Mindfulness Meditation Classes in South African Townships

A Novel Approach to Youth Crime Prevention:

Master Thesis to fulfil the requirements of the M.Sc. in Political Science, main field of Study: Development Studies Submitted on January 3, 2020 by: Katharina Kneip

Supervisors: Hans Blomkvist and Anders Westholm

Department of Government Uppsala University Autumn Term 2019

Word count: 19’922 Abstract

Children growing up in poor areas with high crime rates are shown to easily get involved in violent actions and criminal gangs. In South Africa, despite considerable efforts to reduce youth delinquency, youth crime rates are still disturbingly high – specifically, in the townships of the Cape Flats. This paper points out an important aspect previously unaddressed by most youth crime prevention: the subconscious roots of youth crime. What if we could develop youth crime prevention programs that manage to impact the subconscious behavioral patterns of youth in high crime areas? This paper proposes a promising and cost- effective approach that has great potential to affect multipe causes of crime: mindfulness meditation. Built upon newest findings in Neuroscience, this paper suggests that mindfulness meditation classes are associated with a reduction in aggressive behavior, a risk factor for youth crime, and an increase in self- efficacy, a protective factor. The impact of mindfulness classes at a high school in Khayelitsha, a poor and violent-stricken township of Cape Town, is analyzed. Self-reported aggression and self-efficacy are measured via a psychometric survey questionnaire created from two well-tested and validated scales. Regression analyses of 384 survey answers provided mixed results. Whilst novice meditators were not associated with higher self-efficacy and lower aggression, long-term meditators performed better in several dimensions of self-efficacy and aggression, yet no significant relationship was found. Further research specifically needs to investigate the moderating effect of age (a proxy for psychological development) on meditation. This study aims to bridge the gap between the outdated paradigms of youth crime prevention and ancient wisdom via ground-breaking new evidence from the field of Neuroscience. This study furthermore hopes to point policy makers toward developing new, integrative and sustainable approaches to youth crime prevention – approaches that give back agency to our youth.

KEYWORDS: Youth crime, crime prevention, empowerment, agency, youth development, meditation, mindfulness, resilience, aggression, self-efficacy, Neuroscience

I dedicate this paper to the children of all the coming generations – may we lay the grounds for a better world, a more peaceful world. Acknowledgements

Deep gratitude goes to South Africa: to the NGO WISE (Wellbeing in Schools and Education) and the Tushita Kadampa Meditation Centre for their priceless community work, as well as the Centre of Science and Technology (COSAT) and Sentinel Primary School for their vital help and support.

I specifically wish to extend my appreciation and gratitude to Gen Kelsang Pagpa and Kelsang Sankyong at Tushita Kadampa Meditation Centre for their invaluable help and all the ways in which they tirelessly go out into disavantaged communities and positively impact so many people’s lives. Gen Pagpa, director of the centre, has been holding meditation classes for the students at COSAT in the township of Khayelitsha, Cape Town, since 2013 – without his meaningful work, this project would have not existed, thank you, Pagpa.

Special thanks are directed to Phadiela Cooper, principal at COSAT, for the help in organizing and supervising the distribution of nearly 400 survey questionnaires despite considerable infrastructural setbacks. I also thank her for her heart-felt devotion to the well-being and success of her students, who, along with all the teachers at COSAT and supporters of the school, plays a crucial role in the lives of these young people.

I would also like to thank the IT teachers at COSAT Tiro Motaung, Zahra Majija and Mass Makumbe for supervising and helping with the survey distribution, thank you for your patience and support. I also thank all the students of COSAT who participated in my study, thank you for the laughs and moments spent together, and thank you for your time and meaningful answers to the survey which help us gain greater insight from which coming generations may hopefully benefit.

A big thank you goes to WISE and the wonderful facilitators: Shumi Chimombe, Callie Widd and Carmen Clews, for organizing and coordinating mindful morning assemblies for the students at Sentinel Primary School in Hangberg and to its students who, despite living in very difficult conditions, spread love and joy. I especially want to thank Carmen Clews, co-founder of WISE, for gathering and providing observation reports that were immensely helpful for my project, and for her pure enthuisiasm and kind heart working with the children.

Also, I wish to thank Natalie Buley, Sarah Foale and Thapelo Maila for helping with the meditation classes at COSAT, the moments shared and the meaningful work you all do in the communities.

I am very grateful to Hans Blomkvist for his time, patience, valuable comments and the many words of encouragement whilst supervising this thesis from almost 15’000km away. I would also like to thank Anders Westholm for his incredible support, expertise and answering all my difficult questions regarding factor analysis.

Thank you, Raphael Gurtner, Sophia Wetterblad, Sarah Foale, and Thomas Xavier Fluri, for your time, comments and feedback on the paper. Deep gratitude goes out to Luzia Solothurnmann and Pia Kneip- Livers, thank for your time and meaningful input, and for always being there to remind me to get some sleep.

And lastly, thank you, Universe, for connecting me with so many wonderful and inspiring people on this journey – this is just the beginning.

Table of Contents

1 Introduction ...... 1 Outline of the Paper ...... 2 2 Background ...... 2 2.1 Problem Statement ...... 3 2.2 Policy Situation in South Africa ...... 5 Summary Chapter 2 ...... 6 3 Literature Review ...... 6 3.1 Youth Crime Causes ...... 6 3.1.1 Risk Factors ...... 7 3.1.2 Protective factors ...... 10 3.2 Exploring the Gap: The Subconscious Mind ...... 11 3.2.1 Literature from Neuroscience ...... 13 3.2.2 Literature on the effects of mindfulness meditation on criminal offenders ...... 17 3.2.3 Literature on mindfulness meditation in schools ...... 20 3.2.4 Literature on mindfulness practice with youth in need ...... 22 Summary Chapter 3 ...... 22 4 Theory ...... 23 4.1 Exploratory Study ...... 23 4.2 Risk and Protective Factors impact Resilience ...... 24 4.3 Theoretical Model and Hypotheses ...... 25 Summary Chapter 4 ...... 28 5 Methods ...... 28 5.1 Case Selection and Setting ...... 28 5.2 Research Design ...... 31 5.3 Operationalization ...... 33 5.4 Data Collection ...... 37 5.5 Possible Biases and Limitations ...... 40 Summary Chapter 5 ...... 41 6 Results ...... 41 6.1 Descriptive Statistics ...... 41 6.2 Construct Validity ...... 46

6.2.1 Factor Analysis Self-Efficacy ...... 46 6.2.2 Factor Analysis Aggression ...... 49 6.2.3 Internal consistency / Reliability ...... 51 6.3 Regressions ...... 51 Summary Results ...... 59 7 Discussion ...... 59 Summary Discussion ...... 62 8 Conclusion ...... 62 9 References ...... 64 10 Appendix ...... 72 Appendix A ...... 72 Appendix B ...... 74 Appendix C ...... 77 Appendix D ...... 79 Glossary ...... 92

Tables and Figures

Figure 1 The Mindfulness Meditation Model ...... 27

Table 1 Descriptive Statistics ...... 42 Table 2 Pattern Matrix Factor Loadings Self-Efficacy ...... 47 Table 3 Pattern Matrix Factor Loadings Aggression ...... 50 Table 4 Reliability Statistics Cronbach’s alpha ...... 51 Table 5 Regression Models Hypothesis 1 Total Self-Efficacy (T-SE) ...... 52 Table 6 Regression Models Hypothesis 1 Self-Regulatory Self-Efficacy (SR-SE) ...... 53 Table 7 Regression Models Hypothesis 2 Total Aggression (T-A) ...... 54 Table 8 Regression Models Hypothesis 2 Physical Aggression (P-A)...... 55 Table 9 Regression Models Hypothesis 3 Total Self-Efficacy (T-SE) ...... 56 Table 10 Regression Models Hypothesis 3 Self-Regulated Self-Efficacy (SR-SE)...... 57 Table 11 Regression Models Hypothesis 4 Total Aggression ...... 58 Table 12 Regression Models Hypothesis 4 Physical Aggression ...... 58

1 Introduction

Levels of youth crime and school violence in South Africa have been disturbingly high (Gevers and Flisher 2012: 205). The need for prevention programs has not gone unnoticed over the past decades and, along with policy-level initiatives aiming at youth violence prevention, numerous programs have been implemented in communities and schools around the country (ibid.). Yet, the current statistics and violent incidents involving school children (SAPS Crime Statistics 2018) suggest that youth crime continues to be a major problem in many townships in South Africa. Problematic is the lack of sustainable and inclusive approaches that allow youth enough agency. Whilst a vast body of literature agrees that risk factors for youth crime generally stem from individual, familial and community levels that affect our subconscious behavior, few programs manage to influence this aspect. The current study argues that most common approaches to youth crime prevention have failed to address a very important factor: The subconscious brain. What if we could develop youth crime prevention programs that manage to impact the subconscious behavioral patterns of youth in high crime areas? Based on Neuroscientific research, this paper suggests a technique for students to access their subconscious minds and increase self-regulation: Mindfulness meditation. This dissertation then explores following research question in the context of South African townships:

Can mindfulness meditation increase self-efficacy, a protective factor for youth delinquency, and decrease aggression, a risk factor?

Developing youth crime prevention programs that include mindfulness meditation as a technique would be an innovative and beneficial new approach, especially in the context of South African townships, for following reasons:

- The few current options available to address the subconscious mind, psychotherapy and hypnosis, require long periods of 1:1 treatment over multiple years which is not feasible, specifically in under-resourced areas. - Mindfulness meditation can be taught in large groups and techniques learnt within several weeks or months, and are a thereby an easy-to-implement and non-costly alternative.

- With this approach, policy makers and educators need not struggle to fix children or keep them under control but instead grant agency by allowing them to self-regulate more effectively without force or threats.

The purpose of this study is to offer a new perspective on youth crime prevention and ideas for a novel approach that has the potential to increase long-term resilience and become a viable and (cost-)effective method for youth crime prevention. Whilst much further research is required in the area, the suggested approach offers a priceless potential to create an inter-generational effect that could possibly break the cycle of violence in South Africa, and the world, by giving back agency to our youth.

“Education is the most powerful weapon which you can use to change the world.” – Nelson Mandela

Outline of the Paper This introduction chapter hopes to enable the reader to set off on the journey through this paper with a maximal understanding of the destination. It is followed by the chapter: Background which gives an overview of the problem of high youth crime in South Africa and its policy context. The Literature Review looks at studies analyzing the causes of youth crime and discovers a major gap. The Theory chapter presents the qualitative exploratory study conducted with students to identify possible outcome variables in the context of youth in high crime areas in South Africa. Further, the theoretical model and the hypotheses are developed. In the Methods chapter, the research design for the quantitative study is presented along with the case selection, measurement tool and data collection, and possible biases are discussed. The chapter Results provides the statistical analyses, followed by a Discussion of the findings. Lastly, the Conclusion sums up the findings and implications of this study. The Appendix provides additional information and tables, and at the very end a Glossary offers clarification of important terms/concepts.

2 Background This chapter introduces the problem of youth crime in the South African context which is followed by a short overview of the policy context and legislation.

2.1 Problem Statement

“The stark reality for many young people in South Africa is that violence and crime is a way of life; it insidiously infiltrates every aspect of their lives with both direct and indirect effects on their psychological, emotional, developmental, and physical well being” (Burton et al. 2009: 1).

Children growing up in poor communities with high crime rates are shown to easily get involved in violent actions and criminal gangs, lowering their chances of escaping poverty and the cycle of crime (Farrington and Welsh 2007; Burton and Leoschut 2013; Manaliyo 2014). In South Africa, despite the numerous efforts to reduce youth delinquency, youth crime rates are still shockingly high (Burton and Leoschut 2013). Especially the townships in the Cape Flats, suburbs of Cape Town, are classified as high violence, low resource areas and youth crime is specifically widespread in the many townships such as Guguletu and Khayelitsha (Pernegger and Godehart 2007; Clark 2012: 78, SAPS 2018). These areas have such high crime rates that “despite slower population growth, lower youth unemployment and better income figures, Cape Town still saw a 40% increase in murders and a rise in other crime in the past decade” (Tswanya 2017).

The South African Police Service (SAPS) Crime Statistics 2017/18 show murder rates as high 57 persons per day which, according to the Minister of Police, can be compared to a war zone (Parliamentary Monitoring Group 2018). Especially in the poor townships around Cape Town, violence is high (SAPS 2019).

Most disturbing is the fact that youth aged 12 to 22 “constitute a considerable percentage of both, victims and perpetrators of crime, and in particular violent crime” (Burton 2007: 1). In fact, approximately 35% of the inmates in South African prisons are under 25 years old (Clark 2012: 78). Since over a quarter of South Africa’s population is below 25 years old, it is today still the case that “youth criminality is one of the primary challenges facing contemporary South Africa” (Leoschut and Bonora 2007: 89).

In the townships of the Cape Flats, youth are the most likely perpetrators of violent crime. Findings of a study done in the township Khayelitsha confirms that most crimes are committed by young adults and teenagers (Manaliyo 2014: 600). Whilst (income) inequality and other forms of structural violence is often seen as the main cause of crime in South Africa, youth crime is not

merely a matter of economic and political inequality but also driven by psychological effects (World Bank Group 2015: 26; 2018). Whereas violent environments, abuse, and traumatic experiences have been shown to significantly increase the probability for youth offending,frustrations resulting from persistent, unjustified inequality can further increase reasoning for violent and criminal actions (World Bank Group 2018: 26).

Although the formal segregation ended with the abolition of Apartheid and residents are being allowed to move freely since two decades, the racial/socio-economic composition of many townships has remained much the same. In Khayelitsha for instance, the first illegal settlement for black workers in Cape Town in the 1970s and today the largest single township in Cape Town, the population is still to 98.62% classified as black with a very low socio-economic status (Frith 2011).

Hence, despite the formal end of Apartheid, adolescents in townships continue to carry deep intergenerational roots of violence and discrimination, weak family structures, and lacking role models of people escaping the cycle of poverty and crime – which does not only impact the new generations’ social networks but also the psychological patterns dictating their behavior (World Bank Group 2015: 26; 2018). Such deep societal trauma can very often lead to a lack of agency, i.e. a perceived lack of control over one’s life along with low self-worth (World Bank Group 2015: 26). These feelings of reduced agency can explain school dropout rates and, paired with high youth unemployment, easily lead to so-called ‘crimes of frustration’ or crimes of opportunity (World Bank Group 2018: 26). The persistent lack of opportunity in the townships, “especially when (correctly) perceived as unfair and heightened by extreme poverty”, tends to lead to more impulsive behaviors—such as crime and interpersonal violence (ibid.).

Whereas strutural violence generally “constitutes a significant contextual cause of the phenomenon, a more proximate and specific cause lies in young people’s exposure to direct violence in their schools, homes and communities” (Clark 2012: 77). Clark argues, simply sending young people to prison, where they often experience further and possibly greater levels of violence, is not the answer (ibid.). Unfortunately, this has been the general approach of dealing with youth crime in South Africa. The public discussion on youth crime in South Africa is

predominantly concerned with policing (Ward et al. 2012:1). However, suppression interventions do not attempt to reform individuals at all: Their aim is strictly to suppress, and such approaches tend to backfire (Cooper and Ward 2012: 261,263). The current statistics make evident that youth crime prevention strategies in South African townships have at large failed (Parliamentary Monitoring Group 2018).

2.2 Policy Situation in South Africa The Constitution of the Republic of South Africa says that “the child’s best interests must be considered in every decision made about the child” (Department of Justice and Constitutional Development 2008: 1). The National Youth Commission Act, 1996, established the National Youth Commission as a statutory body responsible for South Africa’s youth policy (Youth Policy Labs 2014: 1-2). This was subsequently replaced by the National Youth Development Agency, the main government agency for youth matters, established via Act 54 of 2008, which advocates mainstreaming of youth development within all governmental spheres at policy level, delivering services, facilitating, and implementing youth development programs (ibid.). The first drafts of the Integrated Youth Development Strategy aimed at streamlining youth economic development whilst integrating policies like the National Industrial Policy Framework into the National Youth Policy. The National Youth Development Agency is then again connected to the National Youth Service Policy Framework, promoting youth volunteering and thereby enabling youth themselves to contribute to development whilst building skills and developing abilities (ibid.). Within The Presidency the Youth Desk coordinates youth development (ibid.). On the civil society side, the South African Youth Council, founded in 1997, is a non-governmental, autonomous civil society umbrella association for youth organizations governed by the National Executive Committee, which consists of provincial secretaries and chairpersons (Youth Policy Lab 2014: 2). It represents youth in forums such as the National Economic Development & Labour Council (NEDLAC) or the National Skills Authority (NSA). In the wider context, South Africa signed the African Youth Charter in 2009, the African Union’s framework for youth empowerment and development in Africa (Youth Policy Lab 2014: 1).

In regard to the legislation regulating youth crime in South Africa, the Child Justice Act (Act 75 of 2008), enacted in 2010, is an effort by the government aimed at protecting the constitutional rights of children in conflict with the law (Department of Justice and Constitutional Development 2008: 1). Youth below the age of 18, suspected of having committed a crime, are no longer under the Criminal Procedure Act (Act 51 of 1977), the normal criminal procedure used for adults - rather, a new child justice process applies (ibid.). Whilst adolescents between 10 and 14 years of age are required to prove criminal capacity, children below the age of 10 years cannot legally be held responsible for their actions (Youth Policy Lab 2014: 1).

De iure, it seems that youth in South Africa are granted some political agency. De facto, South Africa’s youth still lack agency and inclusion in the development and execution of youth crime strategies (Clark 2012).

Summary Chapter 2 This chapter identified the prevailing problem of youth crime in South Africa. Current statistics on youth crime as well as the national policy context and legislation provided background for this study.

3 Literature Review The literature review is divided into different sub-chapters. The first sub-chapter looks at youth crime causes with the help of risk and protective factors. The concept will be taken up again in relation to resilience in chapter 4 to construct the theoretical model. The second sub-chapter explores the gap in literature presenting research from Neuroscience and other fields on the impact of mindfulness meditation in the context of criminal offenders and programs in schools.

3.1 Youth Crime Causes

Juvenile delinquency or juvenile offending, is generally understood as the participation of minors in illegal behavior. Youth crime, on the other hand, as defined in terms of the National Youth Commission Act, is broader and refers to persons between 14 and 35 years old having committed crime (c.f. Frank 2006: 114). From a crime prevention perspective, the definition adopted by the National Youth Development Policy Framework converges rather well with the age group

internationally considered at the greatest risk of offending (Frank 2006: 115). Crime prevention, as defined by the UN Commission on Crime Prevention and Criminal Justice (2002), “involves the disruption of mechanisms which cause crime events”. A large body of crime prevention literature aims at defining what causes crime and how these may be disrupted.

3.1.1 Risk Factors Existing literature widely discusses causes for youth crime in form of risk factors, a very useful way to systematically analyze and measure an else abstract concept.

Decades of research stemming from longitudinal studies documented a set of common risk factors for youth crime and anti-social behavior at the individual, the family, and the community levels (Farrington 1990; Loeber et al. 1998; Wasserman et al. 2003; Burton et al. 2009; Murray and Farrington 2010; Farrington and Welsh 2012; Gardner et al. 2015). “Most professionals agree that early on in a child’s life, the most important risks stem from individual factors (e.g., birth complications, hyperactivity, sensation seeking, temperamental difficulties) and family factors (e.g., parental antisocial or criminal behavior, substance abuse, and poor child-rearing practices). As the child grows older and becomes integrated into society, new risk factors related to peer influences, the school, and the community begin to play a larger role” (Wasserman et al. 2003: 2). According to Farrington and Welsh (2012: 1), amongst the most important risk factors for youth delinquency and crime are high impulsiveness, and low attainment and empathy (individual level), criminal or anti-social parents, disruptive families and parental conflict (family level), and growing up in deprived, low-income and high-crime neighborhoods, associating with criminal peers and attending high-delinquency-rate schools, (community level).

Whilst different opinions exist amongst scholars as to which risk factor is most salient, experts agree that there is no one single risk factor that leads a young person to delinquency, rather, early juvenile offending is caused by a number of risk factors (Wasserman et al. 2003: 1). A study group of 39 experts on child psychopathology and child delinquency convened by the Office of Juvenile Justice and Delinquency Prevention in the US emphasized that despite several risk factors being common for many young offenders, “the patterns and particular combination of risk factors vary from child to child” (ibid.:2).

Researchers have subsequently developed tools to help predict aggressive and disruptive behavior in youth. For example, Augimeri et al. (2001) developed the Early Assessment Risk List for boys (EARL-20B) and further developed it with a second version for girls (Augimeri et al. 2005). EARL- 20B-based assessments showed moderate positive association with aggressive and disruptive behavior (Enebrink et al. 2006: 365). There is however, “a lack of consensus on how to identify meaningful subgroups among young children […] with high risk for persistent aggressive or conduct disordered behavior” (ibid.). Much remains to be learnt about the relationship between individual risk factors identified for aggressive behavior “including how risk factors from multiple domains interact, exert a causal influence on outcome, or are modified by protective factors” (ibid.). Ultimately, as risk factors vary for each child individually, it is not surprising that many measures designed to prevent youth crime struggle addressing all relevant factors for diverse groups of children. Likewise, it can be very different for varying contexts. We will, therefore, look at several studies analysing such factors for youth delinquency in South Africa.

The local context

The first National School Violence Study (NSVS) (Burton and Leoschut 2013: ix) conducted in 2008 had “found that 22% of the secondary school learners surveyed had succumbed to some form of violence in the 12 months preceding the study. In 2012, 22.2% of high school learners were found to have been threatened with violence or had been the victim of an assault, robbery, and/or sexual assault at school in the past year”.

28.7% of all learners surveyed in the Western Cape reported experiencing some form of violent crime in the past year (ibid.: 21). This not only exceeded the national average of 22.2% but made the Western Cape the province with the second-highest rate of violent victimization at schools (ibid.). The high numbers of violent incidents suggest that youth crime continues to be a major problem in vast areas of the Western Cape” (Titus 2006: 3).

Townships

Goldstein and Conoley (1997: 75) state that the quality of education is “severely affected if the child is not in a safe and welcoming learning environment. Teachers cannot teach and students cannot learn in an environment filled with fear and intimidation”. Moreover, the 2012 NSVS

(Burton and Leoschut 2013:95-96) found that students “who had succumbed to violence at school, as well as learners who reported having friends who engaged in violence-related behaviour (such as having done things that could have gotten them in trouble with the police and carrying weapons to school with them), were significantly more likely to” have a higher tolerance attitudes towards violence (p<0.05). This suggests that “exposure to violence contributes to attitudes that are tolerant of violence, which has a significant bearing on the later perpetration of violent and aggressive behaviours” (ibid: 95). Furthermore, the safety in schools is likely to spread and influence the whole community around it (Titus 2006: 3).

''Experience and exposure to violence in any environment at a young age increase the risk of later victimisation, as well as perpetration of violence and other antisocial behaviour. Schools, if considered holistically, are environments where children not only acquire scholastic knowledge, but also where they learn to know, to be, to do and to live together. Violence in schools impacts negatively on all these processes, creating instead, a place where children learn fear and distrust, where they develop distorted perceptions of identity, self and worth, and where they acquire negative social capital, if the violence and safety-related threats are not effectively managed'' (Centre for Justice and Crime Prevention 2016: 6).

Studies measuring the effect of neighbourhood crime show that community and family factors intersect with levels of violence occurring at schools (Burton and Leoschut 2013). Results show that “by the time young people enter secondary school, many of them had already, either as witnesses or victims, been exposed to violence in their communities or homes” (ibid). The 2012 NSVS showed that over one in ten learners had witnessed family members intentionally hurting each another, a tenth of the participants had themselves experienced assault at home, whilst less than one tenth had been robbed or sexually assaulted in their homes. Such experiences significantly increased the risk for violence in the learners’ school environment. Similarly, almost half of the participants had witnessed a physical fight within their community, where victims and perpetrators were often known to them (ibid: xiii). the effect of neighbourhood violence and crime:

Yet, “most people still consider school safety and security issues as the responsibility of the police and not a school governance and management issue. In addition, school safety and security

continue to be viewed in terms of infrastructural reinforcements such as alarms, burglar bars and not developmental strategies such as anger management, conflict resolution, mediation, tolerance and human rights education or integrated partnerships with civil society organisations, state institutions and the broader community” (Titus 2006: 4).

For an overview of youth crime approaches in South Africa see Titus (2006), Clark (2012) and Gevers and Flisher (2012).

3.1.2 Protective factors There are however also protective factors that decrease the risk or probability for youth delinquency and disruptive behavior, building resilience. It is crucial to examine such protective factors in order to identify interventions which are likely to tackle youth delinquency (Wasserman et al. 2003:2). Common protective factors are: prosocial behavior during the preschool years, empathy, and good cognitive performance (ibid.) Ultimately, the “proportion of protective factors to risk factors has a significant influence on child delinquency, and protective factors may offset the influence of children’s exposure to multiple risk factors” (ibid.). If protective factors can counterbalance individual, family and community risk factors, then it is imperative for youth crime prevention to enhance protective factors as much as possible (Burton et al. 2009:103). The authors agree: “proper understanding of risk and resilience among children to violence is essential in the design of appropriate policy, and for the implementation of both policy and programmes” attempting to mitigate effects of youth violence (ibid.).

Even though peer-based prevention programs (e.g. helping adolescents resist peer influences to consume drugs or commit violent acts) as well as community-based prevention programs (including after-school interventions, mentoring, and youth groups) hold wide appeal amongst political leaders and the public, they are very often the first programs that lose funding in times of state budget cuts (Farrington and Welsh 2012:2).

Resilience, built by protective factors, is not inherent in individuals but developed and maintained by specific contexts and experiences (Burton et al. 2009:103). Because individual factors like a child’s temperament, intelligence or to a degree “empathy are constructs that are very difficult to influence positively (with inconclusive arguments as to whether they can be influenced at all

by external forces)”, the “focus of resilience interventions, therefore, should ideally be on those factors that can reasonably be positively influenced”(ibid.).

What if we could develop youth crime prevention interventions that in fact manage to impact those factors which, as Burton et al. (2009:103) suggested, cannot be "influenced at all by external forces"? What if there was a way to influence the subconscious patterns of behavior whilst requiring very low expenditures in order not to fall into the pitfall mentioned above by Farrington and Welsh (2012:2).

3.2 Exploring the Gap: The Subconscious Mind As we have seen, both individual and social factors lie at the root of the complex social phenomenon of criminal behavior (c.f. Wasserman et al. 2003; Burton et al. 2009; Farrington and Welsh 2012; Gardner et al. 2015). As pointed out by Jones et al. (2003:237), programs which focus only on a single risk factor have had only limited success, precisely because of its multiple causes, prevention programs need to effectively address several factors simultaneously. The authors further argue that it is not the case that common youth crime prevention programs are entirely ineffective, the problem, they claim, lies in both the fact that there are multiple causal factors for youth crime and that the identified causes are deeply rooted (ibid.:232). Because criminal behavior “is deeply rooted, approaches that try to reduce crime by having students read, think about, and discuss their behaviors are also unlikely to get at the unspoken and less tractable sources of behavior, sources that lie beneath words and thinking” (ibid.).

Psychology speaks of cognitive and noncognitive skills and studies in child psychology have provided evidence that increasing noncognitive abilities significantly reduces the probability of dropping out of high school for children with only average cognitive abilities, and likewise for many other behavioral outcomes such as reducing the likelihood of incarceration by age 30 (Heckman and Masterov 2004: 20). Hence, whilst I agree with Jones et al. (2003) I would like to take their argument further by pointing out one major factor that has been ignored by most of youth crime prevention, in South Africa and even world-wide – the subconscious brain.

I therefore have two critique points in regard to previous approaches of youth crime prevention:

- Most prevention approaches have struggled to address multiple risk factors simultaneously - and failed to impact the behavioral patterns that are deeply rooted in our subconscious brains.

Neuroscientific evidence suggests that 95% of our behavior is regulated via the subconscious brain, this means, we are not aware of most of our behavior which runs on pre-established behavioral patterns or survival instincts (Zaltman 2003). Moreover, our subconscious mind feeds to a large part on all the things we take in and filter: the news, the conversations and experiences we have (c.f. Wasserman et al. 2003). For example, during a conversation it takes in not only what is being said, but, depending on how our filters are programmed, specific body language, gestures, tone of voice etc. are registered and subjectively judged. Interestingly, a lot of our filters are set very early in our childhood whilst more add onto developing into ‘default behavioral’ patterns as we grow up. But then, how can we alter our behavior? And more specifically: How can youth crime prevention address subconscious behavioral patterns to increase the resilience of youth living in high crime areas more effectively?

Most policies and interventions to date have focused on one or another external factor such as community cohesion, opportunity minimizing such as reducing access to weapons or drugs, public space, and so on. But only a few of these take the much more direct and simple opposite approach: from the inside out. Addressing the internal sphere that brings together all these problematic influences that a person has and is experiencing (and thus has to cope with) in their life: the subconscious mind.

Psychotherapy and hypnosis are amongst the few treatments that are being used to address patterns of the subconscious mind. Subconscious patterns and beliefs are indeed tricky to change by will, which is why it often takes years of therapy or counselling for e.g. alcoholics or drug addicts to become clean, or to heal food disorders. Unfortunately, such interventions are highly time-intensive and create high costs, either for the person receiving treatment or for the donor organization/institution providing it. Moreover, they entail the existence of aggressive or criminal behavior for the children to then be “treated” which would require singling out specific children

instead of addressing all. Such programs are therefore not feasible at a large scale for youth crime prevention in a country like South Africa. Especially in ghettos, townships, or other under- resourced areas, where external risk factors for youth are highest, this is not viable – at least not long-term. What if there were a more direct way to impact the subconscious mind? A more time- and cost-effective method?

Mindfulness Meditation

One method that seems to offer substantial potential is mindfulness meditation. Newest findings in the field of Neuroscience support positive effects of mindfulness meditation on the human psyche, including the subconscious brain (e.g. Meiklejohn et al. 2012; Vago and Silbersweig 2012). A vast amount of evidence from published and peer-reviewed studies shows that mindfulness meditation is associated with simultaneous reduction of multiple risk factors for crime and positively impacting protective factors (e.g. Jones et al. 2003; Parker et al. 2014; Vago and Silbersweig 2012; Tarrash 2017).

Mindfulness meditation can be described as the practice of an intentional and non-judgmental awareness of one’s actions and thoughts in the present moment (Kabat-Zinn 1990). Whilst its origins are connected to ancient spiritual practices from the East (most commonly Buddhism), mindfulness meditation has since the 1970s increasingly gained popularity in the ‘West’ as a secular practice, initially as a second wave cognitive behavioral intervention in form of stress- reduction (ibid.).

Ultimately, providing a technique for children to access their subconscious minds enhancing protective factors and reducing risk factors would be a very cost-and time-effective approach to tackle youth crime with a priceless potential to create an inter-generational effect that could possibly break the cycle of violence.

3.2.1 Literature from Neuroscience Neuroscience has in the past years uncovered how mindfulness meditation practices, manage to change neuronal pathways of the conscious and subconscious brain (Meiklejohn et al. 2012; Vago and Silbersweig 2012). It is the neuroplasticity of the human brain that renders it possible to re- structure old patterns by firing neurons together, even in adults (Evers 2018).

Neurobiological studies and fMRI (functional magnetic resonance imaging) of the brain have shown that mindfulness meditation affects those areas of the brain involved in self-regulation (Holzel et al. 2007;Vago and Silbersweig 2012) and enhances attentional and emotional self- regulation (Meiklejohn et al. 2012:291). Self-regulation are neuronal processes where humans monitor their own behavior, judge it in relation to circumstances and moral values, and thus, regulate their actions (Bandura 1999:193 f.). Very little of our behavior is fully conscious due to the fact that most behavior is activated via sensory triggers, or internalized norms and moral beliefs, so-called affect-laden triggers: “These are changes that occur in the body state (e.g., heart rate, bowel motility, blood pressure) when each option is being considered, assigning a positive or negative connotation to it and influencing the decision process” (Cerqueira et al. 2008:631). Internalized moral motives are part of such affect-laden triggers that arise rapidly in form of intuitive judgements, which then influence the more controlled and slower processes justifying these (in Neuroscience often referred to as system 1 and system 2 of cognition: instinctive, emotional vs. slow, deliberate thinking, cf. Damasio 2000; Kahneman 2011). Hence, whilst self- regulation has cognitive and non-cognitive aspects, by increasing our self-regulatory capacities, we can become more aware of our non-cognitive, or subconscious, affect-triggered and learnt behavioral patterns. Increased self-regulation allows us to switch from the fast system 1 of cognition to system 2, bringing conscious awareness to our emotions and actions. Without awareness, it is difficult to change behavior. Meditation however, not only allows us to bring awareness to our inner worlds, but as Neuroscientific studies discovered, allows us to modulate existing self-narratives and learnt self-biases such as negative self-talk or addictions by accessing and replacing sustained episodic memory with new episodic memory (Vago and Silbersweig 2012) similarly to hypnosis – the difference being that a person can do this by themselves without external interference.

Vago and Silbersweig (2012: 1) explain that meditation modulates self-regulation through an integrative neuronal control network. In fact, the authors describe mindfulness as a systematic mental training which develops a self-awareness with the ability to self-regulate, i.e. effectively modulate one’s behavior (ibid.). Vago and Silbersweig (2012) offer an astoundingly thorough framework explaining the neuronal networks and mechanisms during mindfulness meditation in

relation to self-regulation. Whilst further explanations go far beyond this thesis, their work is highly recommended also for its visual graphics of the different processes at work.

Evans-Chase (2013: 65) argues that it is crucial for youth crime interventions to support the development of self-regulation in order to reduce the negative social and developmental outcomes that are associated with the exposure to adverse childhood experiences such as trauma and violent neighborhoods and may decrease the likelihood of youth to further offending or incarceration. The researcher suggests that “mindfulness meditation may be an effective intervention for incarcerated youth because the mechanisms through which mindfulness meditation affect the practitioner include an increase in self-regulation”. Evans-Chase (2013) points out an important factor, namely that children and adolescents who are disproportionately exposed to adverse childhood experiences related to external (family and community) risk factors for youth crime would benefit significantly from interventions helping them to develop sound self-regulatory mechanisms.

With new technical advances such as fMRI, researchers have in the recent years been able to measure brain activity and display differences in activation. Neuroimaging has produced mounting evidence that adverse childhood experiences impact the development of the prefrontal cortex (PFC) and the neuronal pathways between the amygdala and the PFC (Bremner 2003; Anda et al. 2006). This has important implications for a healthy development of the children’s self-regulation, as Chase-Evens (2013) emphasizes, especially since those pathways are involved in the cognitive control of their emotional impulses (Fareri et al. 2008). Self-regulation strategies in fact emerge at the age of one year and continue developing throughout childhood (Botha 2014: 55).

Research further indicates that mindfulness meditation can impact “the development of those specific brain areas that are both affected by childhood trauma and directly implicated in delinquent and other risk-taking behaviors” (Chase-Evans 2013: 66). High self-regulation has shown to be negatively correlated with delinquent and other crime risk behaviors (Steinberg 2008). According to de Almeida et al. (2015: 126) growing scientific evidence shows that along with the PFC, the ventromedial PFC (vmPFC), the Anterior Cingulate Cortex (ACC), and the

amygdala may be the most important brain regions involved in aggression (c.f. Matthies et al. 2012). This supports Chase-Evans’ (2013) claim as well as my argument that our behavior to a large part is processed by our subconscious brain: the vmPFC is according to Vago and Silbersweig (2012: 10) heavily interconnected with the amygdala, which supports the representation of motivational and affective states “through a gradient of non-conscious and conscious forms of affective appraisal”.

One limitation with brain scanning methods is that they are often in very controlled laboratory environments and it is therefore important to conduct additional behavioral studies in natural environments.

Leung et al. (2014: 1) conducted a review looking not only at neural but also behavioral evidence of the impact of meditation on self-regulation. The authors explain that “long-term meditation practice can trigger meditation-specific neuroplastic changes in the brain regions underlying cognitive control and affective regulation” and also point out that meditation practice as a form of mental exercise can foster affective regulation and may be a cost-effective intervention for mood disorders and thus possibly as well for crime prevention (ibid.1;5).

Fascinating evidence from neuroimaging suggests that mindfulness meditation, in addition to enhancing self-regulation, can also positively impact self-efficacy (c.f. Chang et al. 2004; Meiklejohn et al. 2012; Leung et al. 2014; Vago and Silbersweig 2012) as well as increase empathy and reduce aggression (c.f. Engström and Söderfelt 2010; Vago and Silbersweig 2012). Chang et al. (2004: 144) in a mindfulness-based-stress reduction program found for instance significant improvements in the post-intervention meditation self-efficacy scores [F (1, 25) =14.32, p=0.001]. The participants (n=28) however self-selected due to choosing the course and as there was no control group the application of the study is limited since 1) it becomes difficult to determine whether the differences were a result of the intervention or other factors and 2) the small sample size does not allow generalizability.

Another limitation of neuroimaging studies is the immensely high cost of equipment which renders it very difficult to conduct studies with large samples. Until the equipment becomes more affordable, multi-method studies are desirable.

Nonetheless, advances in Neuroscience are of remarkable methodological significance and may give valuable insight to help the development of educational structures and address social problems. Evers (2018) points out that the way our brains determine social behavior which forms the types of society we create, it is also vice versa, our socio-cultural structures that can influence the development of our brains. With this, Evers, refers to the very novel notion of proactive epigenesis which can be described as directing evolution through influencing the cultural imprints that will be stored in our and future generations’ brains (Evers 2015: 16). Evers and Changeux (2016: 1363) remark that “if new cultural circuits, such as a better ability to control violence, become epigenetically stored in our brains, more peaceful societies might hopefully develop”. Moreover, it may be possible that programs including mindfulness meditation could generate an inter-generational spillover effect and actual neuronal epigenesis, rendering the next generations more peaceful.

3.2.2 Literature on the effects of mindfulness meditation on criminal offenders A continually growing body of research has shown that that mindfulness meditation with incarcerated populations is associated with both physiological and psychological benefits (Himelstein et al. 2015; c.f. Jones et al. 2003; Shonin et al. 2013; Morley 2018). Studies demonstrate that mindfulness and meditation practices with criminal offenders have reduced recidivism, criminal impulsivity, aggression, and violent behavior (Anklesaria and King 2003; Jones et al. 2003; Rainforth et al. 2003; Shonin et al. 2013; Morley 2018), and enhanced self-regulation (Baer 2003; Evans-Chase 2013; Himelstein et al. 2015).

Jones et al. (2003: 253) gathered evidence of a considerable number of studies that show a positive impact of Transcendental Meditation on criminal behavior, recidivism, “and most of the risk factors tied to crime”. More than 600 studies at over 200 independent institutions have been conducted on Transcendental Meditation (ibid.: 237). The authors provide valuable insight as they discuss the positive effects of the meditation technique on twenty-three commonly identified risk factors for crime, divided into physiological, psychological, sociological and substance abuse. For an overview see Jones et al. (2003: 237-238).

Research on other forms of mindfulness-based interventions with juvenile delinquents, incarcerated or at-risk youth also seems promising (for a review see: Shonin et al. 2013; c.f. Himelstein et al. 2015; Morley 2018). Himelstein et al. (2015: 1472) for instance conducted a randomized controlled trial inspecting a “court-mandated substance abuse group treatment program at a juvenile detention camp” where the treatment group alongside psychotherapy received mindfulness training, whereas the control group received solely psychotherapy. The detention camp staff rated each participant’s behavior before and after the intervention (ibid.). Consistent with prior research, the results showed significantly larger increases of good behavior (p < 0.05) and self-esteem (p < 0.05) amongst the treatment group compared to the control (ibid.). Whilst the study employed a randomized controlled trial, the sample included only 35 participants of which all were male and only 27 completed both pre-and post-tests.

Little research exists in the African context. Anklesaria and King (2003) conducted extensive research over two years covering over 11’000 inmates and 900 correctional officers where the Transcendental Meditation was taught in 31 of Senegal’s 34 prisons. Their findings showed a drastic decrease in recidivism and rule infractions via quantitative data as well as a sharp reduction in aggressive behavior and improvement in self-confidence, self-control, and conscientiousness via qualitative data (Anklesaria and King 2003). “Recidivism dropped from 90% in the pre-meditation period to less than 3% after the program was established” whilst most recidivists that year came from the three prisons that had not received the treatment due to remote location (ibid.: 303). The researchers also collected data of one prison on rule infractions and found a significant reduction of infractions post intervention in both the 11-month comparison of prison records (-81%) as well as a 3-month seasonal comparison (-90%) (ibid.: 310- 311). It is important to note, that the study was not a controlled trial, yet, findings “suggests that the program is a viable rehabilitation approach for use in the criminal justice systems of resource- challenged nations” (Anklesaria and King 2003:303). Whilst the authors admit that further empirical research, especially randomized controlled trials, are needed, it is important to emphasize the relevance of such a program which is specifically suitable as a cost-effective rehabilitation method for criminals in developing countries in Africa (ibid.:318).

Evans-Chase (2013) did apply a randomized controlled trial when she studied the impact of internet-based mindfulness meditation on the self-regulation of incarcerated youth. She found an increase in the interactional self-regulatory capacities, especially the older age group scored significantly higher compared to the control group of the same age on interpersonal self-restraint (ibid.: 71). Whereas the researcher followed a rigorous research design (randomized controlled trial: differences between control and treatment groups on posttest scores were analyzed using multiple regression analyses, controlling for pre-test scores, and an interaction term including age group as moderator), her study was limited in its application since, of initially 121 recruited incarcerated youth, only 27 completed pre-and post-tests, rendering once again a very small sample size (ibid.: 67).

As we have seen, a substantial amount of literature exists on programs in correctional settings. Whilst mindfulness meditation has shown to be a viable rehabilitation approach for offenders, its application in crime prevention may be even more wide-ranging and effective, especially when working with children.

Jones et al. 2003 suggest the use of meditation classes in the prevention of youth violence and crime but themselves did not conduct any empirical studies. The authors merely describe two projects that applied the TM via Consciousness-Based education to “children at risk” and whilst one of the projects conducted psychometric pre-and post-tests for a randomly selected treatment and control group, the sample size is only 62 and methods used are not further described (Jones et al. 2003:250). It is unclear whether outcome variables measured were specifically targeted to measure the children’s potential for future delinquency (intelligence, self- concept and computer skills), instead, it was the qualitative interviews that pointed directly towards reduced fights, as did interviews conducted in the second project (n=18): “the dominant impression from the interview tapes was that violence at the school had been significantly reduced” (ibid.: 251-252). Jones et al. (2003) showed that a large body of research exists that indicates that the TM technique can reduce recidivism and criminal behavior (ibid.:253). Nonetheless, the authors admit that “no one seems to have focused in detail on the role of education in crime prevention”. Indeed, its potential in youth crime prevention thus far is untapped.

In addition, this section has made evident that there is a clear need for further quantitative studies with larger sample sizes investigating the effect of meditation classes on risk and protective factors for youth crime.

3.2.3 Literature on mindfulness meditation in schools During the past decade, mindfulness meditation programs were introduced into schools in many countries across the world such as Mindful Schools and MindUp in the US, DotB and Mindfulness in Schools Project in the UK, the Alice Project in India, the Mindful Education in Canada, and the Mindfulness Language in Israel (Waters et al. 2015:104).

In South Africa, there are many small-scale projects, NGOs and private persons offering mindfulness and meditation practices to schools and communities1, e.g. WISE2 (Wellbeing in Schools and Education); Breathwork Africa3; SEELearning4; PhysiFun5; Mindfulness-Based Art classes for youth in Cape Town’s townships; mindfulness meditation classes at the German School in Johannesburg.

Additionally, the Institute for Mindfulness in South Africa (IMISA6) has since 2005 been providing a platform for mindfulness practitioners and trainers. It is one of the few institutions in the world currently offering university level training (one-year, 60 academic credits - collaboration with Stellenbosch University, Faculty of Medicine and Health Sciences) whilst they also offer a wide range of other mindfulness-based programs for individuals, organizations and corporates such as Mindfulness-Based-Stress-Reduction (MBSR) (IMISA 2019). IMISA’s approach is predominantly

1 The author of this thesis is currently involved in the start-up of the Mindful Network, an association of mindfulness practitioners in South Africa to coordinate and synthesize the nationwide efforts of mindfulness in education. 2 http://wise.training/

3 https://www.breathworkafrica.co.za/training

4 https://seelearning.emory.edu/ 5 http://www.physifun.co.za/

6 IMISA holds the annual Mindfulness Conference bringing together the most dedicated researchers and practitioners from a wide range of fields such as Health Care, Neuroscience, Education, Politics. The initial findings from the pilot-study of this project were presented at the 2019 Mindfulness Conference in March 2019, which was held in Maropeng, Johannesburg, where icons such as Jon Kabat-Zinn were invited.

psychology-orientated and aims to mainstream practices of mindfulness-based approaches into health care, education, businesses and communities7.

A vast body of literature exists on the effectiveness of such mindfulness-based interventions (MBIs) that have been implemented since the 1970s in clinical and non-clinical samples (Kabat- Zinn 2003; Segal et al. 2002; Hofman et al. 2010; Spijkerman et al. 2016). Literature in the South African context however is scarce (c.f. Whitesman et al. 2018) and even more so, (peer-reviewed) scientific evaluation of the different programs implemented in local schools. For such programs to expand their full potential, valid research investigating their effectiveness is crucial.

Because “schools need reliable evidence about the outcomes of meditation programs”, Waters et al. (2015) conducted a systematic and evidence-based review of the impact of meditation programs in schools. They reviewed 15 peer-reviewed studies on school meditation programs from around the world and calculated effect sizes for 76 results covering 1,797 participants (ibid:103). 61% of the effect sizes were statistically significant showing small (67%), medium (24%) and large effects (9%) of meditation (ibid.). The most common outcomes, in accordance with Neuroscientific evidence presented, were cognitive functioning and emotional regulation (ibid.:120). Emotion regulation is described as the regulation mechanism that controls, redirects or modifies external and internal factors creating emotional arousal, thus, enables healthy functioning when facing challenging circumstances (Botha 2014). Children’s ability to regulate emotions has been associated with whether the child will develop pathology in times of difficulty (ibid.).

Meditation programs introduced in Australian schools covering 10,000 students between ages of 5 and 18 showed via qualitative analysis of semi-structured individual and group interviews: reduced anger, stress and mobbing (Campion and Rocco 2009). Randomized experiments in elementary schools revealed significantly reduced aggression, social problems, and anxiety

7 “This approach represents a convergence of wisdom teachings and traditions with contemporary applications and modern scientific methods of research (from brain imaging to clinical effectiveness) in a variety of contexts. It can be understood, in a certain respect, as a re-contextualization of Wisdom Teachings for a modern, diverse, and secular audience” (IMISA 2019).

compared to control groups (Parker et al. 2014:184). Problematic is that qualitative studies with such large sample sizes take up much time and are more difficult to compare whilst small sample sizes in randomized experiments do not allow to make inferences, and in general, more research including longitudinal studies are needed.

3.2.4 Literature on mindfulness practice with youth in need Coholic (2011) provides extensive research of an innovative group program that taught mindfulness using arts-based methods to children and adolescents ‘in need’, i.e. involved with child protection, foster homes and/or mental health systems. Findings from this four-year qualitative study with 50 children, aged 8-15, showed increased emotional regulation and coping skills, improving the children’s self-awareness and resilience (Coholic 2011: 303,306). Coholic et al. (2012) then assessed the effectiveness of this program using treatment and control groups. In a mixed-designed MANOVA the researchers analyzed the scores for 36 children aged 8 to 14 and found evidence that the program benefitted the children significantly: post-intervention the participants self-reported lower emotional reactivity, a measure of resilience (Coholic et al. 2012: 833). Coholic et al’s studies provide a unique contribution to the still scarce literature on meditation with children in difficult living situations by looking at adolescents in foster-homes and other special living arrangements. Their findings demonstrate the immense potential when working with youth in difficult environments to foster resilience for those most in need of it. Yet, as pointed out previously, the studies lack large sample sizes and demand further research. Ultimately, long-term studies will have to observe whether such programs affect the neural predispositions of future generations.

Summary Chapter 3 First, this chapter reviewed literature on youth crime causes, identifying an important factor generally overlooked thus far: the subconscious mind. It then suggested mindfulness meditation as a possible form of crime prevention and, in a second part, presented research on its effect on criminal offenders and school children. Whilst existing literature provided evidence of positive effects of mindfulness meditation in youth and adult populations, this paper aims to contribute with further quantitative data from a large sample in the novel context of South African townships whilst uniquely referring to its potential for youth crime prevention.

4 Theory Chapter 4 starts with the presentation of the exploratory study conducted to discover possible outcome variables in the context of Cape Town’s townships. Next, we examine how such risk and protective factors for youth crime relate to resilience. Based upon this, and supporting literature outlined in the literature review, the theoretic model is presented along with the hypotheses.

4.1 Exploratory Study In order to get an understanding of what effects mindfulness meditation may have on children in South African townships, a preparatory/exploratory qualitative study was conducted at a primary school in Hangberg, Hout Bay, a poor and violence-ridden settlement near Cape Town. The NGO WISE (Wellbeing in Schools and Education) has been holding mindful meditation practices in form of morning assemblies for over 600 students in grades R (preschool) to three at Sentinel Primary.

Survey questions are not ideal for kids aged four to nine and interviews with the students rather time-consuming. Thus, observation reports8 that had been professionally developed by an external company and filled out by the facilitators since the start of the program in October 2018 served as measurement instrument.

The observation reports that covered a period of five months were analyzed via a simple qualitative data analysis. Alongside informal interviews with the three facilitators/organizers, the observations revealed increased calmness and self-assurance amongst the majority of the children along with an impressive reduction of aggressive behavior over five months for the classes receiving treatment. The quality of concentration and focus also improved for the treatment groups. Additionally, it was observed that these children developed a sense of resilience. The results highlighted major differences for the classes that received mindful morning assemblies compared to before the intervention and compared to the older grades which had so far not been offered morning assemblies. Findings are summarized as follows:

8 The original reports were not appended to this thesis as they cover 30 pages but can be made available upon request.

Decreased physical and verbal aggression: The level of bullying had decreased tremendously; pushing, shoving and fighting; girls’ physical violence reduced more than boys whilst the verbal aggression decreased for both similarly. In some classes a small group of disruptive boys were still reported, yet the majority of treatment students managed to regulate their emotions and actions in a healthier way.

Increased Self-assurance/esteem: Most students displayed an impressive increase in self- confidence concerning their abilities. Whilst prior to the treatment and in the first month most students were insecure about being able to express emotions, or being able to sit in stillness or be calm, after several months, the students expressed more self-assurance in their capabilities, especially when confronted with difficult or demanding situations.

Increased calmness and concentration: Most children’s ability to focus was increased, resulting in a higher quality of concentration during the morning assemblies and their classes throughout the day. Classrooms of treatment grades were experienced as more calm than prior to the intervention.

Improved resilience: The facilitators commented that over the course of several months, the children seemed to have developed strong resilience when dealing with the harsh realities of their community, such as domestic violence.

Interestingly, the effects were much higher in classes where teachers participated in the dance assembly and began treating the children with more kindness over the course of the five months than classes where the teacher was absent or using force to calm down students. This was not only observed in their behavior during the mindfulness morning practice but throughout the day during classes. This suggests that teachers and caretakers should be actively included in the mindfulness meditation practice.

4.2 Risk and Protective Factors impact Resilience As established in the previous chapter, the concept of risk and protective factors is internationally commonly used to analyze crime causes and prevention. Individual, familial/social, or community factors can, alone or in combination, result in an individual being at a higher risk of becoming an

offender, whereas so-called protective factors can reduce the likelihood. Using this concept, “crime prevention may be thought of as the successful reduction of risk factors, and the strengthening of protective factors” (Frank 2006:16). By reducing risk factors and strengthening protective factors one can promote resilience among youth in high-risk situations as Botha (2014: 33) explains “children at risk can still achieve positive outcomes – a phenomenon that has become known as resilience and is probably best explained by the fact that multiple pathways emerge during the course of development. Any individual trajectory would consist of various interacting variables, such as risk and protective factors”.

“Resilience will give you the strength to stand up for your values and beliefs, enabling you to realise the vision you have for yourself even in the face of adversity and challenge” – Paul Mooney

Resilience in this paper is defined as the potential to successfully adjust to challenging circumstances in the context of youth crime (ibid.: 11).

Previously, we asked the question: What if we could develop youth crime prevention programs that manage to impact the subconscious behavioral patterns of youth? Such programs would grant juveniles agency by empowering them with a technique to impact their subconscious behavioral patterns.

As the literature review revealed, advances in the field of Neuroscience have recently provided new techniques that present evidence of how mindfulness meditation affects both cognitive and non-cognitive areas of the brain. The exploratory study further suggests that mindfulness meditation practice with children in the context of a high crime township can positively impact such risk and protective factors. The understanding of how mindfulness meditation impacts the neural pathways of self-regulation combined with the gained insight about possible outcome variables from the exploratory study conducted now give rise to the research question:

Can mindfulness meditation increase self-efficacy, a protective factor for youth delinquency, and decrease aggression, a risk factor?

4.3 Theoretical Model and Hypotheses

Neuroscientific studies show, mindfulness meditation increases self-regulation, a partially cognitive, partially non-cognitive function of our brain that permits humans to regulate their thoughts and actions (Holzel et al. 2007; Vago and Silbersweig 2012). Self-regulation in turn allows us to become aware of usually unconscious self-narratives and self-biases such as negative self-talk and limiting beliefs (ibid.). Improvements in self-regulation have been associated with enhanced self-esteem and -efficacy (Walsh 2013). Werner (2007) found that building self-esteem and a feeling of being in control of one’s actions to be amongst the most significant protective factors for youth crime in adolescence. Mindfulness meditation then again has been positively associated with increases in protective factors such as self-esteem (Meiklejohn 2012; Vago and Silbersweig 2012; Himelstein 2015). These associations correspond with the findings of the exploratory study described above. It can thus, be hypothesized that mindfulness meditation increases the self-efficacy of youth in high crime areas through enhancing their self-regulatory capacities (H1).

Emotion-regulation is one aspect of self-regulation that allows affective triggers to be controlled or redirected, which is especially important for healthy functioning in the face of challenges (Botha 2014). Existing literature has established that difficulty with self-regulation and strong emotional reactivity correlates with vulnerability to pathology and behavioral problems and is therefore referred to as a risk factor (Botha 2014: 55). Emotional reactivity is considered a risk factor which may decrease resilience (ibid.). Successful emotional regulation allows adolescents to recover well from an emotional reaction and minimize any impairment caused by emotional arousal and can then again be understood as a protective factor for youth crime (ibid.). Aggression has been associated with emotional reactivity and low self-regulation (Walsh 2013). Neuroscientific studies provide evidence of correlation of low self-regulation with high activation of brain areas involved in aggression (Vago and Silbersweig 2012). In addition to Jones et al. (2003), Himelstein (2015) and findings from other scholars the explorative study conducted also found a reduction in aggressive behavior in connection to mindfulness meditation practice. It could then be hypothesized that mindfulness meditation reduces aggressive behavior through increasing self-regulation (H2).

Strengthening the protective factor “self-efficacy” (H1) and reducing the risk factor “aggression” (H2) may play an important role in enhancing resilience of youth in high crime areas.

Tarrasch (2017: 91) found that longer practicing ‘exposure’ lead to higher levels of mindfulness among children. According to Lutz et al. (2008: 2) effects are “modulated by the degree of meditation training”. The authors examined brain activation during meditation for long-term Buddhist meditators vs. novice meditators and found in support for their hypothesis that affective (empathic) response was modulated by the level of meditation training (ibid.). Thus, one could expect exposure time of meditation classes to make a difference, more specifically that long-term meditators show increased self-efficacy (H3) and decreased aggression compared to novice meditators (H4).

In the following, the theoretical model and hypotheses are presented:

Theory: Because Mindfulness meditation increases neurological self-regulatory capacities, it positively influences resilience by reducing a known risk factor for youth crime: aggression, and enhancing a protective factor: self-efficacy. It has thus, the potential to become a (cost-)effective method for youth crime prevention.

Mechanism: Mindfulness meditation positively affects the students’ self-regulatory capacities increasing their self-efficacy, and reducing aggression, which in turn may lead to increased resilience.

Figure 1 The Mindfulness Meditation Model

Hypotheses:

H1: Mindfulness meditation is associated with increased self-efficacy amongst students receiving such classes, compared to the control group.

H2: Mindfulness meditation is associated with decreased aggression amongst students receiving such classes, compared to the control group.

H3: Long-term meditators show increased self-efficacy compared to novice meditators.

H4: Long-term meditators show decreased aggression compared to novice meditators.

Addressing the gap in current literature, this study argues that most common approaches to youth crime prevention failed to address a very important factor: the subconscious patterns of human behavior. Based on findings from Neuroscientific research, and evidence from an exploratory study, this thesis suggests that mindfulness meditation can be used as a technique for students to improve their self-regulation, consequently increasing their self-efficacy whilst reducing aggressive behavior, and by that may increase long-term resilience.

Summary Chapter 4 Chapter 4 began with the exploratory study conducted to determine possible outcome variables. It then presented the research question. Subsequently, the theoretic model and hypotheses were introduced, suggesting that mindfulness meditation can be used as a technique for students to improve self-regulation, reducing a risk factor for youth crime (aggression), and enhancing a protective factor (self-efficacy).

5 Methods This chapter aims to give an outline of the research design and methodology used, and why. It first presents the setting of this study, the sample and the measurement tools. It also discusses the data collection and possible biases and limitations of the design.

5.1 Case Selection and Setting Why a South African township

South Africa has consistently been ranked amongst the countries with the highest violence and crime rates in the world (GPI 2019; c.f. Manaliyo 2014). In 2019, South Africa ranked 127 out of 163 countries in the Global Peace Index, ranking amongst many countries at war (GPI 2019). Additionally alarming are the high levels of youth crime and school violence in South Africa (Gevers and Flisher 2012:205) – specifically, in the townships of the Cape Flats (Burton and Leoschut 2013).

Why Khayelitsha

Khayelitsha, with nearly half a million inhabitants, is the largest township in Cape Town and an area where youth crime rates and youth unemployment are very high (Frith 2011; Pagpa 2018). Despite end of Apartheid, it is still classified as a predominantly black township (98.62% of the population racially identified in the national census as ‘black’) with low income opportunities and lacking infrastructure (Frith 2011). The Centre of Science and Technology (COSAT), a high school in Khayelitsha, the largest township in the Cape Flats, offers a rare opportunity to analyze the impact of mindfulness meditation classes on students in the context of South African townships. Since 2013, the Tushita Kadampa Meditation (TKM) Centre has been offering mindfulness meditation classes as a so-called extramural (a term used in South Africa to denote elective classes) to all students of grades 8 through to 11.

Why a school

As schools represent a vital link between the family and communities and play a crucial role in the development of children and youth, they offer particular potential for youth crime prevention programs (Frank 2006: 25; 117). Frank emphasizes that "extra-mural aspects of formal education” (elective classes) are a crucial “vehicle for crime prevention” (Frank 2006: 117).

Why this age group

Frank argues that crime prevention must reach young people early on in their lives since the experiences during childhood and adolescence “shape the overall well-being of people in their youth, and often create the conditions and constraints by which they have to live” (ibid.: 116). The current study looks at students aged 13 till 18, however, it may be that mindfulness

interventions at a younger age have a more significant impact since older adolescents may have already established rather rigid behavioral patterns and conditioning. This study also intends to look at whether the length of treatment (several months vs. several years) may matter.

The setting

COSAT is a non-fee-paying high school that helps under-privileged adolescents by welcoming students from neighboring secondary schools who performed well in mathematics to attend their program (Center for Education Innovations 2015). With the help of local NGOs, COSAT provides free high-standard education for “the most impoverished and vulnerable students” (ibid.). The school is one of four – by the Western Cape Education Department – listed STEM (Science, Technology, Engineering and Math’s) focused schools in the entire province, and the only of its kind that is based in a township (ibid.).

Treatment: Mindfulness Meditation Classes

Since 2013, the school has been offering weekly mindfulness meditation classes as an elective class to grades 8 through 11. Grade 12 is required to focus on their final year’s exam and does not have electives, hence, grade 12 students are excluded from the study. This provides a suitable setting for the study9.

The mindfulness meditation classes have been taking place on Wednesday afternoons between 3pm and 4.10pm at COSAT, either in the assembly hall or a classroom, depending on availability. The meditation classes have been offered as elective course to all students of COSAT for free by

9 As we saw in chapter 3, mindfulness in South Africa is still an under-researched area, mindfulness meditation practices are not yet being implemented in schools via the national curriculum. Whilst there are several minor projects in operation these are at this point only implemented in very small groups at specific schools and not standardized across different schools. Different forms of meditation and mindfulness interventions are thus difficult to compare along with the age differences and needs of the students ranging from pre-school to university, and naturally, the personal impact of the facilitators. Due to the difficulty in finding comparable cases, I therefore chose to focus my study on one high school and analyze differences between the control group as well as the long-term vs. novice meditators.

the TKM Centre and taught by Generable Kelsang Pagpa, National Spiritual Director for the New Kadampa Tradition in South Africa, who has nearly 25 years of teaching experience as Buddhist monk. Despite the meditation centre being part of Buddhist tradition, the mindfulness meditation classes held at COSAT are kept secular and the students understand and trust that the mindfulness practices need not interfere with any religious faiths. A short description and outline of the mindfulness meditation classes may be found in Appendix A. The mindfulness meditation practice taught at COSAT is based on three basic mental factors: mindfulness, alertness and conscientiousness. Whilst mindfulness in this context was previously described, we will quickly give an understanding of the other two concepts.

Alertness can be understood as a “spy-like function of the mind” that aims to discover whenever the mind gets distracted to then return the attention back onto the object with mindfulness and continue in meditation (Kadampa Meditation Centre Reading 2016). “While mindfulness holds onto its object, alertness observes if there is any wandering from that object or not” (ibid.). Conscientiousness is a mental factor that essentially directs to keep the mind free from negativity, such as downward spirals of hatred, frustration etc. (ibid.). Whilst mindfully focusing on a negative object of thought will evoke negative emotion, such as fears, which is not per se a state that is beneficial to gain peace of mind, conscientiousness aims to direct the focus on an object that will evoke positive emotions and a sense of peace, for instance gratefulness (ibid.). These components can best be understood when the experience is described (see Appendix).

5.2 Research Design As we have seen in the literature review, few studies on the impact of meditation exist that cover large sample sizes. Those studies that follow a rigorous design and analysis are predominantly in highly controlled environments such as clinical studies, and techniques such as fMRI brain scans are tremendously costly. My aim therefore was to uncover how meditation affected students in real life and specifically, in difficult living conditions, whilst providing quantitative evidence with a larger sample size. An aim that may have been slightly over-ambitious, as the initial plan to take measurements at two separate times and measure differences over time (16 weeks) could not

be followed through with due to major delays in the gathering of the data and thus, making causal claims becomes difficult.

Instead of the first round of surveys (baseline) being administered in February 2019, a number of unforeseeable circumstances and events led to several delays in the gathering of the survey data (see Appendix A for details).

By the end of July, four months after the initially planned starting date, 305 surveys were completed though some classes were still missing. Since the remaining time did not allow for a second round of distribution of the survey, the data gathered stems only from the first survey.

Instead of a difference in difference, a cross-sectional, observational research design is employed: There is an intervention with a treatment and control group but randomized sampling was not possible due to the nature of the treatment: mindfulness meditation classes were offered as an elective course, hence students self-selected into treatment and control groups.

Sample Size

According to the 2019 provincial mid-year population estimates the total population of adolescents aged 13-18 living in South Africa is approximately 7.3 million, of which around 756 thousand are estimated to reside in the Western Cape (Statistics South Africa 2019: 18). Taking all adolescents in the country as our population, with a 5% margin of error and a 95% confidence level, the required sample size would be 385, if we want to make inferences only to adolescents in the Western Cape a sample size of 384 would be sufficient. At COSAT there are 560 students in total in grades 8 to 12, of which 107 students are taking meditation classes. Even when excluding grades 12 (107 students – grade 12 students may not take electives during their last year), when surveying all students of grades 8 through 11 (453), it should be possible to acquire a sample size that meets the preferred sample size of 385, respectively 384 for the specified error margin.

5.3 Operationalization The current study aims to measure the effect of mindfulness meditation classes on self-efficacy as well as aggression. There are thus, two main outcomes of interest: One protective factor for youth delinquency: self-efficacy and one risk factor: aggression.

Measurement tool

The measurement tool is a psychometric survey questionnaire built from two psychological scales: The Children’s Self-Efficacy Scale (Bandura 2006) and the Aggression Scale for young adolescents (Orpinas and Frankowski 2001). The survey that was constructed for this study (see Appendix B) has in total 54 items: Part I encompassing 32 items measuring self-efficacy, Part II comprising 11 items measuring self-reported aggression and Part III, anther 11 items measuring demographic data for the descriptive analysis and controls as well as the treatment variable.

Dependent variables

The survey questions measuring self-efficacy are based on Albert Badura’s (2006) Guide for Constructing Self-Efficacy Scales. Bandura is the originator of the Social Cognitive Theory and the theoretical construct of self-efficacy. Items from Bandura’s (2006) Children's Self-Efficacy Scale were used to create Part I of the survey addressing different aspects of (perceived) self-efficacy with a 11-point Likert scale. The battery of items consists of the following seven subscales (Self- Efficacy in Enlisting Social Resources, Self-Efficacy for Self-Regulated Learning, Self-Efficacy for Leisure Time Skills and Extracurricular Activities, Self-Regulatory Efficacy, Self-Efficacy to Meet Others’ Expectations, Social Self-Efficacy and Self-Assertive Efficacy). Appendix B displays how the items are arranged. Factor analysis (section 6.2.1) will show whether the items in this sample behave according to the theoretical expectations or whether different subscales are formed.

Part II measures self-reported aggression via the items of the Aggression Scale for Young Adolescents (Orpinas and Frankowski 2001). It comprises of 11 questions pertaining to how often a student performed a specific behavior within the past seven days, possible answers ranging from “0” to “6 or more”. Orpinas and Frankowski (2001: 53) speak of three different components in their paper (verbal aggression, physical aggression and anger) but then report a two-factor

model collapsing verbal and physical aggression, into one component/factor onto which nine items load and a second component/subscale: anger, onto which two items load. Factor analysis (section 6.2.2) will show whether different subscales are formed for the South African youth in this sample.

Independent variable

The treatment variable, meditation, is measured via the question Are you taking meditation classes? in the demographics part of the survey questionnaire (Part III). The question is embedded in other similar questions asking about the student’s elective classes in order to minimize any discerning of the purpose of this study and consequently possible biased answers. The variable is purposely not constructed as a dichotomous variable so that the answers allow the researcher to differentiate between novice and more long-term meditators which together then make up the treatment group vs. students not taking meditation classes, i.e. the control group. Of 560 students enrolled at the school in 2019, approximately 110 students were taking meditation classes; some of them for several years, others only started this year. Embedded in a set of similar questions (e.g. Are you taking dancing classes?) is the question: Are you taking meditation classes? which is used to divide the anonymous surveys into two groups: meditators (= treatment group) and non-meditators (= control group). The possible answers to the question are: (a) No (b) Since 6 months (c) Since over a year (d) Since over three years. This allows to additionally analyze differences between long-term meditators and beginners compared to non- meditators.

Control Variables

Part III of the survey covers demographic questions concerning: age, gender, grade, religion, residential area and living arrangement, from which also the control variables are obtained.

Gender is one of the most impactful demographic differences in most populations and will be included as control to detect any possible differences. Literature on self-efficacy suggests gender differences specifically for one of the subscales. Usher and Pajares (2008: 444) measured gender differences and found that “students’ self-efficacy for self-regulated learning” typically favors female students. Interviewing students in Grades 5, 8, and 11 they found that the girls expressed

higher goalsetting and planning strategies, thus self-monitoring more frequently than boys (ibid.). Gender is also expected to impact aggression. Neuroscientific studies have found differences in aggression between males and females: Whilst males tend to have a larger propensity for physical aggression, females tend to verbal aggression (Almeida et al. 2015: 125). Moreover, evidence shows greater differences between genders in school-aged children compared to adults whilst no differences have been found between genders in preschool-aged children (ibid.). Orpinas and Frankowski (2001: 55) found significantly higher mean scores on the Aggression Scale for boys (X=19.3, SD=15.5) than for girls (X=13.2, SD=12.9), F (1, 251)=11.7, p=.0007. It can therefore be suggested that male students score higher in the aggression score, or at least in physical aggression. Gender is coded as dummy variables: female, male and others.

Age may well impact both outcome variables. As established in the literature review, psychological and neurological development impacts the self-regulatory capacity of adolescents (c.f. Evans-Chase 2013). Since self-regulation is still very much developing during adolescence (c.f. Fareri et al. 2008) we could expect that older students show higher scores in self-efficacy and lower scores in aggression, and thus age may be an important control variable. Age will be computed as (normalized) ratio variable.

Religion may impact both dependent variables. It could be supposed that religious persons are believers in the sense that they believe in a higher power, ancestors to help them achieve tasks and goals, especially for children this has shown to enhance self-esteem and resilience (Horwath and Lees 2010:84). It could be expected that religious students show higher self-efficacy. Also, such a higher power is usually seen as omnipresent and thus, religious children could be more more-abiding than atheists and not tend to verbal and physical fights as much. Then one could hypothesize that religious students have lower aggression. The variable will be computed as binary: Religious vs. atheists.

Residential area like the other categorical variables will be computed as binary variable for the regressions since the interesting factor is whether the student lives in a township. It is expected that the majority lives in Khayelitsha, a township. Students from more affluent areas are expected to experience lower crime and other external risk factors for youth delinquency. Studies reported

that “children from low socio-economic households are less resilient, mature and independent than children from higher socio-economic groups. The ubiquitous stressors and inappropriate responsibilities put on their shoulders weaken these children’s sense of autonomy and hinder their capacity for adaptation”(Botha 2014:110). Living area is expected to impact both outcome variables. Self-efficacy would be expected to be lower for learners in townships related to a larger number of stressors and challenges and setbacks and a lack of successful role models (Botha 2014: 43). Aggression may due to the same reason and the expected higher exposure to violence and crime in family and neighborhood (Burton and Leoschut 2013; Manaliyo 2014) also be higher for students in townships.

Living arrangement lastly, will purposefully be encoded as binary control variable living alone or with family. This variable is only expected to have an impact on self-efficacy. The notion is that adolescents living alone would be expected to be more self-reliant and self-confident due to higher coping skills. For many children in townships it is not possible modify or leave a negative environment (Werner 2007) and if they live alone,it is often because their parents passed away or left them and other relatives cannot take up the financial responsibility to take them in, or the child left the family in reaction to domestic abuse for example. These children would need to develop extra high coping skills enabling them to handle higher stress, maintain self-esteem and gain access to possible opportunities(Werner 2000). It could then be expected that learners living alone to have higher scores of self-efficacy.

Exposure time

Since longer practicing ‘exposure’ has been observed to lead to higher levels of mindfulness measured among children (Tarrasch 2017:91) and adults (Lutz et al.2008) it would also be interesting to see if "long-term" meditators differ from the more "novice" meditators. Long-term meditators are computed as dummy variable: novice: 6 months to one year; long-term: over 1 year | over 3 years.

Carryover effects

When measuring two independent variables with independent scales in the same survey one must choose how to structure the survey and which scale should be placed first. For this study,

the aggression scale was placed in latter position(Part II) as it comprises of more sensitive questions which are recommended to be put at the end in order to minimize any carryover effects.

5.4 Data Collection The data was collected with the survey questionnaire comprising of 54 questions in the English language distriubuted digitally during computer class.

English is the language of instruction and the students are fluent in English. Therefore, the survey was not translated into Xhosa or any other language as the possible minor gains of accurate understanding may have been lost again through translation. Naturally, without time-constraints or with available funding, the decision may have differed. Instead, much focus was put on the choice of questionnaires and the formulation of the questions to render them as straight-forward and simple as possible using simple and child-friendly language appropriate for the age of the participants. The questionnaires chosen were specifically recommended for children and adolescents in this age group, tested and validated (Orpinas and Frankowski 2001; Bandura 2006; Usher and Pajares 2008; Pajares et al. 2001:214).

Pre-Test

A pre-test was conducted with a representative sample of subjects: 12 teenagers from Khayelitsha (not students at COSAT) and other areas around Cape Town to test the questionnaire and identify questions that may be too difficult for specific age groups or in general inappropriate. After receiving feedback, the questionnaire was adapted following changes were made:

- Bandura’s scale from 0-100 was adapted to 1-10, pilot-tested with1-10 and after feedback requiring a neutral middle adapted to 0-10 to make it odd numbers and allow a neutral opinion. - The multiple-choice option ‘other’ was added in Part III to questions asking for gender and living area.

- The question: Can carry on conversations with others was removed as testers remarked that whether a person is confident to carry on a conversation with others is very personality-and mood-based, and should not be an indicator for self-efficacy.

Pilot Study

Once feedback from the pre-test was incorporated and the questionnaire ready and approved by the school principal and the meditation teacher, the distribution of the survey during computer classes was discussed and planned. In order to prevent technical issues, such as the students not being able to access the survey from the school’s server, a pilot study was conducted with a sample of 35 students from COSAT. The survey distribution took approximately 10-15 minutes at the end of the computer class and ran smoothly. Results were directly available to the researcher on Google Forms and ready to be exported as csv-file into the statistic program STATA, where after cleaning the data, first analyses could be made (summary statistics, Cronbach’s alpha, means and simple regressions).

Survey Distribution

After consulting with the thesis’ supervisor, an ethics inspection via Etikprövning (the Swedish Ethical Review Authority10) was not deemed necessary. The end version of the survey questionnaire was thus reviewed and approved by following persons: the principal of COSAT, the meditation teacher giving the classes and the supervisor of this thesis. It was unanimously agreed that a digital distribution of the survey would be the best solution in order to reduce calculation errors, the chance of students missing a question, or, putting down their names, and by that rendering the survey not anonymous and therefore invalid. The survey was administered anonymously via Google Forms by the IT teachers at COSAT one grade at a time during the students’ official Computer Class taking about 10 to 20 minutes to complete. Whilst the students were informed that the aim of the survey was to gain an understanding for what was difficult for students, any connection to the primary aim: studying the impact of mindfulness meditation

10 An ethics inspection costs between 5 000 and 16 000 SEK, more information at etikprovning.se and via https://mp.uu.se/web/info/forska/etiskafragor/tillstand-och-etisk-provning).

classes was reduced. To meet this end, the questionnaire was built in a way to include a disguised question that – post survey completion – serves to distinguish the treatment group from the control group. After having received back all surveys they can simply be screened for the answer: No to meditation classes, classifying non-meditators, whilst the rest make up the treatment group (three different options, according to ‘length of treatment’).

Advantages of the chosen form of distribution of the survey can be summarized as follows:

1. Allows for strict anonymity: At no point will the names any students be collected. 2. There is no risk for discrimination as to why some students would be selected to take part in the survey and others not (as compared to stratified or systematic sampling). 3. The treatment group is not surveyed separately and the question identifying treatment students (“I am taking meditation classes”) is embedded in other similar questions (“I am taking dance classes”), allowing to minimize any connection of the survey to the meditation extramural that could possibly bias answers. 4. The data will not contain any missing observations, as only complete surveys can be submitted.

Disadvantages are the following:

1. Students self-select into the treatment group. This raises a number of problems and possible biases that will be discussed in section 5.5. 2. Students that choose to withhold any answer will be excluded from the survey as such surveys will not be submitted. On the one hand this renders less work for the researcher and reduced calculation errors due to not thoroughly dropping missing observations. On the other hand, since the surveys never reach the researcher, it will not be possible to determine which questions were not answered and by which parts of the sample.

5.5 Possible Biases and Limitations Limitations

A possible limitation is the utilization of self-report. There is a disagreement amongst scholars as to whether the validity of self-report questionnaires is better than other indices (discussion in: Orpinas and Frankowski 2001). In fact, studies have shown that often, construct validity of self- reports are better than other measurement approaches (c.f. Howard 1994) and self-reports allow for inexpensive and simple data collection (Orpinas and Frankowski 2001:62).

Also, regarding the use of Likert-scales there is dispute amongst researchers. Several scholars point out, that a larger range of responses is desirable because for scales with a fewer number of response options, most people tend to avoid extreme positions which then again decreases the sensitivity of the scale (McMeel et al. 2017; c.f. Pajares et al. 2001; Bandura 2006). Bandura’s (2006) Self-Efficacy Scale for Children for instance instructs: “For each task, the respondent rates his or her confidence level on an 11-point scale (0=cannot do at all, 50=moderately certain can do, and 100=certain can do)” (McMeel et al. 2017:8). For this study, the original Likert-scale with a range of 0-100 was adapted to a range of 0-10 (keeping the 11 intervals) because pre-testing of the scale showed that such large numbers tend to overwhelm the children. In general, it can be argued that a scale from 0-100 with intervals of 10 points is completely unnecessary and offers the exact same number of options. McMeel et al. (2017:8) emphasize that an 11-point-Likert- scale with a 0–100 rage may better “estimate changes in attitudes or performance than a scale with just 5 intervals”. In this context, adapting from a range of 0-100 to a range of 0-10 whilst keeping the number of intervals should not decrease the sensitivity of the measure by any means.

Possible Biases

Since this is a non-random trial, self-selection bias may be of concern. This fact was included in the considerations during the planning of the research project and therefore a number of demographic questions taken up into the questionnaire as Part III. These serve not merely as descriptive statistics but allow further isolation of the treatment effect by controlling for these variables such as gender, age, religion, living situation, living arrangement.

Alternatively, mandatory treatment would reduce possible self-selection bias. In the exploratory study presented in chapter 4.1, primary school children had been receiving mindfulness meditation as morning assemblies. In contrast to COSAT, participation was mandatory. Nonetheless, even with mandatory assignment, bias may arise through the selection of the treatment classes, and, as long as there is no true random sampling the results may always be biased by an unseen factor, hence controlling remains important.

Lastly, self-selection raises the issue of reverse causality: Since random selection was not possible, it may not necessarily be that meditation leads to reduced aggressive behavior, instead, it could be that generally, students who already tend to be less aggressive chose to sign up for meditation classes.

Summary Chapter 5 This chapter introduced the case of COSAT,a high school in Khayelitsha, the largest township in the Cape Flats, where students receive mindfulness meditation classes once a week. The setting and sample were presented and followed by a discussion of the measurement tools: Bandura’s (2009) Children’s Self-Efficacy Scale and Orpinas and Frankowski’s(2001) Aggression Scale for young adolescents along with the operationalization of the variables. Data collection procedure and discussion of possible biases and limitations concluded this chapter.

6 Results Chapter 6 presents the results. First, descriptive statistics are presented. Second, factor analyses investigate the measurement of the outcome variables and the subscales. Lastly, results of the regression analyses are presented in relation to hypotheses.

6.1 Descriptive Statistics Only two cases had to be deleted from the dataset as two learners indicated that they were in Grade 12(which was excluded from the study as Grades 12 do not get to take any elective classes). 384 valid cases were left in the data set. Since the surveys were deliberately administered via a digital survey form that does not allow submission of the survey if any question remains unanswered (surveys with missing cases: students unwilling to answer one or

several questions) were automatically excluded from the dataset11. As no further cases had to be deleted, the final number of observations in our dataset remained at 384, which according to the margin of error and confidence level specified in chapter 5.2, is precisely sufficient for possible inferences to be made for all youth in Cape Town. Descriptive statistics for the measuring instruments were therefore computed with a total sample size of 384 participants. All calculations in the following chapters were performed in STATA 13.0.

Table 1 Descriptive Statistics

Total Non-Meditators Meditators n = 384 %* n = 278 %* n = 106 %*

Gender 228 59% 144 52% 84 80% Female 148 39% 126 45% 22 20% Male 8 2% 8 3% 0 0% Others

Grade 8th 94 24% 65 23% 29 27% 9th 107 28% 93 33% 14 13% 10th 119 31% 72 26% 47 44% 11th 64 17% 48 17% 16 15% Mean (± SD) 9.40 (± 1.03) 9.37 (± 1.03) 9.47 (± 1.05)

Age in years 13 48 13% 29 10% 19 18% 14 101 26% 84 30% 17 16% 15 122 32% 82 30% 40 37% 16 70 18% 48 17% 22 21% 17 37 10% 29 10% 8 8% 18 6 2% 6 2% 0 0% Mean (± SD) 14.91 (± 1.21) 14.94 (± 1.23) 14.84 (± 1.17)

Religion Christian 307 80% 217 79% 90 85% Ancestors (& Chr) 41 11% 30 11% 11 10% African 4 1% 1 3% 3 3% Atheists 8 2% 7 3% 1 1%

11 From the total of 453 students registered at COSAT in grades 8 through 11 for the current school year, it was calculated that 69 students did not complete the survey, either because they chose not to answer at least one question, or because they were ill the day their class was asked to fill in the survey (or due to any technical issues with an individual computer – although the IT Teachers were always present supervising and helping during the distribution).

Rasta/Zionic 4 1% 3 1% 1 1% Islam 2 1% 2 1% 0 0% Other 18 5% 18 6% 0 0%

Residential Area Khayelitsha 333 87% 240 86% 93 88% Another township 47 12% 35 13% 12 11% Not township 4 1% 3 1% 1 1%

Living arrangement With parent(s) 302 79% 215 77% 87 82% Aunt/uncle 24 6% 21 8% 3 3% Grandparent(s) 46 12% 32 11% 14 13% Alone 3 1% 2 1% 1 1% Other 9 2% 8 3% 1 1%

* For ease of reading percentages are rounded to whole numbers and may not always add to 100%

Age. The mean age of the entire sample was 14.91 years. As the answers to the question age included two options that were open-ended (13 or under, 18 or over), the age scale would strictly seen, not count as an interval variable because the point differences are not equal between all options of the scale. However, as there are no students aged less than 13 or over 18, confirmed by the school, age can be used as an interval variable. All participants were in either Grade 8, 9, 10 or 11. Table 1 shows the descriptive statistics.

Gender. 59% of the participants identified as female, whilst 39% referred to themselves as males and ca. 2% made up the category of others. The sample population thus has a larger proportion of female students which seems to reflect national statistic. High school enrollment nationwide has in the recent years been higher for females: According to the World Bank’s development indicators, “ratio of female to male secondary enrollment” is 1.2673 % (Trading Economics 2014). For gender to be an important control variable, we should suspect that gender has an effect on the dependent variables, and is also associated with the independent variable. Table 1 shows that there is a clear (not necessarily very strong) association between the key independent variable meditation and gender: Gender is evenly distributed in the non-meditators group, whereas amongst the meditators in this sample it is appr. 80% female and only 20% male. We

can then suspect that this control variable is not only associated with the key independent variable but affects also the dependent variables.

Grade. 31% students reported being in 11th grade, whilst there were 28% 10th graders and 24% 8th graders. Grade 11 however, held a much smaller number of students (17% of the sample). A possible explanation is the fact that COSAT had received more funding during the more recent years and has been able to admit a larger number of students. An additional explanation as to why there are more students at the school in the younger grades would be the impact of dropouts and students repeating. Whilst several students may have repeated a class, according to the principal there have been hardly any students dropping out in the past years. In 2019, one student was reported to have dropped out whilst another passed away, both were in grade 12 (not affecting any of the current statistics). Because grade is strongly correlated with age it is not recommended to be included as control variable.

Religion. The sample population is predominantly Christian. Approximately 80% of the learners identified as Christians whilst around 16% declared to practice native religious traditions such as Rastafarian, Ancestors, African traditional religions, and only ca. 2% claimed to be atheists or not religious. This seems to be quite an accurate reflection of the distribution of religions across the country as published in the World Factbook: “Christian 86%, ancestral, tribal, animist, or other traditional African religions 5.4%,Muslim 1.9%,other 1.5%,nothing in particular 5.2%”(World Factbook 2019, estimates 2015) although the proportion of students following ancestral and African traditional religions is almost thrice the national average for the general population, specifically Ancestors and Christianity seem to be popular amongst the youth in our sample (ca. 16%). The evidently higher percentage of African religions and comparatively low percentage of Muslims(0.5%) can partly be explained by the strong racial and cultural segregation of South Africa’s population where specific townships are either mainly ‘blacks’ following Christian or African religions (e.g. Khayelitsha) and others such as Mitchells Plain predominantly ‘colored’ with a comparatively higher percentage following Islam (URDR 2017). Religion is intended to be included as a binary control variable divided into: Religious and atheists. Religious participants as opposed to atheists can be assumed to have stronger morals which could suggest lower aggression scores for religious participants. Only about 2% of the total students reported being

atheists or not following any religion which is about half of what the World Factbook estimated for the national average, though this can possibly be explained by the fact that the national population may include a larger number of expatriates coming from countries with less religious tradition than South Africa compared to school students in Khayelitsha. Interestingly, the there are no atheists in the meditators group, whilst 6% of the non-meditators identified as atheists. Yet, since the number of atheists in the sample is very small, the control variable is not expected to show significant impact.

Living area. 87% of the learners reported to be living in the township Khayelitsha. This is not surprising since the school is located in Khayelitsha with the intention to cater to students in the township. Another 12% reported to be living in another township in the area, making it a total of 99% of the students in the sample living in a township. Living area was intended to be used as dichotomous control variable (divided into: township=0 and non-township=1) since living in a township (a high crime, low income area) can be understood as an external risk factor for criminal behavior as described in chapter 3 and thus it can be expected to positively correlate with aggression and negatively with self-efficacy. However, since only 1% (4 students) reported to live in an area not classified as a township, it is not expected to show any significant effects in the regression analyses. Nonetheless, if future research investigates the effect of a mindfulness meditation program in several schools, some located in townships and others in less violence- ridden areas, living area should most certainly be included as control variable.

Living arrangement. 78% of the children reported to be living with at least one parent. The proportion of students in our sample who reported to be living with at least one of their parents is thus very similar to the national statistics that indicate that approximately 75% of children live with at least one parent (Hall and Posel 2012). However, according to Amoateng et al. (2007), this differs a lot according to race as a much smaller proportion of black children are said to live with their parents than white children. Since Khayelitsha is a ‘black’ township, the proportion of students in our sample living without their parents is lower than expected. As control variable, it is interesting whether students live alone. Since merely 1% of both meditators (1 student) and non-meditators (2 students) of our sample live alone, and they are thus equally divided between treatment and control, it is not expected to show any significant relationship.

6.2 Construct Validity Since self-efficacy and aggression are “hypothetical constructs that cannot be directly measured” (i.e. latent variables), they need to be inferred from manifest (directly measurable) variables (MacCallum and Austin 2000). In Social Sciences this often happens through survey questionnaires and/or the construction of mathematical models. Even though theory mainly assumes self-efficacy and aggression to be single constructs, many scholars agree that they are made up of different dimensions, or so-called factors (Bandura 2006; Pajares et al. 2001; Orpinas and Frankowski 2001). A factor can be defined as a set of measured items (observed manifest variables) with similar response patterns and thus similar causal patterns. Factor analysis helps understand and model interrelationships between items with fewer such latent variables (factors).

For the regression analyses (see section 6.3), total scores were thus calculated averaging all items (summated, normalized to a min=0 and max=1). In addition to total scores, summated and normalized factor-based scores for the subscales were computed in order to detect any differing causal patterns that else might be hidden.

6.2.1 Factor Analysis Self-Efficacy Two tests were conducted to determine sampling adequacy, i.e. the suitability of the data for factor analysis. Bartlett’s Test of Sphericity determines if we have sufficient adequate correlations. Bartlett’s test for the Self-Efficacy items is significant (p-value .000), indicating that there are sufficient intercorrelations to conduct a factor analysis. Kaiser-Meyer-Oakland Measure of Sampling Adequacy (KMO) provides an overall measure of the overlap or shared variance between pairs of variables. For the self-efficacy items, the factor test reports a KMO of .849 which is a good value (>.7 is good according to Bühner 2006; Field 2009). Higher values indicate an overlap but not to the point of hindering the analysis due to multicollinearity (Field 2009). The KMO for the self-efficacy scale shows that there is enough overlap but not so much that it would create spurious results.

An exploratory factor analysis (EFA) using the principal-component factors (PCF-) method with orthogonal varimax rotation revealed eight factors (eigenvalues >1). However, whilst most items

gathered precisely along Bandura’s dimensions of self-efficacy (originally nine minus the two subscales/dimensions removed), factor eight seems to be a rest-post, where three items with generally low factors gathered. A second factor analysis (pcf-method, varimax) specifying seven retained factors shows high factor loadings supporting the preference for a 7-factor structure (Table 2).

The 7-factor model explains 58% of the variance. It is common to set the threshold for factor loadings between 0.400 and 0.500 and dropping all items below this threshold (these items usually cross load). Along Fertman and Primack (2009: 12), the threshold was set to .450. As evidence of conversion validity, all factors load on at least one factor above .450, except four items. Items 5, 24, 26 and 27 did not load higher than .450 on any factor, and were thus eliminated to fulfil the aim of getting a parsimonious solution that helps to interpret not only the overall scale but these underlying dimensions. As evidence of discriminant validity there are no strong cross-loadings. Cronbach’s alphas are displayed for each subscale and discussed below in Table 4 the section internal consistency/reliability.

Table 2 Pattern Matrix Factor Loadings Self-Efficacy

Factor Loadings Structure coefficients Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Chronbach’s Alpha 0.8746 α=.9088 α=.7656 α=.7660 α=0.7244 α=0.6559 α=0.4541 α=0.5730

Items

1 Get teachers to help me 0.620676 when I get stuck on schoolwork 2 Get another student to help 0.608466 me when I get stuck on schoolwork 3 Get adults to help me when 0.37178 0.592085 I have social problems 4 Get a friend to help me 0.76744 when I have social problems 5 Finish my homework 0.347589 assignments by deadlines 6 Get myself to study when 0.66541 there are other interesting things to do 7 Always concentrate on 0.754064 school subjects during class 8 Plan and organize my 0.764248 schoolwork for the day 9 Arrange a place to study 0.589374 without distractions 10 Learn sports skills well 0.686205

11 Learn dance skills well 0.720318

12 Learn music skills well 0.303181 0.522395

13 Do regular physical 0.683777 education activities 14 Resist peer pressure to do 0.649871 things in school that can get me into trouble 15 Stop myself from skipping 0.644134 school when I feel bored or upset 16 Resist peer pressure to 0.887971 smoke cigarettes 17 Resist peer pressure to 0.836525 drink beer, wine, or liquor 18 Resist peer pressure to 0.887779 smoke marijuana 19 Resist peer pressure to use 0.875563 pills (uppers, downers) 20 Resist peer pressure to 0.763431 have sexual intercourse 21 Control my temper and 0.525508 resist using physical violence when angry 22 Live up to what my parents 0.774081 expect of me 23 Live up to what my 0.792364 teachers expect of me 24 Live up to what my peers 0.313344 0.323951 expect of me 25 Live up to what I expect of 0.312186 0.511826 myself

6.2.2 Factor Analysis Aggression For the Aggression items, Bartlett’s test is significant (p-value .000) reporting a KMO of .849 indicating that there is enough overlap to conduct a factor analysis.

An EFA with our current sample rendered three factors (eigenvalues > 1). Factor analysis (pcf method) with orthogonal varimax rotation and three factors specified rendered high factor loadings which explain 59% of the variance. Interestingly, in our sample apart from two items, all items load exactly congruent with the three different components of aggression mentioned by Orpinas and Frankowski (2001: 53): “verbal aggression (teasing, name-calling, encouraging students to fight, threatening to hurt or hit) and physical aggression (pushing, slapping, kicking, hitting), as well as information about anger (getting angry easily, being angry most of the day)”.

Whilst the other factors neatly loaded according to the theoretical and semantical differentiation onto Factor 1: Physical aggression, Factor 2: Verbal Aggression and Factor 3: Anger, only items 11 and 6 did not load as expected. Item 11 according to the authors is a form of verbal aggression, in our case however loaded onto Factor 1: Physical Aggression. As I threatened to hit or hurt someone includes both verbal and physical aspects of aggression and loads quite high (.58) on Factor 1, it seems justifiable to keep item 11. Item 6 I pushed or shoved other students though, clearly a form of physical aggression, loaded slightly stronger onto Factor 2: Verbal Aggression. It also cross loads with a loading above .3 on a second factor (physical aggression), whilst loading only slightly higher than .500 on the verbal dimension of aggression.

Due to above-mentioned criteria concerning the cross-loading and general low loadings of item 6, it is suggested that we drop the item. The mismatch of the theoretical dimension further supports the removal of item 6 from the scale. Table 3 displays the factor loadings for the 11 aggression items and alphas for the retained factors (subscales).

Table 3 Pattern Matrix Factor Loadings Aggression

Factor Loadings

Structure coefficients Items Factor 1 Factor 2 Factor 3 α=.7541 α=.7503 α=.4703 1. I teased students to 0.681351 0.42209 make them angry. 2. I got angry very 0.859089 easily with someone. 3. I fought back when 0.502968 0.49892 someone hit me first. 4. I said things about

other kids to make 0.728257 other students laugh. 5. I encouraged other 0.422829 0.493897 students to fight. 6. I pushed or shoved 0.39574 0.544346 other students. 7. I was angry most of 0.388117 0.58744 the day. 8. I got into a physical

fight because I was 0.80731 angry. 9. I slapped or kicked 0.797921 someone. 10. I called other 0.35684 0.723476 students bad names. 11. I threatened to hurt or to hit 0.580919 0.406294 someone. * Factor loadings below .300 are blanked out.

6.2.3 Internal consistency / Reliability To make sure the subscales are conceptionally related and ensure measuring scale quality, Cronbach’s alpha was calculated for both scales with and without the items the factor analysis suggested to drop (Table 4). According to Field (2009) an alpha of >.7 is good and >.9 is great.

Cronbach’s alpha for the whole Self-Efficacy Scale was very good: .87. As shown in Table 2 Self- efficacy subscales 1 through 4 display good alphas (>.7; Field 2009) whilst alphas for factors 5 through 7 are relatively low. A possible explanation could be that the latter two only include two items with not very high loadings. The alpha of the total self-efficacy scale with 0.8746 however, is very good, and hardly gets worse when removing the 4 items (alpha decreases by -.0037).

For the Aggression Scale, when dropping item 6, this does not significantly weaken the internal consistency - Cronbach’s alpha for the entire battery of items (total scale) decreases by a mere .0141 and with .814 maintains a very good reliability of the scale (coefficient >.8).

Table 4 Reliability Statistics Cronbach’s alpha

Cronbach’s alpha N of items Average interim covariance

Self-Efficacy Scale 0.8746 32 1.558149 - Dropping items 5, 24, 26, 27: 0.8709 28 1.70477

Aggression Scale 0.8278 11 1.015164 - Dropping item 5: 0.8137 10 1.051628

Means of each of the subscales derived from the factor analyses shown for meditators and non- meditators are reported, as well as total n can be found in Appendix C.

6.3 Regressions This section presents the regression models testing each of the four hypotheses. H1-Models 1- 16 test the relationship between meditation and self-efficacy (H1). H1-Models 3-16 include the different self-efficacy subscales derived via the factor analysis. Since presenting and discussing 48 models in total would certainly overwhelm the reader, only Models 1-4 per hypothesis (total of 16) are presented, whilst additional models are found in Appendix D.

Shown first, are the models testing H1: Mindfulness meditation is associated with increased self- efficacy amongst students receiving such classes, compared to the control group. In H1-Model 1 the (bivariate) regression coefficients for the dichotomous independent variable meditators (==1:treatment group;==0:control group) measure Total self-efficacy (T-SE) (Table 5). Results show that meditators on average are predicted to score -.0120(-1.20%) on T-SE, adding controls (H1-Model 2) the coefficient becomes slightly more negative -.169(-1.69%), yet remains statistically insignificant.

Table 5 Regression Models Hypothesis 1 Total Self-Efficacy (T-SE)

H1-Model 1 H1-Model 2 OLS OLS Bivariate Controls Variables SeTotalALL se aster SeTotalALL se aster

meditators -0.0120 (0.0152) -0.0169 (0.0158) AGERATIO 0.0251 (0.0288) gender_female 0.0215 (0.0146) gender_other -0.00798 (0.0538) live_alone -0.123 (0.0858) not_township -0.0239 (0.0672) atheists -0.00195 (0.0494) Constant 0.713 (0.00801) *** 0.693 (0.0164) ***

Observations 384 384 R-squared 0.002 0.016 N 384 384 r2 0.00162 0.0159 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table 6 Regression Models Hypothesis 1 Self-Regulatory Self-Efficacy (SR-SE) depicts models testing the first self-efficacy subscale, namely whether meditators show higher Self-Regulatory Self-Efficacy (SR-SE). H1-Model 3 shows that meditators on average scored 0.0038(0.4%) less in SR-SE compared to non-meditators. When adding controls (H1-Model 4), the difference becomes slightly bigger: Meditators now are expected to score 0.0096(0.96%), yet still indicating a very

small difference. Neither model shows statistical significance, which could pertain to the very small differences between the groups. Results for the other self-efficacy subscales (H1-Models 4- 16) can be found in the appendix. As discussed in the Methods chapter, gender was expected to impact Self-Efficacy for Selfregulated Learning (H1-Model 8 in Appendix D) does show a positive coefficient for the control variable female (.136), yet not statistically significant. H1-Models 15 and 16 however show a significant relationship between meditation and SE-ESR(A). The bivariate H1-Model 15 shows that meditators on average scored 0.0478(4.78%) less in ES-ESR(A) compared to non-meditators, significant at p<0.1. When adding controls, the negative relationship between ES-ESR(A) and meditation becomes stronger -.0626 (-6.26%) and significant at p<0.05. Overall, no support was found for the first hypothesis as 14 out of 16 models testing H1 did not show any significant relationship. Only for one subscale, ES-ESR(A), a significant relationship was found, and the relationship goes against expectations.

Table 6 Regression Models Hypothesis 1 Self-Regulatory Self-Efficacy (SR-SE)

H1-Model 3 H1-Model 4

OLS OLS Bivariate Controls Variables SeScale1N se aster SeScale1N Se aster

meditators -0.00380 (0.0303) -0.00962 (0.0309) AGERATIO 0.178 (0.0563) *** gender_female 0.0581 (0.0286) ** gender_other 0.183 (0.105) * live_alone -0.320 (0.168) * not_township 0.0225 (0.132) atheists 0.0462 (0.0968) Constant 0.770 (0.0159) *** 0.666 (0.0320) ***

Observations 384 384 R-squared 0.000 0.043 N 384 384 r2 4.13e-05 0.0429 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

H2- Models 1-8 test H2: Mindfulness meditation is associated with decreased aggression amongst students receiving such classes, compared to the control group. H2-Models 3-8 include the different aggression subscales. H2-Models 1 and 2 in Table 7 show Total Aggression(T-A). H2- Model 1 shows a negative coefficient for meditators (-.0178) compared to non-meditators but when adding controls (H2-Model 2) the coefficient becomes positive (.0052), neither are statistically significant. Even though meditators seem to generally be less aggressive compared to the control group, when adding controls, which increases the r2 of the model drastically, meditators now on average score 0.5% higher on the aggression scale. Since the relationship is not significant on any level, this does not provide much support for the second hypothesis.

Table 7 Regression Models Hypothesis 2 Total Aggression (T-A)

H2-Model 1 H2-Model 2 OLS OLS Bivariate Controls Variables AggTotalALL se aster AggTotalALL se aster

meditators -0.0178 (0.0211) 0.00517 (0.0209) AGERATIO -0.0868 (0.0380) ** gender_female -0.0940 (0.0193) *** gender_other -0.0665 (0.0710) live_alone 0.395 (0.113) *** not_township 0.0947 (0.0888) atheists 0.0866 (0.0653) Constant 0.217 (0.0111) *** 0.295 (0.0216) ***

Observations 384 384 R-squared 0.002 0.105 N 384 384 r2 0.00186 0.105 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table 8 looks at subscales. H2-Model 3 shows a negative coefficient (-0.0151) for the first aggression subscale: Physical Aggression (P-A), which then becomes positive (0.0183) when adding controls (H2-Model 4), neither however are significant. Results for the other aggression subscales (H2-Models 5-8) can be found in the appendix. H2-Model 5 shows that meditators

scored .0433 lower than non-meditators on Verbal Aggression (V-A), significant on p<0.1. Whilst the coefficient hardly becomes smaller (by .0300), significance is lost when adding controls (H2- Model 6). As expected, females do show less P-A (-.128) statistically significant at p<0.01, yet, also lower V-A (-.125) significant at p<0.01 (see Appendix D, H2-Model 6), against the theoretical expectations argued in the methods chapter. The remaining aggression subscale (appendix) does not show any significant relationship between aggression and meditation. Even though most models show lower aggression for meditators, differences are very small and only few models show statistical significance, which usually disappears when adding controls or changes the coefficient to positive. We therefore cannot find enough evidence to reject the null-hypothesis for H2.

Table 8 Regression Models Hypothesis 2 Physical Aggression (P-A)

H2-Model 3 H2-Model 4 OLS OLS Bivariate Controls Variables Agg1N se aster Agg1N se aster

meditators -0.0151 (0.0274) 0.0183 (0.0267) AGERATIO -0.101 (0.0487) ** gender_female -0.128 (0.0247) *** gender_other -0.0416 (0.0911) live_alone 0.571 (0.145) *** not_township 0.0146 (0.114) atheists 0.174 (0.0837) ** Constant 0.196 (0.0144) *** 0.294 (0.0277) ***

Observations 384 384 R-squared 0.001 0.127 N 384 384 r2 0.000791 0.127 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Regressions for the two last hypotheses, H3 (Table 11) and H4 (Table 12), are done with a subset of 106 observations (only meditators), since the comparison is between novice and long-term meditators (H3-Models 1-16).

H3-Model 1 indicates that novice meditators on average scored .0202 (2.02%) lower on total self- efficacy than long-term meditators, when adding the control variables (H3-Model 2) the effect increases by 50% to .0303, neither coefficients are significant.

Table 9 Regression Models Hypothesis 3 Total Self-Efficacy (T-SE)

H3-Model 1 H3-Model 2 OLS OLS Bivariate Controls Variables SeTotalALL se Aster SeTotalALL se aster

SeTotalALL NoviceMeditators -0.0202 (0.0291) -0.0300 (0.0326) o.LongtermMeditators - - AGERATIO -0.0350 (0.0621) gender_female 0.0131 (0.0328) o.gender_other - live_alone 0.000688 (0.138) not_township -0.0344 (0.137) atheists 0.141 (0.137) Constant 0.716 (0.0248) *** 0.725 (0.0479) ***

Observations 106 106 R-squared 0.005 0.021 N 106 106 r2 0.00459 0.0206 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table 10 presents results for the first subscale SR-SE. H3-Model 3 shows that novice meditators scored on average .0613(6.13%) lower on SR-SE than long-term meditators (more than double the coefficient for T-SE). When adding controls (3-Model 4), the difference becomes slightly smaller, though by a mere .0019(0.19%). Neither show any significant relationship.

Results for the other self-efficacy subscales (H1-Models 4-16) are in the appendix, most of them indicating that long-term meditators produced higher scores on the different self-efficacy dimensions. Along with the difference in T-SE(Table 10) this would speak for the third hypothesis

that long-term meditators show increased self-efficacy compared to novice meditators – yet, none of the models depicted statistical significance. Hence, even though 12 out of 16 models showed results supporting H3, we cannot find enough support to reject the null-hypothesis for H3 with 90% or more confidence.

Table 10 Regression Models Hypothesis 3 Self-Regulated Self-Efficacy (SR-SE).

H3-Model 3 H3-Model 4 OLS OLS Bivariate Controls Variables SeScale1N se aster SeScale1N se aster

SeTotalALL NoviceMeditators -0.0613 (0.0586) -0.0594 (0.0650) o.LongtermMeditators - - AGERATIO 0.0701 (0.124) gender_female 0.0982 (0.0654) o.gender_other live_alone -0.121 (0.275) not_township 0.168 (0.273) atheists 0.205 (0.273) Constant 0.810 (0.0500) *** 0.703 (0.0956) ***

Observations 106 106 R-squared 0.010 0.047 N 106 106 r2 0.0104 0.0471 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

H4- Models 1-8 compare levels of aggression for long-term meditators in relation to novice meditators (H4). Table 12 shows models testing H4: Long-term meditators show decreased aggression compared to novice meditators for T-A.

H4-Model 1 shows novice meditators scored on average .0019 less than long-term meditators in T-A, adding controls (H4-Model 2) increases the difference between the two groups of

meditators, novice meditators score even -.0274 (2.74%) less than long-term meditators, neither difference is statistically significant.

Table 11 Regression Models Hypothesis 4 Total Aggression

H4-Model 1 H4-Model 2 OLS OLS Bivariate Controls Variables AggTotalALL se aster AggTotalALL se aster

AggTotalALL NoviceMeditators -0.0190 (0.0369) -0.0274 (0.0396) o.LongtermMeditators - - AGERATIO -0.0558 (0.0755) gender_female -0.101 (0.0399) ** o.gender_other - live_alone 0.150 (0.168) not_township 0.0326 (0.166) atheists 0.320 (0.166) * Constant 0.213 (0.0314) *** 0.315 (0.0583) ***

Observations 106 106 R-squared 0.003 0.098 N 106 106 r2 0.00253 0.0978 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

H4-Model 3 shows that novice meditators also scored less than longterm meditators on average (-.0438), difference increasing to -.0630 when adding controls (H4-Model 4) – none of the coefficients are significant. Results for the other aggression subscales (H4-Models 5-8) can be found in the appendix, neither show a significant relationship between aggression and meditation. Thus, we do not find support for H4.

Table 12 Regression Models Hypothesis 4 Physical Aggression

H4-Model 3 H4-Model 4 OLS OLS Bivariate Controls Variables Agg1N se aster Agg1N se aster

AggTotalALL NoviceMeditators -0.0438 (0.0468) -0.0630 (0.0492)

o.LongtermMeditators - - AGERATIO -0.0986 (0.0938) gender_female -0.109 (0.0495) ** o.gender_other live_alone 0.222 (0.208) not_township -0.131 (0.207) atheists 0.619 (0.207) *** Constant 0.213 (0.0399) *** 0.343 (0.0724) ***

Observations 106 106 R-squared 0.008 0.141 N 106 106 r2 0.00835 0.141 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Summary Results Despite not only testing total scores for both aggression and self-efficacy, but also scores for all the subscales provided by factor analyses in section 6.2, none of the four hypotheses could be confirmed. Results for H1, H2 and H4 showed mixed results with only few significant models. Even though nearly all models testing H3 supported the expectation that long-term meditators would show increased self-efficacy compared to novice meditators none of the models reported statistical significance to corroborate H3.

7 Discussion A clear, statistically significant relationship was neither found between meditation and the protective factor: self-efficacy, nor between treatment and the risk factor: aggression. Whilst the data from the current study did not support any of the hypotheses with enough confidence, this does not mean that there is no important relationship. Especially since previous studies as well as the qualitative study in this paper indicate, mindfulness meditation has been able to positively affect these risk and protective factors for youth delinquency in global and local contexts. Nonetheless, no such effect has been shown for the current sample. Possible reasons that no significant relationship was found are discussed below.

Age as moderator. Even though age was included as control variable, this study did not consider psychological development. Evans-Chase (2013:74) states that in current literature the use of age

as a control variable in investigations of juvenile justice intervention effects is the most common use of the variable – yet, she argues that one should also consider it as a moderator. Evans-Chase suggests using age as moderator in an interaction term and hypothesizes that “the impact of mindfulness meditation on self-regulation will be moderated by age, used as a proxy for level of neurological development” such that the younger age group will experience smaller increases in perceived self-regulation than those in the older age group (ibid.:67). Likewise, emotion regulation is proven to increase with age along with other self-regulation skills that develop throughout childhood and adolescence (Botha 2014). Thus, future studies analyzing the effect of mindfulness meditation on psychological aspects such as self-efficacy and aggression for children and adolescents would do well to inspect a possible moderator effect. Future research would be well-advised to test models with such an interaction effect.

No longitudinal study. After successful piloting, the date and time-slot for each class was scheduled – unfortunately, Khayeitsha is an unpredictable, low-income township and so the gathering of the data was delayed first by several weeks due to broken-down internet connections and electricity cut-offs in the area, then by months due to theft of several computers during the school holiday as well as school closure due to unrests and further internet breakdowns. As mentioned in the Methods chapter, due to this severe delay, the original plan to distribute the survey a first time to all students (baseline) and after 16 weeks a second time, needed to be revised. Adapting the research design to a cross-sectional survey design then makes it more difficult to isolate any effect of treatment, as it raises for instance the problem of reverse causality. Also, it is important to note that there was no pre-treatment survey since some students had been taking meditation classes for years whereas others only for several months. What this setup did allow however is, the comparison between the length of treatment: Is there an observable difference between novice meditators and long-term meditators.

Exposure-time. Also, the exposure-time of treatment may have not been long enough. Lutz et al. (2008) analyzed differences between novice and expert meditators, where experts qualified with a minimum of 10’000hrs of Buddhist meditation (equivalent to 30 years of daily meditation). Since reaching 30 years of mediation experience is not possible when analyzing adolescents,

future research would benefit conducting larger panel studies measuring subjects exposed to meditation practice during childhood, adolescence and adulthood, compared to controls.

Sample size. Likewise, the size of the long-term-meditators-group may have been too small(n=29) for any significant relationship to be detected. Other issues regarding research design and data collection discussed in previous chapters should also be taken into account when constructing future studies. Whilst large sample sizes are preferred, random sampling whenever possible is recommended.

Omitted variables could also bias results. In order to prevent the questionnaire from becoming too long and risk a decrease of the students’ attention rendering inaccurate answers, the section of demographic questions following both psychometric scales was held as short as possible restricted only to the control variables deemed necessary (as well as the questions required to embed the question identifying the independent variable). Whilst the control variables as outlined in chapter 4 where thoroughly chosen, there may still exist an omitted variable bias. In the following, we will discuss possible omitted variables.

Race and language were not measured. Due to the continual strong racial and ethnic segregation of the population in Cape Town, these characteristics were assumed to be very similar therefore not included as control. Still, including race in future studies would allow a more complete picture, especially in larger studies where schools in different (black and colored) townships or even across the country are compared. The inclusion of native language as variable would have also provided a more complete description of the sample population and necessary in a more diverse population. Future research could also consider measuring the frequency of religious practice, a more detailed indicator than mere religion.

Whether students came from a rural or urban area was not measured due to all students residing in the Cape Flats, commonly is classified as urban – although due to lacking infrastructure and safety it is difficult to classify the areas. Instead, the actual residential area was included as control variable, mainly to measure whether living in a township (high violence, often informal settlements, housing without electricity or running water) had an effect on the outcome variables. As 98.96% of the sample reported to be living in one of the townships, no significant

effect could be determined. Future research would nonetheless do well to include the residential area, especially when comparing schools in townships with schools in more affluent areas.

Parents’ marital status and whether both parents were alive was initially considered to be included in the demographic part of the questionnaire but after several considerations were replaced by a less sensitive question asking merely with whom the student lived. According to Hall and Posel (2012), nearly 25% of the children nationwide do not live with any biological parent although this does not mean that the children are necessarily orphans:79% reported to have at least one parent alive but not living with them. Amoateng et al. (2007) further found that only 50% of black children lived with their parents whilst for white children it was 90%, showing large racial differences in the living arrangements.

The inclusion of an item asking for parents’ employment status and/or socio-economic status (SES), would have been beneficial to determine any differences within the sample population and possible correlations, though SES generally being very low within the townships. Future studies are advised to include more demographic information about the parents and households, e.g. whether the student is living in a single-parent household, parent’s employment and education status when possible for both parents separately (including the gender). Since a large portion of the children in South Africa, especially in poor areas, do not live with their parents, also the caretakers’ employment situation would be relevant. According to Botha (2014: 188), around 31% of the South African population receive social grants. Measuring whether families/parents receive grants could also be an important factor in relation to the generally low SES of families living in townships.

Summary Discussion This chapter critically discussed the findings, pointing out important aspects for future studies to enhance this new area of research.

8 Conclusion This paper analyzed the impact of mindfulness classes at a high school in Khayelitsha a township in the Cape Flats. Self-reported self-efficacy and aggression were measured via a psychometric survey questionnaire created from two well-tested and validated scales. Regression analyses of

384 survey answers provided mixed results. Whilst novice meditators were not associated with higher self-efficacy and lower aggression, long-term meditators performed better in several dimensions of self-efficacy and aggression, yet, no significant relationship was found. Further research is required, specifically, the moderating effect of age (psychological development) on meditation needs to be further investigated. This study aimed to bridge the gap between the outdated paradigms of youth crime prevention and ancient wisdom via ground-breaking new evidence from the field of Neuroscience. It furthermore hopes to point policy makers towards developing new, integrative and sustainable approaches to youth crime prevention – approaches that grant agency to our youth, and thus future generations.

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10 Appendix Appendix A

- Additional Information Research Design and Data Collection

I. Short description of mindfulness practice as an experience:

When thoughts or feelings come up in your mind, you don’t ignore them or suppress them, nor do you analyze or judge their content. Rather, you simply note any thoughts as they occur as best you can and observe them intentionally but nonjudgmentally, moment by moment, as the events in the field of your awareness. Paradoxically, this inclusive noting of thoughts that come and go in your mind can lead you to feel less caught up in them and give you a deeper perspective on your reaction to everyday stress and pressures. By observing your thoughts and emotions as if you had taken a step back from them, you can see much more clearly what is actually on your mind. You can see your thoughts arise and recede one after another. You can note the content of your thoughts, the feelings associated with them, and your reactions to them. You might become aware of agendas, attachments, likes and dislikes, and inaccuracies in your ideas. – Kabat-Zinn 2019

II. A short outline of the mindfulness meditation classes:

3.00pm - Intro and settle down for ten minutes.

3.10pm - Guided meditation for ten minutes.

3.20pm - Chanted prayers for ten minutes.

3.30pm - Teaching on mindfulness and meditation practice for fifteen minutes – e.g. how to be more patient and loving in every-day life.

Short meditation based on the day's topic to conclude.

3.45pm - Stretching exercises for five minutes.

3.50pm - Discussion in small groups for five minutes.

3.55pm - Question & Answer and announcements about what's coming up for future classes/events.

4.10pm - Finish.

Data Collection - Delay

- The electricity crisis in Cape Town culminated in load-shedding stages 3 to 7 (intentionally engineered electricity cut-offs by the electricity provider for several hours each day in different parts of the country because of electricity shortage12) for months on end and led to very scarce availability of the computer room, where Computer Classes had to be prioritized. - Unrests and riots13 in Khayelitsha in April required the school to close for a period14. - During school holidays a burglary happened at the school and a large number of computers from the computer classroom were stolen and needed to be replaced. - The generally instable infrastructure provided by the government in South African townships, specifically the internet connection at the school would break down multiple times without repair for weeks and months.

12 For more info on load shedding: https://www.thesouthafrican.com/news/eskom-load-shedding-schedule-2019/ 13 More info on the water protests, rioots and unrests in Khayelitsha: https://www.groundup.org.za/article/water- protests-disrupt-khayelitsha/ and https://www.timeslive.co.za/news/south-africa/2019-04-11-looting-bullets-and- a-volley-of-angry-words-as-protests-ravage-cape-town/ 14 Schools closing due to unrests in April 2019: http://www.capetalk.co.za/articles/344576/21-schools-across-ct- shut-down-due-to-khayelitsha-and-lwandle-protests

Appendix B

Survey Questionnaire – distributed via Google Forms

The Children’s Self-Efficacy Scale (adapted from Bandura 2006: 326-327)

PART I: Please rate your degree of confidence by recording a number from 0 to 10 using the scale given below:

0 1 2 3 4 5 6 7 8 9 10

Cannot do Moderately can do Highly certain can do

Self-Efficacy in Enlisting Social Resources (title of subscales does not appear in the survey) Get teachers to help me when I get stuck on schoolwork Get another student to help me when I get stuck on schoolwork Get adults to help me when I have social problems Get a friend to help me when I have social problems

Self-Efficacy for Self-Regulated Learning Finish my homework assignments by deadlines Get myself to study when there are other interesting things to do Always concentrate on school subjects during class Plan and organize my schoolwork for the day Arrange a place to study without distractions

Self-Efficacy for Leisure Time Skills and Extracurricular Activities Learn sports skills well Learn dance skills well Learn music skills well Do regular physical education activities

Self-Regulatory Efficacy Resist peer pressure to do things in school that can get me into trouble Stop myself from skipping school when I feel bored or upset Resist peer pressure to smoke cigarettes Resist peer pressure to drink beer, wine, or liquor

Resist peer pressure to smoke marijuana Resist peer pressure to use pills (uppers, downers) Resist peer pressure to have sexual intercourse Control my temper and resist using physical violence when angry

Self-Efficacy to Meet Others’ Expectations Live up to what my parents expect of me Live up to what my teachers expect of me Live up to what my peers expect of me Live up to what I expect of myself

Social Self-Efficacy Make and keep friends of the opposite sex Make and keep friends of the same sex Work well in a group

Self-Assertive Efficacy Express my opinions when other classmates disagree with me Stand up for myself when I feel I am being treated unfairly Get others to stop annoying me or hurting my feelings Stand firm to someone asking me to do something that could cause harm/hurt someone or something

* The subscales Self-Efficacy for Academic Achievement and Self-Efficacy for Enlisting Parental and Community Support from the original scale were not included as pretesting suggested items were too similar/repetitive and would render the survey unnecessarily long.

The Aggression Scale (Orpinas and Frankowski 2001)

PART II: Please answer the following questions thinking of what you actually did during the last 7 days. For each question, mark how many times you did that behavior during the last 7 days.

Possible answers: 0-6 (times)

1. I teased students to make them angry.

2. I got angry very easily with someone.

3. I fought back when someone hit me first.

4. I said things about other kids to make other students laugh.

5. I encouraged other students to fight.

6. I pushed or shoved other students.

7. I was angry most of the day.

8. I got into a physical fight because I was angry.

9. I slapped or kicked someone.

10. I called other students bad names.

11. I threatened to hurt or to hit someone.

Demographics

PART III: Please answer the following by choosing an answer:

Age in years: a) 13 or younger b) 14 c) 15 d) 16 e) 17 f) 18 or older

Grade: a) 8 b) 9 c) 10 d) 11 e) 12

Gender: a) female b) male c) other

Religion: a) Christianity b) Islam c) Buddhism e) No religion/atheist f) other

Residential area: a) Khayelitsha b) Guguletu c) Mitchells Plain d) Kuilsriver

e) Philippi f) Nyanga g) Langa h) Crossroads i) other

I live: a) with my parent(s) b) with an aunt/uncle c) with sibling(s)/

grandparents d) alone e) other

I am taking dance classes a) no b) since 6 months c) since over a year d) since > three years

I am taking debating classes a) no b) since 6 months c) since over a year d) since > three years

I am taking meditation classes a) no b) since 6 months c) since over a year d) since > three years

I am taking drama classes a) no b) since 6 months c) since over a year d) since > three years

I am taking soccer a) no b) since 6 months c) since over a year d) since > three years

Appendix C

– Additional Descriptive Statistics

Table 13 Subscales Means Normalized Total

Variable Obs. Mean (SD) Min Max

Self-Regulatory Self- 384 .7685547 (.2648278) 0 1 Efficacy

Self-assertive Self- Efficacy 384 .778125 (.2039109) 0 1

Self-Efficacy for Self- Regulated Learning 384 .6735026 (.2044368) 0 1

Self-efficacy in Social Expectations 384 .7903646 (.173492) 0 1

Self-Efficacy for Leisure Time Skills and Extracurricular 384 .6136068 (.2205694) 0 1 Activities

Self-Efficacy in Enlisting Social Resources 384 .6630208 (.2230801) 0 1 (peers)

Self-Efficacy in Enlisting Social Resources 384 .575651 (.2515479) 0 1 (adults)

Physical Aggression 384 .1917318 (.2398585) 0 1

Verbal Aggression 384 .210612 (.2191994) 0 1

Anger 384 .2983941 (.2595541) 0 1

Subscales Means Normalized Non-Meditators

Subscale Obs Mean Std.Dev. Min Max

Self-Regulatory Self-Efficacy 278 .770 .264 0 1

Self-assertive Self-Efficacy 278 .779 .209 0 1

Self-Efficacy for Self-Regulated Learning 278 .683 .201 0 1

Self-efficacy in Social Expectations 278 .796 .172 0 1

Self-Efficacy for Leisure Time Skills and Extracurricular Activities 278 .611 .225 0 1

Self-Efficacy in Enlisting Social Resources (peers) 278 .665 .226 0 1

Self-Efficacy in Enlisting Social 278 .589 .249 0 1 Resources (adults)

Physical Aggression 278 .196 .249 0 1

Verbal Aggression 278 .223 .226 0 1

Anger 278 .294 .256 0 1

Subscales Means Normalized Meditators

Subscale Obs Mean Std.Dev. Min Max

Self-Regulatory Self-Efficacy 106 .766 .269 0 1

Self-assertive Self-Efficacy 106 .775 .192 .1 1

Self-Efficacy for Self-Regulated Learning 106 .65 .211 .075 1

Self-efficacy in Social Expectations 106 .776 .177 .35 1

Self-Efficacy for Leisure Time Skills and Extracurricular Activities 106 .621 .21 .15 1

Self-Efficacy in Enlisting Social Resources (peers) 106 .659 .218 0 1

Self-Efficacy in Enlisting Social 106 .541 .257 0 1 Resources (adults)

Physical Aggression 106 .181 .215 0 1

Verbal Aggression 106 .179 .198 0 .792

Anger 106 .31 .271 0 1

Appendix D

- Additional Regression Models Hypothesis 1

Total Self-Efficacy (T-SE) H1-Model 1 H1-Model 2 OLS OLS Bivariate Controls Variables SeTotalALL se aster SeTotalALL se aster

SeScale1N meditators -0.0120 (0.0152) -0.0169 (0.0158) AGERATIO 0.0251 (0.0288) gender_female 0.0215 (0.0146) gender_other -0.00798 (0.0538) live_alone -0.123 (0.0858)

not_township -0.0239 (0.0672) atheists -0.00195 (0.0494) Constant 0.713 (0.00801) *** 0.693 (0.0164) ***

Observations 384 384 R-squared 0.002 0.016 N 384 384 r2 0.00162 0.0159

Self-Regulatory Self-Efficacy (SR-SE) H1-Model 3 H1-Model 4 OLS OLS Bivariate Controls Variables SeScale1N se aster SeScale1N se aster

SeScale1N meditators -0.00380 (0.0303) -0.00962 (0.0309) AGERATIO 0.178 (0.0563) *** gender_female 0.0581 (0.0286) ** gender_other 0.183 (0.105) * live_alone -0.320 (0.168) * not_township 0.0225 (0.132) atheists 0.0462 (0.0968) Constant 0.770 (0.0159) *** 0.666 (0.0320) ***

Observations 384 384 R-squared 0.000 0.043 N 384 384 r2 4.13e-05 0.0429

Self-assertive Self-Efficacy (SA-SE) H1-Model 5 H1-Model 6 OLS OLS Bivariate Controls Variables SeScale2N se aster SeScale2N se aster

SeScale1N meditators -0.00497 (0.0233) -0.0157 (0.0242) AGERATIO 0.0306 (0.0440) gender_female 0.0374 (0.0224) * gender_other -0.0767 (0.0823) live_alone -0.00873 (0.131) not_township 0.0717 (0.103)

atheists 0.0650 (0.0756) Constant 0.779 (0.0122) *** 0.748 (0.0250) ***

Observations 384 384 R-squared 0.000 0.014 N 384 384 r2 0.000119 0.0140

Self-Efficacy for Self-Regulated Learning (SE-SRL) H1-Model 7 H1-Model 8 OLS OLS Bivariate Controls Variables SeScale3N se aster SeScale3N se aster

SeScale1N meditators -0.0331 (0.0233) -0.0385 (0.0241) AGERATIO -0.0823 (0.0439) * gender_female 0.0136 (0.0223) gender_other -0.0249 (0.0821) live_alone 0.0953 (0.131) not_township -0.130 (0.103) atheists 0.0644 (0.0755) Constant 0.683 (0.0122) *** 0.707 (0.0250) ***

Observations 384 384 R-squared 0.005 0.023 N 384 384 r2 0.00526 0.0229

Self-efficacy in Social Expectations (SE-SE) H1-Model 9 H1-Model 10 OLS OLS Bivariate Controls Variables SeScale4N se aster SeScale4N se aster

SeScale1N meditators -0.0196 (0.0198) -0.0315 (0.0194) AGERATIO -0.0310 (0.0353) gender_female 0.0278 (0.0179) gender_other -0.0968 (0.0660) live_alone -0.473 (0.105) *** not_township -0.0561 (0.0825) atheists -0.119 (0.0607) *

Constant 0.796 (0.0104) *** 0.803 (0.0201) ***

Observations 384 384 R-squared 0.003 0.123 N 384 384 r2 0.00256 0.123

Self-Efficacy for Leisure Time Skills and Extracurricular Activities (SE- LTSEA) H1-Model 11 H1-Model 12 OLS OLS Bivariate Controls Variables SeScale5N se aster SeScale5N se aster

SeScale1N meditators 0.00987 (0.0252) 0.0220 (0.0259) AGERATIO -0.0728 (0.0472) gender_female -0.0631 (0.0240) *** gender_other -0.105 (0.0883) live_alone 0.0509 (0.141) not_township -0.148 (0.110) atheists -0.0312 (0.0812) Constant 0.611 (0.0132) *** 0.677 (0.0269) ***

Observations 384 384 R-squared 0.000 0.029 N 384 384 r2 0.000402 0.0292

Self-Efficacy in Enlisting Social Resources (peers) (SE-ESR(P)) H1-Model 13 H1-Model 14 OLS OLS Bivariate Controls Variables SeScale6N se aster SeScale6N se aster

SeScale1N meditators -0.00561 (0.0255) -0.0114 (0.0264) AGERATIO 0.0507 (0.0480) gender_female 0.0202 (0.0244) gender_other 0.0569 (0.0898) live_alone 0.168 (0.143) not_township -0.0460 (0.112) atheists -0.157 (0.0825) *

Constant 0.665 (0.0134) *** 0.636 (0.0273) ***

Observations 384 384 R-squared 0.000 0.019 N 384 384 r2 0.000127 0.0193

Self-Efficacy in Enlisting Social Resources (adults) (SE-ESR(A)) H1-Model 15 H1-Model 16 OLS OLS Bivariate Controls Variables SeScale7N se aster SeScale7N se aster

SeScale1N meditators -0.0478 (0.0286) * -0.0626 (0.0296) ** AGERATIO -0.0891 (0.0539) * gender_female 0.0312 (0.0274) gender_other -0.131 (0.101) live_alone 0.182 (0.161) not_township -0.0337 (0.126) atheists -0.0206 (0.0927) Constant 0.589 (0.0151) *** 0.611 (0.0307) ***

Observations 384 384 R-squared 0.007 0.026 N 384 384 r2 0.00724 0.0263

Hypothesis 2

Total Aggression (T-A) H2-Model 1 H2-Model 2 OLS OLS Bivariate Controls Variables AggTotalALL se aster AggTotalALL se aster

Agg1N meditators -0.0178 (0.0211) 0.00517 (0.0209) AGERATIO -0.0868 (0.0380) ** gender_female -0.0940 (0.0193) *** gender_other -0.0665 (0.0710) live_alone 0.395 (0.113) *** not_township 0.0947 (0.0888) atheists 0.0866 (0.0653)

Constant 0.217 (0.0111) *** 0.295 (0.0216) ***

Observations 384 384 R-squared 0.002 0.105 N 384 384 r2 0.00186 0.105

Physical Aggresion (P-A) H2-Model 3 H2-Model 4 OLS OLS Bivariate Controls Variables Agg1N se aster Agg1N se aster

Agg1N meditators -0.0151 (0.0274) 0.0183 (0.0267) AGERATIO -0.101 (0.0487) ** gender_female -0.128 (0.0247) *** gender_other -0.0416 (0.0911) live_alone 0.571 (0.145) *** not_township 0.0146 (0.114) atheists 0.174 (0.0837) ** Constant 0.196 (0.0144) *** 0.294 (0.0277) ***

Observations 384 384 R-squared 0.001 0.127 N 384 384 r2 0.000791 0.127

Verbal Aggression (V-A) H2-Model 5 H2-Model 6 OLS OLS Bivariate Controls Variables Agg2N se aster Agg2N se aster

Agg1N meditators -0.0433 (0.0250) * -0.0133 (0.0247) AGERATIO -0.0958 (0.0450) ** gender_female -0.125 (0.0229) *** gender_other -0.0926 (0.0842) live_alone 0.309 (0.134) ** not_township 0.189 (0.105) * atheists 0.0355 (0.0775) Constant 0.223 (0.0131) *** 0.322 (0.0256) ***

Observations 384 384 R-squared 0.008 0.106 N 384 384 r2 0.00783 0.106 Anger (A) H2-Model 7 H2-Model 8

OLS OLS Bivariate Controls Variables Agg3N se aster Agg3N se aster

Agg1N meditators 0.0157 (0.0297) 0.00265 (0.0308) AGERATIO -0.0601 (0.0560) gender_female 0.0394 (0.0285) gender_other -0.0702 (0.105) live_alone 0.122 (0.167) not_township 0.0562 (0.131) atheists 0.0728 (0.0963) Constant 0.294 (0.0156) *** 0.296 (0.0319) ***

Observations 384 384 R-squared 0.001 0.014 N 384 384 r2 0.000732 0.0139

Hypothesis 3

Total Self-Efficacy (T-SE) H3-Model H3-Model 1 2 OLS OLS Bivariate Controls SeTotalA SeTotalAL Variables LL se aster L se aster

SeTotalALL (0.0291 NoviceMeditators -0.0202 ) -0.0300 (0.0326) o.LongtermMedita tors - - AGERATIO -0.0350 (0.0621) gender_female 0.0131 (0.0328) o.gender_other - live_alone 0.000688 (0.138) not_township -0.0344 (0.137) atheists 0.141 (0.137) (0.0248 Constant 0.716 ) *** 0.725 (0.0479) ***

Observations 106 106 R-squared 0.005 0.021 N 106 106

r2 0.00459 0.0206 Self-Regulatory Self-Efficacy (SR-SE) H3-Model H3-Model 3 4 OLS OLS Bivariate Controls SeScale1 Variables N se aster SeScale1N se aster

SeTotalALL (0.0586 NoviceMeditators -0.0613 ) -0.0594 (0.0650) o.LongtermMedita tors - - AGERATIO 0.0701 (0.124) gender_female 0.0982 (0.0654) o.gender_other live_alone -0.121 (0.275) not_township 0.168 (0.273) atheists 0.205 (0.273) (0.0500 Constant 0.810 ) *** 0.703 (0.0956) ***

Observations 106 106 R-squared 0.010 0.047 N 106 106 r2 0.0104 0.0471 Self-assertive Self-Efficacy (SA-SE) H3-Model 5 H3-Model 6 OLS OLS Bivariate Controls SeScale2 Variables N se aster SeScale2N se aster

SeTotalALL (0.0420 NoviceMeditators -0.00777 ) -0.00909 (0.0471) o.LongtermMedita tors - - AGERATIO 0.0180 (0.0898) gender_female 0.0229 (0.0474) o.gender_other live_alone 0.121 (0.199) not_township -0.0753 (0.198)

atheists 0.175 (0.198) (0.0358 Constant 0.780 ) *** 0.754 (0.0693) ***

Observations 106 106 R-squared 0.000 0.017 N 106 106 r2 0.000329 0.0169 Self-Efficacy for Self-Regulated Learning (SE-SRL) H3-Model 7 H3-Model 8 OLS OLS Bivariate Controls SeScale3 Variables N se aster SeScale3N se aster

SeTotalALL (0.0460 NoviceMeditators 0.0516 ) -0.0103 (0.0492) o.LongtermMedita tors - - AGERATIO -0.268 (0.0939) *** gender_female 0.0134 (0.0496) o.gender_other live_alone 0.217 (0.209) not_township -0.112 (0.207) atheists 0.313 (0.207) (0.0392 Constant 0.612 ) *** 0.741 (0.0724) ***

Observations 106 106 R-squared 0.012 0.112 N 106 106 r2 0.0119 0.112

Self-efficacy in Social Expectations (SE-SE) H3-Model 9 H3-Model 10 OLS OLS Bivariate Controls SeScale4 Variables N se aster SeScale4N se aster

SeTotalALL (0.0387 NoviceMeditators 0.00637 ) 0.0119 (0.0430)

o.LongtermMedita tors - - AGERATIO 0.0335 (0.0820) gender_female 0.0395 (0.0433) o.gender_other live_alone -0.197 (0.182) not_township -0.140 (0.181) atheists 0.160 (0.181) (0.0330 Constant 0.772 ) *** 0.726 (0.0633) ***

Observations 106 106 R-squared 0.000 0.033 N 106 106 r2 0.000260 0.0334 Self-Efficacy for Leisure Time Skills and Extracurricular Activities (SE- LTSEA) H3-Model 11 H3-Model 12 OLS OLS Bivariate Controls SeScale5 Variables N se aster SeScale5N se aster

SeTotalALL (0.0457 NoviceMeditators -0.0569 ) -0.0635 (0.0496) o.LongtermMedita tors - - AGERATIO -0.107 (0.0945) gender_female -0.117 (0.0499) ** o.gender_other live_alone -0.132 (0.210) not_township -0.154 (0.208) atheists -0.0538 (0.208) (0.0389 Constant 0.662 ) *** 0.803 (0.0729) ***

Observations 106 106 R-squared 0.015 0.091 N 106 106 r2 0.0147 0.0914 Self-Efficacy in Enlisting Social Resources (peers) (SE-ESR(P)) H3-Model 13 H3-Model 14 OLS OLS

Bivariate Controls SeScale6 Variables N se aster SeScale6N se aster

SeTotalALL (0.0470 NoviceMeditators -0.0779 ) -0.0627 (0.0524) o.LongtermMedita tors - - AGERATIO 0.0486 (0.0999) gender_female -0.0227 (0.0528) o.gender_other live_alone 0.149 (0.222) not_township -0.241 (0.220) atheists -0.141 (0.220) (0.0401 Constant 0.716 ) *** 0.707 (0.0771) ***

Observations 106 106 R-squared 0.026 0.051 N 106 106 r2 0.0257 0.0505 Self-Efficacy in Enlisting Social Resources (adults) (SE-ESR(A)) H3-Model 15 H3-Model 16 OLS OLS Bivariate Controls SeScale7 Variables N se aster SeScale7N se aster

SeTotalALL (0.0562 NoviceMeditators 0.00665 ) -0.0289 (0.0625) o.LongtermMedita tors - - AGERATIO -0.175 (0.119) gender_female -0.0444 (0.0630) o.gender_other live_alone 0.219 (0.265) not_township 0.0844 (0.263) atheists -0.0156 (0.263) (0.0479 Constant 0.536 ) *** 0.659 (0.0920) ***

Observations 106 106

R-squared 0.000 0.030 N 106 106 r2 0.000135 0.0297

Hypothesis 4

Total Aggression (T-A) H4-Model 1 H4-Model 2 OLS OLS Bivariate Controls Variables AggTotalALL se aster AggTotalALL se aster

Observations 106 106 R-squared 0.003 0.098 N 106 106 r2 0.00253 0.0978

Physical Aggresion (P-A) H4-Model 3 H4-Model 4 OLS OLS Bivariate Controls Variables Agg1N se aster Agg1N se aster

AggTotalALL NoviceMeditators -0.0438 (0.0468) -0.0630 (0.0492) o.LongtermMeditators - - AGERATIO -0.0986 (0.0938) gender_female -0.109 (0.0495) ** o.gender_other live_alone 0.222 (0.208) not_township -0.131 (0.207) atheists 0.619 (0.207) *** Constant 0.213 (0.0399) *** 0.343 (0.0724) ***

Observations 106 106 R-squared 0.008 0.141 N 106 106

r2 0.00835 0.141

Verbal Aggression (V-A) H4-Model 5 H4-Model 6 OLS OLS Bivariate Controls Variables Agg2N se aster Agg2N se aster

AggTotalALL NoviceMeditators 0.00149 (0.0433) 0.00663 (0.0458) o.LongtermMeditators - - AGERATIO -0.0500 (0.0873) gender_female -0.169 (0.0461) *** o.gender_other live_alone 0.116 (0.194) not_township -0.0190 (0.192) atheists -0.0607 (0.192) Constant 0.178 (0.0369) *** 0.326 (0.0674) ***

Observations 106 106 R-squared 0.000 0.124 N 106 106 r2 1.14e-05 0.124 Anger (A) H4-Model 7 H4-Model 8 OLS OLS Bivariate Controls Variables Agg3N se aster Agg3N se aster

AggTotalALL NoviceMeditators -0.0443 (0.0591) -0.0652 (0.0628) o.LongtermMeditators - - AGERATIO 0.0106 (0.120) gender_female 0.0418 (0.0632) o.gender_other live_alone 0.211 (0.266) not_township 0.546 (0.264) ** atheists 0.713 (0.264) *** Constant 0.342 (0.0504) *** 0.306 (0.0924) ***

Observations 106 106 R-squared 0.005 0.120 N 106 106 r2 0.00538 0.120

Glossary

Adolescent: An adolescent can be defined as a child that is in the life stage between puberty and the age of 19 (Botha 2014: 11).

Aggression: The term aggression in general terms can be referred to as hostile behavior intended on inflicting harm or damage (Almeida et al. 2015: 121). A commonly used classification “for human aggression is the way in which it is expressed” and can be divided into physical and verbal aggression (ibid.).

Meditation: Meditation is understood as a state of unattached observing that can occur through mindfulness practices or other forms of non-judgemental attention regulation through the observation of thoughts, body states, or emotions (Zylowska et al. 2008; Black et al. 2009). Although strictly speaking meditation refers to a state, it is commonly used to describe an act. A useful way to prevent confusion is to refer to it as a practice. Mindfulness is listed as a meditation practice for instance in Waters et al. (2015: 104).

Mindfulness: Can be described as the deliberate focusing of attention in the present moment free from judgment and open to the experience that unfolds (Kabat-Zinn 1990).

Mindfulness meditation: In this paper, the term mindfulness meditation (or short meditation) will therefore refer to the practice of a state of deliberate unattached observing of the present moment free from judgment and open to the experience that unfolds. As the preceding chapter revealed, a wide variety of mindfulness practices and interventions exist at present. Whilst mindfulness meditation in current literature typically refers to a “second wave” cognitive behavioral intervention often related to Jon Kabat-Zinn and the development of mindfulness- based stress interventions (MBSI), it has been practiced in different civilizations for thousands of years, its origins most commonly related to spiritual practices from the East, notably Buddhism (Morley 2018: 118). The mindfulness meditation practice used as treatment will be described in detail in chapter 4.

Protective factors: A protective factor is commonly defined as a characteristic which increases chances of a positive outcome through mediating the effect of adversity or risk (Botha 2014: 9).

Protective factors in the youth crime prevention context can then be defined as variables which decrease the risk or probability for youth delinquency and disruptive behavior, building resilience (cf. Farrington and Welsh 2007).

Resilience: In this paper, resilience will be defined in the context of youth crime and referred to as the potential to successfully adjust to challenging circumstances that reduce the chance for a child or adolescent to become criminally active (Botha 2014: 11).

Risk factors: In general, a risk factor is defined as a “measurable characteristic of a certain group of individuals that predict negative outcomes for them” (Botha 2014: 9). In the context of youth crime prevention relevant for this paper, we will use Farrington and Welsh’s (2007) definition of risk factors as variables which predict a higher probability for a person for later offending.

Self-efficacy: Can be understood as beliefs and judgments a person holds regarding their capability to cope effectively with specific challenges and facing demanding situations (Bandura 2001).

Township: A township in the South African context denotes an underdeveloped, usually urban, residential area which during Apartheid had been reserved for non-white population (either for ‘blacks’ or ‘coloreds’) who were not allowed to live in areas where they worked that were designated as ‘whites only’ territories (under the Black Communities Development Act, Section 33; Pernegger and Godehart 2007: 2). Despite the abolition of Apartheid laws, almost thirty years later this racial/socio-economic segregation is still reflected in South Africa’s demographics and most townships struggle with very high crime rates and violence.

Youth crime, juvenile delinquency, crime prevention as well as self-regulation and emotional regulation are defined and discussed in detail in the paper.