1. Section 3: Using longitudinal data for research
From the CLOSER Learning Hub
Module: Introduction to longitudinal studies

2. What longitudinal studies show us
how key life transitions change the course of a person’s life
how behaviour changes as people get older
how early life influences later life
how health, wealth, family, parenting, education, employment and social attitudes are linked
how these aspects of life vary for different people
Each time the studies collect new information about their participants’ lives, they are adding rich new data to what is already known about them. This design allows researchers to uncover important things like:
how key life transitions (such as marriage or divorce, leaving school, or entering or retiring from the labour market) change the course of a person’s life
how patterns of behaviour change as people get older (such as taking up smoking or doing more exercise)
how earlier life circumstances affect later life (for example, how experiencing adversity in childhood affects health in old age)
how different areas of our lives are linked, such as health, wealth, family, parenting, education, employment and social attitudes, and how those relationships change over time
how different aspects of life vary for different people throughout their lives.

3. Social science uses of longitudinal data
Economics: What is the effect of education and training on earnings in later life?
Geography: How well do immigrants entering the UK assimilate into local labour markets?
Political Sciences: What is the impact of cognitive ability and personality on voter turnout?
Sociology: What is the relationship between a child’s family background, and how well they do at school?
Researchers from different disciplines use longitudinal data for different purposes. Here are some examples of the kinds of questions different disciplines might try and answer using longitudinal data.
Economics: What is the effect of education and training on earnings in later life?
Geography: How well do immigrants entering the UK assimilate into local labour markets?
Political Sciences: What is the impact of cognitive ability and personality on voter turnout?
Sociology: What is the relationship between a child’s family background, and how well they do at school?

4. Biomedical science uses of longitudinal data
Genetics: What is the relationship between a particular genetic variant, smoking behaviour and the risk of a person developing lung cancer?
Health: How does the consumption of specific foods and drinks relate to excess weight gain among children?
Psychology: How much does bullying increase the risk of depression and self-harm in adolescence?
Researchers from different disciplines use longitudinal data for different purposes. Here are some examples of the kinds of questions different disciplines might try and answer using longitudinal data.
Genetics: What is the relationship between a particular genetic variant, smoking behaviour and the risk of a person developing lung cancer?
Health: How does the consumption of specific foods and drinks relate to excess weight gain among children?
Psychology: How much does bullying increase the risk of depression and self-harm in adolescence?

5. Strengths of longitudinal data
Detail over the life course
Establishing the order in which events occur
Reducing recall bias
Longitudinal studies have a number of particular advantages in terms of the quantity or quality of the data that they collect:
Detail over the life course. The value of longitudinal studies increases as each sweep builds on what is already known about the study participants. This means that on many topics, longitudinal studies typically contain far more detailed information than could be collected through a one-off survey. For example, many studies collect a detailed array of information about study participants’ education, work histories and health conditions.
Establishing the order in which events occur. Longitudinal data collection allows researchers to build up a more accurate and reliably ordered account of the key events and experiences in study participants’ lives. As we’ll see below, understanding the order in which events occur is important in assessing causation.
Reducing recall bias. Longitudinal studies help reduce the impact of recall error or bias, which occurs when people forget or misremember events when asked about them later. In longitudinal studies, participants provide information about their current circumstances, or are asked to remember events over only a short period of time (that is, since the time of the last sweep).

6. Strengths of longitudinal data
Exploring patterns of change and the dynamics of individual behaviour
Assessing the time-related characteristics of particular events or circumstances
Providing insights into causal processes
Distinguishing between age and cohort effects
Many of the advantages of longitudinal studies relate to the analytic questions their data can help address. For example, longitudinal data help with:
Exploring patterns of change and the dynamics of individual behaviour. Longitudinal data allows researchers to explore dynamic rather than static concepts. This is important for understanding how people move from one situation to another (for example, through work, poverty, parenthood, ill health and so on).
Assessing the time-related characteristics of particular events or circumstances (that is, the duration, frequency or timing). For example, does the impact of a bout of ill health change depending on when in their life someone becomes ill, how long they remain ill, and how often they experience illnesses?
Providing insights into causal processes. Many surveys provide evidence about the association between particular circumstances and outcomes. For example, a cross-sectional study might find that the unemployed have poorer health than those in work (so, in other words, there is an association between health and employment status).
But interpreting this association is more challenging. Might, for example, unemployment be the cause of poor health – or perhaps poor health could lead to unemployment? Longitudinal data cannot definitively ‘prove’ causality, but unlike data from cross-sectional studies, it has a number of important attributes that give more insights into the causal processes that might be involved.
Firstly, longitudinal data allows events to be ordered correctly in time (in our example, this would mean we could establish whether a period of unemployment definitely came before or after an episode of ill health).
Longitudinal data also tend to be much richer in detail than cross-sectional studies, which allows analysts to take a wide array of background characteristics or control variables into account. This reduces the risk of ‘unobserved heterogeneity’.
Finally, there are a range of sophisticated statistical techniques that make use of the repeated observations built up over time in longitudinal studies and allow us to test whether relationships are likely to be causal or the result of other differences.
Distinguishing between age and cohort effects. Longitudinal studies can help researchers to distinguish between changes that happen as people get older, known as ‘age effects’, and generational differences that reflect the historical, economic and social context within which different cohorts grew up, known as ‘cohort’ or ‘generational’ effects. For example, cross-sectional data might show a clear relationship between age and political affiliation (with older age groups being more likely to vote for the Conservative party). Longitudinal data would allow analysts to investigate whether the older generations in the UK are more likely than younger ones to support the Conservative party (a cohort effect), or whether people all people become more likely to vote Conservative as they get older (an age effect). Age and cohort/generational effects also need to be distinguished from ‘period’ effects; these refer to forces that influence everyone – for example, key events in history that affect everyone irrespective of their age or the generation they were born into.

7. Weaknesses of longitudinal data
Attrition
Conditioning
Limits of representivity
Complexity
Timeliness
Cost
Attrition. Over time, participants may cease to take part in a longitudinal study. This is known as attrition. Attrition can result from a range of factors, some of which are unavoidable, while others can be reduced by careful study design or practice.
Unavoidable attrition might be caused by a participant dying, or leaving the country of interest (emigrating). Sometimes attrition occurs when the study team cannot find a participant to invite them to take part. For example, a participant might move house between sweeps and forget to update their contact information. Longitudinal studies use a range of methods to trace their participants in order to prevent this from happening.
Attrition can also occur because participants simply choose not to take part. Participants can choose not to be interviewed in a particular sweep, or can drop out of the study altogether. Again, longitudinal study teams usually have participant engagement strategies to encourage participants to stay involved.
Attrition is problematic for two main reasons. First, attrition reduces the size of the sample, and if the sample becomes too small, it jeopardises statistical power. Second, if attrition is not random, it can lead to non-response bias (which affects the validity of the study findings).
There are a number of ways in which studies try to avoid attrition happening in the first place. Once it has happened, there are analytical approaches that can help compensate for it.
Conditioning. It is possible that participants’ answers to some study questions may be influenced by their participation in the study – in other words, that their responses are ‘conditioned’ by their being members of a longitudinal study. This is referred to as panel conditioning. Examples would include study participants answering questions differently or even behaving differently as a result of their participation in the study.
Limits of representivity. Because cohort studies focus on following a specific group (rather than a representative sample of the population as a whole), they are only representative of that specific group. For instance, the 1970 British Cohort Study yields vital information about people born in that year, but does not tell us anything about the generations that precede or follow it. This attribute of cohort studies can be partly overcome by comparing different cohorts to see how their experiences and circumstances differ. This issue does not affect longitudinal studies that are representative of the population as a whole.
Complexity. The datasets can be more complex to manage and analyse than the data for cross-sectional surveys.
Timeliness. By definition, the value of longitudinal studies builds up gradually over time. However, this means that researchers need to wait until the participants get older before they can answer some key research questions.
Longitudinal studies also face the risk that, by the time participants are old enough to allow researchers to answer certain questions, some of the original measures could appear out of date while the overall social and policy landscape may have changed. Equally, the content and design of a new prospective study will inevitably be shaped by the research context at the point at which it was set up. Questions or measures that, with hindsight, seem obvious ones to have collected at the outset of the study may not have seemed so obvious at that time.
Cost. A longitudinal study with a number of waves of data collection will tend to be more expensive than the equivalent number of fresh cross-sectional studies. This reflects the additional complexity involved in designing, maintaining and carrying out longitudinal data collection.