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Interrupted Time Series Analysis. Vol. 21 [electronic resource]

By: Contributor(s): Material type: TextTextSeries: Quantitative Applications in the Social Sciences Ser ; Vol. 21Publication details: Thousand Oaks : SAGE Publications, Incorporated Aug. 1980Description: 96 pagesISBN:
  • 9780803914933
DDC classification:
  • 519.55 OR 519.55024301 19 40968
LOC classification:
  • HA30.3
Online resources: SAGE Research Methods OnlineSummary: Annotation Describes ARIMA or Box Tiao models, widely used in the analysis of interupted time series quasi-experiments, assuming no statistical background beyond simple correlation. The principles and concepts of ARIMA time series analyses are developed and applied where a discrete intervention has impacted a social system. '...this is the kind of exposition I wished I had had some ten years ago when venturing into the world of autoregressive, moving-average (ARIMA) models of time-series analysis...This monograph nicely lays out a method for assessing the impact of a discrete policy or event of some importance on behavior which can be continuously observed...If widely used, as I hope, it will save a generation of social scientists from the labor of having to learn this methodology the hard way...' -- Helmut Norpoth, State University of New York
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Item type Current library Collection Call number Status Date due Barcode
Books Books Symbiosis International University Central Library Reference Reference 519.55/MCD OR 519.55024301 40968 (Browse shelf(Opens below)) Not For Loan (Restricted Access) siu-b-40968
Browsing Symbiosis International University Central Library shelves, Shelving location: Reference, Collection: Reference Close shelf browser (Hides shelf browser)
519.54/KIM 40960 Introduction to factor analysis : 519.55/BRA 41093 Multiple time series models / 519.55/SAY 41016 Pooled time series analysis / 519.55/MCD OR 519.55024301 40968 Interrupted Time Series Analysis. 519.56/MOH 41018 Understanding significance testing 519.56/CAR OR 300.18 40964 Reliability and validity assessment / 519.5/ASH 40950 Causal modeling /

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Annotation Describes ARIMA or Box Tiao models, widely used in the analysis of interupted time series quasi-experiments, assuming no statistical background beyond simple correlation. The principles and concepts of ARIMA time series analyses are developed and applied where a discrete intervention has impacted a social system. '...this is the kind of exposition I wished I had had some ten years ago when venturing into the world of autoregressive, moving-average (ARIMA) models of time-series analysis...This monograph nicely lays out a method for assessing the impact of a discrete policy or event of some importance on behavior which can be continuously observed...If widely used, as I hope, it will save a generation of social scientists from the labor of having to learn this methodology the hard way...' -- Helmut Norpoth, State University of New York

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