Sehee Hong Department of Education

Recovering Nonlinear Functions of Latent Variables: A Plausible-Value Neural Network Framework

When factor scores replace true latent scores in nonlinear prediction, measurement error attenuates the recoverable variance of any $k$th-order component of the regression function by $\rho^k$ -- the $k$th power of the score's coefficient of determination -- for any linear score type. This study derives the bound via H...