Media Consumption and Support for Capital Punishment

Abstract

Theoretically, the media influences public perceptions of crime and criminality and helps shape perceptions of how the crime problem should be managed. Using a 2010 survey in Washington state, this article tests the theoretical connection between watching television (news, crime dramas, and police-reality programs), reading the newspaper, listening to the radio, interacting with the Internet, and support for capital punishment. Research in the area of perceptions of capital punishment shows that support for capital punishment varies depending on its operationalization; therefore, this study includes both a general measure of support for capital punishment and a measure that provides for the availability of life without parole as an alternative option. Variables including race, sex, age, attitudes toward the police, and perceptual variables such as collective efficacy, economic insecurity, and justice concerns are included as controls in the models. Findings indicate that the relationship between media consumption and capital punishment is dependent on both the media form/channel and the operationalization of capital punishment.

Keywords

capital punishment corrections social constructions of crime/justice crime/delinquency theory crime policy courts/law

Introduction

Scholars in the area of social control have long posited a relationship between media, popular culture, and perceptions of punishment, including punitive attitudes and support for capital punishment (Demker, Towns, Duus-Otterstrom, & Sebring, 2008; Oliver & Armstrong, 2005; Rosenberger & Callanan, 2011; Sotirovic, 2001). In recent years, both Garland (2001) and Simon (2007) argue that several significant historical changes have resulted in a culture of fear that has heightened the public’s sensitivity to risk and led to support for increasingly punitive policies. One driver for changes in public perception may be shifting media consumption patterns that include a steady diet of round-the-clock television news, crime dramas, police-reality programs, talk radio, as well as Internet and newspapers, all of which frequently feature crime stories. Scholars tend to agree that the majority of the public’s knowledge about crime and the criminal justice system come from the consumption of such a media-rich diet (Surrette, 2010; Tonry, 1999). As a result, it is essential to understand how the media influences public attitudes, particularly on the criminal justice system’s ultimate punishment—the death penalty. Understanding the media’s influence in shaping such attitudes is important because some believe that perceptions play a role in policy decisions regarding capital punishment (Vollum, Longmire, & Buffington-Vollum, 2004).

This article examines whether media consumption is related to attitudes toward capital punishment. Specifically, testing whether the specific form/channel of media content (and the genre of specific television programs) influences attitudes toward capital punishment, and whether the operationalization of support for capital punishment influences these relationships.

Literature Review

The Media and Public Policy

Garland (2001) and Simon (2007) both argue that the United States’ punitive turn in the 1970s was a result of many factors, including increasing crime rates, an economic recession, an increasing economic inequality gap, suburbanization, and the media’s blurring of the lines between entertainment and news. These arguments were consistent with the work of media theorist George Gerbner (1970) who asserted that the perceptions cultivated by television have consequences not only for people’s personal and social relationships but also for social policy and social control. Furthermore, media coverage of crime expanded during this time period as a result of 24-hr news, cable programming with a crime focus, and the popularity of police-reality programming and crime dramas (Roman & Chaflin, 2008; Surrette, 2010).

Similarly, Altheide (2009) contends that fear is the driving emotion behind shifts in crime control and government intervention efforts. Research that looks directly at the political effects of the public’s fear in response to crime “waves” indicates that such fears can promote political shifts (Doyle, 2006). “The media suggests that the ‘problem’ can be changed, that mechanisms exist to change it, and that we (as a society) already have an agent and process in place to fix the problem, usually the government” (Kort-Butler & Hartshorn, 2011, p. 40). Kort-Butler and Hartshorn (2011) argue that the more individuals consume media, the less supportive they are of the criminal justice system’s ability to deal with crime, which translates to them being more supportive of punitive measures, like longer sentences and increased use of capital punishment.

A more specified version of Gebner and the Cultural Indicators Project’s (Gerbner et al., 1977) “cultivation hypothesis” posits that both audience characteristics and the specific content (media form/channel and genre of the content) consumed by the audience influence the relationship between media and fear of crime (Callanan & Rosenberger, 2011; Chiricos, Eschholz, & Gertz, 1997; Dowler, 2002). Sotirovic (2001) suggests that the mass media may affect criminal justice policy preferences with both the structure and presentation of its content. Specifically, she finds that the use of complex media content (content that provides a variety of different perspectives on an issue) is related to more complex thinking about crime, and thus a preference for preventive criminal justice policies (Sotirovic, 2001). In line with this argument, Baumgartner, DeBoef and Boydstury (2008), in the book The Decline of the Death Penalty and the Discovery of Innocence, suggest that the news media’s recent focus on innocence cases has led to reduced support of the death penalty on the grounds of fairness. On the other hand, exposure and attention to the simple, infotainment formats of various reality-based pseudo news, talk, and crime drama shows is related to lower levels of complex thinking, and therefore a preference for punitive criminal justice policies (Sotirovic, 2001).

Sotirovic’s findings are echoed in the research conducted by Demker, Towns, Duus-Otterstrom, and Sebring (2008) in Sweden. Demker et al. found a correlation between tabloid consumption and punitiveness, in which regular tabloid readers were more clearly in favor of introducing the death penalty in Sweden than nontabloid readers or those who seldom read tabloids. This was particularly true among males (Demker et al., 2008). Holbert, Shah, and Kwak (2004) studied three genres of crime-related television viewing, including police-reality shows, television news, and crime dramas, and found that all three types of television viewing correlate to sentiments in favor of capital punishment. Furthermore, Holbert et al. found that viewing police reality and crime dramas has a significant direct correlation with attitudes on capital punishment, whereas viewing television news is negatively related to the endorsement of capital punishment. Oliver and Armstrong (1995) find that regular viewing of reality-based crime programs and increased enjoyment of these programs is correlated with punitive attitudes, but contrary to Holbert et al. viewing fictional crime shows is not.

In a study of media use by young adults, Brady (2007) found an association between hours spent viewing violent movies and television and favorable attitudes toward military preparedness and defense among young men. Brady also found that increased time spent viewing violent movies and television was associated with more favorable attitudes toward interpersonal violence and punitive criminal justice policies among women. Brady (2007, p. 519) concurs with previous research (Anderson et al., 2003; Cook, Kestenbaum, Honaker, & Anderson, 2000) that:

Demonstrated effects of media violence exposure include acquired beliefs that violence is an effective way of settling conflicts, emotional desensitization toward real-life violence, increased fear of becoming a victim of violence with a potential increase in self-protective behavior and mistrust of others, and greater likelihood of exhibiting aggressive behavior in the immediate aftermath of violence exposure and later in life.

Similarly, Reith (1999) finds that males who are exposed to crime dramas show increased levels of anger and aggressive attitudes (Reith, 1999).

Kort-Butler and Hartshorn (2011) studied television news, network crime dramas, and police-reality shows, and integrated a fourth type of media known as nonfictional documentary-style programs. The nonfictional documentary-style programs included shows such as American Broadcasting Company’s “Primetime,” A&E’s “The First 48,” and Discovery’s “The New Detectives.” Their results support the notion that program type does matter. Viewing crime dramas predicted support for the death penalty, but other program types did not (Kort-Butler & Hartshorn, 2011). In contrast, Grabe and Drew (2007) found little evidence that any form of media was consistently related to perceptions and opinions about crime and justice.

It is also quite possible, as many media scholars have pointed out, that individuals with certain characteristics and attitudes will seek out media channels and genres that show individuals like themselves in a positive light and that reinforce their preexisting attitudes (Eschholz, Blackwell, Gertz, & Chiricos, 2002; Holbert et al., 2004; Oliver & Armstrong, 1995; Reith, 1999; Surrette, 2010). For example, in terms of television news viewing, Holbert et al. (2004) indicate that age, race, and religion are consistently significant predictors of television news viewing. Specifically, television news appeals to older persons, and those who consider themselves religious, but is least appealing to Caucasians as a source of public affairs information (Holbert et al., 2004). In reference to viewing police-reality programs, females, older persons, Caucasians, and the more educated are less likely to view this type of programming. Yet, frequency of hunting activity is predictive of this type of viewing. As for crime drama viewing, age and race were positively related, with Caucasians viewing this type of programming the least (Holbert et al., 2004).

Support for Capital Punishment and Support for Life Without Parole

In the past 10 years, the U.S. Supreme Court has reexamined the constitutionality of the death penalty under certain circumstances, particularly, the execution of the “mentally retarded” ( Atkins v. Virginia, 2002), the sentencing process ( Kelly v. South Carolina, 2002; Ring v. Arizona, 2002), racial discrimination in jury selection ( Miller-El v. Cockrell, 2003), and the adequacy of legal representation ( Bell v. Cone, 2002; Mickens v. Taylor, 2002: Wiggins v. Smith, 2003). This reexamination by the Court is due, in part, to changing public mores on the subject of state-sanctioned executions.

Public support has an influence on the death penalty’s use as a legitimate form of state-sanctioned punishment (Bohm & Vogel, 2004). In his concurring opinion in Furman v. Georgia (1972), Justice Thurgood Marshall—in what is known as the Marshall hypothesis—postulated that support for the death penalty is contingent upon the degree to which the public is informed about the realities surrounding the administration of capital punishment. Justice Marshall emphasized, “it is imperative for constitutional purposes to attempt to discern the probable opinion of an informed electorate” (p. 362, fn. 145). Justice Marshall assumed that with information, “the great mass of citizens would conclude … that the death penalty is immoral and therefore unconstitutional” (p. 363). This is consistent with Baumgartner et al.’s (2008) hypothesis that the recent reduction in support for the death penalty and changes in death penalty policy in the United States is in large part a result of the news media coverage of innocence cases.

However, Marshall stated that if retribution was the underlying basis for support for the death penalty, then information would have little to no effect. The research of Bohm, Clark, and Aveni (1991) supports Marshall’s view that, to the degree that retribution is the basis for support of the death penalty; knowledge about how it is administered has little effect on public attitudes. This may be one reason why studies that include an alternative measure of support for capital punishment that include the option of life without parole (LWOP) find much lower levels of support for capital punishment (Bohm & Vogel, 2004; Bowers, Vandiver, & Dugan, 1994; Sandys & McGarrell, 1994; Vollum et al., 2004), but that a sizable group continues to support capital punishment.

The media may be one of the major sources of information about crime and the death penalty (Graber, 1980; Roman & Chaflin, 2008; Tonry, 1999), but the veracity of the content presented is likely not consistent across media forms/channels and genres, and much crime-related programming focuses on sensational crimes and espouses a get tough on crime message (Surrette, 2010), rather than providing information about the use of capital punishment. Several studies have supported the hypothesis that if the public had a better understanding of the realities of the death penalty and how the system operates, fewer people would support it as a form of punishment (Bohm, Clark, & Aveni, 1991; Bowers et al., 1994; Longmire, 1996; Sandys & McGarrell, 1994; Sarat & Vidmar, 1976), but the information discussed in these studies often comes from an academic class on the topic, rather than the media. Based on past research, simple media messages about crime would likely lend support for capital punishment, while more complex media messages might reduce support for capital punishment, especially when using a general measure of support for capital punishment.

Research also indicates that many of the previously reviewed relationships may be conditioned by demographic variables. Vollum, Longmire, and Buffington-Vollum (2004) found that males were less supportive of a moratorium and more staunch supporters of the death penalty than were females and that older respondents were more likely to be staunch supporters of the death penalty than younger respondents. The study also found distinct class-related differences, in which low-income households were less confident in the death penalty system than those in high-income households (Vollum et al., 2004). The same was true with level of education, where those with less than a high school education were significantly more likely to support a moratorium than those with greater levels of education (Vollum et al., 2004). Research by Bohm (1991) determined that “Whites, wealthier people, males, Republicans, and westerners have tended to support the death penalty more than Blacks, poorer people, females, Democrats, and Southerners” (p. 135).

Similar to previous research (Bohm et al., 1991), the findings of Unnever and Cullen (2007a) show a racial divide in support for the death penalty, regardless of the control variables introduced into the models. Vollum et al. (2004) found that Blacks reflected the lowest levels of confidence in access to counsel and appeals, in the equitable administration of the death penalty, and in safeguards against the execution of the innocent. Hispanics had slightly more confidence in criminal justice issues and innocence concerns, than in issues concerning class and race equity (Vollum et al., 2004). Conversely, Whites expressed the highest level of confidence in all aspects of capital punishment.

Mallicoat and Brown (2008) find that the rationale for death penalty support among Hispanic/Latino and Asian American populations differs compared to Blacks and Whites. In fact, in reference to the Marshall hypothesis, Mallicoat and Brown’s research concludes that “a lack of knowledge appears to have the strongest significant effects on the mutability of opinion for Asian Americans” (pp. 274–275). Overall, it seems that while Whites, as a whole, are most supportive of the death penalty, support among other racial groups is significantly lower with Asians being more supportive than Hispanics/Latinos, and Blacks being the least supportive (Houston Chronicle Article, 2001; Urbina, 2003).

Perceptual Control Variables

Many criminologists have suggested that perceptual variables like attitudes toward the police (ATP), fear of crime, collective efficacy (COL. EFF.), economic insecurity (ECO. INS.), and justice concerns (JUSTICE) all help shape attitudes toward capital punishment and are important control variables in these models. Additionally, these variables may interact with media variables to influence attitudes toward capital punishment (Kort-Butler & Hartshorn, 2011). Dowler (2003) finds that a favorable view of policing and police effectiveness is, at least in part, the result of law enforcement’s public relations strategy. Researchers suggest that the police and the news media have a codependent relationship; the media are dependent on the police as a regular source of information, while the police are dependent on the media to maintain a positive public image (Ericson, Baranek, & Chan, 1987). Holbert et al. (2004) find that the propolice sentiments, and support for police authority, that are generated from viewing police-reality programs are highly predictive of positive attitudes toward capital punishment.

One of the more interesting elements in the study of fear and crime is the fact that public fear of crime has not waned despite decreasing crime rates since the 1990s (Gallup, 2012; U.S. Department of Justice, 2012). Despite the downward trend in crime, the increased salience of crime has contributed to a number of diffuse anxieties in both the United States and the United Kingdom (Garland, 2001). According to Garland (2001), these diffuse anxieties, which include concerns regarding the state of the economy, big government, welfare, union-led inflation, and affirmative action, can translate into fear of crime and support for punitive policies.

Additionally, politicians chose to manage and downplay these social and economic problems by linking them to crime and, thus, putting the focus on crime control. According to Costelloe, Chiricos, and Gertz (2009), crime control policies were promoted as a way to manage the social and economic risks created by the undeserving poor. Holloway and Jefferson (1997) agree that social and economic uncertainties are expressed as fear of crime. Therefore, fear of crime may translate, on a societal level, into punitive policies, restrictions on civil liberties, concentration of criminal activities in vulnerable communities, and institutional racism (Clear, 2007, Garland, 2001, Simon, 2007).

Research by Costelloe et al. (2009) and Hogan, Chiricos, and Gertz (2005) found a relationship between fear of crime and punitive attitudes and that this relationship is most pronounced for Whites. Dowler (2003) finds that the variables of race, education, income, marital status, and fear of crime are related to punitive attitudes. Kort-Butler and Hartshorn’s (2011) results indicate that fear mediates the relationship between viewing nonfictional documentary-style programs and a lack of support for the criminal justice system (Kort-Butler & Hartshorn, 2011). On the other hand, viewing crime dramas predicted support for the death penalty, but the relationship was not mediated by fear (Kort-Butler & Hartshorn, 2011). Finally, their research indicates that viewing television news was unrelated to either fear or attitudes (Kort-Butler & Hartshorn, 2011).

In reference to economic anxieties, Simon (2007) adds that the fear of downward mobility has lent itself to public support of politicians who claim that social and economic issues can be managed and alleviated with lengthier and more punitive criminal sentences. According to Lyons and Scheingold (2000), “the anxieties associated with unwelcome social, economic, and cultural transformations generate anger, and punishment becomes a vehicle for that anger” (p. 127). A study by Hogan et al. (2005) indicates that the influence of economic insecurity and blame on punitive attitudes depends on the sex and race of respondents, finding the strongest association for White males.

Costelloe et al. (2009) found that when economic insecurity is analyzed in terms of the expectation that one’s financial circumstances will not improve, or will remain stagnant in the coming year, there is little consequence for punitiveness. However, support for punitive criminal measures is consistently elevated for those who anticipate that their financial circumstances will get worse in the coming year (Costelloe et al., 2009), and the results are strongest for lower income White males.

Collective efficacy has also been hypothesized to both increase and decrease punitive sentiments. On one hand, individuals who trust and regularly interact with their neighbors and community members may be more open to rehabilitative methods (Clear, Hamilton, & Cadora, 2003), on the other hand, defying this community trust by committing murder may be seen as the ultimate betrayal and capital punishment may serve a boundary maintenance function (Erikson, 1966). Greer and Jewkes’s (2005) analysis of British media found an overarching message, particularly in the conservative press, that people commit crimes because they are not like the rest of us. In other words, one of the common characteristics found in media explanations of crime is that the media plays into fears of “otherness.” In the United Kingdom, Greer and Jewkes found that the conservative press focused almost exclusively on an individual’s “otherness,” often ignoring other possible explanations, as the sole cause of their deviance.

Finally, some researchers have included measures of a just world (belief that the world is a just place) in models of capital punishment attitudes. These studies show that individuals who feel that the world is a fair place generally believe that offenders should get the punishment they deserve (Freeman, 2006; O’Quin & Vogler, 1989). On the other side, individuals who are concerned with the lack of fairness and justice in the world are less likely to support punitive policies (Rosenberger & Callanan, 2011).

Current Study

This study will specifically test three separate but related media hypotheses derived from the literature review, while including sociodemographic and perceptual controls in the model.

Media consumption is related to support for capital punishment. Gerbner’s (1970) cultivation hypothesis would predict that all media content is positively related to support for capital punishment.

Some media forms and genre influence support for capital punishment while others do not. Sotirovic’s (2001) distinction between simple and complex media content would predict only simple media would increase support for capital punishment.

A positive relationship between media and support for capital punishment is predicted for crime dramas and police-reality programs (simple media), but not for television news, newspaper, and talk radio (complex media), which could produce relationships in either direction (supportive or against capital punishment).

Media consumption effects should be stronger for general support for capital punishment than for support for capital punishment with the LWOP option. General support for capital punishment is more likely to be influenced by simple media messages. The support for LWOP question involves more cognitive processing and is therefore more likely not to be influenced by simple media messages. It is also possible that media consumers may receive complex and contradictory messages about the death penalty, which could either increase or decrease support for capital punishment with the LWOP option.

Method

The data for this study were collected during the winter of 2010. Four-hundred and seventy-seven individuals completed the survey, with 409 of the respondents residing in the State of Washington. A convenience sample was utilized, where 25 student researchers in a research methods in criminal justice class provided the e-mail contacts for 50 individuals they considered acquaintances.¹ The students and the professor collectively decided on this sampling strategy because of its low cost, the ability to reach a sample that was not primarily students, the ability to collect data in a short time frame, and it allowed for a large enough sample size to test theoretical hypotheses. The number 50 was selected for each student because upon review of their various contact lists (social media, work, clubs, etc.) most students had at least this many noncollege peer connections.² The students attended a midsize state institution located in a rural area, but the majority of students were from suburban or urban settings.³ Individuals were contacted with an introductory e-mail describing the study, a second e-mail containing a link to the study in Survey Monkey, and a follow-up thank-you/reminder e-mail. The survey took approximately 10–12 minutes to complete. An altruistic appeal was made to respondents to help assist students in learning how to conduct research and to advance the state of knowledge about crime-related public perceptions, but no incentives were offered. The survey began with an information page and a check box for consent, which was required for an individual to proceed to the survey. The survey concluded with contact information for the primary investigator and the Institutional Review Board office if there were any questions or concerns regarding the survey. The response rate for the survey was 38%, which is comparable to the response rates for most Internet surveys (Sheehan, 2001).

Table 1 shows some basic demographics of the sample as compared to available census data for residents of Washington State.⁴ The sample overrepresents younger individuals, slightly overrepresents women, and slightly underrepresents Asians, Blacks, and Hispanics (although this may be a function of race and ethnicity being a combined measure in this study). Although these deviations from the population are not uncommon in surveys (Tuckel & O’Neill, 2002; Lavrakas, 1987), they were also somewhat expected given the type of sample used in this study. The lack of a probability sample necessitates taking caution with generalizations to a larger population. Nonetheless, the data are useful and appropriate for exploratory theory testing (Piquero & Bouffard, 2007) especially given the size of the sample.

Table 1.

Sample Demographics Compared to the Demographics of Washington State.

Variable	Sample	Washington State
Sex	57.2% Female	50.1% Female
Race/ethnicity	81.3% White	80.5% White
	0.7% Black	3.4% Black
	3.6% Asian	6.5% Asian
	15.9% Other	9.6% Other
	2.0% Hispanic	9.5% Hispanic
Age	Median = 27, M = 33.1	Median = 37.1
Education	33.8% Bachelors	30.5% Bachelors
Income	Median US$15,000–US$30,000	Median US$29,927

Dependent Variables

Measurement of the support for capital punishment has garnered much debate over the years. There are general measures of support for capital punishment (Bohm, 1991) and a variety of specific measures of support for capital punishment for specific crimes or when the alternative of LWOP is also presented in the question (Bohm & Vogel, 2004; Bowers et al., 1994; Sandys & McGarrell, 1994; Vollum et al., 2004). Two measures of support for capital punishment were used in this study. On a 5-point scale, where 1 equals strongly agree and 5 equals strongly disagree, how much do you agree with the following statements:

General Measure: I favor the death penalty for people convicted of murder (reverse coded to consistently reflect high numbers support the death penalty and low numbers oppose the death penalty).

LWOP Measure: If I knew a murderer would stay in prison for the rest of their lives, I would not support the death penalty.

The General Measure of support for capital punishment was reverse coded, so that support for (agreement with) the death penalty increased with the numerical value of the response. The LWOP Measure was stated in the negative (“I would not support the death penalty”) so higher numerical values also indicate increased support for (agreement with) the death penalty. Because the literature shows that perceptions of the death penalty change quite dramatically based on the way the variable is measured (Bohm & Vogel, 2004; Bowers et al., 1994; Sandys & McGarrell, 1994; Vollum et al., 2004), a separate analysis will be run for each operationalization of the variable. It is also possible that the relationship between the media and alternative measures of the dependent variable may be different because assessments of general support for the death penalty and support for the death penalty when the LWOP option is available may activate different cognitive processes for respondents when selecting a response (Sotirovic, 2001). Table 2 shows the frequency distribution of each of these measures.

Table 2.

Frequency Distributions of Support for Capital Punishment Measures.

	General Support (%)	Support With LWOP Option (%)
Strongly disagree	8.7	14.4
Disagree	11.0	21.7
Neutral	17.2	17.2
Agree	25.7	26.6
Strongly agree	37.4	20.1

Note. LWOP = life without parole.

Sixty-three percent of the respondents support (agree or strongly agree) the use of capital punishment with someone convicted of murder, compared to 47% of respondents who support the use of capital punishment when there is a LWOP option. A difference of means test finds these two measurements are statistically significant at p < .001 level ( $\overset{ˉ}{x}$ = 3.7 for General Capital Punishment Support Measure and $\overset{ˉ}{x}$ = 3.2 for LWOP Measure). Because binary logistic regression is utilized in the analysis, both of these variables were dummy coded with strongly disagree, disagree, and neutral being coded as “0” and agree and strongly agree being coded as “1” and representing support for the death penalty.

Independent Variables

Media consumption

Historically, media research focused on general measures of total media consumption (Eschholz, Chiricos, & Gertz, 2003; Gerbner, 1970; Gerbner, Gross, Morgan, & Signorielli, 1980), but media and capital punishment or punitive attitude studies have focused on television consumption, specifying the genre of programs watched, or tabloid news consumption (Brady, 2007; Demker et al., 2008; Oliver & Armstrong, 1995; Reith, 1999; Sotirovic, 2001). Few studies have included measures of newspaper, talk radio, or Internet consumption in models predicting attitudes toward capital punishment. This study includes the variables NEWSPAPER, RADIO (talk radio and radio news), INTERNET, TV (crime-related television content including police-reality programs, crime dramas, local news, national news, and newsmagazine programs), and specific measures of television genre POLICE TV (police-reality programs), NEWS TV (local news, national news, and news magazines), and CDRAMA TV (crime dramas). The television variables were all measured by asking how much time an individual spends watching these types of programs in a given week. The responses available were divided into 30-min units. The newspaper, radio, and Internet variables were measured by asking how much time an individual spends consuming these types of media in a given day. All of the media variables in this study were not normally distributed (as evidenced by their skewness and kurtosis scores) except for Internet consumption. Taking the square root of CDRAMA TV, POLICE TV, NEWS TV, NEWSPAPER, and RADIO corrected this problem.

Control Variables

Demographic controls

Based on past research, numerous demographic controls were included in support for capital punishment models. SEX was dummy coded (female = 1, males = 0) and was predicted to have an inverse relationship with support for capital punishment (Longmire, 1996). RACE was also dummy coded (1 = White, 0 = other). While one of the most consistent findings in the capital punishment literature is that African Americans hold lower support of capital punishment than Whites (Bohm et al., 1991; Unnever & Cullen, 2007b; Urbina, 2003; Vollum et al., 2004), the demographics of Washington State coupled with the nonprobability nature of the sample did not yield a subsample of African Americans large enough to make meaningful comparisons. Similarly, the subsamples of Hispanics, Asians, and other groups were also not large enough to compare each group. Interestingly, the largest non-White racial category in the sample self-identified as mixed race and or ethnicity. Therefore, we divided race into two categories, White and other, but do not predict a direction of the relationship given the composition of our sample.

Education (EDUC) was measured on a 6-item scale from less than high school to graduate or professional degree and was predicted to have an inverse relationship with support for capital punishment (Dowler, 2003). Two dummy-coded variables were used to measure political party preference: Republican (REPUB, coded 1 = Republican, 0 = other) and Independent (INDEP, coded 1 = Independent, 0 = other) were both predicted to be positively associated with support for capital punishment (Bohm, 1991; Cochran & Chamblin, 2006). For each of these dummy codes, Democratic political party preference was the reference category. Residence was dummy coded into two variables: Suburban area (SUBURB, 1 = suburb, 0 = other) and Rural area (RURAL, coded 1 = rural, 0 = other), with urban areas being the reference category. Residents in both suburban and rural areas were predicted to have higher support for capital punishment. Age (AGE), measured by asking the respondent the year they were born, and Income (INCOME), measured on a 6-point scale from less than US$15,000 to over US$100,000, were also both included in the models, but a direction is not specified because results have produced mixed findings (Rosenberger & Callanan, 2011).

Perceptual controls

Five perceptual variables, which are theoretically important in models of support for capital punishment, were included: COL. EFF., ATP, fear of violent crime (FEARVC), ECO. INS., and JUSTICE. Collective efficacy was measured on a 5-point scale, from strongly disagree to strongly agree, how much do you agree with the following statements? I interact regularly with my neighbors. I trust my neighbors. I participate in community activities (such as crime watch, community meetings, religious activities, etc.). The people in my neighborhood do not share the same values (reverse coded). Reliability analysis yielded a Cronbach’s α reliability coefficient of .65. A principle component factor analysis revealed that all items loaded on the same component. Factor-weighted scores were used to create a 4-item COL. EFF. index.

Perception of police performance measures the strength of formal social control operating in a neighborhood. Additionally, support for the police is often a predictor of punitive attitudes (Holber et al., 2004). Attitudes toward police were measured using a scale from 1 (not at all satisfied) to 10 (very satisfied), how satisfied are you in your local police department’s ability to do the following: prevent crime, work with people in the community to solve neighborhood problems, enforce the law, maintain public order, and treat all members of the community equally regardless of sex, race, or religion. Reliability analysis yielded a Cronbach’s α reliability coefficient of .90. A principle component factor analysis revealed that all items loaded on the same component. Factor-weighted scores were used to create a 5-item ATP index.

Following Ferraro and LaGrange (1987) and Ferraro’s (1995) model, respondents were asked: “using a scale of 1 (not at all fearful) to 10 (extremely fearful), how much do you fear being a victim of a mugging; a home break in while you are there; rape or sexual assault; attack with knife, gun or club; or murder.” Reliability analysis yielded a Cronbach’s α reliability coefficient of .91. A confirmatory factor analysis revealed that all items loaded on the same component. Factor-weighted scores were used to create a 5-item FEARVC index. Fear was predicted to be positively correlated with support for capital punishment (Costelloe et al., 2009).

Economic insecurity is measured using a 3-item index combining the questions: How concerned are you with the following issues, with 1 indicating not at all concerned and 10 indicating extremely concerned: the chance of losing your job because of the current economic situation; the chance of advancing in your career because of the current economic situation; and the effect that the decline in the value of stock will have on your retirement? All items loaded on the same component and produced a Cronbach’s α reliability coefficient of .70.

Finally, three questions on the survey assessed whether respondents felt there were fundamental fairness issues with the way the death penalty is carried out in the United States. Concern over these justice issues (JUSTICE) was measured using 3 items: On a 5-point scale, where 1 equals strongly agree and 5 equals strongly disagree, how much do you agree with the following statements. The poor are more likely to receive the death penalty than the middle class or rich. Minorities are more likely to receive the death penalty than Whites. Scientific evidence, such as DNA tests, should be required before a jury sentences someone to the death penalty (reverse coded for consistency). Each item loaded on the same component and produced a Cronbach’s α reliability coefficient of .70.

Analysis

In an effort to understand the possible ways that demographic and perceptual variables may be related to media consumption, separate independent sample t-tests were run to compare nonmedia consumers with media consumers of newspaper, radio, Internet, TV police-reality programs, TV news, and TV crime dramas for the following variables: SEX, RACE, education, AGE, Republican, COL. EFF., ATP, fear of crime, ECO. INS., and JUSTICE. Men, Whites, older individuals, and Republicans were significantly more likely to read the newspaper. Those with high fear levels and high justice concern levels were less likely to read the newspaper. Whites, those with higher education levels, older individuals, and Republicans were more likely to listen to the news on the radio. There was only one significant difference between Internet users and nonusers, with women being significantly less likely to use the Internet. Men, Whites, those with less education, and Republicans were significantly more likely to watch police-reality programs. Whites, older individuals, and Republicans were more likely to watch television news programs. And finally, women and minorities were more likely to watch television crime dramas.⁵ Surprisingly, except for Newspaper readership, there were no significant differences between media consumers and nonconsumers on any of the perceptual measures in this study.

Binary logistic regression was used to analyze the data. Bivariate correlations revealed a strong correlation between AGE and INCOME (.642), but the variance inflation factor scores were below 2.0, so both variables were included simultaneously in the models. Each of the television genre variables were highly correlated with one another and the variance inflation factor scores were well above 2.0 for each of these measures. Consequently, a TV Index was created that combined time spent watching police-reality programs, local and national news, newsmagazines, and crime dramas. Cronbach’s α reliability coefficient for these three measures was .71. The square root of this measure was used to correct for the negative skew of the distribution. Additionally, separate models were run that introduced police-reality programs, television news programs, and crime dramas one at a time.

Findings

Models 1–5 in Table 3 show the original regression models for general support for capital punishment measure. In Model 1, only the control variables (both demographic and perceptual) are included. In Model 2, the four media form variables (NEWSPAPER, RADIO, INTERNET, and TV) are added. In Models 3–5, POLICE TV, NEWS TV, and CDRAMA TV are, respectively, introduced one at a time to test whether the specific genre of programming matters in predicting support for capital punishment. Table 4 replicates these models using the support for capital punishment with the LWOP option.

Table 3.

Logistic Regression Estimates for General Support for the Death Penalty.

	Model 1			Model 2			Model 3			Model 4			Model 5
	Controls Only			General Media			Police TV			News TV			Crime Drama TV
	B	SE	Odds Ratio	B	SE	Odds Ratio	B	SE	Odds Ratio	B	SE	Odds Ratio	B	SE	Odds Ratio
Demographic control variables
SEX	−0.167	0.244	0.846	−0.274	0.257	0.760	−0.278	0.257	0.758	−0.240	0.255	0.786	−0.320	0.259	0.726
RACE	0.027	0.280	1.027	0.096	0.286	1.100	0.085	0.287	1.088	0.091	0.285	1.096	0.110	0.287	1.116
EDUC.	−0.277	0.102	0.758*	−0.274	0.105	0.781*	−0.242	0.105	0.785*	−0.268	0.104	0.765*	−0.252	0.104	0.777*
INCOME	0.073	0.096	1.075	0.037	0.099	1.038	0.062	0.098	1.064	0.025	0.099	1.026	0.056	0.098	1.057
AGE	0.017	0.011	1.017	0.008	0.012	1.008	0.010	0.012	1.010	0.009	0.012	1.009	0.009	0.012	1.010
REPUB.	1.249	0.289	3.486*	1.273	0.297	3.573*	1.256	0.295	3.513*	1.285	0.296	3.613*	1.274	0.029	3.573*
INDEP.	0.474	0.261	1.607*	0.565	0.271	1.760*	0.540	0.269	1.716*	0.573	0.271	1.773*	0.541	0.269	1.717*
SUBURB	0.555	0.298	1.741*	0.517	0.306	1.677*	0.504	0.305	1.656*	0.539	0.305	1.714*	0.513	0.305	1.670*
RURAL	0.526	0.325	1.692	0.502	0.333	1.651	0.454	0.332	1.575	0.554	0.332	1.740	0.457	0.332	1.579
Perceptual control variables
COL. EFF.	0.114	0.115	1.121	0.053	0.118	1.055	0.069	0.118	1.072	0.050	0.118	1.052	0.070	0.117	1.168
ATP	0.146	0.107	1.157	0.172	0.110	1.187	0.165	0.110	1.179	0.170	0.110	1.185	0.155	0.109	1.168
FEAR VC	0.113	0.114	1.120	0.135	0.118	1.144	0.148	0.117	1.160	0.121	0.117	1.129	0.152	0.117	1.165
ECO.INS.	0.022	0.109	1.022	−0.015	0.113	0.985	−0.018	0.113	0.983	−0.013	0.113	0.987	−0.010	0.002	0.990
JUSTICE	−0.332	0.118	0.717*	−0.370	0.122	0.691*	−0.362	0.121	0.696*	−0.356	0.122	0.701*	−0.362	0.121	0.696*
Media variables
NEWSPAPER				−0.044	0.303	0.957	0.044	0.300	1.045	−0.066	0.302	0.936	0.066	0.298	1.068
RADIO				0.367	0.172	1.444*	0.422	0.171	1.525*	0.363	0.171	1.438*	0.439	0.171	1.550*
INTERNET				−0.143	0.030	0.958	−0.042	0.030	0.959	0.039	0.030	0.962	−0.040	0.030	0.961
TV				0.330	1.040	1.931*
POLICE TV							0.355	0.130	1.426*
NEWS TV										0.353	0.124	1.423*
CDRAMA TV													0.327	0.134	1.387*
−2 Log likelihood		537.11			518.54			521.41			520.62			523.05
Cox and Snell R ²		.151			.183			.179			.18			.176

Note. SE = standard error; EDUC. = Education; REPUB. = Republican; INDEP. = Independent; COL. EFF. = collective efficacy; ATP = attitudes toward the police; FEAR VC = fear of violent crime; ECO.INS. = economic insecurity.

*p < .05.

Table 4.

Logistic Regression Estimates for Support for the Death Penalty With LWOP Option.

	Model 1			Model 2			Model 3			Model 4			Model 5
	Controls Only			General Media			Police TV			News TV			Crime Drama TV
	B	SE	Odds Ratio	B	SE	Odds Ratio	B	SE	Odds Ratio	B	SE	Odds Ratio	B	SE	Odds Ratio
Demographic control variables
SEX	−0.676	0.221	0.509*	−0.722	0.227	0.486*	−0.717	0.227	0.488*	−0.712	0.226	0.491*	−0.734	0.230	0.480*
RACE	−0.322	0.268	0.724	−0.324	0.269	0.726	−0.321	0.269	0.726	−0.334	0.269	0.716	−0.315	0.269	0.730
EDUC.	−0.099	0.091	0.905	−0.082	0.092	0.921	−0.086	0.093	0.917	−0.089	0.092	0.915	−0.088	0.092	0.915
INCOME	0.048	0.085	1.049	0.044	0.086	1.045	0.050	0.085	1.051	0.040	0.086	1.040	0.048	0.085	1.049
AGE	0.020	0.010	1.020	0.019	0.011	1.019	0.020	0.011	1.020	0.091	0.011	1.019	0.020	0.011	1.020
REPUB.	0.155	0.266	1.167	0.154	0.267	1.156	0.141	0.267	1.152	0.151	0.268	1.163	0.145	0.267	1.156
INDEP.	0.235	0.258	1.265	0.232	0.261	1.262	0.223	0.261	1.250	0.240	0.261	1.271	0.223	0.261	1.25
SUBURB	−0.089	0.289	0.915	−0.113	0.290	0.893	−0.110	0.290	0.895	−0.105	0.290	0.900	−0.107	0.290	0.898
RURAL	0.38	0.310	1.462	0.364	0.311	1.439	0.354	0.311	1.425	0.388	0.311	1.474	0.352	0.312	1.422
Perceptual control variables
COL. EFF.	0.021	0.106	1.021	0.011	0.107	1.011	0.022	0.107	1.022	0.005	0.108	1.005	0.023	0.107	1.023
ATP	−0.054	0.100	0.947	−0.047	0.101	0.954	−0.052	0.100	0.950	−0.046	0.101	0.955	−0.055	0.100	0.947
FEAR VC	−0.017	0.107	0.983	−0.016	0.107	0.984	−0.010	0.107	0.990	−0.024	0.107	0.976	−0.008	0.107	0.992
ECO.INS.	−0.092	0.099	0.912	−0.085	0.101	0.918	−0.089	0.101	0.914	−0.082	0.101	0.921	−0.087	0.101	0.916
JUSTICE	−0.233	0.108	0.792*	−0.248	0.109	0.780*	−0.243	0.109	0.784*	−0.243	0.109	0.784*	−0.244	0.109	0.784*
Media variables
NEWSPAPER				−0.187	0.270	0.830	−0.146	0.268	0.864	−0.212	0.272	0.607	−0.139	0.268	0.870
RADIO				−0.038	0.150	0.962	−0.013	0.148	0.987	−0.048	0.150	0.953	−0.008	0.148	0.992
INTERNET				−0.007	0.027	0.993	−0.006	0.027	0.994	−0.006	0.027	0.994	−0.006	0.027	0.994
TV				0.127	0.088	1.136
POLICE TV							0.095	0.110	1.099
NEWS TV										0.173	0.105	1.189*
CDRAMA TV													0.097	0.117	1.101
−2 Log likelihood		608.53			606.14			607.5			605.49			607.55
Cox and Snell R ²		.089			.093			.091			.095			.091

Note. EDUC. = Education; REPUB. = Republican; INDEP. = Independent; COL. EFF. = collective efficacy; ATP = attitudes toward the police; FEAR VC = fear of violent crime; ECO.INS. = economic insecurity.

*p < .05.

In Model 1, of Table 3, education, Republican party preference, Independent party preference, living in the suburbs, and justice concerns all show significant relationships with support for capital punishment in the predicted direction. As expected, men, Whites, older individuals, individuals living in rural areas, those most supportive of the police, those with high fear of violent crime and those who are economically insecure are more supportive of the death penalty, but these results were not statistically significant. Income and collective efficacy showed a positive relationship with support for the death penalty, but these relationships were insignificant. Model 2 tests the first hypothesis of this study: Media consumption is related to support for capital punishment. The lack of consistent media effects does not support Gerbner’s cultivation hypothesis, but does provide some support for the second hypothesis in this study. Both RADIO and TV consumption increased support for capital punishment (with logged odds ratios of 1.444 and 1.391, respectively), but there was no relationship for NEWSPAPER and INTERNET consumption. The pseudo R² results for Table 3, Models 1 and 2, as indicated by the Cox and Snell R², were .151 and .183, respectively. This suggests that radio and television consumption have a relatively small, but significant effect on attitudes toward the death penalty. The more one listens to radio news and watches television the more likely one is to support the use of capital punishment.

Models 3–5, of Table 3, test the second hypothesis in this study: Some media forms and genres influence support for capital punishment while others do not. The results of these models indicate that RADIO, POLICE TV, NEWSTV, and CDRAMA TV all increase support for capital punishment, but NEWSPAPER and INTERNET do not. It appears that radio news and television content regardless of the genre contributes to acceptance of the death penalty as a just response to murder, but that other media forms do not significantly alter support for capital punishment.

Table 4 retests Hypothesis 1 (media consumption is related to support for the death penalty) and 2 (some media forms and genres increase support for capital punishment, while others do not) for the support for capital punishment with the LWOP option, and by comparing the results with Table 4 also allows us to test Hypothesis 3 (media consumption effects should be stronger for general support for capital punishment than for support for capital punishment with the LWOP option). In Model 1, the only control variables that were significant predictors of support for capital punishment with the LWOP option were SEX and JUSTICE. Interesting, but insignificant statistically, non-Whites, those with higher income, those in urban areas rather than suburban areas, those with less support for the police, lower fear of violent crime, and less economic insecurity were more supportive of the death penalty with the LWOP option. In Model 2, no media variables are significantly related to support for capital punishment with the LWOP option. The logged odds ratio of 1.135 for TV consumption indicates a positive, but insignificant relationship. The pseudo R² results for Table 4, Models 1 and 2, as indicated by the Cox and Snell R², were .089 and .093, respectively, confirming a negligible addition to explanatory power of the model when media variables are included.

Model 3–5 support Hypothesis 2 predicting some media forms and genre would be related to support for the death penalty, while others would not. Only NEWS TV is positively and significantly related to support for capital punishment with the LWOP option. POLICE TV and CDRAMA TV do not appear to influence this dependent variable. This finding was surprising, given our previous classification of television news as a more complex media form than CDRAMA and POLICE TV.

A comparison of Tables 3 and 4 tests the third hypothesis that media consumption effects should be stronger for general support for capital punishment than for support for capital punishment with the LWOP option. General support for capital punishment is more likely to be influenced by simple media messages. The support for LWOP question involves more cognitive processing and is therefore more likely not to be influenced by simple media messages. In general, the results support this hypothesis with one caveat. There are more and stronger media effects in the general support for the death penalty model, but the one media variable that was related to support for capital punishment with the option of LWOP was TV News, which we had categorized as a complex form of media coverage. No negative media relationships were observed in either model.

Of considerable interest was the fact that the support for the capital punishment with the option of LWOP model had considerably less explanatory power than the general support for the death penalty model. This is consistent with the findings of Bowers, Vandiver, and Dugan (1994), Sandys and McGarrell (1994), and Bohm and Vogel (2004). A number of variables that were significant in the general support model (such as education, Republican party preference, Independent party preference, living in the suburbs or a rural area, and ATP) dropped out of the LWOP model. Additionally, sex was one of the strongest predictors in the LWOP model but was not significant in the general support model. It appears that additional contextual information (options available for alternative, yet severe punishment) is important in terms of deciding support or opposition to the death penalty. This is particularly the case for women.

Discussion and Conclusion

The combined results of this study indicate that the media is a predictor of general support for capital punishment, and plays a limited, but still statistically significant, role in support for capital punishment with the LWOP option. With that being said, not all media content is the same, although when there are media effects they are consistently found in the positive direction.

Television and talk radio consumption predicted general support for capital punishment, while only television consumption predicted support for capital punishment with the LWOP option. When focusing on specific television genres, police-reality programs, news programs, and crime dramas all were associated with higher levels of general support for capital punishment, but only news programs were associated with higher levels of support for capital punishment with the LWOP option. These findings run counter to the argument offered by Baumgartner et al. (2008) that linked recent reductions in support for the capital punishment with the media using the innocence issue to frame much recent coverage of the death penalty. This may be a result of the cross-sectional nature of the current study not allowing for the capture of changes over time, or there could be alternative variables (e.g., social media measures or sociodemographic variables) that explain these changes.

The findings of this study are consistent with past research in noting that attitudes toward capital punishment are complex, and vary depending on both how media consumption and support for capital punishment are measured (Paternoster, Brame & Bacon, 2008). General support measures a global attitude that likely does not involve complex thinking to provide a response, whereas support with the LWOP option requires the respondent to think about capital punishment in a more nuanced way. Another way to think about this is the difference between supporting the death penalty in principle compared to supporting the death penalty in practice (del Carmen, Vollum, Cheeseman, Frantzen, & Miguel, 2005). The dramatic differences between significant control variables in these two models is also indicative of very different processes going on when deciding support for the death penalty in these two circumstances.

These findings further support the Marshall hypothesis that support for the death penalty is dependent upon the general public’s knowledge, and the availability of information, regarding both the administration of the death penalty and available alternatives (Bohm et al., 1991; Bowers et al., 1994; Sandys & McGarrell, 1994; Sarat & Vidmar, 1976). In fact, Bowers et al. (1994) and Sandys and McGarrell (1994) found that the number of people who supported the death penalty decreased when they were presented with the option of LWOP. Overall, studies suggest that death penalty attitudes are highly value expressive (Bohm et al., 1991; Bowers et al., 1994; Sandys & McGarrell, 1994; Sarat & Vidmar, 1976; Vollum et al., 2004) and the wording of death penalty questions can change the magnitude of death penalty support dramatically.

While the findings do not completely support Sotirovic (2001) hypothesis that simple media content, or content with a consistent message, is more likely to drive affective decision making, whereas complex media content is more likely to inform cognitive processes that involves weighing multiple perspectives to make a decision, there was some support. Although television crime news was historically seen as presenting balanced perspectives on issues (Tetlock, 1989), many now see television news as becoming more simplistic and mirroring infotainment programs that attempt to capture news audiences by presenting factual information about specific cases in very stylized ways (Gamson, 1994; Surrette, 2010).

Surrette (2010) finds that television programming overemphasizes crimes of violence and offers stereotypical portrayals of offenders. For example, on television, less common crimes of murder and robbery overshadow more common property crimes (Surrette, 2010). According to Surrette (2010), offenders are portrayed as psychopaths who prey on the weak, helpless, and vulnerable, or as shrewd, ruthless, and violent businessmen or professionals. Other scholars agree that the media often presents offenders as evil and the criminal justice system as an effective and moral authority (Eschholz et al., 2003). Similarly, crime dramas emphasize that while the world may be a complex and confusing place, punitiveness is the simple and straightforward answer (Lyons & Scheingold, 2000).

Newspapers and Internet consumption (depending on the specific choice of sources) would theoretically offer a more balanced perspective and allow consumers to sift through information and activate more cognitive thought processes about the issues. These patterns were consistent with the findings that viewing television news, crime dramas, and police-reality programs were all consistently related to general support for the death penalty, while newspaper and Internet consumption were not. The positive relationship between talk and news radio consumption and general support for the death penalty was somewhat unexpected, but a closer look at a report in Talkers.com (2012) on The Top Talk Radio Audiences provides some clarification. Nine of the top 12 talk radio audiences were for conservative talk radio programs (i.e., Rush Limbaugh and Glenn Beck), which tend to present arguments supportive of punitive policies, including the death penalty.⁶ It should also be noted that news programs on public radio (not ranked on the Talk Radio list) are also quite popular and most likely offer more diverse perspectives on capital punishment. Future research should make sure to differentiate between the different types of talk/news radio that the respondent is consuming. Additionally, more content analysis studies of popular media content that focus on coverage of the death penalty are needed to assess current media messages about the death penalty.

The lack of media effects for the LWOP model, with the exception of TV news, was also generally consistent with Sotirovic's (2001) argument that media content would be less important for complex decision making. Similarly, the lack of effects for newspaper, radio, and Internet (arguably more complex media forms) may be a result of audiences weeding through various perspectives and arguments, combined with their own experience to reach a practice preference on using the death penalty when LWOP is an option.

Finally, we cannot ignore the fact that some of these results may be an artifact of individuals with specific characteristics selecting media content that is consistent with their own perspectives. In line with this possibility, Republicans were more likely to listen to talk radio, watch police-reality programs, and television news and they were also more supportive of the death penalty. Similarly, less educated individuals are more likely to watch police-reality programs and to support the capital punishment. There were other media and demographic variable relationships that were not consistent with the patterns that were found in the support for capital punishment models. For example, those with a higher education are more likely to listen to talk radio, while education and support for capital punishment are negatively correlated.

There were several limitations to the current study. To begin, the convenience sample that was utilized does not allow for specific generalizations to be made to the general population. The sample also lacked enough minority group representations to make meaningful comparisons between African Americans, Asians, Hispanics, and Whites. The cross-sectional data did not allow for adequate tests of the time order between media consumption patterns, support for capital punishment, and a number of perceptual variables. Although the two measures of support for capital punishment are frequently used in the death penalty literature, they are single-item measures. Finally, several media and fear of crime studies have highlighted the importance of disaggregating samples by characteristics such as race, sex, and neighborhood characteristics to control for the possibility that the same media message may differentially impact audience members with different characteristics (Callanan & Rosenberger, 2011; Chiricos et al., 1997; Dowler, 2002; Eschholz et al., 2003). Unfortunately, the size of the current sample made multiple subdivisions of the sample impractical.

More work is needed on both theoretical models of media effects on capital punishment that include nonrecursive relationships and on developing longitudinal data sets that include media consumption and punitive attitude variables. Future research in this area should include measures of complexity of thinking about crime and attitudes toward capital punishment indexes, to help further clarify the relationship between media consumption patterns, and support for capital punishment. Additionally, future research should utilize a probability sampling technique so that generalizations to the general population can be made with greater confidence. It will also be important to separate measures of race from ethnicity, so that differences between and within these groups can be explored in greater detail.

Footnotes

Acknowledgment

The authors wish to express their appreciation to the anonymous reviewers for their careful responses to earlier drafts.

Declaration of Conflicting Interests

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

Funding

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

Notes

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