「data-analysis」の検索結果

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 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 articulating what an Accounting, Organizations and Society (AOS) manuscript contributes — converting findings into a claim that changes how the field understands accounting's social, organizational, behavioral or institutional operation. Frames the payoff; it does not produce new 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 polishing the prose and structure of an Academy of Management Review (AMR) manuscript to AOM house style and an argument-driven voice. Late-stage polish; it does NOT build theory or check logic — do those first with amr-theory-development and amr-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 writing the response document for an Academy of Management Review (AMR) Revise & Resubmit — structuring point-by-point replies that show the theory was genuinely strengthened, not just defended. Drafts the response; revise the manuscript's theory FIRST (amr-theory-development / amr-data-analysis) before writing the letter.

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 actual theory for an Academy of Management Review (AMR) manuscript — turning a positioned puzzle into defined constructs, explicit relationships, propositions, and boundary conditions. Constructs the theory; it does NOT stress-test the argument's logic (that is amr-data-analysis) or design any data collection (AMR has none).

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。

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 an Accounting, Organizations and Society (AOS) revise-and-resubmit arrives — planning revisions that may include recoding field data, new experimental conditions, or deepened theorizing, and drafting the point-by-point response to an interdisciplinary reviewer panel. Drafts the response; it does not run new analysis (aos-data-analysis) or the final preflight (aos-submission).

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 articulating and differentiating the theoretical contribution of an Academy of Management Review (AMR) manuscript — showing precisely what is NEW versus prior theory and why it matters. Frames the contribution; it does NOT build the theory (amr-theory-development) or check its internal logic (amr-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 turning results into an explicit theoretical contribution for an Academy of Management Journal (AMJ) manuscript — the "what new theory do we learn?" statement and the discussion section. Frames the contribution and implications; it does not build the original theory (amj-theory-development) or run the analysis (amj-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 running and reporting the statistical analysis for an Academy of Management Journal (AMJ) manuscript — measurement validity, common-method bias, the right estimator (HLM, SEM, panel, experiments), endogeneity, and robustness. Executes and reports the analysis; it does not design the study (amj-methods) or frame the contribution (amj-contribution-framing).

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 for full-manuscript prose polish of an Academy of Management Journal (AMJ) manuscript — front-loading the argument, active voice, structure, and AOM house style. Polishes language and structure; it does not create the theoretical contribution (amj-contribution-framing) or fix the analysis (amj-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 stress-testing the LOGIC of an Academy of Management Review (AMR) theory manuscript — checking logical coherence, running thought experiments and counterfactuals, addressing alternative explanations and disconfirming cases, and verifying each proposition follows from its argument. This is ARGUMENT DEVELOPMENT, NOT data analysis; AMR publishes no datasets, no statistics, and no empirical results.

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 or cleaning the tables and figures for an Academy of Management Journal (AMJ) manuscript — correlation tables, regression/SEM/HLM result tables, the theoretical-model figure, and interaction plots in AOM house style. Finalizes exhibits; it does not run the analysis (amj-data-analysis) or frame the contribution (amj-contribution-framing).

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 the research design and method are the bottleneck for an Academy of Management Journal (AMJ) manuscript — matching design (archival, survey, experiment, multi-method, field) and level of analysis to the theoretical question. Designs the study; it does not run the estimation or validity checks (amj-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 the theoretical argument and hypotheses are the bottleneck for an Academy of Management Journal (AMJ) manuscript — building a mechanism and deriving testable hypotheses a priori. Constructs the theory; it does not run the analysis (amj-data-analysis) or write the final contribution paragraph (amj-contribution-framing).

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。

Automated exploratory data analysis with statistical summaries and visualizations

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 conversion TRACKING must be set up or fixed before spending — Meta Pixel and Conversions API, Google Ads conversions with Enhanced Conversions, GA4, TikTok Pixel and Events API, server-side GTM, consent mode for GDPR and CCPA, UTM conventions, iOS ATT attribution windows, and a pre-launch verification checklist. Trigger on 'tracking setup', 'pixel setup', 'CAPI', 'GA4 events', 'conversions are not being recorded', 'Meta and Shopify numbers do not match'. Also use when launch is imminent and nothing has been verified. Not for — auditing a live account broadly, see `21-ads-audit-global`; analyzing data once it flows, see `13-data-analysis-global`; the campaign hierarchy, see `52-account-structure-global`.

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 the user needs to WRITE a marketing report someone else will read — a one-page weekly for a CEO, a full monthly, or a quarterly strategy report with TL;DR, results versus target, insight, decisions needed, and next actions with owners. Trigger on 'marketing report', 'monthly report', 'weekly update for my boss', 'report for the client', 'quarterly summary', 'turn these numbers into a report'. Also use when the user says a stakeholder is asking how marketing is doing. Not for — diagnosing why the numbers are bad, see `03-performance-eval-global`; turning a raw export into insight, see `13-data-analysis-global`; a closed campaign retrospective, see `63-campaign-retrospective-global`.

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 marketing numbers look bad and the user needs to know WHY — layered root-cause diagnosis across tracking, delivery, creative, landing page, offer, and audience quality, 5-Whys, regional benchmarks for US, EU, SEA, and LATAM, and a 48-hour action plan. Covers dropshipping ROAS and BE-ROAS. Trigger on 'why is CPA so high', 'my ads are not converting', 'ROAS dropped', 'we are burning budget', 'performance is bad this month', 'diagnose the funnel'. Also use when the user pastes a screenshot of numbers with no question attached. Not for — auditing account configuration and scoring health, see `21-ads-audit-global`; writing a report someone else reads, see `07-marketing-report-global`; turning a raw export into insight, see `13-data-analysis-global`.

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 raw data exists — Meta, Google, TikTok, GA4, Shopify, a CRM export, or a spreadsheet — and has to become insight and decisions: descriptive, diagnostic, predictive, and prescriptive layers, cuts by channel, campaign, creative, audience, and time, cohorts, and a decision log. Trigger on 'analyze this data', 'read these numbers for me', 'what does this export say', 'pull insight from GA4', 'cohort analysis', 'here is the spreadsheet'. Also use when the user pastes a table and asks what it means. Not for — diagnosing ad root cause, see `03-performance-eval-global`; writing the report a stakeholder reads, see `07-marketing-report-global`; auditing account setup, see `21-ads-audit-global`.

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 the user wants a VALID experiment instead of a guess — hypothesis, one variable, sample size and runtime math, statistical significance, primary versus secondary metrics, multi-arm designs, and a results template, across Optimizely, VWO, and native Meta and Google tests. Trigger on 'A/B test', 'split test', 'how long should I run the test', 'is this result significant', 'test two versions', 'which creative is actually better'. Also use when a winner was declared after two days on tiny numbers. Not for — scaling the proven winner, see `55-scaling-ads-global`; analyzing data already collected, see `13-data-analysis-global`; auditing the account, see `21-ads-audit-global`.

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。