DATA ANALYSIS - 2017/8
Module code: SOCM010
FIELDING JL Dr (Sociology)
Number of Credits
FHEQ Level 7
Module cap (Maximum number of students)
Overall student workload
Lecture Hours: 16
|Assessment type||Unit of assessment||Weighting|
|Coursework||COURSEWORK - DATA ANALYSIS (30%)||30%|
|Coursework||COURSEWORK-DATA ANALYSIS (70%)||70%|
Prerequisites / Co-requisites
The module will critically examine the link between theory and research, identifying theoretical, methodological and practical issues, which have an impact on the sociological study of the world. The module focuses on the core elements of quantitative data analysis, data collection and the use of computerised packages (SPSS).
This module elaborates statistical techniques for social research from simple univariate analyses to multiple regression. Emphasis throughout the module is on intuitive understanding rather than rigorous derivation. In addition to the formal lectures there are practical classes at which students acquire skills in the application of techniques and are able to seek clarification of concepts and methods. These classes concentrate on the use of SPSS for data management and analysis.
Introduce students to the basics of data analysis for social research from first principles.
Introduce students to statistical software with which to analyse quantitative data
Give students practical experience of analysing real world problems through secondary analysis of large government data sets, such as the British Social Attitued Survey and the World Bank Indicators of Development.
|Be able to create data sets for statistical analysis using the personal computer||P|
|Be able to carry out advanced data management tasks prior to statistical analysis||KCP|
|Have a comprehensive understanding of both simple and advanced statistical techniques and how to apply them on their own data set or on other secondary data sources||KC|
|Be able to understand regression analysis as a tool for social research||K|
|Have a critical understanding of the logic behind, and the appropriate time to use both bivariate and multivariate analysis||KC|
|Have the technical expertise to know how to conduct statistical analysis using SPSS||CPT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Indicative content includes:
basic concepts and the function of statistical measurement
frequency counts and simple data description
measures of central tendency and dispersion
graphics for display and analysis.
exploratory data analysis
foundations of probability theory
statistical inference (sampling, estimation, hypothesis testing)
measuring association between variables
bivariate correlation and regression, including multiple regression
Methods of Teaching / Learning
The learning and teaching strategy is designed to:
Introduce students to the basic principles and steps involved in the statistical analysis of quantitative data. This will be achieved through lectures and practical exercises on a computer using the latest version of statistical software, SPSS.
The learning and teaching methods include:
16 x one hour lectures
8 x one hour practical computer classes
3 x two hour classes computer classes
The assessment strategy is designed to provide students with the opportunity to demonstrate their understanding of the basic principles of statistics for social research including both statistical concepts and the use of software to demonstrate those concepts practically. Continuous formative assessment in class will allow students to demonstrate their appreciation of the potential of the statistical evaluation, manipulation and interpretation of data, and also allow them to develop skills in the entering and analysing of data with the SPSS package.
Assessed exercises include one short, 30% weighted, written exercise and one longer, 70% weighted, practical exercise to be completed with the aid of computer software.
Thus, the summative assessment for this module consists of:
An individual analysis of World Bank data, using SPSS software, to demonstrate an understanding of the principles, interpretation and limitations of multiple regression analysis in modelling real data.
Formative assessment and feedback
Students receive feedback during practical classes and on feedback sheets provided individually on return of the first shorter exercise.
Reading list for DATA ANALYSIS : http://aspire.surrey.ac.uk/modules/socm010
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 2017/8 academic year.