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Analysis and Interpretation of Qualitative Data 26.11.2001, VeTO, SEMS & Sarcous. Kerttuli Visuri & Jarno Vähäniitty. Topics of this presentation. Some words on qualitative research Data analysis phases and terminology Preparing for analysis Analysis

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kerttuli visuri jarno v h niitty

Analysis and Interpretation of Qualitative Data 26.11.2001, VeTO, SEMS & Sarcous

Kerttuli Visuri & Jarno Vähäniitty

topics of this presentation
Topics of this presentation
  • Some words on qualitative research
  • Data analysis phases and terminology
  • Preparing for analysis
  • Analysis
    • Techniques for analysing qualitative data
    • Differences of single (within-case) and cross-case analyses
    • Tool support for data analysis
  • Interpretation
    • drawing and verifying conlusions
    • confirming the findings
  • Summary
qualitative research basic terminology and concepts
Qualitative research; basic terminology and concepts
  • The aim of the analysis is to understand the research phenomenon from the viewpoint of the research subject
  • Prerequisites
      • Knowledge of the existing literature/research of the selected research field
      • Awareness of the theoretical framework, to which the current research is going to be clung to
    • Progression of a qualitative reseach process
      • The research problem may change during the research process
    • Typical data
      • Derived from interviews
      • Written documents and specifications, magazines, agreements, video tapes, etc.
phases of qualitative research and our focus
Phases of Qualitative Research and Our Focus
  • Data collection
  • Data analysis and interpretation
  • Documenting and reporting
steps in analysis and interpretation
Steps in Analysis and Interpretation
  • Design the data analysis and interpretation phase
  • Data reduction
  • Data display
      • Explore and describe
      • Explain and predict
  • Interpret and draw conlusions
  • Verify the findings
designing the analysis phase
Designing the Analysis Phase
  • Issues to bear in mind while designing the data analysis phase
    • What type of data do you have?
      • Qualitative, quantitative or both?
    • How to link qualitative data to quantitative?
  • Management issues
    • staffing and scheduling?
    • other study participants; verifying the findings; who and when?
    • data mangement; data storage, data analysis techniques?
    • possibilities for computer use in order to facilitate data analysis?

Reserve time and resources for data analysis;

It is the BIGGEST TASK in qualitative research projects!

analysis data reduction
Selecting, focusing, simplifying, abstracting and transforming the data that appear in written-up field notes or transcriptions

Goal: Organise the data in such a way that “final” conclusions can be drawn

Analysis: Data Reduction
displaying reduced data
Displaying Reduced Data
  • Data Display = organised, compressed assembly of information that permits conclusion drawing and action
    • ”You know what you display”
  • Two major approaches for displaying ’reduced’ data
    • matrices
    • networks
  • Displays may sort to data according to
    • chronological sequence (flow) of events, happenings and processes
    • role-ordered positions of the participating personnel
    • conceptual dependences (variables and their interaction)
  • Different display types suited to different analysis problems
    • Also, linked to various tactics for drawing and confirming conclusions
analysis exploring and describing
Analysis: Exploring and describing
  • ”What, where and when?”
    • Making complicated things understandable by showing how their parts fit together according to some rules
    • Plausible reasons for why things are happening as they are
  • Objectives:
    • Compress and display the data in order to permit drawing conclusions and
    • Guard against the overload and potential for bias that appear when analysing unreduced data
data displays for exploring describing purposes
Data Displays for Exploring & Describing Purposes
  • Partially ordered displays
    • Uncover and describe what is happening in a setting, no matter how how messy or surprising
    • Example: Context chart
      • Shows relationships between the roles and groups that make up the context
      • Summarises first understandings and locates questions for next-step data collection
  • Time-ordered displays
    • For understanding flow and sequence of events and processes
    • Example: Event listing
      • Arranges a series of events by time periods and sorts them into categories
      • For understanding extended processes
  • Role-ordered displays
    • Sort people according to their position-related experiences
  • Conceptually ordered displays
    • Emphasise well-defined variables and their interaction
analysis explaining and predicting
Analysis: Explaining and Predicting
  • ”Why and how?”
  • Aim: to allow the researchers to see the underlying mechanisms of influences
  • Two suggested approaches
    • variable-oriented (conceptual approach)
    • process oriented (storylike approach)
data displays for explanation predicting purposes
Data Displays for Explanation & Predicting Purposes
  • Explanatory effects matrix
    • First step towards answering why things happened the way they did
    • Looks at outcomes or results of a process
  • Case dynamics matrix
    • Displays a set of forces and traces the outcomes
    • A way of seeing ”what leads to what”
  • Causal networks
    • Display of the most important variables and their relationships
    • Pulling together independent and dependent variables and their relationships into a coherent picture
  • Straight predictions
    • Inferences that the researcher makes about the probable evolution of case events or outcomes for the future
    • ”Ultimate test of explanatory power”
within case and cross case analysis differences and similarities 1 2
Within-case and cross-case analysis; differences and similarities (1/2)
  • Within-case analysis:
    • one in-depth analysis per one case; may include various viewpoints
  • Cross-case analysis:
    • looking at several cases one after another in order the gain a bigger picture of the research phenomenon
  • The aim of cross-case analyses is to derive good explanations and better theories by looking at multiple cases instead of only one
    • Increases generalisability through deepened understanding of the research phenomenon
  • Summarizing the themes is not enough -> the generalization has to be done across the variable and process factors
    • firstly, individually in each case in order to gain an in-depth analysis of each case
    • are the variables/processes similar in each case?
    • if not, how do they differ from each other in each of the cases?
    • Generalisation possible based on a careful analysis of each case
within case and cross case analysis differences and similarities 2 2
Within-case and cross-case analysis; differences and similarities (2/2)
  • Some suggestions for how to do generalizations:
    • avoid aggregating or smoothing
    • keep the local case configuration (basic conditions) intact
    • join the variable- and process-oriented approaches
    • cases can often be sorted into explanatory groups or families sharing common scenarions
    • However:
      • Deviating cases are at least as important as those that fit nicely
      • Don’t try to fit the case in by force but strive to understand why a certain case deviates from the common stream
        • These findings can support your theory, too
  • Some suggested techniques for exploring and describing the cross-case data
    • partially ordered matrices
    • conceptually ordered matrices
    • case-ordered presentations
    • time-ordered matrices/presentations
tool support for analysis
Tool Support for Analysis
  • Preparing data for analysis
    • Data annotation / memoing
    • Data coding / classification
  • Analysis
    • Data linking
    • Search and retrieval
    • Data display
    • Graphics editing
    • Conceptual / theory development
    • Example:
      • ”find all data referring to ’requirements management’ ”
conclusion drawing and verification
Conclusion Drawing and Verification
  • People make quickly sense of the most chaotic events
    • We keep our world consistent and predictable by organising and interpreting it
    • But, are the meanings found right, valid or repeatable?
      • Qualitative analyses can be evocative, illuminating, masterful – and wrong
  • Coming up: tactics for
    • Generating meaning
    • Testing and confirming meanings
  • Also, look at Hubermann & Miles for a series of questions for the researcher to ask himself when assessing the quality of a study
tactics for generating meaning
”What’s going on?”

Noting patterns and themes

Seeing plausibility (or, lack of it)


Making metaphors


Sharpening the understanding

Making contrasts and comparisons


Partitioning variables


Subsuming particulars into the general


Noting relations between variables

Finding intervening variables

Establishing understanding

Building a logical chain of evidence

Making conceptual / theoretical coherence

Tactics for Generating Meaning
tactics for testing and confirming meanings found
Assessing quality of the data

Checking for


Researcher effects

Triangulating (across data sources and /or methods)

Weighting the evidence

Saying what the found pattern is not like

Checking the meaning of outliers

Using extreme cases

Following up surprises

Looking for negative evidence

Testing our explanations and theories

Making if-then –tests

Ruling out spurious relations

Replicating a finding

Checking out rival explanations

Getting feedback!

Tactics for Testing and Confirming Meanings Found
summary concurrent flows of activity in qualitative data analysis








drawing / verifying

Summary: Concurrent Flows of Activity in Qualitative Data Analysis