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相关概念视频

Bias01:22

Bias

7.4K
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...
7.4K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

1.4K
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:  
1.4K
Correspondence Bias01:17

Correspondence Bias

240
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...
240
Correlation and Causation01:27

Correlation and Causation

43.1K
Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
43.1K
Pedigree Analysis01:35

Pedigree Analysis

89.9K
Overview
89.9K
Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

76.7K
Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
76.7K

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相关实验视频

Updated: Feb 19, 2026

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
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Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Published on: October 24, 2019

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在元分析中的著作权网络偏见.

Marvin Rieck1, Anne-Christine Mupepele2, Carsten F Dormann1

  • 1Department of Biometry and Environmental System Analysis, https://ror.org/0245cg223University of Freiburg, Germany.

Research synthesis methods
|February 18, 2026
PubMed
概括

作者网络偏见可能会扭曲元分析结果. 这项研究引入了一种新方法来检测和纠正这种偏差,提高了定量研究合成的可靠性.

科学领域:

  • 统计 统计 统计 统计
  • 生物统计学 生物统计学
  • 图书统计学 图书统计学

背景情况:

  • 超分析对于定量研究综合至关重要,但容易产生偏见.
  • 作者网络偏见,源于主要研究中的重叠作者,可能会损害元分析的质量.
  • 这种偏见源于效果大小的非独立性,这是由于共享作者权.

研究的目的:

  • 引入一种用于检测和纠正元分析中作者网络偏差的新方法.
  • 提高定量研究综合的可靠性和有效性.
  • 为了解决在元分析研究中经常被忽视的偏见来源.

主要方法:

  • 提出了一种利用作者网络来识别非独立效果大小的新方法.
  • 使用多层模型,将作者集群作为偏差会计的层次层次.
  • 通过对模拟数据和九个示例元分析的分析验证了该方法.

主要成果:

  • 新方法有效地检测和纠正由作者重叠引起的非独立效果大小.
  • 模拟数据分析证实了该方法的有效性.
  • 应用到现实世界的元分析证明了它的实际实用性.

结论:

关键词:
作者的依赖性 作者的依赖性作者的影响力影响着作者.作者网络偏见 创作网络偏见协作网络 协作网络是一个协作网络.这是一个元分析.没有独立性.

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  • 开发的方法提供了一种可靠的方法,以减轻元分析中的作者网络偏见.
  • 这种技术可以很容易地集成到现有的元分析工作流程中,特别是使用R的元喻包.
  • 解决作者网络偏见对于提高元分析结果的整体质量和可信度至关重要.