Author Description
Introduction To Itsa -- Arima Algerbra -- The Noise Component: N(at) -- The Intervention Component: X (it) -- Auxiliary Modeling Procedures -- Into The Future. David Mcdowall, Richard Mccleary, Bradley J. Bartos. Includes Bibliographical References And Index. Electronic Reproduction. Oxford Available Via World Wide Web.
Menrva's Summary
THE THESIS
This text investigates the methodological framework of Interrupted Time Series Analysis (ITSA) as a robust tool for evaluating the impact of interventions in longitudinal data. The authors, experts in quantitative social science research, provide a structured approach to modeling time-dependent data where a specific event or policy change occurs. They utilize ARIMA (Autoregressive Integrated Moving Average) modeling to isolate the effects of interventions from underlying noise and trends in the data.
THE SCOPE MAP
What You Will Find
- Comprehensive introduction to the logic and application of ITSA
- Detailed breakdown of ARIMA algebra and its role in time series modeling
- Analysis of the noise component N(at) and the intervention component X(it)
- Discussion of auxiliary modeling procedures for complex datasets
Scope Limits
- The text focuses primarily on the statistical mechanics of ITSA rather than broad sociological theory
- It assumes a foundational understanding of regression analysis and basic calculus
THE AUTHORITY PERSPECTIVE
Experts frequently cite this work as a foundational technical manual for researchers applying quasi-experimental designs to longitudinal data. Readers often note the high level of mathematical density, making it a specialized resource for those already proficient in statistical modeling.
Page Count:
0
Publication Date:
1900-01-01
Publisher:
Oxford University Press,
ISBN-10:
019094398X
ISBN-13:
9780190943981
Statistics
Data Analysis
Social Science
Econometrics
Time-series Analysis
Statistical methods
Research Methods
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