Topics in Digital and Computational Demography
International Max Planck Research School for Population, Health and Data Science (IMPRS-PHDS)
International Advanced Studies in Demography
Start date: 8 November 2021
End date: 12 November 2021
- Diego Alburez-Gutierrez
- Jisu Kim
- Sophie Lohmann
- Daniela Perrotta
- Emilio Zagheni
Location: Online Course. Link tba.
Rapid increases in computational power and the explosion of Internet, social media and mobile phone use have radically changed our lives, the way we interact with each other and our behavior, including demographic choices and constraints. The digitalization of our lives has also led to the so-called “data revolution” that is transforming the social sciences.
Data science tools offer social scientists the opportunity to address core demographic questions in new ways. At the same time, demographic and social science methods enable researchers to make sense of new and complex data sources for which novel approaches and research designs may be needed.
The main goals for this course are:
- To introduce students to core demographic and social science methods that are essential to interpret digital trace data;
- To introduce students to core data science methods that are key to advance our understanding of population processes in the context of the increasing heterogeneity of data sources useful for demographic research.
- To introduce students to recent substantive advances in the field of Digital and Computational Demography, with emphasis on fostering critical thinking about modern demographic analysis and (big) data-driven discovery.
- To help students identify research questions in their own area of substantive interest that could be addressed with innovative data sources, and support them in the process of devising an appropriate research plan.
The course will be offered online. Each day, there will be one lecture and one discussion session. The lecture will be pre-recorded and made available ahead of time. Students are expected to watch the lecture carefully at their own pace and to complete the assignments before the discussion session, which will be held live every day from 16:00-17:30 CET (Central European Time). During the discussion session, homework assignments including hands-on computing exercises will be reviewed, assigned readings will be discussed and questions about the lecture will be addressed. Active participation of students is expected.
Each day, the lecture and discussion session will be presented by an experienced scholar in the field who will focus on a relevant research topic in which s/he is an expert.
In general students should expect to spend about 6-8 hours per day on the course (lectures, discussion sessions, readings, assignments).
Day 1 (Nov. 8th)
Instructor: Emilio Zagheni
Topics: Welcome and Introduction; Statistical approaches for combining representative data and non-probabilistic samples; Identifying sources of bias in digital trace data and adjusting for them; Collecting data from Advertisement platforms
Day 2 (Nov. 9th)
Instructor: Daniela Perrotta
Topics: Online surveys; Survey participant recruitment via social media; addressing biases in online surveys
Day 3 (Nov. 10th)
Instructor: Sophie Lohmann
Topics: The impact of social media use on health and well-being
Day 4 (Nov. 11th)
Instructor: Diego Alburez-Gutierrez
Topics: Harnessing microsimulations to study demographic processes and kinship dynamics; Potential and drawbacks of crowd-sourced Big Data: the case of online genealogies
Day 5 (Nov. 12th)
Instructor: Jisu Kim
Topics: Using social media data for migration research; Accessing Twitter data via the Academic Twitter API
Diversity of Student Backgrounds
Students in this course have different backgrounds. Some students may have strong computational and statistical skills, some others may not. Some students may be very familiar with demographic methods, some others may only have basic knowledge of population processes. To accommodate the range of backgrounds, the instructors will emphasize substance, and key statistical, mathematical, computational and demographic concepts. There will also be different types of homework assignments. Some of them will involve computing and coding. Some others may involve critical reflections about the readings. In short, we will facilitate the participation of students who do not have extensive background in statistics, or computational methods, but are eager to learn.
Students should be familiar with programming with R/RStudio, Python (Anaconda), or an equivalent programming environment. Homework assignments that require programming can be completed using the programming environment of your choice. Solutions to the assignments will be discussed using R/RStudio or Python (Anaconda).
Instructions on how to download and install R can be found in “A (very) short introduction to R” by Torfs and Brauer (2014):
Python (Anaconda) can be downloaded from the following webpage:
Students will receive a pass/fail grade based on completion of assignments, active participation in class, and/or a multiple-choice final quiz.
There is no tuition fee for this course.
Recruitment of students external to the IMPRS-PHDS network
Applicants should either be enrolled in a PhD program (those well on their way to completion will be favored) or have received their PhD. Applications from advanced master’s students will also be considered.
The selection will be made by the MPIDR based on the applicants’ scientific qualifications.
How to apply
- Applications have to be submitted online via https://www.demogr.mpg.de/go/idem187.
- You will need to attach the following items integrated in *a single pdf file*:
- (1) Curriculum vitae, including a list of your scholarly publications.
- (2) A one-page statement of your research and how it relates to the course. Please include a short description of your knowledge of the programming language R and/or Python.
- Application deadline is 10 October 2021.
- Applicants will be informed of their acceptance by 26 October 2021.
- Applications submitted after the deadline will be considered only if logistically feasible.