Building Applied Models From Data

Applied models are indispensable tools for understanding complex phenomena, enabling predictions, decision-making, and problem-solving in various contexts. Building applied models from data is one of the most powerful methodologies in today's data-driven world, where organizations and researchers across fields increasingly rely on large datasets to inform their models. This chapter will explore the process of building applied models using data, emphasizing the methods, challenges, and considerations that come with this approach. By discussing how data-driven models are constructed, tested, and refined, the chapter will provide a comprehensive overview for administrators, faculty, staff, and researchers working with data to create accurate, generalizable, and actionable models. The chapter will focus on methods for ensuring the robustness, sensitivity, and adaptability of these models to both local and global contexts.