Why use Multivariate Data Analysis
Measurement
Explanation and prediction
Hypothesis testing
Name the basic concepts of Multivariate Analysis
the variate
Measurement scales
Non-metric
Metric
Measurement error
Types of techniques
Explain the concept “variate”
Variate is a linear combination of variables with empirically determined weights
Explain the concept “Measurement Scales”
non-metric:
metric:
Explain the concept “Measurement error”
Measurement error: degree to which the observed values are not representative of the “true” value.
Some causes for measurement error:
imprecision of the measurement
Inability of respondents to provide accurate information
Data entry error
> all variables have some error
Name two important characteristics of measurement and explain them
Which 4 outcomes are there with the null hypothesis, which types of errors are there?
Fill in the table
How are the type I and type II errors related and what is the key learning of it
Type I and type II errors are inversely related. Thus, Type I error become more restrictive as the probability of Type II increases
Key learning:
Reducing type I errors reduce the power of statistical test
Power is determined by three factors, name them
Name the two broad types of Multivariate methods
Dependence - analyze dependent and independent variables at the same time
Interdependence - analyze dependent and independent variables separately
Explain the dependence techniques
Explain interdependence techniques
Name the different dependence techniques
multiple regression
Multiple discriminant analysis
Login/Logistic regression
MANOVA
Conjoint Analysis
Canonical Correlation
SEM
Name the different interdependence techniques
factor analysis
Cluster analysis
Multidimensional scaling
Correspondence analysis
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