Linking students to appropriate interventions: A typology for social workers based on general strain...

Journal of Social Work

13(4) 361–381

! The Author(s) 2012

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DOI: 10.1177/1468017311435446

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Article

Linking studentsto appropriateinterventions: Atypology for socialworkers based ongeneral strain theory

Bridget E WellerDuke University, USA

Natasha K Bowen and Gary L BowenUniversity of North Carolina at Chapel Hill, USA

Abstract

� Summary: The present study employed recent elaborations on general strain theory

(GST) to develop a typology of vicarious and anticipated strain. The typology also

considered gender-race combinations. The study used a sample of 11,222 adolescents

who completed the School Success Profile for school assessment purposes.

� Findings: Latent class analysis identified four patterns of vicarious and anticipated strain

among adolescents. The patterns varied by sample members’ gender-race combinations.

The latent class solution was replicated with two subsamples and validated by testing its

association with behavior problems.

� Application: Evidence-based practices are suggested for students experiencing different

combinations of strain, and specific applications for social work practice are presented.

Keywords

Social work, behavior problems, evidence-based practices, general strain theory

In spite of the efforts of school social workers, behavior problems remain athreat to the school success of many middle school students. In fact, publicschools in the United States reported a 4.1 percent increase in violent crimes

Corresponding author:

Bridget E Weller, Duke University, 718 Rutherford Street, Durham, NC 27710, USA.

Email: [email protected]

between the 1999–2000 to 2007–2008 academic years (National Center forEducation Statistics [NCES], 2009). In addition, middle school students(NCES, 2009), males, and students of color (Centers for Disease Controland Prevention, 2010) often have higher rates of reported behavior problemsat school than their counterparts. Understanding the causes of behavior prob-lems, as well as the likelihood of students with different gender-race charac-teristics being exposed to those causes, may help school social workers respondwith appropriate interventions.

Practitioners in schools are increasingly encouraged to use evidence-basedprograms to address the mental health and behavioral problems evidenced bystudents. Although there are limits to the feasibility and availability of evi-dence-based programs (Powers, Bowen, & Bowen, 2010) and questions aboutthe efficacy of many programs (Powers, Bowen, Webber, & Bowen, in press),helping school social workers identify and implement programs whose effectsare supported by research remains preferable to relying on ‘authority-based’practices (Gambrill, 2011). The success of evidence-based programs and prac-tices, however, also relies on the appropriate matching of programs to students(Thyer & Myers, 2011). Key to appropriate matching is a thorough under-standing of the social environmental contributors to the behavior problems inindividual students. For example, non-compliant behavior that derives from asense of physical danger in a setting requires a different intervention approachthan disruptive school behavior that stems from an association with violentpeers.

Individual characteristics that contribute to optimal matching of students tointerventions include both social environmental experiences and demographiccharacteristics that may be associated with those experiences. Although studieshave shown that prevention and intervention efforts work better for some studentsthan others (Fraser, Richman, Galinsky, & Day, 2009), few assessment tools helppractitioners identify the most appropriate interventions for their students(Gordon, 1983; Powers, Bowen, & Rose, 2005).

The current study furthers the ability of school-based social workers to under-stand and, therefore, appropriately address potential sources of behavior problemsin middle school students. The theoretical framework of general strain theory(GST) is coupled with the statistical methodology of latent class analysis (LCA)to create a typology of social environmental exposures that contribute to problembehaviors. The patterns of exposure represented in the typology are then examinedin terms of gender-race characteristics. Finally, practitioners are referred to onlineresources that link the patterns to appropriate evidence-based prevention and inter-vention strategies.

School behavior problems

The term school behavior problem refers to a specific action that is contrary to rulesand regulations established by school professionals and that, when detected,

362 Journal of Social Work 13(4)

receives a school sanction. Such behaviors include actions such as getting intofights with peers or cutting class. This definition is based on both mental health(DSM-IV-TR, American Psychiatric Association, 2000) and criminological(Hirschi, 2002) literature.

Research has shown an association between school behavior problems anda number of adverse consequences for students and others. For example, stu-dents with behavior problems have more academic difficulty (Bowen, Rose, &Glennie, 2009) and increased likelihood of dropping out of school (Viljoen,O’Neill, & Sidhu, 2005). The lack of a high school education places individualsat a deficit both in terms of finding a job (Leckman, 1995) and earningan income adequate to support a family (Bowen, 2010). Furthermore, stu-dents’ behavior problems create challenges for other students by reducingthe amount of time teachers can spend with other students, thereby, decreasingopportunities for learning among other students (Walker, Ramsey, & Gresham,2004).

General strain theory

GST provides a useful framework for developing a typology of youths’ socialenvironment and the environment’s contribution to school behavior problems.First, it is consistent with social work’s focus on the influence of the social ecologyin youth development and functioning (Fraser, 2004). Second, it specifies the mech-anisms by which individuals’ social experiences affect their behavior. Specifically,GST posits that aversive experiences in the social environment (e.g. witnessingviolence) result in negative emotions (Agnew, 1985, 1992). The aversive experiencesare called ‘strain’. To manage the negative feelings caused by strain, individualsengage in coping strategies that range from legitimate to delinquent behaviors.Greater strain increases the chances of maladaptive coping strategies such as fight-ing with peers.

The type of strain that has received the most attention and is most familiar tosocial workers is experienced strain. Experienced strain includes direct personalinvolvement in aversive situations, such as violence or abuse, discrimination,social exclusion, or unfair treatment (Agnew, 2002). Social workers are generallyattuned to these sources of strain and help school staff support students whoexperience them. However, a growing number of studies have shown that studentsalso react negatively to other types of strain (e.g. Botchkovar, Tittle, & Antonaccio,2009). Two more recently elaborated types of strain that can increase the depth ofsocial workers’ understanding of student experiences in the social environment arevicarious and anticipated strain.

Unlike experienced strain, vicarious strain refers to a person hearing aboutor witnessing the strains of others (Agnew, 2002). Agnew has theorizedthat vicarious strain is most severe when the victim of strain is someone emotion-ally or physically close to the adolescent or is a member of a group to whichthe adolescent belongs. Anticipated strain refers to an individual’s expectation

Weller et al. 363

that strain will be experienced or will continue to be experienced in the future.A small but growing number of studies have shown that youths are exposedto vicarious neighborhood (e.g. Flannery, Singer, & Wester, 2001), school(e.g. Kort-Butler, 2010), and peer (e.g. Baron, 2009) strain as well as anticipatedneighborhood (e.g. Allison, Adalf, & Mates, 1997) and school (e.g. Kort-Butler,2010) strain.

GST posits that vicarious and anticipated strain, as well as being distinctiveconstructs, may be interrelated with each other and with experienced strain(Agnew, 2002). As such, each type of strain may influence the other types. Forexample, a vicarious strain such as witnessing school violence may enhance anindividual’s anticipated strain (i.e. fear of future personal victimization atschool). Few studies have tested the inter-relationships of vicarious and anticipatedstrain.

Relationship of strain to behavior problems

Kort-Butler (2010) has found in a nationally representative sample that vicariousand anticipated strain are associated with behavior problems; however, support forthe relationship between these strains and problem behaviors is found largely out-side of the GST literature itself (e.g. Stewart, Simons, & Conger, 2002). Usingschool-based samples, several studies have shown an association between aversiveneighborhood conditions and behavior problems at school (Bowen, Bowen, &Ware, 2002; Brunson & Miller, 2009; Stewart, 2003; Stewart & Simons, 2010).Such studies, however, have not explicitly examined anticipated strain or usedthe terminology of GST.

Gender differences in strain

GST includes theoretical explanations for gender differences in behavior problems.For example, according to Broidy and Agnew (1997), males and females experiencedifferent types of strain and respond differently to strain. Specifically, the strainsmales experience are more likely to result in delinquency (e.g. they may occur incontexts that are conducive to delinquency) than the strains females encounter.Further, males are more likely to externalize strain (e.g. respond with anger),whereas females are more likely to internalize strain (e.g. experience depression).Last, males are less likely than females to respond to strain using positive copingstrategies.

Studies of gender differences in strain have examined the influence of experi-enced strain on delinquency (Baron, 2007; Broidy, 2001; Hay, 2003; Hoffmann &Su, 1997; Jang, 2007; Kaufman, 2009; Mazerolle, 1998; Piquero & Sealock, 2004).Broidy (2001), for example, found females less likely to respond to experiencedstrain with delinquent acts than males among a sample of college students. Hay


(2003) and Piquero and Sealock (2004) have shown similar results in samples ofjuvenile delinquents. Although studies have not explicitly focused on vicarious oranticipated strain (Agnew, 2002), they suggest that there may also be gender dif-ferences in responses to anticipated and vicarious strain (Bowen & Chapman,1996).

Racial differences in strain

GST has also attempted to explain racial differences in delinquency. For example,Kaufman, Rebellion, Thaxon, and Agnew (2008) have argued that Blacks areexposed to more strains and different types of strain than Whites. Specifically,these authors have contended that Blacks encounter more economic, family, edu-cational, criminal victimization, discrimination, and community strain thanWhites. Further, because of social conditions, Blacks may be more likely to experi-ence elevated negative emotions due to exposure to strain, which may increase theirlikelihood of engaging in delinquency.

Several empirical studies suggest the need for exploring racial differences in thestudy of strain (Jang & Johnson, 2003; Simons, Chen, Stewart, & Brody, 2003).Such studies tend to use race-specific samples. Simons and colleagues, for example,used data collected from 10- to 12-year-old Black adolescents and their caregivers.Their study found that discrimination was positively associated with delinquentbehaviors.

In sum, constructs from GST and research related to strain help explainbehavior problems among youths and behavioral differences among gender andracial groups. Although social workers are familiar with experienced strain andits negative consequences on youth, understanding the potential impact of vic-arious and anticipated strain on youth behavior can improve social workersability to support students appropriately. Finally, being aware of which studentsare more or less likely to be affected by patterns of vicarious and anticipatedstrain can help social workers assess and match students to relevant interventionsmore efficiently.

Latent class analysis

Latent class analysis (LCA) is a person-centered modeling procedure used todevelop typologies. A typology is a tool that provides practitioners with greaterunderstanding of how students might fall into groups with similar experiences.A typology identifies subpopulations within a population or sample based on care-fully selected, theoretically relevant, observed variables. By specifying the natureof commonly shared experiences, a typology also equips practitioners to under-stand problematic student behaviors and to link students to appropriateinterventions.

Weller et al. 365

Hypotheses

Using LCA and based on GST and research on strain, the current study used threeindicators of vicarious strain in the social environment and two indicators ofanticipated strain to develop a typology. Specifically, we included vicarious neigh-borhood, school, and peer strain, as well as anticipated neighborhood and schoolstrain. The following hypotheses were tested:

1. Distinct patterns of exposure to vicarious and anticipated strain exist amongmiddle school youths.

2. Black and White female and male youths will experience different patterns ofstrain. We expected that Black males would be most likely to be in the subgroupreporting the most strain, whereas White females would be most likely to be inthe subgroup reporting the least strain.

3. Patterns of exposure to vicarious and anticipated strain among youths will bepredictive of behavior problems. Specifically, the subgroup with the highestlevel of strain will report the most behavior problems.

Methods

Dataset

Between September 2001 and July 2005, 37,354 middle and high school students in318 schools from six states (Arizona, Florida, Georgia, North Carolina,Pennsylvania, South Carolina, and Virginia) completed the School SuccessProfile (SSP). The SSP, a self-administered survey for middle and high schoolstudents, has repeatedly demonstrated strong reliability and validity (Bowen,Rose, & Bowen, 2005). An eco-interactional developmental model of school per-sistence frames the SSP, which focuses attention on the social environmental ante-cedents to school success (Bowen & Richman, 2010).

Cases missing more than 5 percent of the SSP variables were removed from thedata set before it was made publicly available, resulting in a non probability sampleof 20,749 high school and middle school cases. The procedures of the current studywere approved by the Institutional Review Board of The University of NorthCarolina at Chapel Hill.

Sample

The current study used data from 6th-, 7th-, and 8th-grade students who reportedBlack or White heritage (N¼ 12,209). Given the potential for erroneous resultscaused by missing data, we first conducted a missing data analysis. The data setcomprised 11,903 cases with complete data and 306 cases (2.6%) with at least onemissing value. Males [�2 (1, N¼ 11,903)¼ 74.68; p< .01] and Blacks [�2 (1,N¼ 11,903)¼ 10.69; p< .01] had significantly more missing data than


other youths. Such results suggest that missing data may be missing at random.Because scholars state that researchers may apply listwise deletion when samplesizes are large and the percentage of missing values is small (Acock, 2005), listwisedeletion was used. The final sample had 11,222 cases with complete data. We nextdrew three random subsamples from the 11,222 cases with complete data. Onesample (n¼ 3728) served as the primary analysis sample, and the other twoserved as validation samples (n¼ 3699; n¼ 3795). The primary sample comprised24.5 percent Black males, 28.1 percent Black females, 23.4 percent White males,and 24 percent White females. The two validation samples had similar demo-graphic compositions.

Measures

Gender and race were included in the latent class analysis, which were combinedinto one variable: 0¼White male; 1¼Black male; 2¼Black female; 3¼Whitefemale.

The vicarious neighborhood strain index was based on six items from the SSP.Respondents recalled the past 30 days and indicated ‘yes’ or ‘no’ to six statements:a) someone in your neighborhood was robbed or mugged; b) you heard gunshots;c) you saw someone selling illegal drugs; d) a person was murdered; e) a fight brokeout between two gangs; and f) you saw someone threatened with a weapon such asa gun, knife, or club. Scores were generated by counting ‘yes’ responses and divid-ing the sum by 6. Scores were then dichotomized at the mean (M¼ .15) andassigned values of 0 or 1, with 1 indicating high vicarious neighborhood strain.

The vicarious school strain index was based on nine items from the SSP.Respondents were asked to rate the magnitude of the following school-relatedproblems: a) fights among students; b) destruction of school property; c) studentuse of alcohol; d) student use of illegal drugs; e) students carrying weapons; f)students physically abusing teachers (hitting, pushing); g) students verbally abusingteachers (yelling, name calling); h) racial tension; and i) gang fights. Responseoptions on the SSP were on a three-point scale (1¼ not a problem at all, 2¼ alittle problem, 3¼ a big problem). Composite scores were generated by summingthe scores and dividing the sum by 9. Scores were then dichotomized at the mean(M¼ .44) and given values of 0 or 1, with 1 indicating high vicarious school strain.

The vicarious peer strain composite scale was based on nine items from the SSP(a¼ .89). Respondents reported how well the following statements described them:a) I have peers who get into trouble with the police; b) I have peers who use drugs;c) I have peers who belong to gangs; d) I have peers who drink alcoholic beverages;e) I have peers who often cut class; f) I have peers who carry a weapon such as aknife, gun, or club; g) I have peers who make bad grades in school; h) I have peerswho get into trouble at school; and i) I have peers who probably will not graduatefrom high school. Response options on the SSP were on a three-point scale (1¼notlike me, 2¼ a little like me, 3¼ a lot like me). Composite scores were generated bysumming the scores and dividing the sum by 9. Scores were then dichotomized at

Weller et al. 367

the mean (M¼ .19) and assigned values of 0 or 1, with 1 representing high vicariouspeer strain.

The anticipated neighborhood strain score was based on one SSP question: howoften are you afraid someone will hurt or bother you on the way to or from school?Response options on the SSP were on a three-point scale (1¼ never, 2¼ sometimes,3¼ often). Response options were dichotomized, with 0 indicating never and valuesof 1 indicating responses of ‘sometimes’ or ‘often’ and representing high anticipatedneighborhood strain. Overall, 25.9 percent of the sample had a 1.

The anticipated school strain score was based on one SSP question: how oftenare you afraid that someone will hurt or bother you at school? Response options onthe SSP were on a three-point scale (1¼ never, 2¼ sometimes, 3¼ often). Responseoptions were dichotomized, with 0 indicating never and values of 1 indicatingresponses of ‘sometimes’ or ‘often’ and representing high anticipated schoolstrain. Overall, 31.2 percent of the sample had a 1.

The behavior problem index was based on eight items from the SSP.Respondents were asked to indicate during the past 30 days, how often the follow-ing happened: a) I cut at least one class; b) I cut the entire school day; c) I showedup for school late (unexcused); d) I was sent out of class because I misbehaved; e)my parent(s)/guardian(s) received a warning about my attendance, grades orbehavior; f) I got in a physical fight with another student; (g) I was put on in-schoolsuspension; and (h) I was given an out-of-school suspension. Response options onthe SSP were on a three-point scale (1¼ never; 2¼ once or twice, 3¼more thantwice). Composite scores were generated by summing the scores, subtracting thesum from 8, dividing this number by 16, and then multiplying this number by 100.Thus, results are reported as a percent of the maximum score, with higher scoresrepresenting more behavior problems.

Statistical analyses

Using SPSS version 14.0 (SPSS, Inc., 2005) and the primary sample, we obtaineddescriptive statistics, assessed the reliability, determined cut-points for the threevicarious strain composites, and determined the prevalence of vicarious and antici-pated strain across the four gender-race categories examined. Latent class analysis(LCA) with MPlus version 3.11 was used to develop the typology (Muthen &Muthen, 2004). The three vicarious strain indicators and two anticipated strainindicators were submitted to the LCA. We replicated the typology analysis usingthe two secondary random samples, and then validated the typology by testing itsrelationship to behavior problems. A one-way ANOVA, conducted in SPSS 14.0(SPSS, Inc.), was used to test for significant relationships between the classes andbehavior problems.

Latent class analysis (LCA) is a special case of person-centered mixture model-ing used to develop typologies. LCA identifies subpopulations of a population orsample based on carefully selected indicator variables (Muthen, 2001; Muthen &Muthen, 1998–2004a). Specifically, LCA assigns individuals into latent classes


based on observed categorical variables (Lazarsfeld & Henry, 1968). LCA alsoprovides information about class membership including the size and nature ofeach class and the average likelihood of individuals being assigned to each classgiven their observed scores (Muthen & Muthen, 2000).

Consistent with previous research (Bowen, Lee, & Weller, 2007; Muthen &Muthen, 2000; Weden & Zabin, 2005), we sought the class solution with thefewest number of classes that adequately explained the relationships amongobserved indicator variables. The best model was selected by first developing ameasurement model and then a covariate model. To develop the measurementmodel, we submitted the indicators to an unrestricted LCA with a one-classsolution. Unrestricted LCA allows for an examination of the natural structureunderlining the probabilities of class solutions to emerge rather than imposingconstraints on the model (Bockenholt & Bockenholt, 1990). We then added classesuntil the best-fitting model was identified based on pre-established fit criteria.

Scholars have debated the best ways to assess model fit in LCA (McLachlan &Peel, 2000; Muthen & Muthen, 2000; Nylund, Asparouhov, & Muthen, 2007). Ingeneral, however, researchers commonly have reported three statistical criteriarelated to fit: Bayesian Information Criterion (BIC), average latent class posteriorprobabilities, and the number or percent of subjects in the smallest class solution.The present study applied each of these criteria.

BIC values provide information on relative fit across models. The BIC usesmaximum likelihood estimation to assess how well the model fits the data and‘rewards’ model parsimony (Kuah, 2004). The lower the BIC value, the betterthe model relative to other models. Some scholars have reported that the BIC isone of the more reliable indicators of model fit in LCA (Nylund et al., 2007;Vermunt & Magidson, 2002).

Average latent class probabilities indicate the average probability of correctlypredicting class membership for individuals (Muthen & Muthen, 2000). A modelwith good fit has diagonal probability matrix elements close to 1.0 and off diagonalelements close to 0. Some scholars recommend seeking diagonal elements greaterthan .90 (Muthen & Muthen, 2000; Bowen et al., 2007), whereas other researchersreport that diagonal elements greater than .80 are acceptable (Weden & Zabin,2005).

Class size has also been used to evaluate LCA models. Muthen (1998–2004)warned against generating classes with fewer than 50 cases; however, other studieshave used class solutions with smaller classes (Oxford et al., 2005). Classes with lessthan 5 percent of the sample in the smallest class are also discouraged; however,this criterion may be problematic when smaller groups are expected based ontheory and epidemiology.

After a measurement model was selected based on statistical criteria, covariateswere added to the model. The inclusion of covariates in LCA does not alter theconditional probabilities of the latent classes, but it allows for the examination ofthe distributions of individuals from groups across classes (Vermunt, 1997). Basedon GST, we added our gender-race variable to the model.

Weller et al. 369

Similar to Weden and Zabin (2005), we followed a sequence of steps to deter-mine whether to include theoretically relevant covariates in an LCA model. Thefirst step was to run models in which each covariate is regressed on each latent classto detect significant relationships (CR> 1.96). Significant covariates were retained.The second step was to compare the measurement and covariate models using theBIC and the likelihood ratio chi-square statistic. In addition to the BIC, scholarshave recommended that the likelihood ratio chi-square statistic is appropriate forcomparing measurement and covariate models with the same class solutions(Vermunt & Magidson, 2002). The third step was to identify the model thatmade most theoretical sense – the measurement or covariate model (Muthen,1998–2004). According to a number of LCA experts, substantive meaning andinterpretability of the model may supersede statistical criteria for the selection ofthe final model (Muthen & Muthen, 2000; O’Connor & Colder, 2005). Therefore,we also referred to GST to guide selection of the final model. To establish thereplicability of the typology findings, each step described above was repeated usingthe second and third random samples.

Finally, we validated the model by examining whether classes were associated aspredicted by GST with self-reported behavior problems. One-way ANOVA withLSD post hoc comparisons was used to determine if members of classes with higherstrain reported significantly higher levels of problem behavior.

Results

Development of the model

As shown in Table 1, using the primary sample, a four-class solution emerged asthe best model based on the BIC. The lowest average latent class posterior prob-ability, however, indicated that a two-class solution was best. None of the obtainedclass solutions violated the minimum class size criterion requirements. Given thepreference for the BIC observed in the literature (Nylund et al., 2007) and the need

Table 1. Comparison of measurement and covariate models

BIC Log likelihood

Lowest average

latent class probability

Smallest percent

of sample

Measurement

Two 23237.89 �11573.72 0.87 37.1

Three 22876.73 �11368.46 0.77 23.1

Four 22860.32 �11335.59 0.70 15.5

Five 22889.80 �11325.66 0.67 5.7

Covariate model

Four 22717.16 �11227.00 0.83 16.6


to use the BIC to compare the measurement and covariate models (Vermunt &Magidson, 2002), we determined that the four-class measurement model was thebest. Further, the four-class model is supported by recent substantive advance-ments in GST, which are discussed later in the discussion section.

To create a covariate model, still using the primary sample and the four-classmeasurement model, covariates were regressed on each class. Because the gender-race combinations were significantly associated with each class, each gender-racecombination term was retained in the model. Next the fit statistics from the meas-urement and covariate model were compared. The BIC (Table 1) and the likelihoodratio chi-square (�L2

¼ 11335.59� 11227.00, �d.f.¼ 9, p< 0.001) indicated thatthe covariate model fit the data better than the measurement model.

After deciding that the four-class covariate model was the best model based onstatistical criteria, we then determined that the final model also made most theor-etical sense because, as suggested by Agnew (2002), youths in the sample wereexposed to different types of strain and, as hypothesized by Broidy and Agnew(1997) and Kaufman et al. (2008), exposure to strain varied by gender and race.Log odds ratios for the final model were hand-calculated to generate the probabil-ity of class membership for each demographic group – to determine and comparethe likely distribution of demographic groups across the classes (Muthen, 1998–2004b).

Class characteristics

Figure 1 presents the vicarious and anticipated strain characteristics of each classbased on the primary sample. The five strain indicator variables are along the x-axisand the probabilities of class membership are plotted on the y-axis. The probabilityof class membership represents the average probability that members in a classreported high strain scores (indicator variable¼ 1). For example, Figure 1 shows

0%10%20%30%40%50%60%70%80%90%

100%

VicariousNeighborhood

VicariousSchool

VicariousFriend

AnticipatedNeighborhood

AnticipatedSchool

Pro

babi

lity

Strain Indicator Variable

High Strain Class (17%, n = 634)

Vicarious Strain Class (25.1%, n = 936)

Low Strain Class (40%, 1491)

Anticipation Class (17.8%, n = 664)

Figure 1. Probability of class assignment by indicator variable.

Weller et al. 371

that youths in the High Strain Class (HSC) had an 80 percent probability ofreporting high strain scores on the vicarious neighborhood strain indicator. Thefigure also shows significant differences in average probabilities among classes. Forexample, we see that members of the HSC (80%) had a significantly higher prob-ability of reporting vicarious neighborhood strain than youths in the Low StrainClass (LSC) (24%). The legend for Figure 1 indicates the percentage of the samplethat was assigned to each class. For example, the HSC comprised 17 percent of thesample.

The figure shows that members of the HSC had high strain scores and a 100percent probability of reporting anticipated neighborhood strain. The VicariousStrain Class (VSC) was distinguished by high probability of high vicarious strainscores; and it comprised 25.1 percent of the sample. In contrast to HSC, VSC hadlow probabilities of high anticipated strain scores, including a probability of 0percent for high anticipated neighborhood strain scores. The LSC was character-ized by low probabilities of high scores on all five strain measures, and it comprised40 percent of the sample. The Anticipated Strain Class (ASC) was distinguished bya high probability of high anticipated school strain scores (probability of 100%),and it comprised 17.8 percent of the sample. The ASC had a greater likelihood ofhigh anticipated neighborhood strain scores than the VSC and LSC, but a lowerprobability than the HSC, and relatively low probabilities of high scores on thethree vicarious strain measures. The ASC had as high a probability of high scoreson anticipated school strain as the VSC.

Gender-race combinations were significantly associated with class membership.Figure 2 presents the probability of class assignment for each gender-race group.Each bar indicates the percentage of all members of a gender-race demographicgroup that was found in each class. For example, 4 percent of the Black males inthe sample were in the HSC and 33 percent were in the VSC. The results in Figure 2also can be interpreted within or across classes. For example, a higher percentageof White females (67%) than Black males (24%) were in the ASC. Further, Whitefemales were more likely to be in the ASC than any other class. Conversely, Black

4%

33%38%

24%

5%

16%

34%

45%

2%

12%

28%

58%

1%7%

25%

67%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

High Strain Vicarious StrainLow Strain Anticipated Strain

Pro

babi

lity

Class

African American Males

African American Female

European American Males

European American Female

Figure 2. Probability of class membership by gender-race interactions.


males (38%) were more likely to be members of the LSC than any other class, andreported more exposure to the strain of others close to them than other demo-graphic groups (33% of Black sample members are in the VSC compared to lessthan 20% of each of the other subgroups).

Replication and validation of the model

After the LCA results were replicated with the second and third random samples, afour-class covariate solution emerged with similar strain characteristics and pat-terns of statistical qualities. The BIC and likelihood ratio chi-square statistic sup-ported the solution found in the primary sample. Using the first replication sample,however, one difference was found. The average latent class posterior probabilitymatrix suggested a three-class covariate model solution rather than a four classsolution. In general, the primary results were supported by the two validationsamples and the validation results are available from the first author upon request.

As a validation procedure, we tested the relationship between class membershipand behavior problems. Based on GST, we hypothesized that classes experiencingmore strain would also demonstrate more behavior problems. As shown by theletters in brackets in Table 2, some of the classes were significantly different fromother ones on the behavior problem outcome. For example, members of the a) VSCreported significantly more behavior problems than members of all other classes b)HSC, c) ASC, and d) LSC.

Discussion

The purpose of this study was to develop a typology of strains that contribute tobehavior problems among youths. Based on GST and using LCA, we identified atypology of vicarious and anticipated strain. The typology comprises of fourclasses: High Strain Class (HSC), Low Strain Class (LSC), Vicarious StrainClass (VSC), and Anticipated Strain Class (ASC). Members of the HSC report,on average, high probabilities of exposure to all five sources of strain, suggesting

Table 2. Relationship of class membership to behavior problems

Class

Behavior problems Significant differences

M (group difference) (by class)

(a) High strain 15.55 b, c, d

(b) Vicarious strain 16.97 a, c, d

(c) Anticipated strain 6.08 a, b

(d) Low strain 6.54 a, b

Omnibus statistic F(3,3664) 164.63***

Note. Higher scores indicate more behavior problems. ***p< .001.

Weller et al. 373

that one group of youths has an elevated risk of strain exposure. Members of thisclass would most likely benefit from interventions that address strains in the neigh-borhood, school, and among peers. The SSP intervention database (Bowen &Bowen, n.d.), an online database that links problem areas revealed in youths’SSP scores to evidence-based programs, indicates that one such program is theQuantum Opportunities Program (Lattimore, Mihalic, Grotpeter, & Taggart,1998).

Unlike the HSC, members of the LSC report low probabilities of exposure tofive strains, suggesting that these youths are not only exposed to less strain, but alsomay not benefit from interventions across the multiple strains. Students in the LSC,however, may benefit from universal programs such as All Stars Middle SchoolProgram (McNeal, Hansen, Harrington, & Giles, 2004). This comprehensive pro-gram aims to decrease substance use among middle school students while simul-taneously helping students set goals for the future and develop positive attachmentsto school.

Students in the VSC report high exposure to vicarious strain, whereas studentsin the ASC report high exposure to neighborhood and school anticipated strain butnot vicarious strain. Students in VSC may benefit from the CASASTART (Harrell,Cavanah, & Sridharan, 1999) because youths in this group reported high rates ofvicarious neighborhood and school strain and had significantly more behaviorproblems than the other classes. CASASTART is a community-based programthat has shown to positively impact youths’ experiences at school and decreasebehavior problems. Youths in the ASC, on the other hand, may benefit most fromPositive Action because it uses school and community ‘Kits’ to target specifiedissues such as school and neighborhood safety (Flay, Allred, & Ordway, 2001).

In addition to developing a typology of strain, we also assessed whether eachclass varied in gender-race combinations. Indeed, Black and White male and femalemembership varied across the classes. We found, for example, that White femaleswere significantly more likely than Black males to be members of the ASC, whereasBlack males were significantly more likely than White females to be members of theVSC. These results highlight the need to also consider gender-race when selectinginterventions. For example, Black males may benefit more from interventions thattarget decreasing school and neighborhood violence than White females, who mayrequire interventions related to perceptions of safety.

We validated the typology by comparing class membership with reported behav-ior problems. According to GST, individuals exposed to strain may be more likelyto engage in behavior problems (Agnew, 2001). We hypothesized, therefore, thatmembers of the HSC would have an increased likelihood of engaging in behaviorproblems. Study results partially support this presupposition, in that the HSC hadmore reported behavior problems than the ASC and LSC. However, the VSC hadsignificantly more behavior problems than any other class. This finding, though nothypothesized, is consistent with Agnew’s (2001) argument that vicarious strain maybe especially linked with behavior problems. Such results both support GST andindicate the theory needs to expand to explain why VSC reported significantly more


behavior problems than the HSC. Nonetheless, individuals in the VSC and HSCmay benefit most from interventions that focus on decreasing the occurrence ofbehavior problems.

In addition to providing recommendations for matching students to specificinterventions, results from this study also provide insight into other areas ofsocial work practice. The results indicate that students are exposed to anticipatedand vicarious strain. Moreover, exposure to multiple sources of strain – in particu-lar, multiple sources of vicarious strain – is associated with behavior problems. Toreduce the occurrence of behavior problems, practitioners may find it helpful toinquire about students’ exposure to such strain and use this knowledge to informpractice. In particular, working with students to develop strategies for reducing oravoiding exposure to sources of strain may yield insightful practice ideas. Whileinquiring about strain, practitioners should also consider students’ gender and race.

General strain theory

The findings from the present study provide support for GST and indicate that itrequires further elaboration. For example, in addition to being distinctive con-structs, GST posits that vicarious and anticipated strain can be interrelated(Agnew, 2002). The current study supports this supposition. In the HSC andLSC, probabilities for high scores on the five indicators of strain were either allrelatively high or all relatively low. Similarly, two classes had low probabilities ofboth types of anticipated strain while the other two classes had moderate-to-highprobabilities for both types of anticipated strain. Also, regardless of the class, thethree types of vicarious strain tended to have similar values – either all relativelyhigh or low. These patterns support GST’s claim that types of strain tend to co-occur, even though they are separate entities (Agnew, 2002). Further, the resultsprovide support for GST’s assumptions that exposure to strain varies by gender(Broidy & Agnew, 1997) and race (Kaufman et al., 2008); however, because GSTcurrently does not provide a comprehensive rationale for differences in exposure tovicarious and anticipated strain by gender-race, further theoretical elaboration isnecessary.

Limitations and strengths

A limitation of the study is that it relied on cross-sectional data that were collectedfor purposes other than testing strain hypotheses. We cannot make causal infer-ences based on the results of our cross-sectional analyses. In addition, the studypopulation excluded students not enrolled in school. We could not test some rele-vant GST hypotheses because of the absence of certain variables in the data set,such as experienced strain. The study also is limited because missing data were notmissing completely at random. Nonetheless, the present study provides usefulguidelines for practitioners and lays a foundation for future research to explorethe influence of vicarious and anticipated strain on delinquency.

Weller et al. 375

Methodological strengths of the study include the availability of a large, school-based sample, and reliable and valid measures. Previous studies of GST have usedsmall and gender- and/or race-specific samples (e.g. Simons et al., 2003), or havenot measured strain well (Agnew, 1992). Also, the present study used two sub-samples to replicate and validate its findings. Conceptually, this study is strongbecause it used one of the leading theories of delinquency (Agnew, 2006), addressednew aspects of GST (Agnew, 2002), and explored gender-race differences in classmembership (Broidy & Agnew, 1997; Kaufman et al., 2008).

Implications for future research

Future research should expand GST to account for differences in exposure tovicarious and anticipated strain by gender-race combinations. Future researchalso could test competing hypotheses about which classes of youth are mostlikely to engage in behavior problems. For example, based on Anderson’s (1999)concept of street code, scholars could argue that anticipated strain has the strongestinfluence on individuals’ likelihood of engaging in behavior problems as a pre-emptive strategy. In contrast, Agnew (2001) suggests that vicarious strain is mostlikely to result in delinquency.

Future research also should replicate and expand on the findings of the currentstudy using a nationally representative sample and include other racial/culturalgroups. Studies that include measures of experienced strain are needed to morethoroughly test hypotheses based on GST. Longitudinal studies could elucidate thedifferential effects of the four classes of strain found in this study on various youthoutcomes overtime. The National Longitudinal Study of Adolescent Health offerspromise as a dataset for replicating and extending the present analysis.

Conclusions

We used concepts from GST to identify a typology of vicarious and anticipatedstrain among adolescents that takes into account gender-race combinations. Ourresults support tenets of GST and suggest the value of expanding and testing thetheory further. In particular, it identifies patterns of exposure to two under-studiedsources of strain – anticipated and vicarious strain – and links those patterns toyouths’ gender-racial groups. The findings have implications for how social work-ers approach solutions to behavior problems in schools.

Research ethics

Approval for release of these data for secondary analysis was approved by theBehavioral IRB at the University of North Carolina at Chapel Hill on10 February 2006 (IRB Number: SOCW 2005-054).


Acknowledgements

Special appreciation is expressed to Dr Gary L. Bowen, School of Social Work, The

University of North Carolina at Chapel Hill, who constructed the SSP datafile for researchuse that advances the science of youth development. Responsibility for the ethical use ofthese data, including their coding, analysis, and reporting, remain with user and do not

necessarily reflect the advice, decisions, recommendations or agreement of either Dr Bowenor other members of his research team at the University of North Carolina at Chapel Hill.This study was conducted without financial support.

Funding

This research received no specific grant from any funding agency in the public, commercial,

or not-for-profit sectors.

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