Advanced Regression: Limited and Categorical Dependent Variable Regression SPS4004
- Academic Session: 2019-20
- School: School of Social and Political Sciences
- Credits: 20
- Level: Level 4 (SCQF level 10)
- Typically Offered: Semester 2
- Available to Visiting Students: Yes
- Available to Erasmus Students: Yes
Students will build on the techniques presented in QM1, QM2 and Research Design and Method Selection to gain a more robust skill set to explore quantitative data using limited and categorical dependent variable regression analysis. The course will help students learn about advanced regression methods to examine 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 - lecture led.
10 x 1 hour workshop labs during a 10 week semester - lecture led.
Thursday 12pm (labs can accommodate up to 50 people)
*All level 1 and level 2 lectures in SSPS have been mapped and the above represents the same time as Quantitative Methods in the Social Sciences (using codes: CEES3027; CEES4073; POLITIC3018; POLITIC4137; PUBPOL3010; PUBPOL4137; SOCIO3021; SOCIO4095), so students will not be able to register for both courses in the same term.
Commensurate with other Level 4 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 Science degree. Recommended: successful completion (grade C or above) of QM1 - Measuring Your Social World and QM2 - Analysing Your Social World.
Also Recommended: students who complete Statistics 1A, Geography 2 or Psychology 2 at Grade C or higher will be considered for a place on the above degrees even if they have not completed QM1 and QM2
Recommended: the current Junior Honours cross SSPS course: Quantitative Methods in the Social Sciences (using codes: CEES3027; CEES4073; POLITIC3018; POLITIC4137; PUBPOL3010; PUBPOL4137; SOCIO3021; SOCIO4095)
Research Design and Method Selection.
The assessment will consist of a portfolio made up of four formative assessments throughout the semester. Students will develop models, perform diagnostics, and perform interpretations on secondary data related to core themes for the techniques of binary, ordered, nominal choice and count models. 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
Are reassessment opportunities available for all summative assessments? No
Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below.
The aims of this course are:
•To offer students a presentation of advanced quantitative methods to aid in examining the world around them and to serve as a stepping stone for additional quantitative training that will be available through the Q-Step Programme at Level 4.
•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:
•Identify when different quantitative techniques are appropriate and their limitations.
•Analyse more advanced quantitative data analysis using tests of relationships on secondary data sets using R software.
•Interpret statistical findings in writing, verbally, and graphically.
•Show critical awareness of the production processes of a complete quantitative analysis of data.
•Critically appraise material published within a relevant social science subject area.
Note: additional information about the course content and timetable is available from the document attached - 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 and taught labs; undertake all aspects of the course work (including formative and summative assignments).