Paisley wants to prepare a graph of the rate of growth of a lentil seedling. Which factors would be the independent and dependent variables?
Answers
Answer:In research, variables are any characteristics that can take on different values, such as height, age, species, or exam score.
In scientific research, we often want to study the effect of one variable on another one. For example, you might want to test whether students who spend more time studying get better exam scores.
The variables in a study of a cause-and-effect relationship are called the independent and dependent variables.
The independent variable is the cause. Its value is independent of other variables in your study.
The dependent variable is the effect. Its value depends on changes in the independent variable.
Explanation:
Independent and dependent variables in experiments
In experimental research, the independent variable is manipulated or changed by the experimenter to measure the effect of this change on the dependent variable.
Experiment example
You are studying the impact of a new medication on the blood pressure of patients with hypertension.
To test whether the medication is effective, you divide your patients into two groups. One group takes the medication, while the other group takes a sugar pill placebo.
Your independent variable is the treatment that you vary between groups: which type of pill the patient receives.
Your dependent variable is the outcome that you measure: the blood pressure of the patients.
The independent variable is usually applied at different levels to see how the outcome differs.
You can apply just two levels (e.g. the new medication and the placebo) in order to find out if the independent variable has an effect at all.
You can also apply multiple levels (e.g. three different doses of the new medication) to find out how the independent variable affects the dependent variable.
Variables in other types of research
Outside of an experimental setting, researchers often cannot directly manipulate or change the independent variable that they’re interested in.
Instead, they must find already-existing examples of the independent variable, and investigate how changes in this variable affect the dependent variable.
Research example
You are interested in whether a higher minimum wage impacts employment rates.
You can’t control the minimum wage yourself. Instead, you look at a state that raised its minimum wage last year, and compare it to a neighboring state that did not.
Your independent variable is the minimum wage.
Your dependent variable is the employment rate.
By comparing the difference in outcomes between the two states (and accounting for other factors), you can investigate whether the change in minimum wage had an effect on employment rates.
In non-experimental research, it’s more difficult to establish a definite cause-and-effect relationship, because other variables that you haven’t measured might be influencing the changes. These are known as confounding variables.
In types of research where the exact relationship between variables is less certain, you might use different terms for independent and dependent variables.
Other names for independent variables
Sometimes, the variable you think is the cause might not be fully independent – it might be influenced by other variables. In this case, one of these terms is more appropriate:
Explanatory variables (they explain an event or outcome)
Predictor variables (they can be used to predict the value of a dependent variable)
Right-hand-side variables (they appear on the right-hand side of a regression equation).
Other names for dependent variables
Dependent variables are also known by these terms:
Response variables (they respond to a change in another variable)
Outcome variables (they represent the outcome you want to measure)
Left-hand-side variables (they appear on the left-hand side of a regression equation)