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Time-Series Data: Why Stationarity and Co-Integration Are Not Optional

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Why Stationarity and Co-Integration Are Not Optional in Time-Series Data

Time-series models are among the most powerful tools in empirical research — used to forecast trends, evaluate policy impacts, and assess long-term relationships across economics, finance, climate, and public policy. But too often, researchers focus on sophisticated modeling techniques (like ARDL, VAR, or VECM) without laying the proper statistical foundation.The result? Spurious regressions, invalid inferences, … Read more

Why Using G*Power Isn’t Enough to Justify Sample Size

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Using G Power is not Enough to Justify Sample Size

In quantitative research, justifying sample size is a fundamental requirement. Yet, one of the most misused tools in this process is G*Power — a free and powerful program that helps estimate sample sizes based on power analysis.While G*Power is valuable, it’s often misapplied or overly relied upon, leading to flawed research designs, underpowered studies, and … Read more

The Crisis of Theoretical Underpinning in Qualitative Research

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The Crisis of Theoretical Underpinning in Qualitative Research

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

The Wrong Use of Control Variables is Killing Your Regression Model

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The Wrong Use of Control Variables is Killing Your Regression Model

Regression analysis is one of the most widely used statistical tools in empirical research. Yet, beneath many published models lies a silent killer of validity: the misuse of control variables. Too often, researchers include controls reflexively — without a clear understanding of their role in causal inference or model integrity. This article dives into what … Read more

Why Most Research Gaps Are Superficial

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Why Most Research Gaps Are Superficial — And What Researchers Need to Do Better

In today’s publish-or-perish academic environment, research gaps are a staple of literature reviews. However, many so-called “gaps” are superficial. Weakly framed gaps lead to weak contributions, making manuscripts less likely to survive peer review or gain traction in global journals. This article explains why most research gaps lack depth and how to construct meaningful, impactful … Read more

Impact Factor Obsession: Why Journal Quality is Not Just About Numbers

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𝗜𝗺𝗽𝗮𝗰𝘁 𝗙𝗮𝗰𝘁𝗼𝗿 𝗢𝗯𝘀𝗲𝘀𝘀𝗶𝗼𝗻: 𝗪𝗵𝘆 𝗝𝗼𝘂𝗿𝗻𝗮𝗹 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗶𝘀 𝗡𝗼𝘁 𝗝𝘂𝘀𝘁 𝗔𝗯𝗼𝘂𝘁 𝗡𝘂𝗺𝗯𝗲𝗿𝘀 Too many researchers chase Impact Factor (IF) like it’s the holy grail of academic publishing — but this obsession often leads to poor strategic decisions and missed opportunities. 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗺𝗼𝘀𝘁 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵𝗲𝗿𝘀 𝗶𝗴𝗻𝗼𝗿𝗲: – Impact Factor ≠ Article Quality — a journal’s prestige doesn’t … Read more