Factor analysis helps understand relationships between variables by creating common factors. Maximum likelihood factor analysis can give improper solutions, so a Bayesian approach with prior distributions is used to prevent this. Key aspects include choosing proper parameters and the number of factors. A model selection criterion is derived for Bayesian factor analysis. Simulations show the effectiveness of the approach, and a real data example is provided to demonstrate the method.