Variables “centering” is a procedure that researches ignore quite often working with empirical data.
\nBut what is it? Why can it be very important?<\/p>\n
Let’s look at a trivial example:<\/strong> When we fit a classic linear regression model we get:<\/strong> Let’s say that now you want to interpret the intercept.<\/strong><\/p>\n The intercept is the average income when the value of all the variables is 0. You understand that in this way the value of the intercept is “not interpretable” because at zero years it is impossible to have an income. You don’t have to necessarily subtract the average; you can “centre” your variable in any other way, but using the average is, may be, the most widespread criterion.<\/p>\n
\n10 subjects have an annual income and want to assess if this income is related to:
\nage;
\nsex (0\/males, 1\/females);
\nthe level of education (0 \/”normal” education, 1\/”high” education).<\/p>\n
\nan intercept;
\na coefficient for an age;
\na coefficient for the level of education;
\na coefficient for a sex.<\/p>\n
\nIn our case the intercept would represent the average income of males (sex = 0), of a normal education (education = 0) and … of zero years!<\/p>\n
\nTherefore, to have an interpretable value, you can analyse the data using a “centered” variable of age, obtained by subtracting the average age from each age value instead of direct use of the age variable.<\/p>\n