Information

What is a necessary condition?

A necessary condition is a critical determinant of an outcome: if the condition is not in place the outcome will not occur. For example, AIDS will not develop without HIV, 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. Such single conditions can be a bottleneck for the outcome. If the necessary condition is not in place there is guaranteed failure, and this cannot be compensated by other determinants. But when the condition is in place there is no guaranteed success. The condition is necessary but not sufficient. To prevent failure, each single necessary condition must be in place. Necessary Condition Analysis (NCA) provides the logic and a methodological tool for identifying necessary conditions in different datasets.

Why is NCA important?

Necessary (but not sufficient) conditions are widespread in real life and therefore relevant to various research areas, such as management, business research, sociology, and psychology. But until recently no technique was available to identify necessary conditions in datasets. Traditional (regression based) data analysis techniques fail to do so, even though they are frequently applied for exactly that matter. Necessary Condition Analysis (NCA) is a new technique that can do the job (Dul 2016). For example, it has recently been used to show that contracts and trust are both necessary for successful inter-firm collaboration (Sumo, 2014). Applying NCA has three main advantages:

• It provides new substantive insights in social science phenomena (expressed as necessity rather than average trend); using NCA provides an alternative perspective, may enhance existing research, or falsify theories;
• It has great practical meaning because identified necessary condition 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.

 What participants said about NCA?

• “Simple technique that requires no ‘preparation’, data transformation/manipulation/ correction. A perfect plug-and-play method that can give output in under 10 minutes”
• “It’s a new way of thinking and therefore it may lead to many interesting insights, just reanalyzing old dataset”
•  “Analysis provides insights that cannot be obtained with another method”
• “Insights are very relevant for practice”
•  “I do believe that is has great exploratory value that is congruent with recent emphasis on big data”

Who can attend the course?

The course is primarily open to PhD candidates and junior faculty, but also other researchers (including master students and senior faculty) who are interested in this novel approach are welcome. It is expected that each participant has at least some experience with traditional regression analysis to understand the differences between NCA and regression, and to appreciate how NCA complements regression. Also researchers with a background in Qualitative Comparative Analysis (QCA) can benefit from the course to appreciate how NCA complements QCA. No special methodological or substantive knowledge is required for this course. Examples will be drawn from many different (business) fields. Each participant is expected to bring a laptop computer with a Windows (Microsoft) or IOS (Apple) operating system. The exercises with the NCA software (in R) can be done without prior programming knowledge or experience.

What is the content of the course?

The course consists of four parts:

1. Individual preparations to be done before the course (approximately 16 hours)

• Participant reads literature about necessary conditions (reader)
• Participant installs the NCA software on own laptop (detailed instructions will be provided)
• Participant makes an individual assignment (linked to own research area and own data set)

2. Class 1 (day 1) (approximately 8 hours)

- 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)

3. Class 2 (day2) (approximately 8 hours)

- 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, practical issues to pay attention to

- Necessary conditions in own research area and in own data set (or personally selected dataset

4. Final Assignment (approximately 24 hours)

- Write a short paper about testing a necessary condition hypothesis with NCA using own dataset (or a personally selected existing dataset): Introduction, Methods, Results, Discussion.