Abstract
Autism is commonly measured and conceptualised through self-report questionnaire measures of autistic traits. These tools are widely used in research to advance our theoretical understanding of autism, and in clinical practice to inform screening thresholds for diagnostic referrals. Using measures that can accurately capture autistic traits, therefore, is essential for their appropriate use in clinical settings and for meaningful inferences to be made from research. Despite the high prevalence of self-report measures across research and clinical practice, there is a striking paucity of work investigating the psychometric properties and robustness of these tools.To address this gap in research, the current thesis aims to advance our understanding of how autistic traits should be measured, utilising large sample sizes and novel methodological approaches. After a brief overview of psychometric and statistical methods (Chapter 2), Chapters 3–4 identify misuse in a self-report screening measure, and empirically quantify the consequences of this error. Chapter 5 uses exploratory approaches to identify alternative utility in trait measures beyond clinical cut-offs, and Chapter 6 investigates measurement invariance to sex across a plethora of autistic trait tools.
Drawing on these findings, the latter half of this thesis aims to explore how self-report autistic trait measures can be used to improve our understanding of autism and related neurodevelopmental phenomena, specifically alexithymia (Chapter 7), and ADHD (Chapter 8). Recurring secondary themes around sex-based differences and open science practices will be interwoven throughout the Chapters.
The implications from this thesis for advancing the measurement of autistic traits and wider psychological constructs are discussed in Chapter 9, alongside the limitations and future directions from this work.
| Date of Award | 26 Jun 2024 |
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| Original language | English |
| Awarding Institution |
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| Supervisor | Punit Shah (Supervisor), Mitch Callan (Supervisor) & Esther Walton (Supervisor) |