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This is the study programme for 2020/2021.


The aim of this course is to provide PhD researchers with the necessary skills to produce publishable quantitative analyses. The course covers basic programming in R, as well as key topics in statistical modelling and inference. The course emphasizes careful identification of causal effects and proper modelling of different data generating processes, although the main focus will be on basic linear and generalized linear models. Rather than covering all techniques that could be relevant to a PhD project, the goal is to provide a set of skills that enable researchers to identify and apply more specific methods on their own.

Learning outcome

Knowledge
After completing the course, participants should have a basic understanding of:
  • Programming in R, including loops and functions
  • Frequentist estimator properties and the role of Monte Carlo simulations
  • How Bayesian inference differs from frequentist approaches
  • How missing data may bias quantitative analyses
  • Generalized linear models and relevant link functions
  • Different data structures (e.g. serial and hierarchical) and how they can be modelled
  • Different strategies for identifying causal effects

Competence
After completing the course, participants should be able to:
  • Load, merge and transform data in R
  • Specify and diagnose various quantitative models in R
  • Handle missing data in R
  • Produce publication-ready tables and figures in R
  • Identify, install, and use R packages of relevance to their own research

General Skills
After completing the course, participants should be able to:
  • Interpret and critically evaluate a variety of quantitative analyses
  • Identify the key assumptions of a given statistical model
  • Identify the key assumptions of a given causal identification strategy

Contents

The course covers both foundational knowledges of statistics and practical skills in data analysis. The first part of the course will give an introduction to programming and visualization in R, and use simulations to illustrate statistical concepts. The remaining parts of the course will focus on applied data analysis, and how to specify and diagnose models for different types of data. Because a key goal of the course is to impart practical skills in data analysis, lectures will be mixed with seminars and applied exercises.

Required prerequisite knowledge

Participants must be enrolled in a PhD programme. They should also have basic knowledge of quantitative methods equivalent to having completed a relevant Master’s level course.

Recommended previous knowledge

It is mandatory that the students have basic knowledge of quantitative methods at Master Level.

Exam

Weight Duration Marks Aid
Paper (6-10 pages + complete R code)1/1 Pass - Fail
•Presence and active participation throughout the whole week.
• A one-page note on a tentative topic for the course paper, outlining potential data sources and methods. This note must be submitted one week in advance of the course.
• An individual presentation at the end of the course week, elaborating on the previously submitted note and outlining the course paper.
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Coursework requirements

Each participant will write a short paper on a self-chosen topic. This task includes picking an answerable question, finding relevant data, and conducting an appropriate quantitative analysis. Complete R codes leading from original data to final results should be included as an appendix. In so far as possible, participants are asked to pick a topic of relevance to their ongoing research. The papers should be submitted within six weeks after the course has finished.

Course teacher(s)

Course coordinator
Jørgen Bølstad

Method of work

The course will consist of one intensive week of lectures and applied seminars, Monday through Friday, from 10.15 to 15.00. Participants are expected to review lecture materials on their own.
The relevant software (R and Rstudio) is free and open source. Participants will need to bring well-functioning laptops on which the software can be installed. Participants who use laptops supplied by their university should install the software in advance (and contact IT for administrator rights if necessary).

Open to

PhD candidates enrolled in PhD programmes at the University of Stavanger or accredited universities/university colleges in Norway or abroad.

Single Course Admission to PhD-Courses.

Course assessment

Evalutation according to UiS rules and regulations.

Literature


Readings list


This is the study programme for 2020/2021.

Sist oppdatert: 20.10.2020