This is an archived handbook for a previous year. The current undergraduate and postgraduate handbooks can be viewed using the links above.
Postgraduate Handbook 2020-2021 (archived)
Module SGIA49915: Statistical Exploration and Reasoning
Department: Government and International Affairs
SGIA49915: Statistical Exploration and Reasoning
| Type | Level | Credits | Availability | Module Cap | Location |
|---|---|---|---|---|---|
| Open | 4 | 15 | Available in 2020/2021 | None. | Durham |
Pre-requisites
- None
Corequisites
- None
Excluded Combinations
- None
Aims
- 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
- The nature of data — spreadsheets and what they contain.
- Exploring and describing data.
- Populations, sample data and sampling distributions.
- Basic inference I: point estimates and confidence intervals. Basic inference II: significance tests.
- Cross-tabulation and Chi-Square tests.
- Differences in means: t-tests and ANOVA.
- Correlation and simple linear regression.
- Causal claims, 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 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
- Set up and navigate an SPSS spreadsheet.
- 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 SPSS to execute the procedures covered in the module.
Key Skills
- NOT REQUIRED FOR THIS MODULE
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
- The package SPSS will be used as this is now the best platform for the course content.
- Assessment: Students will be required to produce a report by analysing a real large-scale secondary dataset. enter text as appropriate for the module
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total Hours | Attendance Monitored |
|---|---|---|---|---|---|
| Lectures | 10 | weekly | 1 | 10 | |
| Practicals | 10 | weekly | 1 | 10 | |
| Preparation & Reading | 130 | ||||
| Total | 150 |
Summative Assessment
| Component | Component Weighting | Element | Length/Duration | Element Weighting | Resit Opportunity |
|---|---|---|---|---|---|
| Assessment | 100% | Project based on data that students analyse and interpret | 3,000 words | 100% |
Formative Assessment
This will be a short formative assignment in which students must carry out a series of SPSS operations and interpret the results of the operations. 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.
Students who do not attend monitored activities shown under Teaching Methods
and Learning Hours, or who fail to complete the summative or formative
assessment(s) specified above, may be subject to the Academic Progress
procedures defined in the University's General Regulation V, and may be
required to leave the University.