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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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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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Systematic Error: Methodological and Sampling Errors01:15

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In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
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Motivational Bias01:25

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Cognitive bias results from limitations in thinking and information processing, leading to systematic errors in judgment. Conversely, motivational bias stems from personal desires or emotions, causing distortions in perception to align with self-interest. Motivational bias influences how individuals perceive and attribute causes to events, often shaped by personal needs, goals, and self-esteem preservation. This bias can distort judgment, leading to inaccurate assessments of success, failure,...
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Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

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Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
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Stereotype Content Model02:16

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Algorithmic bias in social research: A meta-analysis.

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Algorithmic bias from importing Boolean optimization algorithms into social sciences has caused a reproducibility crisis. One in three studies using Qualitative Comparative Analysis (QCA) is affected, with one in ten severely impacted.

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Area of Science:

  • Social sciences, including management, political science, and sociology.
  • Interdisciplinary research methodologies.

Background:

  • A significant "reproducibility crisis" is impacting both natural and social sciences.
  • Selective reporting and methodological issues are key contributing factors to this crisis.

Purpose of the Study:

  • To investigate the fusion of selective reporting and methodological problems.
  • To demonstrate how the uncritical adoption of Boolean optimization algorithms has induced "algorithmic bias" in social science research.
  • To assess the scale of this bias in empirical studies using Qualitative Comparative Analysis (QCA).

Main Methods:

  • Analysis of replication material from 215 peer-reviewed QCA articles.
  • Examination of studies published in 109 high-profile management, political science, and sociology journals.
  • Estimation of the prevalence and severity of algorithmic bias in empirical QCA work.

Main Results:

  • An estimated one in three studies employing QCA is affected by algorithmic bias.
  • Approximately one in ten of these studies are severely impacted by the bias.
  • The findings highlight a considerable scale of algorithmic bias over the last 25 years.

Conclusions:

  • The uncritical import of algorithms like Boolean optimization can introduce significant bias across disciplines.
  • Scientists must rigorously evaluate the suitability of methods and algorithms before cross-disciplinary application.
  • Addressing algorithmic bias is crucial for mitigating the reproducibility crisis in social sciences.