What The Application Does

For selected numeric variables, the application supports CSV import, automatic numeric-variable detection, descriptive statistics, missing-value identification, iterative univariate outlier screening, Mahalanobis-distance analysis, graphical diagnostics, normality procedures, optional single imputation, and export of screened or imputed datasets.

• Sample size, missing-data frequency, mean, sample standard deviation, minimum, maximum, quartiles, and median.
• Skewness, excess kurtosis, standardized scores, interquartile-range limits, squared Mahalanobis distances, and Pearson correlations.
• Histograms with fitted normal curves, boxplots, univariate normal Q-Q plots, and multivariate chi-square Q-Q plots.
• Approximate Shapiro-Wilk-type diagnostics, Kolmogorov-Smirnov diagnostics, Mardia's multivariate skewness and kurtosis, and simulation-based Henze-Zirkler-type diagnostics.
• Data-screening summaries, corrective-method suggestions, case review lists, and exportable assessment reports.

Intended Use

PurifyData Pro is intended for preliminary screening and educational use. Results should be interpreted as diagnostic evidence rather than definitive proof that a dataset is normal, non-normal, valid, invalid, suitable, or unsuitable for a particular analysis.

• A flagged observation is not automatically an error.
• Flagged cases may reflect data-entry problems, measurement issues, valid extreme observations, distinct subgroups, or substantively important cases.
• Users should examine every flagged observation before deciding whether correction, retention, transformation, or exclusion is appropriate.
• Normality should be judged using several sources of evidence, including plots, skewness and kurtosis, formal tests, sample size, outliers, measurement features, and the assumptions of the intended model.

Data-Screening Methods

Missing Values

Blank cells, whitespace-only entries, null, undefined, and NA are treated as missing values. Other missing-value codes such as N/A, -99, ., or MISSING should be recoded before import.

Univariate Outliers

Univariate outliers can be screened using absolute standardized scores, interquartile-range limits, or both. Screening is iterative: after flagged cases are removed, the app recalculates the descriptive statistics and screening limits until no additional cases are identified.

Multivariate Outliers

Multivariate outliers are assessed using squared Mahalanobis distance compared with an approximate chi-square cut-off based on the number of selected variables and the user-selected percentile. The Mahalanobis procedure is iterative and uses all selected numeric variables without an arbitrary variable-count cap.

Mahalanobis screening requires complete numerical observations for the selected variables. If the ordinary covariance matrix is singular or high-dimensional, the app uses a small ridge-regularized covariance inverse and reports this caution in the output.

Normality Assessment

Univariate Normality

The app examines skewness and excess kurtosis, an approximate Shapiro-Wilk-type diagnostic, a Kolmogorov-Smirnov diagnostic, histograms, normal Q-Q plots, boxplots, and the relationship between the mean and median.

A variable is classified as approximately normal when at least one active numerical method supports normality. This is a permissive preliminary-screening rule, not a universal statistical definition of normality.

Multivariate Normality

Multivariate normality is assessed using Mardia's multivariate skewness, Mardia's multivariate kurtosis, a simulation-based Henze-Zirkler-type diagnostic, squared Mahalanobis distances, and a chi-square Q-Q plot.

The app combines these findings using graded screening interpretations such as approximately reasonable, mixed evidence, asymmetry, tail-weight departure, mild statistical departure, or clear evidence of non-normality.

Important Statistical Cautions

• The Shapiro-Wilk-type procedure is approximate. It uses normal-order scores and a Monte Carlo p-value rather than the full exact algorithm in specialist statistical software.
• The Henze-Zirkler-type diagnostic uses Monte Carlo simulation; p-values are relatively coarse because simulations are limited for browser responsiveness.
• The Kolmogorov-Smirnov procedure estimates the mean and standard deviation from the analysed data but does not apply a Lilliefors correction.
• Chi-square quantiles and some chi-square probabilities are calculated using numerical approximations.
• For theses, dissertations, journal articles, confirmatory analyses, CFA, SEM, clinical research, or other high-stakes work, users should verify important findings using validated statistical software and the full dataset.

Computational Limits

To maintain acceptable browser performance, the app applies limits to additional diagnostic outputs. These are computational limits, not recommended statistical sample-size thresholds.

• Up to 50 selected variables in the univariate normality computation.
• Up to 24 variables in normality plots.
• Up to 80 variables in multivariate normality assessment.
• A responsive subset of complete cases for some multivariate diagnostics.
• User-selectable univariate formal-test case limit: Fast uses up to 250 Shapiro-Wilk values and 500 Kolmogorov-Smirnov values; Balanced uses 500 and 1,000; Detailed uses 1,000 and 2,000; Full available cases uses all finite values and may be slower.
• Limited Monte Carlo simulations for the Henze-Zirkler-type diagnostic.
• Up to 45 variables in the displayed correlation matrix and up to 1,000 cases in displayed case tables.

Imputation

The application provides optional single-imputation methods: mean imputation, median imputation, random hot-deck imputation, and correlation-weighted regression-style imputation.

• Hot-deck imputation uses a user-specified random seed so identical data and settings produce reproducible donor selections.
• The correlation-weighted method combines predictions from available variables according to their absolute correlations with the target variable. It is not a fitted multiple-regression model and should not be described as multiple imputation.
• All available methods are single-imputation procedures and do not represent uncertainty caused by missing data.
• For confirmatory research or substantial missingness, users should consider validated multiple-imputation or full-information methods.

Data Privacy And Reproducibility

In the local-processing version, uploaded data are processed within the user's browser. The application does not intentionally transmit or permanently store uploaded datasets on an external server.

Researchers should retain exported reports and record the application version, selected variables, missing-value settings, standardized-score cut-off, IQR multiplier, skewness and kurtosis cut-offs, selected Mahalanobis percentile, outlier-exclusion settings, imputation method, and random seed.

Developer

Ady Hameme Bin Nor Azman

Co-founder, Managing Director, and Principal Specialist
ADV Research and Consultancy

ORCID: https://orcid.org/0009-0001-1520-2591

Email: ady@myadvrc.com

Disclaimer

PurifyData Pro is provided as an educational and research-support tool. Although reasonable efforts have been made to ensure computational accuracy, the developer does not guarantee that the application is free from defects or suitable for every dataset, discipline, research design, statistical model, or analytical purpose.

Users are responsible for checking imported data, confirming variable classifications, selecting defensible screening criteria, reviewing flagged observations, documenting data exclusions or modifications, confirming assumptions of the intended analysis, verifying approximate results where necessary, and making the final methodological decisions.