Qualitative Data: Cooking Without a Recipe Forthcoming, Strategic Organization Melissa E. Graebner Jeffrey A. Martin Philip T. Roundy

2 Potential

Jeffrey A. Martin, PhD3 Frustrations

Confusion

Many descriptions of qualitative research: Lump all qualitative research together and offer lists of typical characteristics Nascent theory Naturalistic setting Inductive analytical approach Constructivist, relativist, interpretive stance Acceptance of researcher bias Interest in ordinary or everyday behavior Or group into categories with their own typical characteristics

No recipe or cookbook, but not anything goes While qualitative methods need to be elaborated or modified for each new application, this does not mean that anything goes or that the best method is no method (Gephardt, 2004, p. 458).

This creates some dilemmas: 1.Many studies do not fit in a single school of qualitative research (e.g., Maxwell, 2005) 2.Few studies have all of the typical attributes Researchers may feel compelled to force their work to fit into a mold that isnt appropriate

A potential solution: Focus on why you are using qualitative data There are multiple, distinct reasons for using qualitative data. Understanding why you are using qualitative data helps avoid forcing your study to fit with an inappropriate paradigm This reduces confusion and frustration

How qualitative data are different 1.Open-ended 2.Concrete and vivid 3.Rich and nuanced

Given these advantages, what research goals may call for qualitative data?

Reason #1: Build new theory when prior theory is absent, underdeveloped, or flawed New Theory

Reason #1: Build new theory when prior theory is absent, underdeveloped, or flawed Advantage of qualitative data: open-endedness Findings in a theory-building study can take diverse forms: Variance predictions (e.g., Ozcan & Eisenhart, 2009) Process models (e.g., Graebner, 2009) Typologies that unpack important and poorly understood constructs (e.g., Hite, 2003) And may be aimed at developing objective, positivist theory

Jeffrey A. Martin, PhD13 Reason #2: Capture individuals lived experiences and interpretations Lived experiences

Reason #2: Capture individuals lived experiences and interpretations Again, the advantage of qualitative data is open- endedness But there are important differences vs. theory-building: Interpretive studies: Aim to preserve the subjective nature of their data throughout the analytical process May use qualitative data even when substantial prior theory exists e.g., Creed et al.. use qualitative data to complement and extend previous theoretical work (2010: 1337).

15 Complex processes

Reason #3: Understand complex process issues Phenomena involving complex temporal dynamics or causal mechanisms, often embedded in nuanced social interactions Advantage of qualitative data: Richness In practice, many process studies involve some theory-building but qualitative data can also be used for process studies in areas of relatively mature theory E.g. Martin (2011) top management team processes; Lumineau et al. (2011) organizational learning Process researchers may even use qualitative data to test theory (e.g., Greenwood et al., 1994)

Jeffrey A. Martin, PhD17 Illuminate

Reason #4: Illustrate an abstract idea Advantage of qualitative data: Vividness, concreteness and richness Example: Siggelkow, 2001. The framework proposed in the paper emerged more from a conceptual exercise than from my exposure to Liz Claibornes experiences. However, the case turned out to be a very helpful illustration and was used in that manner after the conceptual framework was presented. Open-endedness is less important these researchers may have well-developed models prior to gathering their data (e.g., Kauppila, 2010).

Jeffrey A. Martin, PhD19 Examine language

Reason #5: Examine narratives, discourse or other linguistic phenomena Phenomena that fundamentally involve words and language May or may not be interested in individuals subjective experiences May examine media accounts, annual reports, websites and press releases And they may code their data in ways that enable statistical analysis E.g., Martens et al.s (2007) analysis of narratives in IPO prospectuses Quantified narratives to test hypotheses using statistical estimation

Jeffrey A. Martin, PhD21 Identifying your reason(s) for using qualitative data can help with navigating the review process: 1.Writing the front end 2.Describing analysis 3.Addressing biases Why is this important?

Writing the front end Illustrating an abstract idea: expect a lengthy front end Process questions, interpretive perspectives or language-related topics may also have a lengthy front end Theory building studies may have a short front end, or a longer one that identifies specific conflicts or other problems in prior theory

Describing analysis Vast majority of qualitative studies describe their analysis as inductive But in reality, they often use a blend of inductive and deductive processes may choose certain constructs or theoretical frames prior to data collection If using qualitative data for a reason other than building theory, no reason to expect a purely inductive approach

Addressing biases Qualitative authors need to convince reviewers they have minimized biases Informant bias Researcher bias Identifying the rationale for working with qualitative data can help For interpretive research, the greater risk may be researcher bias For (positivist) theory-building research, the greater risk is likely to be informant bias Can be addressed through multiple informants, focus on facts, triangulation with archival data, etc.

In summary Qualitative data can serve a number of different purposes Theory-building Interpretive perspective Process issues Illuminate abstract ideas Linguistic phenomena Not all qualitative studies will look alike Identifying the reason for working with qualitative data can help avoid minefields during review process

Questions?

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Qualitative Data: Cooking Without a Recipe Forthcoming, Strategic Organization Melissa E. Graebner Jeffrey A. Martin Philip T. Roundy