social media - The Professional Counselor

Social Connectedness as a Mediator of Racial Trauma Resulting From Exposure to Online Racism

Apr 1, 2026 | Volume 16 - Issue 1

Darius A. Green, Kade Stanzilis, Sierra Roach-Coye, Connor Sullivan

Online racism has increasingly become a mental health concern alongside rapid advancements in digital technology and social media use. This cross-sectional study investigated the associations between exposure to online racism, racial trauma, and social connectedness among a sample of 227 adult social media users in the United States. Using regression and mediation analyses, we found that both exposure to online racism and online social connectedness predicted increased racial trauma symptoms. Additionally, results indicated that the relationship between exposure to online racism was significantly mediated by online social connectedness. These results highlight the existence of online racism as a racially traumatic stressor and the importance of enhancing social connectedness among individuals who may be exposed to online racism.

Keywords: online racism, digital technology, social media, racial trauma, social connectedness

Digital technology use has skyrocketed with advancements in and accessibility of digital technology in the United States. According to Pew Research Center (2024), smartphone ownership has increased from 35% of adults in the United States in 2011 to 91% in 2024, with 41% of adults indicating that they are constantly using the internet (Gelles-Watnick, 2024). Additionally, social media platforms such as Facebook, Instagram, and TikTok have expanded their integration into everyday lives across the globe. It is estimated that there are 4.9 billion social media users worldwide with an anticipated growth to 5.85 billion by 2027 (Wong, 2023). Although use of social media among adults may vary across platforms, estimates highlight that 83% of adults in the United States have used YouTube, and 68% of U.S. adults have used Facebook (Gottfried, 2024). Given the rapid development and expanding use of digital technology, it is essential for counselors to develop awareness of these technologies and their impact on client wellness.

Social media platforms have been the center of scrutiny for several reasons, including online aggression. Online aggression involves various problematic online behaviors such as online hate speech, harassment, and cyberbullying (Rudnicki et al., 2023). Although not a new phenomenon, online racism has emerged in research as a pervasive issue for social media users and People of the Global Majority (PGM; Bliuc et al., 2018; Keum & Miller, 2017). We use PGM to acknowledge Black, Indigenous, Asian, Southwest Asian and North African, and Latine populations that are marginalized by white supremacy despite making up most of the world’s population. Online racism has existed as a concern and stressor for PGM since the inception of the internet. Moreover, the threat of online racism for PGM requires that critical attention be paid to its impact (Keum & Miller, 2017).

As a chronic stressor rooted in racism, it is essential to analyze the connections between exposure to online racism and traumatic stress in order to develop recommendations for clinical practice (Hemmings & Evans, 2018). Moreover, it is essential to examine factors that contribute to the traumatic impact of online racism in our digital era (Keum & Miller, 2017). Notably, social connectedness is of particular interest given the emergence of digital resistance via online counterspaces as tools to combat online racism and its deleterious impact (Gomez & Cabrera, 2025; Hill, 2018; Mosley et al., 2021). Our study seeks to expand upon this growing literature base by examining the connection between exposure to online racism and racial trauma. Moreover, we examine the role of sense of social connectedness in the relationship between online racism and racial trauma.

Online Racism

Online racism refers to the use of electronic and digital communication and media that denigrate and discriminate against PGM because of their racial and ethnic identity (Bliuc et al., 2017; Volpe et al., 2021). Like offline racism, online racism creates a hierarchy of white supremacy (Volpe et al., 2021). Although online and offline racism have common roots, there are notable ways in which online racism is unique. For example, Keum and Miller (2017) noted that online racism is pervasive, permanent, and constantly evolving. Additionally, although PGM may often be the explicit targets of racism, social media allows for a broader audience, including members of a dominant racial identity, to be vicariously impacted by racist content. Moreover, algorithms and artificial intelligence may play a role in disseminating racist content to social media users, which can lead to widespread exposure (Fulmer, 2024; Volpe et al., 2021). As a result of these unique characteristics, the prevalence and impact of online racism may be wide-ranging, particularly as it relates to mental health.

Regarding the impact of online racism on mental health, several studies have documented a connection to a range of mental health symptoms. Most research on the topic has centered on children and adolescents, highlighting associations with anxiety, depression, stress- and trauma-related symptoms, as well as psychological distress (Del Toro & Wang, 2023; Thomas et al., 2023; Tynes et al., 2012). Emerging research on the mental health impact of online racism among adults has demonstrated connections to psychological distress and well-being (Cavalhieri et al., 2024; Keum & Hearns, 2021), substance use (Keum & Ahn, 2021; Keum & Cano, 2021; Keum et al., 2023), suicidal ideation (Keum, 2022), anxiety and depression (Cano et al., 2021; Layug et al., 2022), and trauma (Evans et al., 2024) among adults who experience online racism.

Racial Trauma

Given that racism is a chronic stressor with mental health implications, it is essential to consider the relevance of racial trauma. Racial trauma refers to the psychologically, emotionally, and physically injurious impacts resulting from exposure to racism that may be both directly and vicariously experienced (Carter, 2007; Comas-Díaz et al., 2019; Wright et al., 2023). Following exposure to threatening events, injury, or violence that are racist in nature, individuals may experience symptoms of intrusion, avoidance, altered cognition and mood, as well as impacted arousal and reactivity (American Psychiatric Association, 2022; Williams et al., 2022). Symptoms of racial trauma can also expand beyond these symptoms traditionally associated with post-traumatic stress disorder (PTSD; Williams et al., 2022). For example, some psychological instruments identify depression, intrusion, anger, hypervigilance, physical symptoms, self-esteem, and avoidance as core symptoms to focus on when assessing racial trauma (Carter et al., 2013; Carter, Kirkinis, & Johnson, 2020). Additionally, racial trauma may diverge from this limited categorization of symptoms to encapsulate the cumulative impact of racial discrimination (Williams et al., 2018). Moreover, the impact of racial trauma is also conceptualized as transgenerational (Comas-Díaz et al., 2019; Williams et al., 2018). It must also be noted that emerging research has highlighted that White individuals may endorse symptoms of racial trauma following exposure to racism (Carter & Kirkinis, 2021; Carter, Roberson, & Johnson, 2020). Notably for White individuals, less mature White racial identity attitudes may contribute to how racism and subsequent racial trauma symptoms are experienced (Carter, Roberson, & Johnson, 2020). Additionally, Carter and Kirkinis (2021) found vicarious exposure to racism to be the most frequently reported exposure to racism among White participants. This framing of racial trauma is essential to consider when dissecting how exposure to online racism may produce it.

Technological advancements in social media and digital technology allow for traumatic stress to occur from various manifestations of online racism. Akin to offline racism, online racism may include interpersonal encounters such as microaggressions and cyberbullying (Evans et al., 2024; Green et al., 2023; Keum, 2017). Racially traumatizing stressors may also include exposure to audiovisual media that depicts racialized violence. For example, exposure to police brutality toward Black Americans or imagery from the genocide of Palestinian people in Gaza may result in racial trauma (Green et al., 2024; Keum, 2017; Nasereddin, 2023). To date, there is a paucity of research that connects exposure to online racism and traumatic stress. A study examining race-related online traumatic events in a sample of Black and Latinx adolescents found that online exposure was related to increased PTSD symptoms (Tynes et al., 2019). Emerging research in a sample of young Black adults has also demonstrated connections between exposure to online racism and symptoms of trauma (Maxie-Moreman & Tynes, 2022). Additionally, a study with a sample of Black American adults found evidence of anticipatory traumatic reaction across audiovisual exposure, news reports, and imagined exposure to police violence (Green et al., 2024). Lastly, a study on cyberbullying victimization among PGM adults found that participants experienced racial discrimination that resulted in traumatic stress in alignment with racial trauma (Evans et al., 2024). These studies highlight that traumatic responses to online racism may result in experiences such as traditional symptoms of PTSD, a heightened sense of anticipated vulnerability to racialized violence, and attempts at suppressing the traumatic impact of online racism (Evans et al., 2024; Green et al., 2024; Maxie-Moreman & Tynes, 2022; Tynes et al., 2019). While these studies document the relationship between online racism and traumatic stress, it is important to note that the relationship between racial trauma and online racism has not yet been investigated.

Social Connectedness

French et al. (2020) proposed the psychology of radical healing as a liberatory and multicultural counseling framework for supporting healing from the traumatic impact of racism. This framework emphasizes resisting oppression and envisioning collective liberation despite the pervasive nature of oppressive systems like white supremacy (Adames et al., 2023; French et al., 2020). Moreover, the psychology of the radical healing framework identifies critical consciousness, cultural authenticity and self-knowledge, radical hope, collectivism, and strength and resistance as the foundations of radical healing (Adames et al., 2023; French et al., 2020). Based on the salience of community and social relationships in this framework (French et al., 2020; Mosley et al., 2020), we identified social connectedness as a relevant construct for exploration. Social connectedness is an aspect of sense of belonging and refers to the perceived emotional distance between oneself and others (Lee & Robbins, 1995). Social and peer connectedness are essential resources for coping with and alleviating racial trauma (Holmes et al., 2024). Conversely, having limited social connectedness that fosters radical healing may increase the likelihood of internalizing racist messages communicated online (Adames et al., 2023).

Existing data on offline experiences have indicated that the harmful impact of discrimination may be buffered by social connectedness and related variables. For example, increased social connectedness significantly moderated the relationship between discrimination and post-traumatic cognition among forcibly displaced Muslims (Sheikh et al. 2022). Specifically, lower levels of social connectedness moderated this relationship (Sheikh et al., 2022). In the context of online racism, it is possible that experiencing or witnessing online racism may result in greater emotional distance and disconnection. For example, a study on online racism among professional counselors found that greater exposure to online racism predicted decreased perception of inclusion (Green et al., 2023). Alternatively, seeking counterspaces, such as online communities and interactions that are characterized by cultural affirmation, sense of community and belonging, knowledge sharing, empowerment, resistance, and critical consciousness are theorized to be facilitative of social connectedness and wellness (Case & Hunter, 2012; Gomez & Cabrera, 2025; Hill, 2018; Lopez-Leon & Casanova, 2023). For example, participating with others in counterspaces may provide a sense of connectedness despite the isolating impact of experiencing racial microaggressions (Ong et al., 2018). Relatedly, social connectedness in these counterspaces may enhance radical healing through deconstructing racist narratives and countering self-blame (Adames et al., 2023). Emerging literature on radical healing and its overlap with online counterspaces highlights the importance of investigating how online connectedness might mediate racial trauma associated with online racism.

Present Study

Our study seeks to expand the contemporary literature of racial trauma to online contexts. Specifically, our study seeks answers to the following research questions: 1) What is the relationship between online exposure to racism, social connectedness, and racial trauma symptoms? and 2) Does social connectedness mediate the relationship between exposure to online racism and racial trauma? We hypothesize that 1) exposure to online racism will significantly predict racial trauma symptoms, 2) social connectedness will significantly predict racial trauma symptoms, and 3) social connectedness will significantly mediate the relationship between online racism and racial trauma symptoms.

Methods

Procedures
After receiving IRB approval, participants were recruited to participate in our cross-sectional study using convenience sampling using the MTurk platform. Inclusion criteria included being a U.S. resident, being at least 18 years old, and self-identifying as using social media. A total of 518 participants accessed our online survey. We implemented a racial identity quota to ensure representation of PGM participants. The racial identity quota limited the number of White participants to 170 to prevent oversampling. We chose 170 as a target in an attempt to achieve a final sample comprising approximately 60% White participants to reflect the population of the United States in 2020 (Jones et al., 2021). As a result, a total of 292 participants completed the online survey. Participant responses were screened to remove participants who failed two validity check items, resulting in the removal of 18 participant responses. We took a conservative approach and removed an additional 47 responses from the dataset that were flagged by Qualtrics as potential duplicate responses, leaving a total of 227 participant responses. Participants who completed the online survey were compensated $8.

Participants
Participant age ranged from 19 to 70 years (M = 33.33; SD = 9.04). Regarding race, 19 (8.37%) were Asian, 26 (11.45%) were Black, one (0.44%) was Latinx, one (0.44%) was Middle Eastern and North African, five (2.20%) were Native or Indigenous American, 170 (74.89%) were White, three (1.32%) were multiracial, and two (0.88%) did not indicate their race. As for gender, 164 (72.57%) identified as cisgender men and 62 (27.43%) identified as cisgender women. One (0.44%) had less than a high school diploma or equivalent, seven (3.08%) were high school graduates, seven (3.08%) had some college experience with no degree, 13 (5.73%) had an associate degree, 160 (70.48%) had a bachelor’s degree, 36 (15.68%) had a master’s degree, and three (1.32%) had a doctoral degree. Regarding social media use, one (0.44%) reported never using social media, 13 (5.73%) reported using social media once a week, 44 (19.38%) used social media 2–3 times per week, 24 (10.57%) used social media 4–6 times per week, and 145 (63.88%) used social media daily. Of specific social media platforms, 30 (13.22%) used Discord, 197 (86.78%) used Facebook, 201 (88.55%) used Instagram, 67 (29.52%) used LinkedIn, 49 (21.59%) used Reddit, 41 (20.70%) used Threads, 107 (47.14%) used TikTok, nine (3.96%) used Tumblr, 128 (56.39%) used Twitter/X, and 193 (85.02%) used YouTube.

Measures
Perceived Online Racism
We used the 15-item Perceived Online Racism-Short Form (PORS-SF) to measure experiences of and exposure to online racist interactions and content (Keum, 2021; Keum & Miller, 2017). Participant scores ranged from 1 (not at all) to 4 (extremely). Sample items from the PORS-SF include items such as “received racist insults regarding my online profile (e.g., profile pictures, user ID)” and “seen online videos (e.g., YouTube) that portray my racial/ethnic group negatively” (Keum, 2021; Keum & Miller, 2017). Cronbach’s α for the PORS-SF ranged from .91 to .93 in its validation and was .93 in the current sample (Keum, 2021).

Social Connectedness
The 8-item Social Connectedness Scale (Lee & Robbins, 1995) was used to measure social connectedness in offline and online environments. Scores range from 1 (agree) to 6 (disagree) and were summed to compute a total score. Scores on the scale correspond to the sense of social disconnectedness and detachment experienced, with higher scores indicating greater disconnectedness and detachment (Lee & Robbins, 1995). Reliability for the Social Connectedness Scale was α = .91 in a sample of university students (Lee & Robbins, 1995). To distinguish between offline and online social connectedness, participants completed two versions of the Social Connectedness Scale, one with instructions to focus on offline relationships and another with instructions to focus on online relationships. Example items included, “I feel disconnected from the world around me” and “I catch myself losing all sense of connectedness with society.” Cronbach’s α on the Social Connectedness Scale for the current sample was .94 for both offline and online scores.

Racial Trauma
The 30-item Racial Trauma Scale (Williams et al., 2022) was used to measure participants’ experiences and symptoms of racial trauma. Specifically, the Racial Trauma Scale measures experiences related to impact on safety, negative cognition, and difficulty in coping. Participant responses ranged on each item from 1 (not at all) to 4 (extremely) and were summed for a total score. Sample items included, “thinking the world is unsafe,” “having difficulties connecting with other people,” and “having nightmares about discrimination.” White participants were included in the development of the Racial Trauma Scale and were found to report fewer symptoms of racial trauma compared to PGM (Williams et al., 2022). Internal consistency from three diverse samples of MTurk users for the Racial Trauma Scale ranged from α = .96 to .97. Cronbach’s α for the Racial Trauma Scale in the current sample was .97.

Analytic Plan
Preliminary and regression analyses were conducted using Stata (Version 18.5). We conducted a multiple regression analysis to examine the relationship between perceived online racism and social connectedness on racial trauma. Additionally, we conducted a mediation analysis to determine the mediating role of social connectedness on the relationship between perceived online racism and racial trauma. We used a significance level of α = .05 and pairwise exclusion for each of the analyses. Assumptions for normality, linearity, homoscedasticity, and multicollinearity were tested. Visual inspection of residuals demonstrated no evidence of violations of assumptions of normality, linearity, or homoscedasticity. Participant data showed no evidence of multicollinearity as evidenced by variation inflation factor values being below 10 and tolerance values being above .1; however, offline social connectedness was removed from multiple regression analyses because of the high correlation with online social connectedness (r = .91) as shown in Table 1 (Cohen et al., 2003; Tabachnick & Fidell, 2019).

Table 1

Correlations of Variables

Variable	M	SD	1	2	3
1. Online Social Connectedness	32.48	9.20
2. Offline Social Connectedness	32.32	9.67	.91***
3. Online Racism	51.78	11.29	.61***	.60***
4. Racial Trauma	77.96	20.65	.71***	.68***	.72***

Note. N = 186.
*p < .05; **p < .01; ***p < .001.

Results

Descriptive Analysis
Prior to our primary analyses, we conducted a descriptive analysis for participant responses on the PORS-SF, the Racial Trauma Scale, and the Social Connectedness Scale. For the PORS-SF, participants’ scores ranged from 15 to 69 with a mean of 51.88 and standard deviation of 10.94. Participant scores on the Racial Trauma Scale ranged from 30 to 120 with a mean of 78.40 and standard deviation of 20.63. Participant scores on the Social Connectedness Scale ranged from 8 to 47 with a mean of 32.55 and a standard deviation of 9.28.

Regression Analyses
We used regression analyses to answer our first research question: What is the relationship between online exposure to racism, social connectedness, and racial trauma symptoms? We hypothesized that 1) online racism exposure and 2) social connectedness would significantly predict racial trauma symptoms. Results from the hierarchical multiple regression model analyzing the impact of social connectedness and exposure to online racism on racial trauma symptoms while controlling for race, gender, education, age, and frequency of social media use are presented in Table 2. Among the control variables, age (β = −.19, p = .010) and frequency of social media use (β = −.35, p < .001) significantly predicted less racial trauma in the first step of the model. Race, gender, and education did not significantly predict racial trauma symptoms. There was a significant increase when adding social connectedness and online racism in the second step of the model for predicting racial trauma, F(7, 178) = 47.81, p < .001, ΔR² = .44. Online social connectedness (β = .38, p < .001) and online racism (ꞵ = .45, p < .001) predicted greater racial trauma symptoms. Step 2 accounted for 66% of variance in racial trauma symptoms with the addition of perceived online racism and online social connectedness accounting for a 44% increase in explained variance. Results from the regression analyses demonstrated support for our first two hypotheses that online exposure to racism and social connectedness would significantly predict racial trauma symptoms.

Table 2

Regression Coefficients of Variables on Racial Trauma

Variable	Step 1		Step 2
Variable	B (SE)	β	B (SE)	β
Age	−0.40 (0.15)	−.19**	−0.14 (0.10)	−.07
Race
White (ref)
PGM	3.49 (3.22)	.08	0.98 (2.15)	.02
Gender
Cisgender Man (ref)
Cisgender Woman	−3.66 (3.24)	−.08	1.28 (2.18)	.03
Education
> Bachelors (ref)
< Bachelors	3.22 (4.47)	.05	−2.62 (3.02)	−.04
Social Media Use
> Daily (ref)
Daily	−15.20 (3.01)	−.35***	−5.07 (2.11)	−.12*
Online Social Connectedness			0.86 (0.13)	.38***
Online Racism			0.83 (0.11)	.45***

R²	.21***		.65***
ΔR²			.44***

Note. N = 185.
*p < .05; **p < .01; ***p < .001.

Mediation Analysis
We conducted a simple mediation analysis using Hayes (2018) PROCESS macro (Version 5.0) in RStudio (Version 4.5) to answer our second research question: Does social connectedness mediate the relationship between exposure to online racism and racial trauma? Results from the mediation analysis are presented in Table 3 and depicted in Figure 1. The total effect of online racism exposure on racial trauma was significant, b = 1.32, β = .72, SE = .09, 95% CI [1.13, 1.50], R² = .52. The indirect effect of exposure to online racism on racial trauma through online social connectedness was significant, b = .47, β = .26, SE = .13, bootstrapped 95% CI [0.24, 0.73]. Confirming our third hypothesis, these results indicate that online social connectedness significantly mediated the relationship between online racism and racial trauma among participants.

Table 3

Results of Mediation Analysis

Path	b	SE	β	t	R²	95% CI
Path	b	SE	β	t	R²	LL	UL
Online racism → Social connectedness (a)	0.50	.05	.61***	10.44	.37	0.40	0.59
Social connectedness → Racial trauma (b)	0.95	.13	.70***	7.49		0.70	1.20
Online racism → Racial trauma (c’)	0.85	.10	.72***	8.25		0.65	1.05
Total Effect	1.32	.09	.72***	14.18	.52	1.14	1.50
Indirect Effect	0.47	.13	.26			0.13	0.40

Note. N = 185. The indirect effect was estimated using 10,000 bootstrap samples.
***p < .001.

Figure 1

Mediation Model of Relationships Between Online Racism, Online Social Connectedness, and Racial Trauma

Note. Coefficients presented are unstandardized regression coefficients of direct effects. Confidence interval for the indirect effect is a bootstrapped confidence interval using 10,000 samples.
***p < .001.

Discussion

Prior and emerging literature has documented the significant impact of online racism on mental health and wellness, such as anxiety, depression, stress- and trauma-related symptoms, and psychological distress among children and adolescents (Del Toro & Wang, 2023; Thomas et al., 2023; Tynes et al., 2012) as well as emerging findings of psychological distress, substance use, suicidal ideation, anxiety, and depression among adults (Cano et al., 2021; Cavalhieri et al., 2024; Keum, 2022; Keum & Ahn, 2021; Keum & Cano, 2021; Keum et al., 2023; Keum & Hearns, 2021; Layug et al., 2022). This study sought to build upon these findings to determine if online racism exists as a racially traumatic stressor that may lead to the development of symptoms of racial trauma. Confirming our first hypothesis, the results of our study demonstrated that exposure to online racism was associated with increased racial trauma symptoms among participants. Thus, our findings indicate that such exposure contributes to emotional and psychological injury upon individuals’ racial identity. Our study aligns with emerging research on adults that has demonstrated exposure to racism via online communication and media as a traumatic stressor (Evans et al., 2024; Green et al., 2024; Maxie-Moreman & Tynes, 2022).

Our study builds upon existing research by highlighting that exposure to online racism may be experienced as racially traumatizing by PGM as well as by White individuals. Although not part of our primary research question and analysis, our regression analysis found no statistically significant difference in racial trauma between White and PGM participants. This finding should be interpreted with nuance given that White individuals do not experience racism as a function of white supremacy. Echoing findings from Carter, Roberson, and Johnson (2020) and Carter and Kirkinis (2021), White individuals’ quantitative endorsement of racial trauma from vicarious exposure to racism may be qualitatively different from experiences of PGM. Specifically, racial identity attitudes may play a role in how White individuals perceive racism and experience subsequent distress (Carter, Roberson, & Johnson, 2020). Prior research highlights that White individuals with greater racial awareness endorse fewer racial trauma symptoms despite reporting vicarious exposure to racism (Carter, Roberson, & Johnson, 2020). Moreover, White individuals with color-evasive racial attitudes may view White individuals as targets of online racism (Green et al., 2023). Thus, this finding may be best understood as being connected to racial attitudes that impact measurement of racial trauma symptoms rather than equating White and PGM participant experiences.

Our study also sought to examine the relationship and mediating role of social connectedness with online racism and racial trauma. Confirming our second hypothesis, we found that online social connectedness both significantly predicted racial trauma in our regression model and significantly mediated the relationship between online racism and racial trauma. These findings suggest that feeling disconnected in online contexts following exposure to online racism may relate to increased symptoms of racial trauma. Additionally, individuals who experience challenges in finding and maintaining connectedness and supportive communities with others may be at greater risk of developing racial trauma symptoms. This aligns with findings of suppression and social withdrawal following experiences of racialized cybervictimization (Evans et al., 2024). Conversely, our findings highlight that stronger social connectedness after exposure to online racism may relate to decreased racial trauma symptoms. These findings emphasize the value of resources like online counterspaces that may foster racial identity affirmation, sense of community, social support, and resistance to online racism (Case & Hunter, 2012; Gomez & Cabrera, 2025; Lopez-Leon & Casanova, 2023). In summary, this study highlights the important roles of online interactions, relationships, and communities as they relate to the racial trauma experienced following exposure to online racism.

Clinical Implications and Future Research
As discussed above, our study contributes to existing literature on online racism and symptoms of mental health and wellness. Findings from our study indicate that online racism may be experienced as a racially traumatic stressor. This is noted in participant scores for racial trauma (M = 78.40; SD = 20.63) being above the established clinical cut-off score of 48 (Williams et al., 2022). Thus, counselors should integrate experiences of online racism into assessment and interventions with adult clients, particularly at times when online racist content may be prevalent. This assessment and acknowledgement can incentivize counselors to focus on processing these traumatic experiences. For example, a counselor naming a client’s response as part of racial trauma can support validating their experiences following exposure. Moreover, this may also support client healing by attributing their intrapsychic racial trauma symptoms as responses to external racism while reducing the odds of internalizing racism (Adames et al., 2023). Likewise, counselors can focus on enhancing client social connectedness as a potential protective factor that may support reducing client experiences of self-blame and social withdrawal (Adames et al., 2023; Evans et al., 2024). Counselors may also consider supporting clients in developing digital hygiene to optimize self-care in their therapeutic work.

Considering the significance of social connectedness as a protective factor, counselors can advocate for clients experiencing racial trauma from online racism to actively participate in digital counterspaces that foster social connectedness (Gomez & Cabrera, 2025; Mosley et al., 2021). This may occur through traditional modalities of counseling such as group counseling via telehealth or other community-developed groups across social media platforms. Counselors may encourage client participation in such digital counterspaces during treatment to empower them to engage in community care as a compliment to self-care behaviors. Additionally, these client recommendations may aid in developing critical consciousness, a sense of belonging with others, and in developing resistance strategies despite experiences of online racism (Case & Hunter, 2012; Gomez & Cabrera, 2025; Lopez-Leon & Casanova, 2023). Beyond individual counseling, it is imperative that counselors engage in community-level advocacy in addressing online racism. For example, facilitating workshops, public education, and creating safe spaces in school or university settings may support vulnerable populations in building awareness around online racism and its impact. Such spaces can offer safety and validation of racial trauma experiences (Wright et al., 2023). Moreover, these spaces and events can provide critical insight toward preventing engagement in online racism and healing from its traumatic impact (Wright et al., 2023).

Lastly, counselors might consider ways to support the wellness of White clients who experience distress related to online racism. Counselors should emphasize development in White racial identity and coping practices to navigate vicarious exposure to racism online. This might include supporting White clients in reshaping their cognition of their adverse reactions toward a more critical understanding of white supremacy (Carter, Roberson, & Johnson, 2020). Counselors are encouraged to address color-evasive racial attitudes expressed by White clients during sessions to enhance critical consciousness in alignment with theory on radical healing (Adames et al., 2023; French et al., 2020; Green et al., 2023). Counselors should also encourage White clients who are struggling with their adverse reactions to online racism to build relationships with other White individuals to enhance social connectedness as it pertains to critical consciousness raising. Supporting White clients in appropriately attributing their adverse experiences may also aid in reducing online racism toward PGM. For example, a White client who experiences online backlash from engaging in online racism may attribute their adverse experience to reverse racism, resulting in further engagement in online racism (Green et al., 2023).

Future research may build upon our study by more specifically investigating the role of counterspaces as they relate to mental health, wellness, and exposure to online racism and other forms of online hate. Such research should consider the significance of qualitative methodologies to better understand the lived experiences of coping and resisting through social resources that may reduce the impact of online racism. Further quantitative research on the racial trauma of online racism might compare differences in coping between White and PGM populations to better understand ways for counselors to enhance support for those impacted by online racism. Additionally, it is important to examine online racism experienced by online racial justice activists who may be prone to such exposure. Such research might compare racial trauma symptoms experienced because of online and offline exposure to racism to identify ways in which counselors can support the wellness of those who engage in racial justice efforts via social media and other forms of digital technology. Lastly, future research might examine the impact of long-term exposure to racially traumatizing online content on mental health and wellness in order to better understand the transgenerational impact of online racism alongside advancements in digital technology. Such research might specifically study social, cognitive, and behavioral changes across the lifespan, particularly for parents of PGM children, adolescents, and emerging adults.

Limitations
One limitation of this study lies in its cross-sectional nature. Although the use of mediation analysis was used to better understand how exposure to online racism may result in decreased social connectedness that in turn increases racial trauma symptoms, the cross-sectional design does not allow for causal conclusions. As a result, it is unknown if lower or greater social connectedness reported by participants was preexisting or the result of exposure to online racism. Another limitation of the study lies in the sampling method. First, the use of MTurk as a platform to recruit participants likely introduced bias, particularly given the financial incentive for participants to complete the study. We attempted to mitigate some bias through use of two validity check items in the online survey; however, this may not have counteracted the potential for bias to influence participant data. In addition to the use of convenience sampling with monetary incentive, the use of a quota system introduced a bias in the results for White participants who completed the survey early on while PGM participants did not experience restrictions or any subsequent bias in participation. Although the quota was intended to limit the overrepresentation of White participants that may have occurred through sampling via MTurk, it also introduced uncontrolled bias into the results. Moreover, we did not include exposure to online racism or experiences of racial trauma as criteria in the recruitment process. Using a narrower sample would have been more consistent with the underlying theory of racial trauma and could produce different results. As a result, caution should be taken in generalizing this study’s findings. Future research might mitigate such bias by sampling from multiple sources, such as directly recruiting from social media platform users and groups, to better prevent issues of oversampling as it relates to racial identity.

Conclusion

Our study sought to expand emerging research on exposure to online racism by examining how it relates to racial trauma among adults. Moreover, our study contributed to this emerging research by highlighting the significance of online social connectedness as a mediating variable in the relationship between online racism and racial trauma. Our study indicates that online racism may exist as a racially traumatic stressor that is essential for counselors to attend to in clinical practice for clients who may be vulnerable to such exposure. Lastly, our findings suggest that enhancing sense of connectedness may be an avenue for supporting client wellness among those who experience racial trauma from online racism exposure.

Conflict of Interest and Funding Disclosure
The authors reported no conflict of interest
or funding contributions for the development
of this manuscript.

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Darius A. Green, PhD, NCC, LPCC, is an assistant professor at Bowie State University. Kade Stanzilis, MA, LPCC, is a graduate researcher at the University of Colorado Colorado Springs. Sierra Roach-Coye, MSW, LSW, is a doctoral candidate at Denver University. Connor Sullivan, MA, LPCC, is a graduate researcher at the University of Colorado Colorado Springs. Correspondence may be addressed to Darius A. Green, Department of Counseling and Psychological Studies, Bowie State University, 14000 Jericho Park Road, Bowie, MD, 20715, dgreen2@bowiestate.edu.

Attachment, Ego Resilience, Emerging Adulthood, Social Resources, and Well-Being Among Traditional-Aged College Students

Apr 29, 2020 | Volume 10 - Issue 2

Joel A. Lane

To improve conceptualizations of college student mental health, the present study (N = 538) compared predictors of well-being that comprise both well-established counseling theories (e.g., attachment) and newer models specific to the life experience of the millennial generation and Generation Z. Predictors included internal resources (i.e., attachment security, ego resilience), emerging adulthood identification, and social resources (i.e., social support, social media usage). Each variable set predicted significant variance. The emerging adulthood and social media variables accounted for approximately 7% of variance in both psychological well-being and life satisfaction. Identifying emerging adulthood as a time of negativity and instability was the second strongest predictor of psychological well-being, while identifying emerging adulthood as a time of experimentation and possibilities was the second biggest predictor of life satisfaction. Implications for conceptualizing and treating today’s students are discussed.

Keywords: college counseling, emerging adulthood, social media, attachment, social support

In recent years, higher education personnel have noticed declines in college student emotional health and corresponding increases in stress, depression, and anxiety (Watkins et al., 2012). The rates of students exhibiting frequent anxiety and depression symptoms have nearly doubled over a 30-year period and are now two to three times higher than those of the general population (American College Health Association [ACHA], 2015). Administrators have also described corresponding changes in college counseling services, especially regarding the increased need for crisis intervention and triage services (Watkins et al., 2012).

These trends roughly correspond to the millennial generation and Generation Z entering college. The societal forces that characterize these generational cohorts, including the proliferation of social media (Ellison et al., 2007; McCay-Peet & Quan-Haase, 2017) and increases in parental involvement and corresponding decreases in perceptions of college student maturity and autonomy (Watkins et al., 2012), seem to have substantially altered the psychosocial trajectories for today’s traditional-aged college populations (Arnett, 2004, 2016). The counseling profession has wrestled with how best to respond to these trends, and in many cases has relied on conceptual frameworks and theories of psychosocial development created long before the emergence of the millennial generation. It seems timely to attempt to develop a framework for mental health and well-being during the college years that incorporates theories specific to present generations of traditional-aged college students with more well-established theories of development. Such is the purpose of the present study, in which the contributions to college student well-being of attachment security (Bowlby, 1969/1997), ego resilience (Block & Block, 1980), and social support are integrated with and compared to the theory of emerging adulthood (Arnett, 2004), a conceptualization of psychosocial development occurring from the late teens through the 20s for contemporary generations.

Attachment and Ego Resilience

It is generally accepted that the constructs of attachment security and ego resilience play important roles in college student mental health and well-being (e.g., Lane, 2015; Taylor et al., 2014). According to Bowlby (1969/1997), the quality of our earliest interactions with caregivers provides us with relational templates, or types of attachment, that influence self-worth and interpersonal functioning throughout the life span. Ego resilience is a personality trait reflecting our ability to adapt and thrive amid stress and transition (Block & Block, 1980; Taylor et al., 2014). In the present study, attachment and ego resilience are conceptualized as internal resources because they are instilled early in life, relatively stable over time, and influential to mental health during the college years (Lane, 2016; Lane et al., 2017; Taylor et al., 2014).

Attachment and ego resilience also similarly impact functioning in times of challenge. With secure attachment, individuals are more likely to believe themselves capable of handling adversity and that others can be called upon in times of need (Brennan et al., 1998), presumably because of the consistent responsiveness of their caregivers earlier in life. Conversely, insecure attachment can lead individuals to doubt their own capabilities (i.e., attachment anxiety) or the intentions of others to provide them with support (i.e., attachment avoidance) in times of need. These internalized beliefs can lead to problematic outcomes during distressing situations (Wei et al., 2007), including maladaptive interpersonal dependence or isolation and a heightened focus on the distress (Brennan et al., 1998). Similarly, individuals high in ego resilience are generally able to respond to stressful situations with flexibility and an assortment of healthy coping behaviors (Taylor et al., 2014). Conversely, individuals low in ego resilience may lack the diversity of healthy coping strategies necessary to effectively persevere through a range of life challenges, and they may be prone to giving up when frustrated (Block & Block, 1980). Thus, individuals with attachment insecurity and low ego resilience are at an increased risk of accumulating stress during stressful situations rather than persevering through them (Brennan et al., 1998), which is a likely explanation for the associations of each construct with depression and anxiety symptoms (Taylor et al., 2014).

This latter point is especially important in the context of the present study. The college experience contains numerous life and role transitions, including leaving home, establishing independence, reconstructing social support networks, and developing professional goals (Lane, 2015). Each of these transitions pose opportunities for students high in internal resources to thrive and risks for those who are low in internal resources to accumulate stress and negative mental health symptoms (Lane, 2015). Accordingly, internal resources are conceptualized as the first set of constructs in the present model. That is, they seem to provide a foundation for college student mental health and well-being and perhaps do so by contributing to other potentially relevant aspects of well-being, such as identification with emerging adulthood (Schnyders & Lane, 2018) and social support (Galambos et al., 2006).

Emerging Adulthood

Although attachment and ego resilience have long been considered contributors to college student mental health and well-being, many of the aforementioned factors involved in declining mental health trajectories comprise social forces unique to present-day young adults. Emerging adulthood (Arnett, 2004) is a theory that describes the effects of such factors on psychosocial functioning between the ages of 18 and 29. Specifically, it suggests that this age range now represents a period of life distinct from both adolescence and adulthood. The theory describes several dimensions that are representative of the present-day emerging adult experience, including a prolonged period of identity exploration (i.e., using the emerging adulthood years to consider and audition preferences regarding career, worldviews, romantic relationships, and interpersonal characteristics), significant demographic and relational instability (e.g., increased likelihood of multiple residence changes with respect to previous generations, causing disruptions in social groups), subjectively feeling in between adolescence and adulthood, and idealistic thinking about future possibilities (Arnett, 2004). These dimensions suggest that emerging adulthood is a complex phenomenon with significant individual variation: One’s degree of identification with each dimension can shape their relative satisfaction with the overall emerging adulthood experience (Baggio et al., 2015). Moreover, some evidence suggests that parental attachment quality predicts one’s identification with the various themes of emerging adulthood (Schnyders & Lane, 2018).

Emerging adulthood theory has several implications in the context of college student well-being. First, life transition is a salient theme of emerging adulthood, given that the late teens and 20s are a time of leaving the parental household, creating new attachment and support networks, entering and persisting through college (for many emerging adults), and entering the world of work (Arnett, 2004). These transitions can leave emerging adults vulnerable to distress (Lane et al., 2017) and are central features of the college student experience. Second, emerging adulthood suggests that present traditional-aged college students are at an earlier stage of psychosocial development than prior generations, even though expectations placed on them have remained stable (Arnett, 2004). Thus, emerging adult college students are still expected to navigate the many transitions of the college experience regardless of whether or not they have developed the necessary maturity and life skills. Finally, the emerging adulthood years constitute a high degree of risk-taking behaviors, impulsivity, and psychiatric risk (Arnett, 2004; Baggio et al., 2015). That is, not only is emerging adulthood a time of vulnerability to stress, but also a time of elevated risk for maladaptive stress responses. Thus, in the context of the present study, it is possible that the emerging adult experience uniquely contributes to mental health and well-being with respect to the contribution of internal resources.

Social Resources

Like interpersonal resources and emerging adulthood, social support is a construct with implications for mental health. The degree to which an individual feels supported by their close relationships mitigates distress during stressful situations (Sarason et al., 1991). Individuals who are satisfied with their social support also report less depression, anxiety, and loneliness, and enhanced well-being compared to those low in social support (Galambos et al., 2006).

The aforementioned societal changes impacting emerging adulthood also have implications for college student social support. Today’s emerging adult social support networks have grown in complexity as psychosocial developmental trajectories have continued to evolve (Arnett, 2004) and social media has become an increasingly ingrained aspect of everyday life. These changes necessitate reconsideration of the construct of social support in the 21st century. That is, what are the implications for social support when interpersonal contact is increasingly conducted electronically? Is it possible for one to derive the benefits of social support from social media interactions? To address these questions, Manago et al. (2012) asked a sample of college students to respond to various support-related questions while browsing Facebook. Participants were able to use Facebook to meet certain intimacy needs, especially that of emotional disclosure, and the size of one’s Facebook friends list was positively associated with perceived social support and life satisfaction. Others have suggested that social media sites provide social capital and facilitate sustained connection with potentially beneficial relationships (Ellison et al., 2007). In light of these ideas, the present study conceptualizes social resources to include both social support and social media usage. Assessing the degree to which each construct impacts college student mental health and well-being is important given the ubiquity of social media on college campuses and the current disagreement among scholars regarding its benefits (Manago et al., 2012) and drawbacks (Twenge, 2013). Given that social support seems to facilitate the contributions of internal resources to mental health (Taylor et al., 2014) and emerging adulthood contributes to increasingly complex social networks (Arnett, 2004), social resources are conceptualized as a third level of constructs in the present model, after internal resources and emerging adulthood identification.

Present Study

The present study was designed to address several literature gaps concerning college student mental health and well-being. First, it combines several disparate threads of related research by testing a model including internal resources (i.e., attachment security and ego resilience), identification with the dimensions of emerging adulthood, and social resources (i.e., social support and social media usage). Although some research has examined the additive impact of more than one of these sets of constructs together (e.g., attachment and social support), no existing research has examined all three collectively. Second, the present study examined the mental health implications of emerging adulthood and social media usage: two constructs that are the result of 21st century societal forces. A primary hypothesis of the study was that each predictor variable set would explain unique and additive variance for two characteristics of college student mental health (i.e., psychological well-being [PWB] and life satisfaction). A secondary hypothesis was that emerging adulthood identification and social media usage would predict unique variance in each outcome variable even after accounting for the effects of all other predictor variables in the model.

Method

Participants and Procedure

Participants in this IRB-approved study were traditional-aged undergraduate students from a large, public university in a metropolitan area of the Pacific Northwest. Participants were recruited via a recruitment email sent to a random sample of students meeting the inclusion criteria (i.e., 18 to 25 years old and enrolled as a full-time undergraduate student). An a priori power analysis was conducted to determine appropriate sample size (Faul et al., 2007). Given the large number of variables in the model and the fact that Hypothesis 2 was based on semipartial correlations, a small-to-medium effect size was selected (f ²= .08). Results suggested an ideal sample size of approximately 400 participants. Assuming an approximate 10% response rate (Manfreda et al., 2008), recruitment emails were sent to 4,000 undergraduates.

The recruitment email contained a link to an online survey containing all demographic and study variable items. Surveys were received from 616 undergraduates (15.4% response rate). Data were treated according to the recommendations for multivariate analysis by Meyers et al. (2013). That is, 56 cases (9.1%) were removed because they contained missing data on at least 50% of the items. An additional 17 cases (2.8%) were removed for indicating that they were no longer paying attention at the midpoint of the survey. The remaining missing values were replaced with their respective item mean because no item was missing more than seven cases (1.3%) and no variable contained more than two missing items for any remaining participant. Data were screened for multivariate outliers using Mahalanobis distance, resulting in the removal of five (0.9%) participants. Thus, the study sample consisted of 538 participants.

The study sample had a mean age of 21.72 years (SD = 2.05) and was predominantly female (n = 378, 70.3%), while other participants identified as male (n = 142, 26.4%) or other (n = 16, 3.0%), and two participants declined to answer. The sample was racially diverse, as 341 (63.4%) participants identified as White, 64 (11.9%) as Latinx, 63 (11.7%) as Asian or Pacific Islander, 14 (2.6%) as Black or African American, 11 (2.0%) as Arab American or Middle Eastern, eight (1.5%) as Native American, 27 (5.0%) as multiracial, and seven (1.3%) as other, while three participants declined to answer.

Instruments

Attachment security. As the first internal resources variable, attachment security was measured using the 12-item Experiences in Close Relationship Scale-Short Form (ECR-S; Wei et al., 2007). The items are evenly divided into two subscales: Attachment Anxiety (e.g., “I need a lot of reassurance that I am loved by my partner”) and Attachment Avoidance (e.g., “I am nervous when partners get too close to me”). Items are rated on a 7-point Likert scale. Scores were summed, with higher scores indicating higher attachment insecurity for each dimension. Internal consistencies in the present sample (α = .78 for attachment anxiety, α = .80 for attachment avoidance) mirrored those reported by the ECR-S authors (α = .77 and α = .78, respectively).

Ego resilience. Ego resilience served as the other internal resources variable. It was measured using an 11-item version of Block and Block’s (1980) Ego-Resiliency Scale (Taylor et al., 2014). Items (e.g., “I can bounce back and recover after a stressful or bad experience”) are rated on a Likert scale ranging from one (most undescriptive of me) to nine (most descriptive of me). Higher total scores indicate higher ego resilience. The 11-item version has demonstrated internal consistencies ranging from .63 to .81 across multiple time points with a sample of emerging adults (Taylor et al., 2014). Internal consistency in the present sample was .73.

Emerging adulthood. The second level of predictor variables comprised dimensions of emerging adulthood. Identification with emerging adulthood dimensions was assessed using the 8-item Inventory of Dimensions of Emerging Adulthood (IDEA-8; Baggio et al., 2015). The items are evenly divided into four subscales (i.e., Experimentation/Possibilities, Negativity/Instability, Identity Exploration, and Feeling In Between [adolescence and adulthood]) that each represent dimensions of emerging adult theory (Arnett, 2004). Participants rate the degree to which various statements represent the present time in their lives (e.g., “this is a time of deciding on your own beliefs and values”) on a 4-point scale (1 = strongly disagree, 4 = strongly agree). Scores for each subscale are summed to indicate how participants feel each dimension characterizes their emerging adulthood experience. The IDEA-8 subscales demonstrate internal consistencies ranging from .66 to .76 (Baggio et al., 2015), mirroring the range
found in the present sample (α = .69 to α = .77).

Social support. Social support served as the first social resources variable. It was measured using the 6-item Subjective Social Support subscale of the Duke Social Support Index (Blazer et al., 1990). Items (e.g., “Can you talk about your deepest problems with at least some of your family and friends?”) are rated on a 5-point scale (1 = none of the time, 5 = all of the time), with higher scores indicating higher perceived social support. Internal consistency in the present sample was .85, mirroring estimates found in prior studies (α = .82; Hawley et al., 2014).

Facebook usage. The other social resources variable was social media usage, measured using the 8-item Facebook Intensity Scale (FIS; Ellison et al., 2007). Although numerous social media platforms are popular among college students, developers of social media usage instruments have focused on Facebook. Given its recognizability and ubiquity, it remains the best proxy for assessing overall social media usage (Ortiz-Ospina, 2019). The first FIS item asks participants to approximate their number of Facebook “friends,” while the second item asks them to approximate time spent on Facebook each day. The remaining items ask participants to rate their agreement with various items assessing the importance of Facebook in their lives (e.g., “Facebook has become a part of my daily routine”). Items are first standardized and then summed to create an index of Facebook usage. The FIS authors reported strong convergent validity and internal consistency (α = .83), mirroring that found in the present sample (α = .87).

College student mental health. Operationalizing mental health is challenging given its many existing conceptualizations. Some authors have argued that mental health and mental illness are separate constructs entirely (e.g., Lent, 2004). Lent (2004) suggested that a complete understanding of mental health incorporates both PWB and subjective well-being (i.e., happiness) and added that subjective well-being is best conceptualized as a higher-order outcome of PWB. Others have argued that PWB and depression are opposite ends of the same construct (Bech et al., 2003), suggesting that PWB instruments also measure depressive affect and vice versa. Collectively, and in conjunction with the focus on depressive symptoms in the aforementioned college student mental health research, it seems useful to conceptualize mental health using indices of PWB and life satisfaction (Lent, 2004).

Psychological well-being. PWB was measured using the 5-item World Health Organization-Five Well-Being Index (WHO-5; Bech et al., 2003). Each item is a positively worded self-statement measuring the absence of various symptoms of depression (e.g., “I have felt calm and relaxed”). Because of its ability to measure both well-being and depression, it was selected as an ideal candidate for the present study. The presence of each statement over a 2-week period is rated on a 6-point scale (0 = not present,
5 = constantly present). Scores are multiplied by four to create a 0–100 scale, with higher scores indicating higher PWB, and scores below 28 indicating clinical depression (Bech et al., 2003). The authors reported strong evidence for reliability (α = .82) and validity. In the present sample, internal consistency was .81.

Life satisfaction. Life satisfaction was measured using the Satisfaction with Life Scale (SWLS; Diener et al., 1985). Participants rate agreement with five items (e.g., “In most ways my life is close to my ideal”) on a 7-point scale (1 = strongly disagree, 7 = strongly agree). Internal consistency in both the validation study and present sample was .87.

Results

Table 1 presents the descriptive statistics and intercorrelations for all study variables. With the exception of the emerging adult feeling in between variable, all variables were significantly correlated with each of the outcome variables. Significant correlations ranged from small to large for both PWB (r = .12, p < .05 for Facebook usage and r = .44, p < .001 for ego resilience) and life satisfaction (r = .12, p < .01 for identity exploration and r = .50, p < .001 for social support). Also, the outcome variables were significantly associated with three emerging adulthood variables in different directions. That is, they were positively correlated with experimentation/possibilities and identity exploration, and they were negatively and moderately correlated with negativity/instability; however, neither outcome variable was significantly associated with the feeling in between variable.

To reduce the possibility of confounds in the regression results, several potential covariates were tested for their relatedness to the outcome variables. Based on prior research, age, gender, and race were tested (Galambos et al., 2006; Schnyders & Lane, 2018). Gender and race were dummy coded so that a) 0 = non-woman (i.e., man or other) and 1 = woman, and b) 0 = non-White and 1 = White. Significant differences were present in the Satisfaction with Life Scale scores on the basis of gender: t(537) = -2.841, p < .01. The mean life satisfaction score for women in the sample was 1.91 points higher than for non-women. Thus, all subsequent analyses controlled for the effects of gender. No other significant associations involving the potential covariates were present.

Table 1

Pearson Intercorrelations Among Study Variables

Variables			Intercorrelations
	M	SD		1	2	3	4	5	6	7	8	9	10
1. AAn.	23.42	7.28		–
2. AAv.	17.03	6.76		.07	–
3. ER	69.10	11.94		-.33**	-.08	–
4. EP	7.08	1.16		-.17**	-.04	.24**	–
5. NI	6.90	1.24		.20**	.06	-.25**	-.06	–
6. IE	6.80	1.34		-.05	.04	.12*	.38**	.07	–
7. IB	6.83	1.38		.06	.06	-.01	.22**	.12*	.41*	–
8. SS	21.98	4.27		-.25**	-.27**	.32**	.21**	-.19**	.07	.05	–
9. FB	21.75	8.72		.13*	-.11*	-.05	.08	-.02	.08	.16**	.14*	–
10. PWB	55.35	18.72		-.27**	-.14*	.44**	.32**	-.34**	.16**	.03	.40**	.12*	–
11. LS	21.72	7.21		-.25**	-.25**	.39**	.36**	-.30**	.12*	-.02	.50**	.14*	.61**

Note. N = 538. AAn. = attachment anxiety; AAv. = attachment avoidance; ER = ego resilience; EP = experimentation/possibilities; NI = negativity/instability; IE = identity exploration; IB = feeling in between; SS = social support; FB = Facebook usage; PWB = psychological well-being; LS = life satisfaction.
*p < .05. **p < .001

Hypothesis 1 predicted that internal resources, emerging adulthood identification, and social resources would each predict unique and additive variance in each outcome variable. Thus, two hierarchical regression analyses were conducted (one with PWB as the outcome variable and one with life satisfaction as the outcome). Each set of predictors was entered as an individual level in the hierarchical regression. Table 2 presents the results of these analyses. As can be seen in Table 2, Hypothesis 1 was fully supported. Each predictor variable set predicted significant additive variance in each outcome variable after accounting for the preceding predictor variable sets in the model. It is also useful to note that the social resources variables predicted over twice as much additive variance in life satisfaction (∆R² = .08, p < .001) compared to that of PWB (∆R² = .03, p < .001). The model accounted for 36% of the variance in PWB and 41% of the variance in life satisfaction.

Table 2

Summary of Hierarchical Regression Analyses Predicting PWB and Life Satisfaction Step and Variable	∆R²	∆F	β	t	r_sp
Outcome variable: PWB
Step 1 – Internal resources	.22	38.619***
Attachment anxiety			-.14	-3.438**	-.07*
Attachment avoidance			-.09	-2.308*	-.03
Ego resilience			.40	9.685***	.23***
Step 2 – Emerging adulthood	.09	8.259***
Experimentation/possibilities			.19	4.677***	.14***
Negativity/instability			-.23	-6.177***	-.20***
Identity exploration			.06	1.403	.05
Feeling in between			.00	0.03	-.02
Step 3 – Social resources	.03	1.681***
Social support			.19	4.679***	.16***
Facebook usage			.09	2.361*	.08*
Outcome variable: Life satisfaction
Step 1 – Internal resources	.22	32.966***
Attachment anxiety			-.12	-2.960**	-.03
Attachment avoidance			-.20	-5.185***	-.11**
Ego resilience			.36	8.711***	.17***
Step 2 – Emerging adulthood	.09	8.131***
Experimentation/possibilities			.26	6.571***	.20***
Negativity/instability			-.19	-5.139***	-.15***
Identity exploration			.01	0.304	.02
Feeling in between			-.05	-1.273	-.07*
Step 3 – Social resources	.08	4.872***
Social support			.31	8.138***	.27***
Facebook usage			.08	2.176*	.07*

Note. N = 538. Results control for the effects of gender. r_sp = semipartial correlation. r_sp is reported for the
last step in each model.
* p < .05, ** p < .01, *** p < .001.

Hypothesis 2 predicted that the emerging adulthood variables and Facebook usage would each predict significant individual variance in each outcome variable after accounting for the effects of all other predictor variables. To test this hypothesis, semipartial correlations (r_sp) were examined for all variables at the last step of the hierarchical regression (i.e., the step in which all variables are entered into the model). Semipartial correlations examine the unique variance explained by a single predictor after accounting for the collective variance explained by all other predictors (Meyers et al., 2013). As can be seen in Table 2, significant semipartial correlations predicting PWB included the negativity/instability (r_sp = -.20, p < .001), experimentation/possibilities (r_sp = .14, p < .001), and Facebook usage (r_sp = .08, p < .05) variables. Of all the predictors of PWB in the model, negativity/instability made the second largest individual contribution. Of the predictors of life satisfaction, significant semipartial correlations included experimentation/possibilities (r_sp = .20, p < .001), negativity/instability (r_sp = -.15, p < .001), feeling in between (r_sp = -.07, p < .05), and Facebook usage (r_sp = .07, p < .05). The experimentation/possibilities variable made the second largest individual contribution to life satisfaction. However, because identity exploration was not significant for either outcome variable, and feeling in between was not significant for life satisfaction, Hypothesis 2 was only partially supported. Collectively, the emerging adulthood and Facebook variables accounted for 7.4% of unique variance in PWB and 7.1% of unique variance in life satisfaction.

Discussion

The present findings yield several useful contributions. First, they bridge disparate threads of research by comparing the contributions of well-established mental health predictors with those of constructs unique to present-day college students, each of which contributed uniquely to college student mental health. Although many of the effects of the individual variables were small, the emerging adulthood and Facebook variables collectively explained roughly 7% of unique variance in the mental health variables over and above that explained by the more well-established constructs. As such, the findings are consistent with the assertion that constructs like attachment, ego resilience, and social support, while useful to conceptualizing college student mental health, may nevertheless be aided by also considering factors unique to 21st-century students.

The positive associations between Facebook usage and college student mental health are noteworthy, given the current disagreement regarding the impact of social media use. Contrary to concerns regarding social media overuse (e.g., Twenge, 2013), the present study found that Facebook usage positively predicted PWB and life satisfaction, albeit with a small effect. This was true even after controlling for other predictor variables, suggesting that Facebook provided a small but unique contribution to college student mental health. This finding supports the conclusions of Manago et al. (2012) that Facebook can fulfill certain social support needs for students. There may also be negative implications for societal reliance on social media use (e.g., Twenge, 2013), including its promotion of unhealthy comparison behaviors and cyberbullying. Nevertheless, the present findings and those of Manago et al. demonstrate the positive contributions of social media to college student mental health.

The significance of some of the emerging adulthood variables also warrants discussion. The degree to which participants identified with emerging adulthood being a period of experimentation and possibilities was positively associated with PWB and life satisfaction, while the degree to which they identified with emerging adulthood being a period of negativity and instability was negatively associated with PWB and life satisfaction. Moreover, identifying emerging adulthood as a time of feeling in between adolescence and adulthood was negatively associated with life satisfaction. Even after accounting for all other control and predictor variables, emerging adult instability was the second strongest predictor of PWB (after ego resilience), while emerging adult experimentation/possibilities was the second strongest predictor of life satisfaction (after social support). These findings add important context to prior empirical conclusions that emerging adulthood is associated with negative mental health (Baggio et al., 2015). That is, while each of the dimensions of emerging adulthood represents important developmental processes toward reaching adulthood (Arnett, 2004), only some of these dimensions (especially viewing emerging adulthood as a period of experimentation or instability) seem relevant to college student mental health. Additionally, feeling in between adolescence and adulthood was negatively associated with life satisfaction but unassociated with PWB. This finding underscores the complex contributions of emerging adulthood to college student mental health. Previous research has indicated that life satisfaction decreases during adolescence (Goldbeck et al., 2007). Accordingly, it is plausible that subjectively identifying the emerging adult years as feeling in between adolescence and adulthood results in life satisfaction trajectories that more closely mirror those of adolescence compared to emerging adults who feel less in between adolescence and adulthood. Although such conclusions require further validation, it nevertheless can help college counselors understand which factors of the emerging adult experience are relevant foci of clinical attention.

Implications for Counselors

The present results yield several useful insights that can aid mental health counselors who work with college-aged populations. Most prominently, counselors are encouraged to conceptualize their clients using a blend of foundational and contemporary models. Life for 21st-century college-aged individuals is unprecedentedly complex (Arnett, 2004; Kruisselbrink Flatt, 2013). It is important for college counselors to acknowledge this complexity, as doing so may represent an important form of cultural competence working with millennial generation and Generation Z individuals (Lane, 2015). Counselors are encouraged to utilize emerging adulthood theory when conceptualizing their clients, as this framework contains important departures from other identity development models. For example, counselors are likely to be more familiar with Erikson’s (1959/1994) framework than emerging adulthood theory. The former model suggests that identity development occurs during the teenage years, while the latter model asserts that identity development is a process that now extends well into the 20s (Arnett, 2004). Emerging adulthood theory also suggests that, as a result of this prolonged identity development process, traditional-aged college students are likely to temporarily exhibit heightened self-focus and idealistic thinking. Acknowledging these factors could facilitate a more empathic understanding of the behaviors that contribute to some counselors and scholars endorsing negative stereotypes against millennials and Generation Z individuals (Lane, 2015). Incorporating emerging adulthood theory could help college counselors be more mindful of the evolving nature of the transition to adulthood and its contributions to mental health.

The findings involving the social resources variables also have novel implications for counseling college students. Although social support has long been established as an important target for improving mental health, counselors are encouraged to acknowledge both the unprecedented complexity of emerging adult social support networks (Arnett, 2004) and also the ability of emerging adults to receive social support from face-to-face and electronic interactions (Manago et al., 2012). Accordingly, it is important to continue exploring the potential therapeutic applications of social media and other forms of technology. For example, an exciting direction in this regard is the growing use of informal support groups via social media (Manago et al., 2012), which exist for many counseling-relevant issues. Such groups provide a sense of community and help members remember that they are not alone in their struggles. Moreover, present mental health trajectories among college students have necessitated a shift in focus for many college counseling centers toward crisis intervention and outreach (Watkins et al., 2012). For many college counseling centers, social media remains an underutilized tool, despite the recent development of social media and text-based initiatives for each of these objectives (Evans et al., 2013). Such programs might be especially useful in today’s higher education climate in which symptom severity seems to be increasing while budgetary resources for college counseling centers are often stagnant or decreasing (American College Health Association, 2015; Watkins et al., 2012).

Limitations

Several limitations in the present study warrant consideration. First, the results relied on a convenience sample, and it is impossible to know whether there are group differences between the 15.4% of invited college students who participated compared to those who did not. Second, the findings are correlational in nature, and the directionality of the relationships cannot be assured. Third, although the sample was racially diverse, it was predominantly female. Fourth, it should be noted that the social media variable in this study consisted solely of Facebook usage; the findings may have been different had other prominent social media platforms been represented.

Implications for Future Research

Future research efforts should continue to explore the mental health implications of the study’s variables. First, it would be useful to confirm the findings with a more gender-representative sample. The model should also be explored with a longitudinal sample to determine mental health trajectories through various transitions common during the college experience. It would also be useful to explore potential mediating effects among the variables in the model, which could provide further empirical support for the theoretical sequencing of the variable sets. Other research efforts could further explore the therapeutic applications of social media. Such efforts could aid understanding of the evolving needs of college-aged populations.

Conclusion

The college years constitute considerable mental health risks that seem particularly pronounced for current generations of traditional-aged college students. The present findings suggest that traditional models of college student mental health can be aided by also incorporating generation-specific factors, including emerging adulthood identification and social media usage. Such generation-specific factors seem to predict unique variance in college student mental health characteristics, namely PWB and life satisfaction. The findings underscore the importance that counselors consider contemporary models, including emerging adulthood theory, when conceptualizing and treating traditional-aged college student clients.

Conflict of Interest and Funding Disclosure
The authors reported no conflict of interest
or funding contributions for the development
of this manuscript.

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Joel A. Lane, PhD, NCC, LPC, is an associate professor and department chair at Portland State University. Correspondence may be addressed to Joel Lane, 250G Fourth Avenue Building, 1900 SW 4th Ave., Portland, OR 97201, lanejoel@pdx.edu.

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