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Durham University

Postgraduate Module Handbook 2021/2022

Archive Module Description

This page is for the academic year 2021-22. The current handbook year is 2022-23
No such Code for pgprog: N2I101
No such Code for pgprog: N5I101

Department: Management and Marketing

BUSI4H715: Advanced Quantitative Research Methods (Management and Marketing)

Type Tied Level 4 Credits 15 Availability Not available in 2021/22
Tied to N2I101
Tied to N5I101

Prerequisites

  • None

Corequisites

  • None

Excluded Combination of Modules

  • None

Aims

  • To give students an overview of a wide variety of possible techniques that they might consider as they approach the development of their own studies, to that they can make an informed decision.
  • To provide students with a common base of ‘good data practices’ related to practical aspects of doing any study (including how to maintain various types of data, basics of data set management, transforming data, etc.).
  • To give students ‘hands on’ experience with different analytic softwares such as SPSS, LISREL, Stata, and Mplus.
  • To give students enough information so that they can perform various types of analyses on their own or with minimal guidance.
  • To give students important conceptual information about issues underlying the use of a variety of quantitative techniques (such as making causal inference, logic of hypothesis testing, validity threats, etc.).
  • To develop students’ ability to identify suitable analytical methods to answer their research questions.

Content

  • Construction of a dataset and data management
  • Quantitative data analysis including: Data reduction, validity and reliability, and regression analyses in SPSS; Confirmatory factor analysis and structural equation modelling; Mediation, moderation and quadratic analyses; Logistic regression and non-linear approaches; Multi-level analysis; Panel data and econometric data analysis; Event history analysis.
  • Secondary data use and panel datasets.

Learning Outcomes

Subject-specific Knowledge:
  • Have knowledge of how to appropriately organize and maintain a complex quantitative dataset;
  • Have advanced and up-to-date knowledge of relevant quantitative research methods and their accompanying analytical methods;
  • Have advanced understanding of quantitative analytical techniques;
  • Have a comprehensive understanding of key methodological considerations and challenges for quantitative data generation or collection required for a particular analysis method to be potent;
  • Have advanced understanding of methods for complex quantitative data analysis.
Subject-specific Skills:
  • Ability to skilfully conduct quantitative research and data analysis;
  • Ability to critically assess quantitative existing research and analytical methods used in existing studies;
  • Ability to apply state of the art research methods to analyse data;
  • Ability to defend chosen approaches and objectively critique the application of alternative quantitative methods of research and analysis.
Key Skills:
  • The ability to organize research and analyze data to answer a particular research question;
  • The ability to appropriately manage and maintain a complex dataset;
  • The ability to design the data analysis;
  • The ability to carefully evaluate the data requirements underpinning particular quantitative data analysis methods and to consider the actions needed to execute complex studies.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

  • The module will be delivered in a series of 10 lectures and 10 seminars over term two.
  • By commenting on each other’s suggestions, students will acquire both the capability and the attitude to critically evaluate and improve their research methods.
  • Learning will also occur through tutor-supported, as well as self-supported, learning groups, thus enabling students to develop their own effective research methods strategies.
  • The assessment of the module is by a 3,500 word individual written assignment. Students will be required to construct a ‘notebook’ with sections for each method that is covered during the module. The material should include notes on the important analytical and interpretational aspects of the technique, illustrated examples of outputs, and some sources (or a list of resources) to go to for further information. Students should also include creative content in a variety of ways, which might include the identification and critique of a published paper that uses a particular method, or the design of a fictional study that would be appropriately analysed using a given method. The end notebook should be thought of as their own personalised statistical reference.

Teaching Methods and Learning Hours

Activity Number Frequency Duration Total/Hours
Lecture 10 Weekly 2 hours 20
Seminars 10 Weekly 1 hour 10
Self-supported learning groups (students are expected to form their own discussion groups to reflect on and share their learning about the issues raised in the module) 10 Weekly 1 hour 10
Independent study, preparation and reading 110
Total 150

Summative Assessment

Component: Written Assignment Component Weighting: 100%
Element Length / duration Element Weighting Resit Opportunity
Individual written assignment 3,500 words 100% same

Formative Assessment:

Students will be required to complete attempts to analyse data relating to the various techniques covered during the module ion an on-going basis and will receive feedback on these attempts.


Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University