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Related Concept Videos

Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
Bias in Epidemiological Studies01:29

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:

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Related Experiment Video

Updated: Jun 13, 2026

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
05:02

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

Published on: October 24, 2019

Density-equalizing mapping and scientometric benchmarking in Industrial Health.

Cristian Scutaru1, David Quarcoo, Masaya Takemura

  • 1Department of Information Science, Charité-Universitätsmedizin Berlin, Free University Berlin, Berlin, Germany. cristian.scutaru@charite.de

Industrial Health
|April 29, 2010
PubMed
Summary

This study used bibliometric and scientometric methods to analyze industrial health research. Japan leads in research productivity and citation activity, showing a global pattern.

Related Experiment Videos

Last Updated: Jun 13, 2026

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
05:02

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

Published on: October 24, 2019

Area of Science:

  • Industrial Health
  • Scientometrics
  • Bibliometrics

Background:

  • Bibliometric techniques have been applied to industrial health research over the last two decades.
  • Previous studies utilized quantitative and qualitative measures like impact factor and H-indices.
  • Novel visualization techniques like density-equalizing mapping have not yet been applied to this field.

Purpose of the Study:

  • To combine classical bibliometric tools with novel scientometric and visualizing techniques.
  • To analyze the progression of industrial health research using comprehensive methods.
  • To visualize research activity and collaboration patterns in industrial health.

Main Methods:

  • Screening and analysis of all "INDUSTRIAL HEALTH" entries in the ISI database since 1987.
  • Application of bibliometric approaches to assess quantitative and qualitative markers.
  • Utilization of density-equalizing mapping and radar chart techniques for visualization.

Main Results:

  • Bibliometric analysis revealed a constant increase in qualitative markers (e.g., collaboration, citations).
  • Quantitative markers (e.g., author numbers, publications) remained relatively stable.
  • Density-equalizing mapping identified Japanese institutions as leaders in research productivity and citation activity.
  • Radar charts visualized bi- and multilateral research and institutional cooperations.

Conclusions:

  • This study presents the first scientometric-bibliometric approach to visualize industrial health research activity.
  • A distinct global pattern of research productivity and citation activity was revealed.
  • Japanese institutions hold a leading position in the field of industrial health research.