This thesis discusses the performances of different estimation methods in logistic regression model when covariates are subject to measurement errors. The efficiencies of different methods were evaluated through extensively simulation studies. These simulation studies were conducted under different distribution of covariates, samples size, values of regression parameter, the variance associated with measurement errors and the distribution of measurement errors. Conclusions based on the sample standard deviations were derived.