In an era of rapidly expanding data availability, organizations face a paradox: having more information does not necessarily lead to better insight. This book introduces a novel construct-centered approach to navigating this challenge by integrating structural equation modeling, psychometrics, and data science into a unified analytical framework for contemporary organizational measurement.
Emphasizing the importance of conceptual clarity, the text demonstrates how meaningful analysis depends not only on advanced techniques but also on a deep understanding of measurement, data quality, and organizational objectives. It guides readers through the process of identifying relevant constructs, representing them through appropriate indicators, understanding the underlying structure of organizational phenomena, and informing strategic decision-making. By bridging theory and application, the book highlights the risks of data-rich but conceptually weak analysis and offers a systematic approach to generating interpretable, actionable results.Written for researchers and advanced practitioners in engineering, management, and the social sciences, it provides a rigorous yet accessible foundation for conducting high-quality, theory-driven analysis in data-intensive contexts.
