National Insurance case, Elaboration Model

1. National Insurance case, Elaboration Model Market IntelligenceJulie Edell Britton Session 4 August 22, 2009

2. Today’s Agenda • Announcements • National Insurance • Elaboration Model • Introduction to Survey Research

3. Announcements • No Colgate case • For Friday examine the Comparative Advertising, Measurement Scales & Data Analysis scenario – pg. 52 of your course pack – no slides, we will just discuss • For Sat prepare Milan Food case – download data (Milan.sav) from the platform – no slides • For Sat prepare WSJ/ Harris Survey – no slides

4. National Insurance

5. Today’s Agenda • Announcements • National Insurance • Elaboration Model • Introduction to Survey Research

6. Relationship between Term and Internship

7. Chi-Square Test With (r-1)*(c-1) degrees of freedom Expected number in cell i under independence Observed number in cell i number of columns number of cells number of rows = Column Proportion * Row Proportion * total number observed

8. Expected Cell Counts

9. Chi-Square Test With (r-1)*(c-1) degrees of freedom =(76-56)2/56 + (24-43)2/43 + (142-160)2/160 + (138-121)2/121 = 19.95 with 1 degree of freedom Critical value (alpha=.05) is 3.84 Thus there appears to be a significant relationship between term in which marketing is taken and getting a marketing internship

10. Zero Order Association • Zero Order Association: relationship between two variables without controlling for any other variables. • If every case in a dataset has values on X, Y, Z, the crosstab of X and Z “sums over” the different levels of Y. • Partial association: relationship between two variables controlling for a third

11. Elaboration Model: “Zero Order" and “Partial” Relationships We have a “zero-order” relationship between X and Z we would like to explain -- e.g., (X) Term for Core Marketing and (Z) Getting Desired Internship

12. Confound? Is it just experience? • An Alternate Hypothesis is that Experience causes both taking core marketing in Term 1 and getting desired Internship. 160 of 380 have hi experience, 220 have lo experience. Experience is Y • How would you tell whether experience relates to when you take core marketing? (Hi Experience takes earlier) See if Y is related to X

13. Actual Cell Counts

14. Actual Cell Counts

15. Expected Cell Counts

16. Chi-Square Test With (r-1)*(c-1) degrees of freedom =(80-42)2/42 + (20-58)2/58 + (80-118)2/118 + (200-162)2/162 = 80.42 with 1 degree of freedom Critical value (alpha=.05) is 3.84 Thus there appears to be a significant relationship between term in which marketing is taken and the amount of experience

17. Confound Part 2: Experience v. Getting Desired Marketing Internship More experienced students have more success getting desired marketing internship - See if Y is related to Z

18. Expected Cell Counts

19. Chi-Square Test With (r-1)*(c-1) degrees of freedom =(132-93)2/93 + (28-67)2/67 + (88-127)2/127 + (132-93)2/93 = 67.39 with 1 degree of freedom Critical value (alpha=.05) is 3.84 Thus there appears to be a significant relationship between the amount of experience and get the desired internship

20. “Partial” Relationships: Controlling for Experience, Does Core Term Matter? Lots of Experience Observed / Expected Not Much Experience Observed / Expected

21. “Partial” Relationships: Controlling for Experience, Does Core Term Matter? =(68-66)2/66 + (12-14)2/14 + (64-66)2/66 + (16-14)2/143 + 0 = .69 with 3 degree of freedom Critical value (alpha=.05) is 7.81 Thus there appears NOT to be a significant relationship between the term marketing is taken and getting the desired internship when controlling for experience

22. “Partial” Relationships: Controlling for Term, Does Experience Matter? Term1 Observed / Expected Term 3 Observed / Expected

23. “Partial” Relationships: Controlling for Core Term, Does Experience Matter? =(68-61)2/61 + (12-9)2/9 + (8-15)2/15 + (12-5)2/5 + (64-41)2/41 + (16-41)2/41 + (80-101)2/101 + (120-97)2/97 = .52.84 with 3 degree of freedom Critical value (alpha=.05) is 7.81 Thus there is a significant relationship between experience and getting the desired internship when controlling for term in which core marketing is taken

24. Conclusion on Relationships between Variables • A simple “zero-order” relationship between two variables may not imply causation. • If the true model is X (Experience) causes Y (Term for Core Marketing) and Z (Get Desired Marketing Internship?) • Term will have no “partial” effect on Internship, controlling for Experience. • Experience will have a “partial” effect on Internship, controlling for Term.

25. Today’s Agenda • Announcements • National Insurance • Elaboration Model • Introduction to Survey Research

26. Descriptive Survey Research • Surveys usually used for descriptive research • Provide a snapshot at a point in time • Most analyses univariate or bivariate (but can do elaboration model with control variables) • Would you recommend National to a friend interested in insurance services? Yes 1 No 2 • Bivariate allows for hypothesis testing • Hypothesis: Less educated people more likely to recommend • Descriptive, not causal • Recommendation could be driven by some 3rd factor correlated with education such as income 26

27. Sources of Survey Errors • Population definition • Representativeness of the sample (e.g., Literary Digest) • Respondent Participation: • Willing to participate (Do Not Call) • Comprehend questions • Have knowledge, opinions • Willing & able to respond (language or memory) • Interviewer understands & records accurately 27

28. Raising Willingness to Participate • A good response rate requires persuasion • Survey Introduction • Phone or send letter in advance • Introduce self, give affiliation unless this would bias • Describe purpose briefly, w/o making survey sound threatening or demanding • Make respondent feel that s/he is getting chance to provide opinions that will influence market offerings & that her/his cooperation is extremely important 28

29. Comprehends Questions? Advice on Question Wording • Be simple and precise • Give clear instructions • Check for question applicability • respondent screening • question branching based on prior answers • Avoid leading & double barrel questions

30. What’s the Problem? • “Laws should be passed to eliminate all possibilities of special interests giving huge sums of money to candidates” • “Laws should be passed to prohibit interest groups from contributing to campaigns, as groups do not have the right to contribute to candidates they support?” 30

31. Comprehends Questions? • Literacy, translation considerations • Conversational Norms • How demanding was Term 3? How demanding was Core Finance? • How demanding was Core Finance? How demanding was Term 3? • How demanding was Managerial Accounting? How demanding was Core Finance? How demanding was Global Economic Environment of the Firm? How demanding was Term 3?

32. Do Respondents Have Knowledge? • Retrieve answer from memory vs. construct it on spot • Constructed answers are more likely to be influenced by question wording & prior questions. • When answering later questions or engaging in later behavior, likelihood of using earlier answer input A: • positively related to accessibility of A • positively related to diagnosticity (relevance) of A • negatively related to accessibility, diagnosticity of alternative inputs B, C, etc. (Feldman & Lynch) • e.g., when political poll respondents asked: • issue opinion A, presidential voting intention, issue opinion B, • answers to A predict intention, but only for those who did not vote for either candidate in primary

33. Survey Best Practices: Survey Content, Question Order • Survey Questions • First figure out what questions are needed! • Then order • Lead with interesting, nonthreatening, easy questions • Do you like to play golf? • Can you remember the last time you traveled with your clubs? • Put difficult or sensitive questions well into the interview • How many times did you have to see your doctor for your reconstructive surgery? • What is the size of your company (revenue)? • Usually use funnel order (general to specific) • Use product category? • Brand X? • Do you like Brand X? • Why? 33

34. Question Order (Cont.) • Survey Questions (cont.) • Inverted funnel (specific to general) for complex topics. • Is your company considering offering training courses on word processing over the Internet? • Database? Spreadsheets? • In general, how big is the untapped market for your software training courses if offered over the Internet? • Group questions in logical order • All questions about one subject together, with transitional phrases in between, “Now I’m going to ask you about agricultural applications of GPS systems...” 34

35. Survey Best Practices: Question Order (cont.) • Demographics Questions • Put last—these are less sensitive to prior questions • Seem nosy if put first • Rely on standard approaches for assessing • http://www.norc.org/GSS+Website/ • The Process of Survey Design • Use Backwards Marketing Research to decide what is “need to know” • Draft the survey • Pretest for time, clarity, variability in responses • Revise and retest • Field the survey and keep an eye open for problems 35

37. Survey Best Practices:Choosing a Survey Method • Mail, phone, web, in person? • Cost • Complexity of inquiries (branching) • Need for aids • Issue sensitivity • Control over sample 37

38. Web and Telephone • Web surveys now dominate. To compare web, in person, phone, mail, see • http://knowledge-base.supersurvey.com/ 38

39. Free to Fuqua students: Qualtrics • http://www.qualtrics.com/duke#submit • Set up an account • Build surveys • Allows for complex designs • Available to you during this course 39

40. Multi-Attribute Attitude Model (MAAM) • Liking for a product as a whole = sum of liking for component parts • Attitude toward brand j = (sum from i = 1 to n for salient attributes) •  Importance of Attributei * Evaluationij • Importance • 0 – 100 (allocate 100 points across attributes) • Rating on 1 (unimportant) to 7 (very important) where 0 undefined but implicitly entirely unimportant ) • Evaluation of brand j on attribute I • -4 = poor to +4 = excellent 40

41. MAAM and SUVs 41

42. Diagnostics of Advantage 42

43. Measure Types Revisited • Nominal (Unordered Categories) • Just need unique number for each category • Ordinal: ranking scale, intervals not assumed equal • Interval: Intervals assumed equal, zero is arbitrary • Ratio: Intervals assumed equal, zero means zero • To multiply X * Y, (e.g., importance * evaluation), both X and Y must be on ratio scales. • If X1*Y1 > X2*Y2 (XYbrand 1 >XYbrand 2), it does NOT follow that (X1+a)*Y1 > (X1+a)*Y2…. • e.g., 2*2 > 2*(-2), but (2-4)*2 < (2-4)*(-2) • To say % change in Y, Y must be on ratio scales

44. More on Scaling • To multiply importance x evaluation for each attribute, both must be on ratio scales • 0 on scale must be 0 of underlying quantity • Importance unipolar (all positive). Completely unimportant = 0 weight • Evaluation bipolar (negative to positive). To multiply, must code “neutral” as zero. 44

45. I got these by subtracting 4 from the values three slides back Improper Rescaling 45

46. Consumer Attitudes • We want to be able to predict consumer behavior • However, instead of examining behavior directly (e.g., choice modeling), we often measure attitudes because… • Measuring attitudes is sometimes easier than observing choice • Attitudes are more diagnostic • Attitudes are sometimes easier to interpret • Attitudes can be reasonable predictors of behavior • Attitudes toward products or brands typically derive from beliefs, actions, and perceptions 46

47. Types of Attitude Scales • Semantic differential • Colgate Combo is: • low quality __:__:__:__:__:__:__ high quality • unappealing __:__:__:__:__:__:__ appealing • Constant sum (e.g., Importance) • Purchase intent • Likert scale (Agree-Disagree)

48. Recap • National Insurance Case • Assessing data quality • Comparing Sample to Population • Running SPSS • Survey Design: responses constructed on the spot • Moving parts of a good survey Population definition, choosing a survey method, determining what information needed • Order of questions • Attitude Measurement & multi-attribute attitude model 48