In a science experiment, there are two important things called independent and dependent variables. In this article, we will look into what independent and dependent variables are, including types and examples.
The independent variable is what the scientists change or control in the experiment. They do this to see what happens to the dependent variable.
The dependent variable is the thing that scientists are testing and measuring in the experiment. It depends on what the scientists do with the independent variable. When the scientists change the independent variable, they watch and write down what happens to the dependent variable.
So, in simple words, the independent variable is the one that gets changed, and the dependent variable is the one that shows the result of that change. Scientists look at how the dependent variable reacts when they do things to the independent variable.
What Is an Independent Variable?
An independent variable is something that scientists change on purpose in an experiment to see what happens. It’s like a switch that they turn on or off to see the effects. Scientists can sometimes set this switch to different values to learn more about it. But, in some cases, they can’t directly control it, yet they still watch how it affects the experiment’s outcome.
Scientists may use different words to talk about independent variables. For example, when they do something called linear regression, they might call independent variables “right-side variables” because they show up on the right side of a chart. They might also call them predictor variables because they help scientists make predictions about what will happen in the experiment.
Another name is explanatory variables because they help explain the final results. So, an independent variable is like the key factor that scientists change or observe to understand how it affects an experiment.
Two Types of Independent Variables
- Experimental variables: These are also known as controlled variables because researchers can change or control them during an experiment to see how they affect the outcomes. For example, if scientists want to test how different amounts of sunlight affect plant growth, they can manipulate the amount of sunlight the plants receive.
- Subject variables: Unlike experimental variables, researchers cannot control subject variables. Despite this, they are still valuable in experiments as they can help answer research questions. For instance, if researchers are studying standardized test scores of high school students from various regions, they cannot control or change the regions each student comes from. However, they can still use the regional differences to group the students at the beginning of their study.
Examples of Independent Variables
Let’s look at some examples to understand independent variables better.
First, imagine scientists are curious about how different amounts of fertilizer affect plant growth. In a study, they decide to give different doses of fertilizer to different plants. The amount of fertilizer given to each plant is the independent variable. This variable is something the scientists can change on purpose. They want to see how it might affect the growth of each plant. The growth of the plants is the result, or dependent variable because it depends on the amount of fertilizer.
Now, let’s consider a study about math test results. Researchers are interested in comparing the scores of students who took honours-level algebra with those who took standard algebra. The students’ choices of classes are the independent variables in this study. The researchers can’t control or change which class each student picked. However, they can still study whether the choice of class causes any differences in the students’ standardized test scores. In this case, the standardized test scores are the dependent variable because they depend on the students’ class choices.
So, in both examples, scientists are looking at how one thing they can control (independent variable) might lead to changes in another thing they are observing (dependent variable). This helps them understand relationships and patterns in the world of science.
What Is a Dependent Variable?
A dependent variable is something that changes when you make changes to another thing called an independent variable in a scientific experiment. Some people also call it an “outcome variable” or “response variable” because it depends on what happens to the independent variable.
When scientists do experiments, they follow a rule called the scientific method. One important rule is to only change one thing at a time in an experiment. Everything else should stay the same. This helps scientists see how the change in one thing, the independent variable, affects other things, like the dependent variable.
Scientists don’t directly control or change the dependent variable. Instead, they change the independent variable and see what happens to the dependent variable. It’s like a cause-and-effect relationship. The scientists expect the dependent variable to go up or down based on what they do to the independent variable.
So, in simple terms, a dependent variable is something that changes because of what you do to another thing in a science experiment. Scientists want to see how things are connected and how one thing can make another thing change.
Examples of Dependent Variables
Let’s explore dependent variables in simple terms using two real-life examples:
- Plant Growth Study: Imagine we are conducting a pretend experiment to see how different amounts of fertilizer affect plant growth. The independent variable, the thing we change on purpose, is the amount of fertilizer given to each plant. Now, the dependent variable is what we measure and observe – in this case, it’s the recorded growth of each plant. If we keep everything else the same, like the amount of water, container size, sunlight, and growing time, we can reasonably say that the plant’s growth is directly affected by the independent variable, which is the fertilizer.
- Math Test Analysis: Let’s say we’re interested in how different types of algebra classes influence students’ standardized test scores. The independent variable here is the students’ coursework background – whether they took a regular algebra class or an honours algebra class. The dependent variable, on the other hand, is the scores the students get on the standardized test. We, as researchers, can’t control or change these test scores; we can only observe and compare them after selecting groups of students with different coursework backgrounds.
In both examples, the dependent variable is what we’re watching and measuring, and it changes based on the independent variable we deliberately manipulate. It helps us understand the cause-and-effect relationship between the changes we make and the outcomes we observe.
Independent and Dependent Variables Examples
In scientific experiments, there are things that scientists control and things they observe. Let’s break it down with some examples.
Example 1: Moths and Light
Imagine a scientist studying moths and light. They want to know if the brightness of light affects how moths are attracted to it. The scientist adjusts the light brightness (independent variable) and observes how moths react (dependent variable).
Example 2: Breakfast and Test Scores
Now, think about students and breakfast. Someone wonders if eating breakfast makes a difference in test scores. The experimenter controls breakfast (independent variable) and looks at how test scores change (dependent variable). Even if there’s no connection between breakfast and scores, the test results are still dependent on breakfast.
Example 3: Drugs and Blood Pressure
In another experiment, a scientist checks if one drug is better at controlling high blood pressure than another. The drug type is the independent variable, and the dependent variable is the patient’s blood pressure. To make the experiment more accurate, a control variable (a placebo with no active ingredients) is added. This helps figure out if either drug truly affects blood pressure.
Independent and Dependent Variables in Research
In research, we often use independent and dependent variables, especially in experimental and quasi-experimental studies. Let’s look at examples of research questions and the corresponding independent and dependent variables.
- Which Light is Best for Tomato Growth?
- Independent variable: Type of light the tomato plant is grown under
- Dependent variable: The rate of growth of the tomato plant
- How Does Intermittent Fasting Affect Blood Sugar?
- Independent variable: Presence or absence of intermittent fasting
- Dependent variable: Blood sugar levels
- Can Medical Marijuana Reduce Chronic Pain?
- Independent variable: Presence or absence of medical marijuana use
- Dependent variables: Frequency and intensity of pain
- Does Remote Work Impact Job Satisfaction?
- Independent variable: Type of work environment (remote or in office)
- Dependent variable: Job satisfaction self-reports
When dealing with experimental data, the analysis involves generating descriptive statistics and visualizing findings. The selection of a statistical test depends on the variable types, level of measurement, and the number of independent variable levels.
Typically, t-tests or ANOVAs are employed to analyze data and address research questions. These tests help in drawing conclusions and understanding the relationships between independent and dependent variables.
Learn to Tell Apart Independent and Dependent Variables
To distinguish between independent and dependent variables, follow this simple guide:
- Manipulated or Observed: First, consider whether the variable can be changed or chosen by the researchers (manipulated) or if it’s just watched and measured during the experiment (observed). Variables that researchers control are always independent. Variables that are observed and recorded are dependent. Even if researchers can’t control subject variables, they’re still treated as independent because they influence the dependent variables.
- Graphing: Imagine plotting the variables on a graph with an X-Y coordinate plane. Independent variables, the ones you can change, usually go on the X-axis (horizontal). Dependent variables, the outcomes affected by the changes, go on the Y-axis (vertical).
- A Third Type – Confounding Variables: Sometimes there’s a third type of variable that isn’t independent or dependent but can still mess with the results – these are called confounding variables. They impact the experiment in ways researchers might not expect, like unforeseen independent variables. Sorting variables isn’t always a clear-cut choice between independent and dependent; some variables, like confounding variables, don’t fit neatly into those categories.