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Chapter 15

Chapter 15. Multivariate Data Analysis. Interdependence Techniques Factor Analysis Technique in which researchers look for a small number of factors that could explain the correlation between a large number of variables Cluster Analysis Variables are placed in subgroups or clusters

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Chapter 15

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  1. Chapter 15 Multivariate Data Analysis

  2. Interdependence Techniques • Factor Analysis • Technique in which researchers look for a small number of factors that could explain the correlation between a large number of variables • Cluster Analysis • Variables are placed in subgroups or clusters • Multidimensional Scaling • It encompasses a set of computational procedures that can summarize an input matrix of associations between variables or objects in two dimensional space

  3. Dependence Techniques • Discriminant Analysis • To find a linear combination of independent variables that makes the mean scores across categories of the dependent variable on this linear combination m different • Conjoint Analysis • Deals with the joint effects of two or more independent variables on the ordering of a dependent variable

  4. Factor Analysis • Look for small set of factors to explain correlation • between a large set of variables • Used for data reduction and transformation • Used in personality scales, identification of key • product attributes, etc.

  5. Factor Analysis (contd) Factor: A variable or a construct that is not directly observable but needs to be inferred from input variables Eigenvalue: Amount of variance in the original variables that are associated with the factor

  6. Factor Analysis (contd) Scree Plot: Plot of eigenvalues against number of factors. For factors with large eigenvalues this plot has a steep slope . Percentage of Variance Criteria: The number of factors extracted is determined so that the cumulative percentage of variance extracted by the variance reaches a satisfactory level. Factor Score:Value of each factor for all respondents

  7. Disadvantages of Factor Analysis • Subjective • Does not make use of any standard • statistical tests

  8. Cluster Analysis • Group objects into clusters based on the • attributes they possess. • Objects that are similar placed in one group • Groups have minimum within-group variability and • maximum between-group variability.

  9. Multi-dimensional Scaling • Creates a matrix associations between variables • Used by marketers to study relationships among objects, consumer perceptions, brand preferences, and preferred product attributes.

  10. Discriminant Analysis • Objective is to find a linear combination of • independent variables that make the mean scores • across categories of dependent variables on this linear combination maximally different. • Used to classify objects into two or more • alternative groups on the basis of a set of • measurements

  11. Conjoint Analysis • Measure joint effects of two or more independent variables on the ordering of a dependent variable • Quantitative measure of relative importance of one attribute over another

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