Disciplinary Infractions Behind Bars

Abstract

Using data from one Midwestern state, this article investigates whether or not characteristics of importation and deprivation serve as predictors of receipt of institutional disciplinary infractions. The results of the logistic regression analyses indicate that both importation and deprivation factors were significant indicators of having received disciplinary infractions. Specifically, among 585 adult male inmates, data reveal that two importation measures and two deprivation measures were significant predictors of receipt of any disciplinary infractions. An inmate’s gang membership status and conviction for a sex offense are also found to have been significant indicators of having or not having received at least one serious disciplinary infraction. Policy implications, limitations, and directions for future research are discussed.

Keywords

disciplinary infraction prison misconduct prison violence sex offender gang member importation deprivation

Introduction

Prisons are designed to house convicted criminal offenders and incapacitate such individuals from committing additional criminal offenses. And yet, while confined to prisons, many inmates often continue their refractory behavior through violation of institutional rules and regulations. Identifying institutional resources and focusing such resources on inmates, in order to inhibit their criminal and deviant behavior, are both important and expensive challenges. With some correctional institutions recording thousands of disciplinary infractions annually, resulting costs may be substantial, with each incident averaging approximately $1,000 (Lovell & Jemelka, 1996). This cost may be even more considerable when taking into account the liabilities posed by disciplinary infractions, resulting litigation, and the need for constructing more costly high-security prisons (Goetting & Howsen, 1986; Vaughn & del Carmen, 1995). In addition, disciplinary infractions may have an interactional effect on overcrowding (Gendreau, Goggin, & Law, 1997; McCain, Cox, & Paulus, 1980; Wooldredge, Griffin, & Pratt, 2011). With most state prisons operating near or above capacity (i.e., 1% below to 14% above) and federal prisons operating 34% above their intended capacity, overcrowding enhances opportunities for prison misconduct and may subsequently increase operational and financial tolls on correctional institutions (Harrison & Beck, 2006).

Disciplinary infractions may also serve as indicators for recidivism (Dhami, Ayton, & Loewenstein, 2007; Duwe & Clark, 2011). With state and federal annual correctional budgets estimated at more than $68 billion, finding ways in which to mitigate prison misconduct is crucial (Riordan & McDonald, 2009). Regardless of the violent or nonviolent nature of disciplinary infractions, inmates engaging in any type of institutional misconduct are more likely to return to a prison upon release. These repeated instances of recidivism and costs associated with community supervision further contribute to the $68 billion annual correctional budget and exacerbate existing institutional issues, such as overcrowding. Therefore, ways in which to control or perhaps only reduce such costs is likely to make institutional operations more efficient. One such means of enhancing efficiency is to reduce the number of disciplinary infractions occurring inside a correctional institution. Identification of importation and deprivation characteristics associated with inmates who commonly receive disciplinary infractions is important for both operational efficiency and financial costs. This is the focus of the present study.

Literature Review

Two primary types of characteristics have been examined to attempt to explain and even predict an inmate’s likelihood of committing a disciplinary infraction. These two main categorizations are demographic characteristics (e.g., age, race/ethnicity, education, marital status, and employment) and social characteristics (e.g., criminal history, gang affiliation, amount of time incarcerated, institutional security level, and visitation). Disciplinary infractions come about in many forms, ranging from serious and violent acts (e.g., inmate-on-inmate and inmate-on-staff assaults) to less serious nonviolent infractions (e.g., disobeying a direct order and nonlethal contraband). Infractions may also include alcohol and drug-related offenses. At present, in an effort to reduce disciplinary infractions, prison administrators have only demographic and social characteristics of inmates to aid in making a classification and/or housing assignment.

Throughout the past half century, two chief explanations have been applied to attempt to understand why disciplinary infractions occur inside prisons. These are the theories of importation and deprivation. Importation theory refers to demographic characteristics and socialization experiences that offenders bring with them into correctional institutions (Giallombardo, 1966; Irwin, 1981; Irwin & Cressey, 1962; Schrag, 1961; Wheeler, 1961). It is then posited that these characteristics and experiences are primary causes to inmate misconduct (Poole & Regoli, 1980). Demographic characteristics have been primarily used as proxies for importation theory, since they often have deeper social meanings and experiences attached to them (Irwin & Cressey, 1962; Schrag, 1961; Thomas, 1977). Deprivation theory refers to the occurrence of inmates being removed from society and forced to adapt to the “pains of imprisonment,”¹ subsequently adopting an inmate subculture that is formed often in direct opposition to prison administrators and staff members (Cao, Zhao, & Van Dine, 1997; Clemmer, 1940; Goffman, 1961; Sykes, 1958). Through these “pains of imprisonment” and the adoption of an inmate subculture, the behavior of inmates is believed to be influenced, resulting in institutional misconduct and potential disciplinary infractions (Berg & DeLisi, 2006; Cao et al., 1997; McCorkle, Miethe, & Drass, 1995). Although wide in scope, these theories have developed over the past 50 years to include specific correlates of disciplinary infractions.

Previous research has examined a wide range of specific correlates as possible contributors to disciplinary infractions, under importation and deprivation theories. Such studies have focused on everything from an inmate’s level of testosterone to an inmate’s quality of life prior to incarceration (Dabbs, Frady, Carr, & Besch, 1987; Dhami et al., 2007). Even issues such as specific locations inside an institution where an inmate is housed have been examined in relation to its impact on disciplinary infractions (Smith, 1996). However, the two most examined and supported issues are importation theory’s demographic attributes of inmates and deprivation theory’s institutional characteristics.

Importation Theory

Importation theory’s main premise is to measure factors and characteristics held by inmates prior to incarceration and their effect on inmates’ behavior (including misconduct) while incarcerated (Jiang & Fisher-Giorlando, 2002). The main premise of this theory is that individuals “import” values and socialization experiences with them upon entering prisons. Such values and socialization experiences can include subculture ideologies, lifelong economic deprivation, and race-specific experiences/discrimination. It is these characteristics and values that are believed to have a substantial influence upon an inmate’s behavior while incarcerated, especially violent behavior (Berg & DeLisi, 2006; Cao et al., 1997; Carroll, 1974; Ellis, Grasmick, & Gilman, 1974; Hochstetler & DeLisi, 2005; Irwin, 1981; Irwin & Cressey, 1962; Jacobs, 1974, 1977; Jiang & Fisher-Giorlando, 2002; McCorkle et al., 1995; Poole & Regoli, 1980; Schrag, 1961; Waquant, 2001; Wheeler, 1961). Since demographics help generalize values and overall socialization among various populations, demographic indicators have largely been used as proxies to gauge an inmate’s likelihood for disciplinary infractions (Irwin & Cressey, 1962; Schrag, 1961; Thomas, 1977).

Numerous studies throughout the past three decades have examined the effect of demographic characteristics on the likelihood for disciplinary infractions (Chapman & Alexander, 1981; Dhami et al., 2007; Finn, 1995; Flanagan, 1983; Griffin & Hepburn, 2006; Harer & Steffensmeier, 1996; Hewitt, Poole, & Regoli, 1984; Motiuk, 1991; Poole & Regoli, 1980; Steiner & Wooldredge, 2008; Worrall & Morris, 2011). The above-mentioned studies have demonstrated that demographic information can be used to help identify which inmates have the greatest potential for committing disciplinary infractions. Some of the most commonly examined demographic characteristics include age, race/ethnicity, education, marital status, and employment (i.e., preadmission and while incarcerated). However, some demographics (e.g., education and marital status) have seldom been examined. Perhaps because demographics are static and explain only small variations in disciplinary infraction rates, demographic variables have recently been relegated to the role of control variables (Drury & DeLisi, 2010; Steiner & Wooldredge, 2008). Despite the recent assignment of demographic variables as control variables, understanding the contribution of demographic measures to disciplinary infraction likelihood remains important and merits review.

Age

Age has consistently been found to be the most important indicator for the likelihood of both violent and nonviolent disciplinary infractions (Cunningham & Sorensen, 2006, 2007; Flanagan, 1983; Griffin & Hepburn, 2006; Kuanliang, Sorensen, & Cunningham, 2008; Morris, Longmire, Buffington-Vollum, & Vollum, 2010; Sorensen & Cunningham, 2010; Steiner & Wooldredge, 2008; Welsh, McGrain, Salamatin, & Zajac, 2007; Wooldredge et al., 2001). Specifically, a significant negative relationship has been found with age regarding an inmate’s likelihood for disciplinary infractions (Bales & Miller, 2012; Jiang, Fisher-Giorlando, & Mo, 2005; Morris et al., 2010; Walters, 2007; Walters & Schlauch, 2008). That is, as inmates become older, such individuals are less likely to commit disciplinary infractions of all varieties (Griffin & Hepburn, 2006).

Studies have also examined the relationship that aging has on the decreased likelihood of committing disciplinary infractions. Sorensen and Cunningham (2010) found that for every one-year increase in age the likelihood for disciplinary infractions decreased by 5%. An even greater decrease was reported by Kuanliang, Sorensen, and Cunningham (2008), in their study of juvenile offenders, where an almost 10% decrease in disciplinary infractions occurred for every additional year of age.

In addition, studies have examined the specific ages and age categories regarding how such may be related to disciplinary infraction involvement. One such study indicated that inmates under 25 years old were the most likely individuals to receive disciplinary infractions (Flanagan, 1983). Building upon this notion, Cunningham and Sorensen (2007) found that if inmates were under the age of 21, they were 3.5 times more likely to commit a disciplinary infraction, compared to the inmates in the 31- to 35-year range. Furthermore, if inmates were between 21 and 25 years old, they were 63% more likely, and those between 36 and 40 years old were 13% less likely than inmates who were 31 to 35 years of age (Cunningham & Sorensen, 2007).

Studies examining the youngest and oldest inmates have found similar relationships, with younger inmates being more likely to receive disciplinary infractions. Studies regarding juvenile offenders have indicated that age still has a strong and negative relationship with disciplinary infractions for those under the age of 18 (Dabbs et al., 1987; Kuanliang et al., 2008; McReynolds & Wasserman, 2008). As one increases in age, their propensity to commit infractions decreases, even among the youngest of offenders. At the other extreme, research focused on senior inmates (i.e., age 50 and above) found that younger individuals were more likely to receive disciplinary infractions (McShane & Williams, 1990). In short, the literature has consistently shown that disciplinary infractions are inversely related to age. Although age has held as a consistent demographic indicator of predicting disciplinary infractions, the next demographic indicator of race/ethnicity has shown mostly mixed results.

Race/Ethnicity

Next to age, an inmate’s race and ethnicity have been the most frequently examined demographic characteristic regarding the potential for institutional infractions (Bales & Miller, 2012; Finn, 1995; Drury & DeLisi, 2011; Griffin & Hepburn, 2006; Hewitt et al., 1984; Jiang et al., 2005; Jiang & Winfree, 2006; Lembo, 1969; McReynolds & Wasserman, 2008; Morris et al., 2010; Poole & Regoli, 1980; Steiner & Wooldredge, 2008; Wolff, Shi, & Blitz, 2008; Wooldredge et al., 2001; Worrall & Morris, 2011). However, findings regarding race have demonstrated mixed results, ranging from no differences to large differences between races. Race, as a predictor of disciplinary infractions, has also been shown to have important interaction effects with other demographic variables.

Griffin and Hepburn (2006) reported that Blacks and Hispanics were more likely than Whites to have been found guilty of making threats against others. Also, younger Whites were more likely than younger Blacks and/or Hispanics to commit assault (Griffin & Hepburn, 2006). Further demonstrating existing differences between races regarding disciplinary infractions, Drury and DeLisi (2011) reported that Whites were less likely to commit violent misconduct and possess a weapon. Others have also found that Whites are less likely than all other races to commit disciplinary infractions, especially violent infractions (Bales & Miller, 2012; McReynolds & Wasserman, 2008; Morris et al., 2010; Jiang et al., 2005; Jiang & Winfree, 2006; Steiner & Wooldredge, 2008).

Contrary to the findings discussed previously, however, several earlier studies have demonstrated no relationship between race and an inmate’s likelihood for disciplinary infractions. Poole and Regoli (1980) indicated that White and Black inmates equally committed infractions. Similarly, Hewitt, Poole, and Regoli (1984) found that there was little relationship between the race of inmates and their disciplinary infractions, with White and Black inmates averaging the same number, although Whites were more likely to engage in conspiracy, contraband possession, and sex-related infractions. Finn (1995) also reported that race was not a significant predictor of disciplinary infractions. These mixed results for race as a predicting variable of disciplinary infractions highlights the need for the continued study of race and its impact on disciplinary infractions.

Adding further uncertainty to the use of race/ethnicity as an indicator of disciplinary infractions is the potential for reporting bias of correctional staff members. Poole and Regoli (1980) found that Black inmates were more likely to have been reported by correctional staff members, compared to other races/ethnicities. It was further suggested by Poole and Regoli (1980) that this initial report bias against Black inmates created even more potential for receipt of disciplinary infractions, thus distorting the true numbers of disciplinary infractions by race. Therefore, the true nature of the influence and impact that an inmate’s race/ethnicity may have on explaining and/or understanding disciplinary infractions must be examined with caution. This theme of conflicting results also holds true for the next demographic indicator of an inmate’s educational level prior to incarceration.

Education

Education has been rarely examined as a demographic measure in its relation to predicting disciplinary infractions, and such studies have produced conflicting results. Wright (1989) found that inmates who did not progress beyond high school had fewer instances of physical misconduct, suggesting that more education leads to a higher risk for committing disciplinary infractions. Conversely, Cunningham and Sorensen (2006) found that inmates who scored lower on an education test were more likely to be involved in disciplinary infractions. This was also the case with Kuanliang et al.’s (2008) finding that an inmate’s lower scores on the Test of Adult Basic Education (TABE) exam were related to a higher overall likelihood of involvement in disciplinary infractions. The above-mentioned findings demonstrate that education may be a promising indicator for disciplinary infractions that should be considered further. The lack of research on education as an indicator of disciplinary infractions shares many similarities with the next importation indicator of marital status.

Marital status

Similar to education, marital status has rarely been examined in relation to predicting an inmate’s likelihood for disciplinary infractions. The available studies assessing marital status have produced consistent findings, unlike those examining the effect of education. Jiang, Fisher-Giorlando, and Mo (2005) found that inmates who were married were less likely to receive disciplinary infractions, compared to unmarried inmates. Further, they found that being married decreased an inmate’s overall potential for receiving disciplinary infractions by 14% (Jiang et al., 2005). An even more substantial decrease among married inmates was found by Jiang and Winfree (2006), with a 23% reduction in disciplinary incidents, compared to unmarried inmates.

Employment

One’s employment both prior to and while incarcerated has been shown to have an effect on one’s proclivity for disciplinary infractions. Addressing employment prior to incarceration, Flanagan (1983) found that inmates who were previously employed on the outside were less likely to receive disciplinary infractions. Similarly, Olowu (1985) found that inmates lacking a stable employment history prior to incarceration were more likely to commit disciplinary infractions. Being employed while incarcerated also held strong as an indicator of disciplinary infractions with Gover, Pérez, and Jennings (2008), who reported a 56% reduction in such an inmate’s likelihood of committing a disciplinary infraction. Such findings suggest that those employed, both prior to and during incarceration, are less likely to engage in disciplinary infractions.

Criminal history

Criminal history in relation to an inmate’s likelihood for committing disciplinary infractions is another importation factor of importation that has been previously researched in depth. Study findings highlight that specific offenses on the outside (e.g., homicide) do not necessarily coincide with misconduct on the inside (Bales & Miller, 2012; Cunningham, Sorensen, Vigen, & Woods, 2011; Cunningham & Sorensen, 2007; Sorensen & Cunningham, 2010; Wooldredge et al., 2001). Although specialization in offenses does not appear to occur, those with a history of violent offenses are more likely to engage in disciplinary infractions (Griffin & Hepburn, 2006).

Gang affiliation

Gang affiliation has been identified as one of the strongest importation predictors of inmate disciplinary infractions (Cunningham & Sorensen, 2006; Drury & DeLisi, 2011; Griffin & Hepbrun, 2006; Kuanliang et al., 2008; Cunningham & Sorensen, 2006, 2010; Steiner & Wooldredge, 2008). Both suspected and confirmed gang membership have been shown to increase one’s likelihood of disciplinary infractions by as much as 24% (Sorensen & Cunningham, 2010). Additionally, Kuanliang et al. (2008) found that incarcerated gang members were twice as likely to receive disciplinary infractions compared to other inmates. An inmate’s gang membership has also been shown to interact with other importation characteristics, further exacerbating the effects that gang membership has on the likelihood for disciplinary infractions.

Being affiliated with a gang is associated with an increased likelihood of disciplinary infractions. Drury and DeLisi (2011) found that inmates who were members of a street gang and had a homicide offense were more likely to possess a deadly weapon, commit major institutional violations, disobey officers, and damage property. Similarly, Cunningham and Sorensen (2007) found that one’s gang affiliation interacted with prior prison terms, resulting in a 35% higher likelihood of disciplinary infractions. The importance of prior prison terms points to criminal history as a necessary importation predictor of disciplinary infractions.

Deprivation Theory

Although importation theory is a plausible explanation for prison misconduct, a competing explanation, deprivation theory, suggests that it is the prison environment that encourages deviant behavior within the institutional setting. Deprivation theory refers to situational factors present within a correctional institution that can impact an inmate’s adjustment. Deprivation theory’s main premise is that inmates are isolated from the free world and experience “pains of imprisonment,” and they subsequently develop a subculture generally rooted in violence and opposition against the correctional administration (Cao et al., 1997; Goffman, 1961; Harer & Steffensmeier, 1996; McCorkle et al., 1995; Sykes, 1958; Sykes & Messinger, 1960; Wright, 1991). Furthermore, the slightest institutional change to a policy and/or privilege can have aggressive/violent ramifications from inmates (McCorkle et al., 1995). Because factors associated with deprivation may be situational and institution-specific, common institutional influences are outlined subsequently.

The simple fact of having been previously incarcerated is a significant predictor of disciplinary infractions (Berg & DeLisi, 2006; Cunningham & Sorensen, 2007; Drury & DeLisi, 2010; Jiang et al., 2005; Jiang & Winfree, 2006; Morash, Jeong, & Zang, 2010; Olowu, 1985; Poole & Regoli, 1980; Sorensen & Cunningham, 2010; Welsh et al., 2007). Prior imprisonment has been shown to increase the likelihood of disciplinary infractions as much as 29% (Sorensen & Cunningham, 2010). Prior imprisonment and earlier institutional infractions perhaps serve as the best indicators for an individual’s likelihood of committing a disciplinary infraction, not their current offense (Cunningham & Sorensen, 2007; Gover, Pérez, & Jennings, 2008). Acculturation and exposure to prison life are therefore among the most important predictors of disciplinary infractions in the existing literature.

Although the previously mentioned characteristics have shown to be important indicators of disciplinary infractions, they do not account for all variation in institutional misconduct. Characteristics specific to institutions have also shown to be important to understanding inmate misconduct. Such institutional effects include the amount of time incarcerated and an institution’s security level. Additionally, the opportunity for and participation in programming and activities (e.g., visitation) have shown the potential to reduce disciplinary infractions and ultimately recidivism.

Visitation

Although only rarely examined for a relationship to disciplinary infractions, receipt of visitors while incarcerated is correlated with other outcomes that suggest it may also be related to disciplinary infractions. Visitation has been shown to provide a number of benefits beyond the individual level of inmates maintaining social support structures on the outside. One such benefit is that visitations have been shown to reduce the number of disciplinary infractions (Borgman, 1985; Hensley, Koscheski, & Tewksbury, 2002; Jiang & Winfree, 2006; McShane & Williams, 1990). Included here are conjugal visits, which are related to reduced rates of disciplinary infractions (Hensley et al., 2002). Most recently, Tewksbury and Connor (2012) found that inmates with fewer disciplinary infractions were more likely to receive visits than inmates with more disciplinary infractions. As such, it may be important to include the issues of social support and connections to individuals on the outside in an assessment of predictors of disciplinary infractions.

The Present Study

A review of the pertinent literature suggests that there are multiple demographic, criminal history, and institutional experience measures that should be used in predicting inmates’ likelihood of committing a disciplinary infraction. However, the combination of both importation (i.e., demographics and criminal history) and deprivation (i.e., current institutional experiences) factors being important yet inconsistently linked to institutional behavior highlights the continued need for the study of such factors. Numerous, although by no means all, studies of factors related to inmate behavior/discipline suggest that age and race may be important, but the need remains to better specify if and under what conditions such issues are influential. If demographics are the important influences, there are policy implications, including a possible need for policy revision or development. Disciplinary infractions reduce order, threaten the strength of security and custody, and introduce significant costs to both individual prisons and entire correctional systems. Identifying variables that both contribute to and reduce disciplinary infractions is therefore an important task. The present study looks to contribute to this body of literature by identifying importation and deprivation measures that may influence the receipt of disciplinary infractions. In this way, administrators and policy makers may be better informed in the conduct of their responsibilities.

Method

Data

The target population for this study included adult male inmates continuously incarcerated in state-operated correctional institutions between January 1, 2011, and December 31, 2011, from one Midwestern jurisdiction in the United States. Data for the present study were provided by the Department of Corrections for the respective state. Included in the data were inmate demographics, criminal/incarceration history, visitation records, and number and types of disciplinary infractions received. Prior to receipt of data, all procedures were reviewed by both the Department of Corrections and the authors’ institutional review board to ensure that ethical standards were met.

Sample and Sampling Procedure

The sample consisted of 585 adult male inmates across 10 correctional institutions, all of whom were both (1) admitted to a prison during the 2009 calendar year and (2) incarcerated for all 365 continuous days in the 2011 calendar year. This cohort of inmates was specifically identified so as to have individuals who were acclimated to incarceration and for whom length of time incarcerated was not varying. At the end of 2011, the prison system incarcerated approximately 8,000 male inmates. It was recognized that these inmates constituted a biased (i.e., admitted to a prison during the 2009 calendar year) sample of individuals serving time throughout the entire 2011 calendar year. However, with a focus on predictors of disciplinary infractions received, the sample was appropriate. It was important not to have inmates in the sample who were new to prison life or have been incarcerated for especially lengthy periods, as adjustment to prison life (including behavior) varies across the incarceration period. With inmates who served between 1 and 2 years, prior to the study period of interest, the present study was able to utilize a sample that had “fully adjusted” and acclimated to institutional life and the routines of incarceration (Jones & Schmid, 2000).

The sample was fairly representative of inmates in state prisons across the United States in 2011. Nationally, 37% of inmates entering a prison in 2011 were new commitments, compared to 28% of the present sample (Carson & Sabol, 2012). Demographically, the sample reflected national statistics on inmate race (i.e., 30.1% of the sample was Nonwhite vs. 32.4% of inmates nationally) and age (i.e., mean age of the sample was 32.5 vs. 46.9% of inmates nationally who are under the age of 35; Caron & Sabol, 2012). The sample was more highly educated than state prison inmates nationally in regard to having obtained a high school/GED level of education (i.e., 84.6% of the sample vs. 61% of inmates nationally; Harlow, 2003). When compared to data for all inmates in the state, 75% of inmates were White compared to 69.9% in the sample. In regard to age, the mean age of the statewide population was 36, compared to 32.5 for the sample. Although not completely corresponding, there were no indications of bias or significant skew in the characteristics of the sample.

Measures

Dependent measures

Two dependent variables were used in the analysis: Whether or not an inmate received any disciplinary infractions during the 1-year study period and whether or not an inmate received any serious (defined as A² or B³ level) disciplinary infractions during the 1-year study period. Table 1 shows the descriptive statistics for measures used in the present study. The measure of receipt of any disciplinary infraction was a dichotomous measure that distinguished between inmates with zero and one or more disciplinary infractions. In the sample, fully 52.5% (n = 307) of inmates received at least one disciplinary infraction (range = 0–100, median = 1.00, mean = 3.32).

Table 1.

Descriptive Statistics for Measures Used in Present Study.

Measure	%	M	SD
Any disciplinary infractions
Yes	52.5
No	47.5
Serious disciplinary infractions
Yes	12.6
No	87.4
Importation
Age		32.52	10.99
Race
White	69.9
Nonwhite	30.1
Educational level
High school	84.6
Less than high school	15.3
Marital status
Married, divorced, or widowed	36.8
Single, never married	63.2
Prior prison commitments		0.68	1.14
Gang member
Yes	23.1
No	76.9
Deprivation
Prison admission type
Revocation	28.0
New commitment	72.0
Length of current sentence		20.20	14.03
Sex offender
Yes	21.4
No	78.6
Number of visits received		10.40	20.95

The second dependent variable, whether or not an inmate received any serious disciplinary infractions, was a dichotomous measure that distinguished between inmates with zero and one or more A- or B-level disciplinary infractions. In the sample, 12.6% (n = 74) of inmates received at least one A- or B-level disciplinary infraction. Only 1.7% (n = 10) of inmates received at least one A-level disciplinary infraction, and 12% (n = 70) of inmates received at least one B-level disciplinary infraction.

Importation measures

Independent variables in the present study included six importation characteristics. Importation variables consisted of an inmate’s age at prison admission, race, highest educational level completed, marital status, criminal history (i.e., number of prior prison commitments), and gang membership. Age at prison admission was a continuous, ratio-level variable with a range of 16 to 74, a mean of 32.5, and a median of 31.0. In the original data, the measure of race showed that less than 5% of the sample was classified as neither White nor Black. As such, the race variable was dichotomous, with White inmates (coded as 1) representing 69.9% (n = 409) and non-White inmates (coded as 0) representing 30.1% (n = 176). Educational level was a nominal level measure with two categories. A less-than-high-school completion level of education was reported for 15.3% (n = 89) of the sample. At the same time, 84.6% (n = 493) of inmates had at least a high school diploma or equivalent as their highest level of education. Marital status was a dichotomous variable. Nearly two thirds (63.2%, n = 370) of the sample was reported as single and never married, and 36.8% (n = 215) of the sample was reported as married, divorced, or widowed. Single, never married inmates (coded as 0) served as the reference group. The measure for criminal history was represented by the total number of prior prison commitments served by an inmate. Criminal history was a continuous variable (range = 0–8, mean = 0.68, median = 0). Whether or not an inmate was classified as a gang member was a dichotomous measure. Nearly one quarter (23.1%, n = 135) of the sample was identified by classification officials as a gang member.

Deprivation measures

Deprivation measures consisted of prison admission type, length of current sentence,⁴ criminal offense conviction, and total number of visits received. Prison admission type indicated whether an inmate was serving time for a new commitment (72.0%, n = 421, coded as 0) or serving time for a revocation of parole, probation, or work release (28.0%, n = 164, coded as 1). Length of an inmate’s current sentence was also a continuous variable (mean = 20.20 years, median = 15 years). Whether or not an inmate was serving time for a sex offense conviction was a dichotomous variable. Fully 21.4% (n = 125) of inmates in the sample were incarcerated for at least one sex offense conviction. The final deprivation measure used in the analysis was a continuous, ratio-level variable of the total number of visits received by an inmate during the 1-year study period. This was a count of the number of times a visit was received, rather than the total number of visitors received. The data for the present study originated from a department of corrections with fairly liberal visitation policies. All prisons under supervision of this correctional agency permitted inmates to receive visits at least 4 days per week. In the sample, 66.2% (n = 387) of inmates received at least one visit during the 1-year study period (range = 0–196, mean = 10.40, median = 2.0).

Analysis Strategy

In order to examine the ways in which importation and deprivation measures may differentially predict whether or not an inmate received any disciplinary infractions, as well as any serious (i.e., A- or B-level) disciplinary infractions, during the 1-year study period, logistic regression techniques were utilized. This was the appropriate analytical tool for the present study, as both dependent variables were dichotomous measures and all assumptions for binary logistic regression were satisfied (Pampel, 2000). A linear relationship between the dependent and independent variables does not need to be assumed for logistic regression; thus, a normal distribution of variable values is not assumed. Additionally, in both of the models presented subsequently, computation of tolerance for all measures showed no problems, as all tolerance levels were 0.65 or higher (Menard, 1995). In addition, the data had minimal missing values, as all variables had less than 1% of values missing. Only one measure, length of current sentence, was substantially skewed (i.e., 60% of inmates were serving a sentence of 10 years or more).

Findings

Table 2 presents the results of the logistic regression analysis for the dependent measure of an inmate’s receipt of any type of disciplinary infraction. The model is significant, with 4 of the 10 measures included being significant indicators of having received a disciplinary infraction. Of the four variables, two importation measures and two deprivation measures were statistically significant (p < .05) predictors of an inmate’s receipt of any type of disciplinary infraction. To elaborate, the importation measure of age at prison admission was a significant indicator of an inmate having received a disciplinary infraction during the 1-year study period. For this significant importation measure, as an inmate’s age at the time of prison admission increased by 1 year, the odds that the inmate would receive a disciplinary infraction decreased by 4% (Exp[b] = 0.95). A second importation measure of gang membership was also a significant indicator of an inmate having received a disciplinary infraction during the 1-year study period. Inmates who were gang members were more likely to receive any type of disciplinary infraction. Gang members were 2.14 times more likely than nongang members to receive a disciplinary infraction. The deprivation measure of criminal offense conviction, being whether or not an inmate was serving time for a sex offense conviction, was also a significant indicator. Sex offenders were 1.71 times more likely than other inmates to have received a disciplinary infraction during the 1-year study period. Finally, the deprivation measure of total number of visits received over the 1-year study period was also a significant predictor of an inmate’s receipt of any type of disciplinary infraction. For every additional visit received by an inmate, there was a 1.3% lesser likelihood of that individual receiving a disciplinary infraction.

Table 2.

Logistic Regression Model for Predictors of Receipt of Any Disciplinary Infractions.

Measure	B	SE	Exp (β)	Tolerance
Importation
Age	−0.04*	0.01	0.95	0.65
Race	0.03	0.20	1.03	0.88
Educational level	−0.11	0.25	0.89	0.94
Marital status	−0.25	0.21	0.77	0.76
Prior prison commitments	0.04	0.08	1.05	0.83
Gang member	0.76*	0.24	2.14	0.84
Deprivation
Prison admission type	0.39	0.20	1.47	0.95
Length of current sentence	−0.00	0.00	0.99	0.93
Sex offender	0.53*	0.23	1.71	0.81
Number of visits received	−0.01*	0.00	0.98	0.88
Constant	1.56	0.42

Note. χ² = 78.65 (p = .001). df = 10. n = 585.

*p < .05.

Table 3 shows the results of the logistic regression analysis for the dependent measure of an inmate’s receipt of a serious disciplinary infraction. Again, the model is significant, indicating that these variables, taken as a whole, provide a statistically significant explanation for the change in likelihood of an inmate receiving an A- or B-level disciplinary infraction during the 1-year study period. In this model, only 2 of the 10 assessed measures were statistically significant (p < .05) indicators of an inmate’s receipt of a serious disciplinary infraction. Both importation and deprivation measures emerged as significant. The importation variable of gang membership is once again the most influential. Gang members were 4.03 times more likely than nongang members to receive a serious disciplinary infraction during the 1-year study period. More interesting, however, is the statistical significance of the deprivation measure of criminal offense conviction being whether or not an inmate was serving time for a sex offense conviction. Whereas in the first model sex offenders were more likely than other inmates to receive any type of disciplinary infraction, the second model shows that sex offenders were less likely than other inmates to receive a serious disciplinary infraction. Specifically, inmates serving time for a sex offense conviction were 0.62 times less likely to receive a serious (i.e., A- or B-level) disciplinary infraction than inmates not serving time for a sex offense conviction.

Table 3.

Logistic Regression Model for Predictors of Receipt of Serious Disciplinary Infractions.

Measure	B	SE	Exp (β)	Tolerance
Importation
Age	−0.02	0.01	0.97	0.67
Race	0.39	0.30	1.48	0.65
Educational level	0.03	0.37	1.03	0.83
Marital status	−0.29	0.36	0.74	0.72
Prior prison commitments	−0.28	0.16	0.75	0.76
Gang member	1.39*	0.30	4.03	0.88
Deprivation
Prison admission type	−0.23	0.30	0.78	0.94
Length of current sentence	−0.02	0.01	0.98	0.88
Sex offender	−0.95*	0.46	0.38	0.65
Number of visits received	−0.02	0.01	0.97	0.80
Constant	1.56	0.42

Note. χ² = 64.01 (p = .001). df = 10. n = 585.

*p < .05.

Interestingly, one importation measure (i.e., age at prison admission) and one deprivation measure (i.e., number of received visits) that were statistically significant in the first model are not statistically significant here. And yet, both of these measures were in the same direction, and number of received visits approached statistical significance (p < .07). These results suggest that importation and deprivation factors are not always critical predictors of disciplinary infractions, especially serious infractions. Clearly, the most influential measure was the importation measure of gang membership status. At the same time, while the deprivation measure of having a sex offense conviction was more likely than inmates with other criminal convictions to receive any type of disciplinary infraction, sex offenders were less likely to receive a serious disciplinary infraction.

Discussion

The purpose of this study was to identify whether or not importation and deprivation factors specific to inmates and their incarceration would serve as predictors of receipt of institutional disciplinary infractions. By means of logistic regression analyses, this research identified that both importation and deprivation characteristics were, in fact, significant indicators of having received disciplinary infractions. Specifically, data revealed that two importation and two deprivation measures were significant predictors of receipt of any type of disciplinary infraction. The importation measure of inmate’s gang affiliation status and the deprivation factor of conviction for a sex offense were also found to be significant indicators of having received a serious (i.e., A- or B-level violation, as defined by one Midwestern prison system) disciplinary infraction. The results of this study lend support to the existing literature, provide insights into importation and deprivation characteristics indicative of prison misconduct, and suggest directions for future research.

Examination of the relationship between importation and deprivation characteristics and receipt of any type of disciplinary infraction, throughout one calendar year, revealed that an inmate’s age at prison admission, gang affiliation status, number of received visits, and criminal offense conviction were significant predictors. Consistent with earlier research, which found that younger inmates engaged more frequently in misconduct behind bars (Griffin & Hepburn, 2006; McReynolds & Wasserman, 2008; Walters & Schlauch, 2008; Welsh et al., 2007; Wooldredge et al., 2001), younger inmates in the present study were also more likely to receive any type of disciplinary infraction. Race, educational level, and marital status were not statistically significant in either model. It may be that age takes precedence over other common demographic measures of importation, and the remaining demographics (i.e., race, educational level, and marital status) should only be used as control variables as prior studies have suggested (i.e., Drury & DeLisi, 2010; Steiner & Wooldredge, 2008). From an importation perspective, this advances the notion that inmates are not a unidimensional group, and it indicates that different subgroups of inmates may form based on age, with different attitudes, beliefs, and norms. It may be that defiance and deviation are regarded more favorably among younger individuals on the outside. Once younger individuals are inside prisons, they may import these attitudes and beliefs and continue to refuse to submit to rules and regulations.

At the same time, the deprivation factor of visitation indicates that inmates who received more visits were less likely to receive any type of disciplinary infraction. This is congruent with prior research concerning the effects of social support mechanisms on institutional behavior (Borgman, 1985; Hensley et al., 2002; Jiang & Winfree, 2006; McShane & Williams, 1990). From a deprivation perspective, this makes sense, as inmates who receive more visits are less likely to become completely separated from the outside and experience the various deprivations that lead to negative prison adaptation. Inmates who receive fewer visits are more likely to succumb to the losses of a total institution, and their experience of deprivations consequently facilitates institutional misconduct for those with fewer visits. Future studies should continue to examine the impact of inmate demographics as importation factors, with a focus on age as a significant indicator of their behavior while incarcerated. Additionally, the deprivation factor of visitation should be further examined in subsequent research to better understand its role with respect to inmate misconduct. Such identification of critical importation and deprivation factors may allow correctional leaders to develop and implement strategies and programs that effectively remedy these determinants.

When focusing on serious disciplinary infractions, however, the importation measure of an inmate’s gang membership status and the deprivation measure of conviction for a sex offense were the only significant predictors of such prison misconduct. During the 1-year study period, it is important to note that gang members were more likely than other inmates to receive any type of disciplinary infraction, as well as a serious disciplinary infraction. Accordingly, the most influential characteristic indicative of receipt of disciplinary infractions was gang membership. This finding is consistent with earlier studies, which found that security-threat group membership served as a significant predictor of perpetrated prison violence (Cunningham & Sorensen, 2007; Drury & DeLisi, 2011; Griffin & Hepburn, 2006; Kuanliang et al., 2008; Morris et al., 2010; Sorensen & Cunningham, 2006; Steiner & Wooldredge, 2008). Inmates who are gang members likely have attitudes, beliefs, and norms that are imported to incarceration and oppose prison administrators and staff members. This, in turn, produces institutional misconduct. Again, research should continue to examine the relationship between gang membership and misconduct behind bars, with a specific focus on identifying means for intervention and alleviation of such imported behavior.

Arguably, the most interesting findings concerned the deprivation that sex offenders may experience while incarcerated. Although inmates who served time for a sex offense conviction were significantly more likely than other inmates to receive any type of disciplinary infraction, they were also significantly less likely than other inmates to receive a serious disciplinary infraction. Thus, rather than becoming involved in serious transgressions while incarcerated, these inmates largely participated in more trivial and less serious forms of prison misconduct. This suggests that sex offenders and their behaviors behind bars may be different from other inmates.

Sex offenders are often at the bottom of the prison inmate power/status hierarchy (Clemmer, 1940; Colvin, 1982; Irwin, 1980; Schwaebe, 2005; Waldram, 2012). Throughout their incarceration, sex offenders are commonly rejected and excluded from full participation in prison life. Both other criminal offenders (Akerstrom, 1986) and prison staff members (Connor, 2012; Connor & Tewksbury, 2013; Sapp & Vaughn, 1990) view sex offenders as representing the lowest status in the prison community. As a direct result of their inferior standing, sex offenders are seemingly more prone to neglect, exploitation, and victimization. From a deprivation perspective, it may be that sex offenders experience more acute and distinct “pains of imprisonment” or losses while incarcerated than other inmates, and such may lead to more violations of day-to-day rules and regulations behind bars.

As it relates to receipt of less serious disciplinary infractions, sex offenders who are stigmatized and likely mistreated inside prisons may be actively engaged in avoidance behaviors, in order to escape perceived or actual violence. Perhaps even fully aware that a disciplinary infraction may be issued, a sex offender may, for example, intentionally avoid their prison job assignment or programming, or simply avoid settings where large numbers of inmates congregate, for purposes of self-preservation. When incidents cannot be successfully avoided and violence ensues, a sex offender may still receive a less serious disciplinary infraction. In the case of actual physical violence directed toward a sex offender, for instance, where a sex offender is neither an aggressor nor blameworthy, it is still likely that a sex offender would receive a disciplinary infraction for fighting. Unable to readily differentiate between perpetrators and victims, many prison staff members will issue a disciplinary infraction to each inmate involved.

Both a strong cultural loathing of sex offenders that often permeates the prison environment and sex offenders’ common proclivity toward becoming a target may also lead prison staff members to intentionally or unintentionally ignore these inmates and their requests for assistance. In turn, this may cause incarcerated sex offenders to act out to receive attention. Such overt expressions and attention-seeking behavior that arises from deprivation may lead to receipt of a less severe disciplinary infraction, such as disobeying a direct order. Correctional leaders should remain cognizant of the fact that sex offenders, as the most marginalized and subsequently deprived inmates, may experience unique difficulties inside prisons because of their offenses. Proactive measures from institutional leadership to facilitate the well-being of inmates with sex offense convictions may be necessary. Prison wardens, for instance, should make it a point to closely monitor the safety of their inmates with sex offense convictions through consultation with their correctional officers.

This research was not without limitations. The sample was drawn from only adult male inmates in one Midwestern prison system. As a result, these findings may not apply to juveniles, women, and inmates in other jurisdictions. Because findings were based on inmates who were incarcerated for a minimum of 1 year and a maximum of 2 years, prior to the study period of interest, readers should generalize from this sample with caution. Further, the relationship between visitation and misconduct may not be entirely conclusive, as some inmates with fewer visits may have lost their visitation privileges due to receipt of disciplinary infractions. Future studies should seek to better understand this relationship. At the same time, it is recognized that time incarcerated and institutional security level have been identified in previous research as factors of deprivation (Berk & de Leeuw, 1999; Cunningham & Sorensen, 2007; Dhami et al., 2007; Gaes, Wallace, Gilman, Klein-Saffran, & Suppa, 2002; Goodstein, MacKenzie, & Shotland, 1984; Gover et al., 2008; Griffin & Hepburn, 2006; Jiang et al., 2005; Jiang & Winfree, 2006; Kuanliang et al., 2008; McCorkle et al., 1994; McReynolds & Wasserman, 2008; Proctor, 1994; Steiner & Wooldredge, 2008; Tischler & Marquart, 1989; Wright, 1991). However, the data did not provide for analyses of such measures, thus serving as a limitation of this study. Moreover, it is acknowledged that inmate demographics as measures of importation and criminal/incarceration histories as measures of deprivation may be less than desirable proxies. Nonetheless, the data made this inevitable.

In the end, this study shows that perspectives of importation and deprivation may be important to shed light on key determinants of behavior inside prisons. The merit of importation as an explanation for inmate behavior is made evident by age at prison admission and gang membership. Furthermore, the number of received visits and criminal offense conviction highlight the value of the deprivation perspective. Taken together, it is clear that younger individuals, gang members, individuals without strong social support, and convicted sex offenders may be at a heightened risk for participation in misconduct behind bars. Thus, correctional leaders should strive to focus their efforts and limited resources on these inmates, especially gang members and convicted sex offenders, in order to combat potential refractory behavior that often characterizes ineffective and violent correctional institutions. Meanwhile, the attitudes, beliefs, experiences, and norms, as well as behaviors, that inmates import from the outside and also develop through deprivations on the inside should not be forgotten. In this way, safer prisons may be promoted that are more conducive to inmate rehabilitation, which may lead to decreased recidivism and increased reintegration among formerly incarcerated individuals.

Beyond issues related directly to inmates, correctional institutions largely devoid of inmate misconduct may also feature more efficient management and operation. Staff members employed at prisons with fewer incidents of inmate misconduct, as evidenced by disciplinary infractions, may be less burdened with detection, documentation, and resolution of inmate disciplinary issues, which may allow them to become more productive by devoting more time to additional responsibilities during their shift. This may reduce costs associated with employing large numbers of correctional officers and may help offset the negative impact of high staff member turnover rates. Safer correctional institutions are also likely to be more attractive workplaces to both present and future prison employees. Further, fewer incidents of inmate misconduct may lead to reduced costs associated with the negative repercussions of such behavior, like inmate and staff member injury and destruction of prison property and infrastructure.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Notes

References

Akerstrom

(1986). Outcasts in prison: The cases of informers and sex offenders. Deviant Behavior, 7, 1–12.

Bales

W. D.

Miller

C. H.

(2012). The impact of determinate sentencing on prisoner misconduct. Journal of Criminal Justice, 40, 394–403.

Berg

M. T.

DeLisi

(2006). The correctional melting pot: Race, ethnicity, citizenship, and prison violence. Journal of Criminal Justice, 34, 631–642.

Berk

R. A.

de Leeuw

(1999). An evaluation of California's inmate classification system using a generalized regression discontinuity design. Journal of the American Statistical Association, 94, 1045–1052.

Borgman

(1985). Influence of family visiting upon boys’ behavior in a juvenile correctional institution. Child Welfare, 64, 629–638.

Cao

Zhao

Van Dine

(1997). Prison disciplinary tickets: A test of the deprivation and importation models. Journal of Criminal Justice, 25, 103–113.

Caron

E. A.

Sabol

W. J.

(2012). Prisoners in 2011. Washington, DC: Bureau of Justice Statistics.

Carroll

(1974). Hacks, blacks, and cons. Lexington, MA: Lexington Books.

Carson

E. A.

Sabol

W. J.

(2012). Prisoners in 2011. Washington, DC: Bureau of Justice Statistics.

10.

Chapman

W. R.

Alexander

(1981). Adjustments to prison: A review of inmate characteristics associated with misconducts, victimization, and self injury in confinement (Classification Improvement Project, Working Paper No. 10). New York, NY: State of New York Department of Correctional Services.

11.

Clemmer

(1940). The prison community. New York, NY: Rinehart.

12.

Colvin

(1982). The 1980 New Mexico prison riot. Social Problems, 29, 449–463.

13.

Connor

D. P.

(2012). Prison wardens’ perceptions of sex offenders, sex offender registration, community notification, and residency restrictions (Master’s thesis). University of Louisville, Louisville, KY.

14.

Connor

D. P.

Tewksbury

(2013). Examining prison wardens’ perceptions of inmates incarcerated for sex offenses. Corrections Today, 75, 60–61, 68.

15.

Cunningham

M. D.

Sorensen

J. R.

(2006). Nothing to lose? A comparative examination of prison misconduct rates among life-without-parole and other long-term high-security inmates. Criminal Justice and Behavior, 33, 683–705.

16.

Cunningham

M. D.

Sorensen

J. R.

(2007). Predictive factors for violent misconduct in close custody. The Prison Journal, 87, 241–253.

17.

Cunningham

M. D.

Sorensen

J. R.

Vigen

M. P.

Woods

S. O.

(2011). Correlates and actuarial models of assaultive prison misconduct among violence-predicted capital offenders. Criminal Justice and Behavior, 38, 5–25.

18.

Dabbs

J. M.

Frady

R. L.

Carr

T. S.

Besch

N. F.

(1987). Saliva testosterone and criminal violence in young adult prison inmates. Psychosomatic Medicine, 49, 174–182.

19.

Dhami

M. K.

Ayton

Loewenstein

(2007). Adaptation to imprisonment: Indigenous or imported? Criminal Justice and Behavior, 34, 1085–1100.

20.

Drury

A. J.

DeLisi

(2010). The past is prologue: Prior adjustment to prison and institutional misconduct. The Prison Journal, 90, 331–352.

21.

Drury

A. J.

DeLisi

(2011). Gangkill: An exploratory empirical assessment of gang membership, homicide offending, and prison misconduct. Crime & Delinquency, 57, 130–146.

22.

Duwe

Clark

(2011). Blessed be the social tie that binds: The effects of prison visitation on offender recidivism. Criminal Justice Policy Review, 24, 271–296.

23.

Ellis

Grasmick

Gilman

(1974). Violence in prisons: A sociological analysis. American Journal of Sociology, 80, 16–43.

24.

Finn

M. A.

(1995). Disciplinary incidents in prison: Effects of race, economic status, urban residence, prior imprisonment. Journal of Offender Rehabilitation, 22, 143–156.

25.

Flanagan

T. J.

(1983). Correlates of institutional misconduct among state prisoners: A research note. Criminology, 21, 29–39.

26.

Gaes

G. G.

Wallace

Gilman

Klein-Saffran

Suppa

(2002). The influence of prison gang affiliation on violence and other prison misconduct. The Prison Journal, 82, 359–385.

27.

Gendreau

Goggin

C. E.

Law

M. A.

(1997). Predicting prison misconducts. Criminal Justice and Behavior, 24, 414–431.

28.

Giallombardo

(1966). Society of women: A study of a women’s prison. New York, NY: John Wiley.

29.

Goetting

Howsen

(1986). Correlates of prisoner misconduct. Journal of Quantitative Criminology, 2, 31–46.

30.

Goffman

(1961). Asylums: Essays on the social situation of mental patients and other inmates. Garden City, NY: Anchor Books.

31.

Goodstein

Mackenzie

Shotland

R. L.

(1984). Personal control and inmate adjustment to prison. Criminology, 22, 343–369.

32.

Gover

A. R.

Pérez

D. M.

Jennings

W. G.

(2008). Gender differences in factors contributing to institutional misconduct. The Prison Journal, 88, 378–403.

33.

Griffin

M. L.

Hepburn

J. R.

(2006). The effect of gang affiliation on violent misconduct among inmates during the early years of confinement. Criminal Justice and Behavior, 33, 419–448.

34.

Harer

M. D.

Steffensmeier

D. J.

(1996). Race and prison violence. Criminology, 34, 323–355.

35.

Harlow

C. W.

(2003). Education and correctional populations. Washington, DC: Bureau of Justice Statistics.

36.

Harrison

P. M.

Beck

A. J.

(2006). Bureau of justice statistics. Prisoners in 2005 (NCJ 215092). Retrieved from http://bjs.ojp.usdoj.gov/content/pub/ascii/p05.txt

37.

Hensley

Koscheski

Tewksbury

(2002). Does participation in conjugal visitations reduce prison violence in Mississippi? An exploratory study. Criminal Justice Review, 27, 53–65.

38.

Hewitt

J. D.

Poole

E. D.

Regoli

R. M.

(1984). Research note: Self-reported and observed rule-breaking in prison: A look at disciplinary response. Justice Quarterly, 1, 437–447.

39.

Hochstetler

DeLisi

(2005). Importation, deprivation, and varieties of serving time: An integrated-lifestyle-exposure model of prison offending. Journal of Criminal Justice, 35, 257–266.

40.

Irwin

J. K.

(1981). Sociological studies of the impact of long-term confinement. In Ward

D. A.

Schoen

K. F.

(Eds.), Confinement in maximum custody. Lexington, MA: D.C. Heath.

41.

Irwin

(1980). Prisons in turmoil. Boston, MA: Little, Brown.

42.

Irwin

J. K.

Cressey

(1962). Thieves, convicts, and the inmate culture. Social Problems, 10, 142–155.

43.

Jacobs

(1974). Street gangs behind bars. Social Problems, 21, 395–405.

44.

Jacobs

(1977). Stateville. Chicago, IL: University of Chicago Press.

45.

Jiang

Fisher-Giorlando

(2002). Inmate misconduct: A test of the deprivation, importation, and situational models. The Prison Journal, 82, 335–358.

46.

Jiang

Fisher-Giorlando

(2005). Social support and inmate violations. American Journal of Criminal Justice, 30, 71–86.

47.

Jiang

Winfree

L. T.

(2006). Social support, gender, and inmate adjustment to prison life: Insights from a national sample. The Prison Journal, 86, 32–55.

48.

Jones

Schmid

(2000). Doing time: Prison experience and identity among first-time inmates. Stanford, CT: JAI Press.

49.

Kuanliang

Sorensen

J. R.

Cunningham

M. D.

(2008). Juvenile inmates in an adult prison system: Rates of disciplinary misconduct and violence. Criminal Justice and Behavior, 35, 1186–1201.

50.

Lembo

J. J.

(1969). Research notes: The relationship of institutional disciplinary infractions and the inmate’s personal contact with the outside community. Criminology, 7, 50–54.

51.

Lovell

Jemelka

(1996). When inmates misbehave: The costs of discipline. The Prison Journal, 76, 165–179.

52.

McCain

Cox

V. C.

Paulus

P. B.

(1980). Effect of prison crowding on inmate behavior. Arlington: University of Texas.

53.

McCorkle

Miethe

Drass

(1995). The roots of prison violence: A test of the deprivation, management, and “not-so-total” institution models. Crime and Delinquency, 41, 317–331.

54.

McShane

M. D.

Williams

F. P.

(1990). Old and ornery: The disciplinary experiences of elderly prisoners. International Journal of Offender Therapy & Comparative Criminology, 34, 197–212.

55.

McReynolds

L. S.

Wasserman

G. A.

(2008). Risk for disciplinary infractions among incarcerated male youths: Influence of psychiatric disorder. Criminal Justice and Behavior, 35, 1174–1185.

56.

Menard

(1995). Applied logistic regression analysis. Thousand Oaks, CA: Sage.

57.

Morash

Jeong

S. J.

Zang

N. L.

(2010). An exploratory study of the characteristics of men known to commit prisoner-on-prisoner sexual violence. The Prison Journal, 90, 161–178.

58.

Morris

R. G.

Longmire

D. R.

Buffington-Vollum

Vollum

(2010). Institutional misconduct and differential parole eligibility among capital inmates. Criminal Justice and Behavior, 37, 417–438.

59.

Motiuk

(1991). Antecedents and consequences of prison adjustment: A systematic assessment and reassessment approach (Unpublished doctoral dissertation). Carleton University, Ontario, Canada.

60.

Olowu

E. U.

(1985). Disciplinary infractions & institutional response: A longitudinal study of rule breaking & sentencing in a county prison. Ann Arbor, MI: University Microfilms International.

61.

Pampel

F. C.

(2000). Logistic regression: A primer. Thousand Oaks, CA: Sage.

62.

Poole

E. D.

Regoli

R. M.

(1980). Race institutional rule breaking, and disciplinary response: A study of discretionary decision making in prison. Law & Society Review, 14, 931–946.

63.

Proctor

J. L.

(1994). Evaluating a modified version of the federal prison system inmate classification model: An assessment of objectivity and predictive validity. Criminal Justice and Behavior, 21, 256–272.

64.

Riordan

McDonald

(2009). One in 31 U.S. adults are behind bars, on parole or probation. Retrieved from http://www.pewtrusts.org/news_room_detail.aspx?

65.

Sapp

A. D.

Vaughn

M. S.

(1990). The social status of adult and juvenile sex offenders in prison: An analysis of the importation model. Journal of Police and Criminal Psychology, 6, 2–7.

66.

Schrag

(1961). A preliminary criminal typology. Pacific Sociological Review, 4, 11–16.

67.

Schwaebe

(2005). Learning to pass: Sex offenders’ strategies for establishing a viable identity in the prison general population. International Journal of Offender Therapy and Comparative Criminology, 49, 614–625.

68.

Smith

R. R.

(1996). Warden’s proximity in relation to disciplinary infractions and attempted suicides: A national survey. Journal of Offender Rehabilitation, 23, 37–48.

69.

Sorensen

Cunningham

M. D.

(2010). Conviction offense and prison violence: A comparative study of murderers and other offenders. Crime & Delinquency, 56, 103–125.

70.

Steiner

Wooldredge

(2008). Inmate versus environmental effects on prison rule violations. Criminal Justice and Behavior, 35, 438–456.

71.

Sykes

(1958). The society of captives. Princeton, NJ: Princeton University Press.

72.

Sykes

Messinger

S. L.

(1960). The inmate social system. In Cloward

(Ed.), Theoretical studies in social organization of the prison. New York, NY: Social Science Research Council.

73.

Tewksbury

Connor

D. P.

(2012). Inmates who receive visits in prison: Exploring factors that predict. Federal Probation, 76, 43–46.

74.

Thomas

(1977). Theoretical perspectives on prisonization: A comparison of the importation and deprivation models. Journal of Criminal Law and Criminology, 68, 135–145.

75.

Tischler

C. A.

Marquart

J. W.

(1989). Analysis of disciplinary infraction rates among female and male inmates. Journal of Criminal Justice, 17, 507–513.

76.

Vaughn

del Carmen

(1995). Civil liability against prison officials for inmate-on-inmate assault: Where are we and where have we been? Prison Journal, 75, 69–89.

77.

Waldram

J. B.

(2012). Hound pound narrative: Sexual offender habilitation and the anthropology of therapeutic intervention. Berkley: University of California Press.

78.

Walters

G. D.

(2007). Predicting institutional adjustment with the lifestyle criminality screening form and the antisocial features and aggression scales of the PAI. Journal of Personality Assessment, 88, 99–105.

79.

Walters

G. D.

Schlauch

(2008). The psychological inventory of criminal thinking styles and level of service inventory-revised: Screening version as predictors of official and self-reported disciplinary infractions. Law & Human Behavior, 32, 454–462.

80.

Waquant

(2001). Deadly symbiosis: When ghetto and prison meet and mesh. In Garland

(Ed.), Mass imprisonment: Social causes and consequences (pp. 82–120). London, England: Sage.

81.

Welsh

W. N.

McGrain

Salamatin

Zajac

(2007). Effects of prison drug treatment on inmate misconduct: A repeated measures analysis. Criminal Justice and Behavior, 34, 600–615.

82.

Wheeler

(1961). Socialization in correctional communities. American Sociological Review, 26, 697–712.

83.

Wolff

Shi

Blitz

C. L.

(2008). Racial and ethnic disparities in types and sources of victimization inside prison. The Prison Journal, 88, 451–472.

84.

Wooldredge

Griffin

Pratt

(2001). Hierarchical models of inmate behavior. Justice Quarterly, 18, 203–231.

85.

Worrall

J. L.

Morris

R. G.

(2011). Inmate custody levels and prison rule violations. The Prison Journal, 91, 131–157.

86.

Wright

K. N.

(1989). Race and economic marginality in explaining prison adjustment. Journal of Research in Crime and Delinquency, 26, 67–89.

87.

Wright

K. N.

(1991). A study of individual, environmental, and interactive effects in explaining adjustment to prison. Justice Quarterly, 8, 217–242.