Dependent Variables (DVs) To observe and measure the effect of the independent variable. An independent variable is the variable that you change or control in an experiment to see how it affects the dependent variable. It is called “independent” because its variation does not depend on other variables in the experiment. In simple terms, it’s what you measure in the experiment to see if it changes when you alter something else.
- The graph shows that as the number of members increases the expenditure also increases.
- In any experiment, the dependent variable is observed to measure how it is affected by changes to the independent variable.
- His work has involved designing teacher certification for Trinity College in London and in-service training for state governments in the United States.
- The effects on the variable should not change when repeated—with the same participants, conditions, and experimental manipulations.
Thus, the number of hours studied is the independent variable, also referred to as the control, since it is what the experimenter manipulates in order to determine its effect on the dependent variable. The independent variable is also referred to as the manipulated variable, predictor variable, and explanatory variable, among other things. It follows that the dependent variable may also be referred to as the response variable, predicted variable, explained variable, and so on. A dependent variable is one whose value varies in response to the change in the value of an independent variable. It is the outcome of an experiment or statistical analysis; hence, also termed a left-hand-side variable usually represented as ‘Y’ on the graph.
Which Variable Does the Experimenter Manipulate?
You can set this up as an experiment in which you record food ingested over time. You add up all the calories you eat during a day or you measure the mass of food per day. The relationship between the independent and dependent variables signifies the cause-and-effect phenomenon, where any change in the value of the former triggers a change in the latter’s value.
Operationalization is defined as “translating a construct into its manifestation.” In simple terms, it refers to how a variable will be measured. This is because other factors, called confounding variables, might also influence the result. Yes, it is possible to have more than one independent or dependent variable in a study. An example of a dependent variable is depression symptoms, which depend on the independent variable (type of therapy).
Rely on our powerful data analysis interface for your research, starting with a free trial. Now that we are changing the textbook in the experiment above, we should examine if there are any effects. A doctor changes the dose of a particular medicine to see how it affects the blood pressure of a patient.
What differentiates a dependent variable from an independent variable?
It is important to note that the outcome variable of one research can act as anindependent variable for another. For instance, high production cost results in a lower profit margin for a company. Here, the lower profit margin is a response variable in the first scenario but an independent variable in the second.
Examples of Dependent Variables
A dependent variable is the variable being tested in a scientific experiment. The study environment – with music or without music – is the independent variable, and the number of facts remembered on a test is the dependent variable. One common way of assessing cognitive performance in laboratory rats is by measuring the amount of time it takes to run a maze successfully. It would also be possible to examine the physical effects of car exhaust on the brain by conducting an autopsy. In addition, the scientists randomly assign half of the participants to take a set of vitamins, supplied by the researchers every day for 1 year. The independent variable was the airbag and the dependent variable was the amount of skull damage.
Since the IQs of the groups are being compared, we’re looking to see if IQ depends on a what is a dependent variable person’s gender. That makes ‘gender’ the independent variable since we’re looking to see if a person’s gender influences their IQ. Each gender classification would be considered a level of the independent variable. Thus, we know that we must have the independent and dependent variables switched around.
If you’re still not sure, consult with your professor before you begin to write. Dependent VariableThe variable that depends on other factors that are measured. These variables are expected to change as a result of an experimental manipulation of the independent variable or variables. Finally, independent variables can go by different names such as subject variables or predictor variables.
- Essentially, the dependent variable is the effect, and the independent variable is the cause.
- Independent VariableThe variable that is stable and unaffected by the other variables you are trying to measure.
- Because the researcher controls the level of the independent variable, it can be determined if the independent variable has a causal effect on the dependent variable.
- They represent the outcome or effect that researchers are interested in measuring or predicting.
Because the researcher controls the level of the independent variable, it can be determined if the independent variable has a causal effect on the dependent variable. In the introduction, this information can be presented in a paragraph or two when you describe how you are going to study the research problem. In the methods section, you build on the literature review of prior studies about the research problem to describe in detail background about each variable, breaking each down for measurement and analysis. For example, what activities do you examine that reflect a company’s commitment to environmental sustainability? Levels of employee satisfaction can be measured by a survey that asks about things like volunteerism or a desire to stay at the company for a long time. Since we are looking to see if the different dosage leads to a different blood sugar level, that makes the dosage the independent variable.
This way, only the amount of light is being changed between trials, and the outcome of the experiment can be directly applied to understanding only this relationship. In statistical modeling, dependent variables are used to build predictive models. For example, in linear regression analysis, the dependent variable is modeled as a function of one or more independent variables.