Chapter 1 Summary of NCA

NCA is based on necessity causal logic. If a certain level of the condition is not present, a certain level of the outcome will not be present. Other factors cannot compensate for the missing condition. The necessary condition allows the outcome to exist, but does not produce it. This is different from sufficiency causal logic where the condition produces the outcome.

Conventional theories and methods are based on additive logic. This logic assumes that several factors contribute to the outcome and can compensate for each other. For example, conventional quantitative methods like multiple regression analysis and structural equation modeling describe the complexity of different contributing factors producing the outcome. In these models a low value of one factor can be compensated by a higher value of another factor. When making causal interpretations with these models, factors are usually interpreted as being sufficient causal factors that can help to produce the outcome.

Conventional quantitative methods do not cover necessity logic and are thus not able to identify necessary conditions in data sets. This was the main reason for developing NCA. NCA ensures theory-method fit when the theory includes necessity relations between factors and outcome and these relationships need to be evaluated empirically.

Conducting NCA consists of four stages:

  1. Formulate the necessary condition hypothesis.

  2. Collect the data.

  3. Analyse the data.

  4. Report the results.

Each step is explained below in more detail in sections 1.3, 1.4, 1.5, and 1.6.

In research projects and publications, NCA can be used as a stand-alone method or be used together with conventional methods such as multiple regression analysis, structural equation model, or QCA. The decision to apply NCA as a stand-alone method or a complementary method depends on the goal of the research.

1.1 NCA as stand-alone method

A researcher may have two reasons to use NCA as a stand-alone method in a particular study. First, the researcher may want to employ just a necessity view on the phenomenon of interest. He may have formulated a parsimonious ‘pure necessity theory’ (see Chapter 2) consisting of one or more necessity relations (e.g., Karwowski et al., 2016; Knol et al., 2018). To ensure theory-method fit, NCA is used for testing pure necessity theories. A sufficiency-based method is not appropriate for this purpose. [Similarly, when a researcher wants to test sufficiency theory consisting only of sufficiency relations (most current theories), a conventional sufficiency-based method should be selected and NCA is not appropriate]. A second reason for using NCA as the stand-alone method is that the researcher may want to add a necessity view to an existing sufficiency theory from the literature. Without (re)testing the sufficiency relationships, she may want to test whether some concepts of the sufficiency theory are (also) necessary conditions.

The advantage of using NCA as a stand-alone method is that the study and its theoretical reasoning can focus on the single necessary concepts. There is no need to include other concepts (e.g., contributing factors, control variables) into the reasoning and analysis. This allows a clear storyline and an efficient data collection and analysis.

1.2 NCA as a complementary method

A researcher may also have reasons to use NCA in combination with other methods in a single study. The researcher may want to add a necessity view to a sufficiency theory and to analyse necessity and sufficiency relationships in combination. This can be done in two ways. First, the sufficiency theory is leading and some concepts from this theory are tested for necessity. Second a combined necessity-sufficiency theory is developed with both necessity and sufficiency relationships between concepts.

In the first option, an existing or new sufficiency theory has concepts that are considered being contributing factors to the outcome (including control variables). From these concepts, potential necessary conditions are selected. The sufficiency and necessity relationships of these concepts with the outcome are tested with the appropriate sufficiency-based and necessity-based method, respectively. The methods are conducted successively. The integration occurs when the results are discussed. Several NCA multimethod studies that start with a sufficiency theory have been reported in the literature. For example, when NCA is used in combination with (multiple) regression (e.g., Jain et al., 2021; Klimas et al., 2021; Stek & Schiele, 2021) or structural equation modeling (e.g., Della Corte et al., 2021; Lee & Jeong, 2021; Renner et al., 2022; Richter, Schubring, et al., 2020) “important” factors are identified with the sufficiency-based method, and the necessity of these and other factors is analysed with NCA for identifying whether the factors are necessary or not (see section 4.3). When NCA is used in combination with Qualitative Comparative Analysis- QCA (Kopplin & Rösch, 2021; e.g., Torres & Godinho, 2021) the results of NCA are compared to the sufficient configurations that are identified by QCA (see section 4.4).

In the second option, the researcher starts the study with a combined necessity-sufficiency theory. This is a complex ‘embedded necessity theory’ (see Chapter 2) that consists of both necessity and sufficiency relations from the start. Some concepts may only have a sufficiency but not a necessity relationship with the outcome, other concepts may be necessary and not sufficient, and yet others may be both necessary and sufficient. Embedded necessity theories that combine necessity and sufficiency theorizing for describing relations are still rare. In QCA, most studies theorize from the perspective of sufficiency and test the potential necessity of the single factors, but do not include necessity theorizing from the start. Also, most studies that combine NCA with regression or structural equation modeling do not theorize necessity and sufficiency in combination (for an exception see Dul, 2019). Testing embedded necessity theories requires a multimethod approach with both sufficiency-based method and a necessity-based method.

When combining NCA with regression analysis or QCA, the order of conducting NCA and the other method is not relevant. When NCA is used in combination with structural equation modeling (SEM) the order matters. First SEM is applied followed by NCA. The reason is that the outcome of the SEM measurement model is used to define the constructs to be tested with NCA.

1.3 Formulate the necessary condition hypothesis

NCA starts with a theoretical notion that a necessity relation may exist between \(X\) (the potential condition) and \(Y\) (the outcome). This is usually done by formulating and justifying a hypothesis that is part of a theory (see Chapter 2).

NCA is mainly used in theory-testing research, which starts with formulating the theory and proceeds with empirical testing the theory with data. Hence, theory formulation comes before data collection and analysis. This is also the focus in this book. However, NCA can also be used in theory-building and exploratory research. Then formulating the theory is based on the results of the data analysis, and thus comes after it (e.g., Stek & Schiele, 2021).

1.4 Collect the data

Collecting data in NCA is not different from collecting data in general. The goal of data collection is to have scores (values, levels) for the condition \(X\) and the outcome \(Y\) for each case. The selected research design (e.g., ‘experiment,’ ‘survey,’ ‘case study’) must meet common quality standards. Also the selection of cases for measurement and data analysis must fit the goal of the research (e.g., random sampling, purposive sampling for specific reasons). The data must be ‘good.’ This means that the data must be valid (the measurement scores reflect what they are intended to reflect) and reliable (when measurement is repeated, the results are the same).

NCA has no new requirements on collecting the data. There are a few exceptions. First, the setup of a necessity experiment is different than the setup of the common sufficiency experiment (see section 3.1). Second, in certain situations it is possible to sample just a single case to perform NCA (see section 3.2). Third, the way that NCA is conducted may differ depending on the types of data that are used: quantitative data (section3.3), qualitative data (section 3.4), longitudinal data (section 3.5), and set membership scores (section 3.6). Furthermore, the identification of potential outliers is partly different from the common way of identifying outliers (see section 3.7).

1.5 Analyse the data

under construction

1.6 Report the results

Two types of NCA reports exist: methodological reports and application reports. In methodological reports, based on existing general methodological NCA publications (e.g., Dul (2020) and this book), the method or part of the method is introduced in a specific field, often illustrated with examples from that field. Introducing the NCA method in a new specific field has added value if it can be explained:

  • Why necessity logic is particularly useful for the field in general and for specific topics and challenges in the field (e.g., a list of potential topics/challenges that can benefit from NCA).

  • That necessity thinking already (often implicitly) exists in the literature of the field (e.g., a list of necessity statements from the field).

  • How NCA is different from conventional methods (e.g., by explaining the steps of NCA –theory, data, data analysis, interpretation–, by referring to an example from the field).

  • How NCA and can give new insights to the field (e.g., an example of applying NCA to a topic from the field).

In NCA application reports the focus is on better understanding a specific phenomenon, and necessity logic and NCA are used to serve that goal. The NCA-specific parts of these application publications are (for details see Dul, 2020):

  • Introduction/theory: introduction of necessity logic/theory.

  • Methods: Description of NCA’s data analysis approach.

  • Results: Presentation of scatter plots and NCA parameters (e.g., effect size, p value).

  • Discussion: Description of the importance of including identified necessity in theory and practice (e.g., to avoid failure of the outcome or waste of efforts).

In application publications NCA can be used as a stand-alone method (section 1.1) or in combination with other methods (section 1.2).

Although NCA has already been broadly applied, there are still many fields where the NCA method has not been introduced and applied. Most likely, NCA could be applied in any of the 249 research categories defined by the Journal of Citation Reports. But currently, only about 25% of the categories are covered, with the largest number of publications in the two categories ‘management’ and ‘business.’

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