GRT-40020 - Module Specification
School of Social Sciences
Faculty of Humanities and Social Sciences

Programme/Approved Electives for 2024/25

Available as a Free Standing Elective

Co-requisites

None

Prerequisites

None

Barred Combinations

N/A

Description for 2024/25

The module aims to deliver an introduction to the practices of quantitative social science research, while also examining core principles. It assumes no prior knowledge of statistics or maths beyond GCSE - however it may be useful for those who are less confident with social science numeracy to do some preparatory refreshing of their basic skills.
The module is designed, following a tested pattern of similar models at QStep universities, as an introductory applied quantitative methods approach ¿ using real-world hands-on data analysis which is motivational and confidence building for students. In so doing, it also addresses many of the fundamental conceptual issues in quantitative research such as questions of causality and inference, operationalisation, sampling/representativeness, and testing for significance/association. The hands-on approach works for many QStep and other institutions at Masters¿ level because it seeks to bridge a gap in undergraduate quantitative training for many social scientists, while also offering the option for those who are returning to study from the workplace a `conversion¿ route. Although those who are proficient in quantitative analysis may find this too introductory, but may wish to refresh their knowledge, a `self-study¿ route is possible so they can fast-track the module.
The module approaches introductory descriptive and inferential statistical thinking by exploring and working through key analysis strategies ¿ beginning with univariate and bivariate approaches, which includes: simple percentages, simple tabulation, cross-tabulation and control variables; confidence intervals, t-tests and ANOVA. We also briefly explore data management such as cleaning, recoding etc. Depending on the speed of learning of each cohort, we will add on sessions on linear/logistic regression if appropriate.
In order to understand the context/usage of descriptive/inferential approaches, we will make use of various existing social science datasets (such as the Crime Survey for England and Wales, British Election Survey, British Social Attitudes) to explore and understand how social scientists design/use statistical information to answer a research problem.
The workshop element uses and is designed for SPSS. No prior experience of this package is needed, and all workshops will take place in library PC labs where software is installed.

The module aims to deliver a comprehensive introduction to the principles and practices of quantitative social science research. The module covers major themes in the theoretical appraisal of the methodological terrain of quantitative research methods, including the question of causality, the problematic of operationalisation, and theories of sampling. The module will engage students in a discussion of quantitative research design; the development of research instruments, such as questionnaires, and it will offer an introduction to the statistical analysis of quantitative data sets and the SPSS software.

Intended Learning Outcomes

Demonstrate knowledge of quantitative methods of design, data collection and analysis: 1
Develop critical awareness of the main benefits and limits of a quantitative approach to specific social issues: 1
Understand key quantitative concepts, such as: generalisability, hypothesis testing, variance, statistical significance, sampling, probability.: 1
Access large secondary datasets and be able to prepare them for analysis: 1
Apply simple techniques of data analysis to social science problems using SPSS: to include univariate and bivariate analysis (frequencies, central tendency, tabulation, cross-tabulation, control variables, confidence intervals, t-tests/ANOVA), with the option to add in more intermediate techniques (linear/logistic regression) if the group is able.: 1
Formulate a simple research question, design an appropriate approach, carry out the analysis, use appropriate visualisations and discussion in reporting the outcomes, including understanding how to report when analysis does not work as hoped.: 1
Analyse and interpret results/outcomes of data exploration: 1
Communicate data analysis outcomes professionally, with appropriate academic standards for reporting/visualising/labelling, and while providing suitable social science context/explanation: 1

Study hours

15 hours contact time in workshop format, focused on problem-based learning in an IT lab using SPSS, but also including mini-lecture and discussion elements.
30 hours asynchronous structured practical/worksheet completion
15 hours engagement with supplementary online materials
10 hours asynchronous group discussion/reflection
5 hours optional tutorial drop in time
Independent:
15 hours - class preparation time to include pre-reading,
60 hours - assessment preparation time, to include focused & wider, reading; sourcing data; analysis; writing up; redrafting; proofing.

School Rules

Students should ideally have a prior qualification with some social science or broader numeracy element - this may be a GCSE Maths at grade 4 or above (or equivalent). Alternatively, this might be a social science numeracy/statistical element in their prior degree, or relevant work experience in presenting/handling numerate data.
Although this module is core for certain PGT courses, it needs to be clear to admissions tutors and to those students seeking (and their supervisors advising them) to take it as an option, that it requires the willingness to engage with some mathematical/statistical concepts and operations. The module does work with students with no experience/confidence in these areas; however there does need to be a willingness to engage.

Description of Module Assessment