Management and Business Research M EasterbySmith R Thorpe

Management and Business Research © M. Easterby-Smith, R. Thorpe, P. Jackson & L. Jaspersen

Chapter 8 Framing and interpreting qualitative data

Learning objectives • To get an overview of different approaches to qualitative data analysis, and to understand how they frame qualitative data in different ways • To learn about different methods and techniques for analysing qualitative data • To understand how different software packages can assist with the preparation, management and analysis of qualitative data • To gain insights into how the quality of qualitative research is assessed

Preparing and managing qualitative data • Write up and organize field notes • Transcribe audio and/or video recordings • Digital data should be formatted and labelled in a consistent way • File data systematically and in an appropriate format • Lists of contacts, pseudonyms and an overview of all data should be prepared and regularly updated • Store data in a way that prevents unauthorized access • Back up all data and archive with the same standards of data protection

Framing and interpreting qualitative data: seven approaches • • Content analysis Grounded analysis Visual analysis Discourse analysis Conversation analysis Argument analysis Narrative analysis

Content vs. grounded analysis • Content analysis – aims at drawing systematic inferences from qualitative data that have been structured by a set of ideas or concepts • Grounded analysis – a more intuitive and ‘open’ approach, uses similar techniques in a different way Content analysis Grounded analysis Searching for content Understanding of context and time Casually linked concepts and ideas structure analysis Holistic associations guide analysis Objective/subjective Faithful to views of respondents More deductive More inductive Aims for clarity and unity Preserves ambiguity and illustrates contradiction

Visual, discourse and conversation analysis • Visual analysis – is conducted via transcripts, descriptions and summaries of visual material, which are then studied using methods for the analysis of text • Discourse analysis – covers a range of approaches that focus on language itself or the much wider relationship of how language is used in a particular context • Conversation analysis – stems from an ethnomethodological tradition and was developed to facilitate the formal analysis of the structure of everyday ‘interaction as a continuous process of producing and securing meaningful social order’

Argument and narrative analysis • Argument analysis – is used as an interactive research design in which both data creation and data analysis require the active involvement of research participants • Narrative analysis – is concerned with the ways in which people create and use stories to make sense of the world. It can reveal valuable insight into how organizations shape social life

Analytic process • Interpretation of qualitative data with the aim of making statements about their meaning and what they represent (Uwe Flick) • A code is a word or a short phrase that captures the meaning of a chunk of data (such as a sentence, a paragraph, a section of an image, a sequence of a video, etc. )

Coding • Breaking up data in manageable and meaningful analytical units • Constant comparative analysis across instances Inductive coding Interpreting text and generating codes Data Theory Process of structuration Deductive coding Applying pre-existing codes and indexing

Example of a blog post TEXT Monetary compensation and human-centred initiatives don’t have to be mutually exclusive. Emerging markets are a strategic choice and are repeatedly reported as having explosive growth. Exploding economic growth, often caused by a growing middle class, also creates greater demand on health systems. These health systems need a way to provide care that impacts patients in their own context, otherwise risking inaccessibility to healthcare for large swaths of their patient populations. CODES

Coding is analysis Codes-to-theory model (grounded theory) Open or first-cycle coding Focused or second-cycle coding ‘Qualitative data analysis is rarely a linear process…. ’ (Saldaña, 2009: 12)

CAQDAS What CAQDAS can assist you with: • Filing and organising data • Accessing source data • Exploring data • Annotating and retrieve data fragments (coding) • Searching and ‘interrogating’ data in a systematic way • Comparing data fragments • Structuring your analysis • Writing: organising memos, comments and annotations • Project management

CAQDAS What CAQDAS can’t deliver: • Analyse your data for you • Tell you how to analyse your data • Think! C omputer A ssisted Q ualitative D ata A nalysis S oftware

Computer-aided analysis – choosing the right package • • Availability Requirements Skills and training Teamwork

Quality in qualitative research Criterion Questions to consider Worthy topic Is the research topic relevant, original, timely, significant and/or interesting? Rigour Does the study use appropriate data, concepts and methods? Sincerity Is the study characterized by (self-)reflexivity and transparency? Does it provide me with the information I need to evaluate the study? Credibility Is the study marked by detailed descriptions, the explication of tacit knowledge, and triangulation?

Quality in qualitative research Criterion Questions to consider Resonance Does the research affect readers/audiences through evocative representations, appropriate generalizations, and transferable findings? Contribution Does the research make a significant contribution in one or more of the following areas: theory/concepts, methodology/methods and practical impact? Ethics Does the research consider ethical issues? Meaningful coherence Does the study fulfil its aims? Do the methods and techniques used fit with the stated aims? Does the research meaningfully connect literature, research questions and findings?

Further reading • Banks, M. (2008) Using Visual Data in Qualitative Research. London: Sage. • Bazeley, P. (2007) Qualitative Data Analysis with Nvivo. London: Sage. • Bergmann, J. R. (2004) ‘Conversational analysis’, in U. Flick, E. Kardorff and I. Steinke (eds), A Companion to Qualitative Research. London: Sage, pp. 296 -302. • Boje, D. M. (1995) ‘Stories of the story-telling organization: a postmodern analysis of Disney as “Tamara-land”’, Academy of Management Journal, 38 (4): 997 -1035.

Further reading • Boje, D. M. (2003) ‘Using narratives and telling stories’, in D. Holman and R. Thorpe (eds), Management and Language. London: Sage. • Charmaz, K. (2014) Constructing Grounded Theory in Management Research. London: Sage. • Cunliffe, A. L. (2008) ‘Discourse analysis’, in R. Thorpe and R. Holt (eds), The SAGE Dictionary of Qualitative Management Research. London: Sage. • Dawson, A. and Hjorth, D. (2012) ‘Advancing family business research through narrative analysis’, Family Business Review, 25 (3): 339 -55.

Further reading • De. Cuir-Gunby, J. T. , Marshall, P. L. and Mc. Culloch, A. W. (2011) ‘Developing and using a codebook for the analysis of interview data: an example from a professional development research project’, Field Methods, 23 (2): 136 -55. • Flick, U. (2007) Managing Quality in Qualitative Research. London: Sage. • Flick, U. (2014) SAGE Handbook of Qualitative Data Analysis. London: Sage. • Friese, S. (2012) Qualitative Data Analysis with ATLAS. ti. London: Sage.

Further reading • Galman, S. C. (2013) The Good, the Bad, and the Data: Shane the Lone Ethnographer’s Basic Guide to Qualitative Data Analysis. Walnut Creek, CA: Left Coast Press. • King, N. (2014) Template Analysis Website, University of Huddersfield. Available at www. hud. ac. uk/hhs/research/template-analysis/ (last accessed 14 August 2014). • Knoblauch, H. , Tuma, R. and Schnettler, B. (2014) ‘Visual analysis and videography’, in U. Flick (ed. ), SAGE Handbook of Qualitative Data Analysis. London: Sage, pp. 435 -49. • Lawler, S. (2002) ‘Narrative in social research’, T. May (ed. ), Qualitative Research in Action. London: Sage, pp. 242 -58.

Further reading • Leitch, S. and Palmer, I. (2010) ‘Analysing texts in context: current practices an new protocols for critical discourse analysis in organisational studies’, Journal of Management Studies, 47 (6): 1194 -212. • Miles, M. B. , Huberman, A. M. and Saldaña, J. (2014) Qualitative Data Analysis, 3 rd edn. Thousand Oaks, CA: Sage. • Saladaña, J. (2009) The Coding Manual for Qualitative Researchers. Los Angeles, CA: Sage. • Sidnell, J. (2011) Conversation Analysis: An Introduction. Malden, MA: Wiley-Blackwell.

Further reading • Silver, C. and Lewins, A. (2014) Using Software in Qualitative Research: A Step-by-Step Guide, 2 nd edn. Los Angeles, CA: Sage. • Suddaby, R. (2006) ‘From the editors: what grounded theory is not’, Academy of Management Journal, 49 (4): 633 -42. • Tracy, S. J. (2010) ‘Qualitative quality: eight “big-tent” criteria for excellent qualitative research’, Qualitative Inquiry, 16 (10): 837 -51. • Uriau, V. J. , Reger, R. K. and Pfarrer, M. D. (2007) ‘ A content analysis of the content analysis literature in organizational studies: research themes, data sources, and methodological refinements’, Organizational Research Methods, 10 (1): 534.