The success of a software organization depends upon its ability to deliver a quality software product within time and budget constraints. To ensure the delivery of quality software, software inspections have proven to be an effective method that aid developers to detect and remove problems from artifacts during the early stages of software lifecycle. In spite of the reported benefits of inspection, the effectiveness of the inspection process is highly dependent on the varying ability of individual inspectors. Software engineering research focused at understanding the factors (e.g., education level, experience) that can positively impact the individual’s and team inspection effectiveness have met with limited success. This dissertation tries to leverage the psychology research on Learning Styles (LS) – a measure of an individuals’ preference to perceive and process information to help understand and improve the individual and team inspection performance. To gain quantitative and qualitative insights into the LSs of software inspectors, this dissertation reports the results from a series of empirical studies in university and industry settings to evaluate the impact of LSs on individual and team inspection performance. This dissertation aims to help software managers create effective and efficient inspection teams based on LSs and reading patterns of individual inspectors thereby improving the software quality.