Diego Farren - Thesis Short

UNIVERSITAT ZU KOLN

Violence, Self-Control and Morality: ADual-System Perspective

by

Diego Farren

A thesis submitted in partial fulfillment for the degree ofMaster of Science (M.Sc.)

in theWirtschafts- und Sozialwissenschaftliche Fakultat (WISO)

Institut fur Soziologie und Sozialpsychologie (ISS)Supervisor: Prof. Dr. Clemens Kroneberg

October 2014

Sometimes I am two people.

Johnny is the nice one.

Cash causes all the trouble.

They fight.

- Johnny Cash

i

UNIVERSITAT ZU KOLN

Abstract

Wirtschafts- und Sozialwissenschaftliche Fakultat (WISO)

Institut fur Soziologie und Sozialpsychologie (ISS)

Supervisor: Prof. Dr. Clemens Kroneberg

Master of Science (M.Sc.)

by Diego Farren

An exercise of theoretical elaboration (Thornberry 1989) from a control perspective is

pursued using mainly inputs from the General Theory of Crime (GTC; Gottfredson

and Hirschi 1990), the Situational Action Theory (SAT; Wikstrom et al. 2012; Wik-

strom 2006), the Model of Frame Selection (MFS; Kroneberg 2006), and the Reflective-

Impulsive Model (RIM; Hofmann et al. 2009; Strack and Deutsch 2004). The capability

of the final model of action proposed to predict acts of violence is tested through mul-

tilevel analyses using data from the International Self-Report Study of Delinquency 2

(ISRD-2; Enzmann et al. 2010; Junger-Tas et al. 2012, 2010). The results point in the

expected direction, i.e. action can be modeled through the suggested framework. Theo-

retical, methodological and policy implications derived from the adoption of the proposed

model are discussed. Limitations of the analyses applied and ideas to overcome them in

future researches are also presented.

ii

Acknowledgements

The biggest thanks of all to my boss and friend Dirk Enzmann for all the dedication,

discussions, good advice, good sense of humor and unconditional support.

Also thanks to my college Ilka Kammigan and to the whole Uni Hamburg crew, lead

by Peter Wetzels and Sylvie Billon - thanks for fitting me in, for your support, and for

creating such a nice atmosphere.

Thanks to those who enlighten my way in those dark moments of doubt, whether its

with theory or with statistics or with Stata and R - thanks to Clemens Kroneberg,

Alexander Schmidt and Bernd Wei.

Also thanks to Hans-Jurgen Andre, Ravena Penning and Hawal Shamon for the amaz-

ing two years of profound learning I had as a tutor.

A big thanks to all my friends around the world and especially to my Kumpel David

Bruder, my beloved classmates, and to all the amigos at the Mauer in Koln. And my

special gratitude to Birgit Kastner and Lena Taube for accommodating me in the last

phase of this project, when I no longer had a home.

Last but not least, I thank my family for their support, example and wisdom. Mother

and father, you are both my favorite people in the world.

iii

Contents

Abstract ii

Acknowledgements iii

List of Figures v

List of Tables vi

Abbreviations vii

1 Introduction 1

2 Theoretical Background 3

2.1 Theoretical integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.2 Dual-system perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.3 Conceptualization and measurement . . . . . . . . . . . . . . . . . . . . . 13

2.4 Research question and hypotheses . . . . . . . . . . . . . . . . . . . . . . 17

3 Methods 20

3.1 Data source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.2 Operationalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.3 Statistical analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

4 Results 42

5 General Discussion and Conclusion 56

Bibliography 63

Appendix A: Correlations 74

iv

List of Figures

1.1 Study goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2.1 SAT & MFS complemented . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.2 Clusters of attributes associated with dual systems of thinking . . . . . . 12

2.3 The Reflective-Impulsive Model (RIM) . . . . . . . . . . . . . . . . . . . . 16

3.1 Histogram: impulsive determinants of behavior . . . . . . . . . . . . . . . 24

3.2 Histogram: reflective determinants of behavior . . . . . . . . . . . . . . . 25

3.3 Histogram: school disorganization scale (agg.) . . . . . . . . . . . . . . . . 27

3.4 Mediation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

3.5 Observable and unobservable outcomes . . . . . . . . . . . . . . . . . . . . 39

4.1 Predicted probabilities (95% CI) . . . . . . . . . . . . . . . . . . . . . . . 45

4.2 Marginal probability effects (95% CI) of impulsive determinants of behavior 47

4.3 Predicted probabilities (95% CI) over intoxication . . . . . . . . . . . . . 49

4.4 Discrete probability change (95% CI) over intoxication . . . . . . . . . . . 50

5.1 RIM and SAT/MFS compared . . . . . . . . . . . . . . . . . . . . . . . . 56

v

List of Tables

2.1 Impulsive and controlled outcomes: four scenarios . . . . . . . . . . . . . 18

3.1 Sample description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.2 Violence related prevalences by country . . . . . . . . . . . . . . . . . . . 22

3.3 Descriptive statistics: impulsive determinants of behavior (scale) . . . . . 23

3.4 Descriptive statistics: reflective determinants of behavior (scale) . . . . . 24

3.5 Descriptive statistics: life-time intoxication (scale and items) . . . . . . . 25

3.6 Descriptive statistics: school disorganization scale . . . . . . . . . . . . . . 26

3.7 Descriptive statistics: control variables . . . . . . . . . . . . . . . . . . . . 28

4.1 Regression results (main regressions) . . . . . . . . . . . . . . . . . . . . . 43

4.2 Explained variance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

4.3 Predicted probabilities (95% CI) over impulsive determinants of behaviorcompared (M1 vs M4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

4.4 Marginal probability effects (95% CI) of impulsive determinants of be-havior compared (M1 vs M4) . . . . . . . . . . . . . . . . . . . . . . . . . 48

4.5 Predicted probabilities (95% CI) over intoxication compared (M2 vs M4) . 48

4.6 Discrete probability change (95% CI) over intoxication compared (M2 vsM4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

4.7 Regression results (KHB mediation) . . . . . . . . . . . . . . . . . . . . . 52

4.8 Mediation analysis with impulsive determinants of behavior as treatmentvariable (counterfactuals; no interactions) . . . . . . . . . . . . . . . . . . 53

4.9 Mediation analysis with school disorganization as treatment variable (coun-terfactuals; no interactions) . . . . . . . . . . . . . . . . . . . . . . . . . . 53

4.10 Mediation analysis with impulsive determinants of behavior as treatmentvariable (counterfactuals; with interactions; intoxication = 0) . . . . . . . 54

4.11 Mediation analysis with impulsive determinants of behavior as treatmentvariable (counterfactuals; with interactions; intoxication = 1) . . . . . . . 54

4.12 Mediation analysis with school disorganization as treatment variable (coun-terfactuals; with interactions) . . . . . . . . . . . . . . . . . . . . . . . . . 54

A.1 Descriptive statistics and correlations: self-control/impulse related items . 74

A.2 Descriptive statistics and correlations: violence attitude/reflective deter-minants of behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

A.3 Descriptive statistics and correlations: school climate related items (schooldisorganization) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

A.4 Descriptive statistics and correlations: neighborhood climate related items 75

A.5 Descriptive statistics and correlations: family bonding related items . . . 76

A.6 Correlations of all variables used in the regressions . . . . . . . . . . . . . 76

vi

Abbreviations

GTC General Theory of Crime

ISRD International Self-Report Study of Delinquency

MFS Model of Frame Selection

RIM Reflective-Impulsive Model

SAT Situational Action Theory

vii

Dedicated to my father Fernando Farren

viii

1. Introduction

A dual-system perspective is proposed to test the hypothesis that acts of violence can

be modeled as an outcome that depends mainly on self-control, morality, situational

moderators, and their interplay. Theoretical elaboration (Thornberry 1989) from a con-

trol perspective is applied to develop an action theory that can serve to understand and

predict acts of violence. Data from the International Self-Report Study of Delinquency

2 (ISRD-2; Enzmann et al. 2010; Junger-Tas et al. 2012, 2010) representing populations

of grade 7 to grade 9 (mostly 12 to 14 years students) from 30 different countries is

analyzed. Statistical methods used belong mainly to the family of multilevel logisitc re-

gression models. Also a counterfactual perspective is adopted to test hypotheses about

mediation (see e.g. Imai, Keele and Tingley 2010).

This study has three goals (see Figure 1.1): first, a discussion of the conceptual meaning

of self-control and its place in crime causation. Inputs from different theories in criminol-

ogy and social-psychology will be taken into consideration with the goal of contributing

to a theory of action that can be used as a roadmap for the development of valid mea-

sures and understanding of the variables of interest. An important part of this goal is to

integrate approaches of different disciplines, in this case psichological and criminological

theories (also referred as a postmodernist perspective on integration Barak 2010). In

order to be clear about the type of integration proposed, part of the theoretical chapter

is dedicated to clarifying the meanings of theoretical integration and its state of the art.

Figure 1.1: Study goals

THEORY

Goal 1:Theoretical

model

Goal 2:Measuring

instruments

Goal 3:Statisticalanalysis

ANALYSIS

Once the proposed theory of action has been explained, the next step is to give guidelines

on how to measure the most relevant concepts involved in it. The second goal of this

study is, accordingly, to critically revisit the existent measuring instruments for the key

concepts of the described theory of action. The logical conclusion at this stage, would

be to choose the best measures or to develop new ones and analyze them. Nevertheless,

the data in this case has already been collected, so that no changes to the measuring

1

Introduction 2

instruments can be made. That is, although a critical revision of the instruments being

used to measure the concepts of interest can be done, the data analyses must still rely

on existing instruments. The main intention of the second goal is then, to state the ideal

conditions under which the theoretical model developed in goal one should be tested.

In this sense, the theory of action to be proposed is expected to set the standards for

the measurement of its main concepts. This is considered a relevant goal to avoid mis-

understandings about the meaning of relevant concepts in the guiding theory of action.

Finally the model proposed in goal one will be statistically tested using state of the art

multilevel models. The results will be described and interpreted with special emphasis

on their theory, methodology and public policy implications. It is of main importance to

keep theoretical abstraction and methodological sophistication connected to the prag-

matic goals of public policy. In other words, particular attention will be payed to the

consequences of the proposed theory and methodology for policies to reduce crime and,

specifically, acts of violence.

The next chapter presents the necessary discussion about theoretical integration that

should give the reader a general notion about the meaning of such an exercise and its

relevance for the development of social science. A proposition for theoretical integration

follows, using inputs from relevant criminological and psychological theories. First the

original theories are summarized and then the proposed integrated theory of action is

explained in detail. The chapter ends with the explicit exposure of the research question

and hypotheses to be tested in order to check the predictive power of the action theory

as developed in the previous section.

The methodological chapter presents a short description of the data to be used to test

the model proposed. Also the operationalization of the variables is explained and mea-

sures of reliability are presented. The chapter ends with a general description of the

statistical methods implemented to test the hypotheses.

The results chapter presents the statistical analyses and tests of hypotheses. The findings

are critically discussed in the concluding chapter. The consequences for criminology

theory, methods of data collection and analysis, as well as for public policy, will be

discussed in the final chapter as well.

2. Theoretical Background

The analysis of crime has been characterized by many competing, and at times con-

flicting perspectives on crime and delinquency (Elliott et al. 1979, 3) that deal with

different aspects and levels of reality. Control theories are an important part of crimino-

logical studies. With all the changes they have gone through, they share the same main

proposition: everyone is capable of committing crime and control prevents him/her from

doing so (Bernard et al. 2010, 203f.).

Since the publication of A general theory of crime (GTC; Gottfredson and Hirschi

1990), control theories have placed special attention on self-control. Much has been

discussed about self-control as a predictor of crime (see e.g. Goode 2008; DeLisi and

Vaughn 2007; Geis 2000). Although many scholars criticize this theory, the fact is that

studies including its measure more often than not prove it as a significant predictor of

problematic conduct (for a meta-analysis see Engel 2012).

It seems clear that self-control plays a key role in crime causation. But there are at

least two critical points to be clarified. First, the meaning of self-control is still con-

troversial and should be refined. It is now a commonplace in criminology to state that

Gottfredson and Hirschi (1990) failed to precisely define self-control, and just rested on

a description of behaviors that reflect intrinsically low self-control (e.g. Wikstrom and

Treiber 2007, 243; Tittle et al. 2004, 147).1 Moreover, Gottfredson and Hirschi (1990)

tend to ignore the potential inputs of the conceptualization given by psychological the-

ories of self-regulation. The problem of conceptualization is a critical one, because any

comprehensive test of a theory relies on the congruence between theoretical derivation

and operationalization (Marcus 2004, 35f.). Because Gottfredson and Hirschi (1990)

give no operational definition of self-control (Akers 1991, 204), researchers have been

forced ... to interpret the concept of self-control in their own manner, ... and come

up with their own measures of self-control. (Piquero 2008, 26f.). Although Hirschi

1Gottfredson and Hirschi (1990) define low self-control as the tendency to pursue immediate gratifica-tion while not taking possible negative long-term consequences into account. They characterize peoplehaving low self-control as being impulsive, insensitive, physical (as opposed to mental), risk-taking,short-sighted, and nonverbal (p. 89f.).

3

Theoretical Background 4

and Gottfredson (1993) do manifest preference for behavioral measures of self-control

(p. 48), Akers (1991) makes the point that if offenses themselves were used as measures

of self-control, this would create a problem of tautology (p. 204).

The second critical point refers to integrating self-control theory with other explana-

tory theories that have proved to be good predictors of crime. Theoretical integration

has been fueling discussion for a long time in criminology (see e.g. Bernard et al. 2010,

327f.; Bernard 2001; Bernard and Snipes 1996; Messner et al. 1989). Those in favor of

it (e.g. Elliott et al. 1979) see it as an alternative to theoretical competition and fal-

sification, as this last strategy has failed to reduce the number of criminology theories

(Bernard 2001, 335). However, those opposed to it (e.g. Hirschi 1979) see no compati-

bility between the basic models of delinquency theories -i.e. control, strain, and cultural

deviance models (Kornhauser 1978, 21ff.)- and therefore no possibility of theoretical in-

tegration.

In this chapter the state of the art in the debates about these two critical points is

presented. First the problem of theoretical integration in criminology is revisited to

conclude that a theory of action is needed. Next, a dual-system perspective is proposed

as a suitable theory of action to be applied on acts of crime and violence. In order to

support this statement, a summary of already existing dual-process theories in crimi-

nology is presented and a dual-system model of self-control derived from psychology is

described. The theoretical and methodological benefits of such a perspective are summa-

rized and a revision of existent measuring instruments is presented. Finally, through the

adoption of a dual-system model of self-control, the research questions and hypotheses

will be derived.

2.1 Theoretical integration

Theoretical integration is defined by Thornberry (1989) as the act of combining two

or more sets of logically interrelated propositions into one larger set of interrelated

propositions, in order to provide a more comprehensive explanation of a particular phe-

nomenon. (p. 52, italics from original). The essential and most difficult aspect of

this task is that theoretical integration can only be achieved when many theories form

a new theory that retains the premises of each theory (Bernard and Snipes 1996, p.

308). According to Liska et al. (1989) there are two types of theoretical integration:

propositional and conceptual.


Conceptual integration is when the theorist equates concepts in different theories, argu-

ing that while the words and terms are different, the theoretical meanings and operations

of measurement are similar and is seen by the authors as useful only as a means to

propositional integration (p. 15). Bernard and Snipes (1996) put this nicely into

words: conceptual integration is nothing but playing games with words and meanings

(p. 308).

Hirschi (1979) defines three forms of propositional integration: end to end different

theories get sorted in a sequential order, so that the dependent variable in some prior

theory, becomes the independent variable in a new more general integrated theory (p.

34f.) , side by side the dependent variable of interest is divided into disjunctive parts,

e.g. different types of crimes that can each be explained by different theories (p. 35f.)

and, up and down there is one main theory, and other theories can be subsumed to

it (p.36) . These three types of integration can be further applied to the micro-level,

macro-level, or cross-level (Liska et al. 1989, 5).

Because of the difficulty of achieving propositional integration, some authors have pro-

posed changes or even alternatives to it. Liska et al. (1989) describe the possibility of

a middle range integration, i.e. a more flexible form in which propositions of differ-

ent theories are used to build a new theory, without necessarily retaining all the main

premises or the original definitions of the concepts from each original theory:

We can easily borrow ideas (concepts and propositions) from different theo-

ries and explore how they fit. Some propositions of different theories may be

incompatible because they are tightly linked to incompatible assumptions.

Other propositions, however, may not be so tightly linked to such assump-

tions, and some propositions of different theories may be deduced from a

common set of assumptions, even if they were originally derived from incom-

patible assumptions. (p. 17)

Thornberry (1989) even comes up with an alternative to theoretical integration, i.e.

theoretical elaboration:

Rather than starting with multiple theories and attempting to reconcile their

differences to generate a comprehensive model, theoretical elaboration explic-

itly starts with a particular theoretical model. Accepting its assumptions,

level of explanation, and causal structure, it attempts to build a more and

more comprehensive model by the logical extention of the basic propositions

contained in the model. (p. 56)


Finally, Bernard and Snipes (1996) defend a substantial reinterpretation of criminology

theories (p. 332). They argue that criminology theories should be interpreted and

classified in terms of their location of independent variation and direction of causation

giving raise to two categories of criminology theories: individual difference theories

and structure/process theories (Bernard et al. 2010, 338f.).2 Under this perspective,

full integration could be achieved through a single theory of crime that incorporates

the structural conditions that are associated with higher crime rates, the processes that

explain why normal individuals who experience these structural conditions are likely

to engage in crime, and the individual characteristics that make it more or less likely

that an individual will engage in crime regardless of structural conditions. (Bernard

and Snipes 1996, 342). They also state that although such a full integration should

be theoretically possible, it may not necessarily be desirable, so that it may be best

to attempt integration only within each category of theories, and not between them

(Bernard and Snipes 1996, 304).

The last argument does not mean that theories at one level can not include arguments of

other levels, on the contrary this may be desirable. With respect to individual difference

theories that include macro variables, Bernard and Snipes (1996) declare:

While integrating previously existing theories across levels of explanation

may be undesirable, new theories that incorporate arguments at both of

these levels of explanation may be both useful and feasible....

Such theories explain individual-level variation in criminal behavior using

both individual differences and the interaction between individual differ-

ences and structural position. When explaining individual differences with

a combination of individual and structural explanations, it is easy to avoid

committing an ecological fallacy. (p. 343)

As can be concluded from the exposition so far, what is meant by theoretical integra-

tion is already not always agreed and rarely made explicit (see e.g. Liska et al. 1989,

1; Thornberry 1989, 52). A truly propositional cross-level integration can be thought

as the gold standard. Nevertheless, achieving such an integration is very unlikely and

probably not worth the investment. In other words, cross-level integration in the terms

of Bernard and Snipes (1996) -i.e. a full integrated theory capable of explaining varia-

tion in both crime rates and individual likelihood of engaging in criminal behavior- may

2In the same direction, Nagin and Paternoster (1993) write that [c]riminological theory has developedalong two separate and distinct tracks: in one time-stable individual differences distinguish offendersfrom nonofenders, while other theories attribute crime to circumstances and situations in the socialsetting that are external and proximate to the offender (467f.).


be too complex at the moment and not necessarily useful (p. 342f.).3 But at the same

time, development of theories at the level of the individual needs to account for the

impact that the environment has on the individual differences (Wikstrom et al. 2010,

56). In other words, a general theory of crime should not only explain why people have

different probabilities of committing crime, but need to also account for the role that

the environment has in influencing individual differences and as a trigger that allows

probabilities to become action.

The alternatives to the most purist views on theoretical integration already discussed

here, seem to be closely related to the actual stand of theoretical progress in criminology.

By relying on theories of action, newly developed theories of crime causation integrate

the statements of many existing theories into one theoretical frame. By doing so, they

do not necessarily retain the premises of each original theory. Integration through the

reliance on a theory of action seem much more flexible than a propositional integration

and allows e.g. some modifications to the original concepts and propositions taken from

existing criminology theories.

The next section describes two newly developed theories of action that have been ap-

plied to the explanation of crime. Finally, a dual-system perspective is presented and

promoted as a suitable theory of action to explain acts of violence.

2.2 Dual-system perspective

The demand for a theory of action in social science is not a new one. Coleman (1986)

critically revisited post-Parsonian social theories, and concluded by declaring that a

theory of purposive action that can relate individual actions to systemic functioning

is needed (p. 1332). In criminology, Bernard (1989) made the case for relying on the

relations that different component theories have to a theory of action as a way of in-

tegrating them (p. 140). As stated by Wikstrom et al. (2012), explaining the actual

causal process that directly links a person (crime propensity) and a setting (criminogenic

exposure) to an act of crime ... requires the integration of causally relevant personal and

environmental factors and analysis of their interaction within the context of an adequate

3Bernard and Snipes (1996) state: The integration of individual difference and structure/processtheories is theoretically possible but remains a long-range goal pending the development of more advancetheoretical arguments and statistical techniques. (p. 304). Although such a full integration is not theintention of this study, it is worth mentioning two examples of statistical and theoretical developmentsthat might point at the advance meant by Bernard and Snipes (1996).In the statistical field, Goldstein et al. (2009) present a multivariate-multilevel model that allows for theinclusion of more than one dependent variable at more than one level of analysis.In the theoretical field, Messner (2012) made a proposal for a cross-level integration between Wik-stroms Situational Action Theory (e.g. Wikstrom et al. 2012; Wikstrom 2006) and his and RosenfeldsInstitutional Anomie Theory (e.g. Messner and Rosenfeld 2007, 2001).


action theory. (p. 5f.), so that an action theory is a theory that details the process

(the mechanism) that produces action. (p. 8).

Understanding the mechanism that produce action, means understanding how society

models individual perception and, in turn, how individual action models society (i.e.

the macro-micro-macro relations, see Coleman 1986, 1322ff.). Rational choice theory is

considered to be the first theory of action to integrate person and situation in a con-

vincing way (Lindenberg 2008, 669). Classical criminology has also relied on a reasoning

actor who can estimate costs and benefits when deciding to (or not to) commit crime

(see e.g. Bernard et al. 2010, 14ff.). But because actors do not always act in a rational

way, psychologists have been developing theories that can explain action as the result

of a process that only sometimes follows a rational calculation (see e.g. Chaiken and

Trope 1999; Kahneman 2003; Smith and DeCoster 2000). In criminology this has lead

to some attractive discussions and propositions about the role of human agency in crime

causation (see e.g. Gottfredson 2011; Kroneberg et al. 2010; McCarthy 2002; Paternoster

and Pogarsky 2009; Wikstrom and Treiber 2007). As a consequence, some new theories

of action have been lately proposed that share the reliance on what has been called

variable rationality (see e.g. Kroneberg 2006, 2).

Psychological theories explaining variable rationality are usually labeled as dual-process

theories (see Chaiken and Trope 1999). According to Smith and DeCoster (2000) what

these theories share, is that they all try to explain three main components: how people

process in quick-and-dirty fashion, how they process when willing and able to engage in

extensive thought, and what conditions encourage such effortful processing (p. 108).

On the other side, the main difference between dual-process theories is the temporal and

logical relation assumed between types of process, i.e. whether they occur simultane-

ously or in a sequential way or even in an alternative way (p. 125).

In criminology, Wikstrom and colleagues developed their own theory of action: the Situ-

ational Action Theory (SAT; see e.g Wikstrom 2006; Wikstrom et al. 2012). SAT defines

two perception choice processes that precede action: habit and rational delibera-

tion (Wikstrom et al. 2012, 19ff.). The situation is seen as the core unit of analysis

in criminology, and defined as the perception of action alternatives and process of

choice that follow from the interaction between person and setting (environment is

understood as everything outside the person, so that the analysis should be limited to

the setting, i.e. the part of the environment that is accessible through senses to the

person; Wikstrom et al. 2012, 15). The situation then demarcates how much agency

an actor may apply to the process of action selection, i.e. the situation determines the


likelihood of an action followed by habit or by rational deliberation.

If the situation is at the center of interest, the next step would be to disentangle the

essential elements that conform it. According to Wikstrom et al. (2010) these are: at

the level of the person its crime propensity and, at the level of the setting its crimino-

genic exposure. Both elements can be further decomposed into morality and control.

If the morality of the person and the morality of the context correspond (i.e. both

encourage or both discourage an act of crime4), then the likelihood of an action in the

same direction as the correspondence is high. If the setting encourages acts of crime but

the persons morality discourages it, self-control comes into play. Finally if the context

discourages acts of crime but the persons morality encourages it, then social control in

the form of deterrence plays a role (p. 61).

Following Wikstrom and Treiber (2007), morality is the key individual aspect that filters

what action alternatives an actor will take into consideration in a given situation. If the

setting is familiar to the actor, he or she will tend to act out of habit. But if the setting

is not familiar to the actor, he or she will tend to choose an action after some delib-

eration and self-control may be a part of this process. especially, as stated previously,

when there is a conflict between the moral rules of person and setting in the form of a

setting that encourages an act of crime and a person whos moral rules discourage it,

self-control will be involved in the process of deliberation (p. 245ff.).

In sum, SAT understands that the primary way in which morality causally affects crime,

is by filtering what action alternatives a person will perceive in a given situation, and

only in a secondary order (i.e. when already involved in deliberation) it also affects (to-

gether with controls) the way in which a person chooses between alternatives for action

(Wikstrom 2010, 222).

Another newly developed theory of action that has been applied to crime, is Kronebergs

Model of Frame Selection (MFS; see e.g. Kroneberg 2006, 2007; Kroneberg et al. 2010).

The MFS has emerged as an answer to the absence in social science of a clear definition

of the situation and of variable rationality within a model of action (Kroneberg

2006, 9). Similarly to SAT, it specifies under what conditions behavior follows from

a cost-benefit calculus and under which it will be the result of an automatic reaction

(Kroneberg et al. 2010, 265). This is done by identifying three selections that precede

behavior in any given situation. First an actor needs to make a frame selection in

4Wikstrom (2006) defines act of crime as the act of breaking a moral rule defined in criminal law,where a moral rule is a prescription of what is right and what is wrong in a given context (p. 63).


order to define the situation. That is, through situational objects an actor must get an

idea of in what kind of a situation he or she finds him-/herself. Once the actor has a

general idea of where he or she is, he or she must do a script selection. In this step he

or she searches for possible actions to choose from a list of actions that correspond to

the already selected frame and to his or her interpretation of how he or she is expected

to behave (i.e. that correspond to his definition of the situation). Finally follows an

action selection, i.e. he or she must choose to act (or not to) by selecting one of the

alternatives the chosen script presents (Kroneberg 2006, 10f.).

According to Kroneberg (2006), the way in which each of these selections is done can be

more or less conscious, so that the level of elaboration needed to produce action may vary.

This variation in levels of elaboration is dependent on the modes of information process-

ing (or mode selections in short), which can follow either an automatic-spontaneous

mode (i.e. as-mode) or a reflecting-calculating mode (i.e. rc-mode). Depending on vari-

ables of the situation, the selection will tend to be made in an as-mode or in a rc-mode.

For example, a higher level of familiarity with the situation promotes an automatic an-

swer (i.e. ready-to-use programs are easy to access). Also pressure for a fast reaction will

result in less elaborated selections. On the contrary, being highly motivated to choose

wisely because e.g. of the cost of wrongdoing, will promote the use of the extra effort

needed to apply selections in a rc-mode (p. 11f.). In the case of crime acts, strongly

internalized moral norms will lead to automatic answers (Kroneberg et al. 2010, 261).

MFS and SAT share many arguments and many differences are only terminological. For

example, the modes of selection described by the MFS are equivalent to the forms of

action defined by the SAT. That is, habitual form of action and deliberation in

SAT equate the as-mode and rc-mode in MFS, respectively (Messner 2012, 13).

Also both theories assume that only when values are not strongly internalized, people

proceed to evaluate the possibility of committing crime (Kroneberg et al. 2010, 264).

In this sense, according to both theories action or inaction is primarily an automated

response that depends on the socialization of the individuals and only secondly on vari-

able characteristics of individuals and contexts (Kroneberg et al. 2010, 261).

As can be seen in Figure 2.1, the connection between individual and environment is

already very convincingly explained by the SAT with its definition of the situation. One

particularly interesting consequence of the SAT is that it faces in a very convincing

way the problem of linking self-control with social control as stated by Taylor (2001).

This is done by defining self-control not as a trait -like Gottfredson and Hirschi (1990)

do in their GTC- but as a situational variable (Wikstrom and Treiber 2007, 243). As


Figure 2.1: SAT & MFS complemented

Morality/Norms

Frames/Scripts

situational self-control(executive capabilities)

Mode Selection

CriminogenicExposure

-moral rules of setting-level of enforcement(deterrence)

SAT(situation)

MFS(variable rationality)

EnvironmentalCharacteristicscauses of the causes

(crime propensity)

behavioraloutcome

social/selfcontrol link

stated previously, according to the SAT self-control is the capability to resist temptation

in a situation where the contexts morality promotes an action that conflicts with the

individuals belief of what is good or bad. According to Wikstrom and Treiber (2007),

self-control will vary depending on the situation, but what remains mostly constant is

the trait responsible for the ability to exert self-control, i.e. the executive capability (p.

251). The representation of the environment the person creates is particularly depen-

dent on this capability, too, so that the perception of deterrence will be influenced by

the levels of self-control an individual possesses in a given situation (p. 253).

One essential contribution that the MFS adds to the SAT is a deeper explanation of the

process through which action alternatives get filtered (Messner 2012, 13). The process

which is triggered by Morality/Norms in Figure 2.1 takes place inside the head of

a person. The frames and scripts as defined by MFS are to be found here. The way in

which selections are made, is a consequence of the situation, i.e. depends on character-

istics of the setting and of the person. The individuals morality operates as a filter

of the frames and scripts that a person is going to consider in his selection process, and

also of the way in which these selections will be done (i.e. in a rc-mode or as-mode).

In social and cognitive psychology different dual-process and dual-system models have

been proposed to deal with variable rationality (see e.g. Chaiken and Trope 1999; Evans

2008; Kahneman 2003; Smith and DeCoster 2000). Dual-process theories share the idea

that there are two ways of processing information, with one being slow, conscious and

deliberative, and the other being rapid, unconscious and automatic (Evans 2008, 256).


Whether only different processes are recognized or also different underlying cognitive

systems, distinguishes dual-process from dual-system theories. One of the most influ-

ential works in this area is the one by Kahneman (2003). The author refers to both

systems as system 1 and system 2 (see Figure 2.2). While the names of the systems vary

between theories, the main idea is that system 1 is the more automatic one and system

2 the more reflective one.

Figure 2.2: Clusters of attributes associated with dual systems of thinking

System 1 System 2

Clu

ster

1

(Con

scio

usn

ess)

Unconscious(preconscious)

Implicit

Automatic

Low effort

Rapid

High capacity

Default process

Holistic, perceptual

Conscious

Explicit

Controlled

High effort

Slow

Low capacity

Inhibitory

Analytic, reflective

Clu

ster

2

(Evo

luti

on)

Evolutionarily old

Evolutionary rationality

Shared with animals

Nonverbal

Modular cognition

Evolutionarily recent

Individual rationality

Uniquely human

Linked to language

Fluid intelligence

Clu

ster

3

(Fu

nct

ion

alch

arac

t.) Associative

Domain specific

Contextualized

Pragmatic

Parallel

Stereotypical

Rule based

Domain general

Abstract

Logical

Sequential

Egalitarian

Clu

ster

4

(In

div

.d

iff.)

Universal

Independent ofgeneral intelligence

Independent ofworking memory

Heritable

Linked togeneral intelligence

Limited by workingmemory capacity

Source: Evans 2008, 257

One very interesting theory in this field, is Stracks and Deutschs Reflective-Impulsive

Model (RIM; see e.g. Strack and Deutsch 2004). The authors base their model on

the statements of Smith and DeCoster (2000), who identify two different memory sys-

tems that store different information and also operate in divergent ways. According to

the authors, there is the slow-learning system that operates through an associative

processing mode, and a fast-learning system that operates through a rule-based


processing mode (p. 110). Although the associative processing mode depends only on

the slow-learning system, the rule-based processing mode depends on both systems (p.

111). The general idea of RIM is that by defining two different systems that operate in

parallel and that may operate in accord or conflict with each other, variable rationality

can be explained (Strack and Deutsch 2004, 221f.).

According to Hofmann et al. (2009) the impulsive system is generating impulsive behav-

ior that is the result of the activation of an associative cluster in the long-term memory.

The reflective system, on the other hand, complements the functions of the impulsive

system by adding higher order mental process, like the ones involved in executive func-

tions and deliberation. The RIM also states that in a final step, both systems access the

motor cortex to activate behavior. So that both system are in a constant competition

to trigger behavior and who wins this competition will be decided in this final step and

depends on the strength of the activation of each system and on situational moderators

that may shift the degree of activation in favor of one system (p. 164f.).

In sum, the RIM as defined by Strack and Deutsch (2004) states that behavior follows

from the activation of determinants in an impulsive system or in a reflective system (p.

222). While the impulsive system is always activated and is responsible for automatic

answers applied to familiar settings, the reflective system may be disturbed through

situational moderators (p. 223). Hofmann et al. (2009) conducted a few experiments

to show under what conditions the capability of the reflective system to restrain stan-

dards may be altered. They show that the self-regulatory resources the reflective system

possesses at a given moment can be depleted through repetitive use. This implies that,

keeping everything else constant and within a close time period, with each new similar

impulse that has to be inhibited, the self-regulatory resources get reduced and the likeli-

hood that a new impulse can be inhibited decreases. The authors also show that alcohol

consumption alters the functioning of the reflective system but not of the impulsive one,

so that under the influence of alcohol restraint standards may be affected (p. 168ff.).

2.3 Conceptualization and measurement

One of the influences that the SAT and the MFS have generated is that more and more

studies including some measure of morality or acceptance of norms and also some mea-

sure of deliberation, like self-control or cost-benefit calculation, are been published in

criminology (see e.g. Cops and Pleysier 2014; Kroneberg et al. 2010; Pauwels and Svens-

son 2011; Pauwels 2013; Pauwels and Svensson 2011; Pauwels 2013; Svensson et al. 2010;

van Gelder and deVries 2014; Wikstrom 2009; Wikstrom and Svensson 2010; Wikstrom


2012). Self-control and morality tend to be measured through self-reports (for the op-

erationalization applied by Wikstrom and colleagues in most of their publications, see

Wikstrom et al. 2012, 132ff.). Self-control is mostly analyzed using adapted versions of

the scale proposed by Grasmick and colleagues (Arneklev et al. 1993; Grasmick et al.

1993). Morality on the other side, tends to be measured with much more heteroge-

neous instruments. Usually, people are asked how good or how bad they find a

given behavior and/or how ashamed they would feel if getting caught doing some-

thing wrong (see e.g. Wikstrom and Butterworth 2006).

The problems of self-reports, especially when it comes to measuring sensible topics

through surveys, are quite known (see e.g. Tourangeau 2000, 255ff.). Also particular

problems with the Grasmick et al. (1993) self-control scale and with other instruments

used in criminology to measure self-control, have been largely acknowledged (see e.g.

Marcus 2004; Piquero 2008; Schulz and Beier 2012; Tittle et al. 2004). The first step in

developing good measures is to clearly define the concepts of interest (see e.g. Groves

2004, 50ff.). If the main interest lies in the interaction of some concepts, then both con-

cepts need to be clearly defined as well as their interaction. The SAT makes a nice work

in defining individual morality and self-control and also how they interact. According

to Wikstrom and Treiber (2007) when an actor exerts self-control in the process of de-

liberation, his or her executive capabilities in conjunction with situational variables will

define the levels of self-control he or she possesses, and these executive capabilities reside

in the brains prefrontal cortex. The prefrontal cortex can be further subdivided into

two parts responsible mostly for habit -i.e. the dorsolateral prefrontal cortex (DLPFC)-

and another responsible mostly for deliberation -i.e. the orbital frontal cortex (OFC)-

(p. 253). These two subdivisions are then jointly responsible for the level of agency an

actor will put into action.

Keeping the interaction between propensity and exposition as explained by SAT, RIM

adds a different interpretation of the interaction between self-control and individual

morality. Explained through RIM, what is always activated is the impulsive system

or the long-term memory. The associations created by this system are slowly learned

through the history of the organism and provide quick answers to the environment de-

pending on its needs and learning experiences (Hofmann et al. 2009, 164f.). In other

words, the learned associative clusters are primarily dependent on experience and repe-

tition and only secondarily on the reflective adherence to moral norms. In this sense, the

tendency to impulsive behavior is closer to the perspective of Gottfredson and Hirschi


(1990) on self-control as a trait responsible for criminality.5

The premise made by Smith and DeCoster (2000) that the reflective system operates

as a rule-based processing mode means that the reflective system uses symbolically

represented and intentionally accessed knowledge as rules to guide processing (p. 111).

These internalized rules are socially learned and culturally shared (p. 112), so that they

can be in part equated to the morality and norms in the SAT and the MFS, respectively.

What people believe to be good or bad (independent of their actual behavior) is stored

in this system. The reflective system is also responsible to inhibit impulsive behavior

when necessary (Hofmann et al. 2009, 165), so that the executive functions defined by

SAT (i.e. as the capability to apply self-control when the morality of an actor contra-

dicts the morality of the setting) would be part of this system (see also: Hofmann et al.

2012). That is, self-control as defined by SAT would be the result of operations by the

reflective system, while self-control (or low self-control) as seen by the GTC would be a

consequence of how the impulsive system operates.

In opposition to SAT and MFS, RIM defines no moral filter but an always activated

impulsive system that may or may not trigger behavior depending on the strength of the

activation of both systems and on situational moderators. In other words, there are two

ways of processing information that operate in parallel. Whether the action selection

process follows an automatic response or deliberation will normally depend on which sys-

tem triggers action. The impulsive system is responsible for automatic responses while

the reflective is responsible for deliberation and may be more easily disturbed through

situational moderators (Strack and Deutsch 2004, 223).

The last statement can be seen as contradictory with regard to SAT and MFS, because

according to RIM moral norms get stored in the reflective system that is responsible

for deliberation, whereas the SAT and the MFS state that strongly internalized moral

norms would predict automatic action selection. This contradiction might be explained

through a theoretical and a methodological argument.

From a theoretical point of view, the reflective system relies on the impulsive one to

operate (but not the other way around; Strack and Deutsch 2004, 223). This means

that the reflective system is in part defined by the impulsive one. Strong internalized

values are expected to be stored in both systems (for a very nice in deep discussion

about how rational contents can get stored in the impulsive system and still be seen

5It should be noted that according to Hofmann et al. (2009), trait self-control and impulsive determi-nants of behavior are two different things (p. 170). Nevertheless Gottfredson and Hirschi (1990) includeimpulsive behavior as one of the manifestations of low self-control (p. 90).


as rational, see: Sauer 2012). And even in cases of modifying routines, according to

the RIM the links made in the impulsive system are stable but can be changed gradually

through learning (Strack and Deutsch 2004, 223). This can be achieved through repe-

tition and through a process that is referred as the process of consolidation, i.e. the

process through which newly formed memory is transferred by repeated presentation

from the fast binding to the slow-learning system (Smith and DeCoster 2000, 110).

Kahneman and Frederick (2002) also make the case for the transmission of contents

between systems: complex cognitive operations eventually migrate from System 2 to

System 1 as proficiency and skill are acquired (p. 51).

It can be hypothesized that newly learned moral norms can get strongly internalized

through this process of consolidation that changes the impulsive system. Consequently,

it would be expected that someone who strongly supports right and wrong as defined by

society should also have low impulsivity with respect to the particular behavior, which

leads to the methodological argument. In order for a measure of impulsivity to be valid

it needs to be specific to the temptation of interest, in this case violence, and each sys-

tem should be measured by employing different instruments (Hofmann et al. 2009, 167f.).

Figure 2.3: The Reflective-Impulsive Model (RIM)

ReflectiveSystem (RS)

(executive capabilities)

ImpulsiveSystem (IS)(trait impulsivity)

RS*IS*SM(state self-control)

self-control

outcome

social/selfcontrol link

SituationalModerators (SM)

EnvironmentalCharacteristics

situation

variablerationality

process ofconsolidation

The model to be tested in this study is summarized in Figure 2.3. In short, the impulsive

system produces impulsive behavior that can be restrained by the reflective system.

The outcome of this fight is also affected by situational and dispositional moderators

(Hofmann et al. 2009, 168ff.). The trait responsible for the ability to exert self-control

are the executive capabilities as defined by SAT. The trait responsible for the tendency

to act impulsively, is the trait of impulsivity and can be equated to the underlying


definition of self-control in GTC.6 Impulses (as opposed to trait of impulsivity) arise

when a latent motivation ... meets an activating stimulus (Friese and Hofmann 2009,

796). This implies that measures of impulsive determinants of behavior need to be

specific to the temptation of interest (Hofmann et al. 2009, 167). In the same way, state

self-control is dependent on the characteristics of the situation. Ideal measures of the

complete RIM should point at all these elements and their interplay so that states and

their determinants can be assessed and their effect on crime understood.

2.4 Research question and hypotheses

The perspective on integration adopted in this study follows SATs and MFSs example,

i.e. integration is pursued through the adoption of a theory of action, and not through the

more pure forms of theoretical integration (like a propositional integration respecting

each central argument of the component theories, and a cross-level integration capable of

explaining crime at the level of individuals as well as crime rates). Of the alternatives to

integration revisited in the previous pages, the one to be applied in this study resembles

what Thornberry (1989) defines as theoretical elaboration because a control perspec-

tive is chosen as a starting point. The dependent variable to be explained stays at the

level of individuals but environmental and individual characteristics, proved by crimi-

nology theories to predict crime, are to be linked through a dual-system theory of action.

The theoretical framework to be applied centers on the interaction between individual

and setting as explained by the SAT. However the interpretation of the interaction be-

tween self-control and individual morality differs in the RIM. Although the concepts of

self-control and morality and their operationalizations are similar to the ones applied by

most previous studies and also by the SAT, the framework used here changes the order

in which these factors predict acts of crime. The measure of self-control will be used

as a proxy to impulsive determinants of behavior, while morality is considered to be a

measure of the reflective determinants of behavior.7

The main focus in this study is on the predictive capability of the measures of both

systems (reflective and impulsive) and their interaction as shown in Figure 2.3. Acts of

violence are to be predicted. Accordingly, the main research question is:

6In order to prevent confusion, any use of the word control should be avoided when referring tothe impulsive system, because the reflective is the one that restrains. Nevertheless, whether to definethis trait as self-control or impulsivity is more a question of focus, because both look at the same coin,but from a different perspective (Friese and Hofmann 2009, 796).

7It should also be stressed that according to the RIM impulsive determinants of behavior shouldnot be measured using self-reports. In this study classical measures of self-control are considered to beproxies to the determinants of impulsive behavior, but one should be cautious as to its assumptions.


Can acts of violence be explained as outcomes of self-control that depend mainly on the

interplay of three determinants: reflective and impulsive determinants of behavior and

situational moderators?

The first hypothesis to be tested is that the interaction between determinants of both

systems predict acts of violence better than their non-interacted measures. This hy-

pothesis relies also on the assumption that each system has a significant effect on acts of

violence. With respect to the role of situational moderators, the effect of intoxication

will be tested. It is expected that it has a significant effect on acts of violence and

that the interaction between the determinants of behavior in both systems is moder-

ated by this situational variable. Taking into account the arguments of SAT, MFS and

RIM, this study considers four possible combinations between reflective and impulsive

determinants of behavior that may lead to either more automatically or more controlled

action selection (see Table 2.1). When the norms are strongly internalized (as verbalized

through self-report) and the impulsivity is low, an automatic adherence to the moral

rules of society is expected. When there is rejection to the norms of society and also high

impulsivity, an automatic response is expected in the form of acts of violence. In the two

remaining cases, i.e. strong acceptance of moral norms and high impulsivity, or strong

rejection of moral norms and low impulsivity, a fight between systems is expected in

order to decide who triggers action. As a consequence, a more influential moderation of

situational variables is expected for these last two combinations. The second hypothesis

deals with this idea.

Table 2.1: Impulsive and controlled outcomes: four scenarios

high impulsivity low impulsivity

pro violence automatic (+) fight (social control)

against violence fight (state self-control) automatic ()

Because according to the RIM the impulsive system is always activated while the reflec-

tive system may be disengaged from processing information (asymmetry as defined

by Strack and Deutsch 2004, 223) and the associative processing mode draws solely on

the slow-learning system, [while] the rule-based processing mode uses both memory sys-

tems, not just the fast-learning one (Smith and DeCoster 2000, 111), it is assumed that

the information stored in the impulsive system should precede the information stored

in the reflective system. In other words, it is expected that the effect of the impulsive

determinants of behavior is mediated by the reflective ones. This will be tested in the

third hypothesis.


With respect to the role of the environment, it has been largely argued that charac-

teristics of the context, like school climate, have an impact on acts of crime (see e.g.

Gottfredson 2001; Siegmunt 2012). In this study it is assumed that the school setting

should have a direct effect on the reflective determinants of behavior but not on the im-

pulsive ones. Specifically this means that characteristics of the environment, should have

a direct effect on the morality of pupils (i.e. their opinion about right and wrong) and

their morality should have a causal effect on acts of crime (moderated by self-control).

This is the fourth and final hypothesis.

Formally, the following hypotheses will be tested:

H1: Reflective and impulsive determinants of behavior interact to predict self-control outcomes.

H2: Intoxication moderates the interaction between impulsive and reflective de-terminants of behavior.

H3: The effect of impulsive determinants of behavior is mediated by the reflectiveones.

H4: School climate effect on acts of violence is mediated by reflective determinantsof behavior.

3. Methods

In this chapter the data used is described, the operationalization of variables is ex-

plained and measures of reliability are investigated. Finally the statistical techniques

implemented are generally described.

3.1 Data source

Data from the second International Self-Report Delinquency (ISRD-2) study is used

(Enzmann et al. 2010; Junger-Tas et al. 2012, 2010). The ISRD-2 is the follower of the

ISRD-1 (Junger-Tas et al. 2003). The ISRD project is a cross-national survey with a

comparative design that collects data on juvenile delinquency and victimization (Mar-

shall and Enzmann 2012a, 21ff.). In its second wave, comparability standards have been

improved and assessed (see e.g. Enzmann 2013).

The sampled population are classes of grade 7 to grade 9 (students mostly between 12

and 14 years old) in schools of 30 different countries (see Table 3.1 for the descriptive

values of the general sample and each country). The sampling units are the classes,

weighted by number of pupils. Representation at the level of cities and not of countries

is pursued, with a goal of at least two medium to large cities by country (with exception,

see Table 3.1). In order to keep the results of the different countries comparable, a

standardized self-report survey is applied with emphasis on standardization of meaning

and not of words (see e.g. Harkness et al. 2010). One of the main objectives of the

ISRD-2, is the explanation of delinquent and criminal behavior through the test of

criminological theories (Enzmann et al. 2010, 161).

3.2 Operationalization

3.2.1 Dependent variable

The dependent variable is the life-time prevalence of at least one of three acts of violence:

assault, group fight, carrying a weapon. The prevalences for each question and for the

20

Methods 21

Table 3.1: Sample description

Country Cities Schools Classes % Included N Included

Armenia 5 15 93 .977 1,997Aruba 1 10 29 .833 587Austria 7 45 125 .956 2,863Belgium 4 43 149 .900 2,077Bosnia/H. 2 37 85 .875 1,764Cyprus 5 16 102 .858 1,981Czech Rep. 3 91 160 .941 3,055Denmark 1 65 81 .879 1,210Estonia 3 100 129 .889 2,320Finland 1 44 77 .999 1,363France 3 23 66 .893 2,141Germany 7 68 158 .941 3,271Hungary 2 101 109 .939 2,069Iceland 1 26 32 .927 548Ireland 7 37 73 .884 1,381Italy 15 95 273 .956 5,064Lithuania 5 47 93 .939 2,042NL Antilles 2 33 100 .919 1,583Netherlands 17 40 114 .946 2,204Norway 3 41 83 .867 1,468Poland 7 46 90 .937 1,366Portugal 3 57 120 .944 2,470Russia 5 41 121 .954 2,206Slovenia 5 41 117 .936 2,091Spain 4 80 80 .904 1,617Suriname 2 87 111 .900 2,158Sweden 3 74 119 .894 2,040Switzerland 3 70 205 .969 3,531USA 4 15 148 .888 2,132Venezuela 5 47 94 .758 1,760

Total 135 1,535 3,336 .919 62,359

prevalence of at least one of the three acts (called violence) can be seen in Table 3.2. The

valid cases for each question vary, because of omissions of only particular questions. The

final violence prevalence includes each case that answered at least one of the violence

related questions. The questions were formulated as follows (in the english version for

USA):

assault: Did you ever threaten somebody with a weapon or to beat them up, justto get money or other things from them?

gr.fight: Did you ever participate in a group fight on the school playground, afootball stadium, the streets or in any public place?

weapon: Did you ever carry a weapon, such as a stick, knife, or chain (not apocket-knife)?

Methods 22

Table 3.2: Violence related prevalences by country

Country assault valid gr.fight valid weapon valid violence valid

Armenia .014 1,996 .184 1,983 .057 1,994 .198 1,997Aruba .028 577 .125 584 .107 582 .174 587Austria .022 2,855 .115 2,850 .061 2,850 .146 2,863Belgium .020 2,048 .131 2,046 .104 2,059 .184 2,077Bosnia/H. .009 1,759 .112 1,748 .029 1,752 .120 1,764Cyprus .018 1,965 .124 1,938 .026 1,951 .132 1,981Czech Rep. .006 3,047 .109 3,036 .072 3,041 .158 3,055Denmark .013 1,202 .116 1,194 .110 1,199 .179 1,210Estonia .018 2,311 .074 2,300 .095 2,313 .152 2,320Finland .007 1,363 .072 1,363 .076 1,363 .125 1,363France .023 2,128 .190 2,106 .080 2,124 .218 2,141Germany .043 3,251 .130 3,252 .099 3,247 .191 3,271Hungary .011 2,057 .089 2,038 .061 2,046 .129 2,069Iceland .009 547 .059 546 .057 547 .099 548Ireland .026 1,367 .249 1,361 .122 1,369 .285 1,381Italy .015 5,034 .151 5,028 .050 5,047 .171 5,064Lithuania .009 2,034 .086 2,033 .081 2,031 .141 2,042NL Antilles .020 1,575 .081 1,562 .078 1,568 .137 1,583Netherlands .030 2,196 .154 2,194 .098 2,192 .201 2,204Norway .014 1,466 .057 1,466 .057 1,463 .089 1,468Poland .010 1,363 .073 1,359 .069 1,360 .119 1,366Portugal .005 2,456 .088 2,454 .036 2,464 .109 2,470Russia .011 2,204 .084 2,195 .053 2,200 .117 2,206Slovenia .007 2,079 .068 2,077 .052 2,084 .105 2,091Spain .010 1,600 .096 1,605 .048 1,609 .119 1,617Suriname .013 2,134 .069 2,133 .067 2,137 .113 2,158Sweden .015 2,024 .074 2,017 .064 2,020 .111 2,040Switzerland .013 3,493 .086 3,485 .077 3,499 .135 3,531USA .021 2,112 .094 2,117 .096 2,116 .154 2,132Venezuela .011 1,724 .077 1,732 .028 1,743 .090 1,760

Total .016 61,967 .110 61,802 .069 61,970 .149 62,359

3.2.2 Independent variables

3.2.2.1 Impulsive determinants of behavior

Impulsive determinants of behavior will be measured through a shortened version of the

self-control scale by Grasmick et al. (see Grasmick et al. 1993; Arneklev et al. 1993

for the original scale and Marshall and Enzmann 2012b,a for a detailed description of

the psychometric properties of the questions included in ISRD-2). Although impulsive

determinants of behavior should not be measured through self-reported data (Hofmann

et al. 2009, 167f.), it is expected that the self-control scale can serve as a proxy. The

exercise presented here should be considered as a preliminary test of a dual-system

perspective. The questions and subscales are as follow (answer categories: 1.agree fully;

2.agree somewhat; 3.disagree somewhat; 4.disagree fully):

Impulsivity (IM):1. I act on the spur of the moment without stopping to think.

Methods 23

2. I do whatever brings me pleasure here and now, even at the cost of some

distant goal.

3. Im more concerned with what happens to me in the short run than in the

long run.

Risk-taking (RT):4. I like to test myself every now and then by doing something a little risky.

5. Sometimes I take a risk just for the fun of it.

6. Excitement and adventure are more important to me than security.

Self-centeredness (CE):7. I try to look out for myself first, even if it means making things difficult for

other people.

8. If things I do upset people, its their problem not mine.

9. I will try to get the things I want even when I know its causing problems for

other people.

Volatile temper (TE):10. I lose my temper pretty easy.

11. When Im really angry, other people better stay away from me.

12. When I have a serious disagreement with someone, its usually hard for me

to talk about it without getting upset.

First the four sub-scales previously presented are constructed through simple row mean

calculation. In order for a respondent to get a valid value in each of the sub-scales, he

or she must have answered at least two of the three composing questions. The final

impulse scale is the simple row mean of the sub-scales. For the final scale, only cases

with valid answers in all the sub-scales are included. Finally the impulse scale is me-

dian centered and rescaled to standard deviation unit (see Table 3.3 for the descriptive

statistics of the final scale before and after being standardized, and Figure 3.1 for the

scales histogram; the correlations between items including the final scale is shown in

the appendix in Table A.1). The scale reliability coefficient (Cronbachs ) of the final

scale is .823.

Table 3.3: Descriptive statistics: impulsive determinants of behavior (scale)

Variable Mean Std. Dev. Min Q.25 Median Q.75 Max

impulse 2.165 .601 1.000 1.750 2.167 2.583 4.000impulse (std) -.002 1.000 -1.940 -.693 .000 .693 3.048

Methods 24

Figure 3.1: Histogram: impulsive determinants of behavior

mean = .002

impulsivecontrolled

02

46

perc

ent

2 1 0 1 2 3

3.2.2.2 Reflective determinants of behavior

As a measure of the reflective determinants of behavior, pro-violence values have been

asked through 5 items (see Marshall and Enzmann 2012a, 57; answer categories: 1.agree

fully; 2.agree somewhat; 3.disagree somewhat; 4.disagree fully):

1. A bit of violence is part of the fun

2. One needs to make use of force to be respected

3. If one is attacked, one will hit back

4. Without violence everything would be much more boring

5. It is completely normal that boys want to prove themselves in physical fights with

others

The reflect scale is generated by calculating the simple row mean of the five items. Only

cases with three or more valid answers are included. The reflect scale is then median

centered and rescaled to standard deviation unit (see Table 3.4 and Figure 3.2 for a

description of the final scale; the correlations between items and final scale is shown in

the appendix Table A.2). The scale reliability coefficient (Cronbachs ) is .705.

Table 3.4: Descriptive statistics: reflective determinants of behavior (scale)


reflect 2.998 .661 1.000 2.600 3.200 3.400 4.000reflect (std) -.305 1.000 -3.326 -.907 .000 .302 1.210

Methods 25

Figure 3.2: Histogram: reflective determinants of behavior

mean = .305

reject viol.accept viol.

05

1015

perc

ent

3 2 1 0 1

3.2.2.3 Situational moderators

Situational variables are those that may influence the fight between systems. In other

words, variables that may shift the likelihood of triggering action in favor of one of both

systems. Characteristics of the situation like friends pushing a classmate to do something

wrong may be considered. In this study a variable measuring life-time intoxication is

included under the assumption that the consumption of alcohol and/or drugs should

alter the inhibiting capabilities of the reflecting system. A value of one or yes, is

assigned to someone who has been at least one time drunk (2 items) or has taken drugs

(4 items). Cases having answered at least one of the items are included (see Table 3.5

for the descriptive statistics).

Table 3.5: Descriptive statistics: life-time intoxication (scale and items)

Variable Obs Mean Std. Dev.

life-time prev intoxication (scale) 62,359 .274 .446life-time prev drunk beer/wine 61,641 .234 .423life-time prev drunk spirits 61,556 .163 .369life-time prev hash 61,917 .088 .283life-time prev XTC 61,863 .013 .115life-time prev L/H/C/her/co 61,879 .010 .100

3.2.2.4 Environmental variables

School climate related characteristics (i.e. school disorganization) will be included, ag-

gregated at the level of the school. These characteristics should be capable of influencing

the pro-violence values of the pupils -i.e. their reflective system- and also on the long

Methods 26

run -through the process of consolidation- their impulsive system. The following four

items are included (answer categories: 1.not at all true; 2.not true; 3.true; 4.very true):

1. There is a lot of stealing in my school.

2. There is a lot of fighting in my school.

3. Many things are broken or vandalized in my school.

4. There is a lot of drug use in my school

A scale at the level of pupils is first generated through row means of cases with three

or more valid answers. This scale has an value of .748. A linear multilevel regression

is estimated with this scale as dependent variable and no predictors, in order to get the

value of the scale or its ecological reliability (Raudenbush and Sampson 1999). The

following formula is used:

=2u

2u + (2e/

Nn )

=.152

.152 + (.407/61,9133,336 )= .874

where 2u is the between variance, 2e the within variance, N the size of the complete

sample included in the analyses, and n the number of clusters. This scale is further

aggregated at the level of classes using group means. The final scale at level of classes is

then median centered and standard deviation rescaled (see Table 3.6 and Figure 3.3 for

the descriptive statistics of the scale and Table A.3 for the correlations between items).

Table 3.6: Descriptive statistics: school disorganization scale

Variable Obs Mean Std. Dev. Min Q.25 Median Q.75 Max

school disorg. (L1) 61,913 2.127 .746 1.000 1.500 2.000 2.500 4.000school disorg. (L2) 62,359 2.127 .414 1.000 1.833 2.103 2.409 4.000school disorg. (L2; std) 62,359 .058 1.000 -2.663 -.651 .000 .739 4.580

3.2.3 Control Variables

To avoid spurious relations between the dependent variable and the independent vari-

ables of interest, control variables will be included. The controls represent all relevant

variables in the prediction of acts of violence at hand and also of the reflective determi-

nants of behavior. This is because of the mediation analyses (see the next section).

Controls included are:

Characteristics of individuals: gender: Are you male or female? (1 = male; 0 = female).

age: How old are you? (continuous; between 11-18).

Methods 27

Figure 3.3: Histogram: school disorganization scale (agg.)

mean = .043

disorderorder

02

46

perc

ent

4 2 0 2 4

migration: Generated out of three questions (cases with at least one valid

answer included): Where you born in this country?; In what country was

your mother born?; In what country was your father born? (1 = at least one

member of family born in another country; 0 = else).

Family related variables: violence at home: Have you ever experienced any of the following serious

events? Repeated serious conflicts or physical fights between your parents,

by parents we also mean step- or adoptive parents (1 = yes; 0 = no).

traditional family structure: Are you living with your own mother and

father? (1 = Yes, I live with my own mother and father; 0 = other).

family bonding: Scale out of two questions (cases with at least one valid

answer included): How do you usually get along with the man you live with

(father, stepfather....)?; How do you usually get along with the woman you

live with (your mother or stepmother)? (1 = not well at all, 2 = not so well,

3 = rather well, 4 = very well; row mean, median centered and rescaled to

standard deviation unit).

Others: neighborhood disorganization: Scale out of five questions (cases with at

least three valid answers included): There is a lot of crime in my neighbor-

hood; There is a lot of drug selling; There is a lot of fighting; There are a lot

of empty and abandoned buildings; There is a lot of graffiti (1 = not at all

Methods 28

Table 3.7: Descriptive statistics: control variables


male .486 .500 .000 .000 .000 1.000 1.000migration .230 .421 .000 .000 .000 .000 1.000age 13.948 1.146 11.000 13.000 14.000 15.000 18.000violence at home .110 .313 .000 .000 .000 .000 1.000family bonding 3.642 .535 1.000 3.500 4.000 4.000 4.000family bonding (std) -.669 1.000 -5.612 -.935 .000 .000 .000family structure .732 .443 .000 .000 1.000 1.000 1.000grade 8.019 .817 7.000 7.000 8.000 9.000 9.000neigborhood disorg. 1.631 .704 1.000 1.000 1.400 2.000 4.000neigborhood disorg. (std) .329 1.000 -.568 -.568 .000 .852 3.691

true, 2 = not true, 3 = true, 4 = very true; row mean, median centered and

rescaled to standard deviation unit).

school grade: Meta-data (categorical variable; 7th -reference-, 8th, 9th)

3.3 Statistical analyses

3.3.1 Multilevel analysis

ISRD-2 data is of a multilevel nature. At least five levels can be recognized, from the

lowest to the highest these are: pupils, classes, schools, cities, countries. Also coun-

try clusters could be defined, for example with respect to decommodification (Esping-

Andersen 1990). In this study, the dependent variable is the life-time prevalence of

acts of violence, i.e. the outcome of a Bernoulli trial or a dummy variable. Therefore

nonlinear models are the better choice. Because non-linear models are already complex,

extra complexity should be avoided. In this case, random intercepts only at the level of

schools are included. By clustering at the level of schools, a very big sample size at each

level can be achieved and a better homogenization of the environmental conditions (see

e.g. Oberwittler and Wikstrom 2009).

Following Guo and Zhao (2000), the reliance on multilevel models has at least four ad-

vantages: first multilevel analyses is the adequate framework for analyzing multilevel

data, second it corrects for possible biases resulting from the clustering nature of the

data, third it also corrects for standard errors, and fourth it allows to decompose the

total variances of the dependent variable into variance explained at each level (p. 444f.).

Methods 29

The estimator used in this study is maximum likelihood (MLE; see Winkelmann and

Boes 2006, 44ff.).8 Under random sampling it is possible to assume independence be-

tween observations and that all observations come from the same data generating pro-

cess, so that they are identically independent distributed (iid). The probability of ob-

serving yi given the unknown parameters can be described as the probability or density

function of yi, i.e. f(yi; ). Because the observations are independent of each other, the

joint probability function of the sample is the product of the individual probability func-

tions, i.e. f(y1, ..., yn; ) =ni=1 f(yi; ).

MLE changes the function of the random sample y1, ..., yn given , for a function of

given the random sample y1, ..., yn. This leads to the likelihood function:

L() = L(; y) =

ni=1

L(; yi) =

ni=1

f(yi; )

where L(; yi) is the likelihood contribution of a given observation and L() = L(; y) =

L(; y1, ..., yn) is the likelihood function of the whole sample.

Once the likelihood function is defined, the estimation process consists of finding the pa-

rameter estimates (i.e. ) that produce the maximum value for the likelihood function.

The bigger the value of the likelihood function, the better the estimated parameters

fit the sample. If the assumptions made for the maximum likelihood estimation are

right, then maximum likelihood is consistent, asymptotic normal, and efficient (see e.g.

Amemiya 1985, 115ff.).

In the case of non-linear models, the reliance on multilevel models when the data is

of a multilevel nature is more important than in linear models. The reason is that for

non-linear models estimated with maximum likelihood, the consistency of the estimator

depends on the likelihood function being specified correctly (see e.g. Greene 2012, 712ff.).

In other words, heteroskedasticity in non-linear models does not only affect the standard

errors, but also the consistency of the coefficients. Relying on robust standard errors is

not really an option for non-linear models, because in the presence of heteroskedasticity

the likelihood function is not specified correctly. While robust standard errors may cor-

rect the standard errors, the coefficients may still be inconsistent. That is, while using

robust standard errors for non-linear models that are correctly specified is not necessary,

their use in models that are wrongly specified could produce an appropriate asymptotic

8For the ease of explanation, a one level empty model is explained here (i.e. yi instead of yij andno covariates included). The same results applied here work if covariates are included (i.e. f(yi|xi; )).The extra difficulties of multilevel models are discussed later.

Methods 30

covariance matrix for an estimator that is inconsistent (see Freedman 2006).

At least two different formulations are commonly used to describe non-linear models: as

random utility models for individual choice and as a latent regression model. Here the

latent formulation is explained (taken mostly from Cameron and Trivedi 2005, 475ff.;

Goldstein 2011, 111ff.; Greene 2012, 686ff.; Rabe-Hesketh and Skrondal 2012, 520ff.;

Snijders and Bosker 1999, 223f.; Winkelmann and Boes 2006, 95ff.). The description

that follows is limited to random intercept models only, i.e. each cluster (school) may

differ with respect to the intercept but the slopes are fixed across clusters.

Because the observed outcome is a binary response variable that has a Bernoulli prob-

ability function, i.e.:

f(yij |xij , uj) = piyijij (1 piij)1yij yij = 0, 1

where piij = P (yij = 1|xij , uj), a link function is needed that allows to link thepredictions in the observed outcome to the expected value of a latent (unobserved)

continuous variable:

yij = xij + uj + eij

where xij = 0 +1x1ij + ...+kxkij and xij can be a variable at the level of individuals

or at the higher level (i.e. x.j or generally in the literature zj).

The different models for binary response variables, differ in the way that piij is param-

eterized through the assumption about the distribution of eij . The link between latent

and observed models can be seen as follows:

yij =

1 if yij 00 if yij < 0where yij is the observed binary variable and y

ij the latent one. It follows:

piij = P (yij 0|xij , uj) = P (xij + uj + eij 0|xij , uj)

= P (eij (xij + uj)|xij , uj)= 1 P (eij < (xij + uj)|xij , uj)

where the last equality is valid for symmetric distributions and shows that the observed

variable is used to model the probability of the latent equation of exceeding the threshold

Methods 31

of zero9. When assuming that eij has a standard logistic distribution, i.e. a logistic

distribution with mean 0 and variance pi2/3:

P (eij < a) =exp(a)

1 + exp(a)= (a)

a multilevel logit model is relied on. The conditional function of the logit model is given

by:10

1 P (eij < (xij + uj)|xij , uj) = 1exp[(xij + uj)]

1 + exp[(xij + uj)]=

exp(xij + uj)1 + exp(xij + uj)

so that

piij = G(xij + uj) =

exp(xij + uj)1 + exp(xij + uj)

= (xij + uj)

Applying some algebra,

piij =exp(xij + uj)

1 + exp(xij + uj)

piij + piijexp(xij + uj) = exp(x

ij + uj)

piij = exp(xij + uj) piijexp(xij + uj)

piij = exp(xij + uj)(1 piij)

piij1 piij = exp(x

ij + uj)

ln

(piij

1 piij

)= xij + uj

The logit link function is then defined as logit(piij) = ln(piij/(1 piij)).

This is only one of many possible functions G(xij+uj). For example, assuming that eijhas a standard normal distribution, leads to the probit model with the function (xij+

uj). Independently of the assumption about the distribution of eij , the likelihood of

cluster j would be

nji=1

[G(xij + uj)]yij [1G(xij + uj)]1yij

If the random intercepts uj were observed, this likelihood would be enough to estimate

the parameters in the model. But because they are unobserved, a solution is needed to

solve for the fact that piij is conditional on uj , i.e. that piij = G(xij + uj) = P (yij =

9Using zero as a threshold is an innocent assumption, as far as the model contains a constant term(Greene 2012, 686).

10Keep in mind that,exp(a)

1 + exp(a) 1

1 + exp(a) .

Methods 32

1|xij , uj). Treating the uj as an unobserved normally distributed and uncorrelated ran-dom variables, i.e. uj N(0, 2u),11 and relying on the theorem from Bayes,12 allows tointegrate the random intercepts out, so that piij is no longer conditional on uj (this solu-

tion is known as the Butler and Moffitts method and is also applied to estimate random

effects non-linear panel models; see e.g. Greene 2012, 582). Formally, the joint proba-

bility of all responses for cluster j conditional on the random intercept and covariates is

given by:

P (yj |xj) =P (yj |xj , uj)(uj)duj

However the density unconditional on uj , i.e. P (yj |xj), has no closed expression. Dif-ferent options exist to solve for this problem, e.g. numerical integration relying on

quadrature approximation (like Gauss-Hermite). In this study mean-variance adaptive

GaussHermite quadrature is used (the default in Stata), which has the advantage of

relying on the maximum likelihood estimation, so that fit measures based on maximum

likelihood can be used after estimation (e.g. BIC and AIC criterion).

The marginal likelihood is finally given by the product of the marginal joint probabilities

of the responses in each cluster:

L(, 2u) =Nj=1

P (yj |xj)

With respect to the results presented in this study, three considerations should be kept

in mind. First, multilevel models can produce predictions of: only the fixed part, only

the random part, or both. In this study only the fixed part is included to estimate

predicted probabilities and marginal effects. This implies fixing the random intercept at

zero, its theoretical mean value.

Secondly, the model is non-linear multiplicative (while OLS is linear additive), which

implies that marginal effects are conditional on the values of the variable of interest and

also on the values of all the other covariates in the model (see e.g. Winkelmann and

11The assumption that E(uj) = 0 produce no loss of generality as long as an intercept is included inthe model (see: Wooldridge 2010, 612).

12The theorem from Bayes states that, for two random variables X,Y , the conditional density of Xgiven Y = y, equals the joint density of X,Y divided by the marginal density of Y (see e.g. Mosler andSchmid 2006, 27ff.). Formally,

fx|y(x, y) =fxy(x, y)

fy(y)

so that,fxy(x, y) = fx|y(x, y)fy(y)

Methods 33

Boes 2006, 104f.). For continuous variables this looks as follows:13

pi

xh=G(x)xh

=

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