The independent variable is the thing you are changing/varying. The dependent variable is the thing you are measuring. This variable should be affected by the independent variable. Control variables are anything that must be kept constant. If there are any other factors which affect the dependent variable, then these need to be controlled so that they do not have any significant effect (basically ensuring that you are actually measuring the effects of the independent variable).
either independent or dependent
The answer depends on the context. A variable can be independent in some studies but dependent in others. Time can be an independent variable in distance-time or speed-time studies but the time (to failure of a component) is a dependent variable. Perhaps confusingly, the same two variables can swap places depending upon the context. Suppose I believe that healthier people are taller (their growth is less likely to be stunted by illnesses) then my independent variable is some measure of their health and the dependent variable is their height. If instead, I believe that taller people are healthier (their parents must have had good genes) then the independent variable is height and the dependent is health.
In any experiment there are many kinds of variables that will effect the experiment. The independent variable is the manipulation for the experiment and the dependent variable is the measure you take from that experiment. Confounding variables are things in which have an effect on the dependent variable, but were taken into account in the experimental design. For example, you want to know if Drug X has an effect on causing sleep. The experimenter must take care to design the experiment so that he can be very sure that the subjects in the study fell asleep because of the influence of his Drug X, and that the sleepiness was not caused by other factors. Those other factors would be confounding variables.
Controlled variables are things that must be controlled and kept the same during the experiments in order to prevent them from having an impact on results.
If all the values of the "independent" variable (x) are different then it is a function.If there are any repeats of the independent variable, the corresponding dependent variable, y, must be the same.If all the values of the "independent" variable (x) are different then it is a function.If there are any repeats of the independent variable, the corresponding dependent variable, y, must be the same.If all the values of the "independent" variable (x) are different then it is a function.If there are any repeats of the independent variable, the corresponding dependent variable, y, must be the same.If all the values of the "independent" variable (x) are different then it is a function.If there are any repeats of the independent variable, the corresponding dependent variable, y, must be the same.
yes
In order to be certain that the changing of the independent variable directly affects the dependent variable, a control must be taken where the independent variable is not changed-this ensures that whatever happens to the dependent variable happens because of the independent variable, and is not something that would happen anyway.
The independent variable is the volume of an object. It is the variable that is manipulated or changed in an experiment to observe its effect on the dependent variable. In this case, the dependent variable would likely be the mass of the object.
The independent variable is the thing you are changing/varying. The dependent variable is the thing you are measuring. This variable should be affected by the independent variable. Control variables are anything that must be kept constant. If there are any other factors which affect the dependent variable, then these need to be controlled so that they do not have any significant effect (basically ensuring that you are actually measuring the effects of the independent variable).
Control bias in psychology refers to the influence of a third variable that was not accounted for in a research study, leading to a misinterpretation of results. This bias can occur when an uncontrolled variable affects both the independent and dependent variables, creating a false perception of causality. Researchers must take measures to control for possible biases to ensure the validity and reliability of their findings.
either independent or dependent
You must have a control group, an experimental group, an experimental variable (also called the independent variable), and a response to be measured (also called the dependent variable). The experimental variable is applied only to the experimental group, so that any difference between the control group and experimental group is due only to the experimental variable. Both the control group and experimental group must have the same conditions, except for the experimental variable.
Well, that depends on whether 'customer satisfaction' is the cause or the effect in your analysis. If say, 'prompt attention to customer problems' were the causative (independent) variable, then the 'customer satisfaction' would be the result/outcome/dependent variable. However you could have had a study in which 'customer satisfaction' was the cause/independently variable, and 'likelihood of repeat business' were the result/outcome/dependent variable. You must distinguish between CAUSE and EFFECT. Cause is the independent variable that creates the Effect observed.
When non-experimental variables are held constant, it means keeping factors other than the independent variable the same for all participants or conditions in order to ensure that any observed effects are due to the independent variable and not to any other variable. This helps to isolate the impact of the independent variable on the dependent variable and strengthens the validity of the experiment.
A variable does and must change, but you can only have one variable, otherwise the experiment becomes biased and unfair
Variables that should remain the same in an experiment to have a fair test of the independent variable are called control variables. These include factors such as temperature, time of day, equipment used, and method of measurement. By keeping these control variables constant, any observed effects in the experiment can be confidently attributed to changes in the independent variable.