![]() 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. Type of variableĪ variable that hides the true effect of another variable in your experiment. Some useful types of variables are listed below. 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.īut there are many other ways of describing variables that help with interpreting your results. In these cases you may call the preceding variable (i.e., the rainfall) the predictor variable and the following variable (i.e. However, there might be cases where one variable clearly precedes the other (for example, rainfall leads to mud, rather than the other way around). 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). 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. In this experiment, we have one independent and three dependent variables. The temperature and light in the room the plants are kept in, and the volume of water given to each plant. Variables that are held constant throughout the experiment. Variables that represent the outcome of the experiment.Īny measurement of plant health and growth: in this case, plant height and wilting. The amount of salt added to each plant’s water.ĭependent variables (aka response variables) Variables you manipulate in order to affect the outcome of an experiment. Independent variables (aka treatment variables) Independent vs dependent vs control variables Type of variable You will probably also have variables that you hold constant ( control variables) in order to focus on your experimental treatment. 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. Parts of the experiment: Independent vs dependent variablesĮxperiments are usually designed to find out what effect one variable has on another – in our example, the effect of salt addition on plant growth. This example sheet is color-coded according to the type of variable: nominal, continuous, ordinal, and binary. 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. 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. For example, star ratings on product reviews are ordinal (1 to 5 stars), but the average star rating is quantitative. *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.
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