QM2 - Analysing Your Social World SPS2001

  • Academic Session: 2023-24
  • School: School of Social and Political Sciences
  • Credits: 20
  • Level: Level 2 (SCQF level 8)
  • Typically Offered: Semester 2
  • Available to Visiting Students: Yes

Short Description

Students will build on the techniques presented in QM1-Measuring Your Social World to gain a more robust skill set to exploring quantitative data including regression analysis. The course will help students learn about the data collection processes and various methods to examining differences and patterns of association with links to the school's thematic areas through using key dataset on topical areas such as: inequality, welfare, health, crime and conflict.


10 x 1 hour lectures during a 10 week semester - lecturer led.

Likely to be Monday 10-11am.*


10 x 1 hour taught labs during a 10 week semester - lecturer led.

Mondays 12pm-1pm (labs can accommodate up to 33 people, course is capped at 99, additional times Mondays 1pm-2pm, 2pm-3pm)*


10 x 1 hour supervised labs during a 10 week semester - GTA led.

Wednesday 11am-12pm & 12pm-1pm (labs capped at 33, additional times Thursday 10am-11am)*


*All level 1 and level 2 lectures in SSPS have been mapped and the above represents gaps in timetables so that all students are able to elect to study for a 'with' degree.


Commensurate with other Level 2 courses offered in the SSPS and supported with blended learning via the VLE Moodle.

Requirements of Entry

Mandatory entry requirements in line with those generally required by the MA (Social Sciences) degree programme.

Recommended: achievement of grade D3 or above for either QM1 - Measuring Your Social World course or Statistics 1Y course

Excluded Courses

Recommended: the current Junior Honours cross SSPS course: Quantitative Methods in the Social Sciences (using codes: CEES3027; CEES4073; POLITIC3018; POLITIC4137; PUBPOL3010; PUBPOL4137; SOCIO3021; SOCIO4095)


Normally, QM1 Measuring Your Social World course


The assessment will consist of a portfolio made up of four formative assessments throughout the semester. Students will perform statistical analysis in R on secondary data related to core themes for correlation analysis, data visualisations, linear regression, and linear regression diagnostics. A formative assessment corresponds to each technique. Students will turn in each formative assessment and receive feedback. Students will then turn in the four revised assessments of a four thousand word summative portfolio for a final grade. The portfolio assessment is worth 100%.

Main Assessment In: April/May

Course Aims

The aims of this course are:

■ To offer students a more advanced (relative to QM1) presentation of quantitative methods to aid in examining the world around them and to serve as a stepping stone for more complex quantitative training that will be available through the Q-Step Programme.

■ To develop a critical view of data and statistics that can be applied to published information they are exposed to in their day-to-day lives and academia.

■ To equip students with quantitative and analytical skills to evaluate the data that they are exposed to, and to critically reflect on the impact of this data on social issues and policy formation.

Intended Learning Outcomes of Course

By the end of this course students will be able to:

■ Demonstrate a critical awareness of when different quantitative techniques are appropriate and their limitations

■ Demonstrate the ability to perform more advanced quantitative data analysis using tests of relationships on secondary data sets using R software.

■ Show an understanding of quantitative literacy in order to demonstrate how representative a sample is of the population it claims to represent.

■ Show critical awareness of the production processes of a complete quantitative analysis of data.


Note: additional information about the course content and timetable is available from the document attached 'QM1 & 2 course overview and proposed timetable'.

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.

Students must regularly attend and participate in lectures, taught and supervised labs; undertake all aspects of the course work (including formative and summative assignments).