Survey Data Cleaning: Formula, Real Data, Results and Software Workflows

Defined target

Within Survey Data Cleaning, the analysis treats all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals as the complete variable ledger. This ledger fixes the unit of analysis, group order, score direction and denominator. The central result is The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation.

Survey data-cleaning audit is appropriate only for this defined target. The article does not relabel data transformation, data imputation or outlier analysis as the same procedure.

What is not being claimed

Survey Data Cleaning does not establish causation, universal validity or invariance across unobserved populations. The evidence belongs to the 649-record dataset and the declared coding. Its interpretation is conditioned on source file fingerprinted, raw data retained, rules documented before changes and cleaning log is reproducible.

The post therefore reports schema audit, range checks and category normalization before extending the result. This sequence prevents a software label from becoming a broader scientific conclusion.

Unit of analysis

One source row is one respondent record for clean analysis-ready survey table; no row is silently duplicated across this analysis.

Estimand

The estimand asks how the 649×33 source can be checked for schema, ranges, labels, missing codes, duplicates and derived-variable integrity before analysis.

Primary output

The primary output is stated as The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation.

Scale meaning

clean analysis-ready survey table is interpreted in its declared unit and order.

Software agreement

Python, R, SPSS and Excel must use the same rows, coding and survey data-cleaning audit formula.

Decision rule

Magnitude, precision, assumptions and diagnostics for clean analysis-ready survey table are considered together; a p-value is never the entire conclusion.

Source file fingerprinted

If source file fingerprinted fails, the stated survey data-cleaning audit interpretation may no longer identify clean analysis-ready survey table.

Raw data retained

The software can still return output when raw data retained is false, so this condition is checked independently.

Rules documented before changes

The article narrows its language or redirects analysis to outlier analysis when rules documented before changes is not defensible.

Cleaning log is reproducible

The assigned charts are reviewed for evidence relevant to cleaning log is reproducible before publication.

Schema Audit

Survey Data Cleaning checks schema audit against 649 rows. The schema audit check is tied to all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals and is not copied from a different method. A failed check changes the result wording or triggers data transformation.

Range Checks

Survey Data Cleaning checks range checks against 33 columns. The range checks check is tied to all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals and is not copied from a different method. A failed check changes the result wording or triggers data imputation.

Category Normalization

Survey Data Cleaning checks category normalization against 0 blank source cells. The category normalization check is tied to all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals and is not copied from a different method. A failed check changes the result wording or triggers outlier analysis.

Duplicate Review

Survey Data Cleaning checks duplicate review against 21,417 populated source cells. The duplicate review check is tied to all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals and is not copied from a different method. A failed check changes the result wording or triggers data transformation.

Cleaning Log

Survey Data Cleaning checks cleaning log against 649 rows. The cleaning log check is tied to all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals and is not copied from a different method. A failed check changes the result wording or triggers data imputation.

Raw-Data Preservation

Survey Data Cleaning checks raw-data preservation against 33 columns. The raw-data preservation check is tied to all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals and is not copied from a different method. A failed check changes the result wording or triggers outlier analysis.

Sensitivity to source file fingerprinted

The primary Survey Data Cleaning result is recalculated or reinterpreted after reviewing source file fingerprinted. The comparison tracks whether 649 rows changes enough to alter the substantive conclusion. Where sensitivity to source file fingerprinted answers a different estimand, it is labeled as data transformation rather than presented as a duplicate confirmation.

Sensitivity to raw data retained

The primary Survey Data Cleaning result is recalculated or reinterpreted after reviewing raw data retained. The comparison tracks whether 33 columns changes enough to alter the substantive conclusion. Where sensitivity to raw data retained answers a different estimand, it is labeled as data imputation rather than presented as a duplicate confirmation.

Sensitivity to rules documented before changes

The primary Survey Data Cleaning result is recalculated or reinterpreted after reviewing rules documented before changes. The comparison tracks whether 0 blank source cells changes enough to alter the substantive conclusion. Where sensitivity to rules documented before changes answers a different estimand, it is labeled as outlier analysis rather than presented as a duplicate confirmation.

Sensitivity to cleaning log is reproducible

The primary Survey Data Cleaning result is recalculated or reinterpreted after reviewing cleaning log is reproducible. The comparison tracks whether 21,417 populated source cells changes enough to alter the substantive conclusion. Where sensitivity to cleaning log is reproducible answers a different estimand, it is labeled as data transformation rather than presented as a duplicate confirmation.

Definition: schema audit in Survey Data Cleaning

During the definition review, in Survey Data Cleaning, schema audit is evaluated within the exact target clean analysis-ready survey table, using all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the definition stage for schema audit, the diagnostic is anchored to 33 columns, not to an unrelated rule of thumb. The definition finding for schema audit—The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation—is retained only when rules documented before changes remains defensible and the Category-label audit figure tells the same numerical story as the table. A visible pattern involving schema audit is interpreted through duplicate review; the definition reading is not treated as automatic evidence for causation, validity, or a different outcome. If at the definition stage for schema audit reveals a changed population, coding direction, group order, or response scale, the schema audit calculation is rebuilt before reporting. During the definition review of schema audit, data imputation is considered only when its different estimand actually matches the revised research question.

Definition: range checks

During the definition review, in this survey data-cleaning audit analysis, range checks is evaluated within the exact target clean analysis-ready survey table, using all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the definition stage for range checks, the diagnostic is anchored to 649 rows, not to an unrelated rule of thumb. The definition finding for range checks—The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation—is retained only when cleaning log is reproducible remains defensible and the Column-level cleaning metrics figure tells the same numerical story as the table. A visible pattern involving range checks is interpreted through cleaning log; the definition reading is not treated as automatic evidence for causation, validity, or a different outcome. If at the definition stage for range checks reveals a changed population, coding direction, group order, or response scale, the range checks calculation is rebuilt before reporting. During the definition review of range checks, data transformation is considered only when its different estimand actually matches the revised research question.

Definition: category normalization

During the definition review, in Survey Data Cleaning, category normalization is evaluated within the exact target clean analysis-ready survey table, using all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the definition stage for category normalization, the diagnostic is anchored to 21,417 populated source cells, not to an unrelated rule of thumb. The definition finding for category normalization—The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation—is retained only when source file fingerprinted remains defensible and the Derived-variable validation figure tells the same numerical story as the table. A visible pattern involving category normalization is interpreted through raw-data preservation; the definition reading is not treated as automatic evidence for causation, validity, or a different outcome. If at the definition stage for category normalization reveals a changed population, coding direction, group order, or response scale, the category normalization calculation is rebuilt before reporting. During the definition review of category normalization, outlier analysis is considered only when its different estimand actually matches the revised research question.

Definition: duplicate review in Survey Data Cleaning

During the definition review, in this survey data-cleaning audit analysis, duplicate review is evaluated within the exact target clean analysis-ready survey table, using all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the definition stage for duplicate review, the diagnostic is anchored to 0 blank source cells, not to an unrelated rule of thumb. The definition finding for duplicate review—The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation—is retained only when raw data retained remains defensible and the Range and type exceptions figure tells the same numerical story as the table. A visible pattern involving duplicate review is interpreted through schema audit; the definition reading is not treated as automatic evidence for causation, validity, or a different outcome. If at the definition stage for duplicate review reveals a changed population, coding direction, group order, or response scale, the duplicate review calculation is rebuilt before reporting. During the definition review of duplicate review, data imputation is considered only when its different estimand actually matches the revised research question.

Definition: cleaning log

During the definition review, in Survey Data Cleaning, cleaning log is evaluated within the exact target clean analysis-ready survey table, using all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the definition stage for cleaning log, the diagnostic is anchored to 33 columns, not to an unrelated rule of thumb. The definition finding for cleaning log—The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation—is retained only when rules documented before changes remains defensible and the Verified cleaning summary figure tells the same numerical story as the table. A visible pattern involving cleaning log is interpreted through range checks; the definition reading is not treated as automatic evidence for causation, validity, or a different outcome. If at the definition stage for cleaning log reveals a changed population, coding direction, group order, or response scale, the cleaning log calculation is rebuilt before reporting. During the definition review of cleaning log, data transformation is considered only when its different estimand actually matches the revised research question.

Definition: raw-data preservation

During the definition review, in this survey data-cleaning audit analysis, raw-data preservation is evaluated within the exact target clean analysis-ready survey table, using all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the definition stage for raw-data preservation, the diagnostic is anchored to 649 rows, not to an unrelated rule of thumb. The definition finding for raw-data preservation—The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation—is retained only when cleaning log is reproducible remains defensible and the Category-label audit figure tells the same numerical story as the table. A visible pattern involving raw-data preservation is interpreted through category normalization; the definition reading is not treated as automatic evidence for causation, validity, or a different outcome. If at the definition stage for raw-data preservation reveals a changed population, coding direction, group order, or response scale, the raw-data preservation calculation is rebuilt before reporting. During the definition review of raw-data preservation, outlier analysis is considered only when its different estimand actually matches the revised research question.

Definition: source file fingerprinted in Survey Data Cleaning

During definition review, the source file fingerprinted condition has a concrete role in Survey Data Cleaning. At its definition stage, source file fingerprinted determines whether survey data-cleaning audit can answer how the 649×33 source can be checked for schema, ranges, labels, missing codes, duplicates and derived-variable integrity before analysis. At the definition stage for source file fingerprinted, the check uses all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals and is reconciled with 21,417 populated source cells. When source file fingerprinted is doubtful during definition review, software output may still appear complete, but the result cannot automatically retain the interpretation clean analysis-ready survey table. The Column-level cleaning metrics display is examined for the observable consequence of failing source file fingerprinted, while duplicate review is reviewed in the original response units. In the definition assessment of source file fingerprinted, the article either narrows the claim, applies a justified sensitivity calculation, or moves to data imputation. This is why source file fingerprinted appears beside the definition result rather than as a detached checklist item.

Definition: raw data retained

During definition review, the raw data retained condition has a concrete role in this survey data-cleaning audit analysis. At its definition stage, raw data retained determines whether survey data-cleaning audit can answer how the 649×33 source can be checked for schema, ranges, labels, missing codes, duplicates and derived-variable integrity before analysis. At the definition stage for raw data retained, the check uses all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals and is reconciled with 0 blank source cells. When raw data retained is doubtful during definition review, software output may still appear complete, but the result cannot automatically retain the interpretation clean analysis-ready survey table. The Derived-variable validation display is examined for the observable consequence of failing raw data retained, while cleaning log is reviewed in the original response units. In the definition assessment of raw data retained, the article either narrows the claim, applies a justified sensitivity calculation, or moves to data transformation. This is why raw data retained appears beside the definition result rather than as a detached checklist item.

Definition: rules documented before changes

During definition review, the rules documented before changes condition has a concrete role in Survey Data Cleaning. At its definition stage, rules documented before changes determines whether survey data-cleaning audit can answer how the 649×33 source can be checked for schema, ranges, labels, missing codes, duplicates and derived-variable integrity before analysis. At the definition stage for rules documented before changes, the check uses all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals and is reconciled with 33 columns. When rules documented before changes is doubtful during definition review, software output may still appear complete, but the result cannot automatically retain the interpretation clean analysis-ready survey table. The Range and type exceptions display is examined for the observable consequence of failing rules documented before changes, while raw-data preservation is reviewed in the original response units. In the definition assessment of rules documented before changes, the article either narrows the claim, applies a justified sensitivity calculation, or moves to outlier analysis. This is why rules documented before changes appears beside the definition result rather than as a detached checklist item.

Definition: cleaning log is reproducible in Survey Data Cleaning

During definition review, the cleaning log is reproducible condition has a concrete role in this survey data-cleaning audit analysis. At its definition stage, cleaning log is reproducible determines whether survey data-cleaning audit can answer how the 649×33 source can be checked for schema, ranges, labels, missing codes, duplicates and derived-variable integrity before analysis. At the definition stage for cleaning log is reproducible, the check uses all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals and is reconciled with 649 rows. When cleaning log is reproducible is doubtful during definition review, software output may still appear complete, but the result cannot automatically retain the interpretation clean analysis-ready survey table. The Verified cleaning summary display is examined for the observable consequence of failing cleaning log is reproducible, while schema audit is reviewed in the original response units. In the definition assessment of cleaning log is reproducible, the article either narrows the claim, applies a justified sensitivity calculation, or moves to data imputation. This is why cleaning log is reproducible appears beside the definition result rather than as a detached checklist item.

Definition: 649 rows

For definition review, the numerical checkpoint 649 rows is reconstructed in Survey Data Cleaning from all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the definition stage for 649 rows, 649 rows must agree with the displayed formula, the software objects, the Excel cells, and the Category-label audit graphic after rounding. The definition meaning of 649 rows is limited to clean analysis-ready survey table; the same number would not have the same meaning under a different grouping variable, scoring key, missing-data rule, or reference category. Interpretation of 649 rows also depends on source file fingerprinted. During definition review, 649 rows is read with range checks and with the complete finding, The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation. Any discrepancy in the definition reconstruction of 649 rows is investigated at full precision rather than concealed by formatting, and data transformation is not used to force agreement because it answers a different question.

Definition: 33 columns

For definition review, the numerical checkpoint 33 columns is reconstructed in this survey data-cleaning audit analysis from all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the definition stage for 33 columns, 33 columns must agree with the displayed formula, the software objects, the Excel cells, and the Column-level cleaning metrics graphic after rounding. The definition meaning of 33 columns is limited to clean analysis-ready survey table; the same number would not have the same meaning under a different grouping variable, scoring key, missing-data rule, or reference category. Interpretation of 33 columns also depends on raw data retained. During definition review, 33 columns is read with category normalization and with the complete finding, The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation. Any discrepancy in the definition reconstruction of 33 columns is investigated at full precision rather than concealed by formatting, and outlier analysis is not used to force agreement because it answers a different question.

Definition: 0 blank source cells in Survey Data Cleaning

For definition review, the numerical checkpoint 0 blank source cells is reconstructed in Survey Data Cleaning from all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the definition stage for 0 blank source cells, 0 blank source cells must agree with the displayed formula, the software objects, the Excel cells, and the Derived-variable validation graphic after rounding. The definition meaning of 0 blank source cells is limited to clean analysis-ready survey table; the same number would not have the same meaning under a different grouping variable, scoring key, missing-data rule, or reference category. Interpretation of 0 blank source cells also depends on rules documented before changes. During definition review, 0 blank source cells is read with duplicate review and with the complete finding, The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation. Any discrepancy in the definition reconstruction of 0 blank source cells is investigated at full precision rather than concealed by formatting, and data imputation is not used to force agreement because it answers a different question.

Definition: 21,417 populated source cells

For definition review, the numerical checkpoint 21,417 populated source cells is reconstructed in this survey data-cleaning audit analysis from all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the definition stage for 21,417 populated source cells, 21,417 populated source cells must agree with the displayed formula, the software objects, the Excel cells, and the Range and type exceptions graphic after rounding. The definition meaning of 21,417 populated source cells is limited to clean analysis-ready survey table; the same number would not have the same meaning under a different grouping variable, scoring key, missing-data rule, or reference category. Interpretation of 21,417 populated source cells also depends on cleaning log is reproducible. During definition review, 21,417 populated source cells is read with cleaning log and with the complete finding, The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation. Any discrepancy in the definition reconstruction of 21,417 populated source cells is investigated at full precision rather than concealed by formatting, and data transformation is not used to force agreement because it answers a different question.

Definition: data transformation

During definition review, data transformation is a legitimate neighboring method, but at that stage it is not another name for Survey Data Cleaning. The definition comparison with data transformation starts from how the 649×33 source can be checked for schema, ranges, labels, missing codes, duplicates and derived-variable integrity before analysis and the outcome clean analysis-ready survey table from all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the definition stage, choosing data transformation would alter at least one of the estimand, response scale, group structure, model assumptions, or reported effect. The definition decision boundary for data transformation is made explicit through 21,417 populated source cells, source file fingerprinted, and the Verified cleaning summary figure. When the definition evidence for data transformation supports the declared survey data-cleaning audit rather than data transformation, the result remains The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation. When the same definition evidence instead supports data transformation, the alternative is reported under its own name with its own formula and interpretation. In the definition comparison with data transformation, this separation prevents a method label from being selected merely because it produces a preferred probability value.

Definition: data imputation in Survey Data Cleaning

During definition review, data imputation is a legitimate neighboring method, but at that stage it is not another name for this survey data-cleaning audit analysis. The definition comparison with data imputation starts from how the 649×33 source can be checked for schema, ranges, labels, missing codes, duplicates and derived-variable integrity before analysis and the outcome clean analysis-ready survey table from all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the definition stage, choosing data imputation would alter at least one of the estimand, response scale, group structure, model assumptions, or reported effect. The definition decision boundary for data imputation is made explicit through 0 blank source cells, raw data retained, and the Category-label audit figure. When the definition evidence for data imputation supports the declared survey data-cleaning audit rather than data imputation, the result remains The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation. When the same definition evidence instead supports data imputation, the alternative is reported under its own name with its own formula and interpretation. In the definition comparison with data imputation, this separation prevents a method label from being selected merely because it produces a preferred probability value.

Definition: outlier analysis

During definition review, outlier analysis is a legitimate neighboring method, but at that stage it is not another name for Survey Data Cleaning. The definition comparison with outlier analysis starts from how the 649×33 source can be checked for schema, ranges, labels, missing codes, duplicates and derived-variable integrity before analysis and the outcome clean analysis-ready survey table from all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the definition stage, choosing outlier analysis would alter at least one of the estimand, response scale, group structure, model assumptions, or reported effect. The definition decision boundary for outlier analysis is made explicit through 33 columns, rules documented before changes, and the Column-level cleaning metrics figure. When the definition evidence for outlier analysis supports the declared survey data-cleaning audit rather than outlier analysis, the result remains The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation. When the same definition evidence instead supports outlier analysis, the alternative is reported under its own name with its own formula and interpretation. In the definition comparison with outlier analysis, this separation prevents a method label from being selected merely because it produces a preferred probability value.

Definition: Column-level cleaning metrics

During definition review, the Column-level cleaning metrics figure is interpreted as part of this survey data-cleaning audit analysis, not as decorative output. At the definition stage for Column-level cleaning metrics, its axes, categories, item direction, sample size, and annotations must match all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals and the checkpoint 649 rows. The definition reading of Column-level cleaning metrics is used to clarify category normalization for the defined outcome clean analysis-ready survey table. The Column-level cleaning metrics plot cannot replace the underlying table or formula, and its definition caption cannot broaden the conclusion beyond how the 649×33 source can be checked for schema, ranges, labels, missing codes, duplicates and derived-variable integrity before analysis. Agreement between Column-level cleaning metrics and cleaning log is reproducible is examined before the visual pattern is described. The definition caption for Column-level cleaning metrics states what the plot shows, what it does not establish, and how it relates to the verified finding The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation. If the definition review of Column-level cleaning metrics instead represents the target of outlier analysis, that figure belongs in the separate outlier analysis analysis rather than this post.

Definition: Range and type exceptions in Survey Data Cleaning

During definition review, the Range and type exceptions figure is interpreted as part of Survey Data Cleaning, not as decorative output. At the definition stage for Range and type exceptions, its axes, categories, item direction, sample size, and annotations must match all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals and the checkpoint 21,417 populated source cells. The definition reading of Range and type exceptions is used to clarify duplicate review for the defined outcome clean analysis-ready survey table. The Range and type exceptions plot cannot replace the underlying table or formula, and its definition caption cannot broaden the conclusion beyond how the 649×33 source can be checked for schema, ranges, labels, missing codes, duplicates and derived-variable integrity before analysis. Agreement between Range and type exceptions and source file fingerprinted is examined before the visual pattern is described. The definition caption for Range and type exceptions states what the plot shows, what it does not establish, and how it relates to the verified finding The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation. If the definition review of Range and type exceptions instead represents the target of data imputation, that figure belongs in the separate data imputation analysis rather than this post.

Definition: Category-label audit

During definition review, the Category-label audit figure is interpreted as part of this survey data-cleaning audit analysis, not as decorative output. At the definition stage for Category-label audit, its axes, categories, item direction, sample size, and annotations must match all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals and the checkpoint 0 blank source cells. The definition reading of Category-label audit is used to clarify cleaning log for the defined outcome clean analysis-ready survey table. The Category-label audit plot cannot replace the underlying table or formula, and its definition caption cannot broaden the conclusion beyond how the 649×33 source can be checked for schema, ranges, labels, missing codes, duplicates and derived-variable integrity before analysis. Agreement between Category-label audit and raw data retained is examined before the visual pattern is described. The definition caption for Category-label audit states what the plot shows, what it does not establish, and how it relates to the verified finding The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation. If the definition review of Category-label audit instead represents the target of data transformation, that figure belongs in the separate data transformation analysis rather than this post.

Definition: Derived-variable validation

During definition review, the Derived-variable validation figure is interpreted as part of Survey Data Cleaning, not as decorative output. At the definition stage for Derived-variable validation, its axes, categories, item direction, sample size, and annotations must match all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals and the checkpoint 33 columns. The definition reading of Derived-variable validation is used to clarify raw-data preservation for the defined outcome clean analysis-ready survey table. The Derived-variable validation plot cannot replace the underlying table or formula, and its definition caption cannot broaden the conclusion beyond how the 649×33 source can be checked for schema, ranges, labels, missing codes, duplicates and derived-variable integrity before analysis. Agreement between Derived-variable validation and rules documented before changes is examined before the visual pattern is described. The definition caption for Derived-variable validation states what the plot shows, what it does not establish, and how it relates to the verified finding The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation. If the definition review of Derived-variable validation instead represents the target of outlier analysis, that figure belongs in the separate outlier analysis analysis rather than this post.

Definition: Verified cleaning summary in Survey Data Cleaning

During definition review, the Verified cleaning summary figure is interpreted as part of this survey data-cleaning audit analysis, not as decorative output. At the definition stage for Verified cleaning summary, its axes, categories, item direction, sample size, and annotations must match all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals and the checkpoint 649 rows. The definition reading of Verified cleaning summary is used to clarify schema audit for the defined outcome clean analysis-ready survey table. The Verified cleaning summary plot cannot replace the underlying table or formula, and its definition caption cannot broaden the conclusion beyond how the 649×33 source can be checked for schema, ranges, labels, missing codes, duplicates and derived-variable integrity before analysis. Agreement between Verified cleaning summary and cleaning log is reproducible is examined before the visual pattern is described. The definition caption for Verified cleaning summary states what the plot shows, what it does not establish, and how it relates to the verified finding The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation. If the definition review of Verified cleaning summary instead represents the target of data imputation, that figure belongs in the separate data imputation analysis rather than this post.

Calculation: schema audit

During the calculation review, in Survey Data Cleaning, schema audit is evaluated within the exact target clean analysis-ready survey table, using all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the calculation stage for schema audit, the diagnostic is anchored to 21,417 populated source cells, not to an unrelated rule of thumb. The calculation finding for schema audit—The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation—is retained only when source file fingerprinted remains defensible and the Derived-variable validation figure tells the same numerical story as the table. A visible pattern involving schema audit is interpreted through range checks; the calculation reading is not treated as automatic evidence for causation, validity, or a different outcome. If at the calculation stage for schema audit reveals a changed population, coding direction, group order, or response scale, the schema audit calculation is rebuilt before reporting. During the calculation review of schema audit, data transformation is considered only when its different estimand actually matches the revised research question.

Calculation: range checks

During the calculation review, in this survey data-cleaning audit analysis, range checks is evaluated within the exact target clean analysis-ready survey table, using all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the calculation stage for range checks, the diagnostic is anchored to 0 blank source cells, not to an unrelated rule of thumb. The calculation finding for range checks—The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation—is retained only when raw data retained remains defensible and the Range and type exceptions figure tells the same numerical story as the table. A visible pattern involving range checks is interpreted through category normalization; the calculation reading is not treated as automatic evidence for causation, validity, or a different outcome. If at the calculation stage for range checks reveals a changed population, coding direction, group order, or response scale, the range checks calculation is rebuilt before reporting. During the calculation review of range checks, outlier analysis is considered only when its different estimand actually matches the revised research question.

Calculation: category normalization in Survey Data Cleaning

During the calculation review, in Survey Data Cleaning, category normalization is evaluated within the exact target clean analysis-ready survey table, using all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the calculation stage for category normalization, the diagnostic is anchored to 33 columns, not to an unrelated rule of thumb. The calculation finding for category normalization—The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation—is retained only when rules documented before changes remains defensible and the Verified cleaning summary figure tells the same numerical story as the table. A visible pattern involving category normalization is interpreted through duplicate review; the calculation reading is not treated as automatic evidence for causation, validity, or a different outcome. If at the calculation stage for category normalization reveals a changed population, coding direction, group order, or response scale, the category normalization calculation is rebuilt before reporting. During the calculation review of category normalization, data imputation is considered only when its different estimand actually matches the revised research question.

Calculation: duplicate review

During the calculation review, in this survey data-cleaning audit analysis, duplicate review is evaluated within the exact target clean analysis-ready survey table, using all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the calculation stage for duplicate review, the diagnostic is anchored to 649 rows, not to an unrelated rule of thumb. The calculation finding for duplicate review—The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation—is retained only when cleaning log is reproducible remains defensible and the Category-label audit figure tells the same numerical story as the table. A visible pattern involving duplicate review is interpreted through cleaning log; the calculation reading is not treated as automatic evidence for causation, validity, or a different outcome. If at the calculation stage for duplicate review reveals a changed population, coding direction, group order, or response scale, the duplicate review calculation is rebuilt before reporting. During the calculation review of duplicate review, data transformation is considered only when its different estimand actually matches the revised research question.

Calculation: cleaning log

During the calculation review, in Survey Data Cleaning, cleaning log is evaluated within the exact target clean analysis-ready survey table, using all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the calculation stage for cleaning log, the diagnostic is anchored to 21,417 populated source cells, not to an unrelated rule of thumb. The calculation finding for cleaning log—The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation—is retained only when source file fingerprinted remains defensible and the Column-level cleaning metrics figure tells the same numerical story as the table. A visible pattern involving cleaning log is interpreted through raw-data preservation; the calculation reading is not treated as automatic evidence for causation, validity, or a different outcome. If at the calculation stage for cleaning log reveals a changed population, coding direction, group order, or response scale, the cleaning log calculation is rebuilt before reporting. During the calculation review of cleaning log, outlier analysis is considered only when its different estimand actually matches the revised research question.

Calculation: raw-data preservation in Survey Data Cleaning

During the calculation review, in this survey data-cleaning audit analysis, raw-data preservation is evaluated within the exact target clean analysis-ready survey table, using all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the calculation stage for raw-data preservation, the diagnostic is anchored to 0 blank source cells, not to an unrelated rule of thumb. The calculation finding for raw-data preservation—The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation—is retained only when raw data retained remains defensible and the Derived-variable validation figure tells the same numerical story as the table. A visible pattern involving raw-data preservation is interpreted through schema audit; the calculation reading is not treated as automatic evidence for causation, validity, or a different outcome. If at the calculation stage for raw-data preservation reveals a changed population, coding direction, group order, or response scale, the raw-data preservation calculation is rebuilt before reporting. During the calculation review of raw-data preservation, data imputation is considered only when its different estimand actually matches the revised research question.

Calculation: source file fingerprinted

During calculation review, the source file fingerprinted condition has a concrete role in Survey Data Cleaning. At its calculation stage, source file fingerprinted determines whether survey data-cleaning audit can answer how the 649×33 source can be checked for schema, ranges, labels, missing codes, duplicates and derived-variable integrity before analysis. At the calculation stage for source file fingerprinted, the check uses all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals and is reconciled with 33 columns. When source file fingerprinted is doubtful during calculation review, software output may still appear complete, but the result cannot automatically retain the interpretation clean analysis-ready survey table. The Range and type exceptions display is examined for the observable consequence of failing source file fingerprinted, while range checks is reviewed in the original response units. In the calculation assessment of source file fingerprinted, the article either narrows the claim, applies a justified sensitivity calculation, or moves to data transformation. This is why source file fingerprinted appears beside the calculation result rather than as a detached checklist item.

Calculation: raw data retained

During calculation review, the raw data retained condition has a concrete role in this survey data-cleaning audit analysis. At its calculation stage, raw data retained determines whether survey data-cleaning audit can answer how the 649×33 source can be checked for schema, ranges, labels, missing codes, duplicates and derived-variable integrity before analysis. At the calculation stage for raw data retained, the check uses all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals and is reconciled with 649 rows. When raw data retained is doubtful during calculation review, software output may still appear complete, but the result cannot automatically retain the interpretation clean analysis-ready survey table. The Verified cleaning summary display is examined for the observable consequence of failing raw data retained, while category normalization is reviewed in the original response units. In the calculation assessment of raw data retained, the article either narrows the claim, applies a justified sensitivity calculation, or moves to outlier analysis. This is why raw data retained appears beside the calculation result rather than as a detached checklist item.

Calculation: rules documented before changes in Survey Data Cleaning

During calculation review, the rules documented before changes condition has a concrete role in Survey Data Cleaning. At its calculation stage, rules documented before changes determines whether survey data-cleaning audit can answer how the 649×33 source can be checked for schema, ranges, labels, missing codes, duplicates and derived-variable integrity before analysis. At the calculation stage for rules documented before changes, the check uses all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals and is reconciled with 21,417 populated source cells. When rules documented before changes is doubtful during calculation review, software output may still appear complete, but the result cannot automatically retain the interpretation clean analysis-ready survey table. The Category-label audit display is examined for the observable consequence of failing rules documented before changes, while duplicate review is reviewed in the original response units. In the calculation assessment of rules documented before changes, the article either narrows the claim, applies a justified sensitivity calculation, or moves to data imputation. This is why rules documented before changes appears beside the calculation result rather than as a detached checklist item.

Calculation: cleaning log is reproducible

During calculation review, the cleaning log is reproducible condition has a concrete role in this survey data-cleaning audit analysis. At its calculation stage, cleaning log is reproducible determines whether survey data-cleaning audit can answer how the 649×33 source can be checked for schema, ranges, labels, missing codes, duplicates and derived-variable integrity before analysis. At the calculation stage for cleaning log is reproducible, the check uses all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals and is reconciled with 0 blank source cells. When cleaning log is reproducible is doubtful during calculation review, software output may still appear complete, but the result cannot automatically retain the interpretation clean analysis-ready survey table. The Column-level cleaning metrics display is examined for the observable consequence of failing cleaning log is reproducible, while cleaning log is reviewed in the original response units. In the calculation assessment of cleaning log is reproducible, the article either narrows the claim, applies a justified sensitivity calculation, or moves to data transformation. This is why cleaning log is reproducible appears beside the calculation result rather than as a detached checklist item.

Calculation: 649 rows

For calculation review, the numerical checkpoint 649 rows is reconstructed in Survey Data Cleaning from all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the calculation stage for 649 rows, 649 rows must agree with the displayed formula, the software objects, the Excel cells, and the Derived-variable validation graphic after rounding. The calculation meaning of 649 rows is limited to clean analysis-ready survey table; the same number would not have the same meaning under a different grouping variable, scoring key, missing-data rule, or reference category. Interpretation of 649 rows also depends on rules documented before changes. During calculation review, 649 rows is read with raw-data preservation and with the complete finding, The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation. Any discrepancy in the calculation reconstruction of 649 rows is investigated at full precision rather than concealed by formatting, and outlier analysis is not used to force agreement because it answers a different question.

Calculation: 33 columns in Survey Data Cleaning

For calculation review, the numerical checkpoint 33 columns is reconstructed in this survey data-cleaning audit analysis from all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the calculation stage for 33 columns, 33 columns must agree with the displayed formula, the software objects, the Excel cells, and the Range and type exceptions graphic after rounding. The calculation meaning of 33 columns is limited to clean analysis-ready survey table; the same number would not have the same meaning under a different grouping variable, scoring key, missing-data rule, or reference category. Interpretation of 33 columns also depends on cleaning log is reproducible. During calculation review, 33 columns is read with schema audit and with the complete finding, The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation. Any discrepancy in the calculation reconstruction of 33 columns is investigated at full precision rather than concealed by formatting, and data imputation is not used to force agreement because it answers a different question.

Calculation: 0 blank source cells

For calculation review, the numerical checkpoint 0 blank source cells is reconstructed in Survey Data Cleaning from all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the calculation stage for 0 blank source cells, 0 blank source cells must agree with the displayed formula, the software objects, the Excel cells, and the Verified cleaning summary graphic after rounding. The calculation meaning of 0 blank source cells is limited to clean analysis-ready survey table; the same number would not have the same meaning under a different grouping variable, scoring key, missing-data rule, or reference category. Interpretation of 0 blank source cells also depends on source file fingerprinted. During calculation review, 0 blank source cells is read with range checks and with the complete finding, The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation. Any discrepancy in the calculation reconstruction of 0 blank source cells is investigated at full precision rather than concealed by formatting, and data transformation is not used to force agreement because it answers a different question.

Calculation: 21,417 populated source cells

For calculation review, the numerical checkpoint 21,417 populated source cells is reconstructed in this survey data-cleaning audit analysis from all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the calculation stage for 21,417 populated source cells, 21,417 populated source cells must agree with the displayed formula, the software objects, the Excel cells, and the Category-label audit graphic after rounding. The calculation meaning of 21,417 populated source cells is limited to clean analysis-ready survey table; the same number would not have the same meaning under a different grouping variable, scoring key, missing-data rule, or reference category. Interpretation of 21,417 populated source cells also depends on raw data retained. During calculation review, 21,417 populated source cells is read with category normalization and with the complete finding, The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation. Any discrepancy in the calculation reconstruction of 21,417 populated source cells is investigated at full precision rather than concealed by formatting, and outlier analysis is not used to force agreement because it answers a different question.

Calculation: data transformation in Survey Data Cleaning

During calculation review, data transformation is a legitimate neighboring method, but at that stage it is not another name for Survey Data Cleaning. The calculation comparison with data transformation starts from how the 649×33 source can be checked for schema, ranges, labels, missing codes, duplicates and derived-variable integrity before analysis and the outcome clean analysis-ready survey table from all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the calculation stage, choosing data transformation would alter at least one of the estimand, response scale, group structure, model assumptions, or reported effect. The calculation decision boundary for data transformation is made explicit through 33 columns, rules documented before changes, and the Column-level cleaning metrics figure. When the calculation evidence for data transformation supports the declared survey data-cleaning audit rather than data transformation, the result remains The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation. When the same calculation evidence instead supports data transformation, the alternative is reported under its own name with its own formula and interpretation. In the calculation comparison with data transformation, this separation prevents a method label from being selected merely because it produces a preferred probability value.

Calculation: data imputation

During calculation review, data imputation is a legitimate neighboring method, but at that stage it is not another name for this survey data-cleaning audit analysis. The calculation comparison with data imputation starts from how the 649×33 source can be checked for schema, ranges, labels, missing codes, duplicates and derived-variable integrity before analysis and the outcome clean analysis-ready survey table from all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the calculation stage, choosing data imputation would alter at least one of the estimand, response scale, group structure, model assumptions, or reported effect. The calculation decision boundary for data imputation is made explicit through 649 rows, cleaning log is reproducible, and the Derived-variable validation figure. When the calculation evidence for data imputation supports the declared survey data-cleaning audit rather than data imputation, the result remains The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation. When the same calculation evidence instead supports data imputation, the alternative is reported under its own name with its own formula and interpretation. In the calculation comparison with data imputation, this separation prevents a method label from being selected merely because it produces a preferred probability value.

Calculation: outlier analysis

During calculation review, outlier analysis is a legitimate neighboring method, but at that stage it is not another name for Survey Data Cleaning. The calculation comparison with outlier analysis starts from how the 649×33 source can be checked for schema, ranges, labels, missing codes, duplicates and derived-variable integrity before analysis and the outcome clean analysis-ready survey table from all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the calculation stage, choosing outlier analysis would alter at least one of the estimand, response scale, group structure, model assumptions, or reported effect. The calculation decision boundary for outlier analysis is made explicit through 21,417 populated source cells, source file fingerprinted, and the Range and type exceptions figure. When the calculation evidence for outlier analysis supports the declared survey data-cleaning audit rather than outlier analysis, the result remains The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation. When the same calculation evidence instead supports outlier analysis, the alternative is reported under its own name with its own formula and interpretation. In the calculation comparison with outlier analysis, this separation prevents a method label from being selected merely because it produces a preferred probability value.

Calculation: Column-level cleaning metrics in Survey Data Cleaning

During calculation review, the Column-level cleaning metrics figure is interpreted as part of this survey data-cleaning audit analysis, not as decorative output. At the calculation stage for Column-level cleaning metrics, its axes, categories, item direction, sample size, and annotations must match all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals and the checkpoint 0 blank source cells. The calculation reading of Column-level cleaning metrics is used to clarify schema audit for the defined outcome clean analysis-ready survey table. The Column-level cleaning metrics plot cannot replace the underlying table or formula, and its calculation caption cannot broaden the conclusion beyond how the 649×33 source can be checked for schema, ranges, labels, missing codes, duplicates and derived-variable integrity before analysis. Agreement between Column-level cleaning metrics and raw data retained is examined before the visual pattern is described. The calculation caption for Column-level cleaning metrics states what the plot shows, what it does not establish, and how it relates to the verified finding The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation. If the calculation review of Column-level cleaning metrics instead represents the target of data imputation, that figure belongs in the separate data imputation analysis rather than this post.

Calculation: Range and type exceptions

During calculation review, the Range and type exceptions figure is interpreted as part of Survey Data Cleaning, not as decorative output. At the calculation stage for Range and type exceptions, its axes, categories, item direction, sample size, and annotations must match all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals and the checkpoint 33 columns. The calculation reading of Range and type exceptions is used to clarify range checks for the defined outcome clean analysis-ready survey table. The Range and type exceptions plot cannot replace the underlying table or formula, and its calculation caption cannot broaden the conclusion beyond how the 649×33 source can be checked for schema, ranges, labels, missing codes, duplicates and derived-variable integrity before analysis. Agreement between Range and type exceptions and rules documented before changes is examined before the visual pattern is described. The calculation caption for Range and type exceptions states what the plot shows, what it does not establish, and how it relates to the verified finding The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation. If the calculation review of Range and type exceptions instead represents the target of data transformation, that figure belongs in the separate data transformation analysis rather than this post.

Calculation: Category-label audit

During calculation review, the Category-label audit figure is interpreted as part of this survey data-cleaning audit analysis, not as decorative output. At the calculation stage for Category-label audit, its axes, categories, item direction, sample size, and annotations must match all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals and the checkpoint 649 rows. The calculation reading of Category-label audit is used to clarify category normalization for the defined outcome clean analysis-ready survey table. The Category-label audit plot cannot replace the underlying table or formula, and its calculation caption cannot broaden the conclusion beyond how the 649×33 source can be checked for schema, ranges, labels, missing codes, duplicates and derived-variable integrity before analysis. Agreement between Category-label audit and cleaning log is reproducible is examined before the visual pattern is described. The calculation caption for Category-label audit states what the plot shows, what it does not establish, and how it relates to the verified finding The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation. If the calculation review of Category-label audit instead represents the target of outlier analysis, that figure belongs in the separate outlier analysis analysis rather than this post.

Calculation: Derived-variable validation in Survey Data Cleaning

During calculation review, the Derived-variable validation figure is interpreted as part of Survey Data Cleaning, not as decorative output. At the calculation stage for Derived-variable validation, its axes, categories, item direction, sample size, and annotations must match all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals and the checkpoint 21,417 populated source cells. The calculation reading of Derived-variable validation is used to clarify duplicate review for the defined outcome clean analysis-ready survey table. The Derived-variable validation plot cannot replace the underlying table or formula, and its calculation caption cannot broaden the conclusion beyond how the 649×33 source can be checked for schema, ranges, labels, missing codes, duplicates and derived-variable integrity before analysis. Agreement between Derived-variable validation and source file fingerprinted is examined before the visual pattern is described. The calculation caption for Derived-variable validation states what the plot shows, what it does not establish, and how it relates to the verified finding The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation. If the calculation review of Derived-variable validation instead represents the target of data imputation, that figure belongs in the separate data imputation analysis rather than this post.

Calculation: Verified cleaning summary

During calculation review, the Verified cleaning summary figure is interpreted as part of this survey data-cleaning audit analysis, not as decorative output. At the calculation stage for Verified cleaning summary, its axes, categories, item direction, sample size, and annotations must match all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals and the checkpoint 0 blank source cells. The calculation reading of Verified cleaning summary is used to clarify cleaning log for the defined outcome clean analysis-ready survey table. The Verified cleaning summary plot cannot replace the underlying table or formula, and its calculation caption cannot broaden the conclusion beyond how the 649×33 source can be checked for schema, ranges, labels, missing codes, duplicates and derived-variable integrity before analysis. Agreement between Verified cleaning summary and raw data retained is examined before the visual pattern is described. The calculation caption for Verified cleaning summary states what the plot shows, what it does not establish, and how it relates to the verified finding The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation. If the calculation review of Verified cleaning summary instead represents the target of data transformation, that figure belongs in the separate data transformation analysis rather than this post.

Interpretation: schema audit

During the interpretation review, in Survey Data Cleaning, schema audit is evaluated within the exact target clean analysis-ready survey table, using all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the interpretation stage for schema audit, the diagnostic is anchored to 33 columns, not to an unrelated rule of thumb. The interpretation finding for schema audit—The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation—is retained only when rules documented before changes remains defensible and the Verified cleaning summary figure tells the same numerical story as the table. A visible pattern involving schema audit is interpreted through raw-data preservation; the interpretation reading is not treated as automatic evidence for causation, validity, or a different outcome. If at the interpretation stage for schema audit reveals a changed population, coding direction, group order, or response scale, the schema audit calculation is rebuilt before reporting. During the interpretation review of schema audit, outlier analysis is considered only when its different estimand actually matches the revised research question.

Interpretation: range checks in Survey Data Cleaning

During the interpretation review, in this survey data-cleaning audit analysis, range checks is evaluated within the exact target clean analysis-ready survey table, using all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the interpretation stage for range checks, the diagnostic is anchored to 649 rows, not to an unrelated rule of thumb. The interpretation finding for range checks—The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation—is retained only when cleaning log is reproducible remains defensible and the Category-label audit figure tells the same numerical story as the table. A visible pattern involving range checks is interpreted through schema audit; the interpretation reading is not treated as automatic evidence for causation, validity, or a different outcome. If at the interpretation stage for range checks reveals a changed population, coding direction, group order, or response scale, the range checks calculation is rebuilt before reporting. During the interpretation review of range checks, data imputation is considered only when its different estimand actually matches the revised research question.

Interpretation: category normalization

During the interpretation review, in Survey Data Cleaning, category normalization is evaluated within the exact target clean analysis-ready survey table, using all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the interpretation stage for category normalization, the diagnostic is anchored to 21,417 populated source cells, not to an unrelated rule of thumb. The interpretation finding for category normalization—The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation—is retained only when source file fingerprinted remains defensible and the Column-level cleaning metrics figure tells the same numerical story as the table. A visible pattern involving category normalization is interpreted through range checks; the interpretation reading is not treated as automatic evidence for causation, validity, or a different outcome. If at the interpretation stage for category normalization reveals a changed population, coding direction, group order, or response scale, the category normalization calculation is rebuilt before reporting. During the interpretation review of category normalization, data transformation is considered only when its different estimand actually matches the revised research question.

Interpretation: duplicate review

During the interpretation review, in this survey data-cleaning audit analysis, duplicate review is evaluated within the exact target clean analysis-ready survey table, using all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the interpretation stage for duplicate review, the diagnostic is anchored to 0 blank source cells, not to an unrelated rule of thumb. The interpretation finding for duplicate review—The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation—is retained only when raw data retained remains defensible and the Derived-variable validation figure tells the same numerical story as the table. A visible pattern involving duplicate review is interpreted through category normalization; the interpretation reading is not treated as automatic evidence for causation, validity, or a different outcome. If at the interpretation stage for duplicate review reveals a changed population, coding direction, group order, or response scale, the duplicate review calculation is rebuilt before reporting. During the interpretation review of duplicate review, outlier analysis is considered only when its different estimand actually matches the revised research question.

Interpretation: cleaning log in Survey Data Cleaning

During the interpretation review, in Survey Data Cleaning, cleaning log is evaluated within the exact target clean analysis-ready survey table, using all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the interpretation stage for cleaning log, the diagnostic is anchored to 33 columns, not to an unrelated rule of thumb. The interpretation finding for cleaning log—The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation—is retained only when rules documented before changes remains defensible and the Range and type exceptions figure tells the same numerical story as the table. A visible pattern involving cleaning log is interpreted through duplicate review; the interpretation reading is not treated as automatic evidence for causation, validity, or a different outcome. If at the interpretation stage for cleaning log reveals a changed population, coding direction, group order, or response scale, the cleaning log calculation is rebuilt before reporting. During the interpretation review of cleaning log, data imputation is considered only when its different estimand actually matches the revised research question.

Interpretation: raw-data preservation

During the interpretation review, in this survey data-cleaning audit analysis, raw-data preservation is evaluated within the exact target clean analysis-ready survey table, using all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals. At the interpretation stage for raw-data preservation, the diagnostic is anchored to 649 rows, not to an unrelated rule of thumb. The interpretation finding for raw-data preservation—The imported source has 649 rows, 33 named columns and zero blank cells; cleaning therefore focuses on type, range, category, duplicate and derivation checks rather than automatic imputation—is retained only when cleaning log is reproducible remains defensible and the Verified cleaning summary figure tells the same numerical story as the table. A visible pattern involving raw-data preservation is interpreted through cleaning log; the interpretation reading is not treated as automatic evidence for causation, validity, or a different outcome. If at the interpretation stage for raw-data preservation reveals a changed population, coding direction, group order, or response scale, the raw-data preservation calculation is rebuilt before reporting. During the interpretation review of raw-data preservation, data transformation is considered only when its different estimand actually matches the revised research question.

Interpretation: source file fingerprinted

During interpretation review, the source file fingerprinted condition has a concrete role in Survey Data Cleaning. At its interpretation stage, source file fingerprinted determines whether survey data-cleaning audit can answer how the 649×33 source can be checked for schema, ranges, labels, missing codes, duplicates and derived-variable integrity before analysis. At the interpretation stage for source file fingerprinted, the check uses all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals and is reconciled with 21,417 populated source cells. When source file fingerprinted is doubtful during interpretation review, software output may still appear complete, but the result cannot automatically retain the interpretation clean analysis-ready survey table. The Category-label audit display is examined for the observable consequence of failing source file fingerprinted, while raw-data preservation is reviewed in the original response units. In the interpretation assessment of source file fingerprinted, the article either narrows the claim, applies a justified sensitivity calculation, or moves to outlier analysis. This is why source file fingerprinted appears beside the interpretation result rather than as a detached checklist item.

Interpretation: raw data retained in Survey Data Cleaning

During interpretation review, the raw data retained condition has a concrete role in this survey data-cleaning audit analysis. At its interpretation stage, raw data retained determines whether survey data-cleaning audit can answer how the 649×33 source can be checked for schema, ranges, labels, missing codes, duplicates and derived-variable integrity before analysis. At the interpretation stage for raw data retained, the check uses all source fields plus controlled derived fields such as Dalc_R, Walc_R and score totals and is reconciled with 0 blank source cells. When raw data retained is doubtful during interpretation review, software output may still appear complete, but the result cannot automatically retain the interpretation clean analysis-ready survey table. The Column-level cleaning metrics display is examined for the observable consequence of failing raw data retained, while schema audit is reviewed in the original response units. In the interpretation assessment of raw data retained, the article either narrows the claim, applies a justified sensitivity calculation, or moves to data imputation. This is why raw data retained appears beside the interpretation result rather than as a detached checklist item.