Physiochemical properties of nanomaterials and its nanotoxicity.pptx
Multilevel stable communities esc1
1. A multilevel study of perceived risk of
victimization and avoidance behaviour
Prof. dr. L. Pauwels
Drs. W. Hardyns
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 1
2. Introduction
Fear of crime as a hot issue in theory, empirical
research (Pleysier, 2009; Vanderveen,2006) and
policy (recognizing the cost of fear-policing FOC-
COP; Skogan, Trojanowics- since 1980s, Ponsaers,
2001)
Urban villages often overemphasized as ideal
communities with low levels of crime and fear
(Jacobs, 1961)
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 2
4. Introduction and goals
In this contribution we are interested in further
unravelling the nature of the geographical
differences in feelings of insecurity.
Assessing the nature of observed area differences
in avoidance behaviour, perceived risk of
victimization, perceived disorder and within-
community victimization, controlling for
compositional effects.
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 4
5. Introduction and goals
FOC in this study: NOT fear, but two dimensions of
feelings of insecurity:
Avoidance behaviour and perceived risk of victimization
Descriptive results and hypothesis testing
Theory driven:
Vertical theoretical integration using the end-to-end
strategy
Integrating complementary theoretical models of fear at
two levels:
Community level: CE/SD/BW
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 5
6. Theoretical backdrop
Community structure may either foster or disturb
mechanisms of informal control (social trust and
shared common goals-Bursik and Grasmik, 1993;
Taylor and Harrell, 1996)
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 6
7. Theoretical backdrop
Community characteristics in the present study:
Community stability: structural condition for
the development of local social ties (Sampson,
Raudenbush and Earls, 1997)
Single households: probably the best structural
proxy for lack of control (social eyes) (See
Sampson and Wooldredge, 1987)
Community crime and disorder (Robinson,
Lawton, Taylor and Perkins, 2003) as community
outcome in macro-level theory
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 7
8. Theoretical backdrop
Disorder, crime, victimization and fear are macrolevel
consequences of community structural instability
From a social ecological perspective individuals are
studied within their context: what are the advantages
of stable communities in a Belgian context?
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 8
9. Goals and hypotheses
“Do community stability, low informal control and
crime and disorder leave their marks on
(1) attitudinal (perceived risk of victimization) and (2)
behavioural outcomes of individuals (avoidance
behaviour),
-> independent of indicators of population
composition (vulnerability indicators)?”
And can criminal victimization itself be explained by
the same community mechanisms?
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 9
10. Goals and hypotheses
An analytical approach: what is the “causal link”?
A mechanism-based approach
We introduce multiple mediating links at the
macro level
(1) community instability -> low social ontrol
(2) low social control -> increase in crime and
disorder
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 10
11. Goals and hypotheses
We introduce two mediating links at the micro
level
(1) Criminal victimization: lifestyle and routine
activities (Maxfield, 1987, Hindelang, Gottfredson,
Garofalo, 1978)
(2) Perceived disorder (Ross & Mirowsky,
1991;Sampson &Raudenbush, 2004)
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 11
13. Data and Methods
Federal Survey “Veiligheidsmonitor”: merging of
editions 2002, 2004, 2006
Approx 101,303 respondents clustered in 346
municipalities
Exclusion of municipalities < 40 respondents
(ecological reliability and validity!)
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 13
14. Data and Methods
Hierarchical multilevel models for continuous
variables (ML-estimation)
Block-wise regression models: at each step a
mediating mechanism is introduced
[Quality control: re-analysis with more robust
methods (ordinal hierarchical models, Poisson-based
models and Logistic models, cf violations of
assumptions)
Stable findings of effect parameters]
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 14
15. Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 15
Construct Items Factor
loading
Reliability
(Alpha)
Ecological
reliability
(Lambda)
Unsafety Does it happen that you feel unsafe? -- -- 0.841
Avoidance
behaviour
(3 items)
Does it happen that …
You avoid certain areas in your municipality
because you think they are not safe?
You avoid to open the door for strangers
because you think it is not safe
You avoid leaving home after dark because you
think it is not safe?
0.587
0.615
0.647
0.642 0.881
Perceived risk of
victimization
(4 items)
Within the next 12 months, …
How do you perceive the risk of your household
to become victim of burglary
How do you perceive your personal risk
to become victim of physical violence
or being threatened with physical
violence
How do you perceive your risk of becoming
victim of theft without violence or threats with
violence
How do you perceive your personal risk of
becoming victim of a traffic related offence
0.641
0.697
0.819
0.505
0.756 0.805
Perceived
Disorder and
Crime
(13 items)
Do you consider …as a problem in your
neighbourhood:
Theft of bicycles
Theft from car
Threats
Soiled house fronts/buildings
Incivilities from groups of adolescents
Harassing men and women on the streets
Car accidents
Litter in public
Destruction of phone cabins and bus stops
Burglary in homes or other buildings
Violence
Incivilities related to drug users
Car theft
0.504
0.665
0.704
0.655
0.621
0.754
0.576
0.560
0.660
0.588
0.825
0.729
0.720
0.908 0.917
Measurement issues
Stable
reliability
coefficienst
over time
(2002,
2004,2006)
16. Measuring within area victimization
Total within area victimization is a count variable
that summarizes the different times that a person
(or household) was set out for eight different
phenomena within the municipality of residence.
Eight phenomena: (1) burglary with theft, (2) attempted
burglary, (3) car-theft, (4) theft from car, (5) vandalism on
cars, (6) violent theft, (7) physical violence and (8) being
threatened with violence.
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 16
17. Results of hierarchical ML models
Intra class correlations (empty random intercept
versus controlling for compositional effects)
Avoidance behaviour (6.62%-6.44%)
Perceived risk of victimization (3.72%- 3.83%)
Within area victimization (1.96% - 2.15%)
Perceived disorder (10.04%-10.06%)
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 17
18. Results of hierarchical ML models
Perceived risk of
victimization
ICC
(1) Background variables
-M*
+ age*
-single HH*
- lower education*
Avoidance behaviour
ICC
(1) Background variables
-M *
+ age*
+ single HH*
+ lower education*
- home owners*
+Length of residence
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 18
19. Results of hierarchical ML models
Perceived risk of victimization and avoidance
behavior as dependent variables yield similar
results
(2) Moderating effect of within area victimization
and perceived disorder
(3) Independent contextual effect of community
stability
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 19
20. Results of hierarchical ML models
(4) Community stability also explains individual
differences in within area victimization and perceived
disorder
Suggests that context does matter in different ways.
Some future directions:
Level of analysis? Measurement issues?
Stability across age groups- suggested interaction
affects
Context matters- but: strongly dependent of
explanandum: (“putting space at its place- Titta, 2008”)
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 20
21. Slide 21
"cities are not made from their roofs,
stone walls, bridges and canals but from
men able to grasp opportunities and make
the most of them"
Alcaeus, BC 7th
century