Data Mining for the Social Sciences: An Introduction

We live in a world of big data: the amount of information collected on human behavior each day is staggering, and exponentially greater than at any time in the past. Additionally, powerful algorithms are capable of churning through seas of data to uncover patterns. Providing a simple and accessible introduction to data mining, Paul Attewell and David B. Monaghan discuss how data mining substantially differs from conventional statistical modeling familiar to most social scientists. The authors also empower social scientists to tap into these new resources and incorporate data mining methodologies in their analytical toolkits. Data Mining for the Social Sciences demystifies the process by describing the diverse set of techniques available, discussing the strengths and weaknesses of various approaches, and giving practical demonstrations of how to carry out analyses using tools in various statistical software packages.

Table of Contents

PART 1. CONCEPTS
Chapter 1. What Is Data Mining?
Chapter 2. Contrasts with the Conventional Statistical Approach
Chapter 3. Some General Strategies Used in Data Mining
Chapter 4. Important Stages in a Data Mining Project

PART 2. WORKED EXAMPLES
Chapter 5. Preparing Training and Test Datasets
Chapter 6. Variable Selection Tools
Chapter 7. Creating New Variables Using Binning and Trees
Chapter 8. Extracting Variables
Chapter 9. Classifiers
Chapter 10. Classification Trees
Chapter 11. Neural Networks
Chapter 12. Clustering
Chapter 13. Latent Class Analysis and Mixture Models
Chapter 14. Association Rules