Primary Data Analysis: Differences in Qualitative and Quantitative Variables

The world is filled with enormous amounts of information. Not always is this information understandable to everyone. Information can be perceived in various ways, but the most common one and the one related to the curent subject is in the fom of data. The business industry is also an industry that relies heavily on data. It’s thanks to the business analysts that these industries hire that they can make sense of this data. Business analysts take raw data, and turn into useful information so the company can make further decisions based on this data which basically represents their company in different ways.

For example, for the class project where Big Lebowski clothing was focused on, the students, namely us, were supposed to act as business analysts and translate the data into useful information. Thanks to a short list of data received from only a single day, I was successfully able to determine that the majority of the customers for this store were between the ages of 30-50 and were mainly single women.

Due to this information, the company can now run their promotions where it would be more effective. Also, business analysts can show if two factors are related or unrelated to each other by analyzing data.

For example, if two or more factors such as religion and the net sales are being observed, a business analyst can prove that although they might seem to show a trend that they’ re actually unrelated, or vice versa.. Although most of society perceives population as strictly people, the business world begs to differ.

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In the business industry, a population can be items, groups, people, animals, things, etc.. It’s basically a large group of something that a certain industry or business is trying to study in order to understand it better. However, due to populations” large size, it would be inconvenient to
try to study everyone which is why a sample is randomly chosen in order to study that. Although a sample is substantially smaller than the population, and is a part of the population, it is what’s used to represent that population.

Primary data is data that is collected directly from the one who’s doing the observation. For example, in the assignment done for the class, the data that was being observed was a source of primary data. This is because it came directly from the company’s own database, and hasn’t
been reviewed or analyzed by anyone else. Other examples of primary data are surveys or questionnaires given to the sample of interest. Secondary data, however, is data that has been collected by someone else. Although previously one can imagine that all secondary data was
research papers and tables by others. But now thanks to the internet there’s an endless source of secondary data, for any chart, table, or information that’s used from the website that you didn’t come up with is basically secondary data.

While qualitative data, also called categorical data, focuses on certain characteristics and qualities of a certain thing, quantitative data focuses on the amount. Quantitative data can usually be counted, where it would be pointless and tiresome to count the qualitative data. A good way to remember the differences for these two is looking at their word roots, qualitati ve and quantitative, respectively, quality and quantity, which gives one a general idea of the differences between the two. For example, in the project done for the class, both quantitative and qualitative data were available for analyzation. Some of the quantitative data was the amount of net sales and the items sold. The qualitative data were the gender of the customers, as well as their age, and marriage status.

The difference between discrete and continuous variables are the fact that discrete variables are whole numbers and continuous variables, as the name suggests, are variables that can’t be counted with fingers, and aren’t whole numbers. As the numbers suggest, discrete and
continuous variables are subcategories of quantitative variables. An example of a continuous variable is weight. It’s hard to find people that have an exact number of weight, such as exactly. 119.00 Ibs or 180.00 Ibs. It’s usually numbers like, 110.23 Ibs, which fall under continuous
numbers. A discrete variable example, on the other hand, would be the number of customers who came into the store that day. Since a half person doesn’t exist, it’s impossible for the number to be something like 110.5, which is why this would fall under the discrete variables.

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Primary Data Analysis: Differences in Qualitative and Quantitative Variables. (2023, Mar 10). Retrieved from

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