Chapter 1 Introduction

1.1 Background: Data analysis in educational research

Educational research is hard to do (Berliner, 2002). This is because many educational phenomena are part of a complex system, with multiple, nested levels, and, well, people, many of them developing. Data analysis in education reflects some of the challenges of educational research writ large. In short, both educational research and analysis of educational data is hard. The goal of this book is to share how to make these difficulties less challenging using R, the open-source, free programming language and software.

1.2 Why a book on data analysis in R for educational research

There are at least three reasons why data analysis in educational research is hard:

  • Educational researchers have unique methods: an emphasis on multi-level models, networks, and measurement are just some examples.

  • Educational researchers face unique challenges: coming from myriad backgrounds, and working in fields with greater or lesser emphases on different aspects of data analysis.

  • Finally, there are training challenges. Educational research features some great methodologists: Many advances in the fields mentioned earlier in this session have been made by those working primarily in educational research. Nevertheless, few quantitative classes teach data analysis.