Algorithm

Residual

Probit

Statistics

Probit model

Confounding

Accounting

Conditional probability

Economics

Computer science

Variance (accounting)

Mathematics

Econometrics

Logit

Confidence interval

Programming language

Conditional logistic regression

Logistic regression

Constant (computer programming)

Sociological Methods & Research

The Karlson–Holm–Breen (KHB) method has rapidly become popular as a way of separating the impact of confounding from rescaling when comparing conditional and unconditional parameter estimates in nonlinear probability models such as the logit and probit. In this note, we show that the same estimates can be obtained in a somewhat different way to that advanced by Karlson, Holm, and Breen in their original article and implemented in the user-written Stata command khb. While the KHB method and this revised KHB method both work by holding constant the residual variance of the model, the revised method makes comparisons across multiple nested models easier than the original method.

A Note on a Reformulation of the KHB Method