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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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Strategies for Assessing and Addressing Confounding01:25

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Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
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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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Confirmation Biases01:31

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The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
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Measuring Attentional Biases for Threat in Children and Adults
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Methodological considerations for developing and evaluating response bias indicators.

Danielle Burchett1, Yossef S Ben-Porath2

  • 1Department of Psychology, California State University, Monterey Bay.

Psychological Assessment
|November 26, 2019
PubMed
Summary

Response bias indicators have advanced significantly but require more sophisticated development and study. Future research should focus on improving the evaluation and design of these crucial tools for detecting invalid responding.

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Area of Science:

  • Psychological Measurement
  • Research Methodology

Background:

  • Response bias indicators have a near-100-year history with notable advancements.
  • Current methods for examining the validity of these indicators are established but can be improved.

Purpose of the Study:

  • To discuss definitional issues in response bias.
  • To review common and innovative methods for response bias investigation.
  • To identify areas for future research in response bias indicator development and evaluation.

Main Methods:

  • Review of established and innovative research approaches for response bias.
  • Discussion of definitional issues related to response bias.
  • Focus on considerations for the need, evaluation, and design of response bias indicators.

Main Results:

  • Significant advancements in response bias indicator design and validity examination have occurred.
  • Opportunities exist for greater sophistication in developing and studying these indicators.
  • Specific areas for future research have been identified.

Conclusions:

  • Further sophistication is needed in the development and study of response bias indicators.
  • Continued research is essential to advance knowledge on invalid responding and detection methods.
  • Improved indicators will enhance the utility of detecting invalid response patterns.