Qualitative Methods For

Research

Dr Susan GassonCollege of Information Science & TechnologyDrexel UniversityEmail: sgasson@cis.drexel.edu

Agenda What is qualitative research? Issues of rigor and differences from

quantitative research Methods for qualitative analysis

Data collection methods Analysis methods

A Study of Knowledge Management in a Boundary-Spanning, Global IS Devt. Group

Rigor and validity issues Exercise: coding qualitative data Useful resources and references

What is qualitative analysis?

Non-quantifiable (or non-quantified) data are analyzed using a variety of methods, to understand patterns in the data.

Whereas quantitative data are analyzed statistically, qualitative data are organized, categorized (coded) and then analyzed through inferential reasoning processes.

Organization of qualitative data involves identification of relevant data samples, e.g. sections from tape-recorded interviews time-stamped episodes from a video-recorded activity field notes from observed behavior in the situation being

studied).

Example: Coding Observations Categorize a description of the voting

process in a specific country. Focus is on

(i) how the vote-counting process works,(ii) the reliability of the process(iii) the role of technology.

Code each new idea in the printout (may be a sentence or may be a paragraph) with Category code (may have >1) Attribute(s) of the category

Examples: Coding Voting DescriptionObservation Category

CodeAttribute Code

The officials check that no one person's vote is used more than once, and tally up the total number of ballot papers issued in order to help verify that all the ballots make it safely to the count

??? ???

Note that the count can be observed in the count room by the candidates and their agents; no press or news organization is allowed access, though they can typically watch from a balcony

??? ???

Focus on:(i) How the vote-counting process works,(ii) The reliability of the process

(iii) The role of technology (can you make any observations from this data?).

Example: Coding Voting DescriptionObservation Category

CodeAttribute Code

The officials check that no one person's vote is used more than once, and tally up the total number of ballot papers issued in order to help verify that all the ballots make it safely to the count

Vote-counting process

Reliability

Manual

Secure

Note that the count can be observed in the count room by the candidates and their agents; no press or news organization is allowed access, though they can typically watch from a balcony

Vote-counting process

Reliability

Visible

Trustworthy

Coding SchemeProcess (of vote-counting)

Manual vs. electronic Hidden vs. visible Auditable vs. no-paper-trail

Reliability (of the process) Secure vs. insecure Trustworthy vs. untrustworthy Objective vs. partisan

Technology (role of) Registering vote Counting votes Tallying totals

Philosophical Questions

1. What are you measuring, in a scientific experiment? Does it exist independently of your perception? Is it universal? Is it true?

2. What are you measuring, in an interview or observation study of people performing daily work?

Does it exist independently of your perception? Is it universal? Is it true?

3. If you have 5 different researchers performing the same study, will they reach the same conclusions?

Research Paradigms in IS & Info. Science1. Positivist Research Positivists generally assume that reality is objectively given and can

be described by measurable properties which are independent of the observer (researcher) and his or her instruments.

Positivist studies generally attempt to test theory, to increase the predictive understanding of phenomena (hypothesis testing).

2. Interpretive/Constructivist Research Interpretive researchers start out with the assumption that “reality”

is socially constructed. Phenomena can be understood only through the meanings that people assign to them, accessed via social constructions such as language, consciousness, & shared meanings.

Interpretive research does not predefine dependent and independent variables, but focuses on the full complexity of human sense making in context as the situation emerges.

3. Critical Research Critical researchers assume that social reality is historically

constituted and that people’s ability to change their social and economic circumstances is constrained by various forms of social, cultural and political domination.

Critical research focuses on the oppositions, conflicts and contradictions in organizations and society. It is emancipatory in intent: it seeks to eliminate causes of alienation and domination.

The Research Life-Cycle In Theory Generation

Define research questions

Experimental, observation, action research or case studies

Analysis, using qualitative and/or quantitative methods

Theory suggestion, confirmation, constraints or extension

Review relevant theory (literature)

Determine suitable research method(s) and site(s)

Define research hypotheses or propositions

Hypothesis/proposition testing: experimental or investigative study

Review relevant theory (literature)

(b) Interpretivist approach (a) Positivist approach

Locate or design suitable research instrument

Statistical analysis

Theory verification, refutation, or extension

Publish findings Publish conclusions

Research initiation

Data collection

Data analysis

Synthesis and theory-generation.

Data selection

Research publication

Research lifecycle

Tests/extends theory

Generates/explores theory

Positivist vs. Interpretivist Beliefs

Positivist / Functionalist Interpretive / Constructivist

Ontological (beliefs about the nature of reality)

Real-world phenomena & relationships exist independently of the individual’s perceptions

Phenomena & relationships are viewed as social constructs by which an individual makes sense of the external world/reality

Epistemological (beliefs about knowledge & how we know reality)

Natural laws govern all aspects of existence. These laws may be observed from outside the situation and abstracted to provide generally-applicable models and theories.

Rules governing behavior in various situations are dependent on context. Inferred relationships between contextual factors and observed behaviors may be transferred to similar situations.

Human Nature(how we account for human behavior)

The behavior of individuals en masse (with exceptions that can be explained by a lack of rationality or variance from the mean) can be viewed as determined by the situation.

Human beings have complete autonomy: their actions are dictated by free will (which may be constrained by external forces). So they do not act according to any laws of rational behavior.

Methodological(beliefs about how we apply inquiry methods)

Researchers derive generalizable models or theories of behavior through the analysis of small-scope findings from large samples and systematic methods to construct scientific theories regarding the “real world”.

Researchers infer transferable, in-depth subjective accounts of situations, that analyze observations from small samples in great detail. The presence of the observer is accounted for.

Constructivism: The Hermeneutic Circle

Hermeneutics is (literally) the interpretation of a text: its intent its content, and its context.

Gadamer, H-G (1989), "Text and Interpretation," in Dialogue and Deconstruction: The Gadamer-Derrida Encounter, edited/translated by D. P. Michelfelder and R. E. Palmer, SUNY Press, Albany, NY, pp 21-51.

Methodologically, the assemblage of an understanding of the “whole” through an analysis of its parts, e.g.WHOLE PARTGeneral/typical case Instance of complicated caseLearning process Instances of learningDecision process Instances of decision making

The whole

(the big picture)

The parts (analysis of minutiae or components)

Use Of Multiple Methods

Most often (but not always), the term “qualitative research” refers to qualitative content analysis, performed interpretively.

Tenet of interpretivism is that researcher “interprets” data.

So can use multiple qualitative methods for both data collection and data analysis, e.g. Data collection: observation, formal interviews, interactive

(facilitated analysis) interviews and workshops, document analysis, investigative surveys, etc.

Data analysis: qualitative coding (using different sets of constructs, to examine different aspects of the data), inferential analysis (usually simple frequency co-concurrence), statistical analysis, discourse analysis, etc.

Use Of Mixed Methods

The use of mixed methods indicates the comparison of findings across multiple data collection techniques and analysis methods.

This approach Provides multiple perspectives of the research problem Guards against limiting the scope of the inquiry Yields a stronger substantiation of the derived constructs (Cavaye, 1995; Eisenhardt, 1989; Orlikowski, 1994;

Wolfe, 1994). Mixed methods may (but does not have to)

combine qualitative and quantitative analysis.

Qualitative Data Collection Vs. Qualitative Analysis

DATA

Qualitative Quantitative

Qualitative Interpretive content analysis studies.

Hermeneutics, Phenomenology,

Grounded Theory.

Search for and presentation of meaning in quantitative results.

Explanations of findings Interpretation of statistical results Graphical displays of data Naming factors/clusters in factor

analysis & cluster analysis

Quantitative Post-positivist Content AnalysisTurning words into numbers: Word Counts, Free Lists,

Pile Sorts, etc. Statistical analysis of text

frequencies; code co-occurrence

Positivist Research: Statistical & mathematical

analysis of numeric data (e.g. regression).

Multivariate analysis.

ANALYSIS

Source: Bernard, H.R. (1996) ‘Qualitative Data, Quantitative Analysis’, CAM, The Cultural Anthropology Methods Journal, Vol. 8 no. 1, available at http://www.analytictech.com/borgatti/qualqua.htm

Contributions of Qualitative ResearchThe contribution of qualitative research studies in IS can be:

The development of concepts e.g. “automate vs. informate" (Zuboff, 1988)

The generation of theory e.g. Orlikowski & Robey (1991): organizational consequences of IT.

The drawing of specific implicationse.g. Walsham & Waema (1994): the relationship between design

and development and business strategy.

The contribution of rich insighte.g. Suchman (1987): contrast of situated action with planned

activity and its consequences for the design of organizational IT.

Walsham, G. (1995) ‘Interpretive Case Studies In IS Research: Nature and Method’, European Journal of Information Systems, No. 4, pp 74-81

Distributed Knowledge

Coordination Across Virtual Organization

Boundaries

Dr Susan GassonEdwin M. ElrodDrexel University

Organizational KM viewKnowledge-as-process

Knowledge processes are embedded within Best practices (tacit

knowledge), Contexts (localized

knowledge) and Genres of communication

(legitimate knowledge). Effective knowledge

management depends on sharing understanding that is only meaningful in the context and community of practice within which it is applied.

KM Systems ViewKnowledge-as-thing

Knowledge can be defined independently of human action. Knowledge can be divorced

from practice Knowledge can be abstracted

into rules or algorithms, independent of context

Knowledge can be defined objectively.

Effective KM depends on knowledge capture, codification & transfer across many different places and many different CoPs.

Knowledge Management For Virtual Collaboration

How do we resolve this tension?

Research Question

How are different forms of knowledge managed and coordinated across the boundaries of a virtual,

global organization?

eCommerce Group Functional Boundaries

Executive Management

Technical Operations

BackendApplications

Client FacingApplications

Financial & Client Performance Evaluation

Vendor Projects Europe

Corporate and Geographic Boundaries

VendorCorp

eServCorp eCommerce

ParentCorp

eServCorpEU Operations

eServCorp EU Customer

ServiceeServCorp Asia Pacific

eServCorp N. American Operations

eServCorp Corporate

Field Observations

Researchers observe & transcribe telephone conferences and other (face-to-face) meetings;

Supplemented with monthly ad hoc interviews with management team.

Sample statistics through June 2006 338 conference calls/group meetings;

Average length: 0 :30 Shortest: 0:04 Longest: 1:35

8 group interviews. Over 1000 pages of transcription

Longitudinal, ethnographic, exploratory

Thematic Analysis Of Meetings (Initial)

Thematic analysis: What are the most common themes?

Categories of behavior or phenomena, meaningful in context of the study.

Are there notable exceptions? E.g. individuals who do not discuss specific themes or who say

very different things about particular topics?

What concept-categories or event-categories can be identified ? What is the range of views expressed with regard to a topic?

Can you identify any sub-categories? Variations on your themes, further distinctions/qualifications?

What language is used?

Are there common synonyms or metaphors that indicate a specific meaning or category of behavior?

What respondent characteristics are associated with particular views? Do people with different expertise express different views?

What patterns emerge, across various samples, or over time?

Knowledge Sharing

Boundary Object Mechanism

Knowledge Sharing Form

Know-What

Know-Why

Know-How

Who-knows-what

Repositories

Standardized Forms, Methods, Procedures

Models

Maps

Observed knowledge translation and

transformational activities.

(Star, 1989) (Carlile, 2002)

(Johnson, et al, 2002)(Polanyi, 1958)(Zack, 2001)

Boundary Object Mech.

Knowledge Sharing Form

Know-What

Know-Why

Know-How

Who-knows-what

Repositories

Std Forms, ...

Models

Maps

Make work practices explicit through discussion and

debate.

Know-HowS

tan

da

rdiz

ed

P

roc

edu

res

Ms CorpSys: Some system reports have problems. Mr VendorTech: This was fixed in acceptance, but it didn't move with the release. Mr EVP: How many times does this happen? About 50%. Why are we paying <the vendor> for the same mess up 50% of the time? Ms CorpSys: We go through a rollout plan after every test. Moving code over always catches us.Mr ClientSys: There should be some established best practice. Mr EVP: I'm sure there's a best practice 'cause it's been going on since the 1960s.

Boundary Object Mech.

Knowledge Sharing Form

Know-What

Know-Why

Know-How

Who-knows-what

Repositories

Std Forms, ...

Models

Maps

Establish boundaries of

eCommerce group.

Know-WhyM

ap

s

Mr ClientSys: It turns out that a vendor that the EU office has – is one that everyone else uses.Mr EVP: Yes and develops stuff for everyone else and shares the information. It depends whether we consider that a system for … constitutes a competitive advantage,Ms Europe: I think that outcome analysis and project sourcing has to become a strategic area. ● ● ●

Boundary Object Mech.

Knowledge Sharing Form

Know-What

Know-Why

Know-How

Who-knows-what

Repositories

Std Forms, ...

Models

Maps

Identify relevant stakeholders in other groups.

Who-Knows-What

Ms Europe: Mr Support and June visited the French vendor, so I have asked them to do a write-up for us, so that we understand what the issues are etc. and if there is an opportunity to take some of the stuff like the product site, like the project bank for Europe, since it’s already built. But we need to look at the how we host it, where we do it – so I have asked them to write it up for us.

Mr EVP: OK, let them write it up. Then let’s talk about it – you, me and Mr ClientSys. …The reason I want to discuss this other stuff - you, me and Mr ClientSys - is that I want to make sure that whatever they put together, you have vetted. With a broader understanding of the global perspective than they might have. ...

Ma

ps

Ms Europe: Mr Support and June visited the French vendor, so I have asked them to do a write-up for us, so that we understand what the issues are etc. and if there is an opportunity to take some of the stuff like the product site, like the project bank for Europe, since it’s already built. But we need to look at the how we host it, where we do it – so I have asked them to write it up for us.

Mr EVP: OK, let them write it up. Then let’s talk about it – you, me and Mr ClientSys. …The reason I want to discuss this other stuff - you, me and Mr ClientSys - is that I want to make sure that whatever they put together, you have vetted. With a broader understanding of the global perspective than they might have. ...

Formal knowledge sharing

Informal, distributed, social context

Concept Map Early Themes From Analysis of Meetings

Project Collaboration & Knowledge

Too complex for one person to

understand

Problem emerges thro’

negotiation

Informal, distributed social context of project

Project definition is ad hoc (memory-

dependent)

Diverse set of global groups collaborate according to focus

Who-knows-what more important than

who-can-do-what

Project roles & responsibilities change

frequently

Distribution

Project KnowledgeProblem Organization

Definition of project changes frequently – little coordination or persistence of knowledge (group memory)

Project goals are subjective: various groups

& individuals define project in different ways Group memory of project

changes

Knowledge located in people’s

heads

Formal knowledge often local and undocumented

Analytical Framework: Categorize Collaborations By Modes of Organizational Problem-SolvingWell-Structured Problems Clear problem-structure defines change requirements Unambiguous goals for change Knowledge accessed via pattern recognition (problem-solvers in

similar domains develop repertoire of solutions).

Ill-Structured Problems Uncertain problem-structure indicates multiple alternative solutions Need to bound and structure problem to analyze requirements

(complexity reduction) Explore unfamiliar knowledge-domains through consultation with

experts to resolve ambiguity re change-goals and scope.Wicked Problems Problem emerges: has no objective definition, boundary, or

structure Stakeholders see partial subsets multiple goals for change Problem, solutions, scope of inquiry, and relevant expertise are

negotiated (equivocality reduction) . Explore emergent knowledge-domains thro’ iterative cycles of

inquiry.

Three Spans of Collaboration

(i)  Local coordination of projects Core e-Commerce group manage project: define goals, scope,

timescales, deliverables, and rationale Boundaries: functional, role, geographic.

(ii) Conjoint agency Core e-Commerce group control project: act as hub,

incorporating knowledge/expertise from external groups e-Commerce define goals, scope, and responsibilities Collaboration with hardware or software vendors, other

eServCorp business units, client project groups

(iii) Distributed Collaboration e-Commerce group part of a web of collaborating groups Goals, scope, system definitions, business-process changes

negotiated, implemented, and evaluated jointly e-Commerce group subject to joint or external project-

leadership by groups from eServCorp, ParentCo., associated companies, or vendors.

Problem-Coordination

Distance

Problem-Solving Mode

Collaboration Span

Local Coordination

Conjoint Agency

Distributed Collaboration

Well-structured problems

Ill-structured problems

Wicked problems

Knowledge coordination

strategy depends on problem

coordination-distance

Relative Incidence of Problems

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

40.00%

45.00%

50.00%

Local Conjoint Distributed

Coordination-Span

Ra

tio

of

Pro

ble

ms

Wickedproblems

Ill-structuredproblems

Well-structuredproblems

Modes of Organizational Problem-

Solving Well-Structured

ProblemsIll-Structured

ProblemsWicked

Problems

Local Coordination

Situation interpretation: stories & analogies create shared resource to identify similar problems

Group identity construction: plans, processes & checklists formalize procedural memory

Framing collective strategy: group agrees evolving goals of change, to clarify approach to problem

Conjoint Agency

Scope interpretation: stories & analogies communicate rules, evaluation-criteria, responsibilities at boundary

Delegated knowledge- leadership: domain expert roles assumed. Rules & procedures at coordinate knowledge transfer at boundary

Defining a collective response: delegated boundary-spanner locates knowledge & controls evolving boundary procedures

Distributed Coordination

Coordinating division of labor: functional domain-expert roles and social network leveraged for knowledge exchange

Managing external networks of influence: group domain-experts jointly formulate problem, negotiate group responsibilities

Collective knowledge networking: leader negotiates group role; group members become expert in evolving set of knowledge-domains

Conclusions and Contributions

Knowledge is coordinated by means of a web of: Functional and domain-expert roles Distributed knowledge resources Imposed or negotiated procedures.

Knowledge coordination strategy depends on problem coordination-distance. This concept combinesorganizational span of coordination with problem-type.

Central role of a cohesive group identity: Informs semi-autonomous decision making by group members Provides conceptual patterns for action at group boundaries Adapted collaboratively through distributed, improvisational

sense making to deal with novel situations.

Two Dimensions of KM Coordination

Low Knowledge-Coordination Span High

Deg

ree

of

Pro

ble

m E

mer

gen

ce Wicked

Problems

Ill-Structured Problems

Well-Structured Problems

Local Coordination Conjoint Agency Distributed Collaboration

Collective knowledge networking: Leader frames group identity in terms of role in global network. Multiple group members are delegated to acquire external knowledge, providing a “web” of domain experts who advise the group, acting as a conduit to influential managers & decision-makers, maintaining extra- & inter-group memory.

Delegated knowledge- leadership: Individuals are delegated or self-nominated to become domain experts. Leader defines procedures and rules for action at the interface, selecting relevant social network contacts to maintain inter-group memory.

Situation Interpretation: Group leader manages meaning, providing standardized rules and procedures, communicated through stories and analogies to create a group memory.Low

High

KMS Implications

Knowledge Management Systems must expand beyond communicating management decisions to embrace distributed, emergent, collaborative decision formation: Well-structured problems require rule-based KMS. Ill-structured problems require adaptive KMS. Wicked problems require evolutionary & dynamic KMS,

supplemented by human contact. KMS must be supplemented with face-to-face mechanisms

that permit social networks to be formed and maintained. KMS must be supplemented with face-to-face mechanisms

that permit domain expertise to be acquired and translated across domains.

Analyzing Qualitative Data

Principles and Practice(!)

Qualitative data coding Data are be transcribed into a textual form

(recommended) and/or analyzed in its raw form (e.g. video/audio, with items of interest identified by time-stamp).

Data analysis (coding) can take two forms: Data are classified according to a conceptual

schema or a theoretical model, which leads to explanations dependent upon, or the further development of the conceptual model

Data are classified according to patterns that emerge from interpretation of the data. As themes and patterns emerge from the data, these are tested against further data samples to derive a substantive (grounded) theory.

Let’s find out! Organize in groups of

three(-ish) people.

Discuss themes arising from coded data (10 minutes)

Present findings: 5 minutes per group

A Question

Q: If two researchers are presented with the same data, will they derive the same results if they use the same methods, applied rigorously?

How to “Code” Data RQ: What are differences in the ways that various types of IS

professional or manager define the core problems & skills of IS design & development?

Read the transcript or data record through. Ask yourself “what is it that is going on here?” Make notes about “themes” that you see in the data; Don’t attempt to be systematic/comprehensive at this point

Categorize (“code”) your observations Relate category-codes to research question Define attributes of categories (attribute codes) Define categories and sub-categories (coding “families”)

Ask “so what?” Relate categories and their attributes to contextual factors

and/or type of subject Draw conclusions about what the data tells you, in answer

to the research question.

Issues With Qualitative Research

How much data is enough? How do you know that what you found is

not what you were looking for? Is it difficult to publish qualitative research

studies? Is qualitative research considered less

acceptable than quantitative research? Is this something that a PhD student

should consider?

Intercoder Reliability/Agreement Intercoder reliability is a measure of agreement among coders

in their coding of data High reliability scores indicate that

Categories are well-defined (agreed) and can be replicated by others applying the same schema, OR

Multiple coders are applying a pre-defined set of categories consistently, when coding data samples.

Assess by comparing (co-coding) several data samples (e.g. 10) Or analyze data from a pilot study to see what codes

emerge across researchers before main study starts Measures of intercoder agreement):

Coefficient of reliability (Holsti, 1969, p. 140) Scott’s pi (Holsti, 1969, p. 140) Cohen’s kappa (Krippendorff, 1980, p. 138) Agreement coefficient (Krippendorff, 1980, p. 138) Composite reliability (Holsti, 1969, p. 137)

Good website: http://astro.temple.edu/~lombard/reliability/

Summary: Issues in Qualitative Research

Qualitative research methods are used differently by researchers working within various philosophical approaches and various qualitative traditions.

Data collection methods include action research, case studies, ethnography.

Data analysis methods include statistical sampling of coded data and the inductive generation of relationships between variables.

In the interpretive approach: Rigor is achieved through comparison of findings across data

samples and reflexivity. Validity is communicated through trustworthiness and subject

validation of interpretations, rather than statistical significance. Can protect yourself against allegations of subjective

interpretation (lack of rigor), by testing for co-coder reliability.

The “Qualitative – Quantitative Debate”

Constructivist/Interpretivist Find answers to questions Social science view Explanatory Goal: understand the

subject’s perspective, in context

Investigation oriented Emergent themes and issues Researcher is part of

situation being studied

Realist/Positivist Test hypotheses Natural science view Confirmatory Goal: find probabilities and

correlations Verification oriented Controlled variables Researcher distanced from

situation being studied

BUT Differences are not as simple as this – it is possible to perform qualitative research

in a positivist way, or quantitative analysis of interpreted findings. Positivist research is also subjective – but the subjectivity occurs earlier in the

research “life-cycle”, in selection of theory to be tested and research instrument(s).

Qualitative Quantitative

Denzin, N.K., and Lincoln, Y.S. [Eds.] (2000) The Handbook of Qualitative Research. Sage Books.

Eisenhardt, K.M. (1989) "Building Theories From Case Study Research," Academy of Management Review (14:4), pp 532-550.

Gasson, S (2003) ‘Rigor in Grounded Theory Research’, in M. Whitman and A. Woszczynski (Eds.) Handbook for Info. Sys. Research, Idea Group, Hershey PA

Gasson, S. (2009) ‘ Employing A Grounded Theory Approach For MIS Research’, in Dwivedi et al. (Eds.), Handbook of Research on Contemporary Theoretical Models in Information Systems, Idea Group, Hershey PA.

Glaser, B.G. & Strauss, A.L. (1967) The Discovery of Grounded Theory, Aldine Publishing, New York

Guest, G., Bunce, A., & Johnson, L. (2006). How Many Interviews Are Enough? An Experiment With Data Saturation And Variability. Field Methods, 18(1), 59-82.

Lincoln, Y. S. and Guba, E. G. (1985), Naturalistic inquiry, Sage Publications CAMiles, M.B. and Huberman, A.M. (1994) Qualitative Data Analysis: An Expanded

Sourcebook, (2nd. Edition) Sage Publications, Thousand Oaks, CA Patton, M. Q. (2002). Qualitative research and evaluation methods (3rd ed.).

Thousand Oaks, CA: Sage.. Strauss, A. L., and Corbin, J. (1998) Basics of Qualitative Research: Grounded

Theory Procedures And Techniques. 2nd. edition, Sage Publications, Newbury Park, CA

Yin, R. K. Case Study Research, Design and Methods, 3rd ed. Newbury Park, Sage Publications, 2002.

References (Books and Articles on How-To “Do” Qualitative Research)

More references (recommended examples) – References used in slides are given in notes to slidesBarley, S. (1990) ‘Images Of Imaging: Notes on Doing Longitudinal Field Work’,

Organization Science, Vol. 1, No. 3, pp 220-247 Cavaye, A.L.M. "User Participation In System Development Revisited," Information &

Management (28:5) 1995, pp 311-323. Checkland, P. (1981) Systems Thinking, Systems Practice, John Wiley & Sons, Chichester.Newman, M., and Robey, D.(1992) "A Social Process Model of User-Analyst

Relationships," MIS Quarterly (16:2) 1992, pp 249-266.Orlikowski, W.J. & Robey, D. (1991) ‘Information Technology and the Structuring of

Organizations', Information Systems Research, Vol. 2, No. 2, pp 143-169 Schutz, A.(1962) Collected papers Vol. I. The problem of social reality. Martinus Nijhoff,

The Hague. Suchman, L. (1987) Plans And Situated Action, Cambridge University Press, MA, USA Tannen, D. "What's In A Frame?" in: Framing in Discourse, D. Tannen (ed.), Oxford

University Press, Oxford, UK, 1993.Van Maanen, J. (1988) Tales of the Field, University of Chicago Press, Chicago, IL Walsham, G. (1995) ‘Interpretive Case Studies In IS Research: Nature and Method’,

European Journal of Information Systems, No. 4, pp 74-81Wolfe, R.A. "Organizational Innovation: Review Critique and Suggested Research

Direction," Journal of Management Studies (31:3) 1994, pp 405-431. Yin, R.K.Case Study Research, Design and Methods, 2nd ed. Newbury Park, Sage

Publications, 1994.

ResourcesISWORLD Qualitative Research website:

http://www.qual.auckland.ac.nz/

CAQDAS Qualitative Research resources – lots of software! http://caqdas.soc.surrey.ac.uk/resources.htm

University of Georgia – Qualitative Research Site: http://www.qualitativeresearch.uga.edu/QualPage/

Ethnographic & Qualitative Methods Course Resources

Discourse Analysis (Deborah Tannen, 2004): http://www.lsadc.org/fields/index.php?aaa=discourse.htm

Good discussion of inter-coder reliability in content analysis http://www.temple.edu/sct/mmc/reliability/

Some freeware for qualitative data analysis - Audacity is an audio editor which will record sounds, play sounds,

import, edit and export WAV, AIFF, Ogg Vorbis, and MP3 files Express Scribe provides professional audio playback control software Atlas/ti -- cut-down but usable demo of qualitative analysis software

My web-page – interesting readings for PhD students: http://www.ischool.drexel.edu/faculty/sgasson/IS-readings.html