Sampling techniques

population , sample, sampling process, sampling techniques: probability sampling, non probability sampling

1. DR ANKITA CHATURVEDI

2. SAMPLING TECHNIQUES CONTENTS : Introduction Parameter and statistics Sampling Errors Sampling Process Methods of Sampling >>Probability Sampling >>Non Probability Sampling

3. INTRODUCTION POPULATION SAMPLE STATISTICAL INFERENCE

4. POPULATION The entire aggregation of items from which samples can be drawn is known as a population. In sampling, the population may refer to the units, from which the sample is drawn. Thus, population could consists of all the persons in the country, or those in a particular geographical location or a special economic group, depending on the purpose and coverage of the study. This is one of the first things the analyst needs to define properly while conducting a business research. “N” represents the size of the population.

5. SAMPLING In simple words, sampling consists of obtaining information from a portion of a larger group or an universe. Elements are selected in a manner that they yield almost all information about the whole universe, if and when selected according to some scientific principles and procedures.

6. CENSUS A complete study of all the elements present in the population is known as a census. The national population census is an example of census survey Merits Data obtained from each and every unit of population. Results: more representative, accurate, reliable. Basis of various surveys. Demerits More effort ,money , time. Big problem in underdeveloped countries.

7. SAMPLE A Sample is a selection of units from the entire group called the population or universe of interest. It is Subset of a larger population Merits Less resources (time, money) Less workload. Gives results with known accuracy that can be calculated mathematically. Demerits Difficulties of a representative sample Need for specialized knowledge Chances of bias

8. The population criteria establish the target population ; that is, the entire set of cases about which the researcher would like to make generalizations. Statistical Inference Statistical inference is the process of drawing conclusions about the entire population based on information in a sample.

9. (Population) Parameters A parameter is a value used to describe a certain characteristic of a population. It is usually unknown and therefore has to be estimated. For example, the population mean is a parameter that is often used to indicate the average/typical value of a variable in the population .

10. (Sample) statistics A statistic is an estimate, based on a sample of observed data, of a population parameter. The statistic is drawn from the measurement of the elements of the sample. A statistic is used to estimate a parameter. The more samples you have, the better your estimation.

12. Sampling Error Since a sample does not include all members of the population, parameter estimates generally differ from parameters on the entire population (e.g., use mean height of a sample of 1000 people to estimate mean height of Indian population). The difference between the (sample) statistics estimate and the (population) parameter is sampling error.

13. SAMPLING DESIGN PROCESS

14. PROBABILITY SAMPLING

15. PROBABILITY SAMPLING A probability sampling scheme is one in which every unit in the population has a chance (greater than zero) of being selected in the sample, and this probability can be accurately determined. Universe is identified. . When every element in the population does have the same probability of selection, this is known as an 'equal probability of selection' (EPS) design. Such designs are also referred to as 'self-weighting' because all sampled units are given the same weight. Only Parametric Tests can be applied Results may be generalized

16. PROBABILITY SAMPLING Probability sampling includes: Simple Random Sampling, Stratified Random Sampling, Cluster Sampling Systematic Sampling, Multistage Sampling .

17. SIMPLE RANDOM SAMPLING Applicable when population is small, homogeneous & readily available All subsets of the frame are given an equal probability. Each element of the frame thus has an equal probability of selection. It provides for greatest number of possible samples. This is done by assigning a number to each unit in the sampling frame. A table of random number or lottery system is used to determine which units are to be selected.

18. SIMPLE RANDOM SAMPLING LOTTERY METHOD RANDOM NUMBER TABLE

19. SIMPLE RANDOM SAMPLING Merits No personal bias. Sample more representative of population. Accuracy can be assessed as sampling errors follow principals of chance. Demerits If sampling frame large, this method impracticable Requires completely catalogued universe. Cases too widely dispersed - more time and cost.

20. STRATIFIED RANDOM SAMPLING Stratified random sampling is a method of probability sampling in which the population is divided into different subgroups and samples are selected from each of them. Applicable when population is large and heterogeneous Where population embraces a number of distinct categories, the frame can be organized into separate "strata." Each stratum is then sampled as an independent sub-population, out of which individual elements can be randomly selected. Every unit in a stratum has same chance of being selected.

21. STRATIFIED RANDOM SAMPLING Using same sampling fraction for all strata ensures proportionate representation in the sample. Adequate representation of minority subgroups of interest can be ensured by stratification & varying sampling fraction between strata as required.

22. STRATIFIED RANDOM SAMPLING Merits: It provides better coverage of the population since the researchers have control over the subgroups to ensure all of them are represented in the sampling. A stratified sample can provide greater precision than a simple random sample of the same size. Because it provides greater precision, a stratified sample often requires a smaller sample, which saves money. A stratified sample can guard against an "unrepresentative" sample (e.g., an all-male sample from a mixed-gender population). We can ensure that we obtain sufficient sample points to support a separate analysis of any subgroup.

23. STRATIFIED RANDOM SAMPLING Demerits sampling frame of entire population has to be prepared separately for each stratum when examining multiple criteria, stratifying variables may be related to some, but not to others, further complicating the design, and potentially reducing the utility of the strata. in some cases (such as designs with a large number of strata, or those with a specified minimum sample size per group), stratified sampling can potentially require a larger sample than would other methods

24. CLUSTER SAMPLING Cluster sampling is an example of 'two-stage sampling' . First stage a sample of areas is chosen; Second stage a sample of respondents within those areas is selected. Population divided into clusters of homogeneous units, usually based on geographical contiguity. Sampling units are groups rather than individuals. A sample of such clusters is then selected. All units from the selected clusters are studied.

25. CLUSTER SAMPLING Merits Cuts down on the cost of preparing a sampling frame. This can reduce travel and other administrative costs. Demerit sampling error is higher for a simple random sample of same size.

26. STRATIFIED CLUSTER SAMPLING Combines elements of stratification and clustering First we define the clusters Then we group the clusters into strata of clusters, putting similar clusters together in a stratum Then we randomly pick one (or more) cluster from each of the strata of clusters Then we sample the subjects within the sampled clusters (either all the subjects, or a simple random sample of them) Advantage: Reduce the error in cluster sampling by creating strata of clusters

27. STRATIFICATION v/s CLUSTER Stratification Divide population into groups different from each other: sexes, races, ages Sample randomly from each group Less error compared to simple random More expensive to obtain stratification information before sampling Clustering Divide population into comparable groups: schools, cities Randomly sample some of the groups More error compared to simple random Reduces costs to sample only some areas or organizations

28. With stratified sampling, the best survey results occur when elements within strata are internally homogeneous. However, with cluster sampling, the best results occur when elements within clusters are internally heterogeneous

29. SYSTEMATIC SAMPLING Systematic sampling relies on arranging the target population according to some ordering scheme and then selecting elements at regular intervals through that ordered list. Systematic sampling involves a random start and then proceeds with the selection of every k th element from then onwards. In this case, k =(population size/sample size). It is important that the starting point is not automatically the first in the list, but is instead randomly chosen from within the first to the k th element in the list. A simple example would be to select every 10th name from the telephone directory (an 'every 10th' sample, also referred to as 'sampling with a skip of 10').

30. SYSTEMATIC SAMPLING As described above, systematic sampling is an EPS method, because all elements have the same probability of selection (in the example given, one in ten). It is not 'simple random sampling' because different subsets of the same size have different selection probabilities - e.g. the set {4,14,24,...,994} has a one-in-ten probability of selection, but the set {4,13,24,34,...} has zero probability of selection.

31. SYSTEMATIC SAMPLING Merits: Sample easy to select Suitable sampling frame can be identified easily Sample evenly spread over entire reference population Demerits: Sample may be biased if hidden periodicity in population coincides with that of selection. Difficult to assess precision of estimate from one survey.

32. MULTISTAGE SAMPLING Complex form of cluster sampling in which two or more levels of units are embedded one in the other. First stage, random number of districts chosen in all states. Followed by random number of talukas , villages. Then third stage units will be houses. All ultimate units (houses, for instance) selected at last step are surveyed.

33. MULTISTAGE SAMPLING This technique, is essentially the process of taking random samples of preceding random samples. Not as effective as true random sampling, but probably solves more of the problems inherent to random sampling. An effective strategy because it banks on multiple randomizations. As such, extremely useful. Multistage sampling used frequently when a complete list of all members of the population not exists and is inappropriate. Moreover, by avoiding the use of all sample units in all selected clusters, multistage sampling avoids the large, and perhaps unnecessary, costs associated with traditional cluster sampling.

34. IDENTIFY WHAT TYPE OF SAMPLING IS THIS… A student council surveys 100 students by getting random samples of 25 I year, 25 II year, 25 III year, and 25 PG An airline company wants to survey its customers one day, so they randomly select 5 flights that day and survey every passenger on those flights A teachers puts students' names in a hat and chooses without looking to get a sample of students. A principal takes an alphabetized list of student names and picks a random starting point. Every 20 th student is selected to take a survey

35. 5. Each student at a school has a student identification number. Counselors have a computer generate 50 random identification numbers and those students are asked to take a survey 6. A principal orders t-shirts and wants to check some of them to make sure they were printed properly. She randomly selects 2 of the 10 boxes of shirts and checks every shirt in those 2 boxes. 7. A school chooses 3 randomly selected athletes from each of its sports teams to participate in a survey about athletics at the school.

36. Answers Stratified Random Sampling Cluster Sampling Simple Random Sampling Systematic Sampling Simple Random Sampling Cluster Sampling Stratified Random Sampling

37. Non-PROBABILITY SAMPLING

38. NON PROBABILITY SAMPLING Every element in the universe [sampling frame] does not have equal probability of being chosen in the sample Universe is unidentified It involves the selection of elements based on assumptions regarding the population of interest, which forms the criteria for selection. Hence, because the selection of elements is nonrandom, nonprobability sampling not allows the estimation of sampling errors. Only Non- Parametric Tests can be applied. Results may not be generalized.

39. NON PROBABILITY SAMPLING Non Probability sampling includes: Convenience Sampling Quota Sampling Judgmental Sampling (Purposive Sampling) Snowball sampling

40. CONVENIENCE SAMPLING Convenient sample units selected. Selected neither by probability nor by judgment. Merit useful in pilot studies. Demerit results usually biased and unsatisfactory.

41. QUOTA SAMPLING Most commonly used in non probability sampling. The population is first segmented into mutually exclusive sub-groups, just as in stratified sampling. Then judgment used to select subjects or units from each segment based on a specified proportion.

42. QUOTA SAMPLING Merits Used when research budget is limited Very extensively used/understood No need for list of population elements Disadvantages Variability and bias cannot be measured/controlled Time Consuming Projecting data beyond sample not justified

43. JUDGMENTAL SAMPLING Judgment/Purposive/Deliberate sampling. Depends exclusively on the judgment of investigator . Sample selected which investigator thinks to be most typical of the universe. This is used primarily when there is a limited number of people that have expertise in the area being researched

44. JUDGMENTAL SAMPLING Merits Small no. of sampling units Study unknown traits/case sampling Urgent public policy & business decisions Demerits Personal prejudice & bias No objective way of evaluating reliability of results

45. SNOWBALL SAMPLING A special non probability method used when the desired sample characteristic is rare. The research starts with a key person and introduce the next one to become a chain

46. SNOWBALL SAMPLING- STEPS Make contact with one or two cases in the population. Ask these cases to identify further cases. Ask these new cases to identify further new cases. Stop when either no new cases are given or the sample is as large as is manageable

47. Merit access to difficult to reach populations (other methods may not yield any results). Demerit not representative of the population and will result in a biased sample as it is self-selecting.

48. Types of Sampling Errors Two types of sampling errors Biased Errors- Due to selection of sampling techniques; size of the sample. Unbiased Errors / Random sampling errors- Differences between the members of the population included or not included.

49. METHODS OF REDUCING SAMPLING ERRORS Specific problem selection. Systematic documentation of related research. Effective enumeration. Effective pre testing. Controlling methodological bias. Selection of appropriate sampling techniques