Our paper proposes a method of combining probability and non-probability samples to improve analytic inference on logistic regression model parameters. A Bayesian framework is considered where only a small probability sample is available and the information from a parallel non-probability sample is provided naturally through the prior. A simulation study is run applying several informative priors. Comparisons on the performance of the models are studied with reference to their mean-squared error (MSE). In general, the informative priors reduce the MSE or, in the worst-case scenario, perform equivalently to non-informative priors.