Discussion II

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Women & Criminal Justice

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Victim Blame in Fictional Crime Dramas: An Examination of Demographic, Incident-Related, and Behavioral Factors

Nicole E. Rader, Gayle M. Rhineberger-Dunn & Lauren Vasquez

To cite this article: Nicole E. Rader, Gayle M. Rhineberger-Dunn & Lauren Vasquez (2016) Victim Blame in Fictional Crime Dramas: An Examination of Demographic, Incident-Related, and Behavioral Factors, Women & Criminal Justice, 26:1, 55-75, DOI: 10.1080/08974454.2015.1023487

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Victim Blame in Fictional Crime Dramas: An Examination of Demographic, Incident-Related, and Behavioral Factors

Nicole E. Rader

Department of Sociology, Mississippi State University, Mississippi State, Mississippi, USA

Gayle M. Rhineberger-Dunn

Department of Sociology, Anthropology, and Criminology, University of Northern Iowa, Cedar Falls, Iowa, USA

Lauren Vasquez

Department of Sociology, Mississippi State University, Mississippi State, Mississippi, USA

How victims are portrayed in fictional crime dramas is an important way that individuals come to

understand and interpret what it means to be a victim of crime. We examine how demographic

variables (e.g., gender, race, age), incident variables (e.g., location of offense, relationship between

victim and offender, type of crime), and behavioral variables (e.g., drug use=alcohol use, sexual

promiscuity, negative personality traits, or concealing elements of personality) predict victim blame.

Although some literature has analyzed victims in fictional crime dramas, such literature has been

limited to a single year, a single show, a particular crime, or a particular factor. We extend this litera-

ture by focusing on multiple factors that predict victim blame using data collected from a systematic

sample of 124 episodes from 4 fictional crime dramas (CSI, Law & Order: Special Victims Unit, Criminal Minds, and Without a Trace) over 7 years (2003–2010).

Keywords fictional crime drama, social construction, victim blame

INTRODUCTION

A significant portion of television programming is devoted to crime-related drama, which

constitutes one of the most popular genres on television (Britto, Hughes, Saltzman, & Stroh,

2007; Cavender & Deutsch, 2007). Given the large amount of crime shows available and the

important role of the media in constructing knowledge about social problems (Gamson, Croteau,

Hoynes, & Sasson, 1992; Graziano, Schuck, & Martin, 2010), researchers have investigated

fictional crime shows (Britto et al., 2007; Cuklanz & Moorti, 2006; Rock, 2006). This is impor-

tant because most individuals do not have direct experience with crime or victimization and

therefore gain their knowledge about such topics from the media (Best, 1999).

Correspondence should be sent to Nicole E. Rader, Department of Sociology, Mississippi State University P.O. Box

C, Mississippi State, MS 39762, USA. E-mail: [email protected]

Women & Criminal Justice, 26:55–75, 2016

Copyright # Taylor & Francis Group, LLC

ISSN: 0897-4454 print/1541-0323 online

DOI: 10.1080/08974454.2015.1023487

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Most research examining fictional crime dramas has focused on the social construction of

criminals rather than victims. This body of research has found that viewers tend to have an inac-

curate perception of offenders on television compared to the real-life demographic characteristics

of offenders (Bjornstrom, Kaufman, Peterson, & Slater, 2010). Furthermore, this research has

found that television shows often depict crime as more sensational and stranger induced than

crime that happens in the real world (Britto et al., 2007; Frost & Phillips, 2011; Spitzberge &

Cadiz, 2002). The few studies that have examined victims of crime have explored how victims

are portrayed on television, indicating that oftentimes victims are portrayed inaccurately. Such

studies have usually focused on crimes against women (i.e., crimes whose victims are more likely

to be female than male, such as sexual assault) and have found that television tends to foster

victim blame (when ‘‘individuals find instances within the victims’ behavior, such as drinking

alcohol, to hold the victim at least partially responsible for the incident,’’ Hayes, Lorenz, & Bell,

2013, p. 203) and rape myths (‘‘the false cultural beliefs that mainly serve the purpose of shifting

the blame from perpetrators to victims,’’ Suarez & Gadalla, 2010, p. 2011).

Although this small body of work has offered insight into the portrayal of victims on

television, studies have been limited in both topic and data. For example, previous studies have

not examined various crime types; examined variations across crime shows; or considered varia-

tions in demographic, incident, or behavioral factors simultaneously. In addition, many of these

studies have focused exclusively on one crime show or used only 1 year of data.

In this article, then, we fill several important gaps in the literature by first focusing on victims

of fictional crime dramas over a 7-year time period and assessing the ways in which a variety of

factors (i.e., demographic, incident, and behavioral factors) influence depictions of victims. In

addition, we specifically examine how these factors are differentially applied to male and female

victims, something that has not been done very often in the previous literature. Furthermore, our

study uses a more representative sample of crime shows (n¼ 124) over time (2003–2010) than

has been used in previous research using the Nielsen top 20 ratings. We specifically gauge vic-

tim blame in these shows using a victim blame typology and controlling for variations in crime

type (homicide, physical assault, intimate partner violence, and sexual assault), crime show

(Law & Order: Special Victims Unit [SVU], CSI, Criminal Minds, Without a Trace), and the

relationship between the victim and offender (stranger, family, romantic, other). We also assess

how demographic factors (e.g., gender, race, age), incident-related factors (location, crime type,

relationship with offender), and behavioral factors (drug use=alcohol use, sexual promiscuity,

negative personality traits, or concealing elements of personality) predict victim blame (e.g.,

blameless vs. all other categories). No research to date has examined all three sets of factors

when considering victim blame in fictional crime dramas.

THE MEDIA AS SOCIALLY CONSTRUCTED

Scholars are interested in not only what constitutes social problems but how such socially

constructed problems shape and frame collective knowledge and public opinion (Graziano

et al., 2010; Misra, Moller, & Karides, 2003; Schneider, 1985). The frame is an important socio-

logical tool used to make sense of discourse (Berger & Luckmann, 1966; Gamson et al., 1992;

Goffman, 1974). Particular arenas frame, give life to, and sustain social problems such that

social problems must compete with one another for public attention and have carrying capacity

56 RADER ET AL.

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(Hilgartner & Bosk, 1998). The mass media clearly represents one such arena. Claims makers

provide information to the media that is viewed by the general public and eventually becomes

a normalized topic (Best, 1999; Herda-Rapp, 2003; Schneider, 1985). Therefore, a socially con-

structed reality is facilitated by the mass media, who serve as a powerful outlet for normalizing

public opinion (Best, 1999; Bjornstrom et al., 2010; Graziano et al., 2010).

The Social Construction of Crime Through the Media

Scholars have argued that much of what the general public believes about crime, criminals, and

victims is mythical and contributes to misconceptions about the reality of crime and criminal

justice (Barak, 2007; Gruenewald, Chermak, & Pizarro, 2013). Furthermore, most Americans

have little direct experience with crime and therefore learn much of what they know about crime

from the media (Chadee & Ditton, 2005). This is problematic with high-profile cases, which

often offer the potential to exploit social and public emotion by causing viewers to relate the

story to their own personal life and experience (Machado & Santos, 2009). Furthermore, media

depictions of crime can have particularly significant effects on those who have little experience

with crime, such as women, Whites, or nonvictims (Eschholz, Chiricos, & Gertz, 2003).

Media scholars have explained the importance of what is watched on television using several

theoretical tools. The cultivation hypothesis is one of the tools used to explain the effect of tele-

vision viewing on the general public (Gerbner & Gross, 1976). The general premise is that those

who watch large amounts of television should view the world as a place full of violence and

danger (Gerbner & Gross, 1976; Gerbner, Gross, Morgan, & Signorielli, 1980). Although much

research has examined the merits of the cultivation hypothesis, both supporting the hypothesis

and also finding evidence against this hypothesis (see Heath & Petraitis, 1987, for this

discussion), others have determined that the specific content on television is just as important

as the amount of television watched (which may also greatly affect viewers’ social reality;

Kahlor & Morrison, 2007; Potter & Chang, 1990). For these reasons, researchers in criminology

consider not only how much television is consumed by viewers but also how watching certain

types of programs with certain types of characters, plots, and other factors impact public opinion

about crime and justice (Barak, 2007; Kort-Butler & Sittner-Hartshorn, 2011).

A wide variety of media outlets are used to analyze crime and justice issues, including print

media outlets, such as newspapers (Gruenewald et al., 2013; Machado & Santos, 2009; Van

Brunschot, Sydie, & Krull, 2000), and television media outlets, such as news broadcasts (Barak,

2007; Bjornstrom et al., 2010; Chiricos & Eschholz, 2002), reality television shows (Eschholz

et al., 2003; Prosise & Johnson, 2004), films (Bufkin & Eschholz, 2000; Cecil, 2007; King,

2008), and fictional crime shows (Cavender & Deutsch, 2007; DeTardo-Bora, 2009; Evans &

Davies, 2014; Gans-Boriskin & Wardle, 2005).

In regard to fictional crime shows, the focus of this article, most studies have discovered that

the portrayal of fictional crime drama characters is often inconsistent with real crime and crimi-

nal justice professionals, supplying inaccurate images of crime and the criminal justice system to

its large viewer base. For example, in these shows, offenders are typically portrayed as older,

White, and male, and their crime is often based on sensational types of crimes (Bjornstrom

et al., 2010; Britto et al., 2007). In reality, offenders are more likely to be young, Black, and

male and to commit property crimes (Britto et al., 2007).

VICTIM BLAME IN FICTIONAL CRIME DRAMAS 57

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THE SOCIAL CONSTRUCTION OF CRIME VICTIMS

It is interesting that most of the literature on fictional crime dramas has almost exclusively

studied offenders rather than victims of crime. This is significant in that most criminal events

portrayed in fictional crime shows are violent, interpersonal crimes between a victim and an

offender, and therefore examining victims is an important part of the equation. The few studies

that have analyzed victims in the media have found that victims are typically portrayed as White

women who are young and do not know their attacker (Chiricos, Eschholz, & Gertz, 1997;

Gruenewald et al., 2013). These portrayals frame images of victims inaccurately and may cause

misunderstandings about what a real victim experiences and may also heighten potentially

unnecessary fear of crime (Eschholz et al., 2003; Kort-Butler & Sittner-Hartshorn, 2011; Madriz,

1997; Walklate, 2007; Weitzer & Kubrin, 2004).

A side effect of viewers’ reliance on the media’s portrayal of victims is that victims who do

not fall under the category of normal or worthy (defined by the media in terms of offense type,

demographic characteristics, and=or personality characteristics) are more likely to be scrutinized

by the public (Rye, Greatrix, & Enright, 2006). Characteristics found to make victims more

worthy may include gender (with mixed results—some finding that women are more likely to

be presented as worthy and others finding that men are more likely to be presented as worthy;

Britto et al., 2007; Rader & Rhineberger-Dunn, 2010), race (with Whites more likely to be

presented as worthy victims; Chiricos & Eschholz, 2002), and other behavioral characteristics

(with those not involved in deviant behaviors, such as drinking, doing drugs, or being sexually

promiscuous, considered more worthy; Franiuk, Seefelt, Cepress, & Vandello, 2008).

Viewers may blame victims of crime as a way to internally deal with criminal events by

believing in what is called the just world hypothesis (Hayes et al., 2013). By believing that

people get what they deserve, viewers may be able to mentally distance themselves from victims

of crime (Furnham, 2003; Rye et al., 2006; Sleath & Bull, 2009). Previous literature has found

that viewers are likely to take victim characteristics into consideration, and so the media

becomes one outlet from which individuals form opinions about victims (Anderson, 1999;

Davies, Pollard, & Archer, 2006; Franiuk et al., 2008; Rye et al., 2006; Sleath & Bull, 2009).

Gendered Images in Fictional Crime Dramas

Researchers have argued that women and men learn appropriate behavior for their sex through a

variety of socialization agents (such as the media) and that these rules of gender become a

primary frame for individuals to organize relations in society (Ridgeway, 2009; West &

Zimmerman, 1987). Individuals are held accountable to gender stereotypes by others when they

violate such stereotypes (West & Zimmerman, 1987).

The media is one institution in which gender frames have such effects, especially when one is

observing the role of gender in fictional crime dramas (Britto et al., 2007). Researchers have

ascertained that crime-related shows on television tend to emphasize incorrect gendered depic-

tions of victims of crime. For example, studies analyzing victims in fictional crime dramas have

found that victims are most likely to be portrayed as female instead of male and as White instead

of non-White (Britto et al., 2007; Rader & Rhineberger-Dunn, 2010; Spitzberge & Cadiz, 2002).

This is an inaccurate depiction of victims, because most victims of crime are actually male and

58 RADER ET AL.

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non-White (Britto et al., 2007). Furthermore, victims of crime tend to be portrayed as victims

of violent crime when in fact the majority of crime victims are victims of property crime. In

addition, female victims are often portrayed as victims of sex crimes who are victimized by

strangers, providing inaccurate messages to viewers about crime in general and sex crimes in

particular (Cuklanz & Moorti, 2006).

Furthermore, the media has been criticized by feminist researchers for highlighting common

myths about rape and domestic violence (Cuklanz & Moorti, 2006; Davies et al., 2006; Rader &

Rhineberger-Dunn, 2010; Rye et al., 2006) and for contributing to victim blame by depicting sex

crime victims as ‘‘ideal victims’’ (‘‘those who, when affected by crime, are more frequently

given the ‘legitimate status’ of victim,’’ Madriz, 1997, p. 343). Furthermore, the media has been

criticized by researchers for contributing to a lack of understanding regarding the underreporting

of sexual assault and other crimes against women, as many of the crimes on television are

reported and solved by criminal justice officials (Britto et al., 2007). This can result in the gen-

eral public believing that victims who do not fit the profile of an ideal victim (e.g., date rape,

alcohol involved, less violent than depicted on television, few physical signs of assault, victims

who do not report) are somehow responsible for their victimization (Britto et al., 2007; Bufkin &

Eschholz, 2000; Madriz, 1997; Rye et al., 2006; Walklate, 2007). Thus, as discussed previously,

the depiction of victims of crimes against women by the media is particularly at odds with the

reality of victimization, and these victims have been frequently misrepresented by the media.

This article extends the work discussed previously by hypothesizing that demographic factors

will predict victim blame more than incident-level factors, that behavioral factors will predict

victim blame more than incident-level factors, that demographic factors will predict victim

blame more than behavioral factors, and finally that all of these factors will be more prevalent

for women than men. We base this on the literature that suggests that demographic factors and

behavioral factors matter in determining victim blame, especially for women. We argue that to

fully understand the construction of victims of crime by the media and the role of victim blame

discourse in this process, we must examine victim blame in a multifaceted manner and we must

also examine the intersection of gender and victimization through fictional crime dramas.

METHODOLOGY

Sample

We took several steps to ensure that our data represented popular fictional crime shows on public

networks from the period 2003 to 2010. Our first step was to determine which years we would

analyze for this project. We selected the 2003–2004 season as our starting season. We did so to

avoid the confounding effects of September 11, 2001. In addition, to avoid the immediate impact

of September 11, 2001, we skipped the 2002–2003 season and began with the 2003–2004

season. We selected the 2009–2010 season as our last season to analyze because coding for this

project began during the 2010–2011 season, and therefore not all data were available yet from

that season.

Our second step was to select fictional crime dramas to analyze for our project. We consulted

yearly ratings produced by Nielsen. Using the Nielsen ratings allowed us to view the most

popular shows from each year. Nielsen ratings provide a comprehensive understanding of

VICTIM BLAME IN FICTIONAL CRIME DRAMAS 59

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television viewing, specifically, collecting information on what viewers watch in 100 countries

(Nielsen, 2011a). We consulted the top 20 shows from each season of interest (2003–2004

through 2009–2010). Although a variety of non-crime-related shows made the top 20 list, such

as American Idol and Survivor, we only focused on fictional crime dramas that appeared on at

least one annual list. These included CSI, CSI: Miami, Without a Trace, Law & Order, Cold Case, NCIS, SVU, CSI: New York, and Criminal Minds (Nielsen, 2011b).

To avoid duplication, we only included one CSI show and one Law & Order show. We chose

the original CSI instead of CSI: Miami or CSI: New York because it had been on the air longer.

Even though SVU had been on the air less time than the original Law & Order series, we selected

SVU over the original Law & Order series because it specifically deals with special crime victims,

a particular focus of some of our research. Both CSI (160 episodes) and SVU (157 episodes) were

on the air and available for all seven seasons, and all seasons were included in the population.

Other shows that appeared on the Nielsen list included Cold Case, Criminal Minds, NCIS, and Without a Trace. Cold Case was unavailable for purchase or rent (even on Netflix) for

all seasons and therefore was not included in the population. Criminal Minds did not premier

until the 2005–2006 season. Therefore, the first five seasons were on the air during the study

time period (2005–2006 through 2009–2010), and all five seasons (114 episodes) were included

in the population. Without a Trace began in 2002–2003. Because we did not sample the

2002–2003 season, we did not include Season 1. Without a Trace went off the air in

2008–2009 and was not available for the last 2 years of our time frame. Therefore, only Seasons

2, 3, and 4 of Without a Trace were included in the population (71 episodes). Finally, because

NCIS is significantly different than the other shows listed here in that it focuses on military

courts, we did not include it in the population (see Table 1 for a list of shows and the number

of episodes sampled).

We drew a systematic sample (Babbie, 2008; Corbin & Strauss, 2008) from our population

(n¼ 502), taking every fourth episode to create a sample of 124 episodes. The episodes were

sampled as follows for each show: 40 episodes were sampled from CSI, 39 from SVU, 28 from

Criminal Minds, and 17 from Without a Trace. Finally, we checked reliability by computing a measure of intercoder reliability. First we

interceded 25% of our sample. For this 25%, the episodes were watched by two independent

coders. We took 25% of each show instead of the total sample because each show had a different

sample size. Table 1 shows the intercoding percentage for each show. Once 25% of each show

was selected, a second coder (an independent individual who had not watched any episodes as a

first coder) watched each episode in its entirety and filled out a codebook. The original coder’s

responses were compared with the second coder’s responses to calculate the percent agreement.

TABLE 1

Episodes Sampled of Each Show, 2003–2004 to 2009–2010

Crime show Total episodes No. of episodes sampled 25% sampled for second coder

CSI 160 40 10

Law & Order: Special Victims Unit 157 39 10

Criminal Minds 114 28 7

Without a Trace 71 17 4

Total 502 124 31

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Our intercoder reliability rate for the variables examined in this study was 74%, above minimal

standards of intercoder reliability rates (Lombard, Snyder-Duch, & Bracken, 2002).

Codebook

The codebook provided information about the episode (e.g., crime show, episode number, year)

and was used to assess victim demographics (e.g., gender, race, age) along with information

about the incident (i.e., type of victimization, location of the offense, and the relationship

between the victim and offender). In addition, several behavioral measures were assessed for

each victim. Finally, we assessed three categories of victim blame based on the words and=or

actions of the characters in each episode: blameless, some responsibility, and contributing to

victimization (see ‘‘Victim Blame’’ for definitions).

Before coding each episode, we first transcribed all episodes in the sample. This involved

watching each episode with the closed caption function when available, typing up all words said

in the episode, and noting all speakers. For more visual information that needed to be confirmed

about the actor (such as the age or race=ethnicity of the character), the Web site IMDb.com was

consulted. This Web site keeps a database of most actors and provides information about them

(such as age, name, pictures, and roles). Once all episodes were transcribed, three primary

researchers (two faculty members and one trained graduate student) divided the sample and

coded each episode.

Because the unit of analysis was the victim character instead of the episode, all victim char-

acters who spoke and=or had enough information concerning their characteristics were coded

separately. If there were multiple victims in an episode, for example, the characteristics of each

victim were recorded. Furthermore, even if the character was an offender,1 if the character was

victimized in the episode, he or she was counted as a victim. As shown in Table 2, many epi-

sodes of each show had multiple victims coded for these characteristics. Therefore, although

there were 124 episodes included in the sample, there were actually 263 victims in these 124

episodes, all of whom were used for the analyses in this study. Once codebooks were complete,

variables were assigned values and dropped into an Excel chart. The Excel chart was exported

into SPSS.

TABLE 2

Victims and Offenders by Crime Show

Crime show Victimsa

CSI 30.4% (80)

Law & Order: Special Victims Unit 27% (71)

Criminal Minds 30.8% (81)

Without a Trace 11.8% (31)

Total 100% (263)

Note. Values in parentheses are frequencies. aBecause this article examines each victim as the unit of analysis, and we were

interested in controlling for the relationship between the victim and the offender,

we collected information about offenders only in relation to each victim. Therefore,

only the victim frequency counts are included in this article.

VICTIM BLAME IN FICTIONAL CRIME DRAMAS 61

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Hypotheses

This article tests four hypotheses. Hypothesis 1 suggests that demographic factors (i.e., gender, race,

age) will predict victim blame better than incident-level factors (i.e., the location of the offense, the type

of crime, the relationship between the victim and the offender). We argue that demographic factors will

predict victim blame better than incident-level factors because the previous literature (Britto et al.,

2007; Eschholz et al., 2003) has found strong evidence that demographic variables impact victim

blame. Hypothesis 2 suggests that demographic factors will predict victim blame better than beha- vioral factors (alcohol use, drug use, sexual promiscuity, personality characteristics, concealed ele-

ments of personality). Although behavioral factors have been observed to influence victim blame

(Furnham, 2003; Jordan, 2004; Sleath & Bull, 2009), we believe that because demographic variables

have been found consistently across studies, they will influence victim blame more than behavioral

factors. Hypothesis 3 suggests that behavioral factors will predict victim blame better than

incident-level factors. We believe that this is the case because of the literature that has focused on

the connection between behavioral factors and victim blame and that has shown that behavioral

characteristics of victims are used to determine culpability (Anderson, 1999; Furnham, 2003; Jordan,

2004; Sleath & Bull, 2009). Finally, Hypothesis 4 suggests that all of these factors will be more preva-

lent for women than men. We base this on the notion that the literature indicates that women are more

likely than men to be blamed for victimization experiences (Hayes et al., 2013; Rye et al., 2006).

Measures

Victim Blame

To measure the level of victim blame associated with each character we assessed three categories of

victim blame based on the words and actions of the characters in each episode. We used the words or

actions of the victim, the offender, a criminal justice character, or any other character in the episode to

help make this determination. The episodes were watched multiple times using the codebook to

ensure that all information needed to make the victim blame determination was included. If needed,

the transcript was also consulted to ensure that words were adequately captured. Based on this infor-

mation, we determined whether the preponderance of evidence2 portrayed each victim as (a) blame- less (the victim was not blamed in any way for the victimization experience), (b) having some responsibility (although the victim did not contribute to the victimization experience directly, some-

thing about the victim, such as actions, characteristics, or circumstances, was presented as making the

victim somewhat responsible for his or her victimization), or (c) contributing to the victimization (the

victim’s words, actions, characteristics, or circumstances were presented as contributing to the victi-

mization). A variation of this typology of victim blame was established in our previous research (see

Rader & Rhineberger-Dunn, 2010 for this original typology). In addition, in order to run logistic

regression models, we also created a blameless dummy variable in which the blameless category

was coded 1 and everything else (i.e., some responsibility, contributed to victimization) was coded 0.

Demographic Factors

The previous literature (Britto et al., 2007; Chiricos & Eschholz, 2002) focuses on three

primary demographic variables in regard to victim blame: gender, race, and age. Gender was

62 RADER ET AL.

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coded 1 for male and 0 for female. Race was coded 1 for White and 0 for non-White.3 Age was

recoded from an age range to a dichotomous variable for younger than 19 years of age or not: 1

represented 0–19 years of age and 0 represented any other age group. Although other demo-

graphic variables were available, they were not included for the logistic regression models

because of high missing data (e.g., employment, marital status, socioeconomic status) and a

theoretical focus on the three variables that appear most in the victim blame and media literature

(Britto et al., 2007; Rye et al., 2006; Sleath & Bull, 2009).

Incident-Related Factors

The previous literature (Cavender & Deutsch, 2007; Spitzberge & Cadiz, 2002) has also

noted that the location of the offense, the type of crime, and the relationship between the victim

and the offender may also impact victim blame. Therefore, we included a measure of location of

the offense (1¼ public, 0¼ nonpublic) and four separate measures of crime type: homicide

(1¼ homicide, 0¼ not homicide), sexual assault (1¼ sexual assault, 0¼ not sexual assault),

intimate partner violence (1¼ intimate partner violence, 0¼ not intimate partner violence),

and physical assault (1¼ physical assault, 0¼ not physical assault). All four variables were used

in the logistic regression model because victims could be and often were victims of multiple

crimes (e.g., homicide and sexual assault). Finally, three out of four measures of relationship

between the victim and the offender were put in the regression model, including a measure of

stranger relationship (1¼ stranger relationship, 0¼ not a stranger relationship), romantic

relationship (1¼ romantic relationship, 0¼ not a romantic relationship), and family relationship

(1¼ family relationship, 0¼ not a family relationship). The fourth category, other relationship,

included anything that did not fall under a stranger relationship, family relationship, or romantic

relationship (e.g., friends, acquaintances, neighbors).

Behavioral Factors

Finally, the previous literature (Furnham, 2003; Jordan, 2004; Rye et al., 2006; Sleath & Bull,

2009) has noted that several victim behavior characteristics impact victim blame. We included

measures of alcohol use (1¼ yes, victim’s alcohol use was commented on in the episode; 0¼ no,

it was not), drug use (1¼ yes, victim’s drug use was commented on; 0¼ no, it was not), sexual

promiscuity (1¼ yes, victim’s sexual promiscuity was commented on; 0¼ no, it was not),

negative personality traits (1¼ victim was depicted as having any negative personality traits;

0¼ no, victim was not), and concealed elements of personality (1¼ victim was depicted as

concealing any element of his or her personality; 0¼ no, victim did not). It is important to note

that for all behavioral categories, a speaker must have commented on the category (rather than

the coders making a determination based on the appearance of the character).

Analytic Procedures

The analyses proceeded in two stages. First, through content analysis (‘‘a researcher technique

for making replicable and valid inferences from texts or other meaningful matter to the contexts

of their use,’’ Krippendorff, 2013, p. 18), a variety of descriptive analyses were conducted on the

VICTIM BLAME IN FICTIONAL CRIME DRAMAS 63

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variables. These included a general descriptive analysis of the sample and other variables of

interest and then also included a victim blame descriptive analysis including victim blame by

crime show (and by gender), victim blame by crime type (and by gender), and victim blame

by relationship between the victim and the offender (and by gender). Second, we ran logistic

regression models estimating the effects of demographic factors, incident-related factors, and

behavioral factors on victim blame. Model 1 (the baseline model) examined gender, race, and

age. Model 2 added incident-related factors to the model (i.e., location of crime, crime type,

relationship between the victim and the offender). Model 3 added behavioral factors to the

model (i.e., alcohol use, drug use, sexual promiscuity, portrayal of victim as having negative

personality traits, and concealment of elements of victim’s personality).

RESULTS

Descriptive Statistics

The characteristics of all victims included in the analysis are provided in Table 3. Male victims

made up 44.9% of the sample, White victims made up 91% of the sample, and 29.3% of victims

were younger than 19. As for the incident-related variables, 41.8% of victims were victimized in

public, 62.6% were victims of homicide, 16.3% were victims of sexual assault, 6.1% were

victims of intimate partner violence, and 8.7% were victims of physical assault. As for the

relationship between the victim and the offender, 9.9% were family members, 38.4% were

strangers, 7.6% were romantic partners, and 40.9% were in other relationships (e.g., friends,

acquaintances, or neighbors).

TABLE 3

Description of the Sample

Variable M SD Range

Innocent typology .5247 .50034 0–1

Male .45 .498 0–1

White .91 .285 0–1

Younger than 19 years old .2927 .4559 0–1

Public (location) .43 .496 0–1

Homicide .6274 .48442 0–1

Sexual assault .1635 .37052 0–1

Intimate partner violence .0608 .2394 0–1

Physical assault .0875 .2830 0–1

Stranger (relationship) .3976 .49038 0–1

Romantic (relationship) .0760 .2655 0–1

Family (relationship) .0989 .2990 0–1

Alcohol .07 .253 0–1

Drugs .10 .294 0–1

Sexually promiscuous .14 .344 0–1

Negative personality traits .39 .488 0–1

Concealed characteristics .10 .300 0–1

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When examining the descriptive analyses for victim blame, we found several interesting

findings. Table 4 shows crime show type by victim blame, taking gender into consideration

as well. For Criminal Minds, overall, victims were most likely to be portrayed as blameless

(74%). Female victims were more likely than male victims to be portrayed as either blameless

or having some responsibility, whereas male victims were more likely than female victims to be

portrayed as contributing to their victimization. For CSI, overall, victims were slightly more

likely to be portrayed as having some responsibility for their victimization (48.7%). Female

victims were more likely than male victims to be portrayed as blameless, whereas male victims

were more likely than female victims to be portrayed as either having some responsibility or con-

tributing to their victimization. For SVU, overall, victims were most likely to be portrayed as

having some responsibility for their victimization (50.7%). Female victims were more likely

than male victims to be portrayed as blameless and slightly more likely to be portrayed as having

some responsibility. Male victims were more likely than female victims to be portrayed as con-

tributing to their victimization. For Without a Trace, overall, victims were most likely to be por-

trayed as blameless (61.3%). Female victims were more likely than male victims to be portrayed

as blameless. Male victims were more likely than female victims to be portrayed as either having

some responsibility or contributing to their victimization. Finally, for all shows for all victims,

overall, victims were most likely to be portrayed as blameless (52.9%). Female victims were

more likely than male victims to be portrayed as blameless. Female and male victims were

equally likely to be portrayed as having some responsibility for their victimization. Male victims

were more likely to be portrayed as contributing to their victimization (72.2%).

TABLE 4

Crime Show Type by Gender by Victim Blame

Type Blameless Some responsibility Contributing to victimization Total

Criminal Minds

All victims �70.4%a (57) 25.9% (21) 3.7% (3) 100.0% (81)

Male victims 38.6% (22) 28.6% (6) �66.7% (2) (30)

Female victims �61.4% (35) �71.4% (15) 33.3% (1) (51)

CSI

All victims 46.3% (37) �48.7% (39) 5.0% (4) 100.0% (80)

Male victims 45.9% (17) �61.5% (24) �75.0% (3) (44)

Female victims �54.1% (20) 38.5% (15) 25.0% (1) (36)

Law & Order: Special Victims Unit

All victims 36.6% (26) �50.7% (36) 12.7% (9) 100.0% (71)

Male victims 19.2% (5) 47.2% (17) �77.8% (7) (29)

Female victims �80.8% (21) �52.8% (19) 22.2% (2) (42)

Without a Trace All victims �61.3% (19) 32.3% (10) 6.5% (2) 100.0% (31)

Male victims 42.2% (8) �60.0% (6) 50.0% (1) (15)

Female victims �57.8% (11) 40.0% (4) 50.0% (1) (16)

All shows

All victims �52.9% (139) 40.3% (106) 6.8% (18) 100.0% (263)

Male victims 37.4% (52) 50% (53) �72.2% (13) (118)

Female victims �62.6% (87) 50% (53) 27.8% (5) (145)

Note. Values in parentheses are frequencies. aAsterisks indicate the greatest number in the category (e.g., victim blame or male=female).

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Table 5 examines crime type by victim blame, taking gender into consideration as well. For

homicide, overall, victims were most likely to be portrayed as blameless (50.9%). Female vic-

tims were more likely than male victims to be portrayed as either blameless or having some

responsibility, whereas male victims were more likely than female victims to be portrayed as

contributing to their victimization. For intimate partner violence, overall, victims were most

likely to be portrayed as blameless (48.7%). Female victims were more likely than male victims

to be portrayed as blameless and also as having some responsibility for their victimization. No

male or female intimate partner violence victims were portrayed as contributing to victimization.

For physical assault, overall, victims were most likely to be portrayed as blameless (52.2%).

Female victims were more likely than male victims to be portrayed as blameless and also as con-

tributing to their victimization. Male victims were more likely than female victims to be por-

trayed as having some responsibility for their victimization. For sexual assault, overall,

victims were most likely to be portrayed as blameless (62.8%). Female victims were more likely

than male victims to be portrayed as blameless and as somewhat responsible for their victimiza-

tion. Male victims and female victims were equally likely to be portrayed as contributing to their

victimization. Because the crime categories were not mutually exclusive, an ‘‘all crime’’

category by gender was not calculated.

Table 6 examines relationship type by victim blame, taking gender into consideration as well.

For those victims who had a family relationship with the offender, overall, victims were most

likely to be portrayed as blameless (61.5%). Female and male victims were equally likely to

be portrayed as blameless. Female victims were more likely than male victims to be portrayed

as having some responsibility. No male or female victims with a family relationship with the

offender were portrayed as contributing to their victimization.

TABLE 5

Crime Type by Gender by Victim Blame

Type Blameless Some responsibility Contributing to victimization Total

Homicide

All victims �50.9%a (84) 42.4% (70) 6.7% (11) 100.0% (165)

Male victims 42.9% (36) 48.5% (34) �100.0% (11) (81)

Female victims �57.1% (48) �51.5% (36) 0.0% (0) (84)

Intimate partner violence

All victims �56.3% (9) 43.8% (7) 0.0% (0) 100.0% (16)

Male victims �55.6% (5) 42.9% (3) 0.0% (0) (8)

Female victims 44.4% (4) �57.1% (4) 0.0% (0) (8)

Physical assault

All victims �52.2% (12) 34.8% (8) 13.0% (3) 100.0% (23)

Male victims 25.0% (3) �75.0% (6) 33.3% (1) (10)

Female victims �75.0% (9) 25.0% (2) �66.7% (2) (13)

Sexual assault

All victims �62.8% (27) 32.6% (14) 4.7% (2) 100.0% (43)

Male victims 11.1% (3) 14.3% (2) 50.0% (1) (6)

Female victims �88.9% (24) �85.7% (12) 50.0% (1) (37)

Note. Values in parentheses are frequencies. aAsterisks indicate the greatest number in the category (e.g., victim blame or male=female).

66 RADER ET AL.

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For those victims who had a romantic relationship with the offender, overall, victims were

most likely to be portrayed as blameless (55.0%). Female victims who were romantically

involved with the offender were more likely than male victims to be portrayed as blameless

or as having some responsibility for their victimization. Male victims who were in a romantic

relationship with their offender were much more likely than female victims to be portrayed as

contributing to their victimization. Finally, for all shows for all victims, overall, victims were

most likely to be portrayed as blameless (52.9%). Female victims were more likely than male

victims to be portrayed as blameless. Female and male victims were equally likely to be por-

trayed as having some responsibility for their victimization. Male victims were more likely to

be portrayed as contributing to their victimization (72.2%). For those victims who had an

‘‘other’’ relationship with the offender, victims were most likely to be portrayed as having some

responsibility for their victimization. Female victims were more likely than male victims to be

portrayed as blameless. Male victims were more likely than female victims to be portrayed as

either having some responsibility or contributing to their victimization. Finally, when we exam-

ined all victims by relationship with the offender and victim blame, we found that the majority of

victims were portrayed as blameless (53.8%). Female victims were more likely than male

victims to be portrayed as blameless, female and male victims were equally likely to be

portrayed as having some responsibility, and male victims were more likely than female victims

to be portrayed as contributing to their victimization.

TABLE 6

Victim–Offender Relationship by Gender by Victim Blame

Type Blameless Some responsibility Contributing to victimization Total

Family

All victims �61.5%a (16) 38.5% (10) 0.0% (0) 100.0% (26)

Male victims 50.0% (8) 40.0% (4) 0.0% (0) (12)

Female victims 50.0% (8) �60.0% (6) 0.0% (0) (14)

Romantic

All victims 55.0% (11) �35.0% (7) 10.0% (2) 100.0% (20)

Male victims 9.0% (1) 0.0% (0) �100.0% (2) (3)

Female victims �91.0% (10) �100.0% (7) 0.0% (0) (17)

Stranger

All victims �68.3% (69) 30.7% (31) 1.0% (1) 100.0% (101)

Male victims 36.0% (25) 48.4% (15) �100.0% (1) (41)

Female victims �64.0% (44) �51.6% (16) 0.0% (0) (60)

Other

All victims 38.0% (39) �50.0% (52) 12.0% (13) 100.0% (104)

Male victims 38.4% (15) �59.6% (31) �61.5% (8) (54)

Female victims �61.6% (24) 40.4% (21) 38.5% (5) ()

All relationships

All victims �53.8% (135) 39.9% (100) 6.3% (16) 100.0% (251)b

Male victims 36.2% (49) 50.0% (50) �68.8% (11) (110)

Female victims �63.8% (86) 50.0% (50) 31.2% (5) (141)

Note. Values in parentheses are frequencies. aAsterisks indicate the greatest number in the category (e.g., victim blame or male=female). bTwelve victims had an

unknown relationship with the offender.

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Logistic Regression

The primary purpose of this article is to examine the impact of demographic, incident-related,

and behavioral factors in predicting victim blame. To do this, we conducted logistic regression

analyses on victim blame. For the victim typology, a dummy variable was created with blame-

less equal to 1 and everything else equal to 0. These results appear in Table 7. In Model 1, the

dummy demographic variables were entered in the model. Male and younger than 19 were sig-

nificant. Men were 59% less likely than women to be characterized as blameless, and victims

younger than 19 were 42% less likely to be characterized as blameless. Finally, race was not

significant.

When adding incident-related variables in Model 2 (i.e., public location, homicide victim,

sexual assault victim, intimate partner violence victim, physical assault victim, stranger relation-

ship, romantic relationship, family relationship), we found that men were 51% less likely than

women to be characterized as blameless. Younger than 19 was also still significant—those

younger than 19 were 54% less likely to be portrayed as blameless. In addition, stranger relation-

ship and family relationship were both significant. Those in a stranger relationship with the

offender were 3 times more likely than those in the reference category to be portrayed as blame-

less. Those in a family relationship with the offender were 3 times more likely to be portrayed as

blameless compared to those in the reference category.

TABLE 7

Logistic Regression Results for Blameless Victims

Model 1 Model 2 Model 3

Variables B SE Exp (B) B SE Exp (B) B SE Exp (B)

Male 0.884 0.269 0.413��� �0.705 0.298 0.494�� �0.781 0.391 0.458��

White 0.542 0.476 1.719 0.440 0.495 1.553 0.506 0.619 1.658

Under 19 Years Old 0.538 0.297 0.584� �0.785 0.339 0.456�� 0.107 0.452 1.113

Public Location �0.113 0.296 0.893 �0.257 0.392 0.773

Homicide Victim �0.390 0.340 0.677 �0.442 0.477 0.643

Sexual Asssault Victim 0.371 0.406 1.450 �0.030 0.555 0.970

IPA Victim 0.261 0.755 1.298 0.763 0.971 2.144

Physical Assault Victim �0.278 0.547 0.757 �0.794 0.672 0.452

Stranger Relationship 1.105 0.306 3.020��� 0.961 0.411 2.615��

Romantic Relationship 0.362 0.585 1.435 �0.290 0.683 0.748

Family Relationship 1.171 0.586 3.226�� 0.596 0.761 1.814

Victim Used Drugs �2.845 0.923 0.058��

Victim Used Alcohol �1.229 0.811 0.293

Victim Sex Promiscuous �3.819 1.143 0.022���

Victim Negative Person. �1.930 0.391 0.145���

Victim Conceal Person. �2.146 0.892 0.117��

Constant 0.155 �0.097 1.432�

Nagelkerke R Squared 0.074 0.172 0.581

�p< .10, ��p< .05, ���p< .001.

68 RADER ET AL.

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In Model 3, when adding in behavioral factors (i.e., drugs, alcohol, sexually promiscuous, nega-

tive personality traits, and concealed parts of personality), we found that being male and being a

victim in a stranger relationship were still statistically significant. Men were 54% less likely to be

portrayed as blameless, and victims who had a stranger relationship with their offender were about

2.5 times more likely to be portrayed as blameless. With the addition of the behavioral factor

variables, younger than 19 and having a family relationship with the offender were no longer sig-

nificant. In addition, several behavioral factors were highly significant. Victims who did drugs

were 94% less likely to be portrayed as blameless, and those victims who were sexually promiscu-

ous were 98% less likely to be portrayed as blameless. Victims characterized as having a negative

personality were 86% less likely to be portrayed as blameless. Finally, victims who concealed

elements of their personality were 88% less likely to be portrayed as blameless.

DISCUSSION

Before we discuss the implications of our findings, it is worth noting some characteristics of our

sample more generally that provide misinformation to the general public through fictional crime

dramas. Independent of gender differences, our sample of fictional crime dramas overrepresented

the amount of violent crime. In terms of crime type, more than 60% of victims were homicide vic-

tims. This overrepresents the amount of violent crime in the real world. According to the Bureau of

Justice Statistics, in 2009, there were 15.6 million property crimes in comparison to 4.3 million

violent crimes, which shows that property crime is much more common (Truman & Rand, 2010).

When looking specifically at overall gender findings, a goal of this article, we find gender

differences between actual statistics and the statistics presented by gender in fictional crime dra-

mas. Overall, there were slightly more female (55%) than male (45%) victims, which is out of

touch with reported victimization statistics. According to recent statistics, 18.4 males per 1,000

persons were victims of a violent crime in 2009 compared to 15.8 females per 1,000 persons

(Truman & Rand, 2010).

When looking at gender differences by crime type, we find that although some statistics were

closer to reported victimization (although still not accurate; i.e., sexual assault, men making up

14% of victims), others were far off from the reality of crime. For example, homicide is a

male-dominated crime in the real world. According to the Bureau of Justice Statistics, males

are 4 times more likely than females to be the victim of homicide (Cooper & Smith, 2011).

When looking at our fictional crime drama sample, we find that males were only twice as likely

to be homicide victims, which indicates that females were more likely to be victims of homicide

than they actually are. As another example, intimate partner violence victims, according to

official statistics, were much more likely to be female than male. Data from 1993–2010 show

that 80% of all intimate partner violence victims were female (Catalano, 2012). When looking

at our statistics, we find that 50% of victims were female, which indicates that males were much

more likely to be victims of intimate partner violence than they actually are. These images

confuse viewers and prevent many viewers from having a correct picture of the gendered nature

of victimization. Such differences are indicative of the subtle process of the social construction

of victimization, which may have a powerful influence on viewers. As discussed previously, the

framing of victims greatly impacts viewers’ interpretations and opinions of crime victims,

especially among those groups who have little direct experience with victimization.

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For victim blame specifically, overall, crime shows in our sample were overwhelmingly

likely to depict crime victims as blameless. We were pleasantly surprised to see the blameless

portrayal of victims used most in Criminal Minds and Without a Trace. We were also somewhat

surprised to find that SVU, a show devoted to victims in particular, was the most likely of all four

shows to use the contributing to victimization category for victimization. It is quite possible that

because it focuses specifically on victims, SVU attempts to portray a broad spectrum of victims

to more closely imitate the realistic mixture of victim types that exist (e.g., blameless, some

responsibility, contributing to victimization).

Although blameless was by far the most common portrayal of victims, we found subtle dif-

ferences when analyzing victim blame in more detail. Hypothesis 1 suggested that demographic

factors would predict victim blame better than incident-related factors. When looking at the indi-

vidual indicators of this theoretical set of factors, we found this hypothesis to be supported. Two

of the three demographic factors predicted victim blame in Model 1, and this finding held when

incident-level factors were introduced in Model 2. We discuss the importance of gender later in

this section, but it is important to note that gender held as a significant predictor across all three

models. It is also important to note that being younger than 19 was significant only until

behavioral factors were introduced.

Hypothesis 2 suggested that demographic predictors would be more salient than behavioral

predictors. Although gender stayed significant with the inclusion of behavioral predictors, which

would indicate that some demographic predictors were highly salient, other demographic predic-

tors, such as age, dropped out of the models with the addition of the behavioral factors. This

would suggest that models that include both demographic and behavioral factors better predict

victim blame. No studies to date have made comparisons of these factors. Future studies should

consider combinations of such factors and perhaps use different statistics to help make sense of

these findings.

Hypothesis 3 suggested that behavioral factors would predict victim blame better than

incident-level factors. This hypothesis was also supported. There were several very strong pre-

dictors of victim blame in this category, which included drug use, sexually promiscuous beha-

vior, negative personality traits, and concealment of one’s personality. These findings may be

the most troubling, as they seem to indicate, at least for this sample, that fictional crime shows

are making the connection between behavior and victim blame for the viewer. This may in turn

provide the viewer with the tools to engage in victim blame. Constructing victimization for the

general public in this manner may also mean that viewers come to see victimization as some-

thing that victims should and can prevent themselves, rather than something that we as a society

should see as a social problem. Regardless, victim-blaming behaviors were apparent in these

findings, which mirrors findings provided by other studies that have shown that victim blame

is a fabric of media representations of victimization (Anderson, 1999; Davies et al., 2006;

Rye et al., 2006; Sleath & Bull, 2009).

Hypothesis 4 suggested that females would be blamed for their victimization more than

males. This did not seem to be the case when we examined crime type, crime show, or other

factors. At first glance, this seems to go against what has been found by some of the previous

research on this topic (Cuklanz & Moorti, 2006; Jordan, 2004; Suarez & Gadalla, 2010). How-

ever, when we consider the gender and victimization literature, it is not as surprising as it might

seem. Much of the literature on victimology focuses on the ideal victim (Madriz, 1997; Smolej,

2010). This victim is characterized by a stranger relationship with the offender, is younger and

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female, and has personality characteristics that would lead one to believe that the victim had no

fault in the victimization (Furnham, 2003; Hayes et al., 2013).

This literature has empirically shown that men are not part of the perfect victim stereotype

(Britto et al., 2007; Rye et al., 2006; Sleath & Bull, 2009). Therefore, when men are victims

of crime, especially when they are victims of crimes typically associated with women (i.e.,

sexual assault, intimate partner violence), they may be more likely to be blamed. This could

be because of the belief that men should be able to fight off an attack or that men should not

put themselves in that situation in the first place. When they do not follow these cultural scripts,

they are more likely to be blamed for their victimization than women might be under similar

circumstances. It could also be that men are viewed as more likely to contribute to their own

victimization because they fall outside of the realm of the ideal victim. Our results would support

this notion. Even in the criminology literature today, there is discussion of victim–offender

overlap—or in other words, victims were involved in some sort of activity that contributed to

their victimization (Berg, Stewart, Schreck, & Simons, 2012). This academic perspective, along

with the general public’s sense of what it means to be a victim, may explain why gender was the

most statistically significant finding in this project, with men being blamed more than women for

their victimization overall. Strauss’s (2006) research on male victims of intimate partner violence

and gender symmetry suggests that male involvement in partner violence is an understudied

phenomenon. Given the finding that men are presented by fictional crime dramas at high levels,

we would recommend this as an important next step in the research on media portrayals of

victims of intimate partner violence.

LIMITATIONS AND CONCLUSIONS

Although we believe the findings of this study make a good contribution to the media and gender

and crime literature, the study is not without its limitations. The first limitation of this research is

that we did not directly ask respondents how they interpret characters on fictional crime shows

but instead used characteristics of each character to make sense of fictional crime drama mes-

sages. This is a common limitation of many media studies. However, the previous literature

has outlined that television is an important socialization medium that often teaches the general

public important messages about crime and justice issues. We would suggest that given the

popularity of crime shows, and the consumption of these shows by the general public in parti-

cular, it is important to examine what messages are being given to viewers about victims and

victim blame (Britto et al., 2007; Eschholz et al., 2003; Heath & Petraitis, 1987).

The second limitation is that some of the measures we used were more subjectively

interpreted. We would argue that we took all measures possible to minimize the bias of these

measures, including using our very detailed codebook and an independent second coder. Rather

than having the primary coders come to a consensus, we allowed a completely independent party

to code all variables in this study.

Third, our project focused only on public access fictional crime dramas instead of cable

network shows. As these shows become more widely available on Netflix and other sources,

fictional crime dramas and their viewer base will change. This is an interesting avenue for future

research. Finally, not having access to Cold Case and not including CSI: Miami, Law & Order, and NCIS could be viewed as a limitation.

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Finally, there are likely additional demographic, incident, and behavioral factors that future

research should consider. For example, if the data are available, education levels might be a rel-

evant victim demographic factor. An interesting incident-level factor might include controlling

for the police unit type assigned to the crime (e.g., specialized detail vs. general force). These

and other factors would make an interesting extension to what we have done in this study. Fur-

thermore, an extension of this study would be to qualitatively consider the themes surrounding

demographic, incident, and behavioral factors and how these factors influence victim blame in

fictional crime dramas, with examples of each theme. We see this as a next step of this project.

Although some see media studies as not that relevant to criminology or experts’ understand-

ing of victimization, we would argue differently. We would suggest that the general public, who

often gather information about crime victims from the media because of a lack of personal

experience with crime and victimization, are greatly influenced by the media and all that the

media have to offer about victims and victim blame. Viewers come away from these shows with

the idea of the ideal victim (what that is and is not) and the idea that individuals are in some way

responsible for their victimization if they do not fit the characterization outlined for the ideal

victim. This victim representation may influence criminal justice policy and trial outcomes (Best,

1999; Franiuk et al., 2008; Machado & Santos, 2009).

Overall, our pattern of results clearly indicates that demographic and behavioral indicators of

victim blame matter and that these differences matter in gendered ways. These results provide a

new path for media, crime, and gender scholars to examine, one that is inclusive of the various

factors that predict victim blame (i.e., demographic, incident, and behavioral indicators) and the

ways in which these differences affect male and female victims, which we would argue offers a

more complete picture of the social construction of fictional crime dramas.

NOTES

1. Offender data (demographic information) were collected for the larger project, but for the purposes of this article

(which focuses on victims), we only examine one variable concerning the offender—namely, the relationship between the

victim and the offender (stranger, romantic, family, other).

2. Because there were many statements made about each victim character throughout each episode, we selected the

victim blame category that best fit each victim when at least half (but likely more) of the comments were in that category.

We increased the credibility of our category selection by having a second independent coder code a subsample of

episodes. As Graneheim and Lundman (2004) noted, ‘‘Credibility deals with the focus of the research and refers to

confidence in how well data and processes of analysis address the intended focus . . . Credibility is also a question

of how to judge the similarities within and differences between categories. One way to approach this is to show

representative quotations from the transcribed text. Another way is to seek agreement among co-researchers, experts,

and participants’’ (p. 110). Therefore, the preponderance of evidence allowed us to seek agreement on the victim blame

category and is line with previous content analysis research.

3. Although we initially coded for three non-White racial categories (i.e., African American, Asian, and an other

race category), there were so few cases for each separate category that we collapsed all categories into one and labeled

it non-White.

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