![DR G K KALKOTI81
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](https://image.slidesharecdn.com/samplingtechniquefor2ndyrpbbscnsg-150301220242-conversion-gate02/85/Sampling-technique-for-2-nd-yr-pbbsc-nsg-81-320.jpg)
Sampling technique for 2 nd yr pbbsc nsg
- 2. INTRODUCTION⌠ď Sampling is a process of selecting representative units from an entire population of a study. ď Sample is not always possible to study an entire population; therefore, the researcher draws a representative part of a population through sampling process. ď 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. ď It is a process of obtaining information regarding a phenomenon about entire population by examining3/2/20152
- 4. ďPopulation: ď 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. ď For example, a researcher needs to study the problems faced by postgraduate nurses of India; in this the âpopulationâ will be all the postgraduate nurses who are Indian citizen. 3/2/20154
- 5. Population is defined as The Entire Group under study. Sometimes it is also called as the âUniverse.â Population
- 6. Target Population: 3/2/20156 ď A target population consist of the total number of people or objects which are meeting the designated set of criteria. ď 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. ď For example, a researcher is interested in identifying the complication of diabetes mellitus type-II among people who have migrated to Canada. In this instance, the target population are all the migrants at Canada suffering with
- 7. Accessible population: 3/2/20157 ď It is the aggregate of cases that conform to designated criteria & are also accessible as subjects for a study. ď For example, âa researcher is conducting a study on the registered nurses (RN) working in Father Muller Hospital, Mangalore â. In this case, the population for this study is all the RNs working in Father Muller Hospital, but some of them may be on leave & may not be accessible for research study. Therefore, accessible population for this study will be RNs who meet the designated criteria & who are also available for the research
- 8. Sampling: 3/2/20158 ď Sampling is the process of selecting a representative segment of the population under study.
- 9. Sample: 3/2/20159 ď Sample may be defined as representative unit of a target population, which is to be worked upon by researchers during their study. ď In other words, sample consists of a subset of units which comprise the population selected by investigators or researchers to participates in their
- 10. Element: 3/2/201510 ď The individual entities that comprise the samples & population are known as elements, & an element is the most basic unit about whom/which information is collected. An elements is also known as subject in research. The most common element in nursing research is an individual. The sample or population depends on phenomenon under study
- 11. Sampling frame: 3/2/201511 ď It is a list of all the elements or subjects in the population from which the sample is drawn. ď Sampling frame could be prepared by the researcher or an existing frame may be used. ď For example, a researcher may prepare a list of the all the households of a locality which have pregnant women or may used a register of pregnant women
- 12. Sampling error: 3/2/201512 ďThere may be fluctuation in the values of the statistics of characteristics from one sample to another, or even those drawn from the same population.
- 13. Sampling bias: 3/2/201513 ďDistortion that arises when a sample is not representative of the population from which it was drawn.
- 14. Sampling plan 3/2/201514 ďThe formal plan specifying a sampling method, a sample size, & the procedure of selecting the subjects.
- 15. Sample size 3/2/201515 Size of sample should be determined by a researcher keeping in view: 1. Nature of universe: -homo (small sample) - hetero (large sample). ď 2. No. of classes proposed: directly proportional to the sample size . ď 3. Nature of study: general (large) ď intensive (small). ď 4.Type of sampling: small random sample is better than a large but bad one.
- 16. Contd⌠3/2/201516 Standard of accuracy: high level of precision large sample. 6. Availability of finance: sample size =amount of money available. ď 7. Other considerations: size of population, ď size of questionnaire, ď nature of units, ď conditions.
- 20. Purposes of sampling 3/2/201520 ďąEconomical ďąImproved quality of data ďąQuick study results ďąPrecision and accuracy of data
- 21. ď 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. ď 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. ď 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 ď Precision and accuracy of data: Conducting a study on an entire population provides researchers with voluminous data, & maintaining precision of that data 3/2/201521
- 22. CHARACTERISTICS OF GOOD SAMPLE ďTrue Representative ďFree from sample bias and errors ďNo substitution and incompleteness ďAppropriate sample size(Optimum size (adequately large) 3/2/201522
- 23. Contd⌠ďHas all characteristics that are present in population ďEconomically viable ďResults can be applied to the universe in general with a reasonable level of confidence or reliability
- 24. SAMPLING PROCESS Identifying and defining the target population Describing the accessible population & ensuring sampling frame Specifying the sampling unit Specifying sampling selection methods 3/2/201524
- 25. Count⌠Determining the sample size Specifying the sampling plan Selecting a desired sample 3/2/201525
- 26. Sampling Design Process Define Population Determine Sampling Frame Determine Sampling Procedure Probability Sampling Simple Random Sampling Stratified Sampling Cluster Sampling Systematic Sampling Multistage Sampling Non-Probability Sampling Convenient Judgmental Quota Snow ball Sampling Determine Appropriate Sample Size Execute Sampling Design
- 27. Steps in Sampling Process 1. Define the population 2. Identify the sampling frame 3. Select a sampling design or procedure 4. Determine the sample size 5. Draw the sample
- 29. FACTORS INFUENCING SAMPLING PROCESS Nature of the researcher: ď Inexperienced investigator ď Lack of interest ď Lack of honesty ď Intensive workload ď Inadequate supervision
- 30. Nature of sample ď Inappropriate sampling technique ď Sample size ď Defective sampling frame Circumstances: ď§ Lack of time ď§ Large geographical area ď§ Lack of cooperation ď§ Natural calamities
- 32. 32 METHODS OF SAMPLING ďąThe 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: ď Probability Sampling ďNon Probability Sampling
- 33. 3/2/201533
- 34. Classification of Sampling Methods Sampling Methods Probability Samples Simple Random Cluster Systematic Stratified Non- probability QuotaJudgment Convenience Snowball Multistage
- 36. 36 Probability Sampling Method ď Probability Sampling is also known as Random Sampling or formal sampling ď Probability means chance ď Therefore element of the population has the equal chance or opportunity of being selected in the sample ď Probability samples are more accurate ď 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 /
- 37. 3/2/201537 Concept⌠ď It is based on the theory of probability. ď It involve random selection of the elements/members of the population. ď In this, every subject in a population has equal chance to be selected sampling for a study. ď In probability sampling techniques, the chances of systematic bias is relatively less because subjects are randomly
- 38. Features of the Probability sampling 3/2/201538 ď 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 ď The results of probability sampling more accurate and reliable ď It helps in the formulation of a true representative sample by eliminating human biases ď Under probability method each element of population known in advance about the possibility of being included in the sample ď The advantage of using a random sample is the absence of both systematic & sampling bias. ď 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.
- 39. Probability Techniques Probablity techniques Simple Random Sampling Systemic Sampling Stratified Sampling Disproportionate Proportionate Cluster Sampling Others
- 40. Types of the probability sampling 3/2/201540 1.Simple random sampling 2.Stratified random sampling 3.Systematic random sampling 4.Cluster/multistage sampling 5.Sequential sampling
- 42. DR G K KALKOTI42 Simple Random Sampling ďą It is the basic probability sampling technique and all other methods are variations of simple random method. ďą 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. ď Simple random can be done by A) Lottery Method B) Random Tables c)The use of computer
- 43. Simple random sampling 3/2/201543 ď The entire process of sampling is done in a single step, with each subject selected independently of the other members of the population ď There is need of two essential prerequisites to implement the simple random technique: 1.Population must be homogeneous & 2.Researcher must have list of the elements/members of the accessible
- 45. Steps 3/2/201545 ď The first step of the simple random sampling technique is to ď identify the accessible population & prepare a list of all the elements/members of the population. ď The list of the subjects in population is called as sampling frame & sample drawn from sampling frame by using following methods: ďąThe lottery method ďąThe use of table of random numbers ďąThe use of computer
- 46. 3/2/201546 1.The lottery method⌠ď It is most primitive & mechanical method. ď Each member of the population is assigned a unique number. ď Each number is placed in a bowel or hat & mixed thoroughly. ď The blind-folded researcher then picks numbered tags from the hat. ď All the individuals bearing the numbers picked by the researcher are the subjects for the study.
- 47. 3/2/201547 The use of table of random numbers⌠ď This is most commonly & accurately used method in simple random sampling. ď It provides use of random numbers specially designed for sampling purposes ď Random table present several numbers in rows & columns. ď Researcher initially prepare a numbered list of the members of the population, & then with a blindfold chooses a number from the random table. ď The same procedure is continued until the desired number of the subject is achieved. ď If repeatedly similar numbers are encountered, they are ignored & next numbers are considered until desired numbers of the subject are achieved. ď Such type of random table are mostly found at the end of statistical textbooks
- 48. Random numbers of table 6 8 4 2 5 7 9 5 4 1 2 5 6 3 2 1 4 0 5 8 2 0 3 2 1 5 4 7 8 5 9 6 2 0 2 4 3 6 2 3 3 3 2 5 4 7 8 9 1 2 0 3 2 5 9 8 5 2 6 3 0 1 7 4 2 4 5 0 3 6 8 6
- 49. 3/2/201549 The use of computer⌠ď Nowadays random tables may be generated from the computer , & subjects may be selected as described in the use of random table. ď 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.
- 51. Types of Simple Random Sample ďśWith replacement ďśWithout replacement
- 52. ďśWith replacement The unit once selected has the chance for again selection ďśWithout replacement The unit once selected can not be selected again
- 53. REPLACEMENT OF SELECTED UNITS 53 ď Sampling schemes may be without replacement ('WOR' - no element can be selected more than once in the same sample) or with replacement ('WR' - an element may appear multiple times in the one sample). ď For example, if we catch fish, measure them, and immediately return them to the water before continuing with the sample, this is a WR design, because we might end up catching and measuring the same fish more than once. However, if we do not return the fish to the water (e.g. if we eat the fish), this
- 54. Merits and Demerits 3/2/201554 Merits ď Ease of assembling the sample ď Fair way of selecting a sample ď Require minimum knowledge about the population in advance ď It unbiased probability method ď Free from sampling errors ď Demerits ď It requirement of a complete & up-to-date list of all the members of the population. ď Does not make use of knowledge about a population which researchers may already have. ď Lots of procedure need to be done before sampling ď Expensive & time- consuming
- 56. STRATIFIED SAMPLING ďśPopulation is divided on the basis of characteristic of interest in the population e.g. male and female may have different consumption patterns.
- 57. Stratified Random Sampling 3/2/201557 ď This method is used for heterogeneous population. ď 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. ď 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 specialization, site of care, etc.
- 58. DR G K KALKOTI58 Stratified Random Sampling ď In this method, the population is divided and subdivided with homogeneous or similar characteristics ď For example, a group of 200 college teachers can be first divided into teachers in Arts faculty, Commerce Faculty and Science Faculty. ď 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. ď Stratified random sampling is more accurate and representative as compared to simple random sampling because under this the population is divided into homogeneous groups.
- 59. Stratified Random Sampling Population is divided into two or more groups called strata, according to some criterion, such as geographic location, grade level, age, or income, and subsamples are randomly selected from each strata. Elements within each strata are homogeneous, but are heterogeneous across strata
- 61. Types of Stratified Random Sampling ďśProportionate Stratified Random Sampling Equal proportion of sample unit are selected from each strata ďśDisproportionate Stratified Random Sampling Also called as equal allocation technique and sample unit decided according to analytical consideration
- 62. From a population Based on an attribute divide into strata Select elements from strata by randomizati on
- 65. SYSTEMATIC SAMPLING If a sample size of n is desired from a population containing N elements, we might sample one element for every n/N elements in the population.
- 66. Systematic Random Sampling 3/2/201566 ď It can be likened to an arithmetic progression, wherein the difference between any two consecutive numbers is the same. ď 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. ď Systematic sampling is sometimes used to sample every Kth person entering a bookstore, or passing down the street or leaving a hospital & so forth ď Systematic sampling can be applied so that an essentially random sample is drawn.
- 67. Count⌠3/2/201567 ď 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). Number of subjects in target population (N) K = N/n or K= Size of sample ď 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.
- 68. DR G K KALKOTI68 Systematic Sampling ď It is modification of simple random sampling. It is called as quasi-random sampling. ď It is called quasi because it is in between probability and non-probability sampling. ď 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.
- 69. DR G K KALKOTI69 Illustration of Systematic Sampling ď Selecting a sample of 100 students out of 1000, the sample interval will be 1000 divided by 100 i.e.10. ď Then make small chits bearing numbers 1to 10 and put them into a box ď 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. ď The selected sample consists of elements bearing nos. 5,15,25,..........105,115 and so on . ď It should be noted that up to selecting no.5,Systematic sampling can be treated as probability sampling and afterwards it is non-probability because the chances of other elements are certainly affected ď In this example numbers other than 5 have no chance of being selected
- 70. Systematic Random Sampling Order all units in the sampling frame based on some variable and then every nth number on the list is selected Gaps between elements are equal and Constantď There is periodicity. N= Sampling Interval
- 71. Systematic Sampling ď Order all units in the sampling frame based on some variable and number them from 1 to N ď Choose a random starting place from 1 to N and then sample every k units after that 3. Selecting a Sampling Design
- 72. systematic random sample number the units in the population from 1 to N decide on the n (sample size) that you want or need k = N/n = the interval size randomly select an integer between 1 to k then take every kth unit
- 76. Cluster or Area Random Sampling Clusters of population units are selected at random by dividing the population into clusters (usually along geographic boundaries) and then all or some randomly chosen units in the selected clusters are studied.
- 77. Cluster or multistage Sampling 3/2/201577 ď It is done when simple random sampling is almost impossible because of the size of the population. ď Cluster sampling means random selection of sampling unit consisting of population elements. ď Then from each selected sampling unit, a sample of population elements is drawn by either simple random selection or stratified random sampling. ď 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. ď The important thing to remember about this sampling technique is to give all the clusters equal chances of being selected.
- 78. ď Geographical units are the most commonly used ones in research. For example, a researcher wants to survey academic performance of high school students in India. ďźHe can divide the entire population (of India) into different clusters (cities). Then the researcher selects a number of clusters depending on his research through simple or systematic random sampling. ďźThen, from the selected clusters (random selected cities), the researcher can either include all the high school students as subjects or he can select a number of subjects from each cluster through Count⌠3/2/201578
- 79. DR G K KALKOTI79 Cluster Sampling ď Cluster means group, therefore, sampling units are selected in groups. ď 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. ď 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.
- 80. DR G K KALKOTI80 Illustration of Cluster Sampling ď 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.
- 81. DR G K KALKOTI81 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
- 82. Cluster Sampling ⢠The target population is first divided into mutually exclusive and collectively exhaustive subpopulations, or clusters. ⢠Then a random sample of clusters is selected, based on a probability sampling technique such as SRS. ⢠For each selected cluster, either all the elements are included in the sample (one-stage) or a sample of elements is drawn probabilistically (two-stage). ⢠Elements within a cluster should be as heterogeneous as possible, but clusters themselves should be as homogeneous as possible. Ideally, each cluster should be a small-scale representation of the population. ⢠In probability proportionate to size sampling, the clusters are sampled with probability proportional to size. In the second stage, the probability of selecting a sampling unit in a selected cluster varies inversely with the size of the cluster.
- 83. Types of Cluster Sampling Cluster Sampling One-Stage Sampling Multistage Sampling Two-Stage Sampling Simple Cluster Sampling Probability Proportionate to Size Sampling
- 84. The population is divided into subgroups (clusters) like families. A simple random sample is taken of the subgroups and then all members of the cluster selected are surveyed. Cluster Sampling
- 86. Cluster sampling Section 4 Section 5 Section 3 Section 2Section 1
- 89. 3/2/201589
- 90. DR G K KALKOTI90 Multistage and Multi Phase Sampling ď 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. ď 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.
- 91. DR G K KALKOTI91 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.
- 94. Non Probability Sampling ďś Involves non random methods in selection of sample ďśAll have not equal chance of being selected ďśSelection depend upon situation ďśConsiderably less expensive ďśConvenient ďśSample chosen in many ways
- 95. DR G K KALKOTI95 Non-Probability Sampling ď Non-probability sampling is also called as judgment sampling. ď 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. ď Non-probability sampling suffers defects like personal bias and sampling error cannot be estimated. ď This is an unscientific and less accurate method of sampling, hence it is only occasionally used in research activities.
- 96. 3/2/201596
- 97. Features of the nonprobability sampling 3/2/2015jaympatidar@yahoo.in97
- 98. Uses of Nonprobability Sampling 3/2/201598 ď This type of sampling can be used when demonstrating that a particular trait exists in the population. ď It can also be used when researcher aims to do a qualitative, pilot , or exploratory study. ď It can be used when randomization is not possible like when the population is almost limitless. ď it can be used when the research does not aim to generate results that will be used to create generalizations. ď It is also useful when the researcher has limited budget, time, & workforce. ď This technique can also be used in an initial study (pilot study)
- 99. 3/2/201599
- 100. Purposive Sampling 3/2/2015100 ď It is more commonly known as âjudgmentalâ or âauthoritative samplingâ. ď In this type of sampling, subjects are chosen to be part of the sample with a specific purpose in mind. ď 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. ď 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
- 101. Count⌠3/2/2015101 ď For example, a researcher wants to study the lived experiences of postdisaster depression among people living in earthquake affected areas of Gujarat. ď In this case, a purposive sampling technique is used to select the subjects who were the victims of the earthquake disaster & have suffered postdisaster depression living in earthquake- affected areas of Gujarat. ď In this study, the researcher selected only those people who fulfill the criteria as well as particular subjects that are the typical & representative part of population as per the knowledge of the
- 102. DR G K KALKOTI102 Purposive Sampling ďPurposive sampling means deliberate selection of sample units confirm to some predetermined criteria. This is also known as judgment sampling ďIt 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.
- 104. Convenience Sampling 3/2/2015104 ď It is probably the most common of all sampling techniques because it is fast, inexpensive, easy, & the subject are readily available. ď It is a nonprobability sampling technique where subjects are selected because of their convenient accessibility & proximity to the researcher. ď 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 ď It is also known as an accidental sampling. ď Subjects are chosen simply because they are easy
- 105. 3/2/2015105
- 106. CONVENIENCE SAMPLING ďśSometimes known as grab or opportunity sampling or accidental or haphazard sampling. ďśA type of non probability sampling which involves the sample being drawn from that part of the population which is close to hand. That is, readily available and convenient.
- 107. DR G K KALKOTI107 Convenience Sampling ď In convenience sampling, the sample is selected as per the convenience of the researcher. ď 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. ď 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.
- 108. Merits and Demerits 3/2/2015108 Merits ď This technique is considered easiest, cheapest, & least time consuming. ď This sampling technique may help in saving time, money, & resources. Demerits ď Sampling bias, & the sample is not representative of the entire population. ď It does not provide the representative sample from the population of the study. ď Findings generated from these sampling cannot be generalized on the
- 109. Consecutive Sampling 3/2/2015109 ď It is very similar to convenience sampling except that it seeks to include all accessible subjects as part of the sample. ď 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.
- 110. 3/2/2015110 Count⌠ď In this sampling technique, the investigator pick up all the available subjects who are meeting the preset inclusion & exclusion criteria. ď This technique is generally used in small-sized populations. ď 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.
- 111. Merits and Demerits 3/2/2015111 Merits ď Little effort for sampling ď It is not expensive, not time consuming, & not workforce intensive. ď Ensures more representativeness of the selected sample. Demerits ď Researcher has not set plans about the sample size & sampling schedule. ď It always does not guarantee the selection of representative sample. ď Results from this sampling technique cannot be used to create conclusions & interpretations pertaining to the entire population.
- 112. Quota Sampling 3/2/2015112 ď 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. ď The bases of the quota are usually age, gender, education, race, religion, & socio-economic status. ď 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, 25 third-year, & 25 fourth-year students.
- 113. DR G K KALKOTI113 Quota Sampling ď 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 non-probability method by stratification and the setting of quotas for each stratum. ď 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). ď Quota sampling offer benefits of speed, economy and simplicity. It is widely used in market surveys and public opinion polls.
- 114. Merits and Demerits 3/2/2015114 Merits ď Economically cheap, as there is no need to approach all the candidates. ď Suitable for studies where the fieldwork has to be carried out, like studies related to market & public opinion polls. Demerits ď It not represent all population ď In the process of sampling these subgroups, other traits in the sample may be overrepresented. ď Not possible to estimate errors. ď Bias is possible, as investigator/interviewer can select persons known to
- 115. Snowball Sampling 3/2/2015115 ď 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. ď For example, a researcher wants to conduct a study on the prevalence of HIV/AIDS among commercial sex workers. ď In this situation, snowball sampling is the best choice for such studies to select a sample. ď This type of sampling technique works like chain referral. Therefore it is also known as chain
- 116. 3/2/2015116 Count⌠ď After observing the initial subject, the researcher asks for assistance from the subject to help in identify people with a similar trait of interest ď The process of snowball sampling is much like asking subjects to nominate another person with the same trait. ď The researcher then observes the nominated subjects & continues in the same way until obtaining sufficient number of subjects.
- 117. SNOWBALL SAMPLING ďś Selection of additional respondents is based on referrals from the initial respondents. - friends of friends ďś Used to sample from low incidence or rare populations.
- 118. Merits and Demerits 3/2/2015118 Merits ď The chain referral process allows the researcher to reach populations that are difficult to sample when using other sampling methods. ď The process is cheap, simple, & cost-efficient. ď Need little planning & lesser workforce Demerits ď Researcher has little control over the sampling method. ď Representativeness of the sample is not guaranteed. ď Sampling bias is also a fear of researchers when using this sampling technique.
- 119. PROBLEMS OF SAMPLING 3/2/2015119 ď Sampling errors ď Lack of sample representativeness ď Difficulty in estimation of sample size ď Lack of knowledge about the sampling process ď Lack of resources ď Lack of cooperation ď Lack of existing appropriate sampling frames for larger population
- 120. In A Nut Shell ď Probability Sampling - Simple Random â Selection at Random - Systematic â Selecting every nth case - Stratified â Sampling w/n groups of Populn - Cluster â Surveying whole clusters of P/n - Multistage â Sub samples from large smpl
- 121. ď Non- Probability Sampling - Accidental â Sampling those most convnt - Voluntary â Sample is self selected - Purposive â Handpicking typical cases - Quota â Sampling w/n groups of Ppln - Snowball â building sample thru informnts
- 122. Sampling Error Sampling error refers to differences between the sample and the population that exist only because of the observations that happened to be selected for the sample Increasing the sample size will reduce this type of error
- 124. Types of Sampling Error ďśSample Errors ďśNon Sample Errors
- 125. Sample Errors ďśError caused by the act of taking a sample ďśThey cause sample results to be different from the results of census ďśDifferences between the sample and the population that exist only because of the observations that happened to be selected for the sample ďśStatistical Errors are sample error ďśWe have no control over
- 126. Non Sample Errors ďśNon Response Error ďśResponse Error Not Control by Sample Size
- 127. Non Response Error A non-response error occurs when units selected as part of the sampling procedure do not respond in whole or in part
- 128. Response Errors ďśRespondent error (e.g., lying, forgetting, etc.) ďśInterviewer bias ďśRecording errors ďśPoorly designed questionnaires ďśMeasurement error A response or data error is any systematic bias that occurs during data collection, analysis or interpretation
- 129. Respondent error ďś respondent gives an incorrect answer, e.g. due to prestige or competence implications, or due to sensitivity or social undesirability of question ďś respondent misunderstands the requirements ďś lack of motivation to give an accurate answer ďś âlazyâ respondent gives an âaverageâ answer ďś question requires memory/recall ďś proxy respondents are used, i.e. taking answers from someone other than the respondent
- 130. Interviewer bias ďś Different interviewers administer a survey in different ways ďś Differences occur in reactions of respondents to different interviewers, e.g. to interviewers of their own sex or own ethnic group ďś Inadequate training of interviewers ďś Inadequate attention to the selection of interviewers ďś There is too high a workload for the interviewer
- 131. Measurement Error ďś The question is unclear, ambiguous or difficult to answer ďś The list of possible answers suggested in the recording instrument is incomplete ďś Requested information assumes a framework unfamiliar to the respondent ďś The definitions used by the survey are different from those used by the respondent (e.g. how many part-time employees do you have? See next slide for an example)
- 132. Key Points on Errors Non-sampling errors are inevitable in production of national statistics. Important that:- ďŻ At planning stage, all potential non-sampling errors are listed and steps taken to minimise them are considered. ďŻ If data are collected from other sources, question procedures adopted for data collection, and data verification at each step of the data chain. ďŻ Critically view the data collected and attempt to resolve queries immediately they arise. ďŻ Document sources of non-sampling errors so that results presented can be interpreted meaningfully.
- 134. 134 Questions???