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Module Specifications

Archived Version 2015 - 2016

Module Title Quantitative Research Methods
Module Code MT611
School DCUBS

Online Module Resources

Module Co-ordinatorMr Gerry ConynghamOffice NumberQ144
NFQ level 9 Credit Rating 5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None

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.

Workload Full-time hours per semester
Type Hours Description
Lecture8Formal Lectures
Assignment Completion10No 1
Assignment Completion15No 2
Independent Study53No Description
Directed learning20Specified Readings
Assignment Completion15SPSS Workshops
Assignment Completion4Independent 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 Assessment100% Examination Weight0%
Course Work Breakdown
TypeDescription% of totalAssessment Date
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
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

Programme or List of Programmes
ARAPMMaster of Arts
BSPMMaster of Business Studies
GTBSGraduate Training Visitor Program (BS)