Are Supervision Practices Procedurally Fair? Development and Predictive Utility of a Procedural Justice Measure for Use in Community Corrections Settings

Abstract

This study describes the development and refinement of a practical measure for use by community supervision staff to assess the extent to which individuals under community supervision perceive the supervision process as fair. Seven items functioned statistically and theoretically well. Results showed a clear, one-factor structure. The resulting measure demonstrated significant relationships with supervision outcomes of both crime and technical violations across two independent community supervision samples. This practical measure is grounded in theory and provides supervision agencies with a tool to measure the degree to which the interactions between supervisees and officers are positive and prosocial, and facilitate outcomes that are perceived as legitimate. Findings are framed within the “what works” corrections literature, and the important, yet underresearched theory of procedural justice as it related to community corrections settings.

Keywords

corrections community corrections community supervision procedural justice legitimacy procedural fairness parole probation

A major theme underscoring the recent push toward evidence-based community supervision practices is the working relationship between the community supervision officer and the supervisee (see Andrews, 2011; Dowden & Andrews, 2004; Taxman, 2002, 2008). The concept of the working relationship evolves from the general psychotherapy literature, where a positive relationship, or collaboration, between therapist and client results in part from reciprocated trust, confidence, and acceptance (Horvath & Symonds, 1991; Norcross, 2010). In a handful of empirical studies to date, the views of individuals under community supervision regarding the quality of interactions with their supervising officers do shape supervision outcomes (Taxman & Thanner, 2004), including illegal substance use (Blasko, Friedmann, Rhodes, & Taxman, 2015), treatment participation (P. D. Brown & O’Leary, 2000), jail days (Walters, 2016), and subsequent criminal justice involvement (Blasko et al., 2015; Skeem, Eno Louden, Polasheck, & Cap, 2007; Taft, Murphy, King, Musser, & DeDeyn, 2003).

Taken together, these empirical findings from criminal justice settings mirror the clinical psychology literature that relies largely on interpersonal relationship themes, with emphasis on feelings of reciprocated trust, confidence, and acceptance (see Blasko et al., 2015, and Skeem et al., 2007). In addition to the working relationship, which parallels criminal justice literature to date, clinical psychology literature goes further to emphasize the importance of client–therapist agreement on the goals and tasks or approaches used in treatment (Bordin, 1979, 1994; Norcross, 2010). Over the course of 20 years, four meta-analyses have arguably demonstrated that the working alliance (also referred to as the therapeutic alliance)—the three dimensions (i.e., therapeutic bond, goal, and task agreement) together (Bordin, 1979, 1994; Horvath & Greenberg, 1989)—is one of the most important factors in achieving positive psychotherapy treatment outcomes (Horvath & Bedi, 2002; Horvath, Del Re, Flückiger, & Symonds, 2011; Horvath & Symonds, 1991; Martin, Garske, & Davis, 2000). However, unlike general psychotherapy settings, community supervision settings are typically characterized by authoritarian relationships with emphasis on control and punishment to achieve compliance.

The current study considered another theoretical framework for understanding the working relationship from the viewpoints of individuals under community supervision: procedural justice. Procedural justice is a dimension that has gained currency in the criminal justice literature recently (Franke, Bierie, & MacKenzie, 2010; Paternoster, Bachman, Brame, & Sherman, 1997; Reisig & Mesko, 2009; Tyler, 2006b, 2010). Procedural justice theorizes that individuals under community supervision are more likely to view and accept the rules laid out and decisions made by the authority figures supervising them if interactions with the authority are perceived as fair. Intuitively, it makes sense that the evaluative responses from individuals under community supervision about their supervising officers’ behaviors and procedures would be important for criminal justice outcomes, as compliance—a crucial element of community supervisions—is likely improved when individuals under supervision perceive the supervision rules and practices as a procedurally fair system.

The current study applied theories of legitimacy and procedural justice to the concepts underlying working alliance measures. A short, easy-to-use measure of procedural fairness was designed to assess one-on-one supervision interactions in community supervision settings. A major aim was that the measure could be used easily by supervising officers to gauge supervisee’s perception of the community supervision process.

Discretion in Decision-Making by Community Supervision Officers

National statistics show that approximately 68% of individuals are successfully discharged from community supervision (Bureau of Justice Statistics, 2015); however, this statistic does not describe the extent to which successful discharge is vulnerable to the day-to-day interactions between officers and supervisees. The nature of these interactions can be understood in terms of how the individual perceives the fairness of the criminal justice, or how the individual perceives that their officer is working on their behalf. The reputation of supervision as a “nail ’em, tail ’em, jail ’em” experience emerges from the encounters of those under supervision who feel that the overall supervision requirements were unfair, or made it difficult, if not impossible, to be successful on supervision.

A review of the decisions that supervising officers frequently make about compliance in supervision settings and how officers go about making the decisions illustrates the value of perceived fairness in a community supervision context (Medina, 2016). Rudes (2012), in her 3-year ethnographic study of parole offices in California, described how parole officers would exert their power to address noncompliance (e.g., partner with police to conduct raids on residences, pile on charges to make behavior appear more severe). The actions of the parole officers occurred even when the agency directed officers to use a graduated response guideline to handle various forms of noncompliance. Rather, officers preferred use of power techniques to justify the revocation process (Turner, Braithwaite, Kearney, Murphy, & Haerle, 2012). This is consistent with implementation problems experienced in other states using guidelines to structure technical violation decisions (Steiner, Hester, Makarios, & Travis, 2012). Viglione, Rudes, and Taxman (2015) found that officers held probationers accountable for court conditions even when validated risk and need instruments suggested the condition had no relevance to the individual probationer’s risk for recidivism. It is well documented that discretion is at the heart of the behavior of the supervision officer (Rudes, 2012; Steiner et al., 2012; Turner et al., 2012; Viglione et al., 2015) and defines the experience that the supervisee encounters while under supervision (Blasko et al., 2015; Taxman & Ainsworth, 2009).

Interactions Between Community Supervision Officers and Justice-Involved Individuals Under Supervision

Efforts to date to adequately understand the interactions and relationships between community supervision officer and individuals under community supervision, including subsequent outcomes, largely rely on measures borrowed from general psychotherapy settings. For example, Tatman and Love (2010) adapted the short form of the Working Alliance Inventory (WAI; Horvath & Greenberg, 1989) by inserting “probation officer” as the referent to replace “therapist.” The WAI was operationalized following Bordin’s (1979, 1994) pantheoretical conceptualization of the working alliance, and it measures three main dimensions of the collaboration between client and practitioner: goals, tasks, and bond. Skeem, Encandela, and Eno Louden (2003) and Skeem and her colleagues (2007) argued that the WAI did not adequately capture the unique nature of the authoritarian working relationship between supervision officer and supervisee, where the supervising officer is responsible for both care and control over the individual. Skeem and colleagues developed an alternative relationship measure using a sample of 90 probationers supervised in a specialty mental health unit. The measure was then cross-validated using a sample of 320 probationers diagnosed with mental health disorders (Skeem et al., 2007). The resulting Dual Relationships Inventory–Revised (DRI-R; Skeem et al., 2007) captures three dimensions: (a) caring and fairness, (b) trust, and (c) toughness.

The common tools for measuring working relationship in justice settings are the adapted Working Alliance and the Dual-Relationship tool (e.g., DRI-R). Both tools focus on strictly interpersonal dimensions. For example, Skeem and her colleagues (2007) acknowledged that the DRI-R is a potential “interpersonal form of procedural justice” (p. 399); however, the measure does not incorporate commonly used procedural justice items. What is lacking from current relationship measures is measurement of procedural fairness in the traditional sense—as measured in the form determined important to outcomes at other stages of the criminal justice system (Tyler, 2006b). While this traditional form may not measure interpersonal relationship dynamics, it seems likely many of the facets of procedural justice (for more information about procedural justice, see Tyler, 2010, and Jackson, Tyler, Bradford, Taylor, & Shiner, 2010), and it is possible that these components, including fairness, could have implications for the long-term behavior of individuals under supervision.

Why Procedural Justice? Theory and Research

Although justice organizations are authorized to make decisions in the realm of justice matters, the way in which these decisions are made matters. In recent years, legitimacy of justice organizations—the belief that justice organizations are entitled to make decisions about and should be deferred to in justice matters—is a major theme in criminal justice scholarship (Johnson, Maguire, & Kuhns, 2014; Paternoster et al., 1997; Reisig & Mesko, 2009; Tyler, 2006b). This body of work relies principally on the assumption that internalized judgments about institutions and procedures drive behavior. Within criminal justice scholarship, pathways to legitimacy have primarily focused on (a) distributive justice, or outcome-based satisfaction, which refers to the favorability of the disposition that a person receives at the end of an encounter, and (b) procedural justice, or process-based satisfaction, which is whether an individual approves of how a decision maker arrived at the outcome (Leventhal, 1980; Lind & Tyler, 1988; Tyler & Lind, 1992). From a theoretical perspective, the bond dimension of the working alliance aligns with procedural justice, whereas the goals and tasks dimensions align with distributive justice (Bordin, 1979; Lerner & Clayton, 2011).

The majority of empirical work on legitimacy in the criminal justice system has focused on police (Maguire, Lowrey, & Johnson, 2016; Paternoster et al., 1997; Tyler, Sherman, Strang, Barnes, & Woods, 2007) and court (Canada & Hiday, 2014; Gottfredson, Kearley, Najaka, & Rocha, 2007; McGrath, 2009; Redlich & Han, 2014) contexts. Findings from these studies show individuals are more likely to accept the decisions made by criminal justice actors when they perceive an interaction as fair (Tyler, 2006a), when they are allowed to communicate and provide their own side of the situation (Dai, Frank, & Sun, 2011), and when they are treated with dignity and respect (Liebling, 2004; Mastrofski, Snipes, & Supina, 1996; McCluskey, Mastrofski, & Parks, 1999), concluding procedural justice drives perceived legitimacy. Taken together, these studies show that experiencing procedural injustice in police and court settings has significant influence on an individual’s attitudes, feelings, and behaviors (e.g., Canada & Hiday, 2014; Gottfredson et al., 2007; McGrath, 2009; Paternoster et al., 1997; Redlich & Han, 2014; Tyler et al., 2007).

Recent findings from institutional corrections settings also suggest procedural injustice bears on outcomes of incarcerated individuals. These studies hypothesize that incarcerated individuals will be more likely to cooperate and comply with prison officials if prison officials treat them in a procedurally just manner (Beijersbergen, Dirkzwager, Eichelsheim, Van der Laan, & Nieuwbeerta, 2015; Beijersbergen, Dirkzwager, & Nieuwbeerta, 2016; Reisig & Mesko, 2009; Sparks & Bottom, 1995), which is in line with research from police and court contexts. Other research, however, has suggested there are dimensions of procedural justice that may be unique to corrections contexts. For example, Franke and colleagues (2010), in their comparison of prison and boot camp facilities, found that outcomes were equally as important as process components for prisoner evaluations of fairness (see also Bottoms, 1999). These dimensions have garnered little attention in relation to community corrections contexts.

Despite growing empirical research, several scholars have called into question the measurement of procedural justice, specifically in the context of corrections (Beijersbergen et al., 2015; Henderson, Wells, Maguire, & Gray, 2010; Tyler, 2010). Although Jackson and colleagues (2010) have theorized that the procedural justice model applied in correctional settings would include the dimensions of neutrality, voice, treatment with respect and dignity, and trust, these dimensions have not been tested empirically. Recently, Beijersbergen and colleagues (2015) acknowledged in their study of procedural justice in prisons that there is “no established standard to measure procedural justice, and in the past researchers have used different subscales and items” (p. 109; Colquitt, 2001; Colquitt, Conlon, Wesson, Porter, & Ng, 2001; Thibaut & Walker, 1975; Thibaut, Walker, LaTour, & Houlden, 1973; Tyler, 2006b). It is plausible that the various measures and ways procedural justice operates are a function of the diverse settings, but research is needed to further understand this dynamic (Henderson et al., 2010; Jackson et al., 2010; Wright & Gifford, 2017).

The Current Studies

Drawing on two longitudinal studies of individuals on community supervision, we present a two-part study on the development and refinement of a procedural fairness measure for use in community supervision settings. Relying on a sample of individuals entering parole supervision in the community across six study sites, the purpose of Study 1 was threefold. The first aim was to develop items to capture procedural fairness from the viewpoints of individuals on community supervision. In developing the items, we draw on both legitimacy and procedural justice theories (Beetham, 2013; Bottoms & Tankebe, 2012; Tankebe & Liebling, 2013; Tyler, 2006b) and substantive input from studies conducted at other stages of the criminal justice system. The second aim was to verify the factor structure of the Procedural Justice Measure (PJM) using principal components analysis. It was expected that an interpretable, internally consistent measure would be identified. The third aim was to ascertain whether the PJM predicted subsequent criminal justice outcomes. It was expected that the measure would predict a range of future criminal justice outcomes—in this case, substance use, technical violations, self-reported crime, and official arrests. In Study 2, the PJM was administered to 226 individuals under community supervision across three probation sites to cross-validate the measure developed in Study 1.

Study 1: Development and Validation

Item Generation and Selection

Items were developed by criminal justice experts¹ who relied on legitimacy and procedural justice theories (Beetham, 2013; Bottoms & Tankebe, 2012; Tankebe & Liebling, 2013; Tyler, 2006a, 2006b), modification of items used in studies at other stages of the criminal justice system (i.e., police, courts) to include “probation officer” as the referent, and practical experience. The research cooperative generated an initial pool of 10 items aimed to assess procedural justice among individuals under community supervision specifically. See Table 1 for the initial pool of items. Each item was placed on a 7-point Likert-type scale with response options of 1 = never, 2 = rarely, 3 = occasionally, 4 = sometimes, 5 = often, 6 = very often, and 7 = always. Four of the 10 items were worded such that they would be reverse-scored.

Table 1:

Descriptive Statistics: Initial Pool of Items (N = 480)

Item		M	SD	Minimum	Maximum
Procedural Fairness Items
1	My PO listens to my side of the story when I miss an appointment, have a positive urine, or have other problems with supervision rules.	4.86	2.30	1	7
2	My PO follows clear guidelines when he or she has to punish me for breaking rules.	5.63	1.83	1	7
3	My PO works with other agencies to get the services I need.	4.82	2.20	1	7
4	I feel my PO’s sanctions and punishments are what I should get.	4.31	2.32	1	7
5	My PO makes decisions about how to handle problems in a fair way.	5.25	2.01	1	7
6	I feel that my PO treats me like others on supervision.	5.30	1.96	1	7
7	My PO makes a lot of exceptions to the rules.^a	4.49	1.86	1	7
8	I feel that my PO has favorites and they get special treatment.^a	4.89	1.65	1	7
9	My PO often makes up his or her own rules.^a	5.77	1.47	1	7
10	My PO does not use rewards like I was told they would be used.^a	2.25	2.01	1	7

Note. PO = Probation/Parole Officer.

Reverse-scored item.

Participants

Study 1 consisted of 480 individuals entering parole supervision across six study sites who participated in a multisite randomized control trial (Step’n Out) conducted as part of the Criminal Justice Drug Abuse Treatment Studies (CJ-DATS), a 10-center research cooperative funded by the National Institute on Drug Abuse (NIDA). See Friedmann et al. (2008) for a description of the study, and Friedmann et al. (2011) for study outcomes. Inclusion criteria were (a) at least 18 years of age; (b) English speaking; (c) probable drug dependence immediately prior to incarceration, as determined by a score of 3 or higher on the TCU Drug Screen II (TCUDS II; Knight, Simpson, & Morey, 2002; Simpson, 1995; Simpson, Knight, & Broome, 1997) or mandated drug treatment; (d) substance-use treatment as a mandated or recommended condition of parole; and (e) moderate-to-high-risk of drug relapse and/or recidivism as determined by a Lifestyle Criminality Screening Form (LCSF; Walters, White, & Denney, 1991) score of 7 or greater, or a history of two or more prior episodes of drug abuse treatment or drug-related convictions. Individuals meeting the eligibility criteria were invited to enroll in the study. All participants volunteered and participated in the informed consent process.

The majority of participants were male (85.21%, n = 409), on average 33.52 years old (SD = 8.60, range = 18-59 years), and most identified as Black (51.87%, n = 273) or White (23.75%, n = 114). Regarding criminal justice involvement, 55.83% (n = 268) presented with a prior history of five or more arrests and approximately half were arrested for the first time between ages 14 and 17 years (52.92%, n = 254), whereas the other half were arrested for the first time at 18 years and older (29.17%, n = 140) or 13 years and younger (17.92%, n = 86). Of the sample, 57.29% (n = 275) were deemed high risk for reoffending, as compared with moderate (22.50%, n = 108) or low (20.21%, n = 97).

Procedure

Each parolee signed the informed consent document, and trained interviewers conducted a structured interview at the time of the initial supervision appointment. Three months after the initial appointment, a structured follow-up interview was facilitated by a trained research assistant. Participants responded to a series of items related to the quality of the relationship with their parole officers. At the same time, participants were asked about reported and self-reported criminal behaviors and supervision violations, as well as substance use, via the timeline follow-back method (R. A. Brown et al., 1998; Roberts & Horney, 2010). A third structured interview was conducted 9 months after the initial appointment where participants again self-reported detected and undetected criminal behaviors and supervision violations, as well as substance use, via the timeline follow-back method. Official criminal justice data (e.g., arrests, technical violations) were also obtained from the agency. Participants received a US$20, US$40, and US$60 honorarium, respectively, upon completion of each interview. The study demonstrated a 94% retention rate by the 3-month follow-up and 86% by the 9-month follow-up (Friedmann, Rhodes, & Taxman, 2009).

The study was approved by the Institutional Review Board at all study sites, including the university, the Office of Human Rights Protection (OHRP), the CJ-DATS Steering Committee, and the NIDA Data and Safety Monitoring Board.

Measures

Measures for Study 1 comprised (a) the PJM administered at the 3-month follow-up, (b) criminal justice outcomes at the 9-month follow-up, and (c) case characteristics collected at the time of enrollment.

Criminal Justice Outcomes

Self-reported crime and technical parole violation data were collected at the 3- and 9-month follow-up interviews using the event calendar approach (R. A. Brown et al., 1998; Roberts & Horney, 2010). Trained interviewers asked participants to identify retrospectively on a calendar the days they used illegal substances, were arrested, committed a crime, or violated a parole condition (Sobell & Sobell, 1992). For criminal activity, participants were provided with a list of 25 offenses (e.g., robbery/attempted robbery/mugging, shoplifting/larceny/embezzlement, weapons offenses) and asked to self-report involvement in or arrest for each specific offense. Dichotomous variables were created to represent whether the participant abused illegal substances (1 = yes, 0 = no), self-reported committing a crime or being arrested for a crime (1 = yes, 0 = no), or violated another condition of supervision (1 = yes, 0 = no) during the 9-month follow-up period. Official arrest records were also obtained and a fourth dichotomous variable represented whether the participant was arrested (1 = yes) or not (0 = no) during the follow-up period. In the Study 1 sample, 48.21% of participants used an illegal substance, 39.24% accrued a technical violation (nondrug), 43.52% self-reported a crime or arrest, and 32.33% had record of an official arrest.

Case Characteristics

Study 1 controlled for the following three background characteristics that prior research has suggested may influence reoffending or procedural justice perceptions: age in years (Beijersbergen et al., 2016; Camp, Gaes, Langan, & Saylor, 2003; Reisig & Mesko, 2009), race/ethnicity (White = 1, Other = 0; Beijersbergen et al., 2015), and education in years (Reisig & Mesko, 2009); the background characteristics are as collected at the time of enrollment. The LCSF (Walters et al., 1991) was used to assess criminal justice risk in Study 1. The LCSF is a 17-item instrument scored via file review, capturing four dimensions of a criminal lifestyle: (a) irresponsibility, (b) self-indulgence, (c) interpersonal intrusiveness, and (d) social rule-breaking (Walters et al., 1991, 1998). Scores range from 0 to 22, with scores of 6 and below considered low risk for reoffense, scores of 7 to 9 considered moderate risk, and scores 10 and above considered high risk. Interrater reliability is well established (.81-.96; Walters, 2005), and studies find the LCSF demonstrates moderate accuracy in predicting rearrest (Walters, 1998). In the current study, the total score on the LCSF was used to control for participant criminal justice risk. Participant scores on the LCSF were calculated at the time of enrollment.

Analytic Plan

Guided by previous methodology (e.g., Wnuk, Chapman, & Jeglic, 2006), item intercorrelations were first examined to assess the need to remove poorly correlated items. Items were then subjected to a principal components factor analysis with varimax rotation. Internal consistency reliability was calculated using Cronbach’s α and test–retest was calculated using Pearson correlations (Leech, Barrett, & Morgan, 2005). To determine whether the derived measure predicted criminal justice outcomes, correlations between the measure and outcomes were first calculated. Logistic regression analyses were then used to examine predictive accuracy after controlling for criminal justice risk. Receiver Operating Characteristic (ROC) analyses were then calculated to control for differences between the base rate and the selection ratio (Rice & Harris, 1995). If the resulting PJM could distinguish recidivists (both crime and parole violations) from nonrecidivists, it would demonstrate a ROC area under the curve (AUC) approaching 1.0 (Mossman, 1994). An AUC greater than .70 is considered a good level of accuracy (Blasko & Hiller, 2014; Nicholls, Ogloff, & Douglas, 2004).

Results

Item intercorrelations among the initial item pool are shown in Table 2. Of the 10 items, nine were significantly correlated; one item, “My PO makes a lot of exceptions to the rules,” was not. Additionally, two items (“I feel that my PO has favorites and they get special treatment” and “My PO does not use rewards like I was told they would be used”) were weakly related with other items. As a result, the three items were removed prior to conducting principal components analysis.

Table 2:

Item Intercorrelations: Initial Pool of Items (N = 480)

Item		1	2	3	4	5	6	7	8	9	10
1.	My PO listens to my side of the story when I miss an appointment, have a positive urine, or have other problems with supervision rules.	—
2.	My PO follows clear guidelines when he or she has to punish me for breaking rules.	.44**	—
3.	My PO works with other agencies to get the services I need.	.60**	.50**	—
4.	I feel my PO’s sanctions and punishments are what I should get.	.47**	.38**	.52**	—
5.	My PO makes decisions about how to handle problems in a fair way.	.64**	.53**	.66**	.54**	—
6.	I feel that my PO treats me like others on supervision.	.44**	.40**	.41**	.33**	.47**	—
7.	My PO makes a lot of exceptions to the rules.^a	−.08	.04	−.02	.04	−.01	.02	—
8.	I feel that my PO has favorites and they get special treatment.^a	.26*	.27*	.26*	.25*	.33**	.28*	.29*	—
9.	My PO often makes up his or her own rules.^a	.37**	.35**	.39**	.29*	.49**	.33**	.20	.55**	—
10.	My PO does not use rewards like I was told they would be used.^a	.18	.14	.15	.15	.22*	.20	.12	.27*	.37**	—

Note. PO = Probation/Parole Officer.

Reverse-scored item.

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

Principal components analysis revealed one factor with an eigenvalue greater than 1, accounting for 64.6% of the variance. The factor consisted of seven items and was labeled procedural justice (see Figure 1). The Cronbach’s α was examined to determine the internal consistency of the PJM; it demonstrated an acceptable level (α = .75; Cronbach, 1951; Devellis, 2003). As shown in Table 2, mean values for the seven items making up the PJM ranged from 4.31 to 6.23, with a possible range from 1 to 7. SDs ranged from 1.47 to 2.32, indicating adequate variability for the seven items.

Figure 1:

Factor Loadings and Cronbach Alpha for Items on the PJM

As shown in Table 3, a series of correlations between the total score on the PJM and criminal justice outcomes showed statistically significant relationships between the PJM and each outcome. Pearson’s r correlation coefficients were similar for outcomes of substance use (r = .18, p = .032), technical parole violations (r = .17, p = .006), self-reported crime (r = .19, p = .033), and official arrest (r = .22, p = .007). Lower scores on the PJM were associated with better supervision outcomes.

Table 3:

Correlations Between PJM and Criminal Justice Outcomes by Study

	Outcome
	Substance use	Technical violation (nondrug)	Self-reported crime/arrest	Official arrest
PJM
Study 1	−.18*	−.17**	−.19*	−.22**
Study 2	ns	−.28**	−.28**	−.29**

Note. Outcomes for Study 1 were assessed at nine months, whereas outcomes for Study 2 were assessed at 1 year. N of Study 1 = 480, N of Study 2 = 226. PJM = Procedural Justice Measure; ns = not statistically signficant.

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

After controlling for case characteristics and intervention study condition, logistic regression analyses showed the PJM significantly predicted outcomes of substance use (B = .14, p = .031), technical parole violations (B = .14, p = .030), self-reported crime (B = .11, p = .034), and official arrest (B = .13, p = .031) at the 9-month follow-up. ROC curve analyses computed using the same variables showed that the views of individuals under community supervision with regard to their relationships with their parole officers were better than chance at predicting each of the four outcomes (substance use AUC = .63, technical parole violations AUC = .68, self-reported crime AUC = .68, official arrest AUC = .69) at the 9-month follow-up.

Study 2: Cross-Validation of the PJM

Participants

Study 2 participants were 226 drug-involved individuals sanctioned to probation who enrolled in a randomized clinical trial (RCT) designed to test the efficacy of a seamless model of substance abuse treatment for probationers. Participants were recruited by probation officer referral at three probation sites within the Maryland Division of Probation and Parole. Inclusion criteria were (a) at least 18 years of age, (b) English speaking, (c) sanctioned to probation supervision, and (d) substance-use treatment as a condition of probation. Participation was voluntary. All participants were assessed at baseline and 3 months, and participated in a follow-up interview at 12 months postenrollment. Participants were administered the PJM as part of the 3-month interview. Participants received a US$40 honorarium for each completed interview. All study procedures were approved by both the affiliated Institutional Review Board and the criminal justice agency.

Of the 415 probationers assessed for eligibility, 110 (26.51%) did not meet inclusion criteria, 10 (2.41%) declined to participate, and 44 (10.60%) did not participate for various other reasons. As such, 251 participants were randomized into the treatment (n = 127) and control (n = 124) groups. Of the 251 participants, recidivism data were available for 226 (90.04%). The administrative arrest records of 25 participants were not provided by the agency. There were no significant demographic differences between the 226 participants with official records available and the 25 participants who did not. The final sample for analysis comprised 226 participants. Of the participants, the majority were male (73.45%, n = 166) and Black (68.14%, n = 154). Participants were on average 36.83 (SD = 11.54, range = 18-64) years of age at the time of enrollment.

Measures

For Study 2, measures included (a) the PJM derived from Study 1, which was administered at the 3-month follow-up; (b) criminal justice outcomes at the 12-month follow-up; (c) case characteristics collected at the time of enrollment; and (d) the DRI-R (Skeem et al., 2007), which was administered at the 3-month follow-up and used to assess convergent validity.

Criminal Justice Outcomes

Criminal justice outcomes in Study 2 included substance use, self-reported arrest/crime, official arrest data, and other technical violations (nondrug) during the 12-month follow-up period. During the follow-up interviews, as with Study 1, trained interviewers asked participants to identify retrospectively on a calendar the days they used illegal substances, were arrested, committed a crime, or violated a supervision condition (Sobell & Sobell, 1992).

Dichotomous variables were created to represent whether the participant abused illegal substances (1 = yes, 0 = no), self-reported committing a crime (1 = yes, 0 = no), or violated another condition of supervision (1 = yes, 0 = no) over the 12-month follow-up period. Official arrest records were also obtained and a fourth dichotomous variable represented whether the participant was arrested (1 = yes) or not arrested (0 = no) during the 12-month follow-up period. In the Study 2 sample, 42.32% of participants used an illegal substance, 35.20% accrued a technical violation (nondrug), 44.51% self-reported a crime or arrest, and 30.24% had record of an official arrest.

Case Characteristics

Consistent with Study 1, Study 2 controlled for the following background characteristics as collected at the time of enrollment: age in years, race/ethnicity (White = 1, Other = 0), and education in years. Criminal justice risk was also controlled as assessed at enrollment by a six-item measure shown to be predictive of future reoffense (Austin, 2006; Wooditch, Tang, & Taxman, 2014).

The DRI-R

The DRI-R (Skeem et al., 2007) measures the quality of the relationship between the supervising officer and the supervisee from the perspective of the parolee. The DRI-R is a 30-item instrument used to assess three relationship dimensions: caring/fairness, trust, and toughness. Items are rated on a 7-point Likert-type scale (1 = never, 2 = rarely, 3 = occasionally, 4 = sometimes, 5 = often, 6 = very often, and 7 = always). The reliability and validity of the DRI-R has been demonstrated with probationers (Kennealy, Skeem, Manchak, & Eno Louden, 2012; Skeem et al., 2007); the DRI was reliable in the current sample (Cronbach’s α = .91). The dimensions demonstrated acceptable levels of internal consistency reliability; coefficient alphas ranged from .86 to .91 (caring/fairness α = .91, trust α = .89, toughness α = .86; Cronbach, 1951; Devellis, 2003). In the current study, of interest was the rating of the relationship by the supervisee at the 3-month follow-up.

Results

In Study 2, mean values for the seven PJM items ranged from 4.22 to 6.13 (possible range = 1-7); the items demonstrated factor loadings (range= .58-.82) that were consistent with Study 1 results (range = .53-.86).

As shown in Table 3, a series of correlations between the total score on the PJM and criminal justice outcomes showed statistically significant relationships between the PJM and three of the four outcomes. Pearson’s r correlation coefficients were similar for outcomes of self-reported crime (r = .28, p = .004).² official arrest (r = .29, p = .004), and other parole violations (r = .28, p = .004).2 Lower scores on the PJM were associated with improved outcomes. Substance use, however, did not prove significantly correlated with the PJM in this sample.

After controlling for case characteristics and intervention study condition, logistic regression analyses showed the PJM was significantly related to self-reported crime (B = .13, p = .042), official arrests (B = .14, p = .033), and other parole violations (B = .11, p = .041) over the 12-month follow-up. The PJM was not significantly related to substance use. ROC curve analyses computed using the same variables showed parolee-endorsed views of procedural fairness with regard to the relationship with their parole officer were better than chance at predicting three of the four outcomes (self-reported crime AUC = .69, official arrest AUC = .69, and other parole violations AUC = .66) over the 12-month follow-up.

Examining within-rater types, results showed the PJM and DRI-R were moderately associated. Specifically, ratings on the DRI-R were moderately (r = .46) correlated with PJM ratings, suggesting that the PJM taps something that is related to, but not the same as, the relationship features purportedly captured by the DRI-R. Furthermore, PJM ratings were moderately correlated with each of the three DRI-R subscales of Caring (r = .33), Fairness (r = .43), and Toughness (r = −.38).

Discussion

The working alliance between criminal justice involved individuals and correctional staff is highlighted as a key contributor to positive outcomes in the theoretical framework of dominant criminal justice intervention models (see Andrews, 2011 and Dowden & Andrews, 2004). But existing frameworks offer no consistent guidance about how best to measure this relationship for the purpose of improvement. One approach is to model the general psychotherapy literature with an emphasis on the goals, tasks, and bonds, but this does not capture the context of a correctional supervision setting where punishment is inevitably delivered. Another approach is shaped by legitimacy and procedural justice theories in justice-related settings. However, procedural justice in the traditional sense (see Jackson et al., 2010)—a construct proven important to interactions and outcomes in other criminal justice settings—is not adequately captured in relationship measures commonly recommended for community supervision settings (e.g., DRI-R, WAI). Relying on findings from both approaches, the current studies refined and validated a measure (referred to as the PJM) that incorporates procedural justice and working alliance elements pertinent to day-to-day interactions in community supervision settings. Overall, findings showed when individuals under community supervision perceived that their supervising officers used practices associated with procedural fairness, they were less likely to engage in criminal behavior or violate supervision rules.

As expected, Study 1 revealed an interpretable, internally consistent measure of procedural justice. The items are theoretically coherent and consistent with procedural justice items outlined by other criminal justice studies (e.g., Sunshine & Tyler, 2003; Tankebe, 2013). Examples include “My PO makes decisions about how to handle problems in a fair way” and “I feel that my PO treats me like others on supervision.” The resulting measure consisted of seven items, which is short and practical for use in supervision settings. The measure could feasibly serve as means to leverage and secure the cooperation of individuals under their supervision, but at the same time achieve positive outcomes from the viewpoints of the individuals under community supervision. It is noteworthy that results were consistent across the two studies with regard to factor loadings of the PJM items and correlations between the PJM and outcomes of self-reported and official crime and technical violations.

The PJM diverged slightly from items that to date have emerged as important in procedural justice research in police and courts contexts. The item “I feel my PO’s sanctions and punishments are what I should get,” an item that aligns with distributive justice theory (i.e., outcomes as important rather than process) rather than procedural justice theory, proved important in the development of the PJM in community corrections settings. However, scholars focused on the theoretical development of legitimacy in corrections settings (Bottoms, 1999; Jackson et al., 2010; Tyler, 2010) have not only hypothesized that procedural justice items may differ with policing research (Tyler, 2006b) but have specifically hypothesized that outcomes may be equally as important as process components (for a comprehensive discussion, see Bottoms, 1999). In community corrections settings, where interactions are ongoing and where individuals interact with each other, it is not surprising that supervision officer responses and use of sanctions/punishments proved relevant and statistically significant. It is likely that these outcomes in the ongoing supervision process are similar to those aligned with the goals and tasks of working alliance theory. Results may also differ because, in the context of ongoing supervision, “outcomes” resulting from violations (e.g., positive urinalysis, technical violation) may be seen as part of the supervision process, with the ultimate outcome being completion or failure of supervision.

Findings showed that when the community supervision process was perceived as procedurally fair, individuals under community supervision demonstrated positive criminal justice outcomes, that is, less self-reported criminal behavior, fewer official arrests, and fewer technical parole violations. And, in Study 1, the PJM also predicted less illegal drug use. In Study 2, included for the purpose of cross-validation, findings were consistent with regard to criminal behavior and supervision violations but not illegal drug use. That is, the PJM predicted substance-use outcomes in Study 1, but this did not hold up in Study 2. This may be because the Study 2 population was more homogeneous (concentrated drug users) as compared with Study 1 with regard to prevalence of substance abuse disorders. For the supervisees who are substance abusers in Study 2, it might be that PJM interacts with the type of treatment or other services provided to the person which was not measured in the current study. It is also possible that substance use is related differently to procedural justice as compared with crime or criminal behavior. The relationship between substance use and viewpoints of procedural justice among primary substance users involved in the criminal justice system may be more complex than the criminal behavior-procedural justice relationship. Perhaps this is because mental health factors are differentially impacted by procedural justice and relationship dynamics; however, further research is needed in this area.

Examining the correlations between the DRI-R (Skeem et al., 2007) and the newly developed PJM showed the two were related, but not tapping the same construct. In their development of the DRI-R, Skeem and her colleagues (2007) found that the items tapping goals and tasks were deleted from their analysis. They conclude this is likely because these items are only relevant in psychotherapy settings, where the power imbalance is not present, and clients and therapists can agree with treatment decisions. This study confirms that PJM measure may better capture the underlying aspects of goals and tasks in settings where the probation officer is an authoritarian figure, and therefore, there are aspects of the probationer’s behavior which do not allow the individual officer to have discretion in how to address compliance-related issues. The dual relationship issue is difficult in coerced treatment settings which is what the DRI-R attempts to capture. The current measure suggests it is possible to agree on goals and tasks of supervision as long as the officer is perceived as fair by the supervisee in his or her decision-making.

The current study makes further theoretical contributions to the study of procedural justice in criminal justice settings, particularly community justice. Findings showed procedural justice, as measured by the PJM, is related to criminal justice outcomes of official arrests, as would be expected from existing procedural justice studies in police studies. This is the first study that we know of to explore the connection between procedural justice and criminal justice outcomes in a community supervision setting.

With regard to implications for practice, the PJM tool offers an easy tool for community supervision settings to gauge the experience of supervisees. It may be useful to think of the PJM as measuring perceptions of the working alliance and discretion used by officers. At the individual level, supervision officers can use the results to gauge the degree to which the individuals under supervision are coping with supervision (including their own use of discretion). For example, understanding an individual’s perceptions of the supervision process may provide a mechanism for the officer to intervene before the individual violates supervision (e.g., uses drugs). Another key consideration is that absence of violations is not necessarily indicative of positive postsupervision outcomes. Of the individuals who comply with supervision conditions, officer instructions, and subsequently complete supervision successfully, it is unlikely that everyone will perceive the supervision process and the criminal justice system as legitimate or acceptable (Jackson et al., 2010). For example, individuals under supervision may follow rules for secondary gains, such as gaining decreased supervision levels or approval of valued privileges. Beyond successful completion of supervision requirements, understanding whether individuals perceived the process as fair has implications for long-term compliance and acceptance of authority figures. Community corrections supervisors and administrators can use the PJM to provide valued feedback to officers and intervene in potentially troublesome cases. For example, supervisors can use the information to assess training needs. In fact, this would be analogous to clinical settings where clinical supervisors collect client measures of the therapeutic alliance to assess therapist effectiveness to inform the clinical supervision and feedback provided to the clinicians under their management.

At the aggregate level, the PJM can be used to measure the overall agency environment. The supervision climate and culture affect the implementation of evidence-based practices because perceptions that supervision officers wield unfair discretion may undermine the emphasis on the use of quality practices and programming. It may also affect the outcomes of individuals under supervision of the agency. As noted by Andrews and Bonta (2010), environments that are not individual-centered and do not support procedural fairness are unlikely to be successful in implementing evidence-based practices. The PJM offers a short tool to measure the office environment too that then can be used as part of conceptualizing how change should occur. Collectively, at the officer or office level, procedural justice provides a framework to assess whether supervision practices are fair, a critical component of any justice organization that seeks to emphasize fair treatment and quality practices.

Limitations and Conclusion

When interpreting the findings, there are some potential limitations to consider. First, although the individuals in both samples were recruited from several sites, it is possible that they are not representative of all community supervision populations. Second, in the current study, we did not consider characteristics of the officers. In psychotherapy settings, some therapist characteristics, including therapist gender (Kiesler & Watkins, 1989; Persons, Persons, & Newmark, 1974; Wintersteen, Mensinger, & Diamond, 2005) and age (Connors et al., 2000), have been found to correlate with client perceptions of the relationship. An important next step would be to examine whether officer characteristics play a role in the perceived working relationship. Third, the item response set for the PJM (1 = never to 7 = always) was modeled after the WAI (Horvath & Greenberg, 1989). Therefore, by design, the PJM measures frequency of community supervision officer actions within the seven PJM items as perceived by the supervisee. Future investigations would likely make advances by testing the differential influence of question focus, such as satisfaction with an action rather than frequency. For example, Tankebe’s (2009) legitimacy, procedural fairness, and distributive fairness measures in the context of policing used a Likert-type response set of 1 = strongly disagree to 5 = strongly agree. Another consideration is that the PJM was created as a simple additive index to help practically for scoring by community supervision officers. Future research may consider whether predictive accuracy improves when the items that make a unique contribution are given more weight in scoring.

Procedural fairness is an important construct to consider within community supervision given the discretion afforded to community supervision officers. And it is important given the concerns about the legitimacy of justice actors in making unbiased decisions. Moving forward, more research is needed to better understand the conditions where individuals under community supervision not only comply with supervision requirements but at the same time perceive that their supervising officers handled situations with fairness. The PJM developed as part of the current study can be used by others to better understand how individuals experience community supervision and the culture of supervision. This important construct can add to our understanding of organizational factors that affect individual-level outcomes in community supervision settings.

Footnotes

Data used here were collected under a cooperative agreement from the National Institute on Drug Abuse, National Institutes of Health (NIDA/NIH), with support from the Center for Substance Abuse Treatment, SAMHSA; the Centers for Disease Control and Prevention (CDC); the National Institute on Alcohol Abuse and Alcoholism; and the Bureau of Justice Assistance of the U.S. Department of Justice, and supported by Grants 5U01DA016191, 5U01DA016213, 5U01DA016230, 5U01DA016194, and 5U01DA016211.

Notes

Brandy L. Blasko is assistant professor of criminal justice and criminology at Sam Houston State University. She received her PhD from Temple University’s Department of Criminal Justice and completed a postdoctoral fellowship at George Mason University. Her research focuses on decision-making, discretion, and interpersonal relationships within the context of the criminal justice system with specific emphasis on institutional and community corrections systems.

Faye S. Taxman is university professor in the Department of Criminology, Law, and Society and director of the Center for Advancing Correctional Excellence at George Mason University. She is recognized for her work in probation and community corrections, including the development of seamless systems of care models that link criminal justice systems with other service delivery systems.

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