STATISTICAL MODEL FOR UNDERSTANDING POLITICS: INTRODUCTION TO ECONOMETRICS - 2020/1
Module code: POL2045
In light of the Covid-19 pandemic, and in a departure from previous academic years and previously published information, the University has had to change the delivery (and in some cases the content) of its programmes, together with certain University services and facilities for the academic year 2020/21.
These changes include the implementation of a hybrid teaching approach during 2020/21. Detailed information on all changes is available at: https://www.surrey.ac.uk/coronavirus/course-changes. This webpage sets out information relating to general University changes, and will also direct you to consider additional specific information relating to your chosen programme.
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Econometrics is a research tool that involves the application of statistical methods for the analysis and critical evaluation of datasets. Econometrics is used by social scientists, particularly politics, economics, sociology, in order to explore competing hypotheses and generate predictions applied to real world puzzles. This module is designed for Level 5 undergraduate students specializing in politics and economics who want to work with quantitative data, gain a substantive understanding of the theory and practice of econometrics, learn how to use statistical inference and econometric analysis, and build awareness and expertise in testing hypotheses relying on evidence-based research. Understanding econometrics is recognized by economists and quantitative political scientists as an essential skill, which allows students to obtain an understanding and appreciation of econometric research applications, develop the ability to critically evaluate existing econometric research and policy analysis, and gain the skills to identify shortcomings and problems with studies and assess the confidence that should be placed in the proposed recommendations and conclusions.
NEZI Spyridoula (Politics)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 5
JACs code: L140
Module cap (Maximum number of students): N/A
Prerequisites / Co-requisites
The course consists of lectures and small-group seminars organized as labs. The lectures provide an introduction to causal inference, quantitative research design, descriptive statistics, probabilities and the normal distribution, hypothesis testing, bivariate regression, multiple regression, problems in multiple regression, binary choice models and a brief overview of more advanced topics. The students will be provided with a dataset that will be used along the course in the seminars or lab sessions where the statistical software R is available. The course assessment will be based on a research design (10% of total mark – 1000 words) and two statistical assignments (both 1500 words and 45% of total mark).
|Assessment type||Unit of assessment||Weighting|
|Coursework||RESEARCH DESIGN (1000 WORDS)||10|
|Coursework||STATISTICAL ASSIGNMENT I (1500 WORDS)||45|
|Coursework||STATISTICAL ASSIGNMENT II (1500 WORDS)||45|
The assessment strategy is designed to provide students with the opportunity to demonstrate a comprehensive understanding of the main principles of a research design in Social Sciences and be able to successfully apply and describe the concepts covered during the lectures to the research topic of their choice. In addition, students will also have to demonstrate their ability to conduct independent quantitative analysis using R, identify the appropriate statistical method to be used to answer specific research questions and interpret the results obtained.
Thus, the summative assessment for this module consists of:
Research Design - 10%
Statistical Assignment I - 45%
Statistical Assignment II - 45%
Formative assessment and feedback
Students will receive written feedback for all the assignments submitted and they can also make use of the office hours discuss further problems related to the module and their content.
- The ability to develop a basic understanding of econometrics at the theoretical level
- The hands-on experience of completing practice exercises using R
- The capability to individually engage in applied analysis of cross sectional data.
- The appreciation of the strengths and problems of different statistical methods
|001||On completion of this module, students should be able to:|
|002||Read, understand, and evaluate quantitative analysis published in top journals||PT|
|003||Identify the appropriate statistical method to be used to answer specific research questions||KCPT|
|004||Use a variety of statistical methods and tools||KCP|
|005||Perform and interpret quantitative data analysis using R||KCPT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Overall student workload
Independent Study Hours: 128
Lecture Hours: 11
Seminar Hours: 11
Methods of Teaching / Learning
The learning and teaching strategy is designed to:
Provide students with the theoretical tools to read, understand, and critically evaluate quantitative analysis published in academic journals and; provide students with the practical skills required to conduct independent quantitative research.
The learning and teaching methods include:
11 x 1hr Lectures, 11 x 1hr seminars and statistical application labs
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: POL2045
Programmes this module appears in
|Politics and Economics BSc (Hons)||1||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2020/1 academic year.