{"title":"资讯科技管理局简介","authors":"David McDowall, R. McCleary, Bradley J. Bartos","doi":"10.1093/OSO/9780190943943.003.0001","DOIUrl":null,"url":null,"abstract":"\n Chapter 1 introduces Interrupted Time Series Analysis (ITSA) as a toolbox for researchers whose data consist of a long sequence of observations | say, N ≥15 observations | measured before and after a treatment or intervention. Sometimes the treatment or intervention is implemented by the researcher, other times it occurs naturally or by accident. The chapter also describes a family of impact types, characterized by their onset (abrupt or gradual) and duration (permanent or temporary); and the essential role of counterfactual controls in causal inference. The chapter concludes with an outline and summary of the book's subsequent chapters.","PeriodicalId":180500,"journal":{"name":"Interrupted Time Series Analysis","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Introduction to ITSA\",\"authors\":\"David McDowall, R. McCleary, Bradley J. Bartos\",\"doi\":\"10.1093/OSO/9780190943943.003.0001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Chapter 1 introduces Interrupted Time Series Analysis (ITSA) as a toolbox for researchers whose data consist of a long sequence of observations | say, N ≥15 observations | measured before and after a treatment or intervention. Sometimes the treatment or intervention is implemented by the researcher, other times it occurs naturally or by accident. The chapter also describes a family of impact types, characterized by their onset (abrupt or gradual) and duration (permanent or temporary); and the essential role of counterfactual controls in causal inference. The chapter concludes with an outline and summary of the book's subsequent chapters.\",\"PeriodicalId\":180500,\"journal\":{\"name\":\"Interrupted Time Series Analysis\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Interrupted Time Series Analysis\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/OSO/9780190943943.003.0001\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Interrupted Time Series Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/OSO/9780190943943.003.0001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Chapter 1 introduces Interrupted Time Series Analysis (ITSA) as a toolbox for researchers whose data consist of a long sequence of observations | say, N ≥15 observations | measured before and after a treatment or intervention. Sometimes the treatment or intervention is implemented by the researcher, other times it occurs naturally or by accident. The chapter also describes a family of impact types, characterized by their onset (abrupt or gradual) and duration (permanent or temporary); and the essential role of counterfactual controls in causal inference. The chapter concludes with an outline and summary of the book's subsequent chapters.