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Making Social Work Count Lecture 10 An ESRC Curriculum Innovation and Researcher Development Initiative.

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Presentation on theme: "Making Social Work Count Lecture 10 An ESRC Curriculum Innovation and Researcher Development Initiative."— Presentation transcript:

1 Making Social Work Count Lecture 10 An ESRC Curriculum Innovation and Researcher Development Initiative

2 How can relationships between multiple variables be understood?

3 Learning outcomes understand how multiple variables may interact with one another appreciate the role of intervening variables be aware of how interpretation of statistics may be affected by outliers and misinterpretations

4 How might we describe the students in this class? By gender By age By ethnicity By religion By nationality What might be the limitation of only using one or two of these variables to describe the class?

5 What is multivariate analysis? By using some statistical procedures we can begin to better understand the multiple relationships between a group of obviously related units (eg people, families, households) of which the degree and nature of the relationship is imperfectly known. It also assists to understand that the relationships may not be distributed evenly across the group being studied.

6 Sexual violence – a significant social, health and legal issue Consequences for victims: – physical injury, long-term mental health issues, self-harm and suicide, disruption in intimate relationships, constrained socio-economic opportunities, routes into offending behaviour and wider social exclusion Most national and international government strategies advocate public awareness raising to – deter potential perpetrators – reach out to victims It is estimated that 1 in 4 individuals have been subject to inappropriate sexual behaviour as children or adults

7 Public awareness campaigns Changes in the law relating to criminal offences [Sexual Offences (Northern Ireland) Order 2008] Public awareness campaign from November 2009 – March 2010 Potential for victims to come forward seeking support Adverts on both television and radio, posters on advertising hoardings and in telephone kiosks, bus shelters and washrooms, insertions in the press (including titles catering specifically for Chinese and Polish ethnic minorities), and some targeting of internet websites

8 Objectives of study An analysis of administrative data to: Determine the number and type of calls about sexual violence to a regional helpline support service during the six months of the public awareness campaign (November 2009 – April 2010) Compare the number and type of calls about sexual violence to a regional helpline support service in the six month period prior to the public awareness campaign (May 2009 – October 2009) Compare the number and type of calls about sexual violence to a regional helpline support service in the three months following the public awareness campaign (May 2010 - July 2010)

9 Key questions Who called? When did they call? What prompted them to call? How was this related to the public awareness campaign? What did they call about? What patterns emerged that might inform our understanding of sexual violence?

10 Television campaign Radio Campaign Total unique callers: 102 Callers per week during the public awareness campaign

11 Source of awareness of helpline service N=102

12 Age and gender profile of callers N=98 Mean: 36yrs SD: 14.6

13 Age when sexual abuse started N=99 all categories exclusive

14 Relationship of perpetrator to victim N=76

15 Currency of sexual violence

16 Currency of sexual victimisation Current Sexual VictimisationHistoric Sexual Victimisation

17 Sexual abuse by partner AgeCurrentHistoricalTotal MaleFemaleMaleFemale 0-10yrs old00000 11-20yrs old00000 21-30yrs old03025 31-40yrs old11013 41-50yrs old13239 51-60yrs old00044 61yrs plus00011 Total2721122

18 Frequency of abuse

19 Primary location of abuse

20 Support services accessed N=49 Categories not mutually exclusive

21 Impact of sexual victimisation N=102 Categories not mutually exclusive

22 Comparison of calls about sexual violence over time

23 Controlling for intervening variables An intervening variable is one that is both a product of the independent variable and a cause of the dependent variable

24 PTSD and CBT Little research evidence to inform interventions related to terrorist-related events CBT is widely recognised as an effective method of intervention for PTSD Using an RCT design the study sought to test whether CBT might be effective in this situation 58 consecutive patients with chronic PTSD (median 5.2 years, range 3 months to 32 years) mostly resulting from multiple traumas linked to terrorism and other civil conflict Immediate cognitive therapy compared with a waiting list control condition for 12 weeks followed by treatment.

25 Post Traumatic Stress Disorder in the Context of Terrorism and Other Civil Conflict in Northern Ireland: A randomised control trial Duffy, M., Gillespie, K., Clark, D.M. (2007) Post-traumatic stress disorder in the context of terrorism and other civil conflict in Northern Ireland: randomised controlled trial. British Medical Journal 334: 1147-50.

26 Outcomes At 12 weeks after randomisation, immediate cognitive therapy was associated with significantly greater improvement than the waiting list control group on a range of measures relating to PTSD, depression and self rated occupational and social functioning However, there were differences in the progress made by patients that could not be explained by their age, pre- existing symptoms or the number of sessions they completed It appeared that the therapists themselves had a differential impact on the outcomes, even though they all applied the same model of CBT

27 Controlling for intervening variables An intervening variable is one that is both a product of the independent variable and a cause of the dependent variable CBT Professional Trauma Independent Variable Intervening Variable Dependent Variable

28 IndividualNet WorthSource Bill Gates$46 billionMicrosoft Jeff Bezos$5.1 billionAmazon Craig McCaw$2 billionTelecommunications The impact of outliers Number of households, Medina, Seattle, USA n=1206 Average net worth1206 households$44,253,482 Average net worth(remove Bill Gates)$6,115,934 Average net worth(remove top three)$224,189

29 The prosecutor’s fallacy One morning a woman walking in an expensive part of London had her bag snatched – unfortunately it contained a large amount of cash. She and an eyewitness both described the thief as a very tall man (over 2 metres), between 20 and 30 years old, with red hair and a pronounced limp.

30 Later... Arrest Later that day an eagle-eyed policeman spotted a man fitting this description with a large plasma-screen TV. He was arrested, but denied being the bag-snatcher. He was unable to provide an alibi. Further investigation revealed that the TV was paid for in cash. Prosecution In court the prosecutor said "This case is quite unusual. Because of the nature of the crime, there is no forensic evidence. The thief was wearing gloves, there were no footprints, etc. We can show his guilt using other methods. To illustrate this we call an expert witness."

31 The criminologist The prosecutor produced a criminologist who quoted some statistics from demographic tables: "In London, the probability of having the said characteristics is: CharacteristicProbability Bring male0.51 Being 2m tall0.025 Being between 20 - 30yrs old0.25 Being red headed0.037 Having a pronounced limp0.017

32 “And because these are all independent of each other, we can multiply them together to obtain the probability of one person having all these characteristics: “ 0.51 x 0.025 x 0.25 x 0.037 x 0.017 = 0.000002 He announced with a flourish: "The chance of any random individual sharing all these characteristics is vanishingly small - only 0.000002. The prisoner has them all."

33 Another way of expressing this is to say that the chances of having all of these characteristics is one in half a million people... but there are 10 million people in London... therefore there are likely to be 20 people in London who fit this description. Or to put it another way, the chance of his innocence is 19 in 20, not 1 in half-a-million.

34 What happened here? The expert witness confused two things: the probability of an individual matching the description, and the probability of an individual who does match the description being guilty They are not the same!

35 Numbers and real people It is easier to see the fallacy as soon as the probability of 0.000002 is turned into numbers of real people. When you bear in mind that the population of possible suspects is 10 million, 1 in half a million easily translates into 20 possible suspects - the accused is only one of this group. If we are to be convinced of his guilt with no other evidence, we would want to know that the other 19 had been excluded. And that is without even considering people who might have come up to London for the day!

36 Learning outcomes Completing this session has enabled you to: understand how multiple variables may interact with one another appreciate the role of intervening variables become aware of how the interpretation of statistics may be affected by outliers and misinterpretations


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