Longitudinal Research-Definition, Advantages & Disadvantages

Disadvantages of Longitudinal Studies

• Participants’ lifespan can limit longitudinal studies. If some of the study subjects die during the observation period, the sample size will be reduced. This means that it is only possible to study a small number of people over a long period.
• In some instances, the participants in the longitudinal study may become unresponsive. Unresponsive participants will leave no data for researchers to collect, which can lead to incomplete datasets. The collected data would not be enough to make conclusions.
• Longitudinal studies are expensive to conduct. Researchers need to follow the same participants over a long time which can be costly.
• Longitudinal studies are not able to gather information about different groups. Consequently, they are less effective for comparing relationships between variables.
• The inability to compare two or more groups also makes longitudinal studies less helpful in determining cause and effect.
• It is difficult for longitudinal studies to be representative. This is because it takes time to find enough participants that live in the same areas.
• Longitudinal studies also have long wait times between data collection. This long wait makes longitudinal studies impractical for real-time data collection.
• Longitudinal studies also fail to anticipate study dropout. Participants can choose to drop out at any time, and this will skew the data. The result can be incomplete datasets that may not be representative of the whole group.
• In some instances, participants do not know if they are in a longitudinal or cross-sectional study. Researchers will have to determine if participants are in the wrong group at some point during their study.
• Longitudinal studies also fail to follow participants who join the study late. Researchers will only collect data from the beginning of the study. The information gathered will not represent the participants who joined the study after.
• Longitudinal studies are difficult to generalize since collecting data from a large group takes a long time.

Types of Longitudinal Studies

There are three types of longitudinal studies:

Retrospective study

Retrospective studies examine historical events or existing data to predict future behaviors. For example, a researcher might look at an individual’s past medical records to predict the likelihood of him being diagnosed with a disease in the future.

Conducting a prospective study

This type of longitudinal study follows the same subjects but only collects information at a specific point in time. Researchers can use these data to compare participants’ behaviors, abilities, and attitudes at different points in their lives. These studies help investigate behavior and changes over time.

Panel study

In this study, participants are surveyed over a period to investigate how particular variables affect each other. Researchers collect individual responses from a group of participants and use them to conclude about the effects of variables on one another.

For example, a researcher might survey a group of individuals and collect their height and weight to predict their risk of obesity.

The process of conducting a panel study

It involves a surveyor asking multiple questions to a group of participants. Afterward, the participants are asked similar questions at a later date. The researcher can then compare the responses to see how they change over time.

Cohort study

In cohort studies, groups of participants are observed over a period to see how they develop. Researchers collect individual data from each participant and conclude how variables affect each other over time.

For example, a researcher might survey a group of new parents and collect information about their child’s development over time.

The cohort study procedure

A cohort study involves researchers dividing a group of participants into smaller groups based on particular characteristics. The researcher then collects individual data from the participants in each group. The data is used to conclude how different characteristics affect one another over time.

Longitudinal studies are used to investigate how variables change or develop over time.

Some examples of variables studied in longitudinal studies are:

• socioeconomic status
• intelligence quotient (IQ)
• Psychological well-being
• Healthcare seeking behavior
• Leisure activity participation.
• Happiness
• Social relationships
• Physical health
• Mental state
• Cognitive ability

Applications of Longitudinal Surveys

Longitudinal studies are used in a variety of fields, including:

Medicine

Medical research uses longitudinal observation to track the progression of illnesses or diseases. For example, researchers might study how an illness progresses throughout a lifetime. The participant’s journey is recorded in detail, considering the illness’s effects on their life.

Economics

In economics, longitudinal studies are used to track financial changes over time. For example, a researcher might study how the income of a group of individuals varies from year to year.

Psychology

Researchers use longitudinal studies to track changes in psychological health over time. For example, a researcher might study how anxiety or depression affects an individual over a lifetime.

Education

In education, longitudinal studies are used to track the changes in classroom behavior over time. For example, researchers might study how bullying or aggression affects friendships over time.

Social science

In the social science field, longitudinal studies track changes in opinions and behaviors over time. For example, a researcher might study how voting preferences change over a lifetime.

Forensic science

Forensic science involves using longitudinal studies to track the development of criminals over time. For example, a researcher might study how crime statistics change throughout a prison sentence.

The Steps of Conducting a Longitudinal Study

There are seven steps in conducting longitudinal research.

1. Select a group of participants to study.
2. Choose the type of participants to study and where they will gather data.
3. Define the period that each participant is studied.
4. Choose the variables to study and determine how they will be measured.
5. Design procedures for data collection.
6. Analyze the data and make conclusions.
7. Write the final report of the longitudinal study.

Longitudinal Studies Versus Cross-sectional Studies

Longitudinal studies and cross-sectional studies both involve following a group of individuals for a while. However, there are several key differences between the two types of studies:

• By observing participants over time, longitudinal studies enable researchers to investigate how variables change as time goes on. For example, a researcher might study how physical health changes over time by following a group of individuals with a disease.

A cross-sectional study only allows researchers to look at the state of a variable at a single point in time. For example, a researcher might study the rates of obesity in a particular area by comparing a group of individuals that live in that area.

• Longitudinal studies only follow a group of individuals, not specific individuals. For example, a researcher might study how happiness changes in a group of women rather than tracking a single woman’s happiness over time.

On the other hand, cross-sectional studies are not able to track the development of variables over time. For example, a researcher might study how happiness changes in an individual, but only at a single point in time.

• While cross-sectional studies typically compare two groups, longitudinal studies compare a single group over time.
• Longitudinal studies use individual data from each participant to conclude how variables affect each other over time. For example, a researcher might study how income affects happiness using data from individual participants.

Cross-sectional studies use aggregate data to conclude about two or more groups. For example, a researcher might compare the rates of obesity in two groups using aggregate data.