What is Cross-Sectional Data? How to Analyze It?

Cross-sectional data, or a cross-section of a study population, in statistics and econometrics, is a type of data collected by observing many subjects such as individuals, firms, countries, or regions at one point or time. The analysis might also have no regard for differences in time. Analysis of cross-sectional data usually consists of comparing the differences among selected subjects.

According to dissertation writing services, it can be better understood by knowing what type of data is under observation and what the researchers are trying to achieve by collecting cross-sectional data. If you want to want to measure current obesity levels in a population, you could draw a sample of 1,000 people randomly from that population, also known as a cross-section of that population, measure their weight and height, and calculate what percentage of that sample is categorized as obese.

This cross-sectional sample will provide a snapshot of that population, at that given time. It is necessary to know that we do not know based on one cross-sectional sample if obesity is increasing or decreasing; we can only describe the current proportion and judge by the results that have been obtained. Cross-sectional data differs from time-series data, in which the same small-scale with factors of production aggregate entity is observed at various points in time.

Another type of data, panel or longitudinal data, combines both cross-sectional and time-series data ideas and looks at how the subjects such as firms, individuals, etc. change over a time series. Panel data differs from pooled cross-sectional data across time because it deals with the observations on the same subjects at different times whereas the latter observes different subjects in different periods. Panel analysis uses panel data to examine changes in variables over time and its differences in variables between selected subjects.

Cross-sectional data can be used in cross-sectional regression, which is a regression analysis of cross-sectional data. For instance, the consumption expenditures of various individuals in a fixed month could be regressed on their incomes, accumulated wealth levels, and their various demographic features to find out how differences in those features lead to differences in consumers’ behavior.

How To Analyze Cross-Sectional Data:

Cross-sectional data can be analyzed when you analyze the data set at a fixed point in time.  Surveys and government records are some best examples and sources of cross-sectional data. The datasets record observations of multiple variables at a particular point in time. For instance, financial analysts might want to compare the financial position of two companies at a specific point in time. To do so, they would compare the two companies’ balance sheets.  The analyst will use their balance sheets to look at their past financial position. However, the slight difference in reporting period ending dates could necessitate making a few adjustments.

For cross-sectional data analysis, the investor, analyst, or portfolio manager will compare a particular company to its industry peers. Cross-sectional analysis may focus on a single company for head-to-head analysis with its biggest competitors or it may approach it from an industry-wide lens to identify companies with a particular strength. It is important to understand that cross-sectional analysis is often deployed in an attempt to assess performance and investment opportunities using data points that are beyond the usual balance sheet numbers.

How Cross-Sectional Data Analysis Works:

When conducting a cross-sectional analysis, the analyst uses comparative metrics to identify the valuation, debt-load, future outlook, and/or operational efficiency of the target company. This allows the analyst to evaluate the target company’s efficiency in the specific areas, and to make the best investment choice among a group of competitors within the industry as a whole to achieve better results in the long run.

To do the best, the analysts implement cross-sectional data analysis techniques and strategies that help them identify special characteristics within a group of comparable organizations, rather than establish relationships. It has been observed that cross-sectional data analysis often emphasizes a particular area, such as a company’s war chest, to expose hidden areas of strength and weakness in the sector. This type of analysis is based on information-gathering and seeks to understand the “what” instead of the “why.”

With cross-sectional data analysis, the analysts or the researcher can form assumptions, and then test their hypothesis using research methods.  Cross-sectional data analysis has proved to be a useful tool in many areas of research. By learning more about what is going on in a specific population or organization, researchers are better able to understand relationships that might exist between certain variables and develop further studies that explore these conditions in greater depth.