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35 Cards in this Set

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What is multivariate data analysis?

Research that involves three or more variables, or that is concerned with underlying dimensions among multiple variables, will involve multivariate statistical analysis.
What marketing problems involve multivariate data analysis?
Most psychographic research and most research that seeks to identify variable market segments
A mathamatical way in which a set of variables can be represented with one question.

A linear combination of variables, each contributing to the overall meaning of the variate based upon an empirically derived weight
What are the two very basic classifications of multivariate data analysis procedures?
Dependence methods and interdependence methods
Dependence Techniques
Explain or predict one or more dependent variables. Needed when hypotheses involve distinction between independent and dependent variables
Name some examples of dependence techniques?
Multiple regression analysis
Multiple discriminant analysis
Multivariate analysis of variance
Structural Equation of modeling
Interdependence Techniques

Give meaning to a set of variables or seek to group things together.

Used when reaserchers examine questions that do not distinguish between independent and dependent variables.

What are some examples of interdependence Techniques?
Factor Analysis
Cluster Analysis
Multidimensional scaling
True/False: The nature of the measurement scales will determine which multivariate technique is appropriate for the data
Selection of a multivariate technique requires consideration of what?
Selection of a multivariate technique requires consideration of the types of measures used for both independent and dependent sets of variables
Nominal and ordinal scales are metric or nonmetric?
Interval and ratio scales are metric or nonmetric?
General Linear Modeling
A way of explaining and predicting a dependent variable based on fluctuations (variation) from its mean due to changes in independent variables
Multivariate Analysis of Variance (MANOVA)
A multivariate technique that predicts multiple continuous dependent variables with multiple categorical independent variables
What are the steps involved in interpreting N-way (Univariate) ANOVA?
1)Examine overall model F-Test Result. If significant, proceed

2)Examine invididual F-Tests for invididual Variables

3)For each significant categorical independent variable, interpret the effect by examining the group means

4)For each significant, continous covariate, interpret the parameter estimate (b)

5)For each significant interaction, interpret the means for each combination
Discriminant analysis
A statistical technqiue for predicting the probablity that an object will belong in one of two or more mutually exclusive categories (dependent variable) based on several independent variables
Factor analysis
Statistically indentfies a reduced number of factors from a larger number of measured variables
What are the two types of factor analysis?

Explaratory factor analysis (EFA)

Confirmatory factor analysis (CFA)

Exploratory Factor Analysis
Performed when the researcher is uncertain about how many factors may exist among a set of variables
Confirmatory Factor Analysis
Perfomred when the researcher has strong theoretical expecations about the factor structure bfore performing the analysis
Factor Loading
Indicates how strongly a measured variable is correlated with a factor
Data Reduction Technique
Approaches that summarize the informaiton from many variables into a reduced set of variates formed as linear combinations of measured variables
The rule of parsimony:
an explanation involving fewer components is better than one involving many more
Creating Composite Scales with Factor Results
When a clear pattern of loading exists, the researcher may take a simpler approach by summing the variables with high loadings and creating a summated scale
What does low loadings suggest about a variable?
Very few loadings suggest a variable does not contirbute much to the factor
How is the reliability of each summated scale tested?
The reliability of each summated scale is tested by computing a coefficient alpha estimate
A measure of the percentage of a variable's variation that is explained by the factors
What does a relatively high communality indicate?
A relatively high communality indicates that a variable has much in common with the other variables taken as a group
a measure of how much variance is explained by each factor
Factor Rotation
a mathamatical way of simplifying factor analysis results to better identify which variables "load on" which factors
What is the most common procedure for factor rotation?
Varimax Rotation
Cluster Analysis
A multivariate approach for grouping observations based on similarity among measured variables
Multidimensional scaling
Measures objects in multidimensional space on the basis of similarity of objects
Structural Equations Modeling (SEM)
Combines an inter and dependence technique to allow testing theory by providing an omnibus assessment of fit centered around a goodness of fit test
Partial Least Squares (PLS)
Combines a factor analytic and regression approach to provide path estimates to a model but not a goodness of fit. Usefull when there is only a small amount of data existing or when the measurment quality is not particular strong.