This document provides an overview of a training session on organisational research and case studies for a Doctoral Training Centre in Information Science. The session introduces organisational research and discusses case studies as an approach. It notes some key differences and challenges of organisational research compared to other fields. Examples of past case studies are presented covering topics like intranet implementation, blogs in the classroom, and the impact of information science research. Guidelines for case study design, data analysis, and addressing criticisms of case study research rigour are also covered.
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Data Collection (Methods/ Tools/ Techniques), Primary & Secondary Data, Assessment of Qualitative Data, Qualitative & Quantitative Data, Data Processing
Presentation Contents:
- Introduction to data
- Classification of data
- Collection of data
- Methods of data collection
- Assessment of qualitative data
- Processing of data
- Editing
- Coding
- Tabulation
- Graphical representation
If anyone is really interested about research related topics particularly on data collection, this presentation will be the best reference.
For Further Reading
- Biostatistics by Prem P. Panta
- Fundamentals of Research Methodology and Statistics by Yogesh k. Singh
- Research Design by J. W. Creswell
- Internet
One fundamental problem in sentiment analysis is categorization of sentiment polarity. Given a piece of written text, the problem is to categorize the text into one specific sentiment polarity, positive or negative (or neutral). Based on the scope of the text, there are three distinctions of sentiment polarity categorization, namely the document level, the sentence level, and the entity and aspect level. Consider a review “I like multimedia features but the battery life sucks.†This sentence has a mixed emotion. The emotion regarding multimedia is positive whereas that regarding battery life is negative. Hence, it is required to extract only those opinions relevant to a particular feature (like battery life or multimedia) and classify them, instead of taking the complete sentence and the overall sentiment. In this paper, we present a novel approach to identify pattern specific expressions of opinion in text.
What’s The Difference Between Structured, Semi-Structured And Unstructured Data?Bernard Marr
There are three classifications of data: structured, semi-structured and unstructured. While structured data was the type used most often in organizations historically, artificial intelligence and machine learning have made managing and analysing unstructured and semi-structured data not only possible, but invaluable.
Dear viewers Check Out my other piece of works at___ https://healthkura.com
Data Collection (Methods/ Tools/ Techniques), Primary & Secondary Data, Assessment of Qualitative Data, Qualitative & Quantitative Data, Data Processing
Presentation Contents:
- Introduction to data
- Classification of data
- Collection of data
- Methods of data collection
- Assessment of qualitative data
- Processing of data
- Editing
- Coding
- Tabulation
- Graphical representation
If anyone is really interested about research related topics particularly on data collection, this presentation will be the best reference.
For Further Reading
- Biostatistics by Prem P. Panta
- Fundamentals of Research Methodology and Statistics by Yogesh k. Singh
- Research Design by J. W. Creswell
- Internet
One fundamental problem in sentiment analysis is categorization of sentiment polarity. Given a piece of written text, the problem is to categorize the text into one specific sentiment polarity, positive or negative (or neutral). Based on the scope of the text, there are three distinctions of sentiment polarity categorization, namely the document level, the sentence level, and the entity and aspect level. Consider a review “I like multimedia features but the battery life sucks.†This sentence has a mixed emotion. The emotion regarding multimedia is positive whereas that regarding battery life is negative. Hence, it is required to extract only those opinions relevant to a particular feature (like battery life or multimedia) and classify them, instead of taking the complete sentence and the overall sentiment. In this paper, we present a novel approach to identify pattern specific expressions of opinion in text.
What’s The Difference Between Structured, Semi-Structured And Unstructured Data?Bernard Marr
There are three classifications of data: structured, semi-structured and unstructured. While structured data was the type used most often in organizations historically, artificial intelligence and machine learning have made managing and analysing unstructured and semi-structured data not only possible, but invaluable.
Data Science Tutorial | Introduction To Data Science | Data Science Training ...Edureka!
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You can read the blog here: https://goo.gl/OoDCxz
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
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Data Science Tutorial | Introduction To Data Science | Data Science Training ...Edureka!
This Edureka Data Science tutorial will help you understand in and out of Data Science with examples. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:
1. Why Data Science?
2. What is Data Science?
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This Edureka Data Science course slides will take you through the basics of Data Science - why Data Science, what is Data Science, use cases, BI vs Data Science, Data Science tools and Data Science lifecycle process. This is ideal for beginners to get started with learning data science.
You can read the blog here: https://goo.gl/OoDCxz
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
Just finished a basic course on data science (highly recommend it if you wish to explore what data science is all about). Here are my takeaways from the course.
High level introduction to text mining analytics, which covers the building blocks or most commonly used techniques of text mining along with useful additional references/links where required for background/literature and R codes to get you started.
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Organizational behavior plays an important role in which individuals and groups can interact within entity. This behavior creates a healthy working environment in company that can be positive or negative. An important goal of organizational behavior is to improve the effectiveness of company and the extent to which it is productive and satisfies the demand of its customers. For More Information, read our complete sample written by expert writers of instant essay writing.
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Presented in one of the parallel sessions during the 15th International Conference on Education 2010 at Universiti Brunei Darussalam.
Presenter/courtesy of Michael Moroney, Lecturer, Universiti Brunei Darussalam.
Presented in one of the parallel sessions during the 15th International Conference on Education 2010 at Universiti Brunei Darussalam.
Presenter/courtesy of Michael Moroney, Lecturer, Universiti Brunei Darussalam.
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Training materials used with doctoral students faced with the challenge of writing up their research and asking themselves 'How do I write up my doctoral study?'
Presentation delivered by Professor Hazel Hall at the RIVAL Reunion event in Edinburgh, 25th May 2023. Further details of the event at https://blogs.napier.ac.uk/social-informatics/2023/05/rival-reunion-event-25-may-2023/
Platform to Platform project lightening talkHazel Hall
Lightning talk on the AHRC/Creative Informatics funded Platform to Platform project to create a podcast series based on Lorna Lloyd's 'Diary of the war', and assess audience engagement with archives in two different digital formats - (1) a Blipfoto journal of text and images, and (2) sound in podcast episodes.
Platform to Platform: initial findings from the empirical studyHazel Hall
Initial findings from the empirical study of the Platform to Platform project are presented. The research centred on the creation of a podcast series based on the war diary of Lorna Lloyd (available at https://rss.com/podcasts/lornalloyd/), and the evaluation of audience engagement with it as compared with engagement with online text and images in a Blipfoto journal at http://blipfoto.com/lornal. The research was funded by the AHRC through the Creative Informatics programme.
Digital options: an assessment of audience engagement with a digitised set of...Hazel Hall
Paper presented at the Archives and Records Management conference, 2nd September 2022 on audience engagement with Lorna Lloyd's Diary of the war as a Blipfoto journal, and as a podcast series.
Using a multi-location, longitudinal focus group method to conduct qualitativ...Hazel Hall
Paper presented at 13th Qualitative and Quantitative Methods in Libraries International Conference (QQML2021) (virtual conference), 25-28 May 2021. Full text available at https://www.napier.ac.uk/~/media/worktribe/output-2755729/using-a-multi-location-longitudinal-focus-group-method-to-conduct-qualitative-research.pdf
Research, impact, value and library and information science (RIVAL): developm...Hazel Hall
The research-practice gap in Library and Information Science (LIS) is well documented, especially in respect of the difficulties of translating research into practice, and resultant lost opportunities. While many researchers attempt to explain this research-practice gap, few suggest strategies to address it. The creation of researcher-practitioner networks, however, is one approach that has been proved empirically to bridge the distance between the two communities. Such a network is currently operating in Scotland, funded by the Royal Society of Edinburgh. Research, Impact, Value and Library and Information Science (RIVAL) is part-way through its implementation based around four knowledge exchange events for a network membership of 32 from a wide variety of LIS sectors. RIVAL’s successful delivery depends in part on the project leads’ experience of undertaking, and evaluating the impact of, a UK Arts and Humanities Research Council funded grant: the Developing Research Excellence and Methods (DREaM) project. Already there are indications that RIVAL is delivering value to network members. There is a strong expectation for this to be enhanced, both in the remainder of the funding period and beyond, offering theoretical contributions to the study of social networks, especially in respect of social capital development to support knowledge exchange.
Collaboration and networking: learning from DREaM and RIVALHazel Hall
Discusses the extent of networking and collaboration amongst library and information science researchers and practitioners who took part in the AHRC-funded Developing Research Excellence and Methods (DREaM) project in 2011/12, and the extent to which learning from this grant has influenced the delivery of the Royal Society of Edinburgh funded Research Impact and Value and Library and Information Science project in 2019/20.
Research into Practice case study 2: Library linked data implementations an...Hazel Hall
The research underlying this presentation explored the role that libraries play in the linked data context. Focusing on European national libraries and Scottish libraries, multiple data gathering methods and constant comparative analysis were applied in the study. Amongst the findings, a general lack of awareness within the library community of the Semantic Web and the implications of linked data was identified. At the same time, there is recognition that linked data augments the discoverability and enhances the interoperability of library data. The presentation will include recommendations for the application of the findings of this research in practice.
Catalysing research into practice from the ground upHazel Hall
David Stewart, CILIP President for 2019 and Regional Director of Health Library and Knowledge Services North, presents on his key presidential theme: the importance of evidence to underpin the difference that library services make. He provides an overview of CILIP’s plans for greater collaboration and co-ordination, and also shares details of work undertaken in NHS England. This includes (a) national research on return on investment, and (b) details of the Catalyst scheme in the North of England, which has been designed to develop librarian research capability and a ground-up, small-scale research programme.
Professor Hazel Hall introduces the second networking event of RIVAL - a collaborative network of Scotland-based Library and Information Science (LIS) researchers and practising library and information professionals interested in maximising the impact and value of library and information science research. The project, which runs in 2019 and 2020, is funded by the Royal Society of Edinburgh.
Research, Impact, Value and LIS = RIVAL.
Scotland's school library strategy: advocacy and impact by Martina McChrystalHazel Hall
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Getting research into action: issues, challenges, solutions by Dr Sarah MortonHazel Hall
Sarah Morton has worked across research, policy and practice for most of her career, and will draw on examples from different settings encountered over this time in her presentation. She is keen to interrogate our learning about effective evidence use from the last 20 years, and review how this can be supported from research and practice perspectives. She will present a vision for the effective use of evidence of all kinds to plan, develop and improve policy, practice, and services. As part of this she will explain some of the ways that she is currently developing tools and support for effective evidence use.
Professor Hazel Hall introduces RIVAL - a collaborative network of Scotland-based Library and Information Science (LIS) researchers and practising library and information professionals interested in maximising the impact and value of library and information science research. The project, which runs in 2019 and 2020, is funded by the Royal Society of Edinburgh. This is the first of the four networking events.
Research, Impact, Value and LIS = RIVAL.
Participatory Budgeting, São Paulo, BrazilHazel Hall
Summarises a research project on participatory budgeting in São Paulo, Brazil undertaken by Edinburgh Napier University researchers Dr Wegene Demeke and Dr Bruce Ryan, and supported by the Global Challenge Research Fund.
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3. This session
Theme is organisational research
Context is Information Science
Mix of lecture material and short exercises
Session begins with consideration of the distinctiveness of
organisational research, then moves on to case studies
But first…
5. Organisational research and case studies
at doctoral level within the Centre for
Social Informatics
Organisational
case study
Organisational
case study
Case study
Organisational
research
Organisational
research
6. Research cited in this session
Title Organisational
research
Business
research
Case study
as output
Setting
Intranet implementation in
a corporate environment
X X X Professional
services firm
Blogs in the classroom X X Edinburgh Napier
E-information roles X IM/KM
Outsourcing research &
information services
X Business
information
services
Research in Librarianship
Impact Evaluation Study
(RiLIES)
X Information
science research
8. Organisational research
What makes organisational research
“different/distinctive”?
Practical difficulties in accessing sites for data collection
Information sharing practice of drug dealers
Strategies for dealing with information security breaches in Company X
Legal and ethical issues when setting up studies
Power of the context in sites of data collection
Real-life organisations staffed by humans whose behaviours are influenced by
range of factors – culture, politics, power struggles
Intangibility of phenomena under investigation
Knowledge, value, social capital, goodwill
Expectation of the organisation to derive value from the study
9. Information Science and organisational
research
Borrows from other disciplines
because Information Science is concerned with range of
organisational perspectives
technology, culture, functions…
Requirement to read widely
Sociology, anthropology, management science… even physics?
Intranet implementation: Galison, P. (Ed), (1997). Image and logic: a material
culture of microphysics. Chicago: University of Chicago Press.
As an applied science, organisational partners may expect
return on participation
11. Understanding of the term “case study”
Case study is an approach to research
Empirical enquiry that investigates a contemporary phenomenon
within a bounded, real-life context
especially when boundaries between phenomenon and context are not clearly
evident
• Intranet implementation: “The reasons why they don’t use the intranet to
knowledge share [phenomenon] may be due to cultural issues [context]”
uses multiple methods including, but not exclusively, qualitative techniques,
e.g. participant observation, interviews, document analysis
• Intranet implementation: interviews; document analysis
• RiLIES: interviews; citation “sketching”
12. Alternative understandings
The case study is the output of research
“Story/ies” of the case(s) investigated
The knowledge trap: an intranet implementation in a corporate environment
(http://hazelhall.org/publications/phd-the-knowledge-trap-an-intranet-
implementation-in-a-corporate-environment/)
Hall, H. & Davison, B. (2007). Social software as support in hybrid learning
environments: the value of the blog as a tool for reflective learning and peer
support. Library and Information Science Research, 29(2), 163-187. (DOI
10.1016/j.lisr.2007.04.007.)
Enhancing the impact of LIS Research projects
(Text book exercise)
13. Case study approach for real-life,
contemporary research
Describe - explore – explain – illustrate – provide
examples
Intranet implementation
“Here’s a real information-intensive distributed organisation that hoped an
intranet would support knowledge sharing in the firm. I established that it did
not to the extent anticipated, and propose reasons why with illustrations and
examples.”
Blogs in the classroom
“We wondered if claims that blogs can encourage student reflection were
exaggerated. We tested this by analysing the content of recent student blog
postings in an educational setting, and demonstrated with examples that
reflection is often limited.”
RiLIES
“These five case studies of real LIS research projects show how a range of
factors can increase the impact of the research output on the practice of
librarians.”
14. Case study approach for investigating
“how” and “why” questions
RiLIES
How can LIS research projects be conceived, designed and
implemented to increases the chances that their findings will
influence the practice of librarians?
Intranet implementation
Why don’t staff in this corporate environment use the intranet for
knowledge sharing?
15. Case study approach for triangulation
Collect data on specific cases to triangulate with other
data collected
Case study (or studies) are just part of the project, e.g. RiLIES
Practitioner poll
Focus groups
Validation survey
and case studies
16. Single/multiple case studies as output
A study can include single or multiple cases
Intranet implementation: 1 (big) case study
Blogs in the classroom: 1 case study
RiLIES: 5 case studies
In case of multiple case studies, each should stand on its
own
17. Rationale for single case study
A critical case – likely to have strategic importance for the
general population
Intranet implementation: focus on culture
‘If it is valid for this case, it is valid for all (or many) cases’.
See http://heim.ifi.uio.no/~in166/h00/criticalcase.pdf
An extreme or unique case
RiLIES: 5 case studies chosen were amongst the most frequently
cited in the practitioner poll as having influenced practice
A new/revelatory case
Blogs in the classroom: no empirical studies conducted previously
(although plenty of claims made!)
A prelude to further study, to test ideas
For example, a pilot case
18. Case study research design process
Five elements
1. Identify research questions to be explored
2. Determine propositions or hypotheses
Bearing in mind that case studies themselves often generate hypotheses and
models to be tested in the future – by you, by other researchers
3. Select clear units of analysis
4. Analyse data in a logical fashion so that it can be tied back to
propositions
5. Interpret findings
19. Case study research design process
Five elements: Intranet implementation
1. What is the role of an intranet in knowledge sharing?
2. External and internal organisational factors determine role
This proposition was based on an analysis of sociotechnical literature that dated
back to the 1970s
3. Interviews and document analysis
4. Data analysed and reframed using actor-network theory (more on
this later…)
5. Findings interpreted to uncover underlying explanations of practice
20. Case study research and “rigour”
Accusations of bias and lack of rigour in case study
research because data from which findings derive belong
to a specific context
Poor reliability
Can you be certain that you would report the same findings if you ran the same
study at another time or location?
• Intranet implementation: Perhaps not, but research protocol is such that
the process could be repeated in another large information-intensive
professional services firm (i.e. method is reliable)
Doubtful validity
How can this/these case(s) be generalisable to the wider context? To what
extent is your case study “representative” of the population as a whole?
• Intranet implementation: It can’t, but it does not seek to “generalise”
• RiLIES: Multiple case studies can address this to an extent
21. Other “weaknesses” of case study research
Causal inferences cannot be made, and it’s not possible to
“test” in a “traditional” sense
Chemistry would give you Na2O + 2HCl = H2O + 2NaCl
In case studies only associations and correlations can be made
Processes can be time-consuming and cumbersome
Organising access, non-disclosure agreements
Requirement to be on-site
Willingness of “participants” to participate
Labour in transcribing interviews…
22. Events cannot be controlled
Intranet implementation: access agreed
first week of September 2001 for
interviews to start 1st October 2001…
23. Value of case studies
In-depth studies
“Power of good example” derives from “rich” data
Intranet implementation
Particularly useful for new areas of research, where
there is little/no extant literature and previous empirical evidence
is lacking
Blogging in the classroom
Generate new hypotheses for future testing
Blogging in the classroom
Often inexpensive
Depends on depth of study (and how you transcribe interviews)
24. Resources
Research methods textbooks in business and
management are useful for organisational research in
general
Most general research methods textbooks include
chapters on case study research
Three particularly useful texts
Eisenhardt, K. (1989). Building theories from case studies.
Academy of Management Review, 14, 532-550.
Flyvbjerg, B. (2001). Making social science matter. Cambridge:
Cambridge University Press.
Yin, R.K. (2013). Case study research (5th ed.). London: Sage.
25. Flyvbjerg, B. (2001). Making social science matter.
Cambridge: Cambridge University Press.
28. What are the main “questions” you would need to ask?
Which methods could you use to collect data?
Who would you collect data from?
How will you organise and analyse the data that you have
collected?
An investigation into the impact of UK
information science research
Exercise
29. Analysis of data for organisational
research
Professor Hazel Hall
Institute for Informatics and Digital Innovation/School
of Computing
30. In this section
Data analysis as part of the research process
Data, evidence and findings
Role of coding data in data analysis
Coding exercise
31. Data analysis as a process
Design methods gather evidence present case
Move from description of elements, e.g. object, people,
phenomena “observed” to explanation, i.e. analysis.
Output is tied to purpose of the research and related
research questions, with scope for extension
discover what the research is really about
new research questions may emerge
example: intranets and information sharing power issues and
knowledge management
32. Refine & develop concepts – critical
treatment
Research Established “theory”
Intranet
implementation
Poor understanding of
knowledge sharing with
technologies
Blogs in the
classroom
Blogs promote
reflective learning
Contribution (Action)
Knowledge sharing
practice is local.
Efforts to
knowledge share is
influenced by
power bases
(Adopt communities
approach to KM in
case study
organisation)
Blogs do not
promote reflective
learning to the
extent reported
(Pay attention to
weekly blog hints to
engineer reflection)
33. Refine & develop concepts – critical
treatment
Research Established “theory”
Intranet
implementation
Poor understanding of
knowledge sharing with
technologies
Blogs in the
classroom
Blogs promote
reflective learning
Contribution (Action)
Knowledge sharing
practice is local.
Efforts to
knowledge share is
influenced by
power bases
(Adopt communities
approach to KM in
case study
organisation)
Blogs do not
promote reflective
learning to the
extent reported
(Pay attention to
weekly blog hints to
engineer reflection)
Data analysis
Data analysis
34. Belief in your research results
Research findings are expected
to be grounded in evidence
not to be based on speculation, nor on weak inference
Therefore decisions on data analysis are important
Example from e-information roles study: apparently more
opportunity in the public and voluntary than the corporate sector.
However:
less obligation in corporate sector to advertise posts
public and voluntary sector organisations could be playing “catch up” with the
corporate sector
We could not be confident that this finding was grounded in evidence
because our data collection was not extensive enough
35. Inevitability of “too much” data
Assume it’s murder
safety net
Not all data collected will be analysed - data collectively
“emphasised”
to serve as evidence
to build a case
Tension
present a set of understandable findings
yet acknowledge the complexities of the social world under
investigation
36. Data, evidence and findings
Data + interpretation = evidence
Evidence = social product/artefact of work completed
Evidence findings
Data cannot (normally) speak for itself, so
data ≠ evidence
evidence ≠ mere illustration
evidence is built from multiple data sets
research design should permit multiple collection of “same” data for
triangulation purposes
obligation falls on the researcher to check alternative claims for the
evidence collected
37. Links between findings & research design
Outcome of data analysis (findings) must be understood in
the context of the methods adopted
Example from e-information roles study: globalisation the strongest
driver in the creation of new job roles in the corporate sector.
However:
Research design determined sample selection focused on large, multinational
companies
So we were confident that the finding was grounded in evidence,
within the context of our sample
Obligation to provide detailed and comprehensive account
of both findings and basis on which they were obtained
38. Data analysis: some examples
Research Format of data Coding & analysis
Intranet
implementation
Recorded interviews;
interview notes; archive
of company documents
Interviews transcribed; interview data
coded using Ethnograph; archive details
organised into historical sequence &
coded manually – “content analysis”
Blogs in the
classroom
Students’ blog entries;
survey
Content analysis of blog entries; survey
results not incorporated
e-information roles Focus group notes; job
adverts; job descriptions;
survey; telephone
interview notes
Combined mind-mapping of focus group
notes & job data; survey & telephone data
analysed using Excel
Outsourcing
research &
information services
Interview notes; provider
web sites
Interview data coded & analysed
manually – total of 11 data sets
39. Data analysis options
Analysis using software
Standard packages
Word
Excel
Access
Dedicated software
SPSS - http://www.spss.com/
Ethnograph - http://www.qualisresearch.com/
Nvivo http://www.qsrinternational.com/products_nvivo.aspx
Atlas.ti - http://www.atlasti.com/
Manual analysis
40. Use of Excel to analyse survey &
interview data for the e-information
roles project
Relative ranking of the importance of employee
backgrounds: computing, business, librarianship
Column M records
comments
41. Date Data Source
8 November 2001
History – investment
Budget changes
Named meeting minutes
Use of Word to
analyse document
data for intranet
implementation
Source of information
Date of activity/development
Activity/development
42. H: Who controls 422
the Intranet content, is it 423
controlled by you in XX … rather than 424
from the centre, from the KM group …? 425
#-CONTROL $-RELS KMG
P: Well, in terms of what tools and what 427 -#-$
facilities are made available to us, 428 | |
that's obviously controlled by the 429 | |
central group. But in terms of the XX 430 |-$
content and the XX presence, that's 431 |
entirely controlled by me … simply 432 |
because it wouldn't be relevant to go 433 |
through a central group. 434 -#
H: Yeh, OK. You've told me about 436
#-INT BUY-IN
ownership. How … it sounds as if 437 -#
you've got really good buy-in from 438 |
your own set of people … 439 |
|
P: Absolutely. 441 -#
#-INT BUY-IN
H: What about the Intranet as a whole in 443 -#
the UK? What are your perceptions of 444 |
buy-in there? 445 |
|
$-KM SPONS
P: I think it varies. I mean, I'm very 447 -#-$
fortunate in that I report into the 448 |
KM partner, who's also one of the 449 |
senior partners … 450 -$
H: Which, who …? 452
Use of Ethnograph to analyse
interview data for intranet
implementation
43. “Translating” data for analysis - coding
Coding
records instances of occurrence
organises data into categories
comprises part of the analysis stage in qualitative research
44. Attention to coding in research design
Design of research tool has determined predefined
codes
Indicate the best day of the week for team leader
meetings:
A. Monday
B. Tuesday
C. Wednesday
D. Thursday
E. Friday
F. Don’t know
G. No preference
H. Not applicable
Note also the importance of the last
three options: there is a difference
between not having a preference and not
knowing; if this forms part of a survey of
staff who have nothing to do with team
leader meetings, there needs to be an
option for their response. Attention to
coding at the design stage can help with
asking the “right” questions.
45. Coding down
Data is coded according to predefined categories
identified in range of work brought together in literature review
identified in a single piece of work
commonly deployed, e.g. age breakdowns used in national
statistics
46. Dimension Code Interpretation Evidence
Reflection C Content-free Comment makes no reference to points in the original entry.
U Non-reflective
(U=’unreflective’)
Comment makes reference to the original blog entry, the module content or the
general context in order to state an opinion, emotion or a point of fact or theory.
R Reflective Comment addresses points from the main blog entry and demonstrates a
consideration of the validity of the content, the process or the underlying premise.
Propositional stance A Agree Comment actively supports the point made in the original entry.
I Indifferent Comment neither supports nor challenges original entry.
D Disagree Comment takes up a contradictory position to the original entry.
Affective P Positive Comment is encouraging, approving, accepting, etc.
E Even Comment appears affectively neutral.
N Negative Comment is hostile, discouraging, dismissive, etc.
Scheme based on Kember, D., Jones, A., Loke, A., McKay, J., Sinclair, K., Tse, H., Webb, C., Wong, F., Wong, M. &
Yeung, E. (1999). Determining the level of reflective thinking from students’ written journals using a coding scheme
based on the work of Mezirow. International Journal of Lifelong Education, 18(1), 18–30.
Example coding down: blog posting data
coding scheme
47. Coding up
Data is coded according to categories suggested by the data
Revise codes as new insight is developed through the process of coding - further
discovery of what the research is really about
Example from intranet implementation project: seven broad categories related the
intranet under investigation
Content
Functionality
History
KWorld
Policy
Staffing
Uptake
48. Some data in this spreadsheet fits
with predefined codes, i.e. in
columns D-L. However comments
need to be coded up.
Relative ranking of the importance of employee
backgrounds: computing, business, librarianship
Column M records
comments
49. H: Who controls 422
the Intranet content, is it 423
controlled by you in XX … rather than 424
from the centre, from the KM group …? 425
#-CONTROL $-RELS KMG
P: Well, in terms of what tools and what 427 -#-$
facilities are made available to us, 428 | |
that's obviously controlled by the 429 | |
central group. But in terms of the XX 430 |-$
content and the XX presence, that's 431 |
entirely controlled by me … simply 432 |
because it wouldn't be relevant to go 433 |
through a central group. 434 -#
H: Yeh, OK. You've told me about 436
#-INT BUY-IN
ownership. How … it sounds as if 437 -#
you've got really good buy-in from 438 |
your own set of people … 439 |
|
P: Absolutely. 441 -#
#-INT BUY-IN
H: What about the Intranet as a whole in 443 -#
the UK? What are your perceptions of 444 |
buy-in there? 445 |
|
$-KM SPONS
P: I think it varies. I mean, I'm very 447 -#-$
fortunate in that I report into the 448 |
KM partner, who's also one of the 449 |
senior partners … 450 -$
H: Which, who …? 452
Value of software packages for
coding and generating reports for
analysis
Speaker
Code
Line numbers
Data coded
50. Advice pointers
Be disciplined and systematic when analysing data
especially important to keep records of what you do if you dip in
and out of your research work
Be prepared to account for what you have done in the
report of your work
another reason to keep good records
When designing data collection tools, look forward to data
analysis
good decisions at this stage may save a lot of work at data
analysis stage – as will be demonstrated in the class exercise!
51. The class exercise
is based on the
responses to
questions 3.1, 3.2
and 3.3 in the e-
information roles
survey
52. Ability to align work activities to
business strategy
Ability to connect with
developments
Ability to cope with change
Ability to see the big picture
Ability to translate the needs of
the business at all levels
Abstracting
Adaptability
Analytic mind
Business acumen
Business awareness
Business development
Business focus
Cataloguing
Change management
Classification
Collaboration – non-technical
Collaboration – technical
Commercial awareness
Communication
Computer literacy
Confidence
Contract/supplier management
Creativity
Diplomacy
E-learning facilitation
Empathy
Engaging audiences
Enterprise content management
Enthusiasm
Evaluation of information sources
Facilitation
Flexibility
Grammar
Imagination
Indexing
Influencing
Information analysis
Information delivery
Information governance
Information literacy
Information retrieval
Innovation
Integrity
Intellectual property knowledge
Intelligence
Interviewing
IT savvy
Knowledge harvesting
Knowledge of e-information
arena, new technologies
Knowledge of government policy
Knowledge of information
sources
Knowledge of law
Knowledge of public sector
vocabulary
Languages
Leadership
Literacy
Management
Management of individuals
Management of teams
Marketing
Multi-tasking
Negotiation
Networking
Numeracy
Organisation
Outgoing personality
Political awareness
Presentation skills
Prince 2
Problem solving
Professionalism
Project management
Records management
Relationship building
Relationship management
Repackaging information
Research
Self-management
Small business knowledge
Social computing
Spelling
Stakeholder management
Strategic thinking
Synthesising information
Taxonomy development
Technical ability
Time management
Training
Understanding of
technical tools
Validation of information
sources
Web authoring
Web development
Web usability testing
Working under pressure
Writing
How would you
group these
responses for
coding?
53. Analytical tools and frameworks for
organisational analysis
Professor Hazel Hall
Institute for Informatics and Digital Innovation/School
of Computing
54. In this section
Focus on tools and techniques for organisational and case
study analyses through a consideration of:
Purpose and output of frameworks
Actor-network theory as a framework – with example of its
application in the research into the intranet implementation
NB there is a wide range of tools and techniques for research in
general. Some (or elements of some) are more applicable to
organisational and case study research than others. Actor-network
theory is just one example for illustration purposes here.
55. Purpose of frameworks
Frameworks
help make sense of data collected, and thus of phenomena (e.g.
organisational dynamics) observed
act as a tool for diagnosis
and thus aid the processes of:
acquiring knowledge
reflection
action for change (if appropriate, for example in an action research setting)
56. Output of frameworks
Frameworks provide you with a means of formatting your
findings
e.g. as a graphical representation of the organisation under
investigation
In using a framework you are encouraged to
(re)organise your data
understand what it is that your data represent
present your findings in a format that is understandable to others –
the representation can be used as a short-cut to shared
understanding
57. Actor-network theory as a framework -
example
Background
Optimism associated with the development of systems to promote
knowledge sharing is misguided.
Examples in the literature go back to 1980s.
“Culture” often takes the blame.
Case study organisation wanted explanations as to why the efforts
of its knowledge management staff to promote information
systems for knowledge sharing were sub-optimal.
58. Actor-network theory as a tool of analysis
History
Developed in 1980s
Michel Callon and Bruno Latour
Key concepts
Non-humans, as well as humans, are actors
Relationships between actors shift as they compete for
organisational resources, from tangible, e.g. office space, to
intangible, e.g. corporate attention
Actor-networks grow through successful “translation”
Actor-networks diminish/disintegrate when ties in the network
loosen
59. Relevance of actor-network theory to this
case
The organisation was understood as a mesh of competing
actor-networks.
The success/failure of corporate initiatives was suspected
to be related to the degree to which particular groups
enhanced or diminished their organisational power-base.
Service delivery could be examined with reference to
historical and social context of the organisation.
The approach provided opportunities to reflect, learn, act.
60. Actors in the organisation
External consultants
Senior staff with KM
responsibilities (not KM
specialists)
Knowledge
sharing as a
concept
Intranet
Repositories
Shared
collaboration
space
Mission
statements
Specialist KM staff
members in
centralised unit
Specialist KM staff
members in business
units
Senior sponsors of
KM (not KM
specialists)
External
systems
vendors
Intranet
usage
statistics
“Ordinary” staff
(not KM
specialists)
KM strategy
KM as a
concept
61. Analysis phase 1
Mission
statements
KM as a
concept
Senior sponsor of
KM (not a KM
specialist)
Intranet
Specialist IT/KM staff
member in
centralised unit
Senior specialist
IM/KM staff member
in centralised unit
62. Analysis phase 2
Mission
statements
KM as a
concept
Senior sponsor of KM (not
a KM specialist)
Intranet
Specialist IT/KM staff
member in centralised unit
Senior specialist
IM/KM staff member
in centralised unit
Senior specialist
IM/KM staff member
in centralised unit
Specialist IM/KM
staff members in
centralised unit
Some specialist
IM/KM staff members
in business units
“Ordinary” staff (not
KM specialists)
63. Analysis phase 3
Mission
statements
KM as a
concept
Senior sponsor of KM (not
a KM specialist)
Intranet
Senior specialist
IM/KM staff member
in centralised unit
Senior specialist
IM/KM staff member
in centralised unit
Specialist IM/KM
staff members in
centralised unit
Specialist IM/KM staff
members in business
units
“Ordinary” staff (not
KM specialists)
64. Some findings
Central position of intranet, and its proximity to KM as a
concept, account for confusion over what KM represented
in the organisation.
Distance between policy documentation and “ordinary”
staff explained lack of engagement with KM, and what it
implied in terms of behaviours.
Ties between KM staff in business units and “ordinary”
staff strengthened over time at the expense of their
relationship with the central KM team and the main tool of
the KM implementation. As a result their commitment to
KM weakened, as did that of their “ordinary” colleagues.
65. ESRC Scottish Doctoral Training Centre
Information Science Pathway
Training day 25th June 2014