Why Context Defines Research Quality In academic and applied research, data is often treated as objective truth. Yet, every dataset is shaped by the context in which it is produced—culture, geography, politics, and time. Ignoring context doesn’t just weaken results; it distorts reality. When researchers transfer models from the Global North to the Global South … Read more
Understanding the Problem: Translation Isn’t Enough In global research, translation is often mistaken for localization. Many researchers assume that once a questionnaire or scale is linguistically translated, it becomes universally applicable.However, translation alone rarely guarantees conceptual equivalence—the idea that a question measures the same construct in another language or culture. For example, a “satisfaction survey” … Read more
In data analysis and research, complexity is often mistaken for sophistication. Many assume that the more variables or equations a model has, the stronger it becomes. However, the reality is quite the opposite — simpler models often perform better than overly complex, overfitted ones. Let’s explore why simplicity often wins, both statistically and practically. What … Read more
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
Likert scales remain a staple in social science, management, and policy research. Their simplicity—“strongly disagree” to “strongly agree”—makes them appealing for capturing opinions. Yet, their misuse often leads to statistical pitfalls that undermine credibility. This article explains the risks, provides solutions, and highlights best practices with supporting evidence. Why Likert Scales Are Popular Despite their … Read more
Impact evaluations are often described as the gold standard of evidence-based decision-making. Governments, donors, and organizations rely on them to understand what works, what fails, and why. These evaluations inform billion-dollar policies and shape interventions that affect millions of lives. Yet, one of the most common—and costly—mistakes is attempting to measure impact without establishing a … Read more
In both academic and applied research, the oft-repeated claim—“This study is objective”—masks a deeper reality: objectivity in research design is neither absolute nor devoid of negotiation. Researcher assumptions, frameworks, and methodological decisions inevitably shape outcomes, thus making objectivity constructed and prone to compromise. 1. Framework Choice: The Lens That Filters Reality Every theoretical framework inherently … Read more
Academic publishing has changed rapidly. Research today requires transparency, advanced analytics, and integration with modern data science tools. Yet many researchers still rely on SPSS, a tool designed decades ago for basic statistical work. At Research & Report Consulting (RRC), we often see manuscripts where the research questions are innovative, but the methods are outdated. … Read more
Inferential statistics are powerful tools for uncovering patterns and testing relationships. But they are also easily misused. One of the most frequent and damaging mistakes we see at Research & Report Consulting is treating correlation as causation. This statistical misstep is more than a technical flaw—it undermines credibility, damages publication chances, and can lead to … Read more
