MANAGEMT 7250NA - Research for Decision Makers

Ngee Ann Academy - Quadmester 3 - 2023

This course is designed to develop skills in designing, conducting and evaluating action research in the context of management practice. Action research is a methodology commonly used by management practitioners to affect change. It is an iterative, cyclical, collaborative approach to identifying, critically and reflectively analysing, evaluating and synthesising data in relation to a given problem or question. Practitioners are simultaneously learners, researchers and agents for change. This approach is particularly useful when dealing with complex and complicated systems as it allows practitioners to respond to the emerging and changing needs as the situation changes with each iteration of the process. Students will consider leading theories in this field, the relationship between action research and other common business research methodologies and the relationship between action research and complexity and systems theories.

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Course Details

Course Code	MANAGEMT 7250NA
Course	Research for Decision Makers
Coordinating Unit	Management
Term	Quadmester 3
Level	Postgraduate Coursework
Location/s	Ngee Ann Academy
Units	3

Course Staff

Course Coordinator: Ms Hande Akman

Course Timetable

The full timetable of all activities for this course can be accessed from .

Course Learning Outcomes

The course provides a strong grounding in understanding the research process enabling students to either engage an external research organisation to undertake a study on their behalf in a business environment or, alternatively, action and complete a research project themselves as either the sole researcher or as part of a research team. In addition to the technical skills and knowledge required to be successful in these endeavours, the course also provides students with a clear understanding of the ethical considerations involved in undertaking research and the special challenges evidenced in international and cross cultural studies.
On successful completion of this course students will be able to:

1. Apply an advanced understanding of business research design options, methodologies and analysis methods (both qualitative and quantitative), including respective terms, definitions and applications to the design, implementation and evaluation of a research project.

2. Distil an identified business problem into a succinct research problem (or problems) and articulate this into a comprehensive research brief for investigation by a research team locally or internationally.

3. This brief will include a statement of the resulting research problem and the objectives that need to be achieved to provide the information necessary to tackle the business problem and the decisions that need to be made respective to it.

4. Complete, from the brief created, a research proposal for implementation at either a local or international level.

5. This will include (but not be restricted to), a literature summary at the necessary level of depth to ensure a thorough understanding of what is already known about the problem to be addressed, the proposed research design, data collection, sampling, analysis methods to be employed along with an indicative time frame for each stage of the research proposed and budget.

6. Apply a broad understanding of issues specific to undertaking business research across international boundaries, including cultural, geographical, language and cost related challenges and respective strategies and approaches that may be employed to solve them to the design, implementation and evaluation of a research project.

最新糖心Vlog Graduate Attributes

This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

最新糖心Vlog Graduate Attribute	Course Learning Outcome(s)
Attribute 1: Deep discipline knowledge and intellectual breadth Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.	All
Attribute 2: Creative and critical thinking, and problem solving Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.	2, 3
Attribute 3: Teamwork and communication skills Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.	3, 4, 5
Attribute 4: Professionalism and leadership readiness Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.	All
Attribute 5: Intercultural and ethical competency Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.	3, 4, 5
Attribute 8: Self-awareness and emotional intelligence Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.	3, 4, 5

Learning Resources

Required Resources

Text Book: Gill, John & Johnson, Phil (2010) Research Methods for Managers, 4th edition, London: Sage. 9781487870940

Readings: See Course Materials - Assigned reading material has been provided to generate greater depth of understanding on particular topics and may be discussed during class sessions. It is likely that this material will provide useful examples and references during assessment.

Recommended Resources

3.1 WEEKLY CATCHUP THROUGH ADOBE CONNECT
We will be catching up every Monday night through the Adobe Connect Portal. It is important for you to attend as many of these (direct from your own PC) as you can – although attendance is not compulsory. We will provide the details in the first visit.

3.2 BRING YOUR OWN PC
During the visits we will be using a range of software that requires you to bring your own PC that can connect to NGA wireless. The software is:
• Qualtrics Data Collection Suite (Web Based)
• Statistical Package for the Social Sciences (Web Based through ADAPT)
• nVivo 11 Qualitative data analysis software (PC only – no Mac)

Online Learning

3.4 ONLINE LEARNING
Course Website: www.myuni.adelaide.edu.au

3.5 QUALTRICS DATA COLLECTION SUITE
As an important part of your course, you will need to become familiar with the collection of quantitative data using a web survey. You will need to get yourself an account by going to www.adelaide.qualtrics.com and using your university password.

3.6 “ADAPT” FROM THE UNIVERSITY OF ADELAIDE
ADAPT (Any Device, Any Place and Time) allows staff and students to access their learning and teaching applications on personal devices: desktops, laptops, tablets and smart phones, anywhere:

• On campus via the UofA wireless network; and
• Off campus via broadband access and 3G/4G Mobile networks.

Through this “virtual suite” you will be able to use a range of licensed software products such as Statistical Package for the Social Sciences (SPSS) and the nVIVO Qualitative Data Analysis suite.

Details for ADAPT can be found at:

http://www.adelaide.edu.au/technology/yourservices/learning-teaching/adapt/
Learning & Teaching Activities

Learning & Teaching Modes

Seminars consist of lectures, practical, and student presentations. Essentially, your job in this course is to develop a well thought out, well justified research proposal and an associated set of research methods to embark on a project of your own, under close supervision. We will also strongly support the face to face work with digital content through the MyUni platform. This will include:

• Synchronous “Catchup” sessions each Monday night
• Two Lecturer visits
• Broadcast Emails
• Powerpoints, Readings and Case Studies all posted online
• Lecture recordings on video
• Digital Feedback on assignments – PDF of assignment rubric & video feedback (YouTube Link)

Workload

The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

Our face to face sessions are a total of 36 hours long. You can expect to spend about the same amount of time preparing for each class. Assignments and exam preparation will demand additional concentrated periods of non-classroom study, on your own or with your allocated student group. So you could expect to spend in the order of 120 hours of study time to complete the course, of which 36 hours would be in class.

Learning Activities Summary

Date Week Weekly Catchup Activities Monday Night Assessment
11-Jul 1 Hello and Welcome
16-18 Jul Visit 1 Research Brief presentation
18-Jul 2 Consolidate your written Resarch Brief - Group. Cullen at NAA this week to consult if necessary Research Brief Document
25-Jul 3 Discuss the Quantitative assignment
1-Aug 4 Completing the Quantitative Assignment Quantitative Assignment
5-8 Aug Visit 2
8-Aug 5 Using nVivo to get on top of the literature for your research proposal
15-Aug 6 The important theory of the course
22-Aug 7 Pre-Exam Review
27-Aug Theory Examination
29-Aug 8 Use qualitative methods and nVivo to firm up your ideas
5-Sep 9 Build a solid piece of qualitative investigation
Qualitative Assignment
12-Sep 10 Build a strong literature review
19-Sep 11 Finalising a Research Proposal
26-Sep 12 Research Proposal Document

Specific Course Requirements

Introduction芒聙炉to research
MS Word basic, intermediate & advanced. (esp: formatting, heading styles etc…, captions & cross-references)
MS Excel basic, intermediate & advanced. (esp: charting and formulae)
Library basics & database usage workshop. (including common Business Databases)
a.芒聙炉芒聙炉芒聙炉芒聙炉 … Secondary Sources (most of)
Academic integrity workshop/s (including plagiarism & ethics).
Nature & Value of Research in Business Setting (& ethics – 1st intro: academic & commercial)芒聙炉
a.芒聙炉芒聙炉芒聙炉芒聙炉 Outsourcing research (build a research brief, select a research provider, evaluate provided research)芒聙炉
b.芒聙炉芒聙炉芒聙炉芒聙炉 Designing & conducting research (develop a research proposal & conduct research)芒聙炉
Identifying and elucidating the business problem 芒聙炉
a.芒聙炉芒聙炉芒聙炉芒聙炉 From Defining a research topic to Refining a research question芒聙炉
Dimensions of Research: Approaches; Philosophies & Design (& Morphological Analysis)芒聙炉
a.芒聙炉芒聙炉芒聙炉芒聙炉 Pure/basic vs applied; 芒聙炉
b.芒聙炉芒聙炉芒聙炉芒聙炉 Qualitative & Quantitative & Hybrid, and Mixed Method & Multi-Method (& preview intro to Qualtrics)芒聙炉
i.芒聙炉芒聙炉芒聙炉芒聙炉 Inductive/Deductive/Abductive research (& iterative)芒聙炉
c.芒聙炉芒聙炉芒聙炉芒聙炉 Research ‘question’ vs research ‘objective’ and ‘develop hypotheses’ vs ‘explore & develop’芒聙炉
d.芒聙炉芒聙炉芒聙炉芒聙炉 More than “Qual vs Quant”: Exploratory, descriptive & causal and 芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉
i.芒聙炉芒聙炉芒聙炉芒聙炉 Broader & wider “Research Options” overview (eg: Case Study, Action Research; Systems Research; Systems Dynamics; Design-based research…)芒聙炉
ADAPT - The 最新糖心Vlog of Adelaide's Virtual Suite
Qualtrics data collection suite
SPSS Software: overview of SPSS as a Research Support Tool芒聙炉
NVivo software: Overview of NVivo as a Research Project Management Tool芒聙炉
a. research log 芒聙炉
b. research journal
(Secondary Sources – follow up & complete from Prequel)芒聙炉
Conceptual frameworks & theories:
a.芒聙炉芒聙炉芒聙炉芒聙炉 working with pre-existing conceptual frameworks & theories
b.芒聙炉芒聙炉芒聙炉芒聙炉 incremental/iterative – working with the old and refining to the new
c.芒聙炉芒聙炉芒聙炉芒聙炉 developing new conceptual芒聙炉 frameworks & theories
NVivo software supporting research design & development (and Literature Review; & Data Management):
(Literature Review for Background, Review, Justification, Exploration, Method & Results)
a.芒聙炉芒聙炉芒聙炉芒聙炉 creating & importing folders & documents;
b.芒聙炉芒聙炉芒聙炉芒聙炉 use of literature to define & refine research questions & hypotheses;
c.芒聙炉芒聙炉芒聙炉芒聙炉 use of ‘coding’ as an analytical tool for literature review and data analysis;
i.芒聙炉芒聙炉芒聙炉芒聙炉 a priori coding
ii.芒聙炉芒聙炉芒聙炉芒聙炉 a posteriori (post hoc) coding
d.芒聙炉芒聙炉芒聙炉芒聙炉 5 different methods (mechanically) of coding in NVivo芒聙炉
Intro to Quantitative Research, Scientific Method & Experimental Method
a.芒聙炉芒聙炉芒聙炉芒聙炉 “From Research question to questionnaire items”
b.芒聙炉芒聙炉芒聙炉芒聙炉 “From conceptual framework to questionnaire items”
c.芒聙炉芒聙炉芒聙炉芒聙炉 Variable types: dependent, independent, moderator
d.芒聙炉芒聙炉芒聙炉芒聙炉 Measurement (ratio / interval / ordinal / nominal)
Research Brief
a.芒聙炉芒聙炉芒聙炉芒聙炉 What is it & why have it
b.芒聙炉芒聙炉芒聙炉芒聙炉 Content & focus
c.芒聙炉芒聙炉芒聙炉芒聙炉 Timelines & Budgets
d.芒聙炉芒聙炉芒聙炉芒聙炉 Format & delivery
NVivo software supporting research design & development:
a.芒聙炉芒聙炉芒聙炉芒聙炉 developing use of codes, nodes and classifications;
b.芒聙炉芒聙炉芒聙炉芒聙炉 dealing with demographics;
c.芒聙炉芒聙炉芒聙炉芒聙炉 identifying relationship types & relationships;
d.芒聙炉芒聙炉芒聙炉芒聙炉 working with images
Quantitative Research Design:
a.芒聙炉芒聙炉芒聙炉芒聙炉 Data editing
b.芒聙炉芒聙炉芒聙炉芒聙炉 Data coding
c.芒聙炉芒聙炉芒聙炉芒聙炉 Data transformation
Survey Research Design
Survey Design and Application
a.芒聙炉芒聙炉芒聙炉芒聙炉 Overview introduction to Qualtrics
Qualtrics – Build & Distribute Surveys
a.芒聙炉芒聙炉芒聙炉芒聙炉 Question types
b.芒聙炉芒聙炉芒聙炉芒聙炉 Answer types
c.芒聙炉芒聙炉芒聙炉芒聙炉 Survey flow / logic
d.芒聙炉芒聙炉芒聙炉芒聙炉 Distributing a survey
e.芒聙炉芒聙炉芒聙炉芒聙炉 Time and timing / length
f.芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉 Attention checks
g.芒聙炉芒聙炉芒聙炉芒聙炉 Validity checks (internal & external)
Content Analysis – Qualitative & Quantitative views
Qualtrics – On-board analysis
a.芒聙炉芒聙炉芒聙炉芒聙炉 Built-in histograms
产.芒聙炉芒聙炉芒聙炉芒聙炉&苍产蝉辫;颁丑补谤迟颈苍驳&苍产蝉辫;
c.芒聙炉芒聙炉芒聙炉芒聙炉 Filtering result-views
诲.芒聙炉芒聙炉芒聙炉芒聙炉&苍产蝉辫;颁谤辞蝉蝉-迟补产耻濒补迟颈辞苍蝉&苍产蝉辫;
e.芒聙炉芒聙炉芒聙炉芒聙炉 Exporting data to Excel & SPSS
SPSS software supporting research design & development:
a.芒聙炉芒聙炉芒聙炉芒聙炉 Exploring quantitative data
颈.芒聙炉芒聙炉芒聙炉芒聙炉&苍产蝉辫;迟补产耻濒补迟颈辞苍蝉;&苍产蝉辫;&苍产蝉辫;
ii.芒聙炉芒聙炉芒聙炉芒聙炉 descriptive statistics;
iii.芒聙炉芒聙炉芒聙炉芒聙炉 data transformation (conversions between data types)
SPSS supporting research design & development:
a.芒聙炉芒聙炉芒聙炉芒聙炉 Tests of difference
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉 i.芒聙炉芒聙炉芒聙炉芒聙炉 Paired sample t-test
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉 ii.芒聙炉芒聙炉芒聙炉芒聙炉 Independent samples t-test
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉&苍产蝉辫;颈颈颈.芒聙炉芒聙炉芒聙炉芒聙炉&苍产蝉辫;础狈翱痴础&苍产蝉辫;
Demographics & background – what, where & how much (screener or info?)
SPSS supporting research design & development:
补.芒聙炉芒聙炉芒聙炉芒聙炉&苍产蝉辫;础蝉蝉辞肠颈补迟颈辞苍&苍产蝉辫;
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉&苍产蝉辫;颈.芒聙炉芒聙炉芒聙炉芒聙炉&苍产蝉辫;颁辞谤谤别濒补迟颈辞苍&苍产蝉辫;
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉 ii.芒聙炉芒聙炉芒聙炉芒聙炉 Linear regression
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉 iii.芒聙炉芒聙炉芒聙炉芒聙炉 Crosstabs / non-parametric
Sampling:
补.芒聙炉芒聙炉芒聙炉芒聙炉&苍产蝉辫;笔谤辞产补产颈濒颈迟测&苍产蝉辫;
产.芒聙炉芒聙炉芒聙炉芒聙炉&苍产蝉辫;狈辞苍-辫谤辞产补产颈濒颈迟测&苍产蝉辫;
c.芒聙炉芒聙炉芒聙炉芒聙炉 Expanding purposive & theoretical sampling
d.芒聙炉芒聙炉芒聙炉芒聙炉 What & why (NOT) “convenience”芒聙炉
SPSS supporting research design & development:
a.芒聙炉芒聙炉芒聙炉芒聙炉 Data reduction / Factor Analysis
b.芒聙炉芒聙炉芒聙炉芒聙炉 Multiple regression analysis
NVivo software supporting research design & development:
a.芒聙炉芒聙炉芒聙炉芒聙炉 Data sets;
b.芒聙炉芒聙炉芒聙炉芒聙炉 Data Classification & Analysis;
c.芒聙炉芒聙炉芒聙炉芒聙炉 Data linking;
d.芒聙炉芒聙炉芒聙炉芒聙炉 Basic Queries.
Research Proposal
a.芒聙炉芒聙炉芒聙炉芒聙炉 What is it & why?
b.芒聙炉芒聙炉芒聙炉芒聙炉 Content & focus
肠.芒聙炉芒聙炉芒聙炉芒聙炉&苍产蝉辫;贰迟丑颈肠蝉&苍产蝉辫;
d.芒聙炉芒聙炉芒聙炉芒聙炉 Format & discipline-specificity
e.芒聙炉芒聙炉芒聙炉芒聙炉 How to design & build – customized to YOUR research
Strong research paradigms in the Business World:
a.芒聙炉芒聙炉芒聙炉芒聙炉 Case study
b.芒聙炉芒聙炉芒聙炉芒聙炉 Action Research (& TQM / Sigma / RAD / Design-based … Lewin)
c.芒聙炉芒聙炉芒聙炉芒聙炉 Systems Analysis & Design & Systems Dynamics芒聙炉
NVivo software supporting research design & development: 芒聙炉
a.芒聙炉芒聙炉芒聙炉芒聙炉 Querying literature & raw data to launch
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉 i.芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉Word frequency queries.
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉 ii.芒聙炉芒聙炉芒聙炉芒聙炉 Text search queries
产.芒聙炉芒聙炉芒聙炉芒聙炉&苍产蝉辫;厂别迟蝉&苍产蝉辫;
c.芒聙炉芒聙炉芒聙炉芒聙炉 Memos & Annotations
d.芒聙炉芒聙炉芒聙炉芒聙炉 Links & Hyperlinks
NVivo software supporting research design & development: 芒聙炉
a.芒聙炉芒聙炉芒聙炉芒聙炉 Synthesis & Exploration of Data:
b.芒聙炉芒聙炉芒聙炉芒聙炉 Advanced Queries:
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉&苍产蝉辫;颈.芒聙炉芒聙炉芒聙炉芒聙炉&苍产蝉辫;颁辞诲颈苍驳&苍产蝉辫;
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉&苍产蝉辫;颈颈.芒聙炉芒聙炉芒聙炉芒聙炉&苍产蝉辫;颁辞尘辫辞耻苍诲&苍产蝉辫;
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉&苍产蝉辫;颈颈颈.芒聙炉芒聙炉芒聙炉芒聙炉&苍产蝉辫;惭补迟谤颈虫&苍产蝉辫;
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉&苍产蝉辫;颈惫.芒聙炉芒聙炉芒聙炉芒聙炉&苍产蝉辫;骋谤辞耻辫&苍产蝉辫;
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉 v.芒聙炉芒聙炉芒聙炉芒聙炉 Coding Comparison芒聙炉
Non-reactive data & analysis
Methods, Techniques & Skills in data collection 1:
a.芒聙炉芒聙炉芒聙炉芒聙炉 Developing (& Conducting) interviews A
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉 i.芒聙炉芒聙炉芒聙炉芒聙炉 Structured / semi-structured / unstructured
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉 ii.芒聙炉芒聙炉芒聙炉芒聙炉 Types of question
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉 iii.芒聙炉芒聙炉芒聙炉芒聙炉 Order of questions
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉 iv.芒聙炉芒聙炉芒聙炉芒聙炉 Framing of questions
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉&苍产蝉辫;惫.芒聙炉芒聙炉芒聙炉芒聙炉&苍产蝉辫;笔谤辞产别蝉&苍产蝉辫;
NVivo software supporting research design & development: 芒聙炉
a.芒聙炉芒聙炉芒聙炉芒聙炉 Synthesis & Visualization of Data:
b.芒聙炉芒聙炉芒聙炉芒聙炉 Models – static; dynamic; customized
肠.芒聙炉芒聙炉芒聙炉芒聙炉&苍产蝉辫;肠丑补谤迟蝉&苍产蝉辫;
诲.芒聙炉芒聙炉芒聙炉芒聙炉&苍产蝉辫;驳谤补辫丑蝉&苍产蝉辫;
e.芒聙炉芒聙炉芒聙炉芒聙炉 cluster analysis
Methods, Techniques & Skills in data collection 3:
a.芒聙炉芒聙炉芒聙炉芒聙炉 (Developing &) Conducting interviews B
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉i.芒聙炉芒聙炉芒聙炉芒聙炉 Venue & timing
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉ii.芒聙炉芒聙炉芒聙炉芒聙炉 Rapport & Aura (& empathy) – subjective & objective
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉iii.芒聙炉芒聙炉芒聙炉芒聙炉 Recording (ethics & mechanics)
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉iv.芒聙炉芒聙炉芒聙炉芒聙炉 Verbal communication – listening, studying & interpreting:
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉 1.芒聙炉芒聙炉芒聙炉芒聙炉 ‘Obvious’ meaning
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉 2.芒聙炉芒聙炉芒聙炉芒聙炉 Words & word choice
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉 3.芒聙炉芒聙炉芒聙炉芒聙炉 Underlying meanings & assumptions
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉 4.芒聙炉芒聙炉芒聙炉芒聙炉 Fluency
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉 5.芒聙炉芒聙炉芒聙炉芒聙炉 Cultural,芒聙炉 ethnic, organisational… influences
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉v.芒聙炉芒聙炉芒聙炉芒聙炉 Non-verbal communication – ‘listening’, studying & interpreting:
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉1.芒聙炉芒聙炉芒聙炉芒聙炉 Kinesics (body movement)
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉2.芒聙炉芒聙炉芒聙炉芒聙炉 Proxemics (spatial)
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉3.芒聙炉芒聙炉芒聙炉芒聙炉 Vocalics (paralanguage)
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉4.芒聙炉芒聙炉芒聙炉芒聙炉 Chronemics (time)
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉5.芒聙炉芒聙炉芒聙炉芒聙炉 Haptics (touch)
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉 vi.芒聙炉芒聙炉芒聙炉芒聙炉 Phrasing, probing, affirming…
NVivo software supporting research design & development: 芒聙炉
a.芒聙炉芒聙炉芒聙炉芒聙炉 Transcription (video &/or audio to text).
b.芒聙炉芒聙炉芒聙炉芒聙炉 Types of transcription
c.芒聙炉芒聙炉芒聙炉芒聙炉 Choice of transcriber (self or outsource?)
Methods, Techniques & Skills in data collection 1:
a.芒聙炉芒聙炉芒聙炉芒聙炉 Developing & Conducting Focus Groups A
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉 i.芒聙炉芒聙炉芒聙炉芒聙炉 Multiple participants vs individual interview
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉 ii.芒聙炉芒聙炉芒聙炉芒聙炉 Room design
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉 iii.芒聙炉芒聙炉芒聙炉芒聙炉 Recording issues (ethical & mechanical)
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉 iv.芒聙炉芒聙炉芒聙炉芒聙炉 Building a discussion guide
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉 v.芒聙炉芒聙炉芒聙炉芒聙炉 Facilitation of egalitarian input
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉 vi.芒聙炉芒聙炉芒聙炉芒聙炉 Facilitation of comfort/ confidentiality / security
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉 vii.芒聙炉芒聙炉芒聙炉芒聙炉 Controlling direction of discussion
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉 viii.芒聙炉芒聙炉芒聙炉芒聙炉 Managing ‘difficult’ members
Pulling it all together – toward the final Research Report (or thesis)
NVivo software supporting research design & development: 芒聙炉
a.芒聙炉芒聙炉芒聙炉芒聙炉 Framework Matrices –
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉&苍产蝉辫;颈.芒聙炉芒聙炉芒聙炉芒聙炉&苍产蝉辫;迟丑别辞谤测,&苍产蝉辫;&苍产蝉辫;
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉 ii.芒聙炉芒聙炉芒聙炉芒聙炉 method &
芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉芒聙炉&苍产蝉辫;颈颈颈.芒聙炉芒聙炉芒聙炉芒聙炉&苍产蝉辫;辫谤补肠迟颈肠别&苍产蝉辫;
Research design & development:
a.芒聙炉芒聙炉芒聙炉芒聙炉 Longitudinal 芒聙炉(qual & quant)
b.芒聙炉芒聙炉芒聙炉芒聙炉 [optional ‘advanced topic’?] Temporal Dimensions in Research (including Time Series) 芒聙炉
Methods, Techniques & Skills in data collection 4:
a.芒聙炉芒聙炉芒聙炉芒聙炉 The subtleties of the interview (& focus groups)

The 最新糖心Vlog's policy on Assessment for Coursework Programs is based on the following four principles:

Assessment must encourage and reinforce learning.
Assessment must enable robust and fair judgements about student performance.
Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
Assessment must maintain academic standards.

Assessment Summary

Assessment Weight Basis Related Learning Outcome
Assignment 1: Participation 10% Individual 1, 5, 6, 7

Assignment 2a: Research Brief - Presentation 5% Individual 2, 3

Assignment 2b: Research Brief - Document 10% Individual 2, 3

Assignment 3: Quantitative Assignment 15% Individual 1, 6

Assignment 4: Qualitative Assignment 15% Individual 1, 6

Assignment 5a: Research Proposal Presentation 5% Individual 2, 4, 5

Assignment 5b: Research Proposal Document 35% Individual 2, 4, 5

Assessment Detail

Assignment 1 - Class Discussion and (optional) blog support (Individual grade 10%)
Get involved
By the end of the course, I will know all of you pretty well. I will have spent over 35 hours in a classroom with you, seen some projects and taken attendance (really just as a reminder to me). This is ample opportunity for me to allocate 10% of the course mark to the quality of your contribution. This can be confronting for some people, but part of the transition to senior manager role involves being prepared to comment when the opportunity arises.
So this component of assessment in the course is essentially around the week’s case study, but general engagement within the class is good too.
Why not build a blog?
Grasp the nettle. Many people in business these days are creating a simple reflective blog – allowing it to be public but enhancing their personal brand. If you feel a little uncomfortable about speaking up in class, then why not do some reflection in a blog – even one that is public. This provides a triple benefit; you learn a few new skills, you can supplement the insights you demonstrated in the class, and it pumps you up the Google rankings against your name. In the first session I’ll show you how to get a blog going. It’s a five minute job. For the “tragic” among us, we might feed the blog with an outreach medium such as Twitter.
So for A1 the overarching criterion is as follows:
The instructor’s subjective assessment of the student’s contribution to the discussion of marketing concepts, principles and examples. This can occur in a face-to-face or blog context, or both.

Assignment 2 – Research Brief (15%) total
You are required to submit a research brief that summarises a stated business problem that requires specific information in order for Managers to decide a strategic direction to address the issue.

This will include:

• a background to the problem and
• the implications of not finding a solution.
• The nature of the information that needs to be sourced (from both secondary and primary sources):
• concise and actionable research objectives and
• an indicative time frame and budget for the completion of the resulting study.

This is not meant to be an 'argument' for the research per se - but it must provide any researchers that may take on the task with good understanding of the issues involved and nature of the information that is needed to find a solution.

• This brief (including no more than 10 references) may not exceed 2000 words.
• there is no minimum word limit - this will be dictated by the fewest possible words to contain adequate information to fully communicate the brief.
• A sample brief will be provided to students (see attached) and many others can be found from a variety of sources.

Assignment 3 – Quantitative Assignment (10%) total
Students are required to read a short case study provided in this brief and answer 5 questions, selecting the appropriate statistical test to answer each business problem.

You can download the datafile for this assignment from Myuni. You would also need to install SPSS software to perform the required quantitative data analysis. Detailed instructions on how to install SPSS on personally owned equipment can be found at: /technology/yourservices/software/personal/

This is an individual assignment. So, make sure you conduct data analysis and prepare a summary report individually. The written report represents a summary of key insight from your data analysis, demonstrating an ability to choose an appropriate statistical technique to provide answers to specific business research objectives, as well as an ability to correctly interpret results and develop preliminary conclusions and recommendations.

The title for this report should also include the words Assignment 1_quant_student ID. Please submit this assignment as a Microsoft Word file. The assignment should not exceed 1,500 words (excluding the Appendix). Please use only the key tables or charts in the main body of your report with all support tables attached in the Appendix.

Learning Objectives
This assignment focuses on student’s ability to:
• Develop skills in statistical analysis
• Understand the need to relate research and management objectives to analysis and findings
• Demonstrate an understanding of quantitative research and required analytical skills
• Develop knowledge in the choice and application of inferential and descriptive statistical tests
• Understand how quantitative research fits into the broader management and business planning process
• Apply an advanced understanding of quantitative analysis methods to a specific research problem

In addition, the aim of this assignment is to develop advanced knowledge in relation to the fundamental quantitative business and marketing research processes, such as:
• Data preparation and re-coding
• Descriptive data analysis and charting in SPSS and Excel, use of filters
• Significant difference testing using ANOVA or an Independent-samples T-test
• Factor analysis
• Correlations and regressions
• Provision of a short summary of key findings and recommendations

Assignment 4 – Qualitative Assignment (10%) total
Assessment item: submit an NVivo Project that supports the development of the Literature Review (Background and Introduction) of your Research Proposal, identifying your Research Gap and Research Questions and/or Hypotheses.

Using NVivo software, students will:

1. Import and collate relevant materials to support the development of their Research Proposal. This may include academic literature, government reports, white papers, business articles, survey data, photographs and other images, marketing media, audio and/or video clips.

2. Use the node & coding, classification and query tools to analyse and explore the imported documentation and materials, for the purpose of:
a) summary of “what is already known” about the research topic
b) identification of themes and patterns in “what is already known” about the research topic
c) identification of need for research (research gap/justification)
d) formulation of research question and hypotheses / propositions to address the research gap.

Assignment 5 – Research Proposal (35%) total
This Proposal is not only the culmination of the work in this course, it is also the official proposal that will be used to shape the required research in the 6 unit Research Project for most degree programs (or the equivalent for students in other disciplines).

(as above) In order to pass this course, students must achieve at least 50% overall, and achieve a passing mark of at least 50% for their final research proposal.

This document will be assessed on:
1. Short restatement and justification of the research problem
2. Relevance of objectives to the stated problem
3. Adequate assessment of the relevant secondary data
4. Justification of the research design and proposed methodology, including data collection and sampling methods. Questions to be answered include:
a) can it be done this way?
b) is it an appropriate design - is there a precedent for this design/approach?
c) will it provide the right kind of data and enough of it?
d) will analysis of the data provide the insights required?
e) are projected costs defendable?
5. Proposed analytical methods
6. Research budget and timelines for completion of the project
7. Quality of written presentation (referencing, grammar, punctuation and clarity).

For the purposes of this course, this document is expected to be no more than 15 pages (not including references and appendices), and follow the "Document Guidelines".

Note: where students come from a Discipline that has a specified Research Proposal descriptor, the Proposal must conform to that Discipline’s specifications.

Submission

Presentation of Assignments
• Please must retain a copy of all assignments submitted.
• All assignments must be submitted electronically through the MyUni assignments portal
• Assignments cannot be reworked and resubmitted after marking.

Course Grading

Grades for your performance in this course will be awarded in accordance with the following scheme:

M10 (Coursework Mark Scheme)
Grade	Mark	Description
FNS		Fail No Submission
F	1-49	Fail
P	50-64	Pass
C	65-74	Credit
D	75-84	Distinction
HD	85-100	High Distinction
CN		Continuing
NFE		No Formal Examination
RP		Result Pending

Further details of the grades/results can be obtained from Examinations.

Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

Final results for this course will be made available through .

Student Feedback

The 最新糖心Vlog places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the 最新糖心Vlog to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
Student Support
Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
Fraud Awareness

Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student鈥檚 disciplinary procedures.

The 最新糖心Vlog of Adelaide is committed to regular reviews of the courses and programs it offers to students. The 最新糖心Vlog of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.

Authorised by:	Deputy Vice-Chancellor and Vice-President (Academic)
Maintained by:	Course Outlines Administrator

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