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Related Concept Videos

Bias01:22

Bias

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Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
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Correspondence bias, also referred to as the fundamental attribution error, describes the tendency to attribute another person’s behavior to internal characteristics rather than situational influences. This cognitive bias leads individuals to overlook external factors that may be influencing actions, thereby fostering potentially inaccurate assessments of others’ intentions and dispositions.Empirical Evidence for Correspondence BiasResearch has consistently demonstrated the...
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Bias in Epidemiological Studies01:29

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Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
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Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
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While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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Reflections on modern methods: linkage error bias.

James C Doidge1,2, Katie L Harron2

  • 1Intensive Care National Audit and Research Centre, London, UK.

International Journal of Epidemiology
|October 22, 2019
PubMed
Summary
This summary is machine-generated.

Linkage error in linked data can cause bias in research and policy analysis. This study introduces a framework to identify error mechanisms and support quantitative bias analysis for linked data.

Keywords:
Linkage errorbiasbias analysisdata linkageinformation biasmissing dataquantitative bias analysisrecord linkageselection biassensitivity analysis

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Area of Science:

  • Epidemiology
  • Data Science
  • Public Policy

Background:

  • Linked data are crucial for epidemiological research, primary research enhancement, and public policy evaluation.
  • Linkage error, including missed or false links, can introduce significant bias in analyses.
  • Understanding linkage error mechanisms is vital for accurate quantitative bias analysis.

Purpose of the Study:

  • To introduce a conceptual framework for identifying linkage error mechanisms in data analysis.
  • To provide a study classification system for relevance assessment of these mechanisms.
  • To lay the groundwork for quantitative bias analysis concerning linkage error.

Main Methods:

  • Introduction of five key concepts related to linkage error.
  • Development of a study classification system to identify relevant error mechanisms.
  • Discussion of options for estimating parameters for bias analysis.

Main Results:

  • Identification of various ways linkage error can manifest (e.g., missing data, misclassification, merging/splitting of individuals).
  • Establishment of a link between linkage error, information bias, and selection bias.
  • Provision of a framework to guide quantitative bias analysis for linked data.

Conclusions:

  • A conceptual framework is presented to link linkage error to information and selection bias.
  • The framework aids in identifying relevant mechanisms for quantitative bias analysis.
  • This work supports more robust analysis of linked data in research and policy settings.