2023/24 Undergraduate Module Catalogue

SLSP3065 Quantitative Social Research

20 creditsClass Size: 30

Module manager: Dr Albert Varela
Email: a.varela@leeds.ac.uk

Taught: Semester 1 (Sep to Jan) View Timetable

Year running 2023/24

Pre-requisite qualifications

SLSP2010 Sociology and Social Policy Research Methods (or equivalent) Students who have not taken SLSP2010 should approach the module leader in advance of enrolment to discuss whether prior exposure/knowledge in this area is appropriate for this module.

This module is not approved as a discovery module

Module summary

This module will offer students the opportunity to develop fundamental data analytical skills necessary to conduct quantitative social research. It will introduce students to fundamental techniques in exploratory data analysis and modelling as part of a coherent framework that helps them structure and conduct independently a quantitative research project, from inception to reporting. The focus of the module is eminently applied and based on teaching and learning activities that emphasise hands-on work with widely used social research datasets on topics of interest to sociology, social policy, and the wider social sciences. This module is aimed at students wanting to undertake quantitative dissertations in social science degrees and/or students wanting to develop the key skills to conduct applied quantitative research in academic and industry settings.

Objectives

On completion of this module, students should be able to ...

This research-based module enables students to develop the skills to analyse quantitative data in order to answer a research question of their choice. Over the duration of the module students will:
- familiarise themselves with a variety of existing datasets with which to explore their research question.
- develop the ability to import, manipulate and prepare data for analysis to answer substantive research questions using statistical software.
- learn both the substantive and practical considerations involved in analysing data.
- follow a coherent quantitative research workflow that includes data retrieval and preparation, exploratory data analysis and visualisation, statistical modelling, and reporting.
- reflect substantively and methodologically about the strengths and weaknesses of quantitative data analysis in social research.

Learning outcomes
On completion of the module, students should be better able to:
- Demonstrate understanding and command of standard tools for quantitative social research, such as exploratory data analysis, data visualisation, linear and logistic regression.
- Identify the appropriate methods to analyse different types of data and evaluate their strengths and limitations in both their own analysis and published research.
- Evidence their ability to prepare, analyse and interpret quantitative data to answer substantive research questions using statistical software.
- Effectively present and report findings from quantitative data analysis using graphs and tables in a clear and systematic way to meet the needs of different types of audiences.

Syllabus

The module will run across 11 two-hour workshops taking place in computer labs. These sessions will blend lecturing, individual and collaborative hands-on data analysis exercises and small group discussion. The module will begin with an introduction to the use of quantitative data and the key aspects of statistical inference in social research. The first weeks will introduce the student to fundamental steps in data handling and preparation as well as basic descriptive statistics to consolidate skills that students may have developed in the past. Bivariate and multivariate exploratory data analysis techniques appropriate for different types of data will be introduced next. Data visualisation, as well as its principles and pitfalls, will feature in this part both as useful exploratory tools as well as data communication devices that are increasingly central in research, industry and media. Finally, the module will get students to engage hands-on with standard data modelling techniques, such as linear and logistic regression, that will help them answer substantive research questions that are of interest in their disciplines. This last part will underpin the main assignment component: a research-based report, where students will analyse secondary data to investigate key social research topics such as poverty, wellbeing, crime, social and political attitudes, or electoral behaviour amongst others. Students may choose their projects and datasets based on the substantive interests or from a list of projects and datasets suggested by the convenor.

This module relies on R/R-Studio as the main sofware package to conduct data analysis but does not assume any prior experience or familiarity with this or any other statistical software. Students will progressively learn to implement data analysis techniques on R/R-Studio as the module advances and will have a robust system of technical support from the teaching team as well as learning aids (such as workbooks and videos) to help them develop their methodological and computing skills.

Teaching methods

Delivery type	Number	Length hours	Student hours
On-line Learning	11	1.00	11.00
Computer Class	11	2.00	22.00
Drop-in Session	10	1.00	10.00
Private study hours			157.00
Total Contact hours			43.00
Total hours (100hr per 10 credits)			200.00

Private study

4 hours per week over 10 weeks to work on take home data analysis exercises = 40 hours
Reading and independent research leading to a research report based on the analysis of secondary data (2,000 word) = 67 hours.
Reading and private study = 50 hours

Opportunities for Formative Feedback

Contributions during workshops.
Weekly take home data analysis exercises will help the convenor (and the students themselves) to evaluate their progress.
Ongoing feedback, encouraged and facilitated through open door meetings.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Project	2,500 word written assignment	90.00
Practical	10 x fortnightly take-home data exercises	10.00
Total percentage (Assessment Coursework)		100.00

Take home exercises will be awarded 1% of the final mark on a pass/fail basis. Each week the exercise answers and general feedback will be uploaded on the VLE following the submission of their assignments. This will ensure that students have the opportunity to evaluate their progress. The resit for the take home exercises will be a take-home short test with a set of open-ended questions. To answer all those questions the student will need to perform analyses equivalent to those necessary to complete the take home exercises during the semester.

Reading list

The reading list is available from the Library website

Last updated: 28/04/2023 14:51:52

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