Measuring the sound absorption coefficient (SAC) of sound-absorbing materials in a reverberation room can be expensive and predicting random incidence SACs is a complex and challenging task. This thesis begins by discussing various theoretical models for calculating the acoustic impedance and SAC of porous materials. It then presents a comprehensive analysis of acoustic measurements using techniques such as impedance tube measurement, transmission loss measurement, reverberation room measurement, and air flow resistivity measurement. The accuracy of the measured random incidence SAC in a small-size reverberation room is validated by comparing it with the SAC measured in a full-size reverberation room. The determination of acoustic properties, including the characteristic impedance and complex wave number of porous materials, is explored through different methods such as the two-cavity method, the two-thickness method, the four-microphone transfer matrix method, and the inverse method of the Johnson-Champoux-Allard (JCA) model. The study also investigates the computation of random incidence SACs using the finite transfer matrix method (FTMM), comparing the calculated results with the measurement data obtained in both full-size and small-size reverberation rooms. Recognizing the significant impact of sample size on the measurement and prediction of random incidence SAC, this research describes the concepts of the edge effect phenomenon and the maximum random incidence SAC. Analytical formulae and numerical integration techniques are developed and validated to determine the maximum SAC, demonstrating its potential applications in mitigating the edge effect. Additionally, an empirical function is proposed to account for the influences of sample geometry, based on SAC data from 24 different materials obtained from various laboratories. The study compares the performance of four different methods and the empirical function for estimating the random incidence SAC for samples of different sizes but made from the same material. The results indicate that the analytical method performs best for predicting the SAC of large-size samples from small-size samples, while the empirical function is deemed the most suitable method for predicting the SAC of small-size samples from large-size samples. The research presented in this thesis makes a substantial contribution to the understanding and prediction of SACs of sound-absorbing materials, providing valuable insights that supplement the ISO 354 standard and enhance measurement reproducibility. The experimental findings and developed methods have the potential to improve the efficiency of sound absorption measurement and prediction, reduce resource consumption, and enhance the quality of product development processes.