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Abstract: Understanding and optimizing chemical reactions is important for controlling reaction selectivity, increasing yield, reducing waste, and improving numerous other reaction parameters. Reaction optimization is often informed by mechanistic understanding, which arises from kinetic studies. Kinetic studies aim to understand what impacts the mechanism and rate of a reaction. There are many methodologies for kinetic analysis. There have been recent advances in kinetic methodologies which allow for the interrogation of systems under reaction-relevant conditions, and which require fewer experiments than traditional initial rate methods. These modern kinetic methodologies utilize temporal profiles of chemical reactions that track the course of individual reaction components over the course of a reaction. Time course reaction profiles provide invaluable reaction insight and, coupled with these new kinetic methodologies, are extremely powerful tools for mechanistic elucidation. However, chemists have been slow to onboard these powerful methodologies, which are still considered advanced and niche techniques. This thesis aims to develop protocols to make these methodologies more accessible to the general chemistry community. A set of best practices for collecting robust and high-quality time course reaction profiles for kinetic studies was developed. This guide improves the confidence chemists have in the conclusions of kinetic studies by ensuring that important control reactions have been run and proper optimization of reaction monitoring parameters has been achieved. A protocol for converting data sets of temporal peak area versus time to temporal concentration using nonlinear regression analysis was also developed. This method is rapid, facile, and broadly accessible as it can be done with any nonlinear regression tool, including the Solver plug-in in Microsoft Excel. Lastly, the presented protocols and procedures were applied to gather kinetic data in the Buchwald Hartwig amination of a polyhalogenated arene. Time course data of the amination were gathered with several commonly employed catalyst systems. The time course profiles in combination with reaction modeling enabled delineation of two previously indistinguishable mechanisms, ring walking and diffusion controlled coupling. The resulting mechanistic understanding was leveraged to achieve a specific site selectivity in the amination of the polyhalogenated arene and enabled desymmetrization of the symmetrical starting material.