1 / 9

Multivariate Data Analysis Chapter 1 - Introduction

Multivariate Data Analysis Chapter 1 - Introduction. Chapter 1. What is Multivariate Analysis? Impact of the Computer Revolution Multivariate Analysis Defined. Some Basic Concepts of Multivariate Analysis. The Variate (a linear combination of variables with weights) Measurement Scale

nenet
Download Presentation

Multivariate Data Analysis Chapter 1 - Introduction

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Multivariate Data AnalysisChapter 1 - Introduction

  2. Chapter 1 • What is Multivariate Analysis? • Impact of the Computer Revolution • Multivariate Analysis Defined

  3. Some Basic Concepts of Multivariate Analysis • The Variate (a linear combination of variables with weights) • Measurement Scale • Nonmetric Measurement Scales • Nominal and ordinal scales • Metric Measurement Scales • Interval and ration scales • Measurement Error and Multivariate Measurement • Validity and reliability • Statistical Significance Versus Statistical Power • Type I error (alpha) • Type II error (beta) • Power: Effect size, Alpha, Sample size

  4. Chapter 1Types of Multivariate Techniques • Principal Components and Common Factor Analysis • Multiple Regression • Multiple Discriminant Analysis • Multivariate Analysis of Variance • Conjoint Analysis • Canonical Correlation

  5. Chapter 1Types of Multivariate Techniques • Cluster Analysis • Multidimensional Scaling • Correspondence Analysis • Linear Probability Models • Structural Equation Modeling • Other Emerging Multivariate Techniques

  6. Guidelines for Multivariate Analysis and Interpretation • Establish Practical Significance as well as Statistical Significance • Sample Size Affects All Results • Know Your Data • Influences of outliners • Missing values • Violations of assumptions

  7. Guidelines for Multivariate Analysis and Interpretation (Cont.) • Strive for Model Parsimony • Multicollinearity • Look at Your Errors • Validate Your Results • Splitting the sample • Employing a bootstraping technique • Gathering a separate sample

  8. A structured Approach to Multivariate Model Building • Stage 1: Define the Research Problem, Objectives, and Multivariate Techniques to Be Used • Stage 2: Develop the Analysis Plan • Stage 3: Evaluate the Assumptions Underlying the Multivariate Technique • Stage 4: Estimate the Multivariate Model and Assess Overall Model Fit • Stage 5: Interpret the Variate(s) • Stage 6: Validate the Multivariate Model

  9. Databases • Primary Database • Perceptions of HATCO • Purchase Outcomes • Purchaser Characteristics • Other Databases

More Related