5.3 Qualitative and Mixed-Methods Research for Sport Demands Analysis
Key Takeaways
CPSS Task 2A asks the sport scientist to research sport demands and constraints through both qualitative and quantitative methods; numbers describe what happens, while qualitative data explain why and under which constraints.
Core qualitative methods for a needs analysis are semi-structured interviews, focus groups, structured observation of training and competition, expert consensus such as Delphi rounds, and analysis of rules and documents.
Braun and Clarke's reflexive thematic analysis moves through six phases: familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up.
Qualitative rigor is judged by trustworthiness (credibility, transferability, dependability, and confirmability), supported by triangulation, member reflections, and an audit trail rather than statistical power.
Mixed-methods designs combine both strands on purpose: convergent (parallel and merged), explanatory sequential (numbers first, then interviews to explain them), and exploratory sequential (qualitative first, then measures).
5.3 Qualitative and Mixed-Methods Research for Sport Demands Analysis
Core Principle: Task 2A of the 2026 CPSS outline asks you to research sport performance demands and constraints "through qualitative and quantitative methods." Tracking data, force plates, and match statistics show what happens. Interviews, observation, and document analysis explain why it happens and which constraints shape it. A complete needs analysis uses both.
Sections 5.1 and 5.2 focus on measurable demands. This section covers the qualitative side: how to collect and analyze information from coaches, athletes, and other experts, and how to combine it with numerical data.
Why Qualitative Methods Belong in a Needs Analysis
Many critical constraints never appear in a GNSS export:
- Tactical intent: A coach's game model explains why fullbacks overlap or why a team defends in a low block.
- Technical priorities: Coaches can describe the skills that decide matches at a given level before you have the data to model them.
- Organizational constraints: Facility access, travel, academic schedules, staff numbers, and culture shape what is possible.
- Athlete experience: Perceived fatigue, confidence, injury fears, and life stress affect readiness and adherence.
- Emerging or under-researched sports: For a new event or an under-studied population, little published data exists, so expert knowledge is the starting point.
Quantitative methods measure magnitude and frequency. Qualitative methods explore meaning, context, and mechanisms. Exam questions expect you to match the method to the question being asked.
Core Qualitative Data-Collection Methods
| Method | What It Is | Best Use in a Needs Analysis | Key Pitfall |
|---|---|---|---|
| Semi-structured interview | One-to-one conversation guided by open questions, with follow-up probes | Coach game model, technical priorities, athlete experiences, staff constraints | Leading questions that steer answers toward your hypothesis |
| Focus group | Facilitated discussion with about 5–8 participants | Shared views of a position group or support staff | Dominant voices and conformity |
| Structured observation and notational analysis | Systematic coding of behaviors in training or competition using defined categories, often from video | Frequency and context of key actions such as tackles, duels, or set plays | Poorly defined categories and low inter-rater agreement |
| Expert consensus (Delphi) | Anonymous rounds of rating and feedback among experts until agreement is reached | Agreeing on KPIs or demands where evidence is thin | An unclear consensus threshold, or a panel that lacks real expertise |
| Document and rule analysis | Review of rules, competition schedules, governing-body regulations, and internal reports | Rule changes, fixture density, and eligibility or equipment constraints | Treating documents as complete or neutral |
Good interview practice starts with an interview guide written in advance. Use open questions ("Walk me through what the last 10 minutes of a close match demand from your midfielders"), follow up with neutral probes, and record and transcribe with consent. Sampling is purposive: you choose people with the relevant experience, such as head and position coaches, senior athletes, medical staff, and analysts. You keep sampling until new interviews stop adding new insights, a point often called data saturation.
Analyzing Qualitative Data: Reflexive Thematic Analysis
The most common approach in applied sport science is Braun and Clarke's reflexive thematic analysis. It has six phases:
- Familiarize yourself with the data by reading and re-reading transcripts and notes.
- Generate initial codes that label meaningful features, such as "late-game fatigue in pressing."
- Generate initial themes by grouping related codes into patterns of shared meaning.
- Review themes against the coded extracts and the full dataset.
- Define and name themes so each has a clear, distinct core idea.
- Write the report, using vivid data extracts to support each theme.
For example, interviews with five soccer coaches might produce the themes "transition moments decide matches," "fullbacks fade after the 70th minute," and "travel limits midweek training." Each theme then points to a quantitative check: worst-case-scenario running during transitions, high-speed running by match period, and training load before and after away trips.
Establishing Trustworthiness
Qualitative research is not judged by statistical power. It is judged by trustworthiness, a set of criteria from Lincoln and Guba:
- Credibility: Do the findings represent participants' views? Use triangulation (multiple sources, methods, or analysts) and member reflections (sharing interpretations with participants).
- Transferability: Is the context described richly enough for readers to judge whether the findings apply to their own setting?
- Dependability: Is the process documented in an audit trail of interview guides, codebooks, and analytic decisions?
- Confirmability: Are the findings grounded in the data rather than in the researcher's assumptions? A reflexive journal helps.
Structured observation produces counts, so also report inter-rater reliability, such as Cohen's kappa, for your coding categories.
Integrating Qualitative and Quantitative Findings
Mixed-methods designs combine both approaches on purpose:
- Convergent design: Collect qualitative and quantitative data in parallel, then merge them. Example: Coach interviews and GNSS data both point to declining fullback output late in matches.
- Explanatory sequential design: Collect quantitative data first, then use qualitative data to explain surprising results. Example: Interviews reveal that a drop in midweek load reflects academic exam periods.
- Exploratory sequential design: Start qualitatively to identify what matters, then build and test quantitative measures. Example: A Delphi panel agrees on KPIs for a new event, which you then check against testing data.
When the two strands disagree, do not discard either one; investigate the disagreement. Suppose a coach believes the wingers sprint the most but tracking shows that the fullbacks do. The coach's game-model knowledge and the data's precision together define the next question to answer.
A sport scientist joining a professional rugby sevens program wants to understand why the team concedes tries late in tournament days. GNSS data show modest total distance, but the head coach believes "decision-making collapses when players are tired." Which qualitative method best explores the coach's explanation before further tests are designed?
Semi-structured interviews with coaches and senior players, using open questions about when and how late-game breakdowns happen
Run an isometric mid-thigh pull test on the whole squad, because low maximal force explains most late-game tactical errors
Use a closed yes/no questionnaire asking players whether they agree that fatigue is the cause of their late-game errors
Collect a larger GNSS sample from extra tournaments, because more running data will eventually reveal the cause of the late tries
During reflexive thematic analysis of 12 coach interviews, a sport scientist has labeled transcript segments with short codes such as "fullback fatigue after 70 minutes" and "travel limits midweek sessions." Following Braun and Clarke, what is the next analytic step?
Delete any code that only one coach mentioned, because rare views cannot be reliable
Write the final report immediately, quoting the codes that appeared most often in the data
Generate initial themes by grouping related codes into broader patterns of shared meaning
Run a t-test on the code frequencies to decide which ones are statistically significant
A needs analysis team first analyzes a season of GNSS data and finds an unexpected 25% drop in midweek training load for one position group. They then interview the position coach and athletes to explain the drop. Which mixed-methods design is this?
Exploratory sequential design, because qualitative data came first and were used to build a new tool
A purely quantitative design, because the interviews only confirmed what the GNSS data had shown
Convergent design, because both types of data were collected in parallel and then merged together
Explanatory sequential design, because the interviews followed the quantitative results to explain them
Sections you finish are checked off in the contents.