Examining the Personal and Perceived Organizational Characteristics Associated With Juvenile Probation Staff Job Satisfaction

Abstract

Satisfied employees are essential to an organization, as they are often the primary means for meeting organizational needs. Job satisfaction is particularly important among criminal justice agencies, specifically probation agencies that largely rely on personnel for the supervision and rehabilitation of individuals. Yet, the correlates of job satisfaction among juvenile probation staff are largely unknown. Following organizational climate theory, the current study utilizes baseline data from the Juvenile Justice–Translational Research on Adolescents in the Legal System (JJ-TRIALS) initiative, a project conducted in seven states with 36 participating juvenile probation agencies. Factor analysis and structural equation modeling were utilized to examine the latent structure of organizational characteristics and potential mediating effects. Regression analyses were utilized to examine the direct relationship between job satisfaction and personal and organizational factors. Results highlight the importance of workplace factors and suggest efforts toward improving job satisfaction should focus on the improvement of organizational characteristics.

Keywords

job satisfaction juvenile probation organizational climate theory factor analysis

Introduction

Since 2005, the majority of Americans have been unsatisfied with their jobs (Morris, 2017). Consequences of dissatisfaction include negative organizational outcomes such as higher turnover rates and feelings of burnout, anxiety, and depression. Accordingly, job satisfaction (JS) has been of interest to researchers and practitioners alike. Within the criminal justice field, state and federal prisons have invested a great deal of time and money exploring the correlates of JS among correctional officers. This effort is fueled by the costly outcomes associated with job dissatisfaction, including turnover, turnover intention, and the mental and physical health of employees. This is of interest to probation agencies as well, which rely on personnel, rather than machinery or computers, to accomplish day-to-day tasks and larger organizational objectives. Turnover is a serious issue for probation agencies that report anywhere from 15% to 24% of staff leaving their job each year (Simmons et al., 1997; Texas Juvenile Probation Commission [TJPC], 2003). JS is also essential to organizations because staff morale is good for business. Workers who are more satisfied are less likely to leave or have thoughts of leaving their job, feel less burnt out and stressed, and are more likely to perform well at work. This is imperative for juvenile probation staff who are tasked with the supervision and rehabilitation of at-risk youth.

In 2015, nearly half of all delinquency cases received a sanction of probation, resulting in approximately 294,800 youths on probation (Hockenberry & Puzzanchera, 2018). This is not surprising as juvenile probation is the oldest and most commonly used disposition, being utilized at both the “front-end” of the juvenile justice system for low-risk youth and the “back-end” as an alternative to incarceration for more serious charges (Torbet, 1996). In some instances, communities may even use probation on a voluntary status to monitor at-risk youth, in lieu of formal adjudication. It has even been termed the “workhorse of the juvenile justice system” with probation staff often referred to as the “heart” of the juvenile justice system (Torbet, 1996).

Generally speaking, juvenile probation staff are responsible for conducting intake screenings, investigating predisposition or presentence youth, and supervising youth. Compared with adult probation staff, juvenile probation staff have unique job demands, such as the coordination of multiple entities like parents/guardians, teachers, treatment agencies, and medical providers (Steiner et al., 2004). In some instances, these functions are performed while managing large caseloads (Torbet, 1996). In addition, juvenile probation officers (JPOs) are responsible for addressing the behavioral health needs of their clients. This is a relatively demanding task as an estimated 60% to 70% of justice–involved youth experience mental health and/or substance use–related problems (Fazel et al., 2008).

Similar to most correctional research, however, the majority of studies on JS have focused on institutional corrections staff (e.g., Cheeseman et al., 2011; E. G. Lambert et al., 2005) and, to a lesser degree, adult probation staff (Getahun et al., 2008; Jiang et al., 2016; Leonardi & Frew, 1991; Simmons et al., 1997). Although juvenile probation has received more attention in recent years, few, if any, studies have specifically focused on JS among juvenile probation staff. Given the differences in responsibilities, environment, and management between adult and juvenile probation agencies, this is problematic. Although adult and juvenile probation agencies have similar objectives in regard to the supervision of justice-involved individuals, it is clear that the experiences of juvenile probation staff and the climate in which juvenile probation staff operate are very different from their adult counterparts.

To this end, the current study builds on prior research by assessing the correlates of JS among a sample of juvenile probation staff who participated in a Juvenile Justice–Translational Research on Adolescents in the Legal System (JJ-TRIALS) project. Using data from 2015–2016, the relationship between JS and individual and organizational factors is examined. The potential indirect relationships between organizational factors and JS are also assessed.

Theoretical Framework

Organizational Climate Theory (OCT)

OCT is a long-standing theory utilized within the JS literature (Payne et al., 1976). OCT postulates that organizational and social variables that compromise one’s working environment impact workers’ attitudes toward their jobs. Organizational climate is most commonly defined and measured by workers’ perceptions of their environment, including characteristics of the organization and relationships with other people. JS is often identified as an important variable in organizational climate research and an important topic within correctional literature. More specifically, organizational climate is “the overall meaning derived from the aggregation of individual perceptions of a work environment (i.e., the typical or average way people in an organization ascribe meaning to that organization)” (James et al., 2008, p. 15). Therefore, organizational climate is a concept derived from individual employees. Juvenile probation staff may obtain a sense of climate as soon as entering the organization through things such as physical appearance, the attitudes of other officers, and the treatment of clients and new employees. Climate is a description of what workers see and experience happening to them when, in an organizational situation, this may include an employee’s perception about policies, practices, procedures, and/or rewards (Rentsch, 1990).

OCT has received a wealth of support both within the field of criminal justice and across other occupational domains. To date, correctional research has appraised the work environment through a variety of characteristics ranging from negative aspects such as job stress (Blau et al., 1986; E. G. Lambert, 2004) and role conflict (E. G. Lambert & Paoline, 2008) to positive facets like peer support (Cheeseman et al., 2011), communication (Getahun et al., 2008), organizational support (Armstrong & Griffin, 2004), and many others. Multidimensional measures have been employed to examine features of supervisory support, organizational structure, training, and fear of victimization, among others (Griffin, 2001). Comprehensive or global measures have also been used but with less frequency. For example, Jurik and Halemba (1984) examined the association between JS and a global measure of climate they referred to as “perceived working conditions.” This measure included topics on advancement opportunities, the variety and authority on the job, influence with regard to policy matters, and opportunities to increase one’s knowledge and skills. Stinchcomb and Leip (2013) also utilized a general measure reflecting jail employees’ overall feelings about the workplace. While global measures offer some insight into these relationships, multidimensional measures allow for a more detailed understanding of what specific climate domains are most relevant when studying job attitudes.

The relationship between organizational context and JS among probation staff has yet to be thoroughly explored. However, job-related stress is a common aspect of organizational context studied within a correctional setting. More specifically, a number of probation studies have examined perceptions of job stress as an indicator of organizational context (e.g., Pitts, 2007). Lacking in probation literature is the measurement of additional climate domains, such as innovation and communication, which have been identified as key correlates of JS within institutional corrections settings (E. G. Lambert & Hogan, 2010; Matz et al., 2013).

Job Demands and Resource Model (JD-R)

More recently, research has examined whether the relationship between organizational climate and JS is mediated by feelings of stress. The JD-R provides the theoretical underpinning for examining these relationships. The JD-R theorizes that organizational demands, including role stressors such as role ambiguity and access to resources, are associated with employee health and motivation. Job demands refer to physical, psychological, or social aspects of the job that require sustained physical or mental effort and can, therefore, take a toll on the employee. Employees working in organizations with high job demands are more likely to feel stressed by these pressures. Consequently, feelings of stress are likely to mediate the relationship between other climate domains and an employee’s level of JS.

Conceptually similar to stress, workplace identity and role ambiguity have been identified as mediating the relationship between climate and JS (Cortini, 2016; Pecino-Medina et al., 2017). For example, Cortini (2016) found that workplace identity mediated the relationship between learning climate and JS among apprentices. Pecino-Medina and colleagues (2017) found a similar effect in their study of public administrators. The investigation of stress as a mediating variable is a fairly recent development of OCT, with only a few studies exploring this model. Currently, findings are limited by the global measurement of organizational climate. Further research is needed to parse out the specifics of this relationship. Research among probation staff has established that stress decreases JS (Getahun et al., 2008; Simmons et al., 1997), but there is a dearth of research examining the mediating effects of stress between organizational context and JS.

JS in Corrections

Juvenile probation can be a very difficult yet rewarding occupation, with unique challenges and responsibilities connected to working with youth (Steiner et al., 2004). Officers are often tasked with monitoring and assisting in rehabilitating youth who have entered the justice system. This can be made challenging by a lack of family engagement and struggles associated with treatment initiation and engagement. Job attitudes such as stress and satisfaction have been widely documented among institutional corrections officers, with less discussion focusing on job attitudes among community corrections staff. Interest in JS is often driven by the harmful and costly outcomes associated with job dissatisfaction, including turnover, turnover intention, lesser job performance, and the mental and physical health of employees (Faragher et al., 2005; Griffeth et al., 2000). It has been consistently documented that unsatisfied correctional staff display higher rates of turnover and turnover intention (Wells et al., 2016).

Turnover can be especially costly for correctional agencies, which rely almost exclusively on staff members to supervise youth and address their needs. In 2002, Texas reported that 15% of juvenile probation staff left their job (TJPC, 2003). This creates a number of direct and indirect expenses for agencies, including the cost of recruitment, testing, hiring, and training new employees. Attrition may break down communication between staff members and clients. Job dissatisfaction is also associated with several mental/psychological health concerns including burnout, anxiety, and depression (Faragher et al., 2005). This is particularly concerning for juvenile probation staff who encounter a variety of occupational strains, including large caseloads, role conflict, matching youth to appropriate treatment, and acting as a liaison between various institutions (Steiner et al., 2004; Torbet, 1996).

Absent from the literature is a thorough discussion of the factors associated with JS among juvenile probation staff. Identifying the correlates of JS can be helpful in addressing officer retention and boosting staff morale and job performance. Due to the lack of published work focusing on JS among juvenile probation staff, the literature reviewed primarily focuses on institutional corrections (juvenile and adult) and community corrections (probation/parole).

JS is influenced by a number of personal and work-related factors. To date, organizational climate has been identified as a key correlate of JS across various occupational domains. The majority of research on JS in corrections has largely focused on institutional correctional staff (Blau et al., 1986; Cheeseman et al., 2011; Griffin, 2001; E. Lambert & Hogan, 2009; E. G. Lambert et al., 2005; E. G. Lambert & Paoline, 2008). This is true for both staff who supervise adults (Armstrong et al., 2015; Jurik & Halemba, 1984) and juveniles (Blevins et al., 2006; Matz et al., 2013; Wells et al., 2016). Few studies have examined JS among community correctional staff (i.e., probation), with this limited number of studies primarily focusing on adult probation (Getahun et al., 2008; Jiang et al., 2016; Leonardi & Frew, 1991; Simmons et al., 1997).

The primary focus of correctional research regarding JS has focused on the influence of personal characteristics, such as race, age, tenure, and education (Byrd et al., 2000; Jurik & Halemba, 1984). Generally, these findings are mixed, with personal characteristics explaining little to no variance in JS. Findings are mixed regardless of correctional context (i.e., adult probation, prison, jail, or juvenile detention staff). For example, identifying as White has been found to have a positive, negative, and no relationship with JS among correctional staff (Blau et al., 1986; Simmons et al., 1997; Wells et al., 2016).

Organizational climate has received less attention within the correctional literature. Studies that examined both personal and organizational factors indicate that organizational factors are stronger predictors of JS (Cheeseman et al., 2011; Getahun et al., 2008; Griffin et al., 2010; E. Lambert & Hogan, 2009; Stinchcomb & Leip, 2013). For example, E. G. Lambert and colleagues (2002) found that stress is one of the most frequently examined variables in correctional research on job attitudes. Studies investigating the influence of job-related stress on JS among probation staff largely find a negative association (Getahun et al., 2008; Simmons et al., 1997; L. M. White et al., 2015). Other climate domains have been found to have a positive relationship with JS, including communication (E. G. Lambert et al., 2008; E. G. Lambert & Paoline, 2008; Matz et al., 2013), supervisory support (E. Lambert & Hogan, 2009; E. G. Lambert, 2004), and innovation and flexibility (E. G. Lambert & Hogan, 2010). Taken as a whole, research suggests that an organization’s climate as perceived by employees can significantly influence organizational outcomes and behaviors, such as job attitudes. However, these relationships have not been fully explored among probation staff.

Current Study

Following organization climate theory, empirical research strongly supports the assertion that the work environment is a salient factor related to correctional staff’s JS. However, a broad range of climate characteristics, including dimensions of quality, innovation and flexibility, and organizational support, are either understudied or entirely absent from correctional literature. Prior research has indicated the items of each organizational climate characteristic represent one latent factor, but factor analytic techniques have rarely been used in correctional literature examining organizational climate (Broome et al., 2009; Lehman et al., 2002; Patterson et al., 2005) and are advantageous, when possible, as it allows for estimation and removal of measurement error associated with observed variables (Ullman, 2006). Following the JD-R model, the possibility of mediating relationships warrants further attention, such as whether job-related stress mediates the relationship between organizational climate and JS. Mixed findings regarding the relationship between demographic characteristics and JS requires further examination to determine their influence while controlling for organizational context. Finally, it is imperative to explore these relationships among juvenile probation staff given the uniqueness of their job compared with other community and institutional correctional staff and the negative consequences of low JS. The current study seeks to address these gaps in the literature by accounting for a variety of organizational climate domains often overlooked in correctional research and by examining these relationships among juvenile probation staff, an understudied population.

Accordingly, the current study seeks to answer three interrelated research questions, specifically:

Research Question 1: Do the six measures assessing characteristics of organizational climate (i.e., agency innovation and flexibility, communication, agency quality, supervisory support, job-related stress) and JS represent seven distinct latent factors, respectively?

Research Question 2: Are the relationships between organizational climate factors and JS mediated by job-related stress?

Research Question 3: Are personal characteristics related to probation staffs’ JS? Do these relationships remain after accounting for organizational climate characteristics?

Hypotheses related to organizational climate characteristics are illustrated in Figure 1. Each arrow represents a hypothesized relationship. For example, agency communication is hypothesized to significantly and directly impact job-related stress; in turn, job-related stress is hypothesized to significantly and directly impact JS. Therefore, there is a hypothesized indirect relationship between agency communication and JS.

Figure 1:

Latent Variable Model of the Relationships Between Organizational Climate Characteristics and Job Satisfaction

Method

Data

Data for this study were collected in a National Institute on Drug Abuse (NIDA)–funded research project conducted in seven states. JJ-TRIALS was a 5-year implementation science initiative launched by NIDA in July 2013. Participating states included Texas, Mississippi, Florida, Georgia, Kentucky, Pennsylvania, and New York. Target sites within these states were juvenile justice agencies serving youth on probation under community supervision and associated behavioral health agencies. There were 36 participating juvenile justice sites at baseline. The project was designed to promote change among juvenile justice agencies and associated behavior health care providers and to evaluate the differential effectiveness of two service conditions in 36 sites. The current study focuses only on baseline data gathered from each of the 36 sites. To learn more regarding the research design of JJ-TRIALS, please see Knight et al. (2016).

The current sample comprised data collected in 2015–2016 from the Staff Survey–Juvenile Justice staff (henceforth referred to as the Staff Survey). All sites selected staff to participate in the survey during the baseline phase, as well as three follow-up surveys during the project period.¹ The Staff Survey asked questions regarding organizational characteristics, substance use treatment services, prevention efforts, decision-making, and interagency relationships. Given the focus of the current study, and its aim to assess the correlates of JS among juvenile probation staff, only juvenile justice participants were included in the current study. The selected measures build upon prior research incorporating both organizational climate and personal characteristics of staff. The final data set included a total of 454 probation staff.

Measures

All data were obtained from the Staff Survey and are therefore self-reported and represent perceptions of organizational climate. These Likert-type style questions had four to five response options. Questions with five response options ranged from strongly disagree (coded as 1) to strongly agree (coded as 5). Questions with four response options include definitely false (coded as 1), mostly false, mostly true, and definitely true (coded as 4). All negatively worded items were reverse coded so high values indicated a favorable response for each item. A full list of organizational climate and JS items can be found in Appendix Table A1.

Dependent Variable – Job Satisfaction

JS is theorized to be a latent construct consisting of six items originating from the Survey of Organizational Functioning (SOF). Comprised of six Likert-type style questions, response options ranged from strongly disagree to strongly agree. Prior research has documented the unidimensionality of these items (Broome et al., 2009), as well as their internal consistency (Welsh et al., 2016).

Independent Variables

Job-Related Stress

Job-related stress is theorized to be a latent variable measured by four Likert-type style items that assess perceptions of pressure at the participant’s place of work. These items originate from the Texas Christian University (TCU) organizational readiness for change (ORC) instrument (Institute of Behavioral Research, 2009). The internal reliability and unidimensionality of this measure are documented by Lehman et al. (2002), as a subscale of organizational climate. In addition, scales from the ORC have demonstrated good reliability and validity across multiple studies (Lehman et al., 2012; Taxman et al., 2007; Welsh et al., 2016).

Agency Innovation and Flexibility

This expected latent variable comprised six Likert-type style items based on a subscale of organizational climate designed to assess the degree of innovation and flexibility within an agency (Patterson et al., 2005). Items in this measure are based on concepts of flexibility (King & Anderson, 1995) and innovation (West & Farr, 1990) that reflect readiness for change and innovation.

Agency Quality

Agency quality is theorized to be a latent variable measured by four Likert-type style questions. This measure seeks to address the quality of procedures within an organization. The internal reliability and unidimensionality of this subscale have been demonstrated in prior research (Patterson et al., 2005).

Agency Communication

Originating from the TCU ORC, agency communication is postulated to be a latent variable measured by six Likert-type style items (Lehman et al., 2002). The measure gauged perceptions of the climate of communication within the organization.

Organizational Support

Perceived organization support assessed how organizational effectiveness is felt to be promoted. The eight-item short form (Eisenberger et al., 1986) was utilized in the current study. Prior studies provide evidence of internal consistency and unidimensionality of this measure based on the Survey of Perceived Organizational Support (SPOS; Eisenberger et al., 1986).

Supervisory Support

Supervisory support was originally theorized to be a latent construct based on four Likert-type style items. Items used a common stem referring to “My supervisor” and asked about specific elements of that person’s behavior. For example, items ask respondents whether their supervisor is respectful in handling staff member’s mistakes and whether he or she encourages others’ ideas. However, as the items were so highly correlated, the one item that was most highly correlated with the other three items was selected to represent supervisory support to avoid issues with multicollinearity. That is, “My supervisor acknowledges creative solutions to problems.”

Personal Characteristics

A total of nine variables capturing the personal characteristics of probation staff were included in the current study: gender, ethnicity, race, age, level of education, current job level, years of experience, tenure with current employer, and caseload. Gender (0 = male, 1 = female), ethnicity (0 = non-Hispanic, 1 = Hispanic), and race (0 = non-White, 1 = White) were coded dichotomously. Age was coded as a continuous variable ranging from 23 to 67 years. Level of education refers to the highest degree completed (1 = high school diploma or equivalent; 2 = some college but no degree; 3 = Associate’s degree; 4 = Bachelor’s degree; 5 = Master’s degree; 6 = Doctoral degree or equivalent). Current job level, due to lack of variation and somewhat similar level of positions (e.g., probation officer and counselor have a similar level of authority), was treated as a dichotomous variable (0 = non-JPO; 1 = JPO).²

Respondents were also asked to indicate how many years and months of experience they had working with youth. This was recoded and summed into a single, standardized continuous measure of experience in years. Respondents also reported tenure with their current employer in years and months. Finally, caseload was originally captured as a continuous variable referring to the number of youth under the respondent’s supervision. However, nearly a third (31%) of respondents indicated they were not supervising any youth at the time the survey was administered. Due a lack of variability, caseload was coded as a dichotomous variable (0 = no caseload, 1 = caseload). Table 1 provides a description of the current sample.

Table 1:

Descriptive Statistics of Juvenile Probation Staff (n = 477−492)

Variable	M or frequency (%)	SD	Mdn	Min/max	α
Personal characteristics
Race/ethnicity				0−1	—
White	354 (72.0)	—	—	—	—
Hispanic	59 (12.0)	—	—	—	—
Gender				0−1
Male	201 (40.9)	—	—	—	—
Female	290 (59.1)	—	—	—	—
Age	41.6	9.68	41	23−67	—
Current job—JPO	294 (60.6)	—	—	0−1
Education				1−7
No HS diploma or equivalent	—	—	—	—	—
HS diploma or equivalent	2 (0.4)	—	—	—	—
Some college, but no degree	8 (1.6)	—	—	—	—
Associate’s degree	7 (1.4)	—	—	—	—
Bachelor’s degree	299 (61.0)	—	—	—	—
Master’s degree	161 (32.9)	—	—	—	—
Doctoral degree or equivalent	11 (2.2)	—	—	—	—
Other	2 (0.4)	—	—	—	—
Caseload	331 (68.3)	—	—	0−1	—
Experience	15.20	8.59	14.50	0−44.08	—
Tenure	12.00	8.17	10.75	0−39.92	—
Organizational characteristics^a					—
Job satisfaction	3.94	0.64	4.0	1−5	.81
Stress	3.46	0.86	3.5	1−5	.86
Innovation & flexibility	2.85	0.58	2.8	1−4	.89
Quality	3.22	0.55	3.3	1−4	.81
Communication	3.06	0.81	3.2	1−5	.86
Organizational support	2.85	0.56	2.9	1−4	.89
Supervisory support	3.93	0.86	4.0	1−5	—

Note. JPO = juvenile probation officer; HS = high school.

For ease of interpretation, organizational climate characteristics are displayed as summated scores.

Analyses

Analyses for the current study were carried out in several steps. First, confirmatory factor analysis (CFA) was used to examine the latent structure of organizational climate characteristics and JS, utilizing Bayesian estimation.³ Preliminary CFA suggested issues of multicollinearity within three constructs. Therefore, a second, and final, CFA was run after the most highly correlated item(s) were removed.⁴ Second, the structural equation model (SEM) illustrated in Figure 1 was tested to examine mediating effects. As discussed in the “Results” section, the psychometric analysis results indicated a latent variable modeling approach was not feasible with the data set. Accordingly, a path analysis was utilized to examine mediating effects between organizational climate measures, job-related stress, and JS.

Third, maximum likelihood (ML) regression models were estimated to examine the relationship between JS and personal and organizational characteristics. Multivariate analyses were conducted using additive models. The first model included only personal characteristics, whereas the second model represents the full model including organizational climate characteristics. Factor scores were extracted from the aforementioned CFA and utilized in the regression models. Finally, given the multilevel nature of the data (i.e., staff nested within agencies), the feasibility of conducting a multilevel model was explored. The intraclass correlation (ICC) of the null model was .079. Thus, only 7.9% of the variance of the JS scores is at the group level. This is relatively small, but to account for potential bias in statistical significance tests in regression analyses, the design effect was examined (Cohen et al., 2003). Design effects less than 2.0 indicate suitability for conducting single-level analyses (Heck & Thomas, 2015). Applying this formula to the null model results in the following design effect: (1 + [(13 − 1) × 0.079]) = 1.95. Given the low percentage of variance in JS across agencies, a single-level model was examined in the current study. All models were analyzed using Mplus version 8 (L. K. Muthén & Muthén, 1998–2017). Missing data were addressed using direct ML, or full information ML (FIML), in factor analysis and SEM (n = 489), whereas listwise deletion was used in subsequent regression analyses (n = 454).

Results

The descriptive statistics in Table 1 reveal that the participants were predominately White (72%), female (59%), with a median age of 41 (M = 41.6, SD = 9.68) years. The majority of participants were JPOs (61%). Participants largely held Bachelor’s degrees (61%), with a little over a third holding a Master’s degree or higher. Approximately two thirds (68%) of juvenile probation staff reported having a caseload, supervising at least one youth. Participants varied in their experience, ranging from less than a year to 44 years of experience, with a median of 14.5 (M = 15.20, SD = 8.59) years. Tenure with current employer also had a wide range, with some participants working with their current agency for less than a year and others reporting nearly 40 years with a median of 10.75 (M = 12.00, SD = 8.17) years.

Regarding JS, the dependent variable, participants on average reported that they were satisfied with their job. Approximately half of juvenile probation staff either agreed or strongly agreed that they were satisfied on all six items. Respondents reported a moderate level of stress, with approximately 40% or more agreeing or strongly agreeing with each of the four items. Respondents reported relatively strong feelings of organizational support, with 64% or more reporting mostly true or definitely true for each of the eight items. Similarly, 66% or more of respondents reported mostly true or definitely true to each of the items measuring innovation and flexibility within their agency. Participants perceived a relatively strong climate of quality within their agency, with 83% to 90% reporting mostly true or definitely true to each of the four items. Perceptions of communication among respondents were moderate. Finally, there was a strong perception of supervisory support among respondents. As seen in Table 1, JS and each of the five organizational climate characteristics exhibited good internal consistency with alphas ranging from .81 to .89.

CFA

Next, preliminary CFA models involving Bayesian estimation were conducted, and factor scores generated, for each of the five organizational characteristics and JS. Bayesian estimation was utilized due to the nonnormal distribution of the 34 items. Each latent factor was scaled by fixing the variance of the latent variable to 1.000 (Brown, 2015). Based on initial results, JS (posterior predictive p value [PPP] = .000), organizational support (PPP = .002), and stress (PPP = .029) did not meet acceptable fit criteria and suggested the models should be re-specified. Upon assessment of potential sources of misspecification (e.g., error theory, selection of indicators), multicollinearity of indicators was identified as an issue. To address this, within each construct, the most highly correlated item(s) were removed; analyses are presented in Table 2. Two items (Items 1 and 5) were removed from JS, three items (Items 2, 5, and 6) from organizational support, and one item (Item 2) from job-related stress. All items significantly load onto each latent construct respectively suggesting acceptable fit of a one factor model for each construct.

Table 2:

Factor Analysis of Organizational Climate Characteristics and Job Satisfaction Using Bayesian Estimation and Standardized Loadings (n = 488−492)^a

Items	Factor loadings
Factor 1: Job satisfaction
You would like to find a job somewhere else^b	1.000
You feel appreciated for the job you do	0.888
You like the people you work with	0.496
You are proud to tell others where you work	1.075
Posterior predictive p value	.325
Potential scale reduction	1.005
Factor 2: Stress
Staff members are under too many pressures to do their jobs effectively	1.000
The heavy workload here reduces agency effectiveness	0.631
Staff frustration is common here	0.538
Posterior predictive p value	.509
Potential scale reduction	1.004
Factor 3: Communication
Ideas and suggestions from staff get fair consideration by management	1.000
The formal communication channels work very well here	1.580
The informal communication channels work very well here	0.715
Staff is always kept well informed	1.184
More open discussions about agency issues are needed here^b	0.493
Staff members always feel free to ask questions and express concern	0.986
Posterior predictive p value	.080
Potential scale reduction	1.007
Factor 4: Agency quality
This organization is always looking to achieve the highest standards of quality	1.000
Quality is taken very seriously here	1.762
People believe the organization’s success depends on high-quality work	0.799
This organization does not have much of a reputation for top-quality performance^b	0.456
Posterior predictive p value	.250
Potential scale reduction	1.009
Factor 5: Organizational support
My organization really cares about my well-being	1.000
My organization shows little concern for me^b	1.541
My organization cares about my opinions	1.133
My organization would forgive an honest mistake on my part	1.001
If given the opportunity, my organization would take advantage of me^b	1.242
Posterior predictive p value	.275
Potential scale reduction	1.001
Factor 6: Innovation & flexibility
New ideas are readily accepted here	1.000
This organization is quick to respond when changes need to be made	1.310
Management here is quick to spot the need to do things differently	1.394
This organization if flexible; it can quickly change procedures to meet new conditions and solve problems as they arise	1.339
Assistance in developing new ideas is readily available	1.492
People in this organization are always searching for new ways of addressing problems	1.105
Posterior predictive p value	.060
Potential scale reduction	1.006

Information based on 50,000 iterations. ^bItem was reversed coded prior to analyses.

The PPP for each factor was above .05, the suggested cutoff point for good fit (B. Muthén & Asparouhov, 2012), and the potential scale reduction was less than 1.100 (Brooks & Gelman, 1998). Finally, the Kolmogorov-Smirnov test did not identify any problematic parameters, indicating that JS, organizational support, stress, communication, quality, and innovation and flexibility represent individual latent factors.

Structural Equation Modeling

Next, these factors were used in an SEM to examine whether job-related stress mediated the relationship between the organizational characteristics and JS, as seen in Figure 1. Results indicated that the model did not fit the data, with a PPP of .000 and the Kolmogorov-Smirnov distribution test producing a p value of .001, indicating problematic parameters in the posterior distributions of the different Markov chain Monte Carlo (MCMC) chains. This is likely due to the low ratio of parameters to sample size, which causes complexity and difficulty in estimating the model. Given the complexity of the model, it was appropriate to reconceptualize the model to address the aforementioned research questions. Therefore, factor scores were constructed for each of the five organizational characteristics and JS and used in subsequent path analyses. However, the model did not fit the data well (χ² = 208, p < .001; root mean square error of approximation [RMSEA] = 0.289; comparative fit index [CFI] = 0.565; standardized root mean square residual [SRMR] = 0.133). Modification indices recommended correlating the error terms for JS and job-related stress. Following this recommendation resulted in an acceptable fit of the model to the data (χ² = 25, p < .001; RMSEA = 0.008; CFI = 0.954; SRMR = 0.025; Hu & Bentler, 1995). Although this modification produced an acceptable model fit, it is empirically driven. The conceptual framework for this study did not provide support for this modification and was deemed inappropriate (Kaplan, 1990; Landis et al., 2009). Due to space limitations, path analyses are available upon request.

Multivariate Analyses

Because the mediating effect of stress could not be fully explored in the current sample, Table 3 presents ML regression analyses that estimate the effects of personal and organizational climate factors on JS. Model 1 serves as a baseline model including only the personal characteristics of probation staff. Model diagnostics for each of the models revealed no issues with skewness or kurtosis, as estimates for the dependent variable fell within the appropriate range of 2.0 and 7.0, respectively (Curran et al., 1996). All variance inflation factor (VIF) values were below 10, indicating that multicollinearity was not a problem (Kutner et al., 2003). Robust standard errors were produced in the current models based on White’s and Breusch–Pagan tests.⁵ Overall, personal characteristics account for approximately 9.6% of the explained variance in JS, F(9, 446) = 5.25, p < .001. In Model 1, identifying as White (b = .27, p < .001) was significantly associated with higher JS scores. In addition, managing a caseload (b = −.20, p = .02) was significantly associated with lower scores of JS among juvenile probation staff. Age (b = .01, p = .04) and tenure (b = −.01, p = .04) also had statistically significant relationships with JS; due to low standard errors, these results should be interpreted with caution. The model was rerun without these two variables and produced substantively similar results.

Table 3:

ML Regression Results: Personal and Organizational Characteristics Associated With Job Satisfaction

Variables	Model 1 (n = 456)			Model 2 (n = 454)
Variables	b	RSE	β	b	RSE	β
Personal characteristics
Female	−.09	.06	−.06	−.01	.05	−.01
Age	.01	<.01	.15*	<.01	<.01	.03
White	.27	.08	.20***	.18	.06	.13**
Hispanic	.11	.11	.05	<−.01	.08	<−.01
Juvenile probation officer	−.11	.08	−.08	.02	.06	.01
Education	<.01	.05	<.01	.01	.04	.01
Caseload	−.20	.08	−.14*	−.02	.06	−.02
Experience	<.01	.01	−.06	.01	<.01	.07
Tenure	−.01	<.01	−.12*	−.01	<.01	−.08
Organizational characteristics
Communication				.08	.03	.15*
Innovation & flexibility				<−.01	.04	−.01
Quality				.06	.02	.13**
Supervisory support				.11	.03	.14***
Organizational support				.23	.04	.35***
Job-related stress				−.03	.02	−.08
F	4.90***			30.81
R ²	.096			.484

Note. ML = maximum likelihood; RSE = robust standard error.

Two-tailed p value: *p < .05. **p < .01. ***p < .001.

Model 2 illustrates the full model including both personal and organizational variables. The inclusion of organizational climate characteristics dramatically increased the predictive strength of the model, as it accounts for approximately 48.4% of the explained variance in JS, F(15, 438) = 30.81, p < .001. In the full model, five of the six organizational characteristics were statistically significant. Communication (b = .08, p = .01), quality (b = .06, p = .01), supervisory support (b = .11, p < .001), and organizational support (b = .23, p < .001) were all associated with higher scores of JS. Although nonsignificant, stress (b = −.03, p = .06) was associated with a decrease in JS scores. Interestingly, three of the four personal characteristics (i.e., caseload, age, and tenure) were no longer significantly related to JS once organizational climate measures were included. Finally, identifying as White remained a significant predictor of JS in Model 2, but went from being significant at the p < .001 level in Model 1 to being slightly less significant at the p < .01 level (p = .01).

Discussion

This study moved beyond existing research by examining the association between personal and organizational climate characteristics, and JS among juvenile probation staff, while exploring a diverse variety of climate domains. Three major findings were uncovered in the current study. First, CFA results reveal the psychometric nature of five of the six organizational climate domains and JS. Second, SEM and path analysis results suggest that stress does not mediate the relationship between organizational climate measures and JS in the current sample. Finally, organizational factors are more strongly associated with one’s level of JS compared with personal factors, with the exception of race, suggesting that one’s work environment has a pivotal role in determining their degree of JS. This is an important finding as it suggests the climate or atmosphere of an organization has a more influential role in shaping job attitudes (i.e., JS) than most personal characteristics which employees often bring with them to a job.

Findings from the current study were largely consistent with prior research in that six of the seven concepts each form a unidimensional latent construct. In contrast to prior literature, the items making up supervisory support exhibited signs of multicollinearity and, therefore, were not used to form a latent construct of supervisory support (Broome et al., 2009). This inconsistency may be explained by the adjustments made to the instrument in the current study. That is, the original scale included eight items measuring supervisory support and, although conceptually similar, were worded slightly different. Issues with multicollinearity are likely an artifactual issue where the items are too similarly worded (Brown, 2015). That is, all questions began with the same phrasing, “Your supervisor . . .” which may have been repetitious to the respondent.

Although there was a direct effect of stress on JS, the current study did not find support for an indirect relationship with job-related stress acting as a mediator. This was inconsistent with studies among other occupations where stress, at least partially, mediated the relationship between organizational climate and JS (Cortini, 2016; Pecino-Medina et al., 2017). However, this mediation model is a novel concept and not fully established in the literature. Following the JD-R model, a lack of mediation in the current study might be explained by the presence of job resources. In addition to the importance of job demands, this model also holds that resources may act as buffers to the impact of job demands on stress reactions (Bakker et al., 2005). It could be that job-related stress did not act as a mediator in the current study because respondents felt they had access to range of resources that mitigated job demands. For example, having supervisory support may alleviate the impact of job-related stress (Armstrong et al., 2015; Cheeseman et al., 2011).

Findings from the current study are consistent with prior research on both community and institutional corrections staff, in that compared with personal factors, organizational factors have a greater influence on JS. Personal characteristics accounted for 9.6% of the variation in JS, which increased to 48.4% when organizational characteristics were considered. Furthermore, the significant influence of identifying as White, age, tenure, and having a caseload either decreased or disappeared completely when organizational factors were taken into consideration. The influence of work climate on JS makes an importance distinction that it is not necessarily the personal characteristics that one brings with them to a job, but specific aspects of the working climate that impact their JS.

Interestingly, identifying as White remained statistically significant. This is inconsistent with prior literature involving adult probation staff, which has found no significant impact of race on JS (Getahun et al., 2008; Simmons et al., 1997). However, these findings coincide with a number of institutional corrections studies that show that White staff are generally more satisfied with their jobs (Blevins et al., 2006; Byrd et al., 2000; Wells et al., 2016). Structural explanations may account for this difference, attributing group differences in JS to differential treatment or experiences of White and non-White employees. That is, organizational structures may disadvantage people of color, leading to lower JS. Prior studies have found that compared with White employees, Black employees were more vulnerable to discriminative occupational and educational practices, hindering upward mobility in the workplace (O’Connell, 2012). In addition, organizational structures that limit opportunities for career advancement, do not promote autonomy, and lack Black role models may all contribute to lower levels of JS among Black employees relative to White employees (Koh et al., 2016).

There is greater consensus on the direction and magnitude of relationships between JS and work-related factors compared with personal characteristics. Prior literature has linked JS to general work climate, as well as more specific features, including perceived supervisory support, stress, communication, and innovation. Findings from the current study largely coincide with prior literature examining these organizational domains, with few exceptions. Increased perceptions of communication and supervisory support were associated with greater JS. Open lines of communication are important for staff to do their jobs effectively. Good communication is especially important for juvenile probation staff who must coordinate with multiple entities. The association between supervisory support and JS is not surprising as occupational literature finds that organizational characteristics more closely linked to aspects of the job are more influential on employees’ job attitudes than organizational structure characteristics (Hall et al., 1978). That is, experiences or characteristics which staff experience more closely and regularly will have a greater impact on their attitudes and behavior. Staff typically interact with their supervisor(s) on a day-to-day basis regarding performance, expectations, processes, and youth-related outcomes. Supervisors who are supportive and active regarding work-related issues are likely to increase feelings of satisfaction among subordinates.

While there is a clear consensus on the aspects of quality (e.g., good working relationships, goals and outcomes, resources) across probation staff (Robinson et al., 2014), few, if any, studies have examined its association with JS. This study found that increased perceptions of quality were associated with an increase in JS. This is consistent with occupation literature that has found that organizations that promote quality work systems “enable employees to experience meaningfulness in their work, greater responsibility in their job, and better use of their knowledge and skills leading to increased satisfaction” (Barling et al., 2003, p. 276).

Perceived organizational support (POS) was the strongest predictor of JS. POS is a relatively understudied construct in correctional literature. According to organizational support theory, employees have a tendency to personify organizations, ascribing human-like characteristics to the organization (Levinson, 1965). Because employees personify organizations in this manner, they view favorable or unfavorable treatment indicative of the organization’s caring or spiteful orientation. POS gives employees a sense of fulfillment in terms of socioemotional needs, causing greater psychological well-being and greater identification and commitment to the organization. It is likely that this increased sense of fulfillment and support enhances JS. Juvenile probation is centered around people, requiring intense, sometimes stressful interactions with wayward youth, families, and behavioral health care providers. Although not significant (p = .06), increased levels of stress were linked to decreased JS in the current study. This lack significance may be explained by the large scope of organizational factors included in the current study. To date, no study within the correctional literature has examined all six of the current climate measures. It is possible that these additional domains accounted for a greater proportion of the variance in JS, mitigating the role of stress on JS.

Policy Implications

Similar to other criminal justice agencies, the nature of organizational climate was found to overpower most individual characteristics, resulting in officers developing similar attitudes toward their job. This has several practical implications for juvenile probation agencies, as well as other criminal justice agencies that deal with similar populations or organizational structures. Administrators faced with ensuring employee retention and structure should focus on JS and the climate of the employees’ work environment. This is good news because, compared with personal characteristics, these organizational factors are within their control. It would be advantageous for agencies to strategically build climates while also ensuring these climate changes are communicated to unit members. For example, enhancing organizational support may be a viable option for juvenile probation agencies. Perceptions of organizational support are driven by effective leadership, desirable working conditions, fair treatment, and favorable human resources practices (Eisenberger et al., 2016). Implementing low-cost reward systems like working remotely may be a viable option for agencies to enhance JS through POS. Organizational change within corrections is complicated and made challenging by many obstacles (e.g., centralized decision-making and complex bureaucratic designs). However, in the last 15 years, external changes may be creating opportunities for internal organizational change to occur, such as a move toward implementing evidenced-based practices (Taxman & Belenko, 2011). Although organizational change requires time and effort, substantial payoffs for both the organization and staff should result, including increased JS, lowered stress, and, ultimately, lower turnover and intentions of turnover (Wells et al., 2016).

Limitations

Although this study is one of few to examine the relationship between JS and organizational climate, it is not without limitations. First, the use of secondary data limited the measurement of variables. For example, asking participants about their sense of organizational commitment, a common variable considered in JS literature, was beyond the goals of the original research project. Second, findings should be interpreted with caution as causality could not be determined due to the cross-sectional nature of the data. Third, the results may not be generalizable across different jurisdictions and agencies. The role of probation and processing of youth in the juvenile justice system varies from state to state and even jurisdiction to jurisdiction. Although the current study includes participants from 36 agencies across seven states, the data are not considered to be nationally representative. Participating agencies were not drawn by a probability sample but recruited by each participating Research Center. Furthermore, juvenile probation staff have unique job demands associated with supervising youth (e.g., coordination of multiple entities like parents/guardians, teachers, treatment agencies, and medical providers). Therefore, it is unknown whether the current findings extend to adult probation staff. Finally, data were self-reported and participants may be biased when reporting their experiences or perceptions.

Directions for Future Research

Results from the current study suggest the importance of examining organizational context and climate when studying job attitudes in a correctional setting. Half of the variance in JS is unexplained in the current study; therefore, future studies should examine supplementary domains of organizational climate. We can gain a better understanding of how juvenile probation staff derive satisfaction by considering such variables as formalization, job variety, job autonomy, organizational commitment, family support, and job desirability (Matz et al., 2013; Wells et al., 2016). This information would be valuable to practitioners who are seeking to improve, not only JS, but the costly outcomes associated with low JS (e.g., turnover, turnover intention, mental/psychological health concerns). Additional research is needed to parse out any potential mediating effects of job-related stress. Aside from the current study, it is unclear how findings among the adult probation staff samples compare with those of juvenile probation staff. It could be that the influence of organizational climate on JS transcends occupation within the criminal justice field. Future research should examine the influence of the work environment on JS among both adult and juvenile probation staff to better assess the influence of organizational climate. Continued research among juvenile probation staff is needed to further establish the influence of personal and work-related factors on JS. Finally, research among juvenile probation staff should expand on the current model to investigate turnover and turnover intention. Prior research finds support for E. G. Lambert’s (2001) model, which suggests that JS acts as a mediating variable between personal, work-related factors and turnover/turnover intention among juvenile correctional staff (Wells et al., 2016). It would be advantageous to include additional climate measures within this model to better inform administration on best practices to improve satisfaction and reduce turnover.

Conclusion

The current study contributes to the growing body of literature indicating that the work environment is an important predictor of JS among probation staff. This study included several unexplored or underexplored personal and organizational factors (i.e., caseload, innovation and flexibility, quality, and organizational support) and assessed their relationships with JS. Findings contribute to organizational climate research through the use of advanced statistical techniques to test the unidimensionality of organizational climate measures in a new population (i.e., juvenile probation staff). Although not significant, the current study expands on this body of literature by examining mediating relationships. Administration should focus on organizational characteristics such as providing supportive leadership and organizational support among staff. These are critical objectives in promoting the health and well-being of staff who are responsible for the rehabilitation and supervision of at-risk youth. Agencies can only be as effective as their staff; therefore, it is important to have policies and practices that promote the satisfaction and health of employees.

Footnotes

Appendix

Table A1:

Organizational Climate and Dependent Variable Items

Job satisfaction

1. You are satisfied with your present job.

2. You would like to find a job somewhere else.^a

3. You feel appreciated for the job you do.

4. You like the people you work with.

5. You give high value to the work you do here.

6. You are proud to tell others where you work.

Job-related stress

1. Staff members are under too many pressures to do their jobs effectively

2. Staff members often show signs of stress and strain.

3. The heavy workload here reduces agency effectiveness.

4. Staff frustration is common here.

Agency innovation & flexibility

1. New ideas are readily accepted here.

2. This organization is quick to respond when changes need to be made.

3. Management here is quick to spot the need to do things differently.

4. This organization if flexible; it can quickly change procedures to meet new conditions and solve problems as they arise.

5. Assistance in developing new ideas is readily available.

6. People in this organization are always searching for new ways of addressing problems.

Agency quality

1. This organization is always looking to achieve the highest standards of quality.

2. Quality is taken very seriously here.

3. People believe the organization’s success depends on high-quality work.

4. This organization does not have much of a reputation for top-quality performance.^a

Agency communication

1. Ideas and suggestions from staff get fair consideration by management.

2. The formal communication channels work very well here.

3. The informal communication channels work very well here.

4. Staff is always kept well informed.

5. More open discussions about agency issues are needed here.^a

6. Staff members always feel free to ask questions and express concern.

Organizational support

1. My organization really cares about my well-being.

2. My organization strongly considers my goals and values.

3. My organization shows little concern for me.^a

4. My organization cares about my opinions.

5. My organization is willing to help me if I need a special favor.

6. Help is available from my organization when I have a problem.

7. My organization would forgive an honest mistake on my part.

8. If given the opportunity, my organization would take advantage of me.^a

Supervisory support

1. Your supervisor acknowledges creative solutions to problems.

2. Your supervisor encourages others’ ideas.

3. Your supervisor is respectful in handling staff member mistakes.

4. Your supervisor encourages staff to try new ways to accomplish their work.

Item was reversed coded prior to analyses.

Author’s Note:

The author gratefully acknowledges the collaborative contributions of National Institute on Drug Abuse (NIDA) and support from the following grant awards: Chestnut Health Systems (U01DA03622), Columbia University (U01DA036226), Emory University (U01DA036233), Mississippi State University (U01DA036176), Temple University (U01DA036225), Texas Christian University (U01DA036224), and University of Kentucky (U01DA036158). NIDA Science Officer on this project is Tisha Wiley. The Clinical Trials Registration number is NCT02672150. The contents of this publication are solely the responsibility of the author and do not necessarily represent the official views of the NIDA, National Institutes of Health (NIH), or the participating universities or juvenile justice systems. This study was funded under the Juvenile Justice–Translational Research on Interventions for Adolescents in the Legal System (JJ-TRIALS) cooperative agreement, funded at the NIDA by the NIH.

ORCID iD

Julie M. Krupa

Notes

Julie M. Krupa, PhD, is an assistant professor in the School of Criminal Justice at Michigan State University. Her research focuses on juvenile corrections, health needs of justice-involved youth, and communities and crime. Her recent articles appear in Crime & Delinquency, Justice Quarterly, and Justice Evaluation Journal.

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