Short form Conners’ Adult ADHD Rating Scales: Factor structure and measurement invariance by sex in emerging adults

ABSTRACT

Introduction

The short version of the Conners’ Adult ADHD Rating Scales (CAARS-S:S) is a self-report measure used to identify symptoms of Attention Deficit/Hyperactivity Disorder (ADHD) during adulthood. However, its psychometric properties specifically in emerging adults, or the transitional age group between adolescence and adulthood, remain understudied. This study aimed to validate the factor structure of the CAARS-S:S in a sample of emerging adults.

Method

The CAARS-S:S measure was completed by adults (n = 591) aged 18 to 29 located in English-speaking countries, including Australia, Canada and the United States. Confirmatory factor analysis was used to test a four-factor model of Inattention/Memory Problems, Hyperactivity/Restlessness, Impulsivity/Emotional Lability and Problems with Self-Concept, as well as the model’s invariance by sex. Sex was also included as a covariate in the model to examine differences in males’ and females’ scores on each factor.

Results

Overall, the four-factor structure fit the data and was invariant across males and females. All factors demonstrated high internal reliability (average ω_t and α = .86). It was observed that males tended to score higher on Inattention/Memory Problems while females scored higher on Problems with Self-Concept.

Conclusion

This research establishes the psychometric properties of the CAARS-S:S, placing greater confidence in using it to screen for ADHD symptoms in emerging adults living in a Westernized cultural context. The detailed findings of this research, implications for the use of the CAARS-S:S in this age group and potential future directions for examining the properties of the measure are discussed.

KEYWORDS:

• Attention deficit/hyperactivity disorder
• emerging adult
• adult ADHD
• self-report
• confirmatory factor analysis
• measurement invariance

Introduction

Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder characterized by symptoms of inattention, hyperactivity and impulsivity that impair everyday functioning (American Psychiatric Association [APA], 2013). It persists across the lifespan, with two-thirds of individuals diagnosed in childhood displaying symptoms as adults (Faraone et al., 2006). It is estimated that 2.58% of the global adult population present with ADHD that was diagnosed in childhood, while symptomatic adult ADHD is prevalent in 6.76% of adults (Song et al., 2021).

The diagnosis of ADHD in adulthood may be complicated because symptoms are not stable and can fluctuate over time (Franke et al., 2018). Specifically, whilst inattentiveness tends to persist, hyperactive-impulsive traits that present as externalizing behaviors during childhood tend to shift to an internalized sense of restlessness in adulthood (Biederman et al., 2000; Willcutt, 2012). Moreover, emotion dysregulation, encompassing emotional reactivity and difficulties with emotional control (Retz et al., 2012) can become more prominent, leading some authors to suggest that it is a core feature related to ADHD in adults (Adler et al., 2016; Beheshti et al., 2020). Importantly, such variability in ADHD symptoms has not always been captured by diagnostic criteria. For instance, past and current editions of the Diagnostic and Statistical Manual of Mental Disorders (DSM; American Psychiatric Association, 2013) have been critiqued as not appropriately capturing presentations of ADHD beyond childhood, due to the original development of DSM criteria being based primarily on observations of symptoms in children (Barkley & Biederman, 1997; Lefler et al., 2020; Sibley et al., 2012). The lack of clarity regarding the nature of ADHD changes later in life, and how they are reflected in the diagnostic process, has placed a focus on developing ADHD screening and assessment methods that are suitable for adults who are seeking a clinical diagnosis.

ADHD symptoms in emerging adulthood

One group for whom research on ADHD symptomatology requires greater attention includes those transitioning from adolescence to adulthood. Emerging adulthood describes those between 18 and 29 years living in a Western cultural context (Arnett et al., 2014). They are typically beginning to engage in new educational and occupational pursuits, as well as adjusting to a more independent lifestyle. The prevalence of ADHD symptoms has been shown to be particularly high in this age group, with rates of more than 7% reported (McKee, 2008; Yallop et al., 2015). For those experiencing late-onset symptoms, increases in performance demands combined with decreased external structure and support during emerging adulthood may exacerbate initially unnoticed symptoms (Solanto, 2018). These may involve challenges with executive function (EF), an umbrella term for neuropsychological processes related to self-regulation and problem-solving required to attain future goals (Barkley, 1997; Seidman, 2006). Emerging adults with high-level ADHD symptoms have greater problems with EF than those with lower-level or no symptoms in such domains as sustained attention, response inhibition, working memory and cognitive flexibility (Barkley et al., 1996; Fedele et al., 2009). Poor EF may lead to a struggle to implement skills needed to adapt to the new demands of adulthood (Fleming & McMahon, 2012). Hence, the impact of ADHD symptoms on everyday functioning can be pronounced at this life stage (Abecassis et al., 2017). There is evidence indicating that in postsecondary student samples, those with high levels of ADHD symptoms face greater challenges than those without in the domains of academic performance, work performance, social functioning and maintaining a healthy quality of life (Goffer et al., 2019; Sacchetti & Lefler, 2017; Shifrin et al., 2010; L. Weyandt et al., 2013). Overall, the presence of ADHD symptoms in emerging adulthood may be detrimental without sufficient support. It is important that individuals who present with high levels of ADHD-related characteristics, and therefore have an increased likelihood of an ADHD diagnosis in adulthood, can be accurately identified so that they can access appropriate assessment and treatment.

The Conners’ Adult ADHD Rating Scales

Multiple modes of assessment are typically consulted when testing for ADHD symptoms beyond childhood. Self-report assessments are convenient tools for quantifying subjective experience of symptoms and impairments (Demetriou et al., 2015), including for ADHD (Sibley et al., 2012). Though they are not recommended for sole use when diagnosing ADHD (DuPaul et al., 2001), self-report measures are considered valuable screening tools during the diagnostic process when used in conjunction with other methods such as informant reports, clinical interviews and neuropsychological assessment batteries (Marshall et al., 2021). Thus, it is necessary to ensure that the self-report measures used in the initial screening process are robust.

One of the most widely used self-report measures of ADHD in adults is the self-report version of the Conners’ Adult ADHD Rating Scales (CAARS; Conners, Erhardt & Sparrow, 1999). The CAARS was originally derived from a pool of 93 items that reflected the DSM-IV diagnostic criteria for ADHD (American Psychiatric Association, 1994), the Conners’ Rating Scales-Revised for children and adolescents (Conners, 1997) and conceptualizations of adult ADHD at the time of development. An exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to determine the measure’s latent structure in a sample of 839 typically developed adults aged between 18 and 81 from Canada and the United States of America (US) (Conners, Erhardt, Epstein et al., 1999). These analyses established a set of 66 items that corresponded to a four-factor model of adult ADHD. The factors consisted of Inattention/Memory Problems, Hyperactivity/Restlessness, Impulsivity/Emotional Lability and Problems with Self-Concept, which were represented as distinct yet correlated domains. The four-factor structure is distinct from other common models of adult ADHD that describe a three-factor (Glutting et al., 2005; Vitola et al., 2017), or two-factor structure (Matte et al., 2015) of inattention, hyperactivity and impulsivity.

From the exploratory analyses, the authors of the CAARS have detailed the nature of the measure’s four factors (Conners, Erhardt, Epstein et al., 1999). Firstly, the items on the Inattention/Memory Problems factor of the CAARS are related to behaviors such as sustaining attention toward a goal or initiating and completing tasks, which the authors suggest may point to EF weaknesses associated with ADHD (Conners, Erhardt, Epstein et al., 1999). Responses to the CAARS by postsecondary students have shown that inattentiveness is the most frequently endorsed feature (Harrison et al., 2013), highlighting its importance as a distinct factor for emerging adults. Secondly, the Hyperactivity/Restlessness factor addresses not only hyperactive behaviors, such as fidgeting or seeking out stimulating activities, but also emphasizes cognitive restlessness, such as feeling restless when sitting still. These items reflect the pattern of internalized hyperactivity that has been observed in emerging adulthood (Weyandt et al., 2003). Thirdly, the CAARS treats impulsivity as a distinct factor for adults. Impulsivity is usually attributed to EF deficits in inhibitory control (Logan et al., 1997) and is examined in terms of risk-taking behaviors, such as levels of alcohol consumption (Weafer et al., 2011). However, most of this factor’s items are instead relevant to emotional instability, such as having a hot temper. This suggests that impulsivity later in life is often presented as emotional impulsivity and dysregulation, for which emerging adults with ADHD symptoms have shown elevated levels (Chamberlain et al., 2017). Lastly, the Problems with Self-Concept factor is relevant to the cognitive perception of oneself (Houck & Spegman, 1999). Although problems with negative self-concept are not specific to the DSM-IV criteria of ADHD symptomatology, neither are they specific to the more recent 5^th edition of the DSM (American Psychiatric Association, 2013), they are rather considered as a notable downstream consequence of the primary ADHD symptoms and the effects of ongoing academic, social, and other difficulties (Houck et al., 2011). Indeed, there is evidence that more severe ADHD symptoms are related to lower self-esteem in adulthood (Cook et al., 2014). Thus, while problems with self-concept are not necessarily a unique feature of ADHD, the presence of this factor in the CAARS model identifies this as a significant related problem for adults who also report features of ADHD.

The identification of these distinct factors of the CAARS have resulted in four factor-derived subscales that assess symptoms of ADHD as they typically present in adults, including inattention, hyperactivity and impulsivity, as well as related problems such as difficulties with self-concept. The authors of the CAARS also developed additional subscales and indices to aid with the interpretation of the measure, including three non-factor derived DSM-IV symptom subscales based on DSM-IV diagnostic criteria, as well as an ADHD Index (Conners, Erhardt & Sparrow, 1999).

The four-factor structure of the CAARS has been shown to be reliable as denoted by adequate internal consistency and test-retest reliability (Erhardt et al., 1999). Subsequent studies have continued to support the factor structure the CAARS for adults aged 18 and above in clinical and non-clinical samples (Christiansen et al., 2013, 2011). The four factors have been confirmed in German (Christiansen et al., 2011) and Japanese translations (Someki et al., 2019) and in online and paper formats of the CAARS (Hirsch et al., 2013).

Research involving the CAARS has drawn attention to some of its limitations. Though the measure was initially deemed to have good criterion and concurrent validity with the Wender Utah Rating Scale (WURS; Erhardt et al., 1999; Ward et al., 1993), the WURS itself has been identified as having questionable discriminant validity (Stanton & Watson, 2016). Results from subsequent studies examining the convergent, concurrent and discriminant validity of the CAARS have been variable (Smyth & Meier, 2019). Criticisms have also included its poor sensitivity to non-credible responding (Becke et al., 2021; Suhr et al., 2011). This is a challenge commonly faced in using ADHD self-report measures, given recent findings that ADHD is vulnerable to overdiagnosis in adults, especially younger adults (Lovett & Harrison, 2021). It is estimated that base rates of feigned ADHD symptoms in university undergraduate student samples are approximately 20%, often influenced by motives such as receiving accommodations in work, education or social contexts that typically follow an ADHD diagnosis (Becke et al., 2022). The high risk of non-credible responding is one of the issues raised for ADHD self-report measures that highlights the need for the ongoing appraisal of psychometric tools, such as the CAARS, that are commonly used in the screening and assessment process.

Conners’ Adult ADHD Rating Scales – Self-Report: Short Version

The CAARS Self-Report: Short Version (CAARS-S:S) consists of 26 items derived from the original long version of the CAARS described above. Only a few studies have examined the CAARS-S:S as an independent measure. Amador-Campos et al. (2014) assessed the Catalan translation in a sample of 424 non-clinical adults aged between 18 and 81 years, using CFAs to compare the four-factor model with two alternative models. The first was a second-order model in which the four CAARS factors were accounted for by a global ADHD construct, suggesting that any shared variance between items is attributed to a general category of ADHD. The second was a one-factor model where all items were encompassed by a single ADHD construct. The measure’s original four-factor model best represented the data; it also demonstrated adequate test-retest reliability (average r = 0.80) and internal consistency (average Cronbach’s α = 0.75) for each factor. Similarly, a CFA conducted on the CAARS-S:S Korean version completed by 270 adults aged 19 to 64 years also confirmed the four-factor model (Park et al., 2013). However, some items did not correspond to the same factors as in the English version, possibly due to cultural differences in the interpretation of statements. Nonetheless, all factors demonstrated adequate internal consistency (average Cronbach’s α = 0.73). In another study by Cleland et al. (2006), the factor structure of the CAARS-S:S was examined in an American sample of individuals diagnosed with substance abuse disorders with the aim of determining if comorbid ADHD symptoms could be identified. The authors found general support for the four factors through an EFA, with some items loading on different factors than expected, but found good internal consistency across factors (average Cronbach’s α = 0.83) and ultimately considered the CAARS-S:S a useful measure of ADHD symptomatology for patients with substance abuse disorders. Together, these studies show that, aside from slight variations in item positioning, the CAARS-S:S identified the same factor structure as the original CAARS across adult samples.

Despite the investigations noted above, there is limited research on the psychometrics of the CAARS-S:S in subgroups of adults based on age. One exception was a US study where two groups of adults were examined, one including those aged 18 to 59 (n = 645) and the other including those aged 60 to 85 (n = 233; Callahan & Plamondon, 2019). In this study, the Problems with Self-Concept factor of the CAARS-S:S was not supported and related items were removed. Support was found for the remaining inattention, hyperactivity, and impulsivity factors that were invariant across age. However, it is difficult to ascertain whether this conclusion can be applied to emerging adults since the younger group was relatively broad in its age range of 18 to 59. Measures such as the CAARS-S:S must be evaluated to ensure they can adequately identify emerging adults with increased likelihood of being diagnosed with ADHD during this stage of life. Such evaluations have not, to date, been conducted.

Sex differences in self-reported ADHD symptoms

When discussing self-reported symptoms of ADHD, sex is considered as an influential factor. ADHD is more often diagnosed in males than females, with ratios of 3:1 in community samples (Barkley, 2006). Due to different trajectories of brain maturation, as females’ brains develop up to 3 years ahead of males (Thompson et al., 2005), it may be that males experience greater impairment compared to females of a similar age, for example, in some aspects of EF (Loyer Carbonneau et al., 2020). However, an emphasis on males in ADHD research has led to substantial under-identification of females with ADHD and thus limited knowledge of how symptoms present differently between sexes (Rucklidge, 2010). ADHD may be more easily diagnosed in males during childhood, as they present with more externalized hyperactive-impulsive behaviors than females (Gershon & Gershon, 2002). Females tend to experience more inattentiveness than males in younger years (Slobodin & Davidovitch, 2019), as well as more internalized emotional symptoms (Gershon & Gershon, 2002), both of which are less easily observed in children or are not interpreted as symptoms of ADHD.

Research focusing on adults suggests that ADHD symptoms in males and females present more similarly in adulthood, in terms of prominent inattentiveness and lowered hyperactivity and impulsivity (Biederman et al., 2004; Wilens et al., 2009). However, there seems to be a lasting impact of sex differences, with ADHD being comorbid with conduct disorder and substance use disorder particularly in males, while anxiety and depression are highly comorbid with ADHD in females (Solberg et al., 2018; Wilens et al., 2009). In the transition to adulthood, it is less clear whether the sex differences reported in childhood remain or whether symptoms in males and females are more alike. Such distinctions should considered when determining whether the CAARS-S:S is appropriate for screening ADHD symptoms in emerging adults.

Comparisons of scores on a measure between groups are ideally preceded by tests of measurement invariance, or the degree to which an underlying construct remains equivalent across groups or across time (Putnick & Bornstein, 2016). Determining measurement invariance involves testing different versions of the factor model in question with increasingly constrained parameters and conducting model comparisons to ensure that the model fit remains equivalent. This analysis can provide reinforcement for a measure’s psychometric properties of a measure by demonstrating its invariability between groups with different characteristics, such as samples grouped by sex.

When Conners, Erhardt, Epstein et al., (1999) tested the original CAARS for invariance across sex, the four-factor structure was equivalent for both sexes; subsequent comparisons of responses via ANOVA showed that males scored significantly higher than females on Inattention/Memory Problems, Hyperactivity/Restlessness, and Impulsivity/Emotional Lability. Other studies that have compared the raw scores of males and females on both the CAARS and CAARS-S:S using ANOVA have shown males to be higher in Hyperactivity/Restlessness and Impulsivity/Emotional Lability, whilst females scored significantly higher in Problems with Self-Concept (Amador-Campos et al., 2014; Christiansen et al., 2011; Park et al., 2013; Someki et al., 2019). This suggests that males continue to be prone to externalizing behaviors, and females internalizing behaviors, in adulthood. However, the nuances in sex differences in individuals transitioning from childhood into adulthood specifically were not addressed in detail.

Previous investigations of the initial CAARS and the CAARS-S:S have confirmed their four-factor structure across adulthood from 18 to 85. However, the suitability of this structure as it pertains to emerging adults from Western cultures remains understudied. This study aimed to validate the four-factor structure of the CAARS-S:S in a sample of typically developed emerging adults. If the structure was supported, another aim was to establish the invariance of the model across sex. Provided that invariance was achieved, the final aim was to explore the effect of sex on levels of self-reported ADHD-related characteristics. It was predicted that males would report higher scores for Inattention/Memory Problems, Hyperactivity/Restlessness and Impulsivity/Emotional Lability compared to females, while females would report higher levels of Problems with Self-Concept. The determination of the sample size, all data exclusions, and all measures in the study are reported in the following section.

Method

Participants

In order to acquire a sufficiently large sample of emerging adults (ages 18 to 29), the data were gathered using three methods. The first method sourced data from 155 undergraduate students (males = 64, females = 91) who had previously participated in a larger ongoing study (Nankoo, 2021) and provided permission for their data to be used in subsequent research. The second recruited a convenience sample online via e-mail and social media. This resulted in 94 participants (males = 25, females = 69) who were living in Australia at the time of participation. The third utilized Amazon Mechanical Turk, an online crowdsourcing platform (Buhrmester et al., 2011). This method recruited 425 participants (males = 207, females = 208, other = 10). Those recruited via Amazon Mechanical Turk were provided with an honorarium of 70 cents (USD) for their time. The three samples were combined to form a total sample of 674 participants (males = 296, females = 368, other = 10) from Westernized, predominantly English-speaking countries with a mean age of 24.21 (SD = 3.28), of whom 36.94% were based in Australia, 60.98% in the US and 2.08% in Canada.

Measure

Conners’ Adult ADHD Rating Scales Self-Report: Short Version (CAARS-S:S; Conners, Erhardt & Sparrow, 1999)

The CAARS-S:S is a 26-item self-report screening measure assessing the extent to which one experiences ADHD symptoms and related problems. Each item is scored by the participant on a 4-point scale (0 = “not at all or never”; 1 = “just a little, once in a while”; 2 = “pretty much, often”; 3 = “very much, very frequently”). The measure produces scores for four factor-derived subscales, composed of 5 items each, including A: Inattention/Memory Problems (e.g., “I’m disorganized”), B: Hyperactivity/Restlessness (e.g., “I tend to squirm or fidget”), C: Impulsivity/Emotional Lability (e.g., “I have a short fuse/hot temper”) and D: Problems with Self-Concept (e.g., “I wish I had greater confidence in my abilities”). Subscale E represents an ADHD Index score computed from 12 items, which identifies participants likely to receive an ADHD diagnosis. Higher scores on the subscales indicate greater presence of self-reported ADHD-related characteristics. The Inconsistency Index is a validity indicator calculated from participants’ scores on eight pairs of items; an index value of eight or greater may suggest careless or random responding due to inconsistent ratings between similar items. The psychometrics of the original CAARS, from which the short version is derived, include good criterion validity using the Semi-structured Interview for Adult ADHD developed by Barkley and Poillion (1994), internal consistency (Cronbach’s α = .86 to .92 across subscales) and a median test-retest reliability of .89 (Erhardt et al., 1999).

Procedure

The protocols for the current research received approval from the Human Research Ethics Office of the University of Western Australia (RA/4/1/8003), in accordance with national requirements for ethical conduct and the university’s policies and procedures. Participants either completed the CAARS-S:S in person or online using the survey software Qualtrics. After reading a brief description of the survey and providing informed consent, participants completing the measure online answered a series of demographic questions (i.e., age, sex, level of education, employment status and knowledge of the English language), followed by the CAARS-S:S. Those who completed it in person provided their age and responded to the measure along with a battery of neuropsychological tests relevant to a larger ongoing study. As this research did not pertain exclusively to emerging adults with an ADHD diagnosis, participants were not required to provide information about previous or existing diagnoses.

Data analysis plan

Exclusion criteria

The Inconsistency Index was calculated and 12.31% of participants received an index value of ≥ 8, so their data were excluded, leaving 591 participants. The final number of participants was considered sufficient to conduct an adequately powered CFA and invariance analysis (Bandalos, 2014; Brown, 2015; Koh & Zumbo, 2008; Kyriazos, 2018). Demographic characteristics of the final sample are presented in Table 1.

Table 1. Demographic Characteristics of the Final Sample of Emerging Adults (n = 591).

	Statistics
Characteristic	Sample 1 (existing sample,n = 132)	Sample 2 (convenience sample,n = 85)	Sample 3 (Amazon Turk, n = 374)	Total (n = 591)
Age in years (M [SD])	21.67 [3.70]	22.72 [2.58]	25.51 [2.42]	24.25 [3.24]
Sex (n [%])
Male	55 [41.67]	21 [24.71]	179 [47.86]	255 [43.15]
Female	77 [58.33]	64 [75.29]	185 [49.47]	326 [55.16]
Other	0 [0]	0 [0]	10 [2.67]	10 [1.69]
Location (n [%])
United States	0 [0]	0 [0]	360 [96.36]	360 [60.91]
Canada	0 [0]	0 [0]	14 [3.74]	14 [2.37]
Australia	132 [100]	85 [100]	0 [0]	217 [36.72]
Ethnicity (n [%])
White (of European origin)	-	42 [49.40]	241 [64.44]	283 [47.89]
Asian	-	29 [34.12]	57 [15.24]	86 [14.55]
African	-	1 [1.18]	21 [5.62]	22 [3.72]
Hispanic, Latino or Spanish origin	-	0 [0]	21 [5.62]	21 [3.55]
American Indian or Alaska Native	-	0 [0]	5 [1.33]	5 [0.85]
Native Hawaiian or other Pacific Islander	-	0 [0]	1 [0.27]	1 [0.17]
More than one description	-	11 [12.94]	23 [6.15]	34 [0.68]
Other or unknown	-	2 [2.36]	5 [1.33]	139 [28.59]
Education level (n [%])
Did not complete high school	-	0 [0]	2 [0.53]	2 [0.34]
Completed high school or equivalent	-	3 [3.53]	50 [13.37]	53 [8.97]
Part-way through college/bachelor’s degree	-	23 [27.06]	79 [21.12]	102 [17.26]
College/bachelor’s degree	-	25 [29.41]	191 [51.10]	216 [36.55]
Vocational training/technical school	-	2 [2.35]	9 [2.40]	11 [1.86]
Part-way through postgraduate degree	-	21 [24.71]	10 [2.67]	31 [5.25]
Postgraduate degree	-	11 [12.94]	27 [7.21]	38 [6.41]
Other or unknown	-	0 [0]	6 [1.60]	138 [23.36]
Employment status (n [%])
Employed	-	47 [55.29]	276 [73.80]	323 [54.65]
Unemployed	-	6 [7.06]	55 [14.70]	61 [10.32]
Student	-	31 [36.47]	32 [8.56]	63 [10.66]
Other or unknown	-	1 [1.18]	11 [2.94]	144 [24.37]
English as a first language (n [%])
Yes	-	73 [85.88]	340 [90.91]	413 [69.88]
No	-	12 [14.12]	10 [2.67]	22 [3.72]
Unknown	-	0 [0]	24 [4.42]	156 [26.40]

Data screening

The final data set had no missing values. CFA and invariance testing were conducted using Mplus version 8.1 (Muthén & Muthén, 1998-2017), using a robust weighted least squares mean and variance adjusted (WLSMV) estimator based on a polychoric correlation matrix, as this is considered the most suitable method for ordinal data (Li, 2016). Analyses of internal consistency and descriptive analyses for subscale statistics were conducted in JASP (JASP Team, 2020).

Confirmatory factor analysis

The analysis included the 20 items of the CAARS-S:S corresponding to subscales A, B, C and D. The remaining six items are relevant to the overall ADHD Index and do not correspond to the four factors. CFA was conducted to assess the original four-factor model, including Inattention/Memory Problems, Hyperactivity/Restlessness, Impulsivity/Emotional Lability and Problems with Self-Concept (Conners, Erhardt, Epstein et al., 1999). A one-factor model was also tested in which all 20 items loaded onto a single ADHD factor. Both models are represented in Figure 1.

Figure 1. Competing Factor Models of the CAARS-S:S.

Note: Model 1 depicts the original four-factor model (Conners, Erhardt, Epstein et al., 1999); Model 2 depicts the one-factor model (Amador-Campos et al., 2014). Inat = Inattention/Memory Problems; Hype = Hyperactivity/Restlessness; Impu = Impulsivity/Emotional Lability; Self = Problems with Self-Concept; ADHD = general ADHD construct.

Model fit was evaluated using chi-square (χ²) as a measure of absolute fit, along with the root mean square error of approximation (RMSEA) and the standardized root mean square residual (SRMR) as additional absolute fit indices. The Comparative Fit Index (CFI) and Tucker/Lewis Index (TLI) provided measures of incremental model fit (Schreiber et al., 2006). Suggested cutoff values for acceptable fit were RMSEA < .08, SRMR < .08 and CFI and TLI between .90 – .95 (Hu & Bentler, 1999; MacCallum et al., 1996). Good fit was represented by RMSEA < .05, SRMR < .06 and values ≥ .95 for CFI and TLI (Hu & Bentler, 1999; Kline, 2005). Ideally, items would load significantly and at ≥ .30 onto their respective factors. If the four-factor model did not achieve adequate fit, modification indices produced by Mplus were consulted. If a modification was judged to be theoretically meaningful, the model was adjusted accordingly.

Internal consistency

McDonald’s Omega total (ω_t) coefficient was used to calculate the internal consistency of the factors with ≥ .70 taken to indicate adequate internal consistency (Kline, 2005).

Measurement invariance

Provided that the original four-factor model had acceptable fit, multiple-group confirmatory factor analysis (MGCFA) was conducted to determine whether the CAARS-S:S was invariant across sex using data from n = 581: 255 males and 326 females, excluding those who reported their sex as “other.” Configural invariance was tested by assessing for the adequate fit of the proposed configural model, followed by metric and scalar invariance. Each was tested by examining changes in fit indices when comparing models. When sample sizes are unequal, metric invariance is indicated by a non-significant χ² difference test between the configural and metric model (p > .05) and ΔCFI ≤ −.005, supplemented by ΔRMSEA ≤ .010 or ΔSRMR ≤ .025 (Chen, 2007). Scalar invariance is shown by a non-significant χ² difference test between the metric and scalar model, ΔCFI ≤ −.005, along with ΔRMSEA ≤ .010 or ΔSRMR ≤ .005.

Sex differences and covariates

If the model achieved scalar invariance across sex, sex differences in responses to the CAARS-S:S were explored using the latent factor scores of each subscale. In previous studies, comparisons of sex differences on the CAARS subscales have been conducted with the observed scores of each scale. This may be considered inappropriate at times, as the assumption that all item scores carry equal weightings may lead to inaccuracies when the raw score values are summed together. The use of latent factor scores is known to avoid these potential errors; hence, the current study examined sex differences via factor scores. CFA was conducted on the four-factor model with sex as a covariate, with males coded as “0” and females as “1.” The standardized estimates between sex and the four factors were examined. Statistically significant standardized estimates (p < .05) were interpreted as a noteworthy association of sex with a particular factor.

The CFA also included covariates that would control for additional sampling characteristics while examining sex differences. These covariates consisted of age and method of completion (in-person coded as “0” and online as “1”).

Subscale statistics

The items corresponding to each of the four factors were summed to create raw scores for the four subscales of the CAAR-S:S. Whilst Omega is appropriate for factor score reliability, Cronbach’s α was also calculated to determine the internal consistency of each subscale based on raw scores, with values ≥ .70 indicating acceptable internal consistency (Kline, 2005).

Results

Confirmatory factor analysis

All items loaded significantly and above .30 onto their respective factors. The goodness-of-fit indices of the competing models of the CAARS-S:S are displayed in Table 2. The original four-factor model (Model 1) had less-than-ideal fit. The χ² value was significant, as was expected due to the large sample size (Brown, 2015). An adjusted χ² value based on Z was calculated (Kenny, 2020) confirming poor absolute fit.¹ Although the SRMR and CFI were adequate and the TLI was acceptable, the RMSEA was above the cutoff value of .08 for acceptable fit. The one-factor model (Model 2) showed considerably poorer fit based on the fit indices. Hence, the original four-factor model was found to better represent the CAARS-S:S but did not demonstrate an optimal fit to the data. Item statistics for Model 1 including standardized factor loadings, standard errors, significance and R² values are presented in Table 3; item statistics for Model 2 are included in Appendix 1.

Table 2. Goodness-of-Fit Indices of the Factor Models of the CAARS-S:S.

Model	χ^2	Adjusted χ^2 Z value	df	p	RMSEA [95% CI]	SRMR	CFI	TLI
Model 1	855.72	23.29	164	<.001	.084 [.079-.090]	.052	.952	.945
Model 2	2575.49	53.36	170	<.001	.155 [.149-.160]	.098	.834	.815
Model 3	745.11	20.58	163	<.001	.078 [.072-.083]	.048	.960	.953

Note: Model 1 = original four-factor model; Model 2 = one-factor model; Model 3 = modified four-factor model. RMSEA = root mean squared error of approximation; SRMR = standardized root mean square residual; CFI = Comparative Fit Index; TLI = Tucker-Lewis Index. Acceptable fit is indicated by RMSEA < .08, SRMR < .08, CFI and TLI between .90 – .95; good fit is indicated by RMSEA < .05, SRMR < .06, CFI and TLI ≥ .95.

Table 3. Item Statistics for Model 1 and Model 3.

		Model 1	Model 3
Factor	Item	Estimate (SE)	Estimate (SE)
Inattention/Memory Problems	A1 (Item 3) Disorganized	.74 (.02)‡	.74 (.02)‡
	A2 (Item 5) Hard to keep track of several things	.79 (.02)‡	.79 (.02)‡
	A3 (Item 17) Can’t get things done without a deadline	.88 (.02)‡	.88 (.02)‡
	A4 (Item 18) Has trouble getting started on a task	.87 (.01)‡	.88 (.01)‡
	A5 (Item 21) Absent-minded in daily activities	.84 (.02)‡	.83 (.02)‡
	C1 (Item 1) Interrupts others when talking		.50 (.05)‡
Hyperactivity/Restlessness	B1 (Item 4) Hard to stay in one place for long	.77 (.02)‡	.77 (.02)‡
	B2 (Item 6) Bored easily	.81 (.02)‡	.81 (.02)‡
	B3 (Item 10) Seeks out fast-paced, exciting activities	.45 (.04)‡	.45 (.04)‡
	B4 (Item 11) Feels restless even when sitting still	.83 (.02)‡	.83 (.02)‡
	B5 (Item 23) Tends to squirm or fidget	.83 (.02)‡	.83 (.02)‡
Impulsivity/Emotional Lability	C1 (Item 1) Interrupts others when talking	.70 (.03)‡	.18 (.06)‡
	C2 (Item 7) Has a short fuse/hot temper	.75 (.02)‡	.77 (.02)‡
	C3 (Item 8) Still throws tantrums	.78 (.03)‡	.81 (.03)‡
	C4 (Item 13) Set off easily by many things	.86 (.02)‡	.88 (.02)‡
	C5 (Item 20) Moods are unpredictable	.84 (.02)‡	.87 (.02)‡
Problems with Self-Concept	D1 (Item 9) Lacks faith in own abilities	.75 (.02)‡	.75 (.03)‡
	D2 (Item 15) Gets down on oneself	.87 (.02)‡	.87 (.02)‡
	D3 (Item 16) Unsure of oneself	.87 (.02)‡	.87 (.02)‡
	D4 (Item 25) Wishes they had greater confidence in abilities	.82 (.02)‡	.82 (.02)‡
	D5 (Item 26) Past failures make it hard to believe in oneself	.91 (.02)‡	.91 (.01)‡

Note: ‡ = p < .001.

In order to achieve a better fit for Model 1, Mis with a value > 10 were inspected. It was observed that a reduction in χ² statistic by 119.58 would result from cross-loading Item C1 (“I interrupt others when talking”) on both the Impulsivity/Emotional Lability factor and the Inattention/Memory Problems factor. This made conceptual sense and is discussed in more detail below. No other suggested modifications were deemed to be theoretically meaningful. The modified four-factor model (Model 3), with the cross-loaded Item C1 (Table 2) revealed significant adjusted and unadjusted χ² values. However, the RMSEA satisfied the cutoff of .08 for acceptable fit, while all other indices suggested good fit. As for Model 1, all items loaded significantly and above .30 onto their respective factors. Thus, Model 3 was evaluated as having acceptable fit that was comparatively better than Model 1. Item statistics for Model 3 are presented in Table 3.

Both Models 1 and 3 are represented in Figure 2, including standardized correlations between factors. Given the close-to-acceptable fit for Model 1 and the adequate fit for Model 3, both were included in subsequent analyses.

Figure 2. Model 1 and Model 3 of the CAARS-S:S.

Note: Model 1 depicts the original four-factor model (Conners, Erhardt, Epstein et al., 1999); Model 3 depicts the modified four-factor model. Standardized factor loadings and correlations between factors (all p< .001); Inat = Inattention/Memory Problems; Hype = Hyperactivity/Restlessness; Impu = Impulsivity/Emotional Lability; Self = Problems with Self-Concept.

Internal consistency

McDonald’s ω_t (with 95% confidence intervals) is reported for the four factors in Models 1 and 3 (Table 4) revealing adequate internal consistency for both (Kline, 2005). Cronbach’s α (with 95% confidence intervals) based on the subscale raw scores are also considered adequate (Table 4).

Table 4. Internal Consistency and Mean Subscale Raw Scores for Factors of the CAARS-S:S, Model 1 and Model 3.

Scale	Model 1				Model 3
			Males	Females			Males	Females
	ωt [95% CI]	α [95% CI]	M [SD]	M [SD]	ωt [95% CI]	α [95% CI]	M [SD]	M [SD]
Inattention/Memory Problems	.88 [.86-.90]	.88 [.86-.89]	6.38 [4.21]	5.60 [3.83]	.88 [.87-.90]	.88 [.86-.89]	7.50 [4.73]	6.67 [4.21]
Hyperactivity/Restlessness	.83 [.81-.85]	.83 [.80-.85]	6.62 [3.84]	6.14 [3.48]	.83 [.81-.85]	.83 [.80-.85]	6.62 [3.84]	6.14 [3.48]
Impulsivity/Emotional Lability	.84 [.82-.86]	.83 [.81-.85]	4.11 [3.32]	4.12 [2.93]	.84 [.82-.86]	.83 [.81-.85]	4.11 [3.32]	4.12 [2.93]
Problems with Self-Concept	.90 [.88-.91]	.89 [.88-.91]	6.24 [3.97]	7.26 [4.20]	.90 [.88-.91]	.89 [.88-.91]	6.24 [3.97]	7.26 [4.20]

Note: McDonald’s ω_t calculated using factor loadings in Mplus; Cronbach’s α calculated using raw scores. Values of > .70 indicate adequate internal consistency reliability (Kline, 2005). Males n= 255, Females n = 326. All subscales have a maximum score of 15.

Measurement invariance

MGCFA was conducted for both Models 1 and 3 to test for invariance across sex (males and females only). The fit indices and comparisons between increasingly constrained models for Model 1 are displayed in Table 5. The configural version of Model 1 had an RMSEA value slightly above the cutoff for acceptable fit. However, due to the sufficient values of the SRMR, CFI and TLI and its similarity to the baseline model (in which males and females were combined), the model was deemed to have acceptable configural invariance, suggesting that there was no difference in the four-factor structure between sexes. Further invariance testing demonstrated acceptable fit for both metric and scalar models. A comparison between the configural and metric models revealed that the χ² difference test was non-significant, and the changes in CFI, RMSEA and SRMR met the criteria for invariance. This indicates that there was no difference in the factor loadings between sexes. The metric and scalar model comparison had a non-significant χ² difference test and minimal changes in CFI, RMSEA and SRMR which satisfied the criteria for invariance, indicating that item thresholds were equivalent between sexes. Thus, scalar invariance was established for Model 1.

Table 5. Goodness-of-Fit Indices and Comparative Indices for Measurement Invariance Across Sex, Model 1 and Model 3.

Model	χ^2	df	p	RMSEA [95% CI]	SRMR	CFI	TLI	χ^2 diff. test	df	p	ΔRMSEA	ΔSRMR	ΔCFI
Model 1
Configural	948.935	328	<.001	.081 [.075-.087]	.056	.958	.951
Metric	963.319	344	<.001	.079 [.073-.085]	.057	.958	.954	25.192	16	.067	−.002	.001	.000
Scalar	977.690	380	<.001	.074 [.068-.079]	.057	.960	.960	47.907	36	.089	−.005	.000	.002
Model 3
Configural	858.169	326	<.001	.075 [.069-.081]	.052	.964	.958
Scalar	887.101	379	<.001	.068 [.062-.074]	.053	.966	.966	69.326	53	.066	−.007	.001	.002

Note: RMSEA = root mean squared error of approximation; SRMR = standardized root mean square residual; CFI = Comparative Fit Index; TLI = Tucker-Lewis Index. Metric invariance is indicated by a non-significant χ² difference test (p > .05), ΔCFI ≤ −.005, supplemented by ΔRMSEA ≤ .010 or ΔSRMR ≤ .025. Scalar invariance is indicated by a non-significant χ² difference test (p > .05), ΔCFI ≤ −.005, supplemented by ΔRMSEA ≤ .010 or ΔSRMR ≤ .005 (Chen, 2007).

As for Model 3, the configural version displayed an acceptable model fit, based on the values of RMSEA, SRMR, CFI and TLI, meaning that there was no difference in model structure between sexes. The fit indices for the metric model were unable to be provided by Mplus. However, the scalar model had acceptable fit. Furthermore, a comparison between the configural and scalar models resulted in a non-significant χ² difference test and minimal changes in CFI, RMSEA and SRMR that met the criteria for invariance, indicating that item thresholds were equivalent between sexes. This information was considered satisfactory for establishing scalar invariance for Model 3.

Sex differences and covariates

The mean subscale raw scores for males and females regarding both models are presented in Table 4. CFAs were conducted for Models 1 and 3 in the combined sample, with sex as a covariate, while controlling for the variables of completion method and age. The standardized estimates for sex on the four latent factors are presented in Table 6. In both models, males had higher scores on the Inattention/Memory Problems items than females. Again in both models, females had higher scores on the Problems with Self-Concept items than males. However, the hypothesized sex difference in Hyperactivity/Restlessness and Impulsivity/Emotional Lability was not supported.

Table 6. Item Statistics for Models 1 and 3 with Covariates.

		Model 1		Model 3
Covariate	Factor	B (SE)	p	B (SE)	p
Sex	Inattention/Memory Problems	−.23 (.08)	.006	−.22 (.08)	.007
	Hyperactivity/Restlessness	−.15 (.09)	.084	−.15 (.09)	.084
	Impulsivity/Emotional Lability	.01 (.10)	.919	.01 (.09)	.754
	Problems with Self-Concept	.24 (.09)	.006	.24 (.09)	.006
Recruitment Method	Inattention/Memory Problems	.87 (.10)	<.001	.90 (.10)	<.001
	Hyperactivity/Restlessness	.81 (.10)	<.001	.81 (.10)	<.001
	Impulsivity/Emotional Lability	.55 (.11)	<.001	.46 (.11)	<.001
	Problems with Self-Concept	.40 (.11)	<.001	.40 (.11)	<.001
Age	Inattention/Memory Problems	−.02 (.01)	.217	−.02 (.01)	.274
	Hyperactivity/Restlessness	−.01 (.01)	.421	−.01 (.01)	.421
	Impulsivity/Emotional Lability	−.02 (.02)	.259	−.02 (.02)	.201
	Problems with Self-Concept	−.01 (.02)	.592	−.01 (.02)	.592

Note: Model fit statistics: Model 1: RMSEA = .076, 95% CI = .071-.081; SRMR = .060; CFI = .951; TLI = .942; Model 3: RMSEA = .071, 95% CI = .066-.076; SRMR = .057; CFI = .957; TLI = .949. Coding for Sex: male = 0, female = 1; coding for Recruitment Method: in-person = 0, online = 1.

The standardized estimates for method of completion and age for Models 1 and 3 are also presented in Table 6. This analysis revealed an association between each of the four factors and the completion method, indicating that participants who completed the test online had higher scores overall than those who completed it in person. However, age was not associated with any of the four factors.

Discussion

The transitional period between adolescence and adulthood is a crucial stage of development that may be significantly impacted by high levels of ADHD symptoms. It is imperative that measures designed for adults generally that screen for potential symptoms of ADHD are also established to be suitable for identifying symptoms for emerging adults specifically. The current study aimed to confirm the factor structure of the CAARS-S:S in a sample of typically developed emerging adults, as well as establish sex-related invariance and subsequently examine differences between sexes in self-reported ADHD-related features as per the four dimensions of the CAARS-S:S. It was predicted that males would score higher on the Inattention/Memory Problems, Hyperactivity/Restlessness and Impulsivity/Emotional Lability factors, while females would score higher on the Problems with Self-Concept factor. The results found support for the four-factor structure and its invariance between males and females. Additional analyses of sex differences partially supported initial predictions.

Factor structure of the CAARS-S:S

CFA revealed that the original four-factor structure of the CAARS-S:S had a close-to-acceptable fit to the sample. An adequate fit was achieved after one item adjustment. This implies that, overall, Conners, Erhardt, Epstein et al., (1999) conceptualization of adult ADHD symptoms suitably identifies ADHD-related features in emerging adults between the ages of 18 and 29; the factors of Inattention/Memory Problems, Hyperactivity/Restlessness, Impulsivity/Emotional Lability and Problems with Self-Concept are appropriate for screening for characteristics that are potentially indicative of ADHD symptomatology during this period. The fit of the four-factor model before and after the item adjustment was acceptable, indicating that when using the CAARS-S:S, self-reported ADHD-related features were best represented as four distinct, correlated factors. The one-factor model was a much poorer fit, meaning that a unidimensional structure was considerably less appropriate, which was also observed in the model comparison conducted by Amador-Campos et al. (2014).

The item adjustment that was required for an acceptable fit of the four-factor model involved cross-loading Item C1, “I interrupt others when talking,” on both the Impulsivity/Emotional Lability and Inattention/Memory Problems factors. Modifications to items across factors have been proposed for different translations of the CAARS (Christiansen et al., 2011; Park et al., 2013; Someki et al., 2019); it was unexpected that the English version of the CAARS-S:S would require modification when tested in a sample with similar characteristics to the original study (Conners, Erhardt, Epstein et al., 1999). Our adjustment did not drastically impact the overall model, and in this context, the solution may be considered somewhat idiosyncratic. However, there is some evidence to support that item C1 could be conceptually relevant to both Inattention/Memory Problems and Impulsivity/Emotional lability, which provides insight into alternative interpretations of the item itself. Interrupting others may be viewed as an impulsive behavior linked to response inhibition difficulties (Logan et al., 1997). However, inattentiveness and EF deficits are also linked to inhibitory control (Avisar & Shalev, 2011), so this behavior could also reasonably be construed as related to inattention. Perhaps participants endorsed this item in light of difficulties such as sustaining attention during a conversation, prompting further exploration of how inattentiveness impacts social skills in emerging adults.

In terms of internal reliability, before and after the model modification, McDonald’s ω_t values for factor loadings ranged between .83 and .90, while Cronbach’s α for raw scores ranged between .83 and .89. These are as high as the α values found for the original CAARS (.86 to .92; Erhardt et al., 1999), confirming the internal reliability of the four ADHD constructs when the CAARS-S:S is administered to emerging adults. Invariance testing across sex in the four-factor model indicated that the factor structure, factor loadings and item thresholds were equivalent between males and females and the four ADHD-related dimensions were interpreted in a similar manner across groups. Whilst metric invariance was unable to be determined for the modified model, the demonstration of configural and scalar invariance was deemed as sufficient evidence for sex invariance. Therefore, the four factors are suitable for assessing ADHD-related characteristics in males and females and allow for score comparisons between emerging adults of both sexes to be attributed to true group differences. The initial CAARS validation also established invariance by sex (Conners, Erhardt, Epstein et al., 1999), however, this is the first study to test invariance of the CAARS-S:S specifically for emerging adults.

Sex differences in self-reported symptoms on the CAARS-S:S

As hypothesized, male emerging adults scored significantly higher on items related to Inattention/Memory Problems and females scored higher on Problems with Self-Concept items, but there were no differences in Hyperactivity/Restlessness or Impulsivity/Emotional Lability.

We had predicted that males would score higher on inattentive and EF difficulties often associated with ADHD: a pattern observed in previous studies of the CAARS (Conners, Erhardt, Epstein et al., 1999) and the CAARS-S:S (Park et al., 2013). Although this trend in CAARS scores does not corroborate the research proposing that symptoms of inattentiveness across the sexes become similar in adulthood (Biederman et al., 2004), it may allude to the different developmental trajectories between sexes, with males’ reports possibly attributable to EF deficits that continue into adulthood (Loyer Carbonneau et al., 2020). Perhaps the CAARS-S:S items also highlight differences in how males and females subjectively view inattentiveness or EF difficulties, with males emphasizing these challenges more than females.

The finding that females in this sample scored higher on Problems with Self-Concept also supported initial predictions and was consistent with other CAARS-S:S studies (Amador-Campos et al., 2014; Park et al., 2013). Together, these findings suggest that the challenges of emerging adulthood may have more of an impact on the self-esteem and emotional distress of females with who self-report high levels of ADHD-related features of compared to males who self-report these features – a proposition to be examined in future research. Nonetheless, when a study of college students found females with ADHD symptoms self-reported greater impairment than males, Fedele et al. (2012) interpreted this as a tendency to negatively assess themselves against higher standards of their own capabilities. The under-identification of ADHD-related features in females may also have an accumulated negative effect on self-esteem, due to the failure to have their behaviors recognized as potential symptoms of ADHD (Quinn & Madhoo, 2014). It is also worth noting that the higher Problems with Self-Concept scores coincide with research suggesting that higher levels of internalizing symptoms in females with ADHD continue across the lifespan (Solberg et al., 2018).

Contrary to initial predictions, male emerging adults did not score significantly higher on the Hyperactivity/Restlessness or Impulsivity/Emotional Lability factors. This does not align with other CAARS studies (Christiansen et al., 2011; Conners, Erhardt, Epstein et al., 1999; Park et al., 2013), nor does it support research suggesting that externalizing features are more common or severe in males (Rucklidge, 2010). These results may instead be in line with research showing that males and females become more similar in manifestations of hyperactive-impulsive behaviors over time (Biederman et al., 2004).

Notably, the method by which sex differences were examined in the current study differs from that of Amador-Campos et al. (2014), Christiansen et al. (2011), and Someki et al. (2019), in which sex and age differences were calculated via ANOVA using observed scores. It may be considered inappropriate to report comparisons based on raw scores rather than latent scores, which was the approach used for this study. Moreover, the previous studies did not explicitly test for invariance of the CAARS-S:S before testing for group differences. These different methods of investigating sex differences may be a factor in some of the inconsistencies in findings between studies.

Two covariates, method of measure completion and age, were included during the analysis of sex differences to control for these additional sampling characteristics. One noted limitation is that other sample characteristics were unable to be controlled, due to the unavailability of data for the undergraduate sample. Nevertheless, regarding the statistics of the available covariates, it is of interest that participants who completed the CAARS-S:S online had higher scores than those who completed it in-person – a finding also observed by Hirsch et al. (2013). The latter described several possible explanations that could also apply to the current study, such as online participants potentially using the questions as a means of assessing themselves for ADHD-related characteristics while completing the measure, the online questionnaire reaching a wider demographic due to the absence of geographic barriers, or a greater sense of anonymity when completing the CAARS-S:S online which may have led to more truthful answers. There was no significant associations of any of the factors with age, which was within expectations given the narrow age range that was established for this study.

Implications

The support for the factor structure of the CAARS-S:S, its internal consistency reliability and its invariance across sex in the current sample provides greater confidence in the measure as an appropriate means of screening ADHD-related features in emerging adults in a Westernized cultural context, to be used as an indicator of whether ADHD symptomatology may be present. This screening tool can provide useful information when interpreted in the context of other sources, such as clinical interviews, informant reports and cognitive and neuropsychological testing. Support for the four-factor model of the CAAR-S:S also suggests that ADHD-related characteristics which Conners, Erhardt, Epstein et al., (1999) found to be unique to adult presentations are also relevant during this life stage, such as internalized restlessness, emphasized emotional impulsivity and the cumulative effects of impairments on negative self-evaluations. The findings also revealed insights into how self-reported ADHD-related symptoms compared between males and females in this sample. For males, problem-solving and self-management difficulties due to EF challenges may require consideration. Females may be more prone to internalizing symptoms, showing a need to focus on negative self-reflections and emotional distress as difficulties that may be related to ADHD symptoms.

Previous literature has raised issues over the challenges of identifying symptoms of ADHD beyond childhood (Hartung et al., 2019). From this study, the CAARS-S:S has been reinforced as a valuable initial screening tool for ADHD-related features in emerging adults and can be used alongside other assessments to obtain a comprehensive view of one’s symptom profile (Martel et al., 2017). These findings add considerably to the literature regarding self-report assessments of ADHD in adults, as few studies have examined the CAARS-S:S structure as it pertains specifically to younger adults. Previous studies within this group have often recruited exclusively from postsecondary and college populations (Abecassis et al., 2017). Due to the multiple data collection methods used to obtain a large sample size, the results may be relevant to emerging adults in a wide range of life circumstances, which represents the heterogeneity of this age group. On the other hand, due to the combining three convenience samples with limited opportunity to control for different sampling characteristics, there may be some caution taken when generalizing the results, particularly those regarding sex differences, to other emerging adult populations with self-reported ADHD-related symptoms.

One caveat to the current discussion is that the properties of the CAARS-S:S have not been established for gender-diverse emerging adults, such as those identifying as transgender or non-binary. There are challenges in catering existing psychometric tools to these individuals, in the context of continued use of sex-based standardized norms, and limited research focused on this population, despite increased prevalence of ADHD among transgender and gender-diverse individuals (Goetz & Adams, 2022). While this issue was not within the scope of this study, this highlights an area for consideration in reviewing current ADHD measures or developing new tools.

Limitations

The findings of this study should be considered in the context of some limitations. The sample for this study had an exclusion rate of 12.31%. While this may be considered high, the base rates for feigned ADHD among young adults of around 20% (Becke et al., 2022) suggest that it is not surprising to see increased proportions of non-credible responding in research focused on self-reported ADHD symptoms. However, a note is required regarding the Inconsistency Index as the basis for exclusion criteria. While the index may identify those responding in a careless or random manner, it does not account for cases in which one may intentionally fabricate responses to feign ADHD symptoms (Harrison et al., 2007). While discriminating feigned responses was not a focus of the current study, this nonetheless brings into question the overall effectiveness of the index for distinguishing valid and invalid responses. There have been efforts to develop validity indicators for the CAARS to more accurately carry out this purpose (e.g., Becke et al., 2021; Harrison & Armstrong, 2016), though there are currently no indices that have been tested for use with the short form CAARS.

The high exclusion rate also raises ongoing concerns about the recruitment of research participants via channels such as Amazon Mechanical Turk and student samples, given the susceptibility to careless or insufficient effort responding on self-reported surveys (Ward & Meade, 2023). While these data collection methods are beneficial for research requiring large sample sizes, this highlights the need to carefully review the rigor and adequacy of participant screening in future studies.

The CAARS-S:S as a self-report measure is not without its limitations. In addition to issues of non-credible responding, it has been noted that there is frequent overlap of ADHD symptoms with symptoms of other disorders, which impacts the use of self-reported ADHD measures. Nankoo et al. (2019) found that ADHD symptoms were correlated highly with anxiety and depression symptoms in a postsecondary student sample. This overlap of ADHD symptoms with those of other mental health conditions raises the possibility that responses may be confounded by comorbid symptoms being incorrectly reported as relevant to ADHD (Smyth & Meier, 2019). This signifies a need to carefully consider such overlap and differentiate amongst symptoms. Attempts to test the specificity and sensitivity of the CAARS measures have produced mixed results (e.g., Christiansen et al., 2012; Harrison et al., 2019; Stewart & Liljequist, 2012). Continual investigation of these and other psychometric properties of the CAARS-S:S, such as concurrent and discriminant validity, would provide greater support for the accurate screening of ADHD symptoms in emerging adults, and assist with mitigating non-credible or confounded responses.

In terms of the sample for this study, the majority of participants were of a predominantly White or European background, so the findings may be biased toward these characteristics. Indeed, research suggests that ADHD presentations vary in with different demographics. Previous American-based studies have identified that children and adults from ethnic minorities, such as African American or Hispanic American populations, are less likely to receive an ADHD diagnosis than those of European background (Chung et al., 2019; Morgan et al., 2013). This raises questions as to whether existing measures used today are suitable for those from diverse cultural backgrounds (Asherson et al., 2012). Furthermore, the majority of participants were recruited through social media and Amazon Mechanical Turk, which may suggest a higher socioeconomic status due to factors such as availability of appropriate technology and internet to access online platforms. This may impact one’s ability to participate in online research and therefore limit the representation of those in other socioeconomic circumstances within the sample (Lourenco & Tasimi, 2020). Evidence suggests that socioeconomic disadvantage in both childhood and emerging adulthood is interrelated with a higher risk of symptoms throughout the lifespan (Galéra et al., 2012; Russell et al., 2016). Future studies should consider a broader scope cultural and socioeconomic factors when testing the English CAARS-S:S for emerging adults.

Future research

The findings have highlighted some directions for future investigations of the CAARS-S:S. The adequate fit of the four-factor model was achieved by cross-loading one item between two factors; this modification may be important to consider in future replication studies examining the structure of the CAARS-S:S for young adult age groups. Additional psychometric properties that require assessment include discriminant validity and concurrent validity with other adult ADHD measures. There is evidence of these properties being established for the CAARS in adult samples of broad age ranges (Smyth & Meier, 2019); however, it would be beneficial to confirm the validity and reliability of the CAARS-S:S for emerging adults. Additionally, the current study of the CAARS-S:S was completed in a sample of typically developed emerging adults where a previous diagnosis of ADHD was not specified. The factor structure of the measure would also require testing specifically in clinical ADHD samples to determine its suitability for emerging adults with an existing diagnosis. Additionally, just as invariance across sex was established, future examinations of the CAARS-S:S could focus on its invariance in samples of emerging adults with a greater range of cultural and socioeconomic backgrounds living within Westernized countries. Furthermore, tests of invariance across age would also be beneficial; for example, Callahan and Plamondon’s (2019) research may be built upon to evaluate the complete four-factor structure in older age groups, exploring whether the CAARS-S:S accurately represents ADHD symptomatology in adults at this particular life stage.

To summarize, the adequacy of the theoretical four-factor structure of ADHD, and the internal consistency of the factors, establish the CAARS-S:S as a psychometrically sound tool for identifying ADHD-related features for emerging adults living in Westernized contexts. The demonstration of invariance across sex also displays its value for exploring comparisons in self-reported symptoms between males and females. The confirmation of its factor structure elucidates the nature of ADHD-related characteristics across the lifespan by suggesting that this conceptualization of dimensions of ADHD is suitable during this transitional life stage. Given that it is one of the most widely used measures for screening for ADHD characteristics beyond childhood and adolescence, these findings ultimately provide greater confidence in the CAARS-S:S as a screening tool for use in conjunction with a range of ADHD assessment methods during the crucial developmental period of emerging adulthood.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author (Jasmine Wu, [email protected]) upon reasonable request.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.

Notes

1. To interpret χ² values as an indicator of absolute fit, Kenny (2020) recommends calculating an adjusted value based on Z as follows: Z = √(2χ²) – √(2df – 1).

Appendix 1.

Item Statistics for Model 2.

Table

	Estimate (SE)
Item	ADHD
A1 (Item 3)	.69 (.02)‡
A2 (Item 5)	.72 (.02)‡
A3 (Item 17)	.82 (.02)‡
A4 (Item 18)	.82 (.02)‡
A5 (Item 21)	.77 (.02)‡
B1 (Item 4)	.65 (.03)‡
B2 (Item 6)	.68 (.02)‡
B3 (Item 10)	.35 (.04)‡
B4 (Item 11)	.71 (.02)‡
B5 (Item 23)	.70 (.02)‡
C1 (Item 1)	.60 (.03)‡
C2 (Item 7)	.67 (.03)‡
C3 (Item 8)	.69 (.03)‡
C4 (Item 13)	.76 (.02)‡
C5 (Item 20)	.74 (.02)‡
D1 (Item 9)	.65 (.03)‡
D2 (Item 15)	.78 (.02)‡
D3 (Item 16)	.79 (.02)‡
D4 (Item 25)	.75 (.02)‡
D5 (Item 26)	.82 (.02)‡

Note: ‡ = p < .001.