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People Analytics and Psychometrics Laboratory (PAPsy Lab)

Igor Menezes
Faculty of Business, Law and Politics - Hull University Business School
Igor Menezes
Lecturer in Organisational Behaviour and HRM

The Challenge

The PaPsy Lab seeks to approximate four major fields of research - People Analytics, Psychometrics, Data Science and Organisational Psychology - into a cohesive framework to gain business insights and advance organisational research.

We have currently applied psychometric, computational and modelling techniques to improve the quality of the organisational climate measurement and to better predict individual performance and employee turnover. Our studies have also deployed trailblazing technologies in text analytics such as topic analysis and sentiment analysis for the investigation of organisational culture and customer satisfaction.

The Approach

There is currently a dearth of studies addressing people analytics and its contributions to gaining insights into the workforce through the combination of business and employee data. Also, there is a shortage of skilled people analytics practitioners as they have not been trained in data science, HR metrics and analytics, despite the strong demand for data analysis skills.

The PAPsy Lab has focused on the development of new methods and techniques to deal with HR challenges, namely recruitment and selection, employee turnover, engagement, wellbeing, and individual and organisational performance. Hence, our laboratory intends to help academics and practitioners to overcome the barriers that are holding them back from making better use of HR analytics and deriving insights from people data.


Our work has benefitted society through:

  • The development of guidelines for application of multilevel techniques in public health research;
  • The development and validation of instruments used around the world by academics and professionals in different areas such as organisational psychology, healthcare and educational assessment;
  • The combination of techniques from psychometrics and machine learning for improving the prediction of organisational behaviours such as individual performance and employee turnover.
  • The design of solutions to help managers and executives make informed decisions about their employees and organisational processes.
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  • To develop and apply new technologies that can better explain the relationships between people data and organisational outcomes
  • To help improve the employee's well-being, productivity and performance
  • To design innovative solutions for business such as psychometric instruments, descriptive and prescriptive models and new data visualisation techniques
  • To train students in people analytics, psychometrics and research methods, thereby enhancing their ability to design research studies, deliver oral presentations and write academic papers.


View all projects

Estimating psychological networks for the measurement of organisational climate

This groundbreaking project aims to introduce how the psychological networks technique can be applied to the investigation of organisational climate through the mutual interactions among its dimensions. A dataset comprised of 1,053,322 workers from 160 companies is being used for this study. Even though the research on organisational psychology typically makes use of nonexperimental designs, psychological networks may suggest potential causal structures in a pathway. For example, workers might not rely on the organisational leadership, which in turn will impinge on the team morale and consequently increase turnover intentions. This causal structure indicates that we would be able to predict turnover intentions by knowing the attitudes towards the leadership that could lead a worker to leave his or her organisation. Nonetheless, we can also predict turnover intentions from team morale, making the knowledge on the attitudes towards leadership no longer necessary for the prediction of turnover intentions.

Untangling the performance of automatic video summarisation methods by video compression

To reduce the time users spend watching videos, automatic video summarisation methods seek to detect the most relevant video parts, which are condensed into a shorter video. Performance evaluation of video summarisation methods is still challenging since users are assigned the task of choosing which part of a video is more relevant so that this could ultimately support the development of automatic methods. These subjective choices, and potential cognitive biases may directly influence the actual performance of the automatic methods. Although the performance of video summarisation methods is typically assessed by well-known statistical metrics, the boundaries of these metrics have not yet been explored fully. This project analyses the limitations of the metrics used to evaluate video summarisation methods.

Should I stay or should I go? Predicting intentions to leave the organisation using machine learning algorithms

This study, in partnership with the University of Cambridge, aims to develop a machine learning based application to calculate the risk of leaving an organisation taking into account the interactions among different factors, such as personality traits, decision-making style, financial situation, health status, cognitive processes, among others. Data was collected online via Twitter and Multidimensional Item Response Theory will be combined with a machine learning algorithm (XGBoost algorithm) to predict the intentions to leave the organisation. Two papers will be produced, the first one, more methodological, will address the combination of techniques from psychometrics and machine learning and, the second, more applied, will report the findings of this investigation. This study can help companies to work beyond their turnover rates, mainly on the analyses of their talented employees with a stronger intention to leave the organisation, and then create new measures aimed at worker retention. Moreover, it may provide insights into the field of organisational behaviour and further contribute to the development of this topic and its measurement.


Outputs and publications

Abdalla, K., Oliveira, L., Magidiel, A., Menezes, I.G. (2019). Modelling perceptions on the evaluation of video summarisation. Expert Systems with Applications. 131, 254-265.

Menezes, I.G.; Pires, P.; Zwiegelaar, J., Mendy, J.; Moraes, E (2019). Applying network analysis to measure organisational behaviors using R software. EURAM 2019.

Ruggeri, K, Ivanovic, R., Menezes, I.G., Razum, J., Ondřej, K., Garcia-Garzon, E. (2018).  An evidence-based policy model for improving choice in global health access through medical travel. Health Policy, 122(12), 1372-1376.

Ruggeri, K.; Menezes, I.G.; Kos, M.; Ondřej, K.; Langdon, P.; Miles, J.; Franklin, M.; Parma, L. (2018). In with the new?  Generational differences shape population technology adoption patterns in the age of self-driving vehicles. Journal of Engineering and Technology Management, 50, 39-44.

Menezes, I.G.; Ruggeri, K.; Menezes, A.C.P.G.; Sandbrand, D.; Moraes, E.  Development and validation of the multidimensional turnover intentions scale (2018). Proceedings of British Academy of Management Conference 2018. Bristol, United Kingdom: University of the West of England.

Research Students

Joanna Ilori

Talent Management in the Public Sector

Hugh Scullion, co-supervisor: Igor Menezes

David Osvaldo Huerta Harris

The effects of education, experience, self-efficacy and desirability on students entrepreneurial intentions and the moderating role of cognitive style