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Module Specifications..

Current Academic Year 2021 - 2022

Please note that this information is subject to change.

Module Title Advanced Quantitative Research Methods
Module Code MT612
School DCUBS
Module Co-ordinatorSemester 1: Gerry Conyngham
Semester 2: Gerry Conyngham
Autumn: Gerry Conyngham
Module TeachersGerry Conyngham
Janine Bosak
NFQ level 9 Credit Rating 5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None
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



Workload Full-time hours per semester
Type Hours Description
Lecture24No Description
Workshop24Quantitative Research Case Studies using SPSS, AMOS, R and MPLUS
Assignment Completion24No Description
Independent Study48No 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 Assessment0% Examination Weight100%
Course Work Breakdown
TypeDescription% of totalAssessment Date
AssignmentMulti-Level Modelling Case Study - Analyse a multi-level dataset and interpret and present results of analysis40%n/a
AssignmentStructural 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 analysis60%n/a
Reassessment Requirement Type
Resit arrangements are explained by the following categories;
1 = A resit is available for all components of the module
2 = No resit is available for 100% continuous assessment module
3 = No resit is available for the continuous assessment component
This module is category 2
Indicative Reading List
    Other Resources
    None
    Array
    Programme or List of Programmes
    NSPDPhD
    NSPMMSc
    NSPTPhD-track
    PDPBDBA
    SSPTPhD-track
    Timetable this semester: Timetable for MT612
    Archives:

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