Perceptions of Academic Performance,Impairment,and Mental Health in University Students With and Without ADHD

Abstract

Objective:

This study aims to investigate the impact of positive illusory bias (PIB) on the relationship between ADHD symptoms and functioning in college students, with a focus on gender differences.

Method:

The sample consisted of 195 college students, including 148 with ADHD and 47 without ADHD. Measures of ADHD symptomatology, life satisfaction, affect, gender identity, and impairment were collected, along with cumulative grade point average.

Results:

ADHD symptomatology was inversely linked to subjective well-being, with PIB acting as a significant moderator. Functional impairment was predicted by ADHD symptoms and subjective well-being, while social impairment and academic functioning were predicted by PIB and well-being. Significant gender differences were found, particularly in the interaction between PIB and ADHD symptoms for non-binary individuals.

Conclusion:

This study suggests that PIB is relevant for emerging adults with ADHD enrolled in higher education. Subjective well-being and PIB act as buffers against the detrimental functional and social effects of ADHD symptoms in emerging adult college students. The study highlights the importance of considering gender-specific approaches in understanding and supporting the mental health of this population.

Keywords

adult ADHD positive illusory bias subjective well-being functional impairment

There is strong causal evidence to suggest that executive functioning deficits associated with ADHD may significantly impair academic, social, and behavioral functioning (Barkley & Murphy, 2006; Prevatt et al., 2012). This impairment can manifest in difficulties with focus, planning, completing tasks, managing time, and organization in academic settings. Socially, students with ADHD may struggle with interactions, making friends, and interpreting social cues. The core symptoms of hyperactivity and impulsivity are major threats to controlling emotions and are liabilities to academic and professional success. These difficulties necessitate complex and prolonged interventions (Barkley et al., 2008; Dawson et al., 2020).

ADHD often persists into adulthood, especially affecting emerging adult college students, a population between the ages of 18 and 29 years characterized by identity exploration, instability, self-focus, and feeling in-between adolescence and full adulthood (Arnett, 2015). Emerging adults with ADHD are at a higher risk of academic struggles (DuPaul et al., 2009). This impairment is further exacerbated by co-occurring conditions like depression, anxiety, learning disabilities, and positive illusory bias (PIB); (Hoza et al., 2004; Jia et al., 2016; Jiang & Johnston, 2017; McQuade et al., 2011; Owens et al., 2007).

Positive Illusory Bias

Positive illusory bias (PIB) refers to an exaggerated self-perceived competence compared to objective assessments (Owens et al., 2007). There is evidence to suggest that PIB in individuals with ADHD may be associated with negative outcomes, including reduced prosocial behavior, resistance to treatment, aggression, and social difficulties, especially in young people (Hoza et al., 2010, 2013; Jia et al., 2016; Linnea et al., 2012; Mikami et al., 2010). Most PIB research focuses on ADHD in children and adolescents and uses methods like self-report (vs. using others’ ratings) or comparing predicted performance with actual performance (Owens et al., 2007).

However, there are gaps in our understanding, with limited research in adults with ADHD (Prevatt et al., 2012), raising questions about whether PIB persists into adulthood, its impact on adults, and how best to objectively measure PIB. It also remains unclear whether PIB is adaptive or maladaptive in the context of ADHD. Positive beliefs about one’s own competence and ability are a key driver of motivated behavior according to many theories of motivation, including situated expectancy-value theory (Eccles & Wigfield, 2020) and self-determination theory (Ryan & Deci, 2020). While positive illusions about one’s abilities often motivate individuals without ADHD, this might not hold for those with ADHD due to their challenges with self-regulation and social interactions (Prevatt et al., 2011).

PIB research in children and adolescents with ADHD has linked it to negative outcomes, such as “poorer response to treatment, higher rates of aggression in early adolescence, and less friendly/prosocial behavior,” but the exact reasons for these outcomes are unclear (Bourchtein et al., 2018, p. 1396). Extremely high levels of PIB may be problematic socially, possibly because they undermine the ability to learn from feedback. Several explanations for PIB have been proposed, including cognitive immaturity, neuropsychological deficits, ignorance of incompetence, and self-protection (M. B. Diener & Milich, 1997; Duke et al., 2002; Kruger & Dunning, 1999; Milich, 1994).

Individuals with ADHD, who are more likely to exhibit PIB, may experience compounded psychological and socioemotional impairments (Jia et al., 2016). However, individuals without ADHD can also show PIB and experience similar impairments (Bourchtein et al., 2017). Given this, understanding how PIB and ADHD symptoms impact subjective well-being is crucial (Scholtens et al., 2012). The dual-factor model of mental health (DFM; Suldo & Shaffer, 2008) provides a framework that may help conceptualize how these phenomena may be related.

As depicted in Figure 1, the DFM combines subjective well-being with diagnostic symptomatology, creating a 2 × 2 matrix with four groups: flourishing, symptomatic but content, vulnerable, and troubled (Greenspoon & Saklofske, 2001; Suldo & Shaffer, 2008). The severity of ADHD symptoms may affect subjective well-being, impacting life satisfaction and overall emotional well-being, especially when combined with PIB (Kaiser et al., 2008). Context, such as social or academic settings, may influence this relationship.

Figure 1.

The dual-factor model of mental health.

Examining personal characteristics, such as gender identity, which may be more relevant to self-perception than sex assigned at birth, is essential in understanding PIB, especially in the context of ADHD (Tu et al., 2019). Previous studies, primarily with children and adolescents, showed minimal gender differences in PIB (Chan & Martinussen, 2016; Evangelista et al., 2008; Hoza et al., 2004; Kaiser et al., 2008). Limited research in adults has reported varying ADHD prevalence rates among transgender individuals (Cheung et al., 2018; Dawson et al., 2017; Thrower et al., 2020). Gender differences could impact self-perceptions and moderate the relationship between ADHD, PIB, and mental well-being (Prevatt et al., 2012). Understanding these differences is crucial due to disparities in ADHD diagnosis between genders, as well as the lack of PIB studies focused on women.

The Present Study

The purposes of this study were to explore PIB among emerging adult college students with and without ADHD, as well as the relationships between PIB, ADHD symptomatology, subjective well-being (e.g., life satisfaction and affect), gender identity, and perceived impairment. Based on existing literature, we hypothesized that ADHD symptomatology would negatively correlate with subjective well-being. Students with ADHD often experience challenges in focusing on academics, completing tasks, and managing their time effectively due to inattention, impulsivity, and hyperactivity. These challenges can lead to frustration, stress, and disappointment, ultimately impacting their overall life satisfaction and positive feelings (subjective well-being). We therefore hypothesize that as self-reported severity of ADHD symptoms decreases, self-reported levels of subjective well-being increase. We also hypothesized that PIB would significantly buffer the negative effects of ADHD symptomatology on subjective well-being, such that subjective well-being would be less adversely affected by ADHD symptomatology for people with stronger PIB. Finally, we hypothesized that PIB, ADHD symptomatology, and subjective well-being, would significantly predict functional impairment, social impairment, and cumulative GPA for college students with ADHD. Given the academic challenges associated with ADHD, such as lack of focus, poor organizational skills, and time management difficulties, students with more severe ADHD symptoms may have lower cumulative GPAs. Cumulative GPA was selected due to being a holistic measure of academic functioning which has excellent ecological validity. Owing to lack of prior research with emerging adults, we conducted an exploratory analysis of the impact of gender on ADHD symptoms, subjective well-being, and PIB.

Method

Participants and Procedure

The study involved 195 university students aged 18 to 30 years (average age = 23.94 years, SD = 3.16) with representation across various undergraduate and graduate class standings (see Table 1 for details). This age range encompasses both undergraduate and graduate students. While emerging adulthood was historically defined as encompassing ages 18 to 29 years (Arnett, 2015), we included students up to age 30 years to capture the experiences of graduate students who may face unique academic challenges related to ADHD and the increasing task demands associated with graduate education.

Table 1.

Demographic Characteristics.

Characteristic	Frequency (%)		χ²/t	d/V
Characteristic	ADHD (n = 148)	Non-ADHD (n = 47)	χ²/t	d/V
Age (M, SD)	23.82 (3.32)	24.30 (2.60)	−0.90	−0.15
College Class Standing (grade level)			10.46	0.23
First-year (fewer than 30 credits)	9 (6)	2 (4)
Sophomore (30–60 credits)	38 (26)	15 (32)
Junior (60–90 credits)	28 (19)	17 (36)
Senior (90 or more credits)	35 (24)	4 (9)
Graduate/Professional	36 (24)	9 (19)
Unsure or unknown	2 (1)	—
Gender identity			6.87*	0.19
Woman	77 (40)	18 (9)
Man	51 (26)	15 (8)
Non-binary	20 (10)	14 (7)
Ethnic identity			14.96**	0.28
Non-Hispanic	83 (56)	15 (32)
Mexican, Mexican American, Chicano/a/x	11 (7)	4 (9)
Puerto Rican	18 (12)	11 (23)
Cuban	6 (4)	4 (9)
Another origin	10 (7)	7 (15)
Some other race, ethnicity, or origin	7 (5)	1 (2)
Multiple ethnicities	8 (5)	5 (11)
Not reported	5 (3)	—
Racial identity			31.03**	0.40
African American or Black	9 (6)	5 (11)
Asian	8 (5)	8 (17)
Indigenous American or Alaskan Native	15 (10)	8 (17)
Native Hawaiian or Other Pacific Islander	5 (3)	7 (15)
White	95 (64)	10 (21)
Some other race, ethnicity, or origin	12 (8)	8 (17)
Not reported	4 (3)	1 (2)
School accommodations (n = 190)			31.43**	0.41
Yes	108 (73)	11 (26)
No	38 (26)	28 (67)
Unknown or unsure	2 (1)	3 (7)

Note. χ² = Pearson’s Chi-Square; t = independent samples t-test; d = Cohen’s d; V = Cramer’s V.

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

The sample composition of this study reflects an unusually diverse gender identity distribution of 35% woman, 24% man, 17% non-binary, 14% transgender woman, and 10% transgender man participants (see Table 1). In terms of race and ethnicity, most participants identified as White (72%), followed by Asian (15%) and Hispanic (8%; see Table 1). The sample composition differed significantly from the general population in terms of ADHD prevalence; 76% (148 students) reported having an ADHD diagnosis, significantly higher than the general population’s 4.4% rate (Adamis et al., 2022). This is likely due to the recruitment method, as participants were recruited through ResearchMatch.org, where ADHD status was chosen as a selection criterion. Among those with ADHD, 40% (45 students) self-reported being diagnosed during adolescence, and 43% (64 students) were diagnosed with the primarily inattentive presentation type. Data about the nature of the diagnosis was not collected. Furthermore, 74% (109 students) were currently receiving ADHD medication treatment, and 86% (126 students) had a history of ADHD medication use, as indicated in Tables 2 and 3.

Table 2.

Descriptive Statistics of ADHD Group Characteristics.

Characteristic	ADHD-I (n = 64)	ADHD-H/I (n = 27)	ADHD-C (n = 40)
ADHD Symptomatology, M (SD)	13.73 (3.61)	10.78 (3.58)	15.70 (4.45)
Difficulty concentrating (n = 147)	2.44 (0.87)	1.62 (0.94)	2.62 (1.03)
Remaining seated (n = 147)	1.39 (0.97)	1.65 (1.02)	1.98 (1.14)
Restlessness (n = 146)	2.54 (1.10)	1.69 (1.05)	2.85 (1.12)
Finishing others’ sentences (n = 146)	2.33 (1.05)	2.27 (1.00)	2.97 (0.99)
Procrastination (n = 146)	3.06 (1.13)	2.41 (0.93)	3.31 (0.98)
Dependence on others (n = 147)	2.06 (0.99)	1.14 (0.75)	2.18 (1.21)
Age when diagnosed, M (SD)	14.14 (9.14)	7.67 (9.23)	13.15 (8.55)
Current medication use, n (%)
Yes	52 (85)	17 (65)	30 (77)
No	9 (15)	9 (35)	9 (23)
History of medication use, n (%)
Yes	59 (98)	18 (72)	33 (85)
No	1 (2)	7 (28)	6 (15)
School accommodations, n (%)
Yes	44 (71)	25 (93)	30 (75)
No	18 (29)	2 (7)	10 (25)

Note. ADHD-I = inattentive presentation; ADHD-H/I = hyperactive/impulsive presentation; ADHD-C = combined presentation.

Table 3.

Descriptive Statistics and Correlations for Predictor and Outcome Variables.

Variable	1	2	3	4	5	6
1. ADHD symptomatology	—	−.34**	−.01	.32*	.31*	−.15
2. Subjective well-being	−.33**	—	.12	−.45**	−.58**	.13
3. Positive illusory bias	−.09	.08	—	.20	.30*	−.27*
4. Functional impairment	.39**	−.54**	.04	—	.60**	−.01
5. Social impairment	−.04	−.44**	.11	.48**	—	−.21
6. Cumulative GPA	.02	.25**	−.14*	−.16*	−.33**	—
M	2.30	.00	.00	3.20	2.64	3.77
SD	.82	1.00	1.00	1.09	.91	1.12

Note. ADHD group correlations are on the bottom left and non-ADHD group correlations are on the top right.

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

The study, approved by the university IRB, utilized an online Qualtrics survey, ensuring participant anonymity and data security. Recruitment sources included ResearchMatch.org, social media, and university-based disability/accessibility centers across the United States. The study employed a correlational, passive observational design to investigate the relationship between PIB, ADHD symptoms, well-being, and impairment.

Measures

In an online survey, participants answered questions about their demographics, such as age, gender, transgender identity, birth-assigned sex, ethnicity, race, academic year, and whether they had been diagnosed with ADHD. Those who reported having ADHD were asked additional questions about their diagnosis, including age at diagnosis, type of diagnosis, educational support, and medication use.

ADHD Symptomatology

We used the Adult ADHD Self-Report Screening Scale for DSM-5 (ASRS-5; Ustün et al., 2017) to quantify participants’ ADHD symptoms. This is a commonly used tool to identify adult ADHD symptoms. It consists of six questions (e.g., “How often do you have difficulty concentrating on what people are saying to you even when they are speaking to you directly?”). Participants rate each question from 0 to 4 (never, rarely, sometimes, often, very often). One item, “How often do you leave your seat in meetings or other situations in which you are expected to remain seated?”, was removed due to low reliability. Afterward, the remaining five questions had acceptable reliability (α = .78). These scores were averaged and standardized to provide an overall rating of self-reported ADHD symptoms. Higher ASRS-5 scores (e.g., ≥2) indicate more severe ADHD symptoms (Ustün et al., 2017). Previous research has shown that the ASRS-5 has good internal consistency (α = .87; Genç et al., 2021).

Subjective Well-Being

In this study, we looked at mental health using a two-part model. We used two scales, the Satisfaction with Life Scale (SWLS) and the International Positive and Negative Affect Schedule Short Form (I-PANAS-SF), to measure participants’ subjective well-being. This included their life satisfaction, positive emotions, and negative emotions. To create this composite measure, we followed the approach from previous research (Suldo & Shaffer, 2008), which involved subtracting standardized negative emotion scores from the sum of standardized life satisfaction and positive emotion scores. This composite measure showed acceptable reliability (α = .79).

The Satisfaction with Life Scale (SWLS), developed by E. D. Diener et al. (1985), is a short 5-item questionnaire that assesses life satisfaction on a 7-point scale (1 = strongly disagree to 7 = strongly agree). It asks participants about various aspects of their life, such as whether they are satisfied with their life or if their life is close to their ideal. The average of these responses gives an overall life satisfaction score, with higher scores indicating higher self-reported life satisfaction. According to E. D. Diener et al. (1985), this scale has good reliability (α = .87), and it also showed good reliability in our study (α = .84).

The International Positive and Negative Affect Schedule Short Form (I-PANAS-SF), created by Thompson (2007), measures both positive and negative emotions. This scale is based on the original Positive and Negative Affect Schedule (PANAS; Watson et al., 1988). Participants rate ten emotions like feeling alert, nervous, afraid, or inspired on a 5-point scale from 1 (never) to 5 (always). Previous cross-cultural research found this scale to be reliable (Cronbach’s α = .75 for positive affect and α = .80 for negative affect; Karim et al., 2011), it also demonstrated acceptable reliability in our study (α = .74 for positive affect and α = .77 for negative affect).

Positive Illusory Bias

We created a new model to assess participants’ PIB. This was due to there being no dedicated instrument for this purpose. This model combines pre-task predictions and actual performance. Participants rated how well they expected to do on an academic task from 1 (terrible) to 5 (excellent). Then, they took an academic skills test with math and English language arts and reading (ELAR) questions of varying difficulty. We used questions from various online sources. These questions were presented randomly. The entire set of questions showed good reliability (α = .89). To calculate the composite PIB score, we standardized both the pre-test ratings and the test scores, and then we subtracted the test scores from the pre-test ratings. Higher scores indicated higher levels of PIB.

Impairment

An adapted version of the Impairment Rating Scale (IRS; Fabiano et al., 2006) for adults was employed to gauge participants’ self-reported impairment across different life domains (Dawson et al., 2020). The IRS evaluates impairment or the influence of personal challenges in twelve areas of life, including home, school, work, and relationships. This version of the IRS has demonstrated good validity among college students (Dawson et al., 2020). In the current study, the IRS demonstrated good reliability (α = .81). To enhance clarity, we divided the IRS items into two separate categories based on the literature: one focusing on behavioral functioning at work or school and the other on social or relational functioning. An exploratory factor analysis was conducted to assign items to each subscale, resulting in two factors: one based on behavioral functioning and the other on social functioning.

Functional Impairment

Specific items were employed to gauge participants’ perception of how their problems affect more functional aspects of life. These items included academic progress, work performance, self-esteem, and problem severity. These items showed acceptable reliability (α = .73).

Social Impairment

The remaining items on the IRS pertain to social or relational aspects of life, such as relationships with peers, siblings, parents, family dynamics, teachers, coworkers, supervisors, and partners. These items also demonstrated acceptable reliability (α = .77).

Academic Success

To assess the participants’ current academic performance, we inquired about their cumulative grade point average (GPA). This GPA was evaluated using the following scale: 0 = unsure or unknown, 1 = below 2.0, 2 = 2.0 to 2.4, 3 = 2.5 to 2.9, 4 = 3.0 to 3.4, and 5 = 3.5 to 4.0. To measure a more global dimension of academic functioning, we opted to collect data on participant cumulative GPA, rather than semester GPA.

Gender Identity

Participants were requested to provide information about three aspects related to sex and gender: their biological sex assigned at birth (1 = female, 2 = male, and 3 = intersex), whether they identified as transgender (1 = yes and 2 = no), and their primary gender identity (1 = woman, 2 = man, and 3 = non-binary). After examining descriptive statistics for each aspect, we combined them to create a composite variable, resulting in three gender identity categories: woman (including transgender women), man (including transgender men), and non-binary. Similar to ADHD diagnosis status, gender identity was used to compare whether differences existed between and among each group.

Data Analysis

We conducted all statistical analyses using SPSS version 28, with a significance level set at p < .05 (one-tailed). Missing data, which accounted for less than 5% of the dataset, were shown to be missing randomly. Demographic details for both the ADHD and non-ADHD groups are described in Table 1. To compare these groups on various factors, we used Pearson correlation coefficients, independent samples t-tests, and Chi-square tests. We also ran multiple linear regression models to examine (1) whether PIB has a moderating effect and (2) if we can predict functional impairment, social impairment, and academic success (GPA) based on ADHD symptoms, subjective well-being, and PIB. Each analysis was performed separately for both the ADHD and non-ADHD groups and for each gender identity group.

To determine our sample size, we conducted a power analysis with G*Power 3 (Faul et al., 2007). It revealed that to reliably detect small regression effects (Cohen’s d = 0.17) with a power of .95, we needed at least 130 participants. Our study included 195 participants, which provides sufficient statistical power.

Results

Preliminary Analyses

Table 2 includes descriptive data for those with ADHD. For the entire sample (N = 195), means, standard deviations, and correlations between variables are presented in Table 3. As presented in Table 4, independent samples t-tests indicated significant group differences between those with and without ADHD for ADHD symptomatology, PIB, functional impairment, and GPA. Participants with ADHD reported higher ADHD symptomatology and demonstrated lower PIB than those without ADHD, which was unexpected and inconsistent with the literature which has focused on children and adolescents, not emerging adults (Owens et al., 2007; Prevatt et al., 2012).

Table 4.

ADHD and Non-ADHD Group Differences.

Variable	M (SD)		t	χ²	p
Variable	ADHD (n = 148)	Non-ADHD (n = 47)	t	χ²	p
ADHD symptomatology	2.49 (0.77)	1.70 (0.67)	6.30	—	<.001
Subjective well-being^a	−0.03 (2.22)	0.03 (1.19)	0.05	—	.818
Positive illusory bias	−0.20 (1.12)	0.62 (1.09)	−4.36	—	<.001
Gender identity^b	1.61 (0.71)	1.91 (0.83)	—	6.87	.032
GPA	3.94 (1.05)	3.22 (1.19)	3.96	—	<.001
Functional impairment^a	3.30 (1.14)	2.88 (0.86)	7.08	—	.009
Social impairment	2.57 (0.92)	2.87 (0.85)	−2.00	—	.024

Welch’s t-test was used due to equal variances not being assumed for the independent samples t-test (Sig. < .001).

Cramer’s V = 0.19, p = .032.

A chi-square test of independence was performed to evaluate the relationship between gender identity and ADHD status. The relationship between these variables was significant, χ² (2, N = 195) = 6.87, p = .032. Those who identified as woman were more likely to have ADHD than those who identified as man and non-binary.

Multiple two-way ANOVA were conducted to explore the effects of gender identity and ADHD status on PIB, ADHD symptomatology, subjective well-being, impairment, and GPA. For PIB, the results indicated a significant main effect for ADHD status, F(1, 189) = 15.62, p < .001, partial η² = .08; a significant main effect for gender identity, F(2, 189) = 3.39, p = .036, partial η² = .04; a non-significant interaction between gender identity and ADHD status F(2, 189) = .31, p = .737, partial η² = .003. Results also indicated significant main effects for ADHD status on ADHD symptomatology, F(1, 189) = 30.94, p < .001, partial η² = .14; on functional impairment, F(1, 189) = 4.19, p = .042, partial η² = .02; and on GPA, F(1, 189) = 15.28, p < .001, partial η² = .08.

Main Analyses

As expected, results for the total sample indicated a significant negative correlation between ADHD symptomatology and subjective well-being, r(193) = −.23, p < .001. Participants with greater ADHD symptomatology reported lower subjective well-being. Negative correlations were found for those with a self-reported ADHD diagnosis, r(145) = −.28, p < .001, and those without ADHD, r(45) = −.34, p = .01. Similarly, ADHD symptomatology and subjective well-being were negatively correlated for those who identified as man, r(64) = −.35, p = .002 and woman, r(93) = −.23, p = .012, but not for non-binary, r(32) = −.14, p = .208.

It was hypothesized that PIB would moderate the relationship between ADHD symptomatology and subjective well-being. For the entire sample, results a significant model, R² = .11, F(3, 191) = 8.09, p < .001, with a significant interaction effect between PIB and ADHD symptomatology (i.e., a continuous measure) on subjective well-being, β = .30, t = 2.14, p = .034. Of note, the overall model for the ADHD group was significant, but the interaction effect between PIB and ADHD symptomatology was not significant. For those without ADHD, the overall model was significant, R² = .25, F(4, 42) = 3.56, p = .014, with a significant interaction effect, β = .40, t = 2.31, p = .026. Gender differences were also found, specifically, the overall model was significant for those identifying as man, R² = .14, F(3, 62) = 3.52, p = .020, but the interaction effect was not significant; for those identifying as non-binary the overall model, R² = .14, F(3, 62) = 3.52, p = .020 and interaction effect, β = .53, t = 2.97, p = .006, were significant.

To understand the nature of the moderated relationship, simple slopes for the association between ADHD symptomatology and subjective well-being were analyzed at low (−1 SD below the mean), moderate (mean), and high (+1 SD above the mean) levels of PIB. Simple slopes are visually depicted in Figure 2. Results showed significant conditional effects of ADHD symptomatology on subjective well-being at moderate, β = −.74, t = −4.32, p < .001, and low, β = −1.09, t = −4.55, p < .001, values of PIB. At higher levels of PIB, conditional effects were not significant, β = −.40, t = −1.70, p = .091.

Figure 2.

Interaction plot for positive illusory bias and ADHD symptomatology on subjective well-being.

Three hierarchical multiple regression models were used to test hypotheses predicting functional impairment, social impairment, and academic performance (GPA) from ADHD symptomatology, subjective well-being, and PIB. The predictor variables were added first (step 1) and two-way interactions between each of the predictor variables were added next (step 2). These models were tested for each of the outcome variables for the total sample, the ADHD group, the non-ADHD group, and the three gender identity groups.

Results for the total sample indicated that the overall models were significant for functional impairment, R² = .44, F[8, 188] = 25.01, p < .001, social impairment, R² = .34, F[8, 188] = 15.80, p < .001, and academic functioning, R² = .14, F[8, 188] = 5.14, p < .001. PIB was a significant predictor of GPA, β = −.25, t = −3.56, p < .001; subjective well-being was a significant predictor of functional impairment, β = −.43, t = −6.86, p < .001, social impairment, β = −.59, t = −8.63, p < .001, and academic functioning, β = .23, t = 2.91, p = .004; and ADHD symptomatology was a significant predictor of functional impairment, β = .37, t = 6.40, p < .001.

Results based on ADHD diagnosis revealed similar outcomes. The overall models for functional impairment were significant for participants with ADHD, R² = .46, F[3, 141] = 20.02, p < .001 and those without ADHD, R² = .38, F[3, 40] = 4.00, p = .003. Similarly, models were significant for social impairment for those with ADHD R² = .33, F[3, 141] = 11.42, p < .001 and participants without ADHD, R² = .49, F[3, 40] = 6.42, p ≤ .001. Finally, the overall models for GPA were significant for those with ADHD, R² = .11, F[3, 141] = 2.89, p = .011. Results for main effects are presented in Table 5.

Table 5.

Summary of Hierarchical Multiple Regression Analyses Predicting Functional and Social Impairment and Academic Success.

	1			2			3			4			5
Predictor	ΔR²	β	t	ΔR²	β	t	ΔR²	β	t	ΔR²	β	t	ΔR²	β	t
Functional impairment
Step 1	.43**			.31**			.47**			.46**			.33**
SYMP (centered)		.32**	4.88		.29*	2.21		.28**	3.51		.45**	4.47		.36	2.00
SWB (centered)		−.49**	−7.49		−.37**	−2.79		−.53**	−6.82		−.35**	−3.51		−.53**	−3.14
PIB (centered)		.00	−0.01		.24	1.90		−.13	−1.67		.11	1.20		.21	1.07
Step 2	.03			.07			.01			.02			.19*
SYMP × SWB		−.04	−0.57		.35	1.56		−.04	−0.54		−.01	−0.13		.71*	2.64
PIB × SYMP		.12	1.69		.08	0.45		.13	1.32		.08	0.78		.15	0.84
PIB × SWB		.16*	2.37		.19	1.23		.11	1.19		.13	1.32		.94	3.20
Social impairment
Step 1	.31**			.47**			.38**			.24**			.28*
SYMP (centered)		−.11	−1.45		.18	1.57		−.01	−0.12		.03	0.29		−.37	−1.58
SWB (centered)		−.57**	−7.88		−.58**	−5.02		−.61**	−7.23		−.47**	−3.99		−.30	−0.78
PIB (centered)		.10	1.38		.32*	2.89		.06	0.72		.07	0.62		.24	1.19
Step 2	.02			.02			.01			.12*			.08
SYMP × SWB		.11	1.40		.13	0.67		.06	0.64		.22	1.70		.33	1.08
PIB × SYMP		.06	0.77		−.03	−0.20		−.04	−0.36		.21	1.73		−.09	−0.45
PIB × SWB		.09	1.25		−.08	−0.59		−.06	−0.56		.16	1.42		.50	1.48
Academic success
Step 1	.10**			.11			.09*			.14*			.22
SYMP (centered)		.09	1.04		−.11	−0.72		.08	0.73		.14	1.11		.19	0.98
SWB (centered)		.28**	3.46		.15	1.00		.28**	2.74		.32*	2.54		−.05	−0.27
PIB (centered)		−.15	−1.89		−.28	−1.96		−.10	−1.00		−.25*	−2.05		−.32	−1.49
Step 2	.01			.01			.02			.05			.03
SYMP × SWB		.04	0.47		.11	0.42		.08	0.80		.00	0.03		−.03	−0.08
PIB × SYMP		.03	0.29		.01	0.02		.13	1.04		.01	0.06		−.19	−0.88
PIB × SWB		−.12	−1.37		.03	0.14		−.02	−0.18		−.24	−1.86		−.16	−0.43

Note. Predictors are centered; SYMP = ADHD symptomatology; SWB = subjective well-being; PIB = positive illusory bias; ΔR² = change in R²; 1 = ADHD (n = 148); 2 = Non-ADHD (n = 47); 3 = Woman (n = 95); 4 = Man (n = 66); 5 = Non-Binary (n = 34).

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

Gender differences existed across the overall models. Specifically, for those in the woman category, results were significant for functional impairment, R² = .49, F[6, 88] = 13.93, p < .001 and social impairment, R² = .38, F[6, 88] = 9.10, p < .001, but not for GPA, R² = .11, F[6, 88] = 1.81, p = .106. Similarly, for those in the man group, results were significant for functional impairment, R² = .48, F[6, 59] = 8.99, p < .001, social impairment, R² = .36, F[6, 59] = 5.42, p < .001, and GPA, R² = .20, F[6, 59] = 2.43, p = .036. Finally, for those identifying as non-binary, results were significant for functional impairment, R² = .52, F[6, 27] = 4.84, p = .002 and social impairment, R² = .36, F[6, 27] = 2.56, p = .043, but not for GPA, R² = .25, F[6, 27] = 1.49, p = .218. This non-significant finding for the non-binary group may be related to lower statistical power rather than a substantial difference. Table 5 depicts each regression model split by ADHD status and gender identity.

Discussion

The current study presents several key findings: Significant differences were observed between emerging adult students with and without ADHD in various aspects, including their self-reported gender identity, levels of ADHD symptoms, academic performance, social functioning, and functional impairment (see Table 4). The study also found a relationship between ADHD symptoms and subjective well-being (see Table 3). The research revealed that PIB played a marginal role in the connection between subjective well-being and ADHD symptoms. A more pronounced impact of PIB was expected, especially for those in the ADHD group, given the strong association between ADHD and PIB found in past research. Surprisingly, contrary to expectations, PIB levels were lower in participants self-identifying as having an ADHD diagnosis. While it was expected that higher levels of PIB would be associated with increased functional impairment in those with ADHD, these finding challenges that assumption. Instead, the study found that ADHD symptomatology and subjective well-being significantly predicted functional impairment for individuals with ADHD, supporting the application of a dual-factor model approach to mental health in this population.

Our results suggested that gender identity may play a significant role in one’s mental health status, affecting both subjective well-being and ADHD symptomatology. These results were interpreted in the context of comparisons between those with and without a self-reported ADHD diagnosis. This is a unique finding that warrants further investigation and replication prior to making strong inferences about the relationship between ADHD and gender identify.

As expected, individuals in the ADHD group demonstrated higher ADHD symptomatology (Gudjonsson et al., 2009; Weyandt & DuPaul, 2008). However, the finding of lower PIB levels in the ADHD group was unexpected, as it contradicts previous research findings (Owens et al., 2007; Prevatt et al., 2012). It is important to note that the majority of prior studies on PIB have focused on child and adolescent populations, rather than emerging adults. Furthermore, this study had unique recruiting and methodology compared to prior studies that was necessary to study emerging adults with ADHD and PIB.

As hypothesized, subjective well-being and psychopathology (operationalized as ADHD symptomatology) exhibited a negative correlation. This finding illustrates the importance of examining the subjective experience of ADHD, not just the overt symptoms. This aligns with evidence from the dual-factor model of mental health literature (Suldo & Shaffer, 2008) and contributes to the understanding of emerging adults’ self-perceptions and symptom severity reporting. While concerns exist about the accuracy of self-reporting symptom severity, particularly in children and adolescents with ADHD (Barkley et al., 2002; Fefer et al., 2018; Sibley et al., 2012; Smith et al., 2000), this study found that those who self-reported having an ADHD diagnosis accurately reported higher ADHD symptom severity.

Given the association between PIB and ADHD and the inverse relationship between ADHD symptomatology and subjective well-being in this study, it was anticipated that PIB would further strengthen this relationship. Indeed, this hypothesis received support when considering the entire sample. This implies that as PIB decreases ADHD symptomatology’s negative impact on subjective well-being increases as symptoms intensify. Interestingly, PIB and ADHD symptomatology also predicted the subjective well-being of individuals without an ADHD diagnosis, possibly due to factors such as the disruptive nature of subclinical ADHD symptomatology on cognitive processes like attention regulation and impulse control.

Additionally, emerging adults without an ADHD diagnosis who exhibit ADHD symptomatology may develop adaptive coping strategies, such as PIB, to manage their symptoms and challenges. PIB might help these individuals maintain a positive self-concept despite experiencing difficulties associated with ADHD symptoms. The interaction effect suggests that the combination of ADHD symptomatology and PIB may represent a specific adaptive strategy employed by individuals without ADHD to enhance their subjective well-being.

The findings of this study highlight notable gender differences in the relationship between ADHD symptomatology, subjective well-being, and functional outcomes. Specifically, significant overall models were observed for functional and social impairment across all gender groups, yet the patterns of these relationships varied. For men, the models predicting functional and social impairment were significant, indicating that ADHD symptomatology and subjective well-being are critical factors influencing their daily functioning. In contrast, for non-binary individuals, the interaction effect between PIB and ADHD symptomatology was significant, suggesting that perceptions of personal lack of ability play a more substantial role in their well-being and social functioning. These differences suggest that gender identity may influence how ADHD symptoms and self-perceptions affect overall mental health and functioning. This highlights the importance of considering gender-specific approaches in assessing and supporting individuals with ADHD, as interventions might need to be tailored to address unique challenges faced by different gender identities.

Functional Impairment

This study found evidence linking functional impairment with ADHD symptomatology, consistent with existing literature. Previous correlational studies have revealed associations between ADHD symptoms and lower educational attainment and employment levels (Barkley et al., 2006; Gjervan et al., 2012). Moreover, Goh et al. (2023) found that inattentive symptoms were related to more impairment at home, while hyperactive/impulsive symptoms were related to more impairment outside the home. Due to statistical power considerations, subtype analyses were not conducted in this study, and all results pertain to a general category of ADHD or combined symptomology.

The study also discovered that subjective well-being strongly predicted functional impairment for those with ADHD. Participants with lower life satisfaction and positive affect tended to perceive more impairment in school and work functioning. These impairments may also negatively affect self-esteem and overall mental health from the perspective of the dual-factor model.

Social Impairment

As hypothesized, PIB, subjective well-being, and ADHD symptomatology were significant predictors of social impairment. When looking at the entire sample, these findings indicate that subjective well-being and ADHD symptomatology might mitigate the perceived negative effects of social impairment. Conversely, PIB appears to exacerbate social problems. The negative regression coefficient of subjective well-being can be understood, as lower well-being might lead individuals to perceive themselves as less socially competent or to have significant social difficulties, potentially reducing their willingness to engage in social interactions.

The negative directionality of ADHD symptomatology in this context contradicts expectations. Normally, more severe ADHD symptoms would suggest greater social impairment. However, this result challenges existing research that consistently documents social functioning difficulties in individuals (children and adults) with ADHD (Barkley et al., 2008; Flory et al., 2006; Nijmeijer et al., 2008; Sacchetti & Lefler, 2017). It is possible that age and increased social experience contribute to more accurate self-perceptions of social functioning in individuals with ADHD.

For participants with ADHD, subjective well-being and ADHD symptomatology were significant predictors of social impairment, a unique finding in ADHD research. It was expected that PIB would also predict social impairment for these individuals, which contrasts with past research suggesting a link between PIB and social functioning, primarily in children with ADHD. Age and maturation may be contributing factors, suggesting that individuals with ADHD may develop more accurate perceptions of their social functioning over time.

Among those without ADHD in this study, PIB and subjective well-being were significant predictors of social impairment. These results imply that PIB amplifies perceived social impairment, while the opposite appears to be true for subjective well-being. Within the social context, there seems to be an inverse relationship between PIB and subjective well-being, possibly indicating a more maladaptive function of PIB. This suggests that as emerging adults without ADHD overestimate their competence, they may perceive more problems associated with social impairment.

Academic Success

The study found that for participants with ADHD, subjective well-being and PIB significantly predicted self-reported cumulative GPA (academic success). Past studies have explored the relationships between subjective well-being and academic success, as well as PIB and academic performance. In this study, it appears that higher subjective well-being and lower PIB were associated with higher self-reported GPAs.

Consistently, the finding that subjective well-being predicted GPA aligns with common sense—the more satisfied and emotionally adept a student is, the better they are likely to perform academically. Notably, the study’s discovery that PIB is negatively associated with GPA complements existing research indicating a negative link between PIB and academic success in those with ADHD.

Limitations and Future Research Directions

Though several novel findings emerged, there are limitations to consider, each suggesting avenues for further exploration. Firstly, there are no standardized instruments for quantitatively measuring PIB, which necessitated the use of self-report and performance-based measures. The lack of comparability with commonly used methodologies should be acknowledged.

Additionally, the study focused on a single aspect of functioning, specifically academic performance, when measuring PIB. This might not be suitable for assessing PIB across other domains of functioning, and future research should explore this possibility using a multitrait-multimethod approach.

The study highlights the importance of measuring gender as a heterogeneous category, particularly given the significant portion of participants identifying as transgender or non-binary. Further research should delve into gender identity as both a primary variable and in intersectional analyses with other constructs.

Finally, there are some sampling considerations in this study with the majority of participants self-reporting having ADHD and agreeing to participate in research. Furthermore, the sample had a remarkably diverse gender identity composition. Hence replication of findings with a more typical sample with respect to diagnosis and gender would be interesting. Also, examining nuances such as educational (e.g., undergraduate or graduate enrollment) and developmental considerations (e.g., student vs. work status) would be interesting, but lacks information and power in this study. This could include more objective measures of functioning or collateral report to corroborate subjective personal appraisal.

Conclusion

Outcomes of this novel study emphasize that subjective well-being appears to be a protective factor associated with reduced impairment and greater academic success among emerging adults in enrolled in higher education. It also suggests that PIB may have a more maladaptive role than previously thought. These findings challenge the conventional relationship between ADHD and PIB, particularly for emerging adults. Implications of this study include the recognition that PIB impacts the subjective experience of emerging adult students, regardless of whether they have an ADHD diagnosis. It underscores the clinical relevance of considering PIB, which affects both symptoms and functioning in this population. Moreover, gender differences in these relationships were evident, with significant patterns of functional and social impairment varying by gender identity. For instance, the significant interaction between PIB and ADHD symptomatology in non-binary individuals highlights the need for gender-specific approaches in support and intervention strategies. Additionally, the study supports a dual-factor model of mental health, emphasizing the importance of well-being and its impact on subjective well-being, which may include PIB. This shift in focus from symptoms to well-being is crucial for understanding the mental health of emerging adults.

Footnotes

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Christian M. Stewart

Kristen S. Hassett

Author Biographies

Christian M. Stewart is a post-doctoral clinical psychology fellow at the Boys Town Center for Behavioral Health. Dr. Stewart has a Ph.D. in school psychology and an M.Ed. in counseling from the University of Houston. Prior to this, Dr. Stewart earned an M.S. in marriage and family therapy from the University of Nevada, Las Vegas. Dr. Stewart is specializing in diagnostic assessment for neurodevelopmental and internalizing disorders in children and adolescents.

Allison Master is an Assistant Professor in the University of Houston College of Education, and the director of the Identity and Academic Motivation Lab. Dr. Master holds a Ph.D. in developmental psychology from Stanford University. Dr. Master’s research examines how identity shapes students’ motivation and academic outcomes, especially in K-12 STEM education. Dr. Master’s work has been funded by the National Science Foundation and the Institute of Education Sciences.

Sarah S. Mire is Associate Professor of Educational Psychology at Baylor University and Lead Researcher for the Baylor Center for Developmental Disabilities. She is the founder and director of the School Psychology Autism Research Collaboration (*sparc), where her research broadly focuses on identifying and improving systems-level factors that can positively affect children with neurodevelopmental diagnoses across settings. Her Ph.D. is from the University of Houston in School Psychology (2012). Dr. Mire is also a licensed psychologist and nationally certified school psychologist with more than 15 years’ experience working directly with youth and their families.

Kristen S. Hassett is a Clinical Professor of Special Populations and interim Chair of the Department of Educational Leadership & Policy Studies in the College of Education at the University of Houston. Dr. Hassett has a Ph.D. in school psychology from the University of Houston (2009) and is a licensed psychologist, licensed specialist in school psychology, and nationally certified school psychologist. Dr. Hassett has been on the faculty at UH since 2011. Prior to this, Dr. Hassett was a practicing school psychologist in a local school district and had a private practice from 2012 to 2020. Dr. Hassett specializes in diagnostic assessment for learning disabilities and related conditions, special education assessment, special education law, and clinical supervision.

Bradley H. Smith is a Professor in the Department of Psychological, Health, and Learning Sciences and Director of the School Psychology Program at the University of Houston. Dr. Smith holds a Ph.D. in psychology from the University of Arizona. Dr. Smith specializes in developing and studying innovative interventions for youth in collaboration with schools and community organizations and seeks to make breakthroughs in equitable education. Dr. Smith’s current research interests include research on developmental asset building programs for middle and high school students, particularly those that have an outdoor adventure component and mindfulness interventions that improve well-being, academic performance, and life success.

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