Research and Report Consultancy

Misalignment Between Research Questions and Data Collected

Misalignment Between Research Questions and Data Collected

The Silent Rejection Trigger in Research Many researchers create great research questions (RQs), yet their data cannot answer them. Reviewers spot this quickly and often reject manuscripts for RQ–data misalignment because: These mismatches are subtle yet fatal. A well-aligned study defines the RQ, chooses the correct design, sets proper data needs, and only then asserts … Read more

Why Research Impact Ends When Projects End

Why Research Impact Ends When Projects End

Introduction: Impact Is Not the Same as Publication Most research projects promise impact. Very few deliver it beyond the grant period. Once funding ends, many outputs vanish. Datasets become unreachable. Code breaks. Reports sit unread. If research outputs cannot be found, reused, or maintained, they will not shape policy or practice. This is not a … Read more

Audit Trail Gaps Make Qualitative Research Unreliable

Audit Trail Gaps Make Qualitative Research Unreliable

Qualitative research depends on trustworthiness, not statistical generalization. When researchers omit key components of an audit trail, reviewers cannot see who decided what, when, and why. As a result, credibility, dependability, and confirmability weaken—even when the dataset is rich and quotes appear compelling. A rigorous audit trail acts as the study’s backbone. It links raw … Read more

Power Imbalance Kills Real Participation (Avoid Tokenism)

Power Imbalance Kills Real Participation (Avoid Tokenism)

Power imbalances sabotage true collaboration. They turn “co-creation” into quiet extraction. Hidden hierarchies decide who speaks. They filter what counts as evidence. They direct benefits upward. Teams ignore this at their peril. Real participation demands surfacing power early. This guide uncovers blind spots. It offers fixes. It promotes equitable CBPR and PAR. Read on to … Read more

The Sampling Frame–Population Gap Explained

The Sampling Frame–Population Gap Explained

External validity collapses when the sampling frame (who is reachable or listed) does not match the target population (who the research aims to generalize to). This mismatch—often invisible—creates coverage errors, biased estimates, and misleading conclusions. Understanding and fixing this gap is essential for credible quantitative research, policy studies, market research, and impact evaluations. Studies across … Read more

Hypothesis Overload Hurts Research

Hypothesis Overload Hurts Research

Researchers often believe that adding more hypotheses (H1…H20) makes a paper “comprehensive.” In reality, shotgun hypothesis testing introduces noise, confuses theoretical framing, and triggers statistical inflation. Peer reviewers increasingly flag these issues because journals now demand theoretical clarity, replicability, and transparent analysis decisions. The chart above demonstrates a key reality: false-positive rates increase sharply as … Read more

Why Assuming Variables Are Independent Is Risky

Why Assuming Variables Are Independent Is Risky

Statistical independence is one of the most violated assumptions in social science, economics, public health, and data science. Treating variables—or observations—as independent when they are not can break inference, mislead policymakers, and inflate Type I errors.In real-world datasets, hidden dependence structures arise from clustering, networks, spatial proximity, common raters, and temporal patterns. Ignoring these structures … Read more

Research Without Dissemination Is Wasted

Research-without-dissemination-is-wasted

A DOI is not impact. Publishing a paper does not ensure it reaches policymakers, practitioners, industry leaders, or affected communities. Without intentional dissemination, research becomes static—discoverable to a tiny group of academics and invisible to the people who need it most. The global research ecosystem now produces over 2.5 million papers annually, yet evidence use … Read more

Why Many Research Instruments Lack Face Validity—And How to Fix It

Why Many Research Instruments Fail Face Validity

Face validity is the most intuitive—and often the most ignored—dimension of measurement quality. If respondents cannot immediately understand what a question measures, the resulting data become fragile, regardless of high Cronbach’s alpha, AVE, or composite reliability. Poor face validity leads to misinterpretation, satisficing, social desirability distortions, and ultimately flawed statistical conclusions. In reality, many survey … Read more

Negative Results Are Strategic Assets — Use Them

Negative Results Are Strategic Assets—Use Them

Many researchers treat “null” or negative results as disappointments. But in truth, they are powerful decision fuel. They help prune weak theories, refine approaches, and guide smarter resource allocation. When reported properly, these results contribute to a more honest, efficient, and trustworthy research ecosystem. Why Negative Results Matter Preventing Publication Bias & Waste Enhancing Scientific … Read more