eng Science and Education Publishing American Journal of Applied Mathematics and Statistics 2328-7292 2021-01-31 9 1 28 37 10.12691/ajams-9-1-5 AJAMS2021915 article Oshada Senaweera oshada.senaweera@gmail.com; oshada.s@sliit.lk 1 2 Prasanna S. Haddela 2 Gayan Dharmarathne 2 Department of Information Technology, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka The paper investigates the comparative effects of several random sampling methods on the maximum likelihood estimates of a simple logistic regression model. The study uses simulated data (logistic populations with pre-defined parameter values) that used Monte Carlo methods to simulate. Sampling techniques include Simple Random Sampling (SRS) and six variations of Stratified Sampling where two are single-stage Stratified Sampling and four are choice-based (two-phase) Stratified Sampling. Parameter estimates arising under each sampling technique were compared using performance measures Bias, Standard Error & Percentage of models that are feasibly estimated. The simulation-based analysis found that choice-based sampling with proportional allocation in both phases is the best-suited sampling technique for parameter estimation of a simple logistic regression model. http://pubs.sciepub.com/ajams/9/1/5/ajams-9-1-5.pdf Monte-Carlo simulations random sampling logistic regression maximum likelihood estimates