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. Rating scale responses in a survey, such as Likert scales*.Groups that are ranked in a specific order. Groups with no rank or order between them. Binary vs nominal vs ordinal variables Type of variableīinary variables (aka dichotomous variables) There are three types of categorical variables: binary, nominal, and ordinal variables. They are sometimes recorded as numbers, but the numbers represent categories rather than actual amounts of things. Measurements of continuous or non-finite values.Ĭategorical variables represent groupings of some kind.
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