Multilevel models for predicting
personal victimisation in England
and Wales
Andromachi Tseloni
Analysis of crime data
ESRC Research Methods Festival 2010
St. Catherine’s College, Oxford, 5-8 July 2010
Outline
• Victimisation theory and levels of analysis
• Data
• Dependent variable and covariates
• Statistical specification
• Modelling strategy
• Results
• Conclusions
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Victimisation theory
•Lifestyle & Routine Activity
(Hindelang et al. 1978; Cohen and Felson 1979; Felson 1998)
Individual Characteristics including Lifestyle
Level 1 unit of analysis: individual
•Social Disorganisation
(Shaw and McKay 1942; Sampson and Wooldregde 1987;
Sampson and Groves 1989)
Area Characteristics
Level 2 unit of analysis: quarter postcode sector
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Data
•Crimes, individual & household characteristics
taken from the 2000 British Crime Survey
– Incidents occurred within a 15´ walk from home to
respondents who have not moved house in the previous
year.
– 905 areas (sampling points=quarter postcode sectors)
– 4-29 households per sampling point (mean=10, standard
deviation=5.9)
– 15,774 individuals in total
•Area characteristics taken from the 1991 Census
Small Area Statistics
– 5% random error, standardised values
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Personal crimes
•Common assault
•Wounding
•Robbery
•Theft from person
•Other theft from person
•Sexual offences excluded
•Series incidents are truncated at 5 events.
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Figure 1: Personal Crimes across Areas
(mean=0.8, skewness=2.9, concentration=2.0)
60.0%
50.0%
Percent
40.0%
30.0%
20.0%
10.0%
0.0%
.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
14.00
arpe
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Figure 2: Personal Crimes across Individuals
(mean=0.05, skewness=11.9,
concentration=1.6)
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Covariates: Household Level (1)
•Demographic (male, age, non-white ethnicity)
•Social (marital status, living with children,
education, social class)
•Tenure and accommodation type
•Household Income
•Length of residence in the area
•Routine activities (away from home, going to pubs,
clubs and drinking alcohol)
•Area type (inner city, urban)
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Covariates: Area Level (2)
• 9 Regions of England and Wales (with South East=basis)
• Percent (%) households renting privately
• % Single adult non-pensioner households
• % Afro-Caribbean
• % Indian-subcontinent
• % Population 16-24 years
• % Households in housing association accommodation
• % Households moved in the area last year
• Population density
• Poverty
[0.859 percent lone parent households+0.887 percent households
without car-0.758 nonmanual-0.877 percent owner occupied households+
0.720 mean number of persons per room+0.889 percent households renting
from LA].
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Statistical Model
• Multilevel negative binomial regression with extra negative binomial
variation
Cameron and Trivedi 1986 J. of Appl. Econometrics
Goldstein 1995 Multilevel Statistical Models
Snijders and Bosker 1999 Multilevel Analysis
Tseloni 2000 J. of Quant. Crim.
• ln ij=nij=Xij+q=pq=0 uqjzqij+q=Q-1q=p+1 uqjzqj
i=1,...,15,774, j=1,...,905
(1)
[uqj]~N(0,u)
ln ij= nij+ eij
(2)
where exp(e0ij) follows a gamma probability distribution
• E(Yij)= ij= exp( nij) &
var(Yij)=  ij+ 2ij /
(3)
 /2 overdispersion due to unexplained heterogeneity between
individuals & 2/ precision parameter
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Modelling strategy
Software MLwiN 2.11
Gradually added covariates
The ones with p-value of χ12 < 0.10 were retained
Five models fitted:
Baseline model with just a random intercept
Fixed individual and household effects
Fixed individual, household and routine activities or lifestyle
effects
Fixed individual, household, lifestyle and area effects
Fixed individual, household, lifestyle and area effects with
fixed (cross-cluster) interactions
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Baseline model
Interpretation
• Pecrij denotes number of
personal crimes
• ij =0.046=exp(-3.074) is
the estimated mean of
personal crimes
• α=2.206 & ν are the
parameters of
overdispersion
• var (u0j)=0.187, is the
between areas variance
of personal crimes which
is non- significant
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Final model
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Interpretation of results: constant
• ij =0.016=exp(-2.332-0.0442x51+0.00018x512)
is the estimated number of personal crimes that a
51 years old married white woman without children
is expected to experience per year. This woman
has household income less than £30,000, goes to
pubs less than 3 times per week and to clubs less
often than once a week. Finally, she lives in her
owned detached house for over 2 years in a rural
area of England and Wales with national average
population density and poverty.
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Interpretation of results:
Figure 3: Predicted personal crimes and individual’s age
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Interpretation of results:
significant socio-demographic effects
Positive
Negative
• Single
• Asian or black
• Divorced, especially with children
• Non – definable social class
• Windowed in high population
density areas
• Living in inner city
• Having children
• Social renters
• Private renters
• In terraced houses
• In flats or maisonettes
• Movers (less than 2 years in the
same area)
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Interpretation of results:
significant lifestyle and area positive effects
Area
Lifestyle
• Men who go to clubs at least
once per week
• Poverty
• Population density
• Parents of children who go
to pubs at least 3 times per
week
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Interpretation of results:
Figure 4: Predicted personal crimes and area poverty
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Interpretation of results:
Figure 5: Predicted personal crimes and
area population density
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Interpretation of results:
Figure 6: Predicted personal crimes for widowed
individuals and others across area population density
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Conclusions
• Personal criminal victimisation is predicted by individual and area
characteristics.
• While significant unexplained heterogeneity between individuals
remains, area information fully accounts for the area clustering of
personal victimisation.
• The results partly confirm the assertions of lifestyle /routine
activities theory. Being male, non-white, inner city resident or
having an outgoing lifestyle in general are exceptions.
• The results also confirm the social disorganisation theory with
respect to economic deprivation and population density. But they
also showed that ethnic heterogeneity, residential mobility and high
proportions of young population do not predict personal crimes.
• The two theories should integrate into a single one as the effects of
some risk factors on personal victimisation are communicable
and/or depend on context.
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