Cross Sectional Data

Every time an economic stimulus plan is announced by the government, each newspaper comes out with its survey on how people in general feel about it, is it good or bad for the economy and likewise. In such surveys, they take a cross-section of people and ask them the relevant questions and publish their data. This research is independent of the time component, and a random set of individuals are selected for the research. Then the resulting data gives us information like, say, 60 percent people feel good about it and the rest express their disappointments. This data is called the cross sectional data. It is, primarily, the observation and description of various subjects researched and applied for the purpose of planning and decision-making. Used in a lot of fields for research, cross sectional research is common in financial market research, health surveys, sociological research, and the psychological field.

Cross Sectional Data Definition

There is a basic difference between the terms 'census' and 'survey'. In the census, every person in a given state and country is counted. On the other hand, in a survey, a sample is taken, which means a cross section of people is taken, who are perceived as the representatives of the entire population. Then, extrapolation is done, which acts as an extension to represent the entire population. The data collected is the cross sectional data, since it is akin to taking a snapshot of the concerned population or subjects, as may be the case. Here, comparison is done between the differences in the subjects. Census can be said as the largest cross sectional research with the make-up of the population being understood at a single point in time. Here, what we simply do is eliminate the time dimensions of the data and make it one-dimensional. This data certainly helps in simplifying the comparison of various entities, irrespective of time. It is best expressed with the help of bar graphs and pie charts.

Cross sectional data is different from the longitudinal data considering the time aspect. In case of longitudinal research, we can find out the differences in the entities researched over a period of time. For instance, a dataset with the marks of a student over four years give us longitudinal data, helping us compare his performances over four grades. It can help assess whether there's an improvement or fall in his studies. This type of data judges the long-term phenomena suggesting the change as opposed to cross sectional data, which gives only the short-term ones.

Examples of Cross Sectional Data

Various cross sectional data collection techniques comprise questionnaires, interviews, online surveys, etc. The best example of obtaining the data is prediction of the future president every four years or even the predictions of presidential nominees for a specific political party. What is done is, a cross section of people is taken in every state and their predictions are asked as to who they think is most likely to win the presidential election. This is done at the same point of time in all the states to compare the people's inclinations at a particular instant. The data so collected across all the states form the cross sectional dataset, helping us to compare whose chances are more in various parts of the country.

Some other examples of cross sectional data are health surveys conducted by the Centers for Disease Control and Prevention (CDC) for finding out the prevalence of many diseases and symptoms observed in the population at any given point of time, or the General Social Survey (GSS) conducted every year by the National Opinion Research Center at the University of Chicago to collect data on demographic characteristics and attitudes of the residents of the United States. It is used to correlate many different demographic factors like age, race, gender, as also their belief-system and opinions about various matters of national importance.

Advantages of Cross Sectional Data

• Data collection is done swiftly as it eliminates the time dimension. There's no delay in publishing the results and conclusions of the cross sectional research.
• It is cheaper to collect the cross sectional data as compared to the longitudinal data.
• It is possible to collect large amount of cross sectional data as time instant is fixed beforehand. No dynamic change means lesser on-time hassles.
• There's no need to keep the track of the entire population over a period of time, as in longitudinal research. It certainly minimizes the efforts.

Cross Sectional Data Limitations

• Cross sectional data lacks the detailed analysis of longitudinal research, as it gives us only the differences in the subjects of research but fails to show us the differences over a period of time.
• The biggest drawback of this kind of data is that sometimes the cross section of people taken for the research may not represent the entire population. So, the results can be deceiving if seen in the larger sense, which is technically termed as ecological fallacy.
• Unplanned or sudden changes in the area of research cannot be taken into account, which can have a lasting impact on the entire research.
• As cross sectional data eliminates the past and future comparisons, the causes and effects of the subjects in the study are unknown, hence failing to answer the precise question.

Overall, we can see that cross sectional data gives an entire idea of the subject for a specific point in time, but fails in helping to compare data over the years, like the longitudinal research. But the best part is that it can be collected from different strata of society differentiated into males and females, various age groups and socioeconomic classes, thus helping us to understand the patterns of thoughts and opinions in a well-defined manner.