mailing out surveys or questionnaires, increase sample size by 40-50% to account for lost mails or uncooperative subjects. 4. Big is good, but appropriate is better. 56

57.  Students will discuss and state what they have learned in Lecture 8. 57