Cross Sectional Research

Imagine a team of researchers who want to determine if an increase in flu cases eight years ago was due to age or a specific event and what effect those cases had on the current population. They cannot do an experiment where a variable is manipulated because the event has already happened. Instead, they want to observe the information that has already occurred, describe it, and make inferences about the data. They can essentially take a point in time, analyse the information collected, and then make observations based on that data. The research method described is cross-sectional research.

• We will start by looking at what is meant by the cross-sectional research design.
• Then we will delve into the types of cross-sectional and the cross-sectional research method typically conducted in psychology and explore a cross-sectional research study example.
• Finally, we will explore the cross-sectional research advantages and disadvantages.

Cross-Sectional Research Design

Cross-sectional research is a type of research often used in psychology.

Cross-sectional research in psychology is a non-experimental, observational research design. It is usually used to describe, for example, the characteristics of a population or subgroup of people at a particular point in time. Or for descriptive purposes.

Cross-sectional research is not usually used when the researcher wants to draw analytical or causal conclusions from the research.

A typical cross-sectional study examines individuals who have or have not been exposed to a factor. The research seeks to determine the differences between those exposed to an element and those who have not.

In psychology, cross-sectional research is usually conducted to:

1. Measure the current prevalence rates of mental illness in specific populations.
2. Describe the characteristics of a target population.

Fig. 1 - Twins are the target population for several cross-sectional research studies .

Cross-Sectional Research Method

When conducting cross-sectional studies, the typical methodology commonly used involves:

1. Formulating a research question and hypothesis - at this stage, the researcher must identify the target population.
2. Designing the research - cross-sectional research tends to use observation rather than experimental techniques to collect data. However, other non-experimental methods, such as questionnaires, may be used. Therefore, cross-sectional research methods can collect both qualitative and quantitative data.
3. Conducting the research - the researcher collects data from participants.
4. Analysing the data - how the data is analysed depends on what data collection methods the researcher used.

Types of Cross-Sectional Study

It is important to note that the cross-sectional study is a non-experimental research method. It relies on observations. Rather than manipulating Variables, experimenters observe naturally occurring phenomena.There are three types of cross-sectional studies used in research.

The first type of cross-sectional study we will discuss is a descriptive cross-sectional study. Descriptive cross-sectional research measures characteristics or describes disease prevalence in the target population.

The second is analytical cross-sectional studies. In analytical cross-sectional research, researchers examine whether there is a relationship between two factors at a given time within the target population.

And finally, there is serial cross-sectional research. Serial cross-sectional research is when multiple cross-sectional studies are conducted on different populations/participants at different time points. Each time data are collected, different participants are included in the study.

On the other hand, the disadvantages of cross-sectional research are:

• Cross-sectional research is not conducted in a controlled setting with a standardised procedure (due to the nature of the experimental research method). Therefore, it is difficult for researchers to prevent confounding variables from influencing the factors studied. This reduces the validity of the study.

• Because of the cross-sectional research design, researchers cannot draw causal conclusions. Researchers cannot infer cause and effect from this research design because it does not consider how changes over time affect the data (it focuses on the present).

• According to Wang and Cheng (2020), cross-sectional research requires a large, heterogeneous sample. This increases the risk of sampling bias. If the sample does not meet these requirements, it is unlikely that the results will be generalisable. According to the researchers, sample bias can occur in clinical research when a larger number of individuals come from backgrounds that are more susceptible to developing the disease.

• The period and population the researchers selected for the study may not be truly representative.