Validation of the Fear of Missing Out Scale for Use with African Americans in the United States

Abstract

Social media connects people in a myriad of ways, yet when prevented from staying connected, an experience of missing out on information and events perceived to be integral to one's well-being may ensue. Relatedness, a core construct of self-determination theory, is a primary influencer of motivation, and therefore being cut off from others has a negative impact on one's quality of life. Across diverse groups of people, social media is utilized for a variety of purposes directly related to connectedness, which implies inherent differences in how one's fear of missing out (FoMO) manifests in everyday life. This study employed the previously validated Fear of Missing Out Scale (FoMOS) with a nonclinical sample of African American and Caucasian college students in the United States, with a particular focus on validity of the measure with an African American cohort due to a lack of empirical evidence pertaining to with this demographic. Factor analyses yielded inconsistent findings from the FoMOS initial validation study, and results indicated differences in self-reported FoMO between the two racial groups. We speculate that observed group-based differences are at least partially the result of how individuals from each group understood and internalized the conceptual meaning of FoMO, supporting a hypothesis of practical differences in how anxieties related to missing out manifest. This may be due to individual or group-based differences in motivations for, or general purpose of, using social media.

Introduction

The importance of social media in our digitally connected world cannot be understated. Use of this form of communication allows people to stay abreast of current events from around the world, including sharing information and opinions about diverse topics of interest. This demonstrates the versatility and utility of the relatively new digital medium, which people have come to rely upon for their everyday need for social connectedness. Couched within self-determination theory (SDT), the notion of relatedness as fundamental human need can be thought of as a primary motivator for engaging in digitally mediated communication. However, a lack of connectedness can have a negative impact on one's quality of life due to the anxiety associated with being cut off from others.

When access to the cyberspaces where digital communication platforms such as social media are disrupted, yet the desire of the individual to remain connected persists, a fear of missing out (FoMO) is likely to develop.¹ Social media allows people from around the world to stay connected seemingly without interruption; however, one's true purpose of remaining connected may vary based on a wide range of sociocultural variables. Seemingly uninterrupted accessibility to being online and connected, and the expectation of immediate gratification this encourages, inadvertently creates a predisposition to experience increased stress and anxiety due to the very connectivity it facilitates.² Missing out on specific social events, interactions with friends both online and offline, and access to information in general can be thought of as drivers of the problematic nature of FoMO, especially when someone has a greater desire to stay regularly connected to people within their (online) social networks.

The deleterious effects of FoMO are exacerbated by the very dependence on remaining instantaneously connected. Being connected in this manner was impossible for most people in the general population not more than a few decades ago. During this time, individuals from diverse groups have developed unique ways of interacting with one another and for specific purposes. However, not only the purpose but also the style of those interactions may differ significantly for a myriad of culturally specific reasons.

Social connectivity

FoMO can be conceptualized in terms of the physical manifestation of the anxiety experienced when someone has knowledge of what they believe to be personally rewarding experiences occurring (online) despite not having immediate access.^1,3 FoMO is often understood within the framework of SDT, which assumes that effective regulation of the self and psychological well-being is based on one's sense of competence, autonomy, and relatedness, with the latter referring to connectivity with others.⁴ Inherent to SDT is the exploration of the subjective spectrum between intrinsic and extrinsic motivations that prompt motivations to act.⁵ The easy accessibility and instant gratification social media provides can increase reliance on cyberspace for many of the most fundamental daily life tasks, one of which relates to the motivation to be, and remain, socially connected.⁶

Increased anxiety is experienced when there is desire to be connected, but access is restricted for some reason. Assuaging these anxieties entails engaging in behaviors that serve to increase access to digital platforms such as social media. For example, a smartphone facilitates the accomplishment of being able to access social media and subsequently interact with others. Being able to access social media allows for increased frequency of experiences that facilitate connectedness, which serves to at least momentarily relieve the anxiety associated with the perception of being disconnected. However, the relief in anxiety also serves to reinforce an ongoing predisposition to experience continued anxiety because of the very need to remain connected. As such, ubiquitous connectivity without disruption has become an assumed norm of everyday life.^7–9

Conceptual equivalence

FoMO exacerbates emotional dysregulation,^10–13 leading to poorer behavioral outcomes that result from either state-based stress triggered during acute experiences of FoMO¹⁴ or trait-based stress resulting from personality tendencies consistent with a lower threshold for adaptability.^15,16 Like all anxieties, FoMO cannot be directly observed but can be measured through self-report based on how symptoms that manifest are perceived by someone. The Fear of Missing Out Scale (FoMOS)² is a brief, validated, and a commonly cited questionnaire, which quantifies such situationally based experiences of FoMO. Questionnaire items focus on the frequency of discrete internal experiences of inadequacy, anxiety, irritability, and diminished self-esteem in response to socially derived unmet needs.

Yet, individuals from diverse backgrounds may utilize social media for different purposes.¹⁷ The literature suggests that those who identify as African American tend to use social media more frequently than Caucasians,¹⁸ and are more likely to post and therefore also see more racially charged content.¹⁹ More frequent social media use and interactions online that might elicit race-related anxieties may directly impact perceptions of social connectedness,²⁰ which may also be compounded over time with continued engagement and motivation to post specific types of content. This may allow different (racial) groups to experience varying degrees of feeling connected to others through their online interactions.

Along with notable differences in how or why individuals from diverse racial groups utilize social media and subsequently realize social connectedness are apparent differences in how individuals from different groups experience and report clinical symptoms directly related to connectedness, specifically anxiety.²¹ This is an integral component of FoMO and as such, perceptual differences in how situational factors are internalized across groups should be carefully considered and measured only with established and validated instrumentation. What makes a questionnaire particularly useful, beyond its face and construct validity, is its utility for assessment across groups in order to decipher cross-cultural differences in how a behavioral symptom such as anxiety manifests.

Conceptual equivalence as the basis for cross-cultural comparison is essential because it provides the mechanism whereby self-report data can be examined between groups and along a common metric.^22–24 Crosscultural FoMO-related studies have also concluded that the conceptual nature of FoMO should not be assumed to be equal across groups.^3,25–29 Considering the samples noted in Przybylski's² report on the development and validation of the FoMOS, although relatively large samples were noted, there was no mention of racial or ethnic identity of the participants, which makes it difficult to establish measurement equivalence across diverse groups, such as African Americans and their Caucasian counterparts from the general population.

Purpose of study

A wealth of research has been conducted employing the FoMOS, most of which have reported similar sample descriptive data but not necessarily identical statistical or psychometric results. Additionally, there is a relative absence of empirical support for using the FoMOS specifically with African Americans. During the development of the FoMOS, Przybylski et al.² validated their instrument using a diverse and international sample, although there was no indication that African Americans were adequately represented.

Accordingly, this study sought to examine FoMO within a nonclinical African American sample of adults from the United States, with a corresponding Caucasian sample as a reference group. Considering differences in how members of these two racial groups tend to utilize social media and how they have historically reported perceived stressors and associated anxiety based on unique demographic variables,³⁰ it was hypothesized that African American participants from this sample would self-report lower levels of FoMO than Caucasian participants. The differences in reported FoMO between the two groups may warrant further examination of what was the previously identified unidimensional factor structure of the FoMOS.

Methodology

Data for this study were collected between March and December 2020. Institutional review board approval was obtained before any data collection, and all respondents provided informed consent before administration (IRB approval number: NSU IRB #19-14). Respondents were recruited primarily from a public, research-oriented university in Southeastern Virginia (84 percent), but with several other schools from across the United States included (with sample representation ranging from 0.2 percent to 4.8 percent). The investigators offered no monetary incentive, although students were able to earn course or research participation credit.

Participants

As a subset of a larger data collection effort examining correlates of FoMO for college students in the United States, data from respondents between the ages of 18 and 29 and either African American or Caucasian were included (N = 592). This age group was selected, given (a) Pew Research Center data indicating the majority of individuals utilizing social networking of any kind are college-age individuals roughly corresponding to this range³¹ and (b) to minimize potential confounding effects resulting from a wider age range and generational differences related to attitudes toward digital technology use.³² Twelve cases were removed due to significant missing data, and an additional 13 were removed for not meeting inclusion criteria of endorsing active participation on social media. This resulted in a final sample of 567 African American or Caucasian respondents.

Instrumentation

Respondents were asked to report their age, gender, and racial identification and to complete the 10-item FoMOS²—a self-report inventory with response options rated on a five-point Likert scale. To examine concurrent validity, the following Likert-style instruments were also included: the 60-item Media and Technology Usage and Attitudes Scale (MTUAS³³) to assess attitudes and behavior pertaining to media and technology engagement; the 20-item Center for Epidemiologic Studies Depression Scale (CES-D³⁴) to assess the frequency of depressive symptomology as an indicator of well-being; and the six-item State Trait Anxiety Scale (STAI: Y-6³⁵) to assess experiences of anxiety and well-being.

Procedure

Respondents were undergraduates recruited directly from courses, university email announcements, and formal research participant pools. Psychology majors represented ∼41 percent of the sample, 13 percent were nursing majors, 7 percent biology, 6 percent education, and the remaining 33 percent across several other academic majors. Respondents were administered the surveys through an anonymous link using the secure Qualtrics platform. All analyses were conducted using SPSS (Version 26).³⁶ Descriptive statistics were used to evaluate the internal consistency of the instruments, and correlations were run to examine relationships between demographic variables and survey responses.

Given this study's focus on validation for use with African Americans (who, as a distinct cultural group, may express FoMO differently than their Caucasian counterparts), factor analysis was conducted to examine the component structure of the FoMOS. Principal components analysis (PCA) with varimax rotation and parallel analysis (PA) were run for both groups individually and the combined sample. For reference, PA utilizes the existing dataset and randomly simulates a specified number of sample data in a manner consistent with a Monte Carlo approach. Simulated data are then compared with actual data from the sample to determine if observed eigenvalues are to be interpreted as unique components.³⁷ This approach provides additional insight to the nature of the component matrix that varies somewhat relative to the PCA approach. The syntax for the PA was sourced from O'Connor.³⁸

Results

Exploration of demographic variables indicated an inadvertent oversampling of female respondents (82 percent of African Americans; 76 percent of Caucasians) but similar distribution for racial identification (56 percent Caucasian; 44 percent African American). Average age was 20.70 years (SD = 2.72), with average of the African Americans being 20.57 (SD = 2.60) and average age of the Caucasians being 20.62 (SD = 2.62). Table 1 provides a detailed overview of all descriptive data collected.

Table 1.

Descriptive Statistics of African American and Caucasian Respondents

	Combined		African American		Caucasian
	n (567)	%	n (249)	%	n (318)	%
Gender
Female	445	78.5	203	81.5	242	73.1
Male	117	20.6	45	18.1	72	22.6
Transgender	2	<1	0	0	2	<1
Other	3	<1	1	<1	2	<1
Marital status
Unmarried	530	93.5	240	96.4	290	91.2
Married/committed relationship	29	5.1	5	2.0	24	7.5
Separated/divorced/widowed	8	1.4	4	1.6	4	1.2
Academic status
Freshman	189	33.4	78	31.3	111	35.0
Sophomore	113	20.0	48	19.3	65	20.5
Junior	123	21.7	53	21.3	70	22.1
Senior	141	24.9	70	28.1	71	22.4
Student status
Full-time	487	86.1	223	89.6	264	82.0
Part-time	79	14	26	10.4	53	16.7
Employed
Yes	294	51.9	118	47.4	176	55.3
No	273	48.1	131	52.6	142	44.7

To explore the theoretical implications of, and associations with FoMO as discussed in Przybylski et al.,² concurrent validity was assessed through examination of the weekly social interaction question on the MTUAS (“Over the past week, on average, approximately how much time PER DAY have you spent actively using any form of social media?”) and the General Social Media Usage subscale of the MTUAS (α = 0.95). Psychological well-being was assessed through examination of the STAI-6 (α = 0.84) and the CES-D (α = 0.92).

Bivariate correlations between each scale for the combined sample, African American sample, and Caucasian sample (collectively shown in Table 2) indicated that FoMO was significantly and positively correlated with general and weekly social media use and psychological well-being, with the exception of FoMOS total scale score and weekly social media use correlation for African Americans.

Table 2.

Bivariate Correlations Between Variables: Fear of Missing Out, State Trait Anxiety Scale-6, Center for Epidemiologic Studies Depression Scale, Social Media Engagement (General Social Media Use—GSMU and Social Media Use Weekly—SMW) for Each Grouping by Race

Grouping	Variable	1 FoMo	2 STAI-6	3 CES-D	4 GSMU	5 SMW
Full sample
	1	—	0.343^**	0.341^**	0.133^**	0.204^**
	2		—	0.591^**	—0.016	0.106^*
	3			—	0.059	0.144^**
	4				—	0.319^**
	5					—
African American
	1	—	0.353^**	0.425^**	0.187^**	0.124
	2		—	0.511^**	−0.004	0.076
	3			—	0.099	0.087
	4				—	0.287^**
	5					—
Caucasian
	1	—	0.307^**	0.289^**	0.177^**	0.277^**
	2		—	0.651^**	0.022	0.138^*
	3			—	0.043	0.192^**
	4				—	0.355^**
	5					—

Note: African Americans (n = 249), Caucasian (n = 318), full sample (N = 567).

p < 0.05; ^**p < 0.01.

CES-D, Center for Epidemiologic Studies Depression Scale; GSMU, general social media use; SMW, social media use weekly; STAI, State Trait Anxiety Scale.

Analysis of means was conducted to assess the potential influence of age, gender, and race on FoMO. To assess the relationship between FoMO and age, a one-way ANOVA was performed, and results indicated a significant effect, F(11, 555) = 1.85, p = 0.043. The Tukey post hoc analyses indicated that there was no statistically significant difference in self-reported FoMO scores based on age, with 24- and 25-year-old individuals (M = 2.62, SD = 0.95 and M = 2.61, SD = 1.19, respectively) reporting the highest levels of FoMO, followed closely by those 18 and 19 years old (M = 2.28, SD = 0.82 and M = 2.36, SD = 0.89, respectively).

There was no significant effect for gender, t(218.30) = 1.54, p = 0.124, despite female respondents reporting higher FoMOS scores than their male counterparts (M = 2.29, SD = 0.91 compared with M = 2.16, SD = 0.74). There was a significant effect for race, t(560.02) = −5.12, p < 0.001, with Caucasian respondents demonstrating significantly higher FoMOS scores compared with African American respondents (M = 2.43, SD = 0.91 and M = 2.06, SD = 0.78, respectively), with a small-moderate effect size (g = 0.43).

Internal reliability for the full sample (α = 0.90) as well as racial groupings (α = 0.87 for African Americans and α = 0.91 for Caucasians) suggested strong internal consistency for the FoMOS. Item-total correlations were at least adequate for all items for the combined sample (ranging from 0.47 to 0.78), as well as individually for African Americans (0.50 to 0.72) and Caucasians (0.51 to 0.81).

Other assumptions (of exploratory factor analysis) were met, including homogeneity, sample size, removal of outliers, and presence of multicollinearity. The Kaiser–Meyer–Olkin measure of sampling adequacy was 0.880, and Bartlett's test of sphericity was significant (χ² = 30.94.774, p < 0.001), evidencing adequacy of the data for analysis. Skewness and kurtosis patterns for each item closely resembled one another, suggesting appropriateness of pooling the data for analysis.

PA results are provided in Table 3. Parameters within O'Connor's syntax³⁸ were set to compute 5,000 parallel datasets with a 95 percentile. PCA with normal distribution random data generation was selected. For the combined sample, raw data eigenvalues only exceeded observed percentile values for one component. Likewise, when running the African American and Caucasian samples independently, only one component seemed to be indicated.

Table 3.

Parallel Analysis from the Fear of Missing Out Scale

Root	Combined sample (N = 567)			African Americans (n = 249)			Caucasians (n = 318)
Root	Raw data	Means	Percentile	Raw data	Means	Percentile	Raw data	Means	Percentile
1.00	5.22	1.21	1.27	4.72	1.33	1.42	5.43	1.13	1.37
2.00	1.05	1.15	1.19	1.18	1.22	1.30	1.02	1.20	1.26
3.00	0.82	1.10	1.13	0.83	1.15	1.20	0.85	1.13	1.18
4.00	0.63	1.06	1.09	0.73	1.08	1.13	0.61	1.07	1.11
5.00	0.61	1.02	1.04	0.68	1.02	1.06	0.59	1.02	1.01
6.00	0.58	0.98	1.00	0.60	0.96	1.00	0.56	0.97	1.00
7.00	0.46	0.94	0.97	0.51	0.90	0.95	0.40	0.92	0.95
8.00	0.24	0.90	0.93	0.32	0.85	0.89	0.21	0.86	0.90
9.00	0.21	0.86	0.89	0.24	0.78	0.83	0.20	0.81	0.85
10.00	0.17	0.81	0.84	0.20	0.71	0.77	0.14	0.74	0.80

Although there is debate about the reliability of using eigenvalues >1 as threshold cutoffs in PCA, there is value in interpreting components that account for a significant amount of observed variance, with concerns about the procedure being focused more on the interpretability of components with observed eigenvalues clustered ∼1.0 and which do not account for a high percentage of variance.^39–42 Varimax rotation was utilized because it “…minimizes the number of variables that have high loadings on each factor and works to make small loadings even smaller”⁴³ (p. 84).

Results suggested the possibility of either a one- or two-component solution. Two components were represented by eigenvalues >1.0, although only the first component was well over this threshold and which represented the vast majority of explained variance observed. The same trend was apparent when examining the sample as a whole or for each racial group individually. Table 4 indicates the eigenvalues and variance accounted for by each component, and Table 5 provides component-level summary data and the rotated component matrix (emphasizing factor loadings >0.5 for clarity). Sequence plots based on the PA (Fig. 1) and scree plots based on the PCA (Fig. 2) provide visual representation of the component threshold cutoffs, with a notable elbow immediately after the first component for the whole sample and each group individually.

FIG. 1.

Sequence plots for Caucasian (a), African American (b), and combined (c) samples.

FIG. 2.

Scree plots for Caucasian (a), African American (b), and combined (c) samples.

Table 4.

Eigenvalues and Associated Variance Accounted for by Each Component by Racial Group

FoMOS	Components (combined sample)		Components (African Americans)		Components (Caucasians)
FoMOS	1	2	1	2	1	2
Eigenvalue	3.31	2.96	3.30	2.61	3.31	3.14
Explained variance	33.10%	29.63%	32.95%	26.05%	33.09%	31.40%
Cumulative variance	33.10%	62.73%	32.95%	58.99%	33.09%	64.50%

FoMOS, Fear of Missing Out Scale.

Table 5.

Principal Components Analysis—Rotated Component Matrix

FoMOS item	Factor loadings (combined sample)		Factor loading (African American)		Factor loadings (Caucasian)
FoMOS item	1	2	1	2	1	2
1. I fear others have more rewarding experiences than me.	0.877	0.165	0.860	0.111	0.210	0.900
2. I fear my friends have more regarding experiences than me.	0.899	0.166	0.898	0.091	0.210	0.898
3. I get worried when I find out my friends are having fun without me.	0.765	0.417	0.751	0.347	0.464	0.760
4. I get anxious when I don't know what my friends are up to.	0.492	0.584	0.523	0.464	0.647	0.454
5. It is important that I understand my friends “in jokes.”	0.334	0.626	0.189	0.676	0.606	0.427
6. Sometimes, I wonder if I spend too much time keeping up with what is going on.	0.258	0.632	0.272	0.635	0.607	0.271
7. It bothers me when I miss an opportunity to meet up with friends.	0.616	0.527	0.663	0.461	0.632	0.500
8. When I have a good time, it is important for me to share the details online (e.g., updating status).	0.042	0.753	0.008	0.745	0.715	0.099
9. When I miss out on a planned get-together it bothers me.	0.532	0.523	0.520	0.502	0.596	0.451
10. When I go on a vacation, I continue to keep tabs on what my friends are doing.	0.247	0.696	0.301	0.607	0.767	0.146

Note: Items loading on each component are indicated in bold.

Discussion

This study sought to validate the FoMOS for use with an African American sample from the United States, given the lack of validation data with this specific group. We hypothesized that self-reported experience of FoMO would differ between the two racial groups based on the literature highlighting how cultural differences impact the manifestation of clinical symptomology such as anxiety,²¹ which is a primary characteristic of FoMO. Results generally supported the hypothesis in that statistically significant differences were observed, with significant scale-level scores between Caucasians (who averaged higher FoMOS scores) and their African American counterparts.

The PCA results demonstrated that items from each racial group correlated differently across the two observed components, with the first component significantly more prominent than the second, which was also consistent when examined for each group individually. The initial validation study² suggested that the FoMOS assumes FoMO is a unidimensional construct, but when running the PCA, a single component seemed to better represent the full sample. When running the PCAs separately for each racial group, a single component consisting of different items for each group was also observed, representing the possibility of a conceptual difference in how FoMO manifests within each group, or that the assumption of unidimensionality is faulty.

While results tend to evidence the notion of FoMO being multidimensional, it could also be that it is a unidimensional construct operating differently across groups. This could be the result of group-based differences overshadowing endorsement of more intense symptomology from one group relative to another. It is possible that Caucasians as a categorical grouping were responding in a way that was more associated with a less individually secure attachment to others (e.g., fear and bother resulting from potentially missing out). In contrast, African Americans responded more strongly in ways related to a desire to be connected and share to enhance a more collective secure attachment to one another. Individualistic versus collectivist tendencies might also have influenced how the African Americans in the sample reported their experience of FoMO relative to the Caucasians.

An applicable result from this study was the statistically significant between-group differences for total scale score on the FoMOS, where the Caucasian group's average was statistically significantly higher than the African American group's average. In effect, the more severe reporting observed in the results of the combined sample may have dampened the African American voice, which tended to endorse less FoMO-specific anxiety. Given differences in how and for what purpose diverse groups utilize social media,¹⁷ this could have impacted how FoMO was understood and subsequently reported.

It is possible the racial groups in this study may have a unique internalization of what the concept of FoMO represents since it is a latent construct. For example, some items seem to focus more on assessing anxiety arising from an individual missing out on the functional aspects of social connectivity (which SDT posits influences the quality of life construct), whereas other items seem to focus more on anxiety as it relates to the autonomy of the individual to know what others are doing. Also, items on the FoMOS seem to tap more heavily into state-based anxiety, but these items “ask respondents about interest in other people's lives, but with no mention to [online social media]” (p. 21).⁴⁴

To help dissect between-group differences, item-level analyses may be more advantageous than using scale-level approximations of FoMO, effectively treating each question as an independent assessment of the construct in question.⁴⁵ Anxiety is experienced in a highly subjective manner,⁴⁶ and the behavioral manifestation of it is observable, especially when the frequency and severity of dysfunctional behaviors increase. An example that has been receiving much attention recently is problematic smartphone use⁴⁷ although there is difficulty determining a standardized metric to gauge normality in this area.

However, variability in how different groups interpret and understand FoMO on a conceptual level has not been studied in great depth, which presents a challenge for those seeking to compare results across (racial) groups. In crosscultural research, for example, it is essential not to overemphasize the theoretical explanations surrounding a nonbiological concept such as racial differences (as opposed to, for example, one's sex at birth),^48–50 but instead to consider how cultural relativism—as a manner of understanding how to conceptualize “the role of culture in producing human variation both within and across groups” (p. 1104)⁵¹—might impact measurement invariance.

Given the face validity of the FoMOS items, it is possible that terminology used could have been interpreted differently across the groups as well, impacting how concepts including fear, anxiety, or bothered were understood. For example, fear can be conceptualized as a persistent apprehension or apprehension impacting ongoing motivation to stay engaged, and internalization of the conceptual meaning of the item could manifest as differences in respondents' self-reported and subjective experience of FoMO. Given the negative connotation that tends to dominate how FoMO is conceptualized in the literature, if one group tends to operationalize FoMO as more of a motivating and protective force that operates in a positive and functional manner, more profound between-group differences are likely to be observed.

Limitations and future directions

Results from this study provide a valuable point of comparison between the subjective experience of FoMO as reported by African Americans in the United States and their Caucasian counterparts. However, several limitations impact generalizability of our findings. First, sampling from this study came from a more educated pool of respondents who were generally younger. Therefore, their data may not be representative of individuals from other age groups or levels of education.

This study was also cross-sectional and relied on self-report, nor did it provide a mechanism to compare what respondents said they felt about FoMO relative to their specific social media use. In addition, an unintended oversampling of females could have confounded the results, given existing research demonstrating gender group differences related to social media use.^52–54 However, there were no statistically significant differences in observed FoMOS scores based on gender identity in this study.

We purposefully restricted the age range to minimize the potential confounding impact on results and better represent the average age range for undergraduate college students in the United States; however, examining descriptive analyses between the two groups suggested that age could be an influential factor. Additional research should focus on exploring the relationship between age and FoMO in more depth, especially in the context of a more thorough assessment of digital literacy and the digital divide related to accessibility to social media.³² Furthermore, an in-depth exploration of crosscultural issues that might impact how FoMO is conceptualized between racial groups or even entirely diverse communities is required.

Future research should further examine the latent structure of the measure being used to assess FoMO to develop a more culturally inclusive conceptualization of what constitutes FoMO. This will allow for a greater exploration of how clinically oriented terminology (e.g., fear) is interpreted by respondents. Accordingly, more research is needed to better understand the relationship between FoMO and racial identification as a mediator or moderator of culturally sensitive concepts such as the psychological need to belong.

Conclusion

This study found the FoMOS to be a face valid instrument that demonstrates good internal reliability for use with African Americans. However, the various ways of conducting factor analysis in this study yielded inconsistent dimensionality of components. This suggests that FoMO is more likely to be a multidimensional construct. While FoMOS items seem to be face valid, it is possible that appropriateness of terminology may not transfer between diverse groups.

Further comparison across groups is needed to better understand the uni- or multidimensionality of what underlies the concept of FoMO, and how best to theoretically conceptualize the experience of FoMO to make more accurate measurements that can be used to assess an individual or to compare those scores across groups. Additionally, more research is needed to better understand how culturally relevant phenomena might impact the manifestation of FoMO, and what this might look like when mediated or moderated by unique and culturally relevant factors.

Footnotes

Author Disclosure Statement

No competing financial interests exist.

Funding Information

Funding for this project was supported in part by the U.S. Department of Defense, funding award # FA8750-15-2-0120.

References

Elhai

, Levine

, Dvorak

, et al. Fear of missing out, need for touch, anxiety and depression are related to problematic smartphone use. Computers in Human Behavior, 2016; 63:509–516.

Przybylski

, Murayama

, DeHaan

, et al. Motivational, emotional, and behavioral correlates of fear of missing out. Computers in Human Behavior, 2013; 29:1841–1848.

Al-Menayes

. The Fear of Missing Out scale: validation of the Arabic version and correlation with social media addiction. International Journal of Applied Psychology, 2016; 6:41–46.

Deci

, Ryan

. Self-determination theory: a macrotheory of human motivation, development, and health. Canadian Psychology, 2008; 49:182–185.

Ryan

, Deci

. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 2000; 55:68–78.

Köbler

, Riedl

, Vetter

, et al. (2010). Social connectedness on Facebook: an explorative study on status message usage. In Proceedings of 16th Americas Conference on Information Systems. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1641178 (accessed Mar. 1, 2022).

Chiou

, Tucker

. (2020). Social distancing, internet access and inequality (No. w26982). National Bureau of Economic Research. [Epuba head of print]; DOI: 10.3386/w26982.

De Hert

, Kloza

. Internet (access) as a new fundamental right. Inflating the current rights framework?. European Journal of Law & Technology, 2012; 3. https://cris.vub.be/files/49868990/de_hert_kloza_2012_internet_access.pdf (accessed Mar. 1, 2022).

Piccininni

. Current issues: information access: a right or privilege?. Journal of Information Ethics, 1997; 6:5–7.

10.

Alabi

. A survey of Facebook addiction level among selected Nigerian university undergraduates. New Media & Mass Communication, 2013; 10:70–80.

11.

Alavi

, Maracy

, Jannatifard

, et al. The effect of psychiatric symptoms on the internet addiction disorder in Isfahan's university students. Journal of Research in Medical Sciences, 2011; 16:793–800.

12.

Dempsey

, O'Brien

, Tiamiyu

, et al. Fear of missing out (FoMO) and rumination mediate relations between social anxiety and problematic Facebook use. Addictive Behaviors Reports, 2019; 9:100150.

13.

Roberts

, David

. The Social media party: fear of missing out (FoMO), social media intensity, connection, and well-being. International Journal of Human–Computer Interaction, 2020; 36:386–392.

14.

Afifi

, Zamanzadeh

, Harrison

, et al. WIRED: the impact of media and technology use on stress (cortisol) and inflammation (interleukin IL-6) in fast paced families. Computers in Human Behavior, 2018; 81:265–273.

15.

Balta

, Emirtekin

, Kircaburun

, et al. Neuroticism, trait fear of missing out, and phubbing: the mediating role of state fear of missing out and problematic Instagram use. International Journal of Mental Health and Addiction, 2020; 18:628–639.

16.

Rozgonjuk

, Sindermann

, Elhai

, et al. Fear of missing out (FoMO) and social media's impact on daily-life and productivity at work: do WhatsApp, Facebook, Instagram and Snapchat use disorders mediate that association?. Addictive Behaviors, 2020; 106487. [Epub ahead of print]; DOI: 10.1016/j.addbeh.2020.106487.

17.

Schaffer

, Debb

. Assessing Instagram use across cultures: a confirmatory factor analysis. Cyberpsychology, Behavior, & Social Networking, 2020; 23:100–106.

18.

Chou

WYS

, Hunt

, Beckjord

, et al. Social media use in the United States: implications for health communication. Journal of medical Internet research, 2009; 11:e48.

19.

Bigman

, Smith

, Williamson

, et al. Selective sharing on social media: examining the effects of disparate racial impact frames on intentions to retransmit news stories among US college students. New Media & Society, 2019; 21:2691–2709.

20.

Riedl

, Köbler

, Goswami

, et al. Tweeting to feel connected: a model for social connectedness in online social networks. International Journal of Human-Computer Interaction, 2013; 29:670–687.

21.

Wilson

, Thayer

. Cross-cultural differences in the presentation and expression of OCD in Black individuals: a systematic review. Journal of Obsessive-Compulsive & Related Disorders, 2020; 100592. [Epub ahead of print]; DOI: 10.1016/j.jocrd.2020.100592.

22.

Boateng

, Neilands

, Frongillo

, et al. Best practices for developing and validating scales for health, social, and behavioral research: a primer. Frontiers in Public Health, 2018; 6:149.

23.

Borsa

, Damasio

, Bandeira

. Cross-cultural adaptation and validation of psychological instruments: some considerations. Paidéia, 2012; 22:423–432.

24.

Debb

, Arnold

, Perez

, et al. Validation of the FACT-Gastric cancer quality of life questionnaire for use in Spanish-speaking countries. Psycho-Oncology, 2011; 20:19–27.

25.

Gökler

, Aydin

, Unal

, et al. Determining validity and reliability of Turkish version of Fear of Missing out Scale. Anadolu Psikiyatri Dergisi, 2016; 17:53–60.

26.

Joo

, Jeon

, Shim

. Study on the Validation of the Korean Version of the Fear of Missing Out (K-FoMO) Scale for Korean College Students. The Journal of the Korea Contents Association, 2018; 18:248–261.

27.

Kim

, Lee

, Kim

. Investigating ‘Fear of Missing Out’(FOMO) as an extrinsic motive affecting sport event consumer's behavioral intention and FOMO-driven consumption's influence on intrinsic rewards, extrinsic rewards, and consumer satisfaction. PLoS One, 2020; 15:e0243744.

28.

O'Connell

. How FOMO (Fear of Missing Out), the smartphone, and social media may be affecting university students in the Middle East. North American Journal of Psychology, 2020; 22:83–102.

29.

Traş

, Öztemel

. Examining the relationships between Facebook intensity, fear of missing out, and smartphone addiction. Addicta: The Turkish Journal on Addictions, 2019; 6:91–113.

30.

Nelson

, Balk

, Roth

. Distress, anxiety, depression, and emotional well-being in African-American men with prostate cancer. Psycho-Oncology, 2010; 19:1052–1060.

31.

Hamilton Project. (2017) Age distribution of undergraduate students, by type of institution. https://www.hamiltonproject.org/charts/age_distribution_of_undergraduate_students_by_type_of_institution (accessed Mar. 1, 2022).

32.

Debb

, Schaffer

, Colson

. A reverse digital divide: comparing information security behaviors of generation Y and generation Z adults. International Journal of Cybersecurity Intelligence & Cybercrime, 2020; 3:42–55

33.

Rosen

, Whaling

, Carrier

, et al. The media and technology usage and attitudes scale: an empirical investigation. Computers in Human Behavior, 2013; 29:2501–2511.

34.

Devins

, Orme

, Costello

, et al. Measuring depressive symptoms in illness populations: psychometric properties of the Center for Epidemiologic Studies Depression (CES-D) Scale. Psychology & Health, 1988; 2:139–156.

35.

Marte Marteau

, Bekker

. The development of a six-item short-form of the state scale of the Spielberger State-Trait Anxiety Inventory (STAI). British Journal of Clinical Psychology, 1992; 31:301–306.

36.

IBM Corp. (2019) IBM SPSS statistics for windows, version 26.0. Armonk, NY: IBM Corp.

37.

Çokluk Ö, Koçak

. Using Horn's parallel analysis method in exploratory factor analysis for determining the number of factors. Educational Sciences: Theory & Practice, 2016; 16:537–551.

38.

O'Connor

. SPSS and SAS programs for determining the number of components using parallel analysis and Velicer's MAP test. Behavior Research Methods, Instrumentation, & Computers, 2000; 32:396–402.

39.

Björklund

. Be careful with your principal components. Evolution, 2019; 73:2151–2158.

40.

Hayton

, Allen

, Scarpello

. Factor retention decisions in exploratory factor analysis: a tutorial on parallel analysis. Organizational Research Methods, 2004; 7:191–205.

41.

Ledesma

, Valero-Mora

. Determining the number of factors to retain in EFA: an easy-to-use computer program for carrying out parallel analysis. Practical Assessment, Research, & Evaluation, 2007; 12:2.

42.

Patil

, Singh

, Mishra

, et al. Efficient theory development and factor retention criteria: abandon the ‘eigenvalue greater than one’ criterion. Journal of Business Research, 2008; 61:162–170.

43.

Yong

, Pearce

. A beginner's guide to factor analysis: focusing on exploratory factor analysis. Tutorials in Quantitative Methods for Psychology, 2013; 9:79–94.

44.

Sette

, Lima

, Queluz

, et al. The online fear of missing out inventory (ON-FoMO): development and Validation of a New Tool. Journal of Technology in Behavioral Science, 2020; 5:20–29.

45.

Wright

, Stone

. (2004) Making measures. Chicago, IL: Phaneron Press.

46.

Sue

, Sue

, Neville

, et al. (2019) Counseling the culturally diverse: theory and practice. Hoboken, NJ: John Wiley & Sons.

47.

Mannion

, Nolan

. The effect of smartphones on anxiety: an attachment issue or fear of missing out?. Cogent Psychology, 2020; 7:1869378.

48.

Goodman

. Why genes don't count (for racial differences in health). American Journal of Public Health, 2000; 90:1699–1702.

49.

Saini

. (2019) Superior: the return of race science. Boston, MA: Beacon Press.

50.

Williams

, Lavizzo-Mourey

, Warren

. The concept of race and health status in America. Public Health Reports, 1994; 109:26–41.

51.

Segall

, Lonner

, Berry

. Cross-cultural psychology as a scholarly discipline: on the flowering of culture in behavioral research. American Psychologist, 1998; 53:1101–1110. 1

52.

Krasnova

, Veltri

, Eling

, et al. Why men and women continue to use social networking sites: the role of gender differences. The Journal of Strategic Information Systems, 2017; 26:261–284.

53.

Noguti

, Singh

, Waller

. (2019). Gender differences in motivations to use social networking sites. In Gender economics: breakthroughs in research and practice. Hershey, PA: IGI Global, pp. 676–691.

54.

Twenge

, Martin

. Gender differences in associations between digital media use and psychological well-being: evidence from three large datasets. Journal of adolescence, 2020; 79:91–102.