Missing data is a common problem in longitudinal studies because of the characteristics of repeated measurements. Herein is proposed a latent variable model for nonignorable intermittent missing data in which the latent variables are used as random effects in modeling and link longitudinal responses and missingness process. In this methodology, the latent variables are assumed to be normally distributed with zero-mean, and the values of variance-covariance are calculated through maximum likelihood estimations. Parameter estimates and standard errors of the proposed method are compared with the mixed model and the complete-case analysis in the simulations and the application to the weight gain prevention among women (WGPW) data set. In the simulation results with respect to bias, mean squared error, and coverage of confidence interval, the proposed model performs better than the other two methods in different scenarios. Relatively, the proposed latent variable model and the mixed model do a better job for between-subject effects compared to within-subject effects. The converse is true for the complete case analysis. The simulation results also provide support for application of this proposed latent variable model to the WGPW data set.