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Record W2587781411 · doi:10.4300/jgme-d-16-00693.1

Necessary Groundwork: Planning a Strong Grounded Theory Study

2017· article· en· W2587781411 on OpenAlexaff
Christopher Watling, Sayra Cristancho, Sarah Wright, Lara Varpio

Bibliographic record

VenueJournal of Graduate Medical Education · 2017
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsToronto East General HospitalUniversity of TorontoWestern University
Fundersnot available
KeywordsGrounded theoryComputer scienceData sciencePsychologyManagement scienceSociologyQualitative researchEngineeringSocial science

Abstract

fetched live from OpenAlex

Let's say you are interested in the problem of physician maldistribution—why certain geographical regions face chronic physician shortages, while others experience a surplus. Intrigued by the potential of a qualitative research approach to deepen the understanding of this problem, you decide to interview physicians about how they chose where to practice. You devise a list of 10 open-ended questions and recruit 15 physicians for interviews, which you conduct over the course of a week. Now, you have 15 transcripts sitting on your desk, awaiting analysis.Inspired by recent medical education publications that used grounded theory to build a conceptual understanding of challenging problems like this one, you hope to do a grounded theory study with your data. You contact a colleague with expertise in qualitative research and ask where to start.Your colleague's response is disheartening. You can perhaps do a thematic analysis of your data, but you can't do a grounded theory study.“I wish you'd come to me earlier,” your colleague says.Your choice of methodology must be made earlier in the process. A study's methodology is its backbone. Methodology means the underpinning philosophy that guides how inquiry should proceed; its assumptions and principles guide every step of the research decision-making process (see the Rip Out “Choosing a Qualitative Research Approach” for a review of 3 different qualitative methodologies1). Methods, in contrast, are the tools of the trade—the investigative procedures used to collect and analyze data. For example, methods for data collection include interviews, focus groups, and observations (see the Rip Out “Design: Selection of Data Collection Methods” for a review of 5 common qualitative data collection methods2), while methods for analyzing data include coding, constant comparison, and mapping.3 No methodology claims exclusive ownership of any particular method, although certain methods are more routinely used within some methodologies than others. For example, ethnography tends to rely on observation as a data collection method, while grounded theory typically uses constant comparison as an analytic method.Decisions about methodology shouldn't be made midway through a study—they are foundational decisions that influence each subsequent choice that a researcher makes. In the challenge at hand, it is too late in the game to craft a credible grounded theory study. But why? What necessary groundwork is missing?Although the nuances of grounded theory methodology are hotly debated, there are 2 characteristic features of the methodology that are uncontested: iteration and theoretical sampling. Without these elements, you cannot claim to be conducting a grounded theory study. Iteration and theoretical sampling cannot be injected post hoc into an existing data set; enacting these key features requires careful advance planning.Iteration means that data collection and data analysis “blur and intertwine continually”4; the processes unfold concurrently, each influencing the other. In an interview-based grounded theory study, researchers begin reading and analyzing transcripts early, rather than waiting until all interviews have been completed. They examine data from the first 2 or 3 interviews, asking probing questions as they go. What data require further elaboration? What data are surprising or unexpected? Based on these early analytic forays, they modify and refine the interview approach, adding a follow-up question here and a new probe there in order to explore more fully the ideas they see developing. The process continues as more data are collected and examined, allowing nascent interpretations of data to be tested with later interview participants.The iterative approach required by grounded theory has pragmatic implications for study design. Data cannot be collected all at once. Researchers need to plan for iteration, allowing gaps in data collection that permit concurrent analysis, and they must continuously reflect on how data collection might need to be adjusted to facilitate deeper exploration of key ideas and concepts. Analysis cannot be put off until the end, nor can it be thought of as the second stage of the research. Researchers must create a schedule that fosters not only gathering data but also continual thinking about what data mean, right from the start. In our example, the decision to interview 15 physicians in a single week, though attractive in its seeming efficiency, erased the opportunity for iteration that a grounded theory study would have required.If iteration is the process that ensures you are reflecting on your data from the beginning, then theoretical sampling is the technique that allows you to act on those reflections. In a grounded theory study, your initial sampling strategy is merely a jumping-off point; you target a group of individuals for interviews, for example, who you anticipate will offer insights on the problem at hand. Theoretical sampling means “seeking and collecting pertinent data to elaborate and refine categories in your emerging theory.”5 As you identify key ideas in your data, you consider whether new sources of data are necessary to facilitate your understanding and interpretation of those ideas.How might theoretical sampling play out in a study of how physicians choose where they will practice? Initial interviews might target physicians in their first 3 years in practice, anticipating that those individuals will be most informative because they are closest to the decision-making process. From this starting point, however, the sampling strategy will adapt to the needs of the ongoing analysis. Suppose you identify, in those early interviews, recurring notions of uncertainty, as participants reveal ambivalence about their initial practice choice and speculate about changing practice location in the future. To pursue this uncertainty and how it is resolved, you could (1) add interview probes to explore the idea more in depth with subsequent participants, and (2) recruit from new populations that might offer deeper insights into this particular issue, such as physicians who have been in the same practice location for more than 10 years, or those who have recently changed practice locations. In our example, there was no allowance made for the sampling strategy to shift to accommodate the developing understanding of the data—another opportunity lost.Grounded theory aims to generate theory. Done well, it can advance our understanding of social or psychological processes. But the potential of grounded theory can only be harnessed if its foundational principles guide the study's design decisions from the beginning. Iteration and theoretical sampling allow researchers to act as engaged explorers rather than passive data gatherers. These techniques require deliberate planning; they cannot be added on at the end.Methodologic mastery takes time. You can nurture your research artistry by being reflective about your own work and by sharing your ideas and your struggles with other qualitative researchers. While we regularly discuss results and their interpretation with colleagues to test out the resonance of our ideas, we often neglect in-depth discussions of methodology. Routinely talk not only about what you found, but also about how you found it.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.189
GPT teacher head0.539
Teacher spread0.350 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations16
Published2017
Admission routes1
Has abstractyes

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