Advanced Quantitative Data Analysis

Course content

On this course, the students are introduced to advanced statistical techniques that help them analyse complex data or answer causal questions. A specific feature of advanced quantitative analyses is that they deal with at least one of three challenges. Firstly, contemporary data often entail complex relations between observations that differ from how traditional surveys are set up. Examples are network data, spatial data, data of students who visit the same school or data with repeated observations of the same unit over time. Such relations between observations make the data more relevant and interesting from a sociological point of view, but violate core assumptions of standard statistical techniques taught on the Bachelor’s degree programme. Secondly, contemporary data, especially digital data, frequently come in semi-structured or unstructured formats unlike standardised survey or administrative data. This is especially true of natural-language text data, such as blog posts or newspaper articles. At the same time, the huge quantities of such data make hand-coding impractical. Thirdly, advanced quantitative analyses often aim to test causal claims entailed in sociological theories, which requires more sophisticated statistical techniques than the analyses of associations taught in the Bachelor’s degree programme.

 

The Advanced Quantitative Data Analysis course puts varying focus on one of these three challenges and introduces students to topics such as network analysis, spatial regression, multilevel modelling, panel data analysis, quantitative text analysis or causal identification strategies. As an important element, students learn to apply these techniques by analysing several different datasets in practical exercises throughout the semester. Gaining facility with handling these data is just as much a goal of the course as learning the advanced statistical techniques themselves.

Education

Mandatory MA course 1. Semester

 

The course is closed for credit- and exchange students.

Learning outcome

On completion of the course, students will be able to:

Knowledge

  • account for different types of complex data.
  • account for methodologies that can be applied to complex data.
  • account for the relevance of these data and methods in sociological analyses.
  • account for the relation to other types of methodologies, including qualitative methods.
  • account for the prerequisites for what constitutes a causal connection.

 

Skills

  • handle complex data.
  • use specialised quantitative methods to analyse complex data, and also justify the choice of methodologies in relation to specific data types.
  • apply specialised quantitative methodologies to perform causal analyses.
  • reflect on the possibilities and limitations associated with the application of advanced methodologies for data analysis and causal conclusions in sociological research.
  • interpret and communicate the results/output of such analyses in relation to a given problem.

 

Competencies

  • evaluate critically and reflect on their empirical analysis in relation to a given problem in a way that demonstrates their understanding of the possibilities and limitations of the methodologies used, and
  • read and relate critically to sociological research literature that analyses complex data using quantitative methodologies.
  • translate and transfer their knowledge and skills for research and advisory purposes by being able to plan and perform analyses involving complex data.
  • understand and assess the use of the specific data analysis tools in other sociological studies and advise stakeholders wanting to use a plurality of complex data and methodologies.

type of instruction is a combination of conventional lectures and practical exercises in standard statistical software, such as R or Stata. In connection with selected exercises, learning activities are organised where the students provide feedback to each other on their performance of the exercises. In addition, the students are asked to prepare feedback on another student’s performance. The feedback is provided in groups of three, where one student provides feedback, another receives the feedback and a third notes the content of the feedback and the recipient’s response (feedback triads).

Readings are comprised of peer-reviewed journal articles and one basic textbook.

Students should have a solid understanding of statistics, especially linear regression, as taught in the three statistics courses of the BA program, such as linear regression.

Peer feedback (Students give each other feedback)

We will systematically use student peer-feedback on the three research proposals.

ECTS
7,5 ECTS
Type of assessment
Portfolio, -
Type of assessment details
portfolio assignment, individually or in groups of max. four students.
A portfolio assignment is defined as a series of short assignments that address one or more set questions. The exam is based on the course syllabus. The assignments can be written as the course progresses. Provided students submit their assignments by the stipulated deadlines, feedback is offered during the course. Assignments can be reworked on the basis of the feedback. All of the assignments are submitted together for assessment at the end of the course.
The scope of the combined portfolio assignments is a maximum of 10 pages. For group assignments, an extra 5 pages are added per additional student.
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Criteria for exam assessment

Please see learning outcome

  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 78
  • Project work
  • 100
  • English
  • 206

Kursusinformation

Language
English
Course number
ASOK22103U
ECTS
7,5 ECTS
Programme level
Full Degree Master
Duration

1 semester

Placement
Autumn
Schedulegroup
See timetable.
Capacity
Vejl 40 personer.
Studyboard
Department of Sociology, Study Council
Contracting department
  • Department of Sociology
Contracting faculty
  • Faculty of Social Sciences
Course Coordinators
  • Merlin Schaeffer   (4-7068766643767266316e7831676e)
  • Mengni Chen   (4-7068666b43767266316e7831676e)
Teacher

Merlin Schaeffer, e-mail: mesc@soc.ku.dk
Mengni Chen, e-mail: mech@soc.ku.dk

Saved on the 25-04-2022

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