700-1000 Word post on criminal justice research topic. APA format.

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timer Asked: Mar 23rd, 2017

Question Description

1. What are Independent and Dependent variables? How do they work together in a hypothesis? Using your own research project, highlight what it is that you want to study. Please give one example of a hypothesis using one independent and on dependent variable from your research proposal. Please write the hypothesis as an If/Then statement!

2. Briefly review the research design for either of the two assigned articles for this week. What do you think about how the research was done? Assess and critique the research design.

Reading link

I've attached a link to the week 3 reading. My topic for my research is to define sexting, the activities of sexing, characteristics of those who send these images, the prevalence of cell phone use, the effectiveness of deterrents and consequences. Sexting is something I have to address often in my line of work and under my current assignment, my hope would be that this kind of research might make me more effective in my own attempts to educate and deter the problem. I would also be satisfied if I managed to just know how to address the problem with my own child before he is of that age.

I appreciate help.

Journal of Contemporary Criminal Justice http://ccj.sagepub.com/ Discretionary Decision Making by Probation and Parole Officers : The Role of Extralegal Variables as Predictors of Responses to Technical Violations John J. Kerbs, Mark Jones and Jennifer M. Jolley Journal of Contemporary Criminal Justice 2009 25: 424 originally published online 21 August 2009 DOI: 10.1177/1043986209344556 The online version of this article can be found at: http://ccj.sagepub.com/content/25/4/424 Published by: http://www.sagepublications.com Additional services and information for Journal of Contemporary Criminal Justice can be found at: Email Alerts: http://ccj.sagepub.com/cgi/alerts Subscriptions: http://ccj.sagepub.com/subscriptions Reprints: http://www.sagepub.com/journalsReprints.nav Permissions: http://www.sagepub.com/journalsPermissions.nav Citations: http://ccj.sagepub.com/content/25/4/424.refs.html >> Version of Record - Oct 9, 2009 OnlineFirst Version of Record - Aug 21, 2009 What is This? Downloaded from ccj.sagepub.com by guest on May 1, 2013 Discretionary Decision Making by Probation and Parole Officers Journal of Contemporary Criminal Justice Volume 25 Number 4 November 2009 424-441 © 2009 Sage Publications 10.1177/1043986209344556 http://ccj.sagepub.com hosted at http://online.sagepub.com The Role of Extralegal Variables as Predictors of Responses to Technical Violations John J. Kerbs Mark Jones East Carolina University, Greenville, NC Jennifer M. Jolley Washington University, St. Louis, MO This study examines the predictors of discretionary decisions made by probation and parole officers (PPOs) when they face clients who commit technical rule violations during community supervision. Although prior studies of discretionary decisions in criminal justice systems typically focus on legal predictors of discretion (i.e., offense- and offender-based variables), this study examines extralegal predictors to include PPOs’ sociodemographical, occupational, and organizational characteristics. The study uses data collected from a national self-report survey of 332 PPOs who worked with adults and who were members of the American Probation and Parole Association in 2005. Logistic regression analyses examine extralegal predictors of PPO support for formal hearings (i.e., judicial or parole board hearings) in response to technical rule violations. Analyses identify significant occupational and organizational factors that predicted discretionary decisions. Policy implications and directions for future research are examined. Keywords: probation; parole; violations; decision making; discretion O ver the past quarter century, the number of people under correctional supervision (i.e., incarcerated persons, probationers, and parolees) in the United States increased dramatically. In 1980, there were 501,886 persons in U.S. jails and prisons, 1,118,097 probationers, and 220,438 parolees (U.S. Department of Justice, 1996). Authors’ Note: This publication was made possible by a grant from Department of Criminal Justice at East Carolina University. The authors would like to thank Dr. William Doerner, Dr. Stephanie Bontrager, and Dr. Kristin Winokur for their feedback on earlier drafts of this article. All points of view and opinions in this article are those of the authors and do not necessarily represent the official position or policies of the American Probation and Parole Association (APPA), the Department of Criminal Justice at East Carolina University, the George Warren Brown School of Social Work, the National Institute of Mental Health, or the Winterville Police Department. Please address correspondence to Dr. John J. Kerbs, Department of Criminal Justice, East Carolina University, 244 Rivers Building, Greenville, NC 278584353; e-mail: kerbsj@ecu.edu. 424 Downloaded from ccj.sagepub.com by guest on May 1, 2013 Kerbs et al. / Decision Making by Probation and Parole Officers   425 Twenty-five years later, there were 2,186,230 persons in U.S. jails and prisons, 4,162,536 probationers, and 784,408 parolees (Harrison & Beck, 2006). Given these rising figures, it is no surprise that the United States now incarcerates its citizens at a rate that is the highest in the world (The Sentencing Project, 2006). Of increasing concern is the significant proportion of all U.S. prisoners who are incarcerated due to probation and parole violations for new criminal offenses and/or for technical infractions (i.e., noncriminal infractions) that include conditions of probation and parole that are enforceable by community corrections officers, the courts, and/or parole boards. By definition, technical infractions typically include an offender’s failure to (a) comply with curfews, (b) pass alcohol and drug urinalysis screens, (c) avoid contact with other offenders, (d) maintain employment and/or report unemployment, (e) attend meetings with probation and parole officers (PPOs), (f) make restitution payments and/or perform community service hours, and (g) attend individual and/or group therapy meetings. In terms of national statistics, about 40% of all probationers in the United States were revoked in 2001 and sent to jail and/or prison for technical and/or criminal violations (Nieto, 2003). Research by Travis and Lawrence (2002) suggested that the number of parole violators who were revoked and readmitted to prison increased sevenfold between 1980 and 2000 (27,000 and 203,000, respectively). Langan and Levin (2002) found that about 25.4% of all state prisoners who were released in 1994 were returned to prison within 3 years for criminal violations, and 26.4% were returned for technical violations within 3 years of their release. Research by Glaze and Palla (2004) found similar figures. When probationers, parolees, and others under community supervision commit criminal or technical violations, PPOs can either respond administratively by imposing their own “in-house” sanctions or they can formally seek external assistance from the judiciary or parole board for potential arrest warrants and/or revocation (Champion, 2005). Research shows that discretionary decision making yields different results for offenders who commit the same kinds of infractions. Some probationers and parolees are maintained in the community, often with added restrictions, whereas other probationers and parolees face formal hearings and potential sanctions like revocation (Champion, 2005; Petersilia, 2001; Wilson & Petersilia, 2002). Although it is relatively easy (in theory) for PPOs to justify revocation for those who commit new crimes, it is arguably more difficult to justify formal hearings and the threat of revocation for technical infractions, given that many technical violations are not associated with future criminal activity and/or treatment failure (Jones, 2004). In light of the severe consequences of a potential revocation, it is important for researchers to examine factors that might affect PPOs’ decision making to seek external assistance from the judiciary and parole boards for technical infractions. Even though there have been multiple studies that examined decision making among police (Ho, 2003; Wortley, 2003), court-related personnel (Alschuler, 1979; Bond, 1975; Davis, 1969; Durose & Langan, 2005; Friedman, 1979; Gelsthorpe & Padfield, 2003), and correctional personnel (Poole & Regoli, 1980; Tischler & Downloaded from ccj.sagepub.com by guest on May 1, 2013 426   Journal of Contemporary Criminal Justice Marquart, 1989), few studies have examined PPO decision making (Abadinsky, 1978; Jones, 2004; McCleary, 1975). Moreover, researchers (Harris, Gingerich, & Whittaker, 2004; Rosecrance, 1987; Steen & Opsal, 2007) have noted that most studies look at offender- and offense-based factors as predictors of discretionary decision making while overlooking the contribution of organizational factors. Hence, this study seeks to fill this void in the literature by taking a modest step toward the identification of the PPOs’ sociodemographic, occupational, and organizational factors (i.e., extralegal variables) that may affect the PPOs’ likelihood of pursuing technical violations via formal (external) hearings as compared to addressing such violations administratively (in house). Literature Review Over the past five decades, the scholarly literature has increasingly focused on the discretionary decision-making behavior of criminal justice practitioners. Con­ ceptually, “discretion involves a liberty or power of deciding according to one’s own judgment or discernment” (Gelsthorpe & Padfield, 2003, p. 3). Discretion is potentially based on a number of factors acting in concert to include (a) individual beliefs and preferences, (b) individual training and interpretation of the law, (c) formal and informal workplace policies and practices, (d) individual responses to offender characteristics, and (e) available information at the time. Regardless of whether one looks at decision making among law enforcement officers (Ho, 2003; Wortley, 2003), court-related personnel (Alschuler, 1979; Bond, 1975; Davis, 1969; Durose & Langan, 2005; Friedman, 1979; Gelsthorpe & Padfield, 2003), correctional personnel (Poole & Regoli, 1980; Tischler & Marquart, 1989), or PPOs (Abadinsky, 1978; Jones, 2004; McCleary, 1975), the literature has historically found that legal factors related to the offender (e.g., offense history, offense severity, etc.) were key to predicting the outcomes of discretionary decisions made by criminal justice personnel. However, discretionary decisions have been associated with extralegal factors that have little to do with the offender; instead, they are related to criminal justice personnel and their agencies. These factors have included (a) individual characteristics, such as sociodemographic variables; (b) occupational characteristics, such as job titles and caseload types/sizes; and (c) organizational characteristics, such as the regional location of PPO agencies (Davis, 1969; Gelsthorpe & Padfield, 2003). As early as 1975, McCleary conducted ethnographic studies about the significant impact that personal preferences have on the professional decisions of individual parole officers. Moreover, McCleary believed that a PPO’s personal preferences were as likely to inform decision making as were standard structural or organizational factors (McCleary, 1975, 1978). In an effort to maintain a positive reputation and appear credible to their superiors, parole officers reported pressure to underreport violations; they also believed that high revocation hearing rates might suggest Downloaded from ccj.sagepub.com by guest on May 1, 2013 Kerbs et al. / Decision Making by Probation and Parole Officers   427 that a given parole officer was unable to properly handle cases (McCleary, 1978). In addition, caseload size may affect the officer’s tendency to report minor violations. McLeary (1978) noted that large caseloads may cause officers to underreport minor violations; conversely, reduced caseloads may increase the officers’ rate of reporting for rule violations by probationers and parolees (see, for example, Banks, Porter, Rardin, Silver, & Unger, 1977; Petersilia & Turner, 1990). Furthermore, the discretionary decisions of court-related personnel also appear to be affected by extralegal organizational variables like geography. When examining “Justice by Geography” for sentence length and severity, researchers have observed geographic variations based on region and urbanization. With regard to region, most studies have noted that Southern states punished more severely than non-Southern states (Carroll & Doubet, 1983; Chiricos & Crawford, 1995; Michalowski & Pearson, 1990; Snell, 2000; Weidner & Frase, 2001, 2003), but there were some exceptions to this general trend (Greenberg & West, 2001; McGarrell, 1993; Wiedner, Frase, & Pardoe, 2004). In regard to urbanization, some studies discovered that the level of urbanization was positively associated with sentence length and/or severity (Carroll & Doubet, 1983; Feld, 1991; Greenberg & West, 2001; Myers & Talarico, 1987), whereas other studies reported no association or a negative association (Huang, Finn, Ruback, & Friedman, 1996; Vigorita, 2001; Weidner & Frase, 2003). Furthermore, in one study that looked at sentencing differences in rural versus urban and suburban counties, the authors observed that suburban areas were actually more punitive than rural areas (Ulmer & Kramer, 1996), which suggested the need to move beyond the urban-rural dichotomy to studies that include suburbs. Because Southern states tend to be more rural than states in other regions, concurrent examinations of urbanization and region are important to guard against spurious correlations between region and punitiveness (Michalowski & Pearson, 1990). Finally, many of these regional findings may be related to levels of bureaucracy. For example, one study by Ulmer (1997) discovered that larger courts with greater levels of bureaucracy were less likely to incarcerate (regardless of prior record) than those with lower levels of bureaucracy, perhaps because organizations with lower levels of bureaucracy have been found at times to weigh legal variables more heavily than extralegal variables (Dixon, 1995). Finally, the use of discretion among court-related personnel is perhaps most evident and best studied among prosecutors and judges; an example of court-related discretion can be seen within the process of plea bargaining or the process of deciding which cases should be prioritized for prosecution (Alschuler, 1979; Bond, 1975; Durose & Langan, 2005; Friedman, 1979). Ultimately, the decision to plea bargain cases rests on a mix of prosecutorial and judicial priorities in addition to organizational factors affecting how quickly cases can be brought before the judiciary at any given time. When court calendars become crowded or bogged down, judges can put pressure on prosecutors to plea cases quickly. It is interesting to note that an extralegal factor, such as a packed court calendar, can exert significant influence relative to the process of applying discretion vis-à-vis plea bargaining. Downloaded from ccj.sagepub.com by guest on May 1, 2013 428   Journal of Contemporary Criminal Justice In sum, the literature has yet to provide a detailed quantitative analysis that examines the statistical predictors of discretionary decisions made by PPOs responding to offenders who violate the technical conditions of community supervision. Hence, this study takes an initial step at examining the extralegal predictors (i.e., individual, occupational, and organizational predictors) of PPO decisions regarding offenders who commit technical violations. Method Sample This article is based on a survey of members from the American Probation and Parole Association (APPA). More specifically, this survey targeted line officers and middle managers who supervised adult offenders under pretrial release, probation, parole, or post-release supervision. A membership list was obtained, courtesy of the APPA. This list included all members (N = 2,895) as of October, 2003. The APPA membership roster included the member’s name, job title, agency name, and address. The members’ job titles and addresses were used to select those who were included and excluded from the survey. In total, 1,050 members were surveyed via U.S. mail, whereas 1,845 members were excluded from this survey because they were listed in the APPA roster as one or more of the following: (a) holding a strictly administrative or research position, (b) working for educational institutions, (c) practicing within a federal or private agency (most PPOs work within state or county/municipal systems), or (d) working solely with juvenile offenders (the inclusion of officers who worked solely in the juvenile system would have taken this study in a very different direction, given the differences between the criminal and juvenile justice systems). Although 417 community corrections officers returned surveys for analysis (a 39.7% response rate), this study only examined 332 surveys (a 31.6% response rate for the final analysis) from respondents in 40 states after cases were excluded because of missing data and the reasons noted above. Independent Variables Table 1 that follows contains a detailed description of the sample in relation to all independent variables examined herein. Generally speaking, the sociodemographic backgrounds of the respondents were proportionally similar to other national studies of the PPOs in the United States (Camp & Camp, 2002). The majority (56%) of the study participants were male, White (89.5%), and possessed a 4-year college degree or less (51%). The average officer had worked for 13.3 years in probation and/or parole. With regard to occupational variables, a majority of respondents (62%) self-identified as regular line officers with an average caseload of 141 offenders and a maximum of 4,000 offenders (median = 70). Organizationally speaking, most respondents (78%) were from Downloaded from ccj.sagepub.com by guest on May 1, 2013 Kerbs et al. / Decision Making by Probation and Parole Officers   429 Table 1 Descriptive Analysis of Independent Variables for APPA Respondents Variable Description % Independent variables: sociodemographic measures Gender Male 55.7 Female 44.3 Race White 89.5 Non-White 10.5 Educational background 4-year degree or less 50.9 Graduate-level education 49.1 Average number of years of experience — as officer (range: 1-37) Independent variables: occupational measures Officer’s job title Regular line-level officer 62.3 Middle manager 37.7 Type of caseload Pretrial defendants only 9.3 Probationers only 64.8 Parolees only or parolees and probationers 25.9 Age of caseload Adults only 85.8 Adults and juveniles 14.2 Average number of offenders supervised (range: 0-4000) Independent variables: organizational measures Agency region South 21.7 Non-South 78.3 Agency funding County/municipal 53.0 State 47.0 Agency administrative setting Judiciary 52.4 Correctional department or parole 47.6 authority Community setting Rural or small town 34.9 Suburban 21.7 Urban 43.4 Number of Officers (range: 1-800) — Policy to inhibit formal actions for certain 10.8 minor violations (% yes) Policy requiring formal actions for certain 62.3 violations (% yes) Note: Final sample size: N = 332 cases. Downloaded from ccj.sagepub.com by guest on May 1, 2013 M SD — — — — — — — — — — 13.3 — — 8.3 — — — — — — — — — — — — 141.0 — — 385.2 — — — — — — — — — — — — — — — 39.5 — — — — 97.7 — — — 430   Journal of Contemporary Criminal Justice non-Southern states in agencies that had an average of about 40 PPOs (range 1-800). Please see Table 1 for more information about the remaining predictors not covered here. Dependent Variables Beyond these sample characteristics noted discussed previously, the survey also included measures for PPOs’ preferred response to seven scenarios for different technical violations. Such an approach is congruent with other researchers who have called for the examination of explicit indicators of offender behaviors as dependent variables (Harris et al., 2004; Steen & Opsal, 2007). For each scenario, the surveyed officers indicated whether they supported verbal/written reprimands (coded 1 originally), other administrative sanctions (coded 2 originally), or an arrest warrant or formal hearing for formal sanctions (coded 3 originally). Because of skewed distributions, all questions were recoded as either administrative interventions (coded as 0) or formal hearings (coded as 1) for the logistic regression analyses as discussed below. Administrative interventions were defined hereafter as approaches that required the officer to handle the violation by him or herself as agency-based responses via verbal or written reprimands, increased reporting requirements, required counseling, or other in-house sanctions that represented an officer’s administratively initiated/controlled sanction. All scenarios are presented here in ascending order for the percentage of PPOs who supported the use of formal hearings. In the first scenario, 26% of all PPOs selected formal hearings in response to physically capable offenders who made no effort to seek or obtain employment. Thereafter, the proportions of PPOs who selected formal hearings increased as follows: (a) 29% selected formal hearings for offenders who missed two monthly meetings with PPOs; (b) 34% selected formal hearings for offenders who failed to complete (without explanation) community service for three Saturdays in a row; (c) 40% selected formal hearings for offenders who reported to the PPO smelling of alcohol while registering a .15 for their blood alcohol content; (d) 53% selected formal hearings for offenders who violated house arrest with curfew violations on three occasions; (e) 63% selected formal hearings for offenders defying a PPO’s request to avoid associations with known offenders; and (f) 71% selected formal hearings for offenders who tested positive for marijuana on two occasions, but who did not attend treatment as instructed after the first positive urine. In conclusion, each scenario above acted as a dependent variable for logistic regression analyses as discussed below. For a complete copy of all survey questions, please contact the corresponding author. Multivariate Logistic Regression Analysis This study used logistic regression models to examine the three types of extralegal predictors (i.e., sociodemographic, occupational, and organizational factors) for discretionary decisions involving the use of formal hearings for the seven technical violations. All predictor variables were run simultaneously for each of the seven scenarios. Downloaded from ccj.sagepub.com by guest on May 1, 2013 Kerbs et al. / Decision Making by Probation and Parole Officers   431 To guard against potential problems with multicollinearity, diagnostics were completed using ordinary least square (OLS) models (all variance inflation factors [VIF] <2.5). In addition, Hosmer and Lemeshow (1989) goodness-of-fit tests were examined for all models to determine whether differences existed between the observed and predicted values for all dependent variables; results indicated that the models’ estimates fit the data at an acceptable level (p > .05 for all models). Finally, given the exclusive examination of extralegal predictors in the absence of offender- or offense-based predictors, these models were not expected to explain much variance in the dependent variables. Theoretically, extralegal variables should not affect the outcomes examined here; nonetheless, pragmatically, the literature suggests that extralegal variables may play a role in PPO decisions as applied to technical violations. Results For each of the seven logistic models described above, Table 2 presents the logistic regression coefficients (standard errors are in parentheses), model chi-square statistics, Hosmer and Lemeshow goodness of fit tests, and Nagelkerke R2. Although statistical trends (p < .10) are noted in Table 2, this discussion will focus exclusively on predictors that are significant at the .05, .01, and .001 levels. As expected, this analysis of extralegal variables did not explain a large amount of the variance in any given model regarding discretionary decision making (Nagelkerke R2 ranged from a low of 7.6% in Model 4 to a high of 17.8% in Model 7). Nonetheless, as illustrated by the Model χ2 statistics, the predictors in five of the seven models jointly (adequately) explained the variance in the PPO’s decisions. More specifically, the Model χ2 statistics were significant for Model 1, for employment violations (χ2 = 31.029, df = 17, p < .05); Model 2, for meeting violations (χ2 = 28.671, df = 17, p < .05); Model 3, for community service violations (χ2 = 36.504, df = 17, p < .01); Model 5, for curfew violations (χ2 = 41.385, df = 17, p < .01); and Model 7, for drug screen violations (χ2 = 44.282, df = 17, p < .001). Model χ2 statistics for Model 4 (alcohol violations) and Model 6 (criminal association violations) were not significant, despite each model having one significant predictor and Model 6 coming close to significance (p = .059). With regard to sociodemographic characteristics, gender was significantly associated (p < .05) with decisions regarding community service violations (Model 3); hence, as compared to female PPOs, male PPOs were about 43% less likely (eB = 0.569) to support the use of formal hearings for community service violations. In addition, race was significantly associated (p < .05) with support for formal hearings concerning curfew violations (Model 5); thus, as compared to non-White PPOs, White PPOs were about 57% less likely (eB = 0.428) to support the use of formal hearings with curfew violations. Years of education and years of experience did not significantly affect (p > .05) PPOs’ discretionary decisions in any of the seven models. Downloaded from ccj.sagepub.com by guest on May 1, 2013 432 Downloaded from ccj.sagepub.com by guest on May 1, 2013 Constant Gender (1 = male,    0 = female) Race (1 = White,    0 = non-White) Years of education    (1 = 4-year degree or less,    0 = graduate education) Years of experience (in years) Job title    (1 = regular line officer,    0 = middle manager) Case type: pretrial only    (1 = pretrial only,    0 = parole or mix    parole/probation) Case type: probation only    (1 = probation only,    0 = parole or mix    parole/probation) Age of caseload    (1 = adults only,    0 = adults and juveniles) Size of caseload    (as per quartile) Agency region South    (1 = South, 0 = non-South) -0.018 (0.122) 0.119 (0.306) 0.071 (0.361) -0.507 (0.321) 0.375 (0.476) -0.016 (0.016) -0.068 (0.281) 0.323 (0.249) 0.086 (0.399) -0.311 (0.823) 0.078 (0.246) -0.345 (0.359) -0.493 (0.348) 0.448 (0.403) -0.366 (0.399) -0.139 (0.355) -0.073 (0.349) 0.446 (0.377) -0.073 (0.383) 0.111 (0.505) 0.322 (0.343) 0.793 (0.521) 0.289 (0.541) 0.001 (0.017) -0.733* (0.292) -0.004 (0.128) -0.020 (0.018) -0.151 (0.305) -0.012 (0.019) 0.090 (0.324) 0.043 (0.266) 0.063 (0.131) -0.252 (0.274) -0.402 (0.282) -0.062 (0.433) 1.501† (0.893) -0.564* (0.264) 0.065 (0.314) 0.260* (0.126) -0.407 (0.361) 0.553† (0.326) 0.601 (0.495) 0.005 (0.016) -0.571* (0.286) 0.202 (0.254) -0.849* (0.424) 0.061 (0.843) -0.287 (0.251) 0.383 (0.343) -0.024 (0.331) 0.362 (0.319) 0.113 (0.126) (continued) 0.572† (0.342) 0.080 (0.140) -0.410 (0.428) -0.076 (0.524) -0.561 (0.492) -0.171 (0.364) -0.024 (0.017) -0.078 (0.322) 0.031 (0.280) 0.498 (0.436) 2.028* (0.937) -0.175 (0.276) Model 7: Drug Screens 0.015 (0.017) -0.284 (0.289) -0.017 (0.256) 0.331 (0.399) 0.171 (0.846) 0.074 (0.250) Model 3: Model 4: Model 6: Community Blood Model 5: Criminal Service Alcohol Curfew Associations 0.387** (0.139) 0.559 (0.467) -1.101 (0.930) -0.017 (0.271) -0.486 (0.426) -0.663 (0.922) -0.172 (0.278) Model 1: Model 2: Independent Variables Employment Meetings Table 2 Logistic Regression Coefficients With Standard Errors in Parentheses for Predictors of Support for Formal Responses to Technical Violations 433 Downloaded from ccj.sagepub.com by guest on May 1, 2013 -0.436** (0.151) -1.237* (0.546) 0.289 (0.293) -0.262† (0.152) -0.324 (0.453) 0.588† (0.311) 0.119 0.602 (0.379) -0.031 (0.393) 0.131 0.241 (0.394) 0.348 (0.398) 28.671* 4.426 -0.149 (0.297) -0.302 (0.312) 31.029* 5.152 -0.045 (0.298) 0.078 (0.315) 0.264 (0.343) 0.744* (0.358) 0.241 (0.270) 0.115 (0.273) 0.144 0.076 19.154 12.605 -0.298 (0.270) -0.087 (0.281) 36.504** 4.115 0.082 (0.382) -0.537 (0.464) -0.371** (0.144) -0.071 (0.135) 0.096 (0.362) 0.554 (0.372) 0.073 (0.286) -0.270 (0.287) 0.156 41.385** 5.539 0.973*** (0.283) 0.464 (0.393) 0.044 (0.137) -0.341 (0.346) 0.088 (0.364) 0.116 (0.277) -0.546* (0.274) 0.178 44.282*** 3.178 26.936† 7.923 0.106 0.362 (0.312) -0.320 (0.405) -0.384* (0.156) 0.101 (0.380) 0.528 (0.441) -0.312 (0.305) -0.946***(0.298) Model 7: Drug Screens 0.513† (0.275) -0.410 (0.384) -0.265† (0.142) 0.653† (0.350) 1.156** (0.393) -0.213 (0.275) -0.331 (0.278) Model 3: Model 4: Model 6: Community Blood Model 5: Criminal Service Alcohol Curfew Associations Note: Final sample size, after listwise deletion: N = 332 respondents. Levels of significance for all chi-square statistics were as follows: †p < .10. *p < .05. **p < .01. *** p < .001. Levels of significance for all coefficients were based on Wald statistics with p values as follows: †p < .10 for HO: β = 0. *p < .05 for HO: β = 0. **p < .01 for HO: β = 0. ***p < .001 for HO: β = 0. Agency funding    (1 = state,    0 = county/municipal) Administrative setting    (1 = correctional department    or parole authority,    0 = judiciary) Community setting suburban    (1 = suburban,    0 = rural or small town) Community setting urban    (1 = urban,    0 = rural or small town) Number of officers in agency    (as per quartile) Policy to inhibit formal actions    for certain minor violations    (1 = yes, 0 = no) Policy to mandate formal action    for certain violation    (1 = yes, 0 = no) Model chi-square (df = 17) Hosmer and Lemeshow test    (chi-square w/8 df) Nagelkerke R2 Model 1: Model 2: Meetings Independent Variables Employment Table 2 (continued) 434   Journal of Contemporary Criminal Justice In relation to the occupational variables examined herein, job titles were significantly associated with decisions concerning community service violations in Model 3 (p < .05) and curfew violations in Model 5 (p < .05). As compared to middle managers, regular line officers were about 52% less likely (eB = 0.481) to support the use of formal hearings with community service violations and about 43% less likely (eB = 0.565) to support the use of formal hearings with curfew violations. The size of a PPO’s caseload was positively associated with support for formal hearings with employment violations in Model 1 (p < .01) and curfew violations in Model 5 (p < .05). As for employment violations, a 1-quartile increase in the size of a PPO’s caseload increased the likelihood of supporting formal hearings by about 47% (eB = 1.472). For curfew violations examined, a 1-quartile increase in caseload size increased the likelihood of supporting formal hearings by about 30% (eB = 1.297). The type and age of one’s caseload were not significantly associated with discretionary decisions (p > 0.05) for the seven dependent variables. In relation to organizational variables, agency funding sources were significantly associated with decisions concerning curfew violations in Model 5 (p < .05) and drug screen violations in Model 7 (p < .001). As compared to PPOs in agencies that were funded through county or municipal agencies, PPOs in state-funded agencies were about 42% less likely (eB = 0.579) to support the use of formal hearings with curfew violations and about 61% less likely (eB = 0.388) to support formal hearings with drug screen violations. The number of officers in an agency was also negatively associated with the likelihood of PPOs supporting the use of formal hearings for missed meetings in Model 2 (p < .01), community service violations in Model 3 (p < .01), and drug screen violations in Model 7 (p < .05). Hence, a 1-quartile increase in the number of officers in the PPO’s agency decreased the likelihood of PPOs supporting formal hearings by about 35% for missed meetings (eB = 0.646), 31% for community service violations (eB = 0.690), and 32% for drug screen violations (eB = 0.681). This study also examined variables that measured organizational policies to (a) inhibit responses to certain minor violations, and (b) mandate formal action for certain violations. With regard to the former, PPOs in agencies with policies to inhibit formal action for certain minor violations were 71% less likely (eB = 0.290) to support the use of formal hearings for missed meetings with PPOs in Model 2 (p < .01). With regard to the latter, PPOs in agencies with policies that mandate formal action for certain violations were more than 2.5 times more likely (eB = 2.646) to support using formal hearings for curfew violations in Model 5 (p < .001). Of the eight organizational variables examined, three failed to be associated with any of the dependent variables. First, as compared to PPOs in non-Southern agencies, PPOs in Southern agencies did not differ significantly (p > .05) in their level of support for formal hearings as modeled across the seven dependent variables. In addition, support for formal hearings with technical violations did not differ significantly (p > .05) between PPOs operating in correctional versus judicial settings; Downloaded from ccj.sagepub.com by guest on May 1, 2013 Kerbs et al. / Decision Making by Probation and Parole Officers   435 moreover, there was no difference (p > .05) in decision making between PPOs working in urban settings and those working in rural settings and small towns for the seven models examined herein. In contrast, as compared to PPOs working in rural settings and small towns, suburban PPOs were significantly more likely to support the use of formal hearings for alcohol violations in Model 4 (p < .05) and criminal associations in Model 6 (p < .01). More specifically, suburban PPOs were more than twice as likely (eB = 2.105) to support the use of formal hearings for alcohol violations and more than 3 times as likely (eB = 3.179) to support the use of formal hearings for criminal associations. In sum, the PPOs’ discretionary decisions were predominantly affected by organizational characteristics beyond the PPOs’ daily control. Although only 7.1% of the sociodemographic coefficients (i.e., 2 out of the 28 coefficients) and 11.4% of the occupational coefficients (4 out of 35) demonstrated significant associations with dependent variables (p < .05), 16.1% of the organizational coefficients (9 out of 56) were significantly associated with dependent variables. Furthermore, although no one organizational variable was associated with every type of technical violation examined in the seven models, each violation (with the exception of Model 1) was associated with at least one or more organizational factors. Finally, in additional analyses not presented herein, organizational variables collectively explained more variance in the seven dependent variables than sociodemographic or occupational variables when entered in blocks. Discussion Liebling (2000) described the selective enforcement of rules as a resource used by correctional officers. Officers employed discretionary decision making by selectively enforcing rule violations, not as a primary goal in and of itself, but rather as a resource for building and maintaining their authority within the context of their individual relationships with inmates and within the context of the organization as a whole. PPOs are also in a position to use their discretion as a means of fulfilling higher order goals to include (a) behavioral change among offenders, (b) the assessment of offender risk, (c) the effective management of offender risk and deployment of resources across the aggregate caseload, and (d) the promotion of public safety (Harris et al., 2004; Steen & Opsal, 2007). As PPOs choose if and how to respond to offender actions (e.g., curfew violations) and/or inactions (e.g., unemployment) that are not necessarily illegal but constitute violations of expected behavior, PPOs make discretionary decisions within the context of organizations. As noted by other researchers (Feeley & Simon, 1992; Harris et al., 2004; Steen & Opsal, 2007) and the APPA (1987), PPOs make discretionary decisions in organizations that operate according to two sets of politically charged and perhaps contradictory principles: (a) rehabilitation, and (b) social control through enforcement. Downloaded from ccj.sagepub.com by guest on May 1, 2013 436   Journal of Contemporary Criminal Justice The results described within this article provide a modest but important step toward the contribution of empirical evidence regarding the influence of organizationallevel factors on PPO discretionary decision making in relationship to seven specific kinds of technical violations. In addition, statistically significant associations from this study were often congruent with prior research, providing the beginnings of an integrated backdrop against which to critically examine the influence of extralegal variables on the pursuit of formal hearings for technical violations. As expected, this analysis of extralegal variables only explained a limited amount of variance in PPO decisions that, theoretically, should be devoid of influence from variables measuring the officers’ sociodemographic backgrounds, their occupational characteristics, and their organizational environments. Hence, academics, policy makers, and criminal justice practitioners should be disturbed that extralegal factors played a role in such decisions; moreover, efforts should be made to mediate and/or eliminate the influence of such factors. Additional research and policy implications stemming from this analysis in concert with previous findings include the following suggestions. First, this study documented the presence of high caseloads for PPOs who had an average of 141 offenders and maximum caseloads of 4,000 offenders. Findings from prior studies have estimated the national average at 124 offenders per PPO in 2002 (Camp & Camp, 2003). Although researchers have yet to agree on the “ideal” size for a PPO’s caseload, Champion (2005) suggested that 30 offenders, “is perhaps the closest number to an ideal caseload size” (p. 415). Given the disparity between this purported ideal number and the average number identified by the current study, policy makers and other advocates for community safety should be concerned about the following: (a) the system’s capacity to properly supervise offenders and maintain public safety, and (b) the need to reduce the size of caseloads, which would enhance the quality of community supervision. Second, it is interesting to see that sociodemographic variables did not generally explain the variance in PPOs’ discretionary decisions. This was fairly congruent with the literature (Erez, 1989) but worthy of additional study, given that both the PPO’s gender and race were associated with decisions regarding community service and curfew violations. Third, it is interesting to note that both occupational and organizational variables affected PPO support for formal hearings with technical violators. These findings were congruent with a comment made by Gelsthorpe and Padfield (2003) who said, “‘internal’ organizational and occupational factors may be as important as the legal rules which guide action [related to discretionary decisions]” (p. 9). Given that extralegal factors were significantly associated with decision making in this study, one must ask whether discretionary decisions should be limited by laws and/or procedures to safeguard against influential factors that are not grounded in an offender’s past or present criminal behavior (legal factors). Though this is a philosophical question, it is one that is worthy of debate and further study. Downloaded from ccj.sagepub.com by guest on May 1, 2013 Kerbs et al. / Decision Making by Probation and Parole Officers   437 Fourth, as compared with middle managers, regular line officers were less supportive of using formal hearings for community service and curfew violations. This finding was congruent with prior ethnographic studies (McCleary, 1975, 1978) that documented how line officers underreport violations to maintain credibility with their superiors and to bolster the appearance of being in control of their caseloads. Fifth, as the size of a PPO’s caseload increased, so did support for formal hearings with employment and curfew violations. This finding was a bit confusing. One might expect higher caseloads to be associated with lower support for formal hearings with technical violators, in part because PPOs might feel the need to focus on larger (criminal) violations when time is at a premium. Of course, as caseloads increase, PPOs may be more supportive of formal hearings for technical violations due to a perceived risk of future criminal activity and/or hopes of receiving a temporary reprieve from heavy caseloads of offenders. Conversely, this might not be true for PPOs who get new cases every time an old case closes. Clearly, more research is needed. Sixth, this study did not find evidence of Justice by Geography in relation to PPOs from Southern versus non-Southern agencies. Thus, PPOs from Southern agencies were no more or less supportive of formal hearings than PPOs from nonSouthern agencies. Although this finding was congruent with some of the literature (Greenberg & West, 2001; McGarrell, 1993; Wiedner et al., 2004), it contradicted most studies that found Southern states to be more punitive than non-Southern states (Carroll & Doubet, 1983; Chiricos & Crawford, 1995; Michalowski & Pearson, 1990; Snell, 2000; Weidner & Frase, 2001, 2003). As noted in the literature review of this article, it is important to guard against spurious correlations between region and punitiveness that might be related to urbanization (Michalowski & Pearson, 1990) and the level of bureaucracy in the court system (Ulmer, 1997). Because Southern states tend to be more rural (and perhaps less bureaucratic) than states in other regions, the present study concurrently examined measures for urbanization, bureaucratization, and region. It is interesting to note that, compared to PPOs from rural agencies, PPOs from suburban agencies were more supportive of formal hearings with violations for alcohol use and criminal associations, which was congruent with studies that found suburban areas (i.e., more affluent areas) to be more punitive than rural areas (Ulmer & Kramer, 1996). It is surprising that there was no ruralurban difference, which would have been expected as per the extant literature. Seventh, the levels of bureaucracy in probation and parole agencies appeared to influence PPO’s discretionary decisions. This study examined the number of officers in an agency as a proxy for levels of bureaucracy; findings suggested that increasing the number of officers in an agency decreased PPO support for formal hearings with offenders who missed meetings, failed to complete required community service, and/ or failed to pass drug screens. These findings were congruent with court-based studies that found higher levels of bureaucracy to be associated with a decreased likelihood of incarceration (Ulmer, 1997). Perhaps larger court systems and PPO agencies have more in-house options to stabilize those with minor violations, thereby reducing the need to seek external assistance via formal hearings for such violations. Downloaded from ccj.sagepub.com by guest on May 1, 2013 438   Journal of Contemporary Criminal Justice Eighth, this study has philosophical implications for community corrections officers. Given that sizeable proportions of the sample reported that agency policies mandated or inhibited responses to certain violations, one must question whether such policies are diverting line officers from making discretionary decisions that they feel are in the best interests of community safety and offender rehabilitation. Moreover, PPOs in agencies with policies mandating formal action for certain offenses appeared more supportive of formal hearings for curfew violators than PPOs from agencies without such policies. Given that line officers are in the best position to know the offenders because of their close working relationships with offenders, one must question the efforts of policy makers to remove discretion with such mandates. Study Limitations and Future Directions for Research This study had several limitations that should be noted. First, this survey experienced a “words-versus-deeds” challenge. Officers could only predict what they would do given a certain scenario. What they would actually do may have been another matter. Second, the scenarios that were presented were very brief, and many other factors not presented could have been relevant in decision making. Third, this survey did not present officers with any choices about what sort of formal intervention they might have taken at formal hearings. Merely stating that the officer would have initiated formal sanctions or actions did not indicate whether such action would have included a recommendation for revocation (incarceration) or some lesser sanction. Fourth, these results cannot be generalized because this study did not utilize a random sample; moreover, the APPA membership may not have been representative of all PPOs in the United States, largely because the APPA roster appeared to be top heavy with administrators and short on line-level officers. Hence, future research should aim to replicate and advance these findings with random samples that can be generalized to the county, state, and/or federal levels. Fifth, the survey’s low response rate was a bit problematic but not completely unexpected given that APPA membership is voluntary. Hence, future mail-based surveys should aggressively attempt to increase response rates by using phone- and mail-based techniques to follow up with potential respondents in such studies. Finally, the amount of explained variance in the dependent variables would have increased if this study had employed a factorial vignette design with controls for offender- and offense-based characteristics. Such an approach appears to represent a logical next step in the progression of research regarding PPOs’ discretionary decision making as applied to technical violations. Conclusion Although some academics believe that probation and parole violations are increasing prison admissions, the reality is that PPOs’ discretionary decisions are partly responsible for rising prison admissions in many states (U.S. Department of Downloaded from ccj.sagepub.com by guest on May 1, 2013 Kerbs et al. / Decision Making by Probation and Parole Officers   439 Justice, 2004). Despite the fact that 75% to 80% of parolees commit technical violations, studies have found that states differ in their responses to violations, and dependent on the state, anywhere from 3% to 45% of all such violations result in admission to prison (U.S. Department of Justice, 2004). Such state-by-state variation may be due in part to discretionary decisions that are influenced by extralegal factors (i.e., occupational and organizational factors) that lie outside of codified law. These factors raise Constitutional questions rooted in the 14th Amendment’s declaration that the State shall not, “deprive any person of life, liberty, or property, without due process of law.” In theory, extralegal factors should not influence decisions, but this study suggests that they do explain some of the variance in PPO decisions, despite the 14th Amendment’s call for due process of law. Hence, lawyers, criminal justice practitioners, and policy makers should continue to examine extralegal variables and their implications for technical violations that might result in a loss of liberty. References Abadinsky, H. (1978). Parole history: An economic perspective. Offender Rehabilitation, 2, 275-278. Alschuler, A. W. (1979). Plea bargaining and its history. Law & Society Review, 13, 211-245. American Probation and Parole Association. (1987). Probation. Retrieved June 20, 2008, from http:// www.appa-net.org Banks, J., Porter, A. L., Rardin, R. L., Silver, T. R., & Unger, V. E. (1977). Evaluation of intensive special probation projects. Washington, DC: U.S. Government Printing Office. Bond, J. E. (1975). Plea bargaining and guilty pleas. New York: Clark Boardman. Camp, C. G., & Camp, G. M. (2002). The corrections yearbook 2001: Adult systems. Middletown, CT: Criminal Justice Institute. Camp, C. G., & Camp, G. M. (2003). The corrections yearbook 2002: Adult systems. Middletown, CT: Criminal Justice Institute. Carroll, L., & Doubet, M. (1983). U.S. social structure and imprisonment. Criminology, 21, 449-456. Champion, D. (2005). Probation, parole and community corrections (5th ed.). Upper Saddle River, NJ: Prentice Hall. Chiricos, T. G., & Crawford, C. (1995). Race and imprisonment: A contextual assessment of the evidence. In D. F. Hawkins (Ed.), Ethnicity, race and crime: Perspectives across time and place (pp. 281-309). Albany: State University of New York Press. Davis, K. C. (1969). Discretionary justice: A preliminary inquiry. Baton Rouge: Louisiana State University Press Dixon, J. (1995). The organizational context of criminal sentencing. American Journal of Sociology, 100, 1157-1198. Durose, M. R., & Langan, P. A. (2005). State court sentencing of convicted felons, 2002: Statistical tables (NCJ 208910). Washington, DC: Bureau of Justice Statistics. Erez, E. (1989). Gender, rehabilitation, and probation decisions. Criminology, 27, 307-327. Feeley, M. M., & Simon, J. (1992). The new penology: Notes on the emerging strategy of corrections and its implications. Criminology, 30, 449-475. Feld, B. (1991). Justice by geography: Urban, suburban, and rural variations in juvenile justice administration. Journal of Criminal Law and Criminology, 82, 156-210. Downloaded from ccj.sagepub.com by guest on May 1, 2013 440   Journal of Contemporary Criminal Justice Friedman, L. M. (1979). Plea bargaining in historical perspective. Law & Society Review, 13, 247-259. Gelsthorpe, L., & Padfield, N. (2003). Introduction. In L. Gelsthorpe & N. Padfield (Eds.), Exercising discretion: Decision-making in the criminal justice system (pp. 1-28). Portland, OR: Willan Publishing. Glaze, L. E., & Palla, S. (2004, July). Bureau of Justice Statistics bulletin: Probation and parole in the United States, 2003. Washington, DC: U.S. Department of Justice. Greenberg, D., & West, V. (2001). State prison populations and their growth, 1971-1991. Criminology, 39, 615-654. Harris, P. M., Gingerich, R., & Whittaker, T. A. (2004). The effectiveness of differential supervision. Crime & Delinquency, 50, 235-271. Harrison, P. M., & Beck, A. J. (2006, May). Prison and jail inmates at midyear 2005. Washington, DC: Bureau of Justice Statistics. Ho, T. (2003). The influence of suspect gender in domestic violence arrests. American Journal of Criminal Justice, 27, 183-195. Hosmer, D. W., & Lemeshow, S. (1989). Applied logistic regression. New York: John Wiley. Huang, W. S. W., Finn, M. A., Ruback, R. B., & Friedman, R. R. (1996). Individual and contextual influences on sentence lengths: Examining political conservatism. The Prison Journal, 76, 398-419. Jones, M. (2004). Community corrections. Prospect Heights, IL: Waveland Press. Langan, P. A., & Levin, D. J. (2002, June). Bureau of Justice Statistics special report: Recidivism of prisoners released in 1994. Washington, DC: U.S. Bureau of Justice Statistics. Liebling, A. (2000). Prison officers, policing, and the use of discretion. Theoretical Criminology, 4, 333-357. McCleary, R. (1975). How structural variables constrain the parole officer’s use of discretionary powers. Social Problems, 23, 209-225. McCleary, R. (1978). Dangerous men: The sociology of parole. Beverly Hills, CA: Sage. McGarrell, E. F. (1993). Institutional theory and the stability of a conflict model of the incarceration rate. Justice Quarterly, 10, 7-28. Michalowski, R. J., & Pearson, M. A. (1990). Punishment and social structure at the state level: A crosssectional comparison of 1970 and 1980. Journal of Research in Crime and Delinquency, 27(1), 52-78. Myers, M. A., & Talarico, S. M. (1987). The social contexts of criminal sentencing. New York/Berlin: Springer-Verlag. Nieto, M. (2003). Adult parole and probation in California. Sacramento: California Research Bureau. Petersilia, J. (2001). When prisoners return to communities: Political, economic, and social consequences. Federal Probation, 65(1), 3-8. Petersilia, J., & Turner, S. (1990). Intensive supervision for high risk offenders: Three California experiments. Santa Monica, CA: RAND. Poole, E. D., & Regoli, R. M. (1980). Race, institutional rule breaking, and disciplinary responses: A study of discretionary decision-making in prison. Law and Society Review, 14, 931-946. Rosecrance, J. (1987). Getting rid of the prima donnas: The bureaucratization of a probation department. Criminal Justice and Behavior, 14, 138-155. Snell, T. L. (2000). Bureau of Justice Statistics bulletin: Capital punishment 1999. Washington, DC: U.S. Department of Justice. Steen, S., & Opsal, T. (2007). Punishment on the installment plan: Individual-level predictors of parole revocation in four states. The Prison Journal, 87, 344-366. The Sentencing Project. (2006). New incarceration figures: Thirty-three consecutive years of growth. Washington, DC: Author. Tischler, C. A., & Marquart, J. W. (1989). Analysis of disciplinary infraction rates among male and female inmates. Journal of Criminal Justice, 17, 507-513. Travis, J., & Lawrence, S. (2002). Beyond the prison gates: The state of parole in America. Washington, DC: The Urban Institute. U.S. Department of Justice. (1996, June). Probation and parole population reaches almost 3.8 million. Washington, DC: Author. Downloaded from ccj.sagepub.com by guest on May 1, 2013 Kerbs et al. / Decision Making by Probation and Parole Officers   441 U.S. Department of Justice. (2004). Parole violations revisited. Washington, DC: Author. Ulmer, J. (1997). Social worlds of sentencing: Court communities under sentencing guidelines. Albany: State University of New York. Ulmer, J. T., & Kramer, J. H. (1996). Court communities under sentencing guidelines: Dilemmas of formal rationality and sentencing disparity. Criminology, 34, 383-408. Vigorita, M. (2001). Prior offense type and the probability of incarceration. Journal of Contemporary Criminal Justice, 15, 167-193. Weidner, R. R., & Frase, R. S. (2001). A county-level comparison of the propensity to sentence felons to prison. International Journal of Comparative Criminology, 1(1), 1-22. Weidner, R. R., & Frase, R. S. (2003). Legal and extralegal determinants of intercounty differences in prison use. Criminal Justice Policy Review, 14, 377-400. Weidner, R. R., Frase, R., & Pardoe, I. (2004). Explaining sentence severity in large urban counties: A multilevel analysis of contextual and case-level factors. The Prison Journal, 84, 184-207. Wilson, J. Q., & Petersilia, J. (2002). Crime: Public policies for crime control. Oakland, CA: Institute for Contemporary Studies Press. Wortley, R. K. (2003). Measuring police attitudes toward discretion. Criminal Justice and Behavior, 30, 538-558. John J. Kerbs is an assistant professor in the Department of Criminal Justice at East Carolina University. His current research focuses on the measurement and prevention of violence in prisons and schools. His articles have appeared in the American Journal of Criminal Justice, Annals of the American Academy of Political and Social Science, Crime & Delinquency, Criminal Justice Review, Federal Probation, Hamline Journal of Public Law and Policy, Journal of Adolescent Health, Perspectives: The Journal of the American Probation and Parole Association, and Professional Psychology—Research and Practice. Mark Jones is a professor of criminal justice at East Carolina University, Greenville, North Carolina; he has authored or contributed to more than 40 publications, many in the area of community corrections. He is the author of Criminal Justice Pioneers in U.S. History (Allyn & Bacon, 2005). Jennifer M. Jolley, MSW, is a doctoral student and a National Institute of Mental Health predoctoral fellow in the George Warren Brown School of Social Work at Washington University (St. Louis, Missouri); she is also a police officer and grants specialist with the Winterville (North Carolina) police department. Her work focuses on the design and implementation of community, correctional, and forensic programs that address interpersonal violence. Her articles have appeared in the American Journal of Criminal Justice, Crime & Delinquency, and Criminal Justice Review. Downloaded from ccj.sagepub.com by guest on May 1, 2013

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