Quantitative methods for public policy

Study Board of Political Science, Journalism, Sociology, and European Studies

Teaching language: English
EKA: B450024112, B450024102
Censorship: Second examiner: None, Second examiner: Internal
Grading: Pass/Fail, 7-point grading scale
Offered in: Odense
Offered in: Autumn
Level: Master

Course ID: B450024101
ECTS value: 10

Date of Approval: 17-03-2020


Duration: 1 semester

Course ID

B450024101

Course Title

Quantitative methods for public policy

Teaching language

English

ECTS value

10

Responsible study board

Study Board of Political Science, Journalism, Sociology, and European Studies

Date of Approval

17-03-2020

Course Responsible

Name Email Department
Melike Wulfgramm wulfgramm@sam.sdu.dk Danish Centre for Welfare Studies

Offered in

Odense

Level

Master

Offered in

Autumn

Duration

1 semester

Recommended prerequisites

General knowledge of research design benefits the successful completion of this course. This course requires collaborative skills for group work, academic writing and general computer skills. 

Aim and purpose

The course offers an introduction to quantitative methods for social scientists required to carry out and interpret analyses of quantitative datasets for public policy. This methodological course is designed to introduce core concepts in applied statistics for social scientists and enable students to use statistical software. The sessions are set up to build a core understanding of the concepts and tool-set that can be used (and further extended) in applied statistical analysis. Hence, students working with data and adopting a quantitative analysis for their Master’s thesis or larger research projects will gain training in working on such projects. Furthermore, the course also equips students to interpret and critically assess empirical research that applies quantitative estimation methods. Accordingly, the aim of the course is to make students acquainted with: the possibilities and limitations of quantitative statistical analysis, the quantities of interest in basic statistical analyses, the interpretation of the estimated quantities, and the links between substantive theory and transposition into statistical analysis.

Connection with other courses: By providing students with the basic instruments of statistical analysis, the course is fulfilling a dual aim. On the one hand, students will be able to perform their own analyses for tasks required in other courses or for their own Master thesis. On the other hand, students will be able to understand the arguments, empirical methods and evidence in research papers used for other courses.

Labour market relevance: This course aims at students that would like to work in governmental and non-governmental positions in which they need to form an opinion about and decide on the implementation and modification of specific instruments on the grounds of statistical information and quantitative analyses provided by experts. Furthermore, the course provides an academic base for students that want to pursue a scientific career by providing them with the skills to conduct quantitative research for academic audiences and decision-makers.

Content

The central subject-related topics discussed:

  • Descriptive statistics
  • Statistical inference
  • Analysis of association
  • Hypothesis testing and uncertainty
  • Bivariate regression analysis
  • Multivariate regression analysis
  • Assumptions of OLS and regression diagnostics
  • Conditional relationships: using interactions
  • Models for binary outcomes
This course offers a combination of theory of quantitative methods and its practical application. Topics will first be introduced in lectures with focus on the statistical theory and applied examples. Subsequently, students will apply the introduced statistical methods to analyze real world datasets in lab sessions, using the statistical software program STATA.

Learning goals

To meet the goal of the course, students at the end of the course should have:

Description of outcome - Knowledge

Knowledge that enables students to:

  • understand the principles of statistical analysis
  • discuss and understand core quantities and concepts in statistical analysis
  • interpret results from statistical analysis

Description of outcome - Skills

Skills that enables students to:

  • analyze and critically assess scientific articles using the statistical methods taught in the course
  • find and select suitable data for their own research projects
  • select the appropriate methods and statistical models for their research questions assessed in a quantitative manner

Description of outcome - Competences

Competences that enables students to:

  • comfortably use statistical software for their analyses

Literature

Example

  • Agresti, Alan(latest edition). Statistical Methods for the Social Sciences.
  • Sønderskov, Kim Mannemar (latest edition). Stata: A Practical Introduction. Hans Reitzel Forlag. 

The textbooks stated are preliminary and may be changed as specified in the syllabus.

In addition to the textbooks above, social science articles (on average 30 pages) that use the method(s) discussed at the particular meeting, or specialized methodological articles will be incorporated into the syllabus. The empirical applications will focus on social policy research.

Overall, the reading adds up to around 1200 pages.

Teaching Method

The course consists of a mix of interactive lectures and exercise sessions in which students apply the presented analytical concepts to datasets, using the statistical software STATA. There will be a mid-term exam and a final exam. 

Workload

Scheduled workload:
15 x 2 hours weekly 

A 10 ECTS course entails a total workload of 270 hours. These are divided between different learning activities and below follows an estimation for the average student:

Face-to-face lectures:                                         30 hours (15 sessions of 2 hours each) 
Exercise classes:                                                20 hours (10 sessions of 2 hours each)
Preparation for lectures and exercise classes: 131 hours
Blended lessons:                                                  8 hours
Mid-term exam:                                                  30 hours
Preparation for final exam:                                 50 hours
Final exam:                                                           1 hours
Total:                                                                 270 hours

Examination regulations

Exam

Name

Exam

Timing

Midterm exam (exam part 1)

Exam: during the semester. 

Re-exam: during the semester. 

Exam (exam part 2)

Exam: January

Re-exam: February

Tests

Midterm exam (exam part 1)

Name

Midterm exam (exam part 1)

Form of examination

Take-home assignment

Censorship

Second examiner: None

Grading

Pass/Fail

Identification

Student Identification Card - Exam number

Language

English

Duration

5 days.

Length

The assignment must be maximum 7 pages (each 2,400 key strokes) including spaces, notes and appendixes but excluding title (cover page), table of content, reference list and computer code.

Examination aids

All exam aids are allowed. 

Assignment handin

Hand-in via Digital Exam. 

ECTS value

1

Additional information

The midterm exam (take-home assignment) is a practical one using all concepts covered until the assignment is published in an applied setting. Students are required to use the data supplied with the assignment to answer and interpret the questions. Students are expected to send along the computer code used to carry out the analysis. The midterm exam (take-home assignment) is carried out by the students individually, and is evaluated with pass/fail.

After the deadline, the take-home assignment will be discussed and solved in class in order to prepare for the oral exam.

Re-exam: Students who fail to pass the midterm exam will have a second attempt at passing it during the semester, by resubmitting a revised version of the assignment. Students do not need to register for the second attempt, because their handing-in of the revised paper is the equivalent of registration for the second attempt.

EKA

B450024112

Exam (exam part 2)

Name

Exam (exam part 2)

Form of examination

Oral examination with preparation

Censorship

Second examiner: Internal

Grading

7-point grading scale

Identification

Student Identification Card - Date of birth

Language

English

Preparation

20 minutes.

Duration

20 minutes.

Examination aids

No exam aids allowed.

ECTS value

9

Additional information

Form of examination at the re-exam can be changed.

EKA

B450024102

External comment

New course. 

NOTE - This course is identical with the former course Quantitative methods (97014401 & B450000101).

Used examination attempts in the former identical course will be transferred.
Courses that are identical with former courses that are passed according to applied rules cannot be retaken.

Courses offered

Offer period Offer type Profile Education Semester

Teachers

Name Email Department City
Jonas Havstein Eriksen jeri@sam.sdu.dk Danish Centre for Welfare Studies
Melike Wulfgramm wulfgramm@sam.sdu.dk Danish Centre for Welfare Studies Odense

URL for Skemaplan

Odense
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