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.
Cross-sectional research is a study that measures the relationship between variables by collecting data at a specific point in time in the target population.
A lot of cross-sectional research has been done regarding disease and public health.
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:
- Measure the current prevalence rates of mental illness in specific populations.
- Describe the characteristics of a target population.
The target population is the subgroup of people to whom the research findings will be generalised.
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:
- Formulating a research question and hypothesis - at this stage, the researcher must identify the target population.
- 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.
- Conducting the research - the researcher collects data from participants.
- 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.
An example of a descriptive cross-sectional study is a study examining the prevalence of developmental disabilities in boys in the UK.
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.
An example of an analytic cross-sectional study is an investigation of the side effects of interventions in men at various stages of cancer who have been receiving cancer treatment for three months.
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.
The fact that different participants are used each time is important. If the same participants are used, it is a longitudinal study, not a cross-sectional one.
An example of a serial cross-sectional study is measuring the prevalence rate of mental illness at different time points.
Serial cross-sectional research is useful because it reveals there may be an increase in risk factors in today’s society that increase the incidence of mental illness or that awareness of mental health has now increased. Researchers can determine whether this trend exists but cannot conclude the cause.
Cross-Sectional Research Study Example
The following research scenarios show cross-sectional research study examples that can be conducted in psychology.
Clinical psychology, e.g., research examining the prevalence of diabetes in the South Asian community.
Developmental psychology, e.g., research to investigate the prevalence of symptoms listed in the DSM-5 (the manual used by clinicians to diagnose people with mental illness) in children diagnosed with an autism spectrum disorder in the UK.
Social psychology, e.g., investigating factors that may contribute to educational failure in school children.
Cross-Sectional Research Advantages and Disadvantages
The cross-sectional research advantages are:
This is beneficial because it allows researchers to identify naturally occurring differences/similarities and relationships between variables and subgroups of people.
Cross-sectional research has many practical applications in psychology. For example, it can be used to estimate the prevalence of certain mental illnesses. This is important because it raises the awareness of public health services that changes, such as the support offered, need to be improved.
Huang et al. (2019) found that the prevalence rates of mental illness in China increased in 2013 compared with 1982.
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.
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.
Cross Sectional Research - Key takeaways
- Cross-sectional research is a study that measures the relationship between variables by collecting data at a specific time in the target population.
- Cross-sectional research in psychology is a non-experimental, observational research design. It is usually used to describe things like the characteristics of a population or a subgroup of people or for descriptive purposes. Cross-sectional research is not usually used when the researcher wants to draw analytical or causal conclusions from the research.
- There are three types of cross-sectional research: descriptive cross-sectional research, analytical cross-sectional research, and serial cross-sectional research.
- The advantages of cross-sectional research are that it is a relatively quick and inexpensive research method, can be used to determine the direction of future empirical research, and has many practical applications.
- The weaknesses of cross-sectional research are that the factors measured in cross-sectional research can be easily influenced by confounding factors because researchers lack control over the experiment.
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