Kerttuli Visuri & Jarno Vähäniitty

1. Analysis and Interpretation of Qualitative Data 26.11.2001, VeTO, SEMS & Sarcous Kerttuli Visuri & Jarno Vähäniitty

2. 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

3. 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.

4. Phases of Qualitative Research and Our Focus • Data collection • Data analysis and interpretation • Documenting and reporting

5. 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

6. 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!

7. 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

8. 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

9. 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

10. 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

11. 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)

12. 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”

13. 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

14. 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

15. 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’ ”

16. 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

17. ”What’s going on?” Noting patterns and themes Seeing plausibility (or, lack of it) Clustering Making metaphors Counting Sharpening the understanding Making contrasts and comparisons Differentiation Partitioning variables Abstracting Subsuming particulars into the general Factoring Noting relations between variables Finding intervening variables Establishing understanding Building a logical chain of evidence Making conceptual / theoretical coherence Tactics for Generating Meaning

18. Assessing quality of the data Checking for Representativeness 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

19. Data Collection Data Display Data Reduction Conclusions: drawing / verifying Summary: Concurrent Flows of Activity in Qualitative Data Analysis

20. Let the discussion begin!