The cross-sectional design It is a type of observational research that analyzes and studies data on variables collected over a period of time on a population sample or a predefined set. The data collections are made in the present, that is, at the time of initiating the investigation.
It is also known as a cross-sectional study, a prevalence study, or a cross-sectional study. An example of a cross-sectional design would be how many people went on summer holidays in Barcelona, and how many of them presented symptoms of COVID-19 upon their return.
The people under study are similar in all variables, except the one under study, so that variable is the constant throughout the investigation. It differs from the longitudinal study in that in this the variables may change during the course of the study, while in the cross-sectional design they do not..
Cross-sectional studies can be carried out in various industries, such as retail, medicine, business or education, as it allows collecting a large amount of data that helps make decisions.
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This type of research is based on observing the subjects in their environment, without the researcher intervening.
It means that the period of time is the same for all subjects, who share similar characteristics. For example, the type of purchases made by men and women between 24 and 38 years old in the week of Black Friday 2020 by Mercado Libre Buenos Aires.
Due to its nature of data collection, and to analyze the results, the cross-sectional design makes use of tools similar to statistics: it is common to use average and absolute frequencies, maximum values, etc..
Use graphs and diagrams to represent the results obtained.
It is when it is a cross-sectional study that is based on the sample survey, and with it obtains the necessary data throughout the investigation.
It is when you use two or more samples of the population under study and the data is obtained in a single moment. When the information is collected in two different periods of time, overall comparisons can be made, even if the survey has been applied to different samples of the population..
This type of cross-sectional design is used to evaluate the distribution and frequency of a variable in a specific demographic segment..
An illustrative example could be how many male and female students accessed scientific careers in 2019, at two major universities in Colombia.
It is analytical when the association between two parameters -related or not- is studied. For example, how many workers in an oil company can develop lung problems. For this, the conditions of the oil company and the wells will be observed.
The point is that the study does not explain the causes, since such lung problems could be present in workers from before and for other reasons unrelated to working conditions.
This methodology is not complete, since the risk factors and the results are generated at the same time creating some confusion..
In real life, when a cross-sectional study is started, items of both types are often used.
We will put two examples of cross-sectional design applied to an investigation on COVID-19, to give you an idea of how it can be used.
It will be studied how many patients from two hospitals in Buenos Aires, between July and September 2020, developed pneumonia after testing positive for COVID-19, and who had to be hospitalized.
The demographic segment will be broad: boys and girls up to 14 years old, young people between 15 and 25 years old, and adults onwards. This study will take into account the follow-up surveys carried out by the Ministry of Health to the patients through the physical consultations they made in the hospitals and their own medical records.
It will also determine how many of them had previous health conditions that aggravated the symptoms of COVID-19, how many died as well as the age range.
The variables will be the incidence of pneumonia in COVID-19 patients, and previous conditions (other diseases, such as diabetes, hypertension, kidney problems, asthma, etc.).
A recorded sampling will be made of volunteers who tested negative for COVID-19 in the city of Medellín but who presented similar symptoms, such as cough or fever, which may be the product of other viral processes, such as normal influenza..
Also, another sample will be taken from people who tested positive for the virus and who had a dry cough as one of the symptoms..
The sampling will be taken from the first week of October to the third, and telephone recordings will be accepted where people will cough. The idea of the study is to listen to the coughs of those who have COVID-19 and those who do not, and analyze the differences in order to better diagnose the dry cough of the virus.
The study will be anonymous and simultaneous, and as the information is obtained, a database will be created for the cough of COVID-19 and for the cough of other viral processes..
At the end of the investigation, it will serve as support for doctors and COVID-19 researchers that improve the diagnoses of the virus.
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