Results 311 to 320 of about 11,557,043 (380)
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Toxicology and Industrial Health, 1990
Errors in exposure measures reduce the power in statistical tests in health effect studies, and also bias the estimate of the magnitude of the effect of exposure on health. Several types of error in commonly used exposure measures are described. To study how these errors in exposure measures may obscure exposure-response relationships, an imaginary ...
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Errors in exposure measures reduce the power in statistical tests in health effect studies, and also bias the estimate of the magnitude of the effect of exposure on health. Several types of error in commonly used exposure measures are described. To study how these errors in exposure measures may obscure exposure-response relationships, an imaginary ...
openaire +2 more sources
BMJ (Clinical research ed.), 1996
Abstract A certain amount of error is intrinsic to any measurement process. In the conduct of epidemiologic research, measurement error is potentially a major problem that may invalidate the results of otherwise well-designed studies. For instance, if a case-control study is undertaken to address the issue of whether past dietary fat ...
Jennifer L Kelsey +3 more
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Abstract A certain amount of error is intrinsic to any measurement process. In the conduct of epidemiologic research, measurement error is potentially a major problem that may invalidate the results of otherwise well-designed studies. For instance, if a case-control study is undertaken to address the issue of whether past dietary fat ...
Jennifer L Kelsey +3 more
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Measurement error, fixed effects, and false positives in accounting research
Review of accounting studies, 2023Jared N. Jennings +3 more
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2016
AbstractA main objective of the chapter is to demonstrate how measurement error problems which are non-tractable in cross-section data can be overcome when (balanced) panel data are available. Transformations (including differences) recommended to eliminate fixed effects, can, in measurement error situations, magnify the relative noise–signal variation.
Erik Biørn, Erik Biørn
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AbstractA main objective of the chapter is to demonstrate how measurement error problems which are non-tractable in cross-section data can be overcome when (balanced) panel data are available. Transformations (including differences) recommended to eliminate fixed effects, can, in measurement error situations, magnify the relative noise–signal variation.
Erik Biørn, Erik Biørn
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Evaluating structural equation models with unobservable variables and measurement error.
, 1981C. Fornell, D. Larcker
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Measurement error and the replication crisis
Science, 2017E. Loken, A. Gelman
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Towards complete and error-free genome assemblies of all vertebrate species
Nature, 2021Arang Rhie +2 more
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