In statistics, unstandardized coefficients are the ‘raw’ coefficients that are produced by regression analysis when the analysis is done 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 directly 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 Unstandardized Coefficients
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 done where you regress IQ scores on both years in college and income level, your variables will be on completely different scales and so the unstandardized coefficients (one in IQ/$ and one in IQ/years) can’t be compared with each other. 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.
Wuensch, Karl. Regression Coefficients: Unstandardized versus Standardized. Retrieved from http://core.ecu.edu/psyc/wuenschk/MV/multReg/Standardized_Regression_Coefficients.docx on July 19, 2018.
Janda, Kenneth. Linear Regression. Lecture Notes from Elementary Statistics for Political Research, 310. Retrieved from http://janda.org/c10/Lectures/topic04/L25-Modeling.htm on July 19, 2018
Tindall, David. Some Notes on Statistical Interpretation. Sociology 502 Lecture Notes. Retrieved from http://faculty.arts.ubc.ca/tindall/soci502/overheads+slides/Statistics_I/Notes_on_Interpretation.pdf on July 19, 2018.------------------------------------------------------------------------------
Need help with a homework or test question? With Chegg Study, you can get step-by-step solutions to your questions from an expert in the field. If you'd rather get 1:1 study help, Chegg Tutors offers 30 minutes of free tutoring to new users, so you can try them out before committing to a subscription.
If you prefer an online interactive environment to learn R and statistics, this free R Tutorial by Datacamp is a great way to get started. If you're are somewhat comfortable with R and are interested in going deeper into Statistics, try this Statistics with R track.
Comments? Need to post a correction? Please post a comment on our Facebook page.