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Updated: Jul 31, 2026
07:31
Murine Drinking Models in the Development of Pharmacotherapies for Alcoholism: Drinking in the Dark and Two-bottle Choice
Published on: January 7, 2019
Time series models of individual substance abusers
1Cancer Prevention Research Consortium, University of Rhode Island, Kingston 02881-0808, USA.
NIDA Research Monograph
|January 1, 1994
Summary
Time series analysis uses statistical methods to understand behaviors or evaluate interventions. This approach, including Autoregressive Integrated Moving Average (ARIMA) models, helps analyze single-subject or group data for research and program evaluation.
Area of Science:
- Statistics
- Biostatistics
- Behavioral Science
Background:
- Time series analysis is crucial for analyzing data collected over time from a single subject or unit.
- It aids in understanding underlying processes or assessing intervention impacts.
Purpose of the Study:
- To outline the applications and methodologies of time series analysis.
- To demonstrate its utility in fields like behavioral science and program evaluation.
Main Methods:
- Model identification using Autoregressive Integrated Moving Average (ARIMA) models.
- Intervention analysis to detect changes in series level or direction post-intervention.
- Pooled time series procedures for combining data (cross-sectional analysis, meta-analysis).
Main Results:
- Demonstrated ARIMA model selection for nicotine regulation in smokers.
- Showcased intervention analysis of relaxation therapy on blood pressure.
- Highlighted pooled analysis for aggregated data.
Conclusions:
- Time series analysis offers robust methods for individual and organizational-level research.
- Key considerations include model selection, handling missing data, and seasonal corrections.
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