Using Instrumental Variables to Estimate the Parameters in Unconditional and Conditional Second-Order Latent Growth Models

This article applies Bollen's (1996) 2-stage least squares/instrumental variables (2SLS/IV) approach for estimating the parameters in an unconditional and a conditional second-order latent growth model (LGM). First, the 2SLS/IV approach for the estimation of the means and the path coefficients in a second-order LGM is derived. An empirical example is then used to show that 2SLS/IV yields estimates that are similar to maximum likelihood (ML) in the estimation of a conditional second-order LGM. Three subsequent simulation studies are then presented to show that the new approach is as accurate as ML and that it is more robust against misspecifications of the growth trajectory than ML. Together, these results suggest that 2SLS/IV should be considered as an alternative to the commonly applied ML estimator.