One problem, two kinds of evidence

• Meridian’s board knows that staff turnover has doubled — the HR dashboard says so, precisely and reliably
• The exit interviews hint at why — but they are a handful of unstructured conversations with people already halfway out of the door
• Numbers without stories tell you the size of a problem but not its mechanism; stories without numbers tell you a mechanism but not whether it is typical
• Managers face this gap constantly: the dashboard and the corridor conversation each capture something the other misses

What is mixed methods research?

• Creswell & Creswell define it by three components, all required:
  • collecting both qualitative and quantitative data
  • integrating the two forms of data
  • drawing interpretations from their combined strengths
• The middle component is the one that matters: using two methods is not mixed methods unless the strands actually meet
• The premise: integration yields more than the sum of the parts — each form of data offsets the known weaknesses of the other
• Philosophically, mixed methods sits most comfortably with pragmatism: the question comes first, and methods are tools, not loyalties

Complementary strengths

• Recall the comparison from session 1 — each approach buys its strength by accepting a limitation:

Four classic reasons to mix

• Greene, Caracelli and Graham (1989) catalogued the purposes of mixing; four recur constantly:
  • Triangulation — testing whether different methods converge on the same answer, strengthening confidence in the result
  • Complementarity — using one method to elaborate and illustrate the results of the other
  • Development — using the results of one method to build the other: interviews that shape a questionnaire, a survey that selects interviewees
  • Expansion — widening the study’s range, letting different methods answer different components of one question
• A good justification names which of these you are pursuing — “richer picture” is not a purpose

Reading a design: notation

• Mixed methods has a compact notation (due to Morse) that encodes three decisions at once:
  • Capitalisation = priority: QUAN means the quantitative strand leads; quan means it plays a supporting role
  • + = concurrent: the strands run at the same time
  • → = sequential: one strand finishes before the next begins, and feeds into it
• The three core designs in Creswell & Creswell each get one line:
  • QUAN + QUAL — convergent (parallel) design
  • QUAN → qual — explanatory sequential design
  • QUAL → quan — exploratory sequential design
• Whenever you meet a mixed design, decode these three things: timing, priority, and where the strands meet

Convergent (parallel) design

• Both strands are collected and analysed at the same time, separately, and then merged for comparison — QUAN + QUAL
• The strands are typically given equal priority, and neither depends on the other’s results
• The purpose is usually triangulation (do the two databases agree?) and completeness (statistical pattern plus lived experience of the same phenomenon)
• Its great practical attraction: one data-collection period — the most time-efficient of the three designs
• Use it when you need both kinds of answer to the same question, and you need them now

Convergent design in outline

Convergent design: pitfalls

• Unequal footing — a 1,000-respondent survey merged with three interviews: the strands must each be done properly, or the merge is decorative
• Different constructs — if the survey measures satisfaction and the focus groups discuss loyalty, the strands cannot be meaningfully compared
• No plan for divergence — the strands will sometimes disagree; a design with no strategy for that (collect more data? re-examine one strand?) collapses into cherry-picking
• Team demands — running both strands simultaneously requires qualitative and quantitative expertise at the same time, which usually means a team rather than one researcher
• The pseudo-merge — reporting the two sets of results in adjacent chapters without ever confronting them is the classic failure (we return to this)

Explanatory sequential design

• The quantitative strand comes first and leads; a qualitative phase follows to explain its results — QUAN → qual
• The second phase is deliberately targeted at the first phase’s puzzles: unexpected results, extreme groups, significant differences that beg for a mechanism
• The typical logic: the survey establishes what and for whom; the interviews establish why
• Priority usually lies with the quantitative strand — the qualitative phase is smaller and shaped entirely by what needs explaining
• The natural choice when you already have good numbers — a dashboard, an annual survey — that raise questions they cannot themselves answer

Explanatory sequential in outline

Explanatory sequential: pitfalls

• Time — the phases cannot overlap by definition, so the study takes roughly twice as long; boards and clients must be warned at the start
• Phase 2 cannot be fully specified in advance — who you interview depends on results you do not yet have, which complicates planning and ethical review
• Weak connection — the classic failure: interviews that could have been conducted without the survey, ignoring its puzzles and merely gathering general impressions
• Chasing noise — an “unexpected” subgroup difference may be sampling error; explaining a result qualitatively presupposes the result is real
• Anonymity trade-off — re-contacting respondents for phase 2 requires identifying them in phase 1, which must be disclosed and consented to

Exploratory sequential design

• The qualitative strand comes first and leads; a quantitative phase follows to measure, generalise, or test what it discovered — QUAL → quan
• The classic use is instrument development: interviews establish what the phenomenon is and what words people use for it; the survey then measures it at scale, in the participants’ own vocabulary
• The right design when the phenomenon is new, sensitive, or poorly understood — when you do not yet know enough to write good closed questions
• The middle step is a genuine development phase: turning themes into items, piloting, refining — not a formality
• Inductive logic feeding deductive logic: session 1’s cycle, built into a single study

Exploratory sequential in outline

Exploratory sequential: pitfalls

• The longest design — three genuine stages (explore, build, measure); plan for it, and say so in any proposal
• Skipping the middle — bolting survey items together straight from interview quotes, without piloting, produces an instrument with unknown validity and reliability
• Thin exploration — if the qualitative sample misses a key group, the survey will scale up that blindness: it measures precisely, but measures the wrong things
• Selective carry-forward — deciding which qualitative findings become survey items is a consequential judgement; make it explicit, not silent
• Mission drift — the quantitative phase must test what the qualitative phase found, not wander off to new questions

Embedded (nested) designs

• One strand is nested inside a design led by the other, playing a clearly supporting role — e.g. QUAN(qual)
• The supporting strand answers a secondary question the primary design cannot reach on its own
• Two common organisational forms:
  • a qualitative strand inside an experiment or pilot — Meridian pilots a new bonus scheme in six stores, with embedded interviews asking how staff experienced it and why it worked or failed
  • a quantitative strand inside a case study — a deep single-site study that embeds a short staff survey to check how widely the observed views are held
• Priority is unmistakably unequal, and integration happens at the level of the overall design — the nested strand serves the host
• We note it for completeness: the three core designs cover most of what you will need

Why justification matters

• This course requires not just the principles of combining methods but the justification of the choice — and so will every board, client, reviewer, and examiner you ever face
• A research design is an argument, not a preference: “we like interviews” and “mixed methods is more thorough” justify nothing
• Mixing is expensive — it must earn its cost against the alternative of one method done well
• Any serious justification answers five questions, and the next five slides take them in turn

Fit to question

• The starting point is always the question, never the method: does the question genuinely have both a quantitative and a qualitative component?
• Decompose it and check:
  • How much has turnover risen, and where is it concentrated? — quantitative
  • Why are those employees leaving? — qualitative
  • Is the problem widespread or local? What would make people stay? — both
• If honest decomposition yields only one kind of component, the question does not need mixing — however fashionable mixing may be
• The fit argument names the specific components and shows that neither strand alone can answer the whole question

Sequence logic

• Something must justify the arrow: why this order — or why no order at all?
• Each design carries its own sequence logic:
  • Explanatory (QUAN → qual): the qualitative phase cannot be designed until the quantitative results exist — you do not yet know whom to interview or about what
  • Exploratory (QUAL → quan): the quantitative instrument cannot be written until the qualitative phase supplies constructs and vocabulary
  • Convergent (QUAN + QUAL): neither strand depends on the other’s results — and running them together saves time
• The test: could the second phase have been specified, in full, before the first phase reported? If yes, your sequence has no logic — only a schedule
• “We did the interviews first because they were easier to arrange” is logistics, not sequence logic

Priority: which strand leads?

• In most mixed designs one strand carries the main answer and the other supports it — the capitalisation in the notation is a real decision, not typography
• Priority should follow from two things:
  • the question — is the core of it how many or why?
  • the audience — a board deciding on budget usually needs the generalisable number, with the qualitative strand explaining and humanising it
• Priority drives resources: the leading strand gets the larger share of time, money, and sample
• Declaring priority in advance also protects you later — it settles which strand rules when the two point in different directions and no further data can be gathered

The integration point

• Integration is the moment the strands actually meet — Creswell & Creswell distinguish three forms:
  • Merging — results are brought together and compared, often in a joint display: one table with the statistics and the corresponding themes side by side
  • Connecting — one phase’s results build the other’s design: its sample (explanatory) or its instrument (exploratory)
  • Embedding — one strand is built into the other at the level of the design itself
• Name the integration point in advance, in the proposal: at which step, using what procedure, producing what output
• The brutal test: if you cannot point to the place where one strand changed what the other did or meant, you do not have a mixed design — you have two studies

Practical constraints

• Feasibility is a legitimate part of the justification, not an embarrassing afterthought:
  • Time — sequential designs roughly double the calendar; exploratory adds an instrument-building stage on top
  • Cost — two strands mean two rounds of collection and analysis; CAWI is cheap, CATI and face-to-face interviewing are not
  • Skills — the team must be competent in both traditions; a lone researcher strong in one is a real constraint
  • Access — can you re-contact survey respondents? enter the stores? get release time for interviews?
• Constraints may legitimately push you from the ideal design to a simpler one — say so openly
• What they never justify is faking the integration while keeping the mixed-methods label

The five-question justification

The stapler problem

• The most common failure in mixed methods: two disconnected studies stapled together, with integration consisting of one hopeful paragraph in the conclusion
• The symptoms are recognisable:
  • the strands answer different questions rather than components of one question
  • they are run by separate people who never confront each other’s findings
  • the report presents them in separate chapters, and the “integration” merely summarises each in turn
  • divergent findings are quietly ignored rather than investigated
• The diagnostic question: would either half change in any way if the other were deleted? If not, the staple is all that connects them
• A stapled study costs as much as a mixed one and delivers the insight of neither done well

The costs of mixing

• Mixing is not free, and an honest justification prices it in:
  • Complexity — two designs, two samples, two analyses, plus the integration work that neither strand needs alone
  • Resources — more time and money than a single-method study of the same question
  • Expertise — competence required in both traditions, which usually means a team
  • Interpretation — synthesising two kinds of evidence is genuinely harder than reading one, especially when they disagree
• The uncomfortable implication: a weak mixed study is worse than a strong single-method study — two half-done strands do not add up to one whole answer

When not to mix

• The question is single-strand — has the new rota reduced overtime? needs payroll data, not focus groups
• Resources stretch to one strand done well — a rigorous survey beats a rushed survey plus three token interviews
• The timeline cannot fit the sequence — if the board decides in six weeks, an explanatory sequential design is a promise you cannot keep
• Routine monitoring — a quarterly KPI dashboard needs consistency and comparability, not a fresh qualitative strand each quarter
• Fashion — “mixed methods looks rigorous” is a badge, not a reason; examiners and reviewers see through it immediately
• Choosing not to mix, for stated reasons, is itself a defensible methodological justification

Conclusion

• Mixed methods research is defined by integration: both kinds of data, deliberately combined, interpreted together — not two methods in one binder
• The three core designs are three answers to one pair of questions — when does each strand run, and what does each do for the other: convergent (QUAN + QUAL), explanatory sequential (QUAN → qual), exploratory sequential (QUAL → quan), with embedded designs nesting one strand inside the other
• The choice among them is an argument with five parts: fit to question, sequence logic, priority, integration point, feasibility
• The failure mode to fear is the stapler — and the honest alternative, when mixing cannot be done properly, is one method done well
• In the exercise, Meridian’s board has finally approved the budget: you will design the study, and justify it
• Questions and discussion are welcome

Exercise

Study guide