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Related Concept Videos

Longitudinal Research02:20

Longitudinal Research

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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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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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Observational Studies01:11

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Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
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In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
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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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Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
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Related Experiment Video

Updated: Sep 16, 2025

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
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Equal Opportunity and Luck: Empirical Exploration Using the Canadian Longitudinal Study on Aging.

Yukiko Asada1, Nathan K Smith1, Michel Grignon2

  • 1Department of Bioethics, Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, MD 20892 USA.

Social Indicators Research
|July 7, 2025
PubMed
Summary

Unexplained variation, or luck, in equality of opportunity (EOp) analyses can signal unfairness. Ignoring this luck component may underestimate true inequality, highlighting the need to incorporate luck into EOp frameworks.

Keywords:
CircumstanceEffortEquality of opportunityInequalityLuck

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Area of Science:

  • Social Sciences
  • Economics
  • Health Equity

Background:

  • Equality of opportunity (EOp) research often focuses on explained inequality, categorizing determinants as effort-legitimate or circumstance-illegitimate.
  • Unexplained variation, statistically represented as residuals and often termed luck, is frequently overlooked in empirical EOp studies.

Purpose of the Study:

  • To introduce the playing field framework for assessing unfairness in unexplained inequality within EOp analyses.
  • To empirically explore the role of luck in EOp using a novel framework.

Main Methods:

  • Development and application of the playing field framework to analyze residuals in EOp.
  • Utilizing a large dataset of Canadian older adults for empirical testing.

Main Results:

  • The playing field framework revealed that residual distributions are not consistently fair across all subgroups.
  • No uniform pattern of unfairness was observed across different age-sex groups in the Canadian sample.

Conclusions:

  • Luck significantly influences outcomes and should be explicitly integrated into EOp frameworks via brute luck-effort characterization.
  • Residuals in EOp analyses are not merely statistical noise but can indicate significant unfair inequality, the omission of which leads to underestimation.