MULTIVARIATE STATISTICS

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MULTIVARIATE STATISTICS

• Previous statistics
• Crosstabs (chi-square)
• t-test (means)
• Analysis Of Variance (ANOVA)
• Pearsons correlation coefficient
• Regression
• Multiple regression

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Next couple of statistics

• used less frequently
• (bottom of your tool box)
• more exploratory
• 1. Discriminant analysis
• 2. Factor analysis
• 3. Cluster analysis

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1. Discriminant analysis

• Tests for covariation between
• Categorical dependent variable
• group such as purchaser, non purchaser
• regular, occasional, and infrequent purchasers
• people who like chick flicks, those that like
  guy flicks, those that like both
• Continuous and categorical IVs
• age, income, gender, etc.

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1. Discriminant analysis (continued)

• which variables group people?
• coefficients reveal importance of factors
• larger coefficient, more important
• smaller coefficient, less important
• p-value associated with specific variables
• overall fit assessed by correctly classified

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2. Factor analysis

• examines covariation between
• several different variables
• that are reduced to one or more underlying
  factors or constructs
• e.g., overall intelligence, need to consume,
  etc.
• often used to develop scales of related questions
  -- data reduction
• no information on what causes what

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2. Factor analysis (continued)

• attempts to identify the most shared variation
• the first factor is the largest amount of
  variance
• the second factor is the second largest variance,
  etc.
• Number of factors
• Eigenvalues gt 1.0 (chance)
• bend in the Scree plot

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3. Cluster analysis

• Covariation among a number of variables
• Identifies segments of the sample
• Used frequently in marketing
• Helpful for targeting products and messages

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4. Multi-Dimensional Scaling

• abbreviated MDS
• Subject rate distances or differences between
  objects
• Data are subjected to analysis
• Analysis reveal underlying dimensions
• Used to identify how people differentiate products

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The End