QUANTITATIVE METHODS 2 - 2020/1
Module code: SOC2031
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.
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This module builds upon the year 1 quantitative methods module introducing multivariate quantitative analyses with the help of the software package, SPSS. The module consists of a mix of lectures followed by guided classes where students will investigate the chosen topic, using a data set sourced from the UK Data Service and using SPSS. Students shall also learn how to interpret and present results of quantitative analyses.
SETTY Emily (Sociology)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 5
JACs code: G300
Module cap (Maximum number of students): N/A
Prerequisites / Co-requisites
Quantitative Methods 1
Indicative content includes:
· Creating new data files from survey questionnaires using SPSS
· Sourcing quantitative data for secondary analysis
· Data management procedures using SPSS
· The logic behind simple and multiple regression analysis
· The practical application of simple and multiple regression using SPSS
|Assessment type||Unit of assessment||Weighting|
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.
Thus, the summative assessment for this module consists of:
· Two assessed exercises, including one short, 30% weighted, practical exercise and one longer, 70% weighted, practical and written exercise to be completed with the aid of computer software.
· Deadlines CW1: week 7; CW2: week 12
Formative assessment and feedback
Students receive feedback during practical classes and on feedback sheets provided
Individually on return of both assessed exercises.
- To give students a thorough grasp of how to use SPSS, the most popular and one of the most powerful computer packages for analysing quantitative data
- To explain regression and interval level data analysis in non-technical terms using SPSS
- To be able to read, understand and present quantitative analyses
|1||Be able to create simple data sets for statistical analysis using the personal computer||PT|
|2||Be able to carry out simple statistical analyses on their own data set or on other secondary data sources||CT|
|3||Be able to carry out simple data management tasks prior to statistical analysis||PT|
|4||Be able to understand the logic behind, and the appropriate time to use regression analysis as a tool for social research||KC|
|5||Be able to carry out a simple and multiple regression analysis using SPSS||PT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Overall student workload
Independent Study Hours: 128
Lecture Hours: 11
Practical/Performance Hours: 11
Methods of Teaching / Learning
The learning and teaching strategy is designed to:
Build on the basic principles and steps involved in the statistical analysis of quantitative data learnt in year 1. This module aims to encourage students to develop a practical understanding of when and how to use particular multi-variate statistical techniques to best research social processes using 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:
· 5 x 1 hr Lectures
· 5 x 1 hr classes practical computer classes
· 6 x 2 hr classes practical computer classes
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: SOC2031
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.