This post is part of the continuing mini-series on RQ–RH–D–M across fields. Its purpose is to provide a compact, practical toolkit showing how research questions, research hypotheses or working propositions, data, and methodology can be aligned in one specific discipline. Archaeology is especially suitable for this exercise because it naturally combines material evidence,…
When Mixed Methods Are Mixed in Name Only
Why using both qualitative and quantitative data is not enough if the study never truly integrates them Opening: having two strands is not the same as having one study A study does not become mixed methods merely because it contains both numbers and narratives. Researchers often assume that if a…
When Qualitative Evidence Is Too Thin
Why weak interviews, weak observation, and low analytic yield belong to the same research-design problem Opening: the transcript is long, but the evidence is still thin One of the most misunderstood problems in qualitative research is the belief that a large amount of material automatically counts as strong evidence. It…
When the Method Does Not Fit the Question
Why even a well-executed method weakens a study when it serves the wrong research purpose Opening: the method looks impressive, but the study still misses its target One of the most common mistakes in empirical research is not using a “bad” method, but using the wrong method for the question…
When Data Are Collected Without Design Logic
Why weak sampling, weak case selection, and convenience evidence belong to the same research-design problem 1. Why these mistakes belong together This cluster looks diverse on the surface. Sampling problems are often discussed in quantitative research. Case selection is usually treated as a qualitative issue. Convenience evidence appears across both…
When Concepts Are Poorly Operationalized into Evidence
How strong ideas become weak studies when concepts are translated badly into indicators, variables, and measures 1. Opening: the study sounds impressive, but the evidence is thin Many studies fail not because the topic is weak, but because the central idea is translated badly into evidence. The researcher begins with…
When the Question, Hypothesis, and Variables Do Not Align
How one early design mismatch spreads across the whole study 1. Why these mistakes belong together Some research mistakes arrive alone. This cluster does not. When the research question, hypothesis, and variables do not align, the problem is rarely isolated to one sentence in the proposal. It usually reflects a…
When the Research Question Is Too Broad, Vague, or Overloaded
The upstream design mistake that quietly destabilizes the rest of the study Opening: the first mistake often happens before the study really begins Many weak empirical studies do not begin with a bad dataset or an inappropriate analytic technique. They begin with a research question that is trying to do…
Why Smart Research Still Goes Wrong
A new mini-series on the design mistakes that quietly undermine quantitative, qualitative, and mixed methods studies Why this series? Many research projects do not fail because researchers are careless, unintelligent, or unfamiliar with statistical software. They fail much earlier and much more quietly: at the level of research design. A…
Why Linear Mixed Models Are Usually the Better Choice for Pre–Post Experimental and Control Group Data
A student’s proposal, and a familiar methodological problem A student recently contacted me with a thesis proposal built around a very common design: one experimental group, one control group, and two measurements for each participant, one before and one after the intervention. The student proposed to analyze the data using…