Facemasking Behaviors,Preferences,and Attitudes Among Emerging Adults in the United States During the COVID-19 Pandemic: An Exploratory Study

Abstract

Facemasks have become requisite amid the COVID-19 pandemic. We explore facemasking behaviors, preferences, and attitudes among emerging adults, a “distinct period demographically” within the lifespan. Public opinion polls conducted in May 2020 found that emerging adults were the least compliant when compared to other demographic groups. To understand why, we developed a survey instrument that was administered to a demographically representative quota sample of 1,005 participants. Demographic comparisons revealed that behaviors and attitudes differed significantly by political beliefs, gender, living situation, and race. An exploratory factor analysis revealed six underlying variables: (a) facemask avoidance; (b) concerned adherence (c) vexed faultfinding; (d) statement making; (e) fashion enthusiasm; and (f) hygiene adherence. All factors varied significantly by political affiliation, and in some cases by gender, race, living situation, location, and work/education status. Significant correlations were present between all factors except fashion enthusiasm and vexed faultfinding.

Keywords

facemask COVID-19 apparel design pandemic facial covering emerging adulthood factor analysis personal protective equipment protective apparel

The COVID-19 pandemic has dramatically changed many things about everyday life in the United States, including the way people dress (Aleksander, 2020; Most Americans, 2020). Facial coverings have become a necessary protective garment and fashion accessory that is regulated in many spaces and has transformed the appearance of bodies in public. As Kaiser (1997) has argued, “clothes not only serve individual, social-psychological, and physical needs but also are cultural representations and art forms” (p. 22). In this research, we use an inductive approach to explore the social, aesthetic, and material complexities of the facemask, in particular, choices about whether and in what form and style to wear face coverings. We focus on 18- to 24-year-olds who are in the life stage between adolescence and adulthood, defined by psychologist Jeffrey Arnett (2000) as “emerging adulthood” in the United States.

Our work uncovers important demographic differences and lifestyle conditions that impact facemasking behaviors, preferences, aversions, and attitudes. We surveyed 1,005 emerging adults in the United States and share findings through descriptive statistics, demographic and lifestyle subgroup comparisons, and an exploratory factor analysis that revealed six latent variables related to facemasking attitudes, beliefs, and behaviors. We use inductive reasoning to understand how choices around facemasking are made. We present our findings in the results section and interpret results from the factor analysis in the discussion.

Facemasks and facial coverings have become a new sartorial necessity amid the ongoing pandemic. While facemasks have a long-standing history of use by healthcare professionals, lab technicians, agricultural workers, and others in settings where dangerous particulate matter or airborne pathogens are present, facial coverings have never been a widely adopted accessory for everyday life in the United States. This changed on April 3, 2020, when the U.S. Centers for Disease Control (CDC) issued a recommendation that anyone over the age of two should wear a facemask in populated public settings—a recommendation that was informed by numerous research studies showing that facemasks significantly mitigate the spread of Covid-19 (Howard et al., 2020; Kai et al., 2020). Americans were suddenly faced with a dramatic change to everyday dress, and the choice (in some places) of whether or not to don a mask.

At the time of this publication, the United States government has neither adopted nor implemented a universal facemasking policy at the federal level. Mask regulations vary dramatically across physical spaces and scope, whether by state or county lines, private or public locations, indoor or outdoor venues, and even whether one is standing or seated in a restaurant. However, all people living in the United States have the choice of whether or not to mask in private settings. In other words, choice is still very much present; therefore, the aim of our exploratory research is to better understand the forces that influence individuals’ use of protective facemasks. This understanding is critical because universal facemasking will slow the spread of the virus (Fleisher et al., 2020), and therefore reduce infection rates and lives lost to the virus (Robertson et al., 2020).

The rapid emergence of COVID-19 and the subsequent pandemic has meant that very few peer reviewed studies have been published on the topic of facemasking behaviors and preferences. Therefore, we have used other sources of information, like public opinion polls and news media articles, to understand the contemporary landscape. In fact, this research project was inspired by findings from a public opinion poll of 4,576 adults conducted by Democracy Fund + UCLA Nationscape (conducted between May 7–14, 2020), which asked U.S. Americans about whether they had worn a facemask in public in response to the coronavirus outbreak (Morin, 2020). The poll revealed that only 78% of young people aged 18–29 had worn facemasks, making them the least likely of any group to do so. Male respondents, Republicans, and Independents all tied for second place at 81% (Morin, 2020).

As we anticipated the return of students to our campus, we had a vested interest in understanding why young adults were disproportionately choosing not to wear facemasks. We developed a survey instrument to explore the specific reasons (e.g., physical, psychological, social, aesthetic, economic, etc.) that may have bearing on young people’s facemasking behavior. Using inductive reasoning and an intersectional lens (Crenshaw, 1991; Kaiser, 2012), we hope to provide designers, educators, policymakers, and other stakeholders with information that could improve facemasking compliance and ultimately save lives.

Research Aims and Rationale

Despite the growing body of scientific evidence of facemasks’ efficacy, individuals may not comply or be willing, consciously or subconsciously, to change behaviors and habits of dress (Asadi et al., 2020; Liang et al., 2020; Scheid et al., 2020). To understand facemasking behaviors, preferences, and motivations of emerging adults in the United States, we coordinated with the Survey Research Institute (SRI) at Cornell University to develop a web-based survey. We used quota sampling to ensure a representative sample of 1,005. All respondents were between the ages of 18 and 24, lived within the United States, and were demographically targeted according to gender, race, regional location, and ethnicity.

Researchers and the general public alike do not yet fully understand the motivating forces and mitigating variables that influence adoption of facemasks (Scheid et al., 2020). We also lack information about facemasking behaviors, for example, how often and when a person dons and doffs the facemask, adjusts the facemask while wearing it, launders facemasks, or other behavioral factors that impact the efficacy of facemasks (Tateo, 2020). Because little academic scholarship exists on this topic, we have taken an inductive, rather than deductive, exploratory approach. In other words, we do not present a hypothesis to be tested, but rather an interpretive analysis of our findings. We aim to better understand facemasking practices and attitudes among a population believed to be least compliant: emerging adults. Scerri and Gretch (2020) have suggested that “Instead of continuing to debate technical specifications and efficacy, sociocultural framings should be explored to encourage their use” (p. 3). Our project seeks to understand individual and collective perspectives through an intersectional analysis of behaviors and attitudes. Therefore, we ask: How do facemasking behaviors, attitudes, and compliance vary across demographic groups and living situations? We expect that results of the study will be of benefit to governments, employers, designers, schools, parents, industries and individuals, all of whom are facing at least a year or more of life with facemasks.

Literature Review

Clothes serve a double purpose according to fashion historian James Laver (1937): “They are both self-protective and self-assertive. They serve to merge the individual into his environment” (p. 248). A dramatically changing environment—like the context of a global pandemic—will undoubtedly be reflected through dress. The facemask is functionally about protection, but it has also accrued meaning in its recent adoption. Blumer (1969) has argued that in a world of “anarchic possibilities” (e.g., uncertainty) fashion enables people “to adjust in an orderly and unified way” (p. 290) to a rapidly changing world. As Laver and other scholars have pointed out, changes in fashion are typically incremental—that is, evolutionary rather than revolutionary—and are informed by the zeitgeist (Lehmann, 2000). The facemask, however, was not an incremental change but a dramatic modification to everyday dress. The facemask is thus exemplary and exceptional, and a unique case study with real consequences for human health and wellbeing amid a pandemic. As Sapir (2020) has noted, “Changes in fashion depend on the prevailing culture and on the social ideals which inform it. Under the apparently placid surface of culture there are always powerful psychological drifts of which fashion is quick to catch the direction” (p. 25). In this study, we aim to unearth the “powerful psychological drifts” that influence facemasking among emerging adults.

Emerging Adulthood

Psychologist Jeffrey Arnett (2000) has argued that in the United States, as with other industrialized countries, the post-high school years have become an increasingly distinct period of the life span, “characterized by change and exploration of possible life directions” (p. 469). This is a heterogenous time in life filled with transition and experimentation. By contrast with previous generations, Arnett (2007) has claimed, “Most young people now spen[d] the period from their late teens to their mid-20s not settling into long-term adult roles but trying out different experiences and gradually making their way toward enduring choices in love and work” (p. 69). The exploratory nature of this period, coupled with the fact that it “is the peak age period for many behaviors societies try to discourage, such as binge drinking, illegal drug use, and risky sexual behavior” (p. 72) makes it an important population to understand with regard to facemasking compliance and public health concerns brought about by COVID-19.

Experience in other regions of the world where facemasking compliance tends to be greater has revealed other concerns. A recent study of 18- to 21-year-olds in Taiwan found that even with diligent and regular use of masks, emerging adolescents were not always successful in protecting themselves or others from COVID-19 (Chao, 2020). For example, frequent donning and doffing of masks, adjustments of the mask with hands, and a lack of safe location to store or hold mask when not in use could all inadvertently increase potential transmission (Chao, 2020, p. 39). In a study conducted in Germany, Rieger (2020) found that “young people tend to be more sensitive to the perceived judgement of others,” and were more interested in self-protection (p. 54). These studies reveal the complexity and heterogeneity of this demographic (Arnett, 2007, p. 71). Behaviors are often impacted by variables related to environment, cultural background, and other identities (Osgood et al., 2005), and we must consider the intersectional nature of the identities of this particular age group (Crenshaw, 1991).

Fashion and Intersectional Identities

Tulloch (2010) has used the hyphenated terms style-fashion-dress to refer to the relationships between agency, social forces, and materiality of everyday adornment (p. 274). Kaiser (2012) has drawn upon this conceptualization to argue that people use style-fashion-dress to actively negotiate intersecting and overlapping identities (p. 30). These subject positions (e.g., age, gender, race, dis/ability, sexuality, ethnicity, class, etc.) “defy singular, essentialist ways of being” because of the interplay between different identities (Kaiser, 2012, p. 35). This interplay is often referred to as intersectionality, a concept initially developed by activist and scholar Kimberlé Crenshaw (1991) who argued that, “Through an awareness of intersectionality, we can better acknowledge and ground the differences among us and negotiate the means by which these differences will find expression in constructing group politics” (p. 1299).

An intersectional approach considers how the interplay between different identities impacts behaviors around facemasking. For example, Ma and Zhan (2020) found that “Chinese students in the United States are confronted with the double jeopardy of virus and stigma amid COVID 19” (p. 1). They argued that despite recent destigmitization of mask-wearing in the United States, Chinese students were still alienated and experienced overt racism as a result of “escalating geopolitical tensions between the United States and China” and an overall “lack of social integration of Chinese students in America” (p. 1). In a study of Norwegian students and international academics, Tatao (2020) found that masks simultaneously evoked contradictory senses of safety and fear. These ambivalences were felt differently across different identities and “the meaning-making processes in participants with different age and cultural backgrounds, living in countries that are dealing with the COVID-19 with different timing modalities” (Tatao, 2020, p. 6). The concept of intersectionality allows for a more nuanced interpretation of the complexity of facemasking behavior.

Regulatory Fashion

Negotiating subjectivities through style-fashion-dress is, as Kaiser (2012) has noted, “constrained by what is available in the marketplace, by dress codes and social conventions, by political regimes, and the like” (p. 31). While individuals are afforded some amount of agency in decision-making around everyday appearance, they are also regulated by a number of exogenous forces. Kwon (1988), for example, has found that “the situational influences which affect one’s selection of daily clothing are mostly exogenous in nature” (p. 6), meaning that everyday dress is predominantly impacted by external forces (like the requirement of a facemask during a pandemic). Social norms are a form of implicit regulation, and Broomell et al. (2020) have argued, “if all public health and political leaders deliver the message that wearing masks is necessary, that consistency will reinforce the social norm of mask wearing” (p. 7).

The facemask has become a kind of uniform during the pandemic that has taken on ideological signification. According to Jennifer Craik (2005), uniforms serve as a marker of group membership (p. 22). Craik (2005) further argued that, “Rules about how people should dress are so fundamental to human society that they are frequently encoded in legislation or codes of conduct (e.g., by professional bodies, schools)” (p. 14). Institutions like hospitals, schools, and prisons have a long-standing relationship with regulatory dress. The facemask, on the other hand, is something only recently mandated (and not in all places). Previous scholarship has shown that even when masks are provided to people for safety reasons, they may refuse to wear them because of physical discomfort, perceptions around safety, forgetfulness, or embarrassment (Ferng et al., 2011, p. 19). Facemasks have a number of physiological and psychological consequences for the wearer, which have contributed to “negative attitudes and non-compliance towards mask wearing” (Scheid et al., 2020, p. 7). Discomforts associated with this new regulatory fashion have contributed to psychological reactance. According to psychologists Taylor and Asmundson (2020), psychological reactance “is a motivational response to rules, regulations, or attempts at persuasion that are perceived as threatening one’s sense of control, autonomy, or freedom of choice” (p. 4). They found that anti-masking behavior is a “small but vocal minority of individuals” who believe facemasking is an infringement upon their civil liberties (p. 9).

Facemask Efficacy

Facemasks are a means to mitigate the spread of COVID-19 because, when constructed of appropriate materials and worn properly, they reduce the spread of virus-containing droplets (≤5 μm) and aerosols (>5 to 10 μm) that are expelled through exhalation (Prather et al., 2020). The facemask thus curtails the spread while also protecting individuals from catching the virus. Researchers have argued that, “Public mask wearing is most effective at stopping spread of the virus when compliance is high. The decreased transmissibility could substantially reduce the death toll and economic impact while the cost of the intervention is low” (Howard et al., 2020, p. 1). Models show that facemasking only works if it is adopted by a significant portion of the population with the goal of 80% compliance or higher (Kai et al., 2020).

Method

An exemption from the university’s Institutional Review Board for Human Participant Research was granted on July 15, 2020. The urgent nature of this research, coupled with the many unknowns and uncertainties around COVID-19 in the spring and early summer of 2020, meant that we had to take a rapid, creative, and exploratory approach to developing a survey instrument. Because donning facemasks was a relatively new practice for private citizens in the United States, little scholarly research existed on the topic to date. We instead surveyed a range of news media articles published between March 15 and June 10, 2020, to identify emergent themes and issues related to facemasking to ensure that the questions developed would be aligned with current—yet quickly changing—public sentiment, knowledge, and experience.

Instrument

We developed a survey instrument, which was administered through an online platform to 18- to 24-year-olds in the United States over a 2-week period in August 2020. The opening page of the survey included a consent form, after which demographic and lifestyle information were collected. The latter included age, race, ethnicity, gender identity, political affiliation, student status, employment status, home location, and current living situation. The next set of 17 questions explored facemask behaviors participants had engaged in over the past 2 months, followed by their preferences. These questions were mostly closed-ended with the exception of one open-ended question that allowed participants to specify additional fit problems. The closed-ended behavioral questions asked about where, when, and around whom they chose to wear a mask. The closed-ended questions about preferences explored design proclivities, laundering practices, sizing and fit issues, desired improvements, how participants had acquired masks, and other concerns and desires related to wearing a facemask. The survey concluded with 48 attitudinal statements about facemasking and concerns related to COVID-19 that were evaluated on a 5-point Likert scale.

Sample and Data Collection

Data collection began on August 12, 2020, and completed on August 25, 2020. We used the university’s Survey Research Institute (SRI) to program and build out the survey and secure the sample. We employed a quota sampling method to ensure a demographically representative sample of 18- to 24-year-olds in the United States according to race, ethnicity, gender, and region. A total of 1,339 individuals began the survey, and 1,005 completed, resulting in a 75% completion rate. It took each participant 13 min, on average, to complete the survey.

Data Analysis

Descriptive statistics were calculated for all survey variables. Means and standard deviations were calculated for continuous variables, while counts and percentages were calculated for categorical variables. Descriptive statistics of a subset of the survey variables were also calculated for several participant subgroups, and statistical comparisons between the subgroups were conducted using independent samples t-tests or one-way ANOVAs for continuous variables, and Fisher’s exact test for categorical variables. An exploratory factor analysis was performed on the 48 attitudinal survey questions. The responses to these questions reflected a participant’s agreement with each statement on a 5-point Likert scale, with larger values indicating stronger agreement with the statement. Factor model goodness-of-fit was assessed and an oblimin rotation was used to improve interpretability of the factors. Mean factor scores were compared between participant subgroups using independent sample t-tests or one-way ANOVAs. We assigned each participant a score for each factor and then examined correlations among the factors. All analysis was performed using the R statistical software program (R Core Team, 2020).

Results and Discussion

Results

The results of our study are presented in the order that they were gathered in our survey: demographics; facemask behaviors, preferences, desired improvements; and lastly, an exploratory factor analysis of attitudinal statements. For continuous variables, means and standard deviations are provided, and for categorical variables, counts and percentages are provided. We also performed summary and subgroup statistics of the survey variables by gender identity, race, ethnicity, political affiliation, home location, current living situation, student status, and employment status. We share statistically significant differences between expected and observed frequencies among subgroups to better understand this particular age demographic.

Table 1.

Participant Demographics.

Demographics	N = 1,005^a
Age	21.01 (1.98)
White	773/1,005 (77%)
Black	176/1,005 (18%)
American Indian	7/1,005 (0.7%)
Asian	64/1,005 (6.4%)
Native Hawaiian-Pacific Islander	8/1,005 (0.8%)
Hispanic-Latinx
Hispanic or Latino/a/x	204/1,005 (20%)
Non-Hispanic or Latino/a/x	801/1,005 (80%)
Gender identity
Female	505/1,005 (40%)
Male	484/1,005 (48%)
Other	16/1,005 (1.6%)
Political affiliation
Democrat	400/1,005 (40%)
Republican	205/1,005 (20%)
Libertarian	32/1,005 (3.2%)
Independent	265/1,005 (26%)
I choose not to answer	103/1,005 (10%)
Student
Full time	435/1,004 (43%)
Part time	141/1,004 (14%)
Not a student	428/1,004 (43%)
Employment status
Full time	254/1,005 (25%)
Part time	274/1,005 (27%)
Unemployed	432/1,005 (43%)
Other	45/1,005 (4.5%)
Home location
Urban	338/1,005 (34%)
Suburban	492/1,005 (49%)
Rural	175/1,005 (17%)
Current living situation
Alone	143/1,005 (14%)
At home with parents and/or extended family	583/1,005 (58%)
With housemates and/or roommates	128/1,005 (13%)
With romantic partner	145/1,005 (14%)
Do not have stable/secure living situation	6/1,005 (0.6%)

Note. Gender identity = “Other” includes 13 participants that specified they were non-binary or gender fluid, 1 specified queer, 1 specified other, and 1 that didn’t specify.

^a Statistics presented: Mean (SD); n/N (%).

Table 1 includes demographic information for all 1,005 participants. Of these participants, 91% reported living in a location where they were mandated to wear a mask and 94% reported that they had worn a mask in an indoor public setting over the past 2 months. Compliance was less in outdoor settings, with 84% of participants reporting having worn masks at outdoor public events. If given the choice, 5.1% of participants reported that they would never wear a mask. For the most part, however, results show relatively high mask compliance, with 72% reporting that they always wear a mask when out in public, followed by 21% who said often, and 4.9% who reported sometimes, 1.6% rarely, and just 1.1% who never wear a mask out in public. These results are much different than the public opinion surveys of the late spring and suggest that young people are in fact compliant with mask mandates.

A dataset of this size enables a more granular look at demographic differences around behaviors, preferences, needs, and desires. We conducted statistical comparisons between demographic subgroups where n > 40, with the exception of the “I choose not to answer” category for political affiliation, which included over 103 responses (however, since this was not a salient category it was removed from analysis). We looked for significant differences across demographic groups and different facemasking preferences and behaviors. As other public opinion polls and research studies have shown, political affiliation is a significant predictor of behavior when it comes to mask compliance (Nikolov et al., 2020; Pepinsky et al., 2020). In our study, 83% of Democrats reported that they always wear a mask when out in public, as compared to only 60% of Republicans and 63% of Independents. More specifically, 92% of Democrats reported wearing a mask at a large indoor event over the past 2 months, as compared to 82% of Republicans and 88% of Independents. Home location was also a significant categorical variable in facemasking behavior, with 78% of participants in urban areas reporting that they always wear a mask out in public, as compared to 60% of participants in rural areas. Home location was not significant with regard to behaviors for indoor spaces, but when reporting behavior in attending outdoor events, 89% of urban residents wore a mask to a large outdoor gathering, as compared to 77% of rural residents. Other categorical variables like gender, race, ethnicity, and employment status did not have a significant relationship to self-reported facemasking behavior.

We also aspired to understand preferences for mask styles and any fit issues that might deincentivize facemasking behavior. The most popular style of mask worn by 18- to 24-years olds in the United States at the time of the survey were reusable factory-made cloth masks, with 63% of participants reporting having worn this style. Disposable masks came in second at 61%, and home sewn reusable masks at 47%. Women were more likely than men to have made their own mask (23% of women had made their own, as compared to 17% of men), and women were more likely to have worn home sewn or disposable masks, whereas men were likely to have worn faceshields or bandanas. While political partisanship was significant with regard to facemasking behavior, it did not impact preference for styles of masks, with the exception of homemade cloth facemasks, which were slightly more likely to be worn by 54% of Democrats, as compared to 46% of Republicans and 41% of Independents (p = .003). Preferences for style types were significantly different across racial subgroups. For example, White people were significantly less likely to have constructed their own facemask (only 17% of White people had constructed a mask, as compared to 28% of Asian people and 29% of Black people, p = .002); however, White people were more likely to have worn a home sewn mask over the past 2 months (50% of White people, as compared to 36% of Black people and 44% of Asian people, p = .003). There were no significant differences across racial subgroups with regard to wearing factory-made cloth reusable masks, disposable masks, or N95 respirator styles, Black people reported greater preference for faceshields (p < .001) and bandanas (p = .021) as compared to other subgroups. Full time employees were more likely to have worn faceshields (p < .001) and homemade cloth masks (p = .032), as compared to part-time and unemployed. Other demographic categories were not significant with regard to the type of facemask style adopted and worn.

Many respondents indicated that they desired improvements to the design of face coverings. Of the entire sample, 56% wanted more breathable fabrics, 44% wished for improvements to ties and ear loops, 42% coveted softer fabrics, and 27% wanted stretchier fabrics. Comparisons between demographic sub-groups revealed the following significant differences: women wanted more breathable fabrics (p = .004), more styles (p = .001), and improvements on earloops and ties (p < .001). Significant differences were not observed between categorical values of home location, race, student and employment status. A one-way ANOVA revealed that Democrats, when compared to Republicans and Independents, did indicate a stronger desire for softer fabrics (p = .011) and improvements to ties and earloops (p = .027).

The most common improvement to facemask design was a functional one: better fit. While only 33% of participants asked for more sizes, 68% of participants indicated that they desired better fitting masks. A one-way ANOVA revealed that this number was significantly higher (p = .009) for Asian participants, 83% of whom indicated a desire for improved fit, as compared to 69% of white people and 62% of Black people. However, when asked about specific fit issues there were no observable, significant differences between racial subgroups. Interestingly, while desire for improved fit was not significantly different when comparing gender sub-groups (p = .9), significant differences were observed when asked about specific fit issues. A little over one quarter of men (28%) had trouble with masks sliding up their chins as compared to 18% of women (p = .001), while more women (55%) had trouble with masks sliding down on the chin as compared to 44% of men (p = .015).

An exploratory factor analysis was performed on the 48 attitudinal statements. Each statement had a Kaiser-Meyer-Olkin (KMO) statistic greater than 0.6. Using an eigenvalue cut-off of 1 and the eigenvalue scree plot, eight factors were initially selected. The root mean square of the residuals (RMSR) and the root mean square error of approximation (RMSEA) were both less than 0.05, the Tucker-Lewis index (TLI) was 0.89 and comparative fit index (CFI) was 0.93. These measures of fit indicate that the factor model fits the data well. Factor loadings that were greater than 0.3 were retained. Variables with cross-loadings of 0.3 or greater on two or more factors, or no loadings greater than 0.3, were removed from the analysis. As a result, 43 out of 48 statements were kept because they met this criteria. Factors with loadings on at 3 least attitudinal statements were retained, resulting in a final six factor model. We determined that the six factors represented the following latent attitudes: (a) facemask avoidance; (b) concerned adherence; (c) vexed faultfinding; (d) statement making; (e) fashion enthusiasm; and (d) hygiene adherence. Table 2 contains the exploratory factor analysis result that includes these six factors. Longer attitudinal statements have been abbreviated (e.g., “My concern for personal choice outweighs my concern for public health” has been abbreviated to “Personal choice outweighs public health”).

Table 2.

Exploratory Factor Analysis Loadings.

Attitude	Facemask Avoidance	Concerned Adherence	Vexed Faultfinding	Statement Making	Fashion Enthusiasm	Hygiene Adherence
Facemasks do not need to be worn in outdoor settings	0.77
Facemasks should only be required indoors	0.76
If I meet with a friend not wearing a facemask I will not wear mine	0.72
If I go somewhere people are not wearing facemask I will not wear mine	0.68
I only wear a facemask when I am required to	0.66
When I am out with friends we do not wear facemasks	0.58
People should be able to choose whether to wear a facemask	0.51
Wearing a facemask is a sign of weakness	0.43
My personal choice outweighs public health	0.36
Facemask requirements infringe on my rights	0.35
I am indifferent about wearing a facemask	0.35
I prefer facemasks in solid color rather than print or logo	0.31
I am concerned that I could spread the virus to others		0.76
Facemasks will help slow the spread of COVID-19		0.68
I am concerned people I care about will contract COVID-19		0.65
I will wear a facemask if I plan to go to a populated setting		0.63
I am concerned about contracting COVID-19		0.6
I believe facemasks should be worn while indoors in public spaces		0.59
Wearing a facemask in public is a sign of respect		0.58
I am willing to endure facemask discomfort		0.54
Facemasks can be fashionable		0.35
Facemasks do little to protect against COVID-19		−0.33
I have difficulty breathing when I wear a facemask			0.73
Wearing a facemask irritates my skin			0.64
Wearing a facemask causes discomfort			0.62
Facemasks cause acne			0.53
It is hard to hear people speak through a facemask			0.47
I frequently adjust my facemask with my hands			0.37
I enjoy wearing a facemask			−0.42
I prefer facemasks with a brand logo				0.79
I prefer facemasks with a school-team-organization logo				0.78
I prefer to wear a facemask that has a political message				0.57
I worry that a facemask makes me look intimidating				0.31
I want my facemask to match the outfit I am wearing					0.66
I like to feel fashionable					0.62
I wish there were fashionable cloth facemasks					0.57
I am more likely to wear a facemask if it can be customized					0.49
It is important to have facemasks for different occasions					0.42
Wearing a facemask is an opportunity to express myself					0.38
I always wash my hands before I put my facemask on						0.71
I wash my hands after removing my facemask						0.67
I wash my facemask after each use or wear a disposable each time						0.57
I remove and replace my facemask once it becomes damp						0.4

Discussion

Exploratory factor analysis was used to identify underlying commonalities through correlations between measurable variables. The factors represent groupings of variables—that is, characteristics of respondents rather than a type of respondent, and are not mutually exclusive. In other words, an individual may exhibit characteristics of hygiene adherence and fashion enthusiasm simultaneously. To create the factor scores for each person, we used the factor loadings found in Table 2. The factor scores are a weighted average of the variables that load onto each factor, and the weight for each variable is the loading value in Table 2; therefore, some of the questions contributed more to some factor scores than others, based on the magnitude of loadings. We opted to use factor loadings to create calculated factor scores rather than taking the mean of all the variables that are loaded onto a factor because the selected loadings maximize the amount of variability explained. Table 3 shows correlations between factors, all of which were statistically significant with the exception of fashion enthusiasm and vexed faultfinding. Tables 4 –6 compare the mean factor scores associated with the demographic categories where we observed the most significant differences: gender identity, political affiliation, and current living situation. We also include results of one-way ANOVAS comparing subgroups. We interpret and describe our findings related to the factor analysis in what follows.

Table 3.

Correlations Among Factor Scores.

Characteristic	Facemask Avoidance	Concerned Adherence	Vexed Faultfinding	Statement Making	Fashion Enthusiasm	Hygiene Adherence
Facemask Avoidance	1.00	−0.54	0.52	0.39	0.15	−0.14
Concerned Adherence	−0.54	1.00	−0.34	−0.10	0.24	0.30
Vexed Faultfinding	0.52	−0.34	1.00	0.16	0.05	−0.18
Statement Making	0.39	−0.10	0.16	1.00	0.43	0.21
Fashion Enthusiasm	0.15	0.24	0.05	0.43	1.00	0.26
Hygiene Adherence	−0.14	0.30	−0.18	0.21	0.26	1.00

Table 4.

Factors by Gender Identity.

Characteristic	Female, N = 505^a	Male, N = 404^a	p Value^b
Facemask Avoidance	15.30 (6.54)	17.18 (6.75)	<0.001
Concerned Adherence	22.15 (4.77)	21.13 (5.42)	<0.001
Vexed Faultfinding	9.62 (3.65)	8.79 (3.71)	<0.001
Statement Making	5.64 (2.59)	6.72 (2.87)	<0.001
Fashion Enthusiasm	10.08 (2.89)	9.99 (2.99)	0.6
Hygiene Adherence	8.39 (2.53)	8.38 (2.68)	<0.9

^a Statistics presented: Mean (SD)

^b Statistical tests performed: One-way ANOVA.

Table 5.

Factors by Political Affiliation.

Characteristic	Democrat, N = 400^a	Republican, N = 205^a	Independent, N = 265^a	p Value^b
Facemask Avoidance	14.11 (6.46)	19.30 (6.75)	16.78 (6.44)	<0.001
Concerned Adherence	23.51 (3.50)	19.75 (5.42)	20.86 (4.84)	<0.001
Vexed Faultfinding	8.36 (3.53)	10.65 (3.71)	9.32 (3.66)	<0.001
Statement Making	6.27 (2.71)	6.64 (2.87)	5.66 (2.34)	<0.001
Fashion Enthusiasm	10.61 (2.91)	9.94 (2.99)	9.52 (2.82)	<0.001
Hygiene Adherence	8.89 (2.40)	7.90 (2.68)	8.13 (2.51)	<0.001

^a Statistics presented: Mean (SD)

^b Statistical tests performed: One-way ANOVA.

Table 6.

Factors by Current Living Situation.

Characteristic	Alone, N = 143^a	At Home With Parents and/or Extended Family, N = 583^a	With Housemates and/or Roommates, N = 128^a	With Romantic Partner, N = 145^a	p Value^b
Facemask Avoidance	17.20 (7.25)	15.67 (6.59)	15.54 (6.46)	17.32 (7.11)	0.008
Concerned Adherence	20.81 (5.02)	22.08 (4.69)	22.27 (4.71)	20.65 (4.93)	<0.001
Vexed Faultfinding	8.74 (3.76)	8.99 (3.69)	9.51 (3.46)	10.03 (3.82)	0.007
Statement Making	7.12 (2.98)	5.92 (2.67)	5.79 (2.18)	6.29 (2.42)	<0.001
Fashion Enthusiasm	10.15 (2.97)	9.93 (3.12)	9.86 (2.51)	10.66 (2.76)	0.050
Hygiene Adherence	8.84 (2.48)	8.36 (2.54)	8.03 (2.49)	8.31 (2.61)	0.064

^a Statistics presented: Mean (SD)

^b Statistical tests performed: One-way ANOVA.

Facemask avoidance

This factor is characterized by an overall evasion with regard to facemasking, and a more careless attitude. Wearing a mask (or not) is done so in response to the social and physical environment; in other words, masks are only worn when required and regulated by external forces. Those exhibiting the characteristic of facemask avoidance see no need to wear a facemask outdoors, with friends who choose not to wear a mask, or around others who are not wearing them. There is an element of concern for individual rights, freedom, and choice (e.g., “People should be able to choose whether or not to wear a facemask”), even at the expense of public health (e.g., “My concern for personal choice outweighs my concern for public health”). Men were significantly more likely exhibit facemask avoidance than women and non-binary individuals. White people, Republicans and Independents, people who work full-time, non-students, and those who live alone or with a romantic partner only were also more likely to exhibit facemask avoidance when compared to others in those same demographic categories. Facemask avoidance was negatively correlated with the factors of concerned adherence and hygiene adherence, but most strongly positively correlated with vexed faultfinding, which suggests that avoidance may be linked to discomfort, in addition to a sense of entitlement and concerns related to civil liberties.

Concerned adherence

This factor reveals concern for the health of others (e.g., “I am concerned I could spread the virus to others”), as well as a belief in the effectiveness of facemasks and a willingness to wear a facemask despite discomfort. Concerned adherence is negatively correlated with the factors of vexed faultfinding and facemask avoidance, while positively correlated with fashion enthusiasm and hygiene adherence. Concerned adherence is significantly greater among women, Democrats, people living in urban settings, those who live with extended family or multiple housemates, full-time students and those who are unemployed.

Vexed faultfinding

Vexed faultfinding is a factor that encompasses a variety of physical discomfort issues when wearing a facemask (e.g., “I have difficulty breathing when I wear a facemask”). It is positively correlated with facemask avoidance, indicating that some who only wear facemasks when required are avoiding them due to discomfort. Vexed faultfinding is a characteristic more commonly observed in women, Republicans, full-time employees, and those who live with housemates or romantic partners.

Statement making

Statement making involves a preference for facemasks with a brand logo, a school-team-or organization logo, or a political message. Facemasks offer an opportunity to be noticed and to express individuality or collective identities with a message. Those who live alone or with a romantic partner, full or part-time employees or students, men, and Black people were more likely to exhibit statement making, but also expressed concern about feeling intimidating in a mask.

Fashion enthusiasm

Fashion enthusiasm is defined by interest in facemasks as fashion (e.g., “I want my facemask to match the outfit I am wearing” and “I like to feel fashionable”). No significant differences were observed with regard to gender and fashion enthusiasm, but significant differences were observed with regard to race, home location, living situation, political affiliation, and employment status. Black people, urban dwellers, Democrats, full-time employees, and those living alone or with a romantic partner more likely to express fashion enthusiasm.

Hygiene adherence

Hygiene adherence reflects fastidiousness when donning and doffing a facemask, handwashing in both cases, as well as face washing after facemask use. Hygiene adherence was significantly different across subgroups according to political affiliation (Democrats with highest factor scores), as well as race (with Black, Asian, and Hispanic/Latino/a/x people more frequently expressing this characteristic than white and non-Hispanic/Latino/a/x people), but no significant differences across other demographic categories.

Interpreting factors

Factors are not mutually exclusive; therefore, a participant’s responses may load onto multiple factors, and nearly all correlations between factors are statistically significant. For example, finding both positive correlation between facemask avoidance and vexed faultfinding and negative correlation between facemask avoidance and concerned adherence suggests there are two major factors underlying attitudes toward wearing facemasks.

We interpret the six factors through an intersectional lens in order to consider why we are finding significant differences between individuals with differing lifestyles and identities. As Kaiser (1997) has argued, “certain appearances or material artifacts come to represent shared values within a culture” (p. 49). The facemask and underlying behaviors and attitudes around them also represent shared values and perspectives of different groups and ways of living. While our data are quantitative, we have opted for an inductive approach informed by a cultural, intersectional perspective to gain deeper insights (Crenshaw, 1991; Kaiser, 1997).

Conclusion

In order to increase facemask compliance among emerging adults, we must consider population demographics and their connection to underlying, latent attitudes. Strict regulations, social modeling, and improvements to fit, design, and comfort of masks may be some pathways to ensure compliance among those who exhibit facemask avoidance. Enthusiasm for fashion and a desire to make a statement with masks were positively correlated with facemask avoidance, which also suggests that innovative statement-making designs may help to increase compliance. Interestingly, fashion enthusiasm and statement making were positively correlated with both fashion avoidance and concerned adherence, meaning that aesthetics can play a tremendous role in encouraging compliance. The populations most avoidant among emerging adults tend to be politically conservative white men who either live alone or with a romantic partner only.

Limitations and Suggestions for Future Research

This survey was conducted at a specific point in time, and therefore only provides a single snapshot amid the ever-changing uncertainty of the pandemic. August 2020 was when the initial COVID-19 surge in New York had abated and warm weather allowed considerable social freedoms outdoors with comparatively low risk as compared to indoor social activities. The survey also pre-dated the return to school for most students (43% of the sample identified as full-time students, 14% as part time): once campuses reopened, many students were required to adhere to strict campus facemask protocols, which may have resulted in habituation to the practice. The same survey conducted during the secondary surge, in cooler weather when we tend to congregate indoors, after students have returned to school, and as knowledge of viral transmission mode is clearer, may elicit different responses. Additionally, the statement making factor might have been influenced by the political partisan climate this fall, which had not yet reached a fever pitch early in August. A follow-up study might administer the exact same survey to a similar demographic to analyze change over time.

Footnotes

Acknowledgments

The authors would like to thank the Cornell Atkinson Center for Sustainability for supporting this research with a COVID-19 rapid response grant.

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 project was funded by a 2020 COVID-19 rapid response grant ($10,000) from the Cornell Atkinson Center for Sustainability.

ORCID iD

Frances Holmes Kozen

References

Aleksander

(2020, August 6). Sweatpants forever. The New York Times. https://www.nytimes.com/interactive/2020/08/06/magazine/fashion-sweatpants.html

Arnett

J. J

. (2000). Emerging adulthood: A theory of development from the late teens through the twenties. American Psychologist, 55(5), 469–480.

Arnett

J. J.

(2007). Emerging adulthood: What is it and what is it good for? Child Development Perspectives, 1(2), 68–73.

Asadi

Cappa

C. D.

Barreda

Wexler

A. S.

Bouvier

N. M.

Ristenpart

W. D.

(2020). Efficacy of masks and face coverings in controlling outward aerosol particle emission from expiratory activities. Scientific Reports. https://doi.org/10.1038/s41598-020-72798-7

Blumer

(1969, June). Fashion: From class differentiation to collective selection. The Sociological Quarterly, 10(3), 275–291.

Broomell

S. B.

Chapman

G. B.

Downs

J. S.

(2020). Psychological predictors of prevention behaviors during the COVID-19 pandemic. Behavioral Science & Policy. https://behavioralpolicy.org/journal_issue/covid-19/

Chao

(2020). Adolescents’ face mask usage and contact transmission in novel coronavirus. Journal of Public Health Research, 9, 1771.

Craik

(2005). Uniforms exposed. Berg.

Crenshaw

(July 1991). Mapping the margins: Intersectionality, identity politics, and violence against women of color. Stanford Law Review, 43(6), 1241–1299.

10.

Ferng

Y. H.

Wong-McLoughlin

Barrett

Currie

Larson

(2011). Barriers to mask wearing for influenza-like illnesses among urban Hispanic households. Public Health Nursing, 28(1), 13–23.

11.

Fleisher

Gianordoli

Parshina-Kottas

Patanjali

Peyton

Saget

(2020, October 30). Masks work. Really. We’ll show you how. The New York Times. https://www.nytimes.com/interactive/2020/10/30/science/wear-mask-covid-particles-ul.html

12.

Howard

Huang

Tufekci

Zdimal

van der Westhuizen

von Delft

Price

Fridman

Tang

Watson

G. L.

Bax

C. E.

Shaikh

Questier

Hernandez

Chu

L. F.

Ramirez

C. M.

Rimoin

A. W.

(2020). Face masks against COVID-19: An evidence review. Preprints. https://doi.org/10.20944/preprints202004.0203.v1

13.

Kai

Goldstein

G. P.

Morgunov

Nangalia

Rotkirch

(2020). Universal masking is urgent in the COVID-19 pandemic: SEIR and agent based models, empirical validation, policy recommendations. https://arxiv.org/pdf/2004.13553.pdf

14.

Kaiser

(1997). The social psychology of clothing: Symbolic appearances in context (2nd ed.). Fairchild.

15.

Kaiser

(2012). Fashion and cultural studies. Berg.

16.

Kwon

Y. H.

(1988). Effects of situational and individual influences on the selection of daily clothing. Clothing and Textiles Research Journal, 6(4), 6–12.

17.

Laver

(1937). Taste and fashion: From the French revolution to the present day. George G. Harrap.

18.

Lehmann

(2000). Tigersprung: Fashion in modernity. The MIT Press.

19.

Liang

Gao

Cheng

Zhou

J. P.

Heiner

Sun

(2020). Efficacy of face mask in preventing respiratory virus transmission: A systematic review and meta-analysis. Travel Medicine and Infectious Disease. https://doi.org/10.1016/j.tmaid.2020.101751

20.

Zhan

(2020). To mask or not to mask amid the COVID-19 pandemic: How Chinese students in America experience and cope with stigma. Chinese Sociological Review. https://doi.org/10.1080/21620555.2020.1833712

21.

Morin

(2020). Wear a mask in public? Sure. Majority of Democrats, Republicans say they have, survey shows. USA Today. https://www.usatoday.com/story/news/politics/2020/05/21/coronavirus-wearing-mask-public-common-nationscape-survey-finds/5215365002/

22.

Most Americans Say That Coronavirus Outbreak Has Changed Their Lives. (2020, March 30). Pew Research Center: Demographics and trends. https://www.pewsocialtrends.org/2020/03/30/most-americans-say-coronavirus-outbreak-has-impacted-their-lives/

23.

Nikolov

Pape

Tonguc

Williams

(2020). Predictors of social distancing and mask wearing behavior: Panel survey in seven US states. IZA Institute of Labor Economics, Discussion Paper Series. http://ftp.iza.org/dp13745.pdf

24.

Osgood

D. W.

Foster

E. M.

Flanagan

Ruth

G. R.

(2005). On your own without a net: The transition to adulthood for vulnerable populations. The University of Chicago Press.

25.

Pepinsky

Gadarian

Goodman

(2020). Yes, wearing a mask is partisan now. https://tompepinsky.com/2020/05/13/yes-wearing-a-mask-is-partisan-now/

26.

Prather

K. A.

Wang

C. C.

Schooley

R. T.

(2020). Reducing transmission of SARS-CoV-2. Science. https://doi.org/10.1126/science.abc6197

27.

R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/

28.

Rieger

M. O.

(2020). To wear or not to wear? Factors influencing wearing face masks in Germany during the COVID-19 pandemic. Social Health and Behavior. https://doi.org/10.4103/SHB.SHB_23_20

29.

Robertson

del Rio

G. M. N.

Goldstein

Smith

(2020, November 28). Virus deaths approach spring record amid changing U.S. crisis. The New York Times. https://www.nytimes.com/2020/11/28/us/covid-deaths-united-states.html

30.

Sapir

. (2020). Fashion. In Eicher

Luvaas

(Eds.), The anthropology of dress and fashion: A reader (pp. 25–26). Bloomsbury.

31.

Scerri

Grech

(2020). To wear or not to wear? Adherence to face mask use during the COVID-19 and Spanish influenza pandemics. Early Human Development. https://doi.org/10.1016.j.earlhumdev.2020.105253

32.

Scheid

J. L.

Lupien

S. P.

Ford

G. S.

West

S. L.

(2020). Commentary: Physiological and psychological impact of face mask usage during the COVID-19 pandemic. International Journal of Environmental Research and Public Health. https://doi.org/10.3390/ijerph17186655

33.

Tateo

(2020). Face masks as layers of meaning in times of COVID-19. Culture and Psychology. https://doi.org/10.1177/1354067X20957549

34.

Taylor

Asmundson

G. J. G.

(2020). Negative attitudes about facemasks during the COVID-19 pandemic: The dual importance of perceived ineffectiveness and psychological reactance. https://www.medrxiv.org/content/10.1101/2020.11.17.20233585v1.article-info

35.

Tulloch

(2010). Style-fashion-dress: From black to post-black. Fashion Theory, 10(3), 273–303. https://doi.org/10.2752/175174110X12712411520179