Data plays a crucial role in statistics. In layman terms, it’s all the information you gather to increase your knowledge, reach a conclusion, and consider a hypothesis.
Variables come in different types, each showing certain information. Information’s type indicates what you can pick up from it, and also, what you cannot derive from it. Therefore, it’s important to understand the types of data.
Qualitative and Quantitative Data
Before moving ahead into the types of data, you should know the difference between qualitative and quantitative data. Qualitative data refers to information indicating properties that aren’t measurable through numbers. This information is mostly subjective. For instance, marital status, architectural style, eye color, and taste are all qualitative data.
On the other hand, quantitative data refers to information documented in the form of numbers. It is used to represent a count or an objective measurement. For instance, body mass index (BMI), and temperature are examples of quantitative data. This type of data is also known as numerical data.
Types of Quantitative Data
When you want to represent your data with numbers, it can be done by the collection of quantitative data. This form of data is further split into two categories.
Continuous variables can break down numerical values; it can be contextually split into smaller increments, including decimal and fractional values. There are countless possible numeric values between two values. Generally, a scale is used to measure continuous variables. For instance, temperature and height can be measured through continuous data.
Continuous variables also help you with evaluating properties like standard deviation, range, distribution, median, and mean.
A histogram represents the distribution of the values. This is why they are an effective method to graph continuous variables. With histograms, you can figure out skewed or symmetric values, find the most common values, and comprehend the range of values.
You can use a scatterplot to graph two continuous variables. Regression analysis can help with calculating a line’s equation. Similarly, you can utilize correlation to determine a relationship’s strength.
If you have continuous variables that split into groups, utilize a boxplot to represent the spread and central tendency of each group.
Discrete quantitative data are a total of the occupancy of an activity, item, result, or a property. You cannot split them into smaller increments. For instance, you can have one or two smartphones, but you cannot have 1.5 smartphones. It represents a finite count of possible values that can be recorded in an observation.
Discrete variables can help you with calculating and determining a count’s summary, such as the standard deviation, sum, and mean.
Bar charts are an effective method of graphing discrete variables. You can show distinct values with every, whereas the height shows its proportion to the complete sample.
Qualitative Data: Ordinal, Binary, and Categorical
When you note information with the purpose of categorizing your observations, you collect qualitative data. There are three forms of qualitative variables: ordinal, binary, and categorical.
Pie charts and bar charts are traditional tools for graphing qualitative variables, as they are productive for showing the relative percentage of all the groups from the complete sample.
If there are scenarios where you an option to record property as a qualitative or continuous variable, the most effective method is to document the continuous data as you can understand more.
Ordinal is one where you have three or more categories where each of them is marked by specific order. For instance, you can use it for product review with ‘bad’ to ‘excellent’ rating.
Some experts believe that ordinal variables are made from a combination of quantitative and qualitative characteristics. For example, the Likert-scale is often used to measure satisfaction on a 1-5 scale. You can represent ordinal data through bar graphs.
As the name suggests, binary data is made of two values. This means that if your observation can be analyzed in two categories, then you are working with binary variables. Experts call these variables as both indicator and dichotomous variables. For instance, the result of a course has binary data as it can be either pass or fail.
Binary variables are great for calculating a percentage or proportion. With the pie chart, you can represent the binary yes/no data in the form of continuous data.
Categorical data consists of values that can be included in a countable number of unique groups according to a property. Categorical variables can be used to set categories in cases where they don’t have any natural order.
Analysts call categorical variables as both nominal and attribute variables. For instance, you can select an M.S. program in any of these fields: Computer Science, Software Engineering, Statistics, and Data Science. Pie charts can be used to represent categorical data.
Now that you have an understanding of various types of data, what can be learned from them, and how you can graph them, let’s go ahead and apply this knowledge to your current processes. To improve the quality of your work, you can consider using Image-Charts for creating a wide range of charts.