Introduction to Types of Variables in Statistics
The following article provides an outline on Types of Variables in Statistics. The values that are altering according to circumstances are referred to as variables. A variable can occurs in any form, such as trait, factor or a statement that will constantly be changing according to the changes in the applied environment. Such variables in statistics are broadly divided into four categories such as independent variables, dependent variables, categorical and continuous variables. Apart from these, quantitative and qualitative variables hold data as nominal, ordinal, interval and ratio. Each type of data has unique attributes.
Different Types of Variables in Statistics
In statistics, the variable is an algebraic term that denotes the unknown value that is not a fixed value which is in numerical format. Such types of variables are implemented for many types of research for easy computations. So there are many different types of variables available that can be applied in varied domains. Many other variables are discussed in minimally are listed are active variable which the researcher evaluates. A variable that occurs before the independent variable is called an antecedent variable.
1. Independent Variables
The independent variable is the one that is computed in research to view the impact of dependent variables. It is also called as resultant variables, predictor or experimental variables. For example, A manager asks 100 employees to complete a project. He should know the capacity of the individual employee. He wants to know the reason behind smart guys and failure guys. The first reason is that some will be working hard for day and night to complete the project within the estimated time, and the other one is that some guys are born intelligent and smarter than others. The variable which is similar to an independent variable is called a covariate variable but is impacted by the dependent variable but not as common as a variable of interest.
2. Dependent Variables
The dependent variable is also called a criterion variable which is applied in non-experimental circumstances. The dependent variable has relied on the independent variable. From the above-mentioned example, the project’s productivity or completion is the main criteria that are dependent on estimated time and IQ. Here, the independent variables are IQ and estimated time, which may or may not reflect in an employee’s productivity. So the extension of estimated time or enhancing the IQ of a person doesn’t make any sense in employee’s productivity as it is not predictable.
Hence, the managers’ focus is to work on the independent variables such as allotted time and IQ that leads to certain changes in employee’s productivity that are the dependent variables. So both the variables are connected in some measures. The variables which get affected by other variables in econometrics is termed as endogenous variables. A hidden variable impacts the relationship between the dependent and independent variable called lurking variables. When an independent variable is not impacted by any other variables and is restricted to a certain extent are called an explanatory variable.
3. Categorical Variables
It is a wide category of variable which is infinite and has no numerical data. These variables are called as qualitative variables or attribute variable in terms of statistics software. Such variables are further divided into nominal variables, ordinal and dichotomous variables. Nominal variables don’t have any intrinsic order. For instance, a developer classifies his environment into different types of networks based on their structure, such as P2P, cloud computing, pervasive computing, IoT. So here, the type of network is a nominal variable comprised of four categories. The varied categories present in the nominal variable can be known as the nominal variable levels or groups.Dichotomous variables are also called binary values, which have only two categories.
For example, if we question a person that he owns a car, he would reply only with yes or no. such types of two distinct variables that are nominal are called as dichotomous. It just accounts for only two values, such as 0 or 1. It could be yes or no, short or long, etc.Ordinal variables are nominal variables that include two or multiple categories. If you see any hotel feedback form, it has five ratings such as excellent, good, better, poor and very poor. So we can rank the level with the help of ordinal variables that hold meaning to the research. It is unambiguous, and values can be considered for decision making.
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4. Continuous Variables
The variables which measure some count or quantity and don’t have any boundaries are are termed as continuous variables. It can be segregated into ratio or interval, or discrete variables. Interval variables have their centralized attribute, which is calibrated along with a range with some numerical values. The example can be temperature calibrated in Celsius or Fahrenheit doesn’t give any two different meaning; they display the optimum temperature, and it’s strictly not a ratio variable.
It can account for only a certain set of values, such as several bikes in a parking area are discrete as the floor holds only a limited portion to park bikes. Ratio variables occur with intervals; it has an extra condition that zero on any measurement denotes that there is no value of that variable. In simple, the distance of four meters is twice the distance of two meters. It operates on the ratio of measurements. Apart from these mentioned variables, a dummy variable can be applied in regression analysis to establish a relationship to unlinked categorical variables. For instance, if the user had categories ”has pet” and ”owns a home” can assign as 1 to ”’has pet” and 0 to ”’owns a home”.
A factor that remains constant in an experiment is termed as a control variable. In an experiment, if the scientist wants to test the plant’s light for its growth, he should control the value of water and soil quality. The additional variable which has a hidden impact on the obtained experimental values are called confounding variables.
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