Upcoming SlideShare
×

# Sampling

892 views
756 views

Published on

marketing research ppts . very helpful

Published in: Marketing
1 Like
Statistics
Notes
• Full Name
Comment goes here.

Are you sure you want to Yes No
• Be the first to comment

Views
Total views
892
On SlideShare
0
From Embeds
0
Number of Embeds
4
Actions
Shares
0
31
0
Likes
1
Embeds 0
No embeds

No notes for slide

### Sampling

1. 1. Sampling: Design and Procedures
2. 2.  The objective of most marketing research projects is to obtain information about the characteristics or parameters of a population  Population: The aggregate of all the elements, sharing some common set of characteristics, that comprises the universe for the purpose of the marketing research problem
3. 3.  Census: A complete enumeration of the elements of a population or study subjects  Sample: A subgroup of the elements of the population selected for participation in the study
4. 4. Table 11.1 Conditions Favoring the Use of Type of Study Sample Census 1. Budget Small Large 2. Time available Short Long 3. Population size Large Small 4. Variance in the characteristic Small Large 5. Cost of sampling errors Low High 6. Cost of nonsampling errors High Low 7. Attention to individual cases Yes No
5. 5. Fig. 11.1 Define the Population Determine the Sampling Frame Select Sampling Technique(s) Determine the Sample Size Execute the Sampling Process
6. 6. The target population is the collection of elements or objects that possess the information sought by the researcher and about which inferences are to be made. The target population should be defined in terms of elements, sampling units, extent, and time. ◦ An element is the object about which or from which the information is desired, e.g., the respondent. ◦ A sampling unit is an element, or a unit containing the element, that is available for selection at some stage of the sampling process. ◦ Extent refers to the geographical boundaries. ◦ Time is the time period under consideration.
7. 7.  A sampling frame is a representation of the elements of the target population  It consists of a list or set of directions for identifying the target population  Example – Telephone Directory, Mailing lists etc
8. 8.  Bayesian Approach the elements are selected sequentially  Sampling with Replacement  Sampling Without Replacement
9. 9.  Sample size refers to the number of elements to be included in the study  Determining the sample size is complex and involves several qualitative and quantitative considerations  In general, for more important decisions, more information is necessary and the information should be obtained more precisely  Nature of Research also has some impact on the size
10. 10. Important qualitative factors in determining the sample size ◦ the importance of the decision ◦ the nature of the research ◦ the number of variables ◦ the nature of the analysis ◦ sample sizes used in similar studies ◦ incidence rates ◦ completion rates ◦ resource constraints
11. 11.  Sampling design decisions with respect to the population, sampling frame, sampling unit, sampling technique and sample size are to be implemented
12. 12. Table 11.2 Type of Study Minimum Size Typical Range Problem identification research (e.g. market potential) 500 1,000-2,500 Problem-solving research (e.g. pricing) 200 300-500 Product tests 200 300-500 Test marketing studies 200 300-500 TV, radio, or print advertising (per commercial or ad tested) 150 200-300 Test-market audits 10 stores 10-20 stores Focus groups 2 groups 4-12 groups
13. 13.  Sampling is cheaper than a census survey  Both the execution of the field work and the analysis of the results can be carried out speedily  Sampling results in greater economy of effort  A sample survey enables the researcher to collect more detailed information  Quality of the interviewing, supervision and other related activities can be better
14. 14.  It fails to provide information on individual account  It gives rise to many types of errors  The extrapolation or generalization may not work some times
15. 15. Fig. 11.2 Sampling Techniques Nonprobability Sampling Techniques Probability Sampling Techniques Convenience Sampling Judgmental Sampling Quota Sampling Snowball Sampling Systematic Sampling Stratified Sampling Cluster Sampling Other Sampling Techniques Simple Random Sampling
16. 16.  Non Probability Sampling- Sampling techniques that do not use chance selection procedures.  They rely on personal judgment of the researcher
17. 17.  Probability sampling – A sampling procedure in which each element of the population has a fixed probabilistic chance of being selected for the sample
18. 18.  Convenience sampling attempts to obtain a sample of convenient elements. Often, respondents are selected because they happen to be in the right place at the right time.  The selection of sampling units is left primarily to the interviewer ◦ use of students, and members of social organizations ◦ mall intercept interviews without qualifying the respondents ◦ department stores using charge account lists ◦ “people on the street” interviews
19. 19.  Convenience sampling is least expensive and least time consuming of all sampling techniques  The sampling units are accessible, easy to measure, and cooperative  Many potential sources of selection bias are present  Convenience samples are not representative of any definable population
20. 20. Judgmental sampling is a form of convenience sampling in which the population elements are selected based on the judgment of the researcher. The researcher exercising judgment or expertise, chooses the element to be included in the sample ◦ test markets ◦ purchase engineers selected in industrial marketing research ◦ bellwether precincts selected in voting behavior research ◦ expert witnesses used in court
21. 21. Quota sampling may be viewed as two-stage restricted judgmental sampling. ◦ The first stage consists of developing control categories, or quotas, of population elements. ◦ In the second stage, sample elements are selected based on convenience or judgment. Quota sampling involves fixation of certain quotas, which are to be fulfilled by the interviewer Population Sample composition composition Control Characteristic Percentage Percentage Number Sex Male 48 48 480 Female 52 52 520 ____ ____ ____ 100 100 1000
22. 22. Occupation/ Age-Group Farmers Salaried(Govt.) Salaried(Private) Students Home-makers Business-Men Retired/Unem ployed Total 18-24 50 50 24-35 5 15 15 15 15 65 36-45 15 15 15 15 15 75 46-60 15 15 15 10 10 65 60&above 10 5 5 45 65 Total 45 45 45 50 45 45 45 320 Note: Weshouldhaveatleast30samplesineachcircle,whoownsthe"Smart-phones"acrosscategories Note: TryandCovertherespondentsacrossallSEClevels
23. 23.  ..Telecom questionnaire_23May13_v6.docx
24. 24.  It is economical  It is administratively convenient  When the field is to be done quickly it is the most appropriate method  It is independent of the existence of sampling frames
25. 25.  It is not based on random selection  It may not be possible to get a representative quota within the sample  Since too much of latitude is given to interviewers, the quality of work suffers if they are not competent  It may be extremely difficult to supervise and control the field investigation under quota sampling
26. 26. In snowball sampling, an initial group of respondents is selected, usually at random. ◦ After being interviewed, these respondents are asked to identify others who belong to the target population of interest. ◦ Subsequent respondents are selected based on the referrals.
27. 27.  Are you satisfied with your Mosaic Glasses?  Tera Emotional Atyaachar  ..ShowCard _Corporate Clients (Mid Level)_UTV Bindass_Pure Media_24.08.11(1).docx
28. 28.  Probability sampling techniques vary in terms of sampling efficiency  Trade off between Sampling cost and Precision  The researcher should strive for the most efficient sampling design, subject to the budget allocated
29. 29.  They vary in terms of sampling efficiency  Trade off between sampling cost and precision  Every element is selected independently of every other element  The sample is drawn from a random procedure from a sampling frame
30. 30.  Each element in the population has a known and equal probability of selection.  Each possible sample of a given size (n) has a known and equal probability of being the sample actually selected.  This implies that every element is selected independently of every other element.
31. 31.  The sample is chosen by selecting a random starting point and then picking every ith element in succession from the sampling frame.  The sampling interval, i, is determined by dividing the population size N by the sample size n and rounding to the nearest integer.  When the ordering of the elements is related to the characteristic of interest, systematic sampling increases the representativeness of the sample.  If the ordering of the elements produces a cyclical pattern, systematic sampling may decrease the representativeness of the sample. For example, there are 100,000 elements in the population and a sample of 1,000 is desired. In this case the sampling interval, i, is 100. A random number between 1 and 100 is selected. If, for example, this number is 23, the sample consists of elements 23, 123, 223, 323, 423, 523, and so on.
32. 32.  A two-step process in which the population is partitioned into subpopulations, or strata.  The strata should be mutually exclusive and collectively exhaustive in that every population element should be assigned to one and only one stratum and no population elements should be omitted.  Next, elements are selected from each stratum by a random procedure, usually SRS.  A major objective of stratified sampling is to increase precision without increasing cost.
33. 33.  A variable used to partition population into strata are referred to as “Stratification”  Variables usually used for stratification include demographic characteristics, type of customer, size of firm, type of industry etc
34. 34.  The elements within a stratum should be as homogeneous as possible, but the elements in different strata should be as heterogeneous as possible.  The stratification variables should also be closely related to the characteristic of interest.  Finally, the variables should decrease the cost of the stratification process by being easy to measure and apply.  In proportionate stratified sampling, the size of the sample drawn from each stratum is proportionate to the relative size of that stratum in the total population.  In disproportionate stratified sampling, the size of the sample from each stratum is proportionate to the relative size of that stratum and to the standard deviation of the distribution of the characteristic of interest among all the elements in that stratum.
35. 35.  For example, imagine you want to create a council of 20 employees that will meet and recommend possible changes to the employee handbook. Let's say 40% of your employees are in Sales and Marketing, 30% in Customer Service, 20% of your employees are in IT, and 10% in Finance. You will randomly select 8 people from Sales and Marketing, 6 from Customer Service, 4 from IT, and 2 from Finance. As you can see, each number you pick is proportionate to the overall percentage of people in each category (e.g., 40% = 8 people). Read more: http://www.alleydog.com/glossary/definition.p hp?term=Proportionate+Sampling#ixzz2xawOKRDH
36. 36.  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.
37. 37. Fig. 11.3 Cluster Sampling One-Stage Sampling Multistage Sampling Two-Stage Sampling Simple Cluster Sampling Probability Proportionate to Size Sampling
38. 38.  In some cases, several levels of cluster selection may be applied before the final sample elements are reached. For example, household surveys conducted by the Australian Bureau of Statistics begin by dividing metropolitan regions into 'collection districts' and selecting some of these collection districts (first stage). The selected collection districts are then divided into blocks, and blocks are chosen from within each selected collection district (second stage). Next, dwellings are listed within each selected block, and some of these dwellings are selected (third stage)
39. 39. Surrogate Information Error Measurement Error Population Definition Error Sampling Frame Error Data Analysis Error Respondent Selection Error Questioning Error Recording Error Cheating Error Inability Error Unwillingness Error Fig. 3.2 Total Error Non-sampling Error Random Sampling Error Non-response Error Response Error Interviewer Error Respondent Error Researcher Error
40. 40.  The total error is the variation between the true mean value in the population of the variable of interest and the observed mean value obtained in the marketing research project.  Random sampling error is the variation between the true mean value for the population and the true mean value for the original sample.  Non-sampling errors can be attributed to sources other than sampling, and they may be random or nonrandom: including errors in problem definition, approach, scales, questionnaire design, interviewing methods, and data preparation and analysis. Non-sampling errors consist of non- response errors and response errors.
41. 41.  Non-response error arises when some of the respondents included in the sample do not respond.  Response error arises when respondents give inaccurate answers or their answers are misrecorded or misanalyzed.  Response errors can be made by researchers, interviewers or respondents
42. 42.  Surrogate Information error may be defined as the variation between the information needed for the marketing research problem and the information sought by the researcher  Consumer choice and consumer preference
43. 43.  Measurement error may be defined as the variation between the information sought and the information generated by the measurement process employed by the researcher  Population Definition error may be defined as the variation between the actual population relevant to the problem at hand and the population as defined by the researcher
44. 44.  Sampling Frame Error may be defined as the variation between the population defined by the researcher and the population as implied by the sampling frame  Data analysis error encompasses errors that occur while raw data from questionnaires are transformed into research findings Example – Inappropriate sampling error  Respondent Selection error occurs when interviewers select respondents other than those specified by the sampling design or in a manner inconsistent with the sampling design
45. 45.  Questioning error denotes errors made in asking questions of the respondents or in not probing when more information is needed Example- While asking questions the investigator may not use the exact wording as given in the questionnaire
46. 46.  Recording Error arises due to errors in hearing, interpreting, and recording the answers given by the respondents  Cheating Error arises when the interviewer fabricates answers to a part or all of the interview  Inability Error results from the respondents inability to provide accurate answers  Respondents may provide inaccurate answers because of unfamiliarity, fatigue, boredom, faulty recall, question format, content etc
47. 47.  Unwillingness error arises from the respondents unwillingness to provide accurate information
48. 48.  A common form of cluster sampling in which the cluster consists of geographic area such as countries, housing tracts, blocks or other area descriptions
49. 49. Technique Strengths Weaknesses Nonprobability Sampling Convenience sampling Least expensive, least time-consuming, most convenient Selection bias, sample not representative, not recommended for descriptive or causal research Judgmental sampling Low cost, convenient, not time-consuming Does not allow generalization, subjective Quota sampling Sample can be controlled for certain characteristics Selection bias, no assurance of representativeness Snowball sampling Can estimate rare characteristics Time-consuming Probability sampling Simple random sampling (SRS) Easily understood, results projectable Difficult to construct sampling frame, expensive, lower precision, no assurance of representativeness. Systematic sampling Can increase representativeness, easier to implement than SRS, sampling frame not necessary Can decrease representativeness Stratified sampling Include all important subpopulations, precision Difficult to select relevant stratification variables, not feasible to stratify on many variables, expensive Cluster sampling Easy to implement, cost effective Imprecise, difficult to compute and interpret results Table 11.3
50. 50. Fig. 11.4 Simple Random Sampling 1. Select a suitable sampling frame 2. Each element is assigned a number from 1 to N (pop. size) 3. Generate n (sample size) different random numbers between 1 and N 4. The numbers generated denote the elements that should be included in the sample
51. 51. Fig. 11.4 cont. Systematic Sampling 1. Select a suitable sampling frame 2. Each element is assigned a number from 1 to N (pop. size) 3. Determine the sampling interval i:i=N/n. If i is a fraction, round to the nearest integer 4. Select a random number, r, between 1 and i, as explained in simple random sampling 5. The elements with the following numbers will comprise the systematic random sample: r, r+i,r+2i,r+3i,r+4i,...,r+(n-1)i
52. 52. 1. Select a suitable frame 2. Select the stratification variable(s) and the number of strata, H 3. Divide the entire population into H strata. Based on the classification variable, each element of the population is assigned to one of the H strata 4. In each stratum, number the elements from 1 to Nh (the pop. size of stratum h) 5. Determine the sample size of each stratum, nh, based on proportionate or disproportionate stratified sampling, where 6. In each stratum, select a simple random sample of size nh Fig. 11.4 cont. nh = n H Stratified Sampling
53. 53. Fig. 11.4 cont. Cluster Sampling 1. Assign a number from 1 to N to each element in the population 2. Divide the population into C clusters of which c will be included in the sample 3. Calculate the sampling interval i, i=N/c (round to nearest integer) 4. Select a random number r between 1 and i, as explained in simple random sampling 5. Identify elements with the following numbers: r,r+i,r+2i,... r+(c-1)i 6. Select the clusters that contain the identified elements interval i*.
54. 54.  7. Select sampling units within each selected cluster based on SRS or systematic sampling  8. Remove clusters exceeding sampling interval i. Calculate new population size N*, number of clusters to be selected C*= C-1, and new sampling
55. 55. Repeat the process until each of the remaining clusters has a population less than the sampling interval. If b clusters have been selected with certainty, select the remaining c- b clusters according to steps 1 through 7. The fraction of units to be sampled with certainty is the overall sampling fraction = n/N. Thus, for clusters selected with certainty, we would select ns=(n/N)(N1+N2+...+Nb) units. The units selected from clusters selected under PPS sampling will therefore be n*=n- ns. Cluster Sampling Fig. 11.4 cont.
56. 56.  Sequential Sampling  Double Sampling – Two phase sampling
57. 57. Conditions Favoring the Use of Factors Nonprobability sampling Probability sampling Nature of research Exploratory Conclusive Relative magnitude of sampling and nonsampling errors Nonsampling errors are larger Sampling errors are larger Variability in the population Homogeneous (low) Heterogeneous (high) Statistical considerations Unfavorable Favorable Operational considerations Favorable Unfavorable Table 11.4 cont.
58. 58.  Concept tests, package tests, name tests and copy tests where projections to the population are not generally needed  Highly accurate estimate of market share or sales volume etc
59. 59.  The respondents may not be representative  The internet present with only some part of population  Some products online interviewing is not a feasible option  Online sampling techniques
60. 60.  Any Questions?