28 May Vulnerable Groups, Fieldwork & Missing Data: a qualitative and quantitative approach
9:30 am - 4:00 pm
methodology seminar | level: beginner
registrations and venue via KU Leuven
In this colloquium, both a qualitative and quantitative prospective on vulnerable groups, fieldwork & missing data will be presented. In the morning session, the discussion will focus on the qualitative approach to vulnerable groups research and the challenges to conduct field work for researchers. In the afternoon, the focus will switch on how to deal with data that are incompletely recorded by illustrating sensitivity analysis.
9:00 – 9:30 : Welcome & coffee refreshment
9:30-11:00: "Qualitative researching with vulnerable groups."
Dr. Ana Milosevic, (Leuven International and European Studies (LINES))
- Interviewing techniques focusing on sensitive & vulnerable groups;
- How to tackle GDPR in interviewing these groups.
11:00 – 11:30: Coffee break
11:30-13:00: "Fieldwork and the researcher."
Dr. Andrea Felicetti (Centre for Political Research, KU Leuven)
Fieldwork can be a uniquely rich source of insight for social science research. However, fieldwork also presents researchers with great challenges. In this class we will explore some challenges that all social scientist doing fieldwork face. In particular, we will explore the idea of positionality, multiple identities and power issues. Despite being often undetected, these aspects deeply shape fieldwork research. We will see how researchers can be aware of these issues, manage them and understand how they impact their studies.
13:00 – 14:00: Lunch break
14:00 – 16:00: "Analysis, Sensitivity, and Sensitivity Analysis."
Prof. Geert Molenberghs (Interuniversity Institute for Biostatistics and statistical Bioinformatics, UHasselt & KULeuven)
Statistical analysis often extends beyond the data available. This is especially true when data are incompletely recorded because both ad-hoc as well as model-based approaches are rooted, not only in the observed data and the mechanism governing missingness, but also in the unobserved given the observed data. Other instances of this phenomenon include but are not limited to censored time-to-event data, random-effects models, and latent-class approaches. One needs to be aware of: (1) changes in results and intuition relative to complete-data analysis; (2) the assumptions under which such approaches are valid; (3) the sensitivities implied by departures; and (4) in response to these, what sensitivity analysis avenues are available. This presentation provides a bird's eye perspective on these. Some of the developments are illustrated using real data.
Van den Heuvelinstituut (VHI)
Aula Gaston Eyskens (00.10)
The colloquium is organized in an auditorium, so participants NEED TO BRING THEIR OWN LAPTOP (in case you wish to use it).
No prerequisite for the morning sessions, but a fair knowledge of statistical methods is suggested for the afternoon part.
PhD's or postdocs of a Flemish university: free of charge
Other academics: 60 €
Non-profit/Public sector: 100 €
Private sector: 200 €
For the afternoon session "Analysis, Sensitivity, and Sensitivity Analysis":
Little, R., D'Agostino, R., Cohen, M.L., Dickersin, K., Emerson, S., Farrar, J.T., Frangakis, C., Hogan, J.W., Molenberghs, G., Murphy, S.A., Neaton, z.D., Rotnitzky, A., Scharfstein, D., Shih, W., Siegel, J.P., and Stern, H. (2012). The Prevention and Treatment of Missing Data in Clinical Studies. New England Journal of Medicine, 367, 1355-1360.
Little, R.J.A., D'Agostino, R., Dickersin, K., Emerson, S.S., Farrar, J.T., Frangakis, C., Hogan, J.W., Molenberghs, G., Murphy, S.A., Neaton, J.D., Rotnitzky, A., Scharfstein, D., Shih, W., Siegel, J.P., and Stern, H. National Research Council (2010). The Prevention and Treatment of Missing Data in Clinical Trials. Panel on Handling Missing Data in Clinical Trials. Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, D.C.: The National Academies Press.
Molenberghs, G. (2009). Incomplete data in clinical studies: Analysis, sensitivity, and sensitivity analysis (with discussion). Drug Information Journal, 43, 409-446.
Molenberghs, G. and Kenward, M.G. (2007). Missing Data in Clinical Studies. New York: John Wiley.
Molenberghs, G. and Verbeke, G. (2005). Models for Discrete Longitudinal Data. New York: Springer.
Verbeke, G. and Molenberghs, G. (2000). Linear Mixed Models for Longitudinal Data. New York: Springer.
Dr. Ana Milosevic, Dr. Andrea Felicetti, prof. Dr. Geert Molenberghs