Key research themes
1. How can Necessary Condition Analysis (NCA) complement traditional effect size measures to strengthen inference in social and management sciences?
This research theme investigates the application of NCA as a methodological complement to classical effect size metrics (e.g., Pearson’s r, Cohen’s d), addressing limitations in interpretation thresholds and offering practical insights by identifying necessary but not sufficient conditions in various management and social science contexts.
2. What are the statistical distributions and probabilistic properties of classical and advanced condition numbers (including Demmel’s condition number) in matrix computations and their implications for numerical stability?
This theme centers on the theoretical and probabilistic characterization of condition numbers for matrices, especially in relation to stability and sensitivity of linear algebraic computations, with focus on defining and estimating various metrics such as the Demmel condition number, their distributions for random Gaussian matrices, and applications to numerical algorithms employed in matrix inversion, least squares, and optimization.
3. How do length-weight relationships and condition factors function as biological indicators for fish population health and management in aquatic ecosystems?
Focused on fish biology and ecology, this theme explores empirical studies assessing length-weight relationships (LWR) and condition factors (K) across various species and ecosystems. These metrics are used to evaluate fish growth patterns, wellbeing, reproductive cycles, and habitat suitability, informing fisheries management and conservation strategies in estuarine, freshwater, and aquaculture environments.