Registry

Module Specifications

Archived Version 2016 - 2017

Module Title	Quantitative Research Methods
Module Code	MT611
School	DCUBS
Online Module Resources
Module Co-ordinator	Mr Gerry Conyngham	Office Number	Q144
NFQ level	9	Credit Rating	5
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

Description

This module is designed to instruct the student in quantitative research measurement, multivariate statistical test selection, running multivariate techniques using SPSS and interpreting and presenting the results of these tests.

Learning Outcomes

1. Develop and evaluate multi-item quantitative indicators for measuring theoretical concepts.
2. Select the appropriate statistical techniques for examining the relationships between the different variable types.
3. Apply the most widely used multivariate statistical tests using SPSS.
4. Present the results of a quantitative research analysis in a valid and a readable form.

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Lecture	8	Formal Lectures
Assignment Completion	10	No 1
Assignment Completion	15	No 2
Independent Study	53	No Description
Directed learning	20	Specified Readings
Assignment Completion	15	SPSS Workshops
Assignment Completion	4	Independent SPSS Work using Case Studies
Total Workload: 125

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

Measurement in Quantitative Research
-Measurement Tools -Measurement Validity and Reliability

Data Characteristics and Variable Types
-Data Types -Examining Data - Normal Distribution -Skewed Data -Data Transformations

Statistical Tests/Significance
-What is Statistical Significance -Summary of Bivariate Statistical Techniques -Test Assumptions -Parametric/Non-parametric tests

Multivariate Statistical Tests
-Cause and Effect Independent, Dependent, Moderator and Mediator Variables -Causal Forecasting Techiques Regression Analysis- Hierarchial Regression Logit Models/Discriminant Analysis Structural Equation Modelling Model Assumptions Model Fit Test Diagnostics -ANOVA Models ANCOVA Techniques MANOVA Techniques Multi-level Models

Assessment Breakdown
Continuous Assessment	100%	Examination Weight	0%

Type	Description	% of total	Assessment Date
Course Work Breakdown

Reassessment Requirement
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
Unavailable

Indicative Reading List

Hair J, Black W, Babin B, Anderson E.: 2009, Multivariate Data Analysis: Global Edition, Pearson Education,
Several Quantitative Case Studies will also be provided to students to illustrate the techniques covered.: 0, To be confirmed,

Other Resources

None

Programme or List of Programmes

ALPT	PhD-track
AMPD	PhD
AMPM	MSc
AMPT	PhD-track
ARAPM	Master of Arts
ARPT	PhD-track
BSPD	PhD
BSPM	Master of Business Studies
BSPT	PhD-track
CAPD	PhD
CAPT	PhD-track
EEPD	PhD
EEPT	PhD-track
ESPD	PhD
GTBS	Graduate Training Visitor Program (BS)
MEPD	PhD
MEPT	PhD-track
MPBS	MPhil (Business)
NSPM	MSc
PDPB	DBA
PHPD	PhD
PHPT	PhD-track
PPPT	PhD-track
SFPD	PhD
SFPM	MSc
SGPD	PhD
SGPT	PhD-track
SSPM	MSc
SSPT	PhD-track

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