Which research method should I use for this study?

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    "name": "Which research method should I use for this study?",
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    "description": "Choose the right user research method for your current product question. Guides product managers and researchers through hypothesis clarity, sample size needs, product stage, and behavioural versus attitudinal data requirements. Use at the start of any discovery or validation sprint.",
    "mode": "elimination",
    "entry": "Q1",
    "tags": [
      "product management",
      "user research",
      "ux research",
      "discovery"
    ],
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  "questions": [
    {
      "id": "Q1",
      "text": "Do you already have a specific hypothesis or design solution you need to validate?"
    },
    {
      "id": "A",
      "text": "Yes, I have a clear hypothesis or prototype to test [USABILITY, SURVEY, ANALYTICS]"
    },
    {
      "id": "B",
      "text": "No, I am exploring an unknown problem or opportunity [INTERVIEWS, DIARY]"
    },
    {
      "id": "Q2",
      "text": "How many participants do you need to reach to answer your research question with confidence?"
    },
    {
      "id": "A",
      "text": "Small sample is fine — I need depth and nuance (5–15 people) [INTERVIEWS, DIARY, USABILITY]"
    },
    {
      "id": "B",
      "text": "I need statistical confidence across a large sample [SURVEY, ANALYTICS]"
    },
    {
      "id": "Q3",
      "text": "Is your product or feature currently live and being used by real customers?"
    },
    {
      "id": "A",
      "text": "Yes, it is live with real users [USABILITY, ANALYTICS, SURVEY]"
    },
    {
      "id": "B",
      "text": "No, it is still a concept, prototype, or pre-launch feature [INTERVIEWS, USABILITY, SURVEY]"
    },
    {
      "id": "Q4",
      "text": "Do you primarily need to understand what users do (behaviour) or why they think and feel that way (attitude)?"
    },
    {
      "id": "A",
      "text": "Behaviour — I need to see what users actually do [ANALYTICS, USABILITY]"
    },
    {
      "id": "B",
      "text": "Attitude — I need to understand motivations and perceptions [INTERVIEWS, SURVEY, DIARY]"
    }
  ],
  "outcomes": [
    {
      "id": "INTERVIEWS",
      "label": "User Interviews"
    },
    {
      "id": "SURVEY",
      "label": "Survey"
    },
    {
      "id": "USABILITY",
      "label": "Usability Testing"
    },
    {
      "id": "DIARY",
      "label": "Diary Study"
    },
    {
      "id": "ANALYTICS",
      "label": "Analytics / Data Analysis"
    }
  ],
  "dsl": "dag: Which research method should I use for this study?\nversion: 1.0.0\nimage: https://images.unsplash.com/photo-1516321318423-f06f85e504b3?w=1200&q=80\ndescription: Choose the right user research method for your current product question. Guides product managers and researchers through hypothesis clarity, sample size needs, product stage, and behavioural versus attitudinal data requirements. Use at the start of any discovery or validation sprint.\ntags: product management, user research, ux research, discovery\nentry: Q1\nmode: elimination\n\nQ1: Do you already have a specific hypothesis or design solution you need to validate?\n  hint: A hypothesis is a falsifiable statement like \"Users will complete checkout faster if we surface the discount code field earlier.\" Exploration means you are still trying to understand the problem space — you do not yet know what the right solution looks like. Choosing the wrong framing here leads to confirmation bias in interviews or wasted survey budget testing the wrong thing. If you are unsure, lean toward exploration first.\n  A: Yes, I have a clear hypothesis or prototype to test [USABILITY, SURVEY, ANALYTICS]\n  B: No, I am exploring an unknown problem or opportunity [INTERVIEWS, DIARY]\n\nQ2: How many participants do you need to reach to answer your research question with confidence?\n  hint: Qualitative methods like interviews and diary studies generate deep insight from small samples — typically five to fifteen participants — but cannot provide statistically significant results. Surveys and analytics can reach hundreds or thousands of participants and are suitable when you need to quantify behaviour or attitude at scale. If you need to say \"X% of users do Y\", you need a quantitative method. If you need to say \"users struggle with Y because of Z\", qualitative is more appropriate.\n  A: Small sample is fine — I need depth and nuance (5–15 people) [INTERVIEWS, DIARY, USABILITY]\n  B: I need statistical confidence across a large sample [SURVEY, ANALYTICS]\n\nQ3: Is your product or feature currently live and being used by real customers?\n  hint: If the product is live, you have access to behavioural data — clickstreams, funnels, session recordings — which can answer many questions faster and cheaper than running new research. For concepts or early prototypes, you must rely on methods that work without a live product, such as interviews, surveys with mockups, or moderated usability testing with a prototype. Mixing methods (e.g., analytics to identify where, interviews to understand why) is often the strongest approach.\n  A: Yes, it is live with real users [USABILITY, ANALYTICS, SURVEY]\n  B: No, it is still a concept, prototype, or pre-launch feature [INTERVIEWS, USABILITY, SURVEY]\n\nQ4: Do you primarily need to understand what users do (behaviour) or why they think and feel that way (attitude)?\n  hint: Behavioural data tells you what actually happens — completion rates, drop-off points, feature adoption. Attitudinal data tells you why — motivations, mental models, frustrations, and unmet needs. Both are valuable but answer different questions. Analytics and usability testing capture behaviour; interviews, diary studies, and surveys capture attitude. The strongest research programmes triangulate both, but if you can only do one, choose based on your current knowledge gap.\n  A: Behaviour — I need to see what users actually do [ANALYTICS, USABILITY]\n  B: Attitude — I need to understand motivations and perceptions [INTERVIEWS, SURVEY, DIARY]\n\n[INTERVIEWS]: User Interviews\n  color: #7c3aed\n  description: User interviews are the best method when you are in exploratory discovery and need to understand motivations, mental models, and unmet needs from a small number of participants. Recruit five to eight participants who match your target persona and use a semi-structured discussion guide that starts broad and narrows to specific pain points. Avoid leading questions — your job is to listen, not to validate your own assumptions. Synthesise findings into a structured insight document using an affinity diagram or jobs-to-be-done framework, and share the top three insights with your product and design team within 48 hours of completing fieldwork.\n  code: PM_USER_INTERVIEWS\n\n[SURVEY]: Survey\n  color: #2563eb\n  description: Surveys are appropriate when you have a clear hypothesis and need to quantify attitudes, preferences, or reported behaviours across a large and representative sample. Write questions that are unambiguous and pre-tested on at least five people before broad distribution — bad survey questions are the most common cause of misleading results. Aim for a minimum of 100 responses for directional insight and 300 or more for statistical significance. Pair closed questions (for quantification) with at least one open-ended question per section to capture unexpected themes. Report results with confidence intervals and avoid overstating causality from correlation.\n  code: PM_SURVEY\n\n[USABILITY]: Usability Testing\n  color: #16a34a\n  description: Usability testing is the right method when you have a design, prototype, or live feature that you need to evaluate against real task completion. Run moderated sessions with five to eight participants for qualitative insight, or use unmoderated remote testing platforms for larger samples. Define success criteria before you start — know what \"passing\" looks like so you can make clear go/no-go decisions after the study. Capture both task completion rates and think-aloud commentary, and prioritise findings by severity (blocking, major, minor) so the design team knows what to fix first. Share a findings report with the design and engineering team within one week of fieldwork.\n  code: PM_USABILITY\n\n[DIARY]: Diary Study\n  color: #d97706\n  description: A diary study is best suited to understanding how users engage with a product or behaviour over an extended period — typically one to four weeks — in their natural context. This method reveals temporal patterns, habit formation, and context-of-use details that a one-off interview or lab session cannot capture. Recruit eight to fifteen participants and use a lightweight daily logging tool (an app prompt, a simple form, or even WhatsApp). Brief participants clearly on what to log and check in mid-study to ensure compliance. Diary studies require significant synthesis effort, so plan for at least one full week of analysis time after the study period ends.\n  code: PM_DIARY\n\n[ANALYTICS]: Analytics / Data Analysis\n  color: #0891b2\n  description: When the product is live and you need to understand behavioural patterns at scale, analytics should be your first port of call before commissioning new research. Start by reviewing your funnel data, feature adoption metrics, and session recordings to identify where users are dropping off or succeeding. Use cohort analysis to understand behaviour over time and segment analysis to identify whether problems are universal or concentrated in a specific user group. Analytics answers \"what\" and \"how much\" but rarely \"why\" — plan to follow up with qualitative methods if the data surfaces unexpected patterns that require explanation. Make sure your instrumentation is correctly tagged before drawing conclusions.\n  code: PM_ANALYTICS\n"
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