Tina Hannemann

Tina Hannemann is a lecturer at the department for Social Statistics. She joined the department as part of a NCRM research project in 2016. Currently, she has the role of the director for the MSc program Social Research Methods and Statistics. She teaches several UG and PG modules with focus in survey research methods and theory and applied demography.

Tina Hannemann started her academic career with a MSc degree in Demography from Rostock University in Germany. During that time, she gained applied research experiences at the Max-Planck Institute for Demographic Research (MPIDR), assisting in the conduction and organisation of several research projects. 
Subsequently, she attended the European Doctoral School of Demography program in collaboration between the MPIDR and INED, the French National Institute for Demographic Studies in Paris, France. In 2008 she started a PhD at the Linneaus Centre for Economic Demography at Lund University, Sweden. Her PhD thesis investigated the impact of socio-economic differences on the onset of cardiovascular diseases across migration groups in Sweden. 
In 2013 she joined the European 7th Framework "Families and Societies" project to research the fertility and union formation and dissolution patterns among immigrant groups in the UK and across European countries in collaboration with the Families and Societies research group, which combined the expertise from researchers from all around Europe. 
In 2016, Tina Hannemann took a research position at the University of Manchester, first with the research group CoDE (Centre on Dynamics of Ethnicity) and later with a project from the National Centre of Research Methods (NCRM). The NCRM research project investigated the effectiveness of compensation methods for missing data in bio-marker datasets, with the help of a large-scale simulation study.
Since autumn 2018, she holds the post of Lecturer at the Department of Social Statistics at the University of Manchester, teaching undergraduate and postgraduate modules. From 2018 -2020 she co-organised the Cathie Marsh Centre (CMI) seminar series and contributed to the CMI short courses. 
Currently, she is the director of the MSc program in Social Research Methods and Statistics.

Tina Hannemann teaches a UG module in Theory and Methods of Demography (SOST30012) in collaboration with colleagues from the Social Statistics department. Furthermore, she convenes a PG module in Survey Research Methods (SOST60421) and contributes to a module on Demographic Forecasting for the MSc program in Social Research Methods and Statistics (SOST70102).

Furthermore, she is involved in short courses from the Cathy Marsh Institute by teaching introduction to programming with STATA and Theory and Method of Survival Analysis. She also developed and conducted several workshops on presentation skills and data visualisation.

In the past, Tina Hannemann’s research focused on demographic events in health and mortality and family formation and dissolution, and how they are influenced by ethnic minority status and socio-economical characteristics.

More recently she has worked on more methodological topics. She was involved in a NCRM project which investigated different compensation methods for missing data, specifically for bio-marker datasets. With more datasets expanding to biomarker information, the sensitive nature of that data often leads to higher missing information than traditional items in a social science questionnaire. Those missing information can bias the representativeness of the original sample and if ignored could lead to misleading results in the analysis of such incomplete data.

In general, Tina Hannemann is interested in many applied demographic topics, especially in the field of health and morbidity. She is further interested in the experience and causes for demographic behaviour among ethnic minorities and differences across different age groups.

Additionally, she is interested in the collection and management of large-scale data and bio-marker information. She is interested in statistically literacy and education of understanding and proper use of social science data in research and the public sector, including comprehensive data visualisation.