Qualitative and Quantitative Data Types
Binomial
Binomial distribution distributes possible outcomes from a data series that denoted by the two mutually exclusive clusters. The binomial distribution informs qualitative data distribution with the example of yes or no, right or wrong, and death or alive, among others. Binomial distribution focuses on the probability of events occurrence. In the case of tossing the coin in the air, the binomial distribution determines the probability of having the head or tail in the 10 times the coin gets tossed. Several attributes characterize the situation involving the binomial distribution reliance as denoted bellow;
- ⮚ The duo classifications of observations or trials. The two classes are referred to as success or failure despite one of them having a higher chance of success than the other.
- ⮚ The constant probability of classifying a trial as a success.
- ⮚ The independence characterizing the observations. An example of such a situation is the survey exercise, where one respondent's answer does not affect the other's answer.
Nominal
As the term nominal data denotes, the numbers ac as the name or label, and have no associated meaning. In creating the gender variable, an individual could use 1 to represent the males and 0 to represent the females. The 0 and 1 numerical have no additional meaning and only play the functional role as openly played by M and F in representing males and females, respectively. Several reasons motivate the researchers' utilization of the numerical coding systems. These codes simplify data analysis processes, especially when using statistical packages that do not allow non-numerical values. Numerical coding also eliminates challenges such as conflicting upper and lower letters during data entry.
Ordinal Data
Ordinal data stands for the data whose order has meaning, with the higher values standing for more attributes compared to the lower values. The medical description of conditions such as burns utilize ordinal data. The degree of burns denotes the tissue damage extent following the buns. First-degree burns are the less serious, second-degree burns are moderate, and the third-degree are the most serious. Ordinal data does not have a metric to denote measure the distance between two clusters.
Ratio data
Ratio data replicates the characteristics of the interval data which are; equal intervals and meaningful order. Most of the physical measurements such as age, height, and weight qualify ranking as ratio data. Ratio-level data allows multiplication and division. For instance, it is right to say that an individual aged 40 years is twice as old as another aged 20 years. Ratio-level ranks as the highest data measurement level despite having the same characteristics as interval data. The difference is that the ratio data has absolute zero and the ratio between two numbers has meaning. In the business environment, ratio data include work measurement time, production cycle, and the number of employees. Ratio is often referred to as quantitative or metric data due to reliance on precise instruments during production or engineering activities.
Continuous and discreet data
Continuous data could take any data or data within a given range. Data relying on the interval and ratio scales to measure data rather than counting. Weight, height, and income are some of the examples of continuous data. During data analysis and mode formulation, researchers often rely on larger units to recode data using classifications and larger units. From a statistical aspect, there is no actual instance where data becomes continuous or discrete to facilitate utilization of a specific analytical technique.
