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Related Concept Videos

Bias01:22

Bias

4.8K
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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Convenience Sampling Method00:55

Convenience Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
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Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

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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.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
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Stratified Sampling Method01:16

Stratified Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
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Surveys02:16

Surveys

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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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Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

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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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Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
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How Twitter data sampling biases U.S. voter behavior characterizations.

Kai-Cheng Yang1, Pik-Mai Hui1, Filippo Menczer1

  • 1Observatory on Social Media, Indiana University, Bloomington, Indiana, United States.

Peerj. Computer Science
|July 25, 2022
PubMed
Summary
This summary is machine-generated.

Social media analyses of elections can be biased. Hyperactive accounts, not real voters, often dominate data, spreading misinformation and skewing results. This study identifies voters on Twitter to reveal these biases.

Keywords:
BiasData samplingElectionTwitterVoter

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Area of Science:

  • Computational Social Science
  • Political Science
  • Data Science

Background:

  • Online social media platforms are crucial for public political discourse and election analysis.
  • Inauthentic actors (bots, trolls) compromise the integrity of social media data, creating uncertainty about analysis biases.
  • The prevalence and impact of inauthentic activities in social data streams remain unclear.

Purpose of the Study:

  • To address the gap in understanding inauthentic activities in social media data.
  • To develop an efficient and low-cost method for identifying voters on Twitter.
  • To compare the behavior of identified voters with random samples of accounts during the 2018 U.S. midterm elections.

Main Methods:

  • Utilized Twitter data from the 2018 U.S. midterm elections.
  • Developed and applied a novel method to identify likely voters on Twitter.
  • Systematically compared the behavior of identified voters against various random account samples.

Main Results:

  • Identified "hyperactive" accounts that dominate political content, overshadowing genuine voter voices.
  • Found that hyperactive accounts are over-represented in volume-based sampling methods.
  • Observed that hyperactive accounts exhibit more suspicious behaviors and share lower-credibility information than likely voters.

Conclusions:

  • Social media data analyses of political issues can be significantly biased by hyperactive accounts.
  • The over-representation of hyperactive accounts distorts the characterization of voter behavior and public opinion.
  • Findings highlight the need for methods that distinguish genuine voter activity from inauthentic amplification in social media research.