Show Summary Details
Political ResearchMethods and Practical Skills

Political Research: Methods and Practical Skills (3rd edn)

Sandra Halperin and Oliver Heath
Page of

Printed from Oxford Politics Trove. Under the terms of the licence agreement, an individual user may print out a single article for personal use (for details see Privacy Policy and Legal Notice).

date: 18 October 2021

p. 42216. Patterns of Association: Bivariate Analysislocked

p. 42216. Patterns of Association: Bivariate Analysislocked

  • Sandra HalperinSandra HalperinProfessor of International Relations, Royal Holloway, University of London
  •  and Oliver HeathOliver HeathProfessor of Politics, Royal Holloway, University of London

Abstract

This chapter discusses the principles of bivariate analysis as a tool for helping researchers get to know their data and identify patterns of association between two variables. Bivariate analysis offers a way of establishing whether or not there is a relationship between two variables, a dependent variable and an independent variable. With bivariate analysis, theoretical expectations can be compared against evidence from the real world to see if the theory is supported by what is observed. The chapter examines the pattern of association between dependent and independent variables, with particular emphasis on hypothesis testing and significance tests. It discusses ordinary least squares (OLS) regression and cross-tabulation, two of the most widely used statistical analysis techniques in political research. Finally, it explains how to state the null hypothesis, calculate the chi square, and establishing the correlation between the dependent and independent variables.

You do not currently have access to this chapter

Sign in

Please sign in to access the full content.

Subscribe

Access to the full content requires a subscription