Public Support for Policies to Reduce School Shootings: A Moral-Altruistic Model

Abstract

Objectives:

The recurring mass murder of students in schools has sparked an intense debate about how best to increase school safety. Because public opinion weighs heavily in this debate, we examine public views on how best to prevent school shootings. We theorize that three moral-altruistic factors are likely to be broadly relevant to public opinion on school safety policies: moral intuitions about harm, anger about school crime, and altruistic fear.

Methods:

We commissioned YouGov to survey 1,100 Americans to explore support for a range of gun control and school programming policies and willingness to pay for school target hardening. We test the ability of a moral-altruistic model to explain public opinion, while controlling for the major predictors of gun control attitudes found in the social sciences.

Results:

The public strongly supports policies that restrict who can access guns, expand school anti-bullying and counseling programs, and target-harden schools. While many factors influence attitudes toward gun-related policies specifically, moral-altruistic factors significantly increase support for all three types of school safety policies.

Conclusions:

The public favors a comprehensive policy response and is willing to pay for it. Support for prevention efforts reflects moral intuitions about harm, anger about school crime, and altruistic fear.

Keywords

school shootings gun control public opinion morality emotions

The United States is in an era of mass shootings. Although there are some monthly variations, since 2013 the U.S. has experienced an average of one mass shooting per day (defined as four or more individuals shot, excluding the shooter) (Gun Violence Archive 2020). As mass shootings have become pervasive, they have entered an area of society generally thought to be safe: our nation’s schools. At mid-year of 2018, for example, an alarming average of one school shooting had occurred per week (Ahmed and Walker 2018). Among these was the rampage at Marjory Stoneman Douglas High School in Parkland, Florida, where a former student shot and killed 17 students and staff and injured a dozen more (Ahmed and Walker 2018; Cullen 2019). The severity of this and other similar tragedies in recent years, such as Sandy Hook Elementary School, has led policymakers and members of the public to give sustained attention to the gun control issue and has sparked an ongoing debate about how best to increase school safety.

Pollsters and researchers have recently begun to assess public opinion about different policy responses to school shootings (e.g., Gallup 2018; Mancini et al. 2020), but the evidence remains limited in several ways. First, most polls have only measured support for a handful of gun control policies, while leaving out questions that assess support for other, non-gun-related policies that may prevent school shootings. For example, polls have typically excluded items asking about school-specific measures, such as anti-bullying programs, mental health counseling, and target hardening schools. There is also no evidence about whether the public is willing to pay for such measures. Second, and perhaps most importantly, prior research has not tested major theoretical explanations for cleavages in public attitudes about how best to reduce school shootings. Developing a better understanding of the extent and sources of public support for school safety policies is important, because criminal justice policy and practice are both highly responsive to popular attitudes (Enns 2016; Pickett 2019). Indeed, public attitudes about gun violence appear to have influenced many electoral outcomes in recent years (Yablon and Nass 2018).

The current study provides the most comprehensive analysis to date of public opinion on the major policies often proposed in the aftermath of school shootings: gun control, school programs, and target-hardening. In so doing, it contributes to the literature in several ways. First, using data from a 2018 national survey, we investigate the extent of public support for a wide range of school safety policies. Second, we examine whether the public is willing to pay for school security measures. Assessing willingness-to-pay is important for all manner of reasons (Nagin et al. 2006), not the least of which is that the school safety industry has become a multi-billion dollar enterprise, requiring schools to divert more taxpayer dollars toward safety expenditures versus other traditional educational costs (e.g., hiring staff, better technology) (Cox and Rich 2018). Third, we develop and test a moral-altruistic model of policy preferences. Although many factors are likely to be associated with attitudes toward gun-related policies, we theorize that moral-altruistic factors—specifically, moral intuitions about harm, anger, and altruistic fear—will be broadly important for explaining public support for school safety policies, both gun-related and non-gun-related.

Responses to School Shootings

American schools endured approximately 296 shooting incidents between 2013 and 2018 (Riedman and O’Neill 2019). Nearly one third (97) of those incidents occurred in 2018, which resulted in a total of 55 deaths of students in grades K through 12. From 1966 to 2016, American schools endured 94 mass shootings, in which multiple people were either killed or injured (Schildkraut, Formica, and Malatras 2018). Explicitly focusing on the K–12 settings, 11 shootings since 1989 have resulted in four or more people being killed, with more than half occurring since 2005 (Peterson and Densley 2019). These upward trends in shootings and mass shootings in schools mirror the trend of active shooter incidents in the United States since the year 2000 (Blair and Schweit 2014). Put simply, we are in the age of mass shootings, particularly in schools.

There have been three main responses to prevent and mitigate the harm of school shootings (Bonanno and Levenson 2014; Jonson 2017; Rocque 2012). The first response involves significant campaigns to reduce and limit access to guns (Everytown for Gun Safety 2020; Moms Demand Action 2020; Schildkraut and Hernandez 2014). The second response involves school programs targeting individuals who may carry out a rampage killing, and teaching students, staff, and faculty how to respond to such events. The last response seeks to make these incidents harder to complete. Creating physical barriers in the school environment may thwart attacks by making it difficult for perpetrators to gain access to their intended targets. We elaborate on each of these policy responses below.

Gun Reforms

In the hope to prevent future tragedies, politicians—generally on the political Left—and large segments of the public focus on restricting access to guns after each mass school shooting (Bonanno and Levenson 2014; Schildkraut and Hernandez 2014). As a result, new gun control movements have been born. For example, one day after a gunman took the lives of 20 children and six adults at Sandy Hook Elementary School in December 2012, Moms Demand Action was founded (Moms Demand Action 2020). Flash forward six years later and within days of the murder of 17 people at Marjory Stoneman Douglas High School on Valentine’s Day 2018, the #NeverAgain movement arose (Cullen 2019; Witt 2018).

A common focus to all these movements is the desire to limit access to guns. For example, after the 1999 shooting at Columbine High School, there was a flurry of legislative activity to close gun show loopholes and begin requiring background checks and mandatory waiting periods for firearms purchased gun shows. However, these measures did not pass at the national level (Schildkraut and Hernandez 2014). In response to the Sandy Hook Elementary School shooting, President Obama signed executive orders that initiated 23 separate Executive Actions and called on colleagues in Congress to enact legislation requiring universal background checks, reinstating the assault weapons ban, and limiting high-capacity magazines. But once again, these measures failed to pass (Curtis 2013; Keneally 2017). After the Marjory Stoneman Douglas shooting, there was a renewed focus on mental health and red flag laws, yet few federal laws were actually passed addressing these issues (Kramer and Harlan 2019). Except for a recent 2018 ban on bump stocks, which convert a semi-automatic gun into a nearly fully automatic gun that rapidly fires many bullets, the federal government has taken a rather “hands off” approach to gun control (ATF 2019).

While gun control legislation has been stymied at the federal level, state legislatures have been more amenable to restricting access to firearms, particularly in the aftermath of the school shooting in Parkland, Florida. Heeding the calls of the #NeverAgain movement, the Florida legislature enacted a flurry of new gun control laws within six months of the shooting, including increasing the waiting period to buy a firearm to three days and raising the age to purchase a shotgun or rifle from 18 to 21. This renewed push for gun control not only occured in Florida, but also swept the nation, with 26 other states passing more than 65 new gun control laws in 2018 (Vasilogambros 2018). Beyond laws increasing waiting periods and the age to purchase, states across the country have enacted red flag laws, mandated training classes, capped gun magazine sizes, and allowed law enforcement to seize firearms from individuals engaged in domestic violence crimes (Melendez 2019). With pro-gun-control politicians—those earning F ratings from the NRA—winning races in both red and blue states in the 2018 midterm election, it is likely that this trend toward greater gun control could continue for the foreseeable future, as long as the public remains supportive (Yablon and Nass 2018).

School Safety Programs

The majority of the school programs implemented to reduce school shootings involve the identification of at-risk individuals through threat assessments, increased mental health services, programs to foster inclusive and safe learning environments, and bullying prevention programs (Bonanno and Levenson 2014; Cornell 2020; Everytown for Gun Safety, American Federation of Teachers [AFT], and National Education Association [NEA] 2020; Keels 2018). The goal of school safety programs as a whole is to create an environment that cultivates trusting relationships between students and their teachers and other school personnel, which in turn results in students feeling valued, included, and respected. These relationships are intended to foster lines of communication used by students to confide in trusted adults about potential threats, allowing the school to provide help to at-risk individuals and thwart potential violent incidents (Bonanno and Levenson 2014; Cornell, 2020; Everytown for Gun Safety, AFT, and NEA, 2020; National Threat Assessment Center 2018).

In the event that prevention fails, schools have implemented policies to reduce the toll exacted by an active shooter in a rampage. Active assailant drills—which train students on how to respond to a violent intruder—have been the most common mitigation response. In the 2015 to 2016 school year, 95% of schools conducted drills in lockdown procedures, up from 40% in the 2005 to 2006 academic year (Musu et al. 2019; Nolle et al. 2007). Despite their widespread use, these exercises have proven controversial (Everytown for Gun Safety, AFT, and NEA 2020; Jonson, Moon, and Gialopsos 2020a). Two large teachers’ unions—the AFT and NEA—recently took a stance against including students in active shooter protocols, arguing that such drills are psychologically traumatizing and scarring America’s young people (Everytown for Gun Safety, AFT, and NEA 2020). However, little empirical evidence exists regarding the effectiveness of drills (Huskey and Connell 2020; Jonson et al. 2020a; Jonson, Moon, and Hendry 2020b; Schildkraut, Nickerson, and Ristoff 2020; Zhe and Nickerson 2007).

School Target Hardening

Based on situational crime prevention, communities have also attempted to make schoolhouses harder targets for would-be attackers to penetrate (Addington 2009; Bonanno and Levenson 2014; Clarke 1983; Cornish and Clarke 2003; Jonson 2017; Rocque 2012). Dubbed the “safest school in America,” Southwestern High School in Shelbyville, Indiana spent $400,000 to harden itself against potential attacks by installing bulletproof doors, school-wide alarm systems that can be activated by a special fob worn by teachers, security cameras that are directly tied to the local sheriff’s department, and smoke cannons to distract assailants (Chute and Mack 2018). While few schools may adopt such comprehensive measures to protect against a potential shooter, school security now has grown into nearly a $3 billion industry, selling products such as metal detectors, security cameras, gunshot detection sensors, access control technology, social media trackers that assist schools in flagging potential threats, and bullet-proof whiteboards (Keierleber 2018; Schwartz et al. 2016).

Seeing value in one or more of these security features, many districts have increasingly “hardened” their schools in recent years. In the 1999 to 2000 academic year, 74.6% of the nation’s public schools controlled access into their building by locking and monitoring the door, 25.4% required faculty/staff ID badges to determine who was in the building, and 19.4% had installed security cameras on their campuses. Sixteen years later, in the 2015 to 2016 school year, these percentages drastically increased to 94.1%, 80.6%, and 67.9%, respectively (Musu et al. 2019). Additionally, 10.6% of public high schools conducted random metal detector checks during the 2015/2016 academic year (Musu et al. 2019). Clearly, over the last 15 to 20 years, schools have increasingly implemented target hardening measures to assist in the prevention of the next mass school shooting (Fox and DeLateur 2014; Lassiter and Perry 2009; Snell et al. 2002).

Public Opinion on School Safety Policies: The Moral-Altruistic Model

Public opinion is pivotal in the debate about how best to prevent school shootings, as it is in most debates about crime policy (Pickett 2019). Not only must politicians consider possible electoral consequences when implementing politicized policies, such as gun control, but also the money for school programs and target hardening must come from taxpayers. Additionally, because over 40% of U.S. family households have school-aged children, and because most parents are worried about school shootings, few policy areas have as much “on the ground” relevance to the public as those involving safety at schools (Graf 2018).

The goal in the current study is not just to examine public attitudes toward a wide range of policies—gun control, school programming, and target hardening—aimed at reducing school shootings, but also to explain those attitudes. We draw attention to factors that prior studies focused narrowly on gun control attitudes have overlooked, but which likely are central to attitudes about school safety, given what occurs during school shooting incidents—an armed perpetrator enters a school building with the intent to harm children and staff. Specifically, extending scholarship on moral intuitions and emotions to the school context, we theorize that three factors underpin attitudes toward a wide range of school safety policies, both gun-related and non-gun-related: a belief in the moral wrongfulness of harming others, altruistic fear, and anger. We see these factors key components of what we term the “Moral-Altruistic Model.”

Moral Beliefs about Interpersonal Harm

The dominant theoretical perspective on morality is Haidt’s (2007, 2012) Moral Foundations Theory (MFT), which applies the concept of cognitive “modules” to explain why certain events trigger intuitive reactions and subsequent emotions by those who witness them. Two kinds of triggers exist—original triggers and current triggers (Sperber and Hirschfield 2004). Original triggers are the objects for which a module was enforced. Describing original triggers, Haidt (2012) uses an example of snakes, stating that as a result of learned experiences and evolution, many animals and humans are naturally afraid of snakes because of the high level of threat they pose. Current triggers are all of the things in the world that cause the original trigger to come into consciousness. Staying with the snake example, videos or images of snakes are examples of current triggers.

Extending this basic definition of modules, Haidt (2012) identified sets of adaptative challenges that are found across cultures and time (that is, original triggers that we as humans all share in common). Perhaps the most fundamental adaptive challenge is caring for children, and it should cause depictions or reminders of child harm to serve as current triggers (Haidt 2012). Underneath all adaptive challenges are sets of moral foundations, which Haidt (2007) categorizes into five domains: Harm/Care, Fairness/Reciprocity, Authority/Respect, Ingroup/Loyalty, and Purity/Sanctity. Through their effects on moral intuitions, the different domains affect attitudes toward different types of deviant behavior, with some domains being relevant only for certain offenses directed at specific victim types (Silver 2017).

For the adaptive challenge of protecting children, the Harm/Care domain is most theoretically germane (Haidt 2012). One current trigger for this domain—that is, something that taps that evolutionary module within us that contends it is morally virtuous to care for and protect children—is undoubtedly media coverage of school shootings, which is “wall-to-wall…continuous…[and] all but inescapable” (Schildkraut and Muschert 2013:161). Therefore, we hypothesize that respondents who score high in the Harm/Care domain—that is, those who are compassionate for those who are suffering, especially vulnerable groups such as children, and who believe it is morally virtuous to reduce potential harm whenever and wherever possible—will be significantly more likely to support policies intended to prevent school shootings.

Altruistic Fear for Students and School Employees

The most inexplicable oversight in the literature on public opinion about criminal justice may be the omission of altruistic fear. Countless studies over three decades have examined the relationship between personal crime fear and diverse policy preferences, such as support for the death penalty (Baker et al. 2016; Langworthy and Whitehead 1986), but none, to our knowledge, have included a separate measure of altruistic fear. This omission is surprising. “If substantial numbers of [Americans] routinely experience fear for their own safety,” Warr and Ellison (2000:552) asked, “do they not also feel and respond to fear for other persons…whose well-being they value?” The answer, obviously, is yes. Warr and Ellison (2000:574) found that “altruistic fear is more common and frequently more intense than personal fear.” Notably, 63% of the respondents were concerned or very concerned for their own personal safety, whereas 77% of the respondents reported being worried for their spouse, 83% worried for their sons, and 88% worried for their daughters. Furthermore, Warr and Ellison (2000:560) found that “regardless of the sex of the child, younger children are the greatest objects of concern for their parents.” Other studies have also found higher rates of altruistic than personal fear, especially for female spouses and daughters (Drakulich 2015; Drakulich and Rose 2013; Haynes and Rader 2015).

Research on altruistic fear is still in its infancy. Thus far, scholars have focused mostly on the extent, sources, and behavioral effects of altruistic fear for household members, paying little attention to effects on policy preferences or to other forms of altruistic fear. As Warr and Ellison (2000:576) noted about their study, “a larger and perhaps more important issue is the fact that the analysis has concentrated only on altruistic fear within households, ignoring the fear that individuals may also have on behalf of persons outside their own household—friends, neighbors, coworkers, and relatives.” And yet, there is every reason to expect that altruistic fear for non-household members will also influence attitudes toward criminal justice policies (Dubber 2002; Garland 2001). As Simon (2007:75) explained, “it is as crime victims that Americans are most readily imagined as united; the threat of crime simultaneously de-emphasizes their differences and authorizes them to take dramatic political steps.” The second hypothesis we test is as follows: altruistic fear for students and school employees will increase support for a wide range of policies, both gun-related and non-gun-related, aimed at reducing school shootings.

Anger about School Crime

Anger is an especially powerful emotion (Agnew 1992); it simplifies cognitive processing and creates a strong desire for ameliorative action (Lerner, Goldberg, and Tetlock 1998), its relief is psychologically rewarding (de Quervain et al. 2004), and it is intrinsically linked to crime and punishment (Darley 2009). Experimental evidence shows that when members of the public learn about criminal offending, they frequently become angry (Pickett and Baker 2017). This is consistent with Durkheim’s (1933) influential account of the relationship between emotions, crime, and law, which posited that a violation of values held sacred by society elicits moral outrage and, in turn, social control responses (Garland 1990).

A small but growing number of studies has found that anger about crime increases retributive impulses (Bastian, Denson, and Haslam 2013), more so even than personal fear (Kort-Butler and Ray 2018; Petersen 2010). In one of the first assessments examining how anger influences attitudes toward criminal justice policy, Johnson (2009) found that, while controlling for well-established predictors of punitiveness (e.g., conservative ideology, racial resentment), angrier people were more supportive of “get tough” crime policies. Anger was in fact a stronger predictor than both conservative political ideology and racial resentment. Hartnagel and Templeton (2012) reported that anger also increased punitiveness in a Canadian sample.

Most prior studies have measured general anger about crime, broadly defined, and analyzed its effects on support for punitive policies, such as the death penalty. Theoretically, however, anger about specific types of crime should be equally, if not more, important, and should influence attitudes about both punitive and non-punitive policies. Goodall, Slater, and Myers (2015), for example, found that anger about alcohol-related accidents and crimes increased support for a diverse set of alcohol policies, both individually oriented (e.g., stricter enforcement) and socially oriented (e.g., banning billboard advertisements). Gamson (1992) showed that targeted anger is important for motivating collective action to address specific social problems. In his words, anger “puts fire in the belly and iron in the soul” (p. 32). Similar to anger’s effects in other domains (Agnew 1992, 2010), then, anger about specific crimes should increase both their perceived seriousness and the felt importance of reducing the collective strain they produce (Darley 2009; Pickett and Baker 2017). Simply put, when people are angry about a specific type of collective strain, they “feel bad and want to do something about it” (Agnew 2010:135). School shootings are highly emotional events, not only for the students and staff in the school during the crime, but also for parents, friends, communities, and the entire nation (Cullen 2019). Therefore, the final hypothesis we test is as follows: anger about school crime will increase support for all forms of school safety policies.

Methods

Sample

To test our hypotheses, we commissioned YouGov to survey a nationwide sample of American adults (18 and over). YouGov is a trusted source of survey data for academic research. It fields the Cooperative Campaign Analysis Project as well as the Cooperative Congressional Election Study. Researchers have used survey data from YouGov to examine public opinion about various criminal justice topics (Enns and Ramirez 2018; Lehmann and Pickett 2016; Norris and Mullinix 2019; Simmons 2017), including gun control (Filindra and Kaplan 2016, 2017). YouGov fielded our survey to 1,100 respondents between May 30 and June 6, 2018.

Two approaches to survey inference exist, design-based and model-based; the former relies on random selection to ensure the exogeneity of the sampling design, and the latter relies on modeling (Mercer et al. 2017). YouGov uses a two-stage, sample-matching design for model-based inference. It first selects a matched (on the joint distribution of covariates) sample of respondents from its volunteer online panel (2 million U.S. panelists) using distance matching with a synthetic sampling frame (constructed from probability samples, including the Current Population Survey), and then uses propensity scoring to weight the sample (Ansolabehere and Rivers 2013; Vavreck and Rivers 2008). The assumption is that sample selection is ignorable conditional on the matching and weighting variables. There is strong evidence that findings from YouGov surveys generalize to the U.S. population (Ansolabehere and Schaffner 2014; Sanders et al. 2007; Simmons and Bobo 2015); indeed, several studies have found that YouGov’s sampling design outperforms probability sampling methods (Kennedy et al. 2016; Vavreck and Rivers 2008).

When compared to estimates from the U.S. Census and American Community Survey (in parentheses), our weighted sample looks much like the U.S. population: non-Hispanic White, 63.7% (64.5%); male, 47.8% (48.7%); Bachelor’s degree, 29.3% (28.4%); married, 48.5% (48.2%); Northeast, 16.4% (17.2%); Midwest, 20.2% (20.9%); South 40.9% (38.1%); West, 22.4% (23.8%). When compared to the Pew Research Center’s estimates of party identification among registered voters (in parentheses), our weighted sample also looks similar to the U.S. population: Republican or lean Republican, 38% (42%), Democrat or lean Democrat, 48% (50%). The similarity of our sample to the U.S. population, both demographically and politically, increases our confidence that the findings will generalize to American adults.

Outcome Variables

The dependent variables in our analysis gauge respondents’ support for policies aimed at reducing school shootings and their willingness to pay for school target hardening measures. We measure the variables using responses to two matrix questions. The first asked how much respondents supported (1 = strongly oppose, 5 = strongly support) 15 different policies “that have been proposed to try to reduce school shootings.” The second question presented 11 target hardening measures and asked how much respondents “would be willing to pay, if any, in additional taxes each year to have the following installed at schools in your school district?” There were five response options: $0, $25, $50, $75, and $100. Table 1 shows the question wording for all of the items in both matrix questions. It also shows the loadings from a promax-rotated factor analysis with all 26 items. The factor-analytic results are clear, revealing that the items load on three factors corresponding to support for Gun Control Policies, support for School Safety Programs, and willingness to pay for School Target Hardening. One item failed to load well on any factor (see Table 1; loading < .500) and thus is not included in the indices or multivariate analyses. We used the responses to the other 25 items to create the three mean indices (α = .937, .720, and .955, respectively), which serve as the outcomes in the analyses.

Table 1.

Question Wording and Promax-Rotated Factor Loadings for School Safety Measures, Rank Ordered Within Scales by Level of Support or Willingness to Pay.

Index Survey Question	Percent Support or WTP	Factor 1	Factor 2	Factor 3
Gun Control Policies (α = .937):
“Ban the sale of firearms to anyone who has a severe mental illness.”	85.59	–.115	.252	.464
“Require background checks for private sales of firearms (both in-person and online)…”	81.26	–.006	.615	.251
“Require a five-day mandatory waiting period for the purchase of all guns.”	74.77	–.026	.651	.194
“Use stronger red flag laws to allow family members and/or law enforcement officials to…”	74.75	–.023	.523	.278
“Ban the sale of devices (e.g., bump stocks, binary triggers) that convert a semi-automatic gun…”	70.18	–.035	.802	.038
“Increase the age to purchase any type of firearm from 18 to 21.”	67.39	–.026	.717	.073
“Ban the sale of high-capacity magazines that hold 10 or more bullets.”	61.58	.043	.936	–.083
“Reinstate the assault weapons ban.”	59.20	–.010	.936	–.148
“Ban the sale of AR-15 s.”	59.09	.002	.968	–.141
“Limit the number of firearms that one individual can own.”	51.46	.077	.839	–.125
“Increase federal funding for the study of gun violence.”	50.76	.058	.528	.145
School Safety Programs (α = .720):
“Increasing anti-bulling programs at schools.”	81.98	–.021	.064	.603
“Increasing the number of mental health counselors in every school.”	75.07	.031	.123	.521
“Independent security assessments of every schools by external professionals.”	71.50	.087	–.192	.668
“Increasing active shooter drills conducted at schools.”	68.41	.045	–.170	.672
School Target Hardening (α = .955):
“Security cameras.”	75.57	.815	.044	–.032
“Access control measures (e.g., buzzer systems, front-in check-ins, doors that automatically lock)…”	73.01	.818	–.050	.078
“Metal detectors.”	71.74	.755	–.075	.073
“Steel/bullet-proof classroom doors.”	66.41	.834	–.018	.035
“Better locks on all external doors to the school.”	65.87	.884	.017	–.054
“Better locks on all classroom doors.”	64.77	.862	.047	–.047
“Bullet-proof glass for all windows in the school.”	61.76	.827	–.004	.019
“ID badges for all students, staff, and faculty.”	58.97	.786	.058	–.074
“Social media trackers to assist schools in flagging threats of violence.”	58.61	.755	.073	–.021
“Moveable bullet-proof (e.g., Kevlar) pads/panels in each classroom.”	58.15	.780	–.021	.082
“Perimeter fences surrounding the school.”	53.86	.769	–.028	.034
Variance	—	7.553	6.774	4.276

Note: The WTP column values represent the percentage of respondents that reported they would pay more than zero dollars in extra taxes per year.

Independent Variables: Moral-Altruistic Concerns

Altruistic Fear is a mean index (α = .930, factor loadings: .842 to .891) measured with responses to four items that asked how often (1 = very rarely, 5 = very often) respondents worried about the safety of: 1) “students in primary schools (elementary or earlier),” 2) “students in secondary schools (middle and high),” 3) “students in colleges and universities,” and 4) “school and college faculty/staff.” Moral Foundations—Harm is a mean index (α = .653, factor loadings: .449 to .640) based on responses (1 = strongly disagree, 5 = strongly agree) to four items measuring moral intuitions about harm and care (e.g., “If a saw a mother slapping her child, I would be outraged”; “Compassion for those who are suffering is the most crucial virtue”). We adapted the items from Graham, Haidt, and Nosek (2009). Higher scores on the index indicate a stronger belief in the moral wrongfulness of harming others. Anger is a mean index (α = .857, factor loadings: .655 to .785) measured with responses (1 = not angry at all, 5 = very angry) to five items that asked how angry respondents were about different types of school crime (e.g., “Students getting beat up in schools”; “Students bringing weapons into schools”). See Appendix A for the full question wordings and response options for the moral-altruistic varaibles.

Control Variables

To reduce the risk of omitted variable bias, we control for variables that previous research suggests may influence attitudes about either gun control or school policies. Prior studies have found important, albeit complex, partisan divides in public opinion about both guns and school safety (Filindra and Kaplan 2016; Lee et al. 2020; Mancini et al. 2020), and thus we control for political beliefs. To do so, we adapted questions from Filindra and Kaplan (2016, 2017) to measure Individualism and Libertarianism. The former is a mean index (α = .857, factor loadings: .579 to .775) measured with responses (1 = strongly agree, 5 = strongly disagree) to six items about whether the government should ensure equality (e.g., “It is okay if some people have more of a chance in life than others”; “This country would be better off if we worried less about whether everyone is equal”). To measure Libertarianism (α = .775, factor loadings: .640 to .731), we counted the number of times respondents chose the libertarian option (e.g., “The less government, the better”) over a non-libertarian one (e.g., “There are more things that the government should be doing”) in three forced-choice questions. We also measured partisan identification (Republicanism: 1 = strong Democrat, 7 = strong Republican) and political ideology (Conservatism: 1 = very liberal, 5 = very conservative). These four political indicators—Individualism, Libertarianism, Republicanism, and Conservatism—all loaded on a single factor (factor loadings: .695 to .751). We thus combined them into the variable of Rightward Political Ideology (α = .830), a standardized mean index. Higher scores indicate a more staunchly held rightward political outlook.

Racial Resentment is a mean index (α = .906, factor loadings: .742 to .850) measured with responses (1 = strongly agree, 5 = strongly disagree) to five items adapted from Henry and Sears (2002) “Symbolic Racism 2000” scale (e.g., “Over the past few years, Blacks have gotten less than they deserve”). Personal Fear of Crime (α = .919, factor loadings: .798 to .868) is a mean index based on responses to five questions that asked how afraid respondents were that someone in their household would fall victim to five crimes (theft, burglary, robbery, sexual assault, murder) in the next five years. Following Stroebe, Leander, and Kruglanski (2017), we also include a measure of Dangerous World Beliefs. The variable is a mean index (α = .858, factor loadings: .723 to .803) based on responses (1 = strongly agree, 5 = strongly disagree) to four items that asked about the security and stability of the social order (e.g., “Any day now chaos and anarchy could erupt around us. All signs are pointing to it.”). We recoded the responses so that higher scores indicate a belief that the world is unpredictable and dangerous. We also include a mean index (α = .920, factor loadings: .757 to .838) measuring Confidence in Police. It is based on responses (1 = very dissatisfied, 5 = very satisfied) to six questions that asked about the local police’s performance at accomplishing different goals (e.g., “Prevent crimes”; “Catch people who break the law”).

NRA Membership is a binary indicator of whether anyone in the respondent’s household is a member of the National Rifle Association (0 = no, 1 = yes). We measure Gun Social Network with responses (1 = none to 5 = all) to the question: “Currently, how many of your family members and close friends would you say own guns?” The analysis includes five instrumental variables that tap self-interested concerns about both guns and crime. The first is Gun Ownership, which we measure as the sum of responses to four questions asking how many (0 = none, 4 = four or more) shotguns, rifles, assault rifles, and handguns the respondent owned at the time of the survey (α = .854, factor loadings: .629 to .856). The second, Defense Effectiveness, measures respondents’ beliefs about the effectiveness (1 = very ineffective, 4 = very effective) of “gun possession as a means of protection and self-defense.” We measure Childhood Socialization with responses (0 = no, 1 = yes) to a five-item question that asked: “Please think about your childhood (before the age of 18). Did any of your family members (or guardians) do the following things when you were growing up…1) Keep a firearm in the home; 2) Teach you how to shoot a firearm; 3) Teach you how to clean a firearm; 4) Take you hunting; 5) Take you to a gun show?” We sum the responses to create an index (α = .836, factor loadings: .462 to .826).

We include controls for respondents’ race (1 = White), gender (1 = Male), Age (in years), Education (1 = no high school, 6 = graduate degree), Income (1 = <$10 K, 16 = $500K+), marital status (1 = Married), and household composition (1 = Child in Household and 1 = Victim in Household).¹ We also control for Religiosity and whether the respondent reports being a Born-Again Protestant (0 = no, 1 = yes). Religiosity is a standardized mean index (α = .871, factor loadings: .743 to .873) based on three questions about the importance of religion in respondents’ lives, their frequency of church attendance, and their frequency of praying. Finally, we control for respondents’ region of residence (1 = Southerner). Table 2 provides the descriptive statistics for all of the variables.

Table 2.

Descriptive Statistics.

Correlations with Outcomes
Variables	Mean or %	SD	GunControl	School Programs	Target Hardening
Outcome Variables	—	—	—	—	—
Gun control	3.81	1.07	—	—	—
School programs	4.08	.70	—	—	—
Target hardening	2.37	1.16	—	—	—
Moral-Altruistic Variables	—	—	—	—	—
Moral foundations— harm	3.61	.79	.57*	.30*	.12*
Altruistic fear	3.17	1.06	.42*	.29*	.28*
Anger	4.32	.81	.28*	.40*	.17*
Control Variables	—	—	—	—	—
Rightward political ideology	.000	.81	–.65*	–.14*	–.04
Racial resentment	2.99	1.16	–.58*	–.06	–.03
Personal fear of crime	2.96	1.07	.18*	.16*	.11*
Dangerous world beliefs	3.62	.93	–.03	.25*	.11*
Confidence in police	3.38	.85	.06*	.12*	.07*
NRA membership (%)	15.16	—	–.37*	–.12*	–.01
Gun social network	2.15	1.10	–.43*	–.16*	–.06*
Gun ownership	1.46	2.88	–.43*	–.15*	.01
Defense effectiveness	2.85	.92	–.55*	.04	–.00
Childhood socialization	1.39	1.66	–.42*	–.11*	–.02
White (%)	63.65	—	–.05	.06*	–.06*
Male (%)	47.84	—	–.24*	–.14*	–.10
Age	48.08	17.88	.06*	.10*	–.05
Education	3.24	1.58	.16*	–.03	–.07*
Income	5.56	3.19	.02	–.01	–.01
Married (%)	48.49	—	–.02	.00	.02
Child in household (%)	25.05	—	–.01	.03	.10*
Victim in household (%)	17.41	—	–.06	–.01	.00
Religiosity	.04	.90	–.16*	.05	.08*
BA Protestant (%)	21.08	—	–.17*	.03	.05
Southerner (%)	40.91	—	.00	.03	.08*

* p < .05 (two-tailed).

Results

Table 1 shows that Americans support a range of policies for reducing school shootings and are willing to pay higher taxes to make schools safer. Specifically, most respondents either support or strongly support each of the 15 policies, and most are also willing to pay for each of the 11 target hardening measures. The most supported policies—supported by over 80% of respondents—are banning firearm sales to people with mental illnesses, requiring background checks for all firearm purchases, and increasing anti-bullying programs at schools. Policies that receive almost as much support—supported by approximately 75% of respondents—include mandatory five-day waiting periods for all firearm sales, stronger red flag laws, and increasing mental health counseling in schools. Regarding the target hardening measures, over 70% of respondents are willing to pay more taxes to put security cameras, better access control, and metal detectors in schools, and over 60% are willing to pay for bullet-proof doors, bullet-proof windows, and better door locks. The takeaway is that most Americans: 1) want to take action to prevent school shootings, 2) think a comprehensive policy approach is needed that includes gun control, social-psychological programming, and security measures, and 3) are willing to pay additional taxes to target harden schools.

Why does the public support the aforementioned policies and security measures? To answer this question, we estimate multivariate models predicting our three dependent variables, Gun Control, School Programs, and Target Hardening. Because all three dependent variables are continuous variables, we estimate the models using ordinary least squares (OLS) regression.² Table 3 reports the results for the three models. The variables included in the models explain 65% of the variance in support for gun control (Model 1), 29% of the variance in support for school programs (Model 2), and 14% of the variance in willingness to pay for target hardening.

Table 3.

OLS Regression Analyses Assessing Public Support for Policies to Prevent School Shootings (N = 1,036).

	Model 1:Gun Control		Model 2:School Programs		Model 3:Target Hardening
Variables	b	β	b	β	b	β
Moral-Altruistic Variables
Moral foundations— harm	.157***	.117	.096*	.106	–.029	–.019
Altruistic Fear	.159***	.164	.074**	.114	.228***	.211
Anger	.064	.050	.217***	.253	.171**	.120
Control Variables
Rightward political ideology	–.347***	–.270	–.119**	–.138	.037	.025
Racial resentment	–.124***	–.138	.001	.002	–.018	–.018
Personal fear of crime	.037	.039	–.022	–.034	–.015	–.014
Dangerous world beliefs	.093**	.084	.122***	.162	.062	.050
Confidence in police	.091**	.075	.062*	.076	.107*	.078
NRA membership	–.050	–.017	.004	.002	.080	.024
Gun social network	–.078*	–.082	–.063*	–.099	–.091*	–.085
Gun ownership	–.035**	–.097	–.006	–.026	.024	.059
Defense effectiveness	–.263***	–.233	.064	.085	–.062	–.049
Childhood socialization	–.058**	–.093	.002	.006	.011	.016
White	.077	.036	.075	.052	–.147	–.061
Male	–.029	–.014	–.077	–.055	–.126	–.054
Age	.010***	.173	.003	.070	–.001	–.019
Education	–.000	–.000	–.026	–.060	–.087**	–.118
Income	–.001	–.004	.014	.065	.026	.071
Married	.082	.040	.007	.005	–.076	–.033
Child in household	.145**	.061	.053	.033	.225*	.085
Victim in household	.038	.014	.096	.052	–.048	–.016
Religiosity	–.011	–.010	–.005	–.007	–.015	–.012
BA Protestant	.028	.011	.035	.020	.097	.033
Southerner	.057	.027	.087	.061	.225*	.095
R-squared	.651		.286		.138

Notes: Robust standard errors were used due to heteroskedasticity. Multicollinearity was assessed in two ways—VIFs and bivariate correlations. Across the three models, VIFs ranged from 1.07 to 3.07, with a mean of 1.58 (see Appendix B for all of the VIF values). Furthermore, no bivariate correlations were >.70. *p < .05; **p < .01; ***p < .001 (two-tailed).

Turning to Model 1, two of the three moral-altruistic variables have significant relationships in the expected direction with gun control attitudes: Moral Foundations-Harm and Altruistic Fear. Support for gun control is significantly higher among those who believe it is morally reprehensible to harm others, and among those who express greater altruistic fear for students and school employees. The coefficient for Anger is also in the hypothesized direction, but is not significant. Several of the control variables are significantly related to gun control attitudes. Rightward political ideology, racial resentment, the belief that guns are effective for self-protection, greater embeddedness in the gun culture, gun ownership, and childhood gun socialization are all associated with lower support for gun control. By contrast, dangerous world beliefs and confidence in police are both positively associated with support for gun control. Older respondents and those with young children in the household also tend to be more supportive of gun control.

Model 2 presents the findings for our second dependent variable, School Programs. Consistent with our hypotheses, all three moral-altruistic variables are significantly and positively associated with support for school programs designed to reduce school shootings. Respondents who believe it is morally reprehensible to harm others, who are more afraid for the safety of students and school staff, and who are angrier about school crime, all are more likely to support school programs. In fact, Anger is the strongest predictor in the model. By contrast, most of the control variables that significantly predicted gun control attitudes in Model 1 (e.g., Racial Resentment, Childhood Socialization) do not have significant relationships with views about school programs. Only four controls are significantly related to support for school programs: respondents who have a rightward political ideology and who are embedded in a gun social network are less likely to support school programs, whereas those who believe the world is more dangerous and unpredictable and who express greater confidence in the police are more likely to support school programs.

Model 3 presents the results for our third outcome variable, Target Hardening. Almost none of the variables that significantly predicted gun control attitudes in Model 1 (e.g., Rightward Political Ideology, Racial Resentment, and Dangerous World Beliefs) also significantly predict respondents’ willingness to pay for school security measures. By contrast, two of the three moral-altruistic variables, Altruistic Fear and Anger, have sizable and significant effects in the expected direction. Being afraid for students and school employees and being angry about school crime both increase willingness to pay additional taxes to put security measures (e.g., security cameras, metal detectors) in schools. Altruistic Fear is the strongest predictor in the model and Anger is the second strongest. Only five control variables are significantly related to Target Hardening: having greater confidence in the police, having a child at home, and living in the South all are associated with greater willingness to pay for security measures, whereas greater embeddedness in the gun culture and higher educational attainment are associated with lower willingness to pay.

Discussion

School safety is an evolving and salient issue. Major losses of life from school shootings have led scholars to assess various policy options to keep children safe (see, e.g., Jonson 2017; Jonson et al. 2020b). While evidence is growing, parallel scholarship needs to assess whether the public supports and, more importantly, is willing to pay for such measures, and why. To address these research questions, the current study measured support for a wide range of policy options that are often proposed in response to school shootings. We categorized these as gun control (e.g., increasing the age to purchase a firearm from 18 to 21), school programs (e.g., anti-bullying programs), and target hardening (e.g., bullet-proof glass windows in schools). We also explored the factors that shape public attitudes toward these policies, theorizing that moral-altruistic concerns underlie both support and willingness-to-pay. With few exceptions (e.g., Johnson 2009; Silver 2017), prior studies have mostly neglected the moral and altruistic sources of public opinion on criminal justice policies. This is an important omission, given our finding that moral-altruistic factors matter across a broad range of policies, from gun control to school programming and target hardening. Below, we separate our discussion of the results and their implications by the extent versus sources of public opinion.

Extent of Policy Support

Gun control

Our gun policy questions reveal that a majority of the sample (over 50%) supports all 11 of the gun control measures proposed to reduce school shootings. Six policies receive the most support (all supported by over 65% of the sample), which include, in order of most support: banning the sale of firearms to mentally ill persons, background checks for private sales of firearms, five-day waiting periods when purchasing a gun, stronger red flag laws, banning the sale of gun modifying devices (e.g., bump stocks), and increasing the firearm purchase age from 18 to 21. That these policies received the most support paints a clear picture: the public wants stronger restrictions on who can own guns. More disagreement exists on policies that restrict everyone’s gun ownership rights, by limiting how many or what types of guns (e.g., AR-15 s) someone can own. Special attachments, such as bump stocks are the exception, because these allow someone to change a semi-automatic gun into a near-automatic gun.

School programs

The belief that bullying underlies school shootings is pervasive (Lee et al. 2020; Mears, Moon, and Thielo 2017). Our sample supports this notion: Over 80% of the respondents support increasing the number of anti-bullying programs in schools. In reality, the bullying-school shootings connection is weak at best, given that tens of thousands of kids are bullied every day and never shoot anyone (Mears et al. 2017). Still, these types of programs, along with improved mental health treatment in schools, are likely to prove beneficial for schoolchildren, generally (Farrington and Ttofi 2009; Gaffney, Farrington, and Ttofi 2019). The other programs receiving public support include active shooter drills and security assessments—about 70% of respondents support these policies. While scholars have found that these programs can be successful (Jonson 2017; Jonson et al. 2020b), this is still an underdeveloped area of research that warrants further attention.

Target-Hardening schools

Beyond school programs, environmental criminology tells us that target-hardening should reduce the threat of school shootings by decreasing opportunities for them. The greatest monetary support found for target hardening policies was for security cameras, access control, and metal detectors. Greater willingness to pay for these measures may stem from perceptions that they have worked well to increase safety in other settings, such as airports. While schools have begun to adopt such measures (Mowen and Frang 2019), there is no way of knowing if they are used in accordance with best practices and to what extent they have had a target hardening effect. Nonetheless, even if these measures prove effective, they may also carry unintended consequences such as increasing perceptions of fear and disorder in schools (Jonson 2017).

By contrast, respondents reported less willingness to pay for bullet-proof glass and pads/panels to guard classroom doors, social media trackers, and perimeter fences around schools. The first two policies (bullet proof glass, pads/panels) may have received less support because they are seen as “after the fact” responses (i.e., they mitigate the damage of the shooting once the shooter is already in the school). Thus, the public may wish to put their money toward blocking access to the school in the first place. The public may not view social media trackers favorably because a) past shooters have made social media threats to which law enforcement did not take seriously (e.g., the Marjory Stoneman Douglas shooter) and b) the use of trackers may be viewed as an invasion of youths’ privacy. Fences may be viewed as offering little deterrent effect to someone set on shooting up a school and thus are not worth the investment.

The strong support found for target hardening measures and school programs coincides with Florida’s Marjory Stoneman Douglas High School Public Safety Act. This bill in particular called for approximately $100 million to go toward improving the physical security of school buildings. It also requires law enforcement to develop an app where students and community members can report information regarding unsafe, harmful, and dangerous activities and threats. Relatedly, the bill prohibits individuals from making threats to commit a mass shooting. Although there is vast public support for hardening schools, it is important to address both the collateral consequences and effectiveness of such measures. Transforming schools into fortresses—often equipped with metal detectors and access control measures—may be psychological and emotionally distressing to some students, fostering feelings of fear rather than safety (Hankin et al. 2011; Hirschfield 2008; Jonson 2017; Kupchik 2010). Additionally, the past has shown that some of the target hardening measures that the public favors are imperfect, if not ineffective (Jonson 2017).³ On the other hand, one of the most effective target hardening measures is also one of the simplest and cheapest: better classroom door locks—specifically, locks that can be locked from inside the classroom (Schildkraut and Muschert 2019).

An important next step is to conduct research evaluating the effectiveness and psychological impact of different target-hardening measures and then to compare that evidence with public attitudes to determine which measures are both effective and appealing to tax-paying citizens. Additional research is also needed that unpacks the public’s willingness to pay for target hardening. Our study shows that the public is willing to pay some additional taxes for target hardening, and it showed which measures the public most wants to fund. However, because of both the cross-sectional nature of our survey and the response categories we used, we were unable to examine: 1) how much exactly the public is willing to pay for specific measures, 2) how much the public is willing to pay cumulatively for all the measures, or 3) how the public’s willingness to pay might change as the costs of reforms begin to add up. Future studies should examine these research questions using either longitudinal or experimental survey data.

Sources of Policy Support

Two decades ago, using the example of support for capital punishment, Cullen, Fisher, and Applegate (2000) emphasized that scholars have focused primarily on the negative or socially undesirable correlates of public attitudes (e.g., racial resentment), overlooking prosocial variables that correspond to morality and altruism. These other variables, they explained, may include “a deep respect for the life of the victim, a genuine concern for the pain felt by the victim’s family, and a reluctant but principled belief that an egregious breach of the moral order requires the taking of the offender’s life” (Cullen et al. 2000:15). Even today, scholars have only begun to scratch the surface of how moral-altruistic concerns may shape public attitudes toward criminal justice policies. Only a few studies of policy attitudes have focused on the effects of moral beliefs (e.g., Silver 2017; Silver and Silver 2017), a handful on those of anger (Hartnagel and Templeton 2012; Johnson 2009; Kort-Butler and Ray 2018), and none on those of altruistic fear, despite its prevalence, intensity, and pronounced influence on behavior (Drakulich 2015; Warr and Ellison 2000).

Because of the school context and the victims involved, we theorized that moral-altruistic concerns would be broadly relevant to understanding public opinion toward policies designed to keep kids safe. Across all of our outcomes—gun control, school programs, and target hardening—moral-altruistic variables strongly predicted attitudes. Altruistic fear was significantly and positively related to all three outcomes, moral intuitions about harm and care were significantly and positively related to two, and anger about school crime was significantly and positively related to two. Indeed, for the non-gun related outcomes, moral-altruistic concerns were the strongest predictors of public attitudes—anger in the case of school programming, and altruistic fear in the case of target hardening. What these findings mean, theoretically, is that moral-altruistic concerns may have the ability to overcome existing political and racial divides. For example, regardless of someone’s political ideology, gun ownership status, or racial beliefs, as they become more fearful for students and angrier about crime at school, our findings suggest they may also become more supportive of gun control and of other policies to protect students.

It is clear that future research on school safety policy, as well as on criminal justice attitudes more broadly, should pay greater attention to moral-altruistic variables. Indeed, to our knowledge, no other study to date has included a measure of altruistic fear in a model predicting criminal justice attitudes of any kind. Instead, most prior work has focused on personal fear of crime, despite it being less prevalent and less theoretically germane to attitude formation (Warr and Ellison 2000). An important direction for future research is to explore whether moral-altruistic variables help explain public attitudes toward other criminal justice polices, ranging from criminal record expungement and prisoner reentry to police reform and terrorism prevention. For example, there are strong theoretical reasons to anticipate that anger about sexual offenses, altruistic fear for victims of these crimes, and the moral foundation of purity/disgust will all be important sources of punitiveness toward sex offenders (Pickett, Mancini, and Mears 2013; Silver 2017). Studies are needed that explore this possibility.

Consistent with previous work on gun control attitudes (Filindra and Kaplan 2016, 2017; Kahan and Braman 2003; Kleck 1996; Kleck, Gertz, and Bratton 2009; Wozniak 2017), we found that support for gun control was lower among respondents who owned guns, who believed guns were effective for self-defense, who had been socialized into the gun culture as children, who had larger gun social networks, and who were racially resentful, or held a rightward political ideology. By contrast, gun control support was higher among those who believed the world is dangerous and unpredictable, and those with greater confidence in police. Even with all of these “known predictors” in the model, however, we also found that two variables overlooked entirely in the gun literature—moral foundations and altruistic fear—significantly predicted support for gun control. Again, the key takeaway is that scholars who analyze public attitudes—whether toward gun control, or other policies—and omit moral beliefs and altruistic emotions, as most do, are likely missing an important piece of the story.

Several other findings bear mentioning. First, although a rightward political ideology matters for explaining attitudes for gun control as a means to prevent school shootings, it is only weakly associated with school programs and is not significantly associated with willingness-to-pay for target hardening. Additionally, Mancini et al. (2020) recently found a large political divide in support for arming teachers—Republicans tended to support that specific school safety policy, whereas Democrats tended to oppose it. When considered in light of our findings, it appears that there is a sizable political divide in support for gun-related school safety policies (e.g., gun control, arming teachers), but that the divide shrinks considerably or even disappears when the focus moves to school programing and target hardening. This result conveys the likely bipartisan nature of non-gun-related school safety policies.

Our results also confirm the racialized nature of the gun control debate (Filindra and Kaplan 2016, 2017; O’Brien et al. 2013; Wright, Rossi, and Daly 1983). Independent of political values and other predictors, racial resentment is a strong predictor of opposition to gun control, even when it might increase the safety of students at school. Yet, it is not significantly related to support for school programs and target hardening, likely because school shootings are not themselves racialized, even if guns are. Indeed, recent research shows that whereas traditional street crimes are stereotyped in racial/ethnic terms, school and mass shootings are not (Haner et al. 2020). Because the public does not stereotypically associate school shootings with racial/ethnic minorities (Haner et al. 2020), racial resentment’s influence in the school safety debate appears to be limited to the gun issue. Extant scholarship identifies several possible mechanisms that may link racial resentment to gun control attitudes: fear of minority violence (Wright et al. 1983), the cultural meaning of guns as symbols of racial privilege (Filindra and Kaplan 2016, 2017), and beliefs about whether guns cause crime (Lee et al. 2020). Future research is needed that adjudicates between these and other possible explanations for why racial prejudice so strongly shapes attitudes toward gun laws.

Conclusion

School shootings are a moral stain on modern society—recurring tragedies that remind us of the failures of our culture, our politics, and our social institutions to protect the most vulnerable among us. What policymakers do now can influence the frequency and death toll of future tragedies. Public opinion constrains lawmaking, however, and so understanding it is vital (Pickett 2019). Our study showed that the public supports a comprehensive policy approach for reducing school shootings, one that includes gun control as well as school programs to address bullying and mental illness. We also found that the public is willing to pay to strengthen security measures in schools. What influenced public attitudes toward all of these school safety policies were moral-altruistic concerns, such as anger about school crime and altruistic fear for students and educators. Although other factors strongly influenced views about gun control, no other variable had as powerful of an effect as moral-altruistic concerns on views about school programming and target hardening. We hope that scholars will build on our study by exploring how other moral-altruistic variables, such as empathy for victims, shape views about school safety policies, as well as attitudes toward other criminal justice policies.

Footnotes

Appendix A

Table A1.

Summary of Moral-Altruistic Measures.

Variable	Description and Measure
1. Moral Foundations—Harm	Question Stem: Please tell us the extent to which you agree or disagree with these statements: Items: 1) If I saw a mother slapping her child, I would be outraged; 2) It can never be right to kill a human being; 3) Compassion for those who are suffering is the most crucial virtue; 4) The government must first and foremost protect all people from harm Response options: 1 = strongly disagree, 2 = disagree, 3 = neither agree nor disagree, 4 = agree, 5 = strongly agree
2. Altruistic Fear	Question stem: As far as crime in schools is concerned, how often or rarely do you WORRY about the safety of the following people? Items: 1) Students in primary schools (elementary or earlier); 2) Students in secondary schools (middle and high); 3) Students in colleges and universities; 4) School and college faculty/staff Response options: 1 = very rarely, 2 = rarely, 3 = sometimes, 4 = often, 5 = very often
3. Anger	Question Stem: When you think about each of the following, how ANGRY do you feel? Items: 1) Bullying (e.g., face-to-face, cyber) in schools; 2) Students getting beat up in schools; 3) Students bringing weapons into schools; 4) Students hitting teachers in schools; 5) Shootings in schools Response Options: 1 = not angry at all, 2 = a little angry, 3 = moderately angry, 4 = angry, 5 = very angry

Appendix B

Table B1.

Variance Inflation Factors (VIF) for Multivariate Models.

	VIF
Moral-Altruistic Variables
Moral foundations—harm	1.63
Altruistic Fear	1.64
Anger	1.34
Control Variables
Rightward political ideology	3.07
Racial resentment	2.78
Personal fear of crime	1.46
Dangerous world beliefs	1.51
Confidence in police	1.17
NRA membership	1.34
Gun social network	1.86
Gun ownership	1.74
Defense effectiveness	1.65
Childhood socialization	1.71
White	1.35
Male	1.23
Age	1.35
Education	1.65
Income	1.81
Married	1.46
Child in household	1.27
Victim in household	1.07
Religiosity	1.52
BA Protestant	1.33
Southerner	1.09
Mean	1.58

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.

ORCID iD

Alexander L. Burton

Notes

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