 Shuffle Toggle OnToggle Off
 Alphabetize Toggle OnToggle Off
 Front First Toggle OnToggle Off
 Both Sides Toggle OnToggle Off
 Read Toggle OnToggle Off
Reading...
How to study your flashcards.
Right/Left arrow keys: Navigate between flashcards.right arrow keyleft arrow key
Up/Down arrow keys: Flip the card between the front and back.down keyup key
H key: Show hint (3rd side).h key
A key: Read text to speech.a key
Play button
Play button
35 Cards in this Set
 Front
 Back
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


Variate

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

True


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?

Nonmetric


Interval and ratio scales are metric or nonmetric?

metric


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 Nway (Univariate) ANOVA?

1)Examine overall model FTest Result. If significant, proceed
2)Examine invididual FTests 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


Communality

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


Eigenvalues

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.
