BUSI4H715: Advanced Quantitative Research Methods (Management and Marketing)

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• 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.

• 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.

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.

• The module will be delivered in a series of 10 lectures and 10 seminars over term two.
• By commenting on each others 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.

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.

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