Module: Advanced Quantitative Research Methods

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Advanced Quantitative Research Methods
Module Code	BAA1083 (ITS: MT612)
Faculty	DCU Business School	School	DCU Business School
NFQ level	9	Credit Rating	5

Description

This module is designed for students with a basic knowledge of the General Linear Regression model, to give them a deeper knowledge of this technique and introduce them to more advanced quantitative research analysis. It will examine the assumptions and limitations of these techniques in detail and introduces the students to more advanced alternatives. In particular, the course looks at techniques for dealing with latent variables, multilevel relationships, and multilevel data. The module will expose the students to Jamovi and R for each advanced technique.

Learning Outcomes

1. select the appropriate statistical technique for testing complex relationships between variables
2. identify and analyse multilevel data sets
3. apply a variety of Structural Equation techniques
4. apply a wide variety of diagnostics for testing the assumptions and assessing the limitations of statistical models
5. use appropriate software to apply a number of advanced quantitative research techniques

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	24	No Description
Workshop	24	Quantitative Research Case Studies using JAMOVI and R
Assignment Completion	24	No Description
Independent Study	48	No Description
Total Workload: 120

Section Breakdown
CRN	12058	Part of Term	Semester 1, 2 & 3
Coursework	100%	Examination Weight	0%
Grade Scale	PASS/FAIL	Pass Both Elements	N
Resit Category	RC2	Best Mark	N
Module Co-ordinator	Damien Dupré	Module Teacher	Gerard Conyngham

Type	Description	% of total	Assessment Date
Assessment Breakdown
Assignment	Structural Equation Modelling Case Study Analyse a multi-relationship data set using a variety of SEM techniques and to interpret and present the results of this analysis	100%	n/a

Reassessment Requirement Type
Resit arrangements are explained by the following categories; RC1: A resit is available for both^* components of the module. RC2: No resit is available for a 100% coursework module. RC3: No resit is available for the coursework component where there is a coursework and summative examination element. ^* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Introduction to Structural Equation Modelling
- What is SEM - Latent Variables - Confirmatory Factor Analysis - The Structural Model - Basic SEM using JAMOVI and R

Multi-Level Modelling
- Multilevel structures and classifications - Introduction to multilevel modelling - Multilevel models for binary responses - Multilevel models for ordinal responses - Cross-classified multilevel models - Multiple membership multilevel models - Multilevel Modelling of Repeated Measures Data - Multilevel Modelling using JAMOVI and R

Indicative Reading List

Books:
None

Articles:
None

Other Resources

None