Understanding the Types of Variables
The word, Variables, in general, is mean the feature of an object having changeable value dependent on the other factors .
You can determine a good research design in the study on the basis of the selection of the variables which you intend to measure.
Table of Contents hideExample:
If you want to determine the status of the GDP of a country, you need to judge the factors relevant to the GDP of the country. Like, individual income, market structure, demand of products and supply statistics of the goods and services of the economy. As a reason, with changes in these factors, the GDP of the economy also change . Therefore, the factors having moving value while influencing other factors is known as variables.
Different types of variables would be learned from answering the following questions.
 What is the nature of data the chosen variable ?
 What is the contribution of the variable in the study ?
Types of data:
Quantitative vs Categorical data
Data refers to the value of the specific variable which you have record in the datasheet for use. There are basically two types of data theses are:
 Quantitative data that represents the real value or numeric
 Categorical data involves the grouping and clusters in a group
Variables possessing numeric value or quantitative data are called quantitative variable which you can use for quantification. Variable which you have address with the characteristics of the categorical data is called the categorical variable.
These two variables which you can categorize into other subtypes.
Quantitative variables:
During the collection of quantitative variables, you need to gather real numbers and add, subtract, multiply or divide them to generate value.
Quantitative variables are of two types including Discrete and Continuous
Discrete vs Continuous variables
Variables  Definition  Example 

Discrete variable  Refers to individual value or counts. 

Continuous variable  It is nonfinite values 

Categorical variables:
The categorical variable refers to the groups of some types of categories where those which you need to record and represent as a number.
However, the numeric reveals the specific categories rather than the actual values of the variables. Categorical variables are of three types:
Binary, Nominal and Ordinal Variables
Binary Vs Nominal Vs Ordinal
Variables  Definition  Example 

Binary  Refers to the positive or negative outcomes and coded as two numbers 

Nominal  Refers to the groups having no ranks within them 

Ordinal  It is the group, ranked through specific number on a priority basis 

Example datasheet
Sample  Plant species  Salt added (mg/L water)  Starting height (CM)  Growth (cm) (current height – starting height)  Wilting (rank 010)  Survival (1=survived, 0=died) 

1  A  0  10  
2  A  150  12  
3  A  200  10  
4  B  0  22  
5  B  150  25  
6  B  200  26 
Parts of the experiment
Independent Vs Dependent variable
Researcher generally design and perform experiments to know the effect of one variable on another in an experiment. In the context of our experiment, the effect of salt addition on plant growth is the concern.
You would consider one variable to be the cause (Independent variable) and manipulate it to measure the value or outcomes on the other variable (Dependent variable), that is effect.
Thee maim aim of experiment is to predict the probable effect of one variable on another that will develop theoretical propositions.
Apart from these two, there is another variable (Constant variable/s) which you need to control during the experiment between the previous two variables.
Independent vs Dependent vs control variables
Variables  Definition  Example 

Independent  Variables that move independently without influence to produce outcomes of the experiment 

Dependent  Variable revealing ultimate outcomes of the experiment and rely on circumstances 

Control  Variables kept constant throughout the experiment 

Example dataset
In the current experiment, Researcher has address one independent and three dependent variable. In the following datasheet, you can not segregate the other variables into dependent and independent variables.
However, the value of the variables is identifiable separately to interpret the outcomes of the experiment.
Sample  Plant species  Salt added (mg/L water)  Starting height (CM)  Growth (cm) (current height – starting height)  Wilting (rank 010)  Survival (1=survived, 0=died) 

1  A  0  10  
2  A  150  12  
3  A  200  10  
4  B  0  22  
5  B  150  25  
6  B  200  26 
What about the Correlational research?
In the case of the correlational research, there is no particular definition of dependent and independent variable.
It is necessary to know whether the undertaken variable are statistically related irrespective of the identifying cause and effect of the experiment.
In some cases, one variable proceeds to others where there is no independent and dependent variable rather predictor variable and outcome variable.
For example, excessive temperature has led to drought where the temperature is the predictor variable and drought is the outcome variable.
Receive feedback on Language, Structure, and Layout
 Academic style and content
 Vague and grammatically incorrect sentences
 Fragmented sentences
 Filler sentences
Other common types of variables:
Once you are sure about the dependent and independent variable and ensure the nature of these variables are fall in like quantitative and categorical, you can easily select the statistical test to be executed in the experiment.
Apart from the above descriptions, there are some other ways of defining the variables to interpret the results of the experiments undertaken. Some of the variables are described below:
Variables  Definition  Example 

Confounding  This variable hides the real effect of the other variables in the experiment as the same is closely related to the concerned variable and not controlled properly 

Latent  A variable leads to run the experiment while not being measured directly. These variable are measured via proxy 

Composite  You can develop it through a combination of other variable during data analysis. 
