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Volume 6, Issue 1 • Spring 2017

Table of Contents

Editor's Note

Facility Operations and Juvenile Recidivism

Neighborhood Risks and Resources Correlated With Successful
Reentry of Youth Returning from Massachusetts Detention Centers

Girls Leaving Detention: Perceptions of Transition to Home After
Incarceration

An Innovative Use of Conjoint Analysis to Understand
Decision-Making by Juvenile Probation Officers

“I’d Prefer an Applicant Who Doesn’t Have a Delinquency History”:
Delinquents in the Labor Market

Gender Comparisons in the Processes and Outcomes of Functional
Family Therapy

Achieving Juvenile Justice Reforms Through Decision-Making Structures: The Case of Georgia

The Benefits of Community and Juvenile Justice Involvement in
Organizational Research

An Innovative Use of Conjoint Analysis to Understand Decision-Making by Juvenile Probation Officers

Matthew C. Aalsma, Katherine Schwartz, and Gregory D. Zimet, Section of Adolescent Medicine, Department of Pediatrics, Indiana University School of Medicine; Evan D. Holloway, Department of Psychology, Fordham University; Valerie R. Anderson, School of Criminal Justice, University of Cincinnati.

Correspondence concerning this article should be addressed to Matthew C. Aalsma, Department of Pediatrics, Indiana University School of Medicine, 410 West 10th Street, Suite 1001, Indianapolis, IN 46202. E-mail: maalsma@iu.edu

Acknowledgments: This study was funded by grants from the Indiana Criminal Justice Institute and HRSA/MCHB R40MC08721, provided through the U.S. Department of Health and Human Services, Health Resources and Services Administration, Maternal and Child Health Research Program.

Keywords: juvenile probation, supervision, race, mental health, youth, juvenile, conjoint analysis, decision-making

Abstract

Juvenile probation officers (JPOs) play an important role in the juvenile justice system, and their decisions influence youth outcomes. Conjoint analysis was used to determine the relative influence of youth, case, and family characteristics on JPO decision-making. JPOs (N = 224) were recruited from 18 Indiana counties to review 8 scenarios describing youth probationers. JPOs were randomly assigned to review scenarios depicting either a white youth or a black youth. Within youth probationer race, each scenario varied by 5 dichotomous dimensions commonly associated with differences in decision making among justice system personnel: youth gender, offense severity, mental health screening results, youth age, and family involvement. JPO participants then made recommendations for each probationer regarding (1) placement in the community or secure facility, (2) conditions of probation supervision, and (3) referrals to mental health services. For each recommendation (placement, supervision conditions, and service referrals), mean JPO responses did not differ by probationer race. For both black and white probationers, offense severity was the most influential factor on placement decisions. In contrast, the relative influence of scenario characteristics on JPO recommendations differed by probationer race when JPOs made decisions about conditions of probation and mental health service referrals.

Introduction

Most youth involved in the justice system are sentenced to probation and supervised by a juvenile probation officer (JPO; Sickmund & Puzzanchera, 2014). In their supervisory role, JPOs make decisions that have immediate and lasting implications for youth probationers (Griffin & Torbet, 2002; Leifker & Sample, 2011). JPOs provide input and recommendations related to several aspects of probationer status and care, including whether probationers should be returned to the community, the terms of probation supervision, and which social services probationers should be referred to. Though juvenile court judges are ultimately responsible for many of these decisions, research (Leifker & Sample, 2011) has shown that judicial decisions align with the recommendations of JPOs in the vast majority of cases. JPOs have also been described as gateway providers to behavioral health services for youth offenders, helping identify mental health treatment needs of adolescents (Wasserman et al., 2008) and facilitating youth engagement in behavioral healthcare (Holloway, Brown, Suman, & Aalsma, 2013). Despite the range of decisions to be made by JPOs, and the discretion afforded them, questions remain about how JPOs reach their decisions. Indeed, juvenile probation has been referred to as one of the “black boxes” in justice system decision-making research (Bechtold, Monahan, Wakefield, & Cauffman, 2015, p. 325).

Decisions within the context of the justice system are, ostensibly, to be made in consideration of relevant legal factors, such as the extent of an offender’s criminal history or the severity of a charged offense (Schwalbe, Hatcher, & Maschi, 2009). The juvenile justice system, with its additional mandate to protect the best interests of youth offenders, allows courts to also consider extralegal factors, which are less directly tied to the factual details of a charged offense. For example, judges may consider a youth’s amount of parental support when making determinations about youth culpability and sentencing. In other words, judges make a risk versus needs calculation when considering juvenile offenses (Vincent, Paiva-Salisbury, Cook, Guy, & Perrault, 2012). To this end, decision-makers within the system can be aided by formal risk assessment measures or detailed legal guidelines to weigh complex and potentially conflicting information about an individual youth offender. Some jurisdictions have implemented sentencing rubrics, for example, with the goal of consistently administering punishments proportionate to the offenses committed (Vincent & Lovins, 2015). In the pre-sentencing stage of the juvenile justice system, decision-makers use validated risk and needs assessments to determine an offender’s need for both supervision and services (Grisso, 2007; Vincent et al., 2012). Guidelines, however, vary widely by jurisdiction, and their use is often voluntary. Despite the availability of these determinate processes to increase fairness within the justice system, decision-makers can choose to override prescribed outcomes, diminishing the purpose of guidelines (Wang, Mears, Spohn, & Dario, 2013). This suggests a continued need to study how legal and extralegal factors differentially influence decision-making within the juvenile justice system.

A wide array of interrelated variables have been implicated in decision-making at different stages of the adult and juvenile justice systems, potentially contributing to disparate outcomes among offenders. Past studies have identified many influences on court personnel, including the demographic characteristics of individual offenders (Leiber & Fox, 2005); the severity of the criminal charge (Leiber & Peck, 2015); the mental health of the offender (Cappon & Vander Laenen, 2013); the offender’s family structure or involvement in the legal process (Rodriguez, Smith, & Zatz, 2009); and each decision-maker’s own characteristics, professional orientation, and personal biases (Ricks & Eno Louden, 2015). Many of these factors appear to work in tandem to influence decision-making and depend highly on the context of the decision to be made (Schwalbe & Maschi, 2009).

In addition to the factors described thus far, the role of an offender’s race/ethnicity in decision-making within the justice system has been the subject of a significant body of research, especially given that systemic racial disparities are widespread (Wakefield & Uggen, 2010). Although minority youth comprise about one-third of the general population, just over 60% of individuals involved in the juvenile justice system are youth of color (Desai, Falzer, Chapman, & Borum, 2012). In many cases, racial disparities in arrest and detention persist, even when controlling for other correlates of justice system involvement, including mental illness, propensity for violence, and other social or demographic variables (Desai et al., 2012; Leiber & Johnson, 2008; Pope, Lovell, & Hsia, 2002). JPO recommendations may disproportionately affect youth of color, particularly black youth. In an analysis of JPO case files, offender race influenced JPO assessment of probationers beyond other youth and case characteristics, since black youth were over four times more likely to be documented by JPOs as noncompliant with court orders, despite having fewer prior referrals (i.e., arrests) than other racial/ethnic groups (Smith, Rodriguez, & Zatz, 2009). Black youth ultimately receive more punitive sentences and are removed from their homes at higher rates than white youth (Rodriguez et al., 2009).

Other findings paint a more complicated picture of the influence of offender race within the system. For example, researchers have found that some pre-adjudication race disparities appear to be corrected by post-adjudication decisions. A study (Bechtold et al., 2015) of decision-making within the context of juvenile probation found that, though youth may be sentenced to probation terms at different rates depending on their race, JPOs treated probation violations similarly for both black and white youth.

Theoretical Approach

Social cognition theories, including attribution theory, have offered an approach to understanding legal decisions related to offender culpability (Mears et al., 2014). Attribution theory describes the process by which individual decision-makers attend to, prioritize, and interpret a variety of social and contextual cues to make causal attributions about the behavior of others (Bridges & Steen, 1998; Mears et al., 2014). Causal attributions may align with cultural stereotypes or other developed cognitive heuristics (Graham & Lowery, 2004). Cognitive heuristics are simple and efficient mental tools relied on by individuals to form judgments quickly (Gigerenzer & Gaissmaier, 2011). Heuristics reduce the individual effort required to form a judgment by, for example, eliminating extraneous or conflicting cues from the decision-making process or by ignoring the relative importance or salience of individual attributes (Hilbert, 2012; Shah & Oppenheimer, 2008). Though heuristics can be helpful in making decisions under stress or time constraints, they can contribute to biased decisions if, for example, an individual focuses on one scenario attribute while ignoring other important cues. It has been hypothesized that such biases contribute to disparate outcomes among youth involved in the justice system, since decision-makers appear to rely on heuristics and stereotypes related to offender race, age, mental health status, socioeconomic disadvantage, and complex combinations of these and other extralegal factors (Graham & Lowery, 2004; Rodriguez, 2011).

Purpose

In the current study, we sought to understand the relative importance of various youth, family, and case characteristics in JPO decision-making by employing conjoint analysis, a unique approach described in detail below. Conjoint analysis presents a way to measure the relative influence of multidimensional factors on decision-makers without overtly asking decision-makers about their preferences. We hypothesized that the relative importance of scenario characteristics in JPO decision-making may vary by youth race. Specifically, we anticipated that JPOs would prioritize different data when making decisions for black youth when compared to data that informed their decision-making for white youth.

Methods

Procedures

JPO participants were recruited from counties taking part in a statewide initiative, the Indiana Mental Health Screening Project, to implement standardized, universal mental health screening at detention intake (Aalsma, Schwartz, & Perkins, 2014). Of the 22 Indiana counties in which there is a detention center, 19 counties were involved in the Mental Health Screening Project. All but one of these counties (n = 18) agreed to be included in the present study. Participants were asked to complete surveys and, potentially, follow-up qualitative interviews. A total of 258 JPOs, by virtue of their employment in one of the 18 counties, were eligible to complete the study’s online survey. The Chief JPO of each participating county provided study personnel with the e-mail addresses of all JPO employees within their counties. Though eligible JPOs were not selected randomly from Indiana’s total population of JPOs, the JPOs employed in counties with a detention center receive nearly 70% of the state’s annual referrals to the juvenile justice system (Supreme Court of Indiana, 2015). Therefore, despite reliance on a convenience sample, the participating JPOs likely represented the typical experience of Indiana JPOs.

The participation of JPOs in the study was voluntary. To maintain participant confidentiality, each JPO received a study recruitment e-mail containing a unique web link to the online survey. The JPOs were also assured that their supervisors and coworkers would not be informed about any individual employee’s participation in the study. This study was approved by the Indiana University–Purdue University Institutional Review Board.

Participants

Of the 258 eligible JPOs, 224 (86.8%) consented to study participation and completed survey measures. The sample of JPOs was largely female (67.0%), white (83.3%), and 30–49 years old (64.9%). All participants had received a 4-year college degree, and approximately 30% of JPOs had either begun or completed a master’s program. Participants, on average, had spent 12.7 years (SD = 8.7) working in the juvenile justice system and 7.8 years (SD = 6.7) in their current position. More than 23% were in a management or supervisory position. The mean caseload size per JPO was 40 probationers (SD = 28.4). The JPOs included in the sample supervised youth exhibiting a range of behaviors and needs. A total of 62.5% of the JPOs reported supervising youth categorized as minimum risk; 31.0% supervised sex offenders; and 56.7% supervised high-risk youth. See Table 1 for demographic characteristics of the JPO participants.

Table 1. Demographic Characteristics of Juvenile Probation Officers (N=234)

Participant

n (%)

Age

 

20–29

42 (17.9)

30–39

89 (38.1)

40–49

63 (26.9)

50–59

38 (16.2)

60–69

2 (0.9)

Gender

 

Female

156 (67.0)

Male

77 (33.0)

Race/Ethnicitya

 

White

194 (83.3)

Black

34 (14.6)

Hispanic

6 (2.6)

Native American

4 (1.7)

Multiracial

12 (5.2)

a Subjects were instructed to choose all that apply.

 

Measures

JPO recommendations. The JPO participants were presented with 8 written scenarios, each describing a hypothetical youth probationer. The JPOs were prompted with the statement, “If you had this child on your caseload, which of the following actions would you recommend?” The recommendations to be made included (1) whether the hypothetical probationer should be placed in a secure facility or returned to the community, (2) how intense/restrictive the youth’s probation supervision should be, and (3) whether and to what extent the probationer should receive mental health services. The JPOs indicated their decisions using a scale that ranged from 0 to 100. Greater value responses indicated more restrictive recommendations for youth placement and probation conditions and more intense mental health services. Anchors for the JPO recommendations regarding youth placement included “Community” (0), “Residential” (50), and “Department of Corrections” (i.e., youth prison) (100). Anchors related to probation supervision conditions included “No Action” (0), “Standard” (50), and “Intensive with Restrictions” (100). Mental health services recommendation anchors were “None” (0), “Outpatient” (50), and “Intensive Home-Based” (100). The JPOs were not given specific definitions of each response anchor. However, both the scenarios and the response scales were pilot tested for construct validity with a chief JPO and an assistant chief JPO. Both confirmed that the response anchors corresponded to typical variations in JPO recommendations and would be familiar to JPOs.

Probationer and case characteristics. To assess the relative influence of various probationer, case, and family characteristics on JPO decision-making, the 8 scenarios reviewed by JPOs differed from each other along 5 dimensions, each with dichotomous attributes (see all scenario variations in Table 2). In contrast, for each survey respondent, the race of the hypothetical youth probationers remained consistent across scenarios. Participants were randomly assigned to evaluate one of two groups of scenarios: one group where all scenarios referenced white youth, or one where all scenarios referenced black youth. By incorporating youth race as a between-subjects rather than within-subjects effect, we sought to reduce the likelihood that survey responses would be skewed by social desirability bias. This bias refers to the tendency for research participants to tailor their responses to societal expectations or norms about what is correct (Drakulich, 2015). For example, if the JPOs reviewed scenarios that noticeably differed by youth race, respondents may have felt compelled to appear unbiased by keeping their responses consistent across races of the youth described. Past studies on the use of self-report measures have repeatedly shown that respondents are especially vulnerable to social desirability bias when answering questions about sensitive topics, including questions perceived as related to race or racism (Drakulich, 2015; Krumpal, 2013). By asking each participant to consider youth of a single race, rather than asking them to make side-by-side recommendations for youth of different races, it should be possible to detect the relative influence of other scenario dimensions on JPO decisions. Random assignment was accomplished by using a function built into Qualtrics, the survey development software used in this study.

Table 2. Mean (SD) JPO Recommendation Scores (0–100) for Youth Placement,a Mental Health Services,b and Probation Supervision Conditionsc by Scenario Variations and Youth Probationer Race

Scenario Variations

Youth Placement

Probation Supervision
Conditions

Mental Health Services

Gender

Offense Severity

Positive MHd Screen

Age

Family
Involvement

Black
Probationer

White
Probationer

Black
Probationer

White
Probationer

Black
Probationer

White
Probationer

Female

Burglary

Yes

13

Active

28.35(21.29)

27.54(22.18)

62.19(29.30)

61.18(25.89)

61.08(29.71)

54.14(29.49)

Male

Runaway

Yes

13

Active

17.68(16.19)

15.54(14.64)

58.57(25.12)

60.98(26.38)

62.11(27.31)

56.80(29.67)

Female

Burglary

No

13

Inactive

21.11(19.81)

20.03(17.04)

59.18(25.01)

55.73(24.54)

50.61(31.12)

57.08(29.93)

Female

Runaway

Yes

16

Inactive

18.87(17.81)

16.47(14.11)

55.10(27.78)

57.62(27.60)

52.58(29.15)

59.90(28.67)

Male

Runaway

No

13

Inactive

11.85(12.68)

12.11(13.32)

53.28(27.22)

54.72(25.18)

53.80(29.60)

59.09(27.06)

Female

Runaway

No

16

Active

16.17(14.73)

13.67(11.82)

59.95(27.73)

55.98(24.36)

56.73(26.27)

53.29(28.60)

Male

Burglary

Yes

16

Inactive

28.23(21.80)

26.12(19.07)

61.67(26.48)

55.90(27.25)

57.81(29.09)

55.60(31.49)

Male

Burglary

No

16

Active

32.18(25.93)

28.76(23.90)

60.53(24.79)

62.31(24.02)

60.04(25.00)

58.45(29.48)

a Youth Placement = “Community” (0), “Residential” (50), and “Department of Corrections” (i.e., youth prison) (100).

b Mental Health Services = “None” (0), “Outpatient” (50), and “Intensive Home-Based” (100).

c Probation Supervision Conditions = “No Action” (0), “Standard” (50), and “Intensive with Restrictions” (100).

d MH = mental health.

 

The youth probationers depicted in the 8 scenarios differed systematically across 5 dimensions commonly shown to influence decision-making within the justice system: (1) probationer gender; (2) severity of the charged offense (running away, a status offense vs. burglary, a criminal offense); (3) results of a mental health screen (positive vs. negative for mental health problems); (4) probationer age (age 13 vs. age 16); and (5) the level of family involvement in the probation process (active vs. inactive). For example, one scenario read, “A 16-year-old female has been arrested for running away from home. She has screened positive for mental health problems. Her family has not participated in the probation process in the past.” All scenarios varied by the underlined portions of this example. See Table 2 for a description of all scenario variations included in the surveys.

Analysis

Previous studies of decision-making within the justice system have often turned to retrospective, archival data (e.g., criminal case histories and court hearing transcripts) to identify disparate—and potentially biased—decision-making (Rodriguez, 2011). Other studies have relied on the self-reported attitudes and beliefs of court personnel to gain insight into what may be driving their decisions (Ricks & Eno Louden, 2015). Although survey measures allow researchers to make inferences based on how decision-makers respond to vignette manipulations, “they typically do not allow researchers to determine which components of the manipulation produced the observed effects” (Hainmueller, Hangartner, & Yamamoto, 2015, p. 2). Thus, in the present study, we applied conjoint analysis, which is typically used in business marketing research to evaluate how individual characteristics or dimensions of a product influence product acceptability (Raghavarao, Wiley, & Chitturi, 2011). More recently, conjoint analysis has been employed in the study of healthcare treatment preferences (Bair et al., 2008; Newman, Roungprakhon, Tepjan, & Yim, 2010; Zimet et al., 2005), political science (Hainmueller et al., 2015), and implementation science (Farley, Thompson, Hanbury, & Chambers, 2013). Conjoint analysis is particularly useful for studying the formation of complex judgments, where multiple and interrelated influences are at play (Shamir & Shamir, 1995). This approach also overcomes limitations to more traditional methods of analysis, in that the preferences of the decision-maker are “less declarative and less tainted by social desirability” (Tsang, Chan, & Chan, 2001, p. 137).

Conjoint analysis provides a descriptive model that clarifies the relative preferences of participants for attributes of a variety of dimensions (Bridges et al., 2011). In conjoint analysis, the relative preference of a dimension attribute is called a part-worth utility, which can be interpreted as a relative standardized effect size. The more participants preferentially distinguish among attributes, the wider the range in part-worth utilities. For example, if JPOs strongly preferred prison placement for youth relative to placement in the community, prison placement would have a high positive part-worth utility value; the community placement attribute would have an equally strong proportional negative part-worth utility value. For each dimension, the sum of the part-worth utilities of these attributes is zero (Raghavarao, Wiley, & Chitturi, 2011). The extent to which each dimension contributed to a decision is measured by importance scores, which reflect the relative ranges of the part-worth utilities across scenario-specific dimensions. Thus, importance scores sum to 100.

In the present study, full-profile ratings-based conjoint analysis was applied to assess JPO recommendations. Using a fractional factorial design, JPOs were presented with eight representative scenarios constructed through the SPSS v.21 conjoint procedure. Asking JPOs to consider a full factorial design of this conjoint analysis (e.g., requiring JPOs to make recommendations in response to 32 scenarios) would have been too cumbersome to collect reliable data. It is important to note that one limitation of a fractional factorial design is that analysis is limited to the main descriptive effects of each dimension. However, due to the exploratory nature of the current analysis, we thought that this design was appropriate.

Results

T-tests indicated that, overall, JPO recommendations did not differ significantly by study condition (black or white youth probationers). Likewise, results were not significant for t-tests by probationer race comparing JPO recommendations regarding placement, mental health services, and probation supervision conditions (all p values > 0.20). Table 2 presents all mean JPO recommendation scores by decision-making context (youth placement, probation supervision conditions, and mental health referral), study condition (probationer race), and all eight variations in scenario characteristics.

Youth Placement

Conjoint analysis revealed that JPOs considered scenario characteristics and their attributes similarly for black and white youth probationers when making placement recommendations (see Figure 1). Regardless of youth race, JPOs were more likely to recommend a more restrictive placement (i.e., Department of Corrections/youth prison) if the youth were an older male who had committed a more serious offense, who had received a positive mental health screen, and whose family had been active in the probation process. According to the importance scores associated with each scenario characteristic, offense severity was by far the most influential factor in JPO decision-making regarding youth placement (black youth: 48.9%; white youth: 52.0%). In other words, JPOs were much more likely to recommend a restrictive placement for a youth probationer who committed a burglary than for a youth who ran away from home. All other scenario characteristics, based on their importance scores (range = 5.7%–15.8%), were less influential on JPO recommendations for youth placement.

Figure 1. Probation officer placement recommendations, part-worth utility, and importance scores (IS).

Figure 1. Probation officer placement recommendations, part-worth utility, and importance scores (IS).

Note. MH = mental health.

 

Probation Supervision Conditions

In making recommendations related to probation supervision conditions, JPOs weighed some scenario characteristics differently when considering black versus white youth (see Figure 2). Considering relative importance scores, youth gender was the most important influence on JPO decisions if probationers were black (37.3%), whereas offense severity was the most important factor for white probationers (33.6%). Within each study condition, JPOs were more likely to recommend intensive probation supervision conditions for males (part-worth utility value, black: |1.639|; white: |0.748|); probationers without a positive mental health screen (part-worth utility value: black, |1.185|; white: |0.678|); and 16-year-olds (part-worth utility value, black: |1.326|; white: |1.200|). In contrast, there were differences by probationer race related to offense severity, since black probationers who ran away from home were recommended for more intensive supervision than those who committed burglary (part-worth utility value—black, runaway: 0.122), while white probationers who ran away from home were likely to be recommended for a less intensive probation supervision than those who committed burglary (part-worth utility value—white, runaway: −1.745). There was also a probationer race effect when JPOs considered the family’s involvement with probation. black probationers with families active in the probation process were likely to be recommended for more intensive probation supervision (part-worth utility—black, active: 0.122), while white probationers with active families were likely to be recommended for less intensive probation supervision (part-worth utility—white, active: −0.825).

Figure 2. Probation officer recommendations for probation supervision conditions, part-worth utility, and importance scores (IS). Note that the influence of charged offense and family involvement on JPO recommendations regarding probation supervision conditions varied by youth race.

Figure 2. Probation officer recommendations for probation supervision conditions, part-worth utility, and importance scores (IS). Note that the influence of charged offense and family involvement on JPO recommendations regarding probation supervision conditions varied by youth race.

Note. MH = mental health.

*Note that the influence of charged offense and family involvement on JPO recommendations regarding probation supervision conditions varied by youth race.

 

Mental Health Services

When deciding whether youth probationers should receive mental health services, probationer race affected how JPOs weighed scenario factors (see Figure 3). Specifically, JPOs showed a relative preference for recommending more intensive mental health services for black, male probationers than for black, female probationers (part-worth utility value: |0.288|). In contrast, JPOs demonstrated a relative preference for less intensive mental health services for white, male probationers than for white, female probationers (part-worth utility value: |1.052|). JPOs were more likely to recommend intensive mental health services for youth with a burglary charge than probationers who ran away from home (part-worth utility value, black: |2.218|; white: |0.328|). Likewise, probationers who were 16 years old were more likely to be recommended for intensive mental health services than probationers who were 13 years old (part-worth utility value, black: |1.191|; white: |0.230|). Finally, JPOs were more likely to recommend intensive mental health services for youth without a positive mental health screen than for those with a positive screen (part-worth utility value, black: |2.218|; white, |0.328|).

Figure 3. Probation officer recommendations for mental health (MH) services, part-worth utility, and importance scores (IS). Note that the influence of gender on JPO mental health services recommendations varied by youth race.

Figure 3. Probation officer recommendations for mental health (MH) services, part-worth utility, and importance scores (IS). Note that the influence of gender on JPO mental health services recommendations varied by youth race.

Note. MH = mental health.

*Note that the influence of gender on JPO mental health services recommendations varied by youth race.

 

The relative importance scores of each scenario factor for mental health services also differed depending on probationer race (see Figure 3). For black probationers, offense severity had the greatest influence on JPO recommendations (47.0%), while youth gender was most important for white probationers (53.0%; see Figure 3 for remaining importance scores).

Discussion

The results of this study reflect the complexities of decision-making within the juvenile justice system. Our purpose was to understand the relative influence of various youth, family, and case characteristics on JPO decision-making. Results suggest that JPOs in the current sample attended to probationer race and gender—more so than other individual-level factors—when making decisions about mental health services and probation supervision. Considering the relative influence of race and gender, the JPOs may have relied on common cognitive heuristics or stereotypes related to race and gender, to the exclusion of other empirically supported risk factors including family involvement, age, and offense severity (Hilbert, 2012; Shah & Oppenheimer, 2008). However, the role of race and gender did not explicitly affect JPO recommendations regarding youth placement. These findings have implications for JPO practices and juvenile justice system policy.

We found that the relative influence of legal (e.g., offense severity) and extralegal characteristics (e.g., gender, family involvement) was highly dependent on the type of recommendation to be made by JPOs, meaning that the importance of these factors varied by whether JPOs were making decisions about youth placement, the conditions of probation, or referral to mental health services. When considering a placement recommendation for a youth probationer, offense severity was the most important factor considered by JPOs, while probationer race was not salient. In other words, youth probationers who committed burglary were more often recommended for placement in prison than runaways, regardless of probationer race and other characteristics. Note that the practical distinctions between a status offense (e.g., running away) and a more serious criminal offense (e.g., burglary) are striking; the range of punishments available to a decision-maker for these offenses is likely more prescriptive than discretionary. Clear legal guidelines, and sometimes legal mandates, often guide decision-making related to placement (Wang et al., 2013), which may account for the reliance of JPOs on charge severity to make placement recommendations. This result is also consistent with findings in the context of judicial decision-making with adult populations (Leifker & Sample, 2011). However, probationer race was implicated when JPOs were asked to make recommendations regarding probation supervision conditions or referrals to mental health services.

In terms of their recommendations regarding probation conditions, JPOs were more likely to consider the individual characteristics of black youth (e.g., gender and age) rather than offense severity or family involvement. Although JPOs appeared to consider all scenario characteristics when making recommendations for white youth (importance scores: 13.1%–33.6%), legal factors were especially salient. Specifically, a recommendation for intensive probation supervision for black youth was most likely for older males. For white youth, intensive probation recommendations were more likely for youth who committed more serious offenses and whose families were not active in the probation process. This finding is consistent with previous research showing that justice system decision-makers may view the causes of crime differently for black versus white offenders. Bridges and Steen (1998) found that court officials were more likely to attribute the criminal behavior of black youth to negative internal personality characteristics (i.e., uncooperative; does not admit guilt) and attribute the crimes of white youth to negative external environmental factors (i.e., dysfunctional family, drug/alcohol use). In the current study, one seemingly positive environmental factor (i.e., family involvement with the probation process) was associated with more intensive probation conditions for black youth than for white youth. Family involvement is widely regarded as a protective factor against delinquency, whereas family disenfranchisement from the juvenile justice system can impair the ability of parents to advocate for their children, potentially leading to more punitive outcomes (Arya, 2014). Here, the differential consideration of families of white and black probationers may reflect findings from past research: a common negative attribution that black families are less structurally stable than white families (Pope & Feyerherm, 1995). This underlying structural assumption is one potential reason why black youth have received more intensive probation conditions than their white peers. Future research should examine how the interaction of youth race and perceived family involvement affect JPO decision-making.

Our finding that the combination of probationer gender and race influenced JPO recommendations regarding referrals to mental health services and the intensity of probation conditions may reflect the impact of implicit (rather than explicit) racial bias among decision-makers. Implicit bias against black youth has been demonstrated among police officers in studies of disparate use-of-force by offender race (Goff, Jackson, Di Leone, Culotta, & DiTomasso, 2014). Decision-makers may implicitly view black children more like adults and more culpable for delinquent acts. For instance, in one nationally representative study in which participants were primed to consider a youth who was either black or white, the authors found that adults who were primed to think about a black child were more supportive of life without parole sentences than when they were primed to think about a white child (Rattan, Levine, Dweck, & Eberhardt, 2012). Participants in the black-prime condition were also more likely to perceive the blameworthiness of juveniles as more similar to adults. This pattern has been found among JPOs who were primed with words stereotypically related to black Americans before being presented with a vignette; JPOs “judged the alleged offender to be less immature and more violent . . . more culpable, more likely to reoffend, and more deserving of punishment” when primed with such words (Graham & Lowery, 2004, p. 496).

There is also evidence that prosecutors and JPOs may not select probationers for diversion programs due to attributional stereotypes related to black youth (e.g., family instability; Pope & Feyerherm, 1995), which can also occur when parents seem uncooperative or have trouble making an intake appointment (Henning, 2013). Indeed, one study found that black youth were less likely to be diverted to these programs than youth of other races with similar offense histories and characteristics (Leiber, Johnson, Fox, & Lacks, 2007). However, a more recent study of two model jurisdictions found no racial/ethnic differences in JPO monitoring practices and decisions to file a violation (Bechtold et al., 2015), suggesting that the role of race may be contextually dependent. Indeed, between one-third and one-half of the variance in the use of treatment, deterrence, and restorative justice strategies by JPOs in the current study sample was due to nesting at the county level, indicating that geographical differences in basic training and standard operations may contribute to practice differences among JPOs (Holloway, Cruise, Downs, Monahan, & Aalsma, 2016). Future studies should examine jurisdictional differences associated with racial/ethnic disproportionality in youth outcomes.

In the current study, probationer race was also implicated in JPO recommendations for mental health services. In fact, their recommendations aligned with established stereotypes related to both race and gender in the provision of mental health care among justice-involved youth. Research has shown that black offender youth are less likely to be referred to mental health services (Glisson, 1996; Thomas & Stubbe, 1996). For white youth in the current study, female gender was most influential in mental health service recommendations made by JPOs. For black youth, mental health services were recommended only for older youth who had committed more serious crimes.

Interestingly, the presence of a positive mental health screen was largely ignored in JPO decision-making. JPOs were less likely to recommend intensive mental health services for both black and white youth with positive mental health screens. Research has consistently demonstrated that up to two-thirds of detained youth exhibit symptoms of a mental health or substance use disorder (Fazel, Doll, & Långström, 2008; Teplin, Welty, Abram, Dulcan, & Washburn, 2012). This fact could have the unintended consequence of JPOs perceiving all justice-involved youth as needing care, regardless of screening status, meaning that JPOs rely on other factors in recommending mental health services. Grisso (2007) describes this tendency to perceive all justice-involved youth as needing mental health treatment as “over-interpreting the message” (p. 162). When justice personnel are faced with the possibility that two-thirds of youth need mental health treatment, it may overwhelm their decision-making abilities. Grisso (2007) explained that, “The thought of providing treatment for such a large number of youth seemed to some so daunting that they failed to respond at all” (p. 162).

However, there is a substantial minority of youth in the system who do not meet the diagnostic criteria for a mental health disorder. Moreover, diagnosis of a mental health disorder should not be conflated with the level of treatment need. A helpful model for JPOs when discerning treatment need is to understand the specific risks and needs of juveniles (e.g., Risk-Needs-Responsivity; RNR). Using the RNR model may well improve JPO responses to mental health diagnosis and treatment need (Andrews & Bonta, 2010). An important caveat, however, is that some research findings suggest that risk assessment tools based on the RNR model may be less effective in reducing recidivism with justice-involved females. This highlights the need to consider other gender-specific factors relevant to treatment outcomes for female justice-involved youth. For example, Vitopoulos, Peterson-Badali, and Skilling (2012) found that female adolescents were more likely than males to receive a recommendation for services that targeted personality as a criminogenic need (e.g., short attention span, anger, inadequate guilt) and were scored as higher risk than their male peers on a risk/needs assessment instrument. The authors also found that services matched to risk/needs assessment results were less effective in reducing recidivism risk for female justice-involved youth, presumably due to unidentified criminogenic needs or specific responsivity factors more common among females. Research in this area has consistently recommended that potential factors such as victimization/abuse, trauma exposure, chronic mental health concerns, family dynamics, and social support are factors that deserve greater consideration with justice-involved females (Anderson et al., 2016; Gavazzi, Yarcheck, & Chesney-Lind, 2006; Vitopolous et al., 2012). Such gender-specific factors may interact with more commonly identified criminogenic needs and/or specific responsivity factors to reduce the effectiveness of interventions designed to reduce recidivism risk.

Finally, we found that JPO decision-making was also influenced by probationer gender. Regardless of a probationer’s race, JPOs were more likely to recommend restrictive placements and intensive probation conditions for male than female probationers. This may be partially attributed to JPO reliance on heuristics related to gender and community safety (e.g., JPOs attributing male gender to imply greater community safety needs and recommending placement more frequently for boys). This finding is consistent with research highlighting judicial paternalism in regards to gender (e.g., Corrado, Odgers, & Cohen, 2000; Spivak, Wagner, Whitmer, & Charish, 2014). Probationer gender, along with race, was also implicated in JPO recommendations for mental health services. Referrals for services were more likely for black males than females, but the opposite was true among white youth probationers; white females were referred to mental health services more frequently than males. These gender-based findings may have important implications for service referral and utilization, given previously documented disparities in the juvenile justice system (Grande, Hallman, Underwood, & Rehfuss, 2012; Herz, 2001; Lopez-Williams, 2006). In particular, these findings align with previous research on the cognitive attributions and stereotypes JPOs have applied to describe girls in the justice system (e.g., that girls are highly emotional, manipulative, and have mental health issues; Gaarder, Rodriguez, & Zatz, 2004; Leiber & Peck, 2015). Understanding and addressing these JPO cognitive heuristics (i.e., mental shortcuts) and decision-making processes can inform future training and promote equitable treatment for all justice-involved youth with mental health needs.

Limitations

A few aspects of the current study may limit interpretation of the results. First, the outcomes of interest were assessed using hypothetical scenarios rather than actual cases. Although the vignettes allowed an opportunity to maximize internal validity, the results may have varied if individual cases of actual juvenile probationers were assessed. It is also possible that mere descriptions of probationer characteristics lacked the impact to trigger any associated cognitive heuristics. Again, asking JPOs to make recommendations for actual probationers, or even using photographs of probationers, may have changed the results.

Furthermore, our study only included 5 dimensions that may influence JPO decision-making, though many other factors have been shown to affect JPO recommendations (Steen, Engen, & Gainey, 2005). For example, this study did not account for JPO characteristics or probationer offense history. Also, though we contacted JPOs from many jurisdictions to capture a wide range of caseload sizes, job training, and community cultures, our results were gathered from one state. State-specific factors, such as legislation or political climate, may have influenced JPO decision-making and limited the generalizability of our findings.

Conclusions

This study highlighted the importance of studying JPO decision-making by employing an innovative analytic approach. Conjoint analysis, a technique that has primarily been used in business and medical research, may be an effective method for examining complex decision-making processes. Although many juvenile justice system jurisdictions use standardized measures to guide post-disposition decision-making (e.g., criminogenic risk/needs assessments), decision-makers have been found to often override these decisions (Wang et al., 2013). JPOs may be relying on cognitive heuristics and stereotypes regarding the youth and families with whom they work. Future studies should examine the interaction of how cognitive heuristics and standardized risk measures may affect JPO decision-making. For example, researchers should examine the factors associated with overriding assessment scores and under what conditions JPOs may not follow assessment recommendations.

Family involvement with the probation process was treated differently by the JPOs who were primed to think about a white versus a black probationer. Vignettes about black youth whose families had been actively involved with probation in the past received more intensive probation, whereas those about white youth in the same circumstances received less intense supervision. Future research should examine whether similar patterns are found with real justice-involved youth. If so, differential treatment of family involvement on the basis of race may be a procedural justice issue, since it may disenfranchise black justice-involved youth and their families.

In sum, probationer race and gender were both associated with JPO decision-making regarding probation supervision intensity and mental health recommendations, which is consistent with prior research. Taken together, probationer race, gender, and their interaction should remain a continued focus for future research.

About the Authors

Matthew C. Aalsma, PhD, is a professor of psychology and pediatrics at the Indiana University School of Medicine. His research interests focus on behavioral health screening, assessment, and utilization among adolescents in general and, specifically, with justice-involved youth.

Evan D. Holloway, MA, is a doctoral student in clinical forensic psychology at Fordham University. He is interested in mental health service delivery systems for justice-involved youth and juvenile probation officer decision-making.

Katherine Schwartz, JD, MPA, is a research associate at the Indiana University School of Medicine. Her research interests and experience lie at the intersection of adolescent health and youth involvement in the juvenile justice system.

Valerie R. Anderson, PhD, is an assistant professor in the School of Criminal Justice at the University of Cincinnati. Her research interests include the juvenile justice system, female juvenile delinquency, and domestic minor sex trafficking.

Gregory D. Zimet, PhD, is a professor of pediatrics and clinical psychology at Indiana University School of Medicine and co-director of the Indiana University–Purdue University Indianapolis (IUPUI) Center for HPV Research. His research interests include the exploration of how people make decisions about health care, including immunizations and diagnostic tests.

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