MULTIVARIATE STATISTICS
Previous statistics:
• Crosstabs (chi-square)
• t-test (means)
• Analysis Of Variance (ANOVA)
• Pearson’s 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” > 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
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