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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

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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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Cross-Sectional Research

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...

Introduction To Survival Analysis

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...

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Related Experiment Video

Updated: Jun 9, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

Dealing with short-term fluctuation in longitudinal research.

Timothy A Salthouse¹, John R Nesselroade

¹Department of Psychology, University of Virginia, Charlottesville, VA 22904, USA. salthouse@virginia.edu

The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences

|August 25, 2010

Summary

Measurement-burst designs improve cognitive assessment sensitivity by accounting for short-term fluctuations. This method offers a more accurate way to measure cognitive change in adults.

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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

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Area of Science:

Psychology
Cognitive Science
Gerontology

Background:

Assessing developmental change in cognitive function is challenging due to short-term fluctuations.
Existing analytical methods often lack sensitivity to detect meaningful changes.

Purpose of the Study:

To investigate the measurement-burst design as a solution for enhancing sensitivity in cognitive change assessments.
To compare different methods of incorporating short-term variability in cognitive testing.

Main Methods:

Over 1,200 adults of diverse ages participated in the study.
Participants completed multiple versions of cognitive tests across several sessions within each measurement occasion.

Main Results:

Four methods for incorporating short-term variability were evaluated.
The study examined correlations between ability measures and their relationship with age.

Conclusions:

Measurement-burst designs enhance the sensitivity of cognitive change assessments by considering short-term variability.
Latent constructs representing cognitive level and change provide the most sensitive evaluation of cognitive performance changes.