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Maximum Security Correctional Officers: An Exploratory Investigation into Their Social Bases of Power

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Abstract

Correctional officers are responsible for maintaining prison order, establishing institutional security and managing inmate behavior. To accomplish these goals, officers are sometimes required to deploy available bases of power, which are mechanisms of behavioral control used to achieve certain objectives, and include reward, referent, legitimate, coercive and expert. While power bases have been researched at length across numerous organizational settings, they have received comparatively less attention within corrections. Using questionnaire data from a statewide population of maximum security correctional officers (N = 559), several ordered logistic regression models were estimated in order to explore the power bases upon which officers rely the most, as well as the antecedents to this decision. Referent and legitimate power ranked highest concerning their ability to control inmates, while measures of officer risk perceptions and work-related attitudes significantly predicted their power base reliance. To ensure inmate compliance with institutional regulations, it is recommended that correctional officers utilize softer forms of power such as referent and legitimate.

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Notes

  1. There are a total of 28 state-operated correctional institutions within South Carolina, eight of which are maximum level security. Of these eight, 2 are female only facilities and the remainder are male only. South Carolina’s prison facilities adopt one of three different security level classifications, and they include level-I (minimum), level-II (medium) and level-III (maximum). Lower level security facilities are either community-based pre-release work centers designed to house non-violent offenders serving sentences of 36 months or less, or institutions with double-bunk cubicles surrounded by high perimeter fences that house offenders serving sentences of between 12 and 60 months. High level security facilities (Level-III), instead, house violent offenders who are serving extended sentences of greater than 60 months and who may be exhibiting certain behavioral, mental or cognitive problems that require medical attention. Offenders here are often isolated from one another, have their activities constantly supervised and restricted and are enclosed within single-celled structures that are surrounded by 20’ high perimeter fences with extensive electronic monitoring (SCDC, 2014).

  2. An electronic survey account was purchased using the services of QuestionPro.com. The survey was made available between January 22, 2014 and February 22, 2014. Correctional administrative officials uploaded the survey to computers at all eight maximum security facilities throughout the state via their intranet service, which permitted officers the opportunity to complete the survey during their shift. Completed surveys were submitted through QuestionPro.com, with no identifying information contained within them.

  3. Updated records as of January, 2014 regarding the total number of employed officers were provided by the Research and Development team of the South Carolina Department of Corrections.

  4. When estimating OLM models, it is important to examine whether the effects of explanatory measures are constant across all categories of the dependent variable, which is assessed by running an omnibus Brant test (Hoffmann, 2004; Long, 1997). None of the independent variables within the reward, referent and legitimate power models violated the proportional odds assumption, while instead risk perceptions in the coercive model and officer stress in the expert model were in violation. Under these conditions, it is recommended to employ a generalized ordered logistic regression modelling technique (GOLM) (Williams, 2006). GOLM relaxes the proportional odds assumption and allows the coefficients from “explanatory variables to vary with the level of response category thresholds” (Kaminski et al., 2010, p. 93).

  5. Variance inflation factors across all models ranged from 1.02 to 2.21, while tolerance levels never descended below .45, indicating few multi-collinearity concerns (Hair et al., 2010).

  6. When analyzing clustered data, such as are present here with officers nested within 8 prison facilities, it has been suggested that cluster robust standard errors be estimated as these account for correlated error and produce less biased statistical output (i.e., coefficients and standard errors) (Liang & Zeger, 1986; Rogers, 1993). Angrist and Pischke (2009) cautioned though that this procedure should only be adopted when the number of clusters is both greater than 40 and the total number of independent variables analyzed. Given the nature of the current data, more robust and alternative methods that can accommodate small numbers of clusters include the pairs cluster bootstrapped t-statistic, the wild cluster bootstrapped t-statistic and the cluster adjusted t-statistic standard errors as these each produce more reliable null hypothesis test statistics and standard errors (Horowitz, 1997; Ibragimov & Muller, 2010). However, even these procedures require that the number of clusters be greater than the number of explanatory variables, and in our analyses we have 12 variables to 8 clusters. With these warnings in mind, and to account for heteroskedasticity, robust standard error estimates only are reported (Hoffmann, 2004). It should be noted though that despite these cautionary notes, some comparative analyses between models estimated with robust standard errors and the suggested cluster robust standard error alternatives were conducted. Minimal differences in statistical output were detected between all estimated models.

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Authors and Affiliations

  1. Department of Criminology and Criminal Justice, University of West Florida, Pensacola, FL, 32514, USA

    Frank Valentino Ferdik

  2. Department of Criminology and Criminal Justice, University of South Carolina, Columbia, SC, 29208, USA

    Hayden P. Smith

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  1. Frank Valentino Ferdik
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  2. Hayden P. Smith
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Correspondence to Frank Valentino Ferdik.

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Table 4 Promax rotated pattern matrix table for variables and items used in analysis
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Ferdik, F.V., Smith, H.P. Maximum Security Correctional Officers: An Exploratory Investigation into Their Social Bases of Power. Am J Crim Just 41, 498–521 (2016). https://doi.org/10.1007/s12103-015-9307-5

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