Quantitative Versus Qualitative Data

QUANTITATIVE VERSUS QUALITATIVE DATA When talking about what type of data is this, will usually assume that you are asking whether or not it is mostly quantitative or qualitative. It is likely the most common way of describing the specific characteristics of a dataset. For the most part, when talking about quantitative data, are usually (not always) talking about a structured dataset with a strict row/column structure (because we don't assume unstructured data even has any characteristics). All the more reason why the preprocessing step is so important. These two data types can be defined as follows: 1. Quantitative data: This data can be described using numbers, and basic mathematical procedures, including addition, are possible on the set. 2. Qualitative data: This data cannot be described using numbers and basic mathematics. This data is generally thought of as being described using "natural" categories and language. The four levels of data It is generally understood that a specific characteristic (feature/column) of structured data can be broken down into one of four levels of data. The levels are: 1. The nominal level The first level of data, the nominal level, (which also sounds like the word name) consists of data that is described purely by name or category. Basic examples include gender, nationality, species, or yeast strain in a beer. They are not described by numbers and are therefore qualitative 2. The ordinal level The nominal level did not provide us with much flexibility in terms of mathematical operations due to one seemingly unimportant fact—we could not order the observations in any natural way. Data in the ordinal level provides us with a rank order, or the means to place one observation before the other; however, it does not provide us with relative differences between observations, meaning that while we may order the observations from first to last, we cannot add or subtract them to get any real meaning. 3. The interval level Now we are getting somewhere interesting. At the interval level, we are beginning to look at data that can be expressed through very quantifiable means, and where much more complicated mathematical formulas are allowed. The basic difference between the ordinal level and the interval level is, well, just that—difference. Data at the interval level allows meaningful subtraction between data points. 4. The ratio level Finally, we will take a look at the ratio level. After moving through three different levels with differing levels of allowed mathematical operations, the ratio level proves to be the strongest of the four. Not only can we define order and difference, but the ratio level also allows us to multiply and divide as well. This might seem like not much to make a fuss over, but it changes almost everything about the way we view data at this level. As we move down the list, we gain more structure and, therefore, more returns from our analysis. Each level comes with its own accepted practice in measuring the center of the data. We usually think of the mean/average as being an acceptable form of center, however, this is only true for a specific type of data.