Archival Research

Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships.

Archival research is generally more complex and time-consuming than research that involves the summary, collation and/or synthesis of existing research, and it is frequently also undertaken in other disciplines within the social sciences, including psychology. For example, a psychologist may seek out and extract evidence from original mental institution records from the 1900s in order to determine the prevalence of depressive symptoms in patients at the time. There has been much speculation about modern environments causing an epidemic of depression.^[1] Archival research could provide important information about the increasing prevalence (Figure 1).

Figure 1. A researcher doing archival research examines records, whether archived (a) physically or (b) electronically. (credit “paper files”: modification of work by “Newtown graffiti”/Flickr; “computer”: modification of work by INPIVIC Family/Flickr)

In comparing archival research to other research methods, there are several important advantages and disadvantages.

Advantages of Archival Research

• Archival research minimizes the response biases of subjects because the researcher is not present while the data is recorded.
• Archival data is very plentiful and has already been collected. This makes it easier and often less costly than alternative research methods.
• Archival research is effective in helping to confirm that the results and theories derived from experiments reflect the “real world” and do not just exist in artificial or simplistic laboratory settings.
• This approach can help researchers create new ideas for hypotheses and experiments.

Limitations of Archival Research

• Not all archives endure, and those that do not may not have been randomly lost. When the survival of records is selective, there may be bias in the remaining archival data.
• People make mistakes in entering data in archives or there may be biases when data is recorded. For example, suicides may be recorded as accidental deaths to help maintain the privacy of the victims’ families.
• Sometimes definitions change so that even though long‐term records use the same label, what is being included may change over time. For example, the definition of family may change from comprising a mother, a father, and children to same‐sex couples or single‐parent families.
• Perhaps most importantly, we cannot conclude causal relations from archival research. After all, the researcher did not control and manipulate the variables that may have played a role. Because the data were not assembled to answer a particular research question, it might not be exactly the data the researcher requires.

Longitudinal and Cross-Sectional Research

Sometimes we want to see how people change over time. 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 ages 40, 50, and 60 to possibly find correlations between diet and early-onset dementia. In this case, the longitudinal design is used to uncover risk factors of certain diseases, like Alzheimer’s. Longitudinal studies are also used in social-personality and clinical psychology to study rapid fluctuations in behaviors, thoughts, and emotions from moment to moment or day to day.

Another approach is cross-sectional research. In cross-sectional research, a researcher compares multiple segments of the population at the same time. Using the dietary habits example above, the researcher might directly compare different groups of people by age. Instead of observing a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and then to a group of 40-year-old individuals, and so on. While cross-sectional research requires a shorter-term investment, it is also limited by differences that exist between the different generations (or cohorts) that have nothing to do with age per se, but rather reflect the social and cultural experiences of different generations of individuals that make them different from one another.

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 mental disorders 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.

To illustrate this concept, consider the following research tracked by Alzheimer’s Disease Neuroimaging Initiative (ADNI). Alzheimer’s Disease Neuroimaging Initiative (ADNI) is a multisite study that aims to improve clinical trials for the prevention and treatment of Alzheimer’s disease (AD). Researchers at 63 sites in the U.S. and Canada track the progression of Alzheimer’s disease (AD) in the human brain with neuroimaging, biochemical, and genetic biological markers. This knowledge helps to detect the earliest signs of Alzheimer’s disease (AD) and to track the disease, to find better clinical trials for the prevention and treatment of AD, and to make all data and samples available for sharing with clinical trial designers and scientists worldwide. Alzheimer’s Disease Neuroimaging Initiative (ADNI) enrolls participants between the ages of 55 and 90 who are recruited at 57 sites in the U.S. and Canada. One group has dementia due to AD, another group has mild memory problems known as mild cognitive impairment (MCI), and the final control group consists of healthy elderly participants. Studies using ADNI cross-sectional and longitudinal MRI, PET, genetics, cognitive, biological fluid, and autopsy data have reported that AD pathology is already present in people with no outward sign of memory loss. These cognitively normal people may already have subtle brain atrophy.

Cohort studies are one type of longitudinal study that samples a cohort (a group of people who share a defining characteristic, typically who experienced a common event in a selected period, such as birth or graduation) and perform cross-section observations at intervals through time. The Minnesota Center for Twin and Family Research (or MCTFR) is a series of behavioral genetic longitudinal studies of families with twin or adoptive offspring conducted by researchers at the University of Minnesota. It seeks to identify and characterize the genetic and environmental influences on the development of psychological traits. The primary cohorts of participants include the Minnesota Twin Family Study, Sibling Interaction and Behavior Study, Minnesota Twin Registry, and a variety of other cohorts of participants.

In twin studies, researchers identify individuals with a specific disorder who are members of an MZ or a DZ twin pair and then study the other twin in the pairs. The Minnesota Twin Family Study is a twin study established in June 1989 with 1,300 same-gendered twin pairs ages 11 or 17, with an additional cohort of 500 such pairs recruited around 2004. Twins were born between 1972 and 2000. All twins born in Minnesota at that time were eligible to participate using birth registry data. Both identical and fraternal twins share certain aspects of their environment. This allows researchers to estimate the relative impact of environmental and genetic influences on phenotypes, an individual’s observable or expressed traits. The focus of the Minnesota Twin Family Study (MTFS) is on behavioral phenotypes, such as academic outcomes, cognitive abilities, personality, and interests; family and social relationships; mental and physical health; physiological measurements.

Figure 2. Pedigree chart showing an inheritance pattern consistent with autosomal dominant transmission. Behavioral geneticists have used pedigree studies to investigate the genetic and environmental basis of behavior.

Clearly, this type of research is important and potentially very informative. While the name behavioral genetics connotes a focus on genetic influences, the field broadly investigates genetic and environmental influences, using research designs that allow removal of the confounding of genes and environment. The field has seen renewed prominence with research on inheritance of behavior and mental illness in (typically using twin and family studies) (Figure 2).

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 commitment 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 increase 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.