Cross-Sectional Studies: What Are They?

The cross-sectional study is a type of observational design that allows us to collect and analyze specific variables in the investigation of a problem. In this article, we explore what they are, what their characteristics are, and what advantages and disadvantages they have.
Cross-Sectional Studies: What Are They?

Last update: 21 December, 2021

Any investigation requires an action plan that allows the acquisition of the necessary information to respond to the problem. This plan is known as research design. Among those that stand out in this area are the experimental designs and the non-experimental or observational ones. For example, among the experimental ones we find the pre-experimental studies, the quasi-experiments, and the pure experiments. On the other hand, in observational designs, we can find cross-sectional studies (or transactional studies) and longitudinal or evolutionary studies.

Cross-sectional studies belong to a much broader category of research designs. With these, researchers must visualize in an extremely practical and concrete way how they’ll answer the questions of their research. Furthermore, how they’ll approach the fulfillment of the objectives of their work.

The cross-sectional study

The cross-sectional study is a type of non-experimental research design in which data is collected at a single period of time. In this type of study, as in all observational designs, there’s no intervention on the variables. In fact, they’re not influenced, they’re simply observed.

We can confirm that the purpose of the cross-sectional design is to describe variables and analyze their incidence and interrelation at a given moment (Hernández-Sampieri, 2014). For example, to:

  • Measure the attitude of fourth-graders who missed evaluative exams in the last month of the academic calendar.
  • Measure the level of job satisfaction of the employees of an automotive company.
  • Inquire about the relationship between attraction and trust in dating relationships in young couples.
  • Analyze which population (men or women) buy more sportswear in an online store during one particular week.

Cross-sectional studies can encompass various groups of people, objects, phenomena, events, and situations. However, the data collection is always done at a single moment. The transversal sense of all research corresponds to a single measurement in a period of time, in which it’s planned to analyze the variables or the association relationship between them (Cvetkovic-Vega et al., 2021).

Women conducting cross-sectional studies.


One of the main characteristics of cross-sectional studies is that they’re conducted at a specific moment. This is the main way in which they vary from longitudinal studies. In these kinds, data is collected at different times and the evolution of the values in the variables is analyzed throughout said period. However, in cross-sectional studies, evolution isn’t analyzed. In fact, it’s a snapshot at a specific moment in time.

Another characteristic of these cross-sectional studies is that they don’t manipulate the variables. They’re a variant of the observational design but they only seek to describe, correlate, or explore one or more variables. Indeed, unlike experimental research designs, variables are never manipulated.

Cross-sectional studies allow the researcher to observe numerous characteristics at the same time, but always in a specific period of time. This also allows the study to provide information on what’s happening now.

Types of cross-sectional studies

There are different types of cross-sectional studies. They’re as follows:

Exploratory cross-sectional studies

Their objective is to establish a variable or a set of them. It could be a community, a specific context, an event, or a situation about which little is known. This initial exploration takes place at a specific time. These studies are generally applied to new research problems (Hernández-Sampieri, 2014).

Descriptive cross-sectional studies

These studies aim to know more about the incidence of the modalities of one or more variables in a population and to provide a description. Therefore, they’re purely descriptive studies and, when they formulate their respective hypotheses, these are also descriptive.

Cross-sectional correlational studies

This type of design describes the association between two or more categories, concepts, or variables at a given time. These studies are limited to establishing relationships, not talking about causes and effects.

How to start a cross-sectional study

To start a cross-sectional study, the following four steps can be applied, according to Álvarez-Hernández and Delgado-De la Mora (2015):

  • Ask a research question and delimit the study population, carefully choosing the study sample through rigorous sampling.
  • Decide which variables are relevant to the research question, based on published scientific evidence.
  • Choose the method of measuring the variables, the data collection procedures, and the type of sources to be consulted.
  • Select the analysis plan for the data obtained, clearly establishing the statistical procedures.

Advantages and disadvantages of cross-sectional studies

Among the advantages of cross-sectional studies are the following (Levin, 2006):

  • They’re relatively inexpensive and take little time to complete.
  • They allow the estimation of the prevalence of the outcome of interest. This is because the sample is generally taken from the entire population.
  • Various outcomes and risk factors can be evaluated.
  • They’re extremely useful for public health planning, understanding the etiology of diseases, and generating hypotheses.
  • There are no losses to follow-up, as only one measurement is performed.
Man doing analysis of a cross-sectional study

On the other hand, the disadvantages of these types of designs are the following: (Levin, 2006)

  • It’s impossible to make causal inferences and establish the directionality of associations.
  • Because only a single measurement is performed at a specific time, the situation may give different results if another time frame was chosen.
  • Prevalence-incidence bias (also called Neyman bias ). This is more prevalent in the case of diseases of longer duration. In fact, any risk factor that causes death will be underrepresented among those who suffer from the disease.
  • Risk can’t be measured objectively. Nor can reliable forecasts be made.

In this article, we’ve described a type of study that’s widely used in research. This is due to its power at the descriptive level and its specific cut-off point, as compared to, for instance, a longitudinal study. In many cases, these studies are also used to carry out preliminary analyses, with the aim of assessing whether it’s worth conducting an experimental study.

All cited sources were thoroughly reviewed by our team to ensure their quality, reliability, currency, and validity. The bibliography of this article was considered reliable and of academic or scientific accuracy.

  • Álvarez-Hernández, G., & Delgado-De la Mora, J. (2015). Diseño de estudios epidemiológicos. I. El estudio transversal: Tomando una fotografía de la salud y la enfermedad. Boletín Clínico Hospital Infantil del Estado de Sonora32(1), 26-34.
  • Cvetkovic-Vega, A., Maguiña, J. L., Soto, A., Lama-Valdivia, J., & López, L. E. C. (2021). Estudios transversales. Revista de la Facultad de Medicina Humana21(1), 179-185.
  • Hernández-Sampieri, R., Fernandez, C. y Baptista, M. (2014). Metodología de la investigación(6ª Ed.). McGraw-Hill Interamericana.
  • Levin, K. A. (2006). Study design III: Cross-sectional studies. Evidence-based dentistry7(1), 24-25.

This text is provided for informational purposes only and does not replace consultation with a professional. If in doubt, consult your specialist.