**Summary - ** Cross sectional data deals with populations, groups of students, lists of products in a business, etc.... It mainly analyzes data at a specific point in time. Also, it identifies patterns along with the relationships between the variables.

Cross-sectional data involves collecting data that observes several subjects, such as countries, individuals, and regions in a particular period. One can seek sample cross sectional data of econometrics, economic analysis, public finance, labor market, industrial organization theory, study populations, and statistics.

You can compare the attributive differences among the subjects under analysis. For data about their cross-section, data must be collected from all such subjects at that one point or time.

**Cross Sectional Data Example**

It is always advantageous when you have an example of a theory. So, let’s have a look at a cross-sectional data example.

**Example 1**

Suppose you want to check the blood sugar level of a particular population. Assume 2,000 people are selected randomly from the list of the population. Now, along with their blood sugar level, measurements will be done on their weight and height per their age.

Now, through their **cross sectional data sets, **you will get a snapshot of that population range. You must note that the detailed data will provide only the blood sugar level in the selected 2,000. It is also crucial to note that you cannot judge the blood sugar level of the whole population only with a single cross-sectional sample.

**Example 2**

Let’s take an example of a cross-sectional study on various mocktail flavors. Here, finding out how subjects respond to those flavors is essential. Ask the subjects to sip each mocktail flavor at first. After that, ask them to rate the flavors per their taste and preferences.

Likewise, you can find many such examples. Also, if you are looking for **Nodal Analysis**, there are examples.

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**Random Sampling in Cross-Sectional Data Analysis**

The random sampling framework is one of the statistical frameworks highly recommended in a **cross sectional data analysis**. It usually runs on the concept of assumption. It says there is a close link between the demographics and the sample received from a population.

For example, think about a random cross sectional sample of apple consumption in California. The analysis will be made in terms of money and time. If you wanted a cheaper analysis, it would be simple to analyze only one parameter, for example, taste. However, if you are asked to make cross-sectional data based on time and taste, that will be a complex yet compact idea.

Suppose you want to know the GDPs in American countries; the cross section data will be based on independent random samples. One of the facts that may come from the analysis is that American GDPs will likely correlate with Canada’s.

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**Interpreting Cohort Data**

Cohort data are crucial for examining demographic changes in individuals, the “gross effects.” These are crucial for understanding any potential causal processes because they include data on changes in each cohort’s members and the cohort as a whole. At the macro level, cross-sectional data are longitudinal – we repeatedly observe the same variables over long or short periods – and they allow one to track the “net effects” of societal (or secular) change on a population’s characteristics.

Researchers need to understand the relationships between different life experiences and occurrences, analyzing how people seem impacted by changes in the outside world even while others seem impervious to these changes. For example, there is a cause-and-effect link only if we can prove that low parental aspirations directly contribute to kids’ poor academic performance. A cross-sectional poll could not confirm this direct link; it could only show a link between parents’ educational aspirations and their kids’ academic success.

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**Uses of Cross Sectional Data**

In the social sciences, particularly in economics, cross-sectional datasets are often employed. Cross-sectional datasets are used in applied microeconomics to study health economics, public finance, labor markets, etc.

Political scientists use cross-sectional data to examine electoral campaigns and demographic trends. Financial analysts frequently compare the financial statements of two businesses; however, a cross-sectional study compares the financial statements of the two firms at the same time at the **same point**. In contrast, time-series data analysis evaluates the financial statements of one business over successive equally spaced time intervals.

You can observe the use of cross-sectional data sets in the cross-sectional regression, called **regression analysis**. For example, let’s consider the food expenses of several individuals in a particular month. You can do a regression analysis of their incomes, accumulated wealth level, etc.

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**Merits and Demerits of Cross Sectional Data**

Merits:

- Studies using cross-sectional data can be completed faster.
- All of the study’s variables were gathered at the same time.
- Research can be done simultaneously on several outcomes.
- It is an effective method of collecting data for descriptive analysis.
- It could be helpful when starting new or additional research.

Demerits:

- Timeline-based research cannot be conducted using a cross-sectional study.
- At times, recognizing subjects with common characteristics may be challenging.
- Analyzing associations can be difficult.
- Additionally, the study may be skewed.
- Finding the cause does not assist.

**The Concept Behind Rolling Cross-Section**

In the case of a rolling cross-section, random approaches determine an individual’s inclusion in a sample and the time of inclusion.

A random procedure is used to select each subject from a current population. Each subject is given an arbitrary date after being chosen. On that arbitrary date, the subject gets interviewed and included in the survey.

**Cross Sectional Vs Time Series**

The difference between cross-sectional data and time series is based on the use of data. Also, the nature of data collection is different. Let us find out more about it:

**Differences based on utility and nature**

The time series takes into consideration the same variables over a certain period. But the cross-sectional data picks different data at different points in time.

You must note that the time series data deals with observations collected over **equally spaced time intervals**.** **

**Difference based on observation type**

Since the time series data deals with the observation of data collected in a specific time interval, you can easily categorize it as hourly, daily, monthly, and yearly.

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**What Is Panel Data?**

In a time series cross-sectional data analysis, panel data deals with repeated observations over time. But the data comes from many units from many points in time. The units’ resources include schools, firms, cities, universities, etc.

It is possible to perform a more exciting analysis with panel data compared to the series data and cross-section data.

**Panel Data: Long or Wide?**

There are two structures of panel data: long and wide. Have a look at the following descriptions with an example.

**Wide Data**

Country | population 2000 | population 2001 | population 2002 |
---|---|---|---|

Sweden | 8872284 | 8888675 | 8911899 |

Norway | 4491572 | 4514907 | 4537240 |

Given above is an example where each row in the dataset stands for one country. Also, the columns represent each country and the population size each year. The above table looks good and easy at first glance. But it will be challenging to do a more advanced analysis with several variables here; we will require more columns.

**Long data/longitudinal data**

**Country Year Population**

**Sweden **2000 8872284

**Sweden ** 2001 8888675

**Sweden ** 2002 8911899

**Norway ** 2000 4491572

**Norway ** 2001 4514907

**Norway **2002 4537240

Above, you can get relevant information about each country over the past few years and the population in a single row. Also, each column has several variables. The data we have represented in the columns is also represented in rows. But again, when you work with Stata representation for the rows, it will be easier. It will always be better to get the data in the long form.

**Panel Structure with xtset**

A panel (unit) variable and a time variable must be specified for Stata. In this situation, the panel variable is the country; all observations for Sweden, Norway, and others are related. In this instance, the time variable is a year.

The xtset command in Stata is used to specify these variables. The panel variable comes first, followed by the time variable: we type xtset country year.

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**Time Series Forecasting**

Generating time series data leads to making scientific predictions. These predictions are based on sampled data of historic time. Windowing or the sliding window method is one of the time series forecasting methods where predictions over time take place.

**Windowing**

Windowing converts time series data into a general input dataset for machine learning. A sample windowing and cross-sectional data extraction from a time series dataset is shown below.

The parameters of the windowing procedure define the properties of the windows and the cross-sectional data extractions. The size of the windows, the overlap between subsequent windows, and the forecasting horizon can all be altered using the windowing parameters below.

**1.** **Window Size**: The total number of lag points in one window, excluding the target data point, is the window size.

**2. Step**: It is the number of data points separating the first value of two consecutive panes. Maximum windows can be extracted from the time series dataset if the step is 1.

**3. Horizon width**: The number of time series records that becomes the target variable depends on the forecast horizon. The horizon width typically has a value of 1.

**4. Skip**: It is the offset between the horizon and the window. If the skip is zero, successive data point(s) from the window is used for the horizon.

The window size in the given figure is 6, the step is 1, the horizon width is 1, and the skip is 0.

Thus, the series data has been transformed into a general cross-sectional dataset that may be predicted using learning methods like support vector machines, neural networks, or regression. The power of machine learning techniques can then be applied to a time series dataset after the complete windowing procedure.

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**Journals and Periodical Publications**

Numerous periodicals and journals focus on compilations and review articles, and they frequently include helpful collections of cross-sectional data in a conventional format. In the long run, scientists interested in these data may want to scan them often. For example, journals in atomic and molecular physics developments emphasize atomic and molecule cross-sections, thoroughly analyzing every facet of atomic and molecular physics.

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**Frequently Asked Questions**

**1. What is a cross-sectional example?**

You can quickly draw several examples of cross-sectional analysis. Let’s say 100 patients have cancer. Now, the cross-sectional study will be based on patients of different age groups, geographical locations, and ethnicities.

**2. Is cross-sectional data qualitative or quantitative?**

The cross-sectional data are quantitative.

**3. What are the 2 types of cross-sectional study?**

The two most common types of cross-sectional research studies are descriptive and analytical.

**4. What is cross-sectional data also called?**

There are several names for the cross-sectional data. Those are prevalence studies and transverse studies.

**5. What type of study is cross-sectional?**

According to experts, cross-sectional analyses come within the observational study category.

**6. What is cross-sectional data used for?**

It has a variety of uses. Some applications are in population studies, statistical analyses, econometric studies, etc.

**7. Why use cross-sectional data?**

There are several advantages of using cross-sectional data. Since you can collect data at a single point, these data are relatively cheap, and their time consumption is also pretty low.