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
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 Big Data Isn’t Automatically Unbiased In today’s research world, “big data” is often portrayed as objective, comprehensive and neutral. But the truth is different. Data sets are shaped by human decisions: what to include, what to exclude, how to categorise, what to prioritise.When the assumptions baked into collection or algorithms go unexamined, bias creeps … Read more
In research, content analysis is one of the most powerful methods for decoding meaning from text, media, or communication. Yet many studies collapse under one silent flaw — poor intercoder reliability (ICR). Most researchers assume that once coders agree “most of the time,” their analysis is valid. Unfortunately, that’s a misconception. True intercoder reliability is … Read more
