Analyzing Case Study Data

8/12/2019 Analyzing Case Study Data

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PhD Seminar Series

Qualitative Research Methodology

Defining and conducting case-based

research

Analysing case study data

Klas Eric Soderquist, DBA, Brunel University

&


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A Case of Case Study Research Recalling the Process of Defining the Research Problem

Defining the Unit of Analysis

Research Strategy, Relation to Theory, Methodological

Choice Data Collection

Interviews, Direct observation, Participant observation

From research question to interview guide

Data analysis (case or other qualitative approaches)

Open coding Axial coding

The paradigm model

Validity and Reliability

Seminar Outline

PhD Seminar Series. Qualitative Research MethodologyK.E. Soderquist


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MyCase of Case Study Research

Inside the Tier Model: Product Development Organisation

and Strategies in Automotive Expert Supplier Firms

Carmaker

First tier

Second tier

Third and fourth

tier



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Definition of the Research Problem

Match

REAL WORLD

CONTEXT

RESEARCHQUESTIONS

RESEARCH

OBJECTIVE

OVERALL

PHICALPOSITION

PHILOSO-

Match /Mismatch

Match /Mismatch

Match /Mismatch

Match

REAL WORLD

CONTEXT

RESEARCHQUESTIONS

RESEARCH

OBJECTIVE

OVERALL

PHICALPOSITION

PHILOSO-

Match /MismatchMatch /Mismatch





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MyReal World Context

Background

The Lean Production Framework

The Tier Model

The Partnership Concept Transaction Cost Theory, Strategic

Collaboration Theory, Operational

Coordination Theory

Research Problem Supplier Perspective

Operational Perspective

Overal l

Research

Quest ions



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Overall Research Questions

They tell you what you want to focus on and what you want to

know

They set the rough boundaries of the research: you will study

someissues insomecontext withsomeactors

They are oriented towards action and process

The way they are (implicitly) formulated will determine

research strategy later on

They set the vision for the research project and helps focusingactivities



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MyOverall Research Questions

How does the emergence of new industrial principles take

place in expert supplier firms?

What is the place and role of expert suppliers in the automotive

supply chain?

What lean production techniques are used and how are they

adopted for satisfying the needs of the organisation?

How in practice takes integrated component development

place?

How are organisations and processes designed to support

integration?



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Research Objective

To explain the causality between different observations or

the reasons behind a certain situation concerning the

phenomenon

To explorea vague problem or a new area of

research

To describe, i.e., observe and visualise the situation of

certain phenomena

One can distinguish between mainly three objectives orpurposeswith a

research project:

The research objective does not automatically define a quantitative or

qualitative logic



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Research Objective

WHAT questions of descriptive nature in the sense how

much or how many call for a quantitative approach WHAT questions of explanatory or exploratory nature call for

a qualitative approach

HOW questions and WHY questions call for a qualitative

approach

The research questions implicitly determine the research objective, and together

they indicate quantitative vs. qualitative research:

Qualitative research is needed when we want to come to terms with the meaning,

not the right or wrong with the phenomena under investigation



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Match / Mismatch

Match

ANALYSIS

Match / Mismatch

UNIT OF

Mismatch Mismatch

Methodological choice

Quantitative

Deductive Logic

Quantitative

Inductive Logic

Qualitative Qualitative

Deductive Logic Inductive Logic Data collection and data

Deduction Induction

Predefinition and test of

a theoretical model.

THEORY EXTENSION

Determination of theory

from observations.

THEORY DEVELOPMENT

RELATION TO THEORY

REAL WORLD

CONTEXT

RESEARCHQUESTIONS

RESEARCH

OBJECTIVE

DEFINITION OF THE RESEARCH PROBLEM

OVERALL

Match

STRATEGY

Match / Mismatch

RESEARCH

Match

Mismatch Mismatch

PHICALPOSITION

PHILOSO-

Match /Mismatch

Match /Mismatch

Match /Mismatch

Mismatch

Match

analysis methods, appropriate

for the chosen methodology

Full

Process

Model of

MethodologicalChoice


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Unit of Analysis What is the Case?

It is the Research Objector unit in the real world context

that you will observe

It can be:

An individual,

A role

A group

A process

An organisational entity An organisation

Or any other definable and observable unit



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Unit of Analysis Example

Units of analysis in change management research:

Change efforts (e.g., installing new technology, downsizing, a particular

restructuring, effort to change corporate culture)

Change events (e.g. a crises); The leaders of change (i.e., the individuals that drive through "corporate

revolutions" with research focus on their personal traits, leadership

styles and leadership levers);

Sectors undergoing change (i.e., comparative studies of different

business sectors in search of similarities and specificities basically in thecontent of change)

Employees facing change and acting in change processes.



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Match / Mismatch

Match

ANALYSIS

Match / Mismatch

UNIT OF

Mismatch Mismatch


Quantitative

Deductive Logic

Quantitative

Inductive Logic



Deduction Induction



THEORY EXTENSION


from observations.

THEORY DEVELOPMENT

RELATION TO THEORY

REAL WORLD

CONTEXT

RESEARCHQUESTIONS

RESEARCH

OBJECTIVE


OVERALL

Match

STRATEGY

Match / Mismatch

RESEARCH

Match

Mismatch Mismatch

PHICALPOSITION

PHILOSO-

Match /Mismatch

Match /Mismatch

Match /Mismatch

Mismatch

Match



Full

Process

Model of



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Research Strategy

Five Basic Strategies:

Experiment

Survey

Archival Analysis

History

Case Study



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Case Study Research Strategy

Selecting the Case Study Organization: Criterion Sampling

Purposive rather than random samples

Samples can evolve as research moves on

Identify theory driven criteria in order to frame the research

Inclusive or contradictory samples



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Case Study Research Strategy

MyCriterion Sampling

Case Study Organisations should:

Have a good record for internally generated innovation.

Innovation has a high profile in the organization.

Have established links with research laboratories.

Have an explicit strategy for R&D and R&D driven organizational

development



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Match / Mismatch

Match

ANALYSIS

Match / Mismatch

UNIT OF

Mismatch Mismatch


Quantitative

Deductive Logic

Quantitative

Inductive Logic



Deduction Induction



THEORY EXTENSION


from observations.

THEORY DEVELOPMENT

RELATION TO THEORY

REAL WORLD

CONTEXT

RESEARCHQUESTIONS

RESEARCH

OBJECTIVE


OVERALL

Match

STRATEGY

Match / Mismatch

RESEARCH

Match

Mismatch Mismatch

PHICALPOSITION

PHILOSO-

Match /Mismatch

Match /Mismatch

Match /Mismatch

Mismatch

Match



Full

Process

Model of



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Observations

EmpiricalGeneralizations

HypothesesTest

INDUCTION DEDUCTION

DEDUCTIONINDUCTION

Construction

of theory

Application

of theory

Theoretical work

Empirical work

Theoretical work

Empirical work

Theory

Relation to Theory

Theory ExtensionTheory Development



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Methodological Choice


Quantitative

Deductive LogicQuantitative

Inductive Logic

Qualitative QualitativeDeductive Logic Inductive Logic Data collection and data

analysis methods, appropriatefor the chosen methodology



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Participant Observationlongitudinal presence

The researcher is part of the organisation on a contractual basis and has a

contextual professional role and reporting responsibility Direct Observation(longitudinal) presenceThe researcher is present in the organisation as an external observer

Interviewsoccasional visits (can be longitudinal)

Documentary Analysis - background info and event analysis

Data Collection MethodsCase-Based Research



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The overall research questions starts broadly and are gradually

narrowed by asking sub questions and sub-sub questions

Research questions should be asked crude to managers and

experts in an exploratory phase: Pilot interviews or pilot case

Data Collection

From Research Question to Interview Guide



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Data Collection through InterviewsFrom Research Question to Interview Guide (I)

The process of generating more detailed questions

Sub, sub-sub

Research

Questions

Mental Processing

Researchers

Exploratory Data

(Real World)

Overall Research

Questions

Personal Experience

(Real World)



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Data Collectionthrough InterviewsFrom Research Question to Interview Guide (II)

MyInterview Guide (minor extract). Semi-structured, open ended

How are organisations and processes designed to support integration?

What coordination activities exist in the product development process?

When and how is manufacturing staff involved in new development projects?

What are the benefits of their involvement?

What are the drawbacks?

How is coordination with customers organised?

How is coordination with suppliers organised?

What communication structures exist in the product development process?

Who talks to who in the process?

What channels are used?

Frequency of communication between different players?

How does communication support coordination?



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Data Collection through InterviewsBasic Interview Techniques


Not too active, not too passive - The guide serves as a tool fornavigating the interviews

Use of the critical incident technique to start out the interviewsor enter into new subjects.

It is efficient when managers have difficulties in articulating answers tospecific questions and consists of asking the interviewee to describe specificevents that have had a critical impact on the way a specific issue has beenmanaged

Use re-launch questions:

- Could you tell me more about this?

- Could you specify further?

- What are you thinking about in particular?

It is important in such cases not to give any suggestions tointerviewees


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Between Collection and AnalysisInterview (or observation) Transcripts


For serious research, tape record interviews!

Always keep supportive notes

Proceed to immediate transcript

Transcript should reflect the interview at 100 percent

Ask for feed-back on transcripts

Fill in uncertainties with a second (phone) interview


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Data Analysis / CodingBasics


If we talk about case research, we will have at leastmultiple interviews, possibly a mix of interviews and fieldnotes

Our analytical problem is that of analysing a mass of text

Analysis / Coding represents the operations by which dataare:

Broken down

Conceptualized,

Put back together in new ways

The objective is to build theory from dataa GroundedTheory approach


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Data Analysis / CodingOpen Coding (I)


Labelling events: Consists of placing conceptual labels ondiscrete happenings, events and instances in dataData are broken down and conceptualized by taking apart an observation, asentence, a paragraph and giving each separate incident, idea or event a namethat represents a phenomenon. Incident after incident in the field notes are

compared in order to give similar events the same name -conceptual label- andnuance and enrich each concept.

The process involves asking questions to the data so as to identify propertiesand dimensions of the incidents


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Data Analysis / CodingOpen Coding (II)


Categorising: Consists of grouping conceptual labels intocategoriesConcepts that seem to pertain to the same phenomena are then regrouped -categorized- and given a name that should be more abstract than that givenduring labeling.

Categories have conceptual power because they are able to pull togetheraround them groups of concepts.

Practically, categorising consists of going through all concepts and askingquestions such as 'What is this concept about?' or 'Is this concept similar ordifferent from the one before or after?'

In this way, categories are discovered when concepts are compared against oneanother, and concepts become characteristic components of a category - so

called subcategories.

If a concept seems not to pertain to an already identified category, it is leftaside and might become the entry to a new category as data analysis goes on.

Categorising can be bottom-up or top-down


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Data Analysis / CodingAxial Coding


Condensation of data, i.e., regrouping and linking

categories to each other

Open coding f ractures the data and allows the researcher to identify

some categories, their properties and dimensions. Axial coding puts

these data back together in new ways by making connections between

categories.

The objective is to identify the core category (basically

derived from the unit of analysis) and to logically relate

other main categories to the core The Paradigm Model structures the axial coding process

The Paradigm


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5. Identify the outcomes related to the phenomenon - predictable/intended or not (competences,

ressources, knowledge, know-how...).

CONSEQUENCES

The Paradigm

Model

1. Identify the phenomenon to be studied (activity, business process...).

PHENOMENON

2. Identify the conditions of directive nature, influencing the phenomenon (driving forces,

objectives...).

CAUSAL CONDITIONS

3. Specify the context in which the phenomenon takes place (organization, information....).

CONTEXT

4. Identify the conditions of shaping nature that intervene in carrying out and managing the iphenomenon (reactions from stakeholders, cognitive processes, continuous improvement...).

INTERVENING

CONDITIONS



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Data Analysis / CodingAn Iterative

Process


MyResearch

Process

Initial literature review,

development of research

proposal and topic guide

Pilot interviews, testing

of topic relevance

Suppliers 1, 2, 3 and 4

Final version of

the topic guide

Formal interviews with

product development managers

Suppliers 3 and 4

Formal interviews

with general managers

Suppliers 3 and 4

Data analysis:

open coding

First case study

Supplier 3

Data analysis: open coding,

data reduction and develop-

ment of case study topic guide

Second case study

Supplier 4

1993

1994

1995

1996

Data analysis:Axial coding,

comparison of findings

with relevant literature

Second series of formal

interviews (product development

managers) Suppliers 5, 6, 7 and 8

Data analysis: open coding,

data reduction and comparishon

of concepts with case study one


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Category: Problem Solving

Sub categories: Objectives, Efficiency, Support Structures

Properties of Efficiency:Nature of Topic, Nature of Participation

Dimensions of Nature of Topic: Problems, Problems & Causes,Problems, Causes & possible le actions

Dimensions of Nature of Participation:

Participant Driven Management Driven

Data Analysis / CodingMyOpen Coding


Sentence extracted from interview:

We need performance measurements that enhance thevalue of inter-functional problem solving


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Data Analysis / CodingMyAxial Coding (I)


Core Category: Operational Design

The analysis of the category Problem Solving led to theidentification of four distinctive levels of operational design

Individual workIndividual work

Group work

In t rafunct iona l between des ign

technicians and engineers

Group work

In t rafunct iona l between des ign

technicians and engineers

Project work

In ter funct iona l wi th in the

suppl ier company

Project work

In ter funct iona l wi th in the

suppl ier company

Systemic work

Involving custo mers, interface supp liers

and lower-t ier suppliers

Systemic work

Involving custo mers, interface supp liers

and lower-t ier suppliers


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Data Analysis / CodingMyAxial Coding (II)


Causal Conditions: Means of Guidance

Guiding Visions, Performance Measurements

Context: Design Support Structures

Specifications, Information transmission support technology,

technology scanning, knowledge repositoriesIntervening Conditions

Learning dynamics,learning model

Intra-company,

intra-functional learning

Intra-company,

intra-functional learning

Purchasing

Process

Engineering

Sales

Intra-company, inter-functional learning

Purchasing

Process

Engineering

Sales


Purchasing

Process

Engineering

Sales


Inter-company, intra-functional learning

Designfunction ininterfacing

sup. firms

Designfunction incustomer

firms

Inter-company, intra-functional learning


sup. firms


firms


sup. firms


firms


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Data Analysis / CodingMyAxial Coding (II)


Causal Conditions: Means of Guidance

Guiding Visions, Performance Measurements

Context: Design Support Structures

Specifications, Information transmission support technology,

technology scanning, knowledge repositoriesIntervening Conditions

Learning dynamics,learning model

Consequences

Model of the process ofbuilding core capabilities

S

H

A

R

E

D

V

A

L

U

E

S

Development

Technological

Collective

Learning

Development

Technological

Collective

Learning

Collective

Learning

CAPABILITIES

CORE

CAPABILITIES

CORE

CAPABILITIES

CORE

Adequation with customer needs

Differentiation from competitors

Adequation with customer needs

Differentiation from competitors

Manage-

ment

Sys-

tems

Manage-

ment

Sys-

tems

Strategic

Objec-

tives

Means

of

Guidance

Strategic

Objec-

tives

Means

of

Guidance

Basic Resources

Skills

Basic Resources

Skills

Basic Resources

Skills


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Data Analysis / CodingSelective Coding and Story


The narrative about the core category must identify anddefine the basic content of the other categories

Narrative about each category, crossing the results of axialcoding with research questions

Theoretical sampling: Strengthen weak relationshipsbetween categories inductively and integrate literaturerelevant for the results emerging

Specify action / interaction strategies for how the holisticmodel or theory operates

Example: The answer to one of the research questions emerges from the


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Problem solving 1*

Product

planning

Product

engineering

Process

engineering

Running

Production /

Minor

modifications

Project 1

Project 2

Project 3

Project 4

SUPPLIER CARMAKER

Design

Transfer of learning

through formal and

Shared knowledge base

Component

design

Problem solving 2*

Problem solving 4*

Problem solving 3*

technician S1

Design

technician S2

Design

technician S3

informal group work,

organizational routines

and support structures

Design

Transfer of learning

through formal and

technician C1

Design

technician C3

Design

technician C4

informal group work,

organizational routinesand support structures

* Transfer through supplier - customer

problem solving cycles

Design

technician C2

Transfer of experience

Example:The answer to one of the research questions emerges from thenarratives about the core category, the context category and the intervening

conditions category

How are organisations and processes designed to support integration?


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Reliability and Validity (I)


Reliability consists of demonstrating that the operations of a

study -such as the data collection procedures- can be

repeated, with the same results

It is a question of documenting the research procedure

Reliability is ensured by keeping data in different forms:

- Directly taken field notes - from interviews and observations,

- Expanded typed notes made as soon as possible after the field work

(this includes comments on problems and ideas that arise during each

stage of the fieldwork and that will guide further research),- A running record of analysis and interpretation (open coding and axial

coding).


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Reliability and Validity (II)


Construct validity means establishing correct operationalmeasures for the concepts being studied. It is ensured

through:

- The use of multiple sources of evidence,

- The establishment of a chain of evidence,- Letting key informants review draft result reports

External validity means establishing the domain to which a

study's findings can be generalized. It is ensured through

the use of a replication logic Analytical Generalisation

- Relate case findings to existing or emerging bodies of literature,part

of whichwill have been analysed in the literature section of the thesis


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Sources

Easterby-Smith, M., Thorpe, R., Lowe, A., (1991),Management Research, An Introduction,London: Sage Publications.

Dey, I. (1993), Qualitative Data Analysis. A User-Friendly Guide for Social Scientists,London: Routledge.

Miles, M. B. & Huberman, A. M., (1994), Qualitative Data Analyses, An ExpandedSourcebook, Thousand Oaks, CA: Sage Publications.

Pras, B. & Tarondeau, J-C., (1979), "Typologie de la recherche en gestion" (Typology ofManagement Research, in French),Enseignement et Gestion, Nouveau Serie no. 9, p. 5-11

Silverman, D., (1993),Interpreting Qualitative Data, Methods for Analysing Talk, Text andInteraction, London: Sage Publications.

Strauss, A., & Corbin, J., (1990),Basics of Qualitative Research, Grounded TheoryProcedures and Techniques, Newbury Park CA: Sage Publications

Van Maanen, J., Dabbs, J.M., Faulkner, R.R. (1982), Varieties of Qualitative Research.

Beverley Hills, CA: Sage Publications Van Maanen, J., (1983), Qualitative Methodology, Beverley Hills, CA: Sage Publications.

Yin, R., K., (2003), Case Study Research, Design and Methods, 3rdedition, Newbury Park,CA: Sage Publications.

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Analyzing Case Study Data

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