MEDD8001 Chap.5 Grounded Theory and Gender in Education
Grounded Theory and Gender in Education
An issue whose interesting part is the mechanism
The educational issue here is whether boys or girls learn better at school and what follows from any difference for achievement and aspiration in science, technology, engineering and mathematics. The design is grounded theory, taught as the qualitative methodology used to address that kind of issue.
The pairing is deliberate.
The assigned report on gender gaps argues that the differences in how children do at school, and in the careers they then pick, are not born differences in ability: they come from how learning is regarded, how a pupil behaves at school, what is done with free time, and how much a pupil trusts their own capability, all tied to gender norms that shape student and teacher attitudes.
If that is right, the explanatory work sits in meanings and expectations, and a mechanism nobody has named cannot be measured by an instrument built in advance.
The rule that generates the method
Grounded theory is framed as a series of iterations in which the researcher works forwards and backwards over the material, climbing by degrees from codes to categories and then to theory.
Its governing rule is comparison without pause: as you code something, hold it against everything you have already coded that way, and the properties of the category start to emerge out of the holding. Every comparison either adds a property to an existing code or forces a new one, so the scheme is still moving while data is still arriving.
That is why the design cannot be run as a single analytic pass after fieldwork, and why a proposal that schedules all analysis after all collection has described a different design.
Open, axial and selective coding
Open coding puts a name on what is happening and what is being done in the material, comparing one instance against another to see which belong together, with in vivo codes taking the speaker's own words as the code name.
Axial coding clusters those codes around axes, meaning points of intersection, to form conceptual categories, and this is the first place the researcher's interpretation openly enters. Selective coding treats the clusters selectively, deciding how they relate and what story they tell, which is where themes and then theory begin.
The published exemplar the course assigns is candid about the movement: a code about class size was first placed under a category about classroom issues and later recategorised as administrative, and the authors are explicit that what changed was their interpretation rather than the data.
Six analytic levels, three of them with counts
The exemplar maps its own analysis as six levels read from the bottom up.
A first pass over seventy-one interview transcripts produced a large set of open codes; reviewing them for common features clustered them into conceptual categories; a small set of themes was then developed and, crucially, applied as a frame to all subsequent data, with the authors asking of each new interview and observation to what extent the data supported those themes.
Later levels interrelate the explanations and delineate the theory.
The paper also reports saturation and triangulation through multiple sources, and a limitation students rarely see in print: the heavy collection regime took time from team examination of emerging interpretations, and the later levels were completed after the project officially ended.
What the gender evidence asks a qualitative study to do
The assigned report notes that overall gaps in mathematics and science are quite small while young women remain under-represented in related fields after school, that girls including high achievers more often report anxiety about mathematics and fear of failure, and that parents in every participating economy were more likely to expect a son than a daughter to work in a related field.
Put together, the gap to be explained sits between measured proficiency and expressed expectation, which is a gap in how students read their own situation. That is an account-producing question with no agreed categories, and the categories that exist are imported from psychology rather than generated from students' own words.
What this chapter covers
- 01
Constant comparison as the engine of the method
- 02
Open coding and in vivo codes
- 03
Axial coding, and where interpretation openly enters
- 04
Selective coding, themes and the beginning of theory
- 05
Six analytic levels read from the ground up
- 06
Applying themes as a frame to incoming data
- 07
Saturation, triangulation and an honest limitation
- 08
Where the literature review goes in this design, and why it moves
- 09
Proficiency against expectation: the gap the evidence actually shows
Four interview lines taken to the second analytic level
- 3Give each line an open code that stays close to what was said.
- 3Name what the four have in common that none of them states.
- 2State the conceptual category and the property that makes it useful.
Key terms
- Open Coding
- The first analytic pass, putting a name on what is happening and what is being done, and comparing instance against instance to see which belong together.
- In Vivo Code
- A code whose name is taken from the speaker's own words, which protects a participant's meaning from being abstracted away too early.
- Axial Coding
- Clustering open codes around points of intersection to form conceptual categories, which is the first step where the researcher's interpretation openly shapes the scheme.
- Selective Coding
- Treating the code clusters selectively, deciding how they relate to one another and what story they jointly tell, which is where themes and theory begin.
- Theoretical Saturation
- The point at which new incidents stop adding properties to existing categories, which is a claim about the data rather than a target number of interviews.
- Triangulation
- Using multiple sources or data collection strategies so that a finding does not rest on a single route of access.
- Conceptual Category
- A grouping of codes that names something none of the individual codes states, and which functions as a claim to be tested against incoming data.
Grounded Theory and Gender in Education FAQ
How many interviews does a grounded theory study need?
The method answers with a rule rather than a number: you stop when new incidents stop adding properties to your existing categories, which is saturation. That creates a real problem for a proposal, because a fixed number cannot be promised in advance.
The defensible way to write it is to declare your ceiling as a resource constraint, state the saturation test you will apply as the work proceeds, and commit to reporting whether the ceiling was reached before the test was met.
Is it still grounded theory if I use codes from the literature?
Not as the course defines it. The identifying feature is that the code scheme changes under the data through constant comparison; if the codes existed before the first transcript, what you are doing is closer to content analysis with a qualitative label attached. That is a legitimate design, and naming it correctly is stronger than claiming the more prestigious one and being caught by a reader who asks what moved.
Should I read the literature before or after collecting data?
This design is the one place the course flags a genuine choice. Most designs review the literature first so the researcher understands the field and can clarify scope. In grounded theory researchers may collect and analyse first, so that their own insights can be compared with existing theory rather than shaped by it.
Reading late protects against imposing categories and risks reinventing them, so say which risk you are accepting and why, because a supervisor will ask.
Assessment move
Take twenty lines of any transcript you can get hold of, even a published interview, and code them twice on different days without looking at the first attempt. Compare. The codes that moved are the ones where your interpretation was doing work you had not noticed, and recording that movement is exactly the audit trail this design is judged on.
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