In empirical research, mistaking mediators for moderators (or vice versa) is surprisingly common. Yet the distinction is critical for valid inference, model specification, and publication success. Below, we dig into the conceptual, statistical, and practical nuances—especially those many researchers overlook. Conceptual Foundations In other words: Baron & Kenny’s classic 1986 formulation laid this foundation in … Read more
When researchers layer too many theories, they often think “more breadth = more rigor.” But the opposite often occurs. Below are four critical, but commonly overlooked, issues that weaken research—even when the intent is “comprehensive.” Common but Hidden Pitfalls Conceptual Conflict & Epistemological Clash Every theory carries assumptions about knowledge (epistemology). When researchers mix theories … Read more
What R² Actually Measures Why High R² Is Often Misleading Overfitting & Inflated R² Model Misspecification & Nonlinearity Sampling Issues & Range Effects Better Metrics & Validation Techniques Adjusted R²: A Penalized Alternative Cross-Validation & Leave-One-Out R² Residual Diagnostics & Model Assumptions Practical Tips for Researchers R² is not magic. It’s a limited metric, useful … Read more
Grounded Theory (GT) is one of the most respected qualitative methodologies, designed to move beyond description into theory generation. Yet, many researchers misapply GT as if it were merely a version of thematic analysis. This error weakens contributions, misleads readers, and leads to journal rejection. Grounded Theory is not about “coding until themes appear.” Instead, … Read more
Why Identification Matters in SEM Structural Equation Modeling (SEM) is one of the most powerful tools in quantitative research. It allows scholars to test complex theories, measure latent constructs, and model mediation or moderation effects. Researchers often call SEM the “gold standard” of statistical modeling. However, a hidden truth is often ignored: SEM collapses without … Read more
In research, complex models and advanced statistical techniques often get the spotlight. But here’s the truth: if data is dirty, results are wrong—no matter how advanced the analysis looks. Data cleaning is not optional. It is the backbone of research integrity, transforming raw inputs into credible evidence. Neglecting it risks misleading findings, wasted resources, and … Read more
Impact assessments are widely regarded as the gold standard for evidence-based policymaking. Yet, behind many “causal” claims lies a silent but serious flaw: endogeneity. Most researchers focus on sample size, statistical fit, or significance levels. However, ignoring endogeneity can transform expensive evaluations into misleading exercises. Instead of guiding progress, such studies risk misallocating resources and … Read more
Despite the growing acceptance and institutionalization of qualitative research in academia, many studies still fall into a critical trap — they lack a solid theoretical foundation. While qualitative methods are celebrated for exploring complexity, meaning, and lived experience, they are increasingly being used in ways that are conceptually shallow, methodologically misaligned, and analytically weak. This … Read more
