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.

UV9203 Survey Methodology is the PhD-level version of MAE4053 Survey Methodology, a course within the master's program, Assessment Measurement and Evaluation. The content, schedule and reading list for UV9203 Survey Methodology are the same as for MAE4053 Survey Methodology.

Learning outcome

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 non-ignorable 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

There is a limited number of seats due to joint teaching with the master’s level version of the course.

PhD candidates at the Faculty of Educational Sciences will be given priority, but it is also possible for others to apply for the course.

The deadline for registration is on the corresponding semester page for the course. 

Candidates admitted to a PhD-program at the Faculty of Educational Sciences (UV) can apply in StudentWeb.

Other applicants can apply by filling out and sending in a electronic registration form, which is found on the corresponding semester page for the course. 

Formal prerequisite knowledge

Basic knowledge of the statistical programming environment R is required.

Prior knowledge to 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 course has joint teaching with the master course MAE4053 Survey Methodology.

Schedule and literature: Please see the applicable semester page for the course. 

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 in order 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 pass/fail scale. Read more about the grading system.

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 23, 2024 3:35:17 AM

Facts about this course

Level
PhD
Credits
3
Teaching
Autumn
Examination
Autumn
Teaching language
English

Contact

Centre for Educational Measurement