- This paper investigates the contribution of evidence-based
modelling to grounded theory (GT). It is argued that evidence-based
modelling provides additional sources to truly arrive at a theory
through the inductive process of a Grounded Theory approach. This is
shown by two examples. One example concerns the development of software
ontologies of criminal organisations. The other example is a simulation
model of escalation of ethno-nationalist conflicts. The first example
concerns early to middle stages of the research process. The
development of an ontology provides a tool for the process of
theoretical coding in a GT approach. The second example shows stylised
facts resulting from a simulation model of the escalation of
ethno-nationalist conflicts in the former Yugoslavia. These reveal
mechanisms of nationalist radicalisation. This provides additional
credibility for the claim that evidence-based modelling assists to
inductively generate a theory in a GT approach.
- Grounded Theory, Evidence Based Modelling, Theoretical Coding, Ontologies, Stylized Facts, Theory Development
- In recent years, research in computational social science
has evolved in a way that indicates a certain stage of scientific
maturation. While an initial phase has been characterised by an
experimental stance to explore the possibilities of a new
methodological tool (Deffuant et
al. 2006), attention has now shifted to questions of
empirical credibility of simulation models (Lorscheid
et al. 2012). The purpose of this paper is to contribute to
the research area of a cross-fertilisation between simulation and the
standard methods of empirical social research (Squazzoni 2012). However,
while Lorscheid et al. (2012)
draw on the design of experiments of the classical quantitative
approach in order to enhance the credibility of the analysis of
simulation models, this paper aims at exploring additional sources of
empirical credibility, which evidence-based modelling approaches can
provide to the qualitative account of grounded theory (GT).
- GT is an inductive process of qualitative social research.
It is often questioned whether or not such research might generate
theories. The main thesis of this paper is that the use of simulation
models in an evidence-based modelling approach contributes to arrival
at a theory within a GT framework. For this purpose, the paper will
focus on two main arguments: first, it will be
shown that the research process of evidence-based modelling shares a
number of parallels with the GT approach. This is a rather
unproblematic thesis. However, the parallels can be strengthened even
further if experiences and guidelines of GT approaches are taken into
account explicitly in a research process of evidence-based modelling.
This thesis addresses primarily ICT specialists working in the field of
evidence-based modelling. Following the research programme outlined by
Lorscheid et al. (2012),
it should contribute to an integration of evidence-based modelling into
the canonical methods of empirical social research. Second,
a more surprising thesis may be that evidence-based simulation provides
methodological tools to strengthen the theoretical element of a GT. The
objective of this argument is to inform specialists in the field of GT
about the possibilities of a methodological cross-fertilisation between
GT and evidence-based simulation; that is, to illustrate that GT can
benefit from utilising simulation models—in particular, their formal
precision and explicit representation of social dynamics—in its
research process, as opposed to merely adding another tool to the
toolbox of empirical research methods.
- The paper is organised as follows: firstly, an overview of element of theory is provided. This allows the assessment of whether or not GT research suggests a theory. Secondly, an overview of evidence-based modelling is provided. It follows an overview of the methodology of the GT approach. Thirdly, a comprehensive discussion of the contribution of simulation tools and evidence-based modelling to the GT approach is provided. The discussion builds on two examples. The first example, which concerns the study of organised crime, discusses how so-called theoretical coding benefits from knowledge representation in software ontologies. The second example is an evidence-based model of nationalist radicalisation, which demonstrates how simulation results provide additional explanatory power to a GT. Particular emphasis is put on the notion of stylised facts. Finally, the paper ends with concluding remarks.
Elements of a theory
- It often remains precarious whether any qualitative
empirical study is merely a description of a certain phenomenon, or if
it can be claimed as truly a theory (Corbin
& Strauss 2008; Strübing
2004). Within a qualitative approach, developing a theory
might not even be the aim of research: 'In fact, theory development
these days seems to have fallen out of fashion, being replaced by
description of "lived experience" and "narrative stories"' (Corbin & Strauss 2008,
p. 55). This raises the question of what, if it exists at all, is a theory
in a GT approach. Strauss and Corbin (1998) describe
their notion of theory as follows: 'For us, theory denotes a set of
well-developed categories ... that are systematically related through
statements of relationship to form a theoretical framework that
explains some relevant ... phenomenon.' (Strauss
& Corbin 1998, p. 22). To this end, 'final
integration is necessary. Without it, there might be some interesting
descriptions ... but no theory' (Strauss
& Corbin 1998, p. 155). However, the notion of
integration needs further clarification. It remains ambiguous insofar
as such integration achieves a generalisation from the actual data, or
remains at a level of an 'organization of data in discrete categories' (Corbin & Strauss 2008,
p. 54). Corbin and Strauss (2008,
p. 56) discuss this issue by using the example of an examination of gay
disclosure / nondisclosure of information about their sexual identity
to physicians, which might be expanded to a more general theory of
information management, which encompasses a certain ideal type of human
interaction (Weber 1968).
Thus an explanation involves some kind of generality. This can be
identified as the first element that a theory has to fulfil:
a) Generality. The object of a theory needs to be more than the description of a single phenomenon. Rather, a theory should explain a certain set of phenomena. In order to not be overly restrictive, we will also include middle-range theories of a limited set of phenomena.
- However, this first element of theory characterisation
already implies a further condition: namely, that a theory should
explain something. This refers to the question of what an explanation
actually is. In the social sciences and particularly in qualitative
research, typological classifications, such as the concept of an ideal
type, have a prominent place in scientific research. Typologies have
some kind of generality, insofar as they allow the subsuming of
individual cases into a certain category. However, it remains contested
whether a typological categorisation is sufficient for an explanation
of a phenomenon (Hedström 2005).
As highlighted also by Strauss and Corbin (1998)
that a theory should explain a certain phenomenon, a further element of
theory is that something (i.e., the explanans), explains something else
(i.e., the explanandum). Thus the aim of theory development in social
science is to identify social processes from subjective experiences,
rather than only describing subjective experiences. To provide an
explanans for an explanandum can be characterised as a further core
element of a theory:
b) Explanans and explanandum. A phenomenon X (the explanans) should be identified that explains a different phenomenon Y (the explanandum), which is the subject of scientific inquiry.
- However, identifying explanans and explanandum of an
explanation still leaves space for a number of diverging accounts of
how they are related. The classical concept of philosophy of science is
the deductive-nomological (DN) model (Hempel
& Oppenheim 1948). According to this scheme, a
phenomenon (the explanandum) is explained by a hypothetical theory (the
explanans), if the hypothesis, taken together with specific boundary
conditions of the individual circumstances of a certain case, allows
deducing the phenomenon (i.e., the explanandum). For instance, the
Newtonian laws and the specific knowledge of the mass of the moon and
its position on the day X at midnight, allows deducing its position at
midnight the next day.
Hypothesis A Boundary conditions C Phenomenon B
- The above model of an explanation has the form of a logical
deduction. Note that in this account a theory is only of hypothetical
character, leaving room, for example, for Popper's falsificationism (Popper 1935). The philosophical
debates about the DN model and its decline since the 1960s will not be
pursued here. However, one element of the debate shall be highlighted
because of its relevance for the analysis of social processes, namely
the question of causal mechanisms. A logical implication need not
capture the mechanisms that connect explanans and explanandum. The
following example is based on Salmon's (1989)
famous logical deduction:
Hypothesis: All people taking the pill will not become pregnant Boundary Condition: Steve takes the pill Phenomenon: Steve does not become pregnant
- While this is a correct syllogism, taking the pill is
obviously not the cause of why Steve does not become pregnant. It
overlooks the intervening variable of sex. Thus simple logical
deduction is not enough. One of the various proposals to achieve a
meaningful explanation is the mechanisms approach. Following this
account implies a further condition: that an explanation of an
empirical phenomenon needs to include the mechanisms that connect
explanans and explanandum (Hedström
2005). The debate regarding the definition of mechanisms in
the social world cannot be reviewed here (Hedström
& Ylkoski 2010). A common element of all definitions
of mechanisms is that they include the transform an input x into an
output y with a certain degree of regularity. This
reflects the intuition about causal processes: that, in contrast to
accidental coincidences, a causal relation is characterised by the fact
that similar inputs generate similar outputs.
c) The relation between explanans and explanandum should reveal a mechanism. A relation between an input X and output Y is a mechanism if under similar circumstances a similar input X* reveals similar outputs Y*. Note that this thesis is not universally accepted.
- This section will provide an overview of core principles of
evidence-based modelling. Evidence-based modelling is an umbrella term
for a number of approaches such as participatory modelling, companion
modelling, and others that evolved over the past decade. Edmonds and
Moss (2005) coined the
term 'KIDS principle' (Keep it descriptive, stupid) for such a
modelling account. The common feature of the various methodologies in
this research field is that they follow a descriptive account, rather
than being based on a priori theoretical assumptions.
- The central assumption of this modelling strategy is that
detailed common sense descriptions allow for more valid statements
about the target systems than do analytic propositions. Simplicity is
not an aim in evidence-based modelling. While simple models, such as
game-theoretically inspired ones, can be analysed more easily, it might
be questionable whether the results can provide meaningful information
about a target system (Edmonds
& Moss 2005). In complex systems, it remains
ambiguous which details of the systems' components might be relevant to
the systems' behaviour. For this reason, Edmonds and Moss (2005) suggest keeping models
of systems as empirically descriptive as possible, rather than relying
on a priori theoretical assumptions. To achieve a detailed description,
every source of evidence that can be gathered from the empirical field
should be considered in the model-building process. Empirical evidence
might take the form of classical statistical data, but also includes
field observations and qualitative interviews, stakeholder knowledge (Barreteau 2003; Funtowicz & Ravetz 1994),
textual data, audio and video files, and anecdotal evidence (Edmonds & Moss 2005; Yang & Gilbert 2008).
These methods for data collection, derived from classical qualitative
research, provide additional sources of evidence. In particular, these
methods enable the integration of evidence about mental concepts, i.e.
the meanings that participants ascribe to events, which is outside the
scope of purely quantitative data (Yang
& Gilbert 2008). To build model assumption on this
evidence allows for a micro-validation of behaviour rules of the agents
already involved in the process of the model development (Moss & Edmonds 2005).
Only on the basis of a complex model can it later be decided which
kinds of simplification preserve the properties of the system (Edmonds & Moss 2005).
- Moreover, in participatory accounts the processes of model
building, data gathering and model analysis are not separated, as is
suggested by classical hypothesis testing. In contrast, modelling is a
'bottom-up process', meaning that it is an iterative process, cycling
between modelling and field work (Barreteau
2003). Data generation in participatory accounts includes
stakeholders who are involved in the process of model development. The
process commences with initial stakeholder meetings to arrive at a
basic first model, which is then presented to the stakeholders a second
time in order to gather additional information from the stakeholders.
This additional information is then again input for a revision of the
- The descriptive account is partly supported by the fact
that, in contrast to analytical methods of mathematical modelling,
agent-based modelling allows for a rule-based modelling approach (Yang & Gilbert 2008).
Rule-based modelling enables the implementation of a detailed
description of individual decisions and actions on a social
micro-level, within the rules of the model code (Lotzmann & Meyer 2011).
This technical feature allows that models can 'get away from numbers' (Yang & Gilbert 2008), by
replacing numbers with verbal descriptions in the code. The translation
of empirical field notes into a computer model enables discovery of a
system of rules in the empirical data. This implies that the
transformation of field notes within the rules of a model code is a
process that involves increasing abstraction to gain a consistent and
coherent representation of the most salient features of the target
system. The rules provide a core concept of the mechanisms at work in
the target system.
- In summary, evidence-based modelling applies a research methodology both iterative and inductive, starting from an idiographic description of the field of investigation. Thus the research process does not follow the distinction between logic of discovery and logic of confirmation. Moreover, it shall be noted that simulation models in general enable an analysis of processes, because simulation consists of the observation of the dynamics of the model in simulated time steps. A simulation run allows the generation of statistical patterns that can be observed in the empirical data. This enables a qualitative and quantitative cross-validation (Moss & Edmonds 2005): a qualitative validation in the process of model development, and a quantitative validation in the analysis of simulation results.
- Next, a brief overview of GT will be provided. The
objective of this paper is not to examine the diverging variants of GT
approaches (Kelle 2005).
For this reason, only central tenets will be highlighted briefly. A
comparison between GT and evidence based modelling will demonstrate
that the core assumptions of evidence-based modelling reflect central
tenets of GT, motivating an attempt of a more systematic utilisation of
- The term 'Grounded Theory' is slightly misleading, since it
is not a classical 'grand' theory, for example, a sociological systems
theory; nor is it a middle-range theory of a certain phenomenon, for
example, a theory of deviant behaviour. Rather, it denotes a certain
methodological advice the stimulates the generation of
theories (Flick 2002). The
aim is not to test hypotheses as in the classical design of
experiments, but instead to develop new theory by revealing hidden
structures and relations in the data. Thus the research does not start
with a theory that is subject of hypothesis testing, but instead with
a detailed description of the field, from which relevant insights do
not emerge until later stages in the research process. This research is
conducted through an iterative process, in which data collection and
analysis exist in a reciprocal relationship. The analysis of data
should stimulate new questions posed to the data, which in turn
stimulates new collection of data. Thus data collection is not a random
sample, but rather a so-called theoretical sampling that is already
guided by questions that have emerged during analysis. The process of
switching between data collection and data analysis should be iterated
until a stage of theoretical saturation is reached (Struebing 2004).
Theoretical saturation is reached when further data reveals no further
insights, indicating that the existing categories, which have been
uncovered during the research process, are comprehensive (Goulding 2002; Locke 2001). This first sketch
enables to highlight the following parallel between GT and
- Like evidence-based modelling, GT is an inductive approach to studying social phenomena.
- Both methodologies commence research with a detailed description of the field.
- The concept of theoretical saturation parallels the iterative account in companion or participatory modelling approaches (Barreteau 2003), in which the process of model development is constantly informed by the expertise of stakeholders and vice versa.
- However, distinctive differences shall also be mentioned:
- Simulation enables a representation of the dynamics of social processes.
- Representation of concepts in a computer code for simulation requires a high degree of formal precision.
- GT aims at inductively reaching a theory. In the process of
theory development, the notion of theoretical coding is of central
relevance, as it describes the process of building categories from the
data. The following overview will show that this process corresponds to
the process of developing model assumptions in an evidence-based
- While extracting categories from data has also been denoted
as an art that should not be reduced to a technical execution of
concrete instructions (Corbin
& Strauss 2008), typically elements are denoted as line
coding, focused coding, axial coding and selective
coding (Flick 2002).
For the purpose of this paper, these will be characterised briefly. The
first elements, line coding and focused coding, are closely oriented at
the data. In line coding, single lines of text are assigned to a code
that describes the characteristics of the data, whereas in focused
coding, larger text units are assigned to a code. These two
data-oriented stages are also denoted as open coding. GT is
particularly well-known for the so-called in vivo coding method, which
uses the direct words of the research participants to describe a
category. For instance, in a current research on criminal
organisations, a criminal described the conditions, in which he had
found himself, as a 'rule of terror', which provides a vivid picture of
a war in the underworld. However, beginning with open coding, a process
of increasing abstraction is initiated to integrate the empirical
detail into a coherent picture. Axial coding encompasses the dimensions
of and relation between the codes, and selective coding aims to analyse
the story line that explains the phenomenon, for instance by
identifying the core category or contrasting cases (Corbin & Strauss 2008; Flick 2002). Thus theoretical
coding involves the process of building categories from key terms and
relations in the data, by an increasing abstraction from detail.
Categories do not denote the individual phenomena, but instead relate
certain groups of phenomena into a single concept, that is, they denote
a set. 'Concepts that reach the status of a category are abstractions.
They represent the stories of many persons or groups reduced into …
highly conceptual terms' (Corbin & Strauss 2008, p. 103).
Furthermore, the set of categories is embedded in a web of relations
that describe the properties of the categories in various dimensions.
In the end, the categories themselves might become rather abstract. For
instance, in a study on Vietnam veterans, Corbin revealed that
categories such as 'culture of war' or 'changing self' from the
interviews (Corbin & Strauss
2008) comprised 'physical, psychological, social and moral
problems' (Corbin & Strauss
2008, p. 266) inherent in the phenomenon of war. The research
commenced with a detailed coding of a single interview with volunteers
who worked in an evacuation hospital; it was then enriched by more
interviews with war participants who had been involved in other wartime
situations, for instance, direct combat on the battle fields.
Collectively the set of interviews enabled the development of abstract
categories such as 'changing self'. This parallels evidence-based
modelling, which begins with a detailed description (line coding) for
dissecting rules as mechanisms of salient features of the domain (axial
coding), and only later reaches abstraction (selective coding). Thus
the process of generating model assumptions in evidence-based modelling
can be denoted as a variant of theoretical coding.
- The process of theoretical coding is the central element of
how GT aims to embrace a theory, rather than merely
describing a phenomenon. Representing groups of stories in abstract
conceptual terms fulfils the criterion of generality
in a theory, that is, the criterion 'a)' in this paper. Nevertheless,
the relation between theory and description remains ambiguous. It is
not guaranteed that simply following rules will generate a theory. For
instance, one might fail to identify the core categories, or stick too
closely to the data and retain a more descriptive account. Relying on
the parallel between evidence-based modelling and theoretical coding,
the following examples from ongoing research will be presented, in
order to demonstrate how software tools for knowledge management assist
the process of theoretical coding. For this purpose, software
ontologies will be highlighted.
- Ontologies are used in information systems and knowledge
engineering for purposes of communication, automated reasoning, and
representation of knowledge. In particular, the emergence of the World
Wide Web generated the need for methods to extract information from a
huge body of data (Gruber 2009).
An ontology is defined as a formal, explicit specification of
a conceptualisation (Guarino
et al. 2009; Studer et
al. 1998). The conceptualisation represents concept classes,
which might include a hierarchy of subclasses. A conceptualisation
consists of a triple C: <D, W, R> with D as the universe
of discourse, W the set of possible worlds, which is the maximal set of
observable states of affairs, and ( as the set of conceptual relations
on the domain space <D,W>. The universe of discourse is
the domain of the ontology. Possible worlds represent possible
applications. The structure is a relational structure, as it includes
the relationships between the objects in the domain. The notion of
conceptualisation can be defined as a set of representational
primitives, typically classes, attributes, and relationships, with
which to model a domain of knowledge (Gruber
2009). Here we will draw attention to the fact that automated
reasoning requires the knowledge to be edited with formal precision, so
as to be manageable for computers. For this reason, the development of
an ontology is the mediating step between data analysis and simulation,
by identifying key terms and relations of the domain (Diesner & Carley 2005;
Hoffmann 2013) to
increase the transparency of the derivation of simulation results (Livet et al. 2010). Thus
ontologies concern early and middle stages of the research in the
developmental process of model assumptions.
- The following examples will show how the development of an
ontology can support the processes of theoretical and open coding in a
GT process. The formulation of an ontology enables first a
formal precision and coherence of the description of the domain of
study. Second, utilising software tools such as
Protégé (http://protege.stanford.edu/download/ontologies.html) enables
automatic reasoning to inspect the implications of the logic system.
This in turn enables an examination of the relations that describe the
mechanisms of the processes driving the system. Third,
the reference to classes (Gruber 2009)
parallels the development of categories in a GT approach as sets, which
relates certain groups of phenomena. By defining the sets, the formal
precision contributes to theory development by assisting the
generalisation of empirical findings.
An example of the contribution of ontologies to theoretical coding
- The following example will demonstrate how ontologies
contribute to the process of theoretical coding. It is drawn from
ongoing research, which aims to investigate the global dynamics of
extortion racket systems (ERS). The purpose is to develop a simulation
model for understanding the dynamics of ERS such as the South-Italian
Mafia. A first step in the development of a simulation model is the
development of an ontology that provides the key terms and relations.
Thus we need to define a space of discourse (D) and a set of relations
(R) for our conceptualisation of ERS. This is based on a detailed
analysis of the operations of the Cosa Nostra (the Sicilian Mafia) in
the Sicilian Society (Scaglione
2011). In return for extortion money requested from the
entrepreneurs, the Mafia offered them private protection and
established a monopoly of violence (Franchetti
1876; Gambetta 1993),
owing to a weak state authority and a lack of civil society. With
regard to the question of what ontology development contributes to the
process of theoretical coding, it needs to be emphasised that ontology
development is an inductive and iterative process of code refinement,
starting from data analysis to ontology development and back to the
data. Ontology development enables the identification of gaps in the
data basis, which suggests the need to gather new data. Data analysis
and ontology development are a recursive process.
Figure 1. Reciprocal relation between ontology development and data analysis
Ontologies for theoretical coding
- The ontology (we might call it 'pizzo ontology', as pizzo
is the Italian word for extortion money) had been
developed using the Protégé development tool (http://protege.stanford.edu/).
To show how ontology development supports theoretical coding,
two examples from this ontology will be provided: first, the
representation of relevant organisations in the domain. Organisations
are represented as objects derived from the root class of 'thing'. The
domain of ERS is characterised by three types of organisations (see
fig. 2): the criminal organisation, the private enterprises—which
provide the resources for extortion—and the public administration. In
the case of the Sicilian Cosa Nostra, the criminal organisation of the
Mafia consists of three operational unit classes: the family as the
basic unit, the Mandamento as a regional coordination unit, and the
Cupola (Sicilian Mafia Commission), which is the top echelon of the
hierarchy. For the purpose of characterising the domain of ERS,
enterprises need to be distinguished into three classes, dependent on
their likelihood of becoming victims of extortion: for small shops the
likelihood is very high, whereas for big companies the danger of
extortion is reduced. Construction companies are in high danger,
regardless of whether they are big or small. The sphere of
administration is divided into four professional organisations: the
court, the police, the public administration, and politics. It can be
seen in figure 2 that the ontology is arranged in a set theoretical
manner: the more specific objects are subsumed under more general
classes of objects by the relation 'is a'. The more general classes
represent sets of objects or sub-classes . However, note that the
analysis begins from the reverse direction, namely by identifying the
most specific organisations first. Ontology development is the process
of classifying these specific organisations within more general classes
until they are finally subsumed under the abstract class, 'thing'. In a
GT process, the identification of these general classes can be regarded
as the process of abstraction in theoretical coding. The specification
of subsets and relations between sets can be described as axial coding.
Figure 2. Organisational structure of the Cosa Nostra
- While the objective of this example is to demonstrate the
principle of the development of terminologies in software ontologies,
the objective of the next example is to show how the formal precision
of the set theoretical account of ontologies provides a thinking tool
for the development of terminology in the theoretical coding process.
Figure 3. Example (part) of actions
- This example shows parts of the actions undertaken by the
organisations in the domain; specifically, the elements included in the
extortion of entrepreneurs, and the service of protection offered in
return. Actions are represented as objects. Note that not all
entrepreneurs make use of this service. This is indicated by the black
triangle. Nevertheless, the subsumption of terms looks rather strange
here; it seems counter-intuitive to subsume protection under the
heading 'extortion'. However, the entrepreneur may decide to cooperate
with the Mafia. The service offered by the Mafia includes protection
from other criminals. This is the classical domain of a private
protection company. Additionally, though, the Mafia may also help the
entrepreneur by organising a cartel to hinder competitors looking to
enter the market. Likewise, the Mafia may support illegal activities of
the entrepreneur by using its contacts within the public domain.
Protection generates a win-win situation for both parties. In this
case, extortion is perceived as a kind of taxation, and protection is a
subset of the groups of actions implied by successful extortion. Since
'extortion' and 'protection' are classical terminology used in research
on the Mafia (e.g., Gambetta 1993)
we decided to retain this terminology. Likewise, it seems
counter-intuitive to subsume 'buy' under the term 'protection'.
However, if the entrepreneur decides to cooperate, the Mafia gains a
hold within the company, and the entrepreneur no longer possesses
absolute power. He may even find himself in a situation where the Mafia
takes over the company. This conflicts with the intuitive meaning of
protection. In a GT process, the purpose of theoretical coding is to
achieve the most abstract and general terms to precisely describe the
phenomenon under investigation, as shall be illustrated by the example
of the concept of 'changing self' to describe a situation of exposure
to war. Thus the formal precision required to build a hierarchy of set
theoretic subsumptions of terms reveals possible inconsistencies, and
implausible or unequivocal terms. The objective of figure 3 is to
provide an example of how ontology development stimulates theoretical
Ontologies for open coding
- Following the method of contrasting cases in the GT
approach, this case had been contrasted with data from another case of
organised crime. Here, the CCD tool (Scherer
et al. 2013) has been
utilised for knowledge representation. This is used to achieve a
conceptual model of the data ready for simulation. The paper will draw
on this example to show how formal knowledge representation in software
engineering assists the process of open coding in
GT. The data had been analysed using MaxQDA (http://www.maxqda.com/)
and then imported to CCD. The coding derived with MaxQDA served as the
basis for identifying relations with the CCD tool. CCD provides an
environment for a controlled identification of condition-action
sequences, which represent the micro-mechanisms at work in the
processes described in the data. Whereas the data describes individual
instantiations, the condition-action sequences represent mechanisms
insofar as they describe generalisable event classes. However,
empirical traceability is ensured by tracing the individual elements of
the action diagram that resulted from the identification of
condition-action sequences in the CCD tool, back to text annotation in
the data. These annotations are extracted from the coding derived with
MaxQDA (Neumann &
Lotzmann 2014). The advantage of this formal knowledge
representation is, first, that it enables a
detailed analysis of the dynamics of processes by the condition-action
sequences. Second, whereas the set theoretical
account of finding abstract classes of concepts supports the process of
abstraction in theoretical coding, CCD enables disentangling mechanisms
on a very micro level, derived by single line coding. This assists open
coding. However, by identifying the mechanisms that connect conditions
with actions, the conceptual modelling already infers an element of
theory in the data-driven stage of research. As the following example
will show (see fig. 4), it requires finding concepts in which similar
input corresponds to similar output. This is condition c) of a theory.
- In contrast to the well-established Cosa Nostra, this case
investigates the collapse of a criminal network in relatively early
stages of the network's existence. The data is based on police
interrogations in 2005 and 2006. The network lasted for circa 10 years;
it collapsed when initial conflicts escalated to a degree of violence
that has since been described in the police interrogations as a 'rule
of terror' in which 'old friends were killing each other'. This overall
situation consists of several micro-elements. However, at the time
these were not visible to the members of the group, leading to the
nebulous assumption of a 'rule of terror' that could not be attributed
to individual persons. This has been described in the police
interrogations as a 'corrupt chaos'. In a covert organisation,
commitment to the organisation cannot be secured by formal contracts.
Therefore trust is essential. The following diagram shows a part of the
process that led to a cascading effect in which trust collapsed.
Figure 4. Example of action diagram (part) of the contrasting case
- Figure 4 reveals parts of the escalation process of
conflicts within the criminal group. The starting point is the
condition that some member of the group recognises an act of aggression
performed against him by other members of the group. This triggers the
action to interpret the aggression. It could be a sanction (i.e., norm
enforcement), or self-interested aggression (i.e., violation of the
trust he has in this group member). Interpreting the aggression as norm
violation is the condition for counteraction either in form of betrayal
or in counteraggression. Note that this abstract condition-action
sequence can be traced back to annotations derived from the MaxQDA
coding. An example for aggressive action against member X is the
following annotation from the coding: 'An attack on the life of M.'
Moreover, the data includes the testimony of a member who states that
'M told the newspapers [about my role in the network] because he thought
that I wanted to kill him to get the money.' M had survived an attack
on his life, but he was wrong in the assumption that this particular
member of the organisation was behind this attack. Thus M decided to
interpret the aggression as a violation of his trust in V01, and
reacted by betraying him (Neumann
& Lotzmann 2014). The fact that he was wrong in the
attribution of guilt caused further conflicts within the network. This
is an example of how CCD assists the process of open coding by
dissecting the micro-mechanisms in the data.
Summary: Ontologies as tool for Grounded Theory
- In summary, ontologies provide a tool for thinking in the
processes of theoretical and open coding. Ontologies assist coding by
means of the following features:
- They ensure formal precision and coherence of coding. The precision allows for the detection of gaps in the data.
- Identifying sets contributes to theory building by generalising empirically derived concepts. This corresponds to condition a) of a theory.
- Formal precision allows to check the consistency of the generalisation and to infer if the generalisation is sufficient to subsume the cases.
- They support open coding by disentangling complex verbal concepts from their constituent micro-elements. Identifying condition-action sequences assists the inference of mechanisms that drive a system. This corresponds to condition c) of a theory.
The explanatory power of Grounded Theory: Theoretical sensitivity
- The example of ontologies of criminal organisations
demonstrated how these tools might be utilised in the process of
theoretical coding. This concerns early and middle stages of the
research process. Next, the second criterion to assess the theoretical
quality of results will be addressed, namely the relation between
explanans and explanandum. This is the criterion b) of a theory. A
theory aims to explain something. However, the degree inasmuch such
insights are achieved by a field study remains ambiguous in the GT
account. In the methodological research on GT, terms such as
'theoretical saturation' and 'theoretical sensitivity' provide quality
criteria for the development of a theory. In the literature on GT
methodology in particular, the term 'theoretical sensitivity' is used
to assess the theoretical quality of the research (Corbin & Strauss 2008).
Briefly, theoretical saturation is the criterion for stopping the
iterative process of data collection and analysis. This is indicated if
no more additional categories or properties can be found any more. On
the other hand, theoretical sensitivity indicates the meaningfulness
of the results. Corbin and Strauss (2008)
define sensitivity as 'the ability to pick up on subtle nuances and
cues in the data that infer or point to meaning' (p. 19). It is claimed
that GT cannot be reduced to a routine application of certain methods.
For this reason, in the GT literature (Glaser
1978; Glaser &
Strauss 1967; Strauss
& Corbin 1998) theoretical sensitivity is specified
as being the credibility of the researcher. Thus emphasis is put on the
notion of ability in the definition (i.e., the
ability of the individual researcher), rather than on the subtle
nuances in the data. For instance, the imagination and creativity of
the researcher may be highlighted (Strauss
& Corbin 1998), an action that evaluates the quality
of the researcher rather than the research itself. This is a very
personal conception (Birks &
Mills 2011) and lacks a more objectifiable criterion. While
it can be asserted that creativity and sensitivity in the field of
analysis are essential for the significance of science, the assessment
of the creativity of a researcher is highly subjective, as it depends
to a large degree on the person undertaking the assessment. Moreover,
this does not provide in itself a criterion to determine if the
research achieved an insightful description or explanation of a certain
- The objective of the second example is to show how
simulation contributes to the quality criteria of reaching a
theoretical explanation of a phenomenon, by specifying the explanans
and the explanandum (i.e., criterion b) of a theory). For this purpose
the example will draw on the notion of stylised facts, as developed for
the investigation of simulation results (Heine
et al. 2005, 2007).
It will be shown how the simulation of stylised facts can provide a
means to develop criteria for evaluation of the quality of qualitative
research. Admittedly, this is not the conception of theoretical
sensitivity. Nevertheless, it will be argued that simulation provides a
source of evidence that the inductive research process generated
theoretical insights rather than merely describing the phenomenon under
investigation, by clearly specifying how stylised facts provide the
mechanisms to connect explanans and the explanandum (i.e., criterion c)
of a theory). Arguably this is a criterion for theoretical sensitivity,
as it indicates the meaningfulness of the insights in order to provide
an explanation. This will be demonstrated by a second example to show
how simulation results contribute to the formulation of theoretical
- A simulation model provides a means to clearly specify that
which explains something else; the model assumptions provide the
explanans while simulation results provide the explanandum. Simulation
results are implications of the model assumptions, even if they may be
too complex to be analytically tractable. Thus the assumptions generate
the simulation result. However, as the discussion of theories in
section 2 demonstrated, this does not suffice for a valid explanation.
The example of Steve taking the pill shows that deduction need not be
meaningful. An abstract model might provide sound statistical figures
without providing meaningful information about a target system.
However, in an evidence-based modelling account, model assumptions can
already rely on qualitative empirical evidence. Results of simulation
runs are typically some kind of statistical figures, which can be
compared to empirical data. These two stages of evidence in the
development of the model assumptions and simulation analysis describe
the process of cross-validation (Moss
& Edmonds 2005). The question remains if the model
assumptions and the simulation results are connected by causal
mechanisms. The following example will show that stylised facts might
provide such explanatory mechanisms. It will demonstrate how stylised
facts enable explanation of the results of the simulation runs, by
dissecting the mechanisms that drive the dynamics. Stylised facts
provide a middle-range theory of the domain under investigation. The
relation between evidence-based model assumptions and simulated
stylised facts can be described as the explanatory narrative of the
field, by revealing the explanatory power of the qualitative evidence
of model assumptions.
Figure 5. Structure of an explanation
- However, first the notion of stylised facts will be
explained in more detail. The term 'stylised facts' was coined by
Kaldor (1961) in
macroeconomic growth theory. Heine et al. (2005)
demonstrated that it can be applied beyond macro-economic analysis to
the evaluation of simulation results. The central tenet of stylised
facts is 'to offer a way to identify and communicate key observations
that demanded scientific explanation' (Heine
et al. 2005, 2.2). For this purpose, 'stylised facts' denote
stable patterns that can be found throughout many contexts. Stylised
facts are defined as follows:
- 'Broad, but not necessarily universal generalisations of empirical observations and describe essential characteristics of a phenomenon that call for an explanation' (Heine et al. 2007, p. 583).
- Thus details of concrete empirical cases are left out in
favour of a description of tendencies that have been identified as
robust patterns that can be discerned in a certain class of phenomena.
The fact that they are not restricted to an idiosyncratic description
of a single case, but instead are salient characteristics of a class of
phenomena, enables a generalisation of a particular case. Robust
patterns identified by broad generalisations reflect the characteristic
of mechanisms to regularly reveal similar outputs Y* under similar
circumstances X*. In the case of evidence-based modelling this can be
used as a cross-validation, to check whether the micro assumptions put
in the model assumptions reveal stylised facts characteristic of the
field of investigation. How this may encompass social mechanisms will
be shown in the example below. With regard to the question of what a
theory is in a GT approach, this provides an additional source of
credibility for a GT process: if a simulation reveals stylised facts of
mechanisms, which connect the input of model assumptions with the
output of simulation results, then this would indicate a theoretic
insight generated by the inductive process of evidence-based modelling.
An example for the contribution of stylised facts to the explanatory power of Grounded Theory
- How simulation reveals stylised facts of mechanisms will be
demonstrated by an example of a simulation model of the escalation of
ethno-nationalist. Moreover, in this example it will be possible to
integrate the results in the framework for theories of ethnic conflict.
Thus the stylised facts allow for a final integration as demanded by
Corbin and Strauss (2008),
not only of the data, but also of the resulting theory in the canonical
theoretical discourse of the domain. The example is a model that
investigates the escalation of ethno-nationalist tensions into open
violence. The evidence has been drawn from the case of the former
Yugoslavia. The puzzling question is how and why neighbourhood
relations between people with different ethnic backgrounds changed from
genial and peaceful relations to traumatic and violent ones.
- A simulation model has been developed to study the
dynamics of nationalist radicalisation. Model assumptions were based on
the empirical evidence of historical narratives of this much-studied
case (e.g. Bringa 1995; Gagnon 2004; Melcic 1999; Woodward 1995; Sieber-Egger 2011; Silber & Little 1997; Wilmer 2002). Initially the
conflict started as a power struggle within the Yugoslavian Communist
Party. Formerly communist politicians took advantage of ethnic
sentiments, which allowed them to organise party loyalty with an ethnic
agenda. In the beginning, the degree of ethnic mobilisation in the
population remained small (Calic 1995).
However, very soon civilians were becoming involved in the battles, and
some even took part in war crimes. Ethnic homogenisation was undertaken
by a paramilitary militia of civilians who were not integrated into the
command structure of the Yugoslavian army. These civilians were
responsible for numerous ethnic cleansings. Moreover, normally the
militia pre-warned inhabitants of certain villages—inhabitants who were
of the same ethnic origin as the militia—of the imminent attacks; often
the villagers chose not to pass on the information to their Croatian
neighbors, and also participated in looting afterwards (Bringa 1995; Drakulic 2005; Rathfelder 1999). Thus the
empirical evidence suggests a theoretical mechanism of a recursive
feedback relation, between dynamics on a political level and
socio-cultural dynamics at the population level.
Figure 6. Relation between political actors and political attitudes
- The simulation model cannot be explained here in detail
(see Markisic et al. 2012).
The model is public in the OpenABM archive:
justification of how the model development reflects a GT approach, see
Neumann (2014). The
target of the model assumptions is the change of neighbourhood
relations. The empirical evidence suggests a two-level design of the
model, namely to specify the mechanisms of the escalation dynamics of
ethno-political conflicts as a recursive feedback between political
actors and social identities at the population level. While a focus
purely on the population level (e.g. Horowitz
2001) masks the responsibility of political actors,
explanations that focus purely on the political level (e.g. Gagnon 2004) need to explain
why certain politics were successful. Integrating both accounts
generates a self-organised feedback cycle of political actors and
attitudes. The basic mechanism in the model is an enforcement of the
population's value orientations through political actors. These may be
civil values or national identities. On one hand, politicians mobilise
value orientations in the population to get public support. On the
other hand, politicians appeal to the most popular value orientations
in order to maximise the support. In abstract terms, the feedback
relation can be described as a recursive function. Thus it is a
positive feedback cycle; however, this is damped by the fact that
various politicians compete over different value orientations in the
population. The model was calibrated at the population census of 1991
in Serbia, Croatia, and Bosnia-Herzegovina. Whereas Serbia and Croatia
had a rather homogeneous population, the population of
Bosnia-Herzegovina was highly ethnically mixed.
Figure 7. Use case diagram of the model structure (adapted from Funke 2012)
- Simulation experiments were undertaken with the assumption
ignorance about the empirical distribution of the cognitive components,
namely of the political attitudes of the citizens and the political
agenda of the politicians. Initially both the political agenda and the
value orientation of the citizens are determined by chance, for all
republics. This allows for studying the pure effect of the feedback
- Thus, here we have our explanans:
differences in the simulation results are due to differential
population distribution since all other features are the same for all
republics. For sake of simplicity of the argument, we concentrate on a
single explanandum: the change in the value
orientations of the population. This reflects the research question of
how neighbourhood relations changed in course of the conflicts
escalation. In fact, all republics reach a stage of nationalist
radicalisation during the simulation. However, the dynamics reveals a
crucial difference: while the simulated 'Serbian' and 'Croatian'
population quickly becomes radicalised (see fig. 8 for the Croatian
population), in Bosnia-Herzegovina radicalisation is much slower. Only
in the second half of the simulation can a push towards nationalist
radicalisation be observed (see fig. 9). This difference in the
dynamics is our explanandum, which is explained by
the explanans of the difference in the population
Figure 8. Example of average Croatian value distribution Figure 9. Example of average value distribution in Bosnia-Herzegovina
- However, what are the mechanisms
connecting explanans and explanandum? The example of Steve and how he
did not become pregnant, presumably not because he
took the pill, shows that a purely logical deduction is not sufficient
to dissect the mechanisms. However, the model assumptions are based on
qualitative evidence from the field, which ensures that the basic
elements correspond to an empirical relative. During simulation these
model assumptions generated stylised facts of two basic general
mechanisms of the escalation dynamics. These stylised facts then reveal
the mechanisms that connect the explanans and the explanandum. The
first mechanism concerns political processes; the second mechanism
concerns micro processes of neighbourhood relations. The
interpenetration of the processes reveals a sequential ordering:
- First, on a political level, visibility
of the political appeals plays an essential role in radicalisation, by
stimulating counter-radicalisation in a republic B to initial
radicalisation in a republic A. This is driven by the political level,
and accounts for a rather homogeneous population and nations with a
common or closely related cultural heritage, such as Serbia and
Croatia. Ethnically mixed populations, such as in Bosnia-Herzegovina,
provide more power of resistance against political radicalisation prior
to the outbreak of actual violence. Political radicalisation can be
achieved easier in ethnically homogeneous nations.
- Second, refugees and rumours play an
essential role for later radicalisation in Bosnia-Herzegovina. Here,
radicalisation is imported from outside and is driven predominantly by
the population level. Dense networks increase the likelihood of
- These are theoretical results, derived from a dense
description of a particular case, which has been transformed into a
more abstract code of a simulation model. However, the simulation
results are not limited to an idiographic description of this
particular case, but also provide stylised facts of the mechanisms of
nationalist (or value-driven) radicalisation. This is an example of how
simulation provides a means to analyse an explanation from model
assumptions based on a GT approach, by dissecting the mechanisms that
connect explanans and explanandum. Thus if evidence-based model
assumptions generate meaningful stylised facts, the simulation run
indicates a theory in the sense that something explains something else.
- Moreover, it is possible to achieve a final integration not only of the data, but also of these results, into the theoretical discourse on ethnic conflicts. Note that a main result has indicated that ethnically mixed populations provide more power of resistance against initial political radicalisation. This addresses current theoretical debates in conflict research (Cederman et al. 2010; Fearon & Laitin 2003; Rutherford et al. 2011; Wimmer et al. 2009). Whereas classical sociology of conflicts explained inner state violence via the theory of relative deprivation (Gurr 1970), in the times after the cold war a rise in the number of ethnic conflicts was observed. While it might remain ambiguous whether or inasmuch conflicts simply were perceived differently after the cold war (Wimmer et al. 2009), this nevertheless initiated a research programme in recent decades, in which ethnic conflicts have become a subject of investigation in their own right, as opposed to being subsumed under a broad theoretical umbrella such as the theory of relative deprivation (Neumann 2014). The clash of civilisations (Kaplan 1996) is a prominent catchphrase for these accounts. Various causes have been suggested (e.g. Sambanis 2001) to explain why ethnic groups might be tempted to fall into violent conflicts. Wimmer et al. denote these accounts as a diversity-breeds-conflict theory (Wimmer et al. 2009), according to which it is the diversity that explains the conflicts. Thus 'diversity' is the explanans and 'conflict' is the explanandum of this theoretical account. However, the simulation study of our example reveals a different result: whereas 'diversity' is the explanans, the explanandum is different, namely power of resistance against political radicalisation. This casts doubt over the clash of civilisation thesis. It may be true that self-perpetuating violence on the micro level might indeed be more severe in ethnically heterogeneous territories, once the power of resistance has been broken. The case of Bosnia-Herzegovina perpetuates this statement. Nevertheless, the model shows that diversity in itself is not a sufficient cause to breed conflict. In fact, the outbreak of violence happened later in Bosnia-Herzegovina than in Croatia (Rathfelder 1999). Simple reference to diversity lacks a specification of the mechanisms of conflict escalation. The diversity-breeds-conflict theory is based on statistical data analyses of the large-N research in conflict research (see also Florea 2012). However, statistics cannot reveal mechanisms of social dynamics. Indeed, this model reveals mechanisms that point in a different direction, namely 'power of resistance against political radicalisation'. At least next to diversity, additional mechanisms need to come into play to foster conflicts. In the model, this refers to the second mechanism of imported violence, driven by refugees on the micro level of neighbourhood relations. This discussion demonstrates that simulation of stylised facts allows the achievement of theoretical insights from a GT starting point. In terms of theoretical integration, the integration of the simulation results into the broad scope of theories on ethnic conflict can be regarded as the most abstract framework for describing theories.
- The paper argues that the theoretical element in a GT approach can be strengthened by supplementing the methodology of GT with evidence-based simulation. This is demonstrated by two examples: first, it is shown that the development of an ontology of criminal organisations refines the process of theoretical coding by providing additional precision, which allows to detect gaps in data and concepts and to specify the scope of the domain. Set theory contributes to criterion a) of a theory ("generality"). Identifying condition-action sequences supports open coding by a specification of mechanisms in a system, thereby contributing to criterion c) of a theory ("mechanisms") . Second, by using the example of the escalation of ethno-nationalist conflicts in the former Yugoslavia, it is shown how findings from simulation of an evidence-based model generate stylised facts. Simulation tools enable to derive an explanation from a narrative story by connecting the model assumptions with the simulation results. Model assumptions provide the explanans, and results provide the explanandum. This contributes to criterion b) of a theory ("explanation"). Stylised facts enfold the mechanisms connecting these two, thus contributing to criterion c) ("mechanisms"). These are the basic elements of a theory. If a model, based on the evidence of an empirical case, generates broad patterns that reveal mechanisms which connect the explanans and explanandum, then this shows how a process starting with an idiographic description succeeds in generating a theory. This contributes to a clarification of the precarious relation between a mere description and a strictly theoretical GT.
- The work on this paper is part of the GLODERS project, funded by the European Commission under the 7th Framework Programme. It is an extension and refinement of a paper presented at the 9th Conference of the European Social Simulation Association, Warsaw 2013, in the panel 'Using qualitative data to inform behavioral rules'. The contribution of comments and discussions to improve the paper are greatly acknowledged. The author would like to thank two anonymous reviewers and a proof-reader for critical and constructive hints to improve the paper. All flaws and errors are sole responsibility of the author.
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