Market Research - course slides

1. MARKET RESEARCH 1 Paul Marx

2. DISCLAIMER This Presentation may contain Copyrighted Material, DO NOT DISTRIBUTE 2

3. THE MOST IMPORTANT SKILLS IN MARKETING 3 Source: “7 Habits of Effective Marketing Organizations”, Eloqua (2010)

4. COURSE OBJECTIVES • Understand the role of marketing research in shaping managerial decisions • Get an overview of classical activities in as well as of practical tools and methods of marketing research • Be able to implement marketing research studies, analyze and interpret data, and present the results 4

5. 5 RECOMMENDED READING Malhotra, Naresh K. (2009), “Marketing Research: An Applied Orientation”, 6th edition, Prentice Hall Myers, James H. (1996), “Segmentation & Positioning for Strategic Marketing Decisions”, South-Western Educational Pub Hair, Joseph F. Jr, William C. Black, Barry J. Babin, and Rolph E. Anderson (2009), “Multivariate Data Analysis”, 7th edition, Prentice Hall

6. NICE TO HAVE (READ) 6 Kotler, Philip and Gary Armstrong (2009), “Principles of Marketing”, 13th edition, Prentice Hall Cravens, David and Nigel Piercy (2012), “Strategic Marketing”, 10th edition, McGraw-Hill/Irwin Wedel , Michel, and Wagner A. Kamakura (2000), “Market Segmentation: Conceptual and Methodological Foundations”, 2nd edition, Kluwer Academic Publishers Brunner, Gordon C. II (2012), “Marketing Scales Handbook: A Compilation of Multi-Item Measures for Consumer Behavior & Advertising Research”, Vol. 6, available as PDF at www.marketingscales.com Hoyer, Wayne D., Deborah J. MacInnis (2008), “Consumer Behavior”, South-Western College Pub; 5 edition Ariely, Dan (2010), “Predictably Irrational: The Hidden Forces That Shape Our Decisions”, revised and expanded edition, Harper Perennial Coe, John (2003), “The Fundamentals of Business-to-Business Sales & Marketing”, McGraw-Hill

7. CONTENTS IN BRIEF 1. Introduction 1.1. Marketing Research 1.2. Types of Market Research 1.3. Research Methods 2. Qualitative Research Methods 2.1. Focus Groups 2.2. Depth Interview 2.3. Projective Techniques 2.4. Comparison of Qualitative Techniques 3. Observation Methods 4. Survey: Measurement and Scaling 4.1. Intorduction 4.2. Comparative Scales 4.3. Non-comparative Scales 4.4. Multi-item Scales 4.5. Reliability and Validity 5. Questionnaire 5.1. Asking Questions 5.2. Overcoming Inability to Answer 5.3. Overcoming Unwillingness to Answer 5.4. Increasing Willingness of Respondents 5.5. Determining the Order of Questions 5.6. What’s Next? 6. Sampling 6.1. Non-probability Sampling 6.2. Probability Sampling 6.3. Choosing Non-Probability vs. Probability Sampling 6.4. Sample Size 7. Data Analysis: A Concise Overview of Statistical Techniques 7.1. Descriptive Statistics: Some Popular Displays of Data 7.1.1. Organizing Qualitative Data 7.1.2. Organizing Quantitative Data 7.1.3. Summarizing Data Numerically 7.1.4. Cross-Tabulations 7.2. Inferential Statistics: Can the results be generalized to population? 7.2.1. Hypothesis Testing 7.2.2. Strength of a Relationship in Cross-Tabulation 7.2.3. Describing the Relationship Between Two (Ratio Scaled) Variables 8. Advanced Techniques of Market Analysis: A Brief Overview of Some Useful Concepts 8.1. Conjoint Analysis 8.2. Market Simulations 8.3. Market Segmentation 8.4. Perceptual Positioning Maps 9. Reporting Results 7

8. 8 1.Introduction 1.1.Marketing Research 1.2.Types of Market Research 1.3.Research Methods

9. CASE BEECHCRAFT STARSHIP 9 First civilian aircraft with - carbon fiber composite airframe - canard (“duck”) design - L-shaped wings with rudders in them - Two turbo-prop engines mounted aft to pull - R&D costs est. $500Mio “For the pilot and passengers, it has really got everything... ...for the money, the performance just isn’t there... ...for $5Mio, you can buy a jet. Starship just doesn’t fit in today’s market”1 “The Starship was a $500Mio mistake because of a lack of marketing research”2 1 Dennis Murphy, a sales person at Elliot Flying Services in Des Moines, Iowa 2 Russel Munson in “The Stock Market”, 1991

10. CASE ELECTROLUX 10 Electrolux - a scandinavian manufacturer of inexpensive vacuum cleaners - took its rhyming phrase “Nothing Sucks Like an Electrolux” and brought it in the early 1970s to America from English-speaking markets overseas. They didn’t know that the word “sucks” had become a derogatory word in the US.

11. CASE AMERICAN AIRLINES 11 American Airlines launched a new leather first class seats ad campaign (1977-78) in the Mexican market: "Fly in Leather" (vuela encuero) meant "Fly Naked"

12. CASE FOOD & BEVERAGES 12 In what must be one of the most bizarre brand extensions ever Colgate decided to use its name on a range of food products called Colgate's Kitchen Entrees. Needless to say, the products did not take off and never left U.S. soil. The idea must have been that consumers would eat their Colgate meal, then brush their teeth with Colgate toothpaste. The trouble was that for most people the name Colgate does not exactly get their taste buds tingling. In the 1970s and early 80s, Coke began to face stiff competition from other soft drink producers. To remain in the number one spot, Coke executives decided to cease production on the classic cola in favor of New Coke. The public was outraged, and Coca- Cola was forced to re-launch its original formula almost immediately. Lesson learned -- don't mess with success. Cocaine is a high-energy drink, containing three and a half times the amount of caffeine as Red Bull. It was pulled from U.S. shelves in 2007, after the FDA declared that its producers, Redux Beverages, were "illegally marketing their drink as an alternative to street drugs." The drink is still available, however, online, in Europe and even in select stores in the U.S. Despite the controversy, Redux Beverages does not plan to cease production any time soon. You know what they say -- there's no such thing as bad publicity.

13. RETURNS ON MARKETING ACTIONS • 60-95% of new products fail • 50% of advertising has no effect • 85% of price promotions loose money • 97% brands create 37% $ (Unilever) 13

14. 14 • Marketing Research is there to prevent such things from happening

15. RECALL Marketing Marketing consists of the strategies and tactics used to identify, create and maintain satisfying relationships with customers that result in value for both the customer and the marketer. Marketing Concept A business philosophy based on consumer orientation, goal orientation, and systems orientation. Consumer Orientation Identification of and focus on the people or firms most likely to buy a product and production of a good or service that will meet their needs most effectively. Goal Orientation A focus on the accomplishment of corporate goals; a limit set on consumer orientation. Systems Orientation Creation of systems to monitor the external environment and deliver the marketing mix to the target market. Marketing Mix (a.k.a. 4Ps/Cs and 7Ps Models) The unique blend of product, pricing, promotion, offerings, and distribution designed to meet the needs of a specific group of consumers. 15

16. MARKETING: A VERY PARSIMONIOUS OVERVIEW 16 Quality Satisfaction Profit Needs Wants Preferences Utility function Attitudes Intentions Motives Involvement Beliefs Emotions Lifestyle Habits Buying behavior ... Trust & Loyalty Consumer

18. MARKETING RESEARCH: A CONCISE DEFINITION Marketing Research The planning, collection, and analysis of data relevant to marketing decision making and the communication of the results of this analysis to management. 18

19. 19

20. Why marketing research? THE IMPORTANCE OF MARKETING RESEARCH 20 Improve quality of decision making Trace Problems Focus on keeping existing customers Understand changes in marketplace

21. MARKET RESEARCH VS. MARKETING RESEARCH (STRICTLY SPEAKING...) 21 Market Research Marketing Research Researching the immediate competitive environment of the marketplace, including customers, competitors, suppliers, distributors and retailers Includes all the above plus: - companies and their strategies for products and markets - the wider environment within which the firm operates (e.g., political, social, etc)

22. TOP 10 MARKET RESEARCH ACTIVITIES 22 Market measurement 18% New Product development / concept testing 14% Ad or Brand awareness monitoring / tracking 13% Customer satisfaction (incl. Mystery Shopping) 10% Usage and Attitude studies 7% Media research & evaluation 6% Advertising development and pre-testing 5% Social Surveys for central/local governments 4% Brand/corporate reputation 4% Omnibus studies 3% Source: Business Management Research Associates, Inc.

23. MARKET RESEARCH PROCESS 23 Deﬁne the research problem Decide on budget data sources research approaches sampling plan contact methods methods of data analysis Develop the research plan Collect data Analyze data Report ﬁndings identify and clarify information needs define research problem and questions specify research objectives confirm information value collect data according to the plan or employ an external firm The plan needs to be decided upfront but flexible enough to incorporate changes or iterations This phase is the most costly and the most liable to error If a problem is vaguely defined, the results can have little bearing on the key issues Overall conclusions to be presented rather than overwhelming statistical methodologies Formulate conclusions and implications from data analysis prepare finalized research report Analyze data statistically or subjectively and infer answers and implications 1 2 3 4 5 Type of data analysis depends on type of research Comments Contents

24. WHEN NOT TO CONDUCT MARKET RESEARCH 24 Occasion Comments Lack of resources If quantitative research is needed, it is not worth doing unless a statistically significant sample can be used. When funds are insufficient to implement any decisions resulting from the research. Closed mindset When decision has already been made. Research is used only as a rubber stamp of a preconceived idea. Information not needed When decision-making information already exists. Vague objectives When managers cannot agree on what they need to know to make a decision. Market research cannot be helpful unless it is probing a particular issue. Results not actionable Where, e.g., psychographic data is used which will not help he company form firm decisions. Late timing When research results come too late to influence the decision. Poor timing If a product is in a “decline” phase there is little point in researching new product varieties Costs outweigh benefits The expected value of information should outweigh the costs of gathering an analyzing the data.

26. TYPES OF MARKET RESEARCH 26 By Objectives By Data Source By Methodology Exploratory (a.k.a. diagnostic) Descriptive Causal (a.k.a. predictive, experimental) Qualitative Quantitative Primary Secondary

27. Exploratory (a.k.a. diagnostic) Explaining data or actions to help define the problem What was the impact on sales after change in the package design? Do promotions at POS influence brand awareness? MARKET RESEARCH BY OBJECTIVES 27 Descriptive Gathering and presenting factual statements: who, what, when, where, how What is historic sales trend in the industry? What are consumer attitudes toward our product? Causal (a.k.a. predictive, experimental) Probing cause-and-effect relationships; “What if?” Specification of how to use the research to predict the results of planned marketing decisions Does level of advertising determine level of sales? small scale surveys, focus groups, interviews larger scale surveys, observation, etc. experiments, consumer panels ProblemIdentificationProblemSolving Uncertaintyinfluencesthetypeofresearch

28. UNCERTAINTY SHAPES THE TYPE OF RESEARCH 28 Problem Identification Research Problem Solving Research Market Potential Research Market Share Research Image Research Market Characteristics Research Sales Analysis Research Forecasting Research Business Trends Research Segmentation Research Product Research Pricing Research Promotion Research Distribution Research Exploratory research Descriptive research Causal research AwareUncertain Certain degree of problem/decision certainty

29. MARKET RESEARCH BY DATA SOURCE 29 Primary Secondary Original research to collect new raw data for a specific reason. This data is then analyzed and may be published by the researcher. Research data that has been previously collected, analyzed and published in the form of books, articles, etc.

30. SECONDARY DATA: PROS-AND-CONS 30 Secondary Data Advantages Disadvantages Saves time and money if on target Aids in determining direction for primary data collection Pinpoints the kinds of people to approach Serves as a basis for other data May not give adequate detailed information May not be on target with the research problem Quality and accuracy of data may pose a problem Information previously collected for any purpose other than the one at hand

31. PRIMARY DATA: PROS-AND-CONS 31 Advantages Disadvantages Answers a specific research question Data are current Source of data is known Secrecy can be maintained Expensive “Piggybacking” may confuse respondents Quality declines in interviews are lengthy Reluctance to participate in lengthy interviews Primary Data Information collected for the first time to solve the particular problem under investigation Disadvantages are usually offset by the advantages of primary data

32. Exploratory research Causal research Descriptive research MARKET RESEARCH BY METHODOLOGY 32 Qualitative Involves understanding human behavior and the reasons behind it Focus is on individuals and small groups Objectivity is not the goal, the aim is to understand one point of view, not all points of view. Primary Data Secondary Data Quantitative Involves collecting and measuring data Often requires large data sets. For example, large number of people. Uses statistical methods to analyze data Aims to achieve objective/ scientific view of the subject

34. RESEARCH METHODOLOGY 34 research methodology The searching for and gathering of information and ideas in response to a specific question The set of methods used to address a specific research problem at hand

35. MARKET RESEARCH METHODS 35 Primary Secondary Research Approach Society Groups Individuals Research Source Library Web Database Archive Survey Focus Group Depth Interview Projective Tech. Observation Research Method Literature review

36. SOURCES OF SECONDARY DATA Internal Corporate Information Government Agencies Trade and Industry Associations Business Periodicals News Media Databases Internet Sources … 36 Secondary Data

37. Secondary Data EVALUATING SECONDARY DATA SOURCES 37 Use the C.R.A.P. test Currency Reliability Authority Purpose

38. Secondary Data EVALUATING DATA SOURCES 38 Currency How recent is the information? Are there more recent updates available? Is it current enough for your topic? Reliability Is content of the resource primarily opinion? Is it balanced and evidenced? Does the creator provide references or sources for the data? Authority Who is the creator or author? What are his/her credentials? Is s/he an expert? Who is the publisher os sponsor? Are they reputable? Purpose / Point of View Is it promotional or educational material? Are there advertisements on the website? is this fact or opinion? Who is the intended audience?

39. 39 Quantitative Survey Focus Groups Depth Interview Projective Techniques Observation Qualitative Primary Approaches Survey Observation Depth Interview Projective Tech. Focus Groups Survey Observation

40. 40Robson (1998), Visocky & Visocky (2009) APPARENT TRUTH Literature Review InterviewSurvey Triangulation The combination of methods in the study of the same topic

41. BUT IT IS MESSIER THAN THAT

42. 42 2.Qualitative Research Methods 2.1. Focus Groups 2.2. Depth Interview 2.3. Projective Techniques 2.4. Comparison of Qualitative Techniques

44. FOCUS GROUPS 44 Focus Groups organized discussions with a moderator and limited number of participants qualitative method to gain insights from the appropriate target consumers through studying their perceptions, opinions, beliefs, and attitudes moderator should remain neutral, ask open ended questions, speak only when necessary and record session lasts between 1.5 and 2 hours Focus Groups

45. FOCUS GROUPS 45 I’d like to speak with you all about your opinions on... ------, -----. ----- ! ------. -----? ------! ----. ----? http://www.youtube.com/watch?v=POF3m6ZNoiY http://www.youtube.com/watch?v=cnV1pS7qVD8

46. APPLICATIONS OF FOCUS GROUPS 46 Understanding consumers’ perception, preferences, and behaviors concerning a product category Obtaining impressions of new product concepts Generating new ideas about older products Developing creative concepts and copy material for advertisements Securing price impressions Obtaining preliminary consumer reaction to specific marketing programs ...

47. FOCUS GROUPS: ADVANTAGES 47 I’d like to know what you all think about English Immersion. Do you think we should have more, less or the same amount of it? More but not too much more! About the same. I guess. Less. I think. Less, definitely. Interesting. Why do you all disagree? No, no! Less!

48. FOCUS GROUPS: ADVANTAGES 48 I’d like to know what you all think about English Immersion. Do you think we should have more, less or the same amount of it? More but not too much more! About the same. I guess. Less. I think. Less, definitely. Interesting. Why do you all disagree? No, no! Less! Ability to ask many people about “why” Ability to observe and de-code disagreements Can learn how groups make sense of the topic Synergism Snowballing Stimulation Security Spontaneity Serendipity

49. FOCUS GROUPS: DISADVANTAGES 49 Great. Thank you, Carl. Anyone else? And another one issue I’d l I really hate how high gas prices are! Oh, and don’t get me started about the GST! Ok, Carl. Thanks.

50. FOCUS GROUPS: DISADVANTAGES 50 Great. Thank you, Carl. Anyone else? And another one issue I’d l I really hate how high gas prices are! Oh, and don’t get me started about the GST! Ok, Carl. Thanks. Difficulty in getting people in the same room Difficulty controlling conversations Huge amount of data Dominant personalities Misuse Misjudge Messy Misrepresentation

51. FOCUS GROUPS: PROS-AND-CONS 51 Advantages Disadvantages ability to ask many people about “why” ability to observe and de-code disagreements can learn how groups make sense of the topic synergism snowballing stimulation security spontaneity serendipity difficulty in getting people in the same room difficulty controlling conversations dominant personalities huge amount of data misuse misjudge messy misrepresentation

52. FOCUS GROUPS: SIZE AND WHOM TO RECRUIT 52 Typically 6-10 (Morgan 1998) 8-10 (Malhotra 2004) Small (4-5) when there’s lots to say or a controversity Large (20+) when opinions are likely brief homogenous in terms of target group characteristics (demographics, socio- economics…) experienced with the issue have not participated in many focus groups

53. HOW TO DO FOCUS GROUPS 53 Plot test interview guide General research questions Write interview guide Determine size of group Decide participant qualities Secure facility and moderator Recruit Notes by separate note taker Conduct focus group Interpret data Conceptual and theoretical work Write up findings Recording and/or video Transcript Collection of more data Tighter specification of question

54. INTERNET FOCUS GROUPS 54

55. INTERNET FOCUS GROUPS: ADVANTAGES 55 Geographical constraints are removed Time constraints are lessened Ability to reach hard-to-reach target groups Ability to recontact respondents No travel costs, No videotaping, No facilities to arrange

56. INTERNET FOCUS GROUPS: DISADVANTAGES 56 Difficulty ensuring the person is in the target group Lack of control over environment and distraction Only intangible stimuli Only experienced PC users Not suitable for highly emotional issues

57. INTERNET FOCUS GROUPS 57 Advantages Disadvantages geographical and time constraints are removed or lessened ability to recontact respondents ability to reach hard-to-reach segments lower costs only experienced PC users can be surveyed hard to ensure that a person is a member of a target group lack of control over respondent’s environment and distracting external factors products cannot be touched or smelled inability to explore highly emotional issues or subject matters

58. INTERNET FOCUS GROUPS: USES 58 Banner ads, Copy testing, Concept testing, Usability testing esp. suitable for companies in the online business Multimedia evaluation; Comparisons of icons or graphics

60. DEPTH INTERVIEW 60 Depth Interview method for in-depth probing of personal opinions, beliefs, and values interview is conducted one-on-one lasts between 30 and 60 minutes unstructured (or loosely structured) data is obtained from a relatively small group of respondents data is not analyzed with inferential statistics Depth Interview

61. DEPTH INTERVIEW: TECHNIQUES 61 Laddering start with questions about external objects and external social phenomena, then proceed to internal attitudes and feelings Critical Incident Technique (CIT) A critical incident is one that makes a significant contribution - either positively or negatively - to an activity or phenomenon. respondents are asked to tell a story about an experience they have had Symbolic Analysis attempts to analyze the symbolic meaning of objects by comparing them with their opposites e.g. product non-usage, opposite types of products Hidden Issue Questioning the focus is not on socially share values but rather on personal “sore spots” and “pet peeves”; not on general lifestyles but on deeply felt personal concerns

62. EXAMPLE: LADDERING 62 Laddering start with questions about external objects and external social phenomena, then proceed to internal attitudes and feelings Wide body aircraft I can get more work done I accomplish more I feel good about myself product characteristic user characteristic Advertisement message: You will feel good about yourself when flying our airline. “You’re The Boss”

63. Hidden Issue Questioning the focus is not on socially share values but rather on personal “sore spots” and “pet peeves”; not on general lifestyles but on deeply felt personal concerns EXAMPLE: HIDDEN ISSUE QUESTIONING 63 fantasies, work lives, and social lives historic, elite, masculine- camaraderie, competitive activities Advertisement theme: Communicate aggressiveness, high status, and competitive heritage of the airline.

64. Symbolic Analysis attempts to analyze the symbolic meaning of objects by comparing them with their opposites e.g. product non-usage, opposite types of products EXAMPLE: SYMBOLIC ANALYSIS 64 “What would it be like if you could no longer use airplanes?” “Without planes I would have to rely more on e-mails, letters, and long-distance calls” Advertisement theme: The airline will do the same thing for a manger as Federal Express does for package. Airlines sell to the managers face-to-face communication

65. Critical Incident Technique (CIT) A critical incident is one that makes a significant contribution - either positively or negatively - to an activity or phenomenon. respondents are asked to tell a story about an experience they have had EXAMPLE: CRITICAL INCIDENT TECHNIQUE 65 “What was the worst thing you ever experienced with airlines?” “The snoring guy to m y left who was staring onto my shoes right after he was awake” Lack of privacy

66. 66 Do you go to the cinema? Yes No, no cinema at all What cinema do you usually/most frequently go to? CINESTAR? Yes No Do you remember any particular positive or negative experience regarding CineStar? What do you like about the CineStar (better than other theaters)? What don’t you like that much? In overall, how often do you go to the cinema? INQUIRE UNTIL THE RESPONDENT IS OUT OF IDEAS Do you like watching movies though (e.g. on DVD/TV)? STOP! The respondent does not count! Do you remember any particular positive or negative experience regarding a cinema? Why not go to the cinema? Yes No Which cinema? And how often do you go to CineStar (a year)? Disadvantages/weaknesses of CineStar (vs. your favorite cinema)? Do you remember any particular positive or negative experience regarding CineStar? Example: Laddering + CIT

67. DEPTH INTERVIEW: PROS-AND-CONS 67 Advantages Disadvantages in-depth probing is very useful at uncovering hidden issues very rich depth of information very flexible there is no social pressure on respondents to conform and no group dynamics can be time consuming responses can be difficult to interpret requires skilled interviewers expensive interviewer bias can easily be introduced not representative

69. PROJECTIVE TECHNIQUES 69 Projective Techniques an unstructured, indirect form of questioning that encourages respondents to project their underlying motivations, beliefs, attitudes or feelings regarding the issues of concern they are all indirect techniques that attempt to disguise the purpose of the research respondents are asked to interpret the behavior of others in doing so, they indirectly project their own motivations, beliefs, attitudes, or feelings into the situation Projective Techniques

70. relate the attitudes or feelings of a person (minimize the social pressure to give a pol.cor. response) play the role of someone else (project own feelings or behavior into the role) fill in an empty dialogue balloon of a cartoon character make up a story about the picture(s) complete an incomplete story complete a set of incomplete sentences PROJECTIVE TECHNIQUES 70 Word Association Sentence Completion say the first word that comes to mind after hearing a word Story Completion Picture Response Cartoon Tests Role Playing Third-person Technique a.k.a. thematic apperception tests a.k.a. expressive techniques draw what you are feeling or how you perceive an object Consumer Drawing

71. Word Association respondents are presented with a list of words, one at a time, and asked to respond to each with the first word that comes to mind. only some of the words are test words, the rest are filters to disguise the purpose of the test. good for testing brand names EXAMPLE: WORD ASSOCIATION 71 Analysis by calculating: frequency with which any word is given as a response the amount of time elapsed before the response is given # of respondents who do not response at all washday fresh pure scrub filth bubbles family towels everyday and sweet air husband does neighborhood bath squabbles dirty ironing clean soiled clean dirt soap and water children wash Stimulus Mrs. A Mrs. N

72. 72 A person who shops at Walmart is __________ A person who receives a gift certificate good for Sak’s Fifth Avenue would be ____________ J.C. Penney is most liked by ________________ When I think of shopping in a department store, I _____________________ Sentence Completion Consumer Drawing Consumers of Pillsbury cake-mixes are drawn grandmotherly, whereas Duncan Hills’ consumers look svelte and contemporary Story Completion Hey John, I just received a $500 bonus for suggestion my company is now using on the production line.I’m thinking about putting my money in a credit union. Cartoon Tests ____________ ____________ ____________

73. PROJECTIVE TECHNIQUES: PROS-AND-CONS 73 Advantages Disadvantages disguising the purpose of the study allows to elicit responses that subjects would be unwilling or unable to give otherwise esp. when the issues to be addressed are personal, sensitive, or subject to strong social norms when underlying motivations, beliefs, and attitudes are operating at a subconscious level. requires highly trained interviewers requires skilled interpreters expensive engage people in unusual behavior serious risk of interpretation bias not representative

75. COMPARISON OF QUALITATIVE TECHNIQUES 75 Criteria Focus Groups Depth Interviews Projective Techniques Degree of structure relatively high relatively medium relatively low Probing individual respondents low high medium Moderator bias relatively medium relatively high low to high Interpretation bias relatively low relatively medium relatively high Uncovering subconscious information low medium to high high Discovering innovative information high medium low Obtaining sensitive information low medium high Involve unusual behavior/questioning no to a limited extent yes Overall usefulness highly useful useful somewhat useful

76. 76 3.Observation Methods

77. OBSERVATION METHODS 77 observation in artificial/ experimental environment, such as a test kitchen respondents are aware that they are under observation e.g., eye-tracker, voice pitch analysis, psychogalvanometer observing behavior as it takes place in the natural environment respondents unaware of being observed e.g., one-way mirrors, hidden cameras, mystery shoppers Structured Disguised researcher specifies in detail what is to be observed and how e.g. auditor performing inventory analysis in the store Natural Undisguised Contrived monitor all aspects of the phenomenon that seem relevant for the problem children playing with new toys Unstructured vs vs vs Observation involves recording the behavioral patterns of people, objects, and events in a systematic manner to obtain information about phenomenon of interest The observer does not question or communicate with the people being observed

78. OBSERVATION BY MODE OF ADMINISTRATION 78 Personal observation observe actual behavior as it occurs e.g., record traffic counts, observe traffic flows in a store Audit examining physical records inventory analysis pantry audit Mechanical observation mechanical devices perform observation and recording e.g., people meter, traffic counters, cameras, UPC scanners, eye-tracking, voice pitch analyzer, GSR, response latency Trace analysis physical traces, or evidence of past behavior e.g., erosion of tiles in a museum; pos. of radio dials in cars brought for service; age & condition of cars in a parking lot; # of fingerprints on a page; donated magazines; internet Content analysis when the phenomenon to be observed is communication units: words, characters, topics, length & duration of a message ObservationMethods

79. PERSONAL OBSERVATION 79 Personal observation observe actual behavior as it occurs e.g., record traffic counts, observe traffic flows in a store

80. Mechanical observation mechanical devices perform observation and recording e.g., people meter, counting turnstiles, cameras, UPC scanners, eye-tracking, voice pitch analyzer, GSR, response latency MECHANICAL OBSERVATION 80

81. Audit examining physical records inventory analysis pantry audit AUDIT 81

82. Content analysis when the phenomenon to be observed is communication units: words, characters, topics, length & duration of a message CONTENT ANALYSIS 82

83. Trace analysis physical traces, or evidence of past behavior e.g., erosion of tiles in a museum; pos. of radio dials in cars brought for service; age & condition of cars in a parking lot; # of fingerprints on a page; donated magazines; internet TRACE ANALYSIS 83

84. COMPARISON OF OBSERVATION METHODS 84 Criteria Personal Observation Mechanical Observation Audit Content Analysis Trace Analysis Degree of structure low low to high high high medium Degree of disguise medium low to high low high high Ability to observe in natural setting high low to high high medium low Observation bias high low to high low medium medium Analysis bias high low to medium low low medium General remarks most flexible can be intrusive expensive limited to communications method of last resort

85. OBSERVATION METHODS: PROS-AND-CONS 85 Advantages Disadvantages measurement of actual rather than intended or preferred behavior no interviewer or reporting bias capable of revealing behavior patterns that respondents are unaware of or unable to communicate (e.g., spontaneous purchases, babies’ preferences of toys) may be cheaper and faster than survey methods reasons for the observed behavior may not be determined (underlying motives, beliefs, attitudes, preferences) selective perception bias on the observer’s side may be unethical in certain cases best used as a compliment to survey methods

86. 86 4.Survey: Measurement and Scaling 4.1. Introduction 4.2. Comparative Scales 4.3. Non-comparative Scales 4.4. Multi-item Scales 4.5. Reliability and Validity

87. SURVEY RESEARCH 87 Improve quality of decision making Trace Problems Focus on keeping existing customers Understand changes in marketplace The most popular technique for gathering primary data in which a researcher interacts with people to obtain facts, opinions, and attitudes. Survey Research

88. SURVEY METHODS 8876 Telephone Interviewing traditional (outdated) computer assisted (CATI) Mail Interviewing mail mail panel Personal Interviewing in-home mall intercept computer assisted (CAPI) Electronic Interviewing e-mail internet internet panel SurveyMethods panelizable

90. MEASUREMENT 90 Measurement assigning numbers or other symbols to characteristics of objects according to certain pre-specified rule. one-to-one correspondence between the numbers and characteristics being measured the rules for assigning numbers should be standardized and applied uniformly rules must not change over objects or time Measurement

91. SCALING 91 involves creating a continuum upon which measured objects are located. Scaling Extremely unfavorable Extremely favorable

92. PRIMARY SCALES OF MEASUREMENT 92 differences between objects can be compared zero point is arbitrary numbers indicate the relative positions of objects but not the magnitude of difference between them Ordinal Interval numbers serve as labels for identifying and classifying objects not continuos Nominal zero point is fixed ratios of scale values can be computed Ratio NOT 1 2 or 1 2 1 2 3 1 2 My preference as a snack food less more 0 25 50 75 100 Amount sold (kg) 1 2 3 a.k.a. metric

93. PRIMARY SCALES OF MEASUREMENT 93 Scale Basic Characteristics Common Examples Marketing Examples Permissible StatisticsPermissible Statistics Scale Basic Characteristics Common Examples Marketing Examples Descriptive Inferential Nominal Numbers identify and classify objects Social security numbers, numbering of football players Brand numbers, store types sex, classification Percentages, mode Chi-square, binomial test Ordinal Numbers indicate the relative positions of the objects but not the magnitude of differences between them Quality rankings, ranking of teams in tournament Preference rankings, market position, social class Percentile, median Rank-order correlation, Friedman ANOVA Interval Differences between objects can be compared; zero point is arbitrary Temperature (Fahrenheit, Centigrade) Attitudes, opinions, index numbers Range, mean, standard deviation Product-moment correlations, t- tests, ANOVA, regression, factor analysis Ratio Zero point is fixed; ratios of scale values can be compared Length, weight, time, money Age, income, costs, sales, market shares Geometric mean, harmonic mean Coefficient of variation

94. CLASSIFICATION OF SCALING TECHNIQUES 94 Scaling Techniques Comparative Scales Non-comparative Scales Paired Comparison Rank Order Constant Sum Q-Sort & others Continuous Rating Scales Itemized Rating Scales Likert Semantic Differential Stapel

95. COMPARISON OF SCALING TECHNIQUES 95 Non-comparative Scales each object is scaled independently resulting data is generally assumed to be interval or ratio scaled Comparative Scales involve the direct comparison of stimulus objects. data must be interpreted in relative terms have only ordinal and rank- order properties

98. RELATIVE ADVANTAGES OF COMPARATIVE SCALES 98 same known reference points for all respondents easily understood and can be applied small differences between stimulus objects can be detected involve fewer theoretical assumptions tend to reduce halo or carryover effects from one judgement to another Comparative Scales involve the direct comparison of stimulus objects. data must be interpreted in relative terms have only ordinal and rank- order properties

99. COMPARATIVE SCALES: PAIRED COMPARISON 99 Jhirmack Finesse Vidal Sasoon Head & Shoulders Pert Jhirmack Finesse Vidal Sasoon Head & Shoulders Pert Preferred 3 2 0 4 1 We are going to present you with ten pairs of shampoo brands. For each pair, please indicate which one of the two brands of of shampoo you would prefer for personal use. “ “ (1) indicates that the brand in the column is preferred over the in the corresponding row. “ “ (0) means that the row brand is preferred over the column brand. Recording form: Respondent is presented with two objects and asked to select one according to some criterion

100. EXAMPLES 100 Paired Comp.

101. PROS-AND-CONS 101 Advantages Disadvantages direct comparison and overt choice good for blind tests, physical products, and MDS allows for calculation of percentage of respondents who prefer one stimulus to another can assess rank-orders of stimuli (under assumption of transitivity) possible extensions: “no difference” alternative; graded comparison # of comparisons grows quicker than # of stimuli (for n objects n(n-1)/2 comparisons) violations of transitivity may occur presentation order bias possible preference of A over B does not imply subject’s liking of A little similarity to real choice situation with mult. alternatives Paired Comp.

102. Respondents are presented with several objects simultaneously and are asked to order or rank them according to some criterion COMPARATIVE SCALES: RANK ORDER SCALING 102 Rank the various brands of toothpaste in order of preference. Begin by picking out the one brand that you like most and assign it a number 1. Then find the second most preferred brand and assign it a number 2. Continue this procedure until you have ranked all the brands of toothpaste in order of preference. The least preferred brand should be assigned a rank of 5. No two brands should receive the same rank number. The criterion of preference is entirely up to you. There is no right or wrong answer. Just try to be consistent. Brand Rank Order 1. Crest ___________ 2. Colgate ___________ 3. Elmex ___________ 4. Pepsodent ___________ 5. Aqua Fresh ___________

103. EXAMPLES 103 Paired Comp.Rank Order

106. PROS-AND-CONS 106 Advantages Disadvantages direct comparison more realistic than paired comparison # of comparisons is only (n-1) easier to understand takes less time no intransitive responses can be converted to paired comparison data good for measuring preferences of brands or attributes; conjoint analysis preference of A over B does not imply subject’s liking of A no zero point / separation between liking and disliking only ordinal data Paired Comp.Rank Order

107. Respondents allocate a constant sum of units (points, dollars, chips, %) among a set of stimulus objects with respect to some criterion COMPARATIVE SCALES: CONSTANT SUM SCALING 107 Below are eight attributes of toilet soaps. Please allocate 100 points among the attributes so that your allocation reflects the relative importance you attach to each attribute. The more points an attribute receives, the more important the attribute is. If an attribute is not at all important, assign it zero points. If an attribute is twice as important as some other attribute, it should receive twice as many points. Segment 1 Segment 2 Segment 3 Mildness 8 2 4 Lather 2 4 17 Shrinkage 3 9 7 Price 53 17 9 Fragrance 9 0 19 Packaging 7 5 9 Moisturizing 5 3 20 Cleaning power 13 60 15 Sum 100 100 100 Average response of three segments

108. 108 EXAMPLES Paired Comp.Rank Order Constant Sum

109. EXAMPLES 109 Paired Comp.Rank Order Constant Sum

110. PROS-AND-CONS 110 Advantages Disadvantages allows for for fine discrimination among stimulus objects without requiring too much time ratio scaled results are limited to the context of stimuli scaled, i.e., not generalizable to other stimuli not included in the study relatively high cognitive burden for respondents, esp. when # of items is large prone to calc. errors (e.g., allocation of 108 or 94 points) Paired Comp.Rank Order Constant Sum

111. A rank order procedure in which objects are sorted into piles based on similarity with respect to some criterion. Usually used to discriminate among a relatively large number (60-140) of objects quickly. COMPARATIVE SCALES: Q-SORT SCALING 111 most highly agreed with least highly agreed with

112. EXAMPLE 112 Paired Comp.Rank Order Constant SumQ-Sort

115. Respondents rate objects by placing a mark at the appropriate position on a line that runs from one extreme of the criterion variable to the other. NON-COMPARATIVE SCALES: CONTINUOUS RATING SCALE 115 How would you rate Wal-Mart as a department store? Probably the worst Probably the best Probably the worst Probably the best Probably the worst Probably the best 0 10 20 30 40 50 60 70 80 90 100 Probably the worst Probably the best very bad neither good nor bad very good 0 10 20 30 40 50 60 70 80 90 100 Version 1 Version 2 Version 3 Version 4

116. PERCEPTION ANALYZER 116 Continuous Rating

117. Requires respondents to indicate a degree of agreement or disagreement with each of a series of statements about the stimulus object within typically five to seven response categories. ITEMIZED RATING SCALES: LIKERT SCALE 117 Listed below are different opinions about Sears. Please indicate how strongly you agree or disagree with each by using the following scale: Strongly disagree Disagree Neither agree nor disagree Agree Strongly agree 1 Sears sells high-quality merchandise [1] [x] [3] [4] [5] 2 Sears has poor in-store service [1] [x] [3] [4] [5] 3 I like to shop in Sears [1] [2] [x] [4] [5] 4 Sears does not offer a good mix of different brands within a product category [1] [2] [3] [x] [5] 5 The credit policies at Sears are terrible [1] [2] [3] [x] [5] 6 Sears is where America shops [x] [2] [3] [4] [5] 7 I do not like advertising done by Sears [1] [2] [3] [x] [5] 8 Sears sells a wide variety of merchandise [1] [2] [3] [x] [5] 9 Sears charges fair prices [1] [x] [3] [4] [5] 1 = Strongly agree 2 = Disagree 3 = Neither agree nor disagree 4 = Agree 5 = Strongly agree NOTICE the reversed scoring of items 2,4,5, and 7. Reverse the scale for these items prior analyzing to be consistent with the whole set of items, i.e. a higher score should denote a more favorable attitude.

118. Continuous Rating EXAMPLES 118 Likert

119. SOME COMMONLY USED SCALES IN MARKETING 119 Construct Scale DescriptorsScale DescriptorsScale DescriptorsScale DescriptorsScale Descriptors Attitude Very bad Bad Neither Bad Nor Good Good Very Good Importance Not at All Important Not Important Neutral Important Very Important Satisfaction Very Dissatisfied (Somewhat) Dissatisfied Neither Dissatisfied Nor Satisfied / Neutral (Somewhat) Satisfied Very Satisfied Purchase Intention Definitely Will Not Buy Probably will Not Buy Might or Might Not Buy Probably Will Buy Definitely Will Buy Purchase Frequency Never Rarely Sometimes Often Very Often Agreement Strongly Disagree Disagree Neither Agree Nor Disagree Agree Strongly Agree Continuous RatingLikert

120. EXAMPLES OF LABELING OF 7 AND 9 POINT SCALES 120 Strongly agree Agree to a large extent Rather agree 50/50 Rather disagree Disagree to a large extent Strongly disagree Like extremely Like very much Like moderately Like slightly Neither like nor dislike Dislike slightly Dislike moderately Dislike very much Dislike extremely Continuous RatingLikert

121. A rating scale with end point associated with bipolar labels that have semantic meaning. Respondents are to indicate how accurately or inaccurately each term describes the object. ITEMIZED RATING SCALES: SEMANTIC DIFFERENTIAL 121 This part of the study measures what certain department stores mean to you by having you judge them on a series of descriptive scales bounded at each end by one of two bipolar adjectives. Please mark (X) the blank that best indicates how accurately one or the other adjective describes what the store means to you. Please be sure to mark every scale; do not omit any scale. NOTE: The negative adjective sometimes appears at the left side of the scale and sometimes at the right. This controls the tendency of some respondents, particularly those with very positive or very negative attitudes, to mark the right- or left-hand sides without reading the labels. Powerful [ ] [ ] [ ] [ ] [X] [ ] [ ] Weak Unreliable [ ] [ ] [ ] [ ] [ ] [X] [ ] Reliable Modern [ ] [ ] [ ] [ ] [ ] [ ] [X] Old fashioned Cold [ ] [ ] [ ] [ ] [ ] [X] [ ] Warm Careful [ ] [X] [ ] [ ] [ ] [ ] [ ] Careless Sears is:

122. A SEMANTIC DIFFERENTIAL SCALE FOR MEASURING SELF-CONCEPTS, PERSON CONCEPTS, AND PRODUCT CONCEPTS 122 Rugged [ ] [ ] [ ] [ ] [ ] [ ] [ ] Delicate Excitable [ ] [ ] [ ] [ ] [ ] [ ] [ ] Calm Uncomfortable [ ] [ ] [ ] [ ] [ ] [ ] [ ] Comfortable Dominating [ ] [ ] [ ] [ ] [ ] [ ] [ ] Submissive Thrifty [ ] [ ] [ ] [ ] [ ] [ ] [ ] Indulgent Pleasant [ ] [ ] [ ] [ ] [ ] [ ] [ ] Unpleasant Contemporary [ ] [ ] [ ] [ ] [ ] [ ] [ ] Non-contemporary Organized [ ] [ ] [ ] [ ] [ ] [ ] [ ] Unorganized Rational [ ] [ ] [ ] [ ] [ ] [ ] [ ] Emotional Youthful [ ] [ ] [ ] [ ] [ ] [ ] [ ] Mature Formal [ ] [ ] [ ] [ ] [ ] [ ] [ ] Informal Orthodox [ ] [ ] [ ] [ ] [ ] [ ] [ ] Liberal Complex [ ] [ ] [ ] [ ] [ ] [ ] [ ] Simple Colorless [ ] [ ] [ ] [ ] [ ] [ ] [ ] Colorful Modest [ ] [ ] [ ] [ ] [ ] [ ] [ ] Vain Rating profiles of different objects / respondents / segments. Each point corresponds to a mean or median of the respective scale. LikertSemantic Diff.

123. SEMANTIC PROFILES 123 LikertSemantic Diff.

124. EXAMPLE 124 LikertSemantic Diff.

125. An unipolar rating scale with 10 categories numbered from -5 to +5 without neutral point (zero). ITEMIZED RATING SCALES: STAPEL SCALE 125 Please evaluate how accurately each word or phrase describes each of department stores. Select a plus number for phrases you think describe the store accurately. The more accurately you think the phrase describes the store, the larger the plus number you should choose. You should select a minus number for phrases you think do not describe in accurately. The less accurately you think the phrase describes the store, the larger the minus number you should choose. You can select any number, from +5 for phrases you think are very accurate, to -5 for phrases you think are very inaccurate. Sears:+5 +4 +3 +2 +1 High Quality -1 -2 -3 -4 -5 +5 +4 +3 +2 +1 Poor service -1 -2 -3 -4 -5

126. BASIC NON-COMPARATIVE SCALES 126 Scale Basic Characteristics Examples Advantages Disadvantages Continuous Rating Scale Place a mark on a continuous line Reaction to TV commercials Easy to construct Scoring can be cumbersome, unless computerized Likert Scale Degrees of agreements on a 1 (strongly disagree) to 5 (strongly agree) scale Measurement of attitudes Easy to construct, administer and understand More time- consuming Semantic Differential Seven-point scale with bipolar labels Brand, product, and company images Versatile Controversy as to whether the data are interval Stapel Scale Unipolar ten-point scale, -5 to +5, without a neutral point (zero) Measurement of attitudes and images Easy to construct, administer over telephone Confusing an difficult to apply

127. NON-COMPARATIVE ITEMIZED RATING SCALE DECISIONS 127 Number of categories Although there is no single, optimal number, traditional guidelines suggest that there should be between five and nine categories. Odd/even no. of categories If a neutral or indifferent scale response is possible for at least some respondents, an odd number of categories should be used Balanced vs. unbalanced In general, the scale should be balanced to obtain objective data Verbal description An argument can be made for labeling all or many scale categories. The category descriptions should be located as close to the response categories as possible. Forced vs. non-forced In situations where the respondents are expected to have no opinion, the accuracy of the data may be improved by a non- forced scale

128. Involvement and knowledge more cat. when respondents are interested in the scaling task or are knowledgable about the objects Nature of the objects do objects lend themselves to fine discrimination? Mode of data collection less categories in telephone interviews Data analysis less cat. for aggregation, broad generalizations or group comp. more cat. for sophisticated statistical analysis, esp. correlation based ones Considerations The greater the number of scale categories, the finer the discrimination among stimulus objects that is possible Most respondents cannot handle more than a few categories NUMBER OF SCALE CATEGORIES 128 Number of categories Although there is no single, optimal number, traditional guidelines suggest that there should be between five and nine categories.

129. BALANCED VS. UNBALANCED SCALES 129 Balanced Scale Unbalanced Scale Extremely good Very good Bad Very bad Extremely bad Extremely good Very good Good Somewhat good Bad Very bad Balanced vs. unbalanced In general, the scale should be balanced to obtain objective data

130. The middle option of an attitudinal scale attracts a substantial # of respondents who might be unsure about their opinion or reluctant to disclose it This can distort measures of central tendency and variance Questions that exclude the "don't know" option tend to produce a greater volume of accurate data ODD VS. EVEN / FORCED VS. NON-FORCED 130 Odd/even no. of categories If a neutral or indifferent scale response is possible for at least some respondents, an odd number of categories should be used Forced vs. non-forced In situations where the respondents are expected to have no opinion, the accuracy of the data may be improved by a non- forced scale Do we want/need “contrast” in controversial attitudes? Are respondents unwilling to answer vs. don’t have an opinion? Use "don't know" or better “not applicable” option for factual questions, but not for attitude questions Use branching to ensue concept familiarity on the respondent’s side Considerations

131. Considerations Providing a verbal description for each category may not improve the accuracy or reliability of the data vs. scale ambiguity Peaked vs. flat response distributions VERBAL DESCRIPTION 131 Verbal description An argument can be made for labeling all or many scale categories. The category descriptions should be located as close to the response categories as possible. completely disagree completely agree generally disagree generally agree

133. LATENT CONSTRUCTS 133 A Latent Construct is a variable that cannot be observed or measured directly but can be inferred from other observable measurable variables. Thus, the researcher must capture the variable through questions representing the presence/level of the variable in question. A Latent Construct satisfied [ ] [ ] [ ] [ ] [ ] [ ] [ ] dissatisfied pleased [ ] [ ] [ ] [ ] [ ] [ ] [ ] displeased favorable [ ] [ ] [ ] [ ] [ ] [ ] [ ] unfavorable pleasant [ ] [ ] [ ] [ ] [ ] [ ] [ ] unpleasant I like it very much [ ] [ ] [ ] [ ] [ ] [ ] [ ] I didn't like it at all contented [ ] [ ] [ ] [ ] [ ] [ ] [ ] frustrated delighted [ ] [ ] [ ] [ ] [ ] [ ] [ ] terrible Please indicate how satisfied you were with your purchase of _____ by checking the space that best gives your answer. α=.84

134. LATENT CONSTRUCTS & MULTI-ITEM SCALES 134 Advantages allow to assess abstract concepts make it easier to understand the data and phenomenon reduce dimensionality of data through aggregating a large number of observable variables in a model to represent an underlying concept link observable (“sub-symbolic”) data of the real world to symbolic data in the modeled world Satisfaction Loyalty Trust Service Quality Purchase intention Attitude Toward the Brand Involvement Price Perception Website Ease-of-Use ... Examples

135. SECURE CUSTOMER INDEXTM ASSESSING CONSUMER LOYALTY AND RETENTION 135 Secure Customer Very satisfied Definitely would recommend Definitely will use again D. Randall Brandt (1996), “Secure Customer Index”, Maritz Research Secure Customers % very satisfied/definitely would repeat/definitely would recommend Favorable Customers % giving at least "second best" response on all three measures of satisfaction and loyalty Vulnerable Customers % somewhat satisfied/might or might not repeat/ might or might not recommend At Risk Customers % somewhat satisfied or dissatisfied/probably or definitely would not repeat/probably or definitely would not recommend Overall Satisfaction 4 = very satisfied 3 = somewhat satisfied 2 = somewhat dissatisfied 1 = very dissatisfied Willingness to Recommend 5 = definitely would recommend 4 = probably would recommend 3 = might or might not recommend 2= probably would not recommend 1= definitely would recommend Likelihood to Use Again 5 = definitely will use again 4 = probably will use again 3= might or might not use again 2= probably will not use again 1 = definitely will not use again

136. MULTI-ITEM SCALES: MAKE OR STEAL 136 Develop a theory Generate an initial pool of items: theory, secondary data, and qualitative research Select a reduced set of items based on qualitative judgement Collect data from a large pretest sample Perform statistical analysis Develop a purified scale Collect more data from a different sample Evaluate scale reliability, validity, and generalizability Prepare the final scale Brunner, Gordon C. II (2012), “Marketing Scales Handbook: A Compilation of Multi-Item Measures for Consumer Behavior & Advertising Research”, Vol. 6, available as PDF at www.marketingscales.com Journal of the Academy of Marketing Science (JAMS) Journal of Advertising (JA) Journal of Consumer Research (JCR) Journal of Marketing (JM) Journal of Marketing Research (JMR) Journal of Retailing (JR)

137. MARKETING SCALES HANDBOOK: EXAMPLES 137 Excerpt from Table of Contents: Satisfaction Scales Example of a Scale Copyright material. For educational purposes and use within the current class only!!! Reproduction, copying, and/or dissemination in any form is strictly prohibited by the copyright holder.

138. 138 Scale Variants to Measure a Construct Copyright material. For educational purposes and use within the current class only!!! Reproduction, copying, and/or dissemination in any form is strictly prohibited by the copyright holder.

139. 139 Copyright material. For educational purposes and use within the current class only!!! Reproduction, copying, and/or dissemination in any form is strictly prohibited by the copyright holder.

151. MULTI-ITEM SCALES: MEASUREMENT ACCURACY 151 Measurement A measurement is not the true value of the characteristic of interest but rather an observation of it. XO = XT + XS + XR where XO = the observed score of measurement XT = the true score of characteristic XS = systematic error XR = random error The True Score Model

152. RELIABILITY & VALIDITY 152 XO = XT + XS + XR Reliability extent to which a scale produces consistent results in repeated measurements absence of random error ( XR → 0) reliability of a multi-item scale is denoted as Cronbach’s alpha (0≥α≥1) values of α≥0.7 are conside- red satisfactory Validity extent to which differences in observed scale scores reflect true differences among objects on the characteristic being measured no measurement error ( XO → XT, XS → 0, XR → 0)

153. RELATIONSHIP BETWEEN RELIABILITY & VALIDITY 153 XO = XT + XS + XR validity implies reliability ( XO = XT | XS = 0, XR = 0) unreliability implies invalidity ( XR ≠ 0 | XO = XT +XR ≠ XT) reliability does not imply validity ( XR = 0, XS ≠ 0 | XO = XT +XS ≠ XT) reliability is a necessary, but not sufficient, condition of validity

154. “The purpose of a scale is to allow us to represent respondents with the highest accuracy and reliability. We can’t have one without the other and still believe in our data.” Bart Gamble, vice president, client services, Burke, Inc. 154

155. NET PROMOTER SCORE® COMPETITIVE GROWTH RATES? 155 How likely are you to recommend company/brand/product X to a friend/colleague/relative? Reichheld, Fred (2003) "One Number You Need to Grow", Harvard Business Review Is the scale valid? Is the scale reliable?

156. 156 5.Questionnaire 5.1. Asking Questions 5.2. Overcoming Inability to Answer 5.3. Overcoming Unwillingness to Answer 5.4. Increasing Willingness of Respondents 5.5. Determining the Order of Questions 5.6. What’s Next?

157. QUESTIONNAIRE 157 A Questionnaire is a formalized set of questions for obtaining information from respondents. Objectives of a Questionnaire: translate the information need into a set of specific questions that the respondents can and will answer uplift, motivate, and encourage respondents to become involved in the interview, to cooperate, and to complete the interview minimize response error A Questionnaire

158. ISSUES TO CONSIDER IN QUESTIONNAIRE DESIGN 158 Is the question necessary? Are several questions needed instead of one? Is the respondent informed? Can the respondent remember? Effort required of the respondents Sensitivity of question Legitimate purpose Cultural issues Ease of completion Comprehensiveness Bias in formulation

159. Do you actually believe in the big love? Do you believe in the big love? BIAS IN FORMULATION 159 Q: Do you approve smoking whilst praying? A: No Q: Do you approve praying whilst smoking? A: Yes 0 15 30 45 60 Yes No Uncertain Basis: n = 2100, p <.05 Noelle-Neumann and Petersen (1998), p. 192

161. ASKING QUESTIONS 161 Avoid ambiguity, confusion, and vagueness Avoid jargon, slang, abbreviations Avoid double-barreled questions Avoid leading Avoid implicit assumptions Avoid implicit alternatives Avoid treating respondent’s belief about a hypothesis as a test of the hypothesis Avoid generalizations and estimates “It is not every question that deserves an answer” Publius Syrus (roman, 1st century B.C.)

162. Define the issue in terms of who, what, when, where, why, and way (the six Ws). Who, what, when, and where are particularly important. Example: Which brand of shampoo do you use? Ask instead: Which brand or brands of shampoo have you personally used at home during the last month? In case of more than one brand, please list all the brands that apply. Avoid Ambiguity, confusion and vagueness ASKING QUESTIONS 162

163. The W’s Deﬁning the Question Who The Respondent It is not clear whether this question relates to the individual respondent or, e.g., the respondent’s total household What The Brand of Shampoo It is unclear how the respondent is to answer this question if more than one brand is used When Unclear The time frame is not specified in this question. The respondent could interpret it as meaning the shampoo used this morning, this, week, or over the past year. Where Unclear At home, at gym, on the road? 163 Which brand of shampoo do you use?

164. Example: What brand of computer do you own? ☐ Windows PC ☐ Apple Ask instead: Do you own a Windows PC? (☐ Yes ☐ No) Do you own an Apple computer? (☐ Yes ☐ No) Even better: What brand of computer do you own? ☐ Do not own a computer ☐ Windows PC ☐ Apple ☐ Other Avoid Ambiguity, confusion and vagueness Example: Are you satisfied with your current auto insurance? ☐ Yes ☐ No Ask instead: Are you satisfied with your current auto insurance? ☐ Yes ☐ No ☐ Don’t have auto insurance Even better: 1. Do you currently have a life insurance policy? (☐ Yes ☐ No). If no, go to question 3 2. Are you satisfied with your current auto insurance? (☐ Yes ☐ No) ASKING QUESTIONS 164

165. Example: In a typical month, how often do you shop in department stores? ☐ Never ☐ Occasionally ☐ Sometimes ☐ Often ☐ Regularly Ask instead: In a typical month, how often do you shop in department stores? ☐ Less than once ☐ 1 or 2 times ☐ 3 or 4 times ☐ More than 4 times Avoid Ambiguity, confusion and vagueness ASKING QUESTIONS 165 Whenever using words “will”, “could”, “might”, or “may” in a question, you might suspect that the question asks a time- related question. scales and options should be unambiguous too

166. Use ordinary words Avoid jargon, slang, abbreviations Example: Do you think the distribution of soft drinks is adequate? Ask instead: Do you think soft drinks are readily available when you want to buy them? ASKING QUESTIONS 166 Example: What was your AGI last year? $ _______

167. Are several questions needed instead of one? Avoid double-barreled questions Example: Do you think Coca-Cola is a tasty and refreshing soft drink? Ask instead: 1. Do you think Coca-Cola is a tasty soft drink? 2. Do you think Coca-Cola is a refreshing soft drink? ASKING QUESTIONS 167

168. If you want a certain answer - why ask? Avoid leading Example: Do you help the environment by using canvas shopping bags? Ask instead: Do you use canvas shopping bags? ASKING QUESTIONS 168

169. The answer should not depend on upon implicit assumptions about what will happen as a consequence. Example: Are you in favor of a balanced budget? Ask instead: Are you in favor of a balanced budget it it would result in an increase in the personal income tax? ASKING QUESTIONS 169 Avoid implicit assumptions

170. An alternative that is not explicitly expressed in the options is an implicit alternative. ASKING QUESTIONS 170 Avoid implicit alternatives Example: Do you like to fly when traveling short distances? Ask instead: Do you like to fly when traveling short distances, or would you rather drive?

171. Beliefs are only a biased representation of reality Example: Do you think more educated people wear fur clothing? Ask instead: 1. What is your education level? 2. Do you wear fur clothing? ASKING QUESTIONS 171 Avoid treating beliefs as real facts

172. Don’t task respondents’ memory and math skills Example: What is the annual per capita expenditure on groceries in your household? Ask instead: 1. What is the monthly (or weekly) expenditure on groceries in your household? 2. How many member are there in your household? ASKING QUESTIONS 172 Avoid generalizations and estimates

174. OVERCOMING INABILITY TO ANSWER 174 Can the Respondent Remember? Can the Respondent Articulate? Is the Respondent Informed?

175. Respondents will often answer questions even though they are not informed Example: Please indicate how strongly you agree or disagree with the following statement: “The National Bureau of Consumer Complaints provides an effective means for consumers who have purchased a defective product to obtain relief” 51.9% of the lawyers and 75% of the public expressed their opinion, although there is no such entity as the NBCC Use Filter Questions e.g. ask about familiarity and/or frequency of patronage in a study of 10 department stores Use “don’t know” Option OVERCOMING INABILITY TO ANSWER 175 Is the Respondent Informed?

176. The inability to remember leads to errors of omission, telescoping, and creation Example: How many liters of soft drinks did you consume during the last four weeks? Ask instead: How often do you consume soft drinks in a typical week? ☐ Less than once a week ☐ 1 to 3 times per week ☐ 4 or 6 times per week ☐ 7 or more times per week Use aided recall approach (where appropriate) “What brands of soft drinks do you remember being advertised last night on TV?” vs “Which of these brands were advertised last night on TV?” OVERCOMING INABILITY TO ANSWER 176 Can the Respondent Remember?

177. If unable to articulate their responses, respondents are likely to ignore the question and quit the survey Example: If asked to describe the atmosphere of the department store they would prefer to patronage, most respondents may be unable to phrase their answers. Provide aids, e.g., pictures, maps, descriptions If the respondents are given alternative descriptions of store atmosphere, they will be able to indicate the one they like the best. OVERCOMING INABILITY TO ANSWER 177 Can the Respondent Articulate?

179. OVERCOMING UNWILLINGNESS TO ANSWER 179 Most respondents are unwilling to devote a lot of effort to provide information respond to questions that they consider to be inappropriate for the given context divulge information they do not see as serving a legitimate purpose disclose sensitive information Provide context Legitimate purpose Reduce effort

180. Minimize the effort required of respondents Example: Please list all the departments from which you purchased merchandise on your most recent shopping to a department store. Ask instead: In the list that follows, please check all the departments from which you purchased merchandise on your most recent shopping to a department store. ☐ Women’s dresses ☐ Men’s apparel ☐ Children’s apparel ☐ Cosmetics ……. ☐ Jewelry ☐ Other (please specify) _________________ OVERCOMING UNWILLINGNESS TO ANSWER 180 Reduce effort

181. Some questions may seem appropriate in certain contexts but not in others Example: Questions about personal hygiene habits may be appropriate when asked in a survey sponsored by the Medical Association, but not in one sponsored by a fast-food restaurant Provide context by making a statement: “As a fast-food restaurant, we are very concerned about providing a clean and hygienic environment for our customers. Therefore, we would like to ask you some questions related to personal hygiene.” Provide context OVERCOMING UNWILLINGNESS TO ANSWER 181

182. Explain why the data is needed Example: Why should a firm marketing cereals want to know the respondents’ age, income, and occupation? Legitimate the request information: “To determine how the consumption of cereals vary among people of different ages, incomes, and occupation, we need information on ...” Legitimate purpose OVERCOMING UNWILLINGNESS TO ANSWER 182

184. INCREASING WILLINGNESS OF RESPONDENTS 184 Place sensitive topics at the end of the questionnaire Preface questions with a statement that the behavior is of interest in common Ask the question using third-person technique: phrase the question as if it referred to other people Hide the question in a group of other questions Provide response categories rather than asking for specific figures Sensitive Topics: - money - family life - political and religious beliefs - involvement in accidents or crimes

186. DETERMINING THE ORDER OF QUESTIONS 186 Opening Questions The opening questions should be interesting, simple, and non-threatening. Type of Information As a general guideline, basic information should be obtained first, followed by classification, and, finally, identification information. Difficult Questions Difficult questions or questions which are sensitive, embarrassing, complex, or dull, should be placed late in the sequence.

187. DETERMINING THE ORDER OF QUESTIONS 187 Effect on Subsequent Questions (funneling) General questions should precede the specific questions 1. What considerations are important to you in selecting a department store? 2. In selecting a department store, how important is convenience of location? Logical Order / Branching Questions The question being branched should be placed as close as possible to the question causing the branching. The branching questions should be ordered so that the respondents cannot anticipate what additional information will be reuired.

188. EXAMPLE: FLOWCHART OF A QUESTIONNAIRE 188 Introduction Store Charge Card Ownership of Store, Bank, and/or other Charge Cards Purchased products in a specific department store during the last two months How was payment made? Ever purchased products in a departments store? Bank Charge Card Other Charge Card Intention to use Store, Bank, or Other Charge Cards yes no yes no Cash Other Credit

190. What’s Next? 190 Introduction Catch the respondents’ interest Explain the reasons & objectives Ask for their help Tell that their support is valuable Tell how much time it will last Emphasize the anonymity Incentivize (non-monetary incentives)

191. What’s Next? 191 Pretest! Pretest! Pretest!!! question content wording sequence form and layout question difficulty instructions… analysis procedures

192. RECAP 192 1. Develop a flow chart of the information required based on the marketing research problem Once the entire sequence is laid out, the interrelationships should become clear Match up the actual data you would expect to collect from the questionnaire against the information needs listed in the flow chart Be specific in the objective for each area of information and data. You should be able to write an objective for each area so specifically that it guides your construction of the questions. 2. At this stage, put on your “critic’s” hat and go back over the flowchart and ask Do I need to know it and know exactly what I am going to do with it? or It would be nice to know it but I do not have to have it

193. 193 6.Sampling 6.1. Non-probability Sampling 6.2. Probability Sampling 6.3. Choosing Non-Probability vs. Probability Sampling 6.4. Sample Size

194. 194 The world’s most famous newspaper error President Harry Truman against Thomas Dewey Chicago Tribute prepared an incorrect headline without first getting accurate information Reason? → bias → inaccurate opinion polls

195. 195

196. 196 Yes, dear Dilbert, it was the wrong Sample

197. SAMPLING 197 Population the group of people we wish to understand. Populations are often segmented by demographic or Sample a subset of population that represents the whole Most research cannot test everyone. Instead a sample of the whole population is selected and tested. If this is done well, the results can be applied to the whole population. This selection and testing of a sample is called sampling. If a sample is poorly chosen, all the data may be useless.

198. SAMPLING: TWO GENERAL METHODS 198 This relies on personal judgement of theresearcher (often on people available, e.g.,people passing in the street or walkingthrough a mall). This may yield good estimates of populationcharacteristics, however, doesn’t allow forobjective evaluation of the precision ofsample results. That is, the results are notprojectable to the population. Non- probability Sampling Here, sampling units are selected by chance, i.e., randomly. This randomness allows applying statistical techniques to determine the precision of the sample estimates and their confidence intervals. The results are generalizable and projectable to the population from which the sample is drawn. Probability Sampling

199. CLASSIFICATION OF SAMPLING TECHNIQUES 199 Sampling Techniques Non-probability Probability Convenience Sampling Judgmental Sampling Quota Sampling Snowball Sampling Stratified Sampling Cluster Sampling Other Samp- ling Techniques Systematic Sampling Simple Random Sampling Proportionate Disproportionate

201. CLASSIFICATION OF SAMPLING TECHNIQUES 201 Sampling Techniques Non-probability Probability Convenience Sampling Judgmental Sampling Quota Sampling Snowball Sampling Stratified Sampling Cluster Sampling Other Samp- ling Techniques Systematic Sampling Simple Random Sampling Proportionate Disproportionate

202. CONVENIENCE SAMPLING 202 Depth Interview attempts to obtain a sample of convenient respondents. Often, respondents are selected because they happen to be in the right place at right time. students or members of social organizations mall intercept interviews without qualifying the respondents “people on the street” interviews tear-out questionnaires in magazines Convenience Sampling

203. JUDGMENTAL SAMPLING 203 a form of convenience sampling in which the population elements are selected based on the judgement of the researcher test markets purchase engineers selected in industrial marketing research mothers as diaper “users” Judgmental Sampling

204. Control Characteristic Population Composition Sample CompositionSample Composition Control Characteristic Percentage Percentage Number Sex Male Female 48 52 ------- 100 48 52 ------- 100 480 520 ------- 1000 Age 18-30 31-45 45-60 Over 60 27 39 16 18 ------- 100 27 39 16 18 ------- 100 270 390 160 180 ------- 1000 QUOTA SAMPLING 204 develop control categories, or quotas, of population elements (e.g., sex, age, race, income, company size, turnover, etc.) so that the proportion of the elements possessing these characteristics in the sample reflects their distribution in the population. The elements themselves are selected based on convenience or judgment. The only requirement, however, is that the elements selected fit the control characteristics (quota). Quota Sampling Often used in online surveys

205. SNOWBALL SAMPLING 205 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. Good for locating the desired characteristic in the population: reaching hard-to-reach respondents (e.g., government services, “food stamps”, drug users) estimating characteristics that are rare in the population identifying buyer-seller pairs in industrial research Snowball Sampling Often used in online surveys Very favored by students

207. CLASSIFICATION OF SAMPLING TECHNIQUES 207 Sampling Techniques Non-probability Probability Convenience Sampling Judgmental Sampling Quota Sampling Snowball Sampling Stratified Sampling Cluster Sampling Other Samp- ling Techniques Systematic Sampling Simple Random Sampling Proportionate Disproportionate Require knowledge about the population

208. Each element in the population has a known and equal probability of selection Each possible sample of a given size (n) has a known probability of being the sample actually selected This implies that every element is selected independently of every other element. Simple Random Sampling SRS & SYSTEMATIC SAMPLING 208 The sample is chosen by selecting a random starting point and then picking every i-th element in succession from the sampling frame The sampling interval, i, is determined by dividing the population size N by the sample size n, i.e., i=N/n Systematic Sampling Require knowledge about the population start here take every i-th element select randomly i i i

209. STRATIFIED SAMPLING 209 is obtained by separating the population into non-overlapping groups called strata and then obtaining a proportional simple random sample from each group. The individuals within each group should be similar in some way. Good for: highlighting a specific subgroup within the population observing existing relationships between two or more subgroups representative sampling of even the smallest and most inaccessible subgroups in the population a higher statistical precision Stratiﬁed Sampling Proportionate Stratum A B C Population Size 100 200 300 Sampling Fraction 1/2 1/2 1/2 Final Sample Size 50 100 150 Stratum A B C Population Size 100 200 300 Sampling Fraction 1/5 1/2 1/3 Final Sample Size 20 100 100 Disproportionate Require knowledge about the population

210. CLUSTER SAMPLING 210 the target population is first divided into mutually exclusive and collectively exhaustive subpopulations, or clusters. Than a random sample of clusters is selected, based on SRS. Good for: covering large geographic areas reducing survey costs when constructing a complete list of population elements is difficult when the population concentrated in natural clusters (e.g., blocks, cities, schools, hospitals, boxes, etc.) Cluster Sampling Require knowledge about the population For each cluster, either all the elements are included in the sample (one-stage) or a sample of elements is drawn probabilistically (two-sage).

212. STRENGTHS AND WEAKNESSES OF BASIC SAMPLING TECHNIQUES 212 Technique Strengths Weaknesses Non-probability 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 in the field research 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 Can decrease representativeness Stratified sampling Includes 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

213. 213 The middle option of an attitudinal scale attracts a substantial # of respondents who might be unsure about their opinion or reluctant to disclose it This can distort measures of central tendency and variance Questions that exclude the "don't know" option tend to produce a greater volume of accurate data Do we want/need “contrast” in controversial attitudes? Are respondents unwilling to answer vs. don’t have an opinion? Use "don't know" or better “not applicable” option for factual questions, but not for attitude questions Use branching to ensue concept familiarity on the respondent’s side Non-probability Probability

214. 214 Non-comparative Scales each object is scaled independently resulting data is generally assumed to be interval or ratio scaled Comparative Scales involve the direct comparison of stimulus objects. data must be interpreted in relative terms have only ordinal and rank- order properties nature of the research variability in the population statistical considerations

216. DETERMINING THE SAMPLE SIZE 216 The sample size does not depend on the size of the population being studied, but rather it depends on qualitative factors of the research. desired precision of estimates knowledge of population parameters number of variables nature of the analysis importance of the decision incidence and completion rates resource constraints Determining the Sample Size

217. SAMPLE SIZES USED IN MARKETING RESEARCH STUDIES 217 Type of Study Minimum Size Typical Size Problem identification research (e.g., market potential) 500 1,000 - 2,000 Problem solving research (e.g., pricing) 200 300 - 500 Product tests 200 300 - 500 Test-market studies 200 300 - 500 TV/Radio/Print advertising (per commercial ad tested) 150 200 - 300 Test-market audits 10 stores 10 - 20 stores Focus groups 6 groups 10 - 15 groups

218. MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE 218

219. MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE 219 What is your primary daily media channel?

220. MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE 220 What is your primary daily media channel? How accurate is this statistic? What is the margin of error? The Margin of Error is the measure of accuracy of a survey. The smaller the margin of error, the more accurate are the estimates of a survey.

221. MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE 221 Means use this formula when evaluating estimatesof population means Proportions use this when evaluating estimates of proportions Means Proportions E = z σ n E = z π(1−π) n x = real population parameter x = sample statistic E = margin of error ^ x = ˆx ± E z = z-value for a given level of confidenceσ = standard deviation of a population parametern = sample size z = z-value for a given level of confidence π = estimate of the proportion in the population n = sample size

222. MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE 222 Means use this formula when evaluating estimatesof population means Proportions use this when evaluating estimates of proportions Means Proportions E = z σ n E = z π(1−π) n x = real population parameter x = sample statistic E = margin of error ^ x = ˆx ± E z = z-value for a given level of confidenceσ = standard deviation of a population parametern = sample size z = z-value for a given level of confidence π = estimate of the proportion in the population n = sample size unlikely to be known

223. MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE 223 Means use this formula when evaluating estimatesof population means Proportions use this when evaluating estimates of proportions Means Proportions E = z σ n E = z π(1−π) n x = real population parameter x = sample statistic E = margin of error ^ x = ˆx ± E z = z-value for a given level of confidenceσ = standard deviation of a population parametern = sample size z = z-value for a given level of confidence π = estimate of the proportion in the population n = sample size unlikely to be known has a maximum at π = .5

224. MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE 224 maximum margin of error for 95% level of confidence Proportions E = z π(1−π) n x = real population parameter x = sample statistic E = margin of error ^ x = ˆx ± E z-values z = 1.96 for 95% level of confidence z = 2.58 for 99% level of confidence =1.96 0.5(1− 0.5) n ≈ 1 n

225. MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE 225 What is your primary daily media channel? How accurate is this statistic? What is the margin of error? Margin of Error = 1/√n 48,804 people in sample √48,804 = 220.916 1/221 = 0.0045 *100 = 0.45% x = 61% ± 0.45% 60.55% to 61.45% x = ˆx ± E calculations are approximate values for 95% level of confidence

226. MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE 226 What is your primary daily media channel? How big should the sample be taking margin of error of ±1% into account? Sample Size n = (1/Margin of Error)^2 n±1%= (1/0.01)^2 = (100)^2 = 10,000 n±2%= (1/0.02)^2 = (50)^2 = 2,500 n±5%= (1/0.05)^2 = (20)^2 = 400 n±10%= (1/0.1)^2 = (10)^2 = 100 n ≈ 1 E " # $ % & ' 2 E ≈ 1 n calculations are approximate values for 95% level of confidence

227. MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE 227 What is your primary daily media channel? calculations are approximate values for 95% level of confidence Sample Size n = (1/Margin of Error)^2 Sample Size does not depend on population. n±1%= (1/0.01)^2 = (100)^2 = 10,000 What if the population under study consists of only 100 elements? (e.g., firms producing cars) Corrections needed, when sample size exceeds 10% of the population

228. MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE 228 What is your primary daily media channel? calculations are approximate values for 95% level of confidence Correction of the Sample Size ncorr = n (1+(n −1) / population) Corrections needed, when sample size exceeds 10% of the population

229. MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE 229 What is your primary daily media channel? calculations are approximate values for 95% level of confidence n±1%= (1/0.01)^2 = (100)^2 = 10,000 What if the population under study consists of only 100 elements? (e.g., firms producing cars) ncorr = n (1+(n −1) / population) ncorr = 10,000 (1+(10,000 −1) /100) = 10,000 (1+ 9,999 /100) = 10,000 (100.99) = 99.02 Corrections needed, when sample size exceeds 10% of the population

230. MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE 230 What is your primary daily media channel? calculations are approximate values for 95% level of confidence n±5%= (1/0.05)^2 = (20)^2 = 400 What if the population under study consists of only 100 elements? (e.g., firms producing cars) ncorr = n (1+(n −1) / population) ncorr = 400 (1+(400 −1) /100) = 400 (1+399 /100) = 400 (4.99) = 80.16 Corrections needed, when sample size exceeds 10% of the population

231. MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE 231 What is your primary daily media channel? calculations are approximate values for 95% level of confidence Corrections needed, when sample size exceeds 10% of the population n±10%= (1/0.1)^2 = (10)^2 = 100 What if the population under study consists of only 100 elements? (e.g., firms producing cars) ncorr = n (1+(n −1) / population) ncorr = 100 (1+(100 −1) /100) = 100 (1+ 99 /100) = 100 (1.99) = 50.25

232. A NOTE ON CONFIDENCE INTERVAL 232 A confidence interval estimate is an interval of numbers, along with a measure of the likelihood that the interval contains the unknown parameter. The level of confidence is the expected proportion of intervals that will contain the parameter if a large number of samples is maintained. Conﬁdence Interval & Level of Conﬁdence Suppose we're wondering what the average number of hours that people at Siemens spend working. We might take a sample of 30 individuals and find a sample mean of 7.5 hours. If we say that we're 95% confident that the real mean is somewhere between 7.2 and 7.8, we're saying that if we were to repeat this with new samples, and gave a margin of ±0.3 hours every time, our interval would contain the actual mean 95% of the time.

233. The higher the confidence we need, the wider the confidence interval and the greater the margin of error will be CONFIDENCE INTERVAL, MARGIN OF ERROR, AND SAMPLE SIZE 233 maximum margin of error for 99% level of confidence E = z π(1−π) n z-values z = 1.96 for 95% level of confidence z = 2.58 for 99% level of confidence = 2.58 0.5(1− 0.5) n = 1.29 n

234. The higher the confidence we need, the wider the confidence interval and the greater the margin of error will be CONFIDENCE INTERVAL, MARGIN OF ERROR, AND SAMPLE SIZE 234 maximum margin of error for 99% level of confidence E = z π(1−π) n z-values z = 1.96 for 95% level of confidence z = 2.58 for 99% level of confidence = 2.58 0.5(1− 0.5) n = 1.29 n To reduce the margin of error we have to increase the sample size higher levels of conﬁdence require larger samples smaller margins of error require larger samples

235. 235 7.Data Analysis: A Concise Overview of Statistical Techniques 7.1. Descriptive Statistics: Some Popular Displays of Data 7.1.1. Organizing Qualitative Data 7.1.2. Organizing Quantitative Data 7.1.3. Summarizing Data Numerically 7.1.4. Cross-Tabulations 7.2. Inferential Statistics: Can the results be generalized to population? 7.2.1. Hypothesis Testing 7.2.2. Strength of a Relationship in Cross-Tabulation 7.2.3. Describing the Relationship Between Two (Ratio Scaled) Variables

236. TYPES OF STATISTICAL DATA ANALYSIS 236 Inferential Inferential statistics are techniques that allow making generalizations about a population based on random samples drawn from the population. Allow assessing causality and quantifying relationships between variables. Descriptive Descriptive statistics provide simple summaries about the sample and about the observations that have been made. Include the numbers, tables, charts, and graphs used to describe, organize, summarize, and present raw data.

237. 237 7.Data Analysis: A Concise Overview of Statistical Techniques 7.1. Descriptive Statistics: Some Popular Displays of Data 7.1.1. Organizing Qualitative Data 7.1.2. Organizing Quantitative Data 7.1.3. Summarizing Data Numerically 7.1.4. Cross-Tabulations 7.2. Inferential Statistics: Can the results be generalized to population?

238. FREQUENCY AND RELATIVE FREQUENCY TABLES 238 Original Data A frequency distribution lists each category of data and the number of occurrences for each category The relative frequency is the proportion (or percent) of observations within a category A relative frequency distribution lists each category of data together with the relative frequency of each category. relative frequency = frequency sumof all frequencies Excel how to video: http://faculty.elgin.edu/dkernler/statistics/videos/excel2007/freq-table-1.mov

239. BAR GRAPHS 239 Original Data Bar Graphs / Bar Charts 1. heights can be frequency or relative frequency 2. bars must not touch Excel how to video: http://faculty.elgin.edu/dkernler/statistics/videos/excel2007/bar-graph.mov

240. PIE CHARTS 240 Pie Charts 1. should always include the relative frequency 2. also should include labels, either directly or as a legend Excel how to video: http://faculty.elgin.edu/dkernler/statistics/videos/excel2007/pie-chart.mov