1 national insurance case, elaboration model market intelligence julie edell britton session 4...
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1
National Insurance case, Elaboration Model
Market IntelligenceJulie Edell Britton
Session 4August 22, 2009
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
6
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
7
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
8
Expected Cell Counts
Took Core Marketing
Got Desired Marketing Internship
Did Not Get Desired
Marketing Internship
Term 1 .26*.57*380= 56
43 26%
Term 3 160 121 74%
57% 43% 380
9
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
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
Took Core Marketing
Got Desired Marketing Internship
Did Not Get Desired Marketing
Internship
Term 1 76% 24%
Term 3 51% 49%
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
Term 1 Term 3 Row %Hi Experience 50% 50% 100%Lo Experience 9% 91% 100%
13
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
14
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
15
Expected Cell Counts
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
16
Chi-Square Test
k
i i
ii
E
EO
1
22 )(
With (r-1)*(c-1) degrees of freedom
i
2 =(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
Got Internship
Did Not Get Internship
Lots of Experience
132 28 42%
Not Much Experience
88 132 58%
58% 42% 380
18
Expected Cell Counts
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
19
Chi-Square Test
k
i i
ii
E
EO
1
22 )(
With (r-1)*(c-1) degrees of freedom
i
2 =(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?
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
Got Internship Did Not Get Internship
Term 1 8 / 8 12 / 12 9%
Term 3 80 / 80 120 / 120 91%
40% 60% 220
Not Much Experience Observed / Expected
21
“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
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?
Got Internship Did Not Get Internship
Hi Experience 68 / 61 12 / 19 80%
Lo Experience 8 / 15 12 / 5 20%
76% 24% 100
Term1 Observed / Expected
Got Internship Did Not Get Internship
Hi Experience 64 / 41 16 / 41 29%
Lo Experience 80 / 101 120 / 97 71%
51% 49% 280
Term 3 Observed / Expected
23
“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
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
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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
Introduction to Survey Research
2626
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
27 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
2828
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
3030
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
3333
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?
3434
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...”
3535
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
3737
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
3838
Web and Telephone
Web surveys now dominate. To compare web, in person, phone, mail, see http://knowledge-base.supersurvey.com/
3939
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
4040
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
4141
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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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
4444
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
4646
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)
48
Recap
48
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
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