Statistical Data Collection Methods – Making Sense of The Mine Field

Did you know? Mobile solutions, Radio-frequency identification, and Barcode can simplify most of your business processes. Of course, in the age of constant evolution, technology is at the forefront in simplifying processes in all verticals. Technology has transformed businesses from supply chain processes to App Data Collection and maintaining statistical data.

When employed appropriately, statistical business data can be utilized to improve various areas. From efficiency productivity to the time taken and profit, statistical data can seek processes significantly. However, to make improvements, one must know the ways data collection took place from scratch. Read along to gain a comprehensive understanding of statistical data collection app methods that can be used for your business.

There are four primary types of Statistical data collection methods: Census, Sample survey, Experiment, and Observational study. Each of these come with their set of features, challenges, and drawbacks. To utilize them, an individual must be adept with the characteristics of each data collection method to choose the most appropriate method that fits the situation of the business. Each of them can be defined as follows:

Census: A Census can be defined as a case study created by accumulating data from every member of the population. While a Census collects comprehensive data, it may not necessarily be practical. It also is one of the most time-consuming and money-draining.
Sample survey: A Sample Survey can be defined as a case study that is obtained from a subset of the population. As opposed to a Census, a Sample Survey is more practical and efficient. However, experts find it to be inefficient in precise data collection.
Experiment: Mostly considered a controlled study, an experiment is considered research that can be used to understand how things affect each other.
Observational study: Similar to an Experiment, an Observational Study discovers the cause-effect relationship. However, the subjects and their assignments cannot be controlled during an observational study.

As mentioned above, all the methods used for data collection come with their set of drawbacks and limitations. An individual must know the ways to make decisions regarding the methods that can be applied based on the situation. The decision-making related to data collection methods depends on several factors like resources, causal inference, and generalizability.

If resources are the primary factor, a sample survey is better than a census based on the factors. The sample survey will provide greater accuracy. Similarly, for all other factors, including causal inference and generalizability.

Due to the continuously evolving Information Age, where the quantity of data is complex, statistical data collection methods are crucial for a sustainable economy. They are also important for social and environmental development when data is everywhere.