SWAN-DE-SB

Validation of a Self-Report Version of the German Strengths and Weaknesses of ADHD Symptoms and

Normal Behavior Scale (SWAN-DE-SB)

Friederike Blume 1,2 *, Jan Kühnhausen 1,2,3 *, Lilly Buhr 1, Rieke Köpke 1, Andreas J. Fallgatter 2,4,

Caterina Gawrilow 1,2,3

1 Department , , University of Tübingen, Schleichstr. 4, 72076 Tübingen,

Germany

2 LEAD Graduate School & Research Network, University of Tübingen, Walter-Simon-Str. 12, 72072

Tübingen, Germany

3 Center for Individual Development and Adaptive Education of Children at Risk (IDeA), DIPF | Leibniz

Institute for Research and Information in Education, Rostocker Straße 6, 60323 Frankfurt am Main,

Germany

4 Department of Psychiatry and Psychotherapy, University Hospital Tübingen, Calwer Str. 14, 72076

Tübingen

* equal contribution

Friederike Blume now works at the DIPF | Leibniz Institute for Research and Information in Education,

Rostocker Straße 6, 60323 Frankfurt am Main, Germany and is additionally affiliated with the Center for

Individual Development and Adaptive Education of Children at Risk (IDeA).

Jan Kühnhausen now works at the Department of Child and Adolescent Psychiatry, University Hospital

Tübingen, Osianderstr. 14-16, 72076 Tübingen

Corresponding author:

Dr. Friederike Blume, DIPF | Leibniz Institute for Research and Information in Education, Rostocker Straße

6, 60323 Frankfurt am Main, Germany. E-mail: [email protected] SWAN-DE-SB

Note. This preprint is based on data from 405 adults of whom 17 reported to have received a diagnosis of attention-deficit/hyperactivity disorder (ADHD) earlier, and 14 indicated a current ADHD diagnosis. However, diagnoses were not verified by a clinician through means of a structured diagnostic process. With the help Lydia Weber and Thomas Ethofer (both Department of Psychiatry and

Psychotherapy, University Hospital Tübingen), the study therefore currently recruits an additional clinical sample of patients whose ADHD diagnosis is verified through a qualified clinician. Implications drawn in relation to patients should therefore be considered with caution in the meantime.

SWAN-DE-SB

Abstract

While the excellent psychometric quality of the German third-party report version of the Strengths and Weaknesses of ADHD Symptoms and Normal Behavior Scale (SWAN-DE) for school-aged children was recently demonstrated, a self-report version employable with adults was not available so far. The present study therefore aimed at developing and validating the SWAN-DE-SB, a self-report version of the SWAN-

DE. Based on data obtained from 405 adults, 14 (3.5%) of them with a current ADHD diagnosis, normality, internal consistency, as well as factorial and convergent validity were examined. The SWAN-DE-SB yielded normally distributed scores, high internal consistency, and factorial validity. The scale was shown to discriminate between participants with and without ADHD and to significantly correlate with commonly employed clinical ADHD scales. With the SWAN-DE-SB, we introduce a self-report measure assessing both strengths and weaknesses of ADHD symptoms and normal behavior and demonstrated its excellent psychometric properties.

Key words: SWAN scale, Attention-Deficit/Hyperactivity Disorder, self-regulation, self-report SWAN-DE-SB 1

Adult attention-deficit/hyperactivity disorder (ADHD; American Psychiatric Association, 2013) is characterized by intense symptoms of either inattention (e.g., difficulty sustaining attention at work, during tasks or activities), hyperactivity- (e.g., motor overactivity such as having difficulties remaining seated although social conventions require it, or inner restlessness; interrupting or intruding on others by butting into conversations), or both. These core symptoms were shown to implicate impaired academic (e.g., fewer high school and university degrees), occupational (e.g., lower overall income), as well as social functioning (e.g., fewer friends, higher divorce rates; Frazier, Youngstrom, Glutting, & Watkins, 2007;

Fredriksen et al., 2014; Friedman et al., 2003; Mannuzza, Klein, Bessler, Malloy, & Hynes, 1997). Classified as a neurodevelopmental disorder, symptoms are expected to occur in childhood already, while approximately 2.5% of adults are diagnosed with ADHD (Simon, Czobor, Bálint, Mészáros, & Bitter, 2009).

Empirical evidence, however, clearly suggests that not only individuals diagnosed with ADHD, but all people experiences ADHD symptoms to a certain degree from time to time. This supports the assumption that ADHD comprises a continuum reaching from no or very low to very intense, and thus clinically significant symptomatology rather than a category (i.e., ADHD as opposed to no ADHD; Frazier,

Youngstrom, & Naugle, 2007; Haslam et al., 2006; Levy, Hay, McStephen, Wood, & Waldman, 1997;

Marcus & Barry, 2011; Salum et al., 2014). Relatively asymptomatic individuals can thus be assumed to experience intensities representing the positive extreme (i.e., functionality), while patients with ADHD can thus be assumed to experience symptom intensities representing the negative extreme (i.e., dysfunctionality) of a continuum continuously distributed across the general population.

Nevertheless, common clinical ADHD rating scales usually depict a deficit-oriented view, thereby solely focusing on individuals’ weaknesses while ignoring strengths, hence artificially truncating the full range of behaviors (Swanson et al., 2012). This approach could result in major difficulties, especially when assessing ADHD symptoms in non-clinical samples (cf. Schulz-Zhecheva et al., 2017; Swanson et al., 2012).

First, as the majority of the general population presents with no or only rarely occurring ADHD symptoms and only few individuals anchor at the negative extreme of such scales, any such clinical scale typically produces a skewed distribution. This is problematic as it might result in an overestimation of the prevalence of ADHD when cut-off scores (i.e., scores that separate those with from those without an ADHD diagnosis) SWAN-DE-SB 2 are derived on the basis of data assumed to be normally distributed. Additionally, such distributions result in severe methodological ramifications in studies depicting ADHD as a continuous trait in the general population as, by overlooking meaningful variance at the positive end of the distribution, statistical power and validity would be substantially reduced. Second, when solely focusing on individuals’ deficits, strengths allowing to compensate for one’s weaknesses (e.g., to be organized although one experiences difficulties in staying focused when working on a task) are overlooked, which results in low construct validity of such scales, as only part of the construct is captured. Additionally, strengths cannot directly be reinforced in psychotherapeutic settings, which would be important in terms of improving the patients’ self-esteem.

Consequently, to face these challenges, scales assessing both weaknesses (i.e., dysfunctionality) and strengths (i.e., functionality) of ADHD symptoms are urgently required.

In an attempt to overcome these shortcomings, Swanson and colleagues (2012) introduced the

Strengths and Weaknesses of ADHD-Symptoms and Normal-Behavior (SWAN) Scale assessing symptoms of school-aged children’s inattention and hyperactivity-impulsivity in the form of a third-party report. This scale is based on the symptom criteria outlined in the Diagnostic and Statistical Manual of Mental

Disorders, Fourth Edition (DSM-IV; American Psychiatric Association [APA], 2000, also in accordance with DSM-5, APA, 2013) and comprises 18 items. As they are designed to assess school-aged children’s symptomatology, the items’ wording addresses school and play situations as well as children’s behavior at home. All items are worded positively instead of negatively as clinical scales usually are (e.g., “Remembers daily activities” instead of “Is often forgetful in daily activities”). Accordingly, a 7-point scale anchoring the negative (i.e., “far below average”) and the positive (i.e., “far above average”) end of the dimension allowed to well differentiate between children’s actual strengths and weaknesses. This original SWAN scale comprised the first scale assessing ADHD symptoms in a truly dimensional way. Alongside its translations to Spanish, French, Chinese, and German, it was shown to have excellent psychometric properties such as to result in normally distributed data in the general population, to have high internal consistency and adequate retest reliability, a consistent factor structure, and excellent convergent validity as indicated by high correlations with existing diagnostic instruments (Arnett et al., 2013; Hay, Bennett, Levy, Sergeant, &

Swanson, 2007; Lakes, Swanson, & Riggs, 2012; Polderman et al., 2007; Schulz-Zhecheva et al., 2017; SWAN-DE-SB 3

Swanson et al., 2012). Additionally, its sensitivity and specificity in distinguishing school-aged children with and without ADHD as well as school-aged children with ADHD and other mental disorders were demonstrated (Chan, Lai, Luk, Hung, & Leung, 2014; Lai et al., 2013; Robaey, Amre, Schachar, & Simard,

2007; Schulz-Zhecheva et al., 2017).

Nevertheless, neither the original SWAN scale nor any of its translations can be used with adults.

This is because first, the items’ wording relates to schoolchildren’s activities and environments. Second, while third-party reports interviewing parents and teachers are commonly employed with younger children, self-reports become increasingly important when assessing ADHD symptoms in adults, thus confirming the need for a self-report version of the SWAN. The present study therefore first aimed at adapting the existing

German SWAN-DE (Schulz-Zhecheva et al., 2017) to a self-report version for adults. References of the

SWAN-DE to playing and school were replaced with other activities such as leisure time activities and the occupational context. We additionally added an item inquiring about the informant’s feeling of inner restlessness, as symptoms of hyperactivity-impulsivity are experienced as an inner restlessness rather than motor overactivity in adults (APA, 2013; Rösler et al., 2006). The new scale was termed the German

Strengths and Weaknesses of ADHD and Normal-Behavior Scale Self-Report (SWAN-DE-SB).

Subsequently, the present investigation aimed to assess this scale’s psychometric properties, which were then compared to those reported for the SWAN-DE (Schulz-Zhecheva et al., 2017). In accordance with the

SWAN-DE, we assessed the scale’s internal consistency, factorial and convergent validity with established clinical ADHD scales, and analyses of sensitivity and specificity in differentiating individuals with and without ADHD in an adult population sample containing the percentage of individuals with ADHD diagnosis corresponding to prevalence (i.e., approx. 2.5%; cf. Simon et al., 2009).

Methods

Sample

Participants were recruited via a circular e-mail sent to the university’s mailing list, social media platforms, postings in university buildings, and personal contacts to adults aged at least 18, between April and May 2019. The recruited sample comprised 405 adults (110 male, 293 female, 2 diverse) at an average age of 28.6 years (SD = 11.1, min = 18, max = 71). Among the participants, 17 reported to have received an SWAN-DE-SB 4

ADHD diagnosis earlier, and 14 indicated a current ADHD diagnosis. Ten of the latter participants indicated current treatment with Methylphenidate or Atomoxetine. Asked about current treatment, one participant indicated treatment with Cannabis (i.e., Tetrahydrocannabinol, THC), which is however no approved medication, and might therefore be considered a form of self-medication. In terms of educational attainment,

39.3% of the participants had a university degree (i.e., at least Bachelor's degree or diploma), 54.3% a high school diploma, 4.4% a secondary school leaving certificate, and 0.5% were still in school (information for

1.5% of participants were missing)1.

The study was approved by the Ethics Committee for Psychological Research of the University of X.

Participants were eligible for participation when being at least 18 years old and having provided informed consent for the study participation. Additionally, they were told to only be allowed to participate when having a very good knowledge of the German language. Participants were reimbursed for their study participation by either a certificate for 0.5 test person hours (for university students in psychology programs only) or by participation in a lottery of ten vouchers worth 5.00 EUR each.

Procedure

Data were obtained through an online survey established on the website www.soscisurvey.de (Leiner,

2019). The participants were first presented with the participant information, then indicated informed consent, and finally generated their personal codei. If informed consent was not indicated, participants could not participate in the study. Participants subsequently filled out the SWAN-DE-SB, the ADHS-SB (Rösler,

Retz-Junginger, Retz, & Stieglitz, 2008), and the CAARS-K-SB (Christiansen, Hirsch, Abdel-Hamid, & Kis,

2014), all of which were presented in random order. Thereafter, they were asked to report demographic variables such as age, gender, educational degree, a previous or current ADHD diagnosis, and whether they were currently medicated for ADHD, and if yes with which product. Participants were required to indicate their e-mail address so that they could be contacted in case of a positive ADHD screening result as assessed with the CAARS (Christiansen et al., 2014). Finally, they could indicate either their e-mail address in case they wished to participate in the lottery or their matriculation number and name in case they wished to obtain

1 Years spent in the German education system are: 9-10 years for a secondary school leaving certificate, 12-13 years for a high school diploma, and an additional 3-4 years for a Bachelor’s degree and at least 5 years for a Master’s degree or German diploma. SWAN-DE-SB 5 a certificate for test person hours. E-mail addresses, names, and matriculation numbers were stored separately from the questionnaire data and were deleted after participants were informed about positive

ADHD screening results and after having been reimbursed (i.e., in August 2019). The average time required for answering the items was 12.4 min (SD = 4.4).

Measures

SWAN-DE-SB (see Supplements).

The SWAN-DE-SB questionnaire was developed as an adaptation of the SWAN-DE (Schulz-

Zhecheva et al., 2017). Therefore, activities commonly accomplished by children (e.g., playing, going to school) were replaced with activities commonly associated with adults (e.g., leisure time activities, going to work). Additionally, the wording was changed to first person. In a first attempt, a pilot version comprising

23 items was developed. It comprised five items more than the SWAN-DE as we aimed for a data-driven decision between two different wordings for two items (i.e., concerning the completion of work and instructions [Item 4]; concerning sitting still [Item 10]) and additionally included an item inquiring for the participant’s inner restlessness. This version was piloted by 33 adults (21 female) aged between 18 and 80

(M = 37.2 years, SD = 16.9). Distributions of the total scale and the subscales inattention and hyperactivity- impulsivity as well as reliability indices (Cronbach’s α) were calculated. The results supported the normal distribution of the scales and adequate reliability. Additionally, answer tendencies were calculated for each item. On the basis of this information, the questionnaire employed in the data collection, comprising 19 items, including nine items assessing symptoms of inattention, nine items assessing hyperactivity- impulsivity, and one additional item, assessing inner restlessness, was composed. Participants indicated their answers to the positively worded items on a 7-point scale ranging from -3 (far below average) to +3 (far above average), while 0 indicated average. Lower scores consequently indicated stronger ADHD symptoms.

As a confirmatory factor analysis (CFA) could not show the item on inner restlessness to load on any of the two identified factors (see Table 3), it was dropped from all further analyses presented below. Hence, all results presented are based on mean values of the two subscales inattention (SWAN-AD) and hyperactivity- impulsivity (SWAN-HI), both comprising 9 items each, as well as a total scale (SWAN-TOT). The final SWAN-DE-SB 6 version of the SWAN-DE-SB (see Appendix for both the German scale developed as well as an English translation) consequently comprised 18 items, which were analogous to the items of the original SWAN-DE.

ADHS-SB.

The ADHD self-report scale (ADHS-SB; Rösler, Retz-Junginger, et al., 2008) is a self-report questionnaire assessing ADHD symptoms in adults using 18 items. The items are answered on a 4-point scale ranging from 0 (does not apply) to 3 (severely pronounced). In addition to a total score (ADHS-SB-

TOT), it informs about inattention (ADHS-SB-AD), as well as overactivity-impulsivity (ADHS-SB-HI). Its retest reliability r = .78 - .89, internal consistency α = .72 - .90, and its convergent validity with the CAARS-

K-SB (r = .54 - .79; Christiansen et al., 2014) were shown to be good. Its sensitivity was reported to be 65% while its specificity was 92% (Rösler, Retz-Junginger, et al., 2008).

CAARS-K-SB.

The German version of the Conners Adult ADHD Rating Scales (CAARS-K-SB; Christiansen et al.,

2014) is a self-report questionnaire assessing ADHD symptoms in adults using 26 items. All items are answered on a 4-point rating scale ranging from 0 (does not apply at all) to 3 (applies very strongly/frequently), whereby higher values indicate stronger ADHD symptoms. In addition to an overall symptom score (CAARS-K-SB-TOT), the questionnaire provides values for inattention/memory problems

(CAARS-K-SB-AD) and a composite scale representing hyperactivity-impulsivity calculated as the mean value of the scores on hyperactivity/motor restlessness and impulsivity/emotional lability (CAARS-K-SB-

HI). The psychometric quality of the questionnaire was shown to be adequate, with Cronbach’s α = .64 - .95 and a convergent validity of r = -.51 -.19. No discrimination values were reported for the CAARS-K-SB, but for its corresponding long version. The long version’s sensitivity was reported to range between 61 and 79% while its specificity was between 83 and 88%, depending on the subscale (Christiansen et al., 2014).

Data Analysis

All analyses were computed using the software package R (R Core Team, 2015), and assumed an α- error probability of .05 (two-tailed). All answers provided on the scale ranging from -3 (far below average) to +3 (far above average) were transformed to range between 0 and 6, whereby they could directly be compared to the results presented by Schulz-Zhecheva and colleagues (2017). Mean scores were calculated SWAN-DE-SB 7 on the basis of 18 items (i.e., nine assessing inattention, and nine hyperactivity-impulsivity) for the total scale (SWAN-TOT) and the subscales inattention (SWAN-AD) and hyperactivity-impulsivity (SWAN-HI).

The effects of gender, age, educational level, as well as their interactions were estimated using factorial

Analyses of Variance (ANOVAs). To examine normality of the distribution, histograms and quantile- quantile plots (Q-Q plots) were inspected visually. To assess the questionnaire’s internal consistency, its

Cronbach’s alpha was calculated. The questionnaire’s factorial structure was assessed using principal component analysis (PCA) with promax rotation and confirmatory factor analysis (CFA) with a bifactor model comprising a general ADHD factor (G-ADHD) and one factor each for inattention (SP-AD) and hyperactivity-impulsivity (SP-HI). Its convergent validity was assessed on the basis of Spearman’s correlations between the SWAN-DE-SB and the CAARS-K-SB as well as the ADHS-SB. To assess its clinical utility, differences between participants with and without ADHD were estimated after controlling for age, gender, educational level, and its interactions using factorial Analyses of Covariance (ANCOVAs).

Additionally, a receiver operating characteristics (ROC) curve analysis was computed.

Results

SWAN-Scores, Age and Gender

For our sample, mean SWAN-TOT scores were M = 3.76 (SD = 0.83). Mean scores for the SWAN-

AD were M = 3.76 (SD = 0.93) and M = 3.76 (SD = 0.96) for the SWAN-HI. Mean scores on the item level ranged between 2.61 and 4.38 (SD = 1.22 – 1.56). Mean scores of SWAN-AD, the SWAN-HI, and SWAN-

TOT were highly correlated, rSWAN-AD~SWAN-HI = .54 (p < .05), rSWAN-AD~SWAN-TOT = .87 (p < .05), and rSWAN-

HI~SWAN-TOT = .88 (p < .05). A univariate three-way ANOVA revealed a significant effect of educational level as well as the interaction of Age x Educational Level on the SWAN-TOT (Table 1)2. Additionally, gender and educational level significantly affected SWAN-AD scores and educational level significantly affected

SWAN-HI scores (Table 1).

[insert Table 1 about here]

2 While age significantly predicted the SWAN-TOT score for subjects with a secondary school leaving certificate, b = 0.032, F(1,16) = 6.35, p < .05, it did not do so for subjects with a high school diploma, b = 0.003, F(1,218) = 0.19, p > .05, or a university degree, b = -0.006, F(1,157) = 1.13, p > .05. In contrast, educational degree significantly predicted the SWAN-TOT score for subjects of all ages. SWAN-DE-SB 8

Distribution

SWAN-TOT, SWAN-AD, and SWAN-HI were approximately normally distributed as shown by visual inspection of histograms and quantile-quantile plots (Q-Q plots). Skew and kurtosis were small

(SWAN-TOT: skew = -0.07 (z = -0.60), kurtosis = 0.34 (z = 1.39); SWAN-AD: skew = -0.48 (z = -3.94), kurtosis = 0.65 (z = 2.70); SWAN-HI: skew = 0.14 (z = 1.12), kurtosis = -0.22 (z = -0.92)) and non- significant, except for the kurtosis and skew of the SWAN-AD.

Reliability

Cronbach’s αs were .90 for SWAN-TOT, .85 for the SWAN-AD, and .86 for the SWAN-HI, thereby indicating excellent internal consistency for the total scale as well as both subscales. Additionally, every single item was significantly correlated with the entire (sub)scale (SWAN-TOT (r = .53 - .70), SWAN-AD

(r = .55 - .75), SWAN-HI (r = .55 - .75), all p < .05).

[insert Figure 1 about here]

Validity

A principal component analysis (PCA) revealed four components with eigenvalues above 1. Yet, as the scree-plot (Figure 1) indicated that only two components were visually distinguishable from the remaining ones, a parallel analysis as recommended by Horn (1965) was applied. This parallel analysis uncovered that only the eigenvalues of two factors were above the threshold of randomly generated eigenvalues. Hence, compliant with earlier work (cf. Christiansen et al., 2011; Conners et al., 1999) and comparable with the SWAN-DE (Schulz-Zhecheva et al., 2017), a two-factor structure was assumed. After a promax rotation, the two components explained 47% of the data’s variance. The nine items forming the

SWAN-AD subscale loaded on component 1, explaining 23% of the variance. The nine items forming the

SWAN-HI subscale loaded on component 2, explaining 24% of the variance. The item which had additionally been included in the scale to assess inner restlessness was shown to load little on both factors while all other items showed small cross loadings on the anticipated components (< .30).

The structure of the SWAN-DE-SB was further examined using a confirmatory factor analysis

(CFA). The model fit was examined for four proposed models (Table 2). A bifactor model (Model 2) showed a better model fit than a non-hierarchical two-factor model (Model 1). Since item 19 (“I feel calm SWAN-DE-SB 9 inside”; reflecting inner calmness/restlessness) only indicated small loadings on both factors and was not part of the original version of the SWAN (Swanson et al., 2012), a model without this item was defined, which further improved the model fit (Model 3). An inspection of the modification indices showed that a model including correlated measurement errors between Items 5 (“I organize tasks and activities”) and 7 (“I keep track of things necessary for activities”) as well as between Items 2 (“I sustain attention on tasks”) and

6 (“I engage in tasks that require sustained mental effort”) further increased the model fit (Model 4). This final model presented in Figure 2 showed a tolerable model fit (see Table 2). The standardized loadings of the variables on the general factor (ADHD-G) and the two specific factors SP-AD and SP-HI as well as the

R2 values for the final model of the SWAN-DE-SB can be seen from Table 3. All items showed significant positive loadings on the ADHD-G and all items of the attention deficit subscale loaded significantly on the

SP-AD. However, only four of the nine items of the hyperactivity-impulsivity subscale showed significant loadings on the SP-HI, indicating slightly decreased homogeneity of the HI subscale of the SWAN-DE, in which five of the nine items showed a significant loading (cf. Schulz-Zhecheva et al., 2017). The remaining five items showed very strong loadings on the general factor.

[insert Table 2 about here]

[insert Figure 2 about here]

[insert Table 3 about here]

Convergent validity was assessed by computing Pearson’s correlation coefficients between the

SWAN-TOT, SWAN-AD, and SWAN-HI and the ADHS-SB-TOT, ADHS-SB-AD, and ADHS-SB-HI, respectively. Significant negative correlations were observed for all scales (SWAN-TOT ~ ADHS-SB-TOT: r = -.59, p < .05; SWAN-AD ~ ADHS-SB-AD: r = -.63, p < .05; SWAN-HI ~ ADHS-SB-HI: r = -.53, p <

.05; see Figure 3). The SWAN subscales were also significantly negatively correlated to the CAARS-K-SB-

TOT, CAARS-K-SB-AD, and CAARS-K-SB-HI (SWAN-TOT ~ CAARS-K-SB-TOT: r = -.45, p < .05;

SWAN-AD ~ CAARS-K-SB-AD: r = -.57, p < .05; SWAN-HI ~ CAARS-K-SB-HI: r = -.40, p < .05; see

Figure 4).

[insert Figure 3 about here]

[insert Figure 4 about here] SWAN-DE-SB 10

As for the clinical utility of the SWAN-DE-SB, multifactorial, univariate ANCOVAs controlling for the influence of age, gender, educational level, and their interactions revealed significant differences between participants with and without an actual ADHD. Participants with a self-reported previous ADHD diagnosis scored significantly lower on the SWAN-TOT (MADHD = 2.16 (SD = 0.63), Mno ADHD = 3.81 (SD =

2 0.78), F(1,385) = 39.52, p < .05, hp = .13), the SWAN-AD (MADHD = 1.91 (SD = 0.65), Mno ADHD = 3.82 (SD

2 = 0.87), F(1,385) = 48.47, p < .05, hp = .14), and the SWAN-HI (MADHD = 2.41 (SD = 1.00), Mno ADHD =

2 3.80 (SD = 0.93), F(1,385) = 15.99, p < .05, hp = .071), indicating greater difficulties with regulating attention, and hyperactivity-impulsivity. An ROC analysis revealed large area under the curve (AUC) values

(SWAN-TOT: .96, SWAN-AD: .96, SWAN-HI: .86), hence indicating an excellent differentiation ability of the SWAN-DE-SB between individuals with and without ADHD (Swets, 1988). Optimal cut-off values for specificity and sensitivity were identified as follows: SWAN-TOT: 3.02 with a specificity of 89.5% and a sensitivity of 92.9%; SWAN-AD: 2.61 with a specificity of 92.8% and a sensitivity of 92.9%; SWAN-HI:

2.94 with a specificity of 83.9% and a sensitivity of 71.4%.

[insert Table 4 about here]

Discussion

The aim of the present study was to adapt the existing German version of the Strengths and

Weaknesses of ADHD and Normal Behavior (SWAN) Scale (SWAN-DE; Schulz-Zhecheva et al., 2017), which has recently been validated in a sample of schoolchildren, to a self-report version for adults.

Subsequently, this newly derived SWAN-DE-SB scale was evaluated and validated on the basis of data collected through an online survey from a general population sample comprising adults with and without

ADHD. Additionally, we aimed at comparing the psychometric quality of the SWAN-DE-SB to that of the

SWAN-DE (Schulz-Zhecheva et al., 2017). The results supported the questionnaire’s excellent psychometric properties, which were similar to the original version of the SWAN scale (Swanson et al., 2012), the SWAN-

DE (Schulz-Zhecheva et al., 2017), as well as further translations (Table 4; Arnett et al., 2013; Chan et al.,

2014; Lai et al., 2013; Lakatos, Birkás, Tóth, & Gervai, 2010; Lakes et al., 2012; Polderman et al., 2007;

Robaey et al., 2007). SWAN-DE-SB 11

In accordance with previous evaluations of the original third-party report version of the SWAN scale

(Table 4; Arnett et al., 2013; Chan et al., 2014; Lai et al., 2013; Lakatos et al., 2010; Lakes et al., 2012;

Polderman et al., 2007; Robaey et al., 2007) as well as the SWAN-DE (Schulz-Zhecheva et al., 2017), the results of the present study provided evidence for the assumption that the scores obtained were approximately normally distributed on all subscales. Additionally, the results showed that the SWAN-DE-

SB scale truly captured ADHD symptoms at both ends of a continuously distributed trait. Beyond indicating that the questionnaire may therefore well be used as a self-report measure in studies recruiting general population samples, this result further supports the assumption that ADHD should be conceptualized as a continuum ranging from very low to very intense symptomatology rather than as a category (cf. Coghill &

Sonuga-Barke, 2012).

Additionally, as indicated by high Cronbach’s α values, the SWAN-DE-SB showed excellent internal consistency. The values obtained were similar to those of earlier validation studies of both the original version of the SWAN scale as well as the SWAN-DE (Schulz-Zhecheva et al., 2017), and further translations (Table 4).

Moreover, the results of a PCA and a CFA supported a two-factor structure of the SWAN-DE-SB with all items loading on the expected factors. The structure thereby is in accordance with those of the original third-party report version of the SWAN (Arnett et al., 2013; Swanson et al., 2012) as well as the

SWAN-DE (Schulz-Zhecheva et al., 2017). The amount of the variance explained by the SWAN-DE-SB was slightly lower compared to other studies using the original SWAN scale or the SWAN-DE (see Table

4), and also slightly lower than the values obtained for other commonly employed self-report questionnaires such as the CAARS-K-SD (60%; Christiansen et al., 2014) or the ADHS-SB (63%; Rösler, Retz, et al.,

2008). The CFA likewise supports the bifactor model of ADHD symptoms of the original version of the

SWAN scale (cf. Normand, Flora, Toplak, & Tannock, 2012).

As for its validity, the total scale as well as the SWAN-DE-SB subscales were shown to be significantly related to all scales of the ADHS-SB (Rösler, Retz-Junginger, et al., 2008) and the CAARS-K-

SB (Christiansen et al., 2014), two clinical scales commonly used in the diagnostic process of ADHD. The effect sizes obtained were in the same range as those of earlier studies (Table 4; Arnett et al., 2013; Chan et SWAN-DE-SB 12 al., 2014; Lakatos et al., 2010; Lakes et al., 2012; Polderman et al., 2007; Robaey et al., 2007; Schulz-

Zhecheva et al., 2017), thereby demonstrating the scale’s excellent convergent validity.

Regarding the clinical utility of the SWAN-DE-SB, the total scale as well as both subscales showed to excellently differentiate between individuals with and without ADHD as indicated by high sensitivity (71-

93%) as well as specificity (89-93%) values. The values obtained were at least as high or even higher compared to those obtained through earlier studies examining the original SWAN scale (Arnett et al., 2013), the SWAN-DE (Schulz-Zhecheva et al., 2017), and other translations (see Table 4 for an overview; Chan et al., 2014; Lai et al., 2013; Robaey et al., 2007). They were in the same range or even higher than those reported for further self-report scales assessing ADHD symptoms in adults such as the ADHS-SB (Rösler,

Retz, et al., 2008) or the CAARS (Christiansen et al., 2014). This means that, although an even larger sample with a larger proportion of individuals diagnosed with ADHD should be assessed to derive clinically applicable cut-off values, and these ADHD diagnoses should be confirmed as part of the study, the scale could already now be employed in a clinical diagnostic process. Thus, it could additionally inform about patients’ strengths, which comprise an important starting point for psychotherapeutic approaches focusing on further improving these strengths and thus patients’ self-esteem.

Limitations

The present study was the first to adapt the German SWAN-DE scale (Schulz-Zhecheva et al., 2017) to self-report and to assess its psychometric quality in a sample of adults. While demonstrating its excellent psychometric properties, the concurrent validity could however not be clarified as no cognitive measures were obtained and therefore could not be correlated with the SWAN-DE-SB scores (cf. Schulz-Zhecheva et al., 2017). The SWAN-DE-SB’s concurrent validity should consequently be examined in future studies.

Additionally, before the scale could reliably be employed for diagnostic purposes, the scale’s sensitivity and specificity, as well as cut-off values should be reexamined in a sample comprising a larger number of patients diagnosed with ADHD to thus minimize the influence of randomness on the results.

These patients’ diagnosis should furthermore be confirmed as part of the study to ensure that all diagnoses are met on the basis of the same criteria complying with the current guidelines. SWAN-DE-SB 13

Finally, such a study should additionally examine how well the SWAN-DE-SB discriminates between adults with ADHD and such with other mental disorders. Thus, it will acknowledge that inattention and hyperactivity-impulsivity denote symptoms also occurring in the context of other mental disorders such as depression or borderline personality disorder.

Conclusion

With the SWAN-DE-SB, we introduce a self-report version of the German SWAN-DE scale with excellent psychometric properties. The scale may therefore now be used in diverse research areas (e.g., education and clinical research), and possibly also for the support of diagnostic and therapeutic processes.

Future research may wish to additionally evaluate this scale’s properties in an adolescent sample so that measures assessing strengths and difficulties of ADHD symptoms and normal behavior, thereby applying a dimensional approach, will be available for all age groups.

Declaration of Conflicting Interests

The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publications of this article.

Funding

This research was supported by the Department of School Psychology of the University of X.

Supplement

[Insert link to Appendix (the questionnaire’s instruction and items)].

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Table 1 Influence of age, gender, educational level and their interactions on SWAN-TOT, SWAN-AD, and SWAN-HI SWAN-DE-SB SWAN-TOT SWAN-AD SWAN-HI 2 2 2 df F hp p F hp p F hp p Gender 1 1.79 .008 .18 8.38 .025 .004* 0.24 .000 .62

Age 1 0.04 .000 .785 0.39 .000 .53 0.08 .003 .78 Educational 3 10.00 .070 < .001* 8.15 .057 < .001* 7.27 .052 < .001* Level Gender x Age 1 0.10 .001 .75 0.16 .000 .69 0.03 .003 .87 Gender x Educational 2 1.62 .007 .20 0.47 .002 .62 2.29 .010 .10 Level Age x Educational 2 3.20 .015 .042* 2.63 .012 .073 2.76 .013 .06 Level Gender x Age x Educational 2 0.42 .002 .67 0.42 .002 .66 0.22 .001 .80 Level Note: Gender: female = 1, male = 2; Educational level: higher values indicate higher levels; Residual df for all models: 386; * p < .05.

SWAN-DE-SB 19

Table 2

Confirmatory Factor Analysis of the SWAN-DE-SB – Model Fit Indices

Model 1 Model 2 Model 3 Model 4 Chi Square 627.06 (df = 151)* 436.98 (df = 133)* 376.54 (df = 117)* 320.28 (df = 115)*

Comparative Fit 0.84 0.89 0.91 0.93 Index (CFI) Tucker-Lewis 0.82 0.87 0.88 0.90 Index (TLI) 0.088 0.075 0.074 0.066 RMSEA CI = [0.081, 0.095] CI = [0.067, 0.083] CI = [0.066, 0.082] CI = [0.058, 0.075]

SRMR 0.066 0.044 0.044 0.042 Note. Model 1: Non-hierarchical model; Model 2: Bifactor model with all items included and no correlated measurement errors; Model 3: Bifactor model without item 11; Model 4: Bifactor model without item 19 and with correlated measurement errors allowed between items 5 and 7 and items 2 and 6. * p < .05.

SWAN-DE-SB 20

Table 3

PCA and CFA Results for the SWAN-DE-SB

PCA CFA Item C1 C2 h2 G-ADHD SP-AD SP-HI R2 1. I pay close attention to details and avoid -0.01 0.64 0.40 0.49 (0.07) * 0.61 (0.07)* - .34 careless mistakes. 2. I maintain attention to -0.01 0.79 0.62 0.65 (0.07) * 0.74 (0.07)* - .54 tasks and activities. 3. I listen when I am 0.25 0.44 0.38 0.60 (0.06)* 0.40 (0.06)* - .35 approached directly. 4. I finish work that I have -0.12 0.83 0.59 0.63 (0.08)* 0.91 (0.07)* - .56 started. 5. I organize my tasks and -0.25 0.83 0.52 0.44 (0.08)* 0.87 (0.08)* - .42 activities. 6. I volunteer for tasks that require sustained mental -0.01 0.62 0.38 0.52 (0.07)* 0.54 (0.08)* - .28 effort. 7. I keep track of the items required for my -0.11 0.81 0.56 0.54 (0.07)* 0.70 (0.07)* - .46 activities. 8. I ignore external stimuli. 0.09 0.47 0.28 0.48 (0.07)* 0.41 (0.07)* - .22 9. I keep everyday 0.14 0.58 0.45 0.72 (0.08)* 0.62 (0.08)* - .40 activities in mind. 10. I sit still. 0.50 0.23 0.43 0.99 (0.07)* - -0.12 (0.11) .45 11. I remain seated when rules or social 0.69 0.05 0.52 0.98 (0.06)* - 0.02 (0.11) .53 conventions require it. 12. I regulate my motor 0.65 0.15 0.56 0.99 (0.06)* - -0.12 (0.11) .66 activity. 13. I maintain a noise level appropriate to the 0.76 -0.04 0.55 0.96 (0.07)* - 0.21 (0.11) .51 situation. 14. I come to rest and rest. 0.51 0.22 0.43 0.93 (0.08)* - 0.10 (0.11) .36 15. I regulate my verbal activity and control 0.78 -0.10 0.53 0.82 (0.08)* - 0.52 (0.10)* .48 excessive talking. 16. I think about questions (before I burst out with 0.71 -0.15 0.41 0.66 (0.09)* - 0.67 (0.10)* .41 an answer). 17. I wait patiently until it is 0.89 -0.30 0.59 0.74 (0.10)* - 0.82 (0.12)* .61 my turn. 18. I get into ongoing conversations without 0.68 -0.04 0.43 0.68 (0.07)* - 0.40 (0.09)* .38 interrupting and disturbing them. 19. I feel calm inside. 0.27 0.33 0.29 - - - - Note: For reasons of completeness, Item 19, which has been excluded for all analyses as it was shown not to load on either of the two subscales, is listed here. * p < .05

SWAN-DE-SB 21

Table 4

Overview of Results of Validation Studies of the SWAN Scale (adapted from Schulz-Zhecheva et al., 2017)

Reliability Validity Study Version Distribution Cronbach’s TRR FV CV SP (%) SE(%) α Swanson et Original (T) normal ̶ ̶ 2 ̶ ̶ ̶ al. (2012) factors; 87% Polderman Original (P) normal ̶ ̶ ̶ -.38 – -.42 ̶ ̶ et al. (2007) (CBCL-AP) Robaey et French (P) normal .88 – .91 ̶ ̶ -.57 – -.68 88 86 al. (2007) (DISC-4) -.79 – -.85 (Conner’s Scale P) -.30 – -.32 (Conner’s Scale T) Lakatos, Hungarian normal .87 - .93 – – .40 – .49 – – Birkas, (P) (CBCL-AP) Toth, & .67 – .74 Gervai (SDQ-HI) (2010) Lai et al. Chinese normal .90 – .95 .84 – .87 60% – 66-89 55-83 (2013) (T & P) (P), (P), (P), .97 – .98 .90 – .92 81% (T) (T) (T) Lakes et al. Spanish normal Sp: .91 – Sp: .49 – – -.49 – -.54 – – (2012) (Sp) & .95 .61 (SDQ-HI) Original Or: .92 – Or: .71 – (Or; P) .95 .76 Arnett et al. Original (P) normal .88 .57 – .75 2 .48 – .53 98 58 (2013) factors; (DBRS) 74% Chan et al. Chinese normal – – – .46 – .79 68–95 76–96 (2014) (T & P) (DISC-4) Schulz- German (P) normal .91 – .95 .77 – .81 2 -.43 – -.53 84 75-88 Zhecheva et factors; (CBCL-AP) al. (2017) 65% -.67 – -.68 (FBB-ADHS) Present German (S) normal .85 – .90 – 2 -.63 – -.56 89-93 71-93 study factors; (ADHS-SB) 47% -.57 – -.44 (CAARS-K- SB) Note: SWAN = Strengths and Weaknesses of ADHD-Symptoms and Normal-Behavior; TRR = Test-Retest- Reliability; FV = factorial validity; CV = convergent validity; SP = specificity; SE = sensitivity; P = parent report; T = teacher report; S = self-report; CBCL-AP = Child Behavior Checklist, attention problems subscale; DISC-4 = Diagnostic Interview Schedule for Children Version 4; SDQ-HI = Strengths and Difficulties Questionnaire, hyperactivity-inattention subscale; DBRS = Disruptive Behavior Rating Scale; FBB-ADHS = Fremdbeurteilungsbogen ADHS

SWAN-DE-SB 22

Figure 1. Scree-Plot of the SWAN-DE-SB Eigenvalues and Random Eigenvalues.

SWAN-DE-SB 23

Figure 2: Structural Equation Model for the SWAN-DE-SB.

SWAN-DE-SB 24

Figure 3. Relations between scales of SWAN-DE-SB and ADHS-SB

TOT AD HI

SB - DE - SWAN

ADHS-SB

Figure 4. Relations between scales of SWAN-DE-SB and CAARS-K-SB

SWAN-DE-SB 25

TOT AD HI

SB - DE - SWAN

CAARS-K-SB

SWAN-DE-SB 26

i The personal code is created by each respondent according to specific rules and can (only by the respective respondent) be regenerated at any time according to the same rules so that data can be deleted at any time, for example. This does not include data that has already been reported on in a published paper.