1 National Insurance case, Elaboration Model Market Intelligence Julie Edell Britton Session 4...

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National Insurance case, Elaboration Model

Market IntelligenceJulie Edell Britton

Session 4August 22, 2009

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Today’s Agenda

Announcements

National Insurance

Elaboration Model

Introduction to Survey Research

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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

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National Insurance

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Today’s Agenda

Announcements

National Insurance

Elaboration Model


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Relationship between Term and Internship

Took Core Marketing

Got Desired Marketing Internship

Did Not Get Desired

Marketing Internship

Term 1 76 24 26%

Term 3 142 138 74%

57% 43% 380

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Chi-Square Test

k

i i

ii

E

EO

1

22 )( With (r-1)*(c-1)

degrees of freedom

iO Observed number in cell i i

iE Expected number in cell iunder independence

k number of cells r cnumber of rows number of columns

iE = Column Proportion * Row Proportion * total number observed

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Expected Cell Counts

Took Core Marketing


Did Not Get Desired

Marketing Internship

Term 1 .26*.57*380= 56

43 26%

Term 3 160 121 74%

57% 43% 380

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Chi-Square Test

k

i i

ii

E

EO

1

22 )(

With (r-1)*(c-1) degrees of freedom

i

2 =(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

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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

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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

Took Core Marketing


Did Not Get Desired Marketing

Internship

Term 1 76% 24%

Term 3 51% 49%

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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

Term 1 Term 3 Row %Hi Experience 50% 50% 100%Lo Experience 9% 91% 100%

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Actual Cell Counts

Took Core Marketing

Term 1 Term 3

Lots of Experience

80 80 42%

Not Much Experience

20 200 58%

26% 74% 380

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Actual Cell Counts

Took Core Marketing

Term 1 Not Much Experience

Lots of Experience

80 80 26%

Term 3 20 200 74%

42% 58% 380

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Took Core Marketing

Term 1 Term 3

Lots of Experience

.26*.42*380= 42

118 42%

Not Much Experience

58 162 58%

26% 74% 380

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Chi-Square Test

k

i i

ii

E

EO

1

22 )(


i



Thus there appears to be a significant relationship between term in which marketing is taken and the amount of experience

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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

Got Internship

Did Not Get Internship

Lots of Experience

132 28 42%

Not Much Experience

88 132 58%

58% 42% 380

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Took Core Marketing

Got Internship

Did Not Get Internship

Lots of Experience

.58*.42*380= 93

67 42%

Not Much Experience

127 93 58%

58% 42% 380

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Chi-Square Test

k

i i

ii

E

EO

1

22 )(


i



Thus there appears to be a significant relationship between the amount of experience and get the desired internship

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“Partial” Relationships: Controlling for Experience, Does Core Term Matter?

Took Core Marketing

Got Internship Did Not Get Internship

Term 1 68 / 66 12 / 14 50%

Term 3 64 / 66 16 / 14 50%

83% 17% 160

Lots of Experience Observed / Expected

Took Core Marketing


Term 1 8 / 8 12 / 12 9%

Term 3 80 / 80 120 / 120 91%

40% 60% 220

Not Much Experience Observed / Expected

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“Partial” Relationships: Controlling for Experience, Does Core Term Matter?

2 =(68-66)2/66 + (12-14)2/14 + (64-66)2/66 + (16-14)2/143 + 0= .69 with 3 degree of freedom


Thus there appears NOT to be a significant relationship between the term marketing is taken and getting the desired internship when controlling for experience

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“Partial” Relationships: Controlling for Term, Does Experience Matter?


Hi Experience 68 / 61 12 / 19 80%

Lo Experience 8 / 15 12 / 5 20%

76% 24% 100

Term1 Observed / Expected


Hi Experience 64 / 41 16 / 41 29%

Lo Experience 80 / 101 120 / 97 71%

51% 49% 280

Term 3 Observed / Expected

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“Partial” Relationships: Controlling for Core Term, Does Experience Matter?

2 =(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


Thus there is a significant relationship between experience and getting the desired internship when controlling for term in which core marketing is taken

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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.

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Today’s Agenda

Announcements

National Insurance

Elaboration Model


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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

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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

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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

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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

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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?”

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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?

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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

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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?

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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...”

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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

http://www.norc.org/GSS+Website/

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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

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Web and Telephone

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

http://knowledge-base.supersurvey.com/

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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

http://www.qualtrics.com/duke#submit

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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

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Land Rover RAV Land Rover RAV

Attribute

Importance 1=unimp, 7= important

Brand Evaluation -4 = poor, +4 =

excellent Imp*Eval Imp*EvalSporty Styling 6 1 2 6 12Handling 5 0 1 0 5Cost 2 -2 3 -4 6Ruggedness 4 4 2 16 8Off-Road Ability 2 2 4 4 8Total Attitude 22 39

MAAM and SUVs

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Diagnostics of Advantage

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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

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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.

4545I got these by subtracting 4 from the values three slides back

Land Rover RAV Land Rover RAV

Attribute

Importance -3 =unimp, +3 = imp

Evaluation -4 = poor, +4 = excellent Imp*Eval Imp*Eval Diff

Sporty Styling 2 1 2 2 4 2Handling 1 0 1 0 1 1Cost -2 -2 3 4 -6 -10Ruggedness 0 4 2 0 0 0Off-Road Ability -2 2 4 -4 -8 -4Total Attitude 2 -9 -11

Improper Rescaling

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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

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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)

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Recap

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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

1 National Insurance case, Elaboration Model Market Intelligence Julie Edell Britton Session 4...

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