Violent Victimization in the Prison Context: An Examination of the Gendered Contexts of Prison

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Violent Victimization in the Prison Context: An Examination of the Gendered Contexts of Prison

Brent Teasdale1, Leah E. Daigle1, Shila R. Hawk1, and Jane C. Daquin1

AbstractCurrently there are few published, multilevel studies of physical assault victimization of prisoners. This study builds on the extant research by utilizing a nationally representative sample of correctional facilities (n = 326) and inmates (n = 17,640) to examine the impacts of a large set of theoretically and empirically derived individual- and contextual-level variables on prison victimization, including how the gendered context of prison impacts victimization. Results support the lifestyles/routine activities approach. Inmates who were charged with a violent offense, were previously victimized, were smaller in size, were not married, were without a work assignment, misbehaved, did not participate in programs, used alcohol or drugs, and those who had a depression or personality disorder were more likely to be victimized. In addition, the data suggest that 8% of the variance in victimization is due to the prison context. Prisons with high proportions of violent offenders, males, inmates from multiracial backgrounds, and inmates with major infractions had increased odds of victimization. Moreover, the sex-composition of the prison has significant main and interactive effects predicting victimization. Specifically, we find that the effects of being convicted of a drug crime, drug use, military service, major infractions, and diagnosed personality disorders are all gendered in their impacts on victimization.

Keywordsvictimization, prison, gender, context

1Georgia State University, Atlanta, USA

Corresponding Author:Brent Teasdale, Department of Criminal Justice and Criminology, Georgia State University, P.O. Box 4018, Atlanta, GA 30302-4018, USA. Email: [email protected]

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2 International Journal of Offender Therapy and Comparative Criminology

Despite the charge that prison administrators are to keep prisoners safe, what has been perpetuated is that prisoners face a real risk of being victimized. This perception of prisons as dangerous places may be accurate, yet is surprisingly a relatively neglected area of empirical inquiry. Moreover, the estimates of prison victimization have varied, ranging from 5.8% to 21% of inmates experiencing physical assault during the past 6 to 12 months (James & Glaze, 2006; Lahm, 2009; Wolff, Blitz, Shi, Siegel, & Bachman, 2007; Wooldredge, 1994, 1998).

Although estimates from these studies are instructive, they are limited in the fol-lowing ways. First, almost none of these estimates were derived from national-level data that included both state and federal prisons (for an exception, see James & Glaze, 2006). Some studies have included multiple prisons within a single state (Lahm, 2009; Wolff et al., 2007; Wooldredge, 1998), and three have included prisons from several states (Lahm, 2009; Wooldredge & Steiner, 2012, 2013). As such, national-level esti-mates of the extent of physical victimization in state and federal prisons are needed. A representative picture of the extent of victimization of inmates housed in both state and federal prisons is one contribution of the current project.

Second, save the work by Wolff and colleagues (Wolff et al., 2007; Wolff & Shi, 2009) and Steiner and Wooldredge (2009), the violent (non-sexual) victimization of female inmates has been relatively ignored in the literature. The data collected by Wolff, however, only included one female prison, thus their estimates of victimiza-tion are not generalizable, and institutional factors (given the lack of variation) that could differentially impact victimization risk among females were not examined. Addressing females’ victimization is important for several reasons. Even though females make up only 7% of the incarcerated population—equating to more than 110,000 inmates—they require the same protection as do male inmates. In addition, when females are victimized in prison, they are more likely to have their incident perpetrated by another inmate, whereas male inmates more frequently report being victimized by staff, suggesting the context of female physical victimization is differ-ent (Wolff, Shi, & Siegel, 2009a). The ways gender further interacts with risk factors for prison victimization has yet to be considered, which is a gap in the literature our project addresses.

Literature Review

Risk Factors for Physical Victimization in Prison

According to routine activities/lifestyles theory, personal victimization is likely to occur when persons are exposed to motivated offenders, when they are deemed suit-able targets, and when they lack capable guardianship (Cohen & Felson, 1979). When these three elements coalesce in time and space, victimization is likely to occur. Lifestyles theory is the micro-level counterpart to routine activities theory (Hindelang, Gottfredson, & Garofalo, 1978), which posits that a person’s lifestyle impacts risk. Together, they form the theoretical underpinning for our study of violent victimization in prison.



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Exposure to motivated offenders. Cohen and Felson (1979) noted that motivated offend-ers are everywhere and do not need to be explained—that given the opportunity, there will be people present who will victimize others. The prison victimization literature suggests that prisoners vary in their exposure to these would-be offenders. For exam-ple, spending more time in recreation hours (Wooldredge, 1998), having privileges (e.g., visitation; Wooldredge, 1994), or spending more time working (Wooldredge & Steiner, 2013) increases physical assault risk, whereas inmates who spend more hours in education programs and time studying are less likely to be violently victimized (Wooldredge, 1998). Similarly, misconduct increases the likelihood of victimization for an inmate (Lahm, 2009). The experience of these risks, however, may be gendered. For example, as major rule infractions are received more often by females, and there-fore, are more normative in a female prison setting (Jiang & Winfree, 2006) and because females are more likely than males to be assaulted by fellow inmates, there are arguably more motivated offenders within female facilities than male prisons (Gover, Perez, & Jennings, 2008), which increases the odds that a female inmate will be victimized (Wolff et al., 2009a).

It is possible that inmates will be exposed to (or protected from exposure to) moti-vated offenders in other ways. For instance, being in a prison with more violent offend-ers may impact risk, holding constant whether the individual is himself incarcerated for a violent offense. Exposure to motivated offenders may also vary by sex. For instance, due to the relatively low number of female prisoners, compared with males, most states have only one or two institutions that house women. As a result, all female offenders are often times housed together, regardless of classification (Pollock, 2002), which may increase opportunities for predation.

Suitable targets. Not everyone inside of prison shares the same chance of being victim-ized. According to the tenets of routine activities/lifestyles theory, people who are suitable targets, those who have qualities or characteristics that are deemed valuable or desirable or that make them vulnerable, are at highest risk of personal victimization. For example, having a mental disorder increases an inmate’s vulnerability to victim-ization (Blitz, Wolff, & Shi, 2008). In addition, the type of crime for which an inmate is sentenced can influence victimization risk. Inmates serving time for sexual offenses are at an increased risk of being victimized by fellow prisoners, whereas inmates serv-ing time for violent offenses are especially at risk of being physically assaulted by staff (Wolff, Shi, & Siegel, 2009b). A number of studies suggest that serving time for a violent offense increases risk of victimization for both males and females (Gover et al., 2008; Lahm, 2009; Struckman-Johnson & Struckman-Johnson, 2002).

The risk of victimization for inmates who served in the military may be linked to serving time for a violent offense. Specifically, a study by the Bureau of Justice Statistics found that compared with nonveterans (47%) in state prisons, veterans were more likely to be serving time for a violent offense (57%; Noonan & Mumola, 2007). Thus, serving time for a violent offense may be a stronger predictor of vic-timization for veterans. In addition, younger inmates (Wolff et al., 2009b; Wooldredge, 1998; Wooldredge & Steiner, 2013), those with a slight build (e.g.,




body size; Chonco, 1989; Smith & Batiuk, 1989), inmates who have been previously victimized (Wolff et al., 2009a, 2009b), and alcohol and substance users (Pare & Logan, 2011; Wolff & Shi, 2009; Wolff, Shi, Blitz, & Siegel, 2007b) are at increased risk of victimization.

These factors may differentially affect the degree to which male and female inmates are at risk of victimization. Indeed, a greater proportion of female inmates reported prior physical and sexual abuse, compared with males (Harlow, 1999; Wolff et al., 2009a), which has been found to increase their institutional misconduct (Chen, Lai, & Lin, 2014). These findings suggest that prior victimization increases the risk of being victimized in prison and that victimization may be particularly relevant for women. Although not previously examined in the prison victimization literature, it is possible that other risk factors are gendered. For example, substance use may be differentially related to victimization for males and females, especially because drugs have different effects on males and females (McClellan, Farabee, & Crouch, 1997).

Lack of capable guardianship. Not having effective guardianship also provides ample opportunity for victimization. In prison, guardianship may include having or seeking protection through other inmates or cellmates, which is a form of social guardianship. One of the findings regarding risk of victimization in prison is that compared with non-White inmates, White inmates face an increased risk of being physically victim-ized by other prisoners (Wolff et al., 2009b; Wooldredge, 1998). This increased risk may be due, in part, to the fact that in some prisons White inmates are the minority and have little guardianship from other inmates (Mann & Cronan, 2002). Guardianship can be facilitated formally at the prison level through organization, surveillance, and administration. For instance, Morris, Carriaga, Diamond, Piquero, and Piquero (2012) found environmental factors are significantly related to violent prison behaviors. Moreover, Lahm (2009) found that higher-security level prisons have lower rates of victimization.

The impact of guardianship on victimization may also vary by the nature and qual-ity of relationships that male and female inmates establish. The social organization of male prisons is largely influenced by pseudo-political units, such as gangs (Pollock, 2002). Inmates band together to prevent victimization by joining gangs or creating groups that provide safety and order. Males are concerned with doing their own time and being tough, and therefore rarely do they form personal ties (Jiang & Winfree, 2006). In contrast, the social organization of female prisons is comprised of pseudo-families—small, family-like groups—friendships, and allegiances (Pollock, 2002). As a whole, then, the degree to which male and female prisoners have guardianship may be influenced by the types of relationships in which they engage.

In sum, the literature suggests that at least to some extent the experiences of female and male prisoners are different. Consequently, the factors that increase exposure to motivated offenders, increase target suitability, and reduce capable guardianship may vary by sex, although this idea has not been explored empirically. Thus, a major contribution of the current study is the examination of the gendering of prison victimization.



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Empirical and Theoretical Issues in the Multilevel Study of Prison Victimization

In addition to research on individual-level risk factors, there have been six published studies investigating prison contextual effects on victimization (Lahm, 2009; Wolff et al., 2007; Wolff et al., 2009b; Wooldredge, 1998; Wooldredge & Steiner, 2012, 2013). Despite these studies, as recently as 2009, Lahm concluded, “There is a signifi-cant lack of research regarding the contextual effects of prisons on inmate victimiza-tion” (p. 351). Moreover, Lahm argues, “Not only has context been generally ignored by past researchers, but studies that do include contextual variables include very few prison-level variables” (p. 351). She concludes, “Future research should expand and incorporate more inmates and prisons” and that “research must also be expanded to include women’s prisons” (p. 364). Indeed, the existing multilevel literature on prisons is scant. This is an important omission, as we know from community-based studies of victimization that victimization occurs in context (see, for example, Miethe & McDowall, 1993).

All of the published multilevel studies of prison victimization to date contain sig-nificant data limitations. First, three of the six studies focus on a single state (Wolff et al., 2007; Wolff et al., 2009a; Wooldredge, 1998), two focus on prisons in two states (Wooldredge & Steiner, 2012, 2013), and one focuses on prisons in three states (Lahm, 2009). In addition, most of these studies have a limited sample size of prisons, and thus have limited statistical power to detect contextual effects (for exceptions, see data used in Wooldredge & Steiner, 2012, 2013). Indeed, Wolff and colleagues’ studies utilized 14 prisons in New Jersey (only one of which is a women’s prison). Lahm’s (2009) study sampled inmates from 30 male prisons in Kentucky, Tennessee, and Ohio. Wooldredge’s (1998) study focused on three male prisons but was unable to utilize multilevel modeling techniques given the small number of sites. In the largest study to date, Wooldredge and Steiner (2012, 2013) sampled 3,150 White and 2,403 African American inmates from 46 prisons in Ohio and Kentucky (but there were a compara-bly small number of female inmates, and gendered analyses were not conducted).

In spite of the lack of multilevel studies of victimization in prisons, empirical research and theories of victimization suggest that contexts matter. Routine activities theory provides one possible reason why context matters. Cohen and Felson (1979) posit that legitimate activity patterns and structures create opportunities for crime by lessening guardianship or by bringing motivated offenders and suitable targets together in space and time. Consistent with the routine activities perspective, variations in the “legitimate” prison contextual conditions may create or restrict opportunities for vic-timization. For example, Wolff and colleagues (2009b) found that the percent dissatis-fied with treatment by other inmates at the prison level was a significant predictor of inmate-on-inmate personal victimization, such that contexts in which dissatisfaction is more common have higher rates of victimization.

Little is known about how the gendered context of prison affects victimization experiences. Few studies to date have included female prisons. Moreover, those stud-ies that have included multiple female prisons did not examine variation in victimiza-tion by sex-composition. The exclusion of female prisons (Lahm, 2009; Wolff et al.,




2009b; Wooldredge, 1998) and lack of examination on the impact of sex-composition on victimization (Wolff et al., 2007; Wooldredge & Steiner, 2012, 2013) leaves the need for gendered analyses to capture the differential prison experiences of male and female inmates.

The Current Study

The extant literature has investigated important characteristics of prisons, such as prison architecture (Wooldredge, 1998), racial composition (Lahm, 2009), age compo-sition (Wolff et al., 2007), and prison climate (Wolff et al., 2009b). Multilevel theoriz-ing, such as routine activities theory, has pointed to the important role of context in predicting victimization (Cohen & Felson, 1979; Rountree & Land, 1996) and the conditional effects of individual-level risk factors on victimization by context (Miethe & McDowall, 1993). In the current project, we build on this existing literature by investigating a broader array of contextual-level measures than has previously been included in multilevel studies of prison victimization, including prison racial composi-tion, sex (prisons for men vs. prisons for women), jurisdiction (state vs. federal), and contextual measures of rule infractions and prison programs. In addition, we use data from the 2004 Survey of Inmates in State and Federal Correctional Facilities (SISFCF)—a nationally-representative survey of 17,640 inmates nested within 326 state and federal prisons—to examine how gender affects the context of prison victim-ization. In doing so, we use data from a larger number of prison than has been used in previous studies to answer the following questions:

Research Question 1: Do prison contextual variables predict victimization, hold-ing constant individual-level risk factors?Research Question 2: Do the effects of individual-level risk factors on victimiza-tion vary by gender?

Method

Data and Sampling

This study used data from the SISFCF, 2004 (U.S. Department of Justice, Bureau of Justice Statistics, 2004). The survey instrument has been administered by the Bureau of the Census for the Bureau of Justice Statistics every 5 to 7 years since 1974 in state prisons, and since 1991 in federal facilities. These surveys have been combined to gen-erate a series of data sets that are nationally and geographically representative of pris-oners in the United States. The data we employ were collected from October 2003 to May 2004 through personal and computer-assisted (CAPI) interviews. The sampling procedure was a two-stage design, wherein first the prisons were selected and then the prisoners within them. This process has been described extensively (see Greenberg & Rosenheck, 2008, 2012; James & Glaze, 2006; Wood, 2013). Survey questions were designed to capture information about each inmate’s personal characteristics, prison



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behavior, criminal and family history, gun ownership and use, substance abuse, and involvement in programs and services. With an average response rate of 89.1% for state and 84.6% for federal facilities, the 2004 sample included 18,185 inmates housed within 326 facilities. Prior to public release, each data set was de-identified

Measures

Dependent variable. We used a survey question about prisoner experiences with inten-tional physical assault (e.g., knife or stab wounds, gun-shot wounds, bullet wounds, broken bones, sexual assaults, teeth knocked out or chipped, internal injuries, being knocked unconscious, bruises, black eyes, sprains, cuts, scratches, swelling, welts, and other injuries) since incarceration as our dependent variable. Specifically, prisoners’ responses to the question “Since your admission [most recent admission date], have you been injured in a fight, assault, or incident in which someone tried to harm you?” were coded 1 if victimized since incarceration and 0 if not victimized.

Individual-level demographic variables. Demographic data used in analyses at the indi-vidual-level include prisoner age, race, education, marital status, and pre-incarceration income. The inclusion of these variables is consistent with previous prison-experience literature (Lahm, 2009; Wolf & Shi, 2009; Wooldredge, 1998). Respondent’s age was measured as a continuous variable that has a range of 16 to 84. The survey allowed inmates to indicate their race as White, Black or African American, Spanish, Latino or Hispanic, American Indian or Alaska Native, Asian, Native Hawaiian or Other Pacific Islander, and/or another race. Using White as the referent group, four dummy variables were created: Black or African American, American Indian or Alaskan Native, all Other races, and multiracial. The multiracial variable pools all of the cases in which more than one race was recorded. All of the Hispanic and Latino(a) inmates reported being multiracial.

Education was a dichotomous indicator of whether the respondent has a GED or high school diploma (coded 1) or not (coded 0). The measure of income was a 13-cat-egory ordinal scale based on the amount of money a prisoner made the month before his or her arrest, wherein each level increases in 100-dollar increments, beginning with no income (represented by 0) and ending at 7,000 dollars or more. Inmates’ marital status was recoded into two dummy variables, with one indicating single/never mar-ried and another indicating separated, widowed, or divorced, with married as the refer-ent group.

Individual-level independent variables. We used 17 individual-level independent vari-ables: body mass index (BMI), categorized incarceration offenses, drug use, alcohol abuse, work assignment, military service, privileges received, program participation, minor and major infractions, previous victimization, and general types of disorders. Inmates’ weight and height responses were used to create a BMI scale by using the following standard formula: weight (lb) / (height [in])2 × 703. This measure indicated inmates’ physical size, which ranges from 13 to 80. Out of 88 different offense codes,




we created four dummy variables to account for variation in types of incarceration offenses: property, drug related, sex crimes, and other types of offenses.1 Violent offenders were the referent group.

In addition, inmates indicated if they had any lifetime use of 15 types of drugs, which were recoded into dichotomous variables (use vs. non-use for each substance). We summed the drug responses to represent how many different types of drugs an inmate has used in their lifetime (e.g., reporting having ever tried Cocaine, Marijuana, and Methamphetamines was coded as a 3). Similarly, modeling the Michigan Alcohol Screening Test, we created an alcohol abuse variable using 25 survey questions related to alcoholism.2 After summing the Yes (1) or No (0) response options, the scale ranged from 0 if the respondent had no problem with alcohol to 25, which indicates extreme alcoholism (Cronbach’s α = .928). The work assignment variable was a dichotomous measure of whether prisoners have worked a general or off-grounds job since incar-ceration (coded 1) or not (coded 0). The question “Did you ever serve in the U.S. Armed Forces” was coded 1 for military service and 0 for none.

Privileges were measured using four dichotomous survey questions about access to a television, a telephone, reading materials, and/or being allowed visitors. Those who receive all of these privileges were coded as 1, whereas those who answered 3 or less were coded as 0, because of the skewed nature of these items. Inmates were also asked nine questions related to involvement in any programs while in prison, such as classes, counseling, groups, organizations, and/or any other pre-release oriented planning, which we collapsed into a dichotomous measure of any participation (coded 1). Affirmative answers to the survey question “Since your admission, have you been written up or found guilty of any minor violations relating to facility orderliness and operation, such as use of abusive language, horseplay, failing to follow sanitary regu-lations, etc?” were coded 1 in our dichotomous minor infractions variable. Similarly, affirmative answers to the survey question “Since your admission, have you been writ-ten up or found guilty of any major violation, including work slowdowns, food strikes, setting fires, rioting, and so on?” were coded 1 in our dichotomous major infractions variable. To create a measure of previous victimization, six dichotomous survey ques-tions were collapsed. If the prisoner reported ever being beaten, hit with a fist, having a weapon used on them, being choked, or experiencing other kinds of physical abuse, they were coded as 1, whereas those who said no to all 6 were coded as 0. Finally, three dichotomous variables regarding any diagnoses of depression, psychotic, or personal-ity disorders were also included (coded 1).

Contextual-level demographic variables. At the prison level, we used four demographic measures to characterize the sample of facilities: type, sex, age, and race proportions. Facility type was created so that state facilities were coded 1, whereas federal facilities were coded 0. By aggregating the individual-level age variable, we created a measure of the mean age for each prison, which ranges from 21 to 53. Sex is a dichotomous variable where male prisons were coded 0 and female prisons were coded 1. All of the race categories discussed above were aggregated to the contextual level. These indi-cate the proportion of each race present in a given prison.



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Contextual-level independent variables. We aggregated the individual-level independent variables BMI, the offense categories, drug use, privileges, program participation, and minor and major infractions to the prison level by taking the mean of the individuals’ responses within a facility. These variables represented the average level (or propor-tion of yeses for dichotomous variables) for the prison on that particular variable.

Data Analysis

To answer the proposed research questions, we utilized hierarchical logistic regression models that take into account the nested structure of the SISFCF data. That is, the nest-ing of inmates within the 326 facilities in the SISFCF data violates the assumption of independent observations in traditional regression models, which produces depen-dence in the residuals (correlated errors) and biases standard errors and consequently significance tests in single-level models. As a result, these multilevel models esti-mated unbiased standard errors by modeling a random intercept that captured shared error variance common to each individual within a prison (Raudenbush & Bryk, 2002), allowing the individual-level residuals to be uncorrelated. Because we had a dichoto-mous dependent variable, we estimated multilevel logistic regressions.

Due to missing data on the conviction offense variables primarily, we utilized a multiple imputation procedure using the norm program (Schafer, 1997). The distribu-tions of the variables were preserved using appropriate link functions (e.g., logistic regression for dichotomous variables), and all information from the remaining vari-ables was used to impute missing values. Ten imputed data sets were created and results were pooled using Bryk and Raudenbush’s hierarchical linear modeling (HLM) program.

We found preliminary support for a contextual approach to the study of violent victimization in prison, with significant variation across prisons and a small but statis-tically significant intraclass correlation value (ICC = .08; p < .001). This ICC value indicated that 8% of the variation in victimization was due to the prison level, whereas 92% of the variability in victimization was inmate-level variation.

Results

Sample Description

Table 1 describes the inmates and prisons in our sample, respectively. Out of 17,640 inmates, 13.27% were victimized during their current incarceration, and 30% were victimized prior to their incarceration. The average inmate age was 35.81, and less than half (41%) of the inmates had a GED or high school diploma. On average, the respondents made an income of 1,000 to 1,199 dollars the month before their arrest. The majority (53%) of the sample were single/never married. The majority of the sample was Black (40%), followed by White (35%) and multiracial (21%).

At the individual level, the mean BMI was 27.59, which is considered overweight based on the standards set by the Centers for Disease Control and Prevention (CDC).3




Table 1. Descriptive Statistics.

Variables Minimum Maximum M or % SD

Dependent variable Victimization 0 1 13.27% 0.34Demographic variables Age 16 84 35.81 10.52 GED/HS graduate 0 1 41% 0.49 Income 0 12 6.47 3.28 Marital status Married 0 1 19% 0.39 Single/never married 0 1 53% 0.50 Widowed/divorced/separated 0 1 28% 0.45 Race White 0 1 35% 0.48 Black 0 1 40% 0.49 American Indian and Alaskan native 0 1 2% 0.14 Other races 0 1 2% 0.12 Multiracial 0 1 21% 0.41Individual-level independent variables Body mass index 9 80 27.59 4.84 Count of the types of drugs used 0 15 3.09 3.25 Alcohol abuse 0 25 7.72 6.88 Offenses Violent 0 1 41% 0.49 Property 0 1 16% 0.37 Sex 0 1 10% 0.30 Drug 0 1 25% 0.43 Other 0 1 8% 0.28 Previous victimization 0 1 30% 0.46 Program participation 0 1 69% 0.46 Privileges 0 1 80% 0.40 Military service 0 1 9% 0.29 Work assignment 0 1 71% 0.45 Minor infractions 0 1 8% 0.28 Major infractions 0 1 13% 0.34 Disorders Depression 0 1 20% 0.40 Psychotic 0 1 4% 0.20 Personality 0 1 6% 0.24Contextual-level independent variables Federal prisons 0.00 1.00 0.12 0.32 Age 21.49 53.44 35.69 3.62 Male prisons 0 1.00 0.79 0.41

(continued)



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On average, the inmates had used 3 different types of drugs, and scored 7.72 on the alcohol scale. Approximately 41% of the inmates were incarcerated for violent offenses, 25% for drug offenses, and 16% for property offenses. The majority of respondents had participated in a prison program (69%). Over 80% of the inmates had access to a television, visits, reading materials, and a telephone when they took the survey. About 9% of the sample had served in the military. Slightly over two thirds of the sample (71%) indicated they had a prison work assignment. In addition, 8% of the sample indicated they had been charged with a minor infraction, and around 13% had been charged with a major infraction while incarcerated. Finally, 30% of respondents were previously diagnosed with a disorder.

At the contextual level, approximately 12% of the prisons represented were federal facilities. Almost 80% of the prisons sampled were male prisons. The percentage of White inmates housed in the facilities ranged from 0% to 88%, whereas the percentage of Black or African American inmates ranged from 0% to 89% of the inmates. The percentage of American Indian or Alaskan Native inmates ranged from 0% to 31% of the inmate population; those who identified themselves as some other race ranged from 0% to 17% of the inmate population, and multiracial inmates ranged from 0% to 68% of the inmate population, across the 326 facilities.

Variables Minimum Maximum M or % SD

Race proportion White 0.00 0.88 0.36 0.16 Black or African American 0.00 0.89 0.40 0.19 American Indian or Alaskan Native 0.00 0.31 0.02 0.04 Other races 0.00 0.17 0.01 0.02 Multiracial 0.00 0.68 0.21 0.15 Body mass index 23.66 34.36 27.60 1.19 Offenses Violent 0.00 0.84 0.43 0.14 Property 0.00 0.63 0.16 0.08 Sex 0.00 0.74 0.10 0.10 Drug 0.00 0.80 0.23 0.12 Other 0.00 0.57 0.08 0.06 Drug use 1.22 6.14 3.15 0.91 Program participation 0.13 0.98 0.69 0.16 Prison privileges 0.02 1.00 0.79 0.14 Minor infractions 0.00 0.46 0.09 0.07 Major infractions 0.00 0.53 0.14 0.11

Note. Level 1 = individuals (n = 17,640); Level 2 = prisons (n = 326); income is coded 0 = none, 6 = US$1,000-US$1,199, 12 = US$7,500 or more; privileges is coded 0 = <4, 1 = 4. GED = general equivalency diploma; HS = high school.

Table 1. (continued)




Table 2 shows the main effect results of our multilevel logistic regression model predicting physical assault victimization while incarcerated. There were a number of significant predictors of physical assault victimization at the individual level. Consistent with our theorizing and previous research, larger BMI scores significantly reduced the odds of victimization. Being Black or African American also significantly reduced the odds of victimization, relative to being White. Single/never married inmates had 1.36 times the odds of married inmates of being physically assaulted, while divorced, separated, and widowed inmates had 1.17 times the odds of married inmates of being physically assaulted. Inmates charged with violent offenses had sig-nificantly higher odds of victimization than those charged with drug offenses, property offenses, or other offenses. Violent and sex offenders were not significantly different in their odds of victimization. Alcohol and drug use significantly increased the odds of victimization, whereas a work assignment significantly decreased the odds of victim-ization. Program participation and minor infractions significantly increased the odds of victimization, but major rule infractions multiplied the odds of victimization by almost 6 times. Having a depression or personality disorder increased the odds of vic-timization while in prison.

In terms of contextual effects on victimization, results in Table 2 show that being in a male prison increased the odds of victimization by 45% compared with being in a female prison. Residing in a prison with a higher proportion of multiracial inmates increased the odds of victimization compared with residing in a prison with a lower proportion of multiracial inmates. Residing in a prison with a higher proportion of violent offenders significantly increased the odds of victimization compared with higher proportions of property and other offenses. Finally, residing in a prison with a higher proportion of individuals who had been charged with major rule infractions significantly increased the odds of victimization. All of these effects held while con-trolling for all of the individual-level risk factors included in the model.

Finally, turning to Table 3, we examined whether the gender of the prison moder-ated the individual-level covariates impacts on victimization. Here, we found five sig-nificant cross-level interactions. Specifically, we found that the effects of being convicted of a drug crime, drug use, military service, major infractions, and diagnosed personality disorders are all gendered in their impacts on victimization. That is, the impact of being convicted of a drug offense (as opposed to a violent offense) reduced the risk of victimization more for men than it does for women. Drug use increased the risk of victimization to a greater extent for men than it did for women. Military service was significantly protective for men, but it significantly increased the odds of victim-ization for women. Major rule infractions significantly increased the risk of victimiza-tion for all offenders, but its impact was stronger for women than men. The risk of victimization was significantly higher for men with personality disorders than women.

Discussion

Over the last three decades, researchers have been interested in determining what fac-tors place certain inmates at risk of being victimized. Our study contributes to this



Teasdale et al. 13

Table 2. Prison Victimization Multilevel Results.

Variables b SE OR

Intercept −4.598** 0.993 0.010Level 2 Federal 0.005 0.142 1.005 Male 0.373** 0.129 1.451 Age 0.017 0.011 1.017 Body mass index 0.066 0.034 1.069 Race Proportion Black or African American proportion 0.432 0.250 1.540 American Indian or Alaskan Native proportion 0.324 0.793 1.383 Other races proportion 1.797 1.411 6.033 Multiracial proportion 0.686* 0.301 1.985Offenses Sex offenses 0.336 0.460 1.399 Other offenses −1.754* 0.748 0.173 Property offenses −2.051** 0.667 0.129 Drug offenses −0.742 0.510 0.476 Privileges −0.536 0.298 0.585 Program participation −0.178 0.260 0.837 Minor infractions −0.666 0.492 0.514 Major infractions 1.829** 0.380 6.225Level 1 Age 0.000 0.003 1.000 Body mass index −0.017** 0.006 0.983 Race Black or African American −0.333** 0.073 0.716 American Indian or Alaskan native −0.043 0.198 0.958 Other races −0.293 0.255 0.746 Multiracial −0.064 0.077 0.938 GED/HS graduate −0.036 0.061 0.964 Single/never married 0.309** 0.078 1.362 Separated/divorced/widowed 0.156* 0.078 1.169 Income −0.002 0.009 1.002 Offenses Sex offenses 0.019 0.132 1.019 Other offenses −0.709** 0.162 0.492 Property offenses −0.383** 0.110 0.681 Drug offenses −0.662** 0.102 0.516 Alcohol Abuse Scale 0.010* 0.005 1.011 Sum of drug use 0.328** 0.008 1.033 Work assignment −0.158* 0.063 0.854 Military 0.012 0.095 1.012 Received privileges −0.097 0.081 0.907

(continued)




body of research by considering both contextual- and individual-level predictors of victimization from a lifestyles/routine activities framework and whether individual-level risk factors for victimization varied by gender. Several significant findings resulted from our study.

First, context plays an important role in predicting victimization in prison. Specifically, the odds of being victimized are greater for inmates who reside in a prison that has a higher proportion of violent offenders or those who have been charged with a major rule violation than for prisons with lower rates on these variables. These find-ings support the lifestyles/routine activities perspective, indicating that residing in facilities with a higher proportion of violent offenders and major rule violators increases exposure to motivated offenders. In addition, inmates who reside in a male prison or with a higher proportion of multiracial individuals are more likely than other inmates to be victimized, which suggest a lack of capable guardians.

Second, our contextual analysis showed five significant sex differences in the risk factors that predict victimization. For males, being convicted of a drug offense (com-pared with a violent offense) and having served time in the military reduces the odds of victimization more so than it did for inmates in female prisons. Military service increases the odds of victimization for females. In addition, drug use is a greater pre-dictor of victimization for males compared with females. For both males and females, having major rule infractions increases the odds of victimization; yet, it is a stronger risk factor for females. This finding may be particularly relevant given that females are more likely to be assaulted by fellow inmates (Wolff et al., 2009a). Finally, for males, having a personality disorder increases the risk of victimization compared with females with personality disorders.

It is worth noting that our contextual approach is particularly warranted in light of the changing “meaning” of the independent variables when considered at various lev-els of the analysis. For example, a variable such as whether an individual has been convicted of a violent offense may be an indicator that she or he is (or is not) a suitable

Variables b SE OR

Program participation 0.364** 0.062 1.439 Minor infractions 0.662** 0.080 1.938 Major infractions 1.789** 0.068 5.985 Previous victimization −0.097 0.082 0.907 Disorders Depression disorder 0.367** 0.072 1.444 Psychotic disorder −0.111 0.132 0.895 Personality disorder 0.296** 0.108 1.345

Note. Level 2 = prisons (n = 326); Level 1 = individuals (n = 17,640). b = coefficient; SE = robust standard error; OR = odds ratio/exp(b); GED = general equivalency diploma; HS = high school.*p < .05. **p < .01.




Teasdale et al. 15

Table 3. Prison Victimization—Interactions With Gender.

Variables b SE OR

Intercept −4.723** 0.989 0.009Level 2 Federal 0.001 0.142 1.001 Male 0.556** 0.149 1.743 Age 0.017 0.011 1.017 Body mass index 0.064 0.035 1.066 Race Proportion Black or African American proportion 0.440 0.251 1.553 American Indian or Alaskan native proportion 0.276 0.796 1.318 Other races proportion 1.607 1.434 4.990 Multiracial proportion 0.692* 0.303 1.998Offenses Sex offenses 0.286 0.464 1.331 Other offenses −1.829* 0.762 0.161 Property offenses −2.055** 0.067 0.128 Drug offenses −0.721 0.509 0.486 Privileges −0.521 0.303 0.594 Program participation −0.133 0.260 0.876 Minor infractions −0.646 0.496 0.524 Major infractions 1.828** 0.385 6.223Level 1 Age 0.000 0.003 1.000 Body mass index −0.017** 0.006 0.984 Race Black or African American −0.341** 0.073 0.711 American Indian or Alaskan native −0.032 0.199 0.969 Other races −0.322 0.252 0.725 Multiracial −0.075 0.077 0.927 GED/HS graduate −0.039 0.061 0.962 Single/never married 0.301** 0.077 1.352 Separated/divorced/widowed 0.153* 0.078 1.165 Income 0.001 0.009 1.001 Offenses Sex offenses 0.019 0.133 1.019 Other offenses −0.711** 0.160 0.491 Property offenses −0.373** 0.111 0.689 Drug offenses −0.279 0.197 0.757 Alcohol Abuse Scale 0.010* 0.005 1.011 Sum of drug use −0.022 0.020 0.979 Work assignment −0.161* 0.064 0.852 Military 1.096* 0.450 2.994 Received privileges −0.097 0.081 0.907

(continued)




target at the individual level of the analysis. When aggregated to the prison level, how-ever, the proportion of violent offenders in a prison is an indicator of exposure to motivated offenders. Similarly, being a White person may be considered an indicator of target suitability; though, this depends on the proportion of those who are White in the prison. That is, at the contextual-level, being surrounded by a higher proportion of those who share one’s race may indicate guardianship. This changing “meaning” of the variable across levels is an important feature of our multilevel perspective.

Taken together, these findings suggest that both individual-level and contextual-level factors “matter” in the production of prison victimization and that there are important cross-level interactions. In this regard, prison administrators may be able to identify individuals most at risk of victimization. This identification is especially important given that research suggests being victimized while incarcerated is linked to negative mental health outcomes (Listwan, Colvin, Hanley, & Flannery, 2010), and treatment for mental health problems can significantly reduce inmate misconduct (Houser, Blasko, & Belenko, 2014). Therefore, once identified, evidence-based strate-gies could be developed and instituted to prevent prison victimization.

Importantly, our research also indicates that the context of male and female prisons needs to be considered. The findings show that some individual-level effects operate differently in male institutions compared with female institutions, suggesting that, at least to some extent, the risk factors for victimization while incarcerated differ for males and females. For instances, factors that reduced the risk of victimization for males (e.g., military service) increase the odds of victimization for females. Thus, it is

Variables b SE OR

Program participation 0.367** 0.061 1.443 Minor infractions 0.656** 0.080 1.927 Major infractions 2.226** 0.171 9.264 Previous victimization −0.099 0.082 0.906 Disorders Depression disorder 0.365** 0.071 1.440 Psychotic disorder −0.117 0.133 0.890 Personality disorder −0.238* 0.247 0.788Cross-level interactions Male × Drug offenses −0.464* 0.210 0.629 Male × Drug use 0.062** 0.021 1.064 Male × Military −1.146* 0.460 0.318 Male × Major infractions −0.498** 0.181 0.608 Male × Personality disorder 0.665* 0.267 1.945

Note. Level 2 = prisons (n = 326); Level 1 = individuals (n = 17,640); b = coefficient; SE = robust standard error; OR = odds ratio/exp(b); GED = general equivalency diploma; HS = high school.*p < .05. **p < .01.




Teasdale et al. 17

key for administrators to consider these characteristics when classifying inmates to prisons. Effective classification strategies need to take into account not only individual characteristics but also how these characteristics interact with the gendering of the prison context. Furthermore, knowing that context matters, as well as that risk differs for males and females may aid administrators in developing and facilitating prevention and intervention programs for victimization. For example, we found that drug use was a greater predictor of victimization for males compared with females. Thus, drug use may be a target for change not only as a general risk factor for recidivism but also to reduce male inmates’ risk for victimization. Moreover, as some risk factors for victim-ization differ for males and females, prevention programs should be gender specific and targeted to those specific risk factors.

As with any study, ours is not without limitations. This study faced many of the same issues as those using self-report data have previously noted. The inmates may have over-reported in some areas, underreported in others, or even misinterpreted questions. The validity of responses may also be skewed by fear of sanctions. It is pos-sible that males and females differentially respond to survey items, and as a result, the measures may not be truly indicative of occurrences. In addition, we were limited in what variables we could include in our analysis due to some items relevant for theory not being collected and/or released to the public (e.g., other contextual-level variables that may interact with individual-level factors to influence victimization). Furthermore, our dependent variable, while capturing whether an inmate was victimized, does not take into account motivations for victimization. It is also notable that it was not pos-sible to account for sentence length or to consider tenure of residence at any given facility, which may influence our results. Finally, changes in corrections over the last decade may affect the generalizability, interpretation, and/or significance of this study’s results. Future research should aim to shed more light on these issues.

In addition to concerns about the dependent variable, some of the independent vari-ables may have limitations as well. First, the drug-use measure is limited by its lack of bounding or recall period. It is likely that a person who used drugs at an early age would be at risk of deleterious outcomes, including victimization, because research has shown that age of onset for drug use is linked to drug abuse and dependence (Grant & Dawson, 1998). For individuals who are using multiple different types of drugs at early ages, we would expect them to be at greater risks of drug abuse and dependence, and as research shows, victimization as well. Finally, the alcohol measure is not tem-porally bounded and likely refers to problem drinking prior to the current incarcera-tion. Although current drinking and drug use might be more desirable measures, current use is likely correlated with prior behaviors. Thus, these serve as reasonable proxies for current use behaviors.

As noted above, data limitations prevented the inclusion of other measures that may influence victimization risk, which future research should explore. First, little is known about how sexual orientation may differentially effect males’ and females’ victimiza-tion risk. How an inmate’s sexual orientation affects victimization experiences may differ for males and females. For instance, the hyper-masculine environment in male prisons may increase the likelihood of victimization for homosexual inmates, whereas




pseudo-family units in female prisons may lead to homosexuality being more widely accepted.

Second, future research should examine the role security level of facilities plays in victimization risk. Research shows that high-security facilities have higher rates of victimization (Perez, Gover, Tennyson, & Santos, 2010). It is possible that facility type may differently affect the risk of victimization in female prisons as female inmates are typically housed together regardless of classification. In addition, gang membership may influence victimization risk, as gang affiliation is predictive of assaults in prison (Walters & Crawford, 2013).

In conclusion, this study is the first to examine a multilevel perspective on victim-ization using a nationally-representative sample of prisons and prisoners. We find that male prisons, those with more rule breakers, and prisons with more inmates convicted for violent crimes have higher rates of victimization. At the individual level, those with depression, rule violators, violent offenders, and smaller-statured inmates are at greater risk of victimization. Moreover, we show how gender of the prison interacts with individual-level factors in predicting risk of victimization. As such, our findings indicate the importance of considering not just risk factors at the individual-level for victimization but also how context may interact with individual risk factors in produc-ing victimization. In this way, it lays the groundwork for future research to consider not only men in men’s prisons but also women in women’s prisons.

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

1. Examples of property offenses include charges for burglary, arson, theft, fraud, and forgery. Examples of sexual offenses include rape, sexual assault, lewd acts with children, and sod-omy. Drug crime convictions include delivery, importation, sales, distribution, trafficking, possession, and unlawful disposal of illegal narcotics. The other offense category measures charges such as blackmail, extortion, intimidation, conspiracy, trespassing, flight to avoid prosecution, probation violations, contempt of court, disorderly conduct, immigration vio-lations, racketeering, and espionage. In addition, the reference category (violent offenders) was collapsed to include everyone who indicated they were incarcerated for homicide, kidnapping, aggravated assaults, robbery, or a similar offense.

2. The alcohol abuse variable was based on questions such as “Have you ever felt you should cut down on your drinking,” “Have you ever had a drink first thing in the morning to steady your nerves or get rid of a hangover,” “Have you ever driven a car or any other vehicle after having too much to drink,” “Have you ever had as much as a fifth of liquor in 1 day, in year before admission—got arrested or held at a police station because of drinking,” and



Teasdale et al. 19

“in year before admission—did drinking keep you from doing work, going to school or caring for children” (see survey questions S8Q6A, S8Q5B, S8Q6A_1 through S8Q6A_4, S8Q6B_1, S8Q6B_2, S8Q6D, S8Q6E1_1 through 3, S8Q6E2_4 through 6, S8Q6F1_1 through 5, S8Q6F2_6 through 11).

3. According to the Centers for Disease Control and Prevention (CDC), any body mass index (BMI) below 18.5 is underweight, 18.5 to 24.9 is normal, 25 to 29.9 is overweight, and 30 and above is obese. Retrieved from http://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/index.html

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Violent Victimization in the Prison Context: An Examination of the Gendered Contexts of Prison