What are the best practices in political methodology for applied research? How can political methodology best help with knowledge accumulation in political science? This thesis presents three related papers in applied methods for political science, each addressing a specific issue in the sub-field: model selection in empirical analysis (Paper I), formal and empirical model integration (Paper II), and model selection and aggregation with a common target (Paper III). The papers share the meta-methodological concern of optimizing cumulative learning through optimal modeling practices across two dimensions of model usage: the number (single or multiple) and the type (formal or empirical) of the models. The main point is that knowledge in political science best accumulates when models are both carefully used within and properly aggregated between empirical studies, so that meta-methodological advances can best benefit meta-learning in the discipline. Paper I deals with the issue of model selection in data analysis. Using political deliberation as a case in point, it argues that model choice should take into consideration both the key characteristics of the data under analysis and the expected gains in model insights. For analyzing deliberation data specifically, it recommends the use of network models like the Exponential Random Graph Models (ERGMs) as such models best respect the complex structure of the data and allow for explicit estimation of endogenous influences driving the process. Paper II looks at the issue of model integration between formal and empirical approaches. Using intraparty unity as an example, it proposes that formal models can be used for guiding not only hypothesis testing but also latent variable measurement in empirical studies. For measuring intraparty unity in coalition bargaining, it demonstrates how to turn a formal account of faction-based portfolio allocation into an empirical recipe for measuring this latent variable using real-life data. Paper III focuses on a broader issue of model selection and aggregation across multiple studies. Using crowd-sourced statistical models of COVID-19 mortality as an input set, it field tests multiple methods for the said purpose including both forecasting and algorithmic techniques. For predicting pandemic deaths, it finds that aggregated meta-models generally outperform individual models, and that algorithmic approaches consistently best human experts in achieving such tasks.