QUANTITATIVE METHODS II - 2022/3
Module code: MAND031
In light of the Covid-19 pandemic the University has revised its courses to incorporate the ‘Hybrid Learning Experience’ in a departure from previous academic years and previously published information. The University has changed the delivery (and in some cases the content) of its programmes. Further information on the general principles of hybrid learning can be found at: Hybrid learning experience | University of Surrey.
We have updated key module information regarding the pattern of assessment and overall student workload to inform student module choices. We are currently working on bringing remaining published information up to date to reflect current practice in time for the start of the academic year 2021/22.
This means that some information within the programme and module catalogue will be subject to change. Current students are invited to contact their Programme Leader or Academic Hive with any questions relating to the information available.
This unit builds on the Quantitative Methods I unit in semester 1. Students are exposed to (more) advanced methodological and statistical skills needed to understand and evaluate journal articles that use quantitative methods. This also enables students to have a basic understanding of the different methods, so they are able to choose the appropriate method needed for their research questions. Methods discussed include: logistic regressions, Poisson regression, log linear models, instrumental variables, multilevel analysis, longitudinal data analysis (time series and panel data) and other advanced techniques such as bayesian techniques, cluster analysis and social network analysis.
Surrey Business School
MASSARO Sebastiano (SBS)
Number of Credits: 0
ECTS Credits: 0
Framework: FHEQ Level 8
JACs code: N300
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 117
Seminar Hours: 5
Tutorial Hours: 6
Laboratory Hours: 22
Prerequisites / Co-requisites
Generalized Linear Models.
Generalized Linear Models: Logit Models.
Generalized Linear Models: Poisson Models.
Simple Panel Data Methods.
Advanced Panel Data Methods.
Instrumental Variables. Two Stage Least Squares.
Mediation and Moderation.
Advanced Statistical techniques (Bayesian, SNA, Clustering, Deep Learning)
|Assessment type||Unit of assessment||Weighting|
|Coursework||ASSESSMENT (AS.1)-EMPIRICAL RESEARCH PROJECT||Pass/Fail|
The assessment strategy is designed to provide students with the opportunity to demonstrate their learning and achievement of the unit’s learning outcomes. Both the formative and summative assessments enable the achievement of the learning outcomes. The regular class participation and feedback enhance students’ learning and support their preparation and delivery of the two elements of summative assessment. These are outlined below.
Formative assessment will comprise an ongoing feature of this unit. Students will actively engage in taught sessions and prepare for these via guided readings, discussion topics and other preparatory work issued by the instructor. These will be important for students and will facilitate their critical thinking and applied skills development, as well as enhance their learning and preparation for the three summative assessment elements.
The summative assessment for this unit consists of:
- Assessment 1. Empirical research project.
- To provide a firm grounding in the theory and practice of quantitative data analysis
- To focus on developing skills and knowledge in data management, visualisation and statistical modelling through the analysis of data sets.
- To provide training in a statistical software environment e.g. R, providing the tools for students to develop the skills to use software independently for quantitative analysis in dissertation research.
|001||Understand and critically evaluate quantitative arguments and statistical analyses in business research||KCPT|
|002||Perform a wide variety of data-related tasks in a chosen statistical software environment;||KCPT|
|003||Create, manage and manipulate data sets||KCPT|
|004||Design and produce professional and informative visualisations||KCPT|
|005||Select appropriate statistical tests and models for making predictions and evaluating hypotheses, and apply these to data||KCPT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy is designed to support and achieve the learning outcomes. The learning and teaching methods include class contact sessions which are highly interactive in nature, class discussion, preparatory reading, verbal student presentations, student-led reviews of readings, practical activities, scenario discussion, and individual written assignments.
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: MAND031
Programmes this module appears in
|Management and Business PHD||2||Optional||Each unit of assessment must be passed at 50% to pass the module|
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2022/3 academic year.