Sampling
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This document discusses sampling techniques used in market research. It distinguishes between a census, which surveys all elements in a population, and a sample, which surveys only a subset of a population. The key steps in sampling are identifying the target population, determining the sampling frame, selecting a sampling technique, and determining sample size. Probability sampling techniques like simple random sampling and systematic sampling are preferred as they allow for statistical generalization to the overall population. Non-probability techniques rely more on researcher judgment.
Sampling
- 2. Chapter 10 Sampling: Sample, design and sample size 2
- 3. Learning Objectives • Distinguish between a census and a sample • Describe the steps in the sampling process • Define target population for a given research problem • Identify sampling frames • Differentiate between probability & non- probability sampling techniques • Assess non-response problems
- 4. Review • What should we ask? Relevance & accuracy • Open: free response • Closed: fixed, limited alternative / options responses • Which is better & why? • Also depends on how you will ask eg mail, telephone, personal 4
- 5. Example • How much do you depend on advertising sources to get information about products that you are likely to buy? • Very little 1234567 A lot • What does, Infrequently, occasionally, frequently mean? 5
- 6. The Marketing Research Process
- 7. Sampling terminology • Sample: a subset, or some part, of a larger population. • Population: any complete group of entities that share some common set of characteristics. • Population element: an individual member of a population. • Census: an investigation of all the individual elements that make up a population. 7
- 8. Why sample? • Pragmatic reasons: budget and time constraints • Accurate and reliable results: most properly selected samples give sufficiently accurate results – Relationship between sample size and accuracy • Destruction of test units. 8
- 9. How sampling works 9
- 10. Stages in the selection of a sample 10
- 11. Reasons for Using a Sample • Time • Cost • Inability to study the whole population
- 12. Identify the Target Population Define the Target Population • Precise statement of who should and should not be included in the sample Sampling Elements – Object about which or from which the information is desired e.g., males, females, over 18, buys the groceries etc. Sampling Units – An element, or a unit containing the element that is available for selection at some stage e.g., households, small businesses, schools etc. Extent – Geographical boundaries e.g., western metropolitan region of Melbourne, national, study of two countries Time period under consideration
- 13. Identify the Target Population cont. – Look to the research objectives – Consider the alternatives – Know your market – Consider the appropriate sampling unit – Specify clearly what is excluded – Don’t over-define – Should be reproducible i.e., consider convenience
- 14. Defining the target population • What is the relevant population? – Survey of students’ choice of university might target university students, but should also include friends and parents who exert an influence in the decision. – Operational definition required • Vital to carefully define the target population. 14
- 15. The sampling frame • Sampling frame: a list of elements from which a sample may be drawn. – Example: student email list, membership list • List brokers provide lists of specific populations. • Sampling frame error occurs when certain sample elements are not listed or accurately represented in a sampling frame. 15
- 16. The use of sampling frames in marketing research • An accurate sampling frame will not always exist. – Potentially sacrifice accuracy by using more practical methods like the shopping mall intercept to sample consumers. – Telephone directories may be incomplete. • Using the wrong sample frame will result in the inclusion of respondents who should not be part of the population. 16
- 17. Sampling units • Does not have to be a person. – An airline may wish to select individual passengers as the sampling unit or select certain flights as the sampling unit. 17
- 18. Determine the Sampling Frame • Representation of the target population • List or set of directions for identifying the target population e.g., telephone book, association directory, mail list, map • Sampling frame error – The list may omit some elements of the population or include other elements which do not belong • Dealing with population sampling frame differences – Subset problem – Superset problem – Intersection problem
- 19. Selecting a Sampling Technique: Non-Probability • Personal judgement of the researcher is used rather than chance to select elements • Difficult to generalise result to the population Examples of the methods used with this techniques will be covered shortly
- 20. Selecting a Sampling Technique: Probability • Sampling units are selected by chance • Pre-specifying every potential sample of a given size that could be drawn from the population • Require precise definition of the target population and sampling frame • Able to make inferences about the target population Examples of the methods used with this techniques will be covered shortly
- 21. How do you go about selecting a random sample? William Burlace, Director, Media Services Roy Morgan Research
- 22. Determine the Sample Size: Qualitative Factors • Number of elements to be included in the study • Qualitative factors to consider: – Importance of the decision – Number of variables – Nature of the analysis – Sample size used in similar studies – Incidence rates Quantitative Factors will be – Completion rates covered later in this lecture – Resource constraints
- 23. Execute the Sampling Process • Detail specification of how the sampling, design decisions with respect to the population, sampling frame, sampling units, sampling techniques and sample size are to be implemented.
- 24. Random sampling and non–sampling errors • Random sampling error: the difference between sample result and the result of a census conducted using identical procedures. – Statistical fluctuation due to chance variations in elements selected for a sample. – Example, student population has a true mean income of $20 000 but a sample shows a mean income of $10 000. • Conceivably occurred by accident: random error. 24
- 25. Random sampling and non–sampling errors • Beyond researcher’s ability to control random error. • But, as sample size increases, random sampling error decreases. • The more people we ask, the more likely the sample result is going to reflect the true result. – With statistical inference, we are able to estimate the probability of random error. 25
- 26. Random sampling and non–sampling errors • Non–sampling (systematic) error results from some imperfect aspect of the research design such as mistakes in sample selection, sampling frame error, or non–responses. – Errors not due to chance fluctuations, but due to errors resulting from the researcher. 26
- 27. Less than perfectly representative samples • Non–response error: statistical differences between a survey including only those who responded and a perfect survey that would also include those who failed to respond. – Sample is less likely to be perfectly representative. – Example: consumers who are busier are less likely to respond to a survey than those with more spare time. 27
- 28. Errors associated with sampling 28
- 29. Probability versus non–probability sampling • Probability sampling: a sampling technique in which every member of the population has a known, non–zero probability of selection. • Non–probability sampling: a sampling technique which units of the sample are selected on the basis of personal judgement or convenience. – The probability of any particular member of the population being chosen is unknown. 29
- 30. Probability sampling • Since this process is random, error related to researcher judgement is eliminated. • There are various probability sampling methods: – Simple random sampling – Systematic sampling – Stratified sampling. 30
- 31. Probability Sampling: Simple Random Sampling (SRS) • Each element in the population has a known and equal chance of selection • A sample is drawn by a random procedure from a sampling frame • Easily understood • Generalise to the population • Difficult to construct a sampling frame • May or may not result in representative sample • Accuracy-cost trade-off
- 32. Simple random sampling • A sampling procedure that assures each element in the population of an equal chance of being included in the sample. – Example: drawing names from a hat • Random number generator. 32
- 33. Systematic sampling • A sampling procedure in which a starting point is selected by a random process and then every nth number on the list is selected. • Requires sampling frame • Results appear to be random if there is no other systematic pattern to the list. 33
- 34. Stratified sampling • A sampling procedure in which simple random sub–samples that are more or less equal on some characteristic are drawn from within each stratum of the population. • Involves dividing sampling frame into strata then randomly sampling within each strata. – Example: random sampling within the male and female strata. 34
- 35. Probability Sampling: Stratified Sampling • Population split into sub-populations • Strata are mutually exclusive and collectively exhaustive • Then SRS from each stratum to select the elements – Within stratum – homogeneous – Each(between) stratum - heterogeneous – Proportional stratified sampling – Disproportional324-325 – Proportional stratified sampling Refer to pp. stratified sampling
- 36. Probability sampling 36
- 37. Proportional versus disproportional sampling • Proportional stratified sample: a stratified sample in which the number of sampling units drawn from each stratum is in proportion to the population size of that stratum. • Disproportional stratified sample: a stratified sample in which the sample size for each stratum is allocated according to analytical considerations. 37
- 38. Cluster sampling • An economically efficient sampling technique in which the primary sampling unit is not the individual element in the population but a large cluster of elements. • Clusters are selected randomly. – Example: geographic cluster or area sample • Unlike a strata, a cluster should be as heterogeneous as the population itself. 38
- 39. Probability Sampling: Cluster Sampling • Target population split into mutually exclusive and collectively exhaustive sub-populations • Than random sample of clusters is selected based on SRS • A sample from each cluster – Within cluster – heterogeneous – Each(Between) cluster - homogeneous • Cost effective e.g., area sampling
- 40. Non–probability sampling • Sometimes bias resulting from judgement in selection is necessary because marketers do not always have an accurate list available from which to select respondents. • There are various non–probability sampling methods: – convenience sampling – judgement sampling – quota sampling – snowball sampling. 40
- 41. Convenience sampling • The sampling procedure of obtaining those people or units that are most conveniently available. • Convenient but may be unwilling or unrepresentative – Example: lecturer who uses students • Best use for exploratory research but inappropriate for projecting results. 41
- 42. Non-probability Sampling: Judgmental Sampling • Selection based on researcher judgement • Inexpensive, quick, can be used for exploratory research, or pre-test questionnaire • Selection bias present, not representative, can not generalise to target population
- 43. Judgement sampling • A non–probability sampling technique in which an experienced individual selects the sample based on personal judgement about some appropriate characteristic of the sample member. 43
- 45. Non-probability Sampling: Snowball Sampling • Initial group of respondents is selected • These respondents are asked to identify others who belong to the target population of interest • Referrals will have demographic and psychographic characteristics that are more similar to person referring than would be by chance e.g., minority groups, people involved in a specialist sport or hobby
- 46. Non-probability Sampling: Convenience Sampling • Selection of sampling units is left to the interviewer • Inexpensive, quick, can be used for exploratory research • Selection bias present, not representative, can not generalise to the population e.g., students at uni, shopping centres without qualifying respondents, questionnaires in magazines or restaurants
- 47. Non-Probability Sampling: Quota Sampling • Develop quotas of population elements e.g., gender, age • Respondents are then selected based on convenience or judgement • Not representative but could be relevant • Selection bias • Lower cost and greater convenience
- 48. Non-Response in Sampling • Refusals • Unwillingness / inability of people included in the sample to participate • Reduce refusals by: – Prior notification – Motivating the respondents – Incentives – Good questionnaire design and administration
- 49. Non-response in Sampling cont. • Not at homes or inaccessible • Potential respondents are not at home when contact is attempted • Use ‘call backs’
- 50. Quota sampling • A non–probability sampling procedure that ensures various subgroups of a population will be represented to the extent that the investigator desires. • Introduces bias because quota samples tend to include people who are easily found and willing to be interviewed. 50
- 51. Snowball sampling • A sampling procedure in which initial respondents are selected by probability methods and additional respondents are obtained from information provided by the initial respondents. • Locate members of rare populations by referrals. – Example: stamp collectors and adult croquet players. 51
- 52. What is the appropriate sample design? • There are a number of sampling criteria to evaluate each sample design: – Degree of accuracy • Probability methods are better for conclusive projects that demand accuracy. – Resources • Non–probability methods are better for projects with financial and human resource constraints. – Time • Simple sample design is better for projects with time constraints. 52
- 53. What is the appropriate sample design? • There are a number of sampling criteria to evaluate each sample design: – Advance knowledge of the population • Lack of adequate lists rule out systematic and stratified sampling. – National versus local project • Cluster sample better when population elements are unequally distributed geographically. – Need for statistical analysis • Non–probability methods do not allow statistical analysis to project data beyond the sample. 53
- 54. What is the appropriate sample design? 54
- 55. What is the appropriate sample design? 55
- 56. Random error and sample size • Random sampling error varies with samples of different sizes. – Increasing sample size increases accuracy. • But since every project has a budget, we can ask a certain number of people and be confident of getting the same results had we asked many more people. 56
- 57. Factors in determining sample size • Three factors affect sample size: – The variance or heterogeneity of the population. • Only a small sample is required if the population is homogenous. – The magnitude of acceptable error. • Higher precision requires a larger sample. – The confidence level. • Higher confidence requires a larger sample. 57
- 58. Determining sample size on the basis of judgement • Sample size may also be determined on the basis of managerial judgements. – Rely on experience and sample size similar to previous studies. • Researcher is governed by more practical restrictions such as budget and time limitations. 58
- 59. Appropriate Sample Design • Degree of accuracy • Resources • Time • Advance knowledge of the population • National vs. local project • Need for statistical analysis
- 60. Special Cases • Web-based samples – Yields a high response rate because panel members have agreed to cooperate with the research organisation and are compensated for their time. • Shopping centre sampling – Shopping centre selection – Sample locations within a centre – Time sampling – Sampling people versus shopping visits
- 61. Sampling in an International Context • Key Issues: – Absence of information on sampling frames – May need to consider regions rather than an individual country – Selection of respondents – Generalisability of results across geographic regions – High cost of multi-country research – Appropriateness of different sampling techniques – Use of the same sampling technique in different countries or regions – Sample size
- 62. Chapter 11 Editing and coding: Transforming raw data into information 62
- 63. Stages of data analysis 63
- 64. Editing • Editing is the process of checking the completeness, consistency and legibility of data and making the data ready for coding and transfer to storage. • Coding is the process of assigning a numerical score or other character symbol to previously edited data. 64
- 65. Field editing • Preliminary editing done by field supervisor on the day of interview. • Purpose of field editing is to catch technical omissions, to check legibility of handwriting, and clarify responses that are logically or conceptually inconsistent. 65
- 66. In–house editing • In–house editing rigorously investigates the results of data collection. • In–house editor’s tasks: – Adjust inconsistent or contradictory responses so that the answers will not be a problem for coders and keyboard operators. – Checks for adherence to the data collection framework. – Checks for logically consistent responses. – Edit for completeness. 66
- 67. Facilitating the coding process • Editing and tabulating ‘don’t know’ answers – An alternative is to record all ‘don’t knows’ as a separate category. • Write a code book. – A systematic procedure for assessing the questionnaires should be developed. – Clear and unambiguous instructions on how to deal with each type of response. 67
- 68. Coding • Codes are rules for interpreting, classifying, and recording data in the coding process. • A field is a collection of characters that represent a single type of data. • A record is a collection of related fields. • A file is a collection of related records. 68
- 69. Pre– coding fixed–alternative questions 69
- 70. Pre–coding fixed–alternative questions • The code for each response will be used by keyboard operator for data entry. – Example: a question with three possible answers are pre–coded 1, 2, 3. • Pre–coding can be used if the researcher knows what the answer categories will be prior to data collection. 70
- 71. Coding open–ended questions • Purpose of pre–coding open–ended questions is to reduce the large number of individual responses to a few general categories of answers that can be assigned numerical codes. – Similar answers should be placed in a general category and assigned the same code. – Code building is based on thoughts, not just words. – Test tabulation. 71
- 72. Coding open–ended questions 72
- 73. Computerised data processing • Production coding is the process of transferring the data from the questionnaire to the storage medium. • Optical scanning system and Intelligent Character Recognition are computerised methods to read questionnaire responses directly. • Otherwise, data entry is used to transfer data from the research project to computers. 73
Editor's Notes
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