Public Opinion About Police Weapons and Equipment: An Exploratory Analysis

Abstract

Despite debates about the “material militarization” of the police, relatively little information on mass public opinion about police weapons, equipment, and gear currently exists. We analyze data from a national, opt-in panel of survey participants to assess public opinion regarding police use of 10 different types of weapons and equipment for use in confrontations with citizens. We find that public opinion defies easy classification into “militarized” versus “routine” equipment categories. Multivariate analyses indicate that perceptions of (a) police efficacy and (b) the frequency with which officers experience physical assaults on the job are the most consistent predictors of support for a range of weapons and gear, whereas perceptions of police misconduct and bias predict opposition to some types of tools. Partisan differences in attitudes between Democrats, Republicans, and Independents are less consistent predictors than broader perceptions about policing, but the effects of partisanship that are evident are substantively large.

Keywords

public opinion police militarization weapons politics

Introduction

Beginning in 2014, the practices of police in the United States came under intense public scrutiny following a series of deaths of Black American men and women during encounters with law enforcement that received nationwide news coverage. The killings of Michael Brown in Ferguson, Missouri and Freddie Gray in Baltimore, Maryland, each sparked weeks of mass public protests (Cobbina, 2019; Lowery, 2016). In 2020, additional killings, such as those of Breonna Taylor in Louisville, Kentucky and George Floyd in Minneapolis, Minnesota, caused a new, sustained wave of protests that was called the largest public protest movement in American history (Buchanan et al., 2020; Haines, 2020; Hill et al., 2020). These 21st-century protests were merely the latest in a long history of conflicts between police forces and communities of color that includes other infamous incidents such as the 1992 Los Angeles riots and the numerous urban uprisings that occurred during the “long, hot summers” of the late 1960s (National Advisory Commission on Civil Disorders & Kerner, 1968).

Conflicts between civilian protesters and police forces revitalized a debate about whether police forces in America have become inappropriately “militarized.” This particular debate rose to public salience in the 21st century following coverage of the Ferguson protests by both journalists and smartphone-equipped civilians who posted photos and video on social media. The coverage documented members of the Ferguson police force clad in full riot gear, confronting unarmed civilians with guns drawn (often rifles or machine guns), sometimes from atop armored vehicles. Walter Olson (2014) of the Cato Institute commented, “The dominant visual aspect of the story . . . has been the sight of overpowering police forces confronting unarmed protesters who are seen waving signs or just their hands.” Police violence against civilians resumed en mass during the 2020 protests (Berman & Wax-Thibodeaux, 2020), leading Senator Rand Paul (2020) to editorialize,

In a free society, citizens should be able to easily distinguish between civilian law enforcement tasked with keeping the peace in our communities and the armed forces tasked with protecting our country from foreign adversaries. Unfortunately, thanks to the federal government flooding our neighborhoods with billions of dollars of military equipment and property over the years, the line between peace officer and soldier of war has become increasingly blurry.

Kraska (2007) argued that militarization involves police officers adhering to the belief that coercive force is an appropriate means to solve social problems, as well as efforts by police forces to pattern themselves after the military in regard to training, tactics, arms, and equipment (see also Simckes et al., 2019).¹ Many police forces began the process of militarization decades ago, most notably through the increased use of Special Weapons and Tactics (S.W.A.T.) teams to wage the “war on drugs” (Balko, 2013; Farmer et al., 2019; Hinton, 2016; Kraska & Cubellis, 1997; Kraska & Kappeler, 1997). The creep of militarization received a significant boost following the enactment of the National Defense Authorization Act of 1997. This law established the 1033 Program through which police departments could procure surplus equipment from the U.S. military (Burkhardt & Baker, 2019; Koslicki & Willits, 2018; Phillips, 2016; Ramey & Steidley, 2018). Radil et al. (2017) report that more than 80% of U.S. counties had received equipment through the 1033 Program by the year 2013. This included weapons and tools that critics allege are more appropriate for a war zone, such as machine guns and mine-resistant ambush-protected vehicles. Police use of such equipment raises particularly stark questions about civil liberties when officers deploy them to manage mass protests rather than hostage situations (Maguire, 2015). A growing body of empirical evidence suggests that police militarization has little effect on crime rates and may generate negative collateral consequences (Gunderson et al., 2019; Insler et al., 2019; Lawson, 2019; Masera, 2019; Mummolo, 2018).

Following the Ferguson protests, Members of Congress held committee hearings to debate police officers’ use of surplus military equipment (Bowman, 2014). Although legislative efforts to reform police militarization failed (Turner & Fox, 2019), President Obama issued an executive order curtailing the scope of the 1033 Program on the recommendation of his President’s Task Force on 21st Century Policing (2015). However, at the behest of some police groups who formed a key part of his electoral coalition (Zoorob, 2019), President Trump reversed Obama’s executive order in 2017. The 2020 protests once again put police use of military gear under the spotlight (Penzenstadler & Chen, 2020). The U.S. Senate initially rejected a broad ban on equipment transfers and chose instead to only partially restore some of the Obama restrictions that were undone by Trump (Edmondson, 2020). In light of Congressional gridlock, several state legislatures considered imposing their own restrictions (Fuller, 2020). In the last days of the Trump Administration, Congress did restrict the transfer of bayonets, grenades, combat vehicles, and weaponized drones and implemented a new requirement for law enforcement agencies that receive 1033 equipment to certify that their police officers receive annual training focused on de-escalation tactics and civilians’ constitutional civil liberties. These limited changes to the 1033 program were enacted through an amendment to the 2021 National Defense Authorization Act sponsored by Democratic Senator Brian Schatz (Hager & Eads, 2021). All told, the militarization of police, and particularly police officers’ use of military equipment and weapons when interacting with civilians, has been a source of disagreement among law enforcement and political elites for several years (Turner & Fox, 2019). Surprisingly, though, we know far less about mass public opinion about police weapons, gear, and equipment.

Public attitudes toward police militarization could matter in two ways. First, public opinion constrains and shapes policymaking in democracies, and criminal justice policy is no exception (Canes-Wrone et al., 2011; Enns, 2016). Second, as other scholars have argued (Moule, Burruss, et al., 2019; Moule, Fox, & Parry, 2019; Moule, Parry, & Fox, 2019; Tyler, 2006), police legitimacy depends upon a reservoir of goodwill among the public. If the public perceives the police to be acting in an inappropriate manner, they are less likely to call upon the police for aid or cooperate with the police in the exercise of social control (Brunson, 2007; Carr et al., 2007; Desmond et al., 2016). If citizens perceive that police officers equipped with surplus military weapons and tools are hostile warriors rather than community guardians, such beliefs may have a delegitimizing effect.

In this study, we sought to assess public opinion about the range of equipment that police officers employ. We surveyed the opinions of respondents to an opt-in survey about standard police equipment (such as handguns and handcuffs), tools for crowd control (such as riot shields and tear gas), and weapons and gear that critics frequently argue belong only in a war zone (such as machine guns and armored vehicles). We also analyzed how people’s beliefs, news consumption, and demographic characteristics are related to variation in attitudes toward weapons and gear to better understand patterns of consensus or dissensus among the public in regard to this issue.

Public Opinion About “Militarized” Police Weapons and Gear: Prior Research

Scholars who study public opinion about the police have overwhelmingly focused on people’s perceptions of police efficacy, legitimacy, bias, and/or misconduct, not people’s opinions about which kinds of weapons and gear police should or should not be allowed to utilize. In fact, one widely cited literature review mentions police weapons only in regard to public opinion about police use-of-force (Brown & Benedict, 2002), an issue that is related but not identical to the debate about militarization. It appears that pollsters and scholars have only recently surveyed public opinion about police weapons and equipment.

A series of national polls conducted between 2013 and 2016 indicate that the American public does not support the material militarization of the police. Although the precise question wording varied across polls, between 58% and 72% of respondents objected to police use of military weapons and equipment when explicitly asked to judge whether police versus members of the military should be able to use said tools (De Pinto et al., 2014; Dutton et al., 2014; Ekins, 2013; Murray, 2015). A 2015 Monmouth University Poll found that 72% of respondents objected to police use of “military surplus weapons such as grenade launchers and high-power assault rifles,” whereas a smaller percentage (61%) objected to police use of “military surplus equipment such as mine-resistant armored vehicles” (emphasis added). Question wording seems to matter a great deal. This same poll found that public approval for police use of surplus military weapons and gear varied as a function of situations in which the tools would be used; a higher percentage of respondents expressed support for police using surplus military gear to respond to terrorism versus respond to drugs, gang violence, or riots and demonstrations that turn violent. A 2014 Pew Center survey asked the question, “As you may know, many police departments around the country have military equipment and weapons. Overall, how much confidence do you have in police departments to use this type of equipment appropriately?” Here, 54% of respondents stated that they had either “a fair amount” or “a great deal” of confidence. This suggests that when people take the possession of military weapons and equipment by police officers as a given, rather than a policy choice, a majority of citizens trust the police to use said equipment well.

To date, only a handful of scholars have studied public opinion about police weapons and equipment. Lockwood et al. (2018) analyzed data from the nationally representative Monmouth University survey described above. They found that six factors consistently predicted attitudes toward the use of military weapons or equipment for the purposes of counterterrorism, drug enforcement, gang enforcement, or riot control: satisfaction with police, worries about terrorism or drugs and gangs, gender, education, and political ideology. Men were significantly more supportive than women of police use of military weapons for all purposes, and more supportive of the use of military equipment for gang enforcement. Political liberals expressed less support for police use of military weapons for drug enforcement and riot control than political conservatives, and less support for police use of military equipment in all situations. College graduates were significantly less supportive than people with less formal education of the use of both military weapons and equipment for all four purposes. In contrast, people who expressed greater satisfaction with the police, overall, also expressed significantly more support for the police use of military weapons in all situations except drug enforcement and significantly more support for the use of military equipment in all situations. Likewise, people who expressed greater worry about terrorism also expressed greater support for the police use of military weapons and equipment in all situations, and people who expressed greater worry about drugs or gangs also expressed greater support for the police use of military equipment in all situations and weapons in all situations except riot control. Their analysis revealed surprisingly few divergences across race. Black Americans expressed less support than Whites for police use of military weapons and equipment for riot control, but their opinions did not significantly differ in regard to counterterrorism, drug enforcement, or gang enforcement. The opinions of Whites and Hispanics did not significantly differ.

Bryanna Fox, Richard Moule, and Megan Parry analyzed data from a national sample drawn from the survey firm Qualtrics’s online panel in 2017. In two of their papers, they combined responses to several questions that asked respondents how much they supported or opposed police use of surplus military weapons, vehicles, equipment, and uniforms. Moule et al. (2019) found that stronger perceptions of police legitimacy and stronger belief that the use of surplus military tools will increase police effectiveness at fighting crime were associated with significantly stronger support for police use of military surplus. In contrast, concern about civil liberties violations was associated with significantly less support for police use of military surplus. Men, White respondents, and political conservatives expressed significantly more support for police use of military surplus than women, respondents of color, and political liberals. They found no significant differences across respondents’ age or education. Moule et al. (2019) found that Black respondents expressed significantly less support for police use of military surplus than White respondents, but this racial difference ceased to be significant once perceptions of police legitimacy were added to the model. In their fully specified model, men, older respondents, political conservatives, and respondents who expressed greater belief in the legitimacy of the police also expressed greater support for police use of military surplus. These findings suggest that perceptions of legitimacy may mediate the relationship between race and support for police use of military surplus. The authors found no significant differences across education.

In a fourth paper, B. Fox et al. (2018) conducted a latent class analysis of respondents’ reactions to a global item, “I support the militarization of law enforcement agencies in the United States.” They identified five groups of people: stereotypical supporters, antithetical supporters, fearful supporters, antithetical opponents, and true opponents. These groups differed in their beliefs about the legitimacy of the police, as well as their demographic characteristics. Overall, supporters of police militarization were more likely to express strong belief in the legitimacy of the police, trust in the federal government, and trust in local police, whereas opponents of militarization expressed the opposite sentiments. The only demographic characteristics that consistently differentiated between supporters and opponents were income and political orientation. Their most unexpected finding, though, was that race, education, region of residence, trust in government, and gun ownership were more likely to predict subgroup differences within the global groups of militarization supporters versus opponents rather than differences between supporters versus opponents. They interpreted this to mean that different groups of people have different reasons or motivations to support or oppose police militarization. Finally, Moule et al. (2019) analyzed public support for police use of S.W.A.T. teams in a variety of circumstances. They found that more than 90% of respondents supported S.W.A.T. teams to respond to hostage situations and terrorism events, about 65% supported S.W.A.T. responses to armed and dangerous offenders or civil unrest, about 40% for the serving of drug warrants, about 23% for responding to large-scale public events, and only about 7.5% to respond to peaceful protests. In addition, they found that the ideological or demographic predictors of public support for S.W.A.T. varied considerably across situations.

Ilchi and Frank (2020) analyzed data from a convenience sample of students at the University of Cincinnati. They combined respondents’ answers to a series of questions into an aggregate scale; the constitutive items assessed students’ agreement or disagreement with statements that police in high-crime neighborhoods (a) face similar danger as soldiers in war zones, (b) should be able to do anything they need to in order to control crime, and (c) need military equipment and weapons to control crime. They found that students who expressed more punitive attitudes about criminal justice and held more negative, stereotypical perceptions of residents of high-crime communities, as well as students who held more generally favorable attitudes toward the police, expressed significantly greater agreement with police–military equivalency. White students and older students also expressed significantly greater agreement than non-White students and younger students. Finally, women and students who perceived greater police misconduct expressed significantly less agreement with police–military equivalency than men and students who perceived less police misconduct.

In summary, this small body of literature suggests that perceptions of legitimacy and police effectiveness predict increased support for the material militarization of the police. Gender and political ideology appear to be secondary predictors of attitudes about militarization. Despite strong interracial differences in public opinion about the legitimacy, efficacy, and fairness of the police (Peck, 2015), the extant studies provide inconsistent evidence for a (nonspurious) relationship between race and attitudes toward police militarization. Much remains to be learned. First, the pertinent polls that we were able to find shared a common methodological characteristic. They all asked respondents for their opinions about police use of “military grade weapons,” in general, or provided grouped examples of gear by asking questions like, “Do you think local police forces should have military weapons and vehicles such as assault rifles and armored vehicles . . .” (emphasis added). The former type of question is vague; we do not know which types of weapons the respondents considered to be “military grade” when they provided their answers. The latter type of question is double-barrelled and imposes the pollsters’ idea of what constitutes military-style weapons and equipment upon the respondent—a classification with which a respondent may or may not agree. Questions of this nature help us understand people’s assessment of weapons and gear under explicit “militarization” framing, but they tell us little about the distinctions civilians themselves would draw when grouping weapons and gear into their own “militarized” category. Do civilians feel similarly about police use of guns regardless of firearm type, or would they draw distinctions between a handgun and a sniper rifle? Do people perceive Tasers and tear gas to be comparable because they are both designed to be nonlethal submission tools, or do they perceive the former as “routine” equipment and the latter as an example of militarization? We explore questions like these in this article.

Second, no prior study tested the relationship between exposure to the news or other types of media and support for the material militarization of the police, despite the concerns of public commentators such as Senator Rand Paul and Walter Olson that the images of Ferguson police officers confronting civilian protesters with machine guns and armored vehicles would spark mass public backlash and criticism of the police. Prior studies have found that media consumption is significantly related to perceptions of police legitimacy, efficacy, and bias (Callanan & Rosenberger, 2011; Dowler, 2002; Gauthier & Graziano, 2018; Graziano & Gauthier, 2018), so it stands to reason that media consumption might also influence people’s more specific attitudes toward police weapons and gear. Finally, no prior study has tested whether or not support for police weapons and gear is related to perceptions of danger/risk of harm that police officers face on the job despite the logical premise that police need to be properly equipped to defend themselves (Mac Donald, 2016). We seek to add knowledge about these issues in the present study.

Data and Method

Data

We recruited respondents from Qualtrics’s national, online panel to participate in a survey to “study peoples’ opinions about how well the government and the police are addressing problems facing the nation.” The survey contained a variety of original questions measuring respondents’ attitudes toward the police, their local government, and the federal government. The survey was fielded in early May 2016. We procured responses from 1,100 participants.² We present a comparison of the demographics of our analysis sample against the 2016 U.S. Current Population Survey in Supplemental Online Appendix A.

Qualtrics’s panel reflects the U.S. population in that it includes survey participants drawn from every region of the nation. However, the panel is constituted through an opt-in recruitment strategy, not a probability-based sampling technique. The predominant conclusion reached by survey methodologists is that opt-in, nonprobability samples are proportionately less generalizable to the U.S. population than traditional probability samples (Baker et al., 2010; Chang & Krosnick, 2009; MacInnis et al., 2018; Yeager et al., 2011). However, other scholars have challenged this global conclusion and produced evidence that the sample statistics derived from opt-in panel samples sometimes possess similar sampling error as the same statistics of the same population characteristics derived from random samples (Ansolabehere & Schaffner, 2014; Heen et al., 2014; Kennedy et al., 2016; Simmons & Bobo, 2015). Furthermore, some evidence suggests that probability and opt-in panels produce estimates of the relationships between variables (i.e., correlations, regression coefficients, etc.) that are roughly comparable even if the sample statistic point estimates of each variable diverge from the population parameters (Baker et al., 2010). Thompson and Pickett (2020) replicated this pattern of findings specifically for public opinion about criminal justice issues. Thus, even though opt-in panel samples possess some notable limitations, they also possess enough strengths to facilitate meaningful inquiry, and their relative affordability compared with the contemporary costs of gathering a nationally representative probability sample makes them a viable tool for exploratory research into topics for which no other existing data source includes all the measures necessary to answer a particular research question (see also Ansolabehere & Schaffner, 2018; Pickett et al., 2018). Assessing public opinion about police weapons and equipment is one such case.

Measures

We report the full question wording, operational definitions, and descriptive statistics of all the analysis variables in the appendix.³ In brief, the dependent variables were operationalized by respondents’ answers to the following question: “Please state the degree to which you support or oppose allowing police officers to use each of the following pieces of equipment in confrontations against citizens.” Respondents then reported on a 5-point scale that ranged from strongly oppose through no opinion to strongly support their feelings toward 10 types of weapons and equipment: handguns, automatic machine guns, sniper rifles, Tasers, tear gas, stun grenades, handcuffs, grenade launchers, riot shields, and armored vehicles. We wrote these choices to represent a range of police tools, from those that are traditionally associated with police to those that critics allege are examples of the material militarization of the police.

As independent variables, we included a variety of perceptual, ideological, and demographic characteristics of responds using original questions we wrote ourselves. Five items measured respondents’ general perceptions of local and national police effectiveness, misconduct, and bias. A sixth item measured respondents’ perceptions of the frequency with which police officers suffer a range of violent assaults. We also identified respondents who were stopped by a police officer within the past 12 months. In regard to personal characteristics, we measured respondents’ political party affiliation, political ideology, volume of news consumption, race, age, education, household income, gender, and region of residence.

Finally, this study was part of a larger project designed to test the effect of exposure to media images of policing on public opinion about law enforcement. As such, the survey instrument included an embedded experiment. Full details of the experimental design can be found in Wozniak, Drakulich, and Calfano (2020), but in brief, participants were randomly assigned to view one of three pictures that depicted officers interacting with civilians. One was a picture of the Ferguson protests depicting police officers in military gear sitting atop an armored vehicle pointing a machine gun at protesters with their hands raised over their heads, chosen to represent police militarization. A second was a picture of a police officer smiling and giving a civilian a high-five, chosen to represent community policing. A third was a picture of two officers frisking two men with their hands up against a wall, chosen to represent a stop-and-frisk. Dummy variables identify respondents who were assigned to an image treatment condition and contrast them to control group respondents who saw no picture.

Results

Correlations and Factor Analysis

We begin our analysis by examining Pearson’s correlation matrix of attitudes toward the 10 weapons and tools, presented in Table 1. Our a priori expectation was that members of the public would conceptually group certain weapons and tools together. Handguns, Tasers, and handcuffs are standard tools carried by police officers on routine patrol. Machine guns and sniper rifles are much deadlier weapons that many critics point to as evidence of militarization. Tear gas, stun grenades, and riot shields are specialized tools that are most appropriate for crowd control, but police departments have increasingly deployed these tools in drug raids over the past 30 years (Balko, 2013). Armored vehicles and grenade launchers are archetypal examples of police militarization: tools that critics argue belong on a battlefield, not the nation’s streets. However, this correlation analysis suggests that civilians draw few clear distinctions between different classes of tools for police use. Most of the weapons and tools are correlated between .30 and .60, which indicates covariance of support or opposition that is neither especially weak nor especially strong. The absence of strong correlations (i.e., above .70) is both surprising and notable.

Table 1.

Correlation Matrix of Public Support for Weapons and Equipment.

Weapons & Equipment	Handguns	Machine guns	Sniper rifles	Tasers	Tear gas	Stun grenades	Handcuffs	Riot shields	Armored vehicles	Grenade launchers
Handguns	1.000
Machine guns	.379	1.000
Sniper rifles	.516	.644	1.000
Tasers	.490	.166	.351	1.000
Tear gas	.489	.315	.475	.593	1.000
Stun grenades	.447	.449	.573	.480	.647	1.000
Handcuffs	.353	−.028^a	.160	.534	.326	.249	1.000
Riot shields	.450	.245	.405	.465	.506	.459	.420	1.000
Armored vehicles	.461	.400	.460	.368	.426	.450	.284	.574	1.000
Grenade launchers	.294	.630	.546	.196	.329	.480	.0220^a	.231	.394	1.0000

Note. All correlations significant at p < .001 unless otherwise marked.

p > .10.

Only a few pairings stand out. Respondents seemed to view handcuffs as conceptually distinct from highly lethal weapons; the correlations between handcuffs, machine guns, and grenade launchers were the only relationships that failed to reach statistical significance, and the correlations between handcuffs and sniper rifles and stun grenades were also weak. In contrast, the findings suggest that respondents did perceive a conceptual relationship between highly lethal weapons; the correlations between machine guns, sniper rifles, and grenade launchers were all larger than .60. Finally, the strongest correlation in this analysis, .65, existed between support for tear gas and stun grenades, both tools designed for mass incapacitation and crowd control.

Our original intent was to use opinion about these 10 types of equipment as indicators of latent factors that we a priori assumed would differentiate between “militarized” versus “regular” police tools. As such, we began with confirmatory factor analyses exploring the presuppositions we held about possible latent factors when beginning this project. These fit poorly.⁴ We then turned to exploratory factor analysis (EFAs) in the hopes that meaningful latent factors might emerge that we had not anticipated.⁵ We present the results of this EFA in Table 2. One latent factor—accounting for 46% of the variance—had four factor loadings at .6 or above: Tasers, tear gas, handcuffs, and riot shields. A second factor—accounting for another 16% of the variance—had machine guns, sniper rifles, and grenade launchers with loadings above .6. The other three factors (handguns, stun grenades, and armored vehicles) loaded modestly on both of the two latent factors.

Table 2.

Exploratory Factor Analysis Loadings (N = 1,055).

	Factor
Weapons & Equipment	1	2
Handguns	.530	−.266
Machine guns	−.096	−.875
Sniper rifles	.231	−.685
Tasers	.793	.046
Tear gas	.627	−.231
Stun grenades	.453	−.447
Handcuffs	.715	.254
Riot shields	.646	−.118
Armored vehicles	.441	−.344
Grenade launchers	−.018	−.740
% of variance explained	46	16

Note. Principal axis factor analysis with direct oblimin rotation.

Although these results suggest some commonalities among public opinion on some of the items (particularly along the lines of some of the more potentially fatal vs. nonfatal gear), it also suggests that any attempt to summarize opinion across these items in a simple way would miss potentially interesting and important differences in opinion across the items. We certainly did not want to drop from our analysis the items that cross-loaded on multiple latent factors. Upon reflection, the “messiness” of these results may not be surprising, as each item may reflect multiple dimensions of public opinion. Feelings about riot shields, for instance, may overlap with feelings about other nonlethal police gear like Tasers, but it may also overlap with feelings about more militarized equipment through their common usage in aggressive crowd control efforts. A factor that just captures latent feelings about nonfatal weapons would miss these other factors. Ultimately, although reducing dimensions would have made the analyses easier, we decided to conduct analyses separately for each of the 10 items. This allows us to see evidence of commonalities among some of the items in their relationships with demographic characteristics and other opinions, while also preserving our ability to identify factors that do not fit cleanly into broader patterns.

Table 3 presents Pearson’s correlation matrix between attitudes toward the 10 weapons and tools and key respondent demographic characteristics. Several findings are evident. First, respondents’ support or opposition for weapons and gear was not strongly related to their demographic characteristics; all correlations in this table are less than .3 in magnitude. Second, differences in attitudes across racial groups were only prominent among Black respondents and White respondents (each contrasted against all other racial groups). Black respondents expressed significantly less support for all types of weapons and gear except for handcuffs and riot shields, though these correlations were all less than .17 in magnitude. Conversely, White respondents expressed significantly more support for all types of weapons and gear except for handcuffs, armored vehicles, and grenade launchers, but these relationships were similarly weak with all correlations lower than .18 in magnitude. Hispanic respondents did not express significantly stronger support or opposition than members of other racial groups, and Asian and/or Pacific Islander respondents significantly differed only in their attitudes toward stun grenades and/or riot shields.

Table 3.

Correlations Between Public Support for Weapons and Equipment and Respondent Demographic Characteristics.

Respondent Demographics	Handguns	Machine guns	Sniper rifles	Tasers	Tear gas	Stun grenades	Handcuffs	Riot shields	Armored vehicles	Grenade launchers
Black	−.159 ***	−.160 ***	−.161 ***	−.079 *	−.122 ***	−.106 **	.005	−.064	−.068 *	−.099 **
Hispanic	−.020	.019	−.056	−.022	−.002	−.030	.004	−.053	.035	.039
Asian	−.040	−.001	−.023	−.042	−.064	−.077 *	−.048	−.149 ***	−.065	−.035
Pacific Islander	−.028	−.047	−.018	.035	−.052	−.042	.043	−.068 *	−.041	−.052
White (non-Hispanic)	.138 ***	.106 **	.176 ***	.079 *	.129 ***	.138 ***	.002	.135 ***	.050	.055
Republican	.200 ***	.175 ***	.183 ***	.112 ***	.121 ***	.186 ***	.054	.138 ***	.171 ***	.164 ***
Democrat	−.195 ***	−.143 ***	−.187 ***	−.130 ***	−.175 ***	−.191 ***	−.081 *	−.120 ***	−.169 ***	−.143 ***
Independent	.027	−.008	.035	.040	.082 *	.036	.039	.002	.026	.003
Age	.136 ***	−.009	.143 ***	.098 **	.204 ***	.156 ***	.124 ***	.205 ***	.059	.019
Education	.020	.082 *	.095 **	−.034	.08	.012	−.114 ***	.004	−.004	.006
Income	.055	.128 ***	.138 ***	.056	.135 ***	.111 ***	−.090 **	.056	.037	.053
Female	−.116 ***	−.210 ***	−.225 ***	−.080 *	−.158 ***	−.197 ***	.051	−.115 ***	−.006	−.109 ***
Northeast	−.013	−.070 *	−.024	−.012	−.050	−.054	−.028	−.004	−.031	−.035
South	−.007	.021	.017	.028	.050	.024	.017	−.007	.037	.015
Midwest	.010	−.020	−.043	−.017	−.032	.003	.0109	−.007	−.036	.034
West	.012	.061	.042	−.005	.020	.022	−.001	.018	.020	−.016

Note. Significant correlations in bold.

p < .05. **p < .01. ***p < .001.

Third, political partisanship mattered. Republican respondents expressed significantly more support for all types of weapons and gear except for handcuffs, whereas Democratic respondents expressed significantly less support for all types of weapons and gear without exception. Independents only significantly differed in regard to their attitude toward tear gas, but the magnitude of this correlation was paltry. Third, differences were also evident across gender, age, education, and income. Women expressed significantly less support than men for all types of weapons and gear except handcuffs and armored vehicles. Age was positively correlated with support for handguns, sniper rifles, Tasers, tear gas, stun grenades, handcuffs, and riot shields. Education was positively correlated with support for machine guns and sniper rifles but negatively correlated with support for handcuffs. Income was positively correlated with support for machine guns, sniper rifles, tear gas, and stun grenades but negatively correlated with support for handcuffs. Finally, there was only one significant difference across respondents who lived in different regions of the country. Residents of states in the Northeast expressed significantly less support for machine guns, but the magnitude of this correlation was very low.

Multivariate Analysis

For ease of interpretation, we dichotomized the 10 dependent variables so that a value of 1 equalled responses of “strongly support” or “slightly support” and 0 equalled responses of “no opinion,” “slightly oppose,” or “strongly oppose.”⁶ Table 4 presents the results of logit regression models that tested the relationships between perceptions of the police, contact with the police, political partisanship and ideology, and respondent demographics on support for the 10 types of weapons and gear.⁷ Several findings are notable. First, perceptions about policing are among the most consistent predictors of variation in public support for weapons and gear. The more positively a respondent evaluated the ability of police across the country to complete duties such as preventing crime and responding to victims, the more likely she was to also support police use of all types of weapons and equipment except machine guns, handcuffs, and riot shields; the magnitude of this effect ranged from about an 8% to a 13% increase in the odds of expressing support. The more frequently that a respondent believes police officers experience physical harm during a routine week on the job, the more likely she was to support police use of all types of weapons and gear except handcuffs; the magnitude of this effect ranged from about a 13% to a 25% increase in the odds of expressing support.

Table 4.

Results of Logit Regressions of Perceptions of Police, Politics, and Demographics on Support for Weapons and Equipment.

Ideological & Demographic Characteristics	Handguns	Machine guns	Sniper rifles	Tasers	Tear gas	Stun grenades	Handcuffs	Riot shields	Armored vehicles	Grenade launchers
Local police effectiveness	0.952[0.895, 1.013]	0.989[0.926, 1.056]	0.952[0.898, 1.008]	0.957[0.890, 1.028]	0.943[0.887, 1.001]	0.902*** [0.850, 0.957]	1.013[0.917, 1.119]	0.994[0.929, 1.063]	0.965[0.911, 1.022]	0.967[0.902, 1.036]
Local police misconduct	0.890** [0.818, 0.969]	0.981[0.896, 1.075]	0.918* [0.847, 0.995]	0.937[0.849, 1.034]	0.946[0.870, 1.027]	0.957[0.885, 1.035]	0.812** [0.711, 0.926]	0.995[0.908, 1.091]	0.967[0.893, 1.047]	1.070[0.973, 1.176]
National police effectiveness	1.078* [1.014, 1.146]	1.067[1.000, 1.140]	1.104*** [1.042, 1.170]	1.084* [1.012, 1.160]	1.125*** [1.061, 1.193]	1.130*** [1.067, 1.197]	0.963[0.871, 1.065]	1.052[0.986, 1.122]	1.087** [1.028, 1.149]	1.078* [1.005, 1.157]
National police misconduct	0.963[0.890, 1.041]	1.030[0.948, 1.119]	1.058[0.983, 1.138]	0.966[0.882, 1.059]	0.991[0.918, 1.069]	0.993[0.924, 1.067]	1.058[0.935, 1.196]	0.953[0.876, 1.038]	0.966[0.898, 1.038]	0.953[0.873, 1.040]
National police bias	0.955[0.889, 1.026]	0.845*** [0.788, 0.908]	0.879*** [0.824, 0.937]	1.023[0.943, 1.109]	0.940[0.878, 1.007]	0.869*** [0.814, 0.927]	1.085[0.986, 1.194]	1.032[0.957, 1.112]	0.895** [0.837, 0.957]	0.855*** [0.795, 0.918]
Police stop	0.951[0.589, 1.536]	1.241[0.784, 1.964]	1.045[0.680, 1.608]	1.340[0.744, 2.412]	0.964[0.605, 1.536]	0.873[0.566, 1.347]	1.067[0.539, 2.113]	1.174[0.690, 1.998]	1.256[0.803, 1.966]	1.330[0.829, 2.132]
Perceived police risk	1.183*** [1.085, 1.289]	1.151** [1.058, 1.252]	1.245*** [1.152, 1.345]	1.188*** [1.074, 1.314]	1.128** [1.039, 1.224]	1.192*** [1.105, 1.287]	1.104[0.976, 1.249]	1.136** [1.035, 1.247]	1.154*** [1.067, 1.247]	1.171*** [1.073, 1.278]
Democrat	0.447** [0.244, 0.820]	0.884[0.527, 1.483]	0.665[0.417, 1.062]	0.650[0.326, 1.293]	0.797[0.464, 1.369]	0.501** [0.308, 0.814]	0.510[0.232, 1.120]	0.439* [0.223, 0.865]	0.557* [0.331, 0.937]	1.033[0.604, 1.767]
Independent	0.527* [0.284, 0.976]	0.815[0.493, 1.348]	0.690[0.433, 1.099]	0.869[0.433, 1.747]	1.370[0.777, 2.414]	0.659[0.404, 1.077]	0.864[0.375, 1.991]	0.468* [0.238, 0.919]	0.620[0.368, 1.045]	1.053[0.624, 1.776]
Moderate	1.148[0.758, 1.739]	1.257[0.797, 1.983]	1.279[0.862, 1.898]	1.151[0.706, 1.876]	1.341[0.895, 2.008]	1.303[0.890, 1.907]	0.974[0.521, 1.821]	0.983[0.631, 1.532]	1.477* [1.012, 2.154]	1.042[0.640, 1.699]
Conservative	1.257[0.724, 2.182]	1.424[0.829, 2.447]	1.342[0.833, 2.162]	1.194[0.616, 2.312]	1.507[0.887, 2.559]	1.329[0.825, 2.141]	0.935[0.420, 2.084]	1.448[0.771, 2.720]	1.792* [1.087, 2.955]	2.243** [1.298, 3.878]
Black	0.723[0.443, 1.179]	0.962[0.533, 1.736]	0.951[0.585, 1.547]	1.068[0.590, 1.935]	0.981[0.605, 1.591]	1.687* [1.059, 2.689]	1.376[0.598, 3.169]	1.070[0.611, 1.871]	1.558[0.973, 2.494]	1.504[0.837, 2.704]
Hispanic	0.951[0.589, 1.537]	1.182[0.741, 1.886]	0.747[0.487, 1.147]	0.917[0.532, 1.582]	1.087[0.685, 1.725]	1.163[0.761, 1.779]	0.851[0.439, 1.649]	0.707[0.430, 1.162]	1.190[0.765, 1.849]	1.414[0.875, 2.287]
Asian	0.979[0.392, 2.443]	0.918[0.345, 2.443]	0.700[0.295, 1.660]	1.009[0.367, 2.778]	0.549[0.239, 1.258]	0.524[0.224, 1.224]	0.757[0.228, 2.515]	0.281** [0.122, 0.648]	0.603[0.269, 1.351]	0.835[0.254, 2.746]
News consumption	1.053*** [1.023, 1.085]	1.062*** [1.031, 1.093]	1.020[0.994, 1.046]	0.984[0.952, 1.018]	1.026[0.998, 1.055]	1.027* [1.002, 1.054]	1.029[0.986, 1.074]	0.991[0.960, 1.022]	1.009[0.983, 1.036]	1.025[0.995, 1.057]
Age	1.012* [1.000, 1.024]	0.992[0.980, 1.004]	1.014* [1.003, 1.024]	1.011[0.997, 1.025]	1.020*** [1.009, 1.032]	1.016** [1.005, 1.027]	1.029** [1.010, 1.048]	1.022*** [1.009, 1.035]	1.006[0.996, 1.017]	0.994[0.981, 1.006]
College	1.254[0.754, 2.083]	1.328[0.765, 2.304]	1.374[0.859, 2.199]	0.761[0.401, 1.445]	0.917[0.562, 1.495]	1.448[0.921, 2.277]	0.363[0.121, 1.089]	0.683[0.374, 1.245]	1.019[0.636, 1.635]	0.900[0.531, 1.525]
Grad school	0.943[0.509, 1.746]	1.525[0.807, 2.883]	1.286[0.736, 2.246]	0.713[0.335, 1.518]	0.792[0.437, 1.436]	0.951[0.552, 1.641]	0.228* [0.0703, 0.742]	0.700[0.342, 1.433]	0.922[0.523, 1.626]	0.833[0.441, 1.573]
Household income	0.953[0.865, 1.049]	1.025[0.932, 1.127]	1.039[0.955, 1.131]	1.015[0.910, 1.132]	1.076[0.981, 1.179]	1.043[0.959, 1.135]	0.910[0.789, 1.050]	0.998[0.899, 1.107]	0.999[0.916, 1.088]	1.017[0.921, 1.125]
Female	0.910[0.612, 1.353]	0.566** [0.379, 0.845]	0.616** [0.433, 0.876]	0.969[0.611, 1.538]	0.794[0.545, 1.158]	0.664* [0.469, 0.941]	2.076* [1.143, 3.769]	0.965[0.628, 1.483]	1.383[0.962, 1.988]	0.665[0.439, 1.008]
Midwest	0.933[0.514, 1.693]	1.249[0.690, 2.263]	1.140[0.676, 1.922]	0.967[0.505, 1.852]	1.229[0.713, 2.119]	1.355[0.811, 2.266]	0.713[0.322, 1.577]	0.914[0.490, 1.707]	1.005[0.598, 1.690]	1.442[0.798, 2.605]
South	0.790[0.479, 1.301]	1.249[0.759, 2.056]	1.219[0.786, 1.890]	1.325[0.752, 2.336]	1.556[0.979, 2.470]	1.427[0.928, 2.195]	1.333[0.655, 2.713]	0.997[0.586, 1.694]	1.240[0.799, 1.923]	1.035[0.621, 1.724]
West	0.937[0.542, 1.620]	1.720* [1.008, 2.936]	1.817* [1.125, 2.934]	1.050[0.578, 1.908]	1.761* [1.054, 2.943]	1.753* [1.090, 2.820]	1.262[0.592, 2.688]	1.242[0.691, 2.233]	1.504[0.928, 2.438]	0.966[0.544, 1.714]
Militarized treatment	0.760[0.453, 1.274]	0.967[0.579, 1.616]	1.093[0.699, 1.707]	0.885[0.488, 1.603]	1.352[0.827, 2.211]	1.148[0.737, 1.786]	0.645[0.311, 1.335]	1.478[0.844, 2.589]	1.097[0.693, 1.737]	0.635[0.370, 1.089]
Community treatment	0.623[0.380, 1.021]	1.554[0.950, 2.543]	1.015[0.649, 1.587]	0.591[0.339, 1.031]	0.937[0.587, 1.496]	1.226[0.791, 1.902]	0.922[0.431, 1.970]	1.083[0.642, 1.827]	0.932[0.593, 1.463]	0.999[0.604, 1.652]
Stop and frisk treatment	0.774[0.477, 1.257]	1.134[0.701, 1.833]	1.063[0.699, 1.618]	1.012[0.566, 1.808]	1.146[0.729, 1.802]	1.161[0.767, 1.758]	1.012[0.488, 2.099]	1.075[0.649, 1.780]	0.845[0.552, 1.292]	0.809[0.495, 1.324]
N	817	817	819	818	819	818	819	816	815	817

Note. Odds ratios with 95% confidence intervals in brackets. Significant effects in bold.

p < .05. **p < .01. ***p < .001.

Conversely, perceptions of the frequency with which local police officers engage in misconduct and perceptions that police across the country treat socially advantaged groups better than socially disadvantaged groups were both negatively related to support for several items. Perceptions of local police misconduct decreased respondents’ support for handguns, sniper rifles, and handcuffs, whereas perceptions of bias decreased support for machine guns, sniper rifles, stun grenades, armored vehicles, and grenade launchers. The magnitude of these effects ranged from about an 8% to a 19% reduction in the odds of support. Interestingly, more positive evaluation of local police officers’ ability to complete routine duties was associated with about a 10% reduction in support for stun grenades. This suggests that people may believe that more competent, efficacious officers do not need more extreme submission tools.

Second, in contrast to the bivariate evidence of widespread differences of opinion between Republicans and Democrats presented in Table 3, political ideology and partisanship were significantly related to fewer dependent variables than the police perception variables. However, they were associated with much greater differences in support. Democrats were only about half as likely to support police use of handguns, stun grenades, riot shields, and armored vehicles as Republicans. Independents were about half as likely to support handguns and riot shields as Republicans. Similarly, political moderates were nearly 50% more likely to support police use of armored vehicles than political liberals, and political conservatives were nearly 80% more likely to support armored vehicles and over twice as likely to support grenade launchers as political liberals.

Third, many of the relationships between gender, age, and support for weapons and gear observed through bivariate correlational analysis remained significant even controlling for the political and police perception variables in the multivariate models. In contrast, the bivariate relationship between race and opinions about weapons and gear largely disappeared after controlling for politics and perceptions of the police—both of which prior work suggests are highly correlated with race. The only interracial differences that were significant above-and-beyond the influence of police perceptions and political beliefs were an increased likelihood among Black respondents to support the use of stun grenades and a decreased likelihood of Asian American respondents to support the use of riot shields compared with Whites. Controlling for political beliefs and police perceptions revealed a different regional difference than the one evident in Table 3. Residents of states in the Western region of the nation were about 72% to 82% more likely to express support for machine guns, sniper rifles, tear gas, and stun grenades than residents of Northeastern states.

Finally, frequency of news consumption exerted relatively minimal effects. Respondents who consumed more news were significantly more likely to express support for handguns, machine guns, and stun grenades, but the magnitude of these effects ranged between only a 2.7% to a 6.2% increase in the odds of expressing support. Our policing news image experiment had no significant effect on respondents’ expressed support or opposition toward police weapons and gear.

Discussion

The confrontations between civilian protesters and police officers that occurred during dozens of mass public protests between 2014 and 2020 raised the salience of debates about the “militarization” of the police among both policymakers and the media (Berman & Wax-Thibodeaux, 2020; Bowman, 2014; Lowery, 2016; Paul, 2014; Penzenstadler & Chen, 2020; Turner & Fox, 2019). Although scholars and journalists raised concerns about police adoption of the tactics, weapons, and equipment of the military years ago (Balko, 2013; Kraska, 2007; Kraska & Cubellis, 1997), surprisingly little data exist that allows us to assess mass public opinion about the “material militarization” of police. This study contributes to our understanding of public opinion about police weapons, equipment, and gear. We draw several conclusions from the present findings.

First, previous polls indicated that a majority of Americans oppose police use of military weapons and gear (De Pinto et al., 2014; Dutton et al., 2014; Ekins, 2013; Murray, 2015). Our findings suggest that this conclusion may need to be qualified. As we discussed in the introduction to this article, most prior surveys asked questions that grouped several different kinds of weapons and gear together and/or explicitly labeled particular types of weapons or gear as “military” in nature. Questions of this nature impose the pollster’s categorization upon respondents rather than allowing respondents to express whether they perceive distinctions between which types of weapons and gear should be considered examples of “militarization.” Likewise, double-barrelled questions that ask about several different types of weapons or gear at the same time do not allow respondents to differentiate their support or opposition from item-to-item. In contrast, our exploratory analyses and correlations suggest the possibility that the public’s ideas about how various examples of weapons and gear “go together” are more complicated and nuanced, with each item holding potential meaning across multiple dimensions. People, for instance, may associate tear gas with the more militarized equipment it is often used alongside in confronting protests, or they may view it as a less lethal alternative to automatic weapons. Given our inability to find empirically satisfactory latent factors that could categorize all the weapons and gear queried in our survey without substantial cross-loading, we recommend that people’s definition(s) of “military-style weapons and gear” for police officers should be treated as an empirical question for further inquiry.

Second, our multivariate analyses support the conclusions of prior scholars that broader public perceptions about police legitimacy and efficacy are significant predictors of variation in public attitudes toward police weapons and gear (B. Fox et al., 2018; Lockwood et al., 2018; Moule, Burruss, et al., 2019; Moule, Fox, & Parry, 2019). The two most consistent predictors in our findings were respondents’ evaluation of the efficacy of police across the nation and their perceptions of risk to officers. The more positively a respondent evaluated police officers’ ability to fulfill their standard duties (such as preventing crime and responding to crime victims), the more likely she was to express support for seven of the weapons and equipment we assessed. The more frequently a respondent believed that officers experience physical assaults of varying severity in a typical week on the job, the more likely she was to express support for police use of nine of the weapons and equipment. The odds ratios indicate that perceptions of risk exerted a substantively stronger influence over people’s opinions than their overall evaluation of police. This finding suggests that when members of the public express greater confidence in the competence of police or perceive them to be in greater danger, they are willing to grant officers greater leeway in regard to their selection of weapons and gear.

Third, by contrasting the bivariate correlations in Table 3 against the multivariate analyses in Table 4, our findings replicate those of other scholars that race is largely not a significant predictor of variation in public support for police weapons and gear once one accounts for respondents’ broader beliefs about police and politics (B. Fox et al., 2018; Lockwood et al., 2018; Moule, Fox, & Parry, 2019). In other words, support for police equipment is associated with people’s political beliefs and their more general views of police efficacy, bias, misconduct, and risk, not their race in-and-of itself. This finding is very consistent with prior scholars’ conclusions that Black Americans’s more widespread mistrust of the police is caused by their systematically poorer personal and vicarious interactions with police officers, contrasted to the modal interaction between a police officer and a White American (Braga et al., 2019; Brunson, 2007; Forman, 2017; Fortner, 2015; Peffley & Hurwitz, 2010; Prowse et al., 2020; Rios, 2011).

Fourth, our findings replicate those of other scholars that political ideology and partisanship are related to support or opposition to police use of military weapons and gear (Moule, Burruss, et al., 2019; Moule, Fox, & Parry, 2019). Our findings are most similar to those of Lockwood et al. (2018) who found that political liberals expressed less support for police use of military weapons for the purposes of drug enforcement and riot control than political conservatives, but they found no significant partisan differences in regard to police use of military weapons for counterterrorism or gang enforcement. Similarly, Democrats in our sample were substantially less likely than Republicans to express support for police use of handguns, stun grenades, riot shields, and armored vehicles when confronting civilians. Independents were substantially less likely to express support for handguns and riot shields than Republicans. Self-identified liberals were also substantially less likely to express support for police use of armored vehicles than moderates or conservatives, whereas conservatives were substantially more likely than liberals to express support for grenade launchers.

We hypothesize that these particular partisan divisions may be a function of the particular sociopolitical moment at which this survey was fielded. As discussed in the introduction, there was substantial news coverage of numerous incidents in which police officers killed Black civilians in the preceding 2 years, and several of these fatal incidents sparked mass public protests and confrontations between protesters and police. This survey was fielded in the midst of the 2016 presidential election campaign, and both Democratic candidate Hillary Clinton and Republican candidate Donald Trump talked about issues of race and policing in relation to the Ferguson and Baltimore protests, but in very different ways. Clinton attempted to express support for police but also called for policing reforms to address racially disparate policing practices. Trump expressed categorical support for the police, rejected calls for policing reform, decried protesters, and advanced strong “law and order” themes in his speeches (Tilley, 2020). We think it is plausible that partisan differences emerged in regard to public approval of police use of riot shields, armored vehicles, and stun grenades when confronting civilians at this particular time because respondents associated them with the recent police–civilian confrontations in Ferguson and Baltimore that were politicized over the course of the presidential campaign. While we can only speculate, this hypothesis is consistent with other evidence that public opinion about the police is intertwined with political beliefs and partisan conflicts (Drakulich et al., 2017, 2020; Fix & Fix, 2020; Silver & Pickett, 2015).

Mass protests and the work of numerous grassroots activists have increasingly pushed legislators into public conflict with law enforcement officials over the issue of police weapons and gear (Bowman, 2014; Edmondson, 2020; Fuller, 2020; Penzenstadler & Chen, 2020). Scholars should continue to produce more sophisticated data on public opinion about police weapons, gear, and equipment to better inform elected policymakers as they grapple with this controversial topic in the future.

Limitations

It is important to recognize the limitations of this study because they point the way toward fruitful avenues for future research. We analyzed data sampled from an opt-in, online panel. To reiterate, many survey methodologists have demonstrated that the sample statistics derived from nonrandom, online panels frequently diverge from the true population parameters (Baker et al., 2010; Chang & Krosnick, 2009; MacInnis et al., 2018; Yeager et al., 2011). It is for this reason that we refer to this article as an exploratory analysis; we cannot guarantee that our results fully generalize to the U.S. population. However, given prior evidence that even nonprobability, online panel samples produce relatively valid estimates of the relationships between variables (Baker et al., 2010; Thompson & Pickett, 2020), we argue that our correlational and multivariate findings are worthy of a degree of trust—especially as we replicate many of the relationships found in prior studies of public opinion about police use of military weapons and equipment. Future scholars should measure American public opinion about police weapons and gear with surveys administered to random samples of the U.S. population.

Furthermore, we only queried our participants about their attitudes toward police use of the 10 types of weapons and gear “in confrontations against citizens.” This is a particular type of circumstance (inspired by the police–protester confrontations in Ferguson and Baltimore), and prior scholars found that public opinion about police weapons and gear varied depending upon the purpose for which the equipment would be used (e.g., drug enforcement, riot control; Lockwood et al., 2018; Moule, Parry, & Fox, 2019). It is likely that our findings would have been different if we asked our respondents to assess the 10 types of equipment for a different stated purpose. Future research should replicate our matrix-style question that allows respondents to express support or opposition to each type of weapon or gear individually but vary the purpose for which police would use those tools to better map the boundaries of public opinion about police weapons and gear.

Conclusion

This study suggests that public opinion about what does and does not constitute “militarized” weapons and gear may be more heterogeneous and complex than many pundits assumed. Our findings further suggest that public opinion about police use of weapons and gear is predominantly related to people’s judgments about police officers’ competence, effectiveness, and safety needs, or, conversely, concerns about police misconduct and bias. Political partisanship plays a secondary role that may be limited to a narrower selection of particular types of gear and weapons that have featured most prominently in recent public debates about police militarization.

Supplemental Material

sj-pdf-1-cjp-10.1177_08874034211005005 – Supplemental material for Public Opinion About Police Weapons and Equipment: An Exploratory Analysis

Supplemental material, sj-pdf-1-cjp-10.1177_08874034211005005 for Public Opinion About Police Weapons and Equipment: An Exploratory Analysis by Kevin H. Wozniak, Kevin M. Drakulich and Brian R. Calfano in Criminal Justice Policy Review

Footnotes

Appendix

Variable Operational Definitions and Descriptive Statistics.

Variable	Component question wording^a	Question response option(s) and variable coding	M	SD	N	Range
Weapons and equipment	“Please state the degree to which you support or oppose allowing police officers to use each of the following pieces of equipment in confrontations against citizens.”	1) Strongly oppose 2) Slightly oppose 3) No opinion 4) Slightly support 5) Strongly support
	1) Handguns		3.84	1.24	1,072	1–5
	2) Automatic machine guns		2.13	1.41	1,072	1–5
	3) Sniper rifles		2.60	1.55	1,072	1–5
	4) Tasers		4.15	1.18	1,073	1–5
	5) Tear gas		3.70	1.48	1,076	1–5
	6) Stun grenades		3.16	1.53	1,067	1–5
	7) Handcuffs		4.54	0.92	1,075	1–5
	8) Grenade launchers		1.99	1.35	1,072	1–5
	9) Riot shields		4.11	1.30	1,073	1–5
	10) Armored vehicles		3.60	1.49	1,070	1–5
Local police evaluation	“Do you think that your local police do a good job or a bad job in . . .”1) “. . . dealing with problems that really concern people?” (0.86)2) “. . . preventing crime?” (0.86)3) “. . . responding to people after they have been victims of crime?” (0.85)4) “. . .maintaining order on the streets and sidewalks?” (0.84)Scale eigenvalue = 2.92Cronbach’s α = .92	1) A very bad job2) A somewhat bad job3) A slightly bad job4) A slightly good job5) A somewhat good job6) A very good job	18.51	4.64	1,048	4–24
Local police misconduct	“How often do you think your local police officers . . .”1) “. . . stop people on the streets without good reason?” (0.77)2) “. . . when talking to people, use insulting language against them?” (0.83)3) “. . . use excessive force (more force than is necessary under the circumstances) against people?” (0.84)———————————-4) “How common do you think corruption (such as taking bribes or involvement in the drug trade) is in your local department?” (0.61)Scale eigenvalue = 2.37Cronbach’s α = .87	1) Never2) On occasion3) Fairly often4) Very often————————1) Very uncommon2) Somewhat uncommon3) Fairly common4) Very common	8.56	3.10	1,029	4–16
National police evaluation	“Do you think that the police in general do a good job or a bad job in . . .”1) “. . . dealing with problems that really concern people?” (0.86)2) “. . . preventing crime?” (0.85)3) “. . . responding to people after they have been victims of crime?” (0.87)4) “. . . maintaining order on the streets and sidewalks?” (0.86)Scale eigenvalue = 2.94Cronbach’s α = .92	1) A very bad job2) A somewhat bad job3) A slightly bad job4) A slightly good job5) A somewhat good job6) A very good job	17.47	4.64	1,052	4–24
National police misconduct	“How often do you think police in general . . .”1) “. . . stop people on the streets without good reason?” (0.83)2) “. . . when talking to people, use insulting language against them?” (0.81)3) “. . . use excessive force (more force than is necessary under the circumstances) against people?” (0.82)———————————-4) “How common do you think corruption (such as taking bribes or involvement in the drug trade) is in police departments across the country?” (0.62)Scale eigenvalue = 3.03Cronbach’s α = .89	1) Never2) On occasion3) Fairly often4) Very often————————1) Very uncommon2) Somewhat uncommon3) Fairly common4) Very common	12.58	3.47	1,045	5–20
National police bias	“In general, do you think that police across the country treat _____ better, the same, or worse than _____?”1) Poor people, wealthy people (0.65)2) Black people, White people (0.87)3) Hispanics, White people (0.86)4) Non-English-speaking people, English-speaking people (0.69)Scale eigenvalue = 2.37Cronbach’s α = .85	“Treat [Group A] ____ than [Group B].”1) Much better2) Somewhat better3) The same as4) Somewhat worse than5) Much worse	8.11	2.82	1,044	4–20
Police stop	“Have you been stopped (either while walking or while driving a vehicle) by a police officer or officers within the past 12 months? Please check all the answers that apply to you.”	1 = “Yes, stopped while driving” or “Yes, stopped while walking”0 = “No, I was not stopped by an officer within the past 12 months” or “Prefer not to answer”	0.15	0.36	1,100	0–1
Perceived police risk	“Now were interested in your perceptions about the risk of harm that police officers face. Even if you aren’t 100% sure how to answer, please give us your best guess. In a typical week in the U.S., how often do you think that police officers are . . .”1) “. . . punched, kicked, or otherwise physically assaulted during a typical week on the job?” (.69)2) “. . . shot at but not killed during a typical week on the job?” (.83)3) “. . . killed during a typical week on the job?” (.75)Scale eigenvalue =1.74Cronbach’s α = .83	1) Rare events2) Occur every once in a while3) Occur frequently4) Occur almost all the time	6.82	2.20	1,063	3–12
Political party ID	“Generally speaking, do you think of yourself as a Republican, a Democrat, an Independent, or what?”	1) Republican2) Democrat3) Independent(“Other party” and “No preference” answers dropped)	2.02	0.71	974	1–3
Political ideology	“Now, thinking of your general political views, which of these labels best describes you?”	1 = “Strongly liberal” or “liberal”2 = “Moderate”3 = “Conservative” or “strongly conservative”	1.98	0.77	1,025	1–3
News consumption	“In the past seven days, how many days did you . . .”1) “Watch, read, or listen to news on the internet, not including sports news?2) Watch the news on TV, not including sports news?3) Read news in a printed newspaper, not including sports news?4) Listen to news on the radio, not including sports news?	0 – 7	15.41	6.85	1,076	1–28
Race	1) “Are you Hispanic, Latino, or Spanish Origin?”2) “What is your race? Please check all that apply?”	Responses to these two questions were recoded into the following categories:1 = Non-Hispanic White2 = Non-Hispanic Black3 = Hispanic4 = Non-Hispanic Asian or Pacific Islander	1.75	0.94	1,100	1–4
Age	“What is your current age?”	Open-ended	45.92	17.01	1,100	18–86
Education	“What is the highest grade or year of school you have completed?”	1 = “Eighth grade or less,” “some high school,” “high school graduate”2 = “Some college or technical school” or “college graduate”3 = “Some graduate study” or “graduate degree”	2.06	0.64	1,090	1–3
Income	“Considering the combined income for all household members from all sources, what is your best estimate of your household income for 2015?”	Ordinal categories ranging from “less than $15,000” to “$105, 000” or more.	4.7	2.10	1,040	1–8
Gender	“What is your gender identity?”	1 = “Female”0 = “Male”(Transgender respondents dropped due to tiny N)	0.50	0.50	1,097	0–1
Region	“What is the state in which you currently reside?”	Responses categorized according to U.S. Census categories:1 = Northeast2 = Midwest3 = South4 = West	2.63	1.06	1,090	1–4

When multiple items were combined into a scale variable, the factor loading of each component item is reported in parentheses along with the eigenvalue of a principal factor analysis with orthogonal varimax rotation.

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by a Joseph P. Healey research grant from the University of Massachusetts Boston.

ORCID iD

Kevin H. Wozniak

Supplemental Material

Supplemental material for this article is available online.

Notes

Author Biographies

Kevin H. Wozniak is an associate professor of sociology at the University of Massachusetts Boston. He studies the politics of criminal justice with a particular focus on public opinion. He received his Ph.D. in justice, law, and society and American politics from American University. He is a former Congressional Fellow of the American Political Science Association and a former W.E.B. Du Bois Fellow of the National Institute of Justice.

Kevin M. Drakulich is an associate professor in the School of Criminology and Criminal Justice at Northeastern University. His research broadly focuses on perceptions of race, crime and justice both within communities and as public and political issues. He received his Ph.D. in sociology from the University of Washington. He was named aW.E.B. Du Bois Fellow by the National Institute of Justice and received a “New Scholar Award” from the American Society of Criminology’s Division of People of Color and Crime.

Brian R. Calfano is an associate professor of political science and journalism at the University of Cincinnati. His research interests include media and politics and religion and politics. He is the coauthor of A Matter of Discretion: The Politics of Roman Catholic Priests in the United States and Ireland. He received his Ph.D. in political science from the University of North Texas.

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