Necessary condition analysis (NCA)Methodology courses and philosophy of science
Number of sessions: 2
Hours per session: 7
- free for PhD candidates of the Graduate School
- €440 for non-members
- consult our enrolment policy for more information
NCA is an innovative method to assess and identify “necessary conditions” in a dataset. A necessary condition is a critical determinant of an outcome: if the condition is not in place the outcome will not occur. A student will not be admitted to a PhD program when the GMAT score is too low, creativity will not exist without intelligence, and organizational change will not occur without management commitment.
Necessary conditions are widespread in real life and therefore relevant to various research areas, such as sociology, political science, health, public administration, media and communication, and psychology.
NCA can analyse quantitative scores of potential causes and effects (numbers), or qualitative scores (symbols, words). The data may be obtained from any data collection technique, including questionnaires, interviews, observations, content analysis, archival data and physical measurements.
Advantages of NCA
Applying NCA has three main advantages:
- It provides new substantive insights in social science phenomena; using NCA provides an alternative perspective, may enhance existing research, or falsify theories;
- It has great practical meaning because identified necessary conditions must always be put and kept in place;
- Journal editors and reviewers appreciate that authors use a new solid methodology that provides new insights and contributes to academic rigor and practical relevance.
After completion of this workshop, you will:
- Explain the logic and importance of necessary conditions
- Identify necessary conditions in (your own) datasets
- Combine NCA with other research methods (such as, regression, QCA)
- Report the results in a convincing and attractive way
How to prepare (approximately 16 hours)
Preparatory reading assignments:
- Reader “Introduction to NCA” (will be provided)
- NCA software (www.erim.nl/nca).
- Dul, J. (2016a). Necessary Condition Analysis (NCA): Logic and Methodology of “Necessary But Not Sufficient” Causality. Organizational Research Methods, 19(1), 10-52. (free access)
- Dul, J. (2016b). Identifying single necessary conditions with NCA and fsQCA, Journal of Business Research, 69(4):1516-1523.
- Karwowski, M., et al. (2016). Is creativity without intelligence possible? A Necessary Condition Analysis, Intelligence, 57, 105-117.
- Vis, B. & Dul, J. (2017). Analyzing relationships of necessity not just in kind but also in degree: Complementing fsQCA with NCA. Sociological Methods and Research (in press; free access)
- You will need to bring a laptop for which you have the necessary rights to install software.
- Install the NCA software in advance (detailed instructions will follow).
- Make an individual assignment linked to own research area and own data set (detailed instructions will follow).
The logic of necessary conditions
- Format: lecture with class discussions
- Content: differences between necessity and sufficiency
Data analysis for identifying necessity conditions
- Format: lecture with class discussions
- Content: principles of NCA, comparison NCA with regression analysis, comparison NCA with QCA (Qualitative Comparative Analysis)
Applying NCA to an example dataset
- Format: Participants works on own laptop with assistance by lecturer, and class discussions
- Content: calculate ceiling line, effect size, accuracy, inefficiency, bottleneck table, interpretation of results, handling of problematic or unusual cases, issues to pay attention to.
Necessary conditions in your own research area and in own data set (or personally selected dataset)
Final assignment (approximately 24 hours)
Write a short paper for submission to a conference about testing a necessary condition hypothesis with NCA using your own dataset (or a personally selected existing dataset). The paper should include introduction, methods, results and discussion.
About the instructor
Jan Dul is a professor of technology and human factors at the Rotterdam School of Management (RSM) of Erasmus University Rotterdam (EUR). His research focuses on human factors (ergonomics) and, in particular, the interaction between people and the physical and social environment.