SD5812 Quantitative Methods

Academic year

2024 to 2025 Semester 2

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

15

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 11

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

Availability restrictions

Available only to students studying the PG Cert, PG Dip, and MSc in Data Literacy for Social and Environmental Justice

Planned timetable

Not Applicable

This information is given as indicative. Timetable may change at short notice depending on room availability.

Module coordinator

Dr E O Olamijuwon

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module Staff

Dr L Cole; Dr T Mendo; Dr E Olamijuwon

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module description

This module will be structured around the use of quantitative methods to understand social or environmental processes. It will have two pathways managed through the virtual learning framework – social science vs. environmental science – to ensure students engage with data that are key for their subfield of study. While the content for this module is ambitious, the goal is not that students will become experts in all these analytical techniques, but that they will have a critical understanding of the approaches, including the rationale behind model selection, underlying assumptions, and limitations. Students will have the opportunity to work with large publicly available datasets using open-source software (e.g., R/RStudio and Google Sheets) and will be offered resources for independent study if they choose to pursue further development beyond this module. At the end of this course, students will be familiar with: analysing time-series data; event history modelling; multivariate linear/logistic regression; multilevel/dependent modelling; and causal inference modelling.

Relationship to other modules

Pre-requisites

IN ORDER TO TAKE THIS MODULE YOU MUST TAKE OR HAVE TAKEN SD5510 AND SD5511 OR HAVE PERMISSION FROM THE PROGRAMME DIRECTOR

Assessment pattern

100% Coursework

Re-assessment

100% coursework

Learning and teaching methods and delivery

Weekly contact

This module includes 5 1-hour synchronous tutorial sessions and at least 5 hours of pre-recorded content (e.g., lectures). Students should consider the amount of independent study time this module involves when planning their learning.

Scheduled learning hours

0

The number of compulsory student:staff contact hours over the period of the module.

Guided independent study hours

145

The number of hours that students are expected to invest in independent study over the period of the module.

Intended learning outcomes

  • Understand the use of a range of quantitative models for hypothesis testing;
  • Describe the rationale behind data and model selection, underlying assumptions, and limitations of the data and models covered in the module;
  • Confidently use appropriate software (e.g., R/RStudio and Google Sheets) to manipulate, describe, and analyse large datasets;
  • Present results clearly using a range of formats, e.g., through tables and graphs.

SD5812 Quantitative Methods

Academic year

2024 to 2025 Semester 1

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

15

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 11

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

Availability restrictions

Available only to students studying the PG Cert, PG Dip, and MSc in Data Literacy for Social and Environmental Justice

Planned timetable

Not Applicable

This information is given as indicative. Timetable may change at short notice depending on room availability.

Module coordinator

Dr E O Olamijuwon

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module Staff

Dr L Cole; Dr T Mendo; Dr E Olamijuwon

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module description

This module will be structured around the use of quantitative methods to understand social or environmental processes. It will have two pathways managed through the virtual learning framework – social science vs. environmental science – to ensure students engage with data that are key for their subfield of study. While the content for this module is ambitious, the goal is not that students will become experts in all these analytical techniques, but that they will have a critical understanding of the approaches, including the rationale behind model selection, underlying assumptions, and limitations. Students will have the opportunity to work with large publicly available datasets using open-source software (e.g., R/RStudio and Google Sheets) and will be offered resources for independent study if they choose to pursue further development beyond this module. At the end of this course, students will be familiar with: analysing time-series data; event history modelling; multivariate linear/logistic regression; multilevel/dependent modelling; and causal inference modelling.

Relationship to other modules

Pre-requisites

IN ORDER TO TAKE THIS MODULE YOU MUST TAKE OR HAVE TAKEN SD5510 AND SD5511 OR HAVE PERMISSION FROM THE PROGRAMME DIRECTOR

Assessment pattern

100% Coursework

Re-assessment

100% coursework

Learning and teaching methods and delivery

Weekly contact

This module includes 5 1-hour synchronous tutorial sessions and at least 5 hours of pre-recorded content (e.g., lectures). Students should consider the amount of independent study time this module involves when planning their learning.

Scheduled learning hours

0

The number of compulsory student:staff contact hours over the period of the module.

Guided independent study hours

145

The number of hours that students are expected to invest in independent study over the period of the module.

Intended learning outcomes

  • Understand the use of a range of quantitative models for hypothesis testing;
  • Describe the rationale behind data and model selection, underlying assumptions, and limitations of the data and models covered in the module;
  • Confidently use appropriate software (e.g., R/RStudio and Google Sheets) to manipulate, describe, and analyse large datasets;
  • Present results clearly using a range of formats, e.g., through tables and graphs.