• Sanjukta Moorthy

Quantitative vs Qualitative: Pros and Cons

What strengths and weaknesses do each framework have? Though I dislike the binary and splitting every piece of information into words and numbers, it is the most commonly-referenced framework. I mix the two, and add other frameworks. Because as you know knowledge exists well outside these two - what about art, spoken word, music, nature, sensory knowledge and data?

But the debate about why this binary is so limiting will have to keep for another time! Here's what works and what doesn't about each, which will give you an idea about why I always mix my methods.

Quantitative methods


  • it's scalable, so you can process results from a larger set of data

  • data can be generalised. You can use data from a sample to make assumptions about patterns in a wider set of data or in the wider population

  • there's less personal bias likely in the collection and analysis phases

  • standardised approaches to quantitative data and statistics mean you can compare your results to other data

  • tech-friendly, so you can use digital devices to gather and analyse data

  • it's easy and relatively cheap to collect this data, and because it's the most traditional form of data, numbers are taken more seriously by your funder and the general public


  • reducing the world to statistics and all the typical limitations of numbers

  • the results of the data can't tell you about the depth and complexity of your people, context, or project

  • there's little room for error or agility

  • there's no room for the unexpected - positive or negative factors that affect your communities or your work

Qualitative methods


  • depth and detail about the situation, providing a rich context and painting a vivid picture

  • creates openness for people to expand their responses

  • opens up new areas of inquiry due to its agility

  • looks at peoples' experiences and why they act in certain ways

  • identifies and makes space for the unexpected

  • looks at the world beyond numbers and keeps an open framework for different perspectives on an issue


  • the results are harder to generalise to a larger population

  • this can make sampling tricky, so you may need to spend more time and money to work with a larger dataset

  • the data can be open to different interpretations, adding time to the analysis stage

  • data is susceptible to the perception biases of your analysts and evaluators