Design and Analysis of Time Series Experiments develops methods and models for analysis and interpretation of time series experiments. Drawing on examples from criminology, economics, education, pharmacology, public policy, program evaluation, public health, and psychology, it addresses researchers and graduate students in a wide range of the behavioral, biomedical and social sciences.

• List of Figures and Tables


• Preface


• 1: Introduction


• 2:ARIMA Algebra


• Appendix 2A-Expected Values


• Appendix 2B-Sequences, Series, and Limits


• 3: Noise Modeling


• Appendix 3A-Maximum Likelihood Estimation


• Appendix 3B-The Box-Cox Transformation Function


• 4: Forecasting


• 5: Intervention Modeling


• 6: Statistical Conclusion Validity


• Appendix 6-Probability and Odds


• 7: Internal Validity


• 8: Construct Validity


• 9: External Validity


• Notes


• Bibliography


• Index

Richard McCleary is Professor of Criminology, Law & Society and Planning, Policy & Design at the University of California, Irvine.
David McDowall is Distinguished Teaching Professor in the School of Criminal Justice at the University at Albany.
Bradley J. Bartos is a graduate student in the department of Criminology, Law, and Society at the University of California, Irvine.