Unstandardized coefficients are ‘raw’ coefficients produced by regression analysis when the analysis is performed on original, unstandardized variables. Unlike standardized coefficients, which are normalized unit-less coefficients, an unstandardized coefficient has units and a ‘real life’ scale.
An unstandardized coefficient represents the amount of change in a dependent variable Y due to a change of 1 unit of independent variable X.
Use of Unstandardized Coefficients in Regression
Unstandardized coefficients are usually intuitive to interpret and understand. Since they represent the relation between raw data, they can be used directly in calculations and analysis. They can also be used to make comparisons within the regression equation when just one measurement scale is in use. If several measurement scales are in use, standardized coefficients are preferred for comparison (see below).
Weak Point of an Unstandardized Coefficient
Unstandardized coefficients are less useful for direct comparison when the measurement scales of the independent variables are different. In these cases a larger number may still point to a smaller effect, and to pinpoint the effect size of variables, you may want to standardize your coefficients first.
For instance, in an analysis where you regress IQ scores on both years in college and income level, your variables will be on completely different scales and so each unstandardized coefficient (one in IQ/$ and one in IQ/years) can’t be compared with another unstandardized coefficient. To find out which is the most interesting effect one would want to standardize the coefficients first, which means they would both be in terms of standard deviations and so easily compared with each other.
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