Research can take anything from a few minutes to years or even decades to complete. When a systematic investigation goes on for an extended period, it's most likely that the researcher is carrying out a longitudinal study of the sample population. So how does this work?
In the most simple terms, a longitudinal study involves observing the interactions of the different variables in your research population, exposing them to various causal factors, and documenting the effects of this exposure. It's an intelligent way to establish causal relationships within your sample population.
In this article, we'll show you several ways to adopt longitudinal studies for your systematic investigation and how to avoid common pitfalls.
A longitudinal study is a correlational research method that helps discover the relationship between variables in a specific target population. It is pretty similar to a cross-sectional study, although in its case, the researcher observes the variables for a longer time, sometimes lasting many years.
For example, let’s say you are researching social interactions among wild cats. You go ahead to recruit a set of newly-born lion cubs and study how they relate with each other as they grow. Periodically, you collect the same types of data from the group to track their development.
The advantage of this extended observation is that the researcher can witness the sequence of events leading to the changes in the traits of both the target population and the different groups. It can identify the causal factors for these changes and their long-term impact.
1. Non-interference: In longitudinal studies, the researcher doesn't interfere with the participants' day-to-day activities in any way. When it's time to collect their responses, the researcher administers a survey with qualitative and quantitative questions.
2. Observational: As we mentioned earlier, longitudinal studies involve observing the research participants throughout the study and recording any changes in traits that you notice.
3. Timeline: A longitudinal study can span weeks, months, years, or even decades. This dramatically contrasts what is obtainable in cross-sectional studies that only last for a short time.
A cross-sectional study is a type of observational study in which the researcher collects data from variables at a specific moment to establish a relationship among them. On the other hand, longitudinal research observes variables for an extended period and records all the changes in their relationship.
Longitudinal studies take a longer time to complete. In some cases, the researchers can spend years documenting the changes among the variables plus their relationships. For cross-sectional studies, this isn't the case. Instead, the researcher collects information in a relatively short time frame and makes relevant inferences from this data.
While cross-sectional studies give you a snapshot of the situation in the research environment, longitudinal studies are better suited for contexts where you need to analyze a problem long-term.
Longitudinal studies repeatedly observe the same sample population, while cross-sectional studies are conducted with different research samples.
Because longitudinal studies span over a more extended time, they typically cost more money than cross-sectional observations.
The three main types of longitudinal studies are:
These methods help researchers to study variables and account for qualitative and quantitative data from the research sample.
In a panel study, the researcher uses data collection methods like surveys to gather information from a fixed number of variables at regular but distant intervals, often spinning into a few years. It's primarily designed for quantitative research, although you can use this method for qualitative data analysis.
If you want to have first-hand, factual information about the changes in a sample population, then you should opt for a panel study. For example, medical researchers rely on panel studies to identify the causes of age-related changes and their consequences.
In a retrospective study, the researcher depends on existing information from previous systematic investigations to discover patterns leading to the study outcomes. In other words, a retrospective study looks backward. It examines exposures to suspected risk or protection factors concerning an outcome established at the start of the study.
Retrospective studies are best for research contexts where you want to quickly estimate an exposure’s effect on an outcome. It also helps you to discover preliminary measures of association in your data.
Medical researchers adopt retrospective study methods when they need to research rare conditions.
A cohort study entails collecting information from a group of people who share specific traits or have experienced a particular occurrence simultaneously. For example, a researcher might conduct a cohort study on a group of Black school children in the U.K.
During cohort study, the researcher exposes some group members to a specific characteristic or risk factor. Then, she records the outcome of this exposure and its impact on the exposed variables.
You should conduct a cohort study if you're looking to establish a causal relationship within your data sets. For example, in medical research, cohort studies investigate the causes of disease and establish links between risk factors and effects.
If you're looking to discover the relationship between variables and the causal factors responsible for changes, you should adopt a longitudinal approach to your systematic investigation. Longitudinal studies help you to analyze change over a meaningful time.
There are only two approaches you can take when performing a longitudinal study. You can either source your own data or use previously gathered data.
Collecting your own data is a more verifiable method because you can trust your own data. The way you collect your data is also heavily dependent on the type of study you’re conducting.
If you’re conducting a retrospective study, you’d have to collect data on events that have already happened. An example is going through records to find patterns in cancer patients.
For a prospective study, you collect the data in real-time. This means finding a sample population, following them, and documenting your findings over the course of your study.
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Governmental and research institutes often carry out longitudinal studies and make the data available to the public. So you can pick up their previously researched data and use them for your own study. An example is the UK data service website.
Using previously gathered data isn’t just easy, they also allow you to carry out research over a long period of time.
The downside to this method is that it’s very restrictive because you can only use the data set available to you. You also have to thoroughly examine the source of the data given to you.
How does a longitudinal study work in the real world? To answer this, let's consider a few typical scenarios.
A researcher wants to know the effects of a low-carb diet on weight loss. So, he gathers a group of obese men and kicks off the systematic investigation using his preferred longitudinal study method. He records information like how much they weigh, the number of carbs in their diet, and the like at different points. All these data help him to arrive at valid research outcomes.
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A researcher wants to know if there's any relationship between children who drink milk before school and high classroom performance. First, he uses a sampling technique to gather a large research population.
Then, he conducts a baseline survey to establish the premise of the research for later comparison. Next, the researcher gives a log to each participant to keep track of predetermined research variables.
You decide to study how a particular diet affects athletes' performance over time. First, you gather your sample population, establish a baseline for the research, and observe and record the required data.
Longitudinal studies are primarily a qualitative research method because the researcher observes and records changes in variables over an extended period. However, it can also be used to gather quantitative data depending on your research context.
The biggest challenge with longitudinal research is panel attrition. Due to the length of the research process, some variables might be unable to complete the study for one reason or the other. When this happens, it can distort your data and research outcomes.
Longitudinal data collection is the process of gathering information from the same sample population over a long period. Longitudinal data collection uses interviews, surveys, and observation to collect the required information from research sources.
Because longitudinal studies collect data over a long period, they are often mistaken for time series analysis. So what's the real difference between these two concepts?
In a time series analysis, the researcher focuses on a single individual at multiple time intervals. Meanwhile, longitudinal data focuses on multiple individuals at various time intervals.
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