It depends what you mean. There are pitfalls.
For instance, if you were to measure some other variable at various ages for the same individual then you would need to take that into account. There would be nothing wrong with using age as an independent variable though, as long as you correctly provided for the repeated measurements.
You could also run into trouble if age were confounded with some other variable. For instance, in smokers the likelihood of finding lung cancer would increase with age but this might be because older smokers had, on average, been smoking for longer periods of time.
the variable age has discriptive value but not necessarily explanatory value.
It depends on the experiment. In an experiment, the independent variable is the one whose value is changed by the scientist. The change in the dependent variable is studied to see if it correlates to the change in the independent variable.But because age is only dependent on the passage time, which can't be affected very easily, it's probably the independent variable.
The independent variable is the variable that the scientist controls and can change in an experiment. There should be only one independent variable in an experiment; otherwise the cause-and-effect of the independent variable cannot be determined.The dependent variable is the variable that is affected by the independent variable.EXAMPLE:Students of the same age have been given different sleeping hours (the independent variable)The next day they are tested for their performance (the dependent variable).(Having students the same age is a third type of variable, called the constant variable or the control variable. It is deliberately kept the same to reduce any effects on the outcome.)
An independent variable is exactly what it sounds like. It is a variable that stands alone and isn't changed by the other variables you are trying to measure. For example, someone's age might be an independent variable.
Any variable can be the independent variable. It depends partly on what the dependent variable is, partly on the relationship you are examining. For example, if looking at age and length of children's feet, foot length would be considered the dependent variable. But if looking at foot length and shoe size, then foot length would be the independent variable.
independent
the variable age has discriptive value but not necessarily explanatory value.
It depends on the experiment. In an experiment, the independent variable is the one whose value is changed by the scientist. The change in the dependent variable is studied to see if it correlates to the change in the independent variable.But because age is only dependent on the passage time, which can't be affected very easily, it's probably the independent variable.
The independent variable is the variable that the scientist controls and can change in an experiment. There should be only one independent variable in an experiment; otherwise the cause-and-effect of the independent variable cannot be determined.The dependent variable is the variable that is affected by the independent variable.EXAMPLE:Students of the same age have been given different sleeping hours (the independent variable)The next day they are tested for their performance (the dependent variable).(Having students the same age is a third type of variable, called the constant variable or the control variable. It is deliberately kept the same to reduce any effects on the outcome.)
An independent variable is exactly what it sounds like. It is a variable that stands alone and isn't changed by the other variables you are trying to measure. For example, someone's age might be an independent variable.
Any variable can be the independent variable. It depends partly on what the dependent variable is, partly on the relationship you are examining. For example, if looking at age and length of children's feet, foot length would be considered the dependent variable. But if looking at foot length and shoe size, then foot length would be the independent variable.
Age can be considered an independent variable in a research study if it is being manipulated or controlled by the researcher. However, in many cases, age is treated as a confounding variable because it is often difficult to manipulate and may impact the relationship between other variables being studied.
It allows you to compare two dependent variables when they are related to the same third variable (which may or may not be independent). For example, you could look at the heights and masses of a group of people, with age as the independent variable.
The independent variable would be either gender or age (should choose one or hold 2 experiments) the dependent variable is the phobia the control would be the things similar between every person questioned
The dependent variable would be blood pressure. The independent variable would be age. Of course, there are many factors, other than just age, which determine blood pressure.
They are the variables that you think predict some outcome (which is considered the dependent variable). So you might have a theory that gender and age predicts personal income. Gender and age are the independent variables, and income is the dependent. The choice of whether a variable is independent or dependent often is driven by the question you're trying to answer. So in many cases it's possible that the same variable could be an independent variable in one analysis, but a dependent variable in a different analysis. For example, while income was the dependent variable in the earlier example, if you were trying to predict whether a child goes to college, the parents' income might be an important independent variable in that case.
Independent variable: sickness Dependent variable: taste Controlled variables: type of sickness, age, gender, environment, type of food tasted