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Data Analysis with IBM SPSS Statistics

You're reading from   Data Analysis with IBM SPSS Statistics Implementing data modeling, descriptive statistics and ANOVA

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787283817
Length 446 pages
Edition 1st Edition
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Authors (2):
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Ken Stehlik-Barry Ken Stehlik-Barry
Author Profile Icon Ken Stehlik-Barry
Ken Stehlik-Barry
Anthony Babinec Anthony Babinec
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Anthony Babinec
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Toc

Table of Contents (17) Chapters Close

Preface 1. Installing and Configuring SPSS FREE CHAPTER 2. Accessing and Organizing Data 3. Statistics for Individual Data Elements 4. Dealing with Missing Data and Outliers 5. Visually Exploring the Data 6. Sampling, Subsetting, and Weighting 7. Creating New Data Elements 8. Adding and Matching Files 9. Aggregating and Restructuring Data 10. Crosstabulation Patterns for Categorical Data 11. Comparing Means and ANOVA 12. Correlations 13. Linear Regression 14. Principal Components and Factor Analysis 15. Clustering 16. Discriminant Analysis

Rank order correlations

Although Pearson correlations are central statistical analyses, correlations based on ranking values require fewer assumptions and can capture patterns that are not linear. Bivariate correlations in SPSS include Spearman's Rho and Kendall's Tau-b. Tau-b was introduced as one of the ordinal measures of association in the Crosstabulation discussion (Chapter 10, Crosstabulation Patterns for Categorical Data). Spearman's Rho works well with values that can be ranked, such as the data for the 201 nations used in this chapter.

The following table contains these two sets of coefficients for the two life expectancy variables and the Percent_Urban field:

The Tau-b coefficients differ only slightly from the Pearson correlations shown earlier, while the Spearman coefficients are noticeably higher. This suggests that while GNI_per_Capita and life expectancy...

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