Longitudinal studies

Psychology AS · Research methodology. The idea in plain words, the rules to remember, the mistake to avoid and a worked example, written and checked by Mega Lecture.

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The idea

A longitudinal study follows the same participants over a long period, often years, and measures them on several occasions. This shows how behaviour changes or develops with time or age.

It is a design, not a single method. The data may come from tests, interviews or observations. An experiment can also have a longitudinal design: the researcher sets up an independent variable, such as a treatment group and a control group, and then measures the dependent variable repeatedly over time.

Because each person is compared with their own earlier self, participant variables are controlled. The main risk is attrition: participants drop out as the years pass.

Rules to remember

Longitudinal study: the same people are measured repeatedly over an extended time.
Strength: shows real change and development within individuals, and long-term effects.
Strength: no participant variables between ages or stages, unlike comparing different groups of different ages at one time.
Attrition: participants leave the study. The sample gets smaller and may become biased if those who leave differ from those who stay.
Weakness: takes a long time and costs a lot; results arrive slowly.
Weakness: repeated testing can cause practice effects, and events in the world during the study may affect results.
Ethics: consent should be checked again as the study goes on, the right to withdraw must stay open, and data must be kept confidential for years.

Common mistake

Students call any study that lasts a long time longitudinal. The feature that matters is that the same participants are measured more than once over the period.

Worked example

A longitudinal study of confidence begins with 400 students. Five years later 280 are still taking part. Calculate the attrition rate and explain why it matters.

Number who dropped out = 400 − 280 = 120.
Attrition rate = 120 ÷ 400 × 100 = 30%.
If those who left differ from those who stayed, for example by being less confident, the remaining sample is biased.
The final results would then not represent the group that started.

Answer: The attrition rate is 30%, which makes the sample smaller and possibly unrepresentative.

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