Module: Advanced Quantitative Research Methods

Module Specifications.

Current Academic Year 2024 - 2025

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Date posted: September 2024

Module Title	Advanced Quantitative Research Methods
Module Code	MT612 (ITS) / BAA1083 (Banner)
Faculty	DCU Business School	School	DCU Business School
Module Co-ordinator	Damien Dupré
Module Teachers	Gerard Conyngham
NFQ level	9	Credit Rating	5
Pre-requisite	Not Available
Co-requisite	Not Available
Compatibles	Not Available
Incompatibles	Not Available

Coursework Only

Description

This module is designed for students with a basic knowledge of multivariate analysis techniques, to give them a deeper knowledge of these techniques and introduce them to more advanced quantitative research analysis. It will examine the assumptions and limitations of these techniques in details and introduces the students to more advanced alternatives. In particular the course looks at techniques for dealing with latent variables, multilevel relationships, multilevel data, longditudinal data, non-standard data types and missing values. In addition the course will briefly examine the Bayesian alternatives to standard hypothesis tests. The module will expose the students to a variety of software tools, including SPSS,AMOS, MPLUS and R.

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. model longtitudinal data
5. adjust models to deal with issues like missing values, non-normality and non-numeric data
6. apply a wide variety of diagnostics for testing the assumptions and assessing the limitations of multivariate techniques
7. 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 SPSS, AMOS, R and MPLUS
Assignment Completion	24	No Description
Independent Study	48	No Description
Total Workload: 120

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

Section 6: Intrduction to Structural Equation Modelling
- What is SEM - Latent Variables - Confirmatory Factor Analysis - The Structural Model - Basic SEM using AMOS

Section 7: 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 SPSS, MPLUS and R

Section 8: Latent Growth Modelling
- Introduction to Growth modelling - Latent Growth Modeling - Multilevel latent variable modelling

Section 9: Advanced Structural Equation Modelling
- Multiple-group confirmatory factor analysis - Latent variable modeling with missing data - Multilevel mixture modelling - Cross-sectional mixture modeling - Latent class analysis - Longitudinal mixture modeling – Growth mixture modelling - SEM using MPLUS

Section 10: Advanced Topics
- Multiple-group confirmatory factor analysis - Latent variable modeling with missing data - Multilevel mixture modelling - Cross-sectional mixture modeling - Latent class analysis - Longitudinal mixture modeling – Growth mixture modelling - SEM using R / MPLUS - Traditional Hypothesis Testing vs Bayesian Analysis - Bayesian Estimation and Monte Carlo Simulations - Bayesian alternatives to Independent Sample T-Test and Linear Regression - Bayesian Analysis using R

Assessment Breakdown
Continuous Assessment	0%	Examination Weight	0%

Type	Description	% of total	Assessment Date
Course Work Breakdown
Assignment	Multi-Level Modelling Case Study - Analyse a multi-level dataset and interpret and present results of analysis	40%	n/a
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	60%	n/a

Reassessment Requirement Type
Resit arrangements are explained by the following categories: Resit category 1: A resit is available for both* components of the module. Resit category 2: No resit is available for a 100% continuous assessment module. Resit category 3: No resit is available for the continuous assessment component where there is a continuous assessment and examination element.
* ‘Both’ is used in the context of the module having a Continuous Assessment/Examination split; where the module is 100% continuous assessment, there will also be a resit of the assessment
This module is category -

Indicative Reading List

Other Resources

None