Completion and Recidivism Rates of High-Risk Youth on Probation: Do Home Visits Make a Difference?

Abstract

Probation supervision with and without home visits is an underresearched area for adjudicated higher risk youth. This study compared 287 juvenile probationers receiving police/probation home visits with similar youth (n = 437) who were supervised on regular probation without home visits. Youth performance was measured during supervision and up to 24 months following probation. The most notable finding was that the rearrest rate for youth who had home visits during supervision was 3 times lower after probation supervision ended than for youth who did not have home visits.

Keywords

juvenile probation recidivism intensive probation supervision police partnerships

Introduction

Nearly half a million adjudicated juveniles were under probation or some other form of community supervision in the United States for delinquent behavior (Sickmund & Puzzanchera, 2014). One of the most pressing issues in youth offender supervision today is how to respond in effective ways to minimize later involvement in the criminal justice system, particularly for juveniles on intensive probation who represent a higher risk of recidivism compared with their peers on regular probation supervision. Intensive supervision probation (ISP) represents an intermediate step between regular probation and residential placement because of its focus on increased supervision, drug testing, imposed curfew, and home visits. Intensive probation is for youth who have violated regular probation or have a history of violence, drug, and/or gang activity.

Although more frequent officer–client contact and additional probation conditions may seem appropriate for these youth, results have been mixed relative to the ability of ISP to lower recidivism. The bulk of research found that ISP programs were more likely to lead to an increased revocation rate for technical violations during supervision (Giblin, 2002; Hennigan, Kolnick, Tian, Maxson, & Poplawski, 2010; Hyatt & Barnes, 2017; Petersilia & Turner, 1993). Increased revocation rates ultimately led to a higher likelihood of incarceration and absconding behaviors for high-risk probationers, even though the likelihood of committing new crimes during supervision was no different than with those supervised on regular probation (Hyatt & Barnes, 2017). This is consistent with lower-risk youth supervised on ISP, who were 5 times more likely to commit a status offense at an earlier point in time than youth on regular probation (Land, McCall, & Parker, 1994). Only one study found that ISP programs overall were associated with significantly lower crime/recidivism rates when compared with routine probation supervision (Sarver, Molloy, & Butters, 2012), while one other study found no group differences (MacKenzie, 2006). Most ISP studies, however, have not analyzed how the number of home visits may affect revocation. This study seeks to fill this gap by examining the degree to which intensive probation home visits affect compliance during supervision and postprogram recidivism compared with regular probation supervision for youths.

Theoretical Framework

In his participation process model, Craig Schwalbe (2012) theorized that community supervision was a combination of officer strategies, balanced with offender compliance and participation. In addition, probation success for youth was largely determined by parental compliance and participation. A quality officer–client relationship included casework, communication, and leverage. Good casework strategies assessed problems, established long-range goals with probationers, and motivated them to complete probation conditions (Schwalbe, 2012). Casework strategies on ISP included spontaneous home visits that aimed to enforce probation conditions, and to increase communication between probationers and officers. Home visits can potentially be a good way to involve parents, clarify expectations, encourage desirable behavior, and confront youthful offenders when undesirable behavior occurs (Ahlin, Antunes, & Tubman-Carbone, 2013). Curfews during home visits can also be a strategy to reduce one’s exposure to crime opportunities and situations (Miller, 2014). Another part of quality casework is leverage, or the officer’s ability and willingness to carry out consequences for rule-violating behavior (Schwalbe, 2012). With regard to intensive supervision, leverage involves graduated sanctions and if necessary, the eventual removal of probationers from the community earlier to reduce further opportunities to commit crime.

Within Schwalbe’s theoretical framework, intensive probation programs paired with home visits would predict a decreased revocation rate for new crimes. For example, community supervision of juveniles and home visitation programs that aim to involve parents have been cited as effective program modalities that curtail delinquency of at-risk youth (Greenwood, 2008). One of the responses to youth in the juvenile justice system has been to organize an agency partnership between the city police and the county probation, to share information between the agencies and, if necessary, to conduct evening home visits for specific youth who were involved in gangs and at risk of venturing further into the adult justice system.

Review of Police–Probation Partnerships

Community supervision of high-risk juveniles using police and probation officers emanated from community policing and neighborhood-based community corrections supervision techniques. One of the first model programs of this kind was Operation Night Light (ONL) which began in Boston in the 1990s (Matz & Kim, 2013; Murphy, 2005). In most partnerships that modeled themselves after the Boston ONL program, each agency supplied an officer who jointly conducted evening home visits and curfew checks of youths on probation supervision (Alarid, 2015; Condon, 2003). The juvenile probation officer was able to safely visit youth probationers and their parents at their homes in high crime neighborhoods, while the police officer functioned as a passive security presence. Some teams used uniforms and marked police cars, while others dressed more inconspicuously in civilian clothes and unmarked cars to blend in (Katz & Bonham, 2009).

Most police–probation partnership research can be characterized as either being purely descriptive (Condon, 2003; Corbett, 1998; Leitenberger, Semenyna, & Spelman, 2003) or as an empirical mixed-methods approach that measured perceptions of participants, frontline officers, and/or agency heads (Alarid, 2015; Alarid, Sims, & Ruiz, 2011a, 2011b; Kim, Gerber, & Beto, 2010; Kim, Gerber, Beto, & Lambert, 2013; Kim, Matz, Gerber, Beto, & Lambert, 2013; Matz, 2016). Perceptual studies have been valuable in helping practitioners understand how interagency agency collaborations can work toward a common goal and avoid overstepping their boundaries (Murphy & Worrall, 2007). Through regular meetings, open communication, and information sharing, most probation and police partnerships have been able to maintain their autonomy, while working together to solve crimes, serve warrants, and maintain an active community presence (Alarid, 2015; Alarid et al., 2011a, 2011b; Kim et al., 2010; Matz, 2016).

Few empirical studies of home visits—one of the central components of police–probation partnerships—have been conducted with respect to measuring recidivism of ISP youth. Land, McCall, and Williams (1990) and Land and colleagues (1994) examined recidivism rates of status offenders on ISP compared with regular probation status offenders. They found that ISP decreased the likelihood of recidivism compared with regular probationers during the first 18 months. Of the youth who recidivated, the ISP youth recidivated sooner than youth recidivists on regular probation (Land et al., 1994). In the Land study, youth were adjudicated for runaway, truancy, or ungovernable offenses, and then randomly assigned to an experimental (ISP) or regular probation group. The ISP group had more frequent officer contact, one to two home visits per week, and slightly more treatment services. The regular probation group had at least one home visit every 3 months, with the option for the officer to increase the home visits if needed. Thus, home visits were a part of both status offender groups (Land et al., 1990), and status offenders were a markedly different group than the high-risk offenders on ISP today.

Two studies examined program effects on city-wide crime trends and found some support for decreases in juvenile crime in the area (Jones & Sigler, 2002; Worrall & Gaines, 2006). For example, Worrall and Gaines (2006) used interrupted time series to analyze monthly arrest statistics for all juveniles in San Bernardino before and after program implementation over an 11-year period. They found reductions in juvenile assault, burglary, and theft, and concluded that the home visitation probation program had a general deterrent effect for these crimes, except for robbery and motor vehicle theft which did not decline. Although both city-wide crime data were area-specific, they could not be linked directly to recidivism of individual program participants.

Only two studies were found that examined recidivism outcomes of intensive probation using police–probation partnerships compared with regular probation—of these, one was with youth (Giblin, 2002) and the other examined adults (Piquero, 2003). Giblin (2002) examined the Coordinated Agency Network (CAN) program in Anchorage which used police officers to voluntarily conduct unannounced home visits of low-risk and high-risk youth without a probation officer present. Police notified the probation officer of any noncompliant youth. There were 91 youth who received home visitation/curfew checks on probation, and 64 control youth on standard probation. The CAN program participants incurred more new technical violations, but there were no group differences on likelihood of incurring a new criminal charge. CAN program youth who received four or more visits compared with CAN youth who received less than three home visits by police had more technical violations, but less new charges during the probation period. However, the difference was not large enough to be statistically significant because of the small sample size (Giblin, 2002).

Piquero (2003) compared recidivism patterns of 972 adult probationers in Maryland who were part of a Hot Spot initiative (n = 486) to a matched sample of 486 probationers who were not part of the initiative. Survival analysis indicated that the two groups as a whole did not significantly differ with respect to time until failure for rearrest and technical violations. Adult offenders who were female, older, and who were supervised under regular probation stayed out longer than young males on intensive probation supervision (Piquero, 2003).

In summary, there is a dearth of extant research with respect to recidivism outcomes for ISP models of probation–police partnerships that conduct visits to probationers’ homes. Of these, only one known study examined home visits with youth probationers. Accordingly, the current study adds to this body of literature by drawing on juvenile court data from a large county in the Midwest to examine the effectiveness of home visits within an intensive probation program compared with regular probation supervision for high-risk and gang-involved juveniles. The research questions in this study are as follows:

Research Question 1: What effect does the number of home visits have in predicting probation success or revocation?

Research Question 2: Do home visits decrease the likelihood of rearrest following probation supervision?

Research Question 3: Do youths who received home visits recidivate later than youths who did not receive home visits?

Method

The ONL program involved the formal participation of the juvenile probation department, who functioned as the lead agency, and the local police department in a mid-size city in the Midwest. Probation officers did not carry firearms at the time of data collection, so the police collaborated as security and to share information about the neighborhoods. The juvenile probation department as a whole employed 12 probation officers. Over the course of 1 year, a total of about 2,000 juveniles were on probation, many of whom were being supervised for less than 6 months for misdemeanors. Of the total number of juveniles on probation, about 12% or 150 youth were receiving ONL intensive probation supervision services. Probationers were admitted to the ONL program, if they were between the ages of 10 and 17, and had one or more of the following risk factors:

History of family violence, drug, and/or gang activity

Prior violent offense(s)

Suspected gang affiliation

Friend/family of recent homicide victim/perpetrator

Suspected drug use/involvement in drug sales while on probation

Current warrant/probation violation status

The first four factors related to past behavior that occurred before probation was present, but if one or more was present, the juvenile was placed in the ONL program immediately after being adjudicated. The last two risk factors, if they occurred while on regular probation, could bring the juvenile into ONL at a later point in time if the probation officer and supervisor believed it would be beneficial. While on ONL, curfews were enforced. For unaccompanied minors in middle school, curfew times were 7:00 p.m. on weekdays and 9:00 p.m. on weekends, while for minors in high school, the times were 8:00 p.m. and 11:00 p.m., respectively. Curfew did not apply if the youths were accompanied by their parents or an approved guardian.

Sample

The total sample size was 724 youth, comprised of 287 ONL probationers and a matched comparison group of 437 regular probationers. Youth in both groups were no longer on active supervision, and could be tracked for 24 months after probation ended. The comparison group of probationers was selected from youth who had similar eligibility risk factors, but were on probation the year before the ONL program started. The control group of regular probationers had no home visits, but the number of required probation officer office visits and the expectations of attending treatment remained the same. Both groups were drug tested only on suspicion of drug use, as there was no minimum number of drug tests.

A computer services staff at the probation department generated a list of youth on regular probation who met the ONL criteria. The comparison group would likely have been assigned to ONL had the program been available at that time. Great care was taken to ensure that each individual was a member of only one group. Thus, both probation groups had similar risk factors, but the ONL treatment group (n = 287) had probation with home visits, while the comparison group was supervised on standard probation without home visits (n = 437).

Variables of Interest

Official agency data were obtained from juvenile probation case files for the following variables: age (continuous), gender (male/female), race (Caucasian/African American), and current offense (person, property, drugs, and other) as well as offense severity (felony/misdemeanor). The following measures were collected on youth behavior during probation supervision: number of months on probation, number of home visits, type of home visits (no response, contact with probationer, or collateral contact), whether a new crime was committed while on probation, disposition of the new crime, and type of discharge from probation supervision (successful completion, revocation for a new crime, or revocation for a technical violation). Probation completion or discharge was defined as the final outcome of the supervision, or the reason why probation ended—this reason could be successful or unsuccessful. An unsuccessful completion meant that the court granted the officer’s motion for revocation, and terminated or discharged the probationer from probation. Probationers who committed technical violations or a crime while on probation, but remained on supervision, were not considered discharged until probation ended. Discharge from probation was thus counted only once in the data set. The prevalence of rearrest (yes/no), type of crimes involved in the rearrest, and date of rearrest in relation to probation discharge were measured as a part of the outcome variables. Rearrest is considered to be the best proxy for measuring recidivism and has been used in previous research of probationers (Hyatt & Barnes, 2017; Neithercutt, 1987).

Sample Characteristics

Sample characteristics for both groups are shown in Table 1. The two juvenile groups were similar in terms of percentage breakdown of sex, race/ethnicity, age, and proportion of drug and nonviolent offenses. Between the two groups, about 83% of probationers were males and 17% were females. The average age of juveniles in both groups at the time of data collection was 15 years old. Over half (56%) of probationers were African American, about 37% were Caucasian, and 7% were Latino. ONL juveniles spent an average of 11.3 months on probation, while juveniles in the control group spent 10.4 months. The difference in probation length was not significant.

Table 1.

Sample Characteristics.

Characteristics	Juvenile control(n = 437)	Juvenile ONL(n = 287)
Race/ethnicity
Caucasian/Hispanic	43.7%	46.7%
African American	56.3%	53.3%
Gender
Female	15.3%	20.9%
Male	84.7%	79.1%
Age at time of data collection
10-11 years	4.1%	1.0%
12-14 years	31.1%	28.0%
15-17 years	59.0%	64.0%
18 years^a	5.7%	7.0%
Current adjudication offense
Person/violence	39.1%	22.7%
Property	38.0%	45.5%
Drug possession	20.1%	15.0%
Other	2.7%	16.8%
Adjudication offense severity
Felony	74.6%	50.0%
Misdemeanor	25.4%	50.0%
Months on probation
Range (average)	1-34 (10.4)	2-40 (11.3)
1-6	20.7%	27.8%
7-12	56.8%	39.0%
13-18	13.0%	19.2%
19-24	5.5%	8.4%
25-30	2.7%	4.2%
31-36	1.3%	1.4%
Probation discharge reason^a
Successful completion	71.4%	57.7%
Revocation-tech violation	17.7%	32.1%
Revocation-new crime	10.8%	10.2%

Note. ONL = Operation Night Light.

The case originated with the courts at age 17, but the youth had turned 18 at the time of data collection.

The only significant difference found was the proportion of person/violent offenses between the two groups. Probationers in the control group had significantly more convictions for violent offenses (39%) than juveniles in the ONL group (23%). Upon further inspection of felony and misdemeanor offenses, 75% of juveniles in the control group were on probation for a felony (25% for a misdemeanor), whereas only 50% of juveniles on ONL were felons, and 50% were misdemeanants.

Analytic Strategy

Chi-square was initially used to uncover any potential statistically significant group differences for various cross-tabulated variables. Logistic regression models were run with both treatment and control groups together to determine which variables may be significant predictors of two binary dependent variables: probation completion (success/revocation) and later rearrest (yes/no). In logistic regression, the outcome variable is transformed by taking the logit or the natural logarithm of the variable as an odds ratio of each event—the event either occurs or does not occur.

To analyze factors involved in a youth’s time to rearrest, we used survival analysis and Cox regression. For the survival analysis, a time domain was computed by setting the date of admission to probation as the point of reference (Time 0). The second time frame was the completion of probation (Time 1). The third point of consideration was the 2 year recidivism follow-up (Time 2). Probationers who successfully completed probation and did not commit a crime in the 2 year follow-up were coded a time score of “222.” This arbitrary number was given to allow SPSS to compute the survival analysis without any errors.

The standard Cox regression analysis was used to examine the time until individuals in either group would recidivate after being discharged from probation. The survival distributions for both the ONL and control groups were compared by a binary logistical regression so as to attribute all additional variability into the error of the regression equation. A model comparison approach was conducted to view the specific variability of recidivism explained by group differences.

Findings

During the study period, there were a total of 1,420 home visit attempts made to the 287 youths on ONL. The number of visits to each probationer varied between one and 17, with an average of five home visits per person, or about one home visit every other month. The first home visit occurred within the first 2 months after caseload assignment. About 48% (n = 682 attempts) of these home visits resulted in no one answering the door, and an additional 15 visits (1% of total) resulted in the officer’s discovery that a wrong address had been provided or that the probationer no longer resided there. Of the 723 home visits (51%) that were conducted during a time when someone was at home, 403 resulted in the officer making direct contact with the juvenile probationer, and the remaining 320 visits resulted in a collateral visit, in which the officer was able to talk to one or more family members, but the juvenile was not at home.

Successful Probation Completion

The success rate was 71% for juveniles on regular probation and 58% for ONL juveniles. About the same percent of youth in both groups, 10.2% of ONL and 10.8% of regular probationers, had their probation revoked due to the severity of a new crime. Success rates were lower for probationers supervised on ONL due to the higher rate of technical violators. Juveniles supervised on ONL were significantly more likely than the comparison group to be terminated by the court for technical violations.

About 16% of juveniles on regular probation supervision and 35% of ONL juveniles committed a new crime at some point during their probation supervision. The difference between the two groups was significant (χ² = 12.68, df = 1, p < .00), suggesting that home visits were more likely to uncover misdemeanor offenses of juveniles on ONL compared with juveniles on regular probation. Further analysis revealed that after the new crimes were discovered, many of these offenses did not result in revocation. Of the probation cases that were recommended for revocation, just over half (50.3%) were initially discovered at the ONL home visit, 16.4% were discovered beforehand, and then verified through the home visit, and the remaining one third (33.3%) of violations were discovered at some time other than during the home visit.

In Table 2, the probation outcome (0 = revocation for technical violations or new crimes and 1 = completed probation successfully) was regressed on number of contact visits and months of probation supervision. Other variables in the model included offense severity, age, gender, and race/ethnicity. When control and ONL groups were analyzed together, gender was the most important predictor of success during probation supervision (Wald = 13.57). The odds that boys would successfully complete probation were 62% lower than the odds that a female would complete probation. Another way of stating this is that girls were 2.63 times (1/0.38) more likely than boys to complete probation successfully. Regarding race/ethnicity, the odds that African American youth would successfully complete probation were 34% lower than the odds that Caucasian youth would complete probation. Stated differently, the odds of successful probation for Caucasian youth were 1.52 times more likely than for African American youth. Youth age and offense of conviction had no significant differences in the final probation outcome. When all other variables in the model were held constant, as time on probation supervision decreased by 1 month, the odds of a client completing probation successfully increased by 0.96 times. Similarly, as the number of contact home visits decreased by 1, the odds of completion without revocation increased by 0.91 times.

Table 2.

Logistic Regression: Predictors of Probation Outcome (n = 724).

	Probation outcome(1 = successful completion)
Variable	B (SE)	Exp(B)
Number of contact home visits	−.09 (.03)**	.91
Length of probation supervision (months)	−.04 (.01)**	.96
Offense severity (1 = serious/violent)	−.22 (.06)	—
Age (years)	.05 (.04)	—
Gender (1 = male)	−.96 (.26)**	.38
Race/ethnicity (1 = African American)	−.42 (.17)*	.66
Nagelkerke R²	.18

Note. Odds ratios provided for only the significant variables.

p < .05. **p < .01.

Rearrest After Probation Supervision

The type of offenses committed within 24 months after probation completion are reported in Table 3 for 102 regular probationers and 17 ONL probationers. Note that most recidivists had an average of two offenses within 24 months. The juvenile recidivists appear to be more active in assaults, weapons violations, and theft-related offenses. Of those rearrested for new crimes, there were no significant differences between ONL and control groups in the types of crimes they committed. About half committed a nonviolent property crime, more than one third committed a violent/person crime, and the remaining youth committed a drug-related offense. Among both juvenile groups, boys were significantly more likely than girls to commit a new crime following probation supervision (χ² = 7.37, df = 1, p < .01). Juvenile probationers who were revoked because of a new crime during supervision were more likely to be rearrested in the future (χ² = 10.58, df = 2, p < .01). Juveniles who completed probation successfully and juveniles who were revoked due to technical violations were not as likely to recidivate after probation ended.

Table 3.

Recidivism Offenses Following Probation Discharge.

New rearrest within 24 months	Juvenile control(n = 102)	Juvenile ONL(n = 17)
Person/violence	42	11
Murder/manslaughter	1	1
Rape/sodomy/sex assault	2	—
Aggravated assault	7	—
Simple assault	13	5
Robbery	5	1
Weapon possession	14	4
Property	62	11
Burglary	15	1
Motor vehicle theft	10	2
Theft more than US$500	6	—
Theft below US$500	2	2
Shoplifting	16	4
Vandalism more than US$500	2	—
Vandalism below US$500	8	1
Criminal trespassing	2	1
Fraud/forgery/credit card	1	—
Drugs	18	6
Felony drug possession	5	3
Misdemeanor drug possession	13	3
Other	33	6
Disorderly/disturb. peace	25	3
Threats—phone/letter	5	1
Other felony	3	2

Note. This table reports offenses from juveniles in both groups who were rearrested following probation supervision. Multiple juvenile offenses may be recorded within the same arrest. ONL = Operation Night Light.

Factors Affecting Time to Recidivism

Figure 1 depicts the results of the survival analysis for 724 youth in both groups. The horizontal axis represents time in weeks, and the vertical axis represents the cumulative probability of survival for those who have not yet recidivated. In Figure 1, the two curves differ significantly. For example, after 2 years and 6 weeks, approximately 90% of the ONL group had not recidivated in comparison with 75% of the control group. This is expressed as a hazard ratio of .33, which results in being 3.03 times more likely to not recidivate when belonging to the treatment group. The Cox and Snell R² statistic was .119 with a model comparison pseudo R² (R² logit) of .116.

Figure 1.

Survival function with respect to age, gender, race, and offense type means (n = 724).

Table 4 shows the Cox regression hazard ratios of the recidivism rate of probationers in both control and ONL groups. The model accounted for age, gender, race/ethnicity, and offense type. The stratum, set to compare the two groups, showed that age, gender, and race/ethnicity were significant. The hazard ratio at any given time, given that the individual is a female, is equal to 2.22. This meant that females were 0.45 times less likely to be rearrested (p = .01) in comparison with males (.55 ratio). The ratio for race/ethnicity at any given time, given that the individual is Caucasian was 1.49. This meant that Caucasian youth were 0.67 times less likely to be rearrested (p = .034) in comparison with African Americans (.331 ratio). The final hazard ratio for age was .77 (p = .00). With an increase in age at any given time, individuals are 1.3 times more likely to not recidivate in comparison with younger individuals. The analysis was a hierarchical model where the offense type was added to the second step of the analysis. Table 4 showed that offense type was not significant in explaining recidivism rates in either group.

Table 4.

Cox Regression Hazard Ratios.

Parameter	Parameter estimate	SE	χ²	Significance	Hazard ratio	R ²
Person/violence (n = 216)
Gender	.798	.311	6.562	.010	2.220	.552
Age	−.261	.044	34.453	.000	.770	.129
Race	−.401	.190	4.477	.034	1.494	.002
Other (n = 46)
Offense 1	.222	.454	.239	.625	1.249
Drugs (n = 118)
Offense 2	−.134	.297	.202	.653	.875
Property (n = 264)
Offense 3	.006	.207	.001	.978	1.006
Group (n = 204)^a
ONL	−1.109	.278	15.955	.000	.330	.116^b

Note. ONL = Operation Night Light.

Ratio of group computed with logistic regression.

R² logit statistic.

Discussion

Intensive probation supervision through police–probation partnerships remains an important community corrections response to high-risk and serious juvenile delinquents (Matz & Kim, 2013). Effective community supervision for high-risk offenders is, in theory, a balance between casework strategies that reduce exposure to crime opportunities (Miller, 2014) and leverage strategies that increase offender compliance and participation (Schwalbe, 2012). Closer supervision also results in the likelihood of increased revocation for technical violations, one in which can easily lead to increased justice system involvement and movement further away from the desired goals of juvenile justice (Johnson, Simons, & Conger, 2004).

This study examined whether home visits, an important component of ISP, affected the likelihood of probation success, and whether home visits decreased the time to rearrest following probation supervision. Success rates are largely a combination of officer discretion, along with youth behavior on probation. Succeeding on probation in this study was defined as youth who ended probation supervision without their probation being revoked. Girls were more successful than boys at completing their probation without revocation, and Caucasian youth were more likely to complete their probation successfully than African American and Latino youth. This is consistent with previous research measuring likelihood of probation completion that favors probationers who are White and female (Steinmetz & Henderson, 2015).

This study found that new misdemeanor crimes and technical violations were more likely to be detected on ONL during probation supervision. This was not only an expected outcome, but was consistent with other studies that have found that intensive supervision resulted in a higher number of recorded violations (Giblin, 2002; Hennigan et al., 2010; Hyatt & Barnes, 2017; Petersilia & Turner, 1993). This occurs in part, because intensive supervision probationers have more conditions to follow than regular probationers, and thus, more opportunities to violate their conditions.

Second, our findings suggested that home visits were directly related to points of discovery and early intervention during supervision. Home visits provided preventive leverage, which allowed probation officers the opportunity to intercede at an earlier point in time, immediately following the violation. At times, probation officers have been criticized for failure to discover violations in a timely manner or waiting too long to intervene. Home visits create an opportunity to rectify these criticisms and if the situation warrants, remove probationers from the community to reduce further opportunities to commit crime.

Supervising officers were significantly more likely to terminate ONL probationers for an ongoing pattern of technical violations than the comparison group, but less likely to revoke for a new misdemeanor crime. Despite having twice as many youths on ONL who committed a new crime at some point during their probation supervision, motions to revoke for these new crimes were filed in about the same percent of cases for youth in both groups: 10.2% of ONL and 10.8% in regular probationers. This finding is consistent with Hyatt and Barnes (2017) who found that no group difference in the likelihood of committing new crimes during supervision (at least officially). The findings in the current study suggest that probation officers may have used home visits as a graduated supervision sanction and/or an attempt to reduce offending behavior. Although home visits created a different discretionary option to handle new offending behaviors to keep ONL youth on probation as long as possible, individual probation officer discretion and more insight into the youth’s home life may have also led to probation officers being able to build a stronger case for the patterns of technical violations by ONL youth. Building a stronger case, coupled with the greater likelihood of discovery of violations from the home visit increased the likelihood that the juvenile judge would agree with the officer’s recommendation for revocation. Juvenile probation officers who have a less complete understanding of why the technical violations occurred lack the necessary context to fully convince the courts, and thus may tend to err on the side of not recommending revocation until a new crime occurs (Sieh, 2003).

Rearrest following probation supervision is considered to be a more important outcome variable, as it is less influenced by probation officer and judicial discretion. Probationers who were revoked for a new crime during supervision were more likely to be rearrested after probation ended. However, juveniles who completed probation successfully and even technical violators were significantly less likely to recidivate after probation ended, compared with youth who were revoked during supervision for a new crime.

The most important findings of this study were that juveniles who received home visits were significantly less likely to reoffend for up to 24 months following probation discharge (17 of 287 = 6% recidivism) than juveniles on regular probation supervision who did not receive home visits (102 of 487 = 21% recidivism). Youth were 3 times more likely to stay out of the system following intensive probation with home visits than were youth on regular probation. These findings are opposite to the Land et al. (1994) and Land et al. (1990) study, but both groups in the Land study were status offenders and both groups, to some degree, had home visits. This is qualitatively a different group of youths than the high-risk youth in the current study where one group had home visits and the other group did not. Our findings suggest that home visits may have helped to reduce recidivism after probation for high-risk youth.

Possible reasons for why ONL youth recidivated later could be the nature of the home visit itself. Home visits allowed probation officers to better understand each youth’s family life and how to best monitor each person through the change process (Land et al., 1990; Murphy & Lutze, 2009). One of the primary goals of youth home visits was to garner and increase parental support of their child’s probation. This finding cannot be empirically verified as parental support was not measured for the comparison group. However, when parental support has been directly measured with youth in other community-based programs, parental support contributed to youth success in program completion (Alarid, Montemayor, & Dannhaus, 2012) as well as delinquency reduction of at-risk youth (Greenwood, 2008).

Limitations

Although the results reported here represent an important contribution to the research on youth intensive probation supervision, several limitations of the study must be noted. First, the findings of this study are limited to probationers who met the initial criteria of the ONL program. The comparison group was statistically similar with respect to gender, race/ethnicity, and age. However, the only difference between the groups was the proportion of person/violent offenses. Probationers in the control group were significantly more likely to be convicted for a violent offense than ONL juveniles. We believe this limitation is mediated by the fact that our Cox regression analysis found no recidivism difference by offense type. In other words, the recidivism difference did not seem to be accounted for by the current offense. A second limitation was that the data did not consider differences in individual probation officer discretion which may have played a role in the level of tolerance and ultimately whether the officer recommended revocation. Third, other external treatment or rehabilitation services which may have further contributed to reduced recidivism were not measured. Future research may wish to collect data on cognitive-behavioral therapy programs, such as that outlined by Jewell, Malone, Rose, Sturgeon, and Owens (2015). Finally, the rearrest outcome variable was limited to juvenile rearrests. Given the separate reporting system of juvenile and adults at the time, recidivism could not be tracked past age 18. Areas of inquiry into probation officer home visits include how the home visit affects the family, parental support, and to determine the appropriate number and length of home visits necessary to achieve recidivism reduction (Ahlin et al., 2013).

In conclusion, an intensive supervision program coupled with home visits is more likely to provide probation officers with a more complete picture of environmental and family influences and may also interrupt youths engaged in a more criminally active lifestyle. The characteristics and ideology of ONL (curfew, involvement of the parents and family members, etc.) as a form of ISP seemed quite conducive for juvenile probation (Alarid, 2015). Despite the lower rate of successful probation completion for ISP youth, future research may wish to conduct a post hoc examination of youth who received home visits to determine other factors that lengthened the time to rearrest following probation supervision.

Footnotes

Authors’ Note

The findings were presented at the annual meeting of the Academy of Criminal Justice Sciences, Toronto, Ontario, Canada, March 1-5, 2011. All findings and conclusions represent the authors’ views and may not represent views of the funding agency. Luis M. Rangel Jr is now affiliated to New Mexico State University.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Office of Juvenile Justice and Delinquency Prevention Grant 98-JN-FX-0013.

Author Biographies

Leanne Fiftal Alarid, PhD, is Department Chair and Professor in the Department of Criminal Justice, University of Texas at El Paso. Her research focuses on corrections, gender and criminal behavior, and juvenile justice. She has published in scholarly journal outlets such as Justice Quarterly, Journal of Research in Crime and Delinquency, Law & Society Review, and Journal of Criminal Justice.

Luis M. Rangel Jr., earned his Master’s degree in Psychology from the University of Texas at El Paso. He is currently a graduate student in the Department of Applied Statistics at New Mexico State University. He provides statistical work for all K-12 outreach programs throughout New Mexico.

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