This paper explores the use of K. G. Joreskog's (1970) congeneric modeling approach to reliability using censored quantitative variables. Two Monte Carlo studies were conducted. The first explored the robustness of Normal Theory Generalized Least-Squares (NTGLS) estimates for a single-factor congeneric model across several sample sizes (N=25,50,100,400), model loading sizes, and different levels of censoring (0%, 25%, 50%, 75%). The second study compared alternate estimation procedures for different levels of variable censoring (0%, 25%, 50%, 75%) based on a single-factor large loading congeneric model. NTGLS, asymptotically distribution free (ADF), and latent TOBIT estimators were compared as to their efficiency in estimating model parameters. Results of the first study indicate that convergence rate is inversely related to sample size and to the size of the model loadings. Censoring of variables produced the expected negative bias in estimates using NTGLS methods with the magnitude of the bias somewhat robust against sample size variation. Results from the second study indicate that the TOBIT estimates were robust with respect to model rejection and amount of parameter bias across various levels of variable censoring. Both NTGLS and ADF methods proved to be unsatisfactory both in model rejections and percent of bias. Thirteen graphs and three data tables conclude the document. (Author/TJH)