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Measures of Social Camouflaging

In recent years there have been some attempts to quantify social camouflaging by autistic people. The resultant instruments reflect different ways of defining and operationalising camouflaging, leading to some overlap but also some discrepancies in how camouflaging behaviours are measured.

Livingston and Happé ( 2017) suggest that camouflaging is a component of compensation, the "processes contributing to improved behavioural presentation of a neurodevelopmental disorder, despite persisting core deficit(s) at cognitive and/or neurobiological levels" (p. 8), and hence should be measured at the behavioural, cognitive, and neurobiological levels. We call these approaches to measuring camouflaging 'discrepancy methods', as they seek to measure the gap between how autistic a person is ('internal autistic status') and their overt behaviours ('external autistic presentation'). This approach was used by Lai et al. ( 2017), who measured the discrepancy between self-reported autistic traits on the Autism Spectrum Quotient (AQ) as well as social cognitive abilities on the Reading the Mind in the Eyes test (RMET) (internal autistic status) and scores on the Autism Diagnostic Observation Schedule (ADOS) (external autistic presentation). The study found a greater AQ/RMET-ADOS discrepancy score for female than male participants, and that higher discrepancy scores were associated with greater depressive symptoms for men, but not for women.

A similar result was found by Ratto et al. ( 2017), where autistic females with higher IQ were less likely to meet diagnostic criteria on the Autism Diagnostic Interview (Revised) than males, despite being matched on ADOS scores and having higher levels of parent-report functioning difficulties than males. Other studies have demonstrated autistic females' greater use of camouflaging strategies during communication than males, whether through gesture (Rynkiewicz et al. 2016), or filling pauses in conversation (Parish-Morris et al. 2017), despite overall comparable social skills. These methods measure camouflaging by identifying discrepancies between different measures of social ability or autistic characteristics, such that individuals (especially females) appear less autistic in some settings yet still meet autism diagnostic criteria in others.

A strength of these discrepancy approaches is their conceptual rigour, as they seek to operationalize the key feature of camouflaging; that it is a dissociation between an individual's experience of being autistic and the behaviours they portray to the outside world. In addition, using autism assessment tools as a measure of external autism presentation demonstrates the impact camouflaging can have in a clinical setting, especially for autistic women. However, a key disadvantage is that this approach relies upon an index of how autistic a person is (i.e., their internal autistic status) independent of their behavioural presentation. Given that autism is currently conceptualised at the behavioural level because there are no reliable biomarkers for the condition (American Psychiatric Association 2013; Loth et al. 2015), this represents a significant conceptual and practical challenge. Performance on tests of cognition relevant to autism, or scores on self-reported measures of autism traits can only ever be a proxy measure of internal autistic status: we currently have no way to identify how autistic an individual is meaningfully and accurately.

In addition, the measurement of camouflaging using discrepancy approaches does not allow for unsuccessful camouflaging attempts to be assessed. Some autistic people may use a variety of strategies in an attempt to appear less autistic to others, but these may be only partially or not at all successful. This is especially important when considering the evidence for a link between self-reported camouflaging and poor mental health (Cage et al. 2017; Hull et al. 2017). If individuals attempt to camouflage but are ultimately unsuccessful this may further increase the social, emotional, and psychological harm resulting from their camouflaging efforts.

An alternative to the discrepancy approaches described above is one based on observational recognition of camouflaging; measuring the specific behaviours and experiences which represent camouflaging. Such 'observational/reflective methods' circumvent the limitation of being unable to measure an individual's internal autistic state. Camouflaging can be measured consistently and compared between individuals, and behaviours can be identified regardless of how successful they are. In other words, identification of camouflaging is not reliant on either a proxy measure of internal autistic status, or the need to display a typical social presentation.

Dean et al. ( 2017) used an observational/reflective approach to identify camouflaging strategies used by autistic girls when interacting with peers through behavioural observation. Behavioural techniques, such as standing near to peers who are interacting, but not actually engaging in with them, were classified as camouflaging strategies, and were observed more in autistic girls than in autistic boys or non-autistic girls, in a school setting. This led to the superficial appearance of successful social interaction, but did not actually result in friendships or sustained engagement for the autistic girls using these techniques. Dean et al. operationalisation of camouflaging is based on the idea of blending into the social environment, a strength of which is that the need to camouflage may vary depending on the situation. In addition, this definition of camouflaging emphasises that camouflaging behaviours may be learned or mimicked from non-autistic peers.

This approach to camouflaging has the advantage of allowing for variation in camouflaging behaviours and their success. Techniques learned and used in some situations may not be successful in others, and an individual's overall camouflaging ability may partly depend on their ability to adapt to different situations. The cognitive flexibility enabling this has already been suggested as one explanation for autistic girls' superficially higher social skills (Lehnhardt et al. 2015). However, this measure of camouflaging is based on non-autistic observers' ideas of what camouflaging looks like. Intentions and behaviours of camouflaging which clinicians and researchers may not be aware of, but which may form an important part of autistic individuals' camouflaging strategies, have not yet been measured.

Self-Reported Measurement of Camouflaging

Another observational/reflective approach to the operationalisation of camouflaging addresses some of these remaining issues: asking autistic people themselves about their camouflaging behaviours. Here, camouflaging is conceptualised based on the reported experiences of individuals who have (and have not) camouflaged their autism, and the behaviours and intentions described by these individuals are used to develop a list of camouflaging strategies to measure. Autistic individuals can then report directly on their own camouflaging behaviours, identifying strategies and intentions that might not be visible to an observer without in-depth discussion with the autistic person themselves.

This self-report method, based on an observational/reflective approach, has several strengths. First, identifying camouflaging behaviours based on strategies reported by autistic individuals reduces the potential for introducing bias via researchers' and clinicians' perceptions of autistic behaviours and abilities. Autistic adults have previously reported being told by clinicians that their ability to camouflage (for example, by making or appearing to make eye contact) meant they could not be autistic, despite meeting autism diagnostic criteria in other ways (Hull et al. 2017). Clinicians and researchers may only observe autistic individuals in one structured and limited situation and so may not identify certain behaviours as camouflaging strategies, whereas autistic individuals and those who know them well have a unique insight into their own behaviours across a variety of situations. Second, self-report measures of camouflaging allow for operationalisation of the attempt to camouflage-the intention put into camouflaging autistic characteristics, and the techniques used, which may not result in any observable external change for someone who does not know the person well.

Both the discrepancy and observational/reflective approaches described above offer ways to define and therefore measure camouflaging in autism. All the methods used or suggested have their own strengths and weaknesses, thus combining multiple methods in a triangulation approach allows for greater accuracy in measuring and identifying a complex phenomenon such as camouflaging (Thurmond 2001). Participant report is needed to identify intention to camouflage, behavioural observation to identify how successful that camouflaging is, and measures of cognitive traits and autistic characteristics to identify how much the person is camouflaging their underlying 'autistic-ness' and how they do or do not achieve this. Methods for measuring behavioural camouflaging, and cognitive and autistic-like traits, already exist or have been proposed (Dean et al. 2017; Lai et al. 2017; Livingstone and Happé 2017); however until now, no self-report measures of camouflaging behaviours have been developed.

Camouflaging Across the Dimensions

Autism is a dimensional characteristic; traits are distributed across the entire population, but with a cut-off point at the extreme end requiring clinical identification and support (Constantino 2011; Ruzich et al. 2015; Skuse et al. 2005). All individuals in the general population have some level of autistic traits, and those with an above average number may also camouflage these to varying extent. Camouflaging is similar to impression management, where behaviours which occur in front of others are manipulated in order to make a better impression (Leary and Kowalski 1990). Autistic individuals engage in impression management to a lesser degree than non-autistic individuals (Cage et al. 2013). The combination of underlying autistic characteristics and extent of (successful) camouflaging produces an external 'autistic' presentation, with corresponding variation in general functioning (Livingston and Happé 2017). Thus, it is important to develop measures of camouflaging that are appropriate for both autistic and non-autistic populations.

The Present Study

A psychometrically sound self-report measure of camouflaging behaviours is needed to improve current understanding of the nature, causes and consequences of social camouflaging. Furthermore, existing methods of measuring camouflaging behaviours have not been validated in both autistic and non-autistic populations.

The aim of this study is therefore to develop, psychometrically evaluate, and validate a self-report measure of social camouflaging behaviours (henceforth referred to as the Camouflaging Autistic Traits Questionnaire; CAT-Q), appropriate for both autistic and non-autistic populations.

Development

Preliminary items for the CAT-Q were developed from qualitative responses to a previous study, and were added to and refined by all the authors and several external experts.

Psychometric Evaluation

Exploratory and confirmatory factor analyses were used to identify, refine, and test the underlying factor structure of the CAT-Q in two separate samples. Multi-group measurement invariance analyses were used to compare the underlying factor structure in the male and female autistic and non-autistic samples.

Internal consistency of the measure was estimated using Cronbach's alpha, and test-retest reliability was established by re-sending the CAT-Q to a subsample of 30 autistic participants approximately 3 months after they first completed the survey.

Convergent validity of the new measure was determined by comparing camouflaging scores with scores on theoretically related constructs (Cronbach and Meehl 1955). Individuals with more autistic-like traits are likely to camouflage those traits to a greater extent, although this has not been tested empirically before. Camouflaging has also been associated with increased social anxiety and general anxiety, and decreased wellbeing, in qualitative reports (Hull et al. 2017), as well as with increased depression in quantitative research (Cage et al. 2017; Lai et al. 2017). Accordingly, convergent validity was explored by testing the correlation between camouflaging and autistic-like traits, social anxiety, general anxiety, wellbeing, and depression.

Methods

Participants

Validation of the CAT-Q was conducted in autistic and non-autistic samples which were recruited separately. Autistic participants were recruited via social media, through the Cambridge Autism Research Database (CARD), and through word-of-mouth. Non-autistic participants were recruited via social media and through word-of-mouth. Participants who self-reported as autistic were asked to detail the type of diagnosis, (e.g. Autism, Asperger's Syndrome, Autism Spectrum Disorder), the age they were diagnosed, and the type of healthcare professional who diagnosed them. Those who reported being self-diagnosed were automatically excluded from the study and did not complete any further questions. All participants were at or above the legal age to give informed consent on their own behalf in the UK (16 years).

Of those autistic participants who reported the age they were diagnosed, 12% were diagnosed in childhood (0-17 years) and 72% were diagnosed in adulthood (18 years and over). Of those diagnosed in childhood, 38% were diagnosed by a psychiatrist, 25% by a clinical psychologist, 8% by other specialists including neurologists and specialist nurses, 5% by a multi-disciplinary team, 2% by a Speech & Language Therapist, 2% by their school, and 2% by a paediatrician. Of those diagnosed in adulthood, 55% were diagnosed by a clinical psychologist, 35% by a psychiatrist, 3% by a multi-disciplinary team, 3% by other specialists, 0.7% by a Speech and Language Therapist, 0.7% by a GP, and 0.3% by an occupational therapist.

In the autistic sample, 14% were aged 16-25, 23% were aged 26-35, 20% were aged 36-45, 13% were aged 56-65, 3% were aged 66-75, and 0.3% were aged 75 or over. In the non-autistic sample, 59% were aged 16-25, 16% were aged 26-35, 8% were aged 36-45, 9% were aged 46-55, 6% were aged 56-65, 1% were aged 66-75, and 0.2% were aged 75 or over (proportions may not add up to 1 due to rounding).

Measures

Camouflaging Autistic Traits Questionnaire (CAT-Q)

The measure's operationalisation of social camouflaging is based on the analysis and theoretical model described in the qualitative study by Hull et al. ( 2017). Items for the CAT-Q were identified through multiple routes. A previous study which asked for autistic adults' experiences of camouflaging (Hull et al. 2017) also asked participants to describe specific behaviours they used while camouflaging. These responses were refined to produce a list of behaviours reflecting the two core components of camouflaging identified previously: compensation (i.e. finding ways around the social and communication difficulties associated with autism), and masking (i.e. hiding aspects of one's autistic presentation, or presenting a non-autistic persona to others). Additional camouflaging behaviours were suggested by autism experts, including researchers, clinicians, and autistic adults who were consulted directly.

Once the behaviours were identified, items that described them, including reverse-coded items describing the opposite of these behaviours, were developed. Items were removed or added to ensure there was a roughly even number tapping into 'compensation' and 'masking'. A total of 48 items were produced for inclusion in the study. Participants responded using a seven-point Likert scale, from 'Strongly Disagree' to 'Strongly Agree' with each statement.A total of 832 participants (354 adults with autism and 478 adults without autism) completed the CAT-Q.

Broad Autism Phenotype Questionnaire (BAPQ; Hurley et al. 2007)

A 36-item self-report measure of traits associated with the broader autism phenotype (BAP). BAP characteristics are associated with greater genetic liability for autism, and are found across the population and at especially high levels in relatives of those with an autism diagnosis. Scores for the total questionnaire and three sub-factors (Aloofness, Pragmatic Language, and Rigidity) are averaged across the 36 items in the total questionnaire and 12 in each factor, to produce values in a range of 0-6. A total of 744 participants (299 autistic and 445 non-autistic) completed the BAPQ. The BAPQ has good sensitivity (Sasson et al. 2013) and specificity (Hurley et al. 2007). Internal consistency of the BAPQ in the current study (total sample) was high (α = 0.96). Although the BAPQ was initially developed as a measure of autistic-like traits in relatives of those with autism, it has also been used to measure autistic-like traits in autistic and non-autistic groups (Ingersoll et al. 2011; Nishiyama et al. 2014; although see Piven and Sasson 2014 for an evaluation of this approach). In this case we included the BAPQ as a measure of autism-related characteristics, rather than as a screening tool for autism. Mean BAPQ scores were compared for autistic and non-autistic samples and were found to be significantly different (t[743] = 21.23, p < .001, d = 1.56), with means of 4.31 (SD = 0.69) for autistic participants and 3.18 (SD = 0.73) for non-autistic participants. This suggests that, although the BAPQ was designed for relatives of those with autism, there were no ceiling effects in the autistic sample.

Social Anxiety Scale (LSAS; Liebowitz 1987)

A 24-item self-report questionnaire measuring social anxiety in the general population. The scale requires participants to imagine being in different social situations (such as talking to a sales assistant in a shop) and asks how much fear they would experience and how much they would avoid the situation. The LSAS has demonstrated good test-retest reliability and discriminant validity (Baker et al. 2002). A total of 708 participants (284 adults with autism and 424 adults without autism) completed the LSAS. In the total sample of this study, internal consistency was high (α = 0.97).

Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS; Tennant et al. 2007)

A 14-item self-report questionnaire measuring general wellbeing in the last 2 weeks. The WEMWBS has demonstrated acceptable validity and reliability (Trousselard et al. 2016). A total of 713 participants (289 adults with autism and 424 adults without autism) completed the WEMWBS. Internal consistency in the total sample was high (α = 0.92).

Patient Health Questionnaire (PHQ-9; Kroenke et al. 2001)

A 9-item self-report questionnaire of depressive symptoms in the last 2 weeks, with a clinical cut-off point of 10 for moderate depression. The PHQ-9 has demonstrated good sensitivity and specificity for depressive symptoms (Kroenke et al. 2001). The PHQ-9 was only administered to autistic individuals. A total of 290 autistic participants completed the PHQ-9. Internal consistency in the autistic sample was acceptable (α = 0.89).

Generalised Anxiety Disorder (GAD-7; Spitzer et al. 2006)

A 7-item self-report measure of generalised anxiety symptoms in the last 2 weeks. The GAD-7 has a clinical cut-off point of 10 points and demonstrates good sensitivity and specificity (Spitzer et al. 2006). The GAD-7 was only administered to autistic individuals. A total of 289 autistic participants completed the GAD-7. Internal consistency in the autistic sample was high (α = 0.92).

Procedure

Participants followed a link to the online survey, hosted by Qualtrics, where they read the information sheet and, after contacting the researchers to answer any questions, completed a consent form. They then completed demographic questions and questionnaires.

Participants who had given contact details to researchers were contacted again 3 months later to ask them to re-take the questionnaire for the purpose of estimating test-retest reliability. At that time, adverts were also placed on social media inviting autistic participants who had previously completed the survey to complete it again.

Analyses

All analyses were performed in R (R Core Team 2013).

The total sample was split in two, with the first half utilised for exploratory factor analysis to identify an initial factor structure from which a 25-item final scale was produced ('exploratory sample'; N = 402), and the remainder utilised for confirmatory factor analysis ('confirmatory sample'; N = 430). These two samples had comparable levels of autistic-like traits; however the confirmatory sample was significantly younger on average (partial η ²  = 0.13), and contained proportionally more males (Cramer's V = 0.12), than the exploratory sample.

Exploratory Factor Analysis

Principle components analyses using oblique rotation were performed on the total exploratory sample (N = 402), and separately in the autistic (N = 200) and non-autistic (N = 202) subsamples. Retention of items was based on combined evaluation of the scree plot, following Cattell ( 1966); eigenvalues over 1.0; and parallel analysis techniques to model factor structure (Hayton et al. 2004). Items with loadings below 0.40, or with cross-loadings of greater than 0.40 were excluded.

Confirmatory Factor Analysis

Diagonally Weighted Least Square Means (WLSM) estimators were used to take into account the ordinal nature of the Likert-based responses (DiStefano and Morgan 2014; Wang and Cunningham 2005). The key indices used to assess goodness-of-fit were Comparative Fit Index (CFI), where values of 0.95 or greater indicate good fit; Root Mean Square Error of Approximation (RMSEA), where values of 0.06 or lower indicate acceptable fit; and Standardised Root Mean Square Residual (SRMR), where values of 0.08 or lower indicate acceptable fit (Hu and Bentler 1999).

Multi-group Measurement Invariance

The total sample was recombined and multi-group measurement invariance analysis used to determine whether the same latent variables were measured across four groups: male autistic, female autistic, male non-autistic, and female non-autistic. Participants who identified as a non-binary gender or did not report their gender were excluded from this analysis (n = 92).

Tests of measurement invariance involve the comparison of multiple, nested models (Sass 2011) measuring: (1) Configural Invariance (whether factor structure is equal across groups); (2) Metric Invariance (whether item loading on each factor is equal across groups); (3) Scalar Invariance (item intercepts are equal across groups); and (4) Residual Invariance (item residuals are equal across groups). Each model is compared to the previous in a forward approach to first establish invariance across groups, and then test whether non-invariance has been identified at each additional level. ∆CFI of less than 0.01 is generally used as the most reliable marker of invariance, as Χ ² values can be influenced by sample size (Cheung and Rensvold 2002). Diagonally Weighted Least Square Means (WLSM) estimators were again used, and robust statistics are reported for all results. Satorra-Bentler scaled corrections for multiple comparisons were used.

Reliability and Validity

Internal consistency, test-retest reliability, and convergent validity of the final scale were assessed in a subset of the total sample who had also provided complete responses to at least one of the other measures in the study (N = 706; Autistic N = 306, Non-Autistic N = 400). Internal consistency was measured using Cronbach's alpha, and test-retest reliability using Pearson's r and intra-class coefficients (ICCs). Two-way consistency ICC was used to evaluate absolute consistency between the first and second completion of the questionnaire, (McGraw and Wong 1996), with unity reflecting complete consistency on all items between time one and time two. Values of 0.50 to 0.75 indicate moderate reliability, while values of 0.75 and above indicate good reliability (Koo and Li 2016).

Convergent validity was assessed using correlations between total CAT-Q and factor scores, and measures of autistic-like traits, social anxiety, well-being, generalised anxiety, and depression.

Results

The characteristics of the total sample and all subsamples are described in Table 1.

Exploratory Analyses

Parallel analysis suggested four factors, but examination of the scree plot and eigenvalues suggested that three common factors best fit the data across the autistic, non-autistic, and combined samples, in addition to being a simpler structure. The three factors were labelled Compensation (strategies used to actively compensate for difficulties in social situations), Masking (strategies used to hide autistic characteristics or portray a non-autistic persona), and Assimilation (strategies that reflect trying fit in with others in social situations). These three factors accounted for 38% of variance in the autistic subsample, 41% of variance in the non-autistic subsample, and 45% of variance in the combined exploratory sample. Factor correlations were medium-to-high (Cohen 1988) between all factors in all samples (Table 2).

Items that loaded onto one of the three factors at or above the critical value of 0.40 in both the autistic and non-autistic subsamples, and in the combined sample, were identified. These were reduced to twenty-five items based on the highest factor loadings, which resulted in a total of 8 items each in the Masking and Assimilation factors, and 9 items in the Compensation factor. Table 3 presents the mean scores and internal consistencies of the factors and total scale across the autistic, non-autistic, and combined samples. Autistic participants scored significantly higher than non-autistic participants on the Total CAT-Q ( t [401] = 12.98, p < .001; partial η ² = 0.30) and Compensation ( t [401] = 11.90, p < .001; partial η ² = 0.26), Masking ( t [401] = 2.19, p = .03; partial η ² = 0.01), and Assimilation factors ( t [401] = 16.35, p < .001; partial η ² = 0.40). Factor loadings on all three factors in the final, 25-item Camouflaging Autistic Traits Questionnaire (CAT-Q) are detailed in Table 4.

Confirmatory Analyses

Confirmatory factor analysis was performed on the confirmatory sample (N = 419; Autistic N = 150, Non-Autistic N = 269); the results for the autistic, non-autistic, and combined group analyses for the total scale are reported in Table 5.

Overall the model fit was acceptable; CFI values were above 0.95, and RMSEA and SRMR values were well within the recommended range in all three samples. The model tested is detailed in Fig. 1.

Social Camouflaging model

Invariance Analyses

Measurement invariance (as demonstrated by ∆CFI < 0.01) was found for item loadings (Model 2), intercepts (Model 3), and residuals (Model 4) between the autistic and non-autistic male and female samples (as shown in Table 6). Model fit was close to or within acceptable limits for all models. It was concluded that the CAT-Q demonstrates strict invariance between autistic and non-autistic males and females.

Reliability and Validity

Reliability and validity of the finalised, 25-item scale were assessed in a subset of the total sample that had also provided complete responses to at least one of the other measures included (N = 706; Autistic N = 306, Non-Autistic N = 400).

Reliability

High internal consistency was found for the total scale (Cronbach's α = 0.94), and the Compensation (α = 0.91), Masking (α = 0.85), and Assimilation (α = 0.92) factors. Correlations between each factor and the total score were calculated using item-total correlation, and the corrected factor-total correlations in the total sample were: Compensation α = 0.705; Masking α = 0.483, Assimilation α = 0.627.

Test-retest reliability was calculated in a subsample of 30 autistic participants, who completed the CAT-Q again online three months after initial testing. This subsample was significantly older on average than the total autistic sample (F[1, 331] = 12.61, p < .001; mean difference = 9.23 years [SE = 2.6]). However, there was no significant difference in the distribution of genders (Male, Female, Other Gender, and not stated) (Χ ² [4] = 1.66, p = .80), and no significant difference in mean Total BAPQ score (t[299] = 0.55, p = .59) between this subsample and the total autistic sample. Good stability was found, as measured by Pearson's r and intra-class correlations (ICC) for the total scale and the Compensation factor, while moderate stability was found for the Masking and Assimilation factors (Table 7). No significant difference between scores at Time 1 and Time 2 was found (F[1, 29] = 0.23, p = .63).

Validation

Correlations were performed between the total and factor CAT-Q scores, and scores on autistic-like traits (total BAPQ score and subscale scores), social anxiety (total LSAS score), wellbeing (total WEMWBS score), generalised anxiety (total GAD-7 score), and depression (total PHQ-9 score) in order to investigate convergent validity. Results in the autistic and non-autistic samples are detailed in Table 8. Generalised anxiety and depression scores were available for autistic participants only.

The total CAT-Q score and all CAT-Q factors were significantly positively correlated with autistic-like traits and social anxiety in autistic and non-autistic samples, with the exception of the Masking factor, which was not significantly related to autistic-like traits in the autistic sample. The total CAT-Q and all CAT-Q factors were significantly negatively correlated with wellbeing in the non-autistic sample; however, in the autistic sample, only total CAT-Q and the Assimilation factor were significantly negatively correlated with wellbeing. Depression and generalised anxiety were only measured in the autistic sample; both of these were significantly positively correlated with total CAT-Q and all its factors.

Discussion

This study psychometrically tested the newly developed Camouflaging Autistic Traits Questionnaire (CAT-Q) in autistic and non-autistic samples. Exploratory factor analysis identified a three-factor structure, consisting of Compensation (strategies used to compensate for social and communication difficulties), Masking (strategies used to present a non-autistic or less autistic persona to others), and Assimilation (strategies used to fit in to uncomfortable social situations). The structure of the refined, 25-item CAT-Q (see Online Appendix 1) was corroborated through confirmatory factor analysis, and measurement invariance was established between all four groups, suggesting that the CAT-Q is appropriate for use in clinical and non-clinical populations, and that scores can be compared between males and females. The CAT-Q demonstrated acceptable to good internal consistency and reliability over a period of 3 months. However, as the test-retest reliability analyses were conducted only in the older autistic sample, we report these findings as preliminary and suggest future research replicates these analyses in more diverse autistic and non-autistic samples.

The factors of Compensation and Masking reflect the two components of camouflaging proposed in a previous conceptual model derived from qualitative research (Hull et al. 2017). The third factor ('Assimilation') represents attempts to blend in to social situations in which the individual is uncomfortable, without letting others see this discomfort. These motivations for camouflaging have been described in previous research, although not extensively (Hull et al. 2017; Tint and Weiss 2017). The strategies within the Assimilation factor included avoiding social situations or managing them with the help of others, alongside items reflecting the feeling of not being one's self during interactions. The factor reflects comments made by autistic adults that they often choose to camouflage in situations where they do not know others well, whereas they feel free to be themselves while alone or with trusted others (Hull et al. 2017).

The model tested here provided a good fit in both autistic and non-autistic samples. Total CAT-Q score was positively correlated with autistic-like traits in both samples, suggesting that the higher level of autistic-like traits a person has, the more they will camouflage those traits, regardless of autism diagnosis. As high-level, successful camouflaging may result in missed clinical diagnoses (Tierney et al. 2016), the CAT-Q could be used to identify camouflaging behaviours in individuals considered at-risk for autism, but who do not currently meet diagnostic criteria. Measurement invariance analyses also demonstrated that the underlying structure of the CAT-Q is comparable in male and female autistic and non-autistic samples; in other words, the CAT-Q measures the same latent constructs in both genders and diagnostic groups. However, autistic participants scored significantly higher than non-autistic participants on the total CAT-Q and all three factors in the exploratory sample, demonstrating that the CAT-Q measures behaviours that are more common in individuals who have been diagnosed with autism spectrum conditions.

The Masking factor demonstrated the smallest difference between autistic and non-autistic samples in this analysis, suggesting that there may be more overlap between these two groups than for the other factors. Masking may be less specific to autism than the other components of camouflaging, and may reflect more general self-presentation or impression-management strategies applied to autistic characteristics. However, further research is needed to directly compare masking strategies and other self-presentation strategies in autistic and non-autistic samples to determine similarities and differences. In the autistic sample, masking was not significantly correlated with autistic-like traits, suggesting that it may be a response to the identification of being autistic rather than to the presence of specific autistic characteristics; in contrast, a significant positive relationship between the two was observed for the non-autistic sample, suggesting that the two groups may have been using masking strategies in response to different motivations.

Previous research suggested that camouflaging in autistic adults may be associated with poor mental health outcomes, especially anxiety, depression, and generally poor quality of life (Cage et al. 2017; Hull et al. 2017; Lai et al. 2017). The positive correlations between the CAT-Q and measures of social anxiety, anxiety, and depression, and the negative correlation between the CAT-Q and wellbeing, support this idea and offer convergent validation of the measure. Greater total camouflaging appears to be associated with poorer mental health outcomes overall, although interestingly the Compensation and Masking factors were not significantly associated with wellbeing in the autistic sample. This may reflect individual differences in the impact or success of camouflaging; previous research found that associations between camouflaging and negative outcomes were stronger for autistic men than women (Lai et al. 2017). Further assessment of gender differences and other individual differences in camouflaging behaviours and their association with wellbeing and mental health in this sample is currently underway.

Strengths and Limitations

A significant strength of this approach is that the items were developed based on information from autistic people themselves, describing their own experiences of camouflaging. This ensures that behaviours which may not have been previously identified as part of social camouflaging by non-autistic clinicians and researchers can be measured. The CAT-Q can be used in combination with observed behavioural and cognitive measures of camouflaging to assess all aspects of this complex phenomenon. It may also have clinical implications to identify levels of camouflaging along with other clinical information, including those derived from current autism diagnostic measures, to enhance the sensitivity and specificity of clinical diagnosis, formulation, and support planning; however, the clinical utility requires further clinical research to establish.

In addition, the CAT-Q does not require an official diagnosis of an autism spectrum condition for camouflaging behaviours to be assessed, as the underlying structure shows invariance between autistic and non-autistic populations. This addresses some issues in current autism research, especially that criteria for autistic participants may be based on an overly restricted and potentially inaccurate operational definition of autism. Even if autism diagnostic criteria change in the future, use of the CAT-Q should not vary between clinical and non-clinical groups. The CAT-Q has demonstrated measurement invariance between male and female participants, enabling comparison across genders in future research.

This study is not without its limitations. First, although the BAPQ has demonstrated validity and reliability in clinical and non-clinical samples (Ingersoll et al. 2011; Nishiyama et al. 2014), it was developed for use with relatives of those with an autism diagnosis. Therefore we are cautious about using BAPQ scores as a measure of autistic traits in clinical and general population samples (Piven and Sasson 2014). In future, to accurately examine how camouflaging is related to autistic traits, the CAT-Q should be compared to a measure of autistic traits which has been explicitly developed for use in autistic populations, for example, the severity score of the ADOS-2.

Second, no behavioural measure of social ability was included in the study. Individuals with greater social skills are less likely to need to camouflage in the first place, and may do so more effectively than those with poorer social skills. Further research is needed to identify the extent to which social skills predict camouflaging behaviours, which will have implications regarding prevailing social skills training in autistic individuals. There was also no objective validation of self-reported autism diagnosis. However, only participants who reported receiving a diagnosis from a healthcare professional were included in the autistic sample. Third, responses on the PHQ-9 and GAD-7 were not available for non-autistic participants as these data were collected as part of a separate project; the relationship between camouflaging and depression and anxiety should therefore also be examined in non-autistic adults.

Fourthly, the self-report CAT-Q only measures individuals' own reflections/perceptions of their camouflaging behaviours, and is thus limited in its use to those who are able to reflect on their own behaviours and provide insight to their motivations. The CAT-Q may therefore not be useful for autistic individuals with language difficulties or intellectual disability. By combining this measure with behavioural or informant-report measures of camouflaging, estimates of camouflaging behaviours in those who have less insight or ability to communicate it can also be obtained.

Fifthly, the CAT-Q was created mainly based on reflections from autistic adults, and was psychometrically examined and validated in the present adult sample, in which a substantial proportion of the autistic participants received their diagnoses in adulthood instead of childhood. Hence, although the validity and potential clinical utility are likely ensured in autistic adults, in particular those who are diagnosed in adulthood (Lai and Baron-Cohen 2015), it is still unclear whether the psychometric properties and potential utilities hold for adolescents and older children, with or without autism, or for those with intellectual disability. Further testing of the CAT-Q in samples of varying ages and abilities, including adults who were diagnosed in childhood, should be conducted to measure its factor structure, validity and reliability across these groups. As the confirmatory sample contained more males than females, these analyses should also be replicated in a gender-matched sample. Finally, although the validation of the CAT-Q supports previous research suggesting camouflaging is associated with poorer wellbeing and mental health outcomes, only correlational relationships were identified. Longitudinal or intervention researches are necessary to confirm the causal nature of these relationships, and to establish the mechanisms and individual characteristics that may predict outcomes of camouflaging.

Conclusions

The CAT-Q is a valid and reliable self-report measure of adults' social camouflaging behaviours, suitable for use in autistic and non-autistic male and female populations. It can be used in research settings to quantify camouflaging behaviours and compare between groups; in clinical settings as a potential screening tool for individuals who may be missed under current autism diagnostic criteria because they camouflage; and by autistic and non-autistic people to aid identification of beneficial or harmful behaviours they use in social situations. Further validation of the CAT-Q in more diverse samples is encouraged in the future, alongside comparison with existing measures of camouflaging and broader social skills.

Acknowledgements

The authors would like to thank all those who contributed to the design of the questionnaire, who took part in the study, and who provided comments on the findings. The authors also thank Maya Bowri, Emogen Campbell, Andrew Dunlop, and Lily Levy for assistance with data collection. During the period of the study Meng-Chuan Lai was supported by the University of Toronto Department of Psychiatry Excellence Fund and the O'Brien Scholars Program within the Child and Youth Mental Health Collaborative at the Centre for Addiction and Mental Health and The Hospital for Sick Children, Toronto, Canada; Simon Baron-Cohen was supported by the Autism Research Trust, Autistica, the MRC and Wellcome Trust, and by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care East of England at Cambridgeshire and Peterborough NHS Foundation Trust. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.

Author information

Authors and Affiliations

1. Research Department of Clinical, Educational & Health Psychology, University College London, London, UK

Laura Hull & William Mandy

2. Child and Youth Mental Health Collaborative, Centre for Addiction and Mental Health and The Hospital for Sick Children, Department of Psychiatry, University of Toronto, Toronto, ON, Canada

Meng-Chuan Lai

3. Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK

Meng-Chuan Lai, Simon Baron-Cohen, Carrie Allison & Paula Smith

4. Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan

Meng-Chuan Lai

5. London Psychometric Laboratory, University College London, London, UK

K. V. Petrides

6. Department of Psychology, University College London, 26 Bedford Way, London, WC1H 0AP, UK

Laura Hull

Contributions

LH conceived of the study, participated in the design and coordination of the study, performed analyses, and drafted the manuscript. WM, MCL, and SBC conceived of the study, participated in its design, coordination, and analyses, and revised the manuscript. CA and PS conceived of the study, and participated in its coordination. KVP participated in the design and analyses, and revised the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Laura Hull.

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The authors declare that they have no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

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Hull, L., Mandy, W., Lai, MC. et al. Development and Validation of the Camouflaging Autistic Traits Questionnaire (CAT-Q). J Autism Dev Disord 49, 819-833 (2019). https://doi.org/10.1007/s10803-018-3792-6
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Keywords

Autism

Camouflaging

Masking

Compensation

Coping Strategies

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Development and Validation of the Camouflaging Autistic Traits Questionnaire (CAT-Q) - Journal of Autism and Developmental Disorders

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There currently exist no self-report measures of social camouflaging behaviours (strategies used to compensate for or mask autistic characteristics during social interactions). The Camouflaging Autistic Traits Questionnaire (CAT-Q) was developed from autistic adults' experiences of camouflaging, and was administered online to 354 autistic and 478 non-autistic adults.

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PDF Revisión de Literatura - TEA en Féminas

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Versión en PDF

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Vista de Trastorno del Espectro Autista en Féminas

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Volver a los detalles del artículo Trastorno del Espectro Autista en Féminas Descargar Descargar PDF

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Autism Spectrum Quotient (AQ) (Adult) - Autism Research Centre

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S. Wheelwright, S. Baron-Cohen, N. Goldenfeld, J. Delaney, D. Fine, R. Smith, L. Weil and A. Wakabayashi, (2006) Predicting Autism Spectrum Quotient (AQ) from the Systemizing Quotient-Revised (SQ-R) and Empathy Quotient (EQ) Brain Research 1079:47-56

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A road map for identifying autism spectrum disorder: recognizing and evaluating characteristics that should raise red or “pink” flags to guide accurate differential diagnosis

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A road map for identifying autism spectrum disorder: recognizing and evaluating characteristics that should raise red or “pink” flags to guide accurate differential diagnosis

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A road map for identifying autism spectrum disorder: recognizing and evaluating characteristics that should raise red or "pink" flags to guide accurate differential diagnosis

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Abstract Objective: Given the high population prevalence of Autism Spectrum Disorder (ASD) and overlapping symptoms with medically complex groups, ASD is a common rule out diagnosis for neuropsychologists even when not identified in the referral or initial presenting concerns.

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References

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Autism Spectrum Quotient (AQ) (Adolescent) - Autism Research Centre

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S. Baron-Cohen, R. A. Hoekstra, R. Knickmeyer and S.

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How the ASSQ is used

Recommended cutoff scores vary somewhat depending on the country, whether parents or teachers are used as informants and whether the main priority is high sensitivity, specificity or both. In Sweden, 15 points is considered the cutoff score for non-clinical populations and 19 for clinical populations.

An extended version (ASSQ-REV) has also been developed in order to identify girls with autism.

The most prominent publications on this subject are Ehlers and Gillberg 1993, Ehlers et al 1999, Posserud et al 2006, Hepburn et al 2008, Posserud et al 2009, Kopp and Gillberg 2010, Guo et al 2011 and Mattilla et al 2012.

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ASSQ (Autism Spectrum Screening Questionnaire)

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Recommended cutoff scores vary somewhat depending on the country, whether parents or teachers are used as informants and whether the main priority is high sensitivity, specificity or both. In Sweden, 15 points is considered the cutoff score for non-clinical populations and 19 for clinical populations.

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Adult Asperger Assessment (AAA) - Autism Research Centre

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Copyright © 2021 Autism Research Centre Accessibility | Privacy Policy | FAQ | Relevant links and resources | Patrons Department of Psychiatry, University of Cambridge

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Introduction

The RAADS was designed to address a major gap in screening services for adults with autism spectrum disorders. With the increased prevalence of the condition and the fact that adults are being referred or self-referred for services or diagnosis with increasing frequency, this instrument is a useful clinical tool to assist clinicians with the diagnosis of this growing population of higher functioning individuals in adulthood. This study was designed to standardize the Ritvo Autism Asperger Diagnostic Scale-Revised (RAADS-R) in a large number of subjects at nine centers on three continents.

The RAADS-R is a modified version of the Ritvo Autism Asperger's Diagnostic Scale (Ritvo et al. 2008). The rationale for its development was the need for a clinical adjunct diagnostic tool. While several diagnostic scales for children and adolescents with ASD are available (Attwood 1998; Ehlers et al. 1999), only one peer- reviewed scale, The Autism-Spectrum Quotient (AQ) was designed specifically for adults (Baron-Cohen et al. 2001). Its authors emphasize that it is useful in identifying autistic traits and as a screening instrument but not as a diagnostic tool. The AQ is mailed in by the participant, unlike the RAADS-R, which is designed to be administered by a clinician in a clinical setting. Baron-Cohen et al. ( 2005) propose a diagnostic system as well: The Adult Asperger's Assessment (AAA): A Diagnostic Method. While the AAA is a good system, it is a complete diagnostic system, unlike the RAADS-R. It is lengthy and complicated to administer and is still relatively new. The initial RAADS was developed around the same time period. The original RAADS contains 78 empirically derived questions that assess symptoms based upon DSM-IV-TR (American Psychiatric Association 2000) and ICD-10 World Health Organization ( 1992) diagnostic criteria that were extensively tested prior to inclusion on the scale. It proved accurate in discriminating autistic and Asperger's subjects ( N = 37) from comparison subjects ( N = 57) when tested (e.g., sensitivity = 1, specificity = 1). Seventy-six of the 78 questions significantly distinguished ASD subjects from comparison subjects (Ritvo et al. 2008).

Questions on the initial RAADS assess developmental pathology in three symptom areas: language, social relatedness, and sensory-motor (re: DSM-IV-TR). After critical review and the results of a factor analysis, a revised 80-item version was developed (the RAADS-R) with the addition of a fourth symptom area (circumscribed interests), two questions, and several word clarifications. Like the RAADS, the RAADS-R is a self-report instrument, designed for adults (18 +) with average or above average intelligence.

In order to validate the RAADS-R, a uniform research protocol was developed to be implemented at nine research centers on three continents. The protocol assured uniformity of diagnosis and data acquisition, and its implementation in multiple centers afforded an opportunity to assess the applicability of the scale in a variety of international clinical settings: (a) UCLA; (b) The Child Study Center, Yale University; (c) University of Utah; (d) Mt. Sinai Medical School, New York; (e) The Geneva Center, Toronto, Canada; (f) King's College, London, England; (g) Monash University, Melbourne, Australia; (h) Griffith University, Brisbane, Australia; and (i) ASPECT, Sidney, Australia.

At the time this research was concluding, the American Psychiatric Association's DSM-V committee proposed new diagnostic criteria for Autism Spectrum Disorders (APA DSM-5 2010). These new guidelines propose combining individuals who meet the DSM-IV-TR criteria for Autistic Disorder and Asperger's Disorder into one category called Autistic Spectrum Disorder (ASD). The results of this study will present subjects grouped according to both the DSM-IV-TR and proposed DSM-V criteria. There has been ongoing controversy among clinicians about the relationship of autism and Asperger's disorder. Diagnostic approaches and definitions are described in the literature (Fitzgerald and Corvin 2001; Klin et al. 2005; Volkmar and Lord 1998).

Methods

Participants

Research Centers and Informed Consents

Nine centers on three continents were selected to provide a worldwide spectrum of English speaking subjects. Human Subject Protection Protocol and Consent to Participate Forms were approved by the California Institutional Review Board, Pasadena, California for all UCLA subjects, and served as models for the other centers to assure uniformity. Each center has ongoing clinical and research programs focusing on autism, and each center complied with their rules and regulations concerning the protection of human subjects. Each of the nine centers provided autistic and Asperger's disorder subjects and some also contributed comparison subjects.

Participant Grouping

The study population included 4 groups as follows: (a) subjects with a diagnosis of Autistic Disorder ( n = 66), (b) subjects with a diagnosis of Asperger's Disorder ( n = 135), (c) subjects with no previous DSM-IV-TR diagnosis ( n = 276), and (d) subjects with an axis I DSM-IV-TR diagnosis other than Autistic Disorder, Asperger's Disorder, or PDD NOS ( n = 302). Subjects in this group had the following diagnoses: Social Phobia, Schizophrenia, Major Depressive Disorder, Bipolar Disorder Type I and II, Obsessive Compulsive Disorder, Dysthymic Disorder, Generalized Anxiety Disorder, Post Traumatic Stress Disorder, Psychotic Disorder NOS, Anorexia Nervosa and Polysubstance Dependence. For some data analyses, the above groups were collapsed into two groups as follows: (a) Autism Spectrum Disorder (ASD) subjects ( N = 201), and (b) all comparison subjects (N = 578). No participants from either group declined to participate.

Inclusion Criteria

A standardized research protocol was implemented at each of the nine centers to assure uniformity of diagnosis. All ASD subjects had to meet the following criteria: (a) a clinical DSM-IV-TR diagnosis of autistic or Asperger's disorder, (b) an ADI/ADOS or at least an ADOS module 4 scale to assure they met the cut off for ASD, and (c) a Full Scale IQ score of 80 or above. If prior formal IQ test results were not available, a WAIS or a WASI was administered. Constantino's Social Responsiveness Scale-Adult Research Version (SRS-A) was obtained if a suitable informant accompanied the subject, as it is a second party rating scale (a second party rating scale is administered to a person other than the subject who answers questions about the subject, such as a family member or a friend). The SRS was selected because it is easy to administer and is reported to have good psychometric properties: internal consistency 0.91-0.97, test-retest reliability 0.84-0.97 and interrater reliability 0.74-0.95 (Bolte et al. 2008). The respondents completing the measure in this study were spouses, parents and siblings. A subset of subjects at UCLA was retested after an average of 1 year to determine test-retest reliability.

Two groups of comparison subjects were selected. The first consisted of 276 volunteers who did not have, and never had a DSM-IV-TR diagnosis. They were recruited from clinic staff members and students, and were individually administered the scale by a research staff member. The second group consisted of 302 volunteers recruited at an adult psychiatric out-patient clinic. They were formally diagnosed as having a DSM-IV-TR diagnosis other than ASD by an experienced board certified psychiatrist or a licensed psychologist who administered the scale individually to each. The majority of the comparison cases were recruited by the principal investigator and UCLA.

Materials

Design of the Revised Scale: The RAADS-R contains 64 symptom-based questions and 16 non-symptom based responses (see Appendix 1 for scale and Appendix 2 for scoring instructions). The questions were divided into four sub scales per DSM-IV-TR criteria as follows:

1. Social Relatednesss-total of 39 questions: 1, 6, 8, 11, 14, 17, 18, 25, 37, 38, 3, 5, 12, 28, 39, 44, 45, 76, 79, 80, 20, 21, 22, 23, 26, 31, 43, 47, 48, 53, 54, 55, 60, 61, 64, 68, 69, 72, 77.

2. Circumscribed Interests-total of 14 questions: 9, 13, 24, 30, 32, 40, 41, 50, 52, 56, 63, 70, 75, 78.

3. Language-total of 7 questions: 2, 7, 27, 35, 58, 66, 15.

4. Sensory Motor-total of 20 questions: 10, 19, 4, 33, 34, 36, 46, 71, 16, 29, 42, 49, 51, 57, 59, 62, 65, 67, 73, 74.

Procedures

Administration of the RAADS-R to ASD Subjects

Following initial screening, a clinician interviewed each subject to confirm the diagnostic information and IQ data, completed the ADI/ADOS or ADOS module 4, and then introduced the scale and explained how to answer the questions. The clinician remained with each subject as the questions were being answered to offer clarifications when asked, and to make sure answers were entered in the proper columns on the Likert scale. An SRS-A scale was obtained from cooperating accompanying adults when available.

Administration of the RAADS-R to Comparison Subjects

After initial diagnostic and IQ screening, all were given the RAADS-R to complete in the presence of a clinician. Each of the nine participating centers was asked to score, review, and mail in full packets of ASD subjects. Some of the participating centers contributed data to the comparison group subject pool.

Data Analysis

Data were coded by each center to preserve anonymity prior to being sent to UCLA. Data were then recoded and entered into an Excel spreadsheet and then transferred to SAS and MPlus for statistical analyses. Exploratory factor analysis was carried out using both orthogonal and oblique varimax rotation under maximum likelihood estimation. A scree plot of the eigenvalues was examined to determine the number of factors. A confirmatory factor analysis was carried out on the final factors using robust weighted least squares (WLSMV) estimation methods as the original items are ordinal. The factor loadings were compared across gender and ASD subgroups to assess constancy (differential item function analysis). Cronbach alpha was also computed to assess the internal consistency of the derived factors. Unadjusted means were compared using one-way analysis of variance and demographics adjusted means were compared using analysis of covariance methods. Associations between continuous variables were assessed using Pearson correlations. Sensitivity, specificity and accuracy of a given factor or score for distinguishing between ASD versus non ASD was computed using non-parametric ROC (receiver operator characteristic) methods. Unweighted accuracy is defined as the average of sensitivity and specificity. The optimal threshold (cut point) was defined as the value of the given factor or score that maximized accuracy.

Results

Subjects' Demographics

Table contains the sex distribution and demographic characteristics of the four subject groups.

Table 1

Diagnostic Accuracy (Sensitivity and Specificity)

The total RAADS scores ranged from 44 to 227 in the ASD subjects and from 0 to 65 in the comparison groups. By ROC curve analysis, we determined that the best threshold for distinguishing between the two groups was a score of 65. Using this value, all 578 comparison subjects were correctly predicted with no "false positives" (specificity = 100%).

Six ASD subjects from three of the nine centers scored below the threshold (non-ASD predicted range). These "false negatives" yielded a sensitivity of 97% (see Table ).

Table 2

Table shows the sensitivity, specificity and accuracy for the original four subscale domains and the four factors as well as the total RAADS-R score. As can be seen, the total RAADS-R score has the highest accuracy.

Table 3

Group Comparisons

One-way ANOVA comparisons across all groups, mean RAADS-R scores, and statistical characteristics of subject groups are contained in Table . The means and statistical characteristics obtained after collapsing the four groups into two (all ASD subjects vs. all comparison subjects) are shown in Table . The mean RAADS-R scores of the ASD, the autistic, and the Asperger's disorder groups (Tables and ) were significantly different from the means of the comparison groups ( p < .0001). This was also true at each of the nine research centers. The original subscale domains, the factors and the total score are all significantly different in ASD versus non ASD.

Table 4

Table 5

An age and sex adjusted ANOVA of total RAADS-R scores and subscale scores, across diagnostic groups showed group differences. Sex was not significant as a main effect ( p = .06) but sex-diagnosis was significant ( p = .0043). The effect of diagnosis slightly varied by sex, however, diagnosis was the strongest effect. Age was significant as a main effect ( p = .0035) and age-diagnosis effect was also significant ( p < .05). In ASD subjects, an increase in age was positively correlated with an increase in mean RAADS-R scores. (Spearman correlation 0.37, p = .0003). However, age-sex-diagnosis effect was not significant ( p = .1299). Repeated analyses after adjustment for sex and age between and among the subject groups showed significant differences between ASD and comparison groups ( p < .0001).

Pair-Wise Comparison of Each Question

Post-hoc unequal variance t tests were performed for pair-wise comparison of each of the 80 questions. The ASD subjects had significantly greater scores on each of the individual RAADS-R items ( N = 80) than either of the comparison groups ( p < .0001 for all comparisons). Total RAADS-R mean scores between autistic and Asperger's disorder subject groups were not significantly different.

Validity Measures

All ASD subjects met the research diagnostic criteria of each center and had an ADOS module 4 score in the autism range (these were inclusion criteria). SRS-A scales were administered to 69 first-degree relatives of ASD subjects. The concordance rate was 95.59% (all but three scored in the "autism" range).

Analysis of the Subscales

Mean scores and Cronbach alpha correlation coefficients were computed for each of the four subscale domains described previously: circumscribed interests = .903, language = .789, sensory motor = .905, social relatedness = .923 (for the four participant groups' mean scores of the subscales, see Table ).

Table 6

Factor Analysis

Factor Analysis was carried out using both varimax (orthogonal) and oblique (non-orthogonal) varimax rotation. The oblique rotational method was preferred because it assumes the underlying constructs to be interrelated. In addition, the oblique model had simpler factor structure that was easier to interpret.

Four factors were identified using the oblique rotation method. These factors had a simpler factor structure with much less cross loading compared to the orthogonal results, making for easier interpretation. These final oblique factors also had a higher correlation with the original subscales compared to the orthogonal factors. Factors I and IV correlated most strongly with the social construct ( r = .80). Factor II correlated most strongly with circumscribed interests ( r = .78), factor III with sensory motor ( r = .88). The language construct was not picked up with the four-factor model. The variables determined below are based on factor scores.

1. Factor I: Social Relatedness, containing questions related to empathy, intimacy and social language accounted for 23.9% of the variance. Questions: 1, 6, 8, 11, 13, 14, 15, 16, 18, 31, 37, 38, 43, 48, 52, 53, 58, 62, 66, 68, 72, 74, 77; α = .93.

2. Factor II: Circumscribed Interests, also with questions relating to social blindness, accounted for 25.0% of the variance. Questions: 3, 4, 5, 7, 9, 12, 16, 17, 20, 22, 25, 27, 28, 32, 33, 39, 41, 44, 45, 49, 50, 56, 60, 64, 76, 78, 79, 80; α = .95.

3. Factor III: Sensory Motor accounted for 22.4% of the variance. Questions: 2, 10, 19, 24, 29, 34, 35, 36, 40, 46, 51, 54, 57, 59, 65, 70, 71, 73; α = .87.

4. Factor IV: Social Anxiety accounted for 18.9% of the variance. Questions: 21, 23, 26, 30, 42, 47, 55, 61, 63, 67, 69, 75; α = .89.

A confirmatory factor analysis using these four factors showed that the coefficient of variation of the factor loadings was 20, 18, 18, and 19%, respectively, implying similar loadings for each item within a given factor. A comparison of factor loadings (differential item function) by gender and by ASD vs. non-ASD showed no significant differences with the mean difference of 18% or less.

Test-Retest Reliability

Fifteen ASD subjects and fifteen comparison subjects without another DSM-IV-TR diagnosis were retested at UCLA after a mean interval of 1 year (maximum interval 15 months, minimum interval 10 months) The initial RAADS-R mean score for the ASA subjects was 155, and when repeated was 149. The initial RAADS-R mean score for the comparison subjects was 20, and 21 when repeated (Table contains the statistical analyses for test-retest data).

Table 7

University/Center Effect

The total mean RAADS-R scores for the ASD subjects from each of the 9 centers were significantly different from the comparison groups ( p < .0001) and there were significant differences between the universities (see Table ).

Table 8

Clinical Implications of RAADS-R Scores

A RAADS-R score of 65 or greater is consistent with a clinical diagnosis of ASD. A RAADS-R score of 64 or lower is not consistent with a diagnosis of ASD (sensitivity = 97%, specificity = 100%). It must be emphasized, however, that if a subject has a score of 64 or lower but clinical judgment indicates that ASD is present, the clinical judgment should take precedence. This is due to the many limitations of self-rating scales that will be elaborated upon in the discussion section.

Discussion

The RAADS-R is a highly specific (100%) and sensitive (97%) instrument that is useful as an adjunct clinical diagnostic tool. Based on the results of this study and the RAADS pilot study, a RAADS-R score of 65 or higher is consistent with a diagnosis of ASD. The ASD subjects' scores for each question were significantly different from the comparison subjects'. Additionally, the mean RAADS-R scores of the ASD subjects and the comparison subjects were significantly different in each of the nine centers, as well as all of the centers combined. Both the clinically assigned question-domains and the statistically derived factors demonstrated high internal consistency. The RAADS-R had high test-retest reliability in a limited number of subjects. The RAADS-R was designed to assist clinicians in diagnosing adults (18+) with suspected ASD. It is designed to be administered by clinicians in a clinical setting. It is not intended to be a mail in or an online screening instrument. The questions are designed for individuals with average IQ and above. This is a population with mild or subclinical ASD. These individuals often escape diagnosis. The RAADS-R was specifically designed to capture that population.

Limitations

Scales based on self-reports have inherent limitations: First, a subject may not understand a question, and while answering to the best of their ability, give a misleading answer. This was mitigated in the present study by having a clinician remain with the subject to clarify possible misunderstandings during the administration of the RAADS-R.

Second is the issue of unawareness i.e., "denial" of symptoms or over reporting of symptoms. To control for this we added the SRS-A, which is a second party instrument. The concordance rate was 95.95%, which indicates good concurrent validity. However, since only 69 subjects were available to complete the SRS-A, further studies are warranted.

Six subjects whose clinical diagnoses and ADOS module 4 scores placed them in the ASD group, scored below the RAADS-R ASD cut off point of 65. Their mean age was 19.9 years. Each of these six "false negative" subjects was reported by their family members to be invested in appearing "as normal as they could be." The ANOVA results show a positive correlation with age in the ASD subjects with older subjects scoring higher (see Results section). Possible explanations are: (a) the younger subjects were denying symptoms, (b) the younger subjects received treatment since childhood and now believed they were "normal," and (c) three of the cases from Yale's sample have low functioning siblings with autism. These subjects may see themselves relatively unaffected for this reason. This stresses the importance for clinicians to search very carefully for symptoms when diagnosing young adults, given their propensity to want to "look as normal as possible."

Another confounding factor is that the same symptoms of ASD reported to be unobtrusive by some subjects may be serious to incapacitating in others. For instance, a young man reported that hypersensitivity to lights and sounds caused him to be "completely socially isolated," while another with the same symptoms told us, "I go to carnivals, circuses, and state fairs, every chance I get-I enjoy the flashing lights and sounds of the barkers and noises the merry go round makes." "And I stay as long as I can," he added with a smile.

Factor Analysis

As noted in the Results section, four factors emerged from Factor Analysis. The Cronbach alpha coefficients were satisfactory (social relatedness = .93, circumscribed interests = .95, sensory motor = .87, social anxiety = .89), indicating good internal consistency. For the clinical domain, the Cronbach alpha for language was the weakest. Factor IV contains questions relating to social anxiety and anxiety in general. This factor is important to examine. Targeting the social anxiety questions within the instrument may help the clinician differentiate social anxiety from ASD. The two diagnoses are often difficult to differentiate.

University Effect

As shown in the results section, there were significant differences of the mean RAADS-R scores among the nine research centers. This is most likely due to a large variation in the number of subjects per center (maximum 53 to a minimum of 7). Since the mean RAADS-R score for each center was significantly different from the mean RAADS-R scores of the comparison subjects (p < .0001), it is likely that the differences among centers would diminish if those with fewer subjects were to expand their database. Further validation studies are indicated.

Relevance to DSM-V

Results show significant statistical differences between the autism and Asperger's subjects and comparison subjects with and without another DSM-IV-TR diagnosis. There were no significant differences between the autism and Asperger's groups, which supports the newly proposed DSM-V diagnostic criteria. Recently proposed DSM-V criteria combines the two groups, creating an Autism Spectrum Disorder diagnosis.

Conclusions

The RAADS-R proved to be highly accurate in discriminating between subjects with ASD and those without a DSM-IV-TR diagnosis and with another DSM-IV-TR diagnosis (sensitivity = 97%, specificity = 100%, test-retest reliability = .987, accuracy = 98.5%,). No difficulties were reported in administration even though subjects were evaluated at nine medical centers on three continents. Mean RAADS-R scores were highly significantly different ( p < .0001) between ASD and comparison subjects at each of the nine centers, and all eighty questions were significantly different between ASD and comparison subjects. The fact that this was true even when the comparison cases had a variety of DSM-IV-TR diagnoses other than ASD makes the RAADS-R particularly useful in adult clinical settings.

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The Ritvo Autism Asperger Diagnostic Scale-Revised (RAADS-R): A Scale to Assist the Diagnosis of Autism Spectrum Disorder in Adults: An International Validation Study

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The RAADS was designed to address a major gap in screening services for adults with autism spectrum disorders. With the increased prevalence of the condition and the fact that adults are being referred or self-referred for services or diagnosis with increasing frequency, this instrument is a useful clinical tool to assist clinicians with the diagnosis of this growing population of higher functioning individuals in adulthood.

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Autism Documentary: Inside the UK’s only school for autistic girls

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This was made by the autistic students at Limpsfield Grange School to raise awareness of girls with autism and was sponsored by vInspired. This film has certainly raised awareness and ITV have made a one hour documentary about the school, we await the transmission date.

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Shared via Kindle. Description: M. That's what I'd like you to call me please. I'll tell you why later. Welcome to M's world. It's tipsy-turvy, sweet and sour, and the beast of anxiety lurks outside classrooms ready to pounce. M just wants to be like other ...

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Abstract

The sex difference in the prevalence of autism spectrum disorder (ASD) may be magnified by sex differences on diagnostic measures. The current study compared autistic males and females on items on the gold-standard diagnostic measure, the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2). In a sample of 8-to-17-year old autistic individuals from research (n = 229) and clinical settings (n = 238), females were less likely to show atypicalities on most items related to social-communication behaviors and on total and subscale scores. When controlling for overall intensity of symptomatology, no sex differences survived statistical corrections. Diagnostic criteria and/or gold-standard assessments may be less sensitive to female presentations of ASD and/or autistic females may exhibit fewer or less intense behaviors characteristic of ASD.

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Notes

1. Sex is defined as biological and physiological characteristics related to being male, female, or intersex. Gender refers to the socially constructed characteristics associated with sex (World Health Organization, 2002). As gendered social constructs begin early in development, and gender identity is rarely assessed to appropriately distinguish between the effect of sex or gender on presentation, the term sex/gender will be used in reviews of extant literature (e.g., Lai et al., 2015). Additionally, while we recognize the increased prevalence of gender diversity (e.g., transgender, nonbinary, etc.) among autistic individuals who may not conform to "traditional" gender norms (Janssen et al., 2016; Øien et al., 2018a, b; Strang et al., 2014), the present study focuses on ASD diagnostic assessments based on sex as data was not collected on gender identity.

2. Additionally, we are using "identity-first language" based on consultation with self-advocates, preferences by autistic people (Kenny et al., 2016), and reports that this language is less stigmatizing than person-first language (Bottema-Beutel et al., 2021).

References

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Øien, R. A., Schjølberg, S., Volkmar, F. R., Shic, F., Cicchetti, D. V., Nordahl-Hansen, A., Stenberg, N., Hornig, M., Havdahl, A., & Øyen, A.-S. (2018b). Clinical features of children with autism who passed 18-month screening. Pediatrics. https://doi.org/10.1542/peds.2017-3596

World Health Organization (2002). Report of a technical consultation on sexual health. Geneva, pp 28-31

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Acknowledgments

We wish to thank the families, parents and children, who participated in the GENDAAR study at the four data collection sites, as well as those who participated through Children's National Hospital. Funding was provided by the NIMH R01 MH10028 ACE Network (Pelphrey). This publication was also supported by Award Number 1U54HD090257 from the NIH, District of Columbia Intellectual and Developmental Disabilities Research Center Award (DC-IDDRC) program, and by a private grant to Children's National through the Isadore and Bertha Gudelsky Family Foundation.

The ACE GENDAAR Consortium includes contributions from: Kevin Pelphrey PhD (PI), Katy Ankeman MSW, Elizabeth Aylward PhD, Raphael Bernier PhD, Susan Bookheimer PhD, Sarah Corrigan MA, Mirella Dapretto PhD, Daniel Geschwind MD, Abha Gupta PhD, Allison Jack PhD, Zack Jacokes, Erin Libsack, Jennifer Lowe PhD, Desiree Lussier PhD, Shafali Jeste MD, Anna Kresse MPH, James McPartland PhD, Adam Naples PhD, Charles Nelson PhD, Emily Neuhaus PhD, Megha Santhosh MHE, Matthew State MD, Catherine Sullivan, Carina Torgenson, Jack Van Horn PhD, Pamela Ventola PhD, Julie Wolf PhD.

Funding

The authors did not receive support from any organization for the submitted work.

Author information

Authors and Affiliations

1. Psychiatry & Behavioral Science Department, University of Washington, Seattle, WA, 98195, USA

Hannah M. Rea & Sara Jane Webb

2. UiT-The Arctic University of Norway, PB 6060, 9037, Tromsø, Norway

Roald A. Øien

3. Child Study Center, Yale School of Medicine, New Haven, CT, 06510, USA

Roald A. Øien

4. Center On Child Health, Behavior & Development, Seattle Children's Research Institute, Seattle, WA, 98105, USA

Frederick Shic & Sara Jane Webb

5. Department of Pediatrics, University of Washington, Seattle, WA, 98195, USA

Frederick Shic

6. Children's National Medical Center, 15245 Shady Grove Road, Suite 350, Rockville, MD, 20850, USA

Allison B. Ratto

Contributions

HR conceived of the study, finalized its design, completed analyses, and coordinated and drafted the manuscript; RO complete analyses and drafted the manuscript, FS designed and complete analyses and drafted the manuscript, SJW oversaw data collection and drafted the manuscript, AR contributed to data collection, study design, and drafted the manuscript.

Corresponding author

Correspondence to Allison B. Ratto.

Ethics declarations

Conflict of interest

Hannah M. Rea, Roald A. Øien, Sara Jane Webb, and Allison Ratto report no affiliations with or involvement in any organization or entity with any financial interest in the outcome of this project. Frederick Shic is a paid consultant for Roche and Janssen pharmaceutical companies.

Ethical Approval

Ethical oversight was provided by the Yale Institutional Board (Yale, SCRI), the UCLA Office of Human Research Protection Program (UCLA), Boston Children's Hospital Institutional Review Board (BCH), and USC Office for the Protection of Research Subjects. All procedures performed were in accordance with the ethical standards of the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all parents of children participating in the study; children provided written assent.

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Rea, H.M., Øien, R.A., Shic, F. et al. Sex Differences on the ADOS-2. J Autism Dev Disord 53, 2878-2890 (2023). https://doi.org/10.1007/s10803-022-05566-3
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# data[].attributes.attachment_link.title
Sex Differences on the ADOS-2 - Journal of Autism and Developmental Disorders

# data[].attributes.attachment_link.description
The sex difference in the prevalence of autism spectrum disorder (ASD) may be magnified by sex differences on diagnostic measures. The current study compared autistic males and females on items on the gold-standard diagnostic measure, the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2).

# data[].attributes.headline
Best practices and processes for assessment of autism

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Best practices and processes for assessment of autism

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Bishop, S. L., & Lord, C. (2023). Commentary: Best practices and processes for assessment of autism spectrum disorder–the intended role of standardized diagnostic instruments. Journal of Child Psychology and Psychiatry, 64(5), 834-838. https://doi.org/10.1111/jcpp.13802

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Child Psychology Psychiatry - 2023 - Bishop - Commentary  Best practices and processes for assessment of autism spectrum