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# Sampling and sampling process

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An overview of sampling
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# Sampling and sampling process

Sampling is a process of selecting representative units from an entire population of a study.
Two Types

Probability Sampling Techniques
Non- Probability sampling techniques

Sampling is a process of selecting representative units from an entire population of a study.
Two Types

Probability Sampling Techniques
Non- Probability sampling techniques

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### Sampling and sampling process

1. 1. ravsa Sampling By Mr. Ravi Rai Dangi Assistant Professor Fellowship in Neonatal Nursing MSc. Child Health Nursing
2. 2. ravsa INTRODUCTION  Sampling is a process of selecting representative units from an entire population of a study.  Sample is not always possible to study an entire population; therefore, the researcher draws a representative part of a population through sampling process.  In other words, sampling is the selection of some part of an aggregate or a whole on the basis of which judgments or inferences about the aggregate or mass is made.
3. 3. ravsa Population: Population is the aggregation of all the units in which a researcher is interested. In other words, population is the set of people or entire to which the results of a research are to be generalized. For example, a researcher needs to study the problems faced by postgraduate nurses of India; in this the ‘population’ will be all the postgraduate nurses who are Indian citizen.
4. 4. ravsa Target Population: A target population consist of the total number of people or objects which are meeting the designated set of criteria. In other words, it is the aggregate of all the cases with a certain phenomenon about which the researcher would like to make a generalization. Accessible population: It is the aggregate of cases that conform to designated criteria & are also accessible as subjects for a study.
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6. 6. ravsa Sample: Sample may be defined as representative unit of a target population, which is to be worked upon by researchers during their study. In other words, sample consists of a subset of units which comprise the population selected by investigators or researchers to participates in their research project
7. 7. ravsa Sampling error: There may be fluctuation in the values of the statistics of characteristics from one sample to another, or even those drawn from the same population. Sampling bias: Distortion that arises when a sample is not representative of the population from which it was drawn.
8. 8. ravsa PURPOSES OF SAMPLING  Economical: In most cases, it is not possible & economical for researchers to study an entire population. With the help of sampling, the researcher can save lots of time, money, & resources to study a phenomenon.  Improved quality of data: It is a proven fact that when a person handles less amount the work of fewer number of people, then it is easier to ensure the quality of the outcome.
9. 9. ravsa  Quick study results: Studying an entire population itself will take a lot of time, & generating research results of a large mass will be almost impossible as most research studies have time limits  Precision and accuracy of data: Conducting a study on entire population giver you a voluminous data and maintain the precision of that data become more complex task. So sampling is necessary.
10. 10. ravsa Characteristics of Good Sample  Representative  Free from bias and errors  No substitution and incompleteness  Appropriate sample size
11. 11. ravsa Sampling Process  Sampling process of selecting a part of the assigned population to represent the entire population.
12. 12. ravsa Identifying and defining the target population Describing the accessible population & ensuring sampling frame Specifying the sampling unit Specifying sampling selection methods Determining the sample size Specifying the sampling plan Selecting a desired sample
13. 13. ravsa FACTORS INFLUENCING SAMPLINGPROCESS
14. 14. ravsa Sampling Techniques
15. 15. ravsa Two Types • Probability Sampling Techniques • Non- Probability sampling techniques
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17. 17. ravsa PROBABILITY SAMPLING TECHNIQUE  It is based on the theory of probability.  It involve random selection of the elements/members of the population.  In this, every subject in a population has equal chance to be selected sampling for a study.  In probability sampling techniques, the chances of systematic bias are relatively less because subjects are randomly selected.
18. 18. ravsa Features Of The Probability Sampling  It is a technique wherein the sample are gathered in a process that given all the individuals in the population equal chances of being selected.  In this sampling technique, the researcher must guarantee that every individual has an equal opportunity for selection.
19. 19. ravsa  The advantage of using a random sample is the absence of both systematic & sampling bias.  The effect of this is a minimal or absent systematic bias, which is a difference between the results from the sample & those from the population.
20. 20. ravsa Simple random sampling  This is the most pure & basic probability sampling design.  In this type of sampling design, every member of population has an equal chance of being selected as subject.  The entire process of sampling is done in a single step, with each subject selected independently of the other members of the population
21. 21. ravsa  There is need of two essential prerequisites to implement the simple random technique: population must be homogeneous & researcher must have list of the elements/members of the accessible population
22. 22. ravsa  The first step of the simple random sampling technique is to identify the accessible population & prepare a list of all the elements/members of the population.  The list of the subjects in population is called as sampling frame & sample drawn from sampling frame by using following methods:
23. 23. ravsa The lottery method  It is most primitive & mechanical method.  Each member of the population is assigned a unique number.  Each number is placed in a bowel or hat & mixed thoroughly.
24. 24. ravsa The use of table of random numbers  This is most commonly & accurately used method in simple random sampling.  Random table present several numbers in rows & columns.  Researcher initially prepare a numbered list of the members of the population, & then with a blindfold chooses a number from the random table.
25. 25. ravsa  The same procedure is continued until the desired number of the subject is achieved.  If repeatedly similar numbers are encountered, they22 are ignored & next numbers are considered until desired number of subject are achieved.
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27. 27. ravsa The use of computer  Nowadays random tables may be generated from the computer , & subjects may be selected as described in the use of random table.  For populations with a small number of members, it is advisable to use the first method, but if the population has many members, a computer-aided random selection is preferred.
28. 28. ravsa Merits  Every member have equal opportunity to select  It requires minimum knowledge about the population in advances.  Unbiased  Sample error can be computed
29. 29. ravsa Demerits  Requires all members list  Expensive and time consuming process
30. 30. ravsa Stratified Random Sampling  This method is used for heterogeneous population.  It is a probability sampling technique wherein the researcher divides the entire population into different homogeneous subgroups or strata, & then randomly selects the final subjects proportionally from the different strata.
31. 31. ravsa  The strata are divided according selected traits of the population such as age, gender, religion, socio- economic status, diagnosis, education, geographical region, type of institution, type of care, type of registered nurses, nursing area.
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33. 33. ravsa Merits  It ensures the representation of all group .  To observe the existed fact with two or more groups.  Higher statistical precision .  Save time, money and efforts.
34. 34. ravsa Demerits  It requires accurate information on the portion of population in each stream  Large population needed  Possibility of faulty classification
35. 35. ravsa Systematic Random Sampling  It can be likened to an arithmetic progression, wherein the difference between any two consecutive numbers is the same.  It involves the selection of every Kth case from list of group, such as every 10th person on a patient list or every 100th person from a phone directory.  Systematic sampling is sometimes used to sample every Kth person entering a bookstore, or passing down the street or leaving a hospital & so forth
36. 36. ravsa  Systematic sampling can be applied so that an essentially random sample is drawn.  If we had a list of subjects or sampling frame, the following procedure could be adopted.  The desired sample size is established at some number (n) & the size of population must know or estimated (N).
37. 37. ravsa  For example, a researcher wants to choose about 100 subjects from a total target population of 500 people. Therefore, 500/100=5.  Therefore, every 5th person will be selected.
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39. 39. ravsa Merits  Convenient and simple to carry out.  Distributed of sample is spread evenly over the entire population.  Less time consuming and cost effective also.  Statistically more significant.
40. 40. ravsa Demerits  Subject is not randomly selected so it become non random sampling techniques.  Sometimes this may result in biasness
41. 41. ravsa Cluster or multistage Sampling  It is done when simple random sampling is almost impossible because of the size of the population.  Cluster sampling means random selection of sampling unit consisting of population elements.  Then from each selected sampling unit, a sample of population elements is drawn by either simple random selection or stratified random sampling.
42. 42. ravsa  This method is used in cases where the population elements are scattered over a wide area, & it is impossible to obtain a list of all the elements.  The important thing to remember about this sampling technique is to give all the clusters equal chances of being selected.
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44. 44. ravsa Merits  Cheap, quick and easy for large population  Investigator can use existing division like district, villages or towns.  Same clusters can be used for further studies.
45. 45. ravsa Demerits  Least representative  Some time same characteristics can present in two clusters  Possibility of high sample error  If homogenous then not possible
46. 46. ravsa Sequential Sampling  It is slightly different from others.  Here the sample size is no fixed.  The researcher initially select the small sample and tries out to make inferences ; if not able to draw the result researcher can add more samples.
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48. 48. ravsa Merits  Best possible smallest representative sample  Helps in ultimately finding the inference in the study. Demerits  One point of time study cannon be done  Requires repeated entries
49. 49. ravsa Nonprobability Sampling Technique  Non probability sampling is the techniques wherein the samples are gathered in a process that does not give all the individual in the population equal chance of being selected in the sample.
50. 50. ravsa Features of the nonprobability sampling  Does not give equal chance to select each sample.  Saves time, money and workforce  Samples will be selected by purpose or personal judgement.  If population is unknown then research cannot be used in generalized for entire population.
51. 51. ravsa Uses of non probability sampling  This type of sampling can be used when demonstrating that a particular trait exists in the population.  It can also be used when researcher aims to do a qualitative, pilot , or exploratory study.  It can be used when randomization is not possible like when the population is almost limitless.
52. 52. ravsa  It can be used when the research does not aim to generate results that will be used to create generalizations.  It is also useful when the researcher has limited budget, time, & workforce.  This technique can also be used in an initial study
53. 53. ravsa Purposive Sampling  It is more commonly known as ‘judgmental’ or ‘authoritative sampling’.  In this type of sampling, subjects are chosen to be part of the sample with a specific purpose in mind.  In purposive sampling, the researcher believes that some subjects are fit for research compared to other individual. This is the reason why they are purposively chosen as subject.
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55. 55. ravsa  In this sampling technique, samples are chosen by choice not by chance, through a judgment made the researcher based on his or her knowledge about the population  For example, a researcher wants to study the lived experiences of post disaster depression among people living in earthquake affected areas of Gujarat.
56. 56. ravsa Merits  Simple to draw  Useful in exploratory studies  Save resources  Requires less field work.
57. 57. ravsa Demerits  Requires considerable knowledge abut the population  Not always reliable sample  Purposiveness lead to biasness  Misrepresentation of sample may be possible
58. 58. ravsa Convenience Sampling  It is probably the most common of all sampling techniques because it is fast, inexpensive, easy, & the subject are readily available.  It is a nonprobability sampling technique where subjects are selected because of their convenient accessibility & proximity to the researcher.
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60. 60. ravsa  The subjects are selected just because they are easiest to recruit for the study & the researcher did not consider selecting subjects that are representative of the entire population.  It is also known as an accidental sampling.  Subjects are chosen simply because they are easy to recruit.
61. 61. ravsa Merits  Easiest, cheapest and least time consuming.  In pilots study we use this sampling technique Demerits  Sample is not representative of entire population  Result cannot be generalized for entire population.
62. 62. ravsa Consecutive Sampling  It is very similar to convenience sampling except that it seeks to include all accessible subjects as part of the sample.  This nonprobability sampling technique can be considered as the best of all nonprobability samples because it include all the subjects that are available, which makes the sample a better representation of the entire population.
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64. 64. ravsa  In this sampling technique, the investigator pick up all the available subjects who are meeting the preset inclusion & exclusion criteria.  This technique is generally used in small-sized populations.  For example, if a researcher wants to study the activity pattern of postkidney-transplant patient, he can selects all the postkideney transplant patients who meet the designed inclusion & exclusion criteria, & who are admitted in post- transplant ward during a specific time period.
65. 65. ravsa Merits  Little effort  Saves time, money and material  Ensure more representative from population Demerit  Researcher has not set sampling plans about the sample  Not guarantee for representative sample  Sampling technique cannot be used to create conclusion and interpretation for entire population.
66. 66. ravsa Quota Sampling  It is nonprobability sampling technique wherein the researcher ensures equal or proportionate representation of subjects, depending on which trait is considered as the basis of the quota.  The bases of the quota are usually age, gender, education, race, religion, & socio-economic status.
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68. 68. ravsa  For example,  if the basis of the quota is college level & the research needs equal representation, with a sample size of 100, he must select 25 first- year students, another 25 second- year students,49 25 third-year, & 25 fourth-year students.
69. 69. ravsa Merits  Economically cheap, as there is no need to approach all the candidates.  Suitable for studies where the field work has to be carried out, like studies related to market & public opinion polls. Demits  Not represent the entire population  In the process of sampling these subgroups, other traits in the sample may be overrepresented.  Bias is possible, as investigator/interviewer can select persons known to him.
70. 70. ravsa Snowball Sampling  It is a nonprobability sampling technique that is used by researchers to identify potential subjects in studies where subjects are hard to locate such as commercial sex workers, drug abusers, etc.  For example, a researcher wants to conduct a study on the prevalence of HIV/AIDS among commercial sex workers.
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72. 72. ravsa  This type of sampling technique works like chain  After observing the initial subject, the researcher asks for assistance from the subject to help in identify people with a similar trait of interest.  The process of snowball sampling is much like asking subjects to nominate another person with the same trait.  The researcher then observes the nominated subjects & continues in the same way until obtaining sufficient number of subjects.
73. 73. ravsa Merits  The chain referral process allows the researcher to reach populations that are difficult to sample when using other sampling methods.  The process is cheap, simple, & cost-efficient.  Need little planning & lesser workforce Demerits  Researcher has little control over the sampling method.  Representativeness of the sample is not guaranteed.  Sampling bias is also a fear of researchers when using this sampling technique.
74. 74. ravsa Problems of sampling  Sampling errors  Lack of sample representativeness  Difficulty in estimation of sample size  Lack of knowledge about the sampling process  Lack of resources  Lack of cooperation  Lack of existing appropriate sampling frames for larger population