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Module Catalogue

SCQF Level: 08  

Module Code: ELE016

Credit Value: 20  

Year: 2017/8

Term: Term 2

School: Dundee Business School

Description

An introduction to decision analysis and evidence− based decision making

Aims

The aim of this Module is to provide the student with : an understanding of basic concepts, and a skill set, in decision analysis and support, and evidence−based decision making.

Learning Outcomes

By the end of this module the student should be able to:

1.  Understand key concepts and processes in decision making, analysis, and support

2.  Structure decision problems, elicit preferences and priorities

3.  Analyze, interpret and use different datasets, and evaluate real data problems

4.  Perform basic decision analysis.

Indicative Content

1 *

Attributes and Contexts of Decisions: Attributes of a “good” decision, “bad” vs “wrong” decisions; “Is decision making an art or a science?”; Contexts of considering decisions; The role of the analyst/facilitator.

2 *

Intuition, Individuality, and Self−awareness: The role of intuition in decision making; Individual differences and their impact on making decisions; the role of self−awareness.

3 *

Scientific Approaches to Decision Making: Decision Trees; Multi−Criteria Decision Analysis; Multi−Attribute Value Function model.

4 *

Problem Structuring: Identifying and defining options; Identifying/eliciting criteria; Building a value tree.

5 *

Value Elicitation: Values versus facts as the basis for a decision;The importance of information to support decisions; Eliciting and quantifying preferences (scores); Eliciting and quantifying priorities (weights); the importance of information in decision making.

6 *

Exploring a Decision: Conducting sensitivity analysis, exploring the impact of changes to scores and weights; “Requisite” modelling.

7 *

Exploratory Data Analysis and Vizualization: Methods for exploring and visualizing patterns and trends in data to help generate research hypothesis.

8 *

Introduction to Hypothesis Testing: Basic data analysis methods for hypothesis testing, formulation of research hypothesis, introduction to basic statistical tests (t−test, one way ANOVA, regression analysis) and interpretation of results based on p−values.

Statement on Teaching, Learning and Assessment

Teaching and learning on this module will be interactive and learner− centred, with 50% of content delivered through enquiry based learning. The module will develop the student’s analytical, interpretive and evaluative skills through structured (theoretical) input, and through practice and application, including tutorial and lab based work. In week 7, students will have the opportunity to participate in activities that will provide them with feed−back and feed−forward.

Teaching and Learning Work Loads

Total 200
Lecture 14
Tutorial/Seminar 7
Supervised Practical Activity 14
Unsupervised Practical Activity 0
Assessment 40
Independent 125

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Guidance notes:

Credit Value – The total value of SCQF credits for the module. 20 credits are the equivalent of 10 ECTS credits. A full-time student should normally register for 60 SCQF credits per semester.

Disclaimer: We make every effort to ensure that the information on our website is accurate but it is possible that some changes may occur prior to the academic year of entry. The modules listed in this catalogue are offered subject to availability during academic year 2017/18 , and may be subject to change for future years.

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