Schedule, syllabus and examination date

Course content

In this course, you will learn the core principles of survey design and survey methodology. Surveys usually aim to draw a representative sample from a specified target population with a purpose, for example, to make comparisons between groups or estimate trends in the population.

This course introduces surveys and their different aims, sampling designs and survey error types, sampling weights, and missing data, and methods to handle particular survey characteristics. The course covers both methodological considerations and substantive research implications.

Learning outcome

Upon completion of the course, you:

Knowledge?

  • demonstrate an understanding of the central principles in survey design;?
  • understand how and why surveys are applied in the social sciences;?
  • recognize the differences between different sampling methods, and reflect on their suitable use;?
  • delineate between different types of missing data principles: missing completely at random, missing at random, and nonignorable missing;
  • identify factors that indicate high survey quality.

Skills?

  • conduct statistical analyses with survey data to estimate population parameters while accounting for the survey design (e.g., the use of sampling weights);?
  • use multiple imputation to handle missing data in the statistical environment R.

Competencies?

  • critically read and interpret results from survey reports and secondary analyses of survey data;?
  • Identify and develop a systematic survey approach for given research questions.

Admission to the course

Students who are admitted to study programmes at UiO must each semester register which courses and exams they wish to sign up for?in Studentweb.

If you are not already enrolled as a student at UiO, please see our information about?admission requirements and procedures.

All students in the Assessment, Measurement and Evaluation Master program have equal access to the course. Qualified students from other UiO master's programmes?may be considered if there is capacity. Contact studentinfo@cemo.uio.no if you want to apply for a place in the course. If you are unsure of whether or not you have?sufficient prior knowledge, please let us know about your background and previous courses you have taken.

Formal prerequisite knowledge

Basic knowledge of the statistical programming environment R is required.

MAE4000 Data Science or equivalent.

Overlapping courses

Teaching

This course combines lectures, seminars with group work, and computer labs with data analysis tasks in the R environment.

The proposal for the exam paper has to be submitted and approved to qualify for taking the exam. Once qualified for participating in the exam, you retain this qualification for the next two times the course is offered.

Examination

The exam consists of an individually written paper covering the course contents. The paper should be between 2000 and 2500 words, not including the bibliography and appendices.

Before submitting the exam paper, you are required to hand in a paper proposal as a written assignment. You will be receiving written feedback on your proposal to help improve the quality of the final exam paper. The proposal for the exam paper has to be submitted and approved to qualify for taking the exam.

Language of examination

The examination text is given in English, and you submit your response in English.

Grading scale

Grades are awarded on a scale from A to F, where A is the best grade and F?is a fail. Read more about?the grading system.

The grading bases solely on the final assignment.

Resit an examination

More about examinations at UiO

You will find further guides and resources at the web page on examinations at UiO.

Last updated from FS (Common Student System) May 3, 2024 8:14:17 AM

Facts about this course

Level
Master
Credits
5
Teaching
Autumn
Examination
Autumn
Teaching language
English

Contact

Centre for Educational Measurement