Routine activities and deviant behaviors: American, Dutch, Hungarian, and Swiss youth

Routine Activities and Deviant Behaviors: American,

Dutch, Hungarian, and Swiss Youth1

Alexander T. Vazsonyi,2,7 Lloyd E. Pickering,3 Lara M.

Belliston,4 Dick Hessing,5 and Marianne Junger6

The current investigation examined cross-national similarities and differences inroutine activities, measures of deviance, and their relationship in representativesamples of *7,000 adolescents aged 15–19 years (mean age: 17.5 years) fromHungary, the Netherlands, Switzerland, and the United States. For the majority ofyouth, most of their time was spent in solitary activities, followed by peer activities,community/sports activities, and family activities; Hungarian youth reportedspending a much greater amount of time with the family than adolescents fromother countries, while Dutch youth spent far more time in solitary activities thantheir peers. Rates of total deviance were remarkably similar for American, Dutch,and Swiss youth; Hungarian youth reported substantially lower rates than all otheradolescents. Finally, findings indicated that routine activities accounted for 18%for males and 16% for females of the variance explained in total deviance. Fur-thermore, with the exceptions of alcohol and drug use, country had very little or noexplanatory power in deviance. The current study suggests that the utility and theexplanatory power of the routine activities framework replicates across nationalboundaries.

KEY WORDS: deviant behavior; delinquency; routine activities; cross-culturalresearch.

1Previous versions of this paper were presented at the First Annual Meetings of the European

Society of Criminology in Lausanne, Switzerland (September, 2001) and the 53rd Annual

Meetings of the American Society of Criminology in Atlanta, Georgia (November, 2001).2Department of Human Development and Family Studies, 284 Spidle Hall, Auburn University,

Auburn, AL 36849.

7To whom all correspondence should be addressed at: Department of Human Development and

Family Studies, Auburn University, 284 Spidle Hall, Auburn, Alabama 36849. E-mail:

[email protected]

3Department of Human Development and Family Studies, Auburn University, Auburn, AL.4Department of Human Development and Family Studies, Auburn University, Auburn, AL.5Law Faculty, Erasmus University of Rotterdam, Rotterdam, The Netherlands.6Department of Psychology, Utrecht University, The Netherlands.

Journal of Quantitative Criminology, Vol. 18, No. 4, December 2002 (� 2002 )

397

0748-4518/02/1200–0397/0 � 2002 Plenum Publishing Corporation

1. INTRODUCTION

Based on the routine activities approach to crime and deviance(Felson and Cohen, 1979), criminologists (e.g., Riley, 1987) and devel-opmentalists (e.g., Mahoney and Stattin, 2000) have examined the rela-tionship between how youth spend their time and deviant or antisocialbehavior. As recently noted by Osgood et al. (1996), surprisingly fewempirical investigations have examined implications and basic premisesof the routine activities framework for the relationship between routineactivities and deviance (Agnew and Petersen, 1989; Hawdon, 1996, 1999;Osgood et al., 1996; Riley, 1987), although a larger number of studieshave examined this framework for criminal victimization (e.g., Miethe etal., 1987). Furthermore, with very few exceptions (Swedish youth, Maho-ney and Stattin, 2000 or English/Welsh youth, Riley, 1987), most workthat has been completed in this area has relied on data from theUnited States. Unfortunately, this is very consistent with criminologicalresearch in general (for a discussion, see Barberet, 2001; Farrington, 1999a,1999b).

As pointed out by Gottfredson and Hirschi (1990), explanations ofcrime and deviance should be culture-free in the sense that the sameexplanatory frameworks, if powerful and generalizable enough, shouldalso stand up to cross-cultural (within a country) and cross-national(between countries) comparative efforts (for this argument, see alsoFarrington, 1999a, 1999b; for an empirical study, see e.g., Vazsonyi et al.,2001). In fact, cross-national comparative work has the great advantageof providing a ‘‘naturally’’ large degree of diversity and variability withregard to individual, social, or institutional indicators (Howard et al.,2000). In a recent review on the current state of comparative criminology,Howard and colleagues (2000, p. 183) note that ‘‘self-report surveys at thecross-national level may eventually prove to be a valuable resource onoffenders and patterns of delinquency,’’ acknowledging the value of suchdata for exploring potentially culture-free or ‘‘universal’’ patterns ofbehavior. Farrington (1999a) notes that though cross-national studies areimportant, they are very infrequent; nevertheless, they are perhaps one ofthe only tools to establish true generalizability of explanatory frameworksand theories across local conditions and contexts (for an empiricalexample, see Farrington and Loeber, 1999). Therefore, the primary focusof the current investigation was to further examine the routine activitiesexplanatory framework using self-report data from four countries; morespecifically, we were interested in testing whether the relationshipsbetween different types of routine activities (family, peer, solitary, andcommunity) and various deviance measures were similar or different bycountry.

398 Vazsonyi, Pickering, Belliston, Hessing, and Junger

1.1. Background

In seminal publications introducing the routine activities/lifestylesconcepts, Felson and Cohen (1979) and Hindelang et al. (1978) providednovel ways of thinking about and explaining aggregate crime perpetrationand victimization, respectively, by linking micro- and macro-level vari-ables. Felson and Cohen (1979, p. 593) defined routine activities as ‘‘anyrecurrent and prevalent activities which provide for basic population andindividual needs, whatever their biological or cultural origin.’’ They alsosuggest that these activities ‘‘may occur (1) at home, (2) in jobs away fromhome, and (3) in other activities away from home.’’ In effect, the authorshave argued, in simple terms, that how and where we spend our time mayimpact whether we are victimized and whether we engage in norm-vio-lating conduct or not8 (see also, Garfalo, 1987), independent of social orcultural context. Although perhaps not specifically elaborated by theseauthors, we believe that implicit in this thinking and perspective are ideassimilar to control theories. In fact, Felson and Cohen (1979, p. 590) note‘‘each successfully completed violation minimally requires an offender withboth criminal inclination and the ability to carry out those inclinations, aperson or object providing a suitable target for the offender, and theabsence of guardians capable of preventing violations.’’ They further notethat ‘‘though guardianship is implicit in everyday life, it is usually markedby the absence of violations; hence it is easy to overlook’’ (1979, p. 590). Inthis sense we suggest that the routine activities perspective contains ele-ments of social control as elaborated by Hirschi (1969) when he spoke of‘‘involvement’’ (for a recent empirical test of this idea, see Hawdon, 1999)or ‘‘attachment to parents’’ and which Felson and Cohen termed‘‘guardianship.’’ Furthermore, it may also include the idea of low self-control when, for example, the authors refer to an offender with a‘‘criminal inclination’’ (e.g., Gottfredson and Hirschi, 1990). In fact, in hislater writing on the routine activity approach, Felson (1994, p. 20) speci-fically acknowledged self-control, terming it ‘‘the self-control insight,’’ bysuggesting that individuals differ in their basic propensity to ‘‘get intotrouble, especially by going for the pleasure of the moment.’’ Whetherindividuals violate norms largely depends on the informal controls theindividual encounters in society and in daily life; these controls effectivelyprevent crime.

8In describing their original thinking about the routine activity approach, Felson and Cohen

(1980, p. 403) suggested that ‘‘the routine activity approach might in the future be applied to

the analysis of offenders and their inclinations as well. For example, the structure of primary

group activity may affect the likelihood that cultural transmission or social control of criminal

inclinations will occur, while the structure of the community may influence the extent of peer

group activity influencing crime.’’

Routine Activities and Deviant Behaviors 399

Thus, the underlying premise of the routine activities idea can be tracedto and may be related to control theories, something Gottfredson (1981) hasalluded to previously. Hirschi (1969) suggested that we need not be con-cerned with what makes people deviant or criminal; rather, we need todevelop an understanding of what makes individuals conform or how theyconform. In turn, this will allow a greater understanding of what contributesto individual tendencies to violate social norms and mores. In this sense, theroutine activities or lifestyles frameworks may be useful in gaining anunderstanding of how, where, and perhaps with whom individuals spendtheir time. Put differently, individuals with weak attachments and/or agreater individual propensity to commit a norm-violating act may spendtheir time in systematically different ways than individuals who are lesslikely to commit a deviant act. It is precisely this variability we are interestedin as we believe that variability in routine activities/lifestyles is associatedwith variability in deviant behaviors. In the strictest sense then, we alsosuggest that the routine activities perspective is not necessarily a strongcausal explanation, because it simply focuses on explaining crime and vic-timization by examining the actors, how they spend their time, and theecological controls they encounter in their environment. Rather, we considerit an important theoretical perspective which has inspired a good amount ofrecent empirical investigations (e.g., Agnew and Petersen, 1989; Fox andSobol, 2000; Hawdon, 1996, 1999; Mahoney and Stattin, 2000; Riley, 1987;Wittebrood and Nieuwbeerta, 2000). In the following section, we brieflyreview some of the important empirical work that has focused on therelationship between routine activities and deviant behaviors.

1.2. Previous Investigations

The studies with most relevance to the current investigation haveconceptualized routine activities in one of two main ways. First, activitieswere examined as being either structured vs. unstructured and/or supervisedvs. unsupervised by an adult (Mahoney and Stattin, 2000; Osgood et al.,1996). Second, activities have been examined according to specific content(e.g., housework, games/crafts/hobbies, or music/art; e.g., Agnew andPetersen, 1989). It is important to note here that no previous study hasattempted to model the relationship between routine activities or leisure anddeviance using a composite measure approach; in other words, all work todate has used single item indicators of routine activities.

Perhaps one of the most rigorous studies in this area of research hasbeen the one by Osgood and colleagues (1996). In their large-scale long-itudinal investigation, the authors developed a model of routine activitiesand general deviance (perpetration) for late adolescents and young adults


ages 18 to 26. More specifically, they examined the relationship between avariety of routine activities (categorized as either unstructured activitiesoutside the home, other more structured activities outside the home, or at-home activities; explicitly excluding sustenance activities, school, or work)and several types of deviant conduct. They found that ‘‘the lack of structureleaves time available for deviance; the presence of peers makes it easier toparticipate in deviant acts and makes them more rewarding; and the absenceof authority figures reduces the potential for social control responses todeviance’’ (1996, p. 651). More specifically, regression models including 13different structured and unstructured activities as well as age accounted forbetween 3% (other drug use) and 15% (criminal behavior; average across allfive types of deviance: 8.7%). They were also able to demonstrate howroutine activities conditioned the relationship between background variables(e.g., age, sex, grades, and parental education) and measures of deviance.For example, routine activities accounted for 73% of the observed rela-tionship between parental education and deviance.

Despite its great importance, this investigation also has some limita-tions that we would like to mention. For example, although the authorsmake an explicit attempt to suggest that the measures of routine activitiescarried no connotations of the outcome of interest in their study, namelydeviance, we believe that they did. In fact, ‘‘joy riding’’ and ‘‘attendingparties’’ both inherently include potentially norm-violating conduct. Con-sider party attendance: for many, perhaps even most adolescent and youngadults in the United States, attending a party includes consuming alcoholand/or other illegal substances. Not surprisingly, this ‘‘routine activity’’ wasassociated with all measures of deviance; furthermore, it had the strongestassociation with the deviance measures across all 13 items with the exceptionof dangerous driving. Interestingly, an earlier study assessing the relation-ship between routine activities and victimization by Jensen and Brownfield(1986) resulted in the same finding, namely that ‘‘activities which involve themutual pursuit of fun are more victimogenic’’ (p. 85). The authors had alsofound this strong relationship between these activities and delinquentbehavior. Finally, and perhaps most importantly, this routine activityaccounted for a very large proportion of the 1% to 10% of varianceexplained by unstructured activities in different measures of deviance. Oneimportant implication of this is that ‘‘deviance-free measures’’ of routineactivities may in fact account for less variance than suggested by theauthors. As pointed out earlier, a second issue in this study is the mea-surement of routine activities by single items. We believe that formingcomposites or clusters of related activities to measure some behavioralaspect of how adolescents spend their time might provide more stable orconsistent measurement.


In a recent cross-sectional study of *700 Swedish adolescents byMahoney and Stattin (2000), the authors found that participating inunstructured leisure activities was most associated with antisocial behaviors,for both males and females. Not surprisingly, they also found that thesesame youth experienced the least amount of parental monitoring in com-parison to other youth. Furthermore, they found that these youth spentmore time with older peers, peers who did poorly in school, peers whostayed out on the town at night, and peers who had previously been pickedup by the police. An important limitation of this work was that routineactivity was measured by two dichotomously coded variables based on (1)structured activity measures (e.g., sports, music, hobby, church, etc.) and (2)youth recreation center membership which was considered unstructured.The authors employed ANOVAs and, therefore, did not report the amountof variance explained in deviance by routine activities which makes com-parisons with Osgood et al.’s study challenging.

Agnew and Petersen (1989) examined a similar set of questions basedon a local American sample of 600 adolescents. Using open-ended interviewdata, each respondent indicated five ‘‘favorite ways of spending your freetime’’ (1989, p. 338), who they spent this time with, and how frequently theydid so. Of the 265 activities recorded, the authors developed a typology ofleisure activities (e.g., sports-competitive, sports-noncompetitive, passiveentertainment, hanging out/loafing, etc.). Results suggested that the activityand the company youth kept accounted for 5% to 6% of minor, serious, andtotal delinquency. The authors also found that this relationship was largelyunchanged once they controlled for sex, age, maternal and paternal edu-cation, and the size of the home community. These findings were bothsimilar and different in comparison to Osgood et al.’s study—similar in theamount of variance explained in delinquency or deviance, but different inthat background variables had little or no explanatory power.

Finally, in another important effort based on *700 youth in Englandand Wales, Riley (1987) completed interviews to ascertain the frequency oftime spent with peers, types of peer activities, and the location whereadolescents congregated. He also measured self-reported delinquent beha-viors. Riley tested the notion that juvenile offending is primarily a groupactivity. In effect, some types of delinquency, such as vandalism, are likelyto occur away from parental supervision. He found that individuals whoreported a large amount of delinquent behaviors spent significantly lesstime at home and more time with large groups of peers than adolescentswho reported few such behaviors. This association was true of both malesand females; however, he found that females had lower rates of deviancedue to less time spent in situations conducive to crime. ‘‘Offenders’’ oftencongregated in groups away from home, spent less time in home-based


activities, and less time with parents. Riley concluded that time spent awayfrom home provided the context in which adolescent deviant activityoccurs. It is important to point out that Riley (1987, p. 347) also questionedthe ‘‘causal value’’ of the routine activity framework, namely whether‘‘lifestyle or activity pattern analysis [is] simply another way of presentingwhat we already know about the relationship between delinquency pre-dictors and involvement in crime.’’ Finally, two important limitations ofthis study include that Riley focused on Saturday leisure time which onlyconstitutes a very small proportion of ‘‘free time’’ by adolescents thatrequires analysis. Second, most of Riley’s findings were based on dichot-omized (yes/no) frequency comparisons on offending (behavior), offendingwith peers (with whom), or offending away from home (location).Although an important first step in attempting to understand the rela-tionship between routine activities and deviance, we believe that anexamination of routine activities must also include the time after schoolduring week days; it must also include more complex analyses to fullyexamine the relation between routine activities and deviance.

Together, the studies reviewed suggest that time at home and time spentpursuing personal interests or time in structured activities decreases thelikelihood of deviant behaviors while spending time with peers, especially inunstructured and unsupervised activities, places youth at risk for deviantbehavior. Though the reviewed studies contribute to our understanding ofleisure and deviance in various countries (e.g., England/Wales, Sweden, andthe United States), no studies have directly examined the relationshipbetween routine activities and deviance cross-nationally. The importance ofsuch a comparison lies in the fact that different cultures and countries arelike natural experiments—youth do not live the same way in these nationalcontexts and, therefore, the comparative method provides an excellentmedium to further test the routine activities–deviance relationship andwhether it generalizes cross-nationally.

1.3. The Current Investigation

The current investigation sought to extend this line of research in anumber of important ways:

(1) to reassess the relationship between adolescent routine activitiesand deviance using (a) large samples from four countries (Hungary,the Netherlands, Switzerland, and the United States) and (b)middle and late adolescents ages 15 to 20 years. Based on ourreview, it was important to us to include measures of routineactivities that did not include or imply norm-violating conduct.


(2) to assess the relationship between routine activities and devianceusing a number of multi-item, scalar measures of deviance (rangingfrom vandalism, alcohol use, drug use, school misconduct, generaldeviance, theft, assault, and total deviance).

(3) to examine the importance of national context as a moderator ofthe routine activities–deviance relationship; in other words, doescountry play an important role in explaining this relationship?

2. METHODS

2.1. Procedure

The data for this study were collected as part of the International Studyof Adolescent Development (ISAD), a multinational, multisite investigationconsisting of *8,500 subjects from four different countries (Hungary, theNetherlands, Switzerland, and the United States; see Appendix A). Thepurpose of ISAD is to examine the etiology of adolescent problem behaviorsand deviance using large representative samples from different countries(Vazsonyi et al., 2001; Vazsonyi and Pickering, 2000). A standard datacollection protocol was followed across all study locations. It was approvedby a university IRB and consisted of a self-report data collection instrumentwhich included instructions on how to complete the survey, a description ofthe ISAD project, and assurances of anonymity and confidentiality. Ques-tionnaires were administered to participants during a 1 to 2 hr period. Muchattention was given to the development of the ISAD survey instrument,particularly by developing new or using existing measures that could be usedcross-culturally without losing nuances or changing meanings. This includedan evaluation of survey items as to whether they assessed a readily obser-vable and ‘‘ratable’’ behavior in each of the countries included in the currentstudy. The focus of the current study was to employ measurement in fourdistinctly different countries where previous local efforts can generally notbe compared across national and cultural boundaries (for a discussion, seeArcher and Gartner, 1984). For example, studies have asked about deviantbehaviors that are deviant in a specific national context, but that arebasically nonexistent in others. Consider the following—Americans cannotrelate to theft of mopeds because there exist relatively few or no mopeds inthe United States. On the other hand, Swiss and Dutch youth know mopedsquite well as very many 14 year olds own one. Similarly, while adolescents inall three European countries cannot understand ‘‘trying to cash a phonycheck’’ because checks are not used as payment currency in daily financialdealings, every American knows what ‘‘check writing’’ is quite well. Asidefrom FBI and Interpol categories of index crimes reported in official data


sources, there exists very little work that has attempted to develop com-prehensive, multi-item, multi-factor scales that can reliably assess behaviorscross-nationally (for a discussion on deviance measures, see Moffitt, 1988;Junger-Tas, 1988). Therefore, the current survey was translated from Eng-lish into each of the target languages (Dutch, German, and Hungarian) andback-translated by bilingual translators. Surveys were carefully examined byadditional bilingual translators, and when translation was difficult orambiguous, consensus was used to produce the final translation. We need toacknowledge that using the self-report methodology has been debated fornumerous decades, and we realize that questionnaires, whether used in asingle country or multiple countries, have inherent short-comings andweaknesses. However, we would also like to suggest that the validity andreliability of self report assessment tools have been well established pre-viously (e.g., Farrington, 1988; Hindelang, Hirschi, and Weis, 1981; Junger-Tas and Marshall, 1999; Moffitt, 1988).

2.2. Sample

Valid data for this study were gathered from a total of N ¼ 8;417adolescents from four different countries (Hungary, n ¼ 871; Netherlands,n ¼ 1;315; Switzerland, n ¼ 4;018; United States, n ¼ 2;213). In all loca-tions, medium-sized cities of similar size were selected for participation. Foreach country, different schools were selected for participation to obtainrepresentative samples of the general population. For the Europeansamples, this included schools for university-bound students (Gymnasium)as well as schools specializing in vocational/technical training for students inapprenticeships. In the United States, the samples included high schoolstudents, community college students, and university students (for a detaileddescription of the sample, see Vazsonyi et al., 2001).

We selected a common ‘‘age band’’ including 15 to 19 year olds for thecurrent study across all country samples; this reduced the sample ton ¼ 6;914 (82% of the total sample). The final study sample includedn ¼ 1;516 Americans, n ¼ 1;040 Dutch, n ¼ 797 Hungarians, and n ¼ 3;561Swiss. There were n ¼ 3;913 males (mean age ¼ 17:5, sd ¼ 1:3) andn ¼ 2;939 females (mean age ¼ 17:5, sd ¼ 1:4) in this sample; 62 participantsdid not identify their sex. Of the Hungarian adolescents in the sample,n ¼ 544 were males and n ¼ 242 were female (11 Hungarian subjects did notidentify their sex). The Dutch adolescents were composed of n ¼ 495 malesand n ¼ 540 females (5 Dutch subjects did not identify their sex). Among theSwiss adolescents, n ¼ 2;235 were males and n ¼ 1;291 were females (35Swiss subjects did not identify their sex). Finally, the American adolescents


in the sample consisted of n ¼ 639 who were males, and n ¼ 866 who werefemales (11 American subjects did not identify their sex).

2.3. Measures

Subjects from all countries were asked to fill out the same questionnaireincluding demographic and background variables (age, sex, and socialclass), routine activities, and deviance.

2.3.1. Age

Participants were asked to indicate the month and year in which theywere born. The 15th day of each respective month was used to calculatesubjects’ specific ages.

2.3.2. Sex

Subjects were asked to indicate their sex on a single item: ‘‘What is yourgender?’’ Responses were given as 1 ¼ male and 2 ¼ female.

2.3.3. Social Class

Subjects were asked to indicate the type of work performed by theprimary wage earner in the family. Six categories collapsed from Hollings-head’s (1975) original nine categories and modified to be applicable in eachof the four countries were specified that would readily map on professionsfound in each of the four study countries. Each category containeddescriptions of sample jobs which would fit into each of them. Responseswere given by indicating the number of the category which contained theclosest or most accurate description of the family’s primary wage earner’sjob. The categories, listed here with condensed descriptions, were as follows:1 ¼ owner of a large business, executive; 2 ¼ owner of a small business,professional; 3 ¼ semi-professional, skilled laborer; 4 ¼ clerical staff;5 ¼ semiskilled laborer; and 6 ¼ laborer or service worker.

2.3.4. Country

Participants were each identified in the data according to their nationalmembership (American, Swiss, Hungarian, or Dutch). In some of ourregression analyses, this variable (national membership) was used as adummy-coded predictor.


2.3.5. Routine Activities

We were interested in examining variability in different leisure contextsand not in establishing exact estimates of time adolescents spend in specificactivities or behaviors; therefore, we employed a subjective, molar timerecall self-report methodology of routine activities that excluded time spentat school, at work, or sleeping (see also, Osgood et al., 1996). We focused onthe waking hours after school and before bedtime to examine how adoles-cents spend their time. We also examined routine activities on weekends.Rather than using individual indicators, we employed 2 or more items toassess each area of routine activities.

Subjects answered a total of 11 questions concerning the time theyspent engaging in specific activities. For 8 of these items, subjects were askedto indicate the ‘‘time spent in an average week’’ after school and on weekends(1) playing school or community sports or participating in school clubs, (2)watching TV alone, (3) doing homework or reading alone, (4) hanging outwith friends in a public place, (5) hanging out with friends at someone’shouse, (6) exercising, jogging, working out, other forms of exercise or leisuresports, (7) spending time alone, and (8) participating in community orga-nizations. Each of these items was rated on a 5-point Likert type scaleð1 ¼ none, 2 ¼ 1–5 hr, 3 ¼ 6–10 hr, 4 ¼ 11–19 hr, 5 ¼ 20þ hr). A principalcomponents exploratory factor analysis on these 8 items using varimaxrotation yielded a solution of 3 factors with Eigenvalues greater than 1. Thefirst factor, named Peer Activities, consisted of items 4 and 5 above, whilethe second factor, named Community/Sports Activities, included items 1, 6,and 8, and the third factor, named Solitary Activities, contained items 2, 3,and 7. Three scores were formed by summing individual items.9

A quantitative measure of family time, named Family Activities, wasconstructed by combining 3 items (Pickering and Vazsonyi, 2002).10 Thefirst two items, which were answered on a 5-point Likert type scale ð1 ¼ 0,2 ¼ 1, 3 ¼ 2, 4 ¼ 3, 5 ¼ 4–5) read, (a) ‘‘On the average, how many after-

9The subjective time recall methodology of routine activities required some transformations

prior to analyses. For 8 items these we recoded responses into hour estimates, namely 0, 5, 10,

20, and 25.10For family time, we recoded two items (weekday) to include a floor of zero. Next, we

differentially weighted the three items based on previous work by Csikszentmihalyi and

Larson (1984; for additional detail, see Pickering and Vazsonyi, 2002). This included an

assumption that on average, an adolescent could spend a conservative maximum of 2 hr on a

weekday afternoon and 3 hr on a weekday evening with family. Similarly, we very

conservatively hypothesized based on previous work that an adolescent could spend up to

6 hr per weekend day with family, for a maximum of 12 hr per weekend. We also assumed that

time spent during the week and time spent on weekends are associated. Therefore, we

multiplied time spent during the week by 1.1–1.5 based on the response to the weekend time

item to assess a total measure of family time.


noons during the school week, from the end of school or work to dinner,have you spent talking, working, or playing with members of your family?’’and (b) ‘‘On the average, how many evenings during the school week, fromdinnertime to bedtime, have you spent talking, working, or playing withmembers of your family?’’ (see Warr, 1993, who used a 6-point Likert typescale for these two items). The third item asked, ‘‘On the weekends, howmuch time have you generally spent talking, working, or playing withmembers of your family?’’ and was measured on a 5-point Likert type scale(1 ¼ very little, 2 ¼ not too much, 3 ¼ some, 4 ¼ quite a bit, 5 ¼ a greatdeal). For subsequent mean level comparisons, each of the four routineactivity scores was centered by summing the reports from all four contextsand then dividing each activity by the summed total.

2.3.6. Deviance

Lifetime deviance was measured by the 55-item Normative DevianceScale (NDS; for more detail on the measure, see Vazsonyi et al., 2001). Thescale was developed to measure ‘‘culture-free’’ deviance in general adoles-cent populations and to provide epidemiological data, and, therefore,examined a broader spectrum of deviant activities than just status and indexoffenses. Rather, it also measured less serious forms of norm-violatingconduct that transcend culture. The current investigation examined all sevensubscales of the NDS, namely vandalism (8 items), alcohol (7 items), drugs(9 items), school misconduct (7 items), general deviance (11 items), theft (7items), and assault (6 items). Responses for all items in the NDS were givenon a 5-point Likert type scale and identified lifetime frequency of specificbehaviors (1 ¼ never, 2 ¼ one time, 3 ¼ 2 –3 times, 4 ¼ 4 –6 times, and5 ¼ more than 6 times). Reliability coefficients on the deviance subscales forthe total sample ranged from � ¼ 0:76 (assault) to � ¼ 0:89 (drugs; totaldeviance, � ¼ 0:95Þ; scales were also reliable in each subsample (see Vaz-sonyi et al., 2001).

3. RESULTS

Table I presents the mean ages and the frequencies of the primary wageearner’s job by country. Mean level age differences by country were statis-tically significant (F ¼ 811:62, p � 0:001Þ. Similarly, comparisons by socialclass also indicated significant differences by country (X2 ¼ 700:85,p < 0:001Þ. Therefore, we controlled for both age and social class in sub-sequent regression analyses.

Table II presents mean rates of routine activities and deviance bycountry and sex. Because routine activities variables were standardized


within each country to allow for comparisons, mean values can be inter-preted as actual percentages of time spent in each context of activity. Anexamination of these means indicated that both males and females in all fourcountries reported spending the greatest amount of their time alone (maleaverage: 34.7%; female average: 35.6%). Next, for males, all but the Hun-garians indicated spending the second greatest amount of time with peers(average: 24.0%), then in community/sports activities (average: 22.1%),then finally, with family (average: 19.1%). Both American and Swiss femalesreported spending more time with family than in community/sports activ-ities; in fact, for all four groups of females, community/sports activities(average: 19.0%) ranked last. Both Hungarian males and females, as well asDutch females, reported the same rank ordering of routine activities, namelysolitary, peer, community/sports, and family.

In comparisons on deviance measures, findings suggested that maleswere consistently more deviant than females; this was true in each country.Adolescents indicated that alcohol use was the most common form ofdeviance for both males and females across all countries. Similarly, ado-lescents reported the lowest levels of participation for theft across all groups.

Table II also includes the results of ANOVAs with post-hoc Scheffecontrasts for routine activities and deviant behavior by country and sex. Formale and female family time, Hungarians reported spending a significantlylarger proportion of their time with family than youth from all othercountries, while American adolescents were significantly higher in familytime than both Swiss and Dutch youth. American and Swiss males andfemales were very close in the amount of time spent with peers and were allsignificantly higher than Hungarians. In addition, American and Swissfemales reported a greater amount of time spent with peers than Dutchfemales. Dutch males and females reported spending significantly more timealone than their counterparts in all three other groups; the Swiss were also

Table I. Descriptive Statistics of Demographic Variables by Country

Total sample n ¼ 6;914————————————————————————American Dutch Hungarian Swiss————————————————————————n ¼ 1;516 n ¼ 1;040 n ¼ 797 n ¼ 3;561

Age (mean) 17.9 16.4 16.7 17.9Executive 33.8 19.1 16.0 16.0

Primary Professional 40.3 37.8 22.5 35.0wage Semi-professional 13.4 27.9 17.6 31.7earner’s Clerical 6.9 11.5 33.3 12.5profession Semi-skilled 4.6 2.7 9.0 3.2

Laborer 0.1 0.1 1.5 1.7


Table II. Oneway ANOVAs on Routine Activities and Deviance by Country and Sex (Controlling for Age and Social Class)

Americans Dutch Hungarians Swiss Sig. Sig.—————————— —————————— —————————— —————————— Scheffe ScheffeMales Females Males Females Males Females Males Females post-hoc post-hoc

———— ———— ———— ———— ———— ———— ———— ———— testsb testsma sd m sd m sd m sd m sd m sd m sd m sd (Males) (Females)

Routine activitiesFamily 20.3 12.8 24.4 11.8 15.8 13.4 18.8 14.6 24.2 14.8 29.7 13.1 16.2 11.5 21.0 12.5 acdf abcdefPeers 25.7 13.6 24.9 12.9 23.0 13.1 17.7 11.9 22.0 13.1 19.9 11.0 25.4 13.7 25.3 12.3 aefSolitary 29.9 12.5 28.4 11.7 42.5 15.4 46.2 16.3 32.5 15.3 32.5 13.0 34.0 12.8 35.1 12.1 acdef acdefCommunity 24.2 12.7 22.1 12.2 18.5 12.7 17.4 11.4 21.3 13.3 17.8 10.6 24.3 14.0 18.6 12.2 adef ac

Deviance ScaleVandalism 1.84 0.85 1.30 0.49 1.83 0.83 1.28 0.43 1.77 0.80 1.31 0.46 1.85 0.80 1.32 0.42Alcohol use 2.72 1.35 2.67 1.22 2.50 0.85 2.20 0.78 2.42 0.98 1.86 0.75 2.32 0.95 1.87 0.80 cef abcdeDrug use 2.10 1.16 1.83 0.98 1.84 1.04 1.49 0.70 1.61 0.75 1.22 0.43 2.27 1.13 1.81 0.95 bdef bfSchool

misconduct 2.28 1.02 1.68 0.60 2.38 0.84 1.80 0.56 2.15 0.79 1.46 0.48 2.17 0.79 1.78 0.57 de acdGeneral 2.10 0.83 1.68 0.60 2.26 0.78 1.81 0.56 1.91 0.76 1.47 0.48 2.20 0.82 1.78 0.57 bdf adfTheft 1.61 0.79 1.23 0.46 1.55 0.67 1.25 0.46 1.41 0.62 1.16 0.35 1.69 0.83 1.32 0.47 befAssault 1.75 0.77 1.27 0.49 1.75 0.73 1.40 0.51 1.71 0.70 1.31 0.46 1.80 0.77 1.33 0.46

Total deviance 2.05 0.79 1.69 0.56 2.02 0.67 1.65 0.46 1.84 0.62 1.44 0.40 2.06 0.72 1.66 0.49 bdf d

Note: Analyses used pairwise deletion, therefore, sample sizes slightly varied by analysis: American males, n ¼ 542–563; American females, n ¼ 772–790; Dutch males, n ¼ 417–423; Dutch females, n ¼ 471–477; Hungarian males, n ¼ 526–531; Hungarian females, n ¼ 220–233; Swiss males,n ¼ 2;085–2,166; Swiss females, n ¼ 1;250–1,264.aFor routine activities, group means can be interpreted as a percent since they were computed by scaling each type of routine activity by the total timereported.b Since residualized means are difficult to interpret, numbers listed here indicate group means before entering control variables, while significantScheffe post-hoc comparisons (p < 0:05Þ indicate relationships after controlling for age and social class and are noted by the following designations: a,American vs. Dutch; b, American vs. Hungarian; c, American vs. Swiss; d, Dutch vs. Hungarian; e, Dutch vs. Swiss; f, Hungarian vs. Swiss.

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significantly higher in this category than Americans for both sexes. Also,Hungarian females reported spending more time alone than their Americanpeers. Among females, Americans reported the highest levels of community/sports activities with significantly higher mean values than all three othernationalities. For males, however, the Swiss reported the highest levels ofcommunity/sports activities and were found to be significantly higher thanboth Dutch and Hungarians. Finally, American males reported significantlygreater proportions of time spent in community/sports activities than theDutch.

Results of ANOVAs on the deviance scales revealed that Americanadolescents of both sexes were significantly higher in alcohol use than allthree European groups. In addition, Dutch females reported significantlymore use of alcohol than Swiss or Hungarians. A similar pattern of drug usewas found for both males and females; namely, both Americans and Swissreported significantly higher levels than both Dutch and Hungarians. Dutchmales reported the highest levels of school misconduct and were found to besignificantly higher than Hungarian and Swiss males. For females, theDutch reported the highest levels of school misconduct and, along with theSwiss, were significantly higher than Hungarians. American females repor-ted significantly lower levels of school misconduct than both Swiss andDutch females. Patterns of general deviance were found to be the same forboth sexes where Hungarians reported significantly lower levels than allthree other groups. Swiss males indicated the highest levels of theft and weresignificantly higher than both Dutch and Hungarian males, while Americanmales were also significantly higher than Hungarians. Mean level compar-isons on the total deviance scale indicated that the Hungarians, both malesand females, were significantly lower than adolescents in all three othercountries, who were remarkably similar to each other within sex. No sig-nificant mean level differences were found in the areas of male and femalevandalism and assault or in female theft by country.

Next, in an effort to compare the relationship between routine activitiesand measures of deviance, we compared patterns of associations betweenindependent variables and outcomes, something Rowe and colleagues (1994)termed developmental process. Rowe et al. (1994) suggested comparing entirematrices from each group that include the antecedents, in this case routineactivities variables, and outcomes, in this case measures of deviance. Thisapproach appears to be superior to a large number of pairwise comparisonsfor each association. For example, to compare whether a single relationshipbetween alcohol use and family time differs by country and sex (8 groups), 28pairwise comparisons would have to be computed. This means that for eight(males and females from four countries) 11� 11 matrices (7 deviance scalesand 4 routine activities measures), each containing 55 correlations, 440


pairwise comparisons would have to be computed. Not only is such a‘‘piecemeal’’ approach of pairwise difference testing extremely tedious (not tomention impossible to comprehend), but it is also likely to increase the risk ofType I error (inferring relationships where there are really none). In short,such an approach would be statistically unsound.

For this purpose, controlling for age and social class, we computedeight 11� 11 correlation matrices by sex and by country which were thenused for a model-free comparison using LISREL (Rowe et al., 1994).11

Table III shows partial correlations between routine activities and deviance;however, for the model-free LISREL comparisons, we employed the fullmatrix. Consistent with previous work and with our expectations, routineactivities with peers were positively associated with deviance, while routineactivities in the family, alone, and in the community were negatively asso-ciated with deviance, although due to both small associations as well assample size, a number of the solitary and community routine activities werenot statistically significant.

Based on suggestions by Loehlin (1992), we used standardized measuresof association (correlations) for model-free LISREL analyses because ofknown mean level differences as well as differences in variability in bothroutine activities scores and deviance measures. We also employed randomsamples of equal size from each country as previous research using thismethod has documented that differences in sample size also affected modelfit (Rowe et al., 1994). Therefore, for male comparisons, we randomlyselected n ¼ 274 participants from each country, while for females, weselected n ¼ 186 adolescents; these sample sizes were based on the smalleststudy samples for each sex by country.

Model fit for these analyses was evaluated using the standard chi squarefit statistic and the chi square to degrees of freedom ratio as well as fitindices such as the CFI, GFI, and the RMSEA (Browne and Cudeck, 1993;Loehlin, 1992), because the chi square statistic is overly sensitive to samplesize and almost always significant in large samples. For the CFI and GFI, afit between 0.90 and 1.0 is considered acceptable (Bentler, 1992). Browneand Cudeck (1993) suggest that an RMSEA value of less than 0.05demonstrates excellent fit, while a value between 0.05 and 0.08 suggestsreasonable fit. In general, they also suggest that a value between 0.08 and 0.1demonstrates adequate fit while a model with a value greater than 0.1exhibits poor fit. A well accepted rule of thumb for an acceptable chi squareto df ratio varies between 2 and 3 in the literature (Hayduk, 1987; Loehlin,

11In model-free LISREL comparisons, the program computes a fitted matrix based on the four

input matrices from males and based on the four matrices from females. The more individual

matrices (e.g., Swiss males) deviate from the fitted matrix, the worse statistical fit, both overall

as well as for the individual group (see Rowe et al., 1994 for an illustration).


Table III. Partial Second-Order Correlations (Controlling for Age and Social Class) of Routine Activities with Deviance Scales by Country

SchoolVandalism Alcohol Drug use misconduct

————————————— ————————————— ————————————— ——————————————A D H S A D H S A D H S A D H S

Routine activityMALESFamily �0.10 �0.15 �0.22 �0.16 �0.12 �0.11 �0.18 �0.16 �0.16 �0.12 �0.22 �0.14 �0.14 �0.12 �0.12 �0.16Peers 0.32 0.33 0.31 0.24 0.38 0.31 0.34 0.32 0.42 0.39 0.38 0.41 0.36 0.27 0.28 0.26Solitary �0.01 �0.11 �0.07 �0.02 �0.12 �0.07 �0.10 �0.02 �0.07 �0.10 �0.07 �0.03 �0.05 �0.07 �0.04 �0.01Community �0.06 �0.08 0.03 �0.02 �0.07 �0.13 0.02 �0.08 �0.14 �0.19 0.00 �0.15 �0.11 �0.11 �0.03 �0.03

FEMALESFamily �0.10 �0.11 �0.17 �0.15 �0.06 �0.25 �0.21 �0.22 �0.07 �0.18 �0.08 �0.24 �0.08 �0.13 �0.19 �0.20Peers 0.27 0.33 0.23 0.26 0.35 0.32 0.38 0.37 0.38 0.32 0.23 0.40 0.29 0.31 0.43 0.26Solitary �0.02 �0.15 �0.08 �0.00 �0.08 �0.05 �0.12 �0.00 �0.06 �0.03 �0.11 0.02 �0.00 �0.11 �0.08 0.03Community �0.08 0.01 0.05 �0.05 �0.14 0.06 �0.00 �0.08 �0.14 �0.05 0.01 �0.15 �0.13 �0.01 �0.16 �0.07

General Theft Assault————————————— ————————————— —————————————A D H S A D H S A D H S

MALESFamily �0.15 �0.13 �0.19 �0.20 �0.13 �0.12 �0.18 �0.15 �0.12 �0.08 �0.19 �0.13Peers 0.37 0.30 0.30 0.31 0.33 0.31 0.21 0.26 0.24 0.28 0.17 0.13Solitary �0.06 �0.12 �0.04 �0.02 �0.02 �0.14 0.07 �0.01 �0.00 �0.15 0.03 0.02Community �0.07 �0.05 �0.01 �0.04 �0.08 �0.06 �0.03 �0.05 �0.06 �0.03 0.04 0.02

FEMALESFamily �0.14 �0.22 �0.21 �0.24 �0.09 �0.12 �0.17 �0.20 �0.10 �0.05 �0.13 �0.12Peers 0.30 0.34 0.32 0.34 0.20 0.26 0.12 0.29 0.13 0.25 0.09 0.04Solidary �0.01 �0.09 �0.08 �0.03 0.07 �0.10 0.02 0.02 0.08 �0.11 0.03 0.07Community �0.06 0.07 �0.01 �0.04 �0.08 0.01 0.01 �0.10 �0.06 �0.05 �0.05 �0.00

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andDevia

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1992). Findings indicated that developmental processes were very similaracross the four countries for both males and females (male model fit:w2[198] ¼ 268.54, CFI ¼ 0.98, RMSEA ¼ 0.04; female model fit:w2[198] ¼ 367.06, CFI ¼ 0.94, RMSEA ¼ 0.07). Chi square to df ratios werealso well within the acceptable range, namely 1.4 for the male comparisonand 1.9 for the female comparison. Each group showed minimal deviationfrom the aggregate LISREL model (males: all groups GFI ¼ 0.96; females:GFIs ranged from 0.90 to 0.94).12

Based on findings of similarity, all groups from each country werecombined for a final set of analyses seeking to establish the unique predictivecontributions of both routine activities and country for deviant behavior. Inan initial step, we examined the importance of each routine activity contextor domain on total deviance. For this purpose, we conducted an omnibusregression analysis using all cases and entering country, age, sex, and socialclass as controls. The following relationships were found: family, b ¼ �0.19,p ¼ 0.000; peers, b ¼ 0.35, p ¼ 0.000; solitary, b ¼ 0.02, p ¼ 0.134; andcommunity, b ¼ �0.03, p ¼ 0.026. Next, Table IV presents the results of sethierarchical regression analyses by sex where we included routine activitiesand country (dummy-coded variables) as predictors of adolescent deviantbehavior, while controlling for age and social class. We were interested inestablishing whether country accounted for unique variance above andbeyond how youth spent their time; therefore, we used a set hierarchicalapproach where, in the first series of analyses, a dummy-coded variable forcountry was entered first and routine activities second. Next, we simplyreversed the order and entered routine activities first followed by country.We found that routine activities uniquely explained 18% of the variance inthe total deviance score for males and 16% for females. The amount ofvariance uniquely explained by male routine activities in the deviance sub-scales ranged from 6% for assault to 17% for drug use; for females, itranged from 3% for assault to 14% for drug use. Country uniquelyaccounted for 0% of the variance in total deviance for males, whileaccounting for 1% for females. The amount of variance uniquely explainedby country in the deviance subscales ranged from 0% (vandalism, theft,assault) to 3% (alcohol) for males and from 0% (vandalism, assault) to 11%(alcohol) for females. Also, the interaction term of country and routineactivities accounted for 7% for males and 5% for females. Overall, routineactivities and country together explained from 7% (assault) to 19% (drug

12Due to concerns of the effect of non-normality, the same analyses were also completed where

the deviance measures were log transformed. Findings were almost identical, and for females

suggested even greater similarity: Male model fit: w2[198] ¼ 309.23, CFI ¼ 0.98,

RMSEA ¼ 0.04 (.043), GFIs range: 0.94 to 0.96; female model fit: w2[198] ¼ 238.81,

CFI ¼ 0.98, RMSEA ¼ 0.03 (0.028), GFIs range: 0.93 to 0.97.


use) of the variance in the deviance subscales for males and 3% (assault) to21% (alcohol) for females; they also accounted for 19% of the variance intotal deviance for males and 17% for females.13 Figure 1 summarizes thisinformation graphically.

4. DISCUSSION

The current investigation examined the relationship between adolescentroutine activities and deviance in samples from Eastern and Western Europeas well as the United States. The following important findings were made.First, adolescents from the four countries spent their time in remarkablysimilar ways; most of their time was spent in solitary activities, followed bypeer, family, and community/sports activities. Also, some interesting dif-ferences were found by sex. Consistent with previous work, males spent asmaller proportion of their time in the family context than females, whilefemales spent less time in community/sports activities (Flammer et al.,1999). Second, in comparisons of deviance rates, American, Dutch, andSwiss youth were more deviant than Hungarian adolescents. With theexception of alcohol use, adolescents from Western European countries andthe United States were very similar on most measures of deviance, includingthe total deviance score. This was somewhat unexpected given the largeobserved differences cross-nationally in official rates of crime and delin-quency (see e.g., Gartner, 1990). Furthermore, few differences were foundbetween the two Western European countries. In part, these great similar-ities were also due to the fact that less serious forms of norm violations wereassessed, and that most cross-national comparisons of official data focus onmore serious cases of index crimes. Finally, males were consistently moredeviant than females in each country.

Analyses on developmental processes suggested great similarity formales and for females from the four different countries. In other words, therelationship between how adolescents spend their time in specific routineactivities and whether or not they engage in deviant behaviors was largelyinvariant by national context. This was further supported in subsequentregression analyses which, with the exception of alcohol use, suggested thatnational context had very little or no explanatory power in adolescentdeviant behavior. Our findings on the relationship between routine activ-ities, country, and alcohol use clearly indicate a somewhat different picture;

13Again, to examine the impact of non-normality on regression findings, we also completed

analyses after transforming all dependent variables in two ways, namely log and square root

transformations. Overwhelmingly, we found identical numbers. Therefore, regression-based

ML estimation results appeared robust to violations of normality in the data (for a discussion

of this topic, see Hayduk, 1996).


Table IV. Set Hierarchical Regressions of Deviance (By Sex)

School TotalVandalism Alcohol Drug use misconduct General Theft Assault deviance————— ————— ————— —————— ————— ————— ————— —————Ma Fb M F M F M F M F M F M F M F

Analysis 1Step 1: Countryc 0.00ns 0.00* 0.03 0.11 0.02 0.02 0.01 0.03 0.01 0.02 0.01 0.01 0.00* 0.01** 0.01 0.01Step 2: Activitiesd 0.12 0.08 0.13 0.10 0.17 0.14 0.11 0.09 0.14 0.12 0.11 0.07 0.06 0.03 0.18 0.16

Analysis 2Step 1: Activitiesd 0.12 0.09 0.13 0.10 0.18 0.15 0.10 0.10 0.14 0.13 0.12 0.08 0.07 0.03 0.19 0.17Step 2: Countryc 0.00ns 0.00ns 0.03 0.11 0.01 0.01 0.02 0.02 0.01 0.02 0.00** 0.01** 0.00ns 0.00* 0.00ns 0.01

Total modele 0.12 0.09 0.16 0.21 0.19 0.16 0.12 0.12 0.15 0.15 0.12 0.08 0.07 0.03 0.19 0.17

Note: Figures in this table represent R2 values; all R2 values significant at p < 0:001 unless otherwise noted; * p < 0:05, ** p < 0:01, ns ¼ non-significant; age and social class were both controlled on a first step not shown here.aUsing pairwise deletion, male sample size ranged from n ¼ 3;570–3,913.bUsing pairwise deletion, female sample size ranged from n ¼ 2;716–2,939.cDummy-coded country variables were entered together in one set on this step.dAll routine activities variables (family, peers, community, solitary) were entered together in one set on this step.eSlight differences between sums of steps 1 and 2 and total model R2 are due to rounding error.

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in fact, they suggest that, after controlling for how adolescents spend theirtime, national context accounts for a rather large amount of variance inalcohol use, especially for females. Also, the data suggest that the drug useby country interaction also accounts for 7% and 5% for males and femalesrespectively. These findings suggest that individuals from different nationalcontexts differ systematically with respect to factors contributing to alcoholand drug use. For example, despite the fact that alcohol use is illegal forAmerican youth, their rates of alcohol consumption are the highest in thisstudy. This was found for both males and females and is very consistent withrecent national data which suggest that alcohol use and consumption seemsto be an epidemic problem among teenagers and college students in theUnited States. Conversely, in the countries where alcohol use is legal formost adolescents in the current investigation, both levels of use amongteenagers were lower, and country had limited additional explanatorypower. One potential conclusion from these findings is that the legal pro-visions to protect adolescents from the consumption of alcohol may not beachieving their desired effect. In essence, because alcohol is a forbidden fruit,youth in America develop attitudes and behaviors about alcohol thatuniquely contribute to the consumption of alcohol. While a further test ofthis hypothesis is beyond the scope of the current investigation, we believethat different cultural norms and mores regarding drinking and drug useduring adolescence (i.e., some cultures are more tolerant, while others aremore restrictive) may be contributing to the importance of country in theexplanation of both alcohol and drug use.

On the relationships between routine activities and deviance, we foundthat routine activities accounted for 18% and 16% respectively in male and

Fig. 1. Venn diagram showing unique and shared amounts of variance explained by routine

activities and country (by Sex).


female total deviance, although this leaves a large amount of variance indeviance unexplained. More specifically, we found that how youth spendtheir discretionary free time after school and on weekends is associated witha number of different deviant behaviors ranging from vandalism to assault.We also found that spending time in the family context seemed to bufferadolescents from norm-violating behaviors, while spending time with peersin unstructured and largely unsupervised activities was most predictive ofdeviant behaviors; this latter finding is very consistent with previous work(e.g., Agnew and Petersen, 1989; Junger and Wiegersma, 1995; Osgood etal., 1996; Riley, 1987), although it also adds to the existing evidence foryouth ages 15 to 19 years. Some criminological studies have shown thatjuvenile delinquency is a group activity, and that ‘‘offenders’’ spend theirleisure time differently than ‘‘non-offenders,’’ namely, offenders reportspending their time away from home in settings where there are few or noinformal social controls (e.g., what Riley, 1987, called ‘‘street time’’). Non-offenders, on the other hand, either spend time at home or in conventionalactivities that effectively insulate them from norm-violating conduct (e.g.,Hirschi, 1969). Interestingly, while Riley found a weaker relationshipbetween ‘‘street time’’ and crime for females, the current investigationsuggests that variability in peer time is equally important in the prediction ofdeviance for both males and females. This means that spending time awayfrom home with peers places all adolescents at risk for deviance. On this,Felson (1994) has suggested that we would expect activities which havemoved away from the home to settings lacking guardianship and informalsocial controls to result in more crime and deviance. ‘‘To compensate,people contrive settings to meet their recreational needs. These contrivancesrestore the lost recreation only in part and add more crime in the process’’(p. 112). This implies that human behavior is situational, what Felson callsthe situational insight. He suggests that the process adolescents encounter isthe ‘‘symmetrically bad influence,’’ where human behavior is situational andgiven the right adolescent company, an individual may be more likely tocommit norm violations; however, the individual may also simply be morelikely to be ‘‘tempted’’ in this context. Felson (1994, p. 18) concludes that‘‘the issue is not so much bad company as adolescent company.’’ We believethat this is what the data suggest in our study. An alternative interpretationwhich focuses on ‘‘unidirectional’’ influences of peers include social learningor differential association theory (e.g., Akers, 1977); however, our results donot directly assess time spent with deviant peers which is the central tenet ofsocial learning theory. We also found that spending time alone or spendingtime in community or team sports activities buffers against deviance, thoughthe explanatory power was much smaller and the relationships inconsistentacross the different countries, for males and females, and for the differenttypes of deviance.


In conclusion, the current study suggests that routine activities of youthin the four countries examined are quite similar. Perhaps more importantly,the study also suggests that how youth spend their time, whether males orfemales, appears to be related to deviance in a highly similar fashion cross-nationally. In fact, with the exception of alcohol and drug use, nationalcontext had very little or no explanatory power in adolescent deviance.Therefore, the routine activities perspective seems tenable cross-nationally.In their recent article on cross-national comparative research, Farringtonand Loeber (1999, p. 300) note that ‘‘cross-national comparisons of riskfactors for delinquency are important for addressing the question of how farthe causes of delinquency are similar in different times and places, and hencehow far theories of delinquency can be generalized over time and place.’’In other words, much like Farrington (1999a, 1999b) has suggested, tothoroughly examine theoretical propositions or explanatory frameworks, weneed to employ cross-national comparative data. Future studies need tofurther explore the importance of different cultural and national contexts inadolescent deviance. As social scientists, we need to be interested in estab-lishing the validity and reliability of previous research, guiding frameworks,and theories not only in a single cultural context, but across nationalboundaries. Ultimately, this will lead to a science of human behavior, onethat is genuinely international and intercultural, one that potentially mayprovide evidence of developmental universals or differences cross-nation-ally.

APPENDIX A

In a recent cross-national comparison of teenage sexual and repro-ductive health across five countries (U.S. and European countries) by theAlan Guttmacher Institute (2001), the authors suggest that ‘‘beneath thegeneralizations necessary when making cross-national comparisons, thereare often large differences across areas and groups within a country, andvarying national contexts and histories’’ (p. 1). The same rationale applies tothe current investigation. While all of these countries are currently con-sidered economically developed and democracies (very recent for Hungary),they differ in a number of important respects from each other—legally,politically, economically, and socially. According to the Human Develop-ment Report (United Nations Development Program, UNDP, 1996), verylarge differences exist in crime perpetration and in rates of incarceration,for example, in the number of reported crimes per 100,000 people (e.g.,adult rapes—Hungary: 1.1; Netherlands: 1.2; Switzerland: 0.4; and theUnited States: 90.4) or in the number of incarcerated individuals (Hun-gary: 132; Netherlands: 51; Switzerland: 81; and the United States: 375). A


comparison across these countries in effect is a natural experiment andcomparison of low vs. high crime rate countries. Related to this, legalsystems also differ dramatically, where the United States is based onEnglish common law, while the Dutch system is based on a civil lawsystem incorporating French penal theory. Politically, the United Statesand Switzerland are federal republics, while the Netherlands is a con-stitutional monarchy; finally, Hungary is a parliamentary democracy.Economically, these countries also report very different levels of officialassistance (% of GNP—Hungary: 0.0; Netherlands: 0.76; Switzerland: 0.36;and the United States: 0.15). Similarly, there are also large differences inaverage indicators of socioeconomic status across these countries (real GDPper capita: Hungary: $6,059; Netherlands: $17,340; Switzerland: $22,720;and the United States: $24,680). Again, the implications of these observeddifferences is that they impact individual behavior and associated behavioraloutcomes in members of each respective society. Lastly, these selectedcountries also differ greatly on a number of less tangible and measurablesocial qualities that may also contribute to potential differences, both inrates of behaviors as well as in the relationships between predictors andoutcome variables. For example, Switzerland and the Netherlands are very‘‘liberal’’ with respect to tolerating different lifestyles and fundamentalvalues (e.g., abortion, euthanasia, drugs, etc.); perhaps as a reflection of this,these two countries are the only countries in the world that supply heroin ona medical basis to addicts. On the other hand, both Hungary and the UnitedStates are much more conservative which clearly impacts individualbehavior and behavioral outcomes. Hungary has only recently instituted ademocratic government after having a communist regime for the past halfcentury. While this brief review of some key data provides a sound rationalefor studying and comparing these countries, it is by no means exhaustiveand only provides some brief insights into apparently large differencesbetween countries.

ACKNOWLEDGMENTS

We are indebted to all American, Dutch, Hungarian, and Swiss schools,administrators, and students for their cooperation in this monumentalundertaking. We would also like to thank three anonymous reviewers fortheir feedback on the manuscript.

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Routine activities and deviant behaviors: American, Dutch, Hungarian, and Swiss youth

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