A longitudinal study observes the same individuals repeatedly. Within-person changes distinguish temporal development from persistent differences between people, although calendar-period changes, selective dropout and measurement error still require attention. A random-intercept linear mixed model can represent persistent individual differences and correlated repeated measurements.
Variation over time within an individual, distinguished from differences between individuals. A predictor of which children score higher need not identify what changes a particular child's trajectory.
A model relating repeated measures of variables across occasions. Conventional cross-lagged paths can mix stable between-person differences with within-person change and do not automatically identify causal effects.
A sample selected to include elevated familial or early developmental risk. It can increase information about pathways but its comorbidity and prevalence estimates require qualification before generalising to the population.
A study following a defined group to relate earlier characteristics or exposures to later outcomes. Prospective measurement improves temporal ordering but does not automatically remove confounding, selective attrition or exposure misclassification.
Loss of participants or outcome observations across follow-up. If remaining under observation depends on exposure and determinants of the outcome, analyzing only retained participants can induce selection bias.
Loss of participants or observations during follow-up. It reduces available information and can bias an estimate if continued participation selects individuals in a way relevant to the target relation.
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A longitudinal study is a research design that involves repeated observations of the same variables (such as individuals, groups, or phenomena) over an extended period of time, which can range from months to many years or even decades. Longitudinal studies are often used in various fields, including psychology, sociology, medicine, and education, to track changes and developments, identify trends, and examine causal relationships.