Applied Statistics for Forensic Psychology Students

1. Applied Statistics for Forensic Psychology Students Denny Meyer, Brian Phillips, and Joanna Dipnall Swinburne University of Technology, Australia ISI Hong Kong

2. Sentencing Surveyhttp://www.abc.net.au/mediawatch/transcripts/s3299463.htm • In 2011 the Victorian Government asked people to pass their own sentence on the state’s worst criminals, murderers, rapists and vandals. • Deptof Justice Online Survey • 18,562 responses • 15,816 to full version (17 sceneros) • 2,746 to short version (5 sceneros) • Welcomed by victims of crime advocates. ISI Hong Kong

3. Some Results Some Headlines Victorian government to use survey results as basis for sentencing reforms Lawyers slam sentencing survey Details of the individual case get lost Polling experts lash out at the plan, warning the ''self-selecting'' nature of the online survey - rather than randomly selecting them - will produce highly skewed results. http://www.theage.com.au/victoria/lawyers-slam-sentencing-survey-20110531-1fepv.html#ixzz2aPH6r3VJ This is an example of the need for statistical understanding in the community ISI Hong Kong

4. Statistics for Forensic Psychology Students • At Swinburne, a degree in Bachelor of Social Science (Psychology and Forensic Science) started in 2010. • Students study a number of the regular psychology units • Plus 4 units specifically on forensics, including Statistics. • The regular psychology course includes three statistics units which cover a range of statistical methods including • descriptive statistics, sampling distributions, normal distribution, z and t tests; testing relationships; Pearson's r and the chi-squared test of independence, ANOVA, MANOVA, factor analysis and multiple regression. • A non-mathematical approach is used (IBM SPSS Statistics Package.) • Probability theory is not a feature of these courses. ISI Hong Kong

5. The Students • They have studied at least two statistics units. • They were all on campus. • With about 50 quite capable and motivated students we wanted a course which complimented and extended the students previous statistics experience while incorporating a criminal flavour. • Thus we aimed to develop a suitable unit for students who had a background in data analysis methodologies, especially with continuous data. ISI Hong Kong

6. Some of the questions of interest to researchers in forensic science include: • How does type of offence depend on demographic factors? • Are conclusions stated as a result of a crime survey valid? • Which criminals are likely to reoffend? • When is recidivism likely to occur? • What makes a guilty verdict likely? • What makes a false confession likely? • What patterns do the data show? ISI Hong Kong

7. The Aims in Statistics for Forensics • To enable students to: 1. Critically assess and write crime statistics reports. 2. Choose appropriate analyses to investigate relationships in crime data. 3. Choose appropriate analyses to evaluate various types of evidence. 4. Understand the assumptions and limitations involved in the analyses. 5. Perform data analyses using the IBM SPSS Statistics software. 6. Report the results from this data analyses. ISI Hong Kong

8. What is available? • Despite the increasing availability of large crime data sets (population data, crime reports, police data and survey data), texts dealing with forensic statistics tend to focus on probability rather than data. • Some examples • Evett& Weir (1998), Aitken and Taroni (2004), Bachman and Paternoster (2009) and Lucy (2005) which cover a varying range of statistical topics from introductory statistics to linear models, some including logitmodels. • Curran (2011) extends the treatment of forensic data to count and proportion data and experimental design, and the graphs used are mainly box-plots, histograms, scatterplots and probability curves. ISI Hong Kong

9. Existing texts 2 • These texts tend to use • quite small data sets • and fairly simple graphs (histograms, scatterplots, boxplots). • Probability theory • While these are generally excellent books for their intended readers, most tend to be out of reach of many students, especially those with limited mathematical backgrounds. ISI Hong Kong

10. Structure of the Unit ISI Hong Kong

11. The program • A 2 hour lecture + 2 hour computerlab per week. • The lectures were recorded and all materials were made available on a Blackboard website. • The lecturers wrote extensive course notes and review exercises • Formally assessed by weekly online quizzes, two assignments and a final exam. ISI Hong Kong

12. Types of Forensic Data ISI Hong Kong

13. Some websites used • ABS site 4517.0 - Prisoners in Australia, 2011 • http://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/4517.02011?OpenDocument • Crime Statistics Official Release 2011/2012 • http://www.police.vic.gov.au/content.asp?a=internetBridgingPage&Media_ID=72176 • Victoria Crime Statistics 2011/2012 • http://www.police.vic.gov.au/content.asp?Document_ID=782 • Longitudinal Study of Violence Against Women: (ICPSR 3212) http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/3212 • Prisoners in Australia, 2011 by age, ABS article number 4517.0 - • http://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/4517.02011?OpenDocument • The Swinburne National Technology and Society Monitor” • http://www.swinburne.edu.au/lss/spru/spru-monitor.html • National Registry of Exonerations • http://www.law.umich.edu/special/exoneration/Pages/featured.aspx • US Census: Law Enforcement, Courts, & Prisons: Crimes and Crime Rates • http://www.census.gov/compendia/statab/cats/law_enforcement_courts_prisons/crimes_and_crime_rates.html ISI Hong Kong

14. Datasets and main techniques covered • With the aims and topics in place, a number of crime datasets, which were obtained from other staff members and from relevant web sites, were analysed. • Examples, the ABS site 4517.0 - Prisoners in Australia, 2011. The data was downloaded and converted into formats suitable for analysis in SPSS. ISI Hong Kong

15. ABS site 4517.0 - Prisoners in Australia, 2011http://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/4517.02011?OpenDocument ISI Hong Kong

16. Forensic/Crime Examples Included ISI Hong Kong

17. Preponderance of categorical variables • In particular binaryvariables. • These included • types of offence • sentence outcome (probation or prison), • drug relapse (yes/no) • binge drinking (yes/no), • court verdict (guilty or not guilty), • false confessions (yes/no), • the chance of recidivism (yes or no) an event of some interest to criminologists when a criminal relapses into a previous mode of behaviour after release from sentencing and is re-apprehended. • These variables require analysis using methods designed for categorical variables, • distance methods and dimensional and clustering techniques • and others requiring modelling methods. ISI Hong Kong

18. Some issues noted • The reliance on visual images to portray crime information • The detailed nature of the available data on interesting topics such as drinking, drug taking, self-defence behaviour of women, juvenile crime and public attitudes to issues such as DNA testing, drug laws, the use of CCTV by police for obtaining arrests and convictions. • Where possible such examples were built into the course. • This unit gave an opportunity to explore a number of these data sets, while extending the students' knowledge in statistical procedures, especially in the analysis of categorical data and, where possible, making extensive use of visualisation approaches. ISI Hong Kong

19. Reading Newspaper Articles Critically • Week 1 involved students learning what to look for in survey reports on crime related issues and what questions to ask in order to check the validity of the results claimed. • A number of statistics educators have used similar approaches in teaching statistics literacy egUtts(2005) and Budgett & Pfannkuch (2007). • Students worked in groups of 3 or 4 to study a short newspaper article report based on a survey about crime issues and answered a series of questions which were modified from ideas in Utts (2005). ISI Hong Kong

20. Examples of the titles of articles used. • Victorians asked to pass their own sentence on crime • Survey finds Australian computer crime on the rise • Streets unsafe at night: survey • Pre-dawn survey sheds light on city's hundreds of homeless • Violence threat to school heads • Good ice breaker allowing students to revise a number of important statistical ideas, but may not have yet have applied to on practical examples. ISI Hong Kong

21. Crime Reports • Class 2: Guest lecture, Chief Statistician from the Victoria Police • showed what data the police collected, • the database they used and reports they provide. • The topics included Attitudes to drug laws, doping, DNA testing, sentencing snapshots and regional hotspots. • An emphasis was on the importance for the police to be provided with clear, simple and effective reports. • Of particular interest was the range of graphs that were used to help police understand what was happening on their patch. ISI Hong Kong

22. Example 1: Official Victorian Crime Statistics, 2010/2011eg http://www.police.vic.gov.au/content.asp?Document_ID=782 ISI Hong Kong

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25. An assignment question • Using the Victoria Police Official Release Crime Statistics 2011/2012, pick ONE offence in the “Other Crime” category. Write a brief report for the Commissioner of Police. • Your report should indicate the change in crime rate and single year clearance rate for this offence between 2010/11 and 2011/12. • It should also discuss geographical and location hot-spots for this offence and relative prevalence amongst juvenile and adult offenders. It should include a graph illustrating the most important findings. ISI Hong Kong

26. Main techniques taught • Simple crosstabulations with appropriate percentages and graphs. • Sample data was used to demonstrate a number of contingency table tests including • the Chi-square test, • Fisher’s Exact test, • Inter-rater reliability test, Kappa • McNemar’sTest. ISI Hong Kong

27. Correspondence analysis • This useful descriptive technique can be applied to data in a contingency table with the aim of revealing any underlying relationships. • It is especially useful when the variables have a large number of categories. • Examples used included investigating the type of offence against a race-by-gender variable and looking at blood type by ethnic group. ISI Hong Kong

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29. Non-indigenous females ISI Hong Kong

30. Loglinear Analysis: • This technique is used when we want to simultaneously model relationships between categorical variables, and in particular to look for interactions between variables. • Examples used included • examining relationships between sentences for fraud (probation or prison) by gender and counsel (self or court appointed) • examining relationships between problem high school students’ use of alcohol, cigarettes or marijuana (all yes/no). ISI Hong Kong

31. Binary and Multinomial Logistic Regression: • Cases with categorical outcomes and the predictors either metric or binary. • Often used in forensic /criminology statistics. • For example the outcome variable ‘False confession’ modeled against the predictors time previously served in prison, age, gender, drug use and type of crime. ISI Hong Kong

32. Survival Analysis: • This technique is used to investigate the time taken to an event of interest when the period of observation ended before the event of interest occurred. • Common in biostatistics. • Example used was the recidivism of juvenile delinquents. • The survival time is the time till a re-arrest for re-offenders. Here we only know that recidivism has occurred if a delinquent has been caught re-offending before the end of the study. Otherwise the data must be censored. • This can apply when prisoners disappear (i.e. withdraw voluntarily or involuntarily from the study) and when the study ends before the event of interest (re-arrest) occurs. ISI Hong Kong

33. Multidimensional Scaling: • This technique provides a visual display of a similarity or distance matrix of items/objects. • Examples used included a visual comparison of people in a lineup, of blood samples, and crime characteristics. • Issues of wrong convictions based on eye-witness evidence was discussed (refer to National Registry of Exonerations). ISI Hong Kong

34. Raymond Jacksonhttp://www.law.umich.edu/special/exoneration/Pages/featured.aspx • State: TXDate of Exoneration: 7/16/2012Raymond Jackson (left) and James Williams were exonerated In Dallas after spending almost 30 years in prison for an aggravated sexual assault they did not commit. Multiple mistaken eyewitness identifications were a major factor in the convictions. • In September, 2011, DNA tests evidence eliminated Jackson and Williams. In October, 2011, the DNA profiles matched the DNA profiles of Marion Doll Sayles, 55, and Frederick Anderson, 52. • http://www.law.umich.edu/special/exoneration/pages/casedetail.aspx?caseid=3902 • Mugshotshttp://mugshots.com/US-Counties/Texas/Dallas-County-TX/Marion-Doll-Sayles.8428810.html ISI Hong Kong

35. Example 1. Matching pairs • Match all pairs from most alike to least alike HMA280 Chapter 9 Lecture

36. PairsWomen faces 5.docx HMA280 Chapter 9 Lecture

37. Two Dimensions with names HMA280 Chapter 9 Lecture

38. Cluster analysis: • This technique can be used to identify groups of similar crime situations or clusters based on crime characteristics. • Examples • groupings of similar criminals, prisons and campuses based on crime data can be identified. • the results of chemical analyses of glass samples which could be used by the police to help solve accident crimes. ISI Hong Kong

39. What Method of Analysis is Best? • What analyses do you recommend for the following situations? • Please suggest an analysis and a suitable graph in each case. • Where relevant you should indicate which is the dependent variable. • In the tutorial session we will perform these analyses, interpret the results and discuss any implications of these results. ISI Hong Kong

40. Feedback Opinio Survey on Usefulness of Methods (N=10)(Loglinear and MDS queried!) ISI Hong Kong

41. Feedback Opinio Survey Ratings on Teaching Method (N=10)(quizzes and text need work) ISI Hong Kong

42. Student Results(N=50) ISI Hong Kong

43. Conclusion: • Overall this unit provided students with some very useful tools that can be applied anywhere. • The forensic backdrop has served to catch the interest of the students and the teaching staff. • The Victoria Police are expected to provide more guest lecturers in the future, and we expect that some of their “Intel staff” will be sitting in on some of the lectures, no doubt providing very useful background information for the students. • The approach applied in this unit of letting relevant data shape the unit is currently also being applied in a new Sport Statistics unit, and hopefully this will also be a success. ISI Hong Kong

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45. Introduction to Forensic PsychologyUnit code: HAY120 • A comparative analysis of the Australian criminal justice system • Theoretical perspectives in criminology (psychological, sociological, psychiatric) • Defining and measuring crime: the uniform crime reporting system • Interviewing and deception detection techniques • Research methods in forensic psychology • Risk assessment • Eye-witness testimony and false memories • Developmental risk factors for criminal behaviour social risk factors, parental and family risk factors, psychological risk factors ISI Hong Kong

46. Advanced Topics in Forensic Psychology Unit code: HAY320 • Understanding the impact of crime on victims • The nature of traumatic reactions and post traumatic stress disorder (PTSD) • The impact of specific types of crime (topics may include: intimate partner violence, sexual violence, child abuse physical violence, homicide, indigenous victims of crime • The origins of criminal behaviour • Behaviouralmodels, social learning models, frustration induced criminality • Personality and crime • Human aggression and violence • Criminal psychopathy • Mental disorders and crime • Assessment and treatment of different types of offenders within the criminal justice system (offender types may include fire setters, violent offenders, sex offenders, cultural groups and indigenous offenders, juvenile offenders, offenders with intellectual disabilities) ISI Hong Kong

47. Introduction to Forensic ScienceUnit code: HES1020 • Introduction: key concepts of forensic science.chronological landmarks in the history of forensic science. • Legal aspects of forensic science and the admissibility of forensic evidence in the legal system. • issues related to drugs in racing, sport and the workplace. • Forensic application of Infra Red (IR), Mass Spectroscopy (MS), High Performance Liquid. • Chromatography (HPLC) and Gas Chromatography (GC).Processes involved in DNA fingerprinting ISI Hong Kong