However, although you can show that one variable predicts another, you cannot show that one causes the other. To establish causality, you must show that the change in one variable could only have been due to the influence of the other. However the change may have been due to another, unmeasured variable, which may have been acting on either of the variables you measured. In addition, the direction of the relationship is sometimes difficult to determine. Often used in developmental research, cross-sectional and longitudinal designs allow changes across the lifespan to be assessed.
Longitudinal designs evaluate changes in the same group of subjects across an extended period.
The 7-up series of documentari is an example of a longitudinal study. The advantage in these designs is that the subjects bring their own background and experiences with them. However they are quite expensive to conduct , not only in keeping track of subjects from one test period to another, but in the increase in costs of resources over time. A further problem is the loss of subjects across time, because subjects drop out, or because they cannot be traced. However if the dropouts are not alike then the sample is unlikely to be affected.