Longitudinal Data

Conceptual definition

In life course research, longitudinal data store information on the same individual over a period of time, that can span over several months, several years or several decades. Since it is the case, longitudinal data makes it possible to study how individuals change or stay the same over time. Longitudinal data can be contrasted with cross-sectional data. In the latter, data are collected at one point in time and focus on the current situation of the respondent. If such cross-sectional studies are repeated over time, they allow describing societal or aggregated changes. However, as they do not keep track of respondents’ responses across waves, it is not possible to describe individual changes of behavior and attitudes over time. Moreover, longitudinal data are necessary to explore various dimensions of causality in more depth, since cross-sectional data only reveals associations. In life course research, longitudinal data tracks individuals over time in order to collect their life history. Elder (1992) defines a life history as "a lifetime chronology of events and activities that typically and variably combines data records on education, work-life, family, and residence". To this already complex definition that insists on the timing of life-course events occurring in different life domains experimented by an individual, one could add measurements on many other domains such as the evolution of health or the evolution of cognitive capacities. The literature in life-course research describes two main types of longitudinal data that are collected with the aim to analyze life courses: prospective or retrospective longitudinal data (Scott and Alwin, 1998). In some cases, longitudinal data collections start with an event shared by all units documented, either an exhaustive population or a representative sample. Such data collection efforts are called cohorts. For example, the Swiss Transplant Cohort Study collects since 2008 longitudinal data on all solid organ transplant recipients in Switzerland. Of particular interest to life course studies are birth cohorts, like the cohort ELFE in France which started with a sample of babies born in that country in 2011. Longitudinal data collection can also be launched by a cross-sectional form of data gathering, whose individuals are then followed over time, for example the Swiss Household Panel or the Swiss National Cohort linking administrative records to individuals monitored in the 1990 and 2000 censuses. In low- and middle-income countries demographic surveillance systems also undertake longitudinal data collections. In this case, geographical areas are followed; after an initial census, survey rounds at least once a year collects all vital events (arrival, birth, deaths, departures) in the surveillance areas.
In prospective or panel studies, a sample is followed and observed over time or at different points in time. The information is stored as the sample evolves. For instance, panel data stores the values of the same set of variables for the same sample at different point in time, resulting in several observations per individual units. Similarly, administrative data might record the situation of an individual at different points in time, for instance based on yearly data extraction.
With panel data, individuals are followed over a more or less long period of time, with interviews occurring at regular or irregular intervals. Panel surveys aim to record the successive situations experienced by individuals during their life course, which may also include repeated measurements of biological or psychological markers (cognitive measures, well-being, etc.) (Hauser, 2009). Three main types of prospective data can be distinguished. The first type of panel consists of following a cohort of individuals over time, for instance a birth cohort. An example is the National Child Development Study, in which individuals born between March 3 and 9, 1958 in Great Britain are followed (CLS, 2020). The population taken into account can also be a cohort of individuals who experienced the same event, which is the case with the Wisconsin longitudinal study in which individuals who graduated from a high school in the state of Wisconsin in 1957 (when they were about 18 or 19 years old) are followed (Hauser, 2009). Such prospective surveys are particularly interesting for analyzing divergences in trajectories over time as well as cumulative advantages and disadvantages (Pudrowska & Anikupta, 2014). In this type of survey, data are sometimes collected at irregular intervals. When individuals are followed over a long duration, the questionnaires and the measures carried out can be adapted according to the phase of the life course in which people are (adulthood, old age, etc.).
The second type of prospective data corresponds to surveys in which representative samples of individuals or households from the general population are interviewed regularly, for example once a year, over a long time. A typical example is the Swiss household panel that was created in 1999 (Tillmann et al., 2016). In these surveys, the questionnaire is in principle repeated in each wave, as are the marker measures, to observe changes over time. Because of its regularity, one of the advantages of this type of longitudinal survey is that it allows looking at people's expectations, intentions, or plans for their future life, as expressed in a given survey wave, for example plans to have children, and to see how these plans are being carried out or evolve (Hanappi et al., 2016).
The third type of panel aims to analyze the impact of a stressful event on life courses. One of the first panels carried out by Lazarsfeld (1942, Ruspini, 2002) aimed to analyze the impact of an advertising message on the knowledge of a product by a sample of radio program listeners. The stressful event can be an external event such as an historical event (a war, an economic crisis…,), or a life-course event, such as the birth of the first child. These panels are often composed of a few waves (usually 2 or 3), with at least one of these waves ideally occurring before or during the stressful event and at least one wave after the stressful event. Because of the unpredictability nature of an external event such as an economic crisis, previous waves are taken from a preexisting survey to which new waves are added during and after the stressful event. A recent example is the Swiss household panel in which an additional wave took place in the spring 2020 at the end of the first lockdown due to the pandemic of Covid19, allowing analyzing the impact of the pandemic on life courses in the short and long term (Reffle et al., 2020). Such a design thus makes it possible to analyze the changing situation of individuals, and the resources or reserves they have in order to cope with the stressful event, as well as the effect of the stressful event on their well-being. For example, the Becoming parents survey conducted by researchers from the Universities of Geneva and Lausanne on a sample of couples was composed of three waves, the first when the woman was pregnant of her first child, the second when this child was a few months old and the third when the child was over a year old (Le Goff and Levy, 2011 and 2016). In this type of panel, questions and markers are the same in each wave of the study, the impact of the stressor being assessed by observing the changes in responses given by respondents before and after the stressful event. Specific questions related to the stressful event and how it was experienced can be added to these common questions.
2. Retrospective data corresponds to data that is collected at one point in time on a sample of individuals in order to reconstitute their past life course. Such data can also be collected from traces recorded in archives or registers. Blossfeld and Rohwer (2001) speak of an event-oriented design in the case of such data, since events belonging to different life domains and their time of occurrence are collected, before reconstituting events sequences and then individual trajectories. However, a retrospective survey requires a process of remembering the events that respondents have experienced, which may depend on the situation in which they find themselves at the moment of the survey (Barbeiro & Spini, 2017; Belli, 1998; Gomensoro & Paredes, 2017). Furthermore, Scott and Alwyn (1998) indicate that the term retrospection also encompasses the fact that there may be an assessment by respondents of the events they experienced, depending also on the situation they are in at the time of the survey (Dasoki, 2017).
Retrospective data are collected by reconstituting the past life course of a sample of respondent. This might be achieved by asking the respondents to recall their past. In this case, several tools were developed by life-course researchers to minimize memory errors or bias. Detailed or subjective retrospective questions are generally avoided as there are more prone to recall bias.The NCCR LIVES actively participated in the development of life history calendars (Morselli et al., 2016).

Conclusion

Some surveys combine retrospective and prospective design in order to obtain longer sequences of longitudinal data. This is the case for example of the LIVES-FORS-Cohort survey, which aims to analyze different aspects of vulnerability during the transition to adulthood among a sample of young people born between 1987 and 1997, the sample over-representing the children of migrants (Spini et al., 2019). These young people were followed annually between 2013 and 2019. The questionnaire for the first wave of this survey was essentially a retrospective survey, in the form of a life calendar designed to record the previous life courses of these young people since birth. Then, the questionnaires of the following prospective waves were largely based on those of the Swiss household panel.
The LIVES NCCR heavily rely on longitudinal data, which require specific methods, to study vulnerability over the life course. For instance, it allows understanding how people recover (or not) from a disruptive event overt time. The LIVES NCCR is active in the development of such methods and collected several qualitative and quantitative longitudinal datasets. A full list of these datasets is available here: https://www.centre-lives.ch/fr/lives-data-collections

Authors: Jean-Marie Le-Goff, Clémentine Rossier, Matthias Studer

References

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