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[null,"1.","Record Nr.",null,"UNINA9910822673803321"]
[null,null,"Autore",null,"Weisberg Herbert I. <1944->"]
[null,null,"Titolo",null,"Bias and causation : models and judgment for valid comparisons / / Herbert I. Weisberg"]
[null,null,"Pubbl/distr/stampa",null,"Hoboken, NJ, : Wiley, c2010"]
[null,null,"ISBN",null,"9786612707742"]
[{"$nulls":4},"9781282707740"]
[{"$nulls":4},"1282707744"]
[{"$nulls":4},"9780470631102"]
[{"$nulls":4},"0470631104"]
[{"$nulls":4},"9780470631096"]
[{"$nulls":4},"0470631090"]
[null,null,"Edizione",null,"[1st edition]"]
[null,null,"Descrizione fisica",null,"1 online resource (366 p.)"]
[null,null,"Collana",null,"Wiley series in probability and statistics"]
[null,null,"Classificazione",null,"70.03"]
[null,null,"Disciplina",null,"519.5/35"]
[null,null,"Soggetti",null,"Discriminant analysis"]
[{"$nulls":4},"Paired comparisons (Statistics)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Bias and Causation: Models and Judgment for Valid Comparisons; Contents; Preface; CHAPTER 1: What Is Bias?; CHAPTER 2: Causality and Comparative Studies; CHAPTER 3: Estimating Causal Effects; CHAPTER: 4 Varieties of Bias; CHAPTER 5: Selection Bias; CHAPTER 6: Confounding: An Enigma?; CHAPTER 7: Confounding: Essence, Correction, and Detection; CHAPTER 8: Intermediate Causal Factors; CHAPTER 9: Information Bias; CHAPTER 10: Sources of Bias; CHAPTER 11: Contending with Bias; Glossary; Bibliography; Index"]
[null,null,null,"Sommario/riassunto","A one-of-a-kind resource on identifying and dealing with bias in statistical research on causal effects Do cell phones cause cancer? Can a new curriculum increase student achievement? Determining what the real causes of such problems are, and how powerful their effects may be, are central issues in research across various fields of study. Some researchers are highly skeptical of drawing causal conclusions except in tightly controlled randomized experiments, while others discount the threats posed by different sources of bias, even in less rigorous observational studies. Bias and Causa"]