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Cross-sequential studies

A cross-sequential study is a comparison of two separate but equivalent longitudinal studies, each covering a different period of time. Our study question for this section illustrates this:

How did the proportion of elderly people (aged 65+) moving in with their adult children change between 1971-1981 and 1981-1991?

By looking longitudinally at an elderly person's household circumstances, we can determine whether they have moved in with their children during the follow-up period; then, by comparing the proportions of people making such transitions over each period, we can determine the trend over time.

We should make a distinction between elderly people who are moving in with their children, and the inverse situation, where the children move in with an elderly parent. In the former case, it is likely that the elderly person has moved in order to be supported by their children, whereas in the latter case there may be other reasons for the move.

There are ways to determine the direction of the transition. For example, if the elderly person is head of household, we might assume that they are the incumbent resident, and that the children have moved in with their parent. Conversely, if the child (or their spouse) is head of household, then it is likely that the parent has moved to live with the child. Alternatively, we could look at the geographical data in the LS to see whether the parent is the one who has moved. In this example we will not get into such detail, however.

We need to identify specific types of situation that the elderly people are coming from, and tabulate these against the situation they are moving to. The main categories we might consider are:

  • people living alone,
  • people living with a spouse but no adult children
  • people already living with their adult children,
  • other (includes people cohabiting, living with other people)

Additionally, we will look specifically at those people who are widowed at the end of follow-up, to see if their pattern of transitions differs from that of the rest of the sample.