What is the biggest advantage of a longitudinal study over a cross-sectional study in aging research?

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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. 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).

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).

The link between earlier life circumstances and later outcomes. By building up detailed information over time, longitudinal studies are able to paint a rich and accurate picture of participants’ lives.

In the case of birth cohort studies this has allowed researchers to explore how circumstances earlier in life can influence later outcomes. For example, some of the most well-known findings from the cohort studies describe the long-lasting reach of socio-economic disadvantage in childhood.

Longitudinal data also allow us to assess the time-related characteristics of particular events or circumstances (that is, their duration, frequency or timing). For example, does the impact 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 mechanisms and 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:

Longitudinal data allow 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’ or ‘confounding’ .
Sometimes longitudinal data can be used to exploit ‘natural experiments’. In these cases, analysts take advantage of discontinuities over time or serendipitous events to explore their impact. For example, researchers have exploited the fact that some local authorities in England still have grammar schools, and have used this to examine their impact on children.
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.

Research Over Age and Time
Inside Research: Erika Hoff, Department of Psychology, Florida Atlantic University
Media Matters: An Aging and Able Workforce

Defining Developmental Terms
Some scientists use the word development as an umbrella term to describe any research that studies humans over time, though this is only one of several relevant terms that also include aging, change over time, maturation, and learning. Each of these terms differs subtly but these distinctions influence specific research designs.

Development primarily refers to changes that occur over the first part of the lifespan, which are often thought of as positive, unidirectional, and cumulative. Development is often used to describe advances in such areas as motor skills, language, and cognition.

Maturation refers to growth and other changes in the body and brain that are associated with underlying genetic information. Therefore, maturation is considered to be relatively automatic and development encompasses both maturation and an individual’s life experience.

Aging refers to changes associated over the latter part of the lifespan and is often linked with loss of function in the areas of motor skills, language, and cognition.

Change over time is a more general way to capture temporal changes that is predominantly due to learning and experience. Scientists debate how to define these terms, but learning is often restricted to changes tied to specific instruction or experiences over relatively short periods of time whereas development and aging are thought to be universal processes impacting all members of a species.

Designs to Study Change Over Age and Time
There are a number of designs that allow researchers to understand causes of differences between individuals of various age groups, or within the same individual over time. Four approaches to developmental research discussed in the textbook are: cross-sectional, longitudinal, cross-sequential, and microgenetic.

Cross-sectional Designs
Cross-sectional research designs are the most common types of studies across age and time. They involve simultaneously assessing two or more different age groups. One challenge is determining the spacing between ages (how wide the gap in days, weeks, months, or even years) and results from past research, theoretical arguments, and your research question should all inform that decision.

Advantages of Cross-Sectional Designs
A main advantage of a cross-sectional design is that it allows researchers to gather information about different age groups in a short period of time. They also offer great ways to discover and document age-related differences associated with certain behaviors.

Disadvantages of Cross-Sectional Designs
Cross-sectional designs do not identify the underlying causes of differences across age groups. Researchers cannot tell whether age, maturation, specific learning experiences, or a combination of the above are the root of the difference. It is also possible that a cohort effect, the result of experiences that impact an entire group of individuals, is at play.

A second limitation of a cross-sectional design is verifying that methods are equally good at measuring behaviors for different age groups in the sample, which is known as having equivalent measures.

Lastly, cross-sectional designs tend to underestimate variability within an age group in order to characterize differences between groups. Because the focus is on differences between ages, it is possible that achievements obtained at specific ages gain greater status than they deserve.

Longitudinal Research Designs
Longitudinal research designs tracks groups of participants over a period of time with two or more assessments of the same individuals at different times. These designs can last any amount of time. Short-term designs tend to be used for infants. Most longitudinal designs are conducted over longer periods of times, often for several months, years, or even decades.

Advantages of Longitudinal Research Designs
One advantage is that longitudinal designs can help researchers understand how processes change in individuals. Another advantage is that these designs generate a lot of data and can allow researchers to explore a wide variety of research questions.

Disadvantages of Longitudinal Research Designs
The main challenge of using a longitudinal design is the cost in time and resources. These studies are much more expensive and take much longer to conduct than a cross-sectional study with the same number of participants.

A second issue is the impact of repeated testing. Much like a within-subjects design, researchers need to assess participants several times in a longitudinal study and this might influence participants.

The third limitation of longitudinal research is that it faces subject attrition. Subject attrition poses two issues: 1. It might lead to insufficient number of participants at the end of the study, which may mean not having enough statistical power. 2. It may result in significant changes to the study over the course of multiple assessments in terms of biases in who drops out. There are methods of minimizing attrition such as providing incentives, although these issues related to participant retention are ones that local IRBs will want to know about.

A fourth disadvantage of longitudinal studies is maintaining research personnel over time. In cases where studies last many years, staff might need to be changed and it is critical that the protocol is kept consistent.

A fifth disadvantage is determining whether the outcomes observed are due to developmental processes or to the timing of data collection that impacted all the participants.

Finally, the quality of a longitudinal study depends greatly on the initial sample and the quality of the measures in the earliest assessments. While these are factors critical to all studies, researchers using a longitudinal design have a much more difficult time recruiting a new sample in the middle of their study.

Cross-sequential Designs
Cross-sequential designs combine aspects of both cross-sectional and longitudinal designs. They are also known as sequential, mixed, and accelerated longitudinal designs. This design is when multiple age groups or cohorts are studied over time.

Advantages of Cross-sequential Designs
A key advantage using cross-sequential designs is that it allows researchers to examine multiple age groups in a short period of time, compared to longitudinal designs. It also enables researchers to test for cohort effects, which is often not possible in a usual longitudinal design.

Disadvantages of Cross-sequential Designs
Cross-sequential designs, like longitudinal designs, also face issues of repeated testing and are susceptible to subject attrition. Like cross-sectional designs, they have issues related to having equivalent measures.

Microgenetic Designs
Microgenetic designs aim to capture changes as they occur and attempt to understand mechanisms involved in any observed changes. They often focus on a key transition point or dramatic shift in the behavior of interest. Researchers usually begin observations before this transition point, and make observations until shortly after the transition has stabilized.

Advantages of Microgenetic Methods
One advantage of a microgenetic method is that it may lead to insights about the processes that lead to change. A second advantage is that it allows researchers to examine in detail transitions that occur infrequently.

Disadvantages of Microgenetic Methods
Microgenetic studies are susceptible to repeated testing since participants are observed frequently in a short period of time. Furthermore, because the large numbers of observations, many studies that use a microgenetic design often have a small number of participants and this can influence the representativeness of the sample. Furthermore, small sample sizes can make it difficult to use certain statistical techniques that are common in psychology.

Additional Challenges to Consider in Developmental Designs
Research designs that are used to help scientists understand change over time can be difficult to employ. There are three additional challenges in developmental designs: determining the cause of any observed changes, determining whether the measures used at different times or for different ages are equivalent, and determining the appropriate sample interval.

Determining the Underlying Cause of Change
An important goal of studying change overtime is to determine factors that play important roles in causing those changes. However, changes can be due to age, maturation, learning, specific experiences, and cohort effects. These effects do not always occur independently and can also interact.

Finding Equivalent Measures
From a perspective of creating a well-designed experiment, it would be ideal to use a single assessment to measure a behavior of individuals of different ages. Yet practically, a particular assessment that works best for toddlers may not work so well for young teenagers. A solution to this problem would be to test measures across different ages to find those that provide a reasonable assessment across all different ages.

Determining the Appropriate Sampling Interval
This final issue that confronts researchers examining change over time challenges researchers to determine how frequently they should obtain samples over time. The risk of inadequate sampling is that patterns of change may be mischaracterized or missed altogether. Adolph and Robinson (2011) advocate for frequent sampling, though the appropriateness of their technique involving daily summaries depend on the particular behavior of interest and the resources that you have available.