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‘You do not have to say anything’:

Student Beliefs about the Course of

Events in a Police Interview

Name: Divya Sukumar

Supervisor: Professor David Clarke

Degree: BSc Psychology (Hons)

Student Beliefs about Police Interviews

2

Acknowledgements

I would like to express my deepest gratitude to Professor David Clarke for his endless

guidance, support and most importantly, his sense of humour. His encouragement was an

invaluable source of motivation throughout the project and his unique approach to Psychology will

continue to inspire my further studies.


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Abstract

Since 1985, police interviews in England have been transformed from psychologically

coercive interrogations to non-confrontational, investigative interviews as guided by the PEACE

model. While legal psychologists have mined a wealth of research to empirically support the

success of individual PEACE model tactics, the fundamentally interactive and dynamic nature of

police interviews has been neglected. The method of sequence analysis, which examines the

succession of events along different pathways, is well suited for the study of interactive processes,

such as police interviews. Thus, this study highlighted the utility of sequence analysis in the

domain of legal psychology by mapping out student beliefs about the patterns of events in police

interviews.

Students were given questionnaires in which they reported the events they believed to

occur in police interviews and importantly, provided the order of the events. Sequential analysis

found that despite the potential influence of fictional dramatizations of police interviews, student

beliefs about the course of events in police interviews were remarkably congruent with the PEACE

model. This was the case regardless of whether students had high or low familiarity with police

interviews. In addition, the study employed the Dawkins method to highlight the utility of

identifying extended sequences of events as well as compared complimentary predictability

analyses to traditional sequence analysis. Ultimately, the set of sequence analysis tools employed

are demonstrated to hold great value for the study of actual police interviews.


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Introduction

Police Interviews

Police interviews of suspects are key to the process of criminal investigation. The police

interview may resolve unclear issues in the absence of incriminating evidence, direct attention to

other potential suspects or importantly, generate a confession from the suspect (Hartwig, Anders

Granhag, & Vrij, 2005). In the past, police interviews were conducted in accordance with the Reid

Technique, which involves two stages; the behavioural analysis interview to assess whether the

suspect is guilty or innocent of the crime, followed by an accusatory and confrontational

interrogation for suspects deemed ‘guilty’. The interrogation phase has the sole aim of drawing

out the suspect’s confession and essentially requires psychologically manipulative tactics such as

falsely claiming that incriminating evidence exists, deceiving the suspect with fabricated evidence

and maximising the suspect’s fears (Kassin et al., 2010). However, these coercive tactics have been

called into question as recent DNA exonerations have highlighted that approximately 25% of

wrongful convictions are due to false confessions by innocent suspects, for which duress and

coercion are cited as leading causes ("The Innocence Project," 2014). Similarly, experimental

simulations of police interviews have highlighted that students are more likely to falsely confess to

an act such as cheating or crashing a computer if the interviewer holds a guilt-presumptive bias

(Narchet, Meissner, & Russano, 2011), bluffs about having evidence (Perillo & Kassin, 2011) or

presents fabricated evidence (Nash & Wade, 2009).

While surveys of North American police officers reveal that the Reid technique is still very

much in use (Kassin et al., 2007), the Police and Criminal Evidence Act (1985) in England has

introduced an ethical and fairer template of police interviewing known as the PEACE model which

primarily aims to ascertain the truth rather than gain a confession from the suspect. The

inquisitorial PEACE model comprises of five stages; Preparation and Planning, Engagement and

Explaining, gaining an Account, Closure and finally an Evaluation. Initially, police officers are

expected to plan a “route map” (Snook, Eastwood, Stinson, Tedeschini, & House, 2010) as an

outline of all possible events which may occur in the interview, including the suspect’s denial to

speak. The interview then begins with the delivery of the notice of caution and engaging the

suspect in conversation. Next, the interviewer must elicit an account from the suspect without any

interruptions. Any inconsistencies or discrepancies in the suspect’s account may be challenged in a


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non-aggressive manner before the police officer decides to end the interview. Police officers are

encouraged to self-evaluate at the end of interviews.

The PEACE model prevents interviewers from persuading suspects of unknown guilt

confessing or admitting to a crime they may not have committed, thus avoiding serious

miscarriages of justice such as a wrongful conviction. Moreover, field studies have found that

adherence to the PEACE model enhances interview quality with appropriate delivery of the legally

required notice of caution, rapport building, effective use of pauses and silences and as a result, a

more comprehensive account from the interviewee (Walsh & Bull, 2010) whereas laboratory

studies found that the PEACE model elicited more true confessions and fewer false confessions

compared to the Reid technique (Meissner, Russano, & Narchet, 2010). In particular, two vital

tactics which emerge are rapport building and identifying contradictions in the suspect’s account.

Rapport building has been shown to elicit more accurate information from mock eye-witnesses

(Collins, Lincoln, & Frank, 2002) and is crucial for prompting information from uncooperative

suspects. For this reason, it has been recommended that rapport building tactics such as finding

common ground and meeting the basic needs of the suspect, including water and food, should be

employed early on in the police interview (Kelly, Miller, Redlich, & Kleinman, 2013). Meanwhile,

identifying contradictions in the suspect’s account has been found to successfully prompt

incriminating information in the vast majority of interviews in which it is employed (Leo, 1995). It

is imperative to underscore that police officers have a variety of tactics at hand and must skilfully

and flexibly employ these depending on the suspect’s responses. Thus, despite the success of

certain tactics, police interviews may follow a variety of courses and it is only the start and end of

police interviews which remain relatively uniform (Kelly, et al., 2013).

Crucially, researchers have highlighted that while police officers in England largely follow

the PEACE model which forbids coercive tactics, the use of inappropriate Reid-style tactics

including leading questions and repeating the same question over and over, still occur (Bull &

Soukara, 2010). English police officers appear to resort to such tactics primarily when dealing with

resistant suspects who keep denying their crime (Pearse & Gudjonsson, 1999).


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Lay Beliefs

In contrast to the large body of research on police interviews, very little is known about the

lay beliefs about police interviews. In North America, the beliefs of potential jurors on the

coerciveness of various police interrogation tactics and their likelihood of eliciting true and false

confessions was examined via questionnaires (Leo & Liu, 2009). The majority of participants

recognized that tactics such as false evidence ploys and threats are psychologically manipulative,

however participants did not believe that the tactics would lead to false confessions which

opposes the conclusions of academics (Drizin & Leo, 2003). As most lay individuals never have any

direct experience with legal and forensic processes, their beliefs often tend to be distinct from

actualities. For instance, even individuals with a good education and interests in criminology have

higher expectations of re-offending than actual rates (Redondo, Luque, & Funes, 1996). Similarly,

English university students with an interest in crime overestimate crime rates (Ainsworth & Moss,

2000).

It is hypothesised that such beliefs about the law and crime are greatly influenced by mass

media (Howitt, 2002). As most citizens have minimal interaction with police forces, their beliefs

and expectations tend to be shaped by crime dramas which have established themselves as one of

the most popular television genres in Western societies (Callanan & Rosenberger, 2011), likely due

to their sensationalist and dramatic portrayal of crime, noble police officers who identify neat

solutions and stylized court trials. Due to the implied reality of such shows, the general population

form their own set of beliefs about the mundane routines of police interviews (Dixon, 2010) and

have even been reported to offer advice to police officers (Huey, 2010).

A candidate explanation of such belief formation is cultivation theory which posits that

information transmitted by television is incorporated into the construction of viewers’ social

‘realities’ (Gerbner & Gross, 1976). In essence, much of what the general public believes about

police stations, courtrooms and operating theatres is learnt from the fictional world of television

drama which offers some insight into the workings of such fascinating institutions. However,

cultivation theory’s predictions that increased television viewing leads to conceptions of the world

which are consistent with the images portrayed on TV has been criticized as too simplistic

(Shanahan & Morgan, 1999) and the correlation between heavy television viewing and distorted

perceptions of crime has not been found in the UK (Gunter, 1987). This may be attributed to a

cultural difference or perhaps, a different media environment.


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Compatible with cultivation theory’s explanation of lay beliefs about crime is that of the

availability heuristic which suggests that memorable events are often judged as more probable or

frequent (Tversky & Kahneman, 1973). For instance, people overestimate motor vehicle accidents,

homicide and tornadoes as frequent causes of death and in contrast, underestimate the frequency

of causes such as diabetes, stomach cancer and asthma (Lichtenstein, Slovic, Fischhoff, Layman, &

Combs, 1978). A follow up study found that such beliefs correlate with the disproportionate

reporting of dramatic and catastrophic events by newspapers in which diseases are largely

neglected (Combs & Slovic, 1979). This is highly relevant to beliefs about police interviews as

several of the coercive and manipulative tactics now banned in England are imbued with

entertainment value and are potentially more memorable for the general public.

Sequence Analysis

Sequence analysis is a powerful but underused tool tailor made for studying the dynamic

aspects of behaviour and social interaction, for example, siblings fighting or governments

negotiating (Bakeman & Gottman, 1986). It has previously been used to study a range of

behavioural interactions and their temporal structures such as violent incidents at pubs (Beale,

Cox, Clarke, Lawrence, & Leather, 1998), road accidents (Clarke, Ward, & Jones, 1998), sexual

assault (Fossi, Clarke, & Lawrence, 2005) and marital conflict (J. Gottman, Markman, & Notarius,

1977). The complex and dynamic interactions which compromise a police interview are yet to

benefit from the method of sequence analysis. Thus far, police interviews have predominantly

been studied with regard to the various tactics employed by the police officer and how they

correlate with the types of responses produced by the suspect, such as confessions or denials.

Consequently, the sequential patterns underlying the events in a police interview have been

neglected. By examining how the events unfold over time, sequence analysis has the potential to

offer insight into different pathways police interviews can take and their associated outcomes.

Sequence analysis is based on Markov models which were initially used to mathematically

model learning (J. M. Gottman & Roy, 1990) and involves the study of transitions between pairs of

behavioural events, the first of which is referred to as the antecedent and the second of which is

the sequitor. The commonness of the transitions between these events is known as the

transitional frequency and if this is significantly different from the transitional frequency expected


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by chance, it is likely that the two events are interdependent in that the antecedent is driving the

sequitor. In this manner, probabilistic patterns in the data are uncovered and a rich

characterization of the common sequences of events emerges in the form of a state transition

diagram.

An important characteristic of a sequence is its degree of complexity which is given by its

order. In first order sequences, every event is only dependent on the previous event, in second

order sequences, every event is dependent on the chain of two previous events and so on. Large

amounts of data are required to detect extensive sequences, for example, with the Dawkins’

method in which higher order sequences can be easily identified by locating the most frequent

transition followed by the event most likely to follow or precede that transition and so on (Clarke

& Crossland, 1985).

Finally, it is important to note that thus far in sequence analysis studies, significant

transitions between events have only been used to identify causal relations between the events,

for instance, whether a certain antecedent is causing a sequitor. However, given that the

antecedent has occurred, the most probable event to follow is not necessarily the sequitor which

the antecedent is thought to cause. Forecasting the next event to occur may require a different

analysis. The distinction is essentially between causality, based on contingency statistics, and

predictability, based on conditional probability, an issue which remains unexplored in the domain

of sequence analysis.

Rationale

The present study primarily aims to introduce the method of sequence analysis into the

field of legal psychology. Student beliefs about not only the events which occur in a police

interview but the sequential patterns in which they occur will be examined. Students will be

classified as having high or low familiarity with police interviews as their beliefs about police

interviews may vary as a function of their familiarity. Their responses will then be used to identify

how congruent various lay beliefs of police interview tactics and outcomes are with actual police

interview procedures in England. The utility of a sequential approach for studying psycho-legal

processes will be further highlighted with the Dawkins’ method and an exploratory comparison

between causality and predictability analyses.


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Method

Participants

Approximately 60 students were approached with an announcement during a lecture and a

response rate of 53 was obtained, out of which 51 questionnaires were usable. In addition, 19

students from other disciplines volunteered to take part in the study, resulting in a convenience

sample of 70. All participants were students at the University of Nottingham, the majority of

whom were undergraduates at the School of Psychology (see Appendix 2). The sample consisted

of 9 males and 61 females with an age range of 19 to 24 years (M= 20.61, SD= 0.73 years). No

financial compensation was provided for participation.

Materials

All participants were given a task pack containing the following documents: an information

sheet explaining the nature and purpose of the study, an informed consent form, the

questionnaire and a debrief sheet (see Appendix 1). The debrief sheet summarized the purpose of

the research and provided contact details of support organisations for any participants who found

the task distressing.

First, the questionnaire required participants to provide their age, gender and course of

study. Next, participants were asked to rate their familiarity with police interviews on a 7-point

Likert scale. While it would have been ideal to compare a group of participants with first-hand

experience of police interviews to a group of participants who had very limited knowledge of

police interviews, recruiting actual suspects or convicted offenders was not within the scope of

this project. Thus, as a methodological compromise, participants’ relative familiarity with police

interviews along with their primary source of knowledge on police interviews was assessed. In

addition, a number of incidental 7-point Likert scales were included to assess participants’

attitudes to offender treatment, their beliefs about the causal role of genetics in criminal

behaviour and the success of police officers in upholding the law. For these three measures, the

wording was varied so that participants could not plausibly provide the same rating on all three

scales.


10

Following this, the questionnaire presented participants with an exemplary list of events

which may occur when a student attends a lecture. Some of these events were ticked to indicate

that they do occur and these were numbered in an appropriate order to guide participants with

the main task. Importantly, there were three number columns to give participants the option to

include an event multiple times if necessary. To demonstrate this option, one of the events in the

example list was numbered twice. The questionnaire then requested participants to imagine a

single police interview, with the phrase ‘a single police interview’ underlined to emphasize that

participants should not imagine an aggregated version of several police interviews. The task sheet

then presented 37 potential events which may occur during a police interview in a randomized

order. The initial event of the notice of caution, and the potential final events of the suspect

admitting to part of the crime, confessing to the full crime, denying the crime or providing

information about previous offenses or co-suspects were taken from an observational study of

real police interviews in different police stations in England (Softley, Brown, & Britain, 1980). The

remaining 32 events were common police interview techniques taken from a total of 71

techniques described in Kelly et al. (2013). Only 32 techniques were included as the remaining

techniques were too similar or they took place before, after or during the interview as a whole. As

an additional validation check, an ex-chief inspector from Nottinghamshire provided feedback on

the 37 events, commenting that some events such as the notice of caution were realistic while

others such as presenting fabricated evidence were far-fetched. Moreover, it was deemed that in

reality, “most suspect interviews are somewhat mundane” as are the events in them.

Identically to the example list, a left-hand column was provided to tick the events which

participants believed to occur in police interviews and three number columns on the right hand

side were provided for participants to order the events appropriately. None of the events included

technical jargon, with the exception of the notice of caution for which a footnote quoting the

official English caution was provided. The use of the footnote was to ensure that none of the

events were particularly lengthy and attention-grabbing as this may imply its importance to

participants. In a similar vein, all the events were fitted on a single page to prevent participants

from being biased to events on the initial or later pages.


11

Procedure

Most participants were approached during a lecture with an announcement explaining the

nature of the task with a few participants volunteering outside of lecture time to complete the

task. Participants were allowed to decline to take part in the study, however if they chose to take

part, they returned the task pack to the experimenter immediately after completing the task. To

ensure anonymity and confidentiality of data, all consent forms were separated from the task

packs prior to viewing or processing the response data.

Participants, after signing the consent form, followed instructions and completed the

aforementioned questionnaire. Participants were debriefed and thanked. The whole procedure

lasted no longer than 15 minutes.

Results

Sample Characteristics

Participants’ familiarity with police interviews ranged from 1 (‘No idea at all’) to 7 (‘First-

hand experience’) with a mean of 3.30 (SD = 1.468). Participants who selected 4 or above on the

familiarity scale were placed in the High Familiarity condition while participants who selected 3 or

below on the familiarity scale were placed in the Low Familiarity condition. Television was most

frequently cited as one of the primary sources of information on police interviews with 51

participants either referring to television alone or in combination with, for example, detective

stories (see Appendix 3). For the remaining 7-point scales, participants tended to slightly disagree

that offender treatment in the UK is too harsh (M= 2.96, SD= 1.069) and that criminal behaviour is

largely influenced by genetic factors (M= 3.24, SD= 1.367), while attitudes towards police officers

upholding the law were neutral (M= 3.94, SD=1.190). There was no significant difference between

the responses on these three scales for the High Familiarity and Low Familiarity conditions

(p<0.05).


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Details of Frequency Analysis

Each event was assigned a unique two letter code (see Appendix 4) and using this coding

system, participants’ responses were arranged as data strings using Microsoft Excel and Word (see

Appendices 5 & 6 for all data strings). All event frequencies, which were defined as the number of

times a participant indicated that an event would occur during a police interview, were

determined using Microsoft Word.

Event frequencies for the complete dataset and the High and Low Familiarity conditions

are displayed in Figures 1-3.


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Figure 1. Graph displaying event frequencies for all participants.

0 20 40 60 80

Accuse suspect of being someone else

Touch suspect in a friendly manner

Flatter suspect

Insult suspect

Confront suspect with fabricated evidence

Move interrogation from a formal to a neutral setting

Present self as someone other than the interrogator

Identify and exaggerate fears

Instil hopelessness in suspect

Morally rationalize alleged crime

Make angry/frustrated/impatient gestures

Dismiss suspect's evidence

Bluff suspect about having evidence

Threaten suspect

Misconstrue suspect's words

Move interrogation from a neutral to formal setting

Use polygraphs/physiological measures

Ask a series of questions without allowing suspect to answer

Reduce fear

Use deception

Employ another interrogator (good cop/bad cop)

Directly accuse suspect

Find common ground

Suspect gives information on previous offenses/co-suspects

Suspect's confession to full crime

Stare at suspect in silence

Identify and meet basic needs

Confront suspect without insulting

Suspect's admission to part of the crime

Notice of caution

Show photos from witnesses

Offer rewards for cooperation

Identify contradictions in suspect's story

Reveal evidence to suspect

Repeat question over and over again

Ask unexpected questions

Suspect's denial

Even

t

Frequency


14

Figure 2. Graph displaying event frequencies for high familiarity participants.

0 5 10 15 20 25

Flatter suspect






Insult suspect





Threaten suspect






Reduce fear



Use deception


Find common ground



Suspect gives information on previous offenses or co-suspects


Notice of caution









Suspect's denial

Even

tFrequency


15

Figure 3. Graph displaying event frequencies for low familiarity participants.

0 10 20 30 40 50



Insult suspect


Flatter suspect











Threaten suspect


Use deception



Reduce fear

Find common ground

Suspect gives information on previous offenses or co-suspects









Notice of caution





Suspect's denial

Even

t

Frequency


16

Figure 4 presents event frequencies as a proportion of the total frequency for each

familiarity condition in order to facilitate comparison between the two familiarity conditions.

Figure 4. Graph displaying proportional event frequencies for high and low familiarity conditions. (Events

are shown in order of overall frequency for both familiarity conditions.)


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Frequency Discussion

It is interesting to note from Figure 1 that dramatic, psychologically coercive events such as

instilling hopelessness in suspect, identifying and exaggerating fears and insulting suspect which

are not only commonplace in fiction and media but also quite memorable appear to have the

lowest frequencies. In contrast, events such as the notice of caution, identifying and meeting

basic needs and staring at the suspect in silence which are routine police interview events but

could be argued to lack entertainment value have higher frequencies. This pattern of results is

maintained in both the high and low familiarity conditions as demonstrated by Figures 2-3.

Figure 4 highlights that very similar patterns of event frequencies emerge for both high and

low familiarity participants. For some events, the proportional frequency may appear to vary as a

function of familiarity, for instance, the events of the police interviewer confronting the suspect

without insulting, instilling hopelessness in the suspect or the suspect providing information about

previous offenses or co-suspects. However, the difference in proportions test found that the

frequencies for these three events are not significantly different for high and low familiarity

participants (p<0.05).

Classification

Conducting a sequence analysis on 37 events would require an unnecessarily large matrix

of 1,369 cells. Hence events with a frequency of 11-20 were reclassified as a Medium Frequency

event (with the code MF) while events with a frequency of 10 or below were reclassified as a Low

Frequency Event (with the code LF). As a result, the 17 events shown in Table 1 remained for

sequence analysis.


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Table 1. Table displaying the events, with respective codes and frequencies, used for sequence analysis.

Event Code Frequency

Suspect's denial AA 63

Ask unexpected questions AE 46

Repeat question over and over again AH 45

Reveal evidence to suspect AG 43

Offer rewards for cooperation AO 41

Identify contradictions in suspect's story BD 41

Notice of caution AB 39

Show photos from witnesses AI 39

Suspect's admission to part of the crime BE 36

Identify and meet basic needs AP 35

Confront suspect without insulting AY 35

Stare at suspect in silence AF 33

Suspect's confession to full crime BC 29

Suspect provides information about previous offenses or co-suspects AW 25

Find common ground AR 24

Medium frequency event MF 170

Low frequency event LF 69

Next, the frequency of transitions between each of these events was ascertained from the

data strings using Microsoft Word to produce a transitional frequency matrix for the complete

dataset (see Appendix 7). However, the numbers were quite sparse across the matrix and failed to

meet the criterion for an overall chi-squared test for which less than 20% of the cells must have an

expected count of less than 5.


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Cluster Analysis

In order to further collapse the event categories (excluding the MF and LF events) to meet

the aforementioned chi-squared criterion, single linkage cluster (SLINK) analysis was used. SLINK

analysis, independently suggested by both Sneath (1957) and McQuitty (1957), is a method of

hierarchical clustering in which initially, the two individual events sharing the highest similarity are

linked to each other to form a cluster. One by one, each of the remaining events is then linked to

another event based on their similarity until eventually all the events are linked in one overarching

cluster. For this study, 'similarity' in sequential terms (or more strictly the commutation or

swapablility) of two events, such as A and B, was calculated by taking the frequencies with which A

followed another event (X) and B followed X, together with the frequencies with which A preceded

X and B preceded X, and correlating the A values with the B values over all values of X. Essentially,

if two events frequently followed the same antecedents and frequently preceded the same

sequitors, then the two events could be considered highly similar. All correlations are displayed in

Table 2.

Table 2. Similarity table showing correlations between transition frequencies of pairs of events.

AA AB AE AF AG AH AI AO AP AR AW AY BC BD BE

AA 1.000

AB 0.175 1.000

AE 0.389 0.331 1.000

AF 0.543 0.205 0.260 1.000

AG 0.270 0.122 0.547 0.272 1.000

AH 0.206 0.082 0.354 0.265 0.490 1.000

AI 0.280 -0.104 0.185 0.404 0.058 0.453 1.000

AO 0.130 0.082 0.479 -0.132 0.292 0.283 0.269 1.000

AP 0.450 0.243 0.282 0.407 0.092 0.060 0.095 0.254 1.000

AR 0.152 0.126 0.337 0.268 0.241 0.432 0.379 0.389 0.211 1.000

AW 0.048 -0.002 0.269 -0.075 0.247 0.196 0.023 0.374 0.053 0.271 1.000

AY 0.251 -0.030 0.555 0.292 0.330 0.363 0.329 0.606 0.345 0.390 0.413 1.000

BC -0.194 -0.100 -0.140 -0.243 -0.109 -0.019 0.064 0.063 -0.111 -0.024 0.140 -0.048 1.000

BD 0.007 -0.094 0.334 0.027 0.267 0.369 0.258 0.437 0.004 0.325 0.430 0.489 0.155 1.000

BE 0.089 -0.082 0.252 -0.061 0.287 0.205 0.206 0.423 0.097 0.267 0.444 0.530 0.425 0.486 1.000

The similarity table was then used to create a dendogram with Microsoft Paint, illustrated

in Figure 5, which diagrammatically demonstrates the linkages and clusters of events.


20

Figure 5. Dendogram illustrating events clustered by the correlations between their transition frequencies.

Using the dendogram, the events were collapsed into only three categories; event AB as a

cluster, events AF, AA and AP which were recoded as cluster FP and the remaining events as

cluster RC. The aforementioned criterion for the chi-squared test was met and it was revealed that

there was a sequential structure in the data worthy of further investigation (x2(36) = 1293.6, p <

0.005), see Appendix 8.


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Sequence Analysis

For the sequence analysis, the original events prior to the SLINK analysis, were used in

order to attain sufficient detail. Using SPSS, transitional frequency matrices, which included

standardized residuals, were produced for the complete dataset, high familiarity condition and

low familiarity condition (see Appendix 9). To identify which transitions were occurring at

frequencies higher than expected at chance level, a critical value was calculated for comparison

using Equation 1, (Colgan & Smith, 1978).

= √𝐶ℎ𝑖 𝑆𝑞𝑢𝑎𝑟𝑒 𝑐𝑟𝑖𝑡𝑖𝑐𝑎𝑙 𝑣𝑎𝑙𝑢𝑒

𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑒𝑙𝑙𝑠 𝑖𝑛 𝑡𝑎𝑏𝑙𝑒

Equation 1. For calculating a critical value to compare standardized residuals to.

The critical value was 1.008 for the complete dataset, high familiarity and low familiarity

conditions. However this was treated as a lower bound and a more stringent critical value of 2.000

was used to compare standardized residuals to. In addition to transitions having a standardized

residual above the critical value of 2.000, the researcher added a further criterion of transitions

having a raw occurrence of >5 for the complete dataset and >2 for the high and low familiarity

conditions. As such, only the more important transitions were included in the maps.

Using yEd Graph Editor (yEd Graph Editor, version 3.11, 2014), state transition diagrams

were produced for all conditions as highlighted by Figures 5, 7 and 8. Figure 6 is an illustration of

what the low frequency and medium frequency events in the complete dataset represent.


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Figure 5. State transition diagram for complete dataset.


23

Figure 6. State transition diagram unpacking low and medium frequency events (Numbers refer to raw

occurrences).


24

Figure 7. State transition diagram for high familiarity condition.


25

Figure 8. State transition diagram for low familiarity condition.


26

Sequence Analysis Discussion

Overall, Figures 5, 7-8 highlight that all participants were well aware that police interviews

typically start with the notice of caution, which is also a legal requirement in England.

Interestingly, the suspect’s denial which had been included as a potential outcome of the

interview was not only the most frequent event across conditions but also consistently appeared

as one of the initial events following the notice of caution. Meanwhile the alternative outcomes of

confession or partial admission are closely clustered to the end of the interview. This implies that

students believe that most suspects begin by denying their part in the alleged crime but that

ultimately, police interviews only end when a confession or at minimum, an admission is elicited.

There are two possible explanations of this finding; either students hold a guilt presumptive bias

and are under the impression that despite initial denials, suspects called in for police interviews

are guilty of the crime in question and will eventually confess or alternatively, students are aware

of the high rate of false confessions and believe that police investigators will only terminate an

interview when a confession is elicited. However, it is unlikely that students were aware of false

confessions as these tend to be associated with psychologically manipulative techniques, such as

threats or bluffing, whereas students have mostly associated the ethically approved technique of

identifying contradictions in the suspect’s story as precipitating the suspect’s confession or

admission.

In addition, as underscored by Figure 5, 7 and 8, there are relatively few or no transitions

linking the cluster of final events, including the suspect’s admission, confession and ultimately the

end of the interview, with the remaining events which suggests that students believe that there

are a variety of pathways, as opposed to a single successful route, to reach the interview’s

conclusion.

Finally, a rapport building pathway emerges across conditions in which the suspect’s basic

needs are met and the suspect is engaged in conversation by finding common ground, as

illustrated on the left of all the state transition diagrams.


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Comparison between familiarity conditions

The state transition diagrams in Figures 9-11 illustrate the common and unique transitions in

the two familiarity conditions.

Figure 9. State transition diagram with transitions common to both familiarity conditions.


28

Figure 10. State transition diagram with transitions only found in high familiarity condition.


29

Figure 11. State transition diagram with transitions only found in low familiarity condition.


30

While Figure 9 underscores that a number of event transitions are common to both high

and low familiarity conditions, Figures 10-11 exemplify the key differences between the two

conditions. For example, high familiarity students seem to believe that the police interview does

not always begin with the notice of caution and may instead begin with identifying and meeting a

suspect’s basic needs. This may occur if a police officer chooses not to closely follow the PEACE

model and legal requirements in England. Further, high familiarity students believe that the

suspect’s denial is a recurring event during the interview as highlighted by its multiple transitions

in Figure 10.

Conversely, it is clear from Figure 11 that low familiarity students hold the belief that even

a suspect’s partial admission is sufficient to terminate a police interview unlike high familiarity

students who believe that the only pathway to the interview’s end is via a full confession by the

suspect. Another key difference is that low familiarity students believe that even the act of

revealing evidence will be effective in obtaining a full confession whereas for high familiarity

students, the pathway between revealing evidence and a full confession requires several other

steps. Likewise, confronting the suspect is very closely linked to the final events of confession and

admission for low familiarity students. This may reflect low familiarity students believing in less

resistant suspects and possibly shorter police interviews.

A limitation of the method of sequence analysis used thus far is that first order sequences,

involving transitions only between pairs of events, were used to generate maps which create the

impression that longer chains of events have been identified in the data. These longer chains of

events may or may not exist.

Dawkins’ method

In order to deal with the limitation of using first-order sequences to create maps with

lengthy chains of events, the more rigorous and elaborate Dawkins’ method of sequence analysis

was used to detect extended chains of events in the data (Clarke & Crossland, 1985). This involves

identifying the transition with the highest frequency and then finding the event which is most

likely to follow or precede this transition and so on. As this is a data hungry method, it was only

used on the overall dataset and the resulting transitional frequency matrices are attached in


31

Appendix 10. Two exemplary sequences are shown below with numbers indicating the

frequencies:

(((ST – AB24) AP8) AE2)

Start-> Notice of caution -> Identify and meet basic needs.

(AO2 (AW2 (BE6 (BC25 – EN))))

Offer rewards for cooperation -> Suspect provides information on previous crimes/co-suspects ->

Suspect’s admission to part of the crime -> Suspect’s confession to full crime -> End.

The Dawkins method of sequence analysis is in line with previous findings for the complete

dataset as the two extended chains found map well onto the pathways illustrated in Figure 5.

Moreover, it highlights that according to students, one of the more likely pathways to the

suspect’s admission and confession is through offering rewards for cooperation which was not

revealed in the previous analyses. Of interest is that the two extended chains of events which

emerged (excluding the MF and LF events) with the Dawkins method involved the start and end of

the police interview implying that those are believed to be the most predictable stages.

Predictability Analysis

Traditionally in sequence analysis, row and column totals of transitional frequency matrices

are used to produce an expected frequency for each transition and if the actual frequency is

significantly different from this, it may be inferred that the first event is in some sense causing the

second event. In contrast to such causality analysis, conditional probability can be used to

forecast the following event in a sequence given the current event. The row percentages in the

transitional frequency matrices (see Appendix 9) can be used for the latter analysis. In this study,

there were primarily two instances where the two types of analyses produced different results.

In the low familiarity dataset, traditional sequence analysis uncovered a significant

transition from offering rewards for cooperation to finding common ground however with the

predictability analysis, both suspect’s denial and the end of the interview are equally likely to

follow at 12.5%. Similarly, although there is a significant transition between revealing evidence to


32

suspect and suspect’s full confession, revealing evidence may be followed by the suspect’s denial

or repeating question over and over again at an equal probability of 11.1%.

In both instances, the predictability analysis highlights that even if traditional sequence

analysis finds a causal link between two events, it may not be sufficient to predict the second

event. This approach is particularly useful for analysing actual police interviews where the

likelihood of continued denial by the suspect may be underestimated by traditional sequence

analysis, as in this study.

General Discussion

The present study aimed to introduce the powerful tool of sequence analysis into the field

of legal psychology. Student beliefs about the patterns of events which unfold in police interviews

and how these relate to the final outcome were examined. Remarkably, both high and low

familiarity students held several accurate beliefs about the conduct of police interviews in

England, in accordance with the PEACE model. The Reid-style techniques which were previously in

use in England such as bluffing, presenting fabricated evidence and maximising suspects’ fears, all

of which are associated with false confessions (Narchet, et al., 2011; Nash & Wade, 2009; Perillo &

Kassin, 2011), were rarely cited as events which could occur in a police interview. Meanwhile, the

resulting sequences of police interview events closely resembled what is recommended by the

PEACE model with initial rapport building followed by a number of non-aggressive tactics such as

staring at the suspect in silence and the highly successful tactic of identifying contradictions in the

suspect’s account. Thus, rapport-building and identifying contradictions in the suspect’s story, two

fundamental ways of dealing with resistant suspects in police interviews, as highlighted both by

field and laboratory studies (Collins, et al., 2002; Kelly, et al., 2013; Leo, 1995), are correspondingly

given importance by students. Interestingly, the act of repeating questions, an inappropriate tactic

which is nevertheless still used by police officers in England (Bull & Soukara, 2010; Pearse &

Gudjonsson, 1999), is also reported as a central event in police interviews by students.

The use of the Dawkins method to identify extended sequences of events further

supported the accuracy of student beliefs as the two chains of events which emerged were

located at the beginning and end of the interview. This suggested that the least variable and thus


33

most predictable stages of the police interview were the start and end while the traditional

sequence analysis method highlighted that there was no single pathway to the end of the

interview, implying that the police interview as a whole could follow a number of routes. Once

again, the complex and flexible nature of the police interview, apart from its initial and final stages

has been emphasized in the literature (Kelly, et al., 2013).

The only notable departure in student beliefs from actuality, is the close association

between suspect confessions or admissions and the end of the interview in contrast to suspect

denials which are rarely believed to conclude a police interview. This is distinct from the guidelines

of the PEACE model in which no assumptions about the suspect’s guilt should be made, allowing a

police officer to end the interview even if the suspect continues to claim their innocence (Snook,

et al., 2010). Hence, students either believe that police officers predominantly question guilty

suspects and are determined to gain a true confession or they believe that some of the final

confessions are false. The latter is less likely, particularly with the non-confrontational tactics

found in the sequences as lay individuals rarely believe false confessions occur even with

psychologically coercive tactics (Leo & Liu, 2009).

In terms of familiarity, the key difference between high and low familiarity students was

their beliefs about a suspect’s resistance during a police interview. Low familiarity students

believed that shorter sequences of events, such as revealing evidence or confronting the suspect,

could directly drive a suspect’s confession whereas high familiarity students believed in lengthier

pathways to the suspect’s confession, often involving recurring denials by the suspect.

These findings of accurate beliefs are divergent from related studies in the literature in

which lay beliefs are distinct from actual crime statistics (Ainsworth & Moss, 2000; Redondo, et al.,

1996). Despite the majority of participants citing television and other fictional media, such as

detective stories, as their primary sources of information on police interviews, their beliefs about

the sequential patterns in a police interview are more consistent with actuality than what would

be expected from the dramatic and sensationalist portrayals in the mass media (Callanan &

Rosenberger, 2011). While this study did not intend to test the predictions of cultivation theory

and the availability heuristic, the results are not necessarily incompatible with the two theoretical

frameworks which suggest that recurrent images in the media are likely to play a significant role in

shaping lay beliefs. It is possible that the media environment in England is different as previously

suggested (Gunter, 1987) and that it simply includes more accurate depictions of police


34

interviews. Further, although students reported television as their primary source of information,

it is likely that they were influenced by other reliable sources, such as their course of study since

the majority of the participants were Psychology students and are likely to have covered police

interviews in class.

This study also explored the empirical difference between causality and predictability

analyses in sequence analysis. For the most part, significant transitions between two events as

identified by the traditional sequence analysis, in which the first event appears to be causing a

second event, produced similar results to the conditional probability findings, in which the second

event is most likely to follow the first event. However, a few discrepancies between the two

analyses arose, primarily involving the high likelihood of events being followed by the suspect’s

denial even if those events were found to ‘cause’ another event such as the suspect’s full

confession. In essence, the causal links found between events were not sufficient to forecast the

following event for which conditional probabilities are more appropriate.

The practical implications of the present study centre on how much a sequential approach

has to offer for legal psychology. By applying this analytical tool to student beliefs about police

interviews, previously inaccessible levels of information have been acquired about the interactive

and dynamic nature of police interviews. This has paved the way for future avenues of research

involving sequence analysis of actual police interviews in which various tactics and the outcomes

they lead to may be identified. Common patterns may emerge in the data, for example, the most

frequent extended chain of events which leads to a suspect’s full confession as revealed by the

Dawkins method in this study. Further, similarly to this study’s comparison between high and low

familiarity students’ beliefs, the datasets for actual police interviews can be divided according to

suspects’ demographics and characteristics in order to compare whether different sequences

emerge depending on contextual variables. Moreover, the exploratory comparison between

causality and predictability analyses in this study has spotlighted how useful a complementary

analysis of conditional probabilities can be. For instance, it may expose the suspect’s tendency to

be more resistant in response to certain tactics. Therefore, while the police officer may expect

that revealing evidence will drive the suspect to confess, an equally valid prediction may be the

reverse outcome; the suspect’s denial. Ultimately, the family of sequence analysis methodologies

is highly suitable for preparing police officers in the Preparation and Planning stage of the PEACE

model which encourages the use of “route map[s]” (Snook, et al., 2010). State transition diagrams,


35

such as those created in this study, would enable police officers to familiarize themselves with all

possible directions the interview may take and the likely tactics which could steer the interview in

a more desirable direction.

This study is not without its limitations as the method of sequence analysis is data-hungry

and thus requires extensive data collection in order to identify more complicated patterns of social

interaction. For this reason, the present study primarily identified simple pairwise patterns which

were then arranged in sequences and even the extended sequences identified by the

sophisticated Dawkins method were relatively short. Therefore, future researchers may want to

gather larger data sets in order to unveil more intricate patterns of events in police interviews.

Another limitation is that sequence analysis is a frequency driven method, in that its goal is to find

common ways in which events unfold. While this may be appropriate for the beliefs of students

about police interviews, the study of actual police interviews may benefit from a study of the rarer

and possibly more interesting sequences of events which may transpire for which alternative

sequential approaches may be more suitable.

Despite these limitations, the study has shown that student beliefs about the sequences of

behavioural events in police interviews are surprisingly congruent with policy in England as

opposed to being distorted by fictional portrayals of police interviews. Critically, the study has

presented legal psychology with a novel method of studying police interviews which captures its

fundamentally interactive and dynamic nature.

Word Count: 6669


36

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Appendix 1: Task Pack

University of Nottingham

School of Psychology

Information Sheet

Research Project on Student Beliefs about Police Interviews

Researchers: Divya Sukumar, Professor David Clarke

Contact Details: [email protected]

This is an invitation to take part in a research study on students’ beliefs about police interviews.

The reason you have been approached is because you are a student at the University of Nottingham.

Before you decide if you wish to take part, it is important for you to understand why the research is being

done and what it will involve. Please take time to read the following information carefully.

If you participate, you will be given a list of possible events from police interviews. You will have to imagine

a single police interview, decide which of the events will occur in it and then put them in the order in which

you believe they would occur. The whole procedure will last approximately 15 minutes.

Participation in this study is totally voluntary and you are under no obligation to take part. Thinking about

the events in a police interview may be distressful, particularly if you or someone you know has been

through a police interview. Therefore if this topic may evoke distress or discomfort, please do not

participate in this study. If you do choose to participate in this study, remember that you are free to

withdraw at any point before or during the study. Your responses will be anonymous and all data collected

will be kept confidential and used for research purposes only.

If you have any questions or concerns please don’t hesitate to ask now. We can also be contacted after

your participation at the above address.

Thank you for your time.

mailto:[email protected]


40

CONSENT FORM


Investigators: Divya Sukumar, Professor David Clarke

School of Psychology, University of Nottingham

The participant should complete the whole of this sheet himself/herself. Please cross out as necessary

Have you read and understood the participant information sheet YES/NO

Have you had the opportunity to ask questions and discuss the study YES/NO

Have all the questions been answered satisfactorily YES/NO

Have you received enough information about the study YES/NO

Do you understand that you are free to withdraw from the study:

at any time YES/NO

without having to give a reason YES/NO

Do you agree to take part in the study YES/NO

“This study has been explained to me to my satisfaction, and I agree to take part. I understand that I am free to withdraw at any time.”

Signature of the Participant: Date:

Name (in block capitals)

I have explained the study to the above participant and he/she has agreed to take part.

Signature of researcher Date


41

Task

Please provide the following personal information:

Age: …………………………

Gender: ………………………

Course of Study: …………………………………………………………………

It would be helpful if you answer the following questions:

How much do you know about what goes on during police interviews? (Please circle the

appropriate answer)

1 2 3 4 5 6 7

No idea First-hand

at all experience

Please state your main source of information on police interviews (e.g. television programs, first-

hand experience, detective stories, descriptions by friends):

……………………………………………………………………………………

Do you agree with the following statements? (Please circle the appropriate answer)

‘I think the treatment of offenders by the criminal justice system in this country is too harsh.’

1 2 3 4 5 6 7

Strongly Disagree Strongly Agree

‘Criminal behaviour is largely influenced by genetic factors.’

1 2 3 4 5 6 7


‘Police officers do an excellent job of upholding the law.’

1 2 3 4 5 6 7



42

Information: Now you will see a list of possible events in police interviews. Please

imagine a single police interview and decide which of the events would occur in it.

First, tick the events which you believe occur in the left hand column. The next stage

is to number those events in the order you believe them to occur using the

‘Number’ column on the right. If you believe some events occur more than once in a

police interview, number those events again in the additional ‘Number’ columns.

Example: Attending a lecture

To give you an example of how your form should look like, here is an imaginary

example from a completely different domain of behaviour.

Item Number

Student greets friends 2 5

Student picks up a handout

Student enters lecture theatre 1

Lecturer enters 3

Student takes seat at the front

Student takes seat at the back 4


43

Now, using the example as a guide, do the same for the following list of events.

Item Number

Suspect’s denial

Notice of caution1









Insult suspect


Use deception


Offer rewards for co-operation



Find common ground



Threaten suspect

Flatter suspect

Suspect provides information about previous offences or co-suspects



Reduce fear

Misconstrue suspect’s words


Suspect’s confession to full crime

Identify contradictions in suspect’s story

Suspect’s admission to part of the crime



Dismiss suspect’s evidence


Move interrogation from a formal to neutral setting


Thank you for taking part in this study.

1 “You do not have to say anything. But it may harm your defence if you do not mention when questioned something which you later rely on in court. Anything you do say may be given in evidence.”


44

Debrief Sheet

Thank you for taking part in this study. Your involvement has been of great value in this

project.

This study intends to map out the sequence of events which students believe to occur in a

police interview. All responses submitted will be analysed collectively and the data will remain

anonymous and confidential at all times. This information is for research purposes only; it is not

the intention of this study to pass judgement on any of the opinions expressed by participants and

the collected responses will only be viewed by myself and my supervisor.

Police interviews can be a distressing topic. If you would like to talk to someone about your

own or someone else’s experience with police interviews, please make use of the phone numbers

below:

Victim Support: 0845 3030 900

Confidential and independent helpline offering emotional support and information for victims of

crime

Nottingham University Counselling Service: 0115 951 3695 Confidential counselling service.

Nightline: 0115 951 4985

Confidential listening and information service.

If you would like any more information regarding this study or if you wish to have an executive

summary of the study’s findings in June 2014, please feel free to contact me using the following

details:

Researcher: Divya Sukumar

Email: [email protected]

We would like to take this final opportunity to thank you again for your participation.

mailto:[email protected]


45

Appendix 2: Participants’ Courses of Study

Course

Frequency Percent Valid Percent Cumulative Percent

Valid Architecture 1 1.4 1.4 1.4

Biology 1 1.4 1.4 2.9

Business Management 1 1.4 1.4 4.3

Economics 3 4.3 4.3 8.6

English 3 4.3 4.3 12.9

Environmental Engineering 3 4.3 4.3 17.1

Industrial Economics 1 1.4 1.4 18.6

Law 1 1.4 1.4 20.0

Medicine 1 1.4 1.4 21.4

Natural Sciences 1 1.4 1.4 22.9

Pharmacy 1 1.4 1.4 24.3

Psychology 47 67.1 67.1 91.4

Psychology, Cog. Neuroscience 4 5.7 5.7 97.1

Russian Studies 1 1.4 1.4 98.6

Sustainable Building 1 1.4 1.4 100.0

Total 70 100.0 100.0

Appendix 3: Participants’ Source of Information

Source_of_Information

Frequency Percent Valid Percent Cumulative Percent

Valid Descriptions from dad 2 2.9 2.9 2.9

Detective stories 2 2.9 2.9 5.7

First-hand experience 4 5.7 5.7 11.4

Friends/Family 1 1.4 1.4 12.9

Media, studying Psychology 1 1.4 1.4 14.3

Scripts of Interviews in Literature 1 1.4 1.4 15.7

Studying Law, TV 1 1.4 1.4 17.1

Studying Psychology 7 10.0 10.0 27.1

TV 30 42.9 42.9 70.0

TV, detective stories 14 20.0 20.0 90.0

TV, experience in Sri Lanka 1 1.4 1.4 91.4

TV, friends 1 1.4 1.4 92.9

TV, studying Psychology 4 5.7 5.7 98.6

Working with Victim Support, studying

Psychology 1 1.4 1.4 100.0

Total 70 100.0 100.0


46

Appendix 4: Coding System

Code Event

ST Start of sequence

AA Suspect’s denial

AB Notice of caution2

AC Use polygraphs/physiological measures

AD Touch suspect in a friendly manner

AE Ask unexpected questions

AF Stare at suspect in silence

AG Reveal evidence to suspect

AH Repeat question over and over again

AI Show photos from witnesses

AJ Instil hopelessness in suspect

AK Insult suspect

AL Directly accuse suspect

AM Use deception

AN Accuse suspect of being someone else

AO Offer rewards for co-operation

AP Identify and meet basic needs

AQ Bluff suspect about having evidence

AR Find common ground

AS Morally rationalize alleged crime

AT Employ another interrogator (good cop/bad cop)

AU Threaten suspect

AV Flatter suspect

AW Suspect provides information about previous offences or co-suspects

AX Present self as someone other than the interrogator

AY Confront suspect without insulting

AZ Reduce fear

BA Misconstrue suspect’s words

BB Make angry/frustrated/impatient gestures

BC Suspect’s confession to full crime

BD Identify contradictions in suspect’s story

BE Suspect’s admission to part of the crime

BF Identify and exaggerate fears

BG Move interrogation from a neutral to formal setting

BH Dismiss suspect’s evidence

BI Confront suspect with fabricated evidence

BJ Move interrogation from a formal to neutral setting

BK Ask a series of questions without allowing suspect to answer

EN End of sequence

2 “You do not have to say anything. But it may harm your defence if you do not mention when questioned something which you later rely on in court. Anything you do say may be given in evidence.”


47

Appendix 5: Data Strings for High Familiarity Condition

*003/ AP AB AG AZ AH AA AI AO AW BE BC

*005/ AB AP AG AF AA AY AG AT BE BD AI AO BC

*010/ AC AY AO AI AG AO BD BC

*014/ AB AF AO AY BK AS AR AC AE AH AI AW BD BE BG BH AD

*022/ AZ BK BF AP AR AI AB AD AM

*024/ AE AA AH AP AY AW AI AC BJ AT

*028/ AA AP AR AW AY AE AH AM AT BA BB AF AO BE BC

*029/ AA AE AF AH AK AU BA BK BE BD BE AI

*031/ AA AE AC

*035/ BG AB AF AE AL AA AH AA AH AA AT AG AI AO AW BH BD AL AY BA BE BC

*036/ AG AI AP AR AA AC AW AY BE BD BC

*037/ AB AA AF AO AP AR AZ AE AH AG AM AY BA BB AT BK BE AQ AS AJ AK BC

*039/ AB BG AY AW AG AO AI AS

*040/ AE AG AM AA BD AU AO AW AA AY

*047/ AB BG AP AR AX AY BK AH AZ AI AL AA BD

*050/ AB AP AW AY AE AM AG

*052/ AF AP AE AA AL AG AH AI AT AO AM

*053/ AP AT AB AA AY BD BH AG AI AE BC

*055/ AP AF AL AA AE AR AO BE BD BB AH BA AW

*056/ AF AE AA AG AH AR AX AY AW BD BE BG

*059/ AA AG AA AI AL BD BE BH AO BC

*062/ AB AA AP AW AF AE AH AI AM AN AO AQ AR BE BD BC

*064/ AP AY BA AE AF AG AH AM AR

*067/ AB AF AY AH AA AC AO AT AU AZ BI

*068/ AR AO AB AY AG BD AI

*070/ AB AI BB AH AA AC AO AP AZ BI BC

Appendix 6: Data Strings for Low Familiarity Condition

*001/ BE AC AF AB AL AA AH AI BF AM AE AQ AW BH BD BB BC

*002/ AP AB AA BJ AL AY AZ AC AM AH BD BE

*004/ AB AP AA AZ AE AI AO AW

*006/ AX AP AF AG AI AB AA AR AV AU BA BJ BH BK AJ AH AE AL AW BE BC AZ AP AO

*007/ AB AI AE AH AM BA AL AO AJ BE

*008/ AB AA AH AF AP AO AR AQ AY BD AL AJ BE BC

*009/ AB AY AG AO AH

*011/ BJ AG AE AH AF AQ AT AX

*012/ AX AY AB AA AO AQ AG AW BD AE BE

*013/ BJ AZ AA AB AR AP AG AI BD AY BE

*015/ AL AA AB BG AG AY AZ AW

*016/ AG AA AI AF AE AC AH AT BD BC

*017/ AJ AA AI AA AF AG AQ AH AO AW BE BC

*018/ AZ AE AG AO AR AT AY BC

*019/ AA AG AI BD BF AS Al AO AA AU BE AU BE

*020/ AO AX AS AI BH


48

*021/ AC AP AB AE AA AH AI AG BC

*023/ AB AA AH BI BK BH

*025/ AB AP BG AE AH BD AO

*026/ AB AP AE AR AW AY AF BD

*027/ AE AP AW AY BD AA AH AM AR BB BE BC AB

*030/ AB AA AW AF AI AY BD AG BC AP

*032/ AE AA AF AH AA AY AG AO BD BH

*033/ AA AE AD BB AC AK AM

*034/ AD AI AG AA AE BF AH AZ AP AO BE BK AU BE

*038/ AF AG AI AV AE AT BF

*041/ AP AB AA AR AE BD BE AE BD AG BC

*042/ AB AF AY AS AO AA AG AI AA BA BB BD BG BF BK AU AE AA AG AH AQ

*043/ AE AA AF AT AG AH AM AN AQ AU BA BD BH BE BF BK BB AP AO AL AI AK AJ

*044/ AE AF BD AT AO AA AH AM AQ AU BE BI

*045/ AB AA AI BK AF AO AH BA BD BC

*046/ AB AF AA AE AH AI AK AJ AO AR AS AZ AV AL AT AQ AM BK BA AU AY BD BE BH BC

*048/ AB AF AO AG AY AH AI AL AJ BG BJ BK BE BD BC AZ AC AM AP AS AU AW AQ AR AT

*049/ AO AF AR AQ AE AH BK BA BD AG AI AM AL BE

*051/ AA AG AI AM AO AE AR AV AZ BD BE AE AH BE AZ AE BC

*054/ BG AP AX AL AE AJ AS BD AA AG AO AT BC

*057/ AB AP AR AZ AG AH AF AA AE BG AT AW BC

*058/ AY AA AW AF AA AH AG BD BE BC

*060/ BG AY AB AE AG AI AH AA AL AO BC

*061/ AB AP AE AH AF AC

*063/ AG AA AE AH AI AU BD

*065/ AB AP AR AA AY AA BD AA BE

*066/ AA BB AH AF BI AT BF AO

*069/ BG AP AL AY AW BD BH BK AC


49

Appendix 7: Transitional Frequency Matrix for All Data

ST AA AB AE AF AG AH AI AO AP AR AW AY BC BD BE MF LF EN

ST 0 8 24 6 3 3 0 0 2 6 1 0 1 0 0 1 9 6 0

AA 0 0 2 8 4 7 10 5 1 2 2 2 5 0 3 1 9 2 0

AB 0 11 0 2 6 1 0 2 0 8 1 0 2 0 0 0 4 1 1

AE 0 7 0 0 3 3 12 1 0 1 3 0 0 2 2 1 8 3 0

AF 0 4 1 4 0 4 2 1 5 2 1 0 2 0 2 0 4 1 0

AG 0 4 0 1 1 0 6 11 6 0 0 1 2 3 2 0 5 0 1

AH 0 7 0 1 5 2 0 8 1 1 1 0 0 0 2 1 13 2 1

AI 0 2 2 2 1 3 1 0 4 1 0 1 1 0 2 0 11 6 2

AO 0 3 1 1 1 1 2 2 0 2 3 5 1 3 2 3 6 2 3

AP 0 1 4 3 2 2 0 0 4 0 7 3 2 0 0 0 4 2 1

AR 0 2 0 1 0 0 0 1 2 1 0 2 0 0 0 1 7 6 1

AW 0 1 0 0 3 1 0 1 0 0 0 0 5 1 4 3 3 0 3

AY 0 2 2 2 1 4 2 0 1 0 0 4 0 1 5 2 7 1 1

BC 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 2 0 25

BD 0 3 0 1 0 3 0 2 1 0 0 0 1 6 0 9 9 3 3

BE 0 0 0 2 0 0 0 1 0 0 0 0 0 8 6 0 9 2 8

MF 0 7 2 11 2 8 5 2 10 7 4 7 10 2 9 11 36 24 13

LF 0 1 0 1 1 1 5 2 4 3 1 0 3 3 2 3 24 8 7

EN 69 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Appendix 8: SPSS Output for Clustered Data

Antecedent * Sequitor Crosstabulation

Sequitor Total

AB EN FP LF MF RC ST

Anteceden

t

AB Count 0 1 25 1 4 8 0 39

Expected

Count 1.6 2.9 5.4 2.8 7.0 16.6 2.8 39.0

Std. Residual -1.3 -1.1 8.5 -1.1 -1.1 -2.1 -1.7

EN Count 0 0 0 0 0 0 69 69

Expected

Count 2.8 5.1 9.5 5.0 12.3 29.3 5.0 69.0

Std. Residual -1.7 -2.3 -3.1 -2.2 -3.5 -5.4 28.6

FP Count 7 1 15 5 17 86 0 131

Expected

Count 5.4 9.6 18.0 9.5 23.4 55.6 9.5 131.0

Std. Residual .7 -2.8 -.7 -1.5 -1.3 4.1 -3.1

LF Count 0 7 5 8 24 25 0 69


50

Expected

Count 2.8 5.1 9.5 5.0 12.3 29.3 5.0 69.0

Std. Residual -1.7 .9 -1.5 1.3 3.3 -.8 -2.2

MF Count 2 13 16 24 36 79 0 170

Expected

Count 7.0 12.5 23.4 12.3 30.4 72.1 12.3 170.0

Std. Residual -1.9 .1 -1.5 3.3 1.0 .8 -3.5

RC Count 6 48 53 25 80 192 0 404

Expected

Count 16.6 29.7 55.6 29.3 72.1 171.4 29.3 404.0

Std. Residual -2.6 3.4 -.3 -.8 .9 1.6 -5.4

ST Count 24 0 17 6 9 14 0 70

Expected

Count 2.9 5.1 9.6 5.1 12.5 29.7 5.1 70.0

Std. Residual 12.5 -2.3 2.4 .4 -1.0 -2.9 -2.3

Total Count 39 70 131 69 170 404 69 952

Expected

Count 39.0 70.0 131.0 69.0 170.0 404.0 69.0 952.0

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)

Pearson Chi-Square 1293.617a 36 .000

Likelihood Ratio 719.791 36 .000

N of Valid Cases 952

a. 7 cells (14.3%) have expected count less than 5. The minimum expected count is 1.60.


51

Appendix 9: SPSS Output

All Data


Sequitor Total

AA AB AE AF AG AH AI AO AP AR AW AY BC BD BE EN LF MF ST

Antecedent

AA Count 0 2 8 4 7 10 5 1 2 2 2 5 0 3 1 0 2 9 0 63

Expected Count

4.2 2.6 3.0 2.2 2.8 3.0 2.6 2.7 2.3 1.6 1.7 2.3 1.9 2.7 2.4 4.6 4.6 11.3 4.6 63.0

% within Antecedent

0.0%

3.2%

12.7%

6.3%

11.1%

15.9%

7.9%

1.6%

3.2%

3.2%

3.2%

7.9%

0.0%

4.8%

1.6%

0.0%

3.2%

14.3%

0.0%

100.0%

Std. Residual

-2.0 -.4 2.8 1.2 2.5 4.1 1.5 -1.0 -.2 .3 .3 1.8 -1.4 .2 -.9 -2.2 -1.2 -.7 -

2.1

AB Count 11 0 2 6 1 0 2 0 8 1 0 2 0 0 0 1 1 4 0 39

Expected Count

2.6 1.6 1.9 1.4 1.8 1.8 1.6 1.7 1.4 1.0 1.0 1.4 1.2 1.7 1.5 2.9 2.8 7.0 2.8 39.0

% within Antecedent

28.2%

0.0%

5.1%

15.4%

2.6%

0.0%

5.1%

0.0%

20.5%

2.6%

0.0%

5.1%

0.0%

0.0%

0.0%

2.6%

2.6%

10.3%

0.0%

100.0%

Std. Residual

5.2 -1.3 .1 4.0 -.6 -1.4 .3 -1.3 5.5 .0 -1.0 .5 -1.1 -1.3 -1.2 -1.1 -1.1 -1.1 -

1.7

AE Count 7 0 0 3 3 12 1 0 1 3 0 0 2 2 1 0 3 8 0 46

Expected Count

3.0 1.9 2.2 1.6 2.1 2.2 1.9 2.0 1.7 1.2 1.2 1.7 1.4 2.0 1.7 3.4 3.3 8.2 3.3 46.0

% within Antecedent

15.2%

0.0%

0.0%

6.5%

6.5%

26.1%

2.2%

0.0%

2.2%

6.5%

0.0%

0.0%

4.3%

4.3%

2.2%

0.0%

6.5%

17.4%

0.0%

100.0%

Std. Residual

2.3 -1.4 -1.5 1.1 .6 6.7 -.6 -1.4 -.5 1.7 -1.1 -1.3 .5 .0 -.6 -1.8 -.2 -.1 -

1.8

AF Count 4 1 4 0 4 2 1 5 2 1 0 2 0 2 0 0 1 4 0 33

Expected Count

2.2 1.4 1.6 1.1 1.5 1.6 1.4 1.4 1.2 .8 .9 1.2 1.0 1.4 1.2 2.4 2.4 5.9 2.4 33.0

% within Antecedent

12.1%

3.0%

12.1%

0.0%

12.1%

6.1%

3.0%

15.2%

6.1%

3.0%

0.0%

6.1%

0.0%

6.1%

0.0%

0.0%

3.0%

12.1%

0.0%

100.0%

Std. Residual

1.2 -.3 1.9 -1.1 2.1 .4 -.3 3.0 .7 .2 -.9 .7 -1.0 .5 -1.1 -1.6 -.9 -.8 -

1.5

AG

Count 4 0 1 1 0 6 11 6 0 0 1 2 3 2 0 1 0 5 0 43

Expected Count

2.8 1.8 2.1 1.5 1.9 2.0 1.8 1.9 1.6 1.1 1.1 1.6 1.3 1.9 1.6 3.2 3.1 7.7 3.1 43.0

% within Antecedent

9.3%

0.0%

2.3%

2.3%

0.0%

14.0%

25.6%

14.0%

0.0%

0.0%

2.3%

4.7%

7.0%

4.7%

0.0%

2.3%

0.0%

11.6%

0.0%

100.0%

Std. Residual

.7 -1.3 -.7 -.4 -1.4 2.8 7.0 3.0 -1.3 -1.0 -.1 .3 1.5 .1 -1.3 -1.2 -1.8 -1.0 -

1.8

AH Count 7 0 1 5 2 0 8 1 1 1 0 0 0 2 1 1 2 13 0 45

Expected Count

3.0 1.8 2.2 1.6 2.0 2.1 1.8 1.9 1.7 1.1 1.2 1.7 1.4 1.9 1.7 3.3 3.3 8.0 3.3 45.0

% within Antecedent

15.6%

0.0%

2.2%

11.1%

4.4%

0.0%

17.8%

2.2%

2.2%

2.2%

0.0%

0.0%

0.0%

4.4%

2.2%

2.2%

4.4%

28.9%

0.0%

100.0%

Std. Residual

2.3 -1.4 -.8 2.8 .0 -1.5 4.5 -.7 -.5 -.1 -1.1 -1.3 -1.2 .0 -.5 -1.3 -.7 1.8 -

1.8

AI Count 2 2 2 1 3 1 0 4 1 0 1 1 0 2 0 2 6 11 0 39

Expected Count

2.6 1.6 1.9 1.4 1.8 1.8 1.6 1.7 1.4 1.0 1.0 1.4 1.2 1.7 1.5 2.9 2.8 7.0 2.8 39.0

% within Antecedent

5.1%

5.1%

5.1%

2.6%

7.7%

2.6%

0.0%

10.3%

2.6%

0.0%

2.6%

2.6%

0.0%

5.1%

0.0%

5.1%

15.4%

28.2%

0.0%

100.0%

Std. Residual

-.4 .3 .1 -.3 .9 -.6 -1.3 1.8 -.4 -1.0 .0 -.4 -1.1 .2 -1.2 -.5 1.9 1.5 -

1.7

AO

Count 3 1 1 1 1 2 2 0 2 3 5 1 3 2 3 3 2 6 0 41

Expected Count

2.7 1.7 2.0 1.4 1.9 1.9 1.7 1.8 1.5 1.0 1.1 1.5 1.2 1.8 1.6 3.0 3.0 7.3 3.0 41.0

% within Antecedent

7.3%

2.4%

2.4%

2.4%

2.4%

4.9%

4.9%

0.0%

4.9%

7.3%

12.2%

2.4%

7.3%

4.9%

7.3%

7.3%

4.9%

14.6%

0.0%

100.0%

Std. Residual

.2 -.5 -.7 -.4 -.6 .0 .2 -1.3 .4 1.9 3.8 -.4 1.6 .2 1.2 .0 -.6 -.5 -

1.7

AP Count 1 4 3 2 2 0 0 4 0 7 3 2 0 0 0 1 2 4 0 35

Expected Count

2.3 1.4 1.7 1.2 1.6 1.7 1.4 1.5 1.3 .9 .9 1.3 1.1 1.5 1.3 2.6 2.5 6.3 2.5 35.0

% within Antecedent

2.9%

11.4%

8.6%

5.7%

5.7%

0.0%

0.0%

11.4%

0.0%

20.0%

8.6%

5.7%

0.0%

0.0%

0.0%

2.9%

5.7%

11.4%

0.0%

100.0%

Std. Residual

-.9 2.1 1.0 .7 .3 -1.3 -1.2 2.0 -1.1 6.5 2.2 .6 -1.0 -1.2 -1.2 -1.0 -.3 -.9 -

1.6

AR Count 2 0 1 0 0 0 1 2 1 0 2 0 0 0 1 1 6 7 0 24

Expected Count

1.6 1.0 1.2 .8 1.1 1.1 1.0 1.0 .9 .6 .6 .9 .7 1.0 .9 1.8 1.7 4.3 1.7 24.0

% within Antecedent

8.3%

0.0%

4.2%

0.0%

0.0%

0.0%

4.2%

8.3%

4.2%

0.0%

8.3%

0.0%

0.0%

0.0%

4.2%

4.2%

25.0%

29.2%

0.0%

100.0%

Std. Residual

.3 -1.0 -.1 -.9 -1.0 -1.1 .0 1.0 .1 -.8 1.7 -.9 -.9 -1.0 .1 -.6 3.2 1.3 -

1.3

A Count 1 0 0 3 1 0 1 0 0 0 0 5 1 4 3 3 0 3 0 25


52

W Expected Count

1.7 1.0 1.2 .9 1.1 1.2 1.0 1.1 .9 .6 .7 .9 .8 1.1 .9 1.8 1.8 4.5 1.8 25.0

% within Antecedent

4.0%

0.0%

0.0%

12.0%

4.0%

0.0%

4.0%

0.0%

0.0%

0.0%

0.0%

20.0%

4.0%

16.0%

12.0%

12.0%

0.0%

12.0%

0.0%

100.0%

Std. Residual

-.5 -1.0 -1.1 2.3 -.1 -1.1 .0 -1.0 -1.0 -.8 -.8 4.3 .3 2.8 2.1 .9 -1.3 -.7 -

1.3

AY Count 2 2 2 1 4 2 0 1 0 0 4 0 1 5 2 1 1 7 0 35

Expected Count

2.3 1.4 1.7 1.2 1.6 1.7 1.4 1.5 1.3 .9 .9 1.3 1.1 1.5 1.3 2.6 2.5 6.3 2.5 35.0

% within Antecedent

5.7%

5.7%

5.7%

2.9%

11.4%

5.7%

0.0%

2.9%

0.0%

0.0%

11.4%

0.0%

2.9%

14.3%

5.7%

2.9%

2.9%

20.0%

0.0%

100.0%

Std. Residual

-.2 .5 .2 -.2 1.9 .3 -1.2 -.4 -1.1 -.9 3.2 -1.1 -.1 2.8 .6 -1.0 -1.0 .3 -

1.6

BC Count 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 25 0 2 0 29

Expected Count

1.9 1.2 1.4 1.0 1.3 1.4 1.2 1.2 1.1 .7 .8 1.1 .9 1.2 1.1 2.1 2.1 5.2 2.1 29.0

% within Antecedent

0.0%

3.4%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

3.4%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

86.2%

0.0%

6.9%

0.0%

100.0%

Std. Residual

-1.4 -.2 -1.2 -1.0 -1.1 -1.2 -1.1 -1.1 -.1 -.9 -.9 -1.0 -.9 -1.1 -1.0 15.7 -1.4 -1.4 -

1.4

BD Count 3 0 1 0 3 0 2 1 0 0 0 1 6 0 9 3 3 9 0 41

Expected Count

2.7 1.7 2.0 1.4 1.9 1.9 1.7 1.8 1.5 1.0 1.1 1.5 1.2 1.8 1.6 3.0 3.0 7.3 3.0 41.0

% within Antecedent

7.3%

0.0%

2.4%

0.0%

7.3%

0.0%

4.9%

2.4%

0.0%

0.0%

0.0%

2.4%

14.6%

0.0%

22.0%

7.3%

7.3%

22.0%

0.0%

100.0%

Std. Residual

.2 -1.3 -.7 -1.2 .8 -1.4 .2 -.6 -1.2 -1.0 -1.0 -.4 4.3 -1.3 6.0 .0 .0 .6 -

1.7

BE Count 0 0 2 0 0 0 1 0 0 0 0 0 8 6 0 8 2 9 0 36

Expected Count

2.4 1.5 1.7 1.2 1.6 1.7 1.5 1.6 1.3 .9 .9 1.3 1.1 1.6 1.4 2.6 2.6 6.4 2.6 36.0

% within Antecedent

0.0%

0.0%

5.6%

0.0%

0.0%

0.0%

2.8%

0.0%

0.0%

0.0%

0.0%

0.0%

22.2%

16.7%

0.0%

22.2%

5.6%

25.0%

0.0%

100.0%

Std. Residual

-1.5 -1.2 .2 -1.1 -1.3 -1.3 -.4 -1.2 -1.2 -1.0 -1.0 -1.2 6.6 3.6 -1.2 3.3 -.4 1.0 -

1.6

EN Count 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 69 69

Expected Count

4.6 2.8 3.3 2.4 3.1 3.3 2.8 3.0 2.5 1.7 1.8 2.5 2.1 3.0 2.6 5.1 5.0 12.3 5.0 69.0

% within Antecedent

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

100.0%

100.0%

Std. Residual

-2.1 -1.7 -1.8 -1.5 -1.8 -1.8 -1.7 -1.7 -1.6 -1.3 -1.3 -1.6 -1.4 -1.7 -1.6 -2.3 -2.2 -3.5 28.

6

LF Count 1 0 1 1 1 5 2 4 3 1 0 3 3 2 3 7 8 24 0 69

Expected Count

4.6 2.8 3.3 2.4 3.1 3.3 2.8 3.0 2.5 1.7 1.8 2.5 2.1 3.0 2.6 5.1 5.0 12.3 5.0 69.0

% within Antecedent

1.4%

0.0%

1.4%

1.4%

1.4%

7.2%

2.9%

5.8%

4.3%

1.4%

0.0%

4.3%

4.3%

2.9%

4.3%

10.1%

11.6%

34.8%

0.0%

100.0%

Std. Residual

-1.7 -1.7 -1.3 -.9 -1.2 1.0 -.5 .6 .3 -.6 -1.3 .3 .6 -.6 .2 .9 1.3 3.3 -

2.2

MF

Count 7 2 11 2 8 5 2 10 7 4 7 10 2 9 11 13 24 36 0 170

Expected Count

11.3 7.0 8.2 5.9 7.7 8.0 7.0 7.3 6.3 4.3 4.5 6.3 5.2 7.3 6.4 12.5 12.3 30.4 12.

3 170.0

% within Antecedent

4.1%

1.2%

6.5%

1.2%

4.7%

2.9%

1.2%

5.9%

4.1%

2.4%

4.1%

5.9%

1.2%

5.3%

6.5%

7.6%

14.1%

21.2%

0.0%

100.0%

Std. Residual

-1.3 -1.9 1.0 -1.6 .1 -1.1 -1.9 1.0 .3 -.1 1.2 1.5 -1.4 .6 1.8 .1 3.3 1.0 -

3.5

ST Count 8 24 6 3 3 0 0 2 6 1 0 1 0 0 1 0 6 9 0 70

Expected Count

4.6 2.9 3.4 2.4 3.2 3.3 2.9 3.0 2.6 1.8 1.8 2.6 2.1 3.0 2.6 5.1 5.1 12.5 5.1 70.0

% within Antecedent

11.4%

34.3%

8.6%

4.3%

4.3%

0.0%

0.0%

2.9%

8.6%

1.4%

0.0%

1.4%

0.0%

0.0%

1.4%

0.0%

8.6%

12.9%

0.0%

100.0%

Std. Residual

1.6 12.5 1.4 .4 -.1 -1.8 -1.7 -.6 2.1 -.6 -1.4 -1.0 -1.5 -1.7 -1.0 -2.3 .4 -1.0 -

2.3

Total Count 63 39 46 33 43 45 39 41 35 24 25 35 29 41 36 70 69 170 69 952

Expected Count

63.0 39.0 46.0 33.0 43.0 45.0 39.0 41.0 35.0 24.0 25.0 35.0 29.0 41.0 36.0 70.0 69.0 170.

0 69.

0 952.0

% within Antecedent

6.6%

4.1%

4.8%

3.5%

4.5%

4.7%

4.1%

4.3%

3.7%

2.5%

2.6%

3.7%

3.0%

4.3%

3.8%

7.4%

7.2%

17.9%

7.2%

100.0%

Chi-Square Tests

Value df Asymp. Sig. (2-

sided)

Pearson Chi-Square

2191.390a 324 .000

Likelihood Ratio

1375.813 324 .000

N of Valid Cases

952

a. 319 cells (88.4%) have expected count less than 5. The minimum expected count is .61.


53

High Familiarity


Sequitor Total


Antecedent

AA Count 0 0 3 1 2 3 2 0 2 0 0 3 0 2 0 0 0 5 0 23

Expected Count

1.5 .9 1.0 .8 1.0 1.0 1.1 1.1 1.0 .7 .8 1.1 .7 .9 .8 1.7 1.2 3.9 1.6 23.0

% within Antecedent

0.0%

0.0%

13.0%

4.3%

8.7%

13.0%

8.7%

0.0%

8.7%

0.0%

0.0%

13.0%

0.0%

8.7%

0.0%

0.0%

0.0%

21.7%

0.0% 100.0%

Std. Residual

-1.2 -1.0 2.0 .2 .9 1.9 .8 -1.1 1.0 -.8 -.9 1.8 -.8 1.1 -.9 -1.3 -1.1 .6 -1.3

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

Expected Count

.9 .6 .6 .5 .6 .6 .7 .7 .6 .4 .5 .7 .4 .6 .5 1.0 .8 2.4 1.0 14.0

% within Antecedent

21.4%

0.0%

0.0%

21.4%

7.1%

0.0%

7.1%

0.0%

14.3%

0.0%

0.0%

7.1%

0.0%

0.0%

0.0%

0.0%

7.1%

14.3%

0.0% 100.0%

Std. Residual

2.2 -.7 -.8 3.7 .5 -.8 .4 -.8 1.8 -.7 -.7 .4 -.7 -.7 -.7 -1.0 .3 -.2 -1.0

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

Expected Count

1.0 .6 .6 .5 .7 .7 .7 .7 .6 .5 .5 .7 .5 .6 .6 1.1 .8 2.5 1.1 15.0

% within Antecedent

20.0%

0.0%

0.0%

13.3%

6.7%

26.7%

0.0%

0.0%

0.0%

6.7%

0.0%

0.0%

6.7%

0.0%

0.0%

0.0%

0.0%

20.0%

0.0% 100.0%

Std. Residual

2.0 -.8 -.8 2.1 .4 4.0 -.8 -.8 -.8 .8 -.7 -.8 .8 -.8 -.7 -1.1 -.9 .3 -1.0

AF Count 1 0 3 0 1 1 0 3 1 0 0 1 0 0 0 0 0 1 0 12

Expected Count

.8 .5 .5 .4 .5 .5 .6 .6 .5 .4 .4 .6 .4 .5 .4 .9 .6 2.0 .8 12.0

% within Antecedent

8.3%

0.0%

25.0%

0.0%

8.3%

8.3%

0.0%

25.0%

8.3%

0.0%

0.0%

8.3%

0.0%

0.0%

0.0%

0.0%

0.0%

8.3%

0.0% 100.0%

Std. Residual

.2 -.7 3.5 -.6 .6 .6 -.8 3.2 .7 -.6 -.6 .6 -.6 -.7 -.7 -.9 -.8 -.7 -.9

AG

Count 1 0 0 1 0 3 3 2 0 0 0 0 0 1 0 1 0 4 0 16

Expected Count

1.0 .6 .7 .5 .7 .7 .8 .8 .7 .5 .5 .8 .5 .6 .6 1.2 .9 2.7 1.1 16.0

% within Antecedent

6.3%

0.0%

0.0%

6.3%

0.0%

18.8%

18.8%

12.5%

0.0%

0.0%

0.0%

0.0%

0.0%

6.3%

0.0%

6.3%

0.0%

25.0%

0.0% 100.0%

Std. Residual

.0 -.8 -.8 .6 -.9 2.7 2.5 1.4 -.8 -.7 -.7 -.9 -.7 .5 -.8 -.2 -.9 .8 -1.1

AH Count 5 0 0 0 1 0 3 0 1 1 0 0 0 0 0 0 1 4 0 16

Expected Count

1.0 .6 .7 .5 .7 .7 .8 .8 .7 .5 .5 .8 .5 .6 .6 1.2 .9 2.7 1.1 16.0

% within Antecedent

31.3%

0.0%

0.0%

0.0%

6.3%

0.0%

18.8%

0.0%

6.3%

6.3%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

6.3%

25.0%

0.0% 100.0%

Std. Residual

3.9 -.8 -.8 -.7 .3 -.9 2.5 -.9 .4 .7 -.7 -.9 -.7 -.8 -.8 -1.1 .1 .8 -1.1

AI Count 0 1 1 0 1 0 0 3 1 0 1 0 0 0 0 2 2 5 0 17

Expected Count

1.1 .7 .7 .6 .8 .8 .8 .8 .7 .5 .6 .8 .5 .7 .6 1.3 .9 2.9 1.2 17.0

% within Antecedent

0.0%

5.9%

5.9%

0.0%

5.9%

0.0%

0.0%

17.6%

5.9%

0.0%

5.9%

0.0%

0.0%

0.0%

0.0%

11.8%

11.8%

29.4%

0.0% 100.0%

Std. Residual

-1.1 .4 .3 -.8 .3 -.9 -.9 2.4 .3 -.7 .6 -.9 -.7 -.8 -.8 .7 1.1 1.2 -1.1

AO

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

Expected Count

1.1 .7 .7 .6 .8 .8 .8 .8 .7 .5 .6 .8 .5 .7 .6 1.3 .9 2.9 1.2 17.0

% within Antecedent

0.0%

5.9%

0.0%

0.0%

0.0%

0.0%

11.8%

0.0%

11.8%

0.0%

17.6%

5.9%

11.8%

5.9%

11.8%

0.0%

0.0%

17.6%

0.0% 100.0%

Std. Residual

-1.1 .4 -.8 -.8 -.9 -.9 1.3 -.9 1.5 -.7 3.2 .2 2.0 .4 1.7 -1.1 -1.0 .1 -1.1

AP Count 0 1 1 1 1 0 0 0 0 5 2 2 0 0 0 0 0 2 0 15

Expected Count

1.0 .6 .6 .5 .7 .7 .7 .7 .6 .5 .5 .7 .5 .6 .6 1.1 .8 2.5 1.1 15.0

% within Antecedent

0.0%

6.7%

6.7%

6.7%

6.7%

0.0%

0.0%

0.0%

0.0%

33.3%

13.3%

13.3%

0.0%

0.0%

0.0%

0.0%

0.0%

13.3%

0.0% 100.0%

Std. Residual

-1.0 .5 .5 .7 .4 -.8 -.8 -.8 -.8 6.6 2.1 1.5 -.7 -.8 -.7 -1.1 -.9 -.3 -1.0

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

Expected Count

.7 .4 .5 .4 .5 .5 .5 .5 .5 .3 .4 .5 .3 .4 .4 .8 .6 1.9 .8 11.0

% within Antecedent

9.1%

0.0%

0.0%

0.0%

0.0%

0.0%

9.1%

18.2%

0.0%

0.0%

9.1%

0.0%

0.0%

0.0%

9.1%

9.1%

18.2%

18.2%

0.0% 100.0%

Std. Residual

.3 -.7 -.7 -.6 -.7 -.7 .6 2.0 -.7 -.6 1.0 -.7 -.6 -.7 .9 .2 1.8 .1 -.9

AW

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

Expected Count

.8 .5 .5 .4 .5 .5 .6 .6 .5 .4 .4 .6 .4 .5 .4 .9 .6 2.0 .8 12.0

% within Antecedent

8.3%

0.0%

0.0%

8.3%

8.3%

0.0%

8.3%

0.0%

0.0%

0.0%

0.0%

25.0%

0.0%

16.7%

8.3%

8.3%

0.0%

8.3%

0.0% 100.0%

Std. Residual

.2 -.7 -.7 .9 .6 -.7 .6 -.8 -.7 -.6 -.6 3.2 -.6 2.2 .8 .1 -.8 -.7 -.9

AY Count 0 0 2 0 2 1 0 1 0 0 3 0 0 1 1 1 0 5 0 17


54

Expected Count

1.1 .7 .7 .6 .8 .8 .8 .8 .7 .5 .6 .8 .5 .7 .6 1.3 .9 2.9 1.2 17.0

% within Antecedent

0.0%

0.0%

11.8%

0.0%

11.8%

5.9%

0.0%

5.9%

0.0%

0.0%

17.6%

0.0%

0.0%

5.9%

5.9%

5.9%

0.0%

29.4%

0.0% 100.0%

Std. Residual

-1.1 -.8 1.5 -.8 1.4 .3 -.9 .2 -.8 -.7 3.2 -.9 -.7 .4 .5 -.2 -1.0 1.2 -1.1

BC Count 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 0 0 11

Expected Count

.7 .4 .5 .4 .5 .5 .5 .5 .5 .3 .4 .5 .3 .4 .4 .8 .6 1.9 .8 11.0

% within Antecedent

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

100.0%

0.0%

0.0%

0.0% 100.0%

Std. Residual

-.8 -.7 -.7 -.6 -.7 -.7 -.7 -.7 -.7 -.6 -.6 -.7 -.6 -.7 -.6 11.3 -.8 -1.4 -.9

BD Count 0 0 0 0 0 0 2 0 0 0 0 0 3 0 4 1 1 3 0 14

Expected Count

.9 .6 .6 .5 .6 .6 .7 .7 .6 .4 .5 .7 .4 .6 .5 1.0 .8 2.4 1.0 14.0

% within Antecedent

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

14.3%

0.0%

0.0%

0.0%

0.0%

0.0%

21.4%

0.0%

28.6%

7.1%

7.1%

21.4%

0.0% 100.0%

Std. Residual

-1.0 -.7 -.8 -.7 -.8 -.8 1.6 -.8 -.8 -.7 -.7 -.8 3.9 -.7 4.9 .0 .3 .4 -1.0

BE Count 0 0 0 0 0 0 1 0 0 0 0 0 3 5 0 0 0 4 0 13

Expected Count

.8 .5 .6 .4 .6 .6 .6 .6 .6 .4 .4 .6 .4 .5 .5 1.0 .7 2.2 .9 13.0

% within Antecedent

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

7.7%

0.0%

0.0%

0.0%

0.0%

0.0%

23.1%

38.5%

0.0%

0.0%

0.0%

30.8%

0.0% 100.0%

Std. Residual

-.9 -.7 -.7 -.7 -.8 -.8 .5 -.8 -.7 -.6 -.7 -.8 4.1 6.2 -.7 -1.0 -.8 1.2 -1.0

EN Count 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 25 25

Expected Count

1.6 1.0 1.1 .8 1.1 1.1 1.2 1.2 1.1 .8 .8 1.2 .8 1.0 .9 1.8 1.3 4.2 1.8 25.0

% within Antecedent

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

100.0%

100.0%

Std. Residual

-1.3 -1.0 -1.0 -.9 -1.1 -1.1 -1.1 -1.1 -1.0 -.9 -.9 -1.1 -.9 -1.0 -1.0 -1.4 -1.2 -2.1 17.5

LF Count 0 0 0 1 0 2 0 1 1 1 0 2 2 0 0 3 2 4 0 19

Expected Count

1.2 .8 .8 .6 .9 .9 .9 .9 .8 .6 .6 .9 .6 .8 .7 1.4 1.0 3.2 1.3 19.0

% within Antecedent

0.0%

0.0%

0.0%

5.3%

0.0%

10.5%

0.0%

5.3%

5.3%

5.3%

0.0%

10.5%

10.5%

0.0%

0.0%

15.8%

10.5%

21.1%

0.0% 100.0%

Std. Residual

-1.1 -.9 -.9 .4 -.9 1.2 -1.0 .1 .2 .5 -.8 1.1 1.8 -.9 -.8 1.4 1.0 .4 -1.2

MF

Count 4 2 3 0 4 2 1 5 1 2 2 4 0 2 4 5 10 9 0 60

Expected Count

3.9 2.4 2.5 2.0 2.7 2.7 2.9 2.9 2.5 1.9 2.0 2.9 1.9 2.4 2.2 4.4 3.2 10.2 4.2 60.0

% within Antecedent

6.7%

3.3%

5.0%

0.0%

6.7%

3.3%

1.7%

8.3%

1.7%

3.3%

3.3%

6.7%

0.0%

3.3%

6.7%

8.3%

16.7%

15.0%

0.0% 100.0%

Std. Residual

.0 -.2 .3 -1.4 .8 -.4 -1.1 1.2 -1.0 .1 .0 .7 -1.4 -.2 1.2 .3 3.8 -.4 -2.1

ST Count 4 9 2 2 1 0 0 0 4 1 0 0 0 0 0 0 0 3 0 26

Expected Count

1.7 1.0 1.1 .9 1.2 1.2 1.3 1.3 1.1 .8 .9 1.3 .8 1.0 1.0 1.9 1.4 4.4 1.8 26.0

% within Antecedent

15.4%

34.6%

7.7%

7.7%

3.8%

0.0%

0.0%

0.0%

15.4%

3.8%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

11.5%

0.0% 100.0%

Std. Residual

1.8 7.8 .9 1.2 -.2 -1.1 -1.1 -1.1 2.8 .2 -.9 -1.1 -.9 -1.0 -1.0 -1.4 -1.2 -.7 -1.4

Total Count 23 14 15 12 16 16 17 17 15 11 12 17 11 14 13 26 19 60 25 353

Expected Count

23.0 14.0 15.0 12.0 16.0 16.0 17.0 17.0 15.0 11.0 12.0 17.0 11.0 14.0 13.0 26.0 19.0 60.0 25.0 353.

0

% within Antecedent

6.5%

4.0%

4.2%

3.4%

4.5%

4.5%

4.8%

4.8%

4.2%

3.1%

3.4%

4.8%

3.1%

4.0%

3.7%

7.4%

5.4%

17.0%

7.1% 100.0%

Chi-Square Tests

Value df

Asymp. Sig.

(2-sided)




a. 360 cells (99.7%) have expected count less than 5. The

minimum expected count is .34.


55

Low Familiarity

Antecedent * Sequitor

Crosstabulation

Sequitor Tota

l


Antecedent

AA Count 0 2 5 3 5 7 3 1 0 2 2 2 0 1 1 0 2 4 0 40

Expected Count 2.7 1.7 2.1 1.4 1.8 1.9 1.5 1.6 1.3 .9 .9 1.2 1.2 1.8 1.5 2.9 3.3 7.4 2.9 40.0

% within Antecedent

0.0%

5.0%

12.5%

7.5%

12.5%

17.5%

7.5%

2.5%

0.0%

5.0%

5.0%

5.0%

0.0%

2.5%

2.5%

0.0%

5.0%

10.0%

0.0%

100.0%

Std. Residual -1.6 .3 2.0 1.3 2.4 3.6 1.3 -.5 -1.2 1.2 1.2 .7 -1.1 -.6 -.4 -1.7 -.7 -1.2 -1.7

AB Count 8 0 2 3 0 0 1 0 6 1 0 1 0 0 0 1 0 2 0 25

Expected Count 1.7 1.0 1.3 .9 1.1 1.2 .9 1.0 .8 .5 .5 .8 .8 1.1 1.0 1.8 2.1 4.6 1.8 25.0

% within Antecedent

32.0%

0.0%

8.0%

12.0%

0.0%

0.0%

4.0%

0.0%

24.0%

4.0%

0.0%

4.0%

0.0%

0.0%

0.0%

4.0%

0.0%

8.0%

0.0%

100.0%

Std. Residual 4.9 -1.0 .6 2.3 -1.1 -1.1 .1 -1.0 5.6 .6 -.7 .3 -.9 -1.1 -1.0 -.6 -1.4 -1.2 -1.3

AE Count 4 0 0 1 2 8 1 0 1 2 0 0 1 2 1 0 3 5 0 31

Expected Count 2.1 1.3 1.6 1.1 1.4 1.5 1.1 1.2 1.0 .7 .7 .9 .9 1.4 1.2 2.3 2.6 5.7 2.2 31.0

% within Antecedent

12.9%

0.0%

0.0%

3.2%

6.5%

25.8%

3.2%

0.0%

3.2%

6.5%

0.0%

0.0%

3.2%

6.5%

3.2%

0.0%

9.7%

16.1%

0.0%

100.0%

Std. Residual 1.3 -1.1 -1.3 -.1 .5 5.3 -.1 -1.1 .0 1.6 -.8 -1.0 .1 .5 -.2 -1.5 .3 -.3 -1.5

AF Count 3 1 1 0 3 1 1 2 1 1 0 1 0 2 0 0 1 3 0 21

Expected Count 1.4 .9 1.1 .7 .9 1.0 .8 .8 .7 .5 .5 .6 .6 .9 .8 1.5 1.8 3.9 1.5 21.0

% within Antecedent

14.3%

4.8%

4.8%

0.0%

14.3%

4.8%

4.8%

9.5%

4.8%

4.8%

0.0%

4.8%

0.0%

9.5%

0.0%

0.0%

4.8%

14.3%

0.0%

100.0%

Std. Residual 1.3 .1 -.1 -.9 2.1 .0 .3 1.3 .4 .8 -.7 .5 -.8 1.1 -.9 -1.2 -.6 -.4 -1.2

AG

Count 3 0 1 0 0 3 8 4 0 0 1 2 3 1 0 0 0 1 0 27

Expected Count 1.8 1.1 1.4 .9 1.2 1.3 1.0 1.1 .9 .6 .6 .8 .8 1.2 1.0 2.0 2.3 5.0 1.9 27.0

% within Antecedent

11.1%

0.0%

3.7%

0.0%

0.0%

11.1%

29.6%

14.8%

0.0%

0.0%

3.7%

7.4%

11.1%

3.7%

0.0%

0.0%

0.0%

3.7%

0.0%

100.0%

Std. Residual .9 -1.1 -.3 -1.0 -1.1 1.5 7.0 2.8 -1.0 -.8 .5 1.3 2.4 -.2 -1.0 -1.4 -1.5 -1.8 -1.4

AH Count 2 0 1 5 1 0 5 1 0 0 0 0 0 2 1 1 1 9 0 29

Expected Count 1.9 1.2 1.5 1.0 1.3 1.4 1.1 1.2 1.0 .6 .6 .9 .9 1.3 1.1 2.1 2.4 5.3 2.1 29.0

% within Antecedent

6.9%

0.0%

3.4%

17.2%

3.4%

0.0%

17.2%

3.4%

0.0%

0.0%

0.0%

0.0%

0.0%

6.9%

3.4%

3.4%

3.4%

31.0%

0.0%

100.0%

Std. Residual .0 -1.1 -.4 3.9 -.3 -1.2 3.8 -.2 -1.0 -.8 -.8 -.9 -.9 .6 -.1 -.8 -.9 1.6 -1.4

AI Count 2 1 1 1 2 1 0 1 0 0 0 1 0 2 0 0 4 6 0 22

Expected Count 1.5 .9 1.1 .8 1.0 1.1 .8 .9 .7 .5 .5 .7 .7 1.0 .8 1.6 1.8 4.0 1.6 22.0

% within Antecedent

9.1%

4.5%

4.5%

4.5%

9.1%

4.5%

0.0%

4.5%

0.0%

0.0%

0.0%

4.5%

0.0%

9.1%

0.0%

0.0%

18.2%

27.3%

0.0%

100.0%

Std. Residual .4 .1 -.1 .3 1.0 -.1 -.9 .1 -.9 -.7 -.7 .4 -.8 1.0 -.9 -1.3 1.6 1.0 -1.3

AO

Count 3 0 1 1 1 2 0 0 0 3 2 0 1 1 1 3 2 3 0 24

Expected Count 1.6 1.0 1.2 .8 1.1 1.2 .9 1.0 .8 .5 .5 .7 .7 1.1 .9 1.8 2.0 4.4 1.7 24.0

% within Antecedent

12.5%

0.0%

4.2%

4.2%

4.2%

8.3%

0.0%

0.0%

0.0%

12.5%

8.3%

0.0%

4.2%

4.2%

4.2%

12.5%

8.3%

12.5%

0.0%

100.0%

Std. Residual

1.1 -1.0 -.2 .2 -.1 .8 -.9 -1.0 -.9 3.4 2.0 -.8 .3 -.1 .1 .9 .0 -.7 -1.3

AP Count 1 3 2 1 1 0 0 4 0 2 1 0 0 0 0 1 2 2 0 20

Expected Count

1.3 .8 1.0 .7 .9 1.0 .7 .8 .7 .4 .4 .6 .6 .9 .8 1.5 1.7 3.7 1.4 20.0

% within Antecedent

5.0%

15.0%

10.0%

5.0%

5.0%

0.0%

0.0%

20.0%

0.0%

10.0%

5.0%

0.0%

0.0%

0.0%

0.0%

5.0%

10.0%

10.0%

0.0%

100.0%


56

Std. Residual

-.3 2.4 .9 .4 .1 -1.0 -.9 3.6 -.8 2.4 .9 -.8 -.8 -1.0 -.9 -.4 .3 -.9 -1.2

AR Count 1 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 4 5 0 13

Expected Count

.9 .5 .7 .5 .6 .6 .5 .5 .4 .3 .3 .4 .4 .6 .5 1.0 1.1 2.4 .9 13.0

% within Antecedent

7.7%

0.0%

7.7%

0.0%

0.0%

0.0%

0.0%

0.0%

7.7%

0.0%

7.7%

0.0%

0.0%

0.0%

0.0%

0.0%

30.8%

38.5%

0.0%

100.0%

Std. Residual

.1 -.7 .4 -.7 -.8 -.8 -.7 -.7 .9 -.5 1.3 -.6 -.6 -.8 -.7 -1.0 2.8 1.7 -1.0

AW

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

Expected Count

.9 .5 .7 .5 .6 .6 .5 .5 .4 .3 .3 .4 .4 .6 .5 1.0 1.1 2.4 .9 13.0

% within Antecedent

0.0%

0.0%

0.0%

15.4%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

15.4%

7.7%

15.4%

15.4%

15.4%

0.0%

15.4%

0.0%

100.0%

Std. Residual

-.9 -.7 -.8 2.3 -.8 -.8 -.7 -.7 -.7 -.5 -.5 2.6 1.0 1.8 2.1 1.1 -1.0 -.3 -1.0

AY Count 2 2 0 1 2 1 0 0 0 0 1 0 1 4 1 0 1 2 0 18

Expected Count

1.2 .8 .9 .6 .8 .9 .7 .7 .6 .4 .4 .5 .5 .8 .7 1.3 1.5 3.3 1.3 18.0

% within Antecedent

11.1%

11.1%

0.0%

5.6%

11.1%

5.6%

0.0%

0.0%

0.0%

0.0%

5.6%

0.0%

5.6%

22.2%

5.6%

0.0%

5.6%

11.1%

0.0%

100.0%

Std. Residual

.7 1.4 -1.0 .5 1.3 .1 -.8 -.8 -.8 -.6 1.0 -.7 .6 3.5 .4 -1.2 -.4 -.7 -1.1

BC Count 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 14 0 2 0 18

Expected Count

1.2 .8 .9 .6 .8 .9 .7 .7 .6 .4 .4 .5 .5 .8 .7 1.3 1.5 3.3 1.3 18.0

% within Antecedent

0.0%

5.6%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

5.6%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

77.8%

0.0%

11.1%

0.0%

100.0%

Std. Residual

-1.1 .3 -1.0 -.8 -.9 -.9 -.8 -.8 .5 -.6 -.6 -.7 -.7 -.9 -.8 11.0 -1.2 -.7 -1.1

BD Count 3 0 1 0 3 0 0 1 0 0 0 1 3 0 5 2 2 6 0 27

Expected Count

1.8 1.1 1.4 .9 1.2 1.3 1.0 1.1 .9 .6 .6 .8 .8 1.2 1.0 2.0 2.3 5.0 1.9 27.0

% within Antecedent

11.1%

0.0%

3.7%

0.0%

11.1%

0.0%

0.0%

3.7%

0.0%

0.0%

0.0%

3.7%

11.1%

0.0%

18.5%

7.4%

7.4%

22.2%

0.0%

100.0%

Std. Residual

.9 -1.1 -.3 -1.0 1.6 -1.1 -1.0 -.1 -1.0 -.8 -.8 .2 2.4 -1.1 3.9 .0 -.2 .5 -1.4

BE Count 0 0 2 0 0 0 0 0 0 0 0 0 5 1 0 8 2 5 0 23

Expected Count

1.5 1.0 1.2 .8 1.0 1.1 .8 .9 .8 .5 .5 .7 .7 1.0 .9 1.7 1.9 4.2 1.7 23.0

% within Antecedent

0.0%

0.0%

8.7%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

21.7%

4.3%

0.0%

34.8%

8.7%

21.7%

0.0%

100.0%

Std. Residual

-1.2 -1.0 .7 -.9 -1.0 -1.1 -.9 -1.0 -.9 -.7 -.7 -.8 5.2 .0 -.9 4.8 .1 .4 -1.3

EN Count 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 43 43

Expected Count

2.9 1.8 2.2 1.5 1.9 2.1 1.6 1.7 1.4 .9 .9 1.3 1.3 1.9 1.7 3.2 3.6 7.9 3.1 43.0

% within Antecedent

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

100.0%

100.0%

Std. Residual

-1.7 -1.3 -1.5 -1.2 -1.4 -1.4 -1.3 -1.3 -1.2 -1.0 -1.0 -1.1 -1.1 -1.4 -1.3 -1.8 -1.9 -2.8 22.7

LF Count 1 0 1 0 1 3 2 3 2 0 0 1 1 2 3 4 6 20 0 50

Expected Count

3.3 2.1 2.6 1.8 2.3 2.4 1.8 2.0 1.7 1.1 1.1 1.5 1.5 2.3 1.9 3.7 4.2 9.2 3.6 50.0

% within Antecedent

2.0%

0.0%

2.0%

0.0%

2.0%

6.0%

4.0%

6.0%

4.0%

0.0%

0.0%

2.0%

2.0%

4.0%

6.0%

8.0%

12.0%

40.0%

0.0%

100.0%

Std. Residual

-1.3 -1.4 -1.0 -1.3 -.8 .4 .1 .7 .3 -1.0 -1.0 -.4 -.4 -.2 .8 .2 .9 3.6 -1.9

MF

Count 3 0 8 2 4 3 1 5 6 2 5 6 2 7 7 8 14 27 0 110

Expected Count

7.4 4.6 5.7 3.9 5.0 5.3 4.0 4.4 3.7 2.4 2.4 3.3 3.3 5.0 4.2 8.1 9.2 20.2 7.9 110.

0

% within Antecedent

2.7%

0.0%

7.3%

1.8%

3.6%

2.7%

0.9%

4.5%

5.5%

1.8%

4.5%

5.5%

1.8%

6.4%

6.4%

7.3%

12.7%

24.5%

0.0%

100.0%

Std. Residual

-1.6 -2.1 1.0 -.9 -.4 -1.0 -1.5 .3 1.2 -.3 1.7 1.5 -.7 .9 1.3 .0 1.6 1.5 -2.8

ST Count 4 15 4 1 2 0 0 2 2 0 0 1 0 0 1 0 6 6 0 44

Expected Count

2.9 1.8 2.3 1.5 2.0 2.1 1.6 1.8 1.5 1.0 1.0 1.3 1.3 2.0 1.7 3.2 3.7 8.1 3.2 44.0

% within Antecedent

9.1%

34.1%

9.1%

2.3%

4.5%

0.0%

0.0%

4.5%

4.5%

0.0%

0.0%

2.3%

0.0%

0.0%

2.3%

0.0%

13.6%

13.6%

0.0%

100.0%

Std. Residual

.6 9.7 1.1 -.4 .0 -1.5 -1.3 .2 .4 -1.0 -1.0 -.3 -1.2 -1.4 -.5 -1.8 1.2 -.7 -1.8

Total Count 40 25 31 21 27 29 22 24 20 13 13 18 18 27 23 44 50 110 43 598

Expected Count 40.0 25.0 31.0 21.0 27.0 29.0 22.0 24.0 20.0 13.0 13.0 18.0 18.0 27.0 23.0 44.0 50.0

110.0

43.0 598.

0

% within Antecedent

6.7%

4.2%

5.2%

3.5%

4.5%

4.8%

3.7%

4.0%

3.3%

2.2%

2.2%

3.0%

3.0%

4.5%

3.8%

7.4%

8.4%

18.4%

7.2%

100.0%


57

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)




a. 348 cells (96.4%) have expected count less than 5. The minimum expected count is

.28.

Appendix 10: Transitional Frequency Matrices for Dawkins Method


ST 0 8 24 6 3 3 0 0 2 6 1 0 1 0 0 1 9 6 0

AA 0 0 2 8 4 7 10 5 1 2 2 2 5 0 3 1 9 2 0

AB 0 11 0 2 6 1 0 2 0 8 1 0 2 0 0 0 4 1 1

AE 0 7 0 0 3 3 12 1 0 1 3 0 0 2 2 1 8 3 0

AF 0 4 1 4 0 4 2 1 5 2 1 0 2 0 2 0 4 1 0

AG 0 4 0 1 1 0 6 11 6 0 0 1 2 3 2 0 5 0 1

AH 0 7 0 1 5 2 0 8 1 1 1 0 0 0 2 1 13 2 1

AI 0 2 2 2 1 3 1 0 4 1 0 1 1 0 2 0 11 6 2

AO 0 3 1 1 1 1 2 2 0 2 3 5 1 3 2 3 6 2 3

AP 0 1 4 3 2 2 0 0 4 0 7 3 2 0 0 0 4 2 1

AR 0 2 0 1 0 0 0 1 2 1 0 2 0 0 0 1 7 6 1

AW 0 1 0 0 3 1 0 1 0 0 0 0 5 1 4 3 3 0 3

AY 0 2 2 2 1 4 2 0 1 0 0 4 0 1 5 2 7 1 1

BC 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 2 0 25

BD 0 3 0 1 0 3 0 2 1 0 0 0 1 6 0 9 9 3 3

BE 0 0 0 2 0 0 0 1 0 0 0 0 0 8 6 0 9 2 8

MF 0 7 2 11 2 8 5 2 10 7 4 7 10 2 9 11 36 24 13

LF 0 1 0 1 1 1 5 2 4 3 1 0 3 3 2 3 24 8 7

EN 69 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

(ST-AB) 0 6 0 0 5 0 0 2 0 8 0 0 1 0 0 0 2 0 0

((ST-AB)AP) 0 1 0 2 0 1 0 0 0 0 2 1 0 0 0 0 1 0 0

(((ST-AB)AP)AE) 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0


58


(BC-EN)

(BE(BC-EN))

(AW(BE(BC-EN)))

(AO(AW(BE(BC-EN))))

ST 0 8 24 6 3 3 0 0 2 6 1 0 1 0 0 1 9 6 0 0 0 0 0

AA 0 0 2 8 4 7 10 5 1 2 2 2 5 0 3 1 9 2 0 0 0 0 0

AB 0 11 0 2 6 1 0 2 0 8 1 0 2 0 0 0 4 1 1 0 0 0 0

AE 0 7 0 0 3 3 12 1 0 1 3 0 0 2 2 1 8 3 0 2 0 0 0

AF 0 4 1 4 0 4 2 1 5 2 1 0 2 0 2 0 4 1 0 0 0 0 0

AG 0 4 0 1 1 0 6 11 6 0 0 1 2 3 2 0 5 0 1 2 0 0 0

AH 0 7 0 1 5 2 0 8 1 1 1 0 0 0 2 1 13 2 1 0 0 0 1

AI 0 2 2 2 1 3 1 0 4 1 0 1 1 0 2 0 11 6 2 0 0 0 1

AO 0 3 1 1 1 1 2 2 0 2 3 5 1 3 2 3 6 2 3 3 1 2 0

AP 0 1 4 3 2 2 0 0 4 0 7 3 2 0 0 0 4 2 1 0 0 0 0

AR 0 2 0 1 0 0 0 1 2 1 0 2 0 0 0 1 7 6 1 0 0 0 0

AW 0 1 0 0 3 1 0 1 0 0 0 0 5 1 4 3 3 0 3 1 2 0 0

AY 0 2 2 2 1 4 2 0 1 0 0 4 0 1 5 2 7 1 1 1 0 0 0

BC 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 2 0 25 0 0 0 0

BD 0 3 0 1 0 3 0 2 1 0 0 0 1 6 0 9 9 3 3 1 1 0 0

BE 0 0 0 2 0 0 0 1 0 0 0 0 0 8 6 0 9 2 8 6 0 0 0

MF 0 7 2 11 2 8 5 2 10 7 4 7 10 2 9 11 36 24 13 2 1 0 0

LF 0 1 0 1 1 1 5 2 4 3 1 0 3 3 2 3 24 8 7 3 1 0 0

EN 69 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

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