Correspondence Analysis and Multidimensional Scaling with IBM SPSS Categories Eğitimi

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IBM  » IBM SPSS Statistics Eğitimler
Business Analysis  » Business Analysis Eğitimler

Correspondence Analysis and Multidimensional Scaling with IBM SPSS Categories (0G056G )

IBM Course Code: 0G056G

This course will focus on how to perform Correspondence Analysis and Multi-Dimensional Scaling using procedures in the IBM SPSS Categories add-on module in IBM SPSS Statistics. Learn how to use correspondence analysis to examine the relationship of categorical data and display these relationships on perceptual maps. Learn about multidimensional scaling and preference scaling techniques to examine similarities and dissimilarities among objects such as product brands and features and customer preferences. These techniques are useful in any circumstance where you need to analyze and display graphically the correspondence among categorical data. The course will discuss the basic logic of these techniques, how to setup the analysis and examine the results using a variety of usage examples and hands-on exercises.

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Who Needs to Attend

If you are a IBM SPSS Statistics (formerly SPSS Statistics) user in any application field who wants to learn more about Correspondence analysis, and Multidimensional Scaling, this course will be of interest to you.


Experience with IBM SPSS Statistics or SPSS Statistics or completion of the Introduction to IBM SPSS Statistics (Intermediates) course. Intermediate statistical knowledge or at least one college level course in statistics is helpful.

Follow-On Courses

There are no follow-ons for this course.

Course Outline

  • Introduction to Correspondence Analysis and Perceptual Mapping
  • Simple Correspondence Analysis of Counts
    • Analyzing a Summary Table
    • Adding Supplementary Categories
  • Multiple Correspondence Analysis
  • Means-based Perceptual Maps
  • Introduction to Multidimensional Scaling
  • Individual Differences Multidimensional Scaling
  • Analysis of Preferences: Unfolding Models

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