# Statistical models beyond linear regression - applied statistics for political scientists

### Course content

Political scientists are in high demand as analysts due to their ability to both understand societal questions and answer them using empirical observations. This course puts you in that category of job seekers. You acquire a set of transferable skills that are valuable, regardless of whether you contemplate a career as an academic or a policy analyst for a governmental body, NGO or in a consulting firm. The course also provides a toolkit for students looking use quantitative methods for their master’s thesis.

This is an applied methods course. I explain the statistical theory behind models, but the emphasis is on understanding when different models are useful, how to employ them and interpret the results. The course helps students 1) identify appropriate statistical models that describe different data types and 2) interpret these models in a meaningful way.

Many of the phenomena political scientists take interest in are best classified into categories or events. This includes outcome variables like voters’ choice of party, number of social media posts in a time span, or time between violent events. Furthermore, these events are often not independent from each other. Instead, they are nested in groups. Survey respondents come from several countries, media posts are published by specific social media users, and violent events take place as part of specific conflicts.

The course introduces students to a number of models that are specifically designed to describe the underlying phenomenon that generates such data, while possibly leveraging their nested structure. Our focus will be on observational data. The purpose is to help students gear the statistical analysis towards a realistic description of the data while answering questions of political interest.

Topics include

• introduction to R as a statistical software
• binary outcomes (binomial models)
• categorical outcomes (multinomial and ordered regression)
• count outcomes (poisson, negative binomial and hurdle models)
• event history data (survival models)
• hierarchical (nested) data (hierarchical/multilevel models)
• missing data (imputations)
Education

MSc in Political Science

MSc in Social Science

MSc in Security Risk Management

Bachelor in Political Science

Learning outcome

Knowledge:

Students will acquire an overview of typical data structures in political science and form a mental map over models designed for those structures. They will have an intuition of the statistical process that underpins these models, their assumptions (limitations) and what problem each model seeks to address.

Skills:

Students will obtain a mastery of R as a state-of-the-art software for data analysis. Exiting this course, they can boast hands-on experience with statistical analysis. They will also be able to understand and communicate their findings to policy makers and the wider public in an intuitive way.

Competences:

Students will be able to devise sound strategies for analyzing observational data for which linear models (OLS) is not appropriate. By reading, discussing and replicating research articles, they further have practical knowledge of how researchers use statistical methods to answer substantive political science questions.

We will divide our time between lectures and data labs in which students get to apply what they have learned theoretically by replicating extant research articles. This is a work-intensive class insofar as students are expected to do the readings for each class and work through examples in R.

Hermansen, Silje Synnove Lyder. Forthcoming/2023. Lær Deg R - En Innføring i Statistikkprogrammets Muligheter. 1st ed. Copenhagen: DJØF.

Ward, Michael D., and John S. Ahlquist. 2018. Maximum Likelihood for Social Science: Strategies for Analysis. Analytical Methods for Social Research. Cambridge: Cambridge University Press. https://doi.org/10.1017/9781316888544.

Methods III or equivalent statistics course that provides basic knowledge of linear modeling.

Prior knowledge of R is not a requirement, but students without this experience should prepare to invest time in the three first weeks of class to learn the program.

Oral
Continuous feedback during the course
Peer feedback (Students give each other feedback)

Feedback is also given throughout the semester on the basis of their work. Students will thus have the opportunity to improve on the portfolio on which their grade is based before handing it in. Specifically, students will provide and receive tips and comments that they can later incorporate in the own papers.

ECTS
7,5 ECTS
Type of assessment
Portfolio
Type of assessment details
Portfolio exam
Marking scale
Censorship form
No external censorship
##### Criteria for exam assessment
• Grade 12 is given for an outstanding performance: the student lives up to the course's goal description in an independent and convincing manner with no or few and minor shortcomings
• Grade 7 is given for a good performance: the student is confidently able to live up to the goal description, albeit with several shortcomings
• Grade 02 is given for an adequate performance: the minimum acceptable performance in which the student is only able to live up to the goal description in an insecure and incomplete manner

Single subject courses (day)

• Category
• Hours
• Class Instruction
• 28
• English
• 28

### Kursusinformation

Language
English
Course number
ASTK18414U
ECTS
7,5 ECTS
Programme level
Full Degree Master
Bachelor
Duration

1 semester

Placement
Spring
Studyboard
Department of Political Science, Study Council
##### Contracting department
• Department of Political Science
##### Contracting faculty
• Faculty of Social Sciences
##### Course Coordinator
• Silje Synnøve Lyder Hermansen   (15-766c6f6d68316b6875706471766871436c6976316e7831676e)
Saved on the 31-10-2022

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