Related Experiment Video
Updated: Jul 22, 2026
09:32
Cortical Source Analysis of High-Density EEG Recordings in Children
Published on: June 30, 2014
Interval-censored event history analysis
Population. English Selection
|January 1, 1996
Summary
Event history analysis uses surveys or administrative data. Researchers compared these methods, finding administrative data limitations for tracking job or residential changes, impacting life course analysis accuracy.
Area of Science:
- Sociology
- Demography
- Historical Research
Background:
- Event history analysis is crucial for understanding life course trajectories.
- Two primary data collection methods exist: retrospective surveys and administrative records.
- Administrative data, while comprehensive, may lack crucial event details like job or residential changes.
Purpose of the Study:
- To assess the validity of event history analysis using incomplete administrative data.
- To compare the accuracy of administrative data against dedicated survey data for life course events.
Main Methods:
- Utilized administrative data sources (vital registration, census, residence notifications).
- Conducted an ad hoc survey to collect retrospective life course, family, and occupational histories.
- Performed comparative analysis between the two data sources.
Main Results:
- Administrative data in France does not record job changes or residential moves.
- The study validates the utility of administrative data despite its inherent limitations.
- Comparative analysis revealed discrepancies due to missing event information.
Conclusions:
- Administrative data offers a valuable, albeit incomplete, resource for event history analysis.
- Survey data remains essential for capturing events not registered in administrative systems.
- Future research should explore methods to integrate or compensate for missing administrative data.
Keywords:
Data CollectionDemographic AnalysisDeveloped CountriesEuropeEvent History AnalysisFranceMeasurementMediterranean CountriesMethodological StudiesResearch MethodologySampling StudiesStudiesSurveysValidityWestern EuropeMore Related Videos
09:34Applications of Spatio-temporal Mapping and Particle Analysis Techniques to Quantify Intracellular Ca2+ Signaling In Situ
Published on: January 7, 2019
05:16Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
Published on: June 10, 2025
Related Concept Videos
01:14Relative Frequency Histogram
The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
01:18Introduction To Survival Analysis
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...
The primary goal of survival analysis is to estimate survival time—the time until a...
01:15Assumptions of Survival Analysis
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
01:20Comparing the Survival Analysis of Two or More Groups
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
01:09Truncation in Survival Analysis
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.
01:09Censoring Survival Data
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...