Skip to main content Accessibility help
×
×
Home

Exploring the quantitative nature of empathy, systemising and autistic traits using factor mixture modelling

  • Rachel Grove (a1), Andrew Baillie (a1), Carrie Allison (a2), Simon Baron-Cohen (a3) and Rosa A. Hoekstra (a4)...

Abstract

Background

Autism research has previously focused on either identifying a latent dimension or searching for subgroups. Research assessing the concurrently categorical and dimensional nature of autism is needed.

Aims

To investigate the latent structure of autism and identify meaningful subgroups in a sample spanning the full spectrum of genetic vulnerability.

Method

Factor mixture models were applied to data on empathy, systemising and autistic traits from individuals on the autism spectrum, parents and general population controls.

Results

A two-factor three-class model was identified, with two factors measuring empathy and systemising. Class one had high systemising and low empathy scores and primarily consisted of individuals with autism. Mainly comprising controls and parents, class three displayed high empathy scores and lower systemising scores, and class two showed balanced scores on both measures of systemising and empathy.

Conclusions

Autism is best understood as a dimensional construct, but meaningful subgroups can be identified based on empathy, systemising and autistic traits.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      Exploring the quantitative nature of empathy, systemising and autistic traits using factor mixture modelling
      Available formats
      ×

      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      Exploring the quantitative nature of empathy, systemising and autistic traits using factor mixture modelling
      Available formats
      ×

      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      Exploring the quantitative nature of empathy, systemising and autistic traits using factor mixture modelling
      Available formats
      ×

Copyright

Corresponding author

Rachel Grove, Department of Psychology, Centre for Emotional Health, Macquarie University, Sydney, NSW 2109, Australia. Email: rachel.grove@mq.edu.au

Footnotes

Hide All

Joint senior authors.

Declaration of interest

None.

Footnotes

References

Hide All
1 American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5). APA, 2013.
2 Constantino, JN, Gruber, CP, Davis, S, Hayes, S, Passanante, N, Przybeck, T. The factor structure of autistic traits. J Child Psychol Psyc 2004; 45: 719–26.
3 Frazier, TW, Youngstrom, EA, Speer, L, Embacher, R, Law, P, Constantino, J. Validation of proposed DSM-5 criteria for Autism Spectrum Disorder. J Am Acad Child Adolesc Psychiatry 2012; 51: 2840.
4 Ingram, GD, Takahashi, TN, Miles, JH. Defining autism subgroups: a taxometric solution. J Autism Dev Disord 2008; 38: 950–60.
5 Mandy, WPL, Charman, T, Skuse, DH. Testing the construct validity of proposed criteria for DSM-5 Autism Spectrum Disorder. J Am Acad Child Adolesc Psychiatry 2012; 51: 4150.
6 Frazier, TW, Youngstrom, EA, Sinclair, L, Kubu, CS, Law, P, Rezai, A. Autism spectrum disorders as a qualitatively distinct category from typical behavior in a large, clinically ascertained sample. Assessment 2010; 17: 308–20.
7 Lubke, GH, Muthén, B. Investigating population heterogeneity with factor mixture models. Psychol Med 2005; 10: 2139.
8 Georgiades, S, Szatmari, P, Boyle, M, Hanna, S, Duku, E, Zwaigenbaum, L, et al. Investigating phenotypic heterogeneity in children with autism spectrum disorder: a factor mixture modeling approach. J Child Psychol Psychiatry 2013; 54: 206–15.
9 Georgiades, S, Boyle, M, Szatmari, P, Hanna, S, Duku, E, Zwaigenbaum, L, et al. Modeling the phenotypic architecture of autism symptoms from time of diagnosis to age 6. J Autism Dev Disord 2014; 44: 3045–55.
10 Constantino, JN. The quantitative nature of autistic social impairment. Pediatr Res 2011; 69: 55R62R.
11 Sucksmith, E, Roth, I, Hoekstra, RA. Autistic traits below the clinical threshold: re-examining the Broader Autism Phenotype in the 21st century. Neuropsychol Rev 2011; 21: 360–89.
12 Piven, J, Palmer, P, Jacobi, D, Childress, D, Arndt, S. Broader autism phenotype: evidence from a family history study of multiple-incidence autism families. Am J Psychiatry 1997; 154: 185–90.
13 Baron-Cohen, S. Autism: the empathizing-systemizing (E-S) theory. Ann NY Acad Sci 2009; 1156: 6880.
14 Grove, R, Baillie, A, Allison, C, Baron-Cohen, S, Hoekstra, RA. Empathizing, systemizing, and autistic traits: latent structure in individuals with autism, their parents and general population controls. J Abnorm Psychol 2013; 122: 600–9.
15 Raven, J, Raven, JC, Court, JH. Manual for Raven's Progressive Matrices and Vocabulary Scales. Section 1: General Overview. Harcourt Assessment, 2003.
16 Baron-Cohen, S, Wheelwright, S, Skinner, R, Martin, J, Clubley, E. The Autism-Spectrum Quotient (AQ): evidence from Asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. J Autism Dev Disord 2001; 31: 5.
17 Hoekstra, RA, Bartels, M, Cath, DC, Boomsma, DI. Factor structure, reliability and criterion validity of the Autism-Spectrum Quotient (AQ): a study in Dutch population and patient groups. J Autism Dev Disord 2008; 38: 1555–66.
18 Wheelwright, S, Baron-Cohen, S, Goldenfeld, N, Delaney, J, Fine, D, Smith, R, et al. Predicting Autism Spectrum Quotient (AQ) from the Systemizing Quotient-Revised (SQ-R) and Empathy Quotient (EQ). Brain Res 2006; 1079: 4756.
19 Baron-Cohen, S, Wheelwright, S. The empathy quotient: an investigation of adults with Asperger syndrome or high functioning autism, and normal sex differences. J Autism Dev Disord 2004; 34: 163–75.
20 Baron-Cohen, S, Wheelwright, S, Hill, J, Raste, Y, Plumb, I. The “Reading the mind in the eyes” Test revised version: a study with normal adults, and adults with Asperger syndrome or high-functioning autism. J Child Psychol Psychiatry 2001; 42: 241–51.
21 Lundqvist, D, Flykt, A, Öhman, A. The Karolinska Directed Emotional Faces. Department of Clinical Neuroscience, Psychology section: Karolinska Institutet, 1998.
22 Sutherland, A, Crewther, DP. Magnocellular visual evoked potential delay with high autism spectrum quotient yields a neural mechanism for altered perception. Brain 2010; 133: 2089–97.
23 Hagenaars, J, McCutcheon, AL. Applied Latent Class Analysis. Cambridge University Press, 2002.
24 Meredith, W. Measurement invariance, factor analysis and factorial invariance. Psychometrika 1993; 58: 525–43.
25 Lubke, GH, Muthén, B. Performance of factor mixture models as a function of model size, covariate effects, and class-specific parameters. Struct Equ Modeling 2007; 14: 2647.
26 Muthén, B. Latent variable hybrids: overview of old and new models. In Advances in Latent Variable Mixture Models (eds Hancock, GR, Samuelsen, KM): 124. Information Age Publishing, 2008.
27 Nylund, KL, Asparouhov, T, Muthen, BO. Deciding on the number of classes in Latent Class Analysis and Growth Mixture Modeling: a Monte Carlo simulation study. Struct Equ Modeling 2007; 14: 535–69.
28 Gebregziabher, M, Shotwell, MS, Charles, JM, Nicholas, JS. Comparison of methods for identifying phenotype subgroups using categorical features data with application to autism spectrum disorder. Comput Stat Data Anal 2012; 56: 114–25.
29 Lo, Y, Mendell, NR, Rubin, DB. Testing the number of components in a normal mixture. Biometrika 2001; 88: 767–78.
30 Ramaswany, V, Desarbo, WS, Reibstein, DJ, Robinson, WT. An empirical pooling approach for estimating marketing mix elasticities with PIMS data. Market Sci 1993; 12: 103–24.
31 IBM Corp. IBM SPSS Statistics for Windows. IBM Corp, 2012.
32 Sucksmith, E, Allison, C, Baron-Cohen, S, Chakrabarti, B, Hoekstra, RA. Empathy and emotion recognition in people with autism, first-degree relatives and controls. Neuropsychologia 2013; 51: 98105.
33 Tavassoli, T, Hoekstra, RA, Baron-Cohen, S. The Sensory Perception Quotient (SPQ): development and validation of a new sensory questionnaire for adults with and without autism. Mol Autism 2014; 5: 29.
34 Mandy, W, Charman, T, Puura, K, Skuse, D. Investigating the cross-cultural validity of DSM-5 autism spectrum disorder: evidence from Finnish and UK samples. Autism 2014; 18: 4554.
35 Scheeren, AM, Stauder, JE. Broader autism phenotype in parents of autistic children: Reality or myth? J Autism Dev Disord 2008; 38: 276–87.
36 Greaves-Lord, K, Eussen, ML, Verhulst, FC, Minderaa, RB, Mandy, W, Hudziak, JJ, et al. Empirically based phenotypic profiles of children with pervasive developmental disorders: interpretation in the light of the DSM-5. J Autism Dev Disord 2013: 43: 1784–97.
37 Chakrabarti, S, Fombonne, E. Pervasive developmental disorders in pre-school children. JAMA 2001; 285: 3093–9.
38 Todd, M, Davis, KE, Cafferty, TP. Who volunteers for adult development research? Research findings and practical steps to reach low volunteering groups. Int J Aging Hum Dev 1983; 18: 177–84.
39 Rosenthal, R, Rosnow, RL. The Volunteer Subject. John Wiley and Sons, 1975.
40 Lee, H, Marvin, AR, Watson, T, Piggot, J, Law, JK, Law, PA. Accuracy of phenotyping of autistic children based on internet implemented parent report. Am J Med Genet 2010; 153: 1119–26.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

The British Journal of Psychiatry
  • ISSN: 0007-1250
  • EISSN: 1472-1465
  • URL: /core/journals/the-british-journal-of-psychiatry
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed

Exploring the quantitative nature of empathy, systemising and autistic traits using factor mixture modelling

  • Rachel Grove (a1), Andrew Baillie (a1), Carrie Allison (a2), Simon Baron-Cohen (a3) and Rosa A. Hoekstra (a4)...
Submit a response

eLetters

No eLetters have been published for this article.

×

Reply to: Submit a response


Your details


Conflicting interests

Do you have any conflicting interests? *