Transcript Evaluation of the Observation Method

Chapter 1
Introduction
Instructor : Dr. Shin-Yuan Hung
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The Definition of Multivariate Analysis
 It refers to all statistical methods that
simultaneously analyze multiple measurements
on each individual or object under investigation.
Any simultaneous analysis of more than two
variables can be loosely considered multivariate
analysis.
Terms: variables, observations (cases).
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Measurement Scales
 Nonmetric (qualitative) Scales - Nominal Scale
Ordinal Scale
 Metric (quantitative) Scales ----- Interval Scale
Ratio Scale
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Measurement Error and Multivariate Measurement
 Measurement error is the degree to which the
observed values are not representative of the
“true” values.
 Multivariate measurements are that several
variables are joined in a composite measure to
represent a concept.
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Statistical Significance versus Statistical Power
 Power is the probability of correctly rejecting
the null hypothesis when it should be rejected.
 Power is actually determined by three factors:
effect size, alpha (), sample size.
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Types of Multivariate Techniques
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Principle Components and Common Factor Analysis
Multiple Regression
Multiple Discriminant Analysis
Multivariate Analysis of Variance and Covariance
Conjoint Analysis
Canonical Correlation
Cluster Analysis
Multidimensional Scaling
Correspondence Analysis
Linear Probability Models
Structural Equation Modeling
Other Emerging Multivariate Techniques
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A Structured Approach to Multivariate Model Building
1. Define the research problem, objectives, and
multivariate technique to be used;
2. Develop the analysis plan;
3. Evaluate the assumptions underlying the
multivariate technique;
4. Estimate the multivariate model and assess
overall model fit;
5. Interpret the variates;
6. Validate the multivariate model.
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A Classification of Multivariate Techniques
 Figure 1.2 in textbook
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The End
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