Interpreting the substantive significance of multivariable regression coefficients

A critical objective for applications of multivariable regression analysis is evaluation of both substantive importance and statistical significance, yet many articles focus excessively on inferential statistical tests at the expense of substantive issues. I demonstrate approaches for writing clear sentences to interpret the real-world meaning of estimated coefficients from ordinary least squares regression, taking into account the type of independent variable and the distributions of the dependent and independent variables. After introducing ―the Goldilocks principle‖ – that no one size contrast fits all variables – I use diverse examples to illustrate the importance of considering both the topic and data when evaluating substantive significance. Complementary use of prose, tables and charts to present both statistical and substantive significance are also covered.