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Related Concept Videos

Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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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:
Causality in Epidemiology01:21

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Related Experiment Video

Updated: Jul 15, 2026

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

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Published on: October 24, 2019

Estimating scientific coherence using population-level indicators and research production data: a longitudinal

David A Hernandez-Paez1, Fabriccio J Visconti-Lopez2, Ivan David Lozada-Martınez1,3,4

  • 1Center for Meta-Research and Scientometrics in Biomedical Sciences, Barranquilla, Colombia.

Frontiers in Research Metrics and Analytics
|July 14, 2026
PubMed
Summary

We introduce a new framework to measure the link between scientific research and population outcomes over time. This helps understand how scientific advancements impact global health and development.

Keywords:
biomedical researchepidemiologic methodshealth status indicatorsmeta-researchproof-of-concept study

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Area of Science:

  • Epidemiology
  • Health Services Research
  • Bibliometrics

Background:

  • Scientific research expansion is assumed to improve population health and development.
  • Current methods lack empirical evaluation of this alignment over time.
  • Bibliometric and impact-based approaches do not sufficiently examine longitudinal relationships with population indicators.

Purpose of the Study:

  • Introduce measurable concepts of scientific coherence and development coherence.
  • Propose a framework to analyze associations between research production and population indicators.
  • Provide a methodological basis for studying the co-evolution of science and population dynamics.

Main Methods:

  • Introduce the Data-driven Analysis and Inference of Longitudinal population indicators and research production (DAIL) framework.
  • Utilize a three-step analytical pipeline.
  • Integrate regression models, hierarchical mixed-effects analyses, and moderator screening.

Main Results:

  • Demonstrate a proof-of-concept application using widely available data.
  • Quantify longitudinal associations between research production and global indicators.
  • Acknowledge potential for reverse causality (socioeconomic factors influencing research infrastructure).

Conclusions:

  • The DAIL framework offers a method to study the alignment between scientific activity and population dynamics.
  • This approach enables quantification of longitudinal patterns between research and population indicators.
  • Highlights the complex, co-evolutionary relationship between scientific endeavors and societal development.