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210123 ||| eng |
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|a 1118058208
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|a 0470631090
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|a 9781118058206
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|a 0470631104
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|a 9780470631102
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|a 9780470631096
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4 |
|a QA278.65
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|a Weisberg, Herbert I.
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|a Bias and causation
|b models and judgment for valid comparisons
|c Herbert I. Weisberg
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| 260 |
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|a Hoboken, N.J.
|b Wiley
|c 2010
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| 300 |
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|a xv, 348 pages
|b illustrations
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|a Includes bibliographical references (pages 321-339) and index
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|a What is bias? -- Causality and comparative studies -- Estimating causal effects -- Varieties of bias -- Selection bias -- Confounding : an enigma? -- Confounding : essence, correction, and detection -- Intermediate causal factors -- Information bias -- Sources of bias -- Contending with bias -- Glossary
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| 653 |
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|a Causation / fast
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| 653 |
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|a Paired comparisons (Statistics) / fast
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|a Analyse discriminante
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| 653 |
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|a Statistics as Topic
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| 653 |
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|a Paired comparisons (Statistics) / http://id.loc.gov/authorities/subjects/sh85096859
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| 653 |
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|a Causalité
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| 653 |
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|a Paarsgewijze vergelijkingen / gtt
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| 653 |
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|a Causality
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| 653 |
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|a Causaliteit / gtt
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| 653 |
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|a Méthode des comparaisons par paires
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| 653 |
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|a Causation / http://id.loc.gov/authorities/subjects/sh85021459
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| 653 |
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|a Statistics / http://id.loc.gov/authorities/subjects/sh85127580
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| 653 |
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|a Statistics / fast
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| 653 |
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|a Discriminant analysis / fast
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| 653 |
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|a statistics / aat
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|a Discriminant analysis / http://id.loc.gov/authorities/subjects/sh85038374
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| 653 |
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|a Statistique
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| 653 |
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|a Selection Bias
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| 653 |
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|a Discriminant Analysis
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| 653 |
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|a Bias / gtt
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| 653 |
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|a Statistiques
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| 653 |
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|a Research Design
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| 653 |
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|a Matched-Pair Analysis
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| 653 |
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|a MATHEMATICS / Probability & Statistics / Multivariate Analysis / bisacsh
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|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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|a Wiley series in probability and statistics
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| 776 |
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|z 0470631090
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|z 9781118058206
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|z 0470286393
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|z 0470631104
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|z 9780470286395
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|z 9780470631096
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|z 1118058208
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|z 1282707744
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|z 9780470631102
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|z 9781282707740
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|u https://learning.oreilly.com/library/view/~/9781118058206/?ar
|x Verlag
|3 Volltext
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|a 519.5
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|a 510
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|a 745.4
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|a 519.5/35
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|a 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 Causation pre
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