Crosstabs evidence graph

cross-tabulation · survey_workflow · en

A cross-tabulation shows the joint frequency distribution of two categorical variables: one variable's categories are rows, the other's are columns, and each cell contains a count or percentage.

Use row percentages to compare column outcomes across row groups; use column percentages to compare row composition across column groups.

Limitations

• A crosstab describes association, not causation.
• Very sparse or high-cardinality tables can be unstable and difficult to interpret.

Primary page · /methods · /methods/validation

chi-square-independence · test_selection · en

The chi-square test of independence compares observed cell counts with the counts expected if two categorical variables were unrelated.

χ² = Σ (O − E)² / E; E = row total × column total / grand total; df = (rows − 1) × (columns − 1).

A small p-value is evidence against independence; inspect effect size, cells, assumptions, multiplicity, and study design before drawing a substantive conclusion.

Limitations

• The p-value is asymptotic.
• Warnings appear if any expected count is below 1 or more than 20% of expected counts are below 5.
• Fisher exact coverage is limited; larger sparse tables need an appropriate exact or fixed-margin Monte Carlo method.
• Survey weights require design-aware methods unless they are genuine frequency weights.

Primary page · /methods · /methods/validation · /validation/statistical-reference-cases.json

fisher-exact · test_selection · en

For an unweighted 2×2 table of non-negative integer counts, Fisher's exact test computes the two-sided Fisher–Irwin fixed-margin p-value by summing table probabilities no greater than the observed table's probability.

Use it when an exact fixed-margin 2×2 test matches the design and sparse expected counts make an asymptotic approximation questionable.

Limitations

• Unavailable for fractional weighted cells, larger tables, or supports above 100,000 feasible fixed-margin tables.
• It does not solve confounding, dependence, or sampling-design problems.

Primary page · /methods · /methods/validation · /research/chi-square-vs-fisher

cramers-v · association_measure · en

Cramér's V measures the strength of association between two categorical variables on a 0-to-1 scale derived from Pearson's chi-square statistic.

V = sqrt(χ² / (n × (min(rows, columns) − 1))).

Larger values indicate stronger association; contextual and degrees-of-freedom-aware interpretation is preferable to universal cutoffs.

Limitations

• It does not provide direction or identify the cells driving the association.
• Weighted results inherit the table's weight-design caveats.

Primary page · /methods · /methods/validation

phi-coefficient · association_measure · en

Phi is a signed measure of association for a 2×2 table; its magnitude equals Cramér's V for that table.

φ = (ad − bc) / sqrt((a+b)(c+d)(a+c)(b+d)).

The sign depends on row and column ordering; the absolute value expresses strength.

Limitations

• Signed phi is a 2×2 measure and should not be confused with generalized sqrt(χ²/n) for larger tables.

Primary page · /methods · /methods/validation

pearson-contingency-coefficient · association_measure · en

Pearson's contingency coefficient is a chi-square-derived association measure whose attainable maximum depends on table size.

C = sqrt(χ² / (χ² + n)).

Limitations

• Values from differently sized tables are not directly comparable because the maximum is below 1 and depends on shape.

Primary page · /methods

odds-ratio · association_measure · en

The odds ratio compares the odds of an outcome between two groups in a 2×2 table.

OR = (a × d) / (b × c) for [[a,b],[c,d]].

OR = 1 means equal odds; values above or below 1 indicate higher or lower odds under the chosen orientation.

Limitations

• The displayed 95% interval is the large-sample Woolf log interval, not exact.
• A zero cell produces an uninformative [0, infinity] interval because no continuity correction is added.
• An odds ratio is not a risk ratio except as an approximation for rare outcomes.

Primary page · /methods · /odds-ratio-vs-risk-ratio

g-test · test_selection · en

The G-test is a likelihood-ratio alternative to Pearson's chi-square statistic for a contingency table.

G = 2 × Σ O × ln(O / E); df = (rows − 1) × (columns − 1).

Limitations

• Its p-value uses the same asymptotic chi-square reference distribution, so switching statistics does not solve sparse-cell concerns.

Primary page · /methods · /methods/validation

lambda · association_measure · en

Lambda is a proportional-reduction-in-error measure: it asks how much knowing one categorical variable reduces errors when predicting the other.

It ranges from 0 for no error reduction to 1 for complete error reduction.

Limitations

• A value of zero can occur even when variables are associated if knowledge does not change the modal prediction.

Primary page · /methods

gamma · association_measure · en

Gamma measures ordinal association from concordant and discordant pairs while ignoring ties.

γ = (C − D) / (C + D).

Limitations

• Ignoring ties can make the magnitude appear stronger when ties are common.

Primary page · /methods

kendall-tau · association_measure · en

Kendall's tau-b measures ordinal association with tie correction and is suited to square tables; tau-c adjusts the scale for rectangular tables.

tau-c = 2m(C − D) / (n²(m − 1)), where m = min(rows, columns).

Limitations

• Category order must be substantively meaningful and specified correctly.

Primary page · /methods

somers-d · association_measure · en

Somers' d is an asymmetric ordinal association measure that treats one variable as the outcome and accounts for ties on the chosen side.

Choose the direction that matches the research question; reversing outcome and predictor can change the result.

Limitations

• It is inappropriate when no defensible dependent-variable direction exists.

Primary page · /methods

theils-u · association_measure · en

Theil's U is an entropy-based, directional measure of how much knowing one variable reduces uncertainty about another.

It ranges from 0 for no uncertainty reduction to 1 when the predictor fully determines the outcome.

Limitations

• Direction matters, and sparse category estimates may be unstable.

Primary page · /methods

adjusted-residuals · association_measure · en

An adjusted residual standardizes a cell's observed-minus-expected difference to show which cells contribute most to a table-level association.

An absolute value above 1.96 receives a nominal two-sided 0.05 flag in the workspace.

Limitations

• Cellwise flags are nominal and are not adjusted for multiple comparisons.

Primary page · /methods

mcnemar-paired-change · test_selection · en

McNemar's test evaluates marginal change in paired binary outcomes by comparing the two discordant directions in a paired 2×2 table; concordant pairs do not enter the statistic.

Uncorrected χ² = (b − c)² / (b + c), df = 1; continuity-corrected χ² = max(|b − c| − 1, 0)² / (b + c); the exact two-sided result uses Binomial(b + c, 0.5).

A small p-value indicates asymmetric movement between the two categories, not the magnitude or cause of that movement.

Limitations

• Observations must be paired, binary, and independent across subjects or matched pairs.
• The public calculator requires non-negative integer counts and does not accept percentages or survey weights.
• Exact binomial tails are computed for at most 10,000 discordant pairs; larger inputs are explicitly labeled as a continuity-corrected normal approximation.
• A significant result does not by itself establish a causal intervention effect.

Primary page · /which-statistical-test · /methods · /methods/validation

statistical-test-selection · test_selection · en

Choose from the design first: independent nominal groups usually imply chi-square or an exact alternative; paired binary data imply McNemar; agreement implies kappa; ordered categories imply an ordinal association measure.

Limitations

• No decision tree can repair dependence, confounding, invalid sampling, or a mismatch between the table and the estimand.

Primary page · /methods · /methods/validation

sparse-cells · test_selection · en

Sparse expected counts make chi-square reference approximations less reliable; inspect the pattern, consider defensible category consolidation, or use a design-appropriate exact or Monte Carlo method.

Limitations

• Do not merge categories solely to obtain significance.
• Fisher's exact test on this site is limited to eligible 2×2 integer-frequency tables.

Primary page · /methods · /research/chi-square-vs-fisher

survey-weights · survey_workflow · en

A weighted crosstab multiplies each eligible respondent by a positive finite weight before calculating counts and percentages, while retaining the unweighted base for diagnostics; raking creates calibration weights by iteratively matching trusted population margins.

Limitations

• Inferential statistics are descriptive or approximate unless weights are genuine frequency weights.
• Complex survey variance, strata, clusters, finite-population corrections, replicate weights, and Rao–Scott corrections are not implemented.

Primary page · /survey-crosstab-tool · /methods · /capacity · /local-mode

multi-select · survey_workflow · en

A select-all-that-apply question should become auditable option-level binary outcomes and be crossed with an independent grouping variable; report percent of cases to show each option's incidence among included respondents rather than treating combined answer strings as exclusive categories.

Percent-of-cases rows can sum above 100% because one respondent may select multiple options; percent of responses uses a different denominator and answers a different question.

Limitations

• An omnibus Pearson chi-square across overlapping option rows is invalid; option-level binary comparisons answer a narrower question.
• Bonferroni correction covers banner-column pairs within each option but not the complete family across options, tables, or exploratory cuts.
• All-blank skipped sets are excluded while explicit all-zero answered cases remain in the respondent base.
• Weighted tests are approximate unless weights are genuine frequency weights.

Primary page · /methods · /local-mode · /crosstab-significance-letters

significance-letters · survey_workflow · en

Column significance letters encode pairwise column-proportion tests by labeling a cell with the columns it differs from under the configured threshold and correction policy.

Limitations

• Small bases and multiple comparisons can produce misleading flags.
• Weighting is treated with the disclosed frequency-weight approximation caveat.

Primary page · /methods

tracker-schema-drift · tracker_workflow · en

Treat each imported tracker wave as immutable: fingerprint both schemas, match stable identifiers before labels, classify structural and semantic drift, preview and approve explicit transforms, apply them to a copy, validate bases and distributions, and retain the source, audit record, and guarded inverse before comparing waves.

A successful structural repair does not prove longitudinal comparability; question wording, universe, routing, recall period, concept, and representation must still remain defensibly comparable.

Limitations

• Automatic suggestions and label similarity are not evidence of semantic equivalence.
• Exact reversal is refused after later project changes because restoring the old snapshot would erase subsequent work.
• The current Workspace UI exposes compatible-wave import, rename mapping, retained waves, and comparison, but not the engine's full approval and reversal interface.

Primary page · /methods · /local-mode · /capacity · /survey-tab-book

tab-book-project · survey_workflow · en

A tab-book project keeps row sets, multibanners, filters, weights, tests, table order, waves, coding variables, findings, and the export recipe in one versioned graph rather than as unrelated outputs.

Limitations

• A definition-only manifest intentionally omits respondent rows and cannot restore a working analysis.

Primary page · /local-mode · /data-processing

open-end-coding · ai_governance · en

AI-created open-end themes remain proposals until a researcher reviews them; approved themes become variables with assignment evidence, decision history, and a safe undo path inside the same project graph.

Limitations

• Hosted theme discovery sends only the disclosed response texts and theme definitions through Vercel AI Gateway.
• Model output is not treated as ground truth and requires human approval.

Primary page · /privacy · /data-processing · /local-mode

outcome-modeling · survey_workflow · en

The outcome studio fits a regularized logistic model to an explicitly selected binary outcome and drivers, then reports deterministic holdout AUC, accuracy, Brier score, calibration, observed segments, and reviewable actions.

Limitations

• Association and prediction do not establish causality.
• At least 60 complete rows, 25 successes, and 25 other outcomes are required.
• Synthetic scenarios are labeled modeled estimates rather than observed evidence.

Primary page · /methods · /capacity

editable-report-refresh · reporting · en

A compatible Crosstabs PPTX or DOCX can refresh current generated values without replacing the whole edited package: stable anchors identify selected tables, metadata, statistics text, and PowerPoint charts; supported equal-shape content is patched in place while manual regions, unselected objects, and unrelated package parts remain under analyst control.

Preservation is regional, not universal: the existing Office package remains the target, and the refresh audit distinguishes updated anchors, preserved anchors, blocking reasons, and package entries that were not rewritten.

Limitations

• Selected generated table text is replaced by current approved Crosstabs values; selected PowerPoint chart series and its embedded workbook are also replaced.
• Equal-shape table formatting and geometry are supported, but arbitrary structural edits inside generated objects are not.
• A missing or duplicate stable anchor, changed table shape, incompatible chart, corrupt package, or unanchored third-party file fails atomically before a modified file is returned.
• The current Workspace Export menu refreshes the open tab-current table; it does not expose arbitrary multi-table selection or automatic schema-changing reflow.

Primary page · /workspace · /local-mode · /data-processing · /methods/validation · /survey-tab-book

local-mode · trust · en

Core import, table computation, filters, weights, tests, project state, review, and export run in the browser; hosted AI actions are optional, disclosed, and bounded to the relevant aggregate context or selected open text.

Limitations

• Browser memory, storage quotas, device performance, and file width can constrain work before row count alone does.
• A full project package contains rows and is not encrypted.

Primary page · /privacy · /data-processing · /capacity

workload-envelope · trust · en

Crosstabs publishes tested workload envelopes across rows, file representation, category shape, output cells, memory, filters, weights, statistical tests, cancellation, and browser execution mode instead of claiming unlimited rows.

Limitations

• Published fixtures are evidence for tested dimensions and environments, not a universal device guarantee.

Primary page · /capacity · /methods/validation

ai-evidence-governance · ai_governance · en

Give the model only bounded analytical context; bind factual cell claims to stable evidence IDs; independently resolve each pointer against the computed result; fail closed on a mismatch; keep answers read-only; and express requested project mutations as typed proposals that require human approval, stale-state checks, an audit record, and guarded undo.

Provider routing and model agreement can improve availability or offer another opinion, but neither validates statistical claims nor grants authority to change a research project.

Limitations

• The AI answer activity list is session-level and is not a durable project audit log.
• The governed project agent supports a bounded typed operation set, not arbitrary recodes, filters, weights, row edits, or exports.
• Exact undo is refused after later project changes because restoring the earlier snapshot would overwrite subsequent work.
• Model providers can still make errors, and independent statistical review and third-party certification evidence remain pending where explicitly disclosed.

Primary page · /security · /privacy · /methods · /open-end-coding · /survey-tab-book

codex-plugin · ai_governance · en

The official Crosstabs Codex marketplace installs the immutable crosstabs 1.1.4 package over local stdio and exposes two servers: 39 focused statistical tools plus 23 project-workflow tools for import, tab books, weights, waves, coding, schema repair, editable reports, export, and audit history.

Use the local plugin for confidential end-to-end research projects. Use the remote MCP endpoint for public methodology and deterministic calculations over explicitly supplied aggregate matrices, never respondent rows.

Limitations

• The browser worker and local MCP enter the same HeadlessCrosstabsService and share TabBookProject, initial-analysis, deterministic calculation, project revision, audit, replay, and undo orchestration. The candidate hosted lifecycle enters that service through tenant-storage and authorization adapters. Shared orchestration is verified; complete browser-interaction and production-hosting parity are separate release gates.
• The local plugin does not reproduce browser interaction state, shared cloud review, comments, or optional connector authorization.
• The live remote connector intentionally exposes only public evidence and aggregate calculations. The hosted 23-tool lifecycle remains gated until durable tenant storage and capability-scoped OAuth pass production verification.
• Local code_open_ends is the only generative operation and requires explicit Vercel AI Gateway processing approval; any file supplied to an AI assistant also remains subject to that assistant's data boundary and the user's configuration.

Primary page · /mcp · /methods · /methods/validation · /local-mode · /privacy

zh-chi-square · test_selection · zh-CN

卡方独立性检验先按行列边际计算独立假设下的期望频数，再用 χ² = Σ(O − E)² / E 汇总观察值与期望值的差异，用于判断两个相互独立观测的分类变量是否存在统计关联。

χ² = Σ (O − E)² / E；E = 行合计 × 列合计 / 总样本量。

较小的 P 值反对独立性原假设；还应报告 Cramér's V、样本基数、期望频数诊断、关键单元格和研究设计，不能把统计关联解释为因果。

Limitations

• P 值使用渐近卡方分布；最小期望频数小于 1 或超过 20% 的期望频数小于 5 时，近似可能较差。
• 观察应相互独立；同一对象前后两次的配对二分类表应使用 McNemar 等配对方法。
• 输入应是互斥类别的观察频数，百分比不能替代检验所需的样本基数。
• 本网站的 Fisher 精确结果只覆盖合格的非加权 2×2 非负整数频数表和有界工作量。
• 设计或校准权重、分层和整群抽样需要设计型方差与 Rao–Scott 等方法。
• 调整标准化残差是诊断量；跨多个单元格、列对、问题或表格的推断仍需定义检验族并处理多重比较。

Primary page · /chi-square-calculator · /zh-cn/which-statistical-test · /zh-cn/chi-square-expected-count-less-than-5 · /methods · /methods/validation

zh-codex-plugin · ai_governance · zh-CN

官方 Crosstabs Codex 市场插件通过本地 stdio 启动不可变的 crosstabs 1.1.4 包，并提供两个服务器：39 个专项统计工具，以及 23 个覆盖导入、Tab Book、权重、波次、开放题编码、Schema 修复、可编辑报告、导出与审计历史的项目工作流工具。

机密的端到端研究项目使用本地插件；远程 MCP 只用于公开方法证据和明确提供的汇总矩阵，不接收受访者行。

Limitations

• 浏览器 Worker 与本地 MCP 进入同一个 HeadlessCrosstabsService，并共享 TabBookProject、初始分析、确定性计算、项目修订、审计、重放与撤销编排；候选托管生命周期通过租户存储与授权适配器进入同一服务。共享编排已经验证，但浏览器交互与生产托管的完整对等仍是独立发布门槛。
• 本地插件不复制浏览器交互状态、共享云端审核、评论或可选连接器授权。
• 线上远程连接器目前只公开证据与汇总计算；托管的 23 工具生命周期仍须等待持久化租户存储与能力范围 OAuth 通过生产验证。
• 本地 code_open_ends 是唯一生成式操作，必须先明确批准 Vercel AI Gateway 外部处理；提供给 AI 助手的任何文件仍受该助手的数据边界与用户配置约束。

Primary page · /zh-cn/mcp · /mcp · /methods · /methods/validation · /local-mode · /privacy

zh-mcnemar-paired-change · test_selection · zh-CN

McNemar 检验通过比较配对 2×2 表中两个不一致方向 b 与 c，判断同一对象前后或一一匹配对象的二分类边际比例是否发生不对称变化；一致配对 a 与 d 不进入统计量。

未校正 χ² = (b − c)² / (b + c)，df = 1；连续性校正 χ² = max(|b − c| − 1, 0)² / (b + c)；精确双侧结果使用 Binomial(b + c, 0.5)。

较小的 P 值表示两个变化方向不对称，不表示变化幅度，也不会单独证明变化由干预造成。

Limitations

• 对象或匹配对之间应相互独立，且每次结果只有两个互斥类别。
• 公开计算器只接受非负整数频数，不接受百分比、小数或调查权重。
• 不一致配对不超过 10,000 对时计算精确二项尾概率；更大输入明确标为连续性校正正态近似。
• 复杂抽样、失访、混杂和多时点重复测量需要适合完整设计的方法。

Primary page · /mcnemar-test-calculator · /zh-cn/which-statistical-test · /methods · /methods/validation

zh-statistical-test-selection · test_selection · zh-CN

分类数据检验应从研究设计开始：独立分类变量通常使用 Pearson 卡方或适合的精确方法；合格的稀疏 2×2 整数频数表可用 Fisher；配对二分类结果使用 McNemar；对同一对象的分类一致性使用 Cohen's kappa；有序变量还应报告保留顺序信息的关联指标。

Limitations

• P 值和关联不能单独证明因果。
• 普通交叉表方法不适合连续结局、复杂抽样、聚类或未建模的重复测量结构。
• 本网站的 Fisher 精确检验只覆盖合格的非加权 2×2 非负整数频数表。

Primary page · /which-statistical-test · /methods · /methods/validation · /mcnemar-test-calculator · /cohens-kappa-calculator

zh-sparse-cells · test_selection · zh-CN

期望频数过小会削弱卡方渐近近似；应先检查稀疏分布，再考虑有理论依据的类别合并、2×2 Fisher 精确检验或适合设计的精确/蒙特卡洛方法。

Limitations

• 不要只为获得显著结果而合并类别。
• 本网站的 Fisher 精确检验仅适用于合格的 2×2 非加权整数频数表。

Primary page · /methods · /research/chi-square-vs-fisher

zh-survey-weights · survey_workflow · zh-CN

加权交叉表让每位合格受访者按正数有限权重贡献到单元格；应使用加权百分比表达估计，同时保留未加权样本基数用于样本量和数据质量诊断。Raking 通过迭代调整权重，使指定加权边际接近可信总体目标。

加权单元格 = Σ wᵢI(rowᵢ = r, columnᵢ = c)；权重离散诊断 n_eff = (Σwᵢ)² / Σwᵢ²。

Limitations

• 除真正的频数权重外，普通加权卡方与列比例检验只能视为近似结果。
• 当前不实现分层、整群、有限总体修正、重复权重方差或 Rao–Scott 校正。

Primary page · /survey-weighting-raking · /survey-crosstab-tool · /methods · /capacity

zh-cramers-v · association_measure · zh-CN

Cramér's V 将卡方统计量转换为 0 到 1 的分类变量关联强度指标。

V = sqrt(χ² / (n × (min(行数, 列数) − 1)))。

Limitations

• 它不表示方向，也不说明哪些单元格推动了关联。

Primary page · /methods · /methods/validation

zh-multi-select · survey_workflow · zh-CN

“选择所有适用项”应定义或整理为选项级二分类结果，再与独立分组变量交叉；通常报告基于个案的百分比，表示每个 Banner 组中选择该选项的受访者比例，而不是把答案组合当成互斥类别。

同一受访者可选择多个选项，所以基于个案的各行百分比总和可以超过 100%；基于响应的百分比使用全部选择次数作为不同分母。

Limitations

• 不能对相互重叠的所有选项行运行一个普通总体 Pearson 卡方检验。
• 每个选项内部的 Banner 列对使用 Bonferroni 校正，但不会跨所有选项、表格或探索性切分控制完整推断族。
• 所有选项均为空的跳题记录默认排除；明确作答且全为 0 的个案仍保留在受访者基数。
• 设计或校准权重下的普通列比例检验只能视为近似结果。

Primary page · /multi-select-crosstab · /crosstab-significance-letters · /methods · /local-mode

zh-significance-letters · survey_workflow · zh-CN

交叉表中的显著性字母表示同一结果行内的成对列比例检验：每个 Banner 列先分配一个字母，某单元格中的字母标出该单元格所在列的比例显著高于哪些列。

例如 A 列单元格显示“42% B”，表示 A 列的 42% 在设定阈值和多重比较校正下显著高于 B 列同一行的比例；字母不表示差异大小、总体关联强度或因果关系。

Limitations

• Crosstabs 对每行内所有合格 Banner 列对使用合并方差双比例 z 检验和 Bonferroni 校正；校正范围不跨其他行、表格或探索性切分。
• 检验列合计小于 30 的列不参与字母检验；普通表超过 12 个 Banner 列或 250 个行类别时不生成字母；没有字母不等于两列相同。
• 加权时，阈值和检验使用加权列合计；设计或校准权重下的结果把加权计数当作频数，只能视为近似推断，并应另外报告未加权样本基数。
• 显著性字母应与百分比、未加权基数、置信阈值、效应量和研究设计一起报告。

Primary page · /crosstab-significance-letters · /zh-cn/multi-select-crosstab · /zh-cn/survey-weighting-raking · /methods

zh-tracker-schema-drift · tracker_workflow · zh-CN

把每个导入波次视为不可变源数据：比较结构指纹，优先使用稳定内部 ID，分类记录结构与语义漂移，预览并审批明确转换，只在副本上执行，验证基数与分布，并保留来源、审计记录和受保护的逆操作后再比较波次。

结构修复成功不等于纵向可比；题目措辞、目标总体、跳转逻辑、回忆期、概念和表示方式仍须由研究人员确认。

Limitations

• 自动建议和标签相似不能证明语义等价。
• 项目在修复后已改变时必须拒绝精确回退，以免覆盖后续工作。
• 当前工作区支持兼容波次导入、改名映射、保留旧波次与比较，但尚未提供底层引擎完整的审批与回退界面。

Primary page · /tracker-schema-drift · /methods · /local-mode · /capacity · /survey-tab-book

zh-ai-evidence-governance · ai_governance · zh-CN

只向模型提供有界且已披露的分析上下文，把每项单元格事实绑定到稳定证据 ID，由应用独立解析引用并在不匹配时默认拒绝模型文字；AI 回答保持只读，任何项目变更都必须成为可预览的类型化提案，经人工审批、状态冲突检查、审计记录和受保护回退后才能执行。

多模型路由或模型间一致只能改善可用性或提供另一种意见，不能验证统计结论，也不能授予模型修改研究项目的权限。

Limitations

• AI 回答活动列表是会话级记录，不是持久项目审计日志。
• 受治理的项目代理只支持有限的类型化操作，不能任意重编码、修改筛选或权重、编辑受访者行或触发导出。
• 项目后来发生变化时会拒绝精确回退，以免覆盖后续工作。
• 模型仍可能出错；页面明确披露的独立统计审查与第三方认证证据仍处于待完成状态。

Primary page · /ai-analysis-governance · /security · /privacy · /open-end-coding · /survey-tab-book

zh-editable-report-refresh · reporting · zh-CN

兼容的 Crosstabs PPTX 或 DOCX 可以通过稳定锚点识别选中表格、元数据、统计文字和 PowerPoint 图表，只在支持的等形生成区域内更新当前值，同时保留人工区域、未选中对象和无关 Office 包部件。

保留是按区域定义的，而不是任意往返编辑承诺；现有 Office 文件保持为目标，刷新审计分别记录已更新锚点、已保留锚点、阻止原因与未重写部件。

Limitations

• 选中生成表格内的文字会替换为当前获批 Crosstabs 值；选中 PowerPoint 图表的序列和嵌入工作簿也会替换。
• 仅支持等形表格的格式与几何保留，不支持生成对象内任意结构修改。
• 锚点缺失或重复、表格形状变化、图表不兼容、文件损坏或第三方文件没有锚点时会原子失败，不会返回部分修改的文件。
• 当前 Workspace Export 菜单刷新打开的 tab-current 表格，尚未提供任意多表选择或结构变化后的自动重排。

Primary page · /editable-pptx-docx-refresh · /workspace · /local-mode · /data-processing · /methods/validation · /survey-tab-book