Description
This is a ppt explaining the conjoint analysis.
Conjoint Analysis
Conjoint Analysis
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Conjoint analysis attempts to determine the relative importance consumers attach to salient attributes and the utilities they attach to the levels of attributes. The respondents are presented with combinations of attribute levels and asked to evaluate these stimuli in terms of their desirability.
Statistics and Terms Associated with Conjoint Analysis
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?
?
?
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Part-worth functions. The part-worth functions, or utility functions, describe the utility consumers attach to the levels of each attribute. Relative importance weights. These indicate which attributes are important in influencing consumer choice. Attribute levels. The attribute levels denote the values assumed by the attributes. Full profiles. Complete profiles of brands are constructed in terms of all the attributes and their levels. Pairwise tables. The respondents evaluate two attributes at a time until all the required pairs of attributes have been evaluated.
Conducting Conjoint Analysis
Fig. 1 Formulate the Problem Construct the Stimuli Decide the Form of Input Data Select a Conjoint Analysis Procedure
Interpret the Results
Assess Reliability and Validity
Conducting Conjoint Analysis
Formulate the Problem
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?
?
?
?
Identify the attributes and attribute levels to be used in constructing the stimuli. The attributes selected should be important in influencing consumer preference and choice and should be actionable. A typical conjoint analysis study involves six or seven attributes. At least three levels should be used, unless the attribute naturally occurs in binary form (two levels). The researcher should take into account the attribute levels prevalent in the marketplace.
Conducting Conjoint Analysis
Construct the Stimuli
?
?
?
In the pairwise approach, also called two-factor evaluations, the respondents evaluate two attributes at a time until all the possible pairs of attributes have been evaluated. In the full-profile approach, also called multiplefactor evaluations, complete profiles of brands are constructed for all the attributes and each profile is described on a separate index card. In the pairwise approach, it is possible to reduce the number of paired comparisons by using cyclical designs. Likewise, in the full-profile approach, the number of stimulus profiles can be greatly reduced by means of fractional factorial designs.
Sneaker Attributes and Levels
Table 1
Attribute
Sole
Number
3 2 1 3 2 1 3 2 1
Level
Description
Rubber Polyurethane Plastic Leather Canvas Nylon $30.00 $60.00 $90.00
Upper
Price
Full-Profile Approach to Collecting Conjoint Data
Table 2
Example of a Sneaker Product Profile
Sole Upper Price
Made of rubber Made of nylon $30.00
Conducting Conjoint Analysis
Construct the Stimuli
?
Generally, two sets of data are obtained. One, the estimation set, is used to calculate the part-worth functions for the attribute levels. The other, the holdout set, is used to assess reliability and validity.
Conducting Conjoint Analysis
Decide on the Form of Input Data
?
?
For non-metric data, the respondents are required to provide rank-order evaluations. In the metric form, the respondents provide ratings, rather than rankings.
Sneaker Profiles & Ratings
Table 3
Attribute Levels
Profile No. 1 2 3 4 5 6 7 8 9
Sole 1 1 1 2 2 2 3 3 3
Upper Price 1 1 2 2 3 3 1 2 2 3 3 1 1 3 2 1 3 2
Preference Rating 9 7 5 6 5 6 5 7 6
Conducting Conjoint Analysis
Decide on the Form of Input Data
The basic conjoint analysis model may be represented by the following formula:
U(X ) ? ?
i ?1
m
?? x
j ?1 ij
ki
ij
where
? ij
U(X)
xij = k m
= overall utility of an alternative = the part-worth contribution or utility associated with the j th level ( j = 1, 2, . . . ki) of the i th attribute (i, i = 1, 2, . . . m) 1 if the j th level of the i th attribute is present = 0 otherwise = number of levels of attribute i = number of attributes
Conducting Conjoint Analysis
Decide on the Form of Input Data
The importance of an attribute, Ii, is defined in terms of the range of the part-worths, ? ij across the levels of that attribute: , The attribute's importance relative to other attributes, Wi:
W
? i
I ?I
i m i ?1
m i ?1
i
So that
If an attribute has k levels, it is coded in terms of k-1 dummy variables
?W
i
?1
Conducting Conjoint Analysis
Decide on the Form of Input Data
The model estimated may be represented as: U = b0 + b1X1 + b2X2 + b3X3 + b4X4 + b5X5 + b6X6 where
X1, X2 X3, X4 X5, X6
= dummy variables representing Sole = dummy variables representing Upper = dummy variables representing Price
For Sole the attribute levels were coded as follows:
X1
Level 1 Level 2 Level 3 1 0 0
X2
0 1 0
Sneaker Data Coded for Dummy Variable Regression
Table 4
Preference Ratings Y 9 7 5 6 5 6 5 7 6
Sole X1 X2 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0
Attributes Upper X3 X4 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0
Price X5 X6 1 0 0 0 0 1 0 1 0 0 1 0 1 0 0 0 0 1
Conducting Conjoint Analysis
Decide on the Form of Input Data
The levels of the other attributes were coded similarly. The parameters were estimated as follows:
b0 b1 b2 b3 b4 b5 b6
= = = = = = =
4.222 1.000 -0.333 1.000 0.667 2.333 1.333
Given the dummy variable coding, in which level 3 is the base level, the coefficients may be related to the part-worths:
?11 - ?13 = b1 ?12 - ?13 = b2
Conducting Conjoint Analysis
Decide on the Form of Input Data
To solve for the part-worths, an additional constraint is necessary.
? 11 + ? 12 + ? 13 = 0
These equations for the first attribute, Sole, are:
? 11 - ? 13 = 1. 000 ? 12 - ? 13 = -0. 333
? 11 + ? 12 + ? 13 = 0
Solving these equations, we get,
? 11 ? 12 ? 13
= 0.778 = -0.556 = -0.222
Conducting Conjoint Analysis
Decide on the Form of Input Data
The part-worths for other attributes can be estimated similarly. For Upper we have:
?21 - ?23 = b3 ?22 - ?23 = b4
? 21 + ? 22 + ? 23 = 0
For the third attribute, Price, we have:
?31 - ?33 = b5 ?32 - ?33 = b6
? 31 + ? 32 + ? 33 = 0
Conducting Conjoint Analysis
Decide on the Form of Input Data
The relative importance weights were calculated based on ranges of part-worths, as follows: Sum of ranges of part-worths = (0.778 - (-0.556)) + (0.445-(-0.556)) + (1.111-(-1.222)) = 4.668
Relative importance of Sole Relative importance of Upper Relative importance of Price
= 1.334/4.668 = 0.286 = 1.001/4.668 = 0.214 = 2.333/4.668 = 0.500
Results of Conjoint Analysis
Table 5 Level Attribute No. Description Sole 3 2 1 Rubber Polyurethane Plastic Leather Canvas Nylon $30.00 $60.00 $90.00 Utility 0.778 -0.556 -0.222 0.445 0.111 -0.556 1.111 0.111 -1.222 Importance
0.286
Upper 3 2 1 Price 3 2 1
0.214
0.500
Conducting Conjoint Analysis
Assessing Reliability and Validity
?
?
?
The goodness of fit of the estimated model R2 should be evaluated. Test-retest reliability can be assessed by obtaining a few replicated judgments later in data collection. The evaluations for the validation stimuli can be predicted by the estimated partworth functions and can then be compared with actual.
SPSS Windows
The conjoint analysis approach can be implemented using regression if the dependent variable is metric (interval or ratio). This procedure can be run by clicking: Analyze>Regression>Linear …
doc_338764984.ppt
This is a ppt explaining the conjoint analysis.
Conjoint Analysis
Conjoint Analysis
?
?
Conjoint analysis attempts to determine the relative importance consumers attach to salient attributes and the utilities they attach to the levels of attributes. The respondents are presented with combinations of attribute levels and asked to evaluate these stimuli in terms of their desirability.
Statistics and Terms Associated with Conjoint Analysis
?
?
?
?
?
Part-worth functions. The part-worth functions, or utility functions, describe the utility consumers attach to the levels of each attribute. Relative importance weights. These indicate which attributes are important in influencing consumer choice. Attribute levels. The attribute levels denote the values assumed by the attributes. Full profiles. Complete profiles of brands are constructed in terms of all the attributes and their levels. Pairwise tables. The respondents evaluate two attributes at a time until all the required pairs of attributes have been evaluated.
Conducting Conjoint Analysis
Fig. 1 Formulate the Problem Construct the Stimuli Decide the Form of Input Data Select a Conjoint Analysis Procedure
Interpret the Results
Assess Reliability and Validity
Conducting Conjoint Analysis
Formulate the Problem
?
?
?
?
?
Identify the attributes and attribute levels to be used in constructing the stimuli. The attributes selected should be important in influencing consumer preference and choice and should be actionable. A typical conjoint analysis study involves six or seven attributes. At least three levels should be used, unless the attribute naturally occurs in binary form (two levels). The researcher should take into account the attribute levels prevalent in the marketplace.
Conducting Conjoint Analysis
Construct the Stimuli
?
?
?
In the pairwise approach, also called two-factor evaluations, the respondents evaluate two attributes at a time until all the possible pairs of attributes have been evaluated. In the full-profile approach, also called multiplefactor evaluations, complete profiles of brands are constructed for all the attributes and each profile is described on a separate index card. In the pairwise approach, it is possible to reduce the number of paired comparisons by using cyclical designs. Likewise, in the full-profile approach, the number of stimulus profiles can be greatly reduced by means of fractional factorial designs.
Sneaker Attributes and Levels
Table 1
Attribute
Sole
Number
3 2 1 3 2 1 3 2 1
Level
Description
Rubber Polyurethane Plastic Leather Canvas Nylon $30.00 $60.00 $90.00
Upper
Price
Full-Profile Approach to Collecting Conjoint Data
Table 2
Example of a Sneaker Product Profile
Sole Upper Price
Made of rubber Made of nylon $30.00
Conducting Conjoint Analysis
Construct the Stimuli
?
Generally, two sets of data are obtained. One, the estimation set, is used to calculate the part-worth functions for the attribute levels. The other, the holdout set, is used to assess reliability and validity.
Conducting Conjoint Analysis
Decide on the Form of Input Data
?
?
For non-metric data, the respondents are required to provide rank-order evaluations. In the metric form, the respondents provide ratings, rather than rankings.
Sneaker Profiles & Ratings
Table 3
Attribute Levels
Profile No. 1 2 3 4 5 6 7 8 9
Sole 1 1 1 2 2 2 3 3 3
Upper Price 1 1 2 2 3 3 1 2 2 3 3 1 1 3 2 1 3 2
Preference Rating 9 7 5 6 5 6 5 7 6
Conducting Conjoint Analysis
Decide on the Form of Input Data
The basic conjoint analysis model may be represented by the following formula:
U(X ) ? ?
i ?1
m
?? x
j ?1 ij
ki
ij
where
? ij
U(X)
xij = k m
= overall utility of an alternative = the part-worth contribution or utility associated with the j th level ( j = 1, 2, . . . ki) of the i th attribute (i, i = 1, 2, . . . m) 1 if the j th level of the i th attribute is present = 0 otherwise = number of levels of attribute i = number of attributes
Conducting Conjoint Analysis
Decide on the Form of Input Data
The importance of an attribute, Ii, is defined in terms of the range of the part-worths, ? ij across the levels of that attribute: , The attribute's importance relative to other attributes, Wi:
W
? i
I ?I
i m i ?1
m i ?1
i
So that
If an attribute has k levels, it is coded in terms of k-1 dummy variables
?W
i
?1
Conducting Conjoint Analysis
Decide on the Form of Input Data
The model estimated may be represented as: U = b0 + b1X1 + b2X2 + b3X3 + b4X4 + b5X5 + b6X6 where
X1, X2 X3, X4 X5, X6
= dummy variables representing Sole = dummy variables representing Upper = dummy variables representing Price
For Sole the attribute levels were coded as follows:
X1
Level 1 Level 2 Level 3 1 0 0
X2
0 1 0
Sneaker Data Coded for Dummy Variable Regression
Table 4
Preference Ratings Y 9 7 5 6 5 6 5 7 6
Sole X1 X2 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0
Attributes Upper X3 X4 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0
Price X5 X6 1 0 0 0 0 1 0 1 0 0 1 0 1 0 0 0 0 1
Conducting Conjoint Analysis
Decide on the Form of Input Data
The levels of the other attributes were coded similarly. The parameters were estimated as follows:
b0 b1 b2 b3 b4 b5 b6
= = = = = = =
4.222 1.000 -0.333 1.000 0.667 2.333 1.333
Given the dummy variable coding, in which level 3 is the base level, the coefficients may be related to the part-worths:
?11 - ?13 = b1 ?12 - ?13 = b2
Conducting Conjoint Analysis
Decide on the Form of Input Data
To solve for the part-worths, an additional constraint is necessary.
? 11 + ? 12 + ? 13 = 0
These equations for the first attribute, Sole, are:
? 11 - ? 13 = 1. 000 ? 12 - ? 13 = -0. 333
? 11 + ? 12 + ? 13 = 0
Solving these equations, we get,
? 11 ? 12 ? 13
= 0.778 = -0.556 = -0.222
Conducting Conjoint Analysis
Decide on the Form of Input Data
The part-worths for other attributes can be estimated similarly. For Upper we have:
?21 - ?23 = b3 ?22 - ?23 = b4
? 21 + ? 22 + ? 23 = 0
For the third attribute, Price, we have:
?31 - ?33 = b5 ?32 - ?33 = b6
? 31 + ? 32 + ? 33 = 0
Conducting Conjoint Analysis
Decide on the Form of Input Data
The relative importance weights were calculated based on ranges of part-worths, as follows: Sum of ranges of part-worths = (0.778 - (-0.556)) + (0.445-(-0.556)) + (1.111-(-1.222)) = 4.668
Relative importance of Sole Relative importance of Upper Relative importance of Price
= 1.334/4.668 = 0.286 = 1.001/4.668 = 0.214 = 2.333/4.668 = 0.500
Results of Conjoint Analysis
Table 5 Level Attribute No. Description Sole 3 2 1 Rubber Polyurethane Plastic Leather Canvas Nylon $30.00 $60.00 $90.00 Utility 0.778 -0.556 -0.222 0.445 0.111 -0.556 1.111 0.111 -1.222 Importance
0.286
Upper 3 2 1 Price 3 2 1
0.214
0.500
Conducting Conjoint Analysis
Assessing Reliability and Validity
?
?
?
The goodness of fit of the estimated model R2 should be evaluated. Test-retest reliability can be assessed by obtaining a few replicated judgments later in data collection. The evaluations for the validation stimuli can be predicted by the estimated partworth functions and can then be compared with actual.
SPSS Windows
The conjoint analysis approach can be implemented using regression if the dependent variable is metric (interval or ratio). This procedure can be run by clicking: Analyze>Regression>Linear …
doc_338764984.ppt