CYGNA: Necessary Condition Analysis: What, Why and How?

Reports on our 58th CYGNA meeting where we were enthralled with an emerging powerful methodology that both qualitative and quantitative researchers can use

After a lovely Christmas meeting (CYGNA: Our 4th Christmas meeting - failure & fun) in December and a wonderful on-site meeting on sustainable academic careers organised by the CYGNA North team in January, we decided it was time for a meeting focusing on developing new skills, and more in particular necessary condition analysis (NCA). 

We have done quite a few of these skills meetings over the years (e.g. MBTIbig datasocial mediasocial network analysisnegotiating bettersecondary datamanaging research networks) and it is always great to refresh one's methodological and personal skills toolkit. It is even better to be able to do this in a safe space where there is no such thing as stupid questions.

We were very pleased that 36 CYGNA members joined us to learn about this topic, 32 of which are included in the group picture above; some unfortunately had to leave before this was taken. The meeting included quite a lot of first-timers: Edume Iñigo, Mitaali Katoch, Nada Elnahla, Olivia Tomlinson, Pinkie Mthembu, Ruth Christina Roy, and Siyi Chen. Welcome to CYGNA and we hope it will be an "academic home away from home" for you.

Nicole Franziska Richter (University of Southern Denmark) and Tatiana Andreeva  (Maynooth University, Ireland) did an absolutely brilliant job in a flawless duo-presentation to initiate us into the logic of NCA. You can download their full presentation here. It received uniformly positive feedback and the chat was overflowing with ideas and questions.

In this blogpost Tatiana provides you with an overview of the key topics we discussed and resources that may help you if you want to apply NCA to your own study.

What is Necessary Condition Analysis?

You most likely have seen in the literature statements about necessary conditions – e.g., such as “critical success factors”, “prerequisites” or “bottlenecks”. Until recently, we had no method to evaluate these claims. But now we do: It is called Necessary Condition Analysis (NCA). It is applicable to any discipline and can be used by both qualitative and quantitative researchers.

Theorizing first, technicalities follow

One of the questions asked during the session was about the challenges we faced in learning and applying the method. Both Nicole and I agreed that the technicalities of the necessary conditions analysis are quick and easy to learn. For me, the most challenging part was to shift the mindset in theorizing from the sufficiency logic we are so familiar with (e.g., “the more, the better”), to necessity logic.

Indeed, NCA is not just another method of analysing the data, it is – first and foremost – a different way of thinking about your research problem. Unlike the more common, sufficiency way of thinking, necessity logic focuses on predicting the absence of the outcome.

Therefore, at times I found it challenging to integrate existing literature into my theorizing – until I realized that if it uses a different logic to the necessity one, I should not even try to integrate it. To this end, I personally find Table 1 from Richter & Hauff (2022) particularly useful to support developing arguments about necessary conditions.

To illustrate that NCA is first and foremost about theorizing - it can be used as a lens for conceptual or literature review papers. Yes, you do not necessarily have to work with empirical data to use NCA! Liehr & Hauff (2022) provide an interesting example of the NCA-focused literature review.

When to use Necessary Condition analysis?

NCA answers a different type of research questions compared to other methods of data analysis so it can open new avenues in your research and complements rather than substitutes other methods. To illustrate this point, Nicole shared an example of her research that combines NCA with regression-based methods (for details, check Richter & Hauff, 2022 and Richter et al., 2020).

I find Table 1 from Richter et al. (2020) particularly useful to demonstrate how to interpret different scenarios that may emerge from using NCA and regression-based methods in conjunction. NCA can be also used as a stand-alone method – it all depends on the research questions you’d like to explore.

How large should my sample be?

The question about the sample size is one of the frequently asked questions about NCA, especially by qualitative researchers, who typically think that statistical methods of analysis cannot apply to their data due to sample size limitations. The short answer to it – at least one, but the more the better. Yes, it’s not a typo – depending on your research question and context, your sample size can start from one! (see Allard-Poesi & Dul (2023) for an illustration).

The longer answer is that while the method itself does not put any specific limits on the sample size, there are important aspects to consider in your research design, in particular, in your approach to sampling, to ensure that your NCA findings are meaningful and valid. Dul (2024) provides clear guidelines on how to approach these, for both qualitative and quantitative studies.

NCA is not for academia only

What I really appreciate about NCA, is that – because it identifies “bottlenecks” – its results are usually of high relevance and interest for practitioners. Hence, using NCA as a lens helps researchers to speak to practice and deliver meaningful and actionable insights for managers.

Moreover, as NCA is an easy-to-learn and apply tool, it is very accessible for practitioners themselves. For example, NCA website offers a basic NCA calculator that you can do a quick first analysis of your data in seconds, without the need to install or master any specific software and identify where your main roadblocks are.


Here are several resources on Necessary Condition Analysis that you may find useful:

Core article that describes the method

Top journal papers on application of NCA in different management fields

Step-by-step guidelines / tutorials on how to apply the method

Examples of using NCA in a less common way

Related video

Related pages