Making Social Work Count Lecture 10

An ESRC Curriculum Innovation and Researcher Development Initiative

How can relationships between multiple variables be understood?

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

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?

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.

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

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

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)

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?

0

2

4

6

8

10

12

14

Callers per week

Television campaign Radio Campaign

Total unique callers: 102

Callers per week during the public awareness campaign

Source of awareness of helpline service

Television

RadioPoster/Card

Health Professional

Community/Voluntary Worker

Relative/Friend

OtherNot Specified

0

5

10

15

20

25

30

3529

68

14 1316

8 8

N=102

Age and gender profile of callers

Male Female0

5

10

15

20

25

30

20

2

8

2

18

6

17

7

25

1

8

1 2

0-10 yrs 11-20yrs 21-30yrs 31-40yrs 41-50yrs 51-60yrs61yrs plus

Mean: 37yrs SD: 13.5 N=98

Mean: 36yrs SD: 14.6

Age when sexual abuse started

Pre-school aged

Primary school aged

Post primary school aged

ChildYoung adult (18-25yrs)

AdultBoth child and adult

Not recorded

Perpetrator

0

5

10

15

20

25

30

2

19

13

23

8

24

1

5 4

N=99 all categories exclusive

Relationship of perpetrator to victim

FatherMother

PartnerSibling

Other relative

Neighbour

Family friend

Person with a duty to care

Stranger

Other

0

5

10

15

20

25

11

1 1 1

18

3 36

2 20 1

20

02

03

1 0 1

Child Adult

N=76

Currency of sexual violence

21%

76%

3%

Current Historial Perpetrator

Currency of sexual victimisation

Current Sexual Victimisation

0-10 yrs

11-20yrs

21-30yrs

31-40yrs

41-50yrs

51-60yrs

61yrs plus

01234567

Male Female

Historic Sexual Victimisation

0-10 yrs

11-20yrs

21-30yrs

31-40yrs

41-50yrs

51-60yrs

61yrs plus

02468

101214161820

Male Female

Sexual abuse by partner

Age Current Historical Total

Male Female Male Female

0-10yrs old 0 0 0 0 0

11-20yrs old 0 0 0 0 0

21-30yrs old 0 3 0 2 5

31-40yrs old 1 1 0 1 3

41-50yrs old 1 3 2 3 9

51-60yrs old 0 0 0 4 4

61yrs plus 0 0 0 1 1

Total 2 7 2 11 22

Frequency of abuse

Abuse as a Child Abuse as an Adult

Single Incident

9 8

Multiple Incidents

24 15

Primary location of abuse

Freq of

abuse

Family Home Community Facility

Perpetrators Home if Not Family Home

Public Area Not Recorded

ChildSingleMultipleNR

065

012

100

010

81618

AdultSingle Multiple NR

3147

100

000

000

439

Total 35 4 1 1 58

Support services accessed

GP Mental Health Professional

PoliceSocial Services

NexusWomen's Aid

Substance Misuse Services

LawyerA&E

Other

0102030405060

4843

17 1611 8 4 2 1

6

N=49

Categories not mutually exclusive

Impact of sexual victimisation

Mental health

Suicidality

Deliberate self harm

Physical health

Substance misuse

Sexual relationships

Sexual health

Other

0102030405060708090

100

75

37

90

14 13 9 6

32

N=102

Categories not mutually exclusive

Comparison of calls about sexual violence over time

May 2009 - Oct 2009 Nov 2009 - April 2010 May - July 20100

200400600800

1000120014001600

304

1487

601

Number of Calls to Helpline

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

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.

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.

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

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

Individual Net Worth SourceBill Gates $46 billion MicrosoftJeff Bezos $5.1 billion AmazonCraig McCaw $2 billion Telecommunications

The impact of outliers

Number of households, Medina, Seattle, USA n=1206Average net worth 1206 households $44,253,482Average net worth (remove Bill Gates) $6,115,934Average net worth (remove top three) $224,189

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.

Later...

ArrestLater 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.

ProsecutionIn 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."

The criminologist

The prosecutor produced a criminologist who quoted some statistics from demographic tables: "In London, the probability of having the said characteristics is: Characteristic Probability

Bring male 0.51

Being 2m tall 0.025

Being between 20 - 30yrs old 0.25

Being red headed 0.037

Having a pronounced limp 0.017

“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."

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.

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!

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!

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