Embed Size (px)
Citation preview
Title: Action Research (BMAN 80202)
Credit Rating: 5 credits
Level: (UG 1/2/3 or PG) PG
Delivery: Semester 2
Tutor(s): Prof Peter Kawalek
Aims: The course introduces and evaluates action research for Doctoral Students. Action research has become more popular, with scholars using the method for rapid change situations including technological driven. This method provides access to organisations that otherwise would have declined participation in the research. However, compared to other methods, Action Research has relative strengths and weaknesses including relevance and rigour of the research process and the relationship between the researcher & the client. Learning Outcomes: On completion of the course, students will:
o Understand action research and how it compares to other research methods
o When and when not to employ the method for doctoral research studies o Understand the roles in the research process o Understand the theoretical, practical and methodical implications from a
doctoral study. Content: Action Research, Case Studies and Positivism. The method’s use, concerns dealing with issues and the broader criteria how to choose the method Action Research’s role relationships in the study. Findings and how they relate to theory, practice and methodology. Also recent and current work at Britvic will be brought in from the action researchers’ perspective.
Teaching and learning methods: Lectures Workshops and Discussions Throughout doctoral Action Research studies will be used to illustrate the major points. Preliminary reading: Warmington (1980): ‘Action Research: its methods and its implications’ Journal of Applied Systems Analysis, Vol. 7.
Baskerville and Wood-Harper (1996): ‘A critical perspective on Action Research as
a method for information systems research’, Journal of Information Technology, vol. 11. Eden and Huxham (1996): ‘Action Research for management research’, Journal of Management Studies, Vol. 7. Baskerville R. & Wood-Harper (1998): ‘Diversity in information systems: Action
Research methods’, European Journal of Information Systems, Vol. 7.
Learning hours: Activity Hours allocated
Staff/student contact 5
Tutorials The 5 hours include lectures, workshops, tutorials
Private study 30
Directed reading 15
Total hours 50
Other activities
Optional Assessment:
Optional Assessment activity Length required Weighting within unit
A critique of an Action Research paper or report 2,500-word assignment
100%
Title: Actor Network Theory (BMAN 80370) Credit Rating: 5 Level: (UG 1/2/3 or PG) PG Delivery: (semester 1, 2 or both etc)
Semester 2
Tutor(s): Dr Chris McLean Aims: The main aim of this course is to examine technological change and innovation by exploring a range of issues underlying the Actor Network Approach (ANT or the sociology of translation). The design, development and application of technology within organisations and society will be examined with specific reference to the ANT approach, and the students will gain an insight into how such an approach can provide an alternative understanding of such issues. This course requires in-depth knowledge and understanding of technological determinism, social shaping and the social construction of technology approach. Learning Outcomes: On completion of this unit successful students will be able to:
o Review different methodological issues relating to the design, development and application of technology within organisations and society with specific reference to ANT
o Explore a range of issues relating to our understanding of agency and technological change
o Select and analyse research material from a wide range of sources o Examine and critically reflect upon a range of theories, issues and
perspectives o Work in a group situation to review different ideas and approaches in
relation to the issues explored within the course. Content:
o Section One: An introduction to Actor Network Theory o Section Two: Exploring issues of methodology and ANT.
Teaching and learning methods: Lectures, readings, interactive discussion and group presentations, private study, and essay writing. Preliminary reading: Callon, M., (1986), Some elements of a sociology of translation: Domestication of the scallops and the fishermen of St. Brieuc Bay, in Law, J. Ed., Power, Action, and Belief: A New Sociology of Knowledge?, London: Routledge and Kegan Paul, pp. 196-233.
Latour, B., (1999), Pandora’s Hope: Essays on the Reality of Science Studies, Harvard University Press. Latour, B., (2005), Re-assembling the Social: an introduction to ANT, Oxford University Press.
Law, (1987), Technology and Heterogeneous Engineering: the Case of the Portuguese Expansion, in Bijker, W. E., T. Hughes and T. Pinch, The Social Construction of Technological Systems: New Directions in the sociology and History of Technology, pp. 111- 134.
Learning hours:
Activity
Hours allocated
Staff/student contact 5 Tutorials Private study 30 Directed reading 15 Total hours 50 Other activities e.g. Practical/laboratory work
Optional Assessment:
Assessment activity Length required
Weighting within unit
Essay 2,500 to 3,000 words
100%
1
WORKSHOP OUTLINE Title: Advanced Qualitative Data Analysis with
NVivo Course code: BMAN 97322 Credit Rating: N/A Level: (UG 1/2/3 or PG) PhD Delivery: (semester 1, 2 or both etc) Semester 2 Tutor(s): Dr. Eva Alfoldi ([email protected]) Aims:
The purpose of this workshop is to refresh students’ understanding and application of the QSR NVivo software for qualitative research. The workshop combines a discussion of underlying principles with hands-on application to the students’ own research, thus encouraging the development of ‘disciplined imagination’ (creativity and rigour) in data analysis. NB. Please note that the workshop is only open to those PhD students who have already collected their own qualitative data (e.g. interviews, focus groups, secondary data such as government, media or company documents etc.) for their research. It is a requirement of the workshop that students bring their own data to work on during the workshop. Those students who do not have their own qualitative data to analyse are not eligible to participate in Session 2, although they may be allowed to attend Session 1 at the course tutor’s discretion. Students without their own data to are encouraged to attend the course BMAN 80542 Introduction to Qualitative Data Analysis with NVivo instead. Basic familiarity with the features of NVivo is assumed, although a ‘refresher’ of the software’s key features will be given. Students should either have attended the Introduction to Qualitative Data Analysis with NVivo course before, or familiarised themselves with the software through self-study. See http://www.qsrinternational.com/support_tutorials.aspx).
Learning Outcomes:
On completion of this unit successful students will be able to: • Explore and code their own qualitative (textual, visual and aural) data in NVivo • Apply CAQDAS for their own research project and design strategies to
represent the data analysis process in the methodology section of their theses • Develop an appreciation of the advanced data exploration possibilities offered
by the NVivo software Content:
• Hands-on demonstration and application of the advanced features of the
software (Session 1)
2
• Guided application of specific features to students’ own doctoral research projects, during bookable individual slots (Session 2)
Teaching and learning methods:
NB. Students need to bring their own data to work on.
Learning hours: Activity
Hours allocated
Staff/student contact (group)
4 (intensive computer-based tutorial in Session 1)
Staff/student contact (individual)
30-60 mins per student, depending on total number of participants (guided application and tailored discussion during individual meeting slots – Session 2)
Total contact hours
4.5-5 hrs per student
Private study / practice suggested
5-15 (to be conducted during the 9 days in between Session 1 & Session 2)
Preliminary reading:
• Bazeley, P. (2007). Qualitative Data Analysis with NVivo. London: Sage • Richards, L. (2005). Handling qualitative data - A practical guide. London:
Sage • Sinkovics, Rudolf R. and Eva A. Alfoldi (2012). Progressive focusing and
trustworthiness in qualitative research: The enabling role of computer-assisted qualitative data analysis software (CAQDAS). Management International Review, 52(6), 817-845. DOI: 10.1007/s11575-012-0140-5. Available at: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:137280
1
Title: Advanced Survey Design (BMAN80181)
Level: PG
Delivery: Semester 1, 2015 – 2016
Tutor(s): Dr. Charles C. Cui (e-mail: [email protected])
Aims:
This course aims to introduce:
The process and issues in designing survey research (a quantitative method).
Developing research theories and setting up the survey design.
Principles and techniques for conceptualising research constructs and models.
Principles and techniques for developing measurement scales.
Issues and remedies for method bias/variance.
Issues and techniques for questionnaire design and web-based survey.
Fundamental issues and techniques in sampling.
Issues in data collection.
Issues and techniques in reporting survey research.
Learning Outcomes:
On completion of this unit students are expected to be able to:
present epistemological justifications for the application of survey design;
present well defined research constructs/concepts and research models;
design a survey plan;
design and develop measurement scales;
design a questionnaire (paper-and-pencil and Web-based forms) with solutions to minimising method bias/variance;
select appropriate sampling methods;
code responses from the survey into a database for analysis;
articulate survey analysis issues, results and interpretations of the results.
Content:
Survey design, conceptualisation of research constructs, measurement scale development, questionnaire design, survey instrument translation, solutions to method bias, Web-survey design, pilot test, sampling, administration and data entry, procedural issues in survey data analysis, interpretations of survey analysis results.
Teaching and Learning Methods:
Workshops comprising lectures and group work.
Reading List:
Textbooks:
Mitchell, Mark L. and Jolley, Janina M. (2012), Research Design Explained, International ed. of 8th revised edition, Wadsworth CENGAGE Learning.
De Leeuw, Edith D.; Hox, Joop J.; and Dillman, Don (2008), International Handbook of Survey Methodology, Routledge. Paperback ISBN: 9780805857535.
2
Groves, Robert M.; Fowler, Floyd J., Jr.; Couper, Mick P., Lepkowski, James M.; Singer, Eleanor; Tourangeau, Roger (2009), Survey Methodology, 2nd Edition, Wiley, Paperback ISBN: 978047046546-2
Fowler, Floyd J, Jr. (2009), Survey Research Methods, 4th Edition, Sage Publications, Inc. Paperback ISBN: 9781412958417.
De Vaus, David (2001), Surveys in Social Research, 5th Edition, Routledge. Paperback ISBN: 9780415268585.
De Vellis, Robert F. (2012), Scale Development: Theory and Applications, 3rd Edition, SAGE Publications, Inc. Paperback ISBN: 9781412980449.
Blair, Johnny; Czaja, Ronald F.; and Blair, Edward A. (2014), Designing Surveys: A Guide to Decisions and Procedures, Sage Publications, Inc.
Journal articles:
Forza, Cipriano (2002), “Survey research in operations management: a process-based perspective”, International Journal of Operations & Production Management, 22(2), 152-194.
MacKenzie, Scott B.; Podsakoff, Philip M.; and Podsakoff, Nathan P. (2011), “Construct measurement and validation procedures in MIS and behavioral research: Integrating new and existing techniques”, MIS Quarterly, 35(2), 293-334.
Malhotra, Manoj K. and Grover, Varun (1998), “An assessment of survey research in POM: from constructs to theory”, Journal of Operations Management, 16, 407-425.
Pinsonneault, Alain and Kraemer, Kenneth L. (1993), “Survey research methodology in management information systems: an assessment”, Journal of Management Information Systems, 10(2), 75-105.
Podsakoff, Philip M.; MacKenzie, Scot B.; and Podsakoff, Nathan P. (2012), “Sources of method bias in social science research and recommendations on how to control it”, Annual Review of Psychology, 63, 539-569.
Rindfleisch, Aric; Malter, Alan J.; Ganesan, Shankar; and Moorman, Christine (2008), “Cross-sectional versus longitudinal survey research: Concepts, findings, and guidelines”, Journal of Marketing Research, 45, 261–279.
Learning Hours:
Activity
Hours allocated
Staff/student contact
7
Tutorials
n/a
Private study
43
Directed reading
As shown in the lists of textbooks and articles.
Total hours
50
Other activities e.g. Practical/laboratory work
Within the contact hours there will be some group work exercises.
Title: Bibliometrics, Altmetrics and the Measurement of Science and its Institutions (BMAN 80242)
Credit Rating: 5 Level: (UG 1/2/3 or PG) PG Delivery: (semester 1, 2 or both etc) Semester 2 Tutor(s): Dr John Rigby Aims: To introduce research students to bibliometric and other methods with which to understand and evaluate the development of scientific knowledge and the behaviour of actors in the science system. Learning Outcomes: On completion of this unit, successful students will be able to:
o understand the key assumptions and claims of these methods, their strengths and weaknesses and why such methods are controversial;
o be able to work with bibliometric data and understand the results of bibliometric and altmetric studies.
Content: The course will cover the following topics:
o the development of bibliometrics and examples of its use for policy and evaluation, including key assumptions, methods, and measures;
o derivation of key bibliometric entities, e.g. scientific fields o relation with other forms of codified knowledge, e.g. patents; o the nature of the data and the operation of key publication databases
(e.g. Web of Knowledge, Scopus); o construction of specific datasets.
Teaching and learning methods: The course will comprise two sessions and optional assessment. The first session will be in the form of a lecture and demonstration. The lecture will examine the claims of bibliometrics, new approaches to understanding science using so-called altmetrics, their key assumptions, principal methods, and main claims. There will be a demonstration of Thomson Reuters’ Web of Knowledge, Elsevier’s SCOPUS, including the analysis features offered by the service providers and altmetrics measures. The second session will discuss the claims of altmetrics and the assessment essay will ask students to answer the question: “Do altmetrics offer better ways of understanding science and its institutions than bibliometric methods?” Students must prepare by engaging with the set reading. Preliminary reading: Introductory and Essential: De Bellis, Nicola, (2009) Bibliometrics and citation analysis: From the Science Citation Index to cybermetrics. Lanham, MD: Scarecrow Press
Piwowar, H. (2013). Value all research products. Nature, 493(7431), 159-159. Cronin, B., & Atkins, H.B. (Eds.). (2000) The Web of Knowledge: A Festschrift in. Honour of Eugene Garfield. Medford, NJ: Information Today. Van Raan, A.F.J. (Ed.), (1988) Handbook of Quantitative Studies of Science and Technology, Amsterdam: North-Holland. https://twitter.com/jasonpriem/status/25844968813 Advanced: Qurashi, M. (1984) "Publication rate as a function of the laboratory/group size" Scientometrics 6(1): 19-26. DOI 10.1007/bf02020110 Borgman, Christine L. Ed., (1990) Scholarly Communication and Bibliometrics, Newbury Park, CA: Sage Publications Inc. Boyack, K.W., Klavans, R., & Börner, K., (2005) Mapping the backbone of science, Scientometrics, 64, 3, pp. 351- 374. Small, H.G., (1973) "Co-citation in the scientific literature: a new measure of the relationship between two documents." Journal of the American Society for Information Science, 24, 265-269. van Raan, A.F.J., (2003) The use of bibliometric analysis in research performance assessment and monitoring of interdisciplinary scientific developments, Technikfolgenabschätzung-Theorie und Praxis/Technology Assessment-Theory and Practice, 1, 12, March 2003, pp. 20-29. Also at http://www.cwts.nl/TvR/documents/AvR-TFA2003.pdf.
Learning hours:
Activity
Hours allocated
Staff/student contact 6 (2 sessions of 3 hours each) Tutorials n/a Private study 44 Directed reading Total hours 50 Other activities e.g. Practical/laboratory work
Optional Assessment:
Optional Assessment Activity Length required
Weighting within unit
Mini-project and report 2,500 words 100%
Title: Case Study Research: Method and Methodology
(BMAN 80020) Credit Rating: 5 Level: (UG 1/2/3 or PG) PG Delivery: (semester 1, 2 or both etc)
2
Tutor(s): Prof Robert Scapens NOTE: all students registered for this course will be required to attend both sessions and to take part in the group work (described in the Teaching and Learning Methods section below). There will be pre-reading assigned for this group work, along with other pre-reading. The group work is not assessed and a separate piece of assessed worked will be required from students who elect to be assessed on this course – see Assessment section below. Aims: Case studies are increasingly being used in many areas of business and management research, and it is widely recognised that case research can be powerful in developing, modifying and extending theory in both exploratory and explanatory research designs. However, there can be misunderstanding of the methodological underpinnings of research using case studies. Different methodological perspectives can use case studies in quite different ways. For example, the role of case studies in positive research is quite different to their use by interpretive researchers.
This course focuses on the methodological underpinnings of case study research and the roles of case studies in different methodological traditions within the diverse fields of business and management. Examples will be provided of both positive and interpretive case studies. Categorisations of different methodological bases of case studies will be discussed, and the use of theory in case study research will be explored. In addition, the course will cover the characteristics of good case research design and ways of constructing ‘convincing’ case studies. Learning Outcomes: On completion of this unit successful students will be able to:
o Understand how case study research methods are used within different methodologies.
o Understand the different uses of case studies in different areas of business and management research.
o Design and analyze case studies. o Critique existing case study research papers.
Content:
o The diverse uses of case studies in different research methodologies o What is meant by ‘case study’ and ‘case study research’ and when it is
an appropriate choice of research design – what are the implications of choosing a case study design?
o Examining different uses of case studies in business and management research, and critiquing case study research designs.
o Issues of validity, reliability and generalization. o Practical issues of case study research for doctoral projects.
o Weaknesses in case study design. o Critiquing existing case research papers.
Teaching and learning methods:
The course will comprise two separate half days. The first meeting will be primarily lectures on methodological issues and the methods of case research. At the end of this meeting the group work will be outlined and the groups allocated. Between the two meetings each group will be required to prepare a presentation critiquing an existing case study research paper. These critiques will be presented at the second meeting, and there will be a general discussion about what has been learned from these critiques. Pre-reading of upto five case study research papers (and other pre-reading - 2 papers and 2 book chapters) is a requirement for the course. The readings will be advised about one month before the course.
Optional Assessment:
Participants will select a case study research paper from their own areas and critically evaluate the way in which the case study is used. They will be expected to outline the methodological perspective adopted in the paper, summarise the research questions, explain how the case study is used to address those questions, discuss how theory is used in the case, and assess issues of validity, reliability and generalization.
The length of the critique should not exceed 1500 words; so clarity of analysis and conciseness of presentation are essential.
Preliminary reading: The following two papers, which will be discussed during the lectures, must be read before the course:
Merchant, K.A., and Riccaboni, A., Performance-based Management Incentives in the Fiat Group: A Field, Management Accounting Research, Vol.1 No.4, December 1990, pp.281-303.
Scapens, R.W. and Roberts, J. Accounting and Control: A Case Study of Resistance to Accounting and Change, Management Accounting Research, Vol.4 No.1, March 1993, pp.1-32.
Other pre-reading will be advised about one month before the course.
The following readings provide additional background – other readings will be provided during the course:
Scapens, R. W., (2004), "Doing Case Study Research", in Humphrey, C. & B Lee (Eds), The Real Life Guide to Accounting Research, Elsevier, pp. 257-279. See also other chapters in this book.
Yin, R.K., (2009) Case Study Research: Design and Methods, Fourth Edition, London: Sage
Learning hours:
Activity
Hours allocated
Staff/student contact 12 Tutorials n/a Private study 23 Directed reading 15 Total hours 50 Other activities e.g. Practical/laboratory work
Optional Assessment:
Assessment activity Length required
Weighting within unit
Critique of case study research paper
1,500 words maximum
100%
Title: Comparative Case Study Analysis BMAN80062 Credit Rating: 5 credits Level: (UG 1/2/3 or PG) PG Delivery: (semester 1, 2 or both etc)
Semester 2
Tutor(s): Dr Laszlo Czaban Aims: Introduction to the concept and use of comparative case study analysis. Using case study method is gaining popularity in many disciplines, though it is still regarded with some suspicion because of validity and extrapolation reasons. One of the ways of improving both validity and generalisation is to use case studies in an integrated, comparative manner. The course introduces students to the concepts of comparative case study analysis and the ways it can be used. In addition, epistemological issues will also be addressed. Learning Outcomes: On completion of the course, students will:
o Understand the concept of comparative case study analysis o Use the method in different ways o Understand the limitations of the method o Understand the epistemological implications and the ways in which the
researcher needs to deal with them. Content: Comparative case study analysis. Principles, types, examples. Problems and dealing with them.
Teaching and learning methods: Lectures, workshops Preliminary reading: Depending on the composition of the student group, the course will draw on the works of Max Weber, Barrington Moore, Theda Skcopol, Charles Ragin, Guillen, Djelic
Learning hours:
Activity Hours allocated
Staff/student contact 5 hours Tutorials
The 5 hours include lectures, workshops, tutorials
Private study 30 Directed reading 15 Total hours 50 Other activities
e.g. Practical/laboratory work Assessment:
Optional Assessment activity Length required
Weighting within unit
A report on business analysis/narrative and research/paradigm/stakeholder perspective related to PhD topic
3,500-word assignment
100%
1
Title: Critical Thinking for Scientific Research (BMAN80411)
Credit Rating: 5
Level: PG
Delivery: Semester 1, 2015-2016
Tutor(s): Dr. Charles C. Cui Aims:
This course aims to enable students to achieve understanding and command of fundamental principles and techniques of basic logical reasoning and argumentation for executing high quality research in social sciences. As an elective course with limited classroom time, this course uses an applied approach and helps students to understand how arguments are made and how they are refuted, and how to apply the techniques and skills in constructive critique and developing new, novel thoughts and theories with tremendous potential impacts and power to shape our world.
Learning Outcomes:
On completion of this unit successful students will be able to:
• use logical analysis and rhetoric techniques to identify concepts, argument components and rhetoric structure of an academic discourse;
• identify and differentiate sound and false arguments in a research discourse (e.g. a research article) and critique complicated arguments, theories and research findings;
• apply argumentation techniques to develop and present research ideas, critiques, propositions, case analysis, models, discussions, etc. in valid forms with scientific rigor and eloquence in writing style.
Content:
Scientific research rigor is what distinguishes academic research from consulting and what characterises the nature of a successful PhD thesis and high quality academic conference and journal papers. Scientific rigor is construed as soundness in theoretical and conceptual development based on critical thinking and argumentation, methodological design and execution, interpretation of findings, and use of findings in extending theory or developing new theory with relevance to real life phenomena. One of the major challenges of success in PhD is to be able to develop competence and skills in conducting research and presenting research discourses with scientific (scholarly) rigor. In this course, Dr. Cui will introduce fundamental principles and applied techniques drawn from the fields of argumentation, logic and English rhetoric. Fundamental principles and techniques will be taught through systematic, action-based exercises to equip students with competence and command of techniques. As a methodology course, the contents focus on scientific thinking and applied techniques from logic, argumentation and English rhetoric. This course is complementary but fundamentally different from the courses that introduce epistemology, literature review and research design.
Teaching and Learning Methods:
Three sessions of workshops comprising lectures and group work.
2
Reading List:
Mandatory Textbook:
Inch, Edward S. and Tudor, Kristen H. (2014), Critical Thinking and Communication: The Use of Reason in Argument, 7th edition, global edition, Pearson Education Limited. (http://catalogue.pearsoned.co.uk/educator/product/Critical-Thinking-and-Communication-The-Use-of-Reason-in-Argument-International-Edition/9781292058825.page)
This book is compulsory for attending this course. In-class exercises/activities are based on exercises/tests in this book. You must bring with you a personal copy of this book to the classes. Attendance will not be permitted without this book. You must attend each of the three sessions. Absence from any one session will be regarded as self-withdrawal.
Complementary Textbooks:
Hughes, William; Lavery, Johathan; and Doran, Katheryn (2010), Critical Thinking: An Introduction to the Basic Skills, 6/E, Broadview Press. Paperback ISBN: 9781551111636.
Hurley, Patrick J. (2012), A Concise Introduction to Logic, International Edition of 11th ed., Wadsworth. Paperback ISBN: 9781111185893.
Salmon, Merrilee H. (2013), Introduction to Logic and Critical Thinking, 6th edition, Wadsworth.
Learning Hours: Activity Hours allocated Staff/student contact 10
Tutorials n/a
Private study 65
Directed reading
Total hours 75
Other activities e.g. Practical/laboratory work
n/a
Optional Assessment:
Assessment activity Length required Weighting within unit
A written assignment (in academic journal article style) of individual work that presents a critical analysis of some mainstream theories pertinent to your PhD topic.
2000-2500 words excluding appendices.
100%
COURSE UNIT OUTLINE Title: Diary studies in organisational research Credit Rating: 5 Level: (UG 1/2/3 or PG) PG Delivery: (semester 1, 2 or both etc) Semester 2 Tutor(s): Dr David Holman Aims:
- To introduce students to diary methods in organisational research
- To introduce students to the analysis of quantitative diary data
- To provide students with a method for examining daily and person-level
effects Learning Outcomes: On completion of this unit successful students will be able to:
- Understand the different types of diary methods
- Understand the advantages and limitations of diary methods
- Design a survey-based diary study
- Understand the multi-level structure of quantitative diary data and the
implications of this for statistical analysis
- Structure diary data for SPSS analysis
- Analyse multi-level daily diary data in SPSS Content:
- Introduction to diary methods in organisational research
o Overview of diary methods o Overview of advantages and disadvantages of diary methods
- Designing a survey-based diary study
o Problems and limitations of running diary studies in organisations
o Group exercise to design a survey-based diary study
- Analysing diary studies o Multi-level structure of quantitative diary level data
o Overview of analysis of multi-level data in SPSS o Exercise to introduce students to analysis of diary data in SPSS
Teaching and learning methods:
- Lectures, practical exercises and group discussions
- The basic material will be presented in lectures. Detailed notes will be
supplied in addition to Powerpoint slides. The students will be provided
with a work book explaining in detail how to carry out different analyses
in SPSS applied to real data.
Preliminary reading: Bolger, N., Davis, A., & Rafaeli, E. (2005). Diary Methods: Capturing life as it
is lived. Annual Review of Psychology, 54, 579-616.
Ohly, S., Sonnentag, S., Niessen, C., & Zapf, D. (2010). Diary Studies in
Organizational Research: An Introduction and Some Practical
Recommendations. Journal of Personnel Psychology, 9, 79-93. Learning hours: Activity
Hours allocated
Staff/student contact
7hrs. Six hours will be practical exercises.
Tutorials
Private study
Directed reading
43
Total hours
50
Other activities e.g. Practical/laboratory work
Assessment:
Optional Assessment activity Length required Weighting within unit Students will be required to submit a report that sets out the design and analysis of a survey-based diary study in an organisational context.
1500 words 100%
UNIVERSITY OF MANCHESTER MANCHESTER BUSINESS SCHOOL
s:\research and doctoral services\pgr administration\research training programme\rtp 2015-2016\course outlines\b&m outlines\bman 80222 elite interviewing with senior managers 2013-2014 - copy.docx 1
COURSE UNIT OUTLINE Title: Elite interviewing with senior managers Credit Rating: 5
Level: (UG 1/2/3 or PG) PG
Delivery: (semester 1, 2 or both etc) 2
Tutor(s): Andrew James Aims: To introduce research students to elite interviewing in a business and organizational context, equipping them with skills to undertake elite interviews with senior managers and to appreciate the strengths, weaknesses and challenges of the approach. Learning Outcomes: On completion of this unit successful students will be able to: Provide a critique of elite interviewing as a research method Undertake an elite interview Be aware of the practical, methodological and ethical issues associated with elite interviewing Content: Elite interviewing and examples of its use in research The strengths and weaknesses of elite interviewing Accessing elite interview subjects and the role of gatekeepers Conducting elite interviews Validity and reliability issues in elite interviewing Ethical issues in elite interviewing Teaching and learning methods: The course will comprise two principle elements: 1. A one day taught session introducing the key issues related to elite interviewing; 2. A practical exercise in which each student will be expected to undertake an elite interview – identifying and accessing the elite interviewee; conducting the elite interview; and writing up their findings and reflections on the process. Preliminary reading: Books: G. Moyser & M. Wagstaffe, editors, Research methods for elite studies. London: Allen & Unwin Hertz, Rosanna & Jonathan B. Imber. 1995. Studying elites using qualitative methods. Beverly Hills: Sage Publications.
UNIVERSITY OF MANCHESTER MANCHESTER BUSINESS SCHOOL
s:\research and doctoral services\pgr administration\research training programme\rtp 2015-2016\course outlines\b&m outlines\bman 80222 elite interviewing with senior managers 2013-2014 - copy.docx 2
Journal articles: Aberdach, JD and Rockman, BA (2002) “Conducting and coding elite interviews” PS Online, December: 673-676.. Berry, JM (2002) “Validity and reliability issues in elite interviewing”, PS Online, December: 679-682. Goldstein, K (2002) “Getting in the door: sampling and completing elite interviews” PS Online, December: 669-672. Hertz, R and Imber, JB (1993) “Fieldowrk in elite settings: introduction”, Journal of Contemporary Ethnography, 22 (1): 3-6. Ostrander, SA (1993) “’Surely you’re not in this just to be helpful’: access, rapport and interviews in three studies of elites”, Journal of Contemporary Ethnography, 22 (1): 7-27. Thomas, RJ (1993) “Interviewing important people in big companies”, Journal of Contemporary Ethnography, 22 (1): 80-96. Learning hours: Activity
Hours allocated
Staff/student contact
10
Tutorials
n/a
Private study
40
Directed reading
Total hours
50
Other activities eg Practical/laboratory work
Optional Assessment:
Assessment activity Length required Weighting within unit Mini-project and report 3,000 100%
1
COURSE UNIT OUTLINE Title: Introduction to Qualitative Data Analysis with
NVivo Course code: BMAN 80542 Credit Rating: 5 (if optional assignment is completed) Level: (UG 1/2/3 or PG) PhD / MRes Delivery: (semester 1, 2 or both etc) Semester 2 Tutor(s): Dr. Eva Alfoldi ([email protected]) Aims:
In recent years, qualitative research and the use of computer-assisted qualitative data analysis software (CAQDAS) have gained growing recognition. The purpose of this course is to familiarise students with a specific software package (QSR NVivo 10), which integrates a wide range of tools and enables researches to link theory and data to produce rich insights and in-depth interpretations. NB. This course is suitable for PhD and MRes students who have little or no previous experience of NVivo. Own data is not needed as sample datasets will be used for demonstration. Students who have already collected and transcribed some of their own data and would like help with analysing it should attend the course BMAN 97322 Advanced Qualitative Analysis with NVivo instead.
Learning Outcomes:
On completion of this unit successful students will be able to: • Understand the advantages and limitations of using CAQDAS throughout their
research project • Explore and code generic qualitative (textual, visual and aural) data in NVivo • Determine the applicability of CAQDAS for their own research project and
consider strategies to represent the data analysis process in the methodology section of their theses
Content:
• Introduction to the potential uses of NVivo • Hands-on demonstration and application of the software • Discussion of how students may utilise it during each stage of their doctoral
research project (from literature review through data analysis to writing up) Teaching and learning methods:
8 hours of staff-student contact in the format of computer-based tutorials supplemented by lecture slides covering the logic behind analysis with CAQDAS. Students will be given sample materials to work on in class. The optional assignment can help students to apply what they have learnt during the course,
2
thus allowing them to become more familiar with the software as well as to consider how and why they may use it for their research project.
Preliminary reading:
• Bazeley, P. (2007). Qualitative Data Analysis with NVivo. London: Sage • Richards, L. (2005). Handling qualitative data - A practical guide. London:
Sage • Sinkovics, Rudolf R. and Eva A. Alfoldi (2012). Progressive focusing and
trustworthiness in qualitative research: The enabling role of computer-assisted qualitative data analysis software (CAQDAS). Management International Review, 52(6), 817-845. DOI: 10.1007/s11575-012-0140-5. Available at: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:137280
Learning hours: Activity
Hours allocated
Staff/student contact
8 (intensive computer-based tutorials, held over the course of 2 days)
Private study / practice
8-25 (depending on whether the optional assignment is completed)
Assessment (optional):
Assessment activity Length required Weighting within unit
Coursework (students are required to develop a coding template and visual model in NVivo; as well as write a brief reflective report on the anticipated use the software for their doctoral research)
1,500 words 100%
COURSE UNIT OUTLINE 15/16
Title: Longitudinal Data Analysis Credit Rating: 10 Level: (UG 1/2/3 or PG) PG Delivery: (semester 1, 2 or both etc) 2 Tutor(s): Prof. Paul Irwing, Dr. David Hughes Pre-requisites: Students for this course must have completed prior courses in quantitative methods, structural equation modelling and multi-level analysis. Aims: To introduce students to different longitudinal designs and provide the skills needed to conduct appropriate analyses using longitudinal data. Learning Outcomes: On completion of this unit successful students will be able to:
• Understand the concepts, designs and terminology related to longitudinal research;
• Apply a range of different methods of longitudinal data analysis; • Have a general understanding of how each method represents different kinds
of longitudinal processes; • Choose a design, a plausible model and an appropriate method of analysis in
relation to different research questions. Content: The following content is illustrative:
• Cohort, time series, and repeated measures designs; discrete and continuous event data
• The multilevel model for change including analysis of continuous, discontinuous and non-linear change
• Discrete time Hazard models • Analysis of continuous time event data • Kaplan-Meier method of estimating the continuous time survivor function • Cox regression models • Structural equation modelling approaches to growth curves and survival
analysis
Teaching and learning methods:
The basic material will be presented in lectures. Detailed notes will be supplied in addition to Powerpoint slides. The students will be provided with a work book explaining in detail how to carry out different longitudinal data analyses in Mplus and other programs applied to real data. There will be guidance on how to write up longitudinal data analyses. Students will work in groups, and there will be feedback on this groupwork in class in order to ensure mastery of the material. Preliminary reading: Singer, J. D. and Willett, J. (2003) Applied Longitudinal Data Analysis. New York: OUP. Mplus website: http://www.statmodel.com/ Bryk, A.S. and Raudenbush, S.W. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods, 2nd Ed.. Newbury Park, CA: Sage. Learning hours: Activity
Hours allocated
Staff/student contact
12 hours, 8 hours practical
Tutorials
Private study
88 hours
Directed reading
Total hours
Other activities e.g. Practical/laboratory work
Optional Assessment:
Assessment activity Length required Weighting within unit None
Preferred Session Dates Consecutive Wednesday and Thursday in March or April Session Times Start: 10.00 a.m.
End: 5.00 pm
Please state any specific software/equipment This course requires SPSS and Mplus software. It must, therefore be in Crawford House Computer Cluster, 1.12, because this is the only cluster with this software installed Please state maximum number of students for this course unit 40
COURSE UNIT OUTLINE 15/16
Title: Meta Analysis Credit Rating: Level: (UG 1/2/3 or PG) PG Delivery: (semester 1, 2 or both etc)
2
Tutor(s): Professor Dieter Zapf Aims: Meta-analysis provides estimates of population parameters based on aggregating data from all known extant studies. Meta-analysis provides population estimates which are in principle corrected for study and measurement artefacts. Because meta-analysis does provide more accurate estimates than are obtainable from single studies, meta-analysis has had a revolutionary effect in many fields including business, social science and medicine. The aim of this course is to introduce students to the basic principles and practice of meta-analysis. Learning Outcomes: On completion of this unit successful students will be able to:
• Know the difference between meta-analysis and narrative reviews • Understand the key concepts in secondary data analysis using meta-analysis • Have a basic understanding of how to perform a meta-analysis • Have a knowledge of the key formulas of meta-analysis • Understand how to test for moderators • Understand how to use regression in order to assess the importance of
potential moderator variables • Have a knowledge of typical mistakes in meta-analysis and how to avoid
them Content:
• What a meta-analysis is, and how to perform one • Example meta-analyses • The key differences between meta-analysis and narrative reviews • The difference between psychometric and Glass’s meta-analysis • Typical measures of effect size, the correlation and Cohen’s d.
• The difference between fixed-effect and random-effects models • How to use meta-analysis programs to compute effect sizes, perform a
simple analysis, and create forest plots • Common mistakes in meta-analysis, and how to avoid them
o Mistakes in choosing between fixed-effect and random-effects models o Mistakes in understanding why a meta-analysis appears to conflict
with a clinical trial o Mistakes in using Vote-counting o Mistakes in the goals of meta-analysis
• How to quantify and interpret heterogeneity
• How to compare the effect size in subgroups of studies • How to use regression to assess the relationship between covariates and
effect size • Common mistakes in meta-analysis, and how to avoid them
o Mistakes in interpreting indices of heterogeneity o Mistakes in choosing between fixed-effect and random-effects models
for subgroups-analysis and meta-regression
• How to work with studies that report effects for two (or more) independent subgroups
• How to work with studies that report effects for two (or more) outcomes or time-points
• How to work with studies that compare two (or more) treatments to a common control group
• How to decide whether or not it makes sense to perform a meta-analysis • How to assess the potential impact of publication bias • How to perform a meta-analysis using studies that employed different
designs (matched groups vs. independent groups), formats (some reported means, others reported t-tests) or outcomes (some worked with means, others with risks).
• Common mistakes in meta-analysis, and how to avoid them o Mistakes in working with multiple outcomes from the same sample o Mistakes in interpreting publication bias
Teaching and learning methods: This course will be taught by lecture and demonstration. Preliminary reading: Lipsey, M. W., & Wilson, D. B. (2001). Practical Meta-Analysis. Thousand Oaks: CA.
Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to Meta-analysis. Chichester, west Sussex: Wiley. Hunter, J. E., & Schmidt, F. L. (2004). Methods of meta-analysis: Correcting error and bias in research findings. Thousand Oaks, CA: Sage. Learning hours: Activity
Hours allocated
Staff/student contact
4 hours
Tutorials
Private study
Directed reading
Total hours
Other activities e.g. Practical/laboratory work
Assessment:
Assessment activity Length required Weighting within unit None Preferred Session Dates TBC Session Times Start: 10 am End: 3 pm
Please state any specific software/equipment Normal teaching room. PowerPoint presentation required, plus design of room to be flexible to allow ease of discussion, e.g. movable chairs that can be arranged in circle, as opposed to fixed-seating lecture hall. Please state maximum number of students for this course unit 40
Title: Mixed Methods Approaches in Management and Organization Studies (BMAN 80352)
Credit Rating: 5 Level: (UG 1/2/3 or PG) PG Delivery: (semester 1, 2 or both etc)
Semester 2
Tutor(s): Prof Sven Modell Aims: The aim of the course is to provide an overview and understanding of mixed methods research approaches in management and organization studies, especially research combining qualitative and quantitative modes of data collection and analysis. Learning Outcomes: On completion of this unit successful students will be able to:
o Assess the relative merits and limitations of various mixed methods approaches.
o Understand which mixed methods approaches are relevant for exploring different types of research questions.
o Apply mixed methods approaches in empirical research projects. Content:
The contents of the course focus on field-based mixed methods approaches involving various combinations of qualitative and quantitative data collection and analysis techniques (mixed methods approaches involving experimental or laboratory-based methods will not be covered). A variety of such approaches have gained in popularity over the past decade. Among the more important of these are:
1. Triangulation between survey- and case study-based methods, involving multiple theoretical perspectives. 2. Cross-sectional field studies combining structured qualitative analysis with quantitative methods across medium-sized samples. 3. Case survey methodology relying on coding and statistical analysis of patterns across existing cases studies of specific themes. 4. Meta-triangulation, or inter-paradigmatic approaches based on theories and methods rooted in distinctly different philosophical assumptions. Each of these approaches will be covered in sufficient depth for the students to familiarize themselves with their main strengths and weaknesses. Particular attention will then be paid to (1), which constitutes the dominant mode of mixed methods research in management and organization studies. The course will also address some of the philosophical critiques and debates surrounding mixed methods research in the social sciences and provide an insight into the philosophical approaches available for dealing with such challenges (e.g., pragmatist philosophy, critical realism) and how these may be applied as a basis for such research.
Teaching and learning methods: Seminars, group exercises and student presentations/discussions. Preliminary reading: Blaikie, N.W.H. (1991) A critique of the use of triangulation in social research, Quality and Quantity, 25, 115-136. Brewer, J. and Hunter, A. (1989/2005) Multimethod Research: A Synthesis of Styles, Thousand Oaks: Sage. Larsson, R. (1993) Case survey methodology: quantitative analysis of patterns across case studies, Academy of Management Journal, 36, 1515-1346. Lewis, M.W. and Grimes, A.J. (1999) Metatriangulation: building theories from multiple paradigms, Academy of Management Review, 24, 672-690. Lillis, A.M. and Mundy, J. (2005) Cross-sectional field studies in management accounting research – closing the gap between surveys and case studies, Journal of Management Accounting Research, 17, 119-141. Modell, S. (2005) Triangulation between case study and survey methods in management accounting research: an assessment of validity implications, Management Accounting Research, 16, 231-254. Modell, S. (2009) In defence of triangulation: a critical realist approach to mixed methods research in management accounting, Management Accounting Research, 20, 208-221. Modell, S. (2010) Bridging the paradigm divide in management accounting research: the role of mixed methods approaches, Management Accounting Research, 21, 124-129. Wolfram Cox, J. and Hassard, J. (2005) Triangulation in organizational research: a re-presentation, Organization, 12, 109-133.
Learning hours:
Activity
Hours allocated
Staff/student contact 12 Tutorials Private study 38 Directed reading Total hours 50 Other activities e.g. Practical/laboratory work
Optional Assessment:
Assessment activity Length required
Weighting within unit
Individual essay. 100%
COURSE UNIT OUTLINE 15/16
Title: Multilevel Modelling in Mplus Credit Rating: 10 Level: (UG 1/2/3 or PG) PG Delivery: (semester 1, 2 or both etc) Semester 2 Tutor(s): Dr. David Hughes, Prof. Paul Irwing Pre-requisites: Students for this course must have completed prior courses in quantitative methods, structural equation modelling. Aims: Hierarchically clustered or multilevel data are commonplace in the social sciences, psychology, and business research generally. We are often interested in employees within teams, within departments, within organisations, within industries. We are also often interested in examining the interactions and influences across these levels. In order to appropriately model such data and test such models, specific multilevel methods are needed. This course is designed to provide an introduction to the application of multilevel models and aims to:
• Introduce students to the utility of multilevel modelling techniques. • Help students understand what types of data and designs are suitable for
multilevel analyses. • Provide students with a practical introduction to modelling multilevel data
within Mplus. Learning Outcomes: On completion of this unit successful students will be able to:
• Demonstrate an understanding of what multilevel models are. • Identify when and where multilevel modelling should be used. • Identify and discuss the strengths and weaknesses of different multilevel
modelling approaches (multilevel regression and latent variable models). • Use the above knowledge to design research that is appropriate for
multilevel study. • Specify, run and interpret a latent variable multilevel model within Mplus.
Content: Indicative content
• An introduction to hierarchically structured and nested data – the motivation for multilevel models.
• The theoretical backdrop to multilevel models. • Assumptions, restrictions and limitations of multilevel models. • The use of the intra class correlation coefficient in order to aid decisions
regarding which effects to estimate (e.g. multilevel, cross level). • Multilevel regression models.
◦ Univariate multilevel model example. ◦ Multivariate multilevel model example.
• Comparison of multilevel, random effect mixed linear, and SEM approaches.
• Power analysis for multilevel models. • Estimation of multilevel models. • Assessing the fit of multilevel models. • Multilevel exploratory and confirmatory factor analysis. • Multilevel path analysis. • Multilevel structural models with latent variables. • Multilevel mediation analysis. • Multilevel reliability estimation.
Teaching and learning methods: Both didactic (lecture, group work, discussion) and experiential (specifying, running, and interpreting a multilevel model) methods will be used to facilitate learning and skill acquisition. The basic material will be presented in lecture format. A pack of PowerPoint slides and detailed notes guiding students through the practicalities of multilevel modelling will be supplied. There will be guidance on how to write-up multilevel analyses. Students will work in groups, and there will be feedback on this group work given in class in order to ensure understanding of the material. Preliminary reading:
Gorard, S. (2003). What is multi-level modelling for? British Journal of Educational Studies, 51, 46-63
Heck & Thomas (2015) An Introduction to Multilevel Modeling Techniques: MLM and SEM Approaches Using Mplus, Third Edition.
Hox, J. (2002).Multilevel analysis: techniques and applications, Mahwah, NJ, Lawrence Erlbaum
Mplus website: http://www.statmodel.com/
Preacher, K.J., Zyphur, M.J., & Zhang, Z. (2010). A General Multilevel SEM Framework for Assessing Multilevel Mediation, Psychological Methods, 15, 209-233 Learning hours: Activity
Hours allocated
Staff/student contact
12 hours, 8 hours practical
Tutorials
Private study
60 hours
Directed reading
Total hours
100
Other activities e.g. Practical/laboratory work
Assessment:
Assessment activity Length required Weighting within unit To write a scientific journal style report of the multilevel model estimated within the session consisting of a brief introduction, method and discussion and a thorough results section.
2,000-3,000 100%
Preferred Session Dates Consecutive Wednesday and Thursday in March or April Session Times Start: 10.00 a.m.
End: 5.00 pm
Please state any specific software/equipment
This course requires SPSS and Mplus software. It must, therefore be in Crawford House Computer Cluster, 1.12, because this is the only cluster with this software installed. Please state maximum number of students for this course unit 40
COURSE UNIT OUTLINE Title: Scenario Methods for Research BMAN80322 Credit Rating: 5 Level: (UG 1/2/3 or PG) PG Delivery: (semester 1, 2 or both etc) 2 Tutor(s): Professor Ian Miles [email protected] Aims:
• To introduce research students to the use of scenario methods in a range of different scientific and applied research purposes, equipping them with skills to determine whether and how to use scenarios in their own research ,and to appreciate the strengths, weaknesses and challenges of the approach.
Learning Outcomes: On completion of this unit successful students will be able to:
• Understand the practical and methodological issues associated with using scenario methods
• Begin to use scenario methods in their own research, if and when appropriate
• Critically asses the contributions of scenario analysis to research and in such fields as strategic planning and policy analysis.
Content:
• Introduction to different types ofscenario methods and their wide applications in business strategy and policy making
• Examples of the use of scenario methods in different research and decision-making applications
• The strengths and weaknesses of using scenario methods in research • Practical issues in using scenario methods
Teaching and learning methods: The course will be run as a one-day workshop and will comprise two principal elements: 1. Presentations introducing the key issues related to scenario methods and their uses in research
2. (If sufficient students are enrolled) A group practical exercise in which course participants will develop their own scenarios Preliminary reading: Journals: There are several journals that contain papers on scenarios, including:
• Futures • Foresight • Journal of Forecasting • International Journal of Foresight and Innovation Policy • Technology Forecasting and Social Change • Technology Analysis and Strategic Management
Web resources: Again, there are several web resources that feature scenario work, including:
• UK Foresight Programme: http://www.foresight.gov.uk • GBN International: http://www.gbn.com • OECD International Futures Programme: www.oecd.org/sge/au/ • EC (Brussels): http://www.cordis.lu/foresight • EC (JRC-IPTS, Seville): http://www.jrc.es • APEC Technology Foresight Centre (Bangkok): http://apecforesight.org • African Futures Institute: http://www.africanfutures.net/ • UNIDO Technology Foresight: http://www.unido.org/doc/5216 • UNU Millennium Project: http://www.acunu.org/ • Finland Futures Research Centre:
http://www.tukkk.fi/tutu/default_eng.asp Reading list: On Scenarios: Aligica, P (2005) "Scenarios and the growth of knowledge: Notes on the epistemic element in scenario building," Technological Forecasting & Social Change, vol. 72, pp. 815–824 Chermack, T (2005) "Studying scenario planning: Theory, research suggestions, and hypotheses," Technological Forecasting & Social Change, vol. 72, pp. 59–73 Coates, J. F. (2000) “Scenario Planning”, Technological Forecasting and Social Change vol. 65, 115-123 (This special issue has several useful pieces) European Commission (1995) “Scenario Building: Convergences and Differences”, Profutures Workshop Proceedings, Seville: JRC-IPTS, available at: http://www.jrc.es/home/pages/detail.cfm?prs=38 Mietzner, D and G. Reger (2005) “Advantages and disadvantages of scenario approaches for strategic foresight”, International Journal for Technology Intelligence and Planning, Vol. 1, No. 2, pp. 220-230
Miles, I (2003) “Scenario Planning”, paper presented at UNIDO TF Training Course, Prague, October 2003, available at: http://www.unido.org/file-storage/download/?file%5fid=16957 Ogilvy, J & Schwartz, P (1998) “Plotting your scenarios”, in Fahey, L & Randall, R (eds.), Learning from the Future, John Wiley & Sons, available at: http://www.gbn.com/GBNDocumentDisplayServlet.srv?aid=34550&url=%2FUploadDocumentDisplayServlet.srv%3Fid%3D35520 Postma, T and F. Liebl, (2005) "How to improve scenario analysis as a strategic management tool?", Technological Forecasting & Social Change, vol. 72, pp. 161–173 Roubelat, F (2000) “Scenario Planning as a Networking Process”, Technological Forecasting and Social Change vol. 65, No. 1, available at: http://www.prospective-foresight.com/IMG/pdf/fabriceroublatenglish.pdf UK Office of Science and Technology (OST) (2002) Foresight Futures 2020: Revised scenarios and guidance, London: OST, available at http://www.foresight.gov.uk/Publications/Current%20round%20General%20Publications/Foresight%20Futures%20-%202020%20Revised%20scenarios%20and%20guidance/DTI_FF_web.pdf van der Heijden, K (1997) “Scenarios, Strategy, and the Strategy Process”, available at: http://www.gbn.com/GBNDocumentDisplayServlet.srv?aid=550&url=%2FUploadDocumentDisplayServlet.srv%3Fid%3D12549 Waverley Management Consultants. (2007) Scenario Planning Toolkit. London: Department for Transport. Online at http://www.dft.gov.uk/pgr/scienceresearch/futures/secsceniss/ Wilson, I. (2000) “From Scenario Thinking to Strategic Action”, Technological Forecasting and Social Change vol. 65, 23-29 van’t Klooster, S and van Anselt, M (2006) “Practising the scenario-axes technique”, Futures, vol. 38, pp. 15-30 On Foresight: Becker P. (2002) “Corporate foresight in Europe: A first overview”, EC working paper available at ftp://ftp.cordis.lu/pub/foresight/docs/st_corporate_foresight_040109.pdf Brown et al (2001) “Foresight as a Tool for the Management of Knowledge Flows and Innovation”, Final report of the FORMAKIN project, available at: http://www.york.ac.uk/org/satsu/OnLinePapers/FORMAKIN(VOL1).PDF Daheim, C & Uerz, G (2006) “Corporate Foresight in Europe: ready for the next step?”, Second International Seville Seminar on Future-Oriented Technology Analysis: Impact of FTA Approaches on Policy and Decision-Making, Seville, September 2006, available at: http://forera.jrc.es/documents/papers/Seville-GU-CD-Draft-8-2006-e.pdf Georghiou, L., Cassingena, J., Keenan, M., Miles, I. and Popper, R. (eds.) (2008), The Handbook of Technology Foresight, Cheltenham: Edward Elgar. Miles,I. (2010) “The Development of Technology Foresight: A Review” Technological Forecasting and Social Change Vol 77 issue 9 pp1448-1456
Learning hours: Activity
Hours allocated
Staff/student contact
8
Tutorials
n/a
Private study
35
Directed reading
7
Total hours
50
Other activities e.g. Practical/laboratory work
Optional Assessment:
Assessment activity Length required Weighting within unit Group presentation on scenario practical exercise
15 minutes ppt presentation
40%
Individual report on the potential for application of scenario methods in student’s own research area or topic of study1
1,000 – 2,000 words
60% (100% if no practical exercise undertaken)
1 Submission Date: two weeks after course delivery
Scenario Methods for Research
Wednesday, 27.04.2011, 10.00 – 16.30
Dr. Ozcan Saritas
Introduction 10.00-10.10 Ozcan Saritas Introduction to the course Introduction to Foresight and Scenarios 10.10-10.50 Ozcan Saritas Introduction to Foresight and Scenarios. Applications of
Scenarios in business strategy and policy making Variations in the use of scenarios Scenario planning process
10.50-11.00 Break 11.00-12.00 Denis Loveridge Use of Scenarios: Experience from practice 12.00-12.30 Ozcan Saritas &
Denis Loveridge Questions and discussions
12.30-13.30 Lunch Workshop - Part 1 13:30-13.45 Ozcan Saritas Formation of groups for the practical exercise
Discussions on the topics 13.45-15.00 Scenario groups
facilitated by Ozcan Saritas
1. Scope scenarios 2. Define timeframe for scenarios 3. Identify major actors and stakeholders 4. Map basic trends and drivers around the STEEPV framework 5. Identify key uncertainties and extremes
Workshop: Part 2 15.00-16.00 Scenario groups
facilitated by Ozcan Saritas
7. Define scenarios based on the scenario typology used (e.g. 2x2 matrix, archetypes, success scenario etc.) 8. Sketch out scenarios 9. Discuss further steps to take
Workshop: Part 3 16.00-16.30 Scenario groups Presentation of scenarios
Discussions
Title: Social Network Analysis (BMAN 80102) Credit Rating: 5 Level: (UG 1/2/3 or PG) PG Delivery: (semester 1, 2 or both etc)
Semester 2
Tutor(s): Dr Ronnie Ramlogan and Dr Yanuar Nugroho Aims: The main aim of this course is to present students with a taster of how social network methods and techniques can be applied to a variety of interesting issues in business/management research. The course will introduce some fundamental concepts related to this mode of analysis, raise awareness of various management and innovation literatures where such methods have been successfully applied. Students will also benefit from empirical examples based on research conducted within the Manchester Institute of Innovation Research to demonstrate how SNA can be useful in teasing out the dynamics of innovation. Learning Outcomes: On completion of this unit students will have:
o a broad understanding of fundamental concepts the underline SNA o basic knowledge of kinds of data that can be useful employed in network
analysis o some practical experience of using Pajek, a software that has been
developed for use with large scale datasets Content:
o Part One: Interactive lecture o Part Two: Practical training
Teaching and learning methods: Lecture; interactive discussion; practical workshop and essay writing. A brief look at of the following will be helpful: Wouter de Nooy, Andrej Mrvar, Vladimir Batagelj. 2005. Exploratory Social Network Analysis with Pajek (Structural Analysis in the Social Sciences). Peter Carrington, John Scott, Stanley Wasserman (Eds.) 2005. Models and Methods in Social Network Analysis (Structural Analysis in the Social Sciences Linton C. Freeman. 2004. The Development of Social Network Analysis: A Study in the Sociology of Science. Empirical Press. Robert A. Hanneman and Mark Riddle. 2005. Introduction to social network methods. Riverside, CA: University of California, Riverside (published in digital form at http://faculty.ucr.edu/~hanneman/)
Pre-reading
David Easley and Jon Kleinberg, 2010 Networks, Crowds, and Markets: Reasoning about a Highly Connected World. Cambridge University Press, Complete preprint on-line at http://www.cs.cornell.edu/home/kleinber/networks-book/
Learning hours:
Activity
Hours allocated
Staff/student contact 5 Tutorials Private study 10 Directed reading 10 Total hours 25 Other activities e.g. Practical/laboratory work
Optional Assessment:
Optional Assessment activity Length required
Weighting within unit
(Individual) Essay 1.500 words 100%
Title: Social Shaping of Technology, Innovation and Organizing BMAN80380
Credit Rating: 5 Level: (UG 1/2/3 or PG) PG Delivery: (semester 1, 2 or both etc)
Semester 1
Tutor(s): Dr Chris McLean Aims: The main aim of this course is to examine how the different ways technological change and innovation are examined from a range of perspectives. This will include focusing on issues relating to the design, development and application of technology within organisations and society, and reviewing the range of methodological approaches and perspectives relating to this process. Furthermore, the students will develop team working skills and the ability to apply these ideas to a specific technology of their choice, within a group situation. Learning Outcomes: On completion of this unit successful students will be able to:
o Review different methodological approaches and perspectives relating to the design, development and application of technology within organisations and society
o Explore a range of issues relating to our understanding of agency and technological change
o Select and analyse research material from a wide range of sources o Examine and critically reflect upon a range of theories, issues and
perspectives o Work in a group situation to review different ideas and approaches in
relation to the issues explored within the course. Content:
o Section One: Introduction and Technological Determinism o Section Two: Social Shaping and the Social Construction of
Technology. Teaching and learning methods: Lectures, readings, interactive discussion and group presentations, private study and essay writing. Preliminary reading: Bijker, W.E., T. Hughes, and T. Pinch, (1987), The Social Construction of Technological Systems, Cambridge: MIT Press. Callon, M., (1986), Some elements of a sociology of translation: Domestication of the scallops and the fishermen of St. Brieuc Bay, in Law, J. Ed., Power, Action, and Belief: A New Sociology of Knowledge?, London: Routledge and Kegan Paul, pp. 196-233.
Latour, B., (1999), Pandora’s Hope: Essays on the Reality of Science Studies, Harvard University Press.
Law, (1987), Technology and Heterogeneous Engineering: the Case of the Portuguese Expansion, in Bijker, W. E., T. Hughes, and T. Pinch, The Social Construction of Technological Systems: New Directions in the sociology and History of Technology, pp. 111- 134.
Mackenzie, D. and J. Wajcman, (1985/1999), Social Shaping of Technology, Open University Press: Milton Keynes.
Learning hours:
Activity
Hours allocated
Staff/student contact 4 Tutorials 3 Private study 30 Directed reading 13 Total hours 50 Other activities e.g. Practical/laboratory work
Optional Assessment:
Assessment activity Length required
Weighting within unit
Essay 2,500 to 3,000 words
100%
THE UNIVERSITY OF MANCHESTER
MANCHESTER BUSINESS SCHOOL Academic Year: 2014/2015 Course Unit Title: SEM Course Unit Code: BMAN80502 Programme Titles: Doctoral Training Programme Course Co-ordinator: Dr Paul Irwing Room D4, Ext: 63419 Email: [email protected] Semester: Semester 2 Credit Rating: 5 credits Pre-requisites: Students on this course must also have completed the prior
courses on factor analysis and multiple regression Aims: Most quantitative studies in business involve the measurement of multiple latent variables at either one or multiple points in time. Currently, the most widely accepted analyses of such data depend on structural equation models of various types, the most basic of which are confirmatory factor models and path models. This course will introduce students to such models and provide them with the practical skills to analyze such models in Amos. Intended Learning Outcomes: On completion of this unit successful participants will be able to:
• Understand the basic principles of structural equation modelling • Carry out a confirmatory factor analysis • Test simple path models • Acquire a basic mastery of SEM as implemented in Amos • Apply these basic principles to publishable data sets • Know how to report SEM analyses in journal style
Curriculum Content:
• Sample and model implied covariance matrices • Classical test theory • Confirmatory factor analysis and the concept of latent variables • Path models • Fit statistics and cut-off criteria • Writing up SEM analyses in journal style
2
Teaching and Learning Methods The basic material will be presented in lectures. Detailed notes will be supplied in addition to Powerpoint slides. The students will be provided with a work book explaining in detail how to carry out different SEM analyses in Amos applied to real data. There will be guidance on how to write up SEM analyses. Students will work in groups, and there will be feedback on this groupwork in class in order to ensure mastery of the material. Learning Hours
Activity
Hours allocated
Staff/student contact
7 hours, 4 hours practical
Tutorials
Private study
43 hours
Directed reading
Total hours
50
Other activities e.g. Practical/laboratory work
About 4 hours of the class session will involve practical supervised data analysis applying a variety of SEM models using Amos to data supplied by the lecturer.
Optional Assessment:
Assessment activity Length required Weighting within unit
This will comprise a write up of the class exercises in the form of a journal article. The full coursework will comprise a joint report for the factor analysis, multiple regression and SEM courses. Approximately 1,500 words are required for each course, but due to some rewriting the total length of the coursework should be about 3,500 words.
3,500 words 100%
Assignment The assignment involves a partial write up of the findings from the Work Pressures Survey. Details of the data set have been provided in class. Broadly, the task is to construct a model of turnover based on evidence from research literature and to test that model using factor analysis, multiple regression using the data set provided. Relevant literatures would include;
3
(1) Turnover models. (2) Psychological contract/justice. (3) Empowerment. (4) Work/Family Conflict. (5) Organisational Commitment. (6) Integrative models of (1)-(5).
Some useful references are provided in “Checklist – 2012”, however these will need to be supplemented with your own literature search of the most up-to-date articles, and those relevant to your specific hypotheses. Please incorporate into this report, your previous report on factor analysis and multiple regression so that the report as a whole is complete. For the structural equation modelling coursework you are asked to write:
1. Results of SEM – 750 words. 2. Discussion – 750 words. This should be a rewrite of the discussion originally
provided for factor analysis and multiple regression. You may include any previous material, but the discussion must represent an integrated view based on the factor analysis, multiple regression and SEM results.
Be aware that the greater weight of marks is attached to the results section. Details of the overall structure of the report are contained in a separate document, “Checklist – 2012”. Please note that although this checklist provides some guidance on the method section, nevertheless a method section is not required. The assignment should be handed in to the postgraduate office not later than Monday, 6th May 2013 by 4.00 pm. Evaluation and Feedback The Course is reviewed annually. Student feedback is welcome at any stage. A group discussion is held at the end of the Course and written feedback will be collected from students on an individual basis. If you wish to discuss progress, course content or any other relevant issues, contact us in person, by internal mail or by telephone. READING RECOMMENDED TEXTS Hair, J. F., Jr., Black, W. C., Babin, B.J., & Anderson, R. E., (2006). Multivariate Data Analysis, 7th Ed. Upper Saddle River, NJ: Prentice-Hall. Blunch, N. J., (2008). Introduction to structural equation modelling using SPSS and Amos. Thousand Oaks, CA: Sage. Byrne, B. (2009). Structural equation modelling with AMOS: Basic concepts, application and programming, 2nd Ed. New York, NY: Routledge. Underlying theory - Introductory
4
Kline, R. B. (2005). Principles and Practice of Structural Equation Modelling. London: The Guilford Press. Loehlin, J. (2004). Latent Variable Models: An Introduction to Factor, Path and Structural Equation Analysis. Lawrence Erlbaum Associates. Underlying theory – Advanced Bollen, K. A. (1989). Structural Equations with Latent Variables. New York: Wiley. Selected References Analysis strategies Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modelling in practice
– A review and recommended 2-step approach. Psychological Bulletin, 103, 411-423.
Jöreskog, K. G. (1993). Testing structural equation models. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 294-316). London: Sage.
Tomarken, A. J., & Waller, N. G. (2003). Potential problems with “well fitting” models. Journal of Abnormal Psychology, 112, 578-598.
Estimation Flora, D. B., & Curran, P. J. (2004). An empirical evaluation of alternative methods
of estimation for confirmatory factor analysis with ordinal data. Psychological Methods, 4, 466-491.
Fit Hu, L.T., & Bentler, P.M. (1998). Fit indices in covariance structure modeling:
Sensitivity to underparameterized model misspecification. Psychological Methods, 3, 424–453.
Hu, L.T., & Bentler, P. M. (1999). Cut-off criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.
Marsh, H. W., Hau, K.-T., Grayson, D. (2005). Goodness of fit in structural equation models. In A. Maydeu-Olivares & J. J. McCardle (Eds.), Contemporary psychometrics: A festshrift for Roberick P. McDonald (pp. 275-340). Mahwah, NJ: Erlbaum.
Schermelleh-Engel, K., Moosbrugger, H., & Muller, H. (2003). Evaluating the Fit of Structural Equation Models: Tests of Significance and Descriptive Goodness-of-Fit Measures. Methods of Psychological Research, 8, 23-74.
Yuan, K.-H. (2005). Fit indices versus test statistics. Multivariate Behavioral Research, 40, 115-148.
Parcelling Little, T. D., Cunningham, W. A., Shahar, G., & Widaman, K. F. (2002). To Parcel or
Not to Parcel: Exploring the Question, Weighing the Merits. Structural Equation Modeling, 9, 151-173.
Item level factor analysis Wirth, R. J., & Edwards, M. C. (2007). Item factor analysis: Current approaches and future
directions. Psychological Methods, 12, 58-79.
5
