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Professor Anna Brown

Professor of Psychometrics,
Chair of Ethics
Telephone
01227 (82)3097
Professor Anna Brown

About

Professor Anna Brown is a psychometrician with an established reputation and extensive industry experience. Currently she is teaching psychological methods and conducting research in psychometrics at the School of Psychology. Previously, Anna taught short courses in applied psychometrics at the University of Cambridge, where she also conducted research focusing on modelling response biases in questionnaire data. Anna's industry experiences included research and test development at the research division of the UK largest occupational test publisher, SHL Group, where she had worked as Principal Research Statistician for many years.

Anna holds an MSc degree in Mathematics with distinction and a PhD in Psychology with distinction. Anna's PhD research led to the development of the Thurstonian IRT model described as a breakthrough in scoring of forced-choice questionnaires, and received the 'Best Dissertation' award from the Psychometric Society. Applications of this methodology include the development of an IRT-scored version of Occupational Personality Questionnaire (OPQ32r), the Character Skills Snapshot, Leadership Styles Questionnaire and many others.

Professor Brown has extensive experience in designing, developing and implementing psychometric testing solutions in the workplace, health settings and education, and provides psychometric advice to several organisations in the private and public sectors internationally. She served as an elected member on the Council of the International Test Commission (ITC) for 8 years, chairing its Research and Guidelines Committee. In this role, Anna facilitated publication and translation of ITC guidelines aiming to improve testing standards internationally. She also serves as a member of the editorial Board of the International Journal of Testing, and as an ad-hoc reviewer for countless journals in the field of psychological testing.

Research interests

Anna’s research focuses on psychological measurement and testing, particularly issues in test validity and test fairness. She specialises in modelling response biases and faking, scaling of comparative data, measurement invariance and other measurement models using Item Response Theory (IRT) and Structural Equation Modelling (SEM) frameworks more broadly. She has published widely on these and other methodology topics, producing numerous journal articles, book chapters, psychometric tests and test manuals, many published in top methodology journals such as Psychometrika, Psychological Methods, etc.

Key publications

  • Brown, A. & Maydeu-Olivares, A. (2018). Ordinal Factor Analysis of Graded-Preference Questionnaire Data. Structural Equation Modeling: A Multidisciplinary Journal, 25(4), 516-529. DOI: 10.1080/10705511.2017.1392247
  • Brown, A. (2016). Item Response Models for Forced-Choice Questionnaires: A Common Framework. Psychometrika, 81(1), 135–160. DOI: 10.1007/s11336-014-9434-9
  • Brown, A. & Maydeu-Olivares, A. (2013). How IRT can solve problems of ipsative data in forced-choice questionnaires. Psychological Methods, 18(1), 36-52. DOI: 10.1037/a0030641
  • Brown, A. & Maydeu-Olivares, A. (2011). Item response modeling of forced-choice questionnaires. Educational and Psychological Measurement, 71(3), 460-502. DOI: 10.1177/0013164410375112

Teaching

  • PSYC8010: Statistics and Methodology
  • PSYC5000: Psychology Statistics and Practical

Supervision

Anna is happy to supervise final year and MSc projects related to:

  1. Faking and impression management in high stakes assessments, for example: situational and personal characteristics linked to applicant ‘faking good’ on employment tests; or patient ‘faking bad’ on diagnostic tests for access to treatments; etc.
  2. Unmotivated response biases and their impact on test validity, for example: acquiescence, leniency/severity, halo/horn effects etc.

Current PhD students

  • Miriam Fuechtenhans: In Pursuit of Process in Personality Prevarication: Developing a Model of Faking Behaviour.

Past PhD students

  • Dr Yin Lin PhD Psychology (2020) ESRC funded: Asking the right questions: Increasing fairness and accuracy of personality assessments with Computerised Adaptive Testing
  • Dr Ana Carla Crispim PhD Psychology (2018). Self-funded: Exploring the validity evidence of core affect

Anna welcomes contact from potential Doctoral students interested in modern psychometric modelling (structural equation modelling, questionnaire design, latent trait modelling, and similar).

Professional

  • Chair of the Psychology Research Ethics Committee
  • Consulting Editor of the International Journal of Testing
  • Individual member of the European Association of Methodology (EAM)
  • Individual member of the International Test Commission (ITC)  

Grants and Awards

2022 University of Kent Knowledge Exchange Collaboration Prize £3,000
2021 Innovate UK and YSC Ltd.
“Knowledge Transfer Partnership: Development of a technology-enabled suite of psychometric tests for leadership development”
£184,403
2017 University of Kent Social Science Faculty Teaching Award £1,500
2016 Faculty of Social Sciences
“A pilot study of validity and fairness of 11+ tests”, Principal Investigator
£3,250
2015 Alzheimer's Society
"C-DEMQOL – Measurement of quality of life in family carers of people with dementia: development of a new instrument for evaluation"
£108,223
2014 Department of Health
"Systematic Review of the Psychometric Properties of ASQ (ASQ-SE)"
£5,375
2014 ESRC CASE Studentship
“Asking the right questions: Increasing fairness and accuracy of personality assessments with Computerised Adaptive Testing”
£24,000
2013 University of Barcelona Extraordinary Dissertation Award  
2011 The Psychometric Society Dissertation Award (best dissertation)  
2010-2011 The Isaac Newton Trust grant
“Modern Psychometrics: theoretical and empirical contributions using item response models”: over two financial years 
£31,659
2010 Dissertation support award from Society of Multivariate Experimental Psychology $1,000
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