Demographic analysis for Social Scientists

Course content

Topics in demographic analysis relevant for social scientists include population rates, standardization, decomposition, life tables, event history (survival) analysis, cohort analysis, and demographic data sources. This course will provide you with a basic introduction to these core concepts and basic methods. You can use the concepts and methods thought in this course to study how gender, age, race, family origin, and historical or cultural contexts shape individual lives and opportunities. 


In this course we will cover classic topics including fertility, mortality, and migration, as well as the extensions into fields such as aging, families, and health. You will learn how fertility and mortality rates, lifetables, and life expectancy are calculated.  We will also work with analytical approaches and empirical applications commonly used in social demography including an introduction to event history techniques and cohort analysis.


Traditionally, demographic analysis uses quantitative research methods, including the analysis of survey and register data. Still, ethnographic and other qualitative approaches are increasingly being incorporated in contemporary demographic analyses and in particular so when employed by social scientists. Recently, big data and data science perspectives have also started to emerge in the demographic field. Due to the introductory nature of this course, we will focus on traditional quantitative approaches. However, when relevant we may discuss additional perspectives pertaining to qualitative research strategies and results or new types of knowledge as generated by insights offered by big data and data science.


Elective Course

Course package (MSc 2015)

Welfare, inequality and mobility


Learning outcome


  • explain key concepts used in demographic research
  • formulate analytical perspectives in the study of a given human population
  • explain basic methods used in social demographic and population analysis



  • argue the choice of a given method or model within the realm of demographic and population analysis.
  • Apply the most common analytical tools and calculate basic models used in demographic and population analysis
  • interpret results from basic models used in demographic and population research



  • translate demographic concepts to empirically based arguments

efficiently communicate the acquired knowledge and apply it in the formulation of own research questions and analyses

Lectures, class discussions, exercises, short written assignments, and student presentations.
You are expected to contribute actively to class discussions, solve exercises diligently, and identify analytical questions and demonstrate your ability to critically assess and analyze theoretical arguments and empirical data based on the readings and the examples presented and discussed in class.

Readings are comprised of book chapters, peer-reviewed journal articles, and research notes.

This course is introductory. Still, it will be an advantage to have a basic understanding of quantitative methodology.

If you are uncertain whether you have the requisites required to successfully complete this course, you are welcome to contact Lisbeth Loft via e-mail (

This course will be taught in English

Continuous feedback during the course of the semester

Continuous feedback during the course

7,5 ECTS
Type of assessment
Individual or group.
A portfolio assignment is defined as a series of short assignments during the course that address one or more set questions and feedback is offered during the course. All of the assignments are submitted together for assessment at the end of the course. The portfolio assignments must be no longer than 10 pages. For group assignments, an extra 5 pages is added per additional student. Further details for this exam form can be found in the Curriculum and in the General Guide to Examinations at KUnet.
Marking scale
7-point grading scale
Censorship form
No external censorship
Criteria for exam assessment

Please see the learning outcome

  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 148
  • Exam
  • 30
  • English
  • 206