The effect of perceived threat on human rights: A meta-analysis

Abstract

Individuals express support for civil liberties and human rights, but when threatened tend to restrict rights for both others and themselves. However, the question of whether or not rights are restricted to punish others or protect ourselves remains unclear. This meta-analysis integrates the findings of the effect of perceived threats on support for restrictions of civil liberties from 1997 to 2019. It includes 163 effect-size estimates from 46 different articles involving 91,716 participants. The presence of threat increased support for restrictions against outgroup members significantly more than ingroup members, providing a possible punitive explanation for support for restrictions of civil liberties. These findings contribute to the debate on rights and their relationship with deservingness, suggesting that we delineate those who deserve human rights and those who do not.

Keywords

civil liberties human rights human rights violations meta-analysis perceived threat

In December 1948, the United Nations passed a resolution which contained the Universal declaration of human rights, setting a standard of rights to be universally protected (United Nations General Assembly, 1948). This declaration defined human rights as basic and fundamental rights that uphold our freedom and dignity and are inherently universal and inalienable (Amnesty International, 2012) including in civil, political, economic, social, and cultural domains (Twose & Cohrs, 2015). Yet, 70 years later, human rights violations continue to occur (Cingranelli et al., 2013; Ruhs, 2012). In recent years, Denmark, France, and the Netherlands have all been condemned for their burqa bans that restrict the movement of Muslim women in public spaces (Margolis, 2018). Individuals in Kazakhstan have been arrested and killed for union labor actions (Human Rights Watch, 2018). In the United States, families have been separated at the US–Mexico border, which has been called a cruel practice by the UN High Commissioner of Human Rights (Al Hussein, 2018).

An extensive body of literature demonstrates that the presence of threats – e.g., differences in cultural values, political instability, economic job insecurity – can lead to restrictions of one’s rights¹ as either a punishment for wrongdoing or a preventative measure to avoid further threats (Carriere, 2019a; Carriere et al., 2018). In this research, the focus has been on different types of threat, including cultural threat (Pehrson et al., 2011; Rapp, 2015), economic threat (Feldman & Stenner, 1997; Levin et al., 2012), interfaith threat (Choma et al., 2016), intergroup threat (Cohrs & Asbrock, 2009), safety threat (Abrams et al., 2017), security threat (Lahav & Courtemanche, 2012), outgroup threat (Beck & Plant, 2018; Verkuyten, 2009) and value differences (Hunt, 2011). These different threats can be categorized as realistic, involving one’s tangible resources, including income and one’s own life, or symbolic, involving one’s intangible goods such as culture and values (Stephan, Ybarra, & Bachman, 1999). Importantly, many studies measure a composite of both symbolic and realistic threat together under the construct of “intergroup threat” (Beck & Plant, 2018; Canetti et al., 2009; Djupe & Calfano, 2013; Dunwoody & McFarland, 2018; Seate, 2012; Shitrit et al., 2017; Verkuyten, 2009). Finally, the onset of a new wave of terrorism has shown a third type of threat – terroristic threat. Measures of terroristic threat focus on one’s own fear of dying in a terrorist attack (e.g., “How concerned are you personally about you yourself, a friend, or a relative being the victim of a future terrorist attack in the United States?”) or the collective whole coming under attack (e.g., “How concerned are you that terrorists will attack the US with biological or chemical weapons?”). These questions target both the lives of individuals and their family members. Yet, terroristic threat has stronger correlations with measures of symbolic threat than realistic threat (Crowson, 2009; Hitlan et al., 2007; White et al., 2012).

While research has shown that all measures of threat and support for restrictions of civil liberties (SRCL) are correlated, there is relatively little research on (1) how strong the relationship between threat and SRCL is and (2) whether it matters whose civil liberties are under investigation – that is, are we restricting the rights of ingroup or outgroup members?

Ingroups and outgroups

In many cases, research focuses on the restrictions of the rights of others who are in a different group compared to our own, i.e., an outgroup. Individuals who perceive threat increase their prejudice against outgroups and react with violence and restrictions of resources (Abrams et al., 2017; Thörner, 2014). The perceived expansion of different cultures can lead to restrictions against religious symbols in public (van der Noll, 2010), reductions in support for rights to immigrants (Rapp, 2015; Verkuyten, 2009), and increased demand for cultural adoptions and rejections of the immigrants’ culture (Zagefka et al., 2013). We may perceive an increase in outgroup size (Semyonov et al., 2004) or cultural influence (Newman et al., 2012), and we respond by restricting the outgroup’s rights. This has been measured using a variety of indicators, such as approval of torture to prevent terrorist attacks (Asbrock & Fritsche, 2013), aggressive and hostile policies that specifically target Muslims (Beck & Plant, 2018; Doosje et al., 2009; Dunwoody & McFarland, 2018), policy restrictions against undocumented immigrants (Buckler et al., 2009; Craig & Richeson, 2014), violations of human rights against Palestinians by Israeli populations (David et al., 2016), and opposition to anti-discrimination laws against homosexuals (Feldman & Stenner, 1997).

This is in line with research on ingroup bias, even on a trivial basis (Tajfel et al., 1971; Tajfel & Turner, 1979). Since one’s ingroup is given preferential treatment (Brewer, 1999), the lack of preferential treatment for outgroup members permits a restriction of rights for a target group that is not one’s own. We are less willing to provide negative attributions to ingroup members who are deviant (Harrison & Abrishami, 2004) and give higher approval of misdeeds from ingroup members compared to outgroup members (Schruijer et al., 1994; Tarrant et al., 2012). Individuals show increased levels of trust for ingroup members (Foddy et al., 2009), cooperate more with ingroup members (Balliet et al., 2014) and provide more positive resources to their ingroup within the minimal group paradigm (Tajfel et al., 1971). While our ingroup is trusted, forgiven, and supported, members of outgroups do not receive such benefits and can have their rights removed during times of threat.

This approach to restrictions of civil liberties – where we restrict more rights of outgroup members – can be understood as a punitive approach to human rights restrictions. In response to feelings of threat, we take away rights from others as a punishment. This view of civil liberties presents rights as relatively risk-neutral goods that provide social benefits. The benefit of civil liberties is in having them – and thereby, by restricting access to them, one is selectively discriminating against groups that do not conform to or agree with the culture and norms of the community that otherwise reaps the benefits. If the effect of threat on support for the restriction of civil liberties is driven by punitive-based means, we should expect to see higher effect sizes when looking at restrictions towards outgroup members, not ingroup members.

However, it is not always the case that threat causes us to restrict the rights of outgroup members. Sometimes, threat motivates us to restrict the rights of our own group or both groups equally. When individuals were led to believe there was a high probability of a terrorist attack, they supported harsher punishments against petty crime (Fischer et al., 2006), higher measures of surveillance (Cohrs et al., 2005; Henderson-King et al., 2009), and further withholding of rights (Bozzoli & Müller, 2011; Huddy et al., 2007; Skitka et al., 2004; Welch, 2016). After the Paris attacks in 2015, a state of emergency lasted two years; granting the French government the authority to disband groups, close privately owned venues such as bars and theaters, restrict access to any webpage, search any home at any time, and place any individual under house arrest (Loi n° 2015–1501, 2015). In the United States, there continue to be heated debates over the legitimacy of government surveillance. In 2016, Apple was sued by the Federal Bureau of Investigation (FBI) because they refused to create a backdoor to unlock the phone of the San Bernardino shooter. Apple insisted that the creation of such a backdoor would risk the privacy of their users, despite only 38% of the American public supporting Apple’s stance (Pew Research Center, 2016). The conversation on the right to privacy has been undertaken by academics (Lutz & Ulmschneider, 2019), senators (Daines, 2020; Wyden, 2020), as well as American citizens, with 78% of American citizens supporting federal privacy laws (Internet Association, 2020). Privacy goes beyond just the Patriot Act or the Foriegn Intelligence Surveillance Act (FISA). As fears of a serious epidemic such as COVID-19 increase, support for privacy-invasive policies also increase, regardless of knowledge on the constitutionality of such policies (Chilton et al., 2020; cf. knowledge and support of invasive policies, Best & McDermott, 2007).

Ingroup-based restrictions have been measured by asking questions regarding support for policies directed at citizens (Henderson-King et al., 2009), religious samples banning religious symbols in one’s country (Choma et al., 2016), attitudes towards surveillance (Cohrs et al., 2005; Davis & Silver, 2004), or free speech for teachers (Davis & Silver, 2004). Questions that target one’s ingroup – be it religious, citizenship status, or personal “I”-based questions – direct individuals to engage in considering the restrictions of their own group’s rights. On the other hand, questions that do not specify the target group (Roebroeck & Guimond, 2018), or measures that include both groups (Kossowska et al., 2011), examine the restrictions of ingroup and outgroup rights.

Examining restrictions by looking at times when we restrict rights of ingroup members alongside those of outgroup members would suggest a preventative, not punitive, measure of human rights restrictions. In this way, rights and liberties are resources that by themselves are risk-laden –by removing them, we remove some risk incurred by having liberties. When considering the preventative approach to restricting civil liberties, the focus turns more towards why individuals would revoke their own group’s rights, not the rights of outgroups. In this case, if the effect of threat on support for the restriction of civil liberties is driven by preventative-based means, then restrictions of rights should not vary between groups because there should be no consideration of group membership, only of protection (Hoppe-Graff & Kim, 2005; McFarland, 2015; Moghaddam & Riley, 2005; Passini, 2012; Passini & Emiliani, 2009; Worchel, 2005).

Restrictions that affect both ingroup and outgroup members serve as a useful baseline. If the combination of groups is more similar to either singular target, then this provides evidence that the group which they have in common is an important driving factor in decision making. If our support for COVID-19 movement tracking is not significantly different when comparing “foreigners” (outgroups) to “all individuals” (both groups), but is different when comparing “foreigners” to “citizens of the United States” (ingroups), then it may suggest that the targeted focus of a policy – to selectively impact a single group of people – is extremely important to the outcome. If, on the other hand, “foreigners”, “all individuals”, and “citizens” are all significantly different from each other, it suggests that the three combined is different from the three considered independently.

In practice, neither extreme – purely preventative means or purely punitive means – is likely the answer. For example, one may divide the proportion of rights restricted based on the likelihood that harm may come from the specific group. This logic – that rights are restricted for preventative purposes – still involves bias against an outgroup. By viewing the other group as more threatening and therefore deserving of more restrictions than the ingroup, individuals approach their preventative restrictions with a punitive mindset. On the other hand, research in discrimination and prejudice notes that these types of punitive, outgroup-hate actions are built from a threat against one’s self-concept (Ethier & Deaux, 1994; Tajfel & Turner, 1979). In this way, restrictions as a punitive measure – as a way to put down and isolate the outgroup further – carry preventative justifications in their attempts to bolster the self-esteem and self-concept of the individual. For example, individuals who had their self-image threatened increased their self-esteem through derogating a stereotyped outgroup member (Fein & Spencer, 1997).

Regardless, it is theoretically useful to consider these two ends of a spectrum – preventative or punitive means. If restrictions lean towards a preventive measure, research would need to examine the mental calculus in an individual’s rate of substitutions of rights for security. Individuals are supporting the restrictions of civil liberties because they believe it should increase the amount of security provided. In actuality, countries that violate human rights in order to guard against terrorism are more likely to be targeted by terrorism (Thoms & Ron, 2007; Walsh & Piazza, 2010), suggesting that this preventative-based approach may not work as intended. Apple’s CEO Tim Cook argued that creating the backdoor for the FBI would only make its consumers less safe (Cook, 2016) and the American Civil Liberties Union (ACLU) argued such a demand violated The Fifth Amendment (Brief for American Civil Liberties Union et al., 2016). Research would need to find ways to increase the individual value of rights to the point where no tradeoff would be worth their removal, or emphasize the potential consequences of the removal of rights and see whether knowledge of such consequences would suppress the willingness to restrict them.

Research has not sufficiently concluded whether or not revocation of rights during times of threat is impacted based on which group’s rights are under question, nor whether the relationship is stronger for ingroup members or outgroup members. This binary distinction of preventative/punitive does not dismiss the presence of both – however, it requires us to consider whether threat has a stronger impact when approaching SRCL for outgroups or ingroups. In using a meta-analytic approach, we are able to assess the effect sizes of threat and support for the restriction of civil liberties when targeting outgroup members, ingroup members, and both ingroup and outgroup members.

Other moderators of interest

Gender has been a contested issue when examining SRCL, with some studies showing women to be more intolerant of least-liked groups (Golebiowska, 1999; Parker, 2010; Wemlinger, 2014) while others found the reverse effect (Avery, 1988; Byrne, 2006; Pratto et al., 1997), with women being more likely to reject the use of “advanced interrogation tactics” (Haider-Markel & Vieux, 2008). Age may also play a role, with conservativism increasing with age (Kerr, 1944; Thumin, 1972) and adults showing higher levels of perceived threat than their children (Byrne, 2006), and lower support for equal rights (Verkuyten, 2009). Since perceived threat is focused primarily on the impact of majority groups’ perception of minority groups, it would stand to reason that the number of majority group members in a sample may impact the effects of threat. Caucasians show higher levels of anger after an attack compared to minority groups (Skitka et al., 2004), and are more willing to give up civil liberties when they feel threatened in comparison to Black participants (Davis & Silver, 2004).

No quantitative meta-analysis has yet confirmed the strength of the threat and SRCL relationship nor how the relationship of threat and SRCL differs across groups, populations, and traits (for qualitative review, see Carriere, 2019a). While there has been a meta-analytic study on intergroup threat and general outgroup attitudes (Riek et al., 2006), their outcome variable focused on outgroup evaluations and measurements of prejudice, not support for the restriction of civil liberties. Another recent meta-analysis reviewed the effects of threat on the contact–prejudice relationship (Aberson, 2019), but it focused on how threat mediates the relationship between contact and prejudice, not on the relationship of threat and civil liberty violations. However, this study does provide a strong baseline of a general effect size expected when considering threat’s general effect, showing a medium effect size. This is in line with new argumentation in social psychology arguing for an appreciation of a re-definition of effect sizes in personality characteristics (Funder & Ozer, 2019). This study is unique in that it directly targets the relationship between threat and SRCL.

No experimental research has tested the differences in impact of threat for civil liberties when considering the rights of ingroup and outgroup members. Instead, experimental work has tested either rights of ingroup members, outgroup members, or both ingroup and outgroup members in isolation, not considering the chance that threat may motivate us to protect our ingroup more – and be willing to restrict our own rights heavily – or, that threat may motivate us to punish those who threaten us, and cause us to be more willing to restrict others’ rights more heavily. By synthesizing all research on SRCL and threat, we are able to quantify the effect sizes of threat on SRCL for studies that restrict rights of the ingroup, outgroup, and both ingroup and outgroup to see if there are differences across measurements.

This study aims to quantify the relationship between perceived threat and human rights violations. We conducted a meta-analysis examining the effect of four different measures of perceived threat – realistic, symbolic, intergroup, and terroristic – on support for civil liberty restrictions. We tested to see if there were differential effects between restricting the rights of our ingroup compared to the rights of the outgroup. Finally, in light of personality, gender, and age differences, we tested the effect of threat on these demographic differences on support for civil liberties.

Methods

This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines statement (Moher et al., 2009).²

Literature search

A comprehensive literature search was carried out using PSYCInfo (from 1806–2018), Academic Search Premier (from 1887–2019), Econ Lit (from 1972–2018), Sociological Abstracts (from 1954–2018), Social Sciences Full Texts (from 1983–2018), and Humanities & Social Science Index Retrospective (from 1907–1984). The following keywords were used: threat perception, intergroup threat, inter-group threat, intragroup threat, terrorist threat, threat of terrorism, threat from terrorism, perceived threat, direct threat, external threat, cultural threat, resource deprivation, internal threat, collective threat, sociotropic threat, fear of threat, or symbolic threat and one of (human right*, civil right*, political right*, civil liberty*, political tolerance, political intolerance, tolerance, right*, liberty*, physical integrity, civil constraint, security*, exclusion, restriction, torture, violation*, or repression. This initial search yielded a total of 16,526 articles, from which 2,397 duplicate titles were removed, leaving 14,129 to be reviewed.

Titles of studies were screened according to the following exclusion criteria: (a) genetic-based studies, (b) animal studies, (c) titles not written in English, (d) mentions of qualitative methods, (e) titles that reference co-morbidities (depression, bipolar, injuries), (f) mentions of brain scanning methods, (g) veterans, (h) sports, (i) novels and fiction, (j) familial or romantic relationships, (k) youth populations, (l) governmental-level analyses, (m) health issues and threats of disease, (n) book reviews, (o) patent applications, (p) editorials. In doing so, 11,416 articles were screened out, leaving 2,713 potential studies. These studies were reviewed for the above exclusion criteria and the following inclusion criteria for titles and abstracts: (a) empirical study, (b) mentions of both perceived threat and (c) civil liberties/human rights, (d) that the threat is related to group relations, and (e) the sample is above the age of 18 years. A total of 499 studies met these inclusion criteria. In addition, two studies were published during the peer-review process beyond this set and have been included. From this, 361 studies failed to measure support for restriction of civil liberties or rights, 38 studies failed to have a threat measure, eight studies failed to have both measures, 14 did not include any reported statistical analyses, 24 were not individual level, four were not in English, and six did not have a threat on rights measurement, leaving a final total of 46 studies for 163 effect sizes.

In evaluating which articles included a measure of support for restrictions of civil liberties or rights, coders followed the literature’s definitions of rights – as a “commitment to democratic principles” (Davis & Silver, 2004, p. 28) while using the United Nations definitions of various human rights restrictions (United Nations, 2002). In following this definition, this excluded studies that simply examined negative outgroup attitudes (Riek et al., 2006), candidate preferences (Kinder & Sears, 1981), or least-liked groups (Golebiowska, 1999). While some articles may examine prejudice using questions that highlight rights – ‘Immigrants have become too insistent in their demand for equal rights’ (Shepherd et al., 2018, p. 449–450; see also Akrami et al., 2000) – prejudice measures like these fail to measure support for restrictions of rights. One can easily imagine an answer to such measures that would be supportive of equal rights, if the participant believed the rights were already equal. Instead, studies examined focused on issues of torture, invasive privacy actions (Byrne, 2006), revocation of social service benefits and rights (Buckler et al., 2009; Canetti et al., 2009), freedom of movement and association (Doosje et al., 2009) and censorship (Hetherington & Suhay, 2011). See Table 1 for a full presentation of articles included.

Table 1.

Papers included in meta-analysis. .

First author	Threat type	Sample	Meas	Type of right	Target of RCL	N	Male %	M age
Asbrock (2013)	Terroristic-P	German	N	Torture	Terrorists	144	53.47	22
	Terroristic-C
	Terroristic-P					99	37.37	22
	Terroristic-C
Beck (2018)	Intergroup	US	Y	Torture	Muslims	195	53.00	36.28
Breton (2015)	Symbolic	Canadians	Y	Social rights	Minorities	1035	–	–
Buckler (2009)	Realistic-LJ	US	Y	Social rights	Undocumented	1054	–	–
	Realistic-TJ						–	–
	Symbolic						–	–
Byrne (2006)	Terrorism	US	Y	Surveillance	Many groups	252	49.21	18.88
						196	33.67	48.61
						448	–	–
Canetti (2009)	Intergroup	Jewish Israeli	Y	Social rights	Arabs	190	44.44	37.00
Canetti-Nisim (2009)	Intergroup	Jewish Israeli	Y	Social rights	Arabs	840	42.40	47.36
Carriere (2019)	Terrorism	US	Y	Torture	Terrorists	2019	46.71	46.88
				Surveillance	Many groups	4508	62.85	47.37
				Torture	Terrorists	5283	53.63	47.37
				Torture	Terrorists	3606	48.03	47.37
Carriere (2019b)	Terrorism	US	Y	Torture	Many groups	202	43.56	34.13
	Intergroup
	Terrorism		N	Surveillance	Many groups
					Oneself
					Family
					Community
					Citizens
					Immigrants
					Foreigners
					Terrorists
			Y		Oneself
	Intergroup
	Terrorism				Family
	Intergroup
	Terrorism				Community
	Intergroup
	Terrorism				Citizens
	Intergroup
	Terrorism				Immigrants
	Intergroup
	Terrorism				Foreigners
	Intergroup
	Terrorism				Terrorists
	Intergroup
	Terrorism			Torture	Many groups	167	55.09	32.22
	Intergroup
	Terrorism			Surveillance	Citizens
	Intergroup
	Terrorism			Surveillance	Foreigners
	Intergroup
	Terrorism			Torture	Many groups	89	49.44	32.58
	Intergroup
	Terrorism			General CL	Citizens	45	46.67	32.38
	Terrorism
	Terrorism			General CL	Foreigners	45	51.11	32.80
	Intergroup
Choma (2016)	Symbolic	Canadians	Y	Religion	Citizens	43	14.00	22.88
						115	14.00	19.58
						73	12.30	19.79
Cohrs (2005)	Terrorism	Europeans	Y	General CL	Citizens	275	47.64	30.72
				Surveillance
Conrad (2018)	Terrorism	US	N	Torture	Terrorists	596	–	–
Craig (2014)	Symbolic	US	Y	Citizenship	Undocumented	132	46.20	37.69
	Realistic
Crowson (2005)	Terrorism	US	Y	General CL	Many groups	136	19.85	25.59
Crowson (2006)	Terrorism	US	Y	General CL	Many groups	159	22.64	20.05
Crowson (2007)	Terrorism	US	Y	General CL	Many groups	102	50.00	25.65
David (2016)	Realistic	Jewish Israeli	Y	Torture	Palestinians	517	–	–
Davis (2004)	Terrorism-P	US	Y	Surveillance	Citizens	1142	–	–
	Terrorism-C			Surveillance	Citizens		–	–
Djupe (2013)	Intergroup	US	Y	General CL	Many groups	261	41.00	–
Doosje (2009)	Terrorism	Europeans	Y	Citizenship	Muslims	1409	45.00	46.25
Dunwoody (2018)	Intergroup	US	Y	Religion	Muslims	602	39.04	42.00
				Surveillance	Muslims
Feldman (1997)	Symbolic	US	Y	Gay rights	LGBT	1564	–	–
				Right to life	Criminals		–	–
	Realistic-NE			Gay rights	LGBT		–	–
				Right to life	Criminals		–	–
	Realistic-FU			Gay rights	LGBT		–	–
				Right to life	Criminals		–	–
	Realistic-NPE			Gay rights	LGBT		–	–
				Right to life	Criminals		–	–
Garcia (2016)	Terrorism	US	N	Surveillance	Citizens	161	–	–
Henderson-King (2009)	Terrorism	US	Y	Surveillance	Muslims	297	41.41	–
Hetherington (2011)	Terrorism	US	Y	National ID	Citizens	1422	–	–
				Surveillance	Citizens	952	–	–
						948	–	–
				Social rights	Citizens	949	–	–
						942	–	–
				Torture	Terrorists	1417	–	–
Huddy (2007)	Terrorism	US	Y	National ID	Citizens	1431	–	–
				Surveillance	Citizens	1439	–	–
				Surveillance	Arabs	1334	–	–
				Citizenship	Foreigners	1419	–	–
				General CL	Arabs	1429	–	–
Hunt (2011)	Symbolic	US	Y	General CL	Muslims	243	42.80	–
				Gay rights	LGBT	389	40.36	–
						407	38.57	24.90
						311	50.48	32.59
						324	48.46	31.28
				General CL	Muslims	144	–	–
Kossowska (2011)	Terrorism	Belgian	Y	General CL	Many groups	86	62.79	24.79
		Polish				175	27.43	23.56
		Spanish				121	20.66	22.40
		UK				68	51.47	20.17
Lahav (2012)	Realistic-S	US	N	Surveillance	Citizens	338	61.24	–
	Realistic-E							–
	Symbolic							–
	Realistic-S			Culture	Undocumented			–
	Realistic-E							–
	Symbolic							–
Lopez-Rodiguez (2014)	Realistic	Spanish	Y	Culture	Immigrants	115	38.26	36.41
	Symbolic
	Realistic					89	34.83	34.91
	Symbolic
	Realistic					103	41.75	33.61
	Symbolic
Maoz (2008)	Realistic	Jewish Israeli	Y	Right to citizenship	Palestinians	504	–	–
	Realistic			Torture	Palestinians	501	–	–
Nagar (2014)	Realistic	Jewish Israeli	Y	Right to citizenship	Palestinians	436	–	–
Obaidi (2018)	Realistic	US	Y	Right to life	Muslims	205	54.60	34.77
	Symbolic
	Terrorism
	Realistic	Swedish Islamic	Right to life	Europeans	151	42.60	–
	Symbolic							–
	Realistic	Turkey		Right to life	Many groups	247	29.60	–
	Symbolic							–
	Realistic	Multi-cultural			142	42.90	26.70
	Symbolic
	Realistic					112	51.80	28.67
	Symbolic
	Realistic					155	51.00	23.00
	Symbolic
Raijman (2004)	Symbolic	Israeli	Y	Social rights	Foreigners	1100	–	–
	Realistic						–	–
Raijman (2011)	Realistic	Israeli	Y	General CL	Immigrants	922	50.70	–
Rapp (2015)	Symbolic	Swiss	Y	Social rights	Immigrants	504	–	–
	Realistic						–	–
	Symbolic			Social rights	Balkan Immigrants	493	–	–
	Realistic						–	–
	Symbolic			Social rights	Muslims	103	–	–
	Realistic						–	–
Roebroeck (2018)	Symbolic	France	Y	Religion	Citizens	916	–	–
	Realistic						–	–
Seate (2012)	Intergroup	US	N	General CL	Undocumented	374	22.90	20.14
Sekerdej (2011)	Terrorism	Polish	Y	General CL	Many groups	139	24.46	23.57
Semyonov (2004)	Realistic	German	Y	General CL	Foreigners	1737	49.00	47.17
Shitrit (2017)	Intergroup	Jewish Israeli	Y	Social rights	Palestinians	230	47.00	40.71
	Intergroup					805	47.00	45.93
Skitka (2004)	Terrorism	US	Y	Social rights	Many groups	550	48.00	–
Spry (2007)	Symbolic	Australia	Y	Culture	Immigrants	95	43.16	26.74
van der Noll (2010)	Symbolic	UK	Y	Religion	Muslims	538	–	–
		France				687	–	–
		German				684	–	–
		Netherlands				713	–	–
	Realistic-CE	UK				659	–	–
		France				721	–	–
		German				705	–	–
		Netherlands				737	–	–
	Realistic-IV	UK				668	–	–
		France				722	–	–
		German				710	–	–
		Netherlands				738	–	–
Verkuyten (2009)	Intergroup	Netherlands	Y	Culture	Muslims	887	–	–
Zagefka (2013)	Symbolic	UK	Y	Culture	Pakistani Immigrants	90	24.44	24.08

Note. RCL = restriction of civil liberties; P = Personal; C = Collective; LJ = Lost jobs; TJ = Take jobs; NE = National economic evaluation; FU = Fear of unemployment; NPE = Negativity of personal economic evaluation; S = Security frame; E = Economic frame; CE = Concern of extremism; CL = civil liberties; IV = Islam as violent; LGBT = lesbian, gay, bisexual, and transgender.

Data extraction

Extracted data were converted to Pearson’s R if not already provided. A total of 46 articles with 103 unique samples were collected for 163 different effect sizes. Many articles included multiple studies, measured multiple types of threat, and/or multiple types of right restrictions.

Eighty-nine samples reported and used Pearson’s R for their effect size. For the rest, Pearson’s r was calculated by hand (41 reported beta coefficient and standard error, three reported t statistic, one reported F statistic, 17 reported Cohen’s d; 12 reported Spearman’s Rho: Rupinski & Dunlap, 1996). If multiple models were presented for the same subgroup and outcome (i.e., a hierarchical linear regression with interactions at higher levels), the model in the highest level was chosen. In recognizing the definitional differences of threat, conversions of betas into t statistics into Pearson’s R correlations will estimate a semi-partial correlation, due to various models controlling for various predictors. This allows a very conservative under-estimation of the effect size. For Pearson’s R, we use more contemporary work that argues for personality and individual difference research, that r = .1 is a small effect, r = .2 is a medium effect, and r = .3 is a large effect (Gignac & Szodorai, 2016).

A total of 77 effect sizes also reported right-wing authoritarianism, 10 for social dominance orientation, 38 reported the percentage of the sample White, 91 of the studies reported the mean age of their sample, and 107 reported the percentage of the sample that was male.

Each study was coded for either examining the restrictions of outgroup rights, ingroup rights, or both ingroup and outgroup rights. Two coders independently rated each one based on the target group. If a study was framed towards a specific target outgroup, then it was coded as outgroup restrictions. If a study framed its dependent variables in terms of the participant’s group (I, citizens like me, citizens, Americans for American populations, etc.), the study was coded as ingroup rights. If the dependent variable had mixed group targets, was unclear on who the target was, or was universal in its targeting, it was coded as ingroup and outgroup rights. Any disagreements were to be compared and discussed as a team; however, none were present, so this step was not necessary.

Data analysis

All data analysis was completed using R, with the analyses being run primarily through the MetaFor (Viechtbauer, 2010) and MAd (Del Re & Hoyt, 2014) packages.

Effect sizes

We employed a random-effects approach in order to account for anticipated heterogeneity in effect sizes across the studies included in this meta-analysis due to differences in population and time of study. We assume that the effect of threat in this analysis is heterogeneous because of the multitude of various operationalizations and measures of the treatment (threat), thereby limiting the chances of pinpointing an underlying “true effect” across all studies (Borenstein et al., 2009). Choosing a random-effects model utilizes both within-study variance and between-study variance, leading to wider variances, standard errors and confidence intervals for the treatment effect. While some authors argue that meta-analyses that examine low sample size studies should use fixed-effect models (Poole & Greenland, 1999), with an average N of 562.67, we are not overly concerned about this critique, and instead choose to accept larger confidence intervals with additional controls to the model outlined below.

Heterogeneity

Heterogeneity was tested using $I^{2}$ . In comparison to Cochrane’s Q – which has too much power as a test of heterogeneity when the number of studies is large, and can be underpowered if the studies are too few (Higgins & Thompson, 2002; Higgins et al., 2003) – $I^{2}$ estimates the percentage of variation due to heterogeneity rather than random chance. Due to a high range in sample sizes of reported studies (N = 43 to N = 5,283), confidence intervals with Knapp–Hartung corrections (Knapp & Hartung, 2003) are reported instead of prediction intervals (Partlett & Riley, 2017).

File drawer problem

A weighted fail-safe N (FSN) was used to assess the number of unpublished or novel null findings required to make the combined effect non-significant at p < .05 (Rosenberg, 2005). Models need to receive an FSN greater than 5k + 10 (k = number of effects in model) to display higher tolerance to non-reported null findings (Rosenthal, 1979). This approach could be combined with Egger’s mixed-effects model regression test on funnel plots (Egger et al., 1997; Sterne et al., 2006) to test for asymmetry. However, in the presence of significant heterogeneity, full plot asymmetry is not an appropriate estimate of publication bias, as asymmetry can be attributed to differences in study characteristics or sampling variations (Sterne et al., 2011). Instead, we use a selection method that accounts for certain types of publication bias within a random-effects model (Carpenter et al., 2009; Henmi & Copas, 2010). This method preforms better than general trim-and-fill methods when faced with substantial between-study heterogeneity (Peters et al., 2007; cf. Duval, 2006 on trim-and-fill).

Moderators

There were three demographic moderators: (a) gender, operationalized as percentage male, (b) age, reflecting the average age of the sample, and (c) race, operationalized as the percentage White. There were also five moderators reflecting study characteristics: (a) statistic reported, which was based on the effect sizes used to convert into Pearson’s r correlation coefficients; (b) type of threat measured – either symbolic, realistic, a composite of the two, or terroristic; (c) the group target of restrictions – either ingroups, outgroups, or both ingroup and outgroup; (d) whether the study manipulated or measured threat; and (e) if measured, was the measure a high reliability (α > .7), low reliability (α < .7), not reported, or a single-item measure.

Results

Random intercept models

The overall random-effects model showed a medium effect (b = 0.28, SE = 0.02, t = 13.88, p < .001, CI = [0.24, 0.32]). The overall model showed significant heterogeneity (Q(162) = 4813.85, p < .001, $I^{2}$ = 97.15%), therefore funnel plot asymmetry was not used. Instead, we used the “Copas Selection Model” to explicitly model publication bias for random-effects models. This method models a fixed-effects model while simultaneously modeling the heterogeneity of the random effects and incorporating it into the standard error of the average effect. In doing so, it accounts for the smaller studies with larger standard errors that may have not been published. Running this analysis on our main effect, we found that our effect size did decrease, but stayed at a medium-sized effect (b = 0.20, SE = 0.03, CI = [0.14, 0.26]).The FSN for the overall model is 241,691, meaning 241,691 more studies would need to be taken averaging null results to make the model insignificant. This is above 15 (5k + 10 where k = 1), suggesting the model is robust to potential unpublished or null findings. While results indicate that very large statistical heterogeneity is present, despite this, the random-effect meta-analysis shows a statistically significant effect of threat, even after using Copas Selection. Accordingly, we conclude there is strong evidence for an effect of threat; however, the true size of the effect is unclear due to heterogeneity, yet ranges between r = .20 and r = .28, suggesting a medium-sized effect.

Type of threat

The first subgroup analysis explored whether or not type of threat had differential impacts on SRCL, see Table 2. Running this moderator analysis showed a significant main effect (Q(3) = 75.90, p < .001; F(4, 159) = 76.24, p < .001). All four measurements were significantly different from zero, with intergroup threat (b = 0.57, SE = 0.04, z = 14.40, p < .001, CI = [0.49, 0.65]), realistic threat (b = 0.25, SE = 0.03, z = 8.74, p < .001, CI = [0.20, 0.31]), symbolic threat (b = 0.30, SE = 0.03, z = 9.44, p < .001, CI = [0.23, 0.36]), and terroristic threat (b = 0.17, SE = 0.02, z = 7.13, p < .001, CI = [0.12, 0.22]). Running uncorrected simultaneous tests for linear hypotheses post hoc comparisons indicated the effect on SRCL was significantly higher if measuring the combined measurement of intergroup threat compared to realistic (bdif = –0.31, SE = 0.06, z = –5.39, p < .001), symbolic (bdif = –0.27, SE = 0.06, z = –4.44, p < .001), and terroristic (bdif = –0.40, SE = 0.05, z = –7.26, p < .001). Terroristic threat measurements also reported a lower effect size than symbolic threat measurements (bdif = –0.13, SE = 0.05, z = –2.80, p = .005), but no significant differences were found when comparing symbolic or terroristic to realistic threat (see Figure 1 for plotted betas).

Figure 1.

Effect size of threat on support for the restriction of civil liberties by type of threat measured.

Target group

The second subgroup analysis examines whether or not the relationship between threat and support for the restriction of civil liberties differs depending on the target of the restrictions of civil liberties – the outgroup, the ingroup, or if the target is both the ingroup and the outgroup. Running this moderator analysis showed a significant main effect (Q(2) = 13.98, p < .001; F(3, 160) = 72.69, p < .001). When the ingroup was the target, the effect size was the smallest (b = 0.16, SE = 0.04, z = 3.95, p < .001, CI = [0.08, 0.24]), followed by targeting both groups (b = 0.22, SE = 0.05, z = 4.27, p < .001, CI = [0.12, 0.32]) and then just the outgroup (b = 0.33, SE = 0.02, z = 14.33, p < .001, CI = [0.28, 0.37]). Running uncorrected simultaneous tests for linear hypotheses post hoc comparisons indicated the effect on SRCL was significantly higher if comparing the outgroup to the ingroup (b_dif = –0.17, SE = 0.05, z = 3.37, p < .001), but not the outgroup to both (b_dif = 0.11, SE = 0.06, z = 1.83, p = .067) or the ingroup to both (b_dif = 0.05, SE = 0.07, z = 0.84, p = .403) (see Figure 2 for plotted results).

Figure 2.

Effect size of threat on support for the restriction of civil liberties by target of restrictions.

Specific targeted group

There were 18 targeted groups. These were categorized as follows: comparisons of ingroups (citizens, family, oneself, community); Arabs (Arabs, Palestinians, and Muslims);³ minorities (Balkan immigrants, Pakistani immigrants, immigrants, foreigners, undocumented, minorities); criminals (terrorists, criminals); the lesbian, gay, bisexual, and transgender (LGBT) community; Europeans; and studies that mixed populations.⁴ Studies that targeted Arabs showed significantly higher effect sizes compared to all groups but Europeans (ps < .02), ranging in effect size differences of (b_dif = 0.14, SE = 0.06, z = 2.35, p = .018; many groups) to (b_dif = 0.30, SE = 0.05, z = 5.57, p < .001; ingroups). However, this non-significant result should be read with caution, since the k of Europeans is low (k = 2). Studies targeting minorities showed higher effect sizes than those for criminals (b_dif = 0.19, SE = 0.07, z = 2.75, p = .005) and ingroups (b_dif = 0.14, SE = 0.05, z = 2.65, p = .008). The same pattern was seen for studies that targeted multiple populations (criminals – b_dif = 0.21, SE = 0.07, z = 2.95, p = .003; ingroups – b_dif = 0.17, SE = 0.06, z = 2.86, p = .004). Overall, studies that targeted Arab populations showed the highest effect sizes, followed by studies that studied multiple populations and studies that examined minority groups (see Figure 3). However, since migration patterns differ across populations, studies that did not clarify the home country of the targeted migrants were placed in this category. This could mean that some participants imagined Arab immigrants due to their own personal country’s migration data and yet were not captured in this analysis.

Figure 3.

Effect size of threat on support for the restriction of civil liberties by specific target of restrictions.

Type of right

We examined whether threat’s relationship with restrictions of civil liberties was different for different types of civil liberties. The model was significant (F(9, 153) = 18.48, p < .001) and all rights had a significant effect besides requiring a national ID card (b = 0.13, SE = 0.16, z = 0.79, p = .431, CI = [–0.19, 0.44]) (see Table 2). It is noteworthy that both the two highest and two lowest significant effects have the smallest sample sizes (k ranges from 8 to 9). Linear comparisons revealed that threat’s relationship is stronger for studies that examine rights to citizenship (b_dif = 0.23, SE = 0.10, z = 2.30, p = .02), culture (b_dif = 0.21, SE = 0.10, z = 2.02, p = .043), life (b_dif = 0.23, SE = 0.08, z = 2.72, p = .007), and social rights (b_dif = 0.17, SE = 0.08, z = 2.09, p = .037) compared to religious freedom. Studies that examined “rights to privacy” showed lower effects than “rights to citizenship” (b_dif = –0.18, SE = 0.09, z = –1.96, p = .049) and rights to life (b_dif = –0.17, SE = 0.07, z = –2.43, p = .015).

Table 2.

Results of meta-analysis with subgroup analysis.

Variable and class	Between-classes effect (Q_b)	I ²	k	N	r and [95% CI]
Type of threat	Q_b (3) = 75.90, p < .001	97%
Realistic		97%	41	27688	.25_ab [0.20, 0.31]
Symbolic		95%	36	15316	.30_a [0.24, 0.36]
Intergroup		96%	23	7237	.57 [0.49, 0.65]
Terroristic		77%	63	41475	.17_b [0.12, 0.22]
Target of SRCL	Q_b (2) = 14.44, p < .001	97%
Ingroup		60%	34	15171	.16_a [0.08, 0.24]
Outgroup		98%	107	66908	.33 [0.28, 0.37]
Combined		87%	22	9637	.22_ab [0.12, 0.32]
Specific target of RCL	Q_b (6) = 49.61, p < .001	97%
Ingroups		74%	36	17003	.15_b [0.08, 0.22]
Criminals		87%	16	20269	.10_ab [–0.01, 0.21]
Arabs		98%	35	20268	.45_c [0.38, 0.52]
Minorities		94%	37	16556	.29_d [0.22, 0.36]
Europeans		46%	2	302	.44_abcd [0.14, 0.75]
LGBT		98%	8	7687	.20_abd [0.05, 0.34]
Many groups		89%	29	9631	.31_d [0.23, 0.40]
Type of right	Q_b (9) = 18.48, p = .03	97%
Gay rights		98%	8	7687	.20_abc [0.04 0.36]
Civil liberties		93%	20	6276	.30_abc [0.19, 0.41]
National ID		48%	2	2853	.13_abc [–0.19, 0.44]
Religious		95%	18	10947	.17_b [0.06, 0.28]
Citizenship		98%	9	5046	.40_cd [0.25, 0.55]
Culture		95%	9	1686	.38_ac [0.22, 0.54]
Right to life		97%	17	8485	.40_cd [0.29, 0.51]
Social rights		98%	19	13103	.34_ac [0.24 0.44]
Privacy		95%	43	20545	.22_ab [0.16, 0.29]
Torture		94%	18	15536	.26_abc [0.15, 0.37]
Sample Population	Q_b (1) = 28.57, p < .001	97%
Western		96%	142	82957	.24 [0.20, 0.27]
Middle Eastern		98%	13	7639	.56 [0.45, 0.67]
Threat	Q_b (1) = 12.07, p < .001	97%
Measured		97%	142	86455	.30 [0.26, 0.34]
Manipulated		0%	21	5261	.11 [0.01, 0.21]
Alpha of threat	Q_b (3) = 47.51, p < .001	97%
High		97%	83	26197	.40_a [0.35, 0.45]
Low		93%	8	3180	.35_a [0.21, 0.50]
Single item		79%	34	37996	.16_b [0.10, 0.23]
Unreported		97%	17	19082	.12_b [0.02, 0.21]
Statistic used	Q_b (5) = 47.89, p < .001	97%
Beta		94%	41	40091	.17_ab [0.10, 0.23]
Correlation		97%	89	36404	.39_c [0.34, 0.43]
d		0%	17	4306	.11_ab [0.01, 0.22]
F		100%	1	161	.02_ab [–0.41, 0.46]
Rho		51%	12	8282	.14_ab [0.02, 0.26]
t		97%	3	2472	.28_bc [0.11, 0.60]

Note. rs with shared subscript letters indicate subgroups are not significantly different from each other at p < .05. SRCL = support for restriction of civil liberties; RCL = restriction of civil liberties; LGBT = lesbian, gay, bisexual, and transgender

Middle Eastern vs. Western populations

Finally, with 16 different country-level populations, a comparison was made between Western vs. Middle Eastern samples.⁵ One study (Obaidi et al., 2018, Study 5) was removed from this analysis, because the sample included citizens from both Afghanistan and Denmark. The Western vs. Middle Eastern samples comparison showed significant effects of threat on SRCL (see Table 2), with the effect being significantly higher for Middle Eastern samples (b_dif = 0.32, SE = 0.07, z = 4.72, p < .001). However, of the Middle Eastern samples, 11 out of 13 were studies of Israeli citizens, with eight of those 11 explicitly examining the threats between Palestinians and Israeli populations (Canetti et al., 2009; Canetti-Nisim et al., 2009; David, 2016; Maoz & McCauley, 2008; Nagar & Maoz, 2014; Raijman & Semyonov, 2004; Raijman & Hochman, 2011; Shitrit, 2017). Only Raijman & Semyonov (2004) and Raijman & Hochman (2011) examined the threats between the Israeli population and “foreigners” generally, and Obaidi et al. (2018) was the only non-Israeli Middle Eastern population, having examined a sample of Turkish Muslims and their attitudes towards Europeans. This is an important consideration, since it may be the case that various populations react differently when considering different groups’ rights to restrict due to historical traumas and current conflicts. This is particularly a concern, since 61% of Middle Eastern populations had Arabs as a target group compared to only 19% of Western populations.

Due to low k per group (such that k > number of factors), a full moderation analysis was not possible to examine the posed question. However, a follow-up exploratory analysis controlling for the target of restrictions while examining the sample population resulted in a significant model (F(7, 148) = 39.88, p < .001).⁶ All variables maintained their prior reported significance, but the effect size of having a Middle Eastern sample was reduced (b = 0.21, SE = 0.07, t = 3.21, p = .002, CI = [0.08, 0.34]). So, while we can attribute some of the increased threat < > SRCL relationship to the targeted group, there still seems to be a larger effect of threat on supporting civil liberty restrictions on Middle Eastern samples.

Study characteristics

Measured vs

manipulated.

Running this moderator analysis showed significant main effect (Q(1) = 12.07, p < .001; F(2, 161) = 107.74, p < .001). Studies that manipulated threat showed a small significant effect size (b = 0.11, SE = 0.05, z = 2.13, p = .034, CI = [0.01, 0.21]) but those that measured threat showed medium effect sizes (b = 0.30, SE = 0.02, z = 15.25, p < .001, CI = [0.26, 0.34]). A linear post hoc comparison showed that this effect was higher for studies that measured threat (b_dif = 0.19, SE = 0.06, z = 3.32, p = .001).

When examining just the studies that measured threat, we tested to see whether there were significant differences across the $α$ – if the measurement was a single item, where it was below .7, above .7, or unreported. This model showed a significant effect (Q(3) = 47.51, p < .001; F(4, 138) = 67.48, p < .001). All four categories were significantly different from zero, with high $α$ (b = 0.40, SE = 0.02, z = 17.02, p < .001, CI = [0.35, 0.45]), low alpha (b = 0.35, SE = 0.07, z = 4.71, p < .001, CI = [0.21, 0.50]), single item (b = 0.16, SE = 0.04, z = 4.67, p < .001, CI = [0.10, 0.23]), and unreported (b = 0.12, SE = 0.05, z = 2.30, p = .021, CI = [0.02, 0.22]). Running uncorrected tests for post hoc comparisons indicated the effect on SRCL was significantly higher if the measurement had a high alpha compared to unreported alphas (b_dif = –0.28, SE = 0.06, z = –4.50, p < .001) or compared to a single item measurement (b_dif = –0.24, SE = 0.05, z = –4.92, p < .001). The same was true for lower alphas, being higher than unreported alphas (b_dif = –0.23, SE = 0.10, z = –2.28, p = .022) and single-item measurements (b_dif = –0.19, SE = 0.09, z = –1.98, p = .048). No other effects were significantly different at a p < .05 level (see Figure 4).

Figure 4.

Effect size of threat on support for the restriction of civil liberties by alpha level.

Statistic used

Since we collected and interpreted t-values from beta coefficients to transform into Pearson’s r, subgroup analyses were run in order to see whether there was a significant difference in effect size based on the type of statistic collected. This model showed a significant overall effect (Q(5) = 47.89, p < .001; F(6, 157) = 46.61, p < .001). Effect sizes measured in terms of unstandardized beta coefficients (b = 0.17, SE = 0.03, z = 5.09, p < .001, CI = [0.10, 0.23]), Pearson’s r (b = 0.39, SE = 0.02, z = 16.31, p < .001, CI = [0.34, 0.43]), Spearman’s Rho (b = 0.14, SE = 0.06, z = 2.34, p = .02, CI = [0.02, 0.26]), Cohen’s d (b = 0.11, SE = 0.05, z = 2.12, p = .034, CI = [0.01, 0.22]) and t statistics (b = 0.36, SE = 0.02, z = 2.87, p = .0042, CI = [0.11, 0.60]) were all significantly different than zero, but the one study with an F statistic failed to reach significance. Running post hoc linear combinations, we found that the differences existed within comparisons against correlation-based effect sizes. The effect size of correlation coefficients was larger than betas (b_dif = 0.22, SE = 0.04, z = 5.03, p < .001), Cohen’s d (b_dif = 0.27, SE = 0.06, z = 4.47, p < .001), and Spearman’s Rho (b_dif = 0.24, SE = 0.07, z = 3.50, p < .001), but not significantly different than t or F statistics (see Figure 5).

Figure 5.

Effect size of threat on support for the restriction of civil liberties by statistic reported.

Demographics

There were no significant moderators on percentage of sample White (b = 0.19, SE = 0.27, t = 0.69, p = .49, CI = [–0.36, 0.73]), percentage of the sample that was male (b = 0.12, SE = 0.25, t = 0.49, p = .62, CI = [–0.38, 0.62]), or average age of the sample (b = 0.01, SE = 0.004, t = 1.85, p = .068, CI = [–0.001, 0.01]). However, there was a significant effect of year of study (b = 0.01, SE = 0.003, t = 3.67, p < .001, CI = [0.01, 0.02]), in which a one unit increase from the average (Year_M = 2012.90) was associated with a .01 increase in effect size of threat (see Table 3).

Table 3.

Effect size information for continuous moderator analysis.

Variable	I ²	$τ^{2}$	k	N	b and [95% CI]
Percentage White	99%	.11	38	35045	0.19 [–0.36, 0.73]
Percentage male	96%	.07	107	40333	0.12 [–0.38, 0.62]
Average age	96%	.07	91	34847	0.01 [–0.001, 0.01]
Year of study	97%	.06	163	91716	0.01 [0.01, 0.02]

Discussion

The results of this study provide the first meta-analytic evidence for the effect of threat on civil liberty restrictions. The present research provides a quantitative review of an increasingly large field of research that has examined the relationship between perceived threat on the one hand and individuals’ support for the restrictions of civil liberties (SRCL) on the other. The present study provides meta-analytic results from primary published studies that indicate that perceived threat is overall positively related to SRCL. Moreover, results reveal an instructive estimation of the magnitude of the relationship of r = .28, which is of moderate to large size (Gignac & Szodorai, 2016). Results suggest that while threat impacts support for restrictions on both groups, we see stronger effects on SRCL when targeting outgroups compared to ingroups.

We used several methods to estimate and correct for measurement and publication bias in the identified set of studies. Fail-safe N for effect size analysis (Rosenthal, 1979) indicated that 241,691 unidentified samples with a correlation of zero would be needed to reduce the effect size to non-significant. The Copas Selection Method (Carpenter et al., 2009; Henmi & Copas, 2010) revealed an unbiased effect size of r = .20 after controlling for publication bias, remaining at the threshold for a medium-sized effect (Gignac & Szodorai, 2016).

Due to the heterogeneity of the data, we caution to place a definitive effect size onto the relationship. The mass of heterogeneity may be due to a variety of issues. For instance, we noted the variety of ways in which threat has been defined. It may be that subtle differences between these approaches are increasing the heterogeneity across studies. It also may be that different cultures experience threat in different ways. This concern was addressed by the use of random-effect models instead of fixed-effect models. The extent of heterogeneity may also be due to the variety of effect sizes included in the study, as some were partial correlations while others were strictly the provided Pearson correlations. Further investigation of what else is influencing this relationship needs to be carried out.

We examined several theoretically relevant variables as qualifying conditions of the observed relationship between threat and SRCL. In doing so, we found no effect of gender, age of the sample, or ethnicity of the sample.

We found a significantly smaller effect size when looking at studies in which researchers manipulated, rather than measured, feelings of threat. This result raises intriguing possibilities for future research. It could be the case that correlational studies are overestimating the effect of feelings of threat, or that the true causal variable is being unaccounted for in bivariate correlations. Other important third variables could be important to consider, including various personality characteristics (e.g., right-wing authoritarianism, social dominance orientation, and nationalism). Another possibility is that individual differences, such as sensitivity to threats and belief in a dangerous world (Cook et al., 2018), may moderate responses to threat, whereby the effect is stronger in certain individuals. It could also be that either (or both) threat or SRCL are difficult to effectively manipulate, and future research needs to develop more effective manipulation mechanisms. In brief, this result is worthy of further investigation to parse out the differences between manipulating and measuring feelings of threat.

Regardless, we find the lesser effect of manipulation of threat interesting by itself. This is in line with a growing discussion exploring the possibility that the difficulties of priming studies lie in the current inability to identify which part of the prime is the most compelling, and which prime can bring about the results one expects (Cesario, 2014). Garcia and Geva (2016) failed to find a significant effect of threat by manipulating the amount of death and damages in a described terroristic attack, and this non-significant result was seen again in a later replication (Carriere, 2019b). Describing a suspect being stopped with a rental truck of fertilizer and ammonia compared to being simply uncooperative with proof of citizenship did not significantly increase support for torture (Conrad et al., 2018), but did show significant effects when the individual was Arab, regardless of threat manipulation. Lahav and Courtemanche (2012) found significant effects by comparing threatening articles of the concerns of immigration against a base-control condition where no article was presented to participants, and Seate (2016) also found significant threat manipulations when examining immigration stories. The fact that the two significant manipulations of threat were based on a future, forward-looking effect of threat (i.e., “Immigrants will hurt the community, immigrants will take your jobs”) compared to the non-significant manipulations that focused on fictional stories (describing an imagined terroristic attack or plot) is worthy of future examination.

The present research substantiates assertations that individuals’ perceptions of threat have implications for support for civil liberties for both ingroup and outgroup members. At the same time, it extends our understanding of previous empirical studies by providing a more comprehensive, precise, and quantitative assessment of the threat–liberty relationship. The evidence reveals that perceptions of threat relate to decreased support for civil liberties, and this effect is stronger when considering the civil liberties of outgroup members. In this way, the present research expands upon a wider literature that has asserted that support for civil liberties and human rights is influenced by social factors in general and issues of group membership in particular (Hafer, 2012).

We have shown that the threat–SRCL relationship is strongest when considering the civil liberties of outgroups, suggesting that restrictions of civil liberties may be linked more towards a punitive measure. The result of a weaker effect when considering the rights of one’s own group does not support the notion that rights are removed in order to protect ourselves. If there was a concern for the wellbeing of the group due to the threat, social identity theory would argue its focus would be on protection by supporting the restrictions of rights for all individuals equally. Instead, we found that there were significant differences when outgroups were singled out in the research as targets of restrictions. Further research into exploring the motivations and underpinnings of restrictions of rights against one’s own group should be explored. The non-significant difference of measures that included both groups compared to either singular SRCL target is interesting, as it suggests that there is a boundary condition of threat’s effects when the target moves further away from ourselves. A survey of American citizens showed (32%/49%) that those who heard (a lot/a little ) about US surveillance programs supported the surveillance of American citizens, but that number grew as the survey continued to move further towards various outgroups (47%/61% citizens of other countries, 53%/66% leaders of other countries, 76%/90% suspected terrorists) (Rainie & Madden, 2015).

This result suggests that research surrounding deservingness and removal of civil liberties requires further exploration in the quest to promote civil liberties (Drolet, 2014; Drolet et al., 2016; Hafer, 2012). Deservingness focuses on the assumption that “our ingroup is good and therefore deserves rights” and place it against “that outgroup is bad and therefore does not deserve rights” (Hafer, 2012). Therefore, exploring ways to cognitively expand the ingroup – through thinking in terms of a global community (Hackett et al., 2015) – may reduce restrictions of civil liberties. Formulating one’s ingroup as the global community has been shown to be a strong predictor of solidarity with victims of human rights violations (Barth et al., 2015; McFarland & Mathews, 2005) and concern for human rights (Hackett et al., 2015; McFarland, 2010; McFarland et al., 2013; McFarland et al., 2012).

Of course, it could be the case that such results were muddled by personal subjectivity in question interpretation. While we were rigorous in our examination of all measures to determine whether they explicitly called out ingroups, outgroups, or a mixture of the two, individual respondents may interpret such questions differently (Chiu et al., 2010; Rosenbaum & Valsiner, 2011). Since individuals tend to believe that they are good and only good things should happen to them (Hafer, 2012), they may interpret any “self-imposed” restrictions of rights as only targeting those outgroup “bad citizens”, or others who are “not like me”. However, if this is the case, then there should not have been a significant difference between studies that were coded as ingroup-directed compared to outgroup-directed, or even those that were mixed in their targeting. Since there were significant differences, we can be reasonably confident that there is a qualitative difference between the three types of measurements.

It is important to reflect on the fact, however, that even ingroup-focused policies are not truly unbiased and equitable in their deployment. Laws such as Stop-And-Frisk in New York and SB 1070 in Arizona disproportionately targeted ethnic minorities (Gelman et al., 2007; Sadowski-Smith & Li, 2016) and police recording devices disproportionally have more “malfunctions” in minority majority neighborhoods (Daley, 2014). In the end, policy is made by groups, supported by groups, and applied inconsistently by groups towards other groups.

Furthermore, we recognize our limitation in defining targets in terms of ingroups and outgroups. While many studies made efforts to ensure their samples were not inclusive of the target of the restrictions (e.g., Dunwoody & McFarland, 2018; van der Noll, 2010), it is difficult to assess whether participants also placed themselves within the target category more broadly. The saliency of the targetted identity could impact the extent of these effects. For example, participants who held salient superordinate identities were more supportive of increasing of taxes (Transue, 2007). We cannot be sure of how salient each measurement was in tapping into thoughts of ingroups and outgroups, but would imagine that such effects should be equally randomized across studies.

Instead, by finding that outgroup right limitations have a stronger effect size under times of threat, two main preliminary conclusions can be drawn. First, if the goal of research is to propagate notions that no one deserves to lose their civil liberties, then thinking in terms of a global community (Hackett et al., 2015) or identification with all of humanity (Dunwoody & McFarland, 2018; McFarland et al., 2013) may assist in this endeavor. By reducing the size of the outgroup, and increasing the size of the ingroup, fewer individuals would find themselves on the receiving end of restricted human rights. This leads to the second point – that this result requires we consider where the lines for ingroups and outgroups are drawn when considering human rights and civil liberties. There is some preliminary evidence towards understanding this future direction. Individuals who glorify the idea that “our ingroup is good” – e.g., “The US is better than other nations in all respects” – demand less justice (Leidner et al., 2010) and feel less guilt (Roccas et al., 2006) when faced with human rights violations by their ingroup. Americans, in particular, navigate a complicated relationship between feeling American and sharing human rights. Those who endorsed high importance of “being American” as part of their identity were more likely than those who did not to favor policies that would expel unauthorized immigrants, favor large decreases in immigration levels, favor status checks, and oppose citizenship for illegal immigrants who entered the United States as children (Espinosa et al., 2018; Marshall & Shapiro, 2018). The rise of nationalism is a rise of anti-human-rights sentiment (Skitka, 2005) – with strong positive associations between uncritical acceptance of national authorities and a belief in the superiority and dominant status of one’s nation and greater support for restrictions of civil liberties for one’s ingroup and outgroup (Sekerdej & Kossowska, 2011). Therefore, there may be important considerations around the costs and benefits of “citizenship” as an ingroup. Further research needs to continue to explore the other factors that are at play when considering human rights violations and one’s citizenship.

Conclusion

This meta-analysis showed that the effect size for support for restrictions of civil liberties under threat differed when considering whose rights are restricted. When considering the rights of ingroup members, the effect of threat was significantly smaller than when considering the rights of outgroup members. This is a novel result.

Two studies have tested portions of this result. Hunt (2011) showed that when presenting symbolic threats from either ingroup members or outgroup members, anger towards Muslim immigrants was significantly higher for those in the high-threat outgroup compared to the low-threat outgroup, the control outgroup, and the low-threat ingroup. However, they found no significant differences in anger towards Muslim immigrants when comparing the high-threat ingroup and high-threat outgroup. Hunt (2011) did not test to see whether these conditions then varied the support for willingness to extend civil liberties. On the other hand, Mentovich et al. (2016) showed that support for freedom of speech significantly differed between targets closer to themselves – from self to US citizens – compared to non-US citizens and terrorists (Study 1 & Study 4). However, this study did not consider the effects of threat when examining the relation of ingroup and outgroup differentiation in supporting civil liberties.

That threats lead to different civil liberty restrictions towards ingroup and outgroup members is a new finding that was examined through the collation of all prior literature examining the relationship between threat and civil liberties. This is an important finding because it moves the conversation on civil liberties towards a greater consideration of social identity theory (Tajfel & Turner, 1979) – that the group we identify with is salient and important to considerations of liberty. While prior research showed that individuals are more willing to support torture and are more likely to believe that the torture will be effective when it is to save ingroup members (Houck & Conway, 2013; Houck et al., 2014), this study showed that the effect of threat was largest when considering the removal of others’ rights, not our own, lending preliminary support for a punitive model of restrictions. This suggests that in order to support civil liberties, we need to either cognitively expand the ingroup – so fewer individuals are left in the outgroup position – or suggest alternative punitive measures in lieu of restricting civil liberties.

In being able to compare across many studies and many effect sizes, this meta-analysis addressed a gap in the literature – that threat differentially impacts the restriction of civil liberties for ingroups and outgroups. One limitation in the meta-analysis was defining the boundaries of the ingroup and outgroup. Ingroups and outgroups can be defined in multiple ways (Tajfel & Turner, 1979), and the meta-analysis was not able to tease apart the wide variety of cultural, historical, and social variation that comes with the development of one’s group identity. While we were able to show that threat’s effect on SRCL was stronger for outgroups, we were not able to identify where this distinction of ingroup and outgroup appears.

Supplemental Material

supplementary_material – Supplemental material for The effect of perceived threat on human rights: A meta-analysis

Supplemental material, supplementary_material for The effect of perceived threat on human rights: A meta-analysis by Kevin R. Carriere, Anna Hallahan and Fathali M. Moghaddam in Group Processes & Intergroup Relations

Footnotes

Funding

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

ORCID iD

Kevin R. Carriere

Supplemental material

Supplemental material for this article is available online.

References included in the meta-analysis

Asbrock and Fritsche (2013), Beck and Plant (2018), Breton (2015), Buckler et al. (2009), Byrne (2006), Canetti et al. (2009), Canetti-Nisim et al. (2009), Carriere (2019b), Carriere et al. (2019), Choma et al. (2016), Cohrs et al. (2005), Conrad et al. (2018), Craig and Richeson (2014), Crowson (2007), Crowson et al. (2005), Crowson et al. (2006), David et al. (2016), Davis and Silver (2004), Djupe and Calfano (2013), Doosje et al. (2009), Dunwoody and McFarland (2018), Feldman and Stenner (1997), Garcia and Geva (2016), Henderson-King et al. (2009), Hetherington and Suhay (2011), Huddy et al. (2007), Hunt (2011), Kossowska et al. (2011), Lahav and Courtemanche (2012), Lopez-Rodríguez et al. (2014), Maoz and McCauley (2008), Nagar and Maoz (2014), Obaidi et al. (2018), Raijman and Semyonov (2004), Rapp (2015), Roebroeck and Guidmond (2018), Seate (2012), Sekerdej and Kossowska (2011), Semyonov et al. (2004), Shitrit et al. (2017), Skitka et al. (2004), Spry and Hornsey (2007), Van der Noll (2010), Verkuyten (2009), .

Notes

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