Hey friends,
A few weeks ago, someone asked me a question after one of my workshops:
"How can I publish quantitative research?"
Most quantitative papers get rejected for the same reason. Not because the statistics are wrong. Not because the sample is too small.
But because the story isn't clear.
The first draft of my first paper looked like a report with lots of interesting numbers
It was just like a report
A good quantitative paper is more than tables, charts, p-values, and regression models
It's a logical argument supported by data.
Every section has a job to do.
Here is what I do whenever I write or review quantitative papers.
Start with a title that tells people exactly what the paper is about.
First, many titles are too vague. Instead of writing:
"A Study of Student Performance"
Tell readers what you actually studied.
Include:
• the main variables
• the population
• the study design (when relevant)
A reader should understand your paper before opening it.
And remember, your title is freely available on the internet even when your full text sits behind a paywall
Next comes the introduction.
Think of it as a funnel. Start broad
Why does this topic matter?
Then narrow the focus.
What do we already know?
What don't we know?
Where is the gap?
Finally, explain how your study helps fill that gap.
A good introduction answers one simple question:
"Why should we care about this research?"
Then comes the methods section.
This is probably the most underestimated part of a quantitative paper.
Many researchers rush through it because it feels technical
Reviewers do the opposite. They read it very carefully.
Your methods should allow another researcher to repeat your study.
That means clearly describing:
• your study design
• your participants
• your sampling strategy
• your variables
• your instruments
• your statistical analysis
The more specific you are, the more confidence reviewers have in your work.
Now comes the results section.
This is where many papers become difficult to read.
Researchers often throw numbers at the reader without explaining what they actually mean.
Remember this:
Statistics should tell a story. Don't simply report a correlation coefficient
Explain what that relationship means
Don't only present averages. Explain what readers should notice.
Good figures and tables also make a huge difference. A clear graph often communicates more than three paragraphs of text
Finally, the discussion
This is not where you repeat your results. This is where you explain them
Ask yourself:
So what?
What do these findings actually mean?
How do they compare with previous studies?
Why are they important?
What can researchers, practitioners, or policymakers do differently because of your findings?
And yes, almost every paper ends by saying:
"More research is needed."
Instead, be specific.
What research? On whom? Using which methods?
Good recommendations create the next research project.
Over the years, I have noticed something interesting.
Strong quantitative papers almost always have one thing in common.
They are easy to follow. The statistics may be complex. The writing isn't.
The reader always knows where the paper is going.
here's a simple checklist to follow
• Does your title clearly describe the study?
• Does your introduction explain the research gap?
• Could someone replicate your methods?
• Do your results explain the story behind the numbers?
• Does your discussion explain why the findings matter?
Well, that's all for this week.
I would love to hear from you – any recent experience with qualitative research?
See you next week,
Jamal