When should you Dichotomize a variable?
There are reasons to do it – if a continuous variable falls into two clear groupings anyway, but I tend to avoid dichotomizing unless its a natural form of the variable in the first place. It is often also useful if your field is dichotomizing things anyway to have a dichotomized form of a variable.
How do you Dichotomize a variable?
It means: Take a variable with multiple different values (>2), and transform it so that the output variable has 2 different values. Note that this “thing” can be understood as consisting of two different aspects: Recoding and cutting. Recoding means that value “a” becomes values “b” etc.
What does it mean to Dichotomize a variable?
an item or score that initially had a set of continuous values (e.g., age) but was then separated into two possible values (e.g., younger and older). It may be useful to create a dichotomized variable when there are truncated data.
How to find correlation between dichotomous and continuous variables?
They are either continuous or categorical in nature. My task is to predict a dichotomous variable based on these variables (maybe come up with a logistic regression model). So I thought the initial investigation would involve finding the correlation between dichotomous and a continuous variable.
Why is dichotomization not a natural way to analyze data?
The risk of misclassification because of measurement error is high. In addition, comparing studies that used different cut-points becomes impossible. Hence, for most statisticians, dichotomization is not a natural way of analyzing continuous data.
Why do re-Searchers use dichotomization of independent variables?
Re- searchers may dichotomize independent variables for many reasons—for example, because they believe there exist distinct groups of individuals or because they believe analyses or presentation of results will be simplified.
What is the cost of dichotomizing continuous variables?
The simplicity achieved by creating ≥2 artificial groups has a cost: Grouping may create rather than avoid problems. In particular, dichotomization leads to a considerable loss of power and incomplete correction for confounding factors.