Multidimensional Scaling and Conjoint Analysis
12 important questions on Multidimensional Scaling and Conjoint Analysis
How do you call a class of procedures for representing perceptions and preferences of respondents spatially by means of a visual display?
How do you call in MDS, attribute-based approaches to collecting perception data requiring the respondents to rate the stimuli on the identified attributes using semantic differential or Likert scales?
How do you call a type of multidimensional scaling method that assumes that the input data are metric?
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How do you call a plot of stress versus dimensionality used in MDS?
How do you call a method of configuring a spatial map such that the spatial map represents both brands or stimuli and respondent points or vectors and is derived solely from the preference data?
How do you call a method of configuring a spatial map such that the ideal points or vectors based on preference data are fitted in a spatial map derived from perception data?
How do you call an MDS technique for scaling qualitative data that scales the rows and columns of the input contingency table in corresponding units so that each can be displayed in the same low-dimensional space?
How do you call a technique that attempts to determine the relative importance consumers attach to salient attributes and the utilities they attach to the levels of attributes?
How do you call the mathematical model expressing the fundamental relationship between attributes and utility in conjoint analysis?
How do you call a form of conjoint analysis that can simplify the data collection task and estimate selected interactions as well as all main effects?
What are the seven steps involved in conducting multidimensional scaling?
- Stimuli selection: problem formulation
- Choice of an MDS procedure
- Assessing reliability and validity
- Labeling dimensions
- Input data: metric or nonmetric
- Number of dimensions
- Geometric representation and interpretation
What are the eight steps involved in conducting conjoint analysis?
- Assessing reliability and validity
- Number and levels of attributes: problem formulation
- Attribute importance determination
- Level of analysis: individual vs. Aggregate
- Yaxis: utility values for attribute levels
- Stimuli construction: full profile vs. Pairwise
- Input data: metric or nonmetric
- Selection of a conjoint procedure
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