「qualitative」の検索結果

Create a qualitative risk register and mitigation plan using a Probability x Impact matrix. Use when a project needs structured risk identification, scoring, ownership, and response planning.

日本語の概要は準備中です。原文の説明を表示しています。

Help users define a compelling, qualitative description of a future state that aligns decentralized teams and motivates long-term innovation.

日本語の概要は準備中です。原文の説明を表示しています。

Help users objectively assess where their product stands on the PMF spectrum by triangulating qualitative feedback, quantitative retention benchmarks, and organic growth signals.

日本語の概要は準備中です。原文の説明を表示しています。

Help users convert massive volumes of qualitative and quantitative feedback into high-confidence product decisions through rigorous synthesis and internal immersion.

日本語の概要は準備中です。原文の説明を表示しています。

Use when defending the research design of an American Anthropologist (AA) manuscript — fieldwork and participant observation, interview and archival design for ethnographic/qualitative work, material/contextual analysis for archaeology, and appropriate lab/quantitative design for biological anthropology. AA judges each mode on its own terms with reflexivity. Strengthens the design; it does not write code or run fieldwork.

日本語の概要は準備中です。原文の説明を表示しています。

Use when executing and reporting the analysis/interpretation for an American Anthropologist (AA) manuscript so it survives expert anonymous review — disciplined ethnographic/qualitative inference, honest interpretation, and (for biological/archaeological work) sound quantitative analysis. Guides analysis and interpretation norms; it does not fabricate evidence or quotations.

日本語の概要は準備中です。原文の説明を表示しています。

Use when planning or reporting the analysis for an American Educational Research Journal (AERJ) manuscript — multilevel/HLM and growth models, IRT/measurement, quasi-experimental estimation, or qualitative coding and thematic analysis. Analysis must meet the AERA reporting standards (warrant + transparency). Strengthens analysis reporting; it does not run models for you.

日本語の概要は準備中です。原文の説明を表示しています。

Use when defending the research design of an American Educational Research Journal (AERJ) manuscript — quantitative (multilevel, IRT, quasi-experimental, RCT), qualitative (case study, ethnography, interview), or mixed methods. AERJ judges each tradition on its own terms against the AERA reporting standards. Strengthens the design; it does not write code.

日本語の概要は準備中です。原文の説明を表示しています。

Use when building and refining exhibits for an Administrative Science Quarterly (ASQ) manuscript — qualitative data structures and data-to-theory tables, process models, and quantitative tables. Designs exhibits; it does not run the analysis behind them.

日本語の概要は準備中です。原文の説明を表示しています。

Use when choosing and justifying the research design for an Administrative Science Quarterly (ASQ) manuscript — qualitative (grounded-theory, ethnographic, historical) or quantitative — and setting the rigor bar. Designs the study; it does not run the analysis (see asq-data-analysis).

日本語の概要は準備中です。原文の説明を表示しています。

Use when executing and reporting the analysis for an Administrative Science Quarterly (ASQ) manuscript — qualitative coding and data-to-theory construction, or quantitative estimation and robustness. Makes the evidence-to-theory link transparent; it does not design the study (see asq-methods).

日本語の概要は準備中です。原文の説明を表示しています。

Use when designing the study for an Accounting, Organizations and Society (AOS) manuscript — qualitative field studies (access, case logic, interviews, observation), behavioral experiments, surveys, historical/archival inquiry, or theory-laden archival designs, each with the ethics and site-anonymity groundwork done early. Designs the inquiry; it does not run the coding or estimation (aos-data-analysis).

日本語の概要は準備中です。原文の説明を表示しています。

Use when building the exhibits of an Accounting, Organizations and Society (AOS) manuscript — data-inventory and evidence tables for qualitative work, cell-means and process-test tables for experiments, and figures that carry theoretical weight — in Elsevier-compatible format. Builds exhibits; it does not run the analysis (aos-data-analysis).

日本語の概要は準備中です。原文の説明を表示しています。

Use when analyzing the material of an Accounting, Organizations and Society (AOS) manuscript — coding and interpreting qualitative field data, estimating experimental and survey models, or running theory-laden archival analyses, with an audit trail appropriate to each tradition. Analyzes and reports; it does not design the study (aos-methods).

日本語の概要は準備中です。原文の説明を表示しています。

Select colormaps and qualitative palettes for scientific figures using perceptual-uniformity, color-vision-deficiency safety, and luminance-monotonicity criteria. Covers Crameri scientific colormaps, viridis/cividis/magma, Okabe-Ito categorical, ColorBrewer, and the rainbow/jet critique. Use when choosing palettes for heatmaps, scatter, networks, or any encoding where color carries quantitative or categorical meaning.

日本語の概要は準備中です。原文の説明を表示しています。

Turning research artifacts into actionable PM insight. Customer interviews, user research notes, support ticket reviews, sales call transcripts, survey data, in-app feedback, all synthesized into the decisions they are meant to inform. The discipline of moving from raw discovery data to clear product direction without losing signal in the synthesis or fabricating insight that was not actually there. Triggers on research synthesis, customer interview synthesis, user research analysis, discovery readout, research insights, sales call analysis, support ticket analysis, qualitative data analysis. Also triggers when a team has done research but cannot turn it into decisions, when synthesis is producing pretty decks but no roadmap movement, or when an upcoming PM decision needs to be grounded in research already conducted.

日本語の概要は準備中です。原文の説明を表示しています。

Analyze event logs, clickstreams, user paths, product funnels, retention, behavioral segments, transition graphs, step matrices, sequence patterns, and customer journeys using Retentioneering. Use when the user provides CSV, Parquet, pandas, or database event data containing user, event, and timestamp columns, or asks why users convert, churn, loop, abandon a flow, or follow particular product paths. Do not use for qualitative journey-mapping workshops or aggregate website traffic without user-level event sequences.

日本語の概要は準備中です。原文の説明を表示しています。

Synthesize raw user research (interviews, surveys, tickets) into themed findings and decision-ready briefs. Use when synthesizing user interviews, building a findings brief, or communicating research to stakeholders.

日本語の概要は準備中です。原文の説明を表示しています。

Analyse text complexity across quantitative, qualitative, and reader-task dimensions with scaffolding recommendations. Use when selecting texts, assessing readability, or planning reading support.

日本語の概要は準備中です。原文の説明を表示しています。

Deep qualitative analysis of high-signal sessions. Spawns subagents with v2 template, synthesizes patterns, compares against known findings. Use after /session-scan.

日本語の概要は準備中です。原文の説明を表示しています。

When the user has raw customer interview transcripts, survey responses, support tickets, or other qualitative data and needs to extract actionable insights.

日本語の概要は準備中です。原文の説明を表示しています。

Evaluates market bubble risk through quantitative data-driven analysis using the revised Minsky/Kindleberger framework v2.1. Prioritizes objective metrics (Put/Call, VIX, margin debt, breadth, IPO data) over subjective impressions. Features strict qualitative adjustment criteria with confirmation bias prevention. Supports practical investment decisions with mandatory data collection and mechanical scoring. Use when user asks about bubble risk, valuation concerns, or profit-taking timing.

日本語の概要は準備中です。原文の説明を表示しています。

Synthesize user research findings from interviews, surveys, and analytics. Create insight reports, customer journey maps, and actionable recommendations based on research data and qualitative findings.

日本語の概要は準備中です。原文の説明を表示しています。

Analyze NPS, CSAT, and qualitative customer feedback to extract themes, identify trends, and generate actionable insight reports. Use when the user requests customer feedback analysis or provides relevant inputs for this workflow.

日本語の概要は準備中です。原文の説明を表示しています。