A conceptual-empirical typology of social science research methods pedagogy

Figures & data

Table 1. Participants, methods and dataset.

Starting the dialogue: Expert panel (transcripts and online data)
Phase 1 October– November 2013		Phase 2 May–July 2015
Interviews with 8 UK experts (3 quantitative methods, 3 qualitative methods, 2 mixed methods)	n.8	Interviews with international experts (4 quantitative, 4 qualitative, 4 mixed methods, 1 digital methods, spanning USA, South America, Europe & Africa)	n.13
		Phase 2 July–September 2015
		Online expert forum	n.4
Deepening & broadening the conversation: Focus groups with teachers (transcripts)
Phase 1 October– November 2013		Phase 2 April–October 2016
Focus group 1: Teachers of qualitative methods, natural grouping from 1 institution and department	n.3	Focus group 4: Teachers of qualitative, quantitative and mixed methods, new grouping from 1 institution but different departments	n.5
Focus group 2: Teachers of quantitative methods, natural grouping from 1 institution and department	n.3	Focus group 5: Teachers of qualitative, quantitative and mixed methods, new grouping from universities and social research organisations	n.7
Focus group 3: Teachers of narrative methods, natural grouping from linked set of colleagues	n.8	Focus group 6: Conducted via Skype, new grouping of teachers of research methods online	n.2
		Focus group 7: Conducted via Skype, new grouping of teachers of research methods online	n.3
Moving closer to the action Video stimulated dialogue (focus groups with teachers & learners) (transcripts and observational fieldnotes)
Phase 1 February- April 2014		Phase 2 April 2016–May 2017
Re 1-day course on an aspect of multi-modal analysis	n.13	Re 1-day course on an aspect of survey design	n.10
Re 1-day course on using a computer-assisted qualitative analysis software package	n.5	Re 1 day of 3-day course on data linkage	n.7
Re 3-day course on multi-level modelling	n.7	Re 8-week course on ethnographic technique	n.6
Re 2-day course systematic review	n.7
In-depth case studies (Phase 2 only, March–July 2017) (transcripts and detailed observational fieldnotes)
		1: Teaching ethnographic technique
		2: Teaching advanced computational quantitative methods

Table 2. Typology classifications.

Category	Characteristic
Approach	How the teacher goes about their pedagogic work in a way that coheres around a theory, set of values or principles
Strategy	Goal directed planning for implementing an approach
Tactics	Translation of strategies when the planning becomes procedural and specific to the context
Tasks	What learners (or teachers) are required to do, or actually do

Table 3. Coding principles and definitions for typological categories.

Category	Characteristic	Coding principles
Approach	How the teacher goes about their pedagogic work in a way that coheres around a theory, set of values or principles	Including: established named approaches (e.g. active learning) or emergent unnamed approaches (e.g. visual approach to statistics) Excluding: collected strategies that do not cohere in some clear way Close but not quite: reference to an approach that is one of several strategies employed as part of bigger approach
Strategy	Goal directed planning for implementing an approach	Including: strategies to motivate, engage, manage cognitive load, facilitate reflection, select which data to use as a teaching resource (depending on their approach, purpose and priority) Including: starting points and pedagogic hooks, often with a rationale Excluding: where the purpose/goal is unclear to the teacher Excluding: more generic/abstract descriptions (likely to be approach)
Tactics	Translation of strategies when the planning becomes procedural and specific to the context	Including: context-specific decisions about teaching activities
Tasks	What learners (or teachers) are required to do, or actually do	Including: activities within the pedagogic context that may or may not have pedagogic value or purpose

Table 4. Example of typologically generated matrix of methods teaching.

Approach	Strategy	Tactics	Tasks
Student-centred Putting the student at the heart of pedagogic decisions	Teachers start with where the learners are comfortable and motivated, using themes that connect with the group Teachers show relevance of data and methods to students’ professions/disciplines	Teachers pick up on anything in class that indicates what is meaningful to students personally Build a common vocabulary Use expertise in the room	Students work on their own data Students critique papers in their own discipline
Active learning Valuing learning by doing and application of knowledge	Alternate lecture and exercises so students apply what they hear and learn by doing Work through the statistical knowledge and the software simultaneously	Teachers use learning glitches to reinforce key concepts Teachers often choose their own data for exercises so they can be responsive to queries about it	Students work hands on with any data as long as they can ‘have a play’ and gain ‘flying time’
Experiential approach to teaching qualitative methods Valuing the power of authentic experience	Require students enter the field And attend to the sensory	Teachers exploit opportunities for reflexivity	Students embody data by reading it aloud
Problem-based learning approach Valuing the motivational benefits and cognitive process of approaching tangible research problems	Require students to respond to analysis of research needs by devising projects or action to solve them	Teachers provide support as needed: time for mapping the challenge, collaborating peers, an audience for ideas, tools and materials	Students work collaboratively and intensively on the problem (in parts and holistically) and present their solution
Standpoint-led approach to teaching qualitative methods Valuing reflexivity and critical engagement	Bring teachers’ and students’ standpoints to the foreground for examination	Teachers encourage dialogue and model reflexivity	Students reflect upon, articulate, share and defend standpoints
Visual approach to teaching statistics Valuing the power of the visual, putting it up front	Teachers use visual scaffolds to reduce the cognitive load	Students see data-related things quicker	Students work with visual metaphors and visual software
Verbal approach to teaching statistics Cohering premise that learning stats is like learning a foreign language	Teachers make the concepts understandable and backfill technical skills later	Teachers translate between statistical and non-technical terms	Students develop glossaries