Basically, if you have enrolled for a master’s or doctoral degree in economics, psychology, medicine, epidemiology, or social sciences course, then to receive your graduation, you must perform in-depth research on any topic related to your field of study and submit an outstanding dissertation or thesis. Especially, to make that possible, you must possess strong knowledge of cross-sectional data and several other new research methodologies. In case, you are unaware of what cross-sectional data means, then keep on reading this blog. For your better understanding, here, we have explained in detail about cross-sectional data with examples.
What is Cross Sectional Data?
Cross-sectional information is a type of statistical data collected in a specific time period by monitoring various subjects like companies, firms, religions, individuals, etc. You can analyze cross-sectional data by evaluating differences within the subject.
To elaborate, you can collect cross-sectional facts from all participants at the same time. Here, time is not involved as a study variable for cross-sectional research. In contrast, you may use it to bring variance in unabashed results. However, you can never ignore that all participants never share data at the same time. Though, a lot of time is not involved in participant’s data collection.
You can expand the short time frame for information collection, also known as the field period to include daily sales revenue and operating costs over a few months to get a time series for expenditure and sales.
Now, let us see a few examples of cross-sectional figures to understand the concept better.
Examples of Cross-Sectional Data
Take a look at the two examples to know what is cross-sectional data and what it looks like in practical or academic scenarios.
Example 1
Suppose you want to measure the current blood sugar levels in a population. For that, you select 2000 people randomly from the population. Now when you measure the blood sugar you must also take note of their height, weight, age, and other biological factors.
This cross-sectional statistics offers you a glimpse of the specific population. The information collected will give you the correct percentage of the blood sugar level. Based on this sample you can analyze whether the blood sugar levels are rising, lowering, or at regular levels. However, you can get an idea of the scenario.
Example 2
Next, let’s read the second example of cross-sectional figures collected from a study performed on various ice cream flavors from a specific ice cream store outlet. Then, see how people respond to the flavors. Now, similar to the method applied in the data collection process mentioned in sample 1, you can collect the data from a random class of school students. You just have to ask them to grade their favorite flavors in a specific test. Again, using this data, you can analyze the average likes or dislikes of people towards specific ice cream flavors.
Example 3
Another example of the cross-sectional numbers can be a month’s data collection on revenue and volume of sales, expense, and count of customer purchases from a specific coffee shop. If you enhance the sales collection process and collect revenue and expenses every day for a span of a few months, you can create a cross-sectional records series for costs and sales.
By now, you must have a glimpse of the areas where you can use cross-reference data. Let’s learn more about this from the areas where you can use reference data.
Where Can You Use Cross-Sectional Data?
Once you know what is cross-sectional data and see real examples of it, you may be inquisitive about where you can use it. Well, you can use cross-sectional data in multiple areas. Some of the most common areas are:
1. Regularity of outcome
Cross-reference data come to great use when you want to examine the prevalence of outcomes at a given moment.
2. Practical grounds
At times cross-reference is the best choice for practical reasons like if the only data that you can search for to find a solution to your research question was collected at a single point in time.
3. Collect data for other research
Cross-reference facts and research conducted on them are available at low cost and can be found in less time. Hence they can be used as the basis of further research or for research on other subjects.
Cross-sectional Data vs. Time Series Data
Cross-sectional data and time series data are for use by researchers of social science subjects. Both of them come in various sizes and shapes and serve the specific interests of financial analysts. However, there are some prominent differences between the two.
1. Concept
Here are the differences in concept:
- Data on cross-section: It is the observation of multiple subjects over a given point in time.
- Time series data: It is an observation of a single subject over numerous intermissions of time.
2. Primary Focus:
Find the difference of concept here:
- Data on cross-section: Focuses on multiple variables at the same time
- Time series data: Centers on the same variable over a longer stretch of time
3. Examples
Here are some differences in the examples:
- Data on cross-section: The minimum temperature of several cities around the globe on December 25, 2022, is an example of cross-sectional figures.
- Time series data: The temperature of California from 2018 to 2022 is an example of time series data.
Cross-sectional vs. Time Series Data: Comparison in Tabular Form
The tables below sum up the differences between cross-sectional statistics and time series data.
| Parameters | Data on Cross-Sectional | Time Series Data |
| Definition | A data type that contains observations of multiple subjects at the same time | A form of data that includes single-subject observations at multiple intervals |
| Primary Focus | Centers on several variables at the same point in time | Focuses on multiple variables over a while |
| Common Examples | Minimum temperature of several cities of the globe on December 25, 2022 | The temperature of California from 2018 to 2022 |
Study of Data on Cross-Section: Merits and Demerits
Analysis of cross-sectional facts has multiple merits and demerits. Let’s explore them.
Let’s see the merits first.
Merits of Cross-Sectional Figure Study
There are multiple merits of exploring cross-sectional statistics. Here are a few prominent ones:
- Study cross-sectional numbers quickly
- Exclude the need to wait long to collect all the cross-sectional study variables since all information is available at the same time
- You can conduct research on multiple objectives at the same time.
- A useful method of data collection for descriptive analysis
- Helpful to begin further research
Read more: Best Humanities Research Topics To Explore and Write About
Demerits of Cross-Sectional Numbers Study
Cross-sectional statistics also has some flaws. Here are a few common ones:
- You cannot use it for timeline-based research
- Recognizes the objects or population that come under the same variable
- Examining the associations is difficult
- It can be biased
- Fails to determine the cause
Conclusion
Hopefully, by now, you will have understood the basics of cross-sectional data along with its merits and demerits. In case, you have any doubts regarding it or if you are unsure how to use cross-sectional data in your research, call us right away.
