This is the second of two presentations on time series analysis. The morning workshop introduced time series methods and their utility for examining social science data. This afternoon workshop will discuss research that employs time series methods to answer a substantive question of interest to social science scholars, namely the connection between theories of crime rate change and observable characteristics of US crime rate trends. It focuses in particular on theoretical efforts to explain how the levels or means of serious crimes fluctuate over time. Although not always formulated in a way consistent with time series concepts, existing explanations generally yield clear predictions about how rates should behave. Empirical methods can then help adjudicate between theories, and the talk presents results from recent analyses of major national crime rates. More generally, the talk argues for a strategy that exploits broad stylized facts about crime rate variations to help guide and discipline theoretical development.
Dr. David McDowall is Professor in the School of Criminal Justice, University at Albany, SUNY. He teaches quantitative methods in the School’s graduate program. He is past editor of the Journal of Quantitative Criminology. With Richard McCleary, Errol Meidinger, and Richard Hay, he coauthored the Sage green book, Interrupted Time Series Analysis. Much of his research has examined issues related to the social distribution of criminal violence, and most recently he has been engaged in studying patterns in U.S. crime rate trends. Dr. McDowall is a Fellow of the American Society of Criminology.