Abstract This study assesses Autism-Spectrum Quotient (AQ) scores in a ‘big data’ sample collected through the UK Channel 4 television website, following the broadcasting of a medical education program. We examine correlations between the AQ and age, sex, occupation, and UK geographic region in 450,394 individuals. We predicted that age and geography would not be correlated with AQ, whilst sex and occupation would have a correlation. Mean AQ for the total sample score was m = 19.83 (SD = 8.71), slightly higher than a previous systematic review of 6,900 individuals in a non-clinical sample (mean of means = 16.94) This likely reflects that this big-data sample includes individuals with autism who in the systematic review score much higher (mean of means = 35.19). As predicted, sex and occupation differences were observed: on average, males (m = 21.55, SD = 8.82) scored higher than females (m = 18.95; SD = 8.52), and individuals working in a STEM career (m = 21.92, SD = 8.92) scored higher than individuals non-STEM careers (m = 18.92, SD = 8.48). Also as predicted, age and geographic region were not meaningfully correlated with AQ. These results support previous findings relating to sex and STEM careers in the largest set of individuals for which AQ scores have been reported and suggest the AQ is a useful self-report measure of autistic traits.

Citation: Ruzich E, Allison C, Chakrabarti B, Smith P, Musto H, Ring H, et al. (2015) Sex and STEM Occupation Predict Autism-Spectrum Quotient (AQ) Scores in Half a Million People. PLoS ONE 10(10): e0141229. https://doi.org/10.1371/journal.pone.0141229 Editor: Masako Taniike, Osaka University, JAPAN Received: July 22, 2015; Accepted: October 6, 2015; Published: October 21, 2015 Copyright: © 2015 Ruzich et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Data Availability: Data from the My Health Checker application were accessed by the research team in collaboration with the Channel Four Television Corporation and 4 Ventures Limited, who own the data. Information from these parties indicate that anonymised test results may be made available upon request. For access to this data, please contact Adam Gee (AGee@Channel4.co.uk). Funding: The authors of this work were supported by grants from the UK Medical Research Council (MRC), the Wellcome Trust, the National Institute for Health Research Collaboration for Leadership in Applied Health Research & Care (NIHR CLAHRC) East of England and the Autism Research Trust during the period of this work. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.

Introduction The Autism-Spectrum Quotient (AQ) is a brief self-report measure suitable for adults with average or above average IQ [1]. It has been used both in research [2] and to aid in clinical practice, during referral to specialist autism diagnostic clinics [3]. The AQ has also been used in the general population to quantify autistic traits along a quantitative dimension of individual differences [2]. Here we examine the distribution of AQ scores in a very large population sample, collected online, by Channel 4, a UK national television production company. Before describing the rationale for the study, it is important to acknowledge the benefits [4] and the risks [5] of ‘big data’. The term ‘big data’ refers to large sets of digital records, and to a developing research paradigm [6,7]. The analysis of big data is now common in business and finance, social media, advertising, medicine and epidemiology (see for example Google flu trends [8] and the recent Ebola outbreak [9]). Big data can be particularly useful to examine the relationship of seemingly unrelated variables, such as in a recent study of adverse drug reactions via social media [10]. Big data also has appeared in pure research, such as genome sequencing or neuroimaging to uncover the human connectome. Big data has the potential to test the robustness of previous findings but one needs to be aware not just of volume (data size) and velocity (rate of data accrual) [11], but also variety (data type and scope), value (data worth), and veracity (the reliability of the data) [7,12]. Drawbacks of big data include ethical considerations of privacy and security, as well as storage and processing limitations [7,13,14]. In addition, standard statistical methods may not be appropriate for use with big data [7], and the ability of big data studies to identify subtle correlations often considered a strength, can sometimes be disadvantageous [15]. There is a tendency to view big data as accurate and objective, without considering potential biases in data collection and cleaning [5]. Large sample sizes have the potential to amplify error, so care needs to be taken in data interpretation to clarify in what way large samples may not be representative. Many of these drawbacks can be overcome as needed, for instance by planning for heavy computational processing, using specialised storage and visualisation platforms, using scaling algorithms or other adjustments, correcting for multiple comparisons, calculating effect size in addition to significance, and thoroughly characterising the population sample to ensure sample representativeness. With these caveats in mind, the present study uses big data to examine correlations between the AQ and selected available demographic variables (age, sex, occupation, and UK geographic region). We examined a very large set of AQ data collected following the screening of the UK Channel 4 television program Embarrassing Bodies: Live from the Clinic. A study of this nature is important because it offers the opportunity to test to findings reported in smaller samples. Secondly, it offers the opportunity to investigate the AQ in a large data set, since signals of small effect size might not be apparent in smaller samples. Finally, it offers the opportunity to use regression modelling to disentangle the influence of several factors on AQ. We hypothesized that sex and occupation would predict autistic traits, whilst age and geographical region would not. These predictions were based on previous research showing that, in the general population, males on average have higher AQ scores than do females [16]. In addition, individuals who work in fields that require high ‘systemizing’ (the drive to analyse or build rule-based systems) such as science, technology, engineering, and mathematics (STEM), show higher levels of autistic traits as measured by AQ than do those working in non-STEM fields [1,17–20]. The increased prevalence of individuals with autism in regions that are rich in jobs in Information Technology (IT) [21] also leads to the prediction that AQ will be higher in those working in STEM than in non-STEM jobs. In contrast, previous studies have not found that AQ is influenced by age and whilst small fluctuations in AQ have been reported across cultures [22,23], these may reflect subtle differences in the meanings in translations of items into languages (the AQ has now been translated into approximately 30 languages (http://www.autismresearchcentre.com/arc_tests), and we had no reason to expect that geographic region within the UK would correlate with AQ.

Discussion A dataset from the Channel 4 television website was used to examine the relationship between AQ and sex, occupation, age, and UK geographic region. Our predictions were strongly supported in that previously reported findings about different distributions of scores between males and females and between individuals working in STEM vs. non-STEM careers were replicated. In a sample of nearly half a million individuals, we found a moderate effect of sex on AQ, with males scoring higher than females by an average of 2.5 points. This replicates similar findings [2,16]. Further, with regard to occupation, we found that people working in STEM careers scored higher than people reporting non-STEM careers, again corroborating earlier findings [1,19,30]. In contrast, we found only a very small effect of age on AQ that is likely an artefact produced by the age-biased recruitment pool of the Embarrassing Bodies audience, summarized in Fig 1. We found no meaningful association of AQ with geographic location. When sex, age, and occupation are entered into a multiple linear regression model, less than 4% of the variance in AQ in the current sample is explained, indicating that other unmeasured variables contribute to autistic traits as measured by the AQ. From previous work, we know that these unmeasured variables may include common genetic polymorphisms [31], prenatal testosterone [32] and brain structure [33]. The AQ was implemented on the Channel 4 website without the research team’s input, as initially this was not conceived as a research study, although the Ethics Committee retrospectively approved its use for research because the wording on the Channel 4 website alerted participants that the data would be used for research. The mean AQ score for the cleaned sample was 19.83 (SD = 8.71) is higher than that reported in the literature by approximately 3 points [2]. The dataset also has a positive skew. These findings reflect that there may be a bias in this self-selecting sample toward people who suspect they may be on the autism spectrum, as well as the likely inclusion of individuals with a clinical diagnosis of ASC in the sample. Participants were not asked as a part of the survey if they had ever received a diagnosis of ASC. Lack of this participant diagnostic information has made interpretation of novel results difficult, and has rendered it impossible for the binary logistic model described above to be assessed on predictive power of diagnosis, though we were able to use the cut-off values reported in the AQ literature. Variables such as whether participants have, or suspect they have, a diagnosis of ASC, or if they have a family member with a confirmed diagnosis, would be simple to collect in a future replication study, and useful for interpreting results. The Channel 4 team have agreed to include this information so that in a future replication of the current study, the effects of sex and STEM occupation can be studied in cases of ASC and controls separately. In future, research would also benefit from itemized response scores rather than AQ scores out of 50, so that subscale and item-level data could be analysed. From this study we were able to replicate previous findings concerning the effect of sex and occupation on AQ scores, despite this not being a representative sample from the general population. This suggests these effects are robust and universal and allows us to conclude that traits commonly associated with autism are strongly linked to traits associated with being male and with STEM occupations, regardless of other factors. More generally, the use of a large data set in the current study demonstrates the value of collaborating with groups that have access to national and international survey platforms, and of collaboration between researchers and non-researchers. Limitations to the current study include that the respondents were self-selecting rather than randomly selected. In addition, the only individuals who would have been aware of the invitation to volunteer to take the AQ were those who watched the late-night medical TV program Embarrassing Bodies, which appeals particularly to viewers who are not deterred by graphic details of medical syndromes, or who tuned in for this particular episode because it included an item on autism. The sample is relatively young compared to the UK population, and contains twice as many females as males. This is thus not a representative sample from the general population. A further potential improvement in the design of the survey is in the selection options for demographic categories. The list of possible career options to select from was not compiled from national or international survey data, and nearly a quarter of participants selected the “Other” category. Given the age distribution of participants, it is possible that a large proportion of “Other” selectors were students, but this could not be confirmed. The “Other” category is likely a mixed group comprised of STEM and nonSTEM individuals. The same limitation applies to the location variable; locations were coarse-grained, and omitted large areas (e.g. London; East England). The presence of both an “Other” option and a “Prefer not to say” option caused a relatively large number of entries to be discarded during data cleaning. This means there was no opportunity to test the ‘Silicon Valley’ effect [21,34] in geographical areas that are enriched in jobs in Information Technology, which would be an interesting modification to include in a future replication study. Another limitation to the current dataset is that there was no mechanism in the online platform by which to limit the number of times individuals could take the AQ, and no participant identifier in the data transferred to the research team that could be used to individuate results or remove potential duplicates. However, we assume that, in this case, the signal is greater than any noise. Evidence of an acceptable signal-to-noise ratio can be taken from the webmaster’s report, which states that the survey was taken 63,000 times during the hour-long show broadcast, suggesting that, at least during the first hour during which responses could be made, the majority of completed data entries are not duplicates. Regarding statistical methodology, the ability of big data studies to identify subtle trends in a population can be misleading [15]. Historically, there has been criticism of null hypothesis significance testing, where it is argued that the method is an amalgamation of disparate techniques, that the results can be misinterpreted, that the test is employed in place of replication, and that the resultant conclusions could be unreliable [35–38]. Researchers concerned with the lack of reproducibility in the field of psychology have, among other things, criticized an overreliance on p-values, or the probability of rejecting the null hypothesis. In this study, we found that p-values, combined with large sample sizes, led to results that were difficult to interpret. For this reason, we also considered effect size, or practical significance, as a measure of the strength of an observed finding, as this measure is independent of sample size. Where appropriate, we also reported confidence intervals and odds ratios.

Conclusions In this study we confirm the effect of sex and STEM occupation on AQ, supporting earlier studies, and found no statistically meaningful effects of either age or geographical region on AQ. This study awaits replication in a big data study that controls for audience bias and diagnostic status. We conclude that autistic traits are consistently higher in males than females, and in those working in STEM than in non-STEM fields. These findings may have importance for understanding the male-biased sex ratio in autism [39,40] as well as the hyper-systemizing theory of autism [21,34,41].

Acknowledgments We are grateful to Adam Gee and Channel 4 for providing the data, and to David Greenberg and Bonnie Auyeung for valuable discussions.

Author Contributions Conceived and designed the experiments: ER BC SBC. Analyzed the data: ER HM PS. Wrote the paper: ER CA HR SBC. Obtained permission for use of data: SBC CA PS BC.