candidate: amy walsh supervisor: professor david clarke subject ... · exploring behavioural...

Exploring Behavioural Scripts of Street Robbery: A Sequence Analysis Investigation

Candidate: Amy Walsh

Supervisor: Professor David Clarke

Subject: Psychology and Cognitive Neuroscience B.Sc.


Acknowledgements

I wish to extend my deepest gratitude to Professor David Clarke, who never failed to make the time to guide and encourage me throughout every step of this project. His outlook on Psychology as a discipline has been a great inspiration for my work and is something I will take forward with me in my future studies and career.

Additionally, I wish to thank Dr Danielle Ropar and Dr Nadja Heym for finding space in their already stretched lecturing time to give me the opportunity to make contact with participants and Miss Jess Purchase for her helpful feedback whilst drafting the final report.


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Abstract

Although street robbery is phenomenon, many resources have been poured into

awareness campaigns to help educate the public in precautionary behaviours. In order to

be more effective, these campaigns would benefit from assessing the public’s behaviour

scripts of street robberies. This study aimed to assess behavioural scripts of street

robbery. 70 respondents gave imagined accounts of an incident of street robbery, in which

either assailants were armed with a knife but did not use it to inflict injury or assailants

were armed with a knife and did use to it injure the victim. Next, they identified the order in

which behaviours appeared in the account using a pre-determined list of events taken from

real life incident. Data were analysed using sequence analysis methodology. State

transitions diagrams for both conditions were constructed and compared. Additional

analysis and comparisons were also made between responses from participants who

identified as having either high or low familiarity with the concept of street robbery. The

results indicated points of interest within the behaviour scripts and the effect these could

have on behaviour are discussed. The advantages of assessing behavioural scripts in

future campaigns are considered.

Literature Review

The Nature of Street Robbery

Street robberies (more colloquially known as ‘muggings’) are incidents in which the

theft of property from a person occurs, in a public place, with the threat of or actual use of

violence. Becoming a victim of such incidents can have massive impacts on an individual.

Alongside the obvious practical and financial implications to consider (such as the

replacement of stolen items or recovering from physical injuries (Shapland and Hall, 2005),

there are often profound emotional and psychological consequences, such as increased

fear of victimisation (Gale and Coupe, 2005), reductions in positive affect and increased

negative affect (Davis and Friedman, 1985) and in particularly severe cases’ a resulting

diagnoses of post-traumatic stress disorder (Wohlfarth, Winkel and van den Brink, 2002) .

It is no surprise that street robbery is of great public concern (Monk et al. 2010).

From the offset, it is apparent that the official documentation of street robbery is

relatively hazy. From a legal perspective, street robbery is not defined as an offence within

its own right, but comes under section 8 (1) of the Theft Act 1968 definition of Robbery.


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Whilst this is a functional definition for the courts, it makes fully understanding the nature

of street robbery specifically more difficult. The British Crime Survey’s record of

‘muggings’ reached 321,000 incidents in the last year (The Home Office, 2011). On the

other hand, police records of personal property theft were nearer 69,000 incidents (The

Home Office, 2011), suggesting a clear issue with official definitions. The following

limitations of these statistics must be considered. The British Crime Survey’s definition of

mugging considers all counts and types of robbery, including less relevant offences such

as armed bank robbery and does not account for victims under the age of 16.

Furthermore, it is estimated that some 50% of street robbery offenses go unreported to the

police (The Home Office, 2011). Still, according to these publications, robbery is

recognised as a low volume crime in the United Kingdom.

Despite this, a host of resources have been pledged in order to study and

understand street robbery as a social phenomenon. Previous research has been

repeatedly evidenced that street robbery is a ‘young crime’, disproportionately involving

young adult offenders and young adult victims (Smith, 2003; Baker et al., 1993, but see

Deakin et al., 2007). Some have suggested that street robbery in the United Kingdom is

best described as a ‘professional’ crime motivated by an offender’s desire for financial gain

and mediated by cost/benefit decision making (Matthews, 2002). However, Wright et al.

(2006) noted that whilst instrumental purposes were the most common cause of street

robberies, according to offenders, non-instrumental reasons were also commonplace.

These reasons include enjoying the ‘buzz/excitement’ of being dominant over the victim,

satisfying an internal anger or desire to fight, obtaining ‘status and honour’ in the eyes of

peers and rivals or in order to achieve informal justice.

When placed on the spectrum of criminal activity as a whole, street robbery is

relatively rare. Despite this, street robbery cannot simply be regarded as a subset of a

legal definition, but is in fact a widely recognised social problem that holds a significant

level of concern for the general public, worthy of extensive scientific study.

Weaponry in Street Robbery

Perhaps the key reason for street robbery being such a concern is the fear of injury

and threat to life it can cause. Zimring and Hawkins (1997) posit that the fear of a crime is

directly related to 1) the importance of the object threatened, 2) the ability to recuperate

losses (e.g. through insurance companies) and 3) the extent to which an individual feels

they can control their risk of loss. In a violent incident of street robbery, the object of


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concern becomes the individual’s life, which obviously can not be restored if lost. If a

weapon, such as a knife, is involved then the threat is even further increased. The

prevalence of public unease regarding knife crime can be partially traced back to a recent

so-called ‘knife epidemic’ in 2007. A relatively recent surge of media reports on fatal knife

injuries cast the problem of knife crime in the United Kingdom in to the spotlight:

Is Britain in the grip of a Knife Crime Epidemic? (Headline: The Telegraph, 2007)

Between 2010-2011, robbery offences involving the use of a knife or sharp

instrument increased by seven per cent, a greater proportional increase than the overall

increase of robbery offenses (The Home Office, 2011) and interviews with an offender

sample group revealed that knives were the most commonly selected weapon of choice

when planning a street robbery (Baker et al., 2003). Whilst some remain sceptical to the

credibility of a proposed ‘epidemic’ (ACPO/Home Office (2007); Perpetuity Research &

Consultancy International (2007), but see Koing et al., 2007), media coverage such as

news reports hold a powerful influence on opinions, despite the fact it may not be reflective

of reality, as within the media there is a tendency to select most hard hitting stories, most

commonly those involving severe levels of violence (Warr, 1994).

Furthermore, thought must be given to the likelihood of offender actually intending

to inflict injury. Wright et al. (2006) demonstrated that the primary cause of street robbery

was to acquire cash and valued property. It has been seen that offenders felt they were

more likely to escalate violence when victims were non-compliant or when offenders

feared not being able to control the situation, rather, weapons would be used as a threat to

get compliance in the first instance (Barker et al. 1993; Bennett and Brookman, 2008).

Thus escalation to violence is not necessarily the primary goal of a street robbery, but is a

method that may well be used. None the less, as a victim may never be truly certain of the

intentions of their attacker, the possibility of being confronted with a knife is an

understandable concern and warrants further investigation and understanding.

Current Street Robbery Awareness Campaigns

As further testament to the gravity of the problem, work from local councils and

governments and charities has resulted in an array of safety awareness and victimisation

prevention campaigns for street robbery. Current campaigns go to great lengths to inform

members of the community of particular preventative behaviours they can use to help keep

them safe:


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Be discreet with your belongings; displaying expensive jewellery or electronic

devices, like mobile phones or cameras, could attract unwanted attention (The

Metropolitan Police, 2012).

However, whilst undoubtedly informative, these methods of reaching out to the

community have limitations. Monk et al. (2010) noted that general public safety campaigns

for street robbery are ineffective as the message will often be overlooked by those that fall

into a more susceptible category. Instead, they argue that prevention strategies should be

targeted at those who are the most at risk. Perhaps more fundamentally, whilst the take-

home message of the campaign may well be observed, this does not cross the barrier

between knowing what should be done and actually undertaking the safety behaviour

prescribed.

The Role of Behavioural Scripts

Beliefs have a significant role to play in an individual’s attitudes, perceptions and

the subsequent elicitation of behaviour (for example, Azjen, 1991). When it comes to

finding oneself in social interaction, the individual relies on mental representations, known

as schemas (see Bartlett, 1932) to help make sense of and predict the world around them.

Specifically, behavioural scripts are groups of beliefs and expectations of how events in

the real world will unfold (Abelson, 1981), and are developed from previous experience as

well as through social learning (e.g. Bandura, Ross and Ross, 1963). Scripts allow us to

function in novel situations with relatively high levels of efficiency by providing a mental

framework on which to interpret the situation, ultimately affecting the behaviours we decide

to elicit (Anderson, 1983; Abelson, 1981). If this is considered from a prevention strategy

perspective, it stands to reason that such schemas could have a large impact on the

acceptance and integration of safety campaign messages into everyday behaviour. For

example, consider the two scripts in table 1. The first assumes an offender will not use

violent tactics until the victim has demonstrated non-compliance, whereas the second

script expects violence to occur at the first point of contact between a victim and assailant.


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Table 1. Examples of possible excerpts from behavioural scripts of street robbery

These two scripts will have very different influences on behaviour: the former may

allow the individual to feel confident that they can handle the situation should it arise,

therefore feel less inclined to take note of the tips provided, whereas the latter may feel

less in control and as such will be more likely to heed the advice.

Given this, it seems intuitive that assessing the scripts of street robbery in the public

would be a useful tool in helping to improve current campaigns’ efficacy. Specifically, if one

can uncover the expectations of how behaviours in street robbery occur, there is a better

opportunity to understand the possible resulting behaviours in real life incidents and

discover more beneficial ways of imparting knowledge and information about

precautionary behaviour to those who need it.

Sequence Analysis

Social interactions are complex and dynamic phenomena. Harre (1993) argues the

interdependent and sequential development of behaviour during interactions are

necessary to understand, as by breaking down social behaviour into isolated events, any

resulting analytic outcomes will not be truly reflective of the nature of that behaviour.

Therefore, in order to best understand the patterns of action that develop during a street

robbery, a methodology that can assess and account for all elements of social interaction

is ideal.

Sequence analysis, based on the mathematical Markov model (Ivanouw, 2007),

allows for such study of interdependent behaviours. Transitions between behaviour events

are analysed to determine the probability of a certain behavioural event being succeeded

by another. Data analysis under this method requires three steps: parsing continual

streams of behaviour into discrete events, classifying these events by similarity and

Script 1 Script 2

Assailant approaches Victim

Assailant demands cash/ items of value

from Victim

Victim is non-compliant

Assailant assaults Victim…

Assailant approaches Victim

Assailant assaults Victim

Assailant demands cash/ items of

value from Victim….


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identifying the frequency at which the events occur sequentially across the data set

(Clarke and Crossland, 1985). The transitions which can be said to occur at a level

surpassing chance expectation can then be represented in ‘state transition diagrams’ in

order to visually represent the interconnected behavioural events. In incidents such as

criminal activity or violent confrontations, the sequence in which behaviours are carried out

can have large ramifications on the outcomes (for example, the successful acquisition of

property in a street robbery); understanding the temporal order and connection between

behaviour events could be a vital tool in developing knowledge on the topic of choice.

Sequence analysis has already made large contributions to understanding forensic

behaviour patterns, including incidents of stranger sexual assault (Fossi et al., 2005) and

night-time violence (Levine et al., 2007, cited in Taylor et al., 2008). Despite this, sequence

analysis remains an underused tool in the forensic field (Taylor et al., 2008).

Whilst other methodology such as vignettes can be useful in assessing beliefs

within a sample group (for example, see Carlson, 1999), behavioural scripts are not just a

collection of expected actions, but have a sequential order that is meaningful and

important to the event it mentally represents (Anderson and Godfrey, 1987). This ordering

can be best preserved by using the sequence analysis methodology.

Rationale

It is the aim of this study to conduct a preliminary investigation into behavioural

scripts of street robbery using sequence analysis methodology. Specifically, this study will

make comparisons between scripts for incidents in which a knife is used to inflict injury and

incidents in which they are not, as not only does knife crime have profound effect on

victimisation fear levels but the difference between the two types of incidents could be the

difference between life and death outcomes. As such, assessing behaviour which may

affect this is vital. Further comparisons will be made between scripts obtained from groups

with either high or low levels of familiarity with street crime in order to assess the influence

experience/ knowledge may have on behavioural scripts.


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Design

Participants

Approximately 300 potential participants were approached as a sample of

opportunity to take part in the study. A response rate of 82 questionnaires was obtained,

70 of which were deemed usable for analysis. Responders (10 male) were aged between

18 and 40 years old (mean: 20.3 years standard deviation: 2.83) and all were

undergraduates at the University of Nottingham, the majority of whom were from the

School of Psychology. Participants received no payment for their participation.

Materials

Participants received task packs including the following: an information sheet

explaining the nature of the study, a consent form, the street robbery task and a

concluding summary sheet including contact details of the researcher and information

regarding appropriate organisations the participant should contact if they felt distressed or

wished to report information (see appendix A) .

Prior to starting the task participants were asked to give their age, gender and

subject of study. Finally, participants were asked to identify how familiar they were with the

concept of street robbery from 6 options ranging from ‘I have a personal experience with

street robbery’ to ‘I have no idea what a street robbery is’. The street robbery task itself

consisted of 2 sections. Section 1 contained a brief description of the opening details of a

fictional street robbery scenario:

The Scenario: The victim is a 23 years old male. At the time of the incident, he is

walking alone on the street. Two men (the assailants) notice him. They approach the

young man.

These fictional victim/assailant characteristics were selected for use based on their

statistical prevalence in actual incidents of street robbery (Smith, 2003). Below the

description, participants were given space to write out the rest of an envisioned incident,

ending at the point in which the Assailants flee the scene. Creating the start and end point

of a sequence is an innovative modification to sequence analysis methodology that has, to

the author’s knowledge, not been used before. By setting parameters for the event, more

refined comparison can be made between experimental conditions.


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Section 2 compromised of a list of 22 event categories of behaviours seen in real-

life street robberies. These categories were found through an analysis of 42 online BBC

news reports dating from between March 2010 and October 2011(BBC News, 2011), a TV

documentary of street robberies (Hill, 2011) and an analysis of previous literature studying

the nature of street robbery (Barker et al., 1993; Smith, 2003). Each item was analysed for

behaviours of both victim and assailants. These events were then combined in categories

based on similarity (for example, ‘Assailants surround victim’ and ‘Assailant pulls victim by

shirt’ are combined into the category ‘Assailants physically dominate Victim’). Upon first

analysis over 80 separate categories of behaviour could be distinguished (see appendix

B). However, due to the limitations of the selected methodology, this list was too extensive.

In order to condense the list to a more optimal number, the author was posed with the

problem of ensuring that there were enough event categories to show the varied nature of

street robberies, but avoid selecting categories that were too specific to single cases that

they had no relevance to either the participant or the task. Therefore, categories were

combined further if they were deemed similar enough that vital information would not be

lost by doing so (e.g. ‘Victim is knocked unconscious’ and ‘Victim is incapacitated’ are

combined to the category ‘Victim is incapacitated by an injury) and omitted if they were not

consistent with the opening details of the account in section 1 (e.g. ‘Assailants threaten

members of the Victim’s group’) or were related to bystander intervention as this was

thought to create even more complex behaviour dynamics that may confuse analysis. As a

result, 22 items remained. Whilst this was still too many for optimum analysis, it was

decided that further category changes would be made after data collection based on

frequency of selection.

In order to create the two knife scenarios (Knife Used/Knife Not Used), half of the

packs informed the participants that in the imagined account ‘the assailants are armed with

a knife and use it to injure the victim’ and the other half informed that ‘the assailants are

armed with a knife but do not use it to injure the victim’. Furthermore, the event category

‘Assailants assault Victim with knife’ was removed from the Knife Used task packs.

A small pilot study (n= 10) was conducted to ensure the task was easy to

understand and complete without the aid of a researcher. After a few minor alterations to

the phrasing of the questionnaire, the task was distributed.


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Procedure

Equal amounts of task packs of both conditions were printed and randomly shuffled

by a peer to ensure the researcher was blind to the recipient of each condition. Packs

were distributed to potential participants via lectures or by approaching undergraduates

who were available to take part. The nature of the task was briefly explained. To ensure

participants were given adequate opportunity to decline without pressure, they were asked

to take the packs away to complete in their own time. Packs were returned anonymously

to a specified location before a set deadline. Participants removed the consent form from

the pack and returned it separately to the task. After distribution, one reminder e-mail was

circulated re-confirming the date of the submission deadline.

Task instructions informed participants to imagine the rest of the fictional

incident up until the assailants flee from the scene. Following this, they were told to

indicate which of the events in the given list matched the events in their imagined incident

and then assign numbers to these events to determine the sequential order they had

imagined them happening during the incident. Overall, the task took approximately 10

minutes.

Ethics

Ethical approval was obtained for this study by University of Nottingham Ethics Board.

Data were collected from Section 2 of the questionnaire and collated using Microsoft Word

and Microsoft Excel.

Data collation and pre-analysis manipulation

Events were referred to by an alphabetic coding system (see appendix C) and the event

orders obtained from participants were arranged into strings (see appendix D). If there

were any uncertainties as to the ordering of the sequences presented, it was verified by

the reports written in section 1 of the task pack.

Frequency of selection for each event was totalled. As it was deemed prior to analysis that

22 events would be too many for an effective sequence analysis, any category of

behaviour with a frequency lower than 10 (events: Assailants move Victim to another

location, Victim Physically Assaults Assailants, A fight between Victim and Assailant

Ensues, Assailants reject/ return property, Assailant Chases Victim, Victim Chases

Assailant, and Assailants assess the property) were compiled into a new category entitled


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Low Frequency Events (LFE). If at any point two LFE followed one another, they were

collapsed again into a single LFE event. This only occurred 3 times within the data, and as

such it is not felt that any substantial data was lost by doing so.

Sequences were split into experimental conditions; the use of a knife and levels of

familiarity (see appendix E for respondent N within each group). High and low familiarity

groups were created by splitting the data based on the level of familiarity participants that

indicated on a 6 point scale(see appendix F for frequencies): those who answered 1 and

2(I have personal experience of street robbery, Someone I know has a personal

experience of street robbery) were grouped as High Familiarity, as these options indicated

a more personal knowledge/ experience and those who answered 3-6 (Street robbery is a

topic I take an interest in and have studied, Street robbery is a topic I am aware of from

day to day life, I am vaguely aware of the concept of street robbery) were grouped as Low

Familiarity as this knowledge was more factual and impersonal. No respondent answered

with 6 (I have no understanding of what street robbery is).

Results and Discussion

Frequency Analysis

Figures 1- 3 show frequency of event selection in each condition. Due to the large

discrepancy in sample sizes, percentages were calculated to make the data more

comparable.


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12


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ExploringBehaviouralScriptsofStreetRobbery:ASequenceAnalysisInvestigation

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Frequency Analysis Discussion

Figure 1 shows frequency of event selection across all accounts. High levels of non-

compliance events imply that many believed victims would in some way attempt to avoid

handing over their property. Additionally, despite the Knife Not Used condition not

necessarily requiring the role of a knife at any point the graph indicates that participants

largely believed that a knife would be produced.

Figure 2 displays the frequency of event selection between conditions of knife use.

It is apparent that under knife use conditions, both conditions show a range of variability in

the types of events expected to occur and almost all behaviour events listed were relevant

to both conditions. Further, many events were selected at similar frequencies, although it

is notable that there is a greater proportion of selections of ‘non-compliance’ in the Knife

Used condition. This is consistent with offender opinions that victim resistance is likely to

initiate more extremely aggressive behaviour (Bennett, 2009). Further, participants in the

Knife Used condition more frequently reported incapacitation resulting from an injury

suggesting the belief that such injuries sustained during knife assaults are far more

severe. Additionally, Knife Not Used conditions show that a greater number of participants

envisioned a more frequent use of threats of physical violence and initial engagement of

the Victim in conversation. It may be that these methods are used as control tactics,

whereas in Knife Used conditions the control is gained from the use of a weapon (Bennett,

2009).

Figure 3 displays the difference in frequencies between participants who identify as

having high or low familiarity with street robbery. There are similar frequencies for many

events. However, victim non-compliance and knife attack were markedly higher in low

familiarity groups, yet physical assault frequencies were greater in high familiarity groups,

in contrast to the previously discussed ideas of violence escalation. This could suggest

that low familiarity groups hold greater beliefs that non-compliance is perhaps associated

with more violent outcomes; whereas high familiarity groups may identify that there is not

necessarily an obvious victim response that will instigate some form of violent behaviour.

Some small areas of interest about participants’ beliefs can be gleaned from these

frequencies, such as the selection of non-compliance in the Low familiarity group and the

Knife Used. However, standing alone, it is evident that there is not much difference

between many of the events in both conditions. To explore the above points of interest


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further, and to assess whether the sequential development of these events can provide

further information, a sequence analysis was conducted.

Sequence Analysis

Chi- squared and Standard Normal Residuals: Matrices (appendix G) were created

identifying the frequency of each event on the list being followed by another (known as

transitions). Chi-squared tests (see appendix H) were conducted on the transition matrices

(see table 2 for chi squared results).

Table 2. Chi-Squared results for all conditions

This indicated that there were transitions within the data that occurred at a level

greater than chance expectation. In order to identify these transitions of importance an

informal statistical analysis was undertaken. Standardised Normal Residuals were

calculated and then compared to a critical value set by the following calculation:

According to Colgan and Smith (1978), the transitions that identified as higher than

this set threshold are worthy of further investigation. The critical value for all conditions

was >1.1. However, a threshold of greater than 1.5 was set by the researcher. It was

hoped that by using more stringent conditions, only the most important transitions would

be identified.

Condition Chi-square value Degrees of Freedom Significance Level

All Sequences 1258.36 225 <0.001

Knife Not Used 762.435 196 <0.001

Knife Used 652.992 210 <0.001

High Familiarity 801.994 256 <0.001

Low Familiarity 854.419 225 <0.001

Equation 1. Calculation for Standard Normal Residuals critical value


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Sequence mapping: The resulting state transitions diagrams were mapped Using yEd

Graph Editor (yEd Graph Editor, version 3.9, 2012). Figures 4- 8 show the transition maps

composed for all conditions.


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19


20


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Sequence Analysis Discussion

Figure 4 shows event transitions for all sequences. Transitions from the start point

(see figure 9a.) show that outright demands of property and engaging victim in

conversation are thought to be the most common forms of initial interaction between victim

and assailant. Such behaviour is characteristic of confrontation and con type incidents

rather than blitz or snatch types (Smith, 2003). Further, transitions between non-

compliance and some sort of violence escalation (see figure 9b.) implies that non-

compliance will often trigger violence, in line with offender perceptions of violence

escalation (Barker et al. 1993; Bennett and Brookman, 2008). Finally, the acquisition of

property, through whichever means, shows numerous links to Assailants flee scene, which

was the designated end point (see figure 9c), reflecting the idea that street robbery is an

instrumental behaviour based on obtaining property. Ultimately, the graph shows that

street robbery is thought of as a highly violent crime driven by the desire of property,

showing some confirmation of Bennett (2009)’s theories of reasons for street robbery.

Figure 1. state transition diagram breakdown for all sequences

a)

b)

c)


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Comparison of transitions between knife use conditions

Figures 5 and 6 show state transition diagrams for incidents in which a knife is used

to inflict injury and a knife is not used to inflict injury. It is apparent that many of the

behaviour transitions are similar between the two groups, which would suggest there is

perhaps not a set event that would indicate to a victim that assault with a knife was

imminent. However, under the Knife Not Used conditions, assault was shown to link with

either incapacitation or victim handing over property, whereas under the Knife Used

condition the link is between physical assault and a Low Frequency Event (LFE). Further,

LFE shows a strong transition to knife attack (see figure 10).

Figure 2. State transition graph showing differences between Knife Used and Knife Not Used conditions. Transitions in blue identify those seen only in Knife Not Used diagram, transitions in green identify those only seen in Knife Used diagram, transition in black identify those that are the dame between conditions

It is interesting to note that the key difference between the two graphs is

involvement of a low frequency event prior to assault with a knife. Whilst only tentative,

there is the possibility that the diagram is displaying an explicit behaviour event that would

increase the probability a knife would be used. Specifically, it may be that a Low

Frequency Event can explain beliefs about violence escalation from assault to knife use.

After surveying the original data, however it was evident that there was no particular event

that appeared frequently enough between assault and knife use events to explain this

pattern. As such, no causality could be inferred. However, it remains evident that there are


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no significant links between physical assault and incapacitation in the Knife Used

condition. This could be taken to suggest that knife attack is believed to be reflective of a

need to fully ensure a Victim is not in a position to retaliate or become non-compliant,

perhaps where assault was insufficient.

Comparison of transitions between familiarity groups

Figures 7 and 8 show the state transition diagrams created for behavioural scripts

of high and low familiarity groups. Low Familiarity responses show escalating from

physical assault directly into a knife attack suggesting a strong belief that violence will

rapidly escalate into extreme levels (see figure 11a). On the other hand, High Familiarity

group patterns show two separate between transitions to incapacitation and knife attack

indicating that those with a more personal familiarity with street robbery behaviour hold

beliefs of more variable patterns of violence (see figure 11b). Additionally, transitions

between the production of a knife and the use of a knife are evident in the low familiarity

group (see figure 11c) but are not apparent in the High Familiarity. Low familiarity groups

are demonstrating a belief that the appearance of a knife is highly likely to result in its use,

whilst on the other hand high familiarity groups may believe it may be produced in advance

at an unrelated point, perhaps as a threat tactic, or never revealed at all to the victim.

Figure3.Statetransitiondiagrambreakdownsforhighandlowfamiliaritygroups.

a) b)

c)


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General Discussion

This study aimed to explore and assess behaviour scripts about street robbery

within a student sample. Specifically, the study compared beliefs differed between

incidents in which a knife is used to inflict injury and incidents in which it is not, and made

comparisons between responses obtained from those who had high or low familiarity with

the topic of street robbery. The findings indicated that, in general, participants saw street

robbery as a means by which to primarily acquire someone else’s property Furthermore,

non-compliance from a victim is expected to be followed by an escalation to some violent

act and street robberies were most likely to take the form of a con or a confrontation

(Smith, 2003). Comparisons between knife conditions demonstrated that behaviour scripts

were mostly similar regardless of whether or not a knife was used to inflict injury. However,

the use of a knife would frequently result in victim incapacitation for the purpose of

rendering them powerless, perhaps where physical aggression was unsuccessful at

reducing victim retaliation or not deemed to be enough. Low Familiarity respondents

showed beliefs of rapid violent escalation, compared to the High Familiarity group. Further,

Low Familiarity expected the knife to be visible before use; High Familiarity identified that a

knife attack did not necessarily follow knife production.

Whilst these findings can be interesting purely at face value, the potential effects

that they could have on an individual’s behaviour are even more profound. A general belief

that street robbery is for acquisition of valuable property, though valid, means that people

less typically consider ulterior motives of street robbery such as expression of anger or ‘for

the buzz’ (Bennett and Brookman, 2009). As a result, individuals could well assume they

are not likely to be a target as they are not in possession of anything deemed worthy,

resulting in a false sense of security and less vigilance. Assumptions that non-compliance

will result in violence escalation, whilst consistent with previous literature (Barker et al.

1993; Bennett and Brookman, 2008), may create a belief that serious danger will be

avoided by simply complying. However, this does not take into account incidents in which

violence is not caused by victim behaviour; as such, precautionary behaviours to avoid

becoming a target in the first place may be missed. Further, the fact that there were so

many similarities between the two knife conditions suggests that it is difficult to predict

whether any given incident is more or less likely to result in knife use, but should a knife be

used, it is highly likely that the outcome will be incapacitating injury. Subsequently, one

may be inclined to think that any incident should be met with equal concern and anxiety.

However, perhaps the most striking finding lies within the differences between familiarity


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groups. Those with little knowledge of street robbery may presume that simply because a

knife is not visible, there will not be any knife attack. Such an assumption could entirely

alter the Victim’s perceptions of threat, thus affecting the likelihood to comply with

demands and possibly result in a more violent outcome.

The above results and their implications are testament to the idea that behaviour

scripts should be considered in greater detail when attempting to promote safety

awareness and victimisation prevention. Whilst current strategies undeniably provide

important and well-founded information to the public, such methods can fail to reach the

heart of the matter. Understanding the current beliefs within the community about how

street robberies unfold can reveal a host of inaccurate ideas that need to be challenged or

updated so individuals are better equipped to avoid becoming victims or making ill-advised

decisions should the find themselves in that scenario.

Furthermore, it is possible that developing knowledge of current beliefs can help to

pinpoint and assess particular fears or concerns within the community, which could lead to

appropriate action for teaching knowledge or skills that may help alleviate anxiety. For

example, the findings demonstrate a large belief that street robberies are more often than

not immersed with physically violent acts. However, this opinion may be based on

exposure to extreme reporting (Warr, 1994). Instead, it would be beneficial to make clear

the rarity of street robbery as a crime and perhaps example incidents in which violence

does not occur. Naturally, this is not to say that potential for violence must not be taken

seriously, but in this instance, creating a balanced view of the nature of street robbery

could provide individuals with knowledge that will lessen fear and help promote feelings of

security.

However, there are a few limitations to the study that should be considered. Whilst

prevalence statistics were used in order to create a prototypical scenario, it is obvious that

there are many assailant and victim characteristics (such as age or gender, size of

assailant/victim group) that would change the dynamics of the interaction between victims

and assailants. Additional study in this area could consider altering such characteristics

and determining the effects they have on beliefs. Furthermore, due to the nature of a

sample of opportunity, and the potentially upsetting nature of street robbery, response rate

was relatively low and particularly sparse for some conditions. Therefore, whilst the insight

gleaned are certainly of value to understanding street robbery beliefs, additional testing


27

may be beneficial to yield additional interactions. The current findings are by no means

exhaustive.

This study has identified a host of complex behavioural events underpinning beliefs

about street robbery and has merely touched upon some concepts of particular

importance. It is possible that any of these events could be broken down and analysed

through additional experimental methodologies in order to help build up a greater

knowledge of street robbery. As an expansion to this study, it would be ideal to investigate

in full the extent to which the beliefs expressed in behavioural scripts can or will influence

decision making, although the concept of behavioural scripts is compelling and well

documented, it would be best to empirically evaluate how important their role is once an

individual finds themselves in a high risk incidents, as factors such as subjective fear

responses may have a more powerful effect on behaviour. Furthermore, it would be

beneficial to compare the beliefs that are expressed in this preliminary investigation with

real life accounts of street robbery, ranging from police statements to offender interviews.

This would assess how reflective beliefs really are of real world incidents.


28

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31

Appendices

Appendix A- Participant Task Pack

InformationSheet

Title:BeliefsaboutStreetRobbery

Investigator:AmyWalsh

Supervisor:ProfessorDavidClarke

SchoolofPsychology,UniversityofNottingham

Thisexperimentaimstostudybehaviourpatternsinstreetrobberies.Inthisstudyyouaregiventheopeninginformationaboutanincidentofstreetrobbery.Fromthis,youwillbeaskedtoimaginetherestoftheincidentindetail.Youwillthenseealistofeventsthathavebeenreportedinreal‐lifestreetrobberyincidents.Youwillbeaskedtoidentifyalltheeventsfromthatlistthatcorrespondwithyourenvisionedincidentandthennumbertheeventsyouhaveselectedintheorderthatyouenvisionedthemhappening.Itisrequestedthatyouworkonthistaskindependently,onlygivingyourpersonalthoughts.

Thinkingaboutanddiscussingcrimecanbeadistressingmatter,particularlyforthosewhohavehadapersonalexperienceorknowapersonwhohas.Thereforeweaskthatifyoufeelthistopicmayevokefeelingsofdistressordiscomfortforyou,forwhateverreason,pleasedonotparticipateinthisstudy.Ifyoudowishtocontinuewiththeactivity,pleaserememberthatyouarefreetowithdrawatanytimewithouthavingtogiveareason.Yourresponseswillremainanonymousandconfidential;resultswillonlybereportedingroupsandthereforewillremainunidentifiable.

Weaskthatyoutaketheconsentformandtaskawaywithyoutofillinduringyourowntime,andreturnittoA12intheschoolofPsychologybeforeFriday17thFebruary.Togetthere,enterthedepartment,turnright,turnrightagainandfollowthecorridortotheend.Foryourconvenience,theconsentformisattachedtoyouranswerbooklet,howeverweaskthatwhensubmittingyourresponsesthatyoupleaseteartheconsentformawayandsubmititseparatelyfromtherestofthepacktomaintainconfidentiality.Pleasefeelfreetoapproachmyselformysupervisorwithanyquestionsyoumayhavepriortoorduringfillingoutthequestionnaire.Contactdetailscanbefoundatthebottomofthesheet.


32

Thankyouforyourtime.

ConsentForm

Pleasereadthefollowingstatementsbelowandindicateyouranswerbycirclingasnecessary.

• Haveyoureadandunderstoodtheparticipantinformationsheet YES/NO

• Haveyouhadtheopportunitytoaskquestionsanddiscussthestudy YES/NO

• Haveallthequestionsbeenansweredsatisfactorily YES/NO

• Haveyoureceivedenoughinformationaboutthestudy YES/NO

• Doyouunderstandthatyouarefreetowithdrawfromthestudy:• atanytime YES/NO• withouthavingtogiveareason YES/NO

• Doyouagreetotakepartinthestudy YES/NO

“Thisstudyhasbeenexplainedtometomysatisfaction,andIagreetotakepart.IunderstandthatIamfreetowithdrawatanytime.”

SignatureoftheParticipant: Date

Name(inblockcapitals)

Ihaveexplainedthestudytotheaboveparticipantandhe/shehasagreedtotakepart.


33

Signatureofresearcher: Date

Task

PersonalInformation

Pleasegivethefollowingdetailsaboutyourself:

Gender:…………….....

Age:………………..

Occupation(ifastudent,pleaseidentifyyourmainsubject):..........................……………………………………..

Howfamiliarareyouwiththenotionofstreetrobbery?(Pleasecircletheappropriateanswer)

1. Ihavepersonalexperienceofstreetrobbery2. SomeoneIknowhasapersonalexperienceofstreetrobbery3. StreetrobberyisatopicItakeaninterestinandhavestudied4. StreetrobberyisatopicIamawareoffromdaytodaylife5. Iamvaguelyawareoftheconceptofstreetrobbery6. Ihavenounderstandingofwhatstreetrobberyis

Information:Thetaskwillcontinueasfollows:youwillbegivensomebackgroundinformationandanopportunity(andsomewritingspace)tocreateasingleincidentofstreetrobbery,inwhichtheassailantsarearmedwithaknifeanduseittoinjurethevictim.Next,youwillseealistofreal‐lifeeventsfromstreetrobberies,bywhichyouwillbeaskedtoindicatewhetherornoteachitemisconsistentwithyouraccount,andifso,orderthoseitemsyouhaveselected(usingnumericalvalues)inorderinwhichtheyoccur.Taketimetofamiliariseyourselfwithwhateachsectionisaskingandhowtoanswerappropriately.


34

Imagine

TheScenario:Thevictimisa23yearsoldmale.Atthetimeoftheincident,heiswalkingaloneonthestreet.Twomen(theassailants)noticehim.Theyapproachtheyoungman.

Pleasenowtakeafewmomentstoimaginetherestoftheeventsinthisincident,inwhichtheassailantsis/arearmedwithaknifeanduseittoinjurethevictim.Completetheincidentupto(andincluding)thepointatwhichtheassailantsexitthescene.Itmaybeonecreatedentirelyfromimaginationormaybeinspiredreallifeexperiencesyouknowabout/haveheardof.Considertheactionsofboththevictimandtheassailantsandaddasmuchdetailaspossible.Usethespacebelowtomakenotesifyouwish.Pleaserememberthereisnorightorwronganswer.


35

Nextyouwillseealistofreal‐lifeeventsfromstreetrobberyreports.Inthelefthandboxes,indicatealltheeventsinthelistthatmatchyouraccountofastreetrobbery.After,usetherighthandsidetonumbertheeventsintheordertheyhappeninyouraccount.Ifaneventappearsmorethanonceusetheadditionalboxestonumberitagain.

Example:Goingtoarestaurant

Imaginedaccount:awomanenterstherestaurant.Sheiswelcomedbythehost,whoshowshertoatable.Shereadsthemenutodecidewhatshewants.Whenthewaitresscomesovertotakeherorder,sheisnotsureandsoreadsthemenuagain.

Match Item: NumberX Waitressarrivestotakeorder 4 X Customerentersrestaurant 1 Customerseatsself X Customerisgreetedbyhostandseated 2 X Customerreadsmenu 3 5

Now,usingtheexampleaboveasaguide,dothesameforyourimaginedincidentusingthelistbelow.

Match Item: Number AssailantsengageVictiminconversation AssailantsdemandpropertyfromVictim AssailantsthreatenVictimwithphysicalviolence Assailantsproduceaknife Assailantsassaultvictimwithknife AssailantsphysicallydominateVictim(restrain/pushtoground) AssailantsphysicallyassaultVictim(e.g.kickorpunch) Assailantsmovevictimtoanotherlocation VictimphysicallyassaultsAssailants AfightbetweenVictimandAssailantsensues Victim does not comply with Assailants demand (attempts to flee area, explicitly refuses,

statestheydonothaveproperty)

Victimappearsincapacitatedbyinjurysustained(fallstofloor,knockedunconscious) AssailantssnatchpropertyfromVictimthatisondisplay VictimhandsoverpropertytoAssailants Assailantssearch/patdownVictimandtakeproperty Assailantsrejects/returnsproperty Victimfleesscene Assailantsfleescene AssailantchasesVictim VictimchasesAssailants AssailantsthreatenVictimwithphysicalviolenceiftheygotothepolice Assailantsassesstheproperty

Thatistheendofthetask.Thankyouforyourhelp.PleasereturnthistaskandconsentformtoA12(SchoolofPsychology)byFriday17thFebruary


36

SummarySheet

Thankyoufortakingpartinthisstudy.Yourinvolvementhasbeenofgreatimportanceinthisproject.

Itistheintentionofthisstudytoinvestigatebehaviourpatternsinstreetrobbery.Allresponsescollectedwillbeanalysedcollectivelyandwillbekeptconfidentialandanonymousatalltimes.Theinformationisforresearchpurposesonly;itisnottheintention,northeplace,ofthisstudytopassjudgementonanyoftheopinionsexpressedbyparticipantsandthoseresponsescollectedwillonlybeviewedbymyselfandmysupervisor.

Crimecanbeadistressingtopic.Ifyouwouldliketotalktosomeoneaboutyourownorsomeoneelse’sexperiencesofstreetrobberyoranyothercrimepleasemakeuseofthephonenumbersbelow:

VictimSupport:08453030900Confidentialandindependenthelplineofferingemotionalsupportandinformationforvictimsofcrime.

NottinghamUniversityCounsellingService:01159513695Confidentialcounsellingservice.

CrimeStoppers:0800555111.Ifyouhaveinformationonacrimeyou’dliketoreportanonymously.

Formoreinformationandtipsoncrimeprevention(includingbutnotlimitedtostreetrobbery)pleasevisittheCrimeStopperswebsite:http://www.crimestoppers‐uk.org/crime‐prevention/

Ifyouwouldlikeanymoreinformationregardingthisstudy,orwishtoviewtheresultsofthestudyoncetheyareavailable,pleasefeelfreetocontactmyselformysupervisorusingthefollowingcontactdetails:

Researcher:AmyWalshe‐mail:[email protected]

Supervisor:ProfessorDavidClarkee‐mail:[email protected]

Wewouldliketotakethisfinalopportunitytoonceagainthankyouforyourparticipation.

‐PLEASEREMOVETHISSHEETANDRETAINFORYOURRECORDS‐


37

Appendix B- Original list of events taken from research

Victim is alone

Victim is walking on the street

Victim uses a cashpoint Victim appears distracted (ipod/phone/house keys)

Victim is displaying property

Victim appears vulnerable (drunk/ lost)

Victim becomes aware of Assailant(s)

Victim continues with original route

Victim chooses alternative route

Assailant(s) follow Victim

Assailant(s) wait for Victim

Assailant(s) approach Victim from behind

Assailant(s) approach Victim from front

Assailant engages Victim in distractor conversation

Assailant(s) engage Victim in conversation about property

Victim approaches Assailant

Victim engages Assailant in (innocuous) conversation

Assailant(s) demand property from Victim

Assailant(s) surround victim/ block exit routes

Assailant(s) threatens Victim with physical violence if non-compliant

Assailant(s) draws weapon

Assailant(s) elude to weapon

Assailant(s) uses weapon on Victim

Assailant(s) restrain Victim

Assailant(s) force Victim to ground


38

Assailant(s) physically assault Victim (non weapon)

Assailant(s) move Victim to another location using physical force

Assailant(s) demand Victim to go with them to another location

Assailant threatens to hurt a witness/ bystander

Victim physically assaults Assailant(s)

Victim retaliate physical assault received from Assailant(s)

A fight between Victim and Assailant(s) ensues

Victim verbally refuses Assailant(s) demand ‘No’

Victim attempts to placate situation ‘Ok, ok’

Victim attempts to flee from Assailant(s)

Victim falls to ground (unintentional)

Victim is knocked unconscious

Victim receives injury

Victim is incapacitated

Victim states they do not own/have [property]

Victim responds to question

Assailant snatches property from Victim with force

Victim hands property to assailant

Victim throws property to ground

Property falls to floor

Assailant(s) search/ pat down Victim

Assailant(s) remove property from Victim’s person

Assailant(s) reject property

Assailant(s) return some of the property to Victim

Assailant(s) return all of the property to Victim

Victim exits scene

Assailant exits scene

Victim alerts bystander


39

Assailant chases Victim

Victim chases Assailant bystander intervention: calls police bystander intervention: chase/ follow assailant

Bystander intervention: assess victim ‘are you Ok? What happened?’

Assailant physically attacks Bystander attempting to intervene

Police arrive to scene

Assailant(s) comfort victim

Assailant(s) threaten victim with physical violence if property is worthless

Assailant(s) threaten Victim with physical violence if they go to the police

Assailant assaults victim to ensure ‘seriousness’

Victim gives fake details victim gives real details

friend/witness removes self

V Utterance: ‘Just take it’ A utterance: ‘ why are you disrespecting my brother?’ Assailant(s) pose as authority figures

Assailant tests the property

Victim does not respond to question (laughs thinking it is a joke)

Assailant utterance: ‘whats so funny?


40

Appendix C- Coding for events

Code: Event: a Assailants engage Victim in conversation b Assailants demand property from Victim c Assailants threaten Victim with physical violence d Assailants produce a knife e Assailants physically dominate Victim (restrain/ push to ground) f Assailants physically assault Victim (e.g. kick or punch) g Assailants move victim to another location h Victim physically assaults Assailants i A fight between Victim and Assailants ensues j Victim does not comply with Assailants demand (attempts to flee area, explicitly

refuses, states they do not have property) k Victim appears incapacitated by injury sustained (falls to floor, knocked

unconscious) l Assailants snatch property from Victim that is on display m Victim hands over property to Assailants n Assailants search/ pat down Victim and take property o Assailants rejects/returns property p Victim flees scene q Assailants flee scene r Assailant chases Victim s Victim chases Assailants t Assailants threaten Victim with physical violence if they go to the police u Assailants assess the property v Assailants assault victim with knife w Low Frequency Event


41

Appendix D- Data Sequences

*001/ d v e f k m q

*002/ a b c e d f k l n q

*003/ a d b j f h p r i v k l q

*004/ a b c j e j d e m q

*006/ a b c d m l n u t q

*007/ b j f e i n l u q

*009/ a b d c m t q p

*010/ e b d j f v n q

*011/ a b d t j q

*012/ b j d c n q p

*013/ b m d c g m q p

*015/ d c b m q

*016/ b c d v k m p q

*017/ j e d c b m c j v f q

*018/ b j d c j f m u o t q

*019/ b j b d m b j l v k q

*020/ a d b m n q

*021/ e b j d m j v k q

*022/ a e b c l p q

*023/ a d c b l n m q

*024/ b c d e f v h i l q

*025/ a b j c d n v q

*027/ a b d j e f i v k n q

*028/ b d f i v k n t q

*030/ a b j c d m q t

*031/ a d c m q

*032/ d a b m

*033/ b j d m b j v q

*034/ a b j d c m t q

*035/ a d b m q

*036/ b c d v m q u

*037/ e d c f b l n q

*038/ a b d a m p q

*039/ b d v f j l m p q

*040/ b j c j d j e v k l q

*041/ a b j c j d v l q

*043/ a b c d l v e q

*045/ b d c e j n l u t q

*046/ a d b c n m u t q *047/ a d b j e f k l q s

*050/ a b j c f c d v k l q

*051/ b e b j d m t q

*052/ b c j e f d n f q

*054/ b m v

*055/ d c b m q p

*056/ a b j c e d m p q

*057/ a b j c a d f e k l n q

*059/ b j v n q

*060/ b j c j d v k l q

*061/ a b d j v k l q

*062/ a b c d j v i f e n l q

*063/ a b d m q p

*064/ d b l m u q

*065/ a e d b m e q p

*066/ a b c d v k l n t q

*067/ a b j d v n q

*068/ a b e c d m q p

*069/b c d j h p q

*070/ b d c j e n u p q

*071/ a b j d j e m q p u

*073/ a b j d m q

*074/ r b e c d f m l n t q

*075/ a d c b m q

*076/ d b j v k n l q

*077/ a b c h l p q

*078/ a c d g e m q u

*079/ a b d l m c f q p

*080/ e b j h d m q

*081/ d c b j e f m o q

*082/ a b d c f j l n q


42

Appendix E- Frequency table of respondent N across groups

Appendix F – Frequency table of respondent levels of street crime familiarity

Familiarity level N 1.I have personal experience of street robbery 7 2.Someone I know has a personal experience of street robbery 18 3.Street robbery is a topic I take an interest in and have studied 2 4.Street robbery is a topic I am aware of from day to day life 30 5.I am vaguely aware of the concept of street robbery 13 6.I have no understanding of what a street robbery is 0

Knife Used Knife Not used Total

High Familiarity 7 18 25

Low Familiarity 19 26 45

Total 26 44 70


43

Appendix G -Transition matrices

All sequences

a b c d e f j k l m n p q t v w

a 0 27 1 9 2 0 0 0 0 1 0 0 0 0 0 0

b 0 0 14 14 3 0 27 0 3 10 0 0 0 0 0 0

c 1 6 0 14 3 4 8 0 1 3 2 0 0 0 0 2

d 2 8 16 0 2 4 7 0 2 11 2 0 0 1 9 1

e 0 5 2 5 0 6 2 1 0 3 2 0 2 0 1 1

f 0 1 1 1 3 0 2 3 0 3 0 0 3 0 2 3

j 0 1 8 13 9 4 0 0 3 0 1 0 1 0 7 2

k 0 0 0 0 0 0 0 0 9 2 3 0 2 0 0 0

l 0 0 0 0 0 0 0 0 0 3 8 2 10 0 2 2

m 0 2 2 1 1 0 1 0 2 0 1 4 17 3 1 4

n 0 0 0 0 0 1 0 0 4 2 0 0 10 3 1 2

p 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 2

q 0 0 0 0 0 0 0 0 0 0 0 9 0 1 0 3

t 0 0 0 0 0 0 1 0 0 0 0 0 10 0 0 0

v 0 0 0 0 2 2 0 12 1 1 3 0 2 0 0 2

w 0 1 0 1 1 1 1 0 2 1 1 3 3 4 3 0


44

Knife Used


a 0 9 0 1 0 0 0 0 0 0 0 0 0 0 0 0

b 0 0 6 6 0 0 14 0 0 2 0 0 0 0 0 0

c 0 1 0 8 0 1 4 0 0 0 0 0 0 0 0 0

d 0 2 1 0 1 1 5 0 1 3 1 0 0 0 9 0

e 0 2 0 1 0 3 0 0 0 0 1 0 1 0 1 0

f 0 0 1 0 1 0 1 1 0 0 0 0 1 0 2 3

j 0 1 5 6 3 2 0 0 2 0 0 0 0 0 7 0

k 0 0 0 0 0 0 0 0 6 2 3 0 2 0 0 0

l 0 0 0 0 0 0 0 0 0 1 1 0 9 0 2 0

m 0 2 1 0 0 0 1 0 0 0 0 2 2 0 1 0

n 0 0 0 0 0 0 0 0 2 0 0 0 4 2 1 0

p 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 1

q 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

t 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0

v 0 0 0 0 2 2 0 12 1 1 3 0 2 0 0 2

w 0 0 0 0 0 1 0 0 1 0 0 1 0 0 3 0

Knife Not Used


a 0 18 1 8 2 0 0 0 0 1 0 0 0 0 0 0

b 0 0 8 8 3 0 13 0 3 8 0 0 0 0 0 0

c 1 5 0 6 3 3 4 0 1 3 2 0 0 0 0 2

d 2 6 15 0 1 3 2 0 1 8 1 0 0 1 0 1

e 0 3 2 4 0 3 2 1 0 3 1 0 1 0 0 1

f 0 1 0 1 2 0 1 2 0 3 0 0 2 0 0 0

j 0 0 3 7 6 2 0 0 1 0 1 0 1 0 0 2


45

k 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0

l 0 0 0 0 0 0 0 0 0 2 7 2 1 0 0 2

m 0 0 1 1 1 0 0 0 2 0 1 2 15 3 0 4

n 0 0 0 0 0 1 0 0 2 2 0 0 6 1 0 2

p 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 1

q 0 0 0 0 0 0 0 0 0 0 0 9 0 1 0 2

t 0 0 0 0 0 0 1 0 0 0 0 0 8 0 0 0

v 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

w 0 1 0 1 1 0 0 0 1 1 1 2 3 4 0 0

High Familiarity responses


a 0 8 0 3 0 0 0 0 0 1 0 0 0 0 0 0

b 0 0 6 5 2 0 7 0 1 4 0 0 0 0 0 0

c 0 4 0 4 1 2 3 0 0 1 1 0 0 0 0 1

d 1 3 8 0 0 2 1 0 0 5 2 9 0 0 2 0

e 0 3 1 3 0 3 0 0 0 0 0 0 0 0 0 0

f 0 1 0 1 0 0 1 2 0 2 0 0 2 0 0 2

j 0 0 1 3 3 2 0 0 1 0 0 0 0 0 2 2

k 0 0 0 0 0 0 0 0 2 1 1 0 1 0 0 0

l 0 0 0 0 0 0 0 0 0 0 3 1 1 0 0 0

m 0 0 1 0 0 0 1 0 0 0 1 1 7 2 0 3

n 0 0 0 0 0 1 0 0 0 1 0 0 4 1 1 0

p 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 1

q 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 1

t 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0

v 0 0 0 0 1 1 0 3 0 1 0 0 1 0 0 0

w 0 0 0 1 0 0 0 0 1 0 0 2 1 2 2 0


46

Low Familiarity responses


a 0 19 1 6 2 0 0 0 0 0 0 0 0 0 0 0

b 0 0 8 9 1 0 20 0 2 6 0 0 0 0 0 0

c 1 2 0 10 2 2 5 0 1 2 1 0 0 0 0 1

d 1 5 8 0 2 2 6 0 2 6 0 0 0 1 7 1

e 0 2 1 2 0 3 2 1 0 3 2 0 2 0 1 1

f 0 0 1 0 3 0 1 1 0 1 0 0 1 0 2 1

j 0 1 7 10 6 2 0 0 2 0 1 0 1 0 5 0

k 0 0 0 0 0 0 0 0 7 1 2 0 1 0 0 0

l 0 0 0 0 0 0 0 0 0 3 5 1 9 0 2 2

m 0 2 1 1 1 0 0 0 2 0 0 3 10 1 1 1

n 0 0 0 0 0 0 0 0 4 1 0 0 6 2 0 2

p 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 1

q 0 0 0 0 0 0 0 0 0 0 0 5 0 1 0 2

t 0 0 0 0 0 0 1 0 0 0 0 0 5 0 0 0

v 0 0 0 0 1 1 0 9 1 0 3 0 1 0 0 2

w 0 1 0 0 1 1 0 0 1 1 1 1 2 2 1 0


47

Appendix H- SPSS outputs

All Sequences


Total

Count 0 27 1 9 2 0 0 0 0 1 0 0 0 0 0 0 40

Expected 0.2 4 3.5 4.6 2.1 1.7 3.9 1.3 2.1 3.2 1.8 1.4 5.4 0.9 2.1 1.9 40 a

Std. Residual -

0.5 11.5

-1.3

2.1 0 -

1.3 -2 -1.1

-1.5

-1.2

-1.3

-1.2 -

2.3 -1

-1.4

-1.4

Count 0 0 14 14 3 0 27 0 3 10 0 0 0 0 0 0 71

Expected 0.4 7.1 6.2 8.1 3.6 3.1 6.9 2.2 3.8 5.6 3.2 2.5 9.5 1.7 3.6 3.4 71 b

Std. Residual -

0.6 -2.7 3.2 2.1

-0.3

-1.8

7.7 -1.5 -

0.4 1.9

-1.8

-1.6 -

3.1 -

1.3 -

1.9 -

1.8

Count 1 6 0 14 3 4 8 0 1 3 2 0 0 0 0 2 44

Expected 0.3 4.4 3.8 5 2.3 1.9 4.3 1.4 2.3 3.5 2 1.6 5.9 1 2.3 2.1 44 c

Std. Residual 1.4 0.7 -2 4 0.5 1.5 1.8 -1.2 -

0.9 -

0.3 0 -1.2

-2.4

-1 -

1.5 -

0.1

Count 2 8 16 0 2 4 7 0 2 11 2 0 0 1 9 1 65

Expected 0.4 6.5 5.6 7.4 3.3 2.8 6.3 2.1 3.5 5.1 2.9 2.3 8.7 1.5 3.3 3.1 65 d

Std. Residual 2.6 0.6 4.4 -

2.7 -

0.7 0.7 0.3 -1.4

-0.8

2.6 -

0.6 -1.5 -3

-0.4

3.1 -

1.2

Count 0 5 2 5 0 6 2 1 0 3 2 0 2 0 1 1 30

Expected 0.2 3 2.6 3.4 1.5 1.3 2.9 0.9 1.6 2.4 1.4 1.1 4 0.7 1.5 1.4 30 e

Std. Residual -

0.4 1.1

-0.4

0.8 -

1.2 4.1

-0.5

0.1 -

1.3 0.4 0.5 -1 -1

-0.8

-0.4

-0.4

Count 0 1 1 1 3 0 2 3 0 3 0 0 3 0 2 3 22

Expected 0.1 2.2 1.9 2.5 1.1 1 2.1 0.7 1.2 1.7 1 0.8 3 0.5 1.1 1 22 f

Std. Residual -

0.4 -0.8

-0.7

-1 1.8 -1 -

0.1 2.8

-1.1

1 -1 -0.9 0 -

0.7 0.8 1.9

Count 0 1 8 13 9 4 0 0 3 0 1 0 1 0 7 2 49

Expected 0.3 4.9 4.3 5.6 2.5 2.1 4.7 1.5 2.6 3.9 2.2 1.7 6.6 1.2 2.5 2.3 49

j

Std. Residual -

0.5 -1.8 1.8 3.1 4.1 1.3

-2.2

-1.2 0.2 -2 -

0.8 -1.3

-2.2

-1.1

2.8 -

0.2


48

Count 0 0 0 0 0 0 0 0 9 2 3 0 2 0 0 0 16

Expected 0.1 1.6 1.4 1.8 0.8 0.7 1.5 0.5 0.9 1.3 0.7 0.6 2.1 0.4 0.8 0.8 16 k

Std. Residual -

0.3 -1.3

-1.2

-1.4

-0.9

-0.8

-1.2

-0.7 8.8 0.7 2.7 -0.8 -

0.1 -

0.6 -

0.9 -

0.9

Count 0 0 0 0 0 0 0 0 0 3 8 2 10 0 2 2 27

Expected 0.2 2.7 2.3 3.1 1.4 1.2 2.6 0.9 1.4 2.1 1.2 1 3.6 0.6 1.4 1.3 27 l

Std. Residual -

0.4 -1.6

-1.5

-1.8

-1.2

-1.1

-1.6

-0.9 -

1.2 0.6 6.1 1.1 3.4

-0.8

0.5 0.6

Count 0 2 2 1 1 0 1 0 2 0 1 4 17 3 1 4 39

Expected 0.2 3.9 3.4 4.5 2 1.7 3.8 1.2 2.1 3.1 1.8 1.4 5.2 0.9 2 1.8 39 m

Std. Residual -

0.5 -1

-0.8

-1.6

-0.7

-1.3

-1.4

-1.1 -

0.1 -

1.8 -

0.6 2.2 5.1 2.2

-0.7

1.6

Count 0 0 0 0 0 1 0 0 4 2 0 0 10 3 1 2 23

Expected 0.1 2.3 2 2.6 1.2 1 2.2 0.7 1.2 1.8 1 0.8 3.1 0.5 1.2 1.1 23 n

Std. Residual -

0.4 -1.5

-1.4

-1.6

-1.1

0 -

1.5 -0.9 2.5 0.1 -1 -0.9 3.9 3.3

-0.2

0.9

Count 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 2 10

Expected 0.1 1 0.9 1.1 0.5 0.4 1 0.3 0.5 0.8 0.5 0.4 1.3 0.2 0.5 0.5 10 p

Std. Residual -

0.2 -1

-0.9

-1.1

-0.7

-0.7

-1 -0.6 -

0.7 -

0.9 -

0.7 -0.6 5.7

-0.5

-0.7

2.2

Count 0 0 0 0 0 0 0 0 0 0 0 9 0 1 0 3 13

Expected 0.1 1.3 1.1 1.5 0.7 0.6 1.3 0.4 0.7 1 0.6 0.5 1.7 0.3 0.7 0.6 13 q

Std. Residual -

0.3 -1.1

-1.1

-1.2

-0.8

-0.8

-1.1

-0.6 -

0.8 -1

-0.8

12.6 -

1.3 1.2

-0.8

3

Count 0 0 0 0 0 0 1 0 0 0 0 0 10 0 0 0 11

Expected 0.1 1.1 1 1.3 0.6 0.5 1.1 0.3 0.6 0.9 0.5 0.4 1.5 0.3 0.6 0.5 11 t

Std. Residual -

0.3 -1.1 -1

-1.1

-0.8

-0.7

-0.1

-0.6 -

0.8 -

0.9 -

0.7 -0.6 7

-0.5

-0.8

-0.7

Count 0 0 0 0 2 2 0 12 1 1 3 0 2 0 0 2 25

Expected 0.1 2.5 2.2 2.9 1.3 1.1 2.4 0.8 1.3 2 1.1 0.9 3.4 0.6 1.3 1.2 25 v

Std. Residual -

0.4 -1.6

-1.5

-1.7

0.6 0.9 -

1.6 12.6

-0.3

-0.7

1.8 -0.9 -

0.7 -

0.8 -

1.1 0.8

Count 0 1 0 1 1 1 1 0 2 1 1 3 3 4 3 0 22

Expected 0.1 2.2 1.9 2.5 1.1 1 2.1 0.7 1.2 1.7 1 0.8 3 0.5 1.1 1 22

w

Std. Residual -

0.4 -0.8

-1.4

-1 -

0.1 0

-0.8

-0.8 0.8 -

0.6 0 2.5 0 4.8 1.8 -1


49

Count 3 51 44 58 26 22 49 16 27 40 23 18 68 12 26 24 507

Total Expected Count

3 51 44 58 26 22 49 16 27 40 23 18 68 12 26 24 507

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)

Pearson Chi-Square 1258.360a 225 .000

Likelihood Ratio 907.372 225 .000

N of Valid Cases 507

a. 238 cells (93.0%) have expected count less than 5. The minimum

expected count is .06

Knife Used

VAR00002

b c d e f j k l m n p q t v w Total

Count 9 0 1 0 0 0 0 0 0 0 0 0 0 0 0 10

Expected .8 .7 1.1 .3 .5 1.2 .6 .6 .4 .4 .1 1.2 .1 1.3 .3 10.0

a

Std. Residual 8.9 -.8 -.1 -.6 -.7 -1.1 -.8 -.8 -.7 -.7 -.4 -1.1 -.3 -1.1 -.6

Count 0 6 6 0 0 14 0 0 2 0 0 0 0 0 0 28

Expected 2.4 1.9 3.0 1.0 1.4 3.5 1.8 1.8 1.2 1.2 .4 3.5 .3 3.6 1.0 28.0

b

Std. Residual -1.5 2.9 1.7 -1.0 -1.2 5.7 -1.3 -1.3 .7 -1.1 -.6 -1.9 -.5 -1.9 -1.0

Count 1 0 8 0 1 4 0 0 0 0 0 0 0 0 0 14

Expected 1.2 1.0 1.5 .5 .7 1.7 .9 .9 .6 .6 .2 1.7 .1 1.8 .5 14.0

c

Std. Residual -.2 -1.0 5.2 -.7 .4 1.7 -.9 -.9 -.8 -.8 -.5 -1.3 -.4 -1.3 -.7

Count 2 1 0 1 1 5 0 1 3 1 0 0 0 9 0 24

d

Expected 2.0 1.7 2.6 .8 1.2 3.0 1.5 1.5 1.1 1.1 .4 3.0 .2 3.1 .8 24.0


50

Std. Residual .0 -.5 -1.6 .2 -.2 1.2 -1.2 -.4 1.9 -.1 -.6 -1.7 -.5 3.4 -.9

Count 2 0 1 0 3 0 0 0 0 1 0 1 0 1 0 9

Expected .8 .6 1.0 .3 .4 1.1 .6 .6 .4 .4 .1 1.1 .1 1.2 .3 9.0

e

Std. Residual 1.4 -.8 .0 -.6 3.8 -1.1 -.8 -.8 -.6 .9 -.4 -.1 -.3 -.1 -.6

Count 0 1 0 1 0 1 1 0 0 0 0 1 0 2 3 10

Expected .8 .7 1.1 .3 .5 1.2 .6 .6 .4 .4 .1 1.2 .1 1.3 .3 10.0

f

Std. Residual -.9 .4 -1.0 1.1 -.7 -.2 .4 -.8 -.7 -.7 -.4 -.2 -.3 .6 4.5

Count 1 5 6 3 2 0 0 2 0 0 0 0 0 7 0 26

Expected 2.2 1.8 2.8 .9 1.3 3.2 1.7 1.7 1.2 1.2 .4 3.2 .3 3.3 .9 26.0

j

Std. Residual -.8 2.4 1.9 2.2 .6 -1.8 -1.3 .3 -1.1 -1.1 -.6 -1.8 -.5 2.0 -.9

Count 0 0 0 0 0 0 0 6 2 3 0 2 0 0 0 13

Expected 1.1 .9 1.4 .5 .6 1.6 .8 .8 .6 .6 .2 1.6 .1 1.7 .5 13.0

k

Std. Residual -1.0 -.9 -1.2 -.7 -.8 -1.3 -.9 5.6 1.9 3.2 -.4 .3 -.4 -1.3 -.7

Count 0 0 0 0 0 0 0 0 1 1 0 9 0 2 0 13

Expected 1.1 .9 1.4 .5 .6 1.6 .8 .8 .6 .6 .2 1.6 .1 1.7 .5 13.0

l

Std. Residual -1.0 -.9 -1.2 -.7 -.8 -1.3 -.9 -.9 .6 .6 -.4 5.8 -.4 .3 -.7

Count 2 1 0 0 0 1 0 0 0 0 2 2 0 1 0 9

Expected .8 .6 1.0 .3 .4 1.1 .6 .6 .4 .4 .1 1.1 .1 1.2 .3 9.0

m

Std. Residual 1.4 .5 -1.0 -.6 -.7 -.1 -.8 -.8 -.6 -.6 5.1 .8 -.3 -.1 -.6

Count 0 0 0 0 0 0 0 2 0 0 0 4 2 1 0 9

Expected .8 .6 1.0 .3 .4 1.1 .6 .6 .4 .4 .1 1.1 .1 1.2 .3 9.0

n

Std. Residual -.9 -.8 -1.0 -.6 -.7 -1.1 -.8 1.9 -.6 -.6 -.4 2.7 6.4 -.1 -.6

Count 0 0 0 0 0 0 0 0 0 0 0 2 0 0 1 3

Expected .3 .2 .3 .1 .1 .4 .2 .2 .1 .1 .0 .4 .0 .4 .1 3.0

p

Std. Residual -.5 -.5 -.6 -.3 -.4 -.6 -.4 -.4 -.4 -.4 -.2 2.7 -.2 -.6 2.8


51

Count 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1

Expected .1 .1 .1 .0 .0 .1 .1 .1 .0 .0 .0 .1 .0 .1 .0 1.0

q

Std. Residual -.3 -.3 -.3 -.2 -.2 -.4 -.3 -.3 -.2 -.2 -.1 -.4 -.1 -.4 5.2

Count 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 2

Expected .2 .1 .2 .1 .1 .2 .1 .1 .1 .1 .0 .2 .0 .3 .1 2.0

t

Std. Residual -.4 -.4 -.5 -.3 -.3 -.5 -.4 -.4 -.3 -.3 -.2 3.5 -.1 -.5 -.3

Count 0 0 0 2 2 0 12 1 1 3 0 2 0 0 2 25

Expected 2.1 1.7 2.7 .9 1.2 3.1 1.6 1.6 1.1 1.1 .4 3.1 .2 3.2 .9 25.0

v

Std. Residual -1.5 -1.3 -1.7 1.2 .7 -1.8 8.2 -.5 -.1 1.8 -.6 -.6 -.5 -1.8 1.2

Count 0 0 0 0 1 0 0 1 0 0 1 0 0 3 0 6

Expected .5 .4 .7 .2 .3 .7 .4 .4 .3 .3 .1 .7 .1 .8 .2 6.0

w

Std. Residual -.7 -.6 -.8 -.5 1.3 -.9 -.6 1.0 -.5 -.5 3.1 -.9 -.2 2.5 -.5

Count 17 14 22 7 10 25 13 13 9 9 3 25 2 26 7 202 Total

Expected Count 17.0 14.0 22.0 7.0 10.0 25.0 13.0 13.0 9.0 9.0 3.0 25.0 2.0 26.0 7.0 202.0

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)




a. 240 cells (100.0%) have expected count less than 5. The

minimum expected count is .01.


52

Knife Not Used

a b c d e f j k l m n p q t w Total

Count 0 18 1 8 2 0 0 0 0 1 0 0 0 0 0 30

Expected .3 3.4 3.0 3.6 1.9 1.2 2.3 .3 1.4 3.1 1.4 1.5 4.2 1.0 1.7 30.0

a

Std. Residual -.5 8.0 -1.1 2.4 .1 -1.1 -1.5 -.5 -1.2 -1.2 -1.2 -1.2 -2.1 -1.0 -1.3

Count 0 0 8 8 3 0 13 0 3 8 0 0 0 0 0 43

Expected .4 4.8 4.2 5.1 2.7 1.7 3.3 .4 2.0 4.4 2.0 2.1 6.1 1.4 2.4 43.0

b

Std. Residual -.7 -2.2 1.8 1.3 .2 -1.3 5.4 -.7 .7 1.7 -1.4 -1.5 -2.5 -1.2 -1.6

Count 1 5 0 6 3 3 4 0 1 3 2 0 0 0 2 30

Expected .3 3.4 3.0 3.6 1.9 1.2 2.3 .3 1.4 3.1 1.4 1.5 4.2 1.0 1.7 30.0

c

Std. Residual 1.3 .9 -1.7 1.3 .8 1.7 1.1 -.5 -.3 .0 .5 -1.2 -2.1 -1.0 .2

Count 2 6 15 0 1 3 2 0 1 8 1 0 0 1 1 41

Expected .4 4.6 4.0 4.9 2.6 1.6 3.1 .4 1.9 4.2 1.9 2.0 5.8 1.3 2.3 41.0

d

Std. Residual 2.5 .7 5.4 -2.2 -1.0 1.1 -.6 -.6 -.6 1.9 -.6 -1.4 -2.4 -.3 -.9

Count 0 3 2 4 0 3 2 1 0 3 1 0 1 0 1 21

Expected .2 2.3 2.1 2.5 1.3 .8 1.6 .2 1.0 2.1 1.0 1.0 3.0 .7 1.2 21.0

e

Std. Residual -.5 .4 -.1 1.0 -1.1 2.4 .3 1.7 -1.0 .6 .0 -1.0 -1.1 -.8 -.2

Count 0 1 0 1 2 0 1 2 0 3 0 0 2 0 0 12

Expected .1 1.3 1.2 1.4 .8 .5 .9 .1 .6 1.2 .6 .6 1.7 .4 .7 12.0

f

Std. Residual -.3 -.3 -1.1 -.4 1.4 -.7 .1 5.5 -.7 1.6 -.7 -.8 .2 -.6 -.8

Count 0 0 3 7 6 2 0 0 1 0 1 0 1 0 2 23

j

Expected .2 2.6 2.3 2.7 1.4 .9 1.7 .2 1.1 2.3 1.1 1.1 3.3 .8 1.3 23.0


53

Std. Residual -.5 -1.6 .5 2.6 3.8 1.1 -1.3 -.5 -.1 -1.5 -.1 -1.1 -1.2 -.9 .6

Count 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 3

Expected .0 .3 .3 .4 .2 .1 .2 .0 .1 .3 .1 .1 .4 .1 .2 3.0

k

Std. Residual -.2 -.6 -.5 -.6 -.4 -.3 -.5 -.2 7.7 -.6 -.4 -.4 -.7 -.3 -.4

Count 0 0 0 0 0 0 0 0 0 2 7 2 1 0 2 14

Expected .1 1.6 1.4 1.7 .9 .6 1.1 .1 .6 1.4 .6 .7 2.0 .5 .8 14.0

l

Std. Residual -.4 -1.3 -1.2 -1.3 -.9 -.7 -1.0 -.4 -.8 .5 7.9 1.6 -.7 -.7 1.4

Count 0 0 1 1 1 0 0 0 2 0 1 2 15 3 4 30

Expected .3 3.4 3.0 3.6 1.9 1.2 2.3 .3 1.4 3.1 1.4 1.5 4.2 1.0 1.7 30.0

m

Std. Residual -.5 -1.8 -1.1 -1.4 -.6 -1.1 -1.5 -.5 .5 -1.7 -.3 .4 5.2 2.0 1.8

Count 0 0 0 0 0 1 0 0 2 2 0 0 6 1 2 14

Expected .1 1.6 1.4 1.7 .9 .6 1.1 .1 .6 1.4 .6 .7 2.0 .5 .8 14.0

n

Std. Residual -.4 -1.3 -1.2 -1.3 -.9 .6 -1.0 -.4 1.7 .5 -.8 -.8 2.9 .8 1.4

Count 0 0 0 0 0 0 0 0 0 0 0 0 6 0 1 7

Expected .1 .8 .7 .8 .4 .3 .5 .1 .3 .7 .3 .3 1.0 .2 .4 7.0

p

Std. Residual -.3 -.9 -.8 -.9 -.7 -.5 -.7 -.3 -.6 -.8 -.6 -.6 5.0 -.5 1.0

Count 0 0 0 0 0 0 0 0 0 0 0 9 0 1 2 12

Expected .1 1.3 1.2 1.4 .8 .5 .9 .1 .6 1.2 .6 .6 1.7 .4 .7 12.0

q

Std. Residual -.3 -1.2 -1.1 -1.2 -.9 -.7 -1.0 -.3 -.7 -1.1 -.7 10.9 -1.3 1.0 1.6

Count 0 0 0 0 0 0 1 0 0 0 0 0 8 0 0 9

Expected .1 1.0 .9 1.1 .6 .4 .7 .1 .4 .9 .4 .4 1.3 .3 .5 9.0

t

Std. Residual -.3 -1.0 -.9 -1.0 -.8 -.6 .4 -.3 -.6 -1.0 -.6 -.7 6.0 -.5 -.7

Count 0 1 0 1 1 0 0 0 1 1 1 2 3 4 0 15

Expected .1 1.7 1.5 1.8 .9 .6 1.1 .1 .7 1.5 .7 .7 2.1 .5 .8 15.0

w

Std. Residual -.4 -.5 -1.2 -.6 .1 -.8 -1.1 -.4 .4 -.4 .4 1.5 .6 5.0 -.9


54

Count 3 34 30 36 19 12 23 3 14 31 14 15 43 10 17 304 Tota

l

Expected 3.0 34.0 30.0 36.0 19.0 12.0 23.0 3.0 14.0 31.0 14.0 15.0 43.0 10.0 17.0 304.0

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)





expected count is .03.

High Familiarity

VAR00002

a b c d e f j k l m n p q t v w Total

Count 0 8 0 3 0 0 0 0 0 1 0 0 0 0 0 0 12

Expected .1 1.2 1.1 1.3 .5 .7 .8 .3 .3 1.0 .5 1.1 1.6 .3 .5 .6 12.0

a

Std.

Residual

-.3 6.1 -1.0 1.5 -.7 -.8 -.9 -.6 -.6 .0 -.7 -1.0 -1.3 -.6 -.7 -.8

Count 0 0 6 5 2 0 7 0 1 4 0 0 0 0 0 0 25

Expected .1 2.6 2.3 2.7 .9 1.5 1.7 .7 .7 2.2 1.1 2.3 3.4 .7 .9 1.3 25.0

b

Std.

Residual

-.4 -1.6 2.5 1.4 1.1 -1.2 4.0 -.8 .4 1.3 -1.0 -1.5 -1.8 -.8 -1.0 -1.2

Count 0 4 0 4 1 2 3 0 0 1 1 0 0 0 0 1 17

Expected .1 1.7 1.6 1.8 .6 1.0 1.2 .5 .5 1.5 .7 1.6 2.3 .5 .6 .9 17.0

c

Std.

Residual

-.3 1.7 -1.2 1.6 .5 1.0 1.7 -.7 -.7 -.4 .3 -1.2 -1.5 -.7 -.8 .1


55

Count 1 3 8 0 0 2 1 0 0 5 2 9 0 0 2 0 33

Expected .2 3.4 3.0 3.5 1.2 2.0 2.3 .9 .9 2.8 1.4 3.0 4.4 .9 1.2 1.8 33.0

d

Std.

Residual

2.0 -.2 2.9 -1.9 -1.1 .0 -.9 -.9 -.9 1.3 .5 3.4 -2.1 -.9 .7 -1.3

Count 0 3 1 3 0 3 0 0 0 0 0 0 0 0 0 0 10

Expected .1 1.0 .9 1.1 .4 .6 .7 .3 .3 .9 .4 .9 1.3 .3 .4 .5 10.0

e

Std.

Residual

-.2 2.0 .1 1.9 -.6 3.1 -.8 -.5 -.5 -.9 -.7 -1.0 -1.2 -.5 -.6 -.7

Count 0 1 0 1 0 0 1 2 0 2 0 0 2 0 0 2 11

Expected .1 1.1 1.0 1.2 .4 .7 .8 .3 .3 .9 .5 1.0 1.5 .3 .4 .6 11.0

f

Std.

Residual

-.2 -.1 -1.0 -.2 -.6 -.8 .3 3.1 -.5 1.1 -.7 -1.0 .4 -.5 -.6 1.8

Count 0 0 1 3 3 2 0 0 1 0 0 0 0 0 2 2 14

Expected .1 1.4 1.3 1.5 .5 .8 1.0 .4 .4 1.2 .6 1.3 1.9 .4 .5 .8 14.0

j

Std.

Residual

-.3 -1.2 -.2 1.2 3.4 1.3 -1.0 -.6 1.0 -1.1 -.8 -1.1 -1.4 -.6 2.0 1.4

Count 0 0 0 0 0 0 0 0 2 1 1 0 1 0 0 0 5

Expected .0 .5 .5 .5 .2 .3 .3 .1 .1 .4 .2 .5 .7 .1 .2 .3 5.0

k

Std.

Residual

-.2 -.7 -.7 -.7 -.4 -.5 -.6 -.4 5.1 .9 1.7 -.7 .4 -.4 -.4 -.5

Count 0 0 0 0 0 0 0 0 0 0 3 1 1 0 0 0 5

Expected .0 .5 .5 .5 .2 .3 .3 .1 .1 .4 .2 .5 .7 .1 .2 .3 5.0

l

Std.

Residual

-.2 -.7 -.7 -.7 -.4 -.5 -.6 -.4 -.4 -.7 6.0 .8 .4 -.4 -.4 -.5

Count 0 0 1 0 0 0 1 0 0 0 1 1 7 2 0 3 16

Expected .1 1.6 1.5 1.7 .6 .9 1.1 .4 .4 1.4 .7 1.5 2.2 .4 .6 .9 16.0

m

Std.

Residual

-.3 -1.3 -.4 -1.3 -.8 -1.0 -.1 -.7 -.7 -1.2 .4 -.4 3.3 2.4 -.8 2.3


56

Count 0 0 0 0 0 1 0 0 0 1 0 0 4 1 1 0 8

Expected .0 .8 .7 .9 .3 .5 .6 .2 .2 .7 .3 .7 1.1 .2 .3 .4 8.0

n

Std.

Residual

-.2 -.9 -.9 -.9 -.5 .8 -.7 -.5 -.5 .4 -.6 -.9 2.8 1.7 1.3 -.7

Count 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 1 4

Expected .0 .4 .4 .4 .2 .2 .3 .1 .1 .3 .2 .4 .5 .1 .2 .2 4.0

p

Std.

Residual

-.1 -.6 -.6 -.7 -.4 -.5 -.5 -.3 -.3 -.6 -.4 -.6 3.4 -.3 -.4 1.7

Count 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 1 5

Expected .0 .5 .5 .5 .2 .3 .3 .1 .1 .4 .2 .5 .7 .1 .2 .3 5.0

q

Std.

Residual

-.2 -.7 -.7 -.7 -.4 -.5 -.6 -.4 -.4 -.7 -.5 5.2 -.8 -.4 -.4 1.4

Count 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 5

Expected

Count

.0 .5 .5 .5 .2 .3 .3 .1 .1 .4 .2 .5 .7 .1 .2 .3 5.0

t

Std.

Residual

-.2 -.7 -.7 -.7 -.4 -.5 -.6 -.4 -.4 -.7 -.5 -.7 5.3 -.4 -.4 -.5

Count 0 0 0 0 1 1 0 3 0 1 0 0 1 0 0 0 7

Expected

Count

.0 .7 .6 .8 .3 .4 .5 .2 .2 .6 .3 .6 .9 .2 .3 .4 7.0

v

Std.

Residual

-.2 -.8 -.8 -.9 1.4 .9 -.7 6.5 -.4 .5 -.5 -.8 .1 -.4 -.5 -.6

Count 0 0 0 1 0 0 0 0 1 0 0 2 1 2 2 0 9

Expected

Count

.0 .9 .8 1.0 .3 .5 .6 .2 .2 .8 .4 .8 1.2 .2 .3 .5 9.0

w

Std.

Residual

-.2 -1.0 -.9 .0 -.6 -.7 -.8 -.5 1.5 -.9 -.6 1.3 -.2 3.6 2.9 -.7

Count 1 19 17 20 7 11 13 5 5 16 8 17 25 5 7 10 186 T

ot

al % of Total .5 10.2 9.1 10.8 3.8 5.9 7.0 2.7 2.7 8.6 4.3 9.1 13.4 2.7 3.8 5.4 100.0


57

% % % % % % % % % % % % % % % % %

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)




a. 256 cells (100.0%) have expected count less than 5. The

minimum expected count is .02.

Low Familiarity

VAR00002


Total

Count 0 19 1 6 2 0 0 0 0 0 0 0 0 0 0 0 28

Expected .2 2.7 2.3 3.2 1.6 .9 3.0 .9 1.9 2.0 1.3 .9 3.7 .6 1.6 1.2 28.0 a

Std. Residual -.4 9.9 -.9 1.5 .3 -1.0 -1.7 -1.0 -1.4 -1.4 -1.1 -.9 -1.9 -.8 -1.3 -1.1

Count 0 0 8 9 1 0 20 0 2 6 0 0 0 0 0 0 46

Expected .3 4.5 3.8 5.3 2.7 1.5 4.9 1.5 3.1 3.4 2.1 1.4 6.0 1.0 2.7 2.0 46.0 b

Std. Residual -.5 -2.1 2.2 1.6 -1.0 -1.2 6.8 -1.2 -.6 1.4 -1.4 -1.2 -2.5 -1.0 -1.6 -1.4

Count 1 2 0 10 2 2 5 0 1 2 1 0 0 0 0 1 27

Expected .2 2.6 2.2 3.1 1.6 .9 2.9 .9 1.8 2.0 1.2 .8 3.5 .6 1.6 1.1 27.0 c

Std. Residual 2.1 -.4 -1.5 3.9 .4 1.2 1.3 -1.0 -.6 .0 -.2 -.9 -1.9 -.8 -1.2 -.1

d Count 1 5 8 0 2 2 6 0 2 6 0 0 0 1 7 1 41


58

Expected .2 4.0 3.4 4.7 2.4 1.4 4.4 1.4 2.7 3.0 1.9 1.2 5.4 .9 2.4 1.7 41.0

Std. Residual 1.5 .5 2.5 -2.2 -.2 .5 .8 -1.2 -.4 1.7 -1.4 -1.1 -2.3 .1 3.0 -.6

Count 0 2 1 2 0 3 2 1 0 3 2 0 2 0 1 1 20

Expected .1 1.9 1.6 2.3 1.2 .7 2.1 .7 1.3 1.5 .9 .6 2.6 .4 1.2 .9 20.0 e

Std. Residual -.3 .0 -.5 -.2 -1.1 2.9 -.1 .4 -1.2 1.3 1.1 -.8 -.4 -.7 -.1 .2

Count 0 0 1 0 3 0 1 1 0 1 0 0 1 0 2 1 11

Expected .1 1.1 .9 1.3 .6 .4 1.2 .4 .7 .8 .5 .3 1.4 .2 .6 .5 11.0 f

Std. Residual -.3 -1.0 .1 -1.1 3.0 -.6 -.2 1.0 -.9 .2 -.7 -.6 -.4 -.5 1.7 .8

Count 0 1 7 10 6 2 0 0 2 0 1 0 1 0 5 0 35

Expected .2 3.4 2.9 4.0 2.0 1.2 3.7 1.2 2.3 2.6 1.6 1.1 4.6 .7 2.0 1.5 35.0 j

Std. Residual -.5 -1.3 2.4 3.0 2.8 .8 -1.9 -1.1 -.2 -1.6 -.5 -1.0 -1.7 -.9 2.1 -1.2

Count 0 0 0 0 0 0 0 0 7 1 2 0 1 0 0 0 11

Expected .1 1.1 .9 1.3 .6 .4 1.2 .4 .7 .8 .5 .3 1.4 .2 .6 .5 11.0 k

Std. Residual -.3 -1.0 -1.0 -1.1 -.8 -.6 -1.1 -.6 7.3 .2 2.1 -.6 -.4 -.5 -.8 -.7

Count 0 0 0 0 0 0 0 0 0 3 5 1 9 0 2 2 22

Expected .1 2.1 1.8 2.5 1.3 .7 2.3 .7 1.5 1.6 1.0 .7 2.9 .5 1.3 .9 22.0 l

Std. Residual -.4 -1.5 -1.3 -1.6 -1.1 -.9 -1.5 -.9 -1.2 1.1 4.0 .4 3.6 -.7 .6 1.1

Count 0 2 1 1 1 0 0 0 2 0 0 3 10 1 1 1 23

Expected .1 2.2 1.9 2.7 1.3 .8 2.4 .8 1.5 1.7 1.0 .7 3.0 .5 1.3 1.0 23.0 m

Std. Residual -.4 -.2 -.6 -1.0 -.3 -.9 -1.6 -.9 .4 -1.3 -1.0 2.8 4.0 .7 -.3 .0

Count 0 0 0 0 0 0 0 0 4 1 0 0 6 2 0 2 15

Expected .1 1.5 1.2 1.7 .9 .5 1.6 .5 1.0 1.1 .7 .5 2.0 .3 .9 .6 15.0 n

Std. Residual -.3 -1.2 -1.1 -1.3 -.9 -.7 -1.3 -.7 3.0 -.1 -.8 -.7 2.9 3.0 -.9 1.7

Count 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 1 6

p

Expected .0 .6 .5 .7 .3 .2 .6 .2 .4 .4 .3 .2 .8 .1 .3 .3 6.0


59

Std. Residual -.2 -.8 -.7 -.8 -.6 -.4 -.8 -.4 -.6 -.7 -.5 -.4 4.8 -.4 -.6 1.5

Count 0 0 0 0 0 0 0 0 0 0 0 5 0 1 0 2 8

Expected .0 .8 .7 .9 .5 .3 .9 .3 .5 .6 .4 .2 1.0 .2 .5 .3 8.0 q

Std. Residual -.2 -.9 -.8 -1.0 -.7 -.5 -.9 -.5 -.7 -.8 -.6 9.6 -1.0 2.0 -.7 2.8

Count 0 0 0 0 0 0 1 0 0 0 0 0 5 0 0 0 6

Expected .0 .6 .5 .7 .3 .2 .6 .2 .4 .4 .3 .2 .8 .1 .3 .3 6.0 t

Std. Residual -.2 -.8 -.7 -.8 -.6 -.4 .5 -.4 -.6 -.7 -.5 -.4 4.8 -.4 -.6 -.5

Count 0 0 0 0 1 1 0 9 1 0 3 0 1 0 0 2 18

Expected .1 1.8 1.5 2.1 1.0 .6 1.9 .6 1.2 1.3 .8 .5 2.4 .4 1.0 .8 18.0 v

Std. Residual -.3 -1.3 -1.2 -1.4 .0 .5 -1.4 10.8 -.2 -1.1 2.4 -.7 -.9 -.6 -1.0 1.4

Count 0 1 0 0 1 1 0 0 1 1 1 1 2 2 1 0 12

Expected .1 1.2 1.0 1.4 .7 .4 1.3 .4 .8 .9 .5 .4 1.6 .3 .7 .5 12.0

w

Std. Residual -.3 -.2 -1.0 -1.2 .4 .9 -1.1 -.6 .2 .1 .6 1.1 .3 3.5 .4 -.7

Count 2 32 27 38 19 11 35 11 22 24 15 10 43 7 19 14 329

Total

Expected 2.0 32.0 27.0 38.0 19.0 11.0 35.0 11.0 22.0 24.0 15.0 10.0 43.0 7.0 19.0 14.0 329.0

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)





expected count is .04.

candidate: amy walsh supervisor: professor david clarke subject ... · exploring behavioural...

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