What Is a Variable in Science?
Understanding Variables in a Science Experiment
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- Ph.D., Biomedical Sciences, University of Tennessee at Knoxville
- B.A., Physics and Mathematics, Hastings College
Variables are an important part of science projects and experiments. What is a variable? Basically, a variable is any factor that can be controlled, changed, or measured in an experiment. Scientific experiments have several types of variables. The independent and dependent variables are the ones usually plotted on a chart or graph, but there are other types of variables you may encounter.
Types of Variables
- Independent Variable: The independent variable is the one condition that you change in an experiment. Example: In an experiment measuring the effect of temperature on solubility, the independent variable is temperature.
- Dependent Variable: The dependent variable is the variable that you measure or observe. The dependent variable gets its name because it is the factor that is dependent on the state of the independent variable . Example: In the experiment measuring the effect of temperature on solubility, solubility would be the dependent variable.
- Controlled Variable: A controlled variable or constant variable is a variable that does not change during an experiment. Example : In the experiment measuring the effect of temperature on solubility, controlled variable could include the source of water used in the experiment, the size and type of containers used to mix chemicals, and the amount of mixing time allowed for each solution.
- Extraneous Variables: Extraneous variables are "extra" variables that may influence the outcome of an experiment but aren't taken into account during measurement. Ideally, these variables won't impact the final conclusion drawn by the experiment, but they may introduce error into scientific results. If you are aware of any extraneous variables, you should enter them in your lab notebook . Examples of extraneous variables include accidents, factors you either can't control or can't measure, and factors you consider unimportant. Every experiment has extraneous variables. Example : You are conducting an experiment to see which paper airplane design flies longest. You may consider the color of the paper to be an extraneous variable. You note in your lab book that different colors of papers were used. Ideally, this variable does not affect your outcome.
Using Variables in Science Experiment
In a science experiment , only one variable is changed at a time (the independent variable) to test how this changes the dependent variable. The researcher may measure other factors that either remain constant or change during the course of the experiment but are not believed to affect its outcome. These are controlled variables. Any other factors that might be changed if someone else conducted the experiment but seemed unimportant should also be noted. Also, any accidents that occur should be recorded. These are extraneous variables.
Variables and Attributes
In science, when a variable is studied, its attribute is recorded. A variable is a characteristic, while an attribute is its state. For example, if eye color is the variable, its attribute might be green, brown, or blue. If height is the variable, its attribute might be 5 m, 2.5 cm, or 1.22 km.
- Earl R. Babbie. The Practice of Social Research , 12th edition. Wadsworth Publishing, 2009.
- What Is a Dependent Variable?
- What Is an Experiment? Definition and Design
- Six Steps of the Scientific Method
- Examples of Independent and Dependent Variables
- How To Design a Science Fair Experiment
- The Role of a Controlled Variable in an Experiment
- Scientific Variable
- What Are the Elements of a Good Hypothesis?
- Dependent Variable vs. Independent Variable: What Is the Difference?
- What Is the Difference Between a Control Variable and Control Group?
- Independent Variable Definition and Examples
- Null Hypothesis Examples
- What Is a Controlled Experiment?
- DRY MIX Experiment Variables Acronym
- Scientific Method Vocabulary Terms
- What Is the Difference Between Hard and Soft Science?
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- Types of Variables in Research & Statistics | Examples
Types of Variables in Research & Statistics | Examples
Published on September 19, 2022 by Rebecca Bevans . Revised on June 21, 2023.
In statistical research , a variable is defined as an attribute of an object of study. Choosing which variables to measure is central to good experimental design .
If you want to test whether some plant species are more salt-tolerant than others, some key variables you might measure include the amount of salt you add to the water, the species of plants being studied, and variables related to plant health like growth and wilting .
You need to know which types of variables you are working with in order to choose appropriate statistical tests and interpret the results of your study.
You can usually identify the type of variable by asking two questions:
- What type of data does the variable contain?
- What part of the experiment does the variable represent?
Table of contents
Types of data: quantitative vs categorical variables, parts of the experiment: independent vs dependent variables, other common types of variables, other interesting articles, frequently asked questions about variables.
Data is a specific measurement of a variable – it is the value you record in your data sheet. Data is generally divided into two categories:
- Quantitative data represents amounts
- Categorical data represents groupings
A variable that contains quantitative data is a quantitative variable ; a variable that contains categorical data is a categorical variable . Each of these types of variables can be broken down into further types.
Quantitative variables
When you collect quantitative data, the numbers you record represent real amounts that can be added, subtracted, divided, etc. There are two types of quantitative variables: discrete and continuous .
| Type of variable | What does the data represent? | Examples |
|---|---|---|
| Discrete variables (aka integer variables) | Counts of individual items or values. | |
| Continuous variables (aka ratio variables) | Measurements of continuous or non-finite values. |
Categorical variables
Categorical variables represent groupings of some kind. They are sometimes recorded as numbers, but the numbers represent categories rather than actual amounts of things.
There are three types of categorical variables: binary , nominal , and ordinal variables .
| Type of variable | What does the data represent? | Examples |
|---|---|---|
| Binary variables (aka dichotomous variables) | Yes or no outcomes. | |
| Nominal variables | Groups with no rank or order between them. | |
| Ordinal variables | Groups that are ranked in a specific order. | * |
*Note that sometimes a variable can work as more than one type! An ordinal variable can also be used as a quantitative variable if the scale is numeric and doesn’t need to be kept as discrete integers. For example, star ratings on product reviews are ordinal (1 to 5 stars), but the average star rating is quantitative.
Example data sheet
To keep track of your salt-tolerance experiment, you make a data sheet where you record information about the variables in the experiment, like salt addition and plant health.
To gather information about plant responses over time, you can fill out the same data sheet every few days until the end of the experiment. This example sheet is color-coded according to the type of variable: nominal , continuous , ordinal , and binary .
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Experiments are usually designed to find out what effect one variable has on another – in our example, the effect of salt addition on plant growth.
You manipulate the independent variable (the one you think might be the cause ) and then measure the dependent variable (the one you think might be the effect ) to find out what this effect might be.
You will probably also have variables that you hold constant ( control variables ) in order to focus on your experimental treatment.
| Type of variable | Definition | Example (salt tolerance experiment) |
|---|---|---|
| Independent variables (aka treatment variables) | Variables you manipulate in order to affect the outcome of an experiment. | The amount of salt added to each plant’s water. |
| Dependent variables (aka ) | Variables that represent the outcome of the experiment. | Any measurement of plant health and growth: in this case, plant height and wilting. |
| Control variables | Variables that are held constant throughout the experiment. | The temperature and light in the room the plants are kept in, and the volume of water given to each plant. |
In this experiment, we have one independent and three dependent variables.
The other variables in the sheet can’t be classified as independent or dependent, but they do contain data that you will need in order to interpret your dependent and independent variables.
What about correlational research?
When you do correlational research , the terms “dependent” and “independent” don’t apply, because you are not trying to establish a cause and effect relationship ( causation ).
However, there might be cases where one variable clearly precedes the other (for example, rainfall leads to mud, rather than the other way around). In these cases you may call the preceding variable (i.e., the rainfall) the predictor variable and the following variable (i.e. the mud) the outcome variable .
Once you have defined your independent and dependent variables and determined whether they are categorical or quantitative, you will be able to choose the correct statistical test .
But there are many other ways of describing variables that help with interpreting your results. Some useful types of variables are listed below.
| Type of variable | Definition | Example (salt tolerance experiment) |
|---|---|---|
| A variable that hides the true effect of another variable in your experiment. This can happen when another variable is closely related to a variable you are interested in, but you haven’t controlled it in your experiment. Be careful with these, because confounding variables run a high risk of introducing a variety of to your work, particularly . | Pot size and soil type might affect plant survival as much or more than salt additions. In an experiment you would control these potential confounders by holding them constant. | |
| Latent variables | A variable that can’t be directly measured, but that you represent via a proxy. | Salt tolerance in plants cannot be measured directly, but can be inferred from measurements of plant health in our salt-addition experiment. |
| Composite variables | A variable that is made by combining multiple variables in an experiment. These variables are created when you analyze data, not when you measure it. | The three plant health variables could be combined into a single plant-health score to make it easier to present your findings. |
If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.
- Student’s t -distribution
- Normal distribution
- Null and Alternative Hypotheses
- Chi square tests
- Confidence interval
- Cluster sampling
- Stratified sampling
- Data cleansing
- Reproducibility vs Replicability
- Peer review
- Likert scale
Research bias
- Implicit bias
- Framing effect
- Cognitive bias
- Placebo effect
- Hawthorne effect
- Hindsight bias
- Affect heuristic
You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .
In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:
- The independent variable is the amount of nutrients added to the crop field.
- The dependent variable is the biomass of the crops at harvest time.
Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .
A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.
A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.
In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.
Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).
Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).
You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .
Discrete and continuous variables are two types of quantitative variables :
- Discrete variables represent counts (e.g. the number of objects in a collection).
- Continuous variables represent measurable amounts (e.g. water volume or weight).
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An experiment uses the scientific method to test a hypothesis and establish whether or not there is a cause and effect relationship between two variables: the independent and dependent variables. But, there are other important types of variables, too, including controlled and confounding variables.
Basically, a variable is any factor that can be controlled, changed, or measured in an experiment. Scientific experiments have several types of variables. The independent and dependent variables are the ones usually plotted on a chart or graph, but there are other types of variables you may encounter.
In an experiment, all of the things that can change are called variables. There are three types of variables in a good experiment: independent variables, dependent variables, and controlled variables.
The independent variable is the factor the researcher controls, while the dependent variable is the one that is measured. The independent and dependent variables are key to any scientific experiment, but how do you tell them apart?
Variables are factors that influence an experiment or that are of interest as a result. These include variables you change to test a hypothesis, variables you measure to determine results and variables you hold constant to produce a valid experiment.
These factors that change in a scientific experiment are variables. A properly designed experiment usually has three kinds of variables: independent, dependent, and controlled. What is an Independent Variable? The independent variable is the one that is changed by the scientist. Why just one?
At the core of any scientific investigation are two primary types of variables: independent and dependent variables. These variables are crucial for defining the relationships between factors within an experiment or study and determining the cause-and-effect relationships that underpin scientific knowledge.
Variables are the factors, traits, and conditions you can modify and measure. You’ll find different variables in all types of subjects. But, the most common variables found in a science experiment include dependent, independent, and controlled. Check out what each is through examples.
An experimental inquiry typically has three main types of variables: an independent variable, a dependent variable and controlled variables. We will look at each of these three types of variables and how they are related to experimental inquiries involving plants.
There are three types of categorical variables: binary, nominal, and ordinal variables. *Note that sometimes a variable can work as more than one type! An ordinal variable can also be used as a quantitative variable if the scale is numeric and doesn’t need to be kept as discrete integers.