Territorial functioning and victimisation: conceptualisation and scale development

Territorial functioning and victimisation: conceptualisationand scale development

Aldrin Abdullah & Massoomeh Hedayati Marzbali &Helen Woolley & Azizi Bahauddin &

Mohammad Javad Maghsoodi Tilaki

# Springer Science+Business Media Dordrecht 2013

Abstract The purpose of this study was to develop and validate territorial functioningmeasures and to examine the link between territorial functioning and victimisation in ahigh-crime context. To this end, four sequential stages of scale development wereundertaken: conceptual model development, item generation and content validation,exploratory study and confirmatory study. Confirmatory factor analysis confirmed thethree dimensions of territorial functioning, namely, neighbourhood attitudes, sense ofcontrol and marking behaviour, as dimensions of the second-order territorial functioningconstruct. The results of the structural model support findings reported in the literaturethat associate high territorial functioning with low victimisation. The theoretical andpractical implications of the study and directions for future research are discussed in theconcluding sections of this study.

Introduction

There is substantial empirical evidence that links territorial functioning (TF) to lowcrime rates. Territorial functioning originates from the action of protecting a space and

Crime Law Soc ChangeDOI 10.1007/s10611-013-9490-6

A. Abdullah :M. Hedayati Marzbali (*) : A. Bahauddin :M. J. Maghsoodi TilakiSchool of Housing, Building & Planning, Universiti Sains Malaysia, 11800 Penang, Malaysiae-mail: [email protected]

A. Abdullahe-mail: [email protected]

A. Bahauddine-mail: [email protected]

M. J. Maghsoodi Tilakie-mail: [email protected]

H. WoolleyDepartment of Landscape, The University of Sheffield, Floor 13, The Arts Tower,Sheffield S10 2TN, UK

H. Woolleye-mail: [email protected]

defending it against intrusion. It is based on the notion that residents are likely to protectspaces that belong to them and over which they have some means of control. Althoughthe concept has been widely employed as a crime prevention measure in residentialneighbourhoods, there is a lack of empirical evidence that develops and validates theterritorial functioning scale systematically. Oscar Newman [1] was the first theorist topropose that the layout and design of an environment can influence criminal activity. Hisdefensible space theory, influenced in part by Jane Jacobs [2], refers to the systematicmanner in which the physical design of an urban environment can be manipulated toreduce criminal activity by increasing residents’ control over their space and theirdefence of the space, if required. Newman’s work was influential and became areference point for other studies on the relationship between crime and design [3–10].His theoretical framework implies that defensible space is activated through territorial-ity, natural surveillance and image/milieu. These elements rely heavily on environmen-tal design to function effectively as crime prevention tools.

For the concept of territorial functioning to be useful in reducing crime, it mustoperate in low-income high-crime contexts in particular, where residents often do nothave the economic means to install safety devices such as sturdy locks, light sensorsand CCTV cameras. Several studies have found that certain defensible space andterritoriality features are associated with lower levels of crime and fear [2, 6, 11–14].Perkins et al. [15] argued that the built environment, territoriality and physicaldisorder are significantly correlated with crime and social climate. Foster et al. [16]concurred, suggesting that natural surveillance and the display of territorial featuresdiscourage incivilities and encourage perceived safety. Researchers have measuredterritorial functioning through a variety of methods over the years [16, 17], but littleattention has been paid to the cross validation of these features.

Taylor [18:356] suggests that territorial functioning exists along a centralitycontinuum and defines it as “…an interlocked system of sentiments, cognitions,and behaviours that are highly place specific, socially and culturally determinedand maintaining…”. According to Taylor [18], territorial functioning consists of threemain elements, attitudes, behaviour and markers. The study of TF in a neighbourhoodinvolves statements such as, “I know my neighbour’s name” or, “I feel comfortableliving in my neighbourhood”. The responses to such fundamental statements aremeasured because they reflect residents’ sense of control over their residential areaand willingness to become involved in community events. To be effective, all threeelements must respond to, support and stimulate one another. This contention issupported by previous studies [19, 20]. However, research has found that territorialattitudes do not always translate into territorial behaviours [21]. The purpose of thepresent study was to develop and validate these three dimensions and their respectiveindicators, which measure the territorial functioning construct. Therefore, our re-search model contributes to the literature by incorporating the integral roles ofattitude, markers and behaviour into the territorial functioning measurement. In thecurrent study, territorial functioning is defined as a set of attitudes and behaviour thatrefers to how people manage, occupy and use the space that they own. In addition toextending previous research into territorial functioning and the defensible spaceconcept, the study contributes to the existing body of knowledge by examining themultidimensional TF construct, encompassing neighbourhood attachment, sense ofcontrol and marking behaviour.

A. Abdullah et al.

Territorial functioning studies

Hunter and Jeffery [22] argued that Newman’s descriptions suggest that territorialityis the central concept of defensible space, in which natural surveillance, image andmilieu are all mechanisms that facilitate the territoriality that gives residents control oftheir environment. The concept of territoriality generated further interest amongresearchers, notably Taylor [18], who coined the term ‘territorial functioning’. Basedon Newman’s [1] defensible space theory, TF is a crime prevention strategy thatfocusses on the manipulation of physical design to reduce crime by giving residentscontrol over their surrounding environment. Altman [23] identified the followingthree types of territory based on the criteria of duration of occupancy and psycho-logical centrality: primary territories, secondary territories and public territories.Territorial functioning plays a vital role in the residential environment, especially inprimary territories such as the home, due to its centrality to the lives of the occupants.Intrusion into primary territories such as the home is critical not only because itaffects the owners’ quality of life but also because the owners have no other spaces towhich they can retreat.

There is substantial evidence that territorial functioning correlates with low crimeareas. Research has shown that the crime of violence against persons is lower in areaswhere residents display higher territorial attitudes and sense of control [14]. Thisfinding is significant because it links the appropriation of territorial functioning to thedeterrence of crime. With regard to territorial attitudes, Brown et al. [24] found thatstronger place attachment among residents fosters attitudes and behaviours thatcontribute to protect the residents against crime.

Perkins et al. [25] found that physical disorder in the built environment isassociated with a decrease in informal social control. Research suggests that a largenumber of territorial markers are needed in areas with high threat levels [26]. Thiscontention is supported by Brower et al. [11] through the use of drawing-basedtechniques. Respondents who perceived themselves as living in a low-problem areafelt that a fence was a sufficient security feature and that the home could be leftunattended without risk. When respondents perceived that their neighbourhood hadmany problems, both of these elements were considered important. Thus, territorialmarkers were valued differently by residents according to their perception of the scaleof problems in their neighbourhood (i.e., the fear of crime, conflicts between neigh-bours, bad elements moving into the area). Respondents in the high-problem areaperceived that loiterers made their property appear less private, whereas respondentsin the low-problem area felt that the property continued to appear private. However,Greenberg et al. [27] concluded that territorial functioning is an expression of fear ofexisting crime rather than a strategy for maintaining control or safety.

Taylor et al. [20] suggested that territorial functioning is more likely to develop instable neighbourhoods (as opposed to unstable neighbourhoods) because of theclearer distinction between insiders and outsiders that exists in this context andbecause residents are more invested in their home and are thus more willing tomanage the local environment. Residents of blocks with a higher household income,length of residence and home ownership status have a greater material stake in theirproperties. Because these residents are more territorial and often engaged in crimeprevention efforts, they are expected to experience less crime. Other studies have also

Territorial functioning and victimisation

consistently revealed a link between neighbourhood stability and territorial function-ing [19, 28–30]. Research has often found a correlation between high crime levelsand lower population stability, and it remains to be determined whether territorialfunctioning effectively prevents crime in high-crime neighbourhoods with low pop-ulation stability [27, 31].

Similar to Newman’s idea, Taylor and Harrell [10] emphasised the manipulation ofphysical design to create spaces less vulnerable to crime by providing inhabitantswith more opportunities to control their space. Aside from social elements, Taylor andcolleagues [20] conceptualised the physical potential for control, similarly to whatwas done in Newman’s work. They measured defensible space and territorial func-tioning in terms of surveillance opportunities and real and symbolic barriers butomitted measures of image and milieu. Although influential, Newman’s theory hasbeen criticised for its methodology and conceptual content [20, 29, 32–38]. Forexample, Bottoms [32] criticised Newman for analysing only two housing projectsin detail and suggested that these sites were selected because they provided the bestresults. The original method of analysis, the multivariate analysis of the data, did notlend much support to Newman’s theory.

Although Newman’s claim of a relationship between physical design and crimemay have some foundation, Bottoms [32] concluded that the methodological flaws ofthe study are such that the relationship between design and crime cannot be fullyestablished. Wilson [38], however, argued that Newman’s theory contains muchjargon, meaning that several details in the work are unclear and contradictory. Forinstance, Newman [1] failed to distinguish controlled access from limited access anddid not make a distinction between overlooking and constant use surveillance.Moreover, his work provided no guidelines regarding when barriers were real orsymbolic and may have assumed that crimes were committed by outsiders (i.e., he didnot consider whether offences were being committed by those who lived within thestudy area) [9, 39]. In summary, the similarities between Newman’s [1] and Taylorand colleagues’ [20] studies refer to the use of physical design to create lessvulnerable spaces. The differences refer to Newman’s failure to acknowledge theeffects of the residents’ social functioning in the creation of a defensible residentialenvironment [40], although in later work, he found that social factors influencedcrime more than physical design did [41].

Reynald and Elffers [42] provided a detailed and comprehensive account of theevolution of Newman’s original theoretical model and the subsequent theoreticaldevelopments that have taken place over the last three decades. They argued thatsome of the conceptual ambiguities of the defensible space theory lie in the failure toidentify, define and measure the territoriality concept and its mechanisms. Theysupport this assertion by exhibiting how Perkins et al. [25] conceptualised defensiblespace in terms of physical design variables that are separate and distinct from imageand territoriality. Moreover, they describe how Booth [43] combined territoriality,image and milieu to construct the variable ‘accessibility’. Reynald and Elffers [42]concluded that the inconsistencies in the definition and measures of defensible spaceresult in various subjective interpretations of this concept. The research gaps andinconsistencies described above justify the development of an instrument to measureterritorial functioning through both exploratory factor analysis (EFA) and confirma-tory factor analysis (CFA). Therefore, the objective of the current study was to

A. Abdullah et al.

develop and validate territorial measures that can be used to determine whether theTF construct effectively reduces crime in a high-crime context.

To examine the factor structure of the TF construct, we conducted both exploratoryand confirmatory factor analyses. We divided the sample into two subsamples andconducted exploratory factor analysis in one subsample and then verified the factorstructure with confirmatory factor analysis in the other sample. EFA was chosenbecause we were interested in exploring the factor structure of the TF by identifyingunderlying common factors that could then be validated and tested in a separatesample using confirmatory factor analysis. The resulting EFA model was subjected toCFA to further test this latent structure. Based on the literature, which has confirmedattitude, behaviour and markers as important factors influencing territorial function-ing, the current research extends the study of this construct by assessing physicalfactors and residents’ perception using both on-site observation and a questionnairesurvey.

Conceptual model development

The proposed model includes an analytical framework based on structural equationmodelling (SEM), allowing for the simultaneous inclusion of higher-order(multidimensional) constructs and their dimensions as latent variables. In line withthe defensible space theory, the model argues that territorial functioning is associatedwith victimisation. Based on prior knowledge [14, 19, 44], TF is composed of threeinterrelated first-order factors (neighbourhood attachment, sense of control and mark-ing behaviour) modelled as manifest variables. According to Taylor [18], these threeelements are interwoven and therefore support and stimulate one another. The linkbetween the concept of territorial functioning and the deterrence of crime is based onthe assumption that offenders perceive care and maintenance of outdoor residentialspaces by the occupants as likely to be defended. In conclusion, there are three proposeddimensions of the TF construct. Although the terminology of the dimensions may begeneric in the existing literature, the constructs measured in each dimension are specif-ically relevant to the neighbourhood environment. The definition of each dimension isprovided in Table 1. Thus, the following hypotheses are proposed:

H1 The three dimensions (neighbourhood attachment, sense of control and markingbehaviour) and their respective indicators provide a valid measurement of theterritorial functioning construct.

H2 The territorial functioning construct is negatively associated with victimisation.

Methods

Sample and procedure

This study focusses on council houses in Sheffield, England, which are predominant-ly occupied by low-income people. The most recent British Crime Survey reportsindicated higher burglary rates, fear of crime and perceived risk among council


tenants than among other types of tenants [45–47]. The council estate that was chosenfor the current study had both the highest recorded offense and offender rates inSheffield based on the Police Offence database provided by the South YorkshirePolice Force. The main wage earner or the spouse was identified as the surveyrespondent in each household.

The respondents were selected using a systematic sampling method. The samplinglist was drawn from the list of all properties obtained from the Area Housing Offices.A total of 217 residents of the study area participated in the survey, and the overallresponse rate was 66.8 %. We randomly divided the participants into two datasets,108 cases for the exploratory analysis and the remaining 109 cases for the confirma-tory analysis. Based on the parameter estimate ratio of 5, the sample size for bothdatasets was considered adequate. The sample characteristics for each dataset areoutlined in Table 2.

Item generation and content validation

The aim of this research was to generate and validate the territorial functioning con-struct. A number of studies have focussed on environmental assessment and environ-mental perception [25, 48–54]. Several studies have reported the direct or indirect effectof neighbourhood conditions and neighbourhood safety on personal health [48, 55, 56].The neighbourhood is one of several spaces that provide social and physical space forinteractions between residents, which may influence crime rates and consequentlyindividuals’ health. However, research into the relationship between the physical char-acteristics of the built environment and its influence on crime rate has produced mixedresults. Therefore, devising a reliable, valid and contextual measure is an important issuefor this body of research [17].

The present study adapted the measures that previous related studies used tooperationalise the study model constructs. Following Taylor and colleagues [14, 20],the three dimensions of the TF conceptual model (as defined in Table 1) were used as abasis to generate scale items [18]. Based on the literature, multiple items were generatedfor each dimension to ensure reliability [57] and internal consistency [58]. According toHair et al. [59], content validity must be ensured prior to any measurement and testing ofthe theory. Items from existing scales that had been empirically tested were considered

Table 1 Territorial functioning construct, dimensions and definitions

Dimensions Definition

Neighbourhoodattachment

Neighbourhood attachment is related to people’s perception of their relationship to aparticular delimited location or their perception of conditions in such a locus. Thisinvolves care-taking and surveillance behaviour in outdoor residential settingsclose to the home.

Sense of control Sense of control refers to the extent to which people perceive control overtheir territories.

Marking behaviour Marking behaviour refers to a variety of physical elements that are observableconsequences of behaviour and serve as a medium for residents to communicatewith each other. It covers a broad range of non-verbal and verbal efforts,including surveillance, maintenance, beautification and modification work.

A. Abdullah et al.

and adapted for the newly developed measure. A pool of 15 items was established forthe purpose of content validation.

In the first round, a panel of experts with relevant expertise was asked to review thesurvey instruments, examine the effectiveness of the selected items in measuring theunderlying variables and evaluate the overall content of the survey instruments. Thisprocess was employed because the items used to measure a specific variable should berepresentative of that variable. Three content experts agreed to participate in the study.First, they were asked to rate the importance of each of the three dimensions using a five-point rating scale ranging from ‘extremely unimportant’ to ‘extremely important’. Themean values for each of the three dimensions were greater than 3, which confirmed thatall dimensions were essential for measuring the TF construct.

In the second round, following Lynn’s [60] recommendation, we estimated two typesof content validity. The first type involves the determination of content validity of eachindividual item, and the second involves the content validity of the overall scale. A panelof experts was asked to rate the relevance of each item to the respected dimension using a4-point rating scale ranging from ‘not relevant’ to ‘highly relevant’. Following Lynn’s [60]recommendation, a four-point rating scale was used to calculate the Content ValidationIndex (CVI). Each item had to be scored 3 or 4 to be considered content valid. The CVIwas computed as the number of experts giving a rating of either 3 or 4 divided by the totalnumber of experts. In this study, each item had a value of 3 or 4 and the CVI value for theoverall scale was more than the expected minimum CIV 0.80 [60]. Corroborating the firstand second rounds, the panel experts confirmed the content validity of the entire scale.

Table 2 Demographic breakdown for exploratory and confirmatory analyses

Exploratory analysis (108 cases) Confirmatory analysis (109 cases)

Percentage Percentage

Gender

Male 37.0 41.3

Female 63.0 58.7

Age

Under 25 years 8.3 13.8

25– 34 years 19.4 25.6

35– 44 years 18.5 16.5

45– 54 years 9.3 11.0

55– 64 years 24.1 19.3

65 and over 20.4 13.8

Length of residence

1 but less than 2 years 15.7 17.4



10 years or more 46.3 37.6

Ethnicity

White 95.4 93.6

Non-white 4.6 6.4


Once content validity was established, the questionnaire and observation checklistwere refined through rigorous pre-testing. This pre-testing emphasised the clarity,validity and wording of the survey instruments. During the pre-testing, 20 respon-dents were selected and the questionnaire was administered to them orally. Therespondents were purposely selected to represent various levels of territorial func-tioning based on the display of territorial cues in their front gardens. The resultsobtained from this pilot procedure indicated that some modifications to the format ofthe questions in the territorial attitudes section were needed. Only a few questionsfrom the other sections of the questionnaire required minor changes, mainly byclarifying the meaning of ambiguous words. The feedback collected from the expertsand the pilot study was used to amend the survey instruments where appropriate. Thisprocess was employed to confirm that the development and adaptation of the finalinstrument were appropriate and that the instrument could be understood by respon-dents in the target population.

Survey instrument

The first part of the data collection involved an on-site observation of residents’ frontgardens to identify and evaluate marking behaviour. Many studies have linked crimeand neighbourhood characteristics, the latter of which can be derived from observa-tion based primarily on the work of Taylor and his colleagues [14, 20]. Thisobservable neighbourhood characteristic has been defined as “markers which conveya non-verbal message of control, separation from outsiders, and investment in thelocale” [25:22]. The present indicators of marking behaviour were adapted based onthe work of Dunstan and colleagues [17] and include both markers of territoriality(i.e., signs of personalisation, maintenance and symbols of protection) and markers ofdefensible space (i.e., physical barrier). As shown in Table 3, these actions wereclassified into four categories based on measures adapted from previous studies [12,17, 21, 28, 52]. The first three categories were measured on a five-point Likert scale.Markers, as the last category, were based on the total number of items present.

The second part of the study involved asking the residents to answer a self-administered questionnaire that contained several sections intended to ascertain back-ground information regarding the respondents, their territorial attitudes and theirvictimisation experience. As shown in Table 3, seven statements refer to the level ofneighbourhood attachment to the respondents’ current neighbours, adapted based onprevious studies [14, 17, 20, 44]. Four statements refer to the sense of control [14, 19,20]. Each respondent indicated his/her amount of agreement with each of the 11 state-ments. For each statement, he/she used a four-point Likert scale ranging from ‘disagreestrongly’ to ‘agree strongly’. Lastly, respondents were asked whether they had been thevictim of any household or personal crime in the past 12 months. Responses were codedone for victims and zero for non-victims.

To evaluate the reliability of the observational measure, the consistency withwhich observers make ratings is a key criterion that is important for subsequentanalysis [61]. Consistent with methods employed in previous studies [48], the secondobserver scored 50 similar samples (nearly 25 % of the samples to monitor inter-raterreliability) to evaluate whether two independent observers would judge the samebuilding to have the same levels of marking behaviours. An analysis of inter-rater

A. Abdullah et al.

reliability was performed to assess the degree that observers consistently assignedlevels of marking behaviour ratings to subjects in the study using Cohen’s (1960)Kappa [62]. The resulting kappa indicated that coders had substantial agreement,κ=0.86, in marking behaviour ratings [63]; therefore, the ratings were deemedadequate for use in the subsequent tests of the present study.

Results

Exploratory analysis

The 108 cases in the first dataset were subjected to principle component analysis. Due tothe self-reported nature of the data, there was potential for common method variance(CMV); therefore, the Harman one-factor test was conducted to determine CMV.According to Podsakoff and Organ [64], common method bias is problematic if a singlelatent factor can account for the majority of the explained variance. The un-rotated factoranalysis showed that the first factor accounted for only 24.38 % of the total 57.60 %variance; thus, common method bias was not a serious threat in the study.

Principal component analysis with Varimax rotation was used for data analysis.Following the literature [65–67], the present study employed two common decisionrules to identify the number of factors underlying the TF construct. Items with lessthan 0.50 loading and that cross-loaded onto two or more factors at 0.50 or abovewere excluded. Based on Kaiser’s [68] criterion, an eigenvalue of 1 was used as the

Table 3 Victimisation and territorial functioning dimensions with respective indicators

Construct Indicators Description of instrument

Territorialfunctioning


1. Conversation: I spend time talking with neighbours in my garden;2. Watch: I keep an eye on what occurs in front of my house daily;3. Recognise: I can tell if a person standing in front of my house lives in

the neighbourhood;4. Know: I know the names of most of my neighbours;5. Responsible: I feel responsible for watching over my neighbours’

house when they are on vacation;6. Comfort: I feel comfortable living among my neighbours;7. Belong: I feel that I belong in this neighbourhood.

Sense ofcontrol

1. Fears: I am not frightened if I am alone in my house at night;2. Litter: Not much litter is thrown into my garden;3. Maintain: I maintain my garden so that other people know it is my

territory;4. Strangers: It is easy to keep strangers out of my garden if I don’t

want them there.

Markingbehaviour

1. Gardening2. Maintenance3. Physical barrier4. Markers (e.g., personalised decoration, security grills, garden

furniture, etc.)

Victimisation Householdcrime

1. Number of crime incidents


cutoff value for extraction. The iterative sequence of factor analysis and item deletionwas repeated, resulting in a final scale of 14 items belonging to three distinct factorsassociated with the TF construct, namely, neighbourhood attachment, sense ofcontrol and marking behaviour. The exploratory analysis demonstrated that all itemsmeasuring the constructs loaded onto the respective factors, thereby retaining theoriginal name. It should be noted that one item (maintain) cross-loaded onto twofactors, and thus, this item was excluded. Appendix Table 7 summarises the factorloadings for the condensed 14-item scale. As shown in Appendix Table 7, the alphavalues were in the range of 0.62 to 0.82, which were above 0.6 as suggested byNunnally and Berstein [69], indicating acceptable reliability. The significant loadingof all of the items on the single factor indicates unidimensionality. The results furtherillustrated that no item had multiple cross-loading, indicating preliminary discrimi-nant validity.

Confirmatory analysis

The present study employed SEM to evaluate the territorial functioning factor structurevia a CFA using the maximum likelihood method for the remaining 109 cases. Thismethod detects the unidimensionality of each construct and indicates the presence of asingle construct underlying a set of measures [70]. According to Hair et al. [59], the CFAneeds a measurement theory to help the researcher specify an a priori number of factorsand determine which variables load onto these factors. In specifying a model, theconceptual constructs in a measurement model are operationalised objectively [59].Following Byrne [71], we developed a full measurement model that included theidentified three factors as first-order factors. The hypothesised three-factor measurementmodel was an acceptable model. To achieve the best factor structure, we considered themodification indices [71]. A final re-specified model, incorporating correlations oferrors between conversation and responsible (r=0.39), resulted in the best-fit model.

The results from the measurement models suggest that the model fits the data well.Several indices were employed to determine whether the model fits the data, such as thechi-squared statistic, chi-squared/degree of freedom ratio and goodness-of-fit indices. It isrecommended that the relative/normed chi-squared (χ2/df) be less than or equal to 3 [61].The conventional method for examining model fit is the chi-squared (χ2) statistic, whichshould be non-significant in a well-fitting model. The multivariate distribution was non-normal in the current study, based on the large Mardia’s coefficient of multivariatekurtosis of 35.5 [72]. Therefore, the bootstrap method was used in further analyses andthe Bollen-Stine p statistic was calculated, which should be non-significant in a well-fitting model [73]. Because SEM parameters with maximum likelihood (ML) estimationare sensitive to non-normality [74], the bootstrap estimation was used to create asampling distribution that was not dependent on the normality assumption [74]. Re-searchers advocate using the bootstrapping procedure to test both direct and indirecteffects [75–77]. Bootstrapping of ML estimates (n=1,000 samples) was employed toevaluate potential bias using repeated resampling of the original sample [78]. This is analternative method to Sobel’s [79] large-sample test, which allows the distribution of theindirect effect to be examined empirically [80].

An acceptable fit of the data to the model is indicated when the comparative fitindex (CFI) >0.9, the Tucker-Lewis index (TLI) >0.9 and the root-mean square error

A. Abdullah et al.

of approximation (RMSEA) <0.08 [81]. The cut-off value for the accepted factorloading employed in this study was 0.50 [59]. The full measurement model using MLestimation resulted in 73° of freedom, χ2=95.19, p=0.04, χ2/df=1.304. BecauseMardia’s coefficient revealed multivariate non-normality, the model was rerun with1,000 bootstraps. The Bollen-Stine p statistic for the model was 0.274, which is non-significant and suggests a good model fit. The model fit indicators GFI = 0.918, CFI= 0.957, TLI = 0.946 and RMSEA = 0.053 also support the fit results obtained fromthe measurement model. Therefore, the non-significant Bollen-Stine p statistic andthe underlying model fit indices suggested that the model was a good fit for the data,demonstrating factor validity [82]. This result indicates that the measurement scalesemployed in the model can be considered a valid operationalisation of the latentconstructs.

Investigation of higher-order factor model

Cheung [83] defined a higher-order construct as a multidimensional construct that hasa higher level of abstraction than its dimensions; it is a latent model in which itsdimensions act as indicators. However, competing model analyses were used tocorroborate the correlated three-factor model [84, 85]. Four plausible alternativemodels were specified. Confirmatory maximum likelihood factor analysis was usedto test the goodness of fit of competing models. Model 1 tested a single general factorincluding all 14 items in the item pool. In Model 2, the 14 items were loaded ontothree-correlated factors. Based on the results of EFA, this was the proposed model inthe study. Model 3 tested a nested model within Model 4, testing an uncorrelatedthree-factor first-order model. Lastly, Model 4 tested a correlated three-factor first-order model.

The fit indices for the four models are shown in Table 4. The fit for Model 3 wasslightly better than that for Model 1; however, neither model indicated a reasonable fitwith the empirical data. Model 2 provided substantial improvement over Model 1 anddemonstrated a reasonable fit, as indicated by the goodness-of-fit indices. RegardingModel 4, the majority of the fit indices were close to the ideal thresholds. Thus,Model 2 and Model 4 were both adequate for representing the underlying structure ofthe TF construct.

Table 4 Fit indices for competing models of the structure of territorial functioning

Threshold Model 1 Model 2 Model 3 Model 4

χ2 Smaller is better 247.44 95.19 128.387 95.19

df 76 73 76 73

p value Non-significant p<0.01 p>0.01 p<0.01 p>0.01

χ2/df 1<χ2/df <3 3.26 1.304 1.69 1.304

GFI >0.9 0.71 0.92 0.86 0.92

CFI >0.9 0.67 0.96 0.90 0.96

TLI >0.9 0.60 0.95 0.88 0.95

RMSEA <0.08 0.14 0.05 0.08 0.05


It is believed that even though the higher-order model is able to explain the constructcovariations, its goodness-of-fit indices will never surpass the corresponding correlatedfirst-order model. However, Model 4 provided the optimum fit for the TF scale andserved as the target model. In addition, the paths (regression weights) from the second-order to first-orders were all significant. This result demonstrates that the second-orderfactor model is a more accurate representation of the TF construct. However, the modelof territorial functioning proposed in the present study serves as a second-order factormodel. Territorial functioning was hypothesised to be represented by three first-orderfactors, namely, neighbourhood attachment, sense of control and marking behaviour.Therefore, the first research hypothesis was confirmed.

Assessment of construct validity

Construct validity is divided into two categories, convergent and discriminant validity.Three methods are used to assess convergent validity, namely, factor loading, compositereliability and average variance extracted (AVE). Composite reliability estimates thedegree to which the respective indicators indicate the latent construct [86], which in thisstudy ranged from 0.70 to 0.88 (see Table 5). A cut-off value of 0.70 and above issuggested for composite reliability, representing good reliability [59]. The general rulesuggested for AVE is that it be equal to or greater than 0.50, indicating adequateconvergence [87]. In the present study, the AVE values were in the range of 0.44 to0.61, which were close to the recommended value of 0.50 [59]. We then testeddiscriminant validity for the three constructs of TF. One of the most common ways toexamine discriminant validity is to compare the square-root of AVEs for any two

Table 5 Assessment of convergent validity of the TF dimensions

Construct Items Convergent validity

Factor loading (>0.5) Composite reliability(>0.70)

AVE (>0.50)


Comfort 0.71 0.88 0.51

Responsible 0.69

Know 0.61

Recognise 0.72

Conversation 0.72

Watch 0.79

Belong 0.74

Sense of control Fears 0.70 0.85 0.61

Strangers 0.73

Litter 0.57

Marking behaviour Maintenance 0.95 0.70 0.44

Physical barrier 0.52

Markers 0.56

Gardening 0.97

A. Abdullah et al.

constructs and the correlation estimate between the same constructs, where the formermust be greater than the latter [59, 88]. As shown in Table 6, the measure has adequatediscriminant validity. Overall, the CFA model demonstrated adequate reliability, con-vergent validity and discriminant validity. The results from the measurement modelssuggest that these models fit the data well based on the normed chi-squared statistic andgoodness-of-fit indices.

Structural model

The associations between the independent variable (territorial functioning) and thedependent variable (victimisation) were assessed using AMOS. The SEM techniqueusing bootstrapping of ML estimates was performed to examine the validity of thehypothesised model. As shown in Fig. 1, there was a significant relationship betweenterritorial functioning and victimisation (β=−0.46, p<0.05), confirming the secondresearch hypothesis. The final structural model resulted in 86° of freedom, χ2=106.53, p=0.07, χ2/df=1.239. The Bollen-Stine p statistic for the model was 0.320,which is non-significant and suggests good model fit. The model fit indicators GFI =0.92, CFI = 0.96, TLI = 0.95 and RMSEA = 0.05 also support the fit results from thestructural model. The result indicates that the model fit the data well, and thestandardised regression weights were all significant. The final model demonstrated agood fit to the data, accounting for 21 % of the variance associated with victimisation(R2=0.21).

Discussion

This study aimed to investigate the dimensionality of territorial functioning and tospecifically test the three-dimensional model proposed by Taylor [18] and others. Inaddition, the impact of territorial functioning in reducing crime was addressed.Exploratory and confirmatory factor analyses were used in two ways. First, first-order factors were extracted from the 15 items using EFA. Three primary factors wereextracted. These factors presented content similarities to factors based on the con-ceptual definitions and goodness-of-fit indices, supporting them as TF first-orderfactors. Then, a second-order TF construct was tested. The CFA undertaken in thisstudy confirmed the stability of these three first-order factors.

The competing analysis suggested the existence of the second-order TF factor,providing an acceptable fit to the data that can be used for further analysis. The

Table 6 Discriminant validity of the TF dimensions

Neighbourhood attachment Sense of control Marking behaviour

Neighbourhood attachment 0.71

Sense of control 0.38 0.78

Marking behaviour 0.36 0.51 0.66

Diagonals (in bold) represents square root of AVE and off-diagonals represent correlations


findings reveal that high levels of territorial functioning are associated with low crimerate. Consistent with a large body of studies [18–20, 89], we found a strong positivecorrelation between territorial attitudes and marking behaviours. On the other hand,contrary to a study conducted by Pollack and Patterson [21], the findings of thepresent study revealed that people who displayed more territorial markers perceivedthemselves as being more territorial about their home than those with low territorialmarkers. Moreover, respondents with a better ability to identify between insiders andoutsiders were more engaged in high-demand gardening and had better-kept dwell-ings and were also found to have lower victimisation experience. The current studyexamined whether attitudes, behaviour and markers correspond to one another. Whenthe model was tested in a high-crime context, the results supported the inter-relatedness of the territorial functioning components.

A notable strength of the current study is that it was able to construct a measurementmodel of territorial functioning that comprehensively covers most TF items used inprevious studies, therefore reducing the influence of any particular item that undulyaffects the outcome. This advantage provides a strong foundation for future studiesassociated with territorial functioning measurements. The results of the present studyfurther revealed that neighbourhood attachment is an important component of territorialfunctioning. Newman [90] clearly highlights the interactions between social and phys-ical factors and their correlations with crime rates. Studies show that physical factors canexert a direct influence on social interactions between neighbours. Taylor [18] revealedthat some specific architectural elements affect residents’ social life in outdoor locationsand influence the amount of territorial control exercised by residents. Based onNewman’s defensible space concept, the underlying theme of territorial functioning isthat neighbourhood ties enhance the sense of control among residents as well as theapplication of physical elements, such as markers.

Another important strength of the current study is the use of both observations anda questionnaire survey to collect data, covering residents’ perception of the surround-ing environment and the physical characteristics of the residence area. Although the

Fig. 1 Results of structural model

A. Abdullah et al.

TF scale has been utilised in crime prevention studies as an appropriate strategy toreduce crime and fear of crime, research into the subject of territorial functioning hasseldom taken into consideration territorial attitudes [16]. Therefore, the present studysuggests that future work on crime prevention by design strategies should incorporateboth interpersonal and contextual influences of territorial functioning in relation tocrime-related outcomes.

Altman’s definition of territoriality contains several indices of neighbourhood attach-ment, in which territorial cues are believed to enhance residents’ privacy by allowingthem to control their territory [23, 91]. The current findings lead to the conclusion thatterritorial functioning is a combination of territorial cognitions expressed throughattitudes and territorial behaviour, including the distribution of territorial cues to indicatethat a space is owned, used or cared for. In summary, one of the most effective ways tocombat criminal behaviour is to allow a greater opportunity for socialisation [92].Therefore, territoriality can be the foundation for the development of neighbourhoodattachment.

Research shows that street blocks without continuous occupancy create ‘holes’ thatweaken the fabric of resident-based territorial functioning in spaces surrounding thehome [93]. This behaviour can be observed in areas with numerous vacant properties. Insuch places, residents show less territorial control and responsibility. The current studyfocussed on territorial functioning in precisely such a context. The results show thatterritorial functioning has a negative and significant influence on crime, leading to theconclusion that territoriality features serve as a deterrent of crime, even in a high-crimearea. However, the theory of defensible space is based on the modification and im-provements of the physical environment to enhance people’s interaction and socialintegration in communities, resulting in a reduction of crime and fear among residents.

In summary, this study has conceptually defined the domain of the TF constructand operationalised this construct into conceptually distinct indicators that could beobserved and assessed. The findings provide a better understanding of the multidi-mensionality of the TF construct, which comprises three empirically distinguishabledimensions. The existence of the second-order factor in the TF construct alsoconfirms the view that the territorial functioning should not be treated as a singleconstruct. This study may contribute to and stimulate further interest in the study ofcrime prevention in residential environments and its link to crime and even otheroutcome variables, such as fear of crime. Regarding the practical implications of thestudy, the 14-item TF scale is an accessible and easily administered measure of TF.Because local authorities have a responsibility to design and beneficially use publicareas and residential neighbourhoods, the results of the current study have conse-quences for local authorities and public policy aimed at crime reduction, enhancingresidents’ interactions and, lastly, promoting healthier communities.

Study limitations and directions for future research

Despite the abovementioned strengths, the present study has several limitations. First,the study focussed only on territorial functioning and does not address the concept ofdefensible space in its entirety. Previous research has indicated that, ideally, alldimensions of defensible space must be examined together [42]. In examining thedefensible space concept in a particular situation, in addition to territorial functioning


and victimisation rate, researchers should take into consideration the level of fear ofcrime in their conceptual model. Second, studies have linked territorial functioning tothe social structure of neighbourhoods where the majority of households are inhabitedby elder, married, owner-occupied and long-term-occupied people with a high edu-cational and income level [20, 52]. By incorporating the three TF dimensions, thefinal model accounts for 21 % of the variance of the occurrence of crime; thus, manyother variables can account for variance in crime occurrence. Therefore, understand-ing the patterns of demographic factors in relation to perceived territorial attitudes andengaged in territorial features can provide new insights.

The current study was conducted in a homogeneous community. Therefore, thestudy results may not necessarily be generalisable to low-crime-context residentsliving in heterogeneous communities. It is well known that ethnicity influencesresidents’ attitudes and crime rate, although there is debate over why this is the case.Such a perspective also indicates that attitudes vary across cultures due to forces ofcultural evolution and adaptation to specific ecologies. Support for this assumptioncomes from work by Brower [26] and Scheflen [94], indicating that different culturalor ethnic groups utilise different systems of attitudes and marking behaviours tomaintain control. It can be concluded that residents from different cultures mayperceive the contents of the TF items differently given that neighbourhood attach-ment, sense of control and even marking behaviour could vary across cultures. Futurestudies should focus on the mechanisms of territorial functioning in heterogeneous,high-crime settings.

Appendix

Table 7 Exploratory analysis: factor loading of the 14 final items

Factor

1 2 3

Neighbourhood attachment

Conversation 0.55

Watch 0.59

Recognise 0.65

Know 0.84

Responsible 0.73

Comfort 0.67

Belong 0.53

Sense of control

Fears 0.63

Litter 0.77

Strangers 0.72

Marking behaviour

Gardening 0.90

A. Abdullah et al.

References

1. Newman, O. (1972). Defensible space; Crime prevention through urban design. New York: Macmillan.2. Jacobs, J. (1961). The death and life of great American cities. New York: Random House.3. Baran, P., Smith, W. R., & Toker, U. (2006). Conflict between space and crime: exploring the

relationship between spatial configuration and crime location. Paper presented at the 37th AnnualConference of EDRA, Atlanta.

4. Beavon, D. J. K., Brantingham, P. L., & Brantingham, P. J. (1994). The influence of street networks onthe patterning of property offenses. Crime Prevention Studies, 2, 115–148.

5. Clarke, R. V. (1992). Situational crime prevention: Successful case studies. Albany: Harrow and Heston.6. Cozens, P., Hillier, D., & Prescott, G. (2001). Crime and the design of residential property- exploring

the theoretical background- Part 1. Property Management, 19(2), 136–164.7. Feelei, M. M. (2004). Actuarial justice and the modern state. In G. Bruinsma, H. Elffers, & J. D. Keijser

(Eds.), Punishment, places and perpetrators: Developments in criminology and criminal justiceresearch (p. 62). Portland: Willan Publishing.

8. Jeffery, C. R. (1999). CPTED: past, present and future. Paper presented at the 4th Annual InternationalCPTED Association Conference, Ontario, Canada.

9. Mawby, R. I. (2001). Burglary. Portland: Willan Publishing.10. Taylor, R. B., & Harrell, A. V. (1996). Physical environment and crime. Social Problems, 40(3), 374–395.11. Brower, S., Dockett, K., & Taylor, R. B. (1983). Residents’ perceptions of territorial features and

perceived local threat. Environment and Behavior, 15(4), 419–437.12. Brown, B. B., & Altman, I. (1983). Territoriality, defensible space and residential burglary: an

environmental analysis. Journal of Environmental Psychology, 3(3), 203–220.13. Clontz, K. A. (1997). Spatial Analysis of Residential Burglaries in Tallahassee, Florida. Paper

presented at the Annual Environmental Systems Research Institute (ESRI).14. Taylor, R. B., Gottfredson, S. D., & Brower, S. (1984). Block crime and fear: defensible space,

local social ties, and territorial functioning. Journal of Research in crime and delinquency, 21(4),303–331.

15. Perkins, D. D., Florin, P., Rich, R. C., Wandersman, A., & Chavis, D. M. (1990). Participation and thesocial and physical environment of residential blocks: crime and community context. AmericanJournal of Community Psychology, 18(1), 83–115.

16. Foster, S., Giles-Corti, B., & Knuiman, M. (2011). Creating safe walkable streetscapes: does housedesign and upkeep discourage incivilities in suburban neighbourhoods? Journal of EnvironmentalPsychology, 31(1), 79–88.

17. Dunstan, F., Weaver, N., Araya, R., Bell, T., Lannon, S., Lewis, G., et al. (2005). An observation tool toassist with the assessment of urban residential environments. Journal of Environmental Psychology,25(3), 293–305.

18. Taylor, R. B. (1988). Human territorial functioning: An empirical, evolutionary perspective on individualand small group territorial cognitions, behaviours and consequences. Cambridge: Cambridge UniversityPress.

Table 7 (continued)

Factor

1 2 3

Maintenance 0.87

Markers 0.67

Physical barrier 0.70

Cronbach’s α 0.81 0.62 0.82

Eigenvalue 4.91 2.08 1.63

Cumulative variance explained (%) 22.59 44.80 57.60


19. Edney, J. J. (1972). Property, possession and permanence: a field study of human territoriality. Journalof Applied Social Psychology, 2(3), 275–282.

20. Taylor, R. B., Gottfredson, S. D., & Brower, S. (1981). Territorial cognitions and social climate inurban neighborhoods. Basic and Applied Social Psychology, 2(4), 289–303.

21. Pollack, L. M., & Patterson, A. H. (1980). Territoriality and fear of crime in elderly and nonelderlyhomeowners. The Journal of Social Psychology, 111(First Half), 119–129.

22. Hunter, R. D., & Jeffery, C. R. (1997). Preventing convenience store robbery through environmentaldesign. In R. V. Clarke (Ed.), Situational crime prevention: Successful case studies (2nd ed.). Albany,NY: Harrow and Heston.

23. Altman, I. (1975). Environment and social behavior: Privacy, personal space, territory, crowding.Monterey: Brooks/Cole.

24. Brown, B. B., Perkins, D. D., & Brown, G. (2004). Incivilities, place attachment and crime: block andindividual effects. Journal of Environmental Psychology, 24(3), 359–371.

25. Perkins, D. D., Meeks, J. W., & Taylor, R. B. (1992). The physical environment of street blocks andresident perceptions of crime and disorder: implications for theory and measurement1. Journal ofEnvironmental Psychology, 12(1), 21–34.

26. Brower, S. N. (1980). Territory in urban settings. In I. Altman, A. Rapoport, & J. F. Wohlwill (Eds.),Culture and environment. Human behavior and environment (Vol. 4, pp. 179–207). New York: Plenum.

27. Greenberg, S. W., Rohe, W. M., & William, J. R. (1982). Safety in urban neighborhood: a comparisonof physical characteristics and informal territorial control in high and low crime neighborhoods.Population and Environment, 5(3), 141–165.

28. Greenbaum, P. E., & Greenbaum, S. D. (1981). Territorial personalization: group identity and socialinteraction in a Slavic-American neighborhood. Environment and Behavior, 13(5), 574–589.

29. Macdonald, J. E., & Gifford, R. (1989). Territorial cues and defensible space theory: the burglar’s pointof view. Journal of Environmental Psychology, 9(3), 193–205.

30. Rainwater, L. (1966). Fear and house-as-haven in the house class. Journal of the American Institute ofPlanners, 32(1), 23–31.

31. Greenberg, S. W., & Rohe, W. M. (1984). Neighbourhood design and crime: a tale of two perspectives.Journal of the American Planning Association, 50(1), 48–61.

32. Bottoms, A. E. (1974). Review of defensible space. British Journal of Criminology, 14(2), 203–206.33. Hillier, B. (1973). In defense of space. Royal Institute of British Architects Journal, 80(11), 539–

544.34. Hillier, B., & Shu, S. (2000). Do burglars understand defensible space? New Evidence on the Relation

Between Crime and Space. http://www.spacesyntax.org.35. Mawby, R. I. (1977). Defensible space: a theoretical and empirical appraisal. Urban Studies, 14(2),

169–179.36. Mayhew, P. (1979). Defensible space: the current status of a crime prevention theory. The Howard

Journal of Criminal Justice, 18(3), 150–159.37. Merry, S. E. (1981). Defensible space undefended: social factors in crime control through environ-

mental design. Urban Affairs Review, 16(4), 397–422.38. Wilson, S. (1981). A new look at Newman. RIBA Journal, 50–51.39. Brantingham, P. L., & Brantingham, P. J. (1993). Environment, routine and situation: Toward a pattern

theory of crime. In R. V. G. Clarke & M. Felson (Eds.), Routine activity and rational choice (Vol. 5, pp.259–294). New Jersey: Transaction Publishers.

40. Wilson, S. (1980). Vandalism and defensible space on London housing project. In R. V. G. Clarke & P.Mayhew (Eds.), Designing out crime (pp. 39–66). London: HMSO.

41. Newman, O. (1976). Design guidelines for creating defensible space. Washington: National Institute ofLaw Enforcement and Criminal Justice.

42. Reynald, D. M., & Elffers, H. (2009). The future of Newman’s Defensible Space Theory: linkingdefensible space and the routine activities of place. European Journal of Criminology, 6(1), 25–46.

43. Booth, A. (1981). The built environment as a crime deterrent. Criminology, 18(4), 557–570.44. Patterson, A. H. (1978). Territorial behavior and fear of crime in the elderly. Environmental Psychology

and Nonverbal Behavior, 2(3), 131–145.45. Flatley, J., Kershaw, C., Smith, K., Chaplin, R., & Moon, D. (2010). Crime in England and Wales

2009/10. London: Home Office Statistical Bulletin.46. Kershaw, C., Nicholas, S., & Walker, A. (2008). Crime in England and Wales 2007/8: Findings from

the British crime survey and police recorded crime. London: Home Office Statistical Bulletin.47. Walker, A., Flatley, J., Kershaw, C., & Moon, D. (2009). Crime in England and Wales 2008/09.

London: Home Office Statistical Bulletin.

A. Abdullah et al.

http://www.spacesyntax.org

48. Caughy, M. O., O’Campo, P. J., & Patterson, J. (2001). A brief observational measure for urbanneighborhoods. Health & Place, 7(3), 225–236.

49. Cohen, D., Spear, S., Scribner, R., Kissinger, P., Mason, K., & Wildgen, J. (2000). “ Broken windows”and the risk of gonorrhea. American Journal of Public Health, 90(2), 230–236.

50. Craik, K. H., & Appleyard, D. (1980). Streets of San Francisco: Brunswick’s lens model applied tourban inference and assessment. Journal of Social Issues, 36(3), 72–85.

51. Hedayati Marzbali, M., Abdullah, A., Razak, N. A., & Maghsoodi, M. J. (2012). The effects of CrimePrevention Through Environmental Design (CPTED) on Fear of Crime and Perception of Safety.Journal of Environmental Psychology, 32(2), 79–88.

52. Hedayati Marzbali, M., Abdullah, A., Razak, N. A., & Maghsoodi, M. J. (2012). The relationshipbetween socio-economic characteristics, victimization and CPTED principles: evidence from theMIMIC model. Crime, Law and Social Change, 58(3), 351–371.

53. Sampson, R. J., & Raudenbush, S. W. (1999). Systematic social observation of public spaces: a newlook at disorder in urban neighborhoods. American Journal of Sociology, 105(3), 603–651.

54. Skjaeveland, O., & Garling, T. (1997). Effects of interactional space on neighbouring. Journal ofEnvironmental Psychology, 17(3), 181–198.

55. Baum, F. E., Zierscha, A. M., Zhangb, G., & Osborne, K. (2009). Do perceived neighbourhood cohesionand safety contribute to neighbourhood differences in health? Health and Place, 15(4), 925–934.

56. Chandola, T. (2001). The fear of crime and area differences in health. Health and Place, 7(2), 105–116.57. Allen, M. J., & Yen, W. M. (1979). Introduction to measurement theory. Monterey: Brooks/Cole.58. Nunnally, J. C. (1978). Psychometric theory (2nd ed.). New York: McGrew-Hill Inc.59. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: A global

perspective (7th ed.). United States: Pearson.60. Lynn, M. R. (1986). Determination and quantification of content validity. Nursing Research, 35(6), 382.61. Carmines, E. G., & McIver, J. P. (1981). Analyzing models with unobserved variables. In G. W.

Bohrnstedt & E. F. Borgatta (Eds.), Social measurement: current issues (pp. 65–115). Beverly Hills:Sage Publications, Inc.

62. Di Eugenio, B., & Glass, M. (2004). The kappa statistic: a second look. Computational Linguistics,30(1), 95–101.

63. Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data.Biometrics, 33, 159–174.

64. Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: problems andprospects. Journal of Management, 12(4), 531–544.

65. Comrey, A. L., & Lee, H. B. (1992). A first course in factor analysis (2nd ed.). Hillsdale: LawrenceErlbaum.

66. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (1998). Multivariate data analysis (5th ed.).Upper Saddle River: Prentice-Hall.

67. Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). New York: Allyn andBacon.

68. Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational andPsychological Measurement, 20, 141–151.

69. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill.70. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: a review and

recommended two-step approach. Psychological Bulletin, 103(3), 411–423.71. Byrne, B. M. (2001). Structural equation modeling with AMOS: Basic concepts, applications, and

programming. Mahwah: Lawrence Erlbaum Associates, Inc.72. Mardia, K. V. (1974). Applications of some measures of multivariate skewness and kurtosis in testing

normality and robustness studies. Sankhyā: The Indian Journal of Statistics, Series B, 36(2), 115–128.73. Fassinger, R. E. (1987). Use of structural equation modeling in counseling psychology research.

Journal of Counseling Psychology, 34(4), 425–436.74. Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.75. Briggs, N. E. (2006). Estimation of the standard error and confidence interval of the indirect effect in

multiple mediator models. The Ohio State University.76. Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and

comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891.77. Williams, J., & MacKinnon, D. P. (2008). Resampling and distribution of the product methods for

testing indirect effects in complex models. Structural Equation Modeling, 15(1), 23–51.78. Lockwood, C. M., & MacKinnon, D. P. (1998). Bootstrapping the standard error of the mediated

effect. Paper presented at the 23rd Annual Meeting of SAS Users Group International, Cary, NC.


79. Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models.In S. Leinhart (Ed.), Sociological methodology (pp. 290–312). San Francisco: Jossey-Bass.

80. Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and nonexperimental studies: newprocedures and recommendations. Psychological Methods; Psychological Methods, 7(4), 422–445.

81. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis:conventional criteria versus new alternatives. Structural Equation Modeling: A MultidisciplinaryJournal, 6(1), 1–55.

82. Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invarianceliterature: suggestions, practices, and recommendations for organizational research. OrganizationalResearch Methods, 3(1), 4–70.

83. Cheung, G. W. (2008). Testing equivalence in the structure, means, and variances of higher-orderconstructs with structural equation modeling. Organizational Research Methods, 11(3), 593–613.

84. Milfont, T. L., & Duckitt, J. (2004). The structure of environmental attitudes: a first-and second-orderconfirmatory factor analysis. Journal of Environmental Psychology, 24(3), 289–303.

85. Tojib, D. R., Sugianto, L. F., & Sendjaya, S. (2008). User satisfaction with business-to-employee portals:conceptualization and scale development. European Journal of Information Systems, 17(6), 649–667.

86. Lin, H. F. (2007). Predicting consumer intentions to shop online: an empirical test of competingtheories. Electronic Commerce Research and Applications, 6(4), 433–442.

87. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of theAcademy of Marketing Science, 16(1), 74–94.

88. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variablesand measurement error. Journal of Marketing Research, 18(1), 39–50.

89. Taylor, R. B., & Brower, S. N. (1985). Home and near-home territories. In I. Altman & C. Werner (Eds.),Human behavior and environment: Current theory and research (Vol. 8, pp. 183–212). NewYork: Plenum.

90. Newman, O. (1996). Creating defensible space, Office of Policy Development and Research.Washington: US Department of Housing and Urban Development.

91. Brown, B. B., & Werner, C. M. (1985). Social cohesiveness, territoriality, and holiday decorations: theinfluence of cul-de-sacs. Environment and Behavior, 17(5), 539–565.

92. Moffatt, R. E. (1983). Crime prevention through environmental design-a management perspective.Canadian Journal of Criminology, 25(1), 19–31.

93. Ley, D., & Cybriwsky, R. (1974). Urban Graffiti as territorial markers. Annals of the Association ofAmerican Geographers, 64(4), 491–505.

94. Scheflen, A. E. (1971). Living space in an urban ghetto. Family Process, 10(4), 429–450.

A. Abdullah et al.

Documents

Territorial functioning and victimisation: conceptualisation and scale development