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Durham University

Postgraduate Module Handbook 2021/2022

Module Description

Please ensure you check the module availability box for each module outline, as not all modules will run in each academic year. Each module description relates to the year indicated in the module availability box. Please be aware that modules may change from year to year, and may be amended to take account of, for example: changing staff expertise, disciplinary developments, the requirements of external bodies and partners, and student feedback.

No such Code for pgprog:

Department: Government and International Affairs

SGIA49915: Quantitative Methods and Analysis

Type Open Level 4 Credits 15 Availability Available in 2021/22
Tied to

Prerequisites

  • None.

Corequisites

  • None.

Excluded Combination of Modules

  • None.

Aims

  • To provide an opportunity for students with a range of disciplinary backgrounds and experiences, to assimilate and develop their knowledge, critical understanding and skills in quantitative data analysis.
  • To enable students to understand how to use statistical techniques for exploration and description of data sets.
  • To enable students to make appropriate statistical inferences about associations between social phenomena.

Content

  • Indicative content as follows:
  • Foundational statistical methods for quantitative inquiry of data
  • Performing quantitative data analysis using statistical software
  • The nature of quantitative data and levels of measurement
  • Exploring and describing data with statistics and graphs.
  • Populations, sampling and sampling distributions.
  • Point estimates and confidence intervals.
  • Significance tests.
  • Association, cross-tabulation and Chi-Square tests.
  • Association and differences in means: t-tests and ANOVA.
  • Correlation and simple linear regression.
  • Dealing with alternative explanations – the role of research design, statistical control and multiple linear regression.
  • Discussion of formative assignments, review, questions and answers.

Learning Outcomes

Subject-specific Knowledge:
  • At the end of this module students will be able to:
  • Understand the fundamentals of applied statistical analysis for social data.
  • Understand the concept of statistical inference and carry out basic inferential procedures.
  • Understand the concept of statistical association and how to use statistical methods to test for such association.
Subject-specific Skills:
  • Use statistical software to explore quantitative social data.
  • Calculate basic descriptive statistics for a set of data and construct appropriate graphical representations.
  • Obtain and interpret confidence intervals and significance tests for single variables.
  • Construct and interpret contingency tables.
  • Carry out and interpret tests for differences in means across two or more groups.
  • Estimate and interpret simple and multiple linear regression models.
  • Distinguish correlation and cause.
  • Use statistical software to execute the procedures covered in the module.
Key Skills:
  • By the end of this module, students will be able to demonstrate:
  • An ability to undertake and interpret statistical analyses of social data;
  • An ability to critically explain and evaluate quantitative evidence;
  • Specialist IT and research skills
  • Basic written communication skills;
  • Basic learning and study skills;
  • An ability to plan and manage time effectively.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

  • The lectures on the module will outline the key statistical methods and concepts, how they can be used in social science, and their appropriate interpretation
  • The computer classes will involve students undertaking practical data analysis exercises using statistical software, which will enable students to develop skills in data analysis and in interpreting and evaluating the results of this analysis
  • The summative data analysis exercise for the module requires students to demonstrate their knowledge and understanding of applied statistics for social science by carrying out an analysis of pre-specified secondary quantitative data and writing up the results in the form of a report.
  • The formative assessment provides students with an opportunity to perform, write up and obtain feedback on a series of analyses of a pre-specified secondary data set.

Teaching Methods and Learning Hours

Activity Number Frequency Duration Total/Hours
Lectures 10 weekly 1 hour 10
Practicals 10 Weekly 2 hours 20
Preparation and Reading 120
Total 150

Summative Assessment

Component: Assessment Component Weighting: 100%
Element Length / duration Element Weighting Resit Opportunity
Report based on data provided to students and analysed and interpreted by themselves 3,000 100%

Formative Assessment:

This will be a short formative assignment in which students must carry out a series of analyses on pre-specified dataand interpret the results of the analyses. Although not all procedures will be covered in this work, and different data will be used, the formative assignment follows the similar format as the summative assignment. It is therefore aimed to assist students to become familiar with requirements and expectations of summative work. To be submitted in session eight. Students will receive individual feedback on the formative.


Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University


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