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Related Concept Videos

Bias

Bias

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...

Bias in Epidemiological Studies

Bias in Epidemiological Studies

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:

Confirmation Biases

Confirmation Biases

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?

Motivational Bias

Motivational Bias

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,...

Correspondence Bias

Correspondence Bias

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 prevalence of...

Surveys

Surveys

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

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Related Experiment Videos

Berkson's bias, selection bias, and missing data.

Daniel Westreich¹

¹Department of Obstetrics and Gynecology and Duke Global Health Institute, Duke University, Durham, NC 27710, USA. daniel.westreich@duke.edu

Epidemiology (Cambridge, Mass.)

|November 15, 2011

Summary

This summary is machine-generated.

Berkson's bias, often overlooked, is a key model for understanding selection bias and missing data issues in epidemiology. Recognizing its causal structure is crucial for addressing data problems in research.

Related Experiment Videos

Area of Science:

Epidemiology
Biostatistics
Causal Inference

Background:

Berkson's bias is a recognized issue in epidemiologic literature.
It is often underappreciated as a model for selection bias and missing data bias.
Understanding its mechanisms is vital for accurate data analysis.

Purpose of the Study:

To illustrate Berkson's bias as a model for selection bias and missing data.
To demonstrate the connection between Berkson's bias, collider bias, and general selection bias.
To highlight the importance of causal structure in missing data processes.

Main Methods:

Utilizing simple causal diagrams.
Employing 2x2 tables for illustration.
Analyzing the causal structure of missing data processes.

Main Results:

Berkson's bias is shown to be analogous to general selection bias and bias due to missing data.
Causal diagrams and tables clarify the relationships between different types of bias.
The causal structure of missing data is emphasized over missingness mechanisms (MAR/MNAR).

Conclusions:

Berkson's bias offers valuable insights into selection bias and missing data.
Causal reasoning provides a framework for understanding and mitigating these biases.
Intuition from simple examples aids in addressing real-world missing data challenges.