This is an archived handbook for a previous year. The current undergraduate and postgraduate handbooks can be viewed using the links above.

Postgraduate Handbook 2015-2016 (archived)

Module SOCI57615: Categorical Data Analysis with SPSS and R

Department: Applied Social Sciences

SOCI57615: Categorical Data Analysis with SPSS and R

Type	Level	Credits	Availability	Module Cap	Location
Open	4	15	Available in 2015/2016		Durham

Pre-requisites

Statistical Exploration and Reasoning.

Corequisites

None

Excluded Combinations

None.

Aims

To introduce students to the statistical methods for analysing categorical data that are collected from real social surveys.
To introduce students to some of the computing functions in SPSS and R that are useful for graphing and analysing categorical data.

Content

Statistical inference for a single categorical variable;
Measuring and graphing the relationship between two categorical variables;
Statistical methods for studying multi-way contingency tables;
Binary logistical regression models;
Log-linear models.

Learning Outcomes

Subject-specific Knowledge

Subject-specific Skills

Manage social survey data with specific functions of SPSS and R;
Produce relevant statistics on categorical variables with SPSS and R;
Interpret the meaning of statistics in the context of the data;
Construct logistic models and interpret the results;
Construct and choose log-linear models and interpret the results.

Key Skills

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

Lectures and computer lab sessions.
At the end of the module students will be given materials suitable for the conduct of categorical data analysis. Having done this they will be expected to carry out appropriate tasks and comment on the findings they generate. Students will also be given a set of real survey data. They will be expected to carry out appropriate analyses, and to comment on the findings of those analyses.

Teaching Methods and Learning Hours

Activity	Number	Frequency	Duration	Total Hours	Attendance Monitored
Lectures	10	weekly	1 hour	10
Computer Sessions	9	weekly	1 hour	9	Yes
Preparation & Reading				131
Total				150

Summative Assessment

Component	Component Weighting	Element	Length/Duration	Element Weighting	Resit Opportunity
Project	100%	Essay including tables and graphs	3000 words	100%

Formative Assessment

Feedback on work in class.

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.