When researchers fit statistical models to multiply imputed datasets, they have to fit the model separately for each imputed dataset. Since there are multiple datasets, there are always multiple sets of model results. It is possible for some of these sets of results not to converge while some do converge. This study examined occurrence of such a problem--partial convergence problem and inspected four outcomes of partially convergent models: proportion of convergence, percent parameter bias, root mean square error, and coverage rate of the true population parameter in the confidence interval of the parameter estimates. In this Monte Carlo simulation study complete data with standard normal distribution were simulated based on a single CFA model with 3 factors each with 3 indicators. True factor loadings for each factor varied as 0.4, 0.6, and 0.8. A single factor covariance of 0.4 was set as the true population parameter. In addition to variation of the factor loadings, sample size levels varied as 60, 80, 100, 150, 200, 300, and 400 and rate of missingness varied as 5%, 10%, 25%, and 50%. Partial convergence problem was not observed for sample size levels of 300 and 400. Thus, these conditions were not examined any further. It was examined that partial convergence problem was more likely to occur when the sample size was small and rate of missingness was high. It was observed that factor loading estimations were unbiased, however, there were two cells (sample of 60 with 25% missingness and sample size of 150 with 50% missingness) where factor covariance estimations were biased. Even though, overall for most cells parameter estimates were unbiased, root mean square errors were high across sample sizes for the 50% missingness condition which indicates that the parameter estimates for the 50% missingness were inconsistent across replications. Almost for all conditions, confidence intervals of the parameter estimates did not contain the true population parameter at acceptable rate--90% or higher. A different solution to the partial convergence problem was examined in this study. For the sets of results that did not converge, additional imputations were conducted to examine whether any of the outcomes would be improved. The only improvement was observed in the proportion of convergence outcome, indicating, that re-imputation may result in fully convergence of models, however, this will have no effect on percent parameter bias, root mean square error or the coverage rate of the confidence intervals. Recommendations for researchers who deal with the partial convergence problem are provided. Limitations of the study are acknowledged and suggestions for future research are given. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]