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The Overlooked Importance of Positionality and Reflexivity in Qualitative Research

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The Overlooked Importance of Positionality and Reflexivity Statements in Qualitative Research

In qualitative research, validity is not merely technical — it is deeply interpretive, ethical, and relational. Yet, while researchers meticulously detail methods, tools, and coding frameworks, they often gloss over or omit a critical component: positionality and reflexivity. This oversight undermines not only the credibility of the study but also its epistemological integrity. At Research … Read more

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