Download SINGS educational activities fall semester 2017

SINGS educational activities fall semester 2017
During fall semester 2017 the following educational activities will be arranged within SINGS. There is
one core course and one elective course. Costs for travel and lodging will be covered for SINGS
people outside the Stockholm area, and will be arranged by SINGS academic administrator Johanna
Bergman.
SINGS Core course
Please note that this is a core course. Reasons for non-attendance are specific aspects such as illness
or attending a conference. If you are prevented from attending, state the reason not being able to
attend and include a confirmation letter from your supervisor.
“Longitudinal research methods: panel, growth curve, and sequence analysis”
Number of higher education credits: 1.5 hp
Dates: November 6–10, 2017
Venue: Karolinska Institutet (KI), Stockholm
Content: This course serves as an introduction to longitudinal research methods used in registerbased research. More specifically, we will cover regression analysis of panel (repeated measures)
data, growth curve analysis, and sequence analysis. Longitudinal data, in which subjects are
measured at multiple time points, are common both in the social and the medical sciences.
The many uses of such data include controlling for unobserved confounding, and analyzing change
over time and the ways in which individual life course trajectories unfold. These features make
longitudinal data an indispensable resource for answering a multitude of questions central to both
the social and the medical sciences.
We will discuss the nature of longitudinal data and how the above-mentioned methods can be used
to answer different types of research questions, demonstrate the use of these methods and practice
their use within a specified research question. Both continuous and discrete outcome variables are
discussed. The statistical software used in the lectures and labs is Stata.
Teachers:
Juho Härkönen (course leader), PhD, Professor of Sociology, Demography Unit, Department of
Sociology, Stockholm University
Karin Modig (course director), PhD, Research Associate, Institute of Environmental Medicine, Unit of
Epidemiology, KI
Sven Drefahl, PhD, Researcher, Demography Unit, Department of Sociology, Stockholm University
and Institute of Environmental Medicine, KI
Christian Brzinsky-Fay, PhD, Research Fellow, Research Unit Skill Formation and Labor Markets, WZB
Berlin Social Science Center, Berlin, Germany
SINGS Elective course
This course can be applied through the KI course catalogue for autumn 2017, which will be open April
13 – May 15. In the course application (motivation for attending the course) you should state that
you are a SINGS student.
“Design and analysis of twin and family-based studies”
Number of higher education credits: 1.5 hp
Dates: October 23–27, 2017
Venue: Karolinska Institutet (KI), Stockholm
Content: This course is relevant for doctoral students in the fields of epidemiology, public health,
quantitative sociology, demography, psychology, statistics, health economics, and other medical and
social sciences.
The aim of empirical research is often to estimate the causal effect of a particular exposure on a
particular outcome. A complicating feature of observational studies is that the exposure-outcome
association is typically confounded, and cannot be given a causal interpretation. The standard
approach to deal with confounding is to control for confounders in the analysis, e.g. by regression
modeling. However, many confounders may be difficult to measure, or unknown to the investigator.
An appealing solution is to study within-family associations, which are automatically controlled for all
factors that are shared within the family (e.g. socioeconomic status, genetic factors). In this course
we will focus on the theory and practice of within-family analyses.
In many studies, the research question is to what extent a phenotype is caused by genetic factors.
Frequently though, there may be no obvious candidate gene, and financial limitations may prohibit a
genome wide scan. An appealing solution is to study whether the phenotype tends to run in families;
the stronger genetic influence, the larger familial heredity. A commonly used methodology to
estimate the fraction of variation in an outcome which may be attributable to genes and
environment is the classic twin methodology. In this course we will cover the concept of heritability,
its underlying assumptions, and applications in the classic twin method.
Teachers:
Ralf Kuja-Halkola, PhD, Department of Medical Epidemiology and Biostatistics, KI
Arvid Sjölander, PhD, Associate Professor, Department of Medical Epidemiology and Biostatistics, KI
Paul Lichtenstein, PhD, Professor, Department of Medical Epidemiology and Biostatistics, KI
Brian D’Onofrio, PhD, Professor, Department of Psychological and Brain Sciences, Indiana University,
USA