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Longitudinal Research

Longitudinal Research

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...

Longitudinal Studies

Longitudinal Studies

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...

Observational Studies

Observational Studies

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.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One example of...

Assumptions of Survival Analysis

Assumptions 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.

Introduction To Survival Analysis

Introduction 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...

Actuarial Approach

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...

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Related Experiment Video

Updated: May 25, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Predicting long-term absenteeism from work in construction industry: a longitudinal study.

Peter Hoonakker¹, Cor van Duivenbooden

¹Center for Quality and Productivity Improvement, University of Wisconsin-Madison, 3128 Engineering Centers Building, 1550 Engineering Drive, Madison, WI 53706, USA.

Work (Reading, Mass.)

|February 10, 2012

Summary

The Work Ability Index (WAI) offers some predictive value for long-term absenteeism in the construction sector. However, its explained variance is low, potentially due to how absenteeism is defined in the Netherlands.

Related Experiment Videos

Last Updated: May 25, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Area of Science:

Occupational Health
Workplace Safety
Construction Industry Studies

Background:

Long-term absenteeism poses significant challenges in physically demanding industries like construction.
Accurate prediction of absenteeism is crucial for workforce management and intervention strategies.

Purpose of the Study:

To evaluate the additional predictive value of the Work Ability Index (WAI) for long-term absenteeism.
To assess the WAI's utility in the specific context of the construction industry.

Main Methods:

The study examined the relationship between the Work Ability Index (WAI) and long-term absenteeism.
Data was collected from the construction industry in The Netherlands.

Main Results:

The Work Ability Index (WAI) demonstrated additional value in predicting long-term absenteeism.
The proportion of variance explained by the WAI was found to be low.
The definition of absenteeism in The Netherlands may influence the explained variance.

Conclusions:

While the WAI shows potential, its current predictive power for long-term absenteeism in construction is limited.
Further research may be needed to refine predictive models or consider contextual factors like absenteeism definitions.