Edited by Ian Shapiro
Edited by Rogers M. Smith
Edited by Tarek E. Masoud
Publisher: Cambridge University Press
Print Publication Year: 2004
Online Publication Date:September 2009
Chapter DOI: http://dx.doi.org/10.1017/CBO9780511492174.012
Empirical studies of cause and effect in social science may be divided into two broad categories, experimental and observational. In the former, individuals or groups are randomly assigned to treatment and control conditions. Most experimental research takes place in a laboratory environment and involves student participants, but several noteworthy studies have been conducted in real-world settings, such as schools (Howell and Peterson 2002), police precincts (Sherman and Rogan 1995), public housing projects (Katz, Kling, and Liebman 2001), and voting wards (Gerber and Green 2000). The experimental category also encompasses research that examines the consequences of randomization performed by administrative agencies, such as the military draft (Angrist 1990), gambling lotteries (Imbens, Rubin, and Sacerdote 2001), random assignment of judges to cases (Berube 2002), and random audits of tax returns (Slemrod, Blumenthal, and Christian 2001). The aim of experimental research is to examine the effects of random variation in one or more independent variables.
Observational research, too, examines the effects of variation in a set of independent variables, but this variation is not generated through randomization procedures. In observational studies, the data generation process by which the independent variables arise is unknown to the researcher. To estimate the parameters that govern cause and effect, the analyst of observational data must make several strong assumptions about the statistical relationship between observed and unobserved causes of the dependent variable (Achen 1986; King, Keohane, and Verba 1994). To the extent that these assumptions are unwarranted, parameter estimates will be biased.