000			01742nam a22001817a 4500
999			_c65590 _d65590
008			200610b2017 ||||| |||| 00| 0 eng d
020			_a9780190661564
082			_a519.55 McC-R
100			_aMcCleary, Richard
245			_aDesign and analysis of time series experiments / _cRichard McCleary, David McDowall and Bradley J. Bartos
260			_aIndia _bOxford _c2017
300			_a368 p.
365			_aUSD _b47.95
500			_aDesign and Analysis of Time Series Experiments present the elements of statistical time series analysis while also addressing recent developments in research design and causal modeling. A distinguishing feature of the book is its integration of design and analysis of time series experiments. Readers learn not only how-to skills but also the underlying rationales for design features and analytical methods. ARIMA algebra, Box-Jenkins-Tiao models and model-building strategies, forecasting, and Box-Tiao impact models are developed in separate chapters. The presentation of the models and model-building assumes only exposure to an introductory statistics course, with more difficult mathematical material relegated to appendices. Separate chapters cover threats to statistical conclusion validity, internal validity, construct validity, and external validity with an emphasis on how these threats arise in time series experiments. Design structures for controlling the threats are presented and illustrated through examples. The chapters on statistical conclusion validity and internal validity introduce Bayesian methods, counterfactual causality, and synthetic control group designs.
650			_aTime-series analysis
650			_aExperimental design
650			_aSocial sciences--Statistical methods