The ABC of Cross-Sectional Data

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Written By Charlotte Miller

Although we think that we know almost everything about certain things, there may be these ‘things’ that we actually don’t have knowledge about. The topic to be discussed in this article, is one of such ‘things’.

Cross sectional data is taken somewhat like statistics and is collected by taking into consideration many varied subjects (like countries, regions, institutions, etc.) at a given point of time. 

A comparative study is brought out among these subjects and study is conducted. For example: If the heart patients in a particular region are to be studied, data is collected for 1000 people in the area. 

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Then this data is analyzed for the height, weight, blood pressure, etc. of these people. Resulting analysis would help identify the age heart problems affect people in, their eating habits and their lifestyle. 

This would not give you the idea of when the heart attack would occur but inform you about the healthy lifestyle that can be followed. People are chosen randomly from that area (this is known as cross-section) and analysis is done on this selected lot.

Another example of cross-sectional data can be a study of the various varieties of fast food available in McDonalds and the customer’s response to them. 

The study can indicate what food is liked by customers and which one is disliked thus giving an idea to the company about how they could bring change in their business for its betterment. 

A lot of people resort to quora answers to find out about various things but they don’t know that this one is a better method of understanding anything.

Cross sectional data does not give cause and effect of the particular topic of study. It just provides the characteristics in the study. This data can support further research and development on this topic. So let us understand a few facts about the cross-sectional data, which could help us in a better understanding of the overall concept:

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Stable Change in Variables: 

The variables used in the data do not change immediately and are thus not manipulative. For example: The age, height and weight of a person does not change in a day or two. Therefore, if we use them as a base, proper results can be found out.

Time of collection of the Data: 

This data is collected at a single point of time which gives credibility to the variables.

Factors considered during data collection: 

Researchers take into consideration several factors while doing the research thus these factors can also be studied in the research. These factors may depend on the demand of the research and what is its end goal.

Time it applies to: 

It considers the present trends and not the ones that have passed or will come in future. And present is what matters in the result.

Information provided: 

Since it considers all the factors related to the present time, hence it can provide information about what is happening around in the present times. 

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Advantages of cross-sectional study:

The Research is Faster: 

The research in a cross-sectional data can be done quickly without wasting much time as data has to be collected in a set area from among a decided number of people. Also, online surveys can serve the purpose of making the process even faster.

Inexpensive Method: 

The data collected for cross sectional study is gathered in an inexpensive manner using self-report surveys. So, one does not have to worry about the expenditure.

Inclusion of Multiple Variables: 

Data collected takes into consideration multiple variables like age, sex, height, weight and the like. For example: If you are studying about heart issues you collect data for blood pressure patients, height, etc.

The Further use of Data: 

This data can be further used to facilitate studies and research into the field. For example: If I collect cross sectional data for people who are obese, then I will have to take into consideration certain diseases that these people face, as well. 

Thus, the data to study obesity would also provide me data for prevalence of diseases in such people and even the frequency of its occurrence. Not only this, but the data can also be used by several researchers and journal writers to warn people of the results of being obese.

Challenges to cross sectional data collection:

Chances to misleading information: 

People may sometimes tend to provide you misleading information which could affect the accuracy of the study. Thus, the cross-sectional data may sometimes be false and made out.

May vary from area to area: 

Considering its collection from one geographical area, the cross-sectional data may vary in other areas among the same age groups. For example: collection of data for obesity provides different trends in elite houses where people walk less and sit more and different ones in villages where farmers work hard and do physical activities to earn a living.

No cause-and-effect relationship established: 

Cross sectional data does not provide for cause and effect thus limiting itself to just bringing out facts and figures. Multiple variables in this study do not do the needful. It does not tell us about why certain things have happened and what can happen as a result of an event. 

Long-term changes not considered: 

No eye at the long-term changes and effects. Unlike the longitudinal studies, the cross-sectional study gives observation only for a short time thus not taking into consideration long term results. 

But there are various studies and research that demand a long-term knowledge of certain things rather than the short-one.

Therefore, the above points, provided in this article, can help you understand why the cross-sectional data is a better prerogative than the quora answers. This can help you have a better perspective and understanding of certain things. 

Cross sectional data can help facilitate study in any and every field. Be it economics where trends in business patterns can be compared among different countries, or psychology where reactions of different people to a particular stimulus is observed, cross sectional data provides a comfortable medium of study. 

Its focus on the present trends helps the problem to be treated then and there, leaving no or less scope for further discrepancies. So now if you have to focus on people facing disorders due to drug abuse, people in a certain age group being addicted to smoking and drinking, 

people with a set behavioral pattern towards certain things or people with certain comorbidities, you can either draw a cross sectional data yourself or refer to the one already compiled. 

Hence cross-sectional data is used in study of micro economics, social sciences, in political research to figure out how to influence people in the area and create vote banks, 

in comparison of finances of various institutions, in the area of retail- to calculate trends of expenditure by males and females, in business, in medical and healthcare and the like. 

Its various applications add up to its requirements in these fields. Not only this, for all you students who need help in psychology assignment help or accounting assignment, cross sectional data can provide the best case studies and information on it.