Systematic dataset hygiene, missing value imputation, and multivariate outlier detection—transforming messy raw research spreadsheets into pristine, audit-ready data.
Data scientists frequently note that 80% of analytical effort is spent cleaning raw data. In academic research, feeding unvetted datasets into regression models or ANOVA tables produces distorted coefficients, deflated p-values, and severe bias caused by unengaged survey respondents or unhandled missing values.
At IJRDO, our data management specialists perform forensic dataset audits. We clean, normalize, and organize your raw empirical files while generating an exhaustive audit trail so your methodology is transparent, reproducible, and ready for peer review.
Determining Missing Completely at Random (MCAR) versus Missing at Random (MAR) with Little’s test before deploying modern imputation.
Flagging unengaged survey respondents (straight-liners, speeders) and extreme empirical measurement anomalies.
Combining disparate longitudinal datasets, cross-sectional waves, and relational multi-source tables.
Full documentation script tracking every edit from raw source to cleaned analytical dataset.
We deliver an exhaustive reference codebook indexing every variable, scale description, measurement level (Nominal, Ordinal, Interval, Ratio), missing value codes, and computation formula used for composite constructs.
Unsure about missing data patterns in your spreadsheet? Send your file for a complimentary diagnostic health check.
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