1.6:
Longitudinal Research
Sometimes the goal of a psychological study may be to understand how people’s attitudes and behaviors change over time, or to determine what factors may predict future abilities.
These objectives can be accomplished using a longitudinal design—a research study where data are repeatedly collected from the same group of individuals for a period of time, whether it’s as short as a few weeks or months or as long as several decades.
For example, if a researcher wants to know whether college students’ exercise routines change over the course of their first semester of college, she can use a longitudinal approach and ask students to repeatedly report their workout regiments. She may find that as students get more caught up in their studies, they go to the gym less often.
In addition, the same researcher may keep track of a group of people for twenty years, because she wants to explore how their exercise routines shift across their 20s, 30s and 40s. This approach allows her to best measure changes, within individuals, over time.
In this case, she may discover that those who enjoyed running outdoors in their 20s maintain blood pressure levels, display low amounts of stress, and are more likely to do yoga in their 40s.
While longitudinal research can provide informative results, the method also has its drawbacks. For instance, long-running studies can be very expensive and require a significant time-commitment from the research team and their participants.
Because of this commitment, attrition rates tend to be higher—meaning, more participants dropout. For this reason, the researcher would have to recruit more individuals at the start of the study, expecting a certain number to dropout. Attrition may also cause the study’s sample to be less representative of the population.
Despite its disadvantages, longitudinal research has the power to help us understand variation across human development and the lifespan.
One of the longest-running studies—following people over 80 years as opposed to comparing different groups at different ages—provides a robust measure of human growth—even revealing factors, like close relationships, that lead to people living healthy and happy lives.
1.6:
Longitudinal Research
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again at age 40.
Let's consider another example. In recent years there has been significant growth in the popular support of same-sex marriage. Many studies on this topic break down survey participants into different age groups. In general, younger people are more supportive of same-sex marriage than are those who are older (Jones, 2013). Does this mean that as we age we become less open to the idea of same-sex marriage, or does this mean that older individuals have different perspectives because of the social climates in which they grew up? Longitudinal research is a powerful approach because the same individuals are involved in the research project over time, which means that the researchers need to be less concerned with differences among cohorts affecting the results of their study.
Often longitudinal studies are employed when researching various diseases in an effort to understand particular risk factors. Such studies often involve tens of thousands of individuals who are followed for several decades. Given the enormous number of people involved in these studies, researchers can feel confident that their findings can be generalized to the larger population. The Cancer Prevention Study-3 (CPS-3) is one of a series of longitudinal studies sponsored by the American Cancer Society aimed at determining predictive risk factors associated with cancer. When participants enter the study, they complete a survey about their lives and family histories, providing information on factors that might cause or prevent the development of cancer. Then every few years the participants receive additional surveys to complete. In the end, hundreds of thousands of participants will be tracked over 20 years to determine which of them develop cancer and which do not.
Clearly, this type of research is important and potentially very informative. For instance, earlier longitudinal studies sponsored by the American Cancer Society provided some of the first scientific demonstrations of the now well-established links between increased rates of cancer and smoking (American Cancer Society, n.d.).
As with any research strategy, longitudinal research is not without limitations. For one, these studies require an incredible time investment by the researcher and research participants. Given that some longitudinal studies take years, if not decades, to complete, the results will not be known for a considerable period of time. In addition to the time demands, these studies also require a substantial financial investment. Many researchers are unable to commit the resources necessary to see a longitudinal project through to the end.
Research participants must also be willing to continue their participation for an extended period of time, and this can be problematic. People move, get married and take new names, get ill, and eventually die. Even without significant life changes, some people may simply choose to discontinue their participation in the project. As a result, the attrition rates, or reduction in the number of research participants due to dropouts, in longitudinal studies are quite high and increases over the course of a project. For this reason, researchers using this approach typically recruit many participants fully expecting that a substantial number will drop out before the end. As the study progresses, they continually check whether the sample still represents the larger population, and make adjustments as necessary.
This text is adapted from OpenStax, Psychology. OpenStax CNX.