Independent and Dependent Variables

In any experiment or study, researchers are interested in understanding how one factor affects another. To explore this relationship, they use the concept of independent and dependent variables. These variables help in analyzing cause-and-effect connections clearly. In this blog, we will explain independent and dependent variables, their categories, and examples, making it easier to identify and use them in real-life situations.

What is an Independent Variable?

The independent variable is a variable that you can choose to change or control in an experiment. It is the cause or the reason that might make something happen or change it. It is called independent because it does not depend on anything else in the experiment; instead, it is a factor that influences or affects the outcome.

You can identify an independent variable with the key points as follows:

• Look at what you are changing on purpose: The independent variable is the one thing you change on purpose to see what happens in an experiment, and it is important in experiments to test what happens when you change something.
• See if it does not depend on another variable: The independent variable is chosen by the user, the experimenter, and is not determined by something else in the experiment. For example, you choose the type of fertilizer (A, B, or none). The fertilizer type is not chosen because of plant height, so the fertilizer type is independent

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Types of Independent Variables 

There are the following types of independent variables.

1. Experimental Variables: These variables are directly related to the social and physical environment of the participants and affect how participants in an experiment respond. For example, a bright or dim light in an experiment.

2. Subject Variables or Participant Variables: These variables are the characteristics that are inherent to the participants, and they cannot be changed by the researcher, but they can be used to compare groups. They are used to see how differences affect the outcomes. For example, age (children vs adults), gender (male vs female). 

3. Categorical Independent Variables: These variables represent the categories or groups rather than numerical values. For example, teaching methods can be categorized as online, classroom, or hybrid.

4. Situational Variables: These variables come from the environment or conditions that are part of an environment. They can be changed by researchers and can be part of the experimental setup. For example, the temperature of a room, hot, cold, or moderate.

5. Instructional Variables: These variables are the different sets of instructions that are given to the participants to see how they affect the outcome. These variables affect the performance, understanding, and behavior of the experiment. 

6. Time Variables: These variables involve the timing in the experiment. They can include the duration, frequency, or number of days. For example, measuring experiment outcomes after 1 week vs 1 month.

What is a Dependent Variable?

A dependent variable is the factor in an experiment that you observe or measure. The value of the dependent variable is directly related to the independent variable, due to which it is called the dependent variable. You can think, 

• an independent variable as the cause (you change it)
• a dependent variable as the effect (you measure it)

You can identify a dependent variable with the key points as follows:

• It is the factor you measure: The dependent variable is always something that you collect data on the basis of the characteristics, but you cannot control it, as it depends on the independent variable. For example, testing how different amounts of water will affect plant growth.
• It is the outcome or result: It is the answer to what result I want to measure? And the result you observe to check the effect of your change is the dependent variable. 
• It can be quantitative or qualitative: Quantitative are the measurable numbers like height, weight, exam score, heart rate, and qualitative are the descriptive observations like color change, behavior type, and mood. For example, 

Types of Dependent Variables

There are the following types of dependent variables.

1. Continuous Variables: Continuous variables are those that can take any value within a given range, and can be measured with high precision and with small differences between values. They are represented as numbers with decimals. For example, the height of a person can be 170.5 cm, 171.2 cm, or any value within the possible range of human heights.

2. Discrete Variables: Discrete variables only take specific or separate values, and are usually countable numbers. For instance, the number of students in a class is a discrete variable because you can have 20, 21, or 22 students, but not 20.5 students.

3. Binary Variables or Dichotomous Variables: Binary variables have exactly two possible values, and only one can occur at a time. For example, in an experiment testing task success, a participant may either succeed or fail.

4. Categorical Variables or Nominal Variables: Categorical variables represent the variables in different categories or groups, and one category or group is not considered higher or lower as compared to another. For example, the type of animal, such as a dog, a cat, or a bird.

5. Ordinal Variables: Ordinal variables represent the categories with a meaning of order, but the difference between the categories is not necessarily equal. These variables allow us to arrange outcomes, but do not allow any precise numerical comparisons between the levels. For example, survey responses such as unsatisfied, neutral, and satisfied form an ordinal variable.

6. Interval Variables: Interval variables are the variables that have a meaning between the values, but they do not have a zero point, i.e., you can measure the difference between values, but you cannot say one value is “twice as much” as another. For example, temperature measured in Celsius or Fahrenheit is an interval variable, and the difference between 20°C and 30°C is the same as the difference between 30°C and 40°C, but 0°C does not mean that there is no temperature. 

7. Ratio Variables: Ratio variables are similar to interval variables, but they also have a zero point. For example, an income of $0 represents no income.

Identifying Independent and Dependent Variables

When you perform an experiment, you try to find out how one factor affects the other factor. To do this, we categorize the variables into two types: independent variables and dependent variables. 

• The independent variable is the factor that you change or control on purpose. 
• The dependent variable is the factor that you measure or observe.

Let us discuss how we can identify an independent and dependent variable with the help of an example. 

Imagine you are performing an experiment to see how sugar dissolves in hot water faster than in cold water. You take two water samples: one hot and one cold, i.e., you are changing the temperature. You are putting the sugar in both containers, the cold and hot water, and measuring the time in different cases to see at which temperature the sugar dissolves faster. This is the result you are watching to see if the temperature change made a difference. Hence, in this example, the temperature is the independent variable, and the time taken for the sugar to dissolve is the dependent variable

Dependent vs Independent Variables

Below are the key differences between independent and dependent variables. 

Graph of Dependent and Independent Variables

When creating a graph to show the relationships between variables, the independent variable is always placed on the X-axis and the dependent variable is placed on the Y-axis. 

The independent variable represents the factor that is changed in the experiment, while the dependent variable represents the result that is measured. By plotting them in this manner, the graph clearly shows how changes in the independent variable affect the dependent variable. 

The above graph shows the relationship between the independent variable and the dependent variable. The x-axis represents the month, and the y-axis represents the number of ice-creams sold. The independent variable gets larger (moving right on the graph), and the dependent variable also reliably gets larger (moving up on the graph). Because the data points are clustered so tightly together in a straight line, this means that two variables increase together at a near-constant rate.

The relationship between the two axes is given by the formula

y=mx+c

Where, 

• y is the dependent variable
• x is the independent variable
• m is the slope of the line, which represents the rate of change of y with respect to x
• c is the constant

Note: For experiments with a linear relationship, the dependent variable y can be modeled as y = mx + c, where x is the independent variable.

Conclusion

From the above article, we can conclude how one factor affects the other factor, while performing an experiment, we categorize the variables into two categories: independent variables and dependent variables. The independent variable represents the factor you change or control in an experiment, whereas the dependent variable represents the factor you measure or observe as a result. Independent variables are divided into many categories, such as experimental, subject, situational, categorical, and time. Dependent variables are divided into many  categories, such as continuous, discrete, binary (dichotomous), categorical (nominal), ordinal, interval, and ratio.

Useful Resources:

• What is Machine Learning
• Machine Learning Interview Questions

Independent and Dependent Variables – FAQs