Olla Peeps! In this article, I will discuss Cross-sectional data. Cross sectional data is a part of Cross sectional study. It is a cross-section of a study population in econometrics and statistics. I will be discussing these facts in an easy form which you will easily understand.
I will cover the following points:
- What is Cross sectional data?
- Cross sectional data example
- Cross sectional data vs. time-series data
- Uses of Cross sectional data
So, let’s get started with our discussion on cross sectional data.
What is Cross sectional data?
Cross sectional data is a part of the cross sectional study. A cross sectional data is data collected by observing various subjects like (firms, countries, regions, individuals), at the same point in time. A cross sectional data is analyzed by comparing the differences within the subjects.
Basically, Cross sectional is a data which is collected from all the participants at the same time. Time is not considered as a study variable during cross sectional research. Though, this is also a fact that, during a cross sectional study, all the participants don’t give the information at the same moment.
Cross sectional data is collected from the participants within a shorter time frame. This time frame is also known as field period. Time only produces a variance in the results, but it’s not biased.
If you expand your data collection process to involve daily sales revenue and expenses over a span of time of few months, you will now be having a time series for expenditures and sales.
Cross sectional data example
Take an example. Say, suppose you want to measure current blood pressure levels in a population. 1000 people will be selected randomly from that population. It is also called a cross section of that particular population range). Now, their Blood Pressure will be measured. Their height, weight and other health factors will also be noted.
This cross sectional data provides you with a snapshot of that population. This data will only provide the current proportion of the Blood pressure levels. On the basis of just one cross sectional sample, you can’t judge whether the rate of Blood pressure raising is low or high. But, it will surely give you an idea of the scenario.
Another cross sectional data example can be a cross sectional study performed on the variations of ice cream flavours at a particular store and how people are responding to those flavours. You can also obtain cross sectional data from a list of grades scored by a class of students on a particular test.
Data collected on sales revenue, sales volume, expenses for the last month and number of customers at a particular coffee shop. This is also a type of cross-sectional data. If you expand your data collection process to involve daily sales revenue and expenses over a span of time of a few months, you will now be having a time series for costs and sales.
Cross Sectional Data vs. Time Series Data
Data comes in various sizes and shapes. This data measures many things at different times. Well, both time-series data and cross-sectional data are a specific interest of financial analysts.
Various methods are used to analyze different types of data. It is, therefore, crucial to be able to identify both time series and cross sectional data sets. Let’s discuss both one by one and analyze the difference between both.
Cross sectional data
These are the observations which come from different groups or individuals at a single point of time. The underlying population should have members with similar characteristics. For example, if you want to know how many companies are spending their money on development and research?
Some of the companies spend less amount, and some spend a lot of research and development. This will provide different data as there are various companies belonging to different groups. Rather, you can analyze the companies belonging to a similar group and then do a cross sectional analyses on them. Let’s now talk about Time-series data.
These are observations which are collected at equally spaced time intervals. For example, you can consider the daily closing price of a particular stock recorded over the past four weeks. One thing is to be noted, and that is, too short or too long time can lead towards time bias.
Other examples of Time series data can be, weekly sales graph of an ice-cream sold during a holiday period at some shop. Another example can be, staff numbers noted at a college, which was taken on a monthly basis. It was done to assess the turnover rates of the staff. These examples can be used to showcase data patterns in the near future.
Let’s make it easier to understand. When the data is collected for the same variable over time, like months, years, then this type of data is called as time-series data. The data might be collected over months, years, but virtually, any time interval can be seen.
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Uses of Cross sectional data
Cross-sectional data is used in differential equations and statistical techniques. Primarily it is used for cross-sectional regression. It is a kind of regression analysis for this data. For instance, each individuals’ usage expenditure in a specific month can be regressed on the basis of different aspects.
These aspects can be; incomes, wealth and their various demographic characteristics as well. It is to judge how distinctions amongst those characteristics, result in the ultimate behaviour of the consumers.
Some practical examples of cross-sectional data
- Cross-sectional datasets are utilized mostly in Finance, economics and various areas of social sciences.
- In applied microeconomics, cross-sectional data is used to study labour markets, public funds, industrial organization theory, and health finance.
- Political researchers utilize cross-sectional information to break down demography and electoral engagements.
- Cross-sectional data also has a role in comparing financial statements of two or more companies. Financial analysts carry out this job. In a cross-sectional analysis, the comparison is carried out at the same time. Whereas in time series data analysis, a comparison between the financial statement of the company takes place in several time periods.
- In retail, cross-sectional data plays a significant role. It can study the expenditure trend of males and females of any age group.
- In business, cross-sectional data can be used to study the response to a single change from people coming from different socio-economic status from a specific geographical section.
- In medical and healthcare also cross-sectional data can be used to analyze how many kids of age between 4 to 14 are prone to low calcium deficiency.
Cross-sectional data allows collecting large information that further helps in quick decision making. For more information regarding the cross-sectional data visit here.
The concept behind Rolling Cross-Section
In case of a rolling cross-section, the existence of an individual in a sample along with the time period at which he was enrolled in that sample, both are defined through random techniques.
The individual is chosen through a random technique from the existing population. After the selection, a random date is allotted to each. It is a random data on whose basis an individual’s interview is conducted and hence, it is made a part of the survey.
Merits and demerits of cross-sectional studies
- A cross-sectional data study can be conducted in less time.
- Al the variables of this study are collected at the same point in time.
- Research can be conducted on multiple outcomes at the same time.
- It is a good form of data collection for descriptive analysis.
- It can be helpful in commencing new or further research.
- The cross-sectional study cannot be used for timeline-based research.
- It could be tough at times to identify the people that come under similar variables.
- Associations are tough to analyze.
- The study can be biased as well.
- It does not help to determine the cause.
So, this was about Cross-sectional data. It is data collected for some variables at the same point of time. If you liked this information or want me to add something to this article, then kindly let me know through your valuable comments below.
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