network analysis - applications of networks in psychology ...keywords :r, networks, correlations,...
TRANSCRIPT
Network AnalysisApplications of networks in Psychology and beyond
Sacha Epskamp
University of AmsterdamDepartment of Psychological Methods
12-05-2014TNO
Outline
I What is a network?I What can we do with networks?I How can we obtain a network structure?I Example of network analysis on radicalization data
MDInsomnia
Fatigue
Concentration
Worry
MDInsomnia
Fatigue
Concentration
Worry
MDInsomnia
Fatigue
Concentration
Worry
MDInsomnia
Fatigue
Concentration
Worry
MDInsomnia
Fatigue
Concentration
Worry
MDInsomnia
Fatigue
Concentration
Worry
Psychology as a complex system
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Disorders usually first diagnosed in infancy, childhood or adolescenceDelirium, dementia, and amnesia and other cognitive disordersMental disorders due to a general medical conditionSubstance−related disordersSchizophrenia and other psychotic disordersMood disordersAnxiety disordersSomatoform disordersFactitious disordersDissociative disordersSexual and gender identity disordersEating disordersSleep disordersImpulse control disorders not elsewhere classifiedAdjustment disordersPersonality disordersSymptom is featured equally in multiple chapters
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Disorders usually first diagnosed in infancy, childhood or adolescenceDelirium, dementia, and amnesia and other cognitive disordersMental disorders due to a general medical conditionSubstance−related disordersSchizophrenia and other psychotic disordersMood disordersAnxiety disordersSomatoform disordersFactitious disordersDissociative disordersSexual and gender identity disordersEating disordersSleep disordersImpulse control disorders not elsewhere classifiedAdjustment disordersPersonality disordersSymptom is featured equally in multiple chapters
What is a network?
What is a network?
I A network is a set of nodes connected by a set of edges
Edge
Node 1
Node 2
What is a network?
I A network is a set of nodes connected by a set of edgesI A node represents an entity, this can be anything:
I PeopleI CitiesI Symptoms
I An edge represents some connection between two nodes.Again, this can be anything:
I Friendship / contactI DistanceI Comorbidity
Anne is friends with Laura:
Friendship
Anne
Laura
Anne is friends with Laura and Roger, but Laura is not friendswith Roger:
Anne
LauraRoger
Networks can be weightedAnne is better friends with met Roger than Laura:
Anne
LauraRoger
Weights can be signedAnne is friends with Roger and Laura, but Roger and Lauradon’t like each other at all!
Anne
LauraRoger
Networks can be directedAnne likes Laura, but Laura doesn’t like Anne:
Anne
Laura
Networks can model causalityIf it rains the grass becomes wet:
Rain
Grass wet
If the sprinkler is on the grass also becomes wet:
Rain
Grass wetSprinkler
Directed Undirected
Unw
eigh
ted
Wei
ghte
d
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What can we do with networks?
What can we do with networks?
Besides providing a interpretable structure of a complexsystem, we can also use networks to compute uniquemeasures such as:
I DistanceI How fast can symptom A influence symptom B?
I CentralityI Which symptom is the most important?
I Connectivity?I How well are symptoms connected?
DistanceNode A is much further away from node B than node C:
C
A
B
CentralityNode A is much more influential than node B:
A
B
Connectivity
The left network is much denser connected than the right one:
Friendship
Friendship
Relationships
Sexual contacts
Networks can be simulated given sufficient information about apopulation:
Psychopathology as a virus...
How to get a network?
JSS Journal of Statistical SoftwareMay 2012, Volume 48, Issue 4. http://www.jstatsoft.org/
qgraph: Network Visualizations of Relationships in
Psychometric Data
Sacha EpskampUniversity of Amsterdam
Angelique O. J. CramerUniversity of Amsterdam
Lourens J. WaldorpUniversity of Amsterdam
Verena D. SchmittmannUniversity of Amsterdam
Denny BorsboomUniversity of Amsterdam
Abstract
We present the qgraph package for R, which provides an interface to visualize datathrough network modeling techniques. For instance, a correlation matrix can be rep-resented as a network in which each variable is a node and each correlation an edge; byvarying the width of the edges according to the magnitude of the correlation, the structureof the correlation matrix can be visualized. A wide variety of matrices that are used instatistics can be represented in this fashion, for example matrices that contain (implied)covariances, factor loadings, regression parameters and p values. qgraph can also be usedas a psychometric tool, as it performs exploratory and confirmatory factor analysis, usingsem and lavaan; the output of these packages is automatically visualized in qgraph, whichmay aid the interpretation of results. In this article, we introduce qgraph by applying thepackage functions to data from the NEO-PI-R, a widely used personality questionnaire.
Keywords: R, networks, correlations, data visualization, factor analysis, graph theory.
1. Introduction
The human visual system is capable of processing highly dimensional information naturally.For instance, we can immediately spot suggestive patterns in a scatterplot, while these samepatterns are invisible when the data is numerically represented in a matrix.
We present qgraph, a package for R (R Development Core Team 2012), available from the Com-prehensive R Archive Network at http://CRAN.R-project.org/package=qgraph. qgraphaccommodates capacities for spotting patterns by visualizing data in a novel way: throughnetworks. Networks consist of nodes (also called ‘vertices’) that are connected by edges
qgraph
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NeuroticismExtraversionOpennessAgreeablenessConscientiousness
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NeuroticismExtraversionOpennessAgreeablenessConscientiousness
Epskamp, Cramer, Waldorp, Schmittmann, and Borsboom (2012)
Correlations
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Cutoff: 0.4Minimum: 0.25 Maximum: 0.77
Correlation networks
I Correlation networks are useful as a visualization toolI But correlations are not easily interpreted as networks due
to many spurious connectionsI A partial correlation network, in which you display the
correlations conditional on all other variables in thenetwork, is easier interpretable:
I Two nodes are connected if and only if there is covariancebetween those nodes that can not be explained by anyother variable in the network
I Such a network is called a Markov Random Field
Markov Random Fields
I A specific type of network is called a Markov Random FieldI Undirected networks with the property that a node is
independent of all other nodes given its neighbors(connected nodes)
I InterpretableI High predictive powerI If data is assumed normal these are called Gaussian
random fields or Partial correlation matricesI If the data is binary these are called the Ising modelI Typically we want to estimate a sparse structure, which we
can do using a LASSO penalty
Markov Random Fields
A
BC
I A predicts C and B, but C betterI C predicts A, and B via AI B predicts A, and C via AI B and C are independent given A
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AfoAge
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A. Correlation network
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B. Partial Correlation network
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C. Adaptive lasso network
Paper by Giulio Costantini, Sacha Epskamp, Denny Borsboom, MarcoPerugini, René Mõttus, Lourens J. Waldorp & Angelique O. J. Cramersubmitted
# Load packages:library("qgraph")library("parcor")
# Read data:Data <- read.csv("HEXACOfacet.csv")
# Plot correlations:qgraph(cor(Data), layout = "spring")
# Plot partial correlations:qgraph(cor(Data), layout = "spring", graph = "concentration")
# Plot LASSO network:adls <- adalasso.net(Data)network <- as.matrix(forceSymmetric(adls$pcor.adalasso))qgraph(network, layout = "spring")
Partial correlations (using adaptive lasso)
Paper by Jolanda J. Kossakowski, Jacobien M. Kieffer, Sacha Epskamp &Denny Borsboom in preparation
Zooming in: Physical Functioning en Item 2
Three datasets
Figure: Left: AMC cancer. Middle: NKI cancer. Right: NKI healthy.
Ising Network
fal slewak
hyp
sad
irr
anx
rmo
mod
qmo
app
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Paper by Claudia D. van Borkulo, Denny Borsboom, Sacha Epskamp TessaF. Blanken, Lynn Boschloo, Robert A. Schoevers & Lourens J. Waldorpsubmitted
Individual networks
C
E
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P
!"#$%&%! + "' (')$*'%+ "+ (+)$*' + ... + #!
Bringmann, L., Vissers, N., Wichers, M., Geschwind, N., Kuppens, P., Peeters, F., Borsboom, D., & Tuerlinckx, F. (2013). A network approach to psychopathology: New insights into clinical longitudinal data. PLoS ONE.
Wednesday, October 2, 13
A subject over time...
Example of network analysis inRadicalization
Journal of Social Issues, Vol. 69, No. 3, 2013, pp. 586--604
Determinants of Radicalization of Islamic Youth in theNetherlands: Personal Uncertainty, PerceivedInjustice, and Perceived Group Threat
Bertjan Doosje∗University of Amsterdam
Annemarie Loseman and Kees van den BosUtrecht University
In this study among Dutch Muslim youth (N = 131), we focus on the process ofradicalization. We hypothesize that this process is driven by three main factors: (a)personal uncertainty, (b) perceived injustice, and (c) perceived group threat. Usingstructural equation modeling, we demonstrate that personal uncertainty, perceivedinjustice, and group-threat factors are important determinants of a radical beliefsystem (e.g., perceived superiority of Muslims, perceived illegitimacy of Dutchauthorities, perceived distance to others, and a feeling of being disconnectedfrom society). This radical belief system in turn predicts attitudes toward violenceby other Muslims, which is a determinant of own violent intentions. Results arediscussed in terms of the role of individual and group-based determinants ofradicalization.
When people decide to join radical groups, this can have dramatic conse-quences. For example, radical groups can decide to let their actions speak. Radicalgroups as diverse as Tamil Tigers, IRA, Al-Qaeda, FARC, and ETA all have in
∗Correspondence regarding this article should be addressed to Bertjan Doosje, Department ofSocial Psychology and Department of Political Science, University of Amsterdam, Weesperplein 4,Amsterdam 1018 XA, The Netherlands. Tel: +31 20-5256885 [e-mail: [email protected]].
We thank the Ministry of Justice of the Netherlands (NCTb/WODC) for awarding a grant tofinance this research. We thank the head (A. W. A. Erkens) and members of the supervising committee(F. Beijaard, D. Carabain, J. Dagevos, F. van Gemert, J. J. van Miert, C. Nassau, and C. J. de Poot)for their useful comments during this research project. This research was also funded in part by aVICI innovational research grant from the Netherlands Organization for Scientific Research (NWO,453.03.603) awarded to Kees van den Bos.
Annemarie Loseman and Kees van den Bos, University of Utrecht, Van Unnik Building,Heidelberglaan 2, Utrecht, 3584 CS, The Netherlands; [email protected]; [email protected].
586
C© 2013 The Society for the Psychological Study of Social Issues
Radicalization example
Doosje, Loseman, and Bos (2013) investigated the process ofradicalization of Dutch youth. They looked at possibledeterminants for adopting a radical beliefs system, which inturn can cause the basis for violent attitudes. An oversimplifiedversion of their model is:
Determinants → Radical beliefs system → Violent attitudes
To test this model they measured several constructs in 131Islamic youths in the Netherlands.
Radical belief system:I Perceived illegitimacy of authoritiesI Perceived in-group superiorityI Perceived distance to other peopleI Perceived societal disconnectedness
Determinants:I Personal uncertaintyI Perceived injusticeI Perceived group threat
Background variables:I In-group identificationI individualistic relative deprivationI collective relative deprivation
Determ
inantsof
Radicalization
ofIslam
icY
outhin
theN
etherlands595
Table 1. The Means, Standard Deviations, and Inter-Correlations of All the Constructs
M SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14
1. Identification 4.56 0.85 – − .19* .08 − .25* .42* .07 .08 − .06 − .28* .09 − .17 − .25* − .04 − .072. Ind. Rel. Depri. 2.39 0.81 – .49* .36* .23* .50* .21* .50* .25* .12 .17 .21* .12 .093. Col. Rel. Depri. 3.31 0.92 – .11 .54* .62* .26* .38* .21 .31* .18* .09 .20* .104. Int. Anxiety − 0.20 0.17 – .01 .15 .19* .21* .35* .08 .22* .26* .18* .145. Symbolic Threat 3.46 0.76 – .64* .21* .24* .07 .39* .01 .04 .17 − .016. Realistic Threat 3.10 0.88 – .27* .34* .16 .35* .19* .14 .26* .167. Per. Em. Uncertain. 2.84 0.67 – .10 .08 .29* .18 .00 .30* .148. Perc. Proc. Injustice 2.38 0.68 – .15 .01 .03 .23* .04 .069. Perc. Illegitimacy 2.37 0.02 – .22* .17* .35* .35* .24*
10. Perc. Ingr. Super. 3.26 0.93 – .34* .08 .53* .30*11. Distance 2.32 0.66 – .08 .44* .39*12. Disconnected 2.79 0.96 – .24* .0013. Moslim Violence 2.89 1.06 – .47*14. Violent Intentions 2.08 0.91 –
Note. 2 = Individual Relative Deprivation, 3 = Collective Relative Deprivation, 4 = Intergroup Anxiety, 5 = Symbolic Threat, 6 = Realistic Threat, 7 =Personal Emotional Uncertainty, 8 = Perceived Procedural Injustice, 9 = Perceived Illegitimacy, 10 = Perceived Ingroup Superiority. *p < .05.
CorrelationsThe qgraph package can be used to visualize correlations as anetwork (Epskamp et al., 2012):
1
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3
4
5
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1314
1: In−group Identification2: Individual Deprivation3: Collective Deprivation4: Intergroup Anxiety5: Symbolic Threat6: Realistic Threat7: Personal Emotional Uncertainty8: Perceived Injustice9: Perceived Illegitimacy authorities10: Perceived In−group superiority11: Distance to Other People12: Societal Disconnected13: Attitude towards Muslim Violence14: Own Violent Intentions
To test the hypothesized model, Doosje et al. (2013) usedStructural Equation Modelling (SEM). SEM is a powerfulmodeling framework that has been one of the main focuses ofPsychometrics for decades. In essence, it can be seen asconfirmatory testing a network of observed and unobservedvariables where typically is assumed that:
I All paths are directed and indicate linear effectsI All variables are assumed normally distributedI The network is acyclic
596 Doosje, Loseman, and van den Bos
Symbolic Threat
.21
.18
R2=.39
R2=.17
R2=.18
R2=.26
R2=.16
R2=.05
In-group Identification
Individual Deprivation
PersonalEmotional
Uncertainty
Perceived Injustice
Realistic Threat
Perceived Illegitimacy Authorities
Perceived In-group
Superiority
Distance to Other People
Societal Dis-Connected
Attitude towards Muslim
Violence
Own Violent Intentions
R2=.05
R2=.43
R2=.40
-.18
.29
.23
.37 .36
.28
.15
.35
Collective Deprivation
Intergroup Anxiety
.25
.26
.16
.35
.23
-.21
.51
R2=.27
R2=.07
.41
.17
.26
.53
.33
Background Determinants Radical Belief ViolentVariables System Intentions
Fig. 1. Final structural equation model. All paths are significant. R2 = % variance explained.
Determinants of perceived injustice, personal uncertainty, and groupthreat. As hypothesized, perceived injustice is predicted most strongly by in-dividual deprivation. When people feel personally deprived, they experience thisas unjust. Collective deprivation was another significant predictor of perceivedinjustice. Collective deprivation was the only predictor of personal uncertainty: tothe extent that people experience deprivation as group members, they feel person-ally uncertain. As expected, realistic group threat was most strongly predicted bycollective deprivation, and individual deprivation constituted another significantpredictor. Again in line with hypotheses, when people perceived a high level ofcollective deprivation and in-group identification, they experienced high levels ofsymbolic threat. Finally, high intergroup anxiety was associated with high lev-els of individual deprivation, and unexpectedly, low in-group identification wasassociated with higher levels of intergroup anxiety.
Determinants of radical belief system. As expected, perceived illegitimacyof Dutch authorities was predicted by symbolic threat as well as intergroup anx-iety. Thus, to the extent that people feel symbolically threatened and experience
Interpreting SEM results
SEM can be used to test if the data rejects a theorized model.But care should be taken in that a fitting SEM model does notmean the model is correct. Many equivalent models could fitthe data just as well.
Equivalent model
anxiety
sym_threat
real_threat
uncertainty
injustice
illegitemacy
superiority
distance
disconnected
muslim_violence
own_violentidentification
in_dep
col_dep
Equivalent models
anxiety
sym_threat
real_threat
uncertainty
injustice
illegitemacy
superiority
distance
disconnected
muslim_violence
own_violent
identification
in_dep
col_dep
anxiety
sym_threat
real_threat
uncertainty
injustice
illegitemacy
superiority
distance
disconnected
muslim_violence
own_violent
identification
in_dep
col_dep
Equivalent models
anxiety
sym_threat
real_threat
uncertainty
injustice
illegitemacy
superiority
distance
disconnected
muslim_violence
own_violent
identification
in_dep
col_dep
anxiety
sym_threat
real_threat
uncertainty
injustice
illegitemacy
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disconnected
muslim_violence
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disconnected muslim_violence
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disconnected muslim_violence
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uncertainty
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illegitemacy
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distance
disconnected muslim_violence
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in_dep
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anxiety
sym_threat
real_threat
uncertainty
injustice
illegitemacy
superiority
distance
disconnected
muslim_violence
own_violent
identification
in_dep
col_dep
Equivalent models
anxiety
sym_threat
real_threat
uncertainty
injustice
illegitemacy
superiority
distance
disconnected
muslim_violence
own_violent
identification
in_dep
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sym_threat
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injustice
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muslim_violence
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disconnected
muslim_violence
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muslim_violence
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muslim_violence
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identification
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uncertainty
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illegitemacy
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distance
disconnected
muslim_violence
own_violent
identification
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anxiety
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uncertainty
injustice
illegitemacy
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distance
disconnected muslim_violence
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uncertainty
injustice
illegitemacy
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distance
disconnected
muslim_violence
own_violent
identification
in_dep
col_dep
anxiety
sym_threat
real_threat
uncertainty
injustice
illegitemacy
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distance
disconnected
muslim_violence
own_violent
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in_dep
col_dep
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muslim_violence
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Equivalent models
anxiety
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uncertainty
injustice
illegitemacy
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distance
disconnected
muslim_violence
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in_dep
col_dep
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disconnected
muslim_violence
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disconnected
muslim_violence
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muslim_violence
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muslim_violence
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muslim_violence
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muslim_violence
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muslim_violence
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Equivalent models
anxiety
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injustice
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disconnected
muslim_violence
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in_dep
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superiority
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disconnected
muslim_violence
own_violent
identification
in_dep
col_dep
anxiety
sym_threat
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uncertainty injustice
illegitemacy
superiority distance
disconnected
muslim_violence
own_violent
identification
in_dep
col_dep anxiety
sym_threat
real_threat
uncertainty
injustice
illegitemacy
superiority
distance
disconnected
muslim_violence
own_violent
identification
in_dep
col_dep
anxiety
sym_threat
real_threat
uncertainty injustice
illegitemacy
superiority distance
disconnected
muslim_violence
own_violent
identification
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col_dep
anxiety
sym_threat
real_threat
uncertainty
injustice
illegitemacy
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disconnected
muslim_violence
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injustice
illegitemacy
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disconnected
muslim_violence
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col_dep
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sym_threat
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illegitemacy
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disconnected
muslim_violence
own_violent
identification
in_dep
col_dep
anxiety
sym_threat
real_threat
uncertainty injustice
illegitemacy
superiority distance
disconnected
muslim_violence
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identification
in_dep
col_dep
anxiety
sym_threat
real_threat
uncertainty injustice
illegitemacy
superiority distance
disconnected
muslim_violence
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injustice
illegitemacy
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muslim_violence
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disconnected
muslim_violence
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injustice
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disconnected
muslim_violence
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muslim_violence
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muslim_violence
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own_violentidentification
in_dep
col_dep
anxiety
sym_threat
real_threat
uncertainty injustice
illegitemacy
superiority distance
disconnected
muslim_violence
own_violent
identification
in_dep
col_dep
anxiety
sym_threat
real_threat
uncertainty
injustice
illegitemacy
superiority
distance
disconnected
muslim_violence
own_violent
identification
in_dep
col_dep
anxiety
sym_threat
real_threat
uncertainty
injustice
illegitemacy
superiority
distance
disconnected
muslim_violence
own_violent
identification
in_dep
col_dep
anxiety
sym_threat
real_threat
uncertainty
injustice
illegitemacy
superiority
distancedisconnected
muslim_violence
own_violent
identification
in_dep
col_dep
anxiety
sym_threat
real_threat
uncertainty
injustice
illegitemacy
superiority
distancedisconnected
muslim_violence
own_violent
identification
in_dep
col_dep
anxiety
sym_threat
real_threat
uncertainty
injustice
illegitemacy
superiority
distance
disconnected
muslim_violence
own_violent
identification
in_dep
col_dep
anxiety
sym_threat
real_threat
uncertainty
injustice
illegitemacy
superiority
distancedisconnected
muslim_violence
own_violent
identification
in_dep
col_dep
anxiety
sym_threat
real_threatuncertainty
injustice
illegitemacy
superiority
distance
disconnected
muslim_violence
own_violent
identification
in_dep
col_dep
anxiety sym_threat
real_threat
uncertaintyinjustice
illegitemacy
superioritydistance
disconnected
muslim_violence
own_violent
identification
in_dep
col_depanxiety
sym_threat
real_threat
uncertainty
injustice
illegitemacy
superiority
distance
disconnected
muslim_violence
own_violent
identification
in_dep
col_dep
Interpreting SEM results
I Direction of causation can often not be inferredI Assumption of no cycles is very strict and in psychology
often not tenableI SEM is very powerful in testing strict theories where the
acyclic assumption is met. In more exploratory settingshowever, using partial correlation networks can bepreferred:
I Shows relationships present in the dataI No equivalent modelsI Naturally cyclicI Optimally predicts each node given all others
Correlation network
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1: In−group Identification2: Individual Deprivation3: Collective Deprivation4: Intergroup Anxiety5: Symbolic Threat6: Realistic Threat7: Personal Emotional Uncertainty8: Perceived Injustice9: Perceived Illegitimacy authorities10: Perceived In−group superiority11: Distance to Other People12: Societal Disconnected13: Attitude towards Muslim Violence14: Own Violent Intentions
Partial correlation network
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1: In−group Identification2: Individual Deprivation3: Collective Deprivation4: Intergroup Anxiety5: Symbolic Threat6: Realistic Threat7: Personal Emotional Uncertainty8: Perceived Injustice9: Perceived Illegitimacy authorities10: Perceived In−group superiority11: Distance to Other People12: Societal Disconnected13: Attitude towards Muslim Violence14: Own Violent Intentions
Partial correlation networkAfter glasso (Friedman, Hastie, & Tibshirani, 2011):
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1: In−group Identification2: Individual Deprivation3: Collective Deprivation4: Intergroup Anxiety5: Symbolic Threat6: Realistic Threat7: Personal Emotional Uncertainty8: Perceived Injustice9: Perceived Illegitimacy authorities10: Perceived In−group superiority11: Distance to Other People12: Societal Disconnected13: Attitude towards Muslim Violence14: Own Violent Intentions
Centrality
Betweenness Closeness Strength
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Attitude towards Muslim Violence
Collective Deprivation
Distance to Other People
Individual Deprivation
In−group Identification
Intergroup Anxiety
Own Violent Intentions
Perceived Illegitimacy authorities
Perceived In−group superiority
Perceived Injustice
Personal Emotional Uncertainty
Realistic Threat
Societal Disconnected
Symbolic Threat
0.00 0.25 0.50 0.75 1.000.5 0.6 0.7 0.8 0.9 1.00.00 0.25 0.50 0.75 1.00
Shortest path length
Muslim Violence Violent IntentionsIn-group Identification 16.12 19.89Individual Deprivation 20.10 23.87Collective Deprivation 17.05 20.82
Intergroup Anxiety Inf InfSymbolic Threat 11.44 15.21Realistic Threat 14.81 18.58
Personal Emotional Uncertainty 10.73 14.50Perceived Injustice 24.83 28.60
Perceived Illegitimacy authorities Inf InfPerceived In-group superiority 3.16 6.93
Distance to Other People 5.70 8.81Societal Disconnected 9.15 12.92
Attitude towards Muslim Violence 0.00 3.77Own Violent Intentions 3.77 0.00
Shortest path length
Muslim Violence Violent IntentionsIn-group Identification 16.12 19.89Individual Deprivation 20.10 23.87Collective Deprivation 17.05 20.82
Intergroup Anxiety Inf InfSymbolic Threat 11.44 15.21Realistic Threat 14.81 18.58
Personal Emotional Uncertainty 10.73 14.50Perceived Injustice 24.83 28.60
Perceived Illegitimacy authorities Inf InfPerceived In-group superiority 3.16 6.93
Distance to Other People 5.70 8.81Societal Disconnected 9.15 12.92
Attitude towards Muslim Violence 0.00 3.77Own Violent Intentions 3.77 0.00
Conclusion
Preliminary network analysis agrees with Doosje et al. (2013)that Perceived In-group superiority, Distance to Other Peopleand to a lesser extent Societal Disconnected are strongpredictors for attitudes towards Muslim violence which in turnpredicts own violent intents. The fourth element of the radicalbeliefs system, Perceived Illegitimacy authorities, plays a lesserrole in the network.Personal Emotional Uncertainty is the only variable whosepredictive power on the violent variables is not mediated by theradical beliefs system.
Thank you for your attention!
References I
Doosje, B., Loseman, A., & Bos, K. (2013). Determinants ofradicalization of islamic youth in the netherlands:Personal uncertainty, perceived injustice, and perceivedgroup threat. Journal of Social Issues, 69(3), 586–604.
Epskamp, S., Cramer, A. O. J., Waldorp, L. J., Schmittmann,V. D., & Borsboom, D. (2012). qgraph: Networkvisualizations of relationships in psychometric data.Journal of Statistical Software, 48(4), 1–18. Retrievedfrom http://www.jstatsoft.org/v48/i04/
Friedman, J., Hastie, T., & Tibshirani, R. (2011). glasso:Graphical lasso- estimation of gaussian graphical models[Computer software manual]. Retrieved fromhttp://CRAN.R-project.org/package=glasso(R package version 1.7)