Dirty data produces unreliable results. Before your analysis begins, AcademiQ's specialists clean, prepare, and validate your dataset — so every statistical test you run is built on a solid foundation.
A data cleaning service — also called data preparation or data pre-processing — is the systematic process of detecting and correcting errors, inconsistencies, and gaps in a raw dataset before statistical analysis begins. In academic research, this stage is not optional: examiners and journal reviewers increasingly scrutinise the handling of missing data, outliers, and variable coding as part of their assessment of methodological quality. A poorly prepared dataset can invalidate even the most sophisticated analysis.
AcademiQ's PhD-qualified data specialists provide comprehensive dataset preparation for quantitative research across all disciplines. Our process begins with a full data audit — examining variable types, distributions, range violations, and the extent and pattern of missing data. We then apply the appropriate treatment for each issue, following published methodological standards and documenting every decision so you can defend your choices in your methodology chapter or to a journal reviewer.
Missing value treatment is one of the most consequential decisions in data preparation. Listwise deletion (complete case analysis) is acceptable when data is missing completely at random (MCAR) and the proportion is small. Mean or median imputation is defensible under MCAR at low rates. Multiple imputation using MICE (Multivariate Imputation by Chained Equations) in R or Python is the gold standard for MAR (missing at random) data and is increasingly required by high-impact journals. We assess your data pattern using Little's MCAR test and apply the appropriate method with full documentation.
Outlier detection uses z-scores, IQR fences, and Mahalanobis distance for multivariate outliers. Each outlier is assessed individually — not blindly removed — and the decision to retain, winsorise, or exclude is justified based on the nature of the variable and its likely impact on model results. Variable recoding covers reverse-scoring Likert items, creating dummy variables for nominal predictors, collapsing response categories, and generating composite scores. The cleaned SPSS data cleaning output is delivered in your preferred format with full variable labels and a cleaning log.
Comprehensive inspection of your raw dataset: sample size, variable types, range checks, frequency distributions, and proportion of missing data.
Listwise deletion, mean/median imputation, or multiple imputation (MICE/MI) — justified and documented for your methodology chapter.
Z-score, IQR, and Mahalanobis distance methods. Every outlier flagged, assessed, and handled with documented justification.
Reverse-scoring, scale collapsing, dummy variable creation, and full variable labelling for clarity and reproducibility.
Shapiro-Wilk, Kolmogorov-Smirnov, Q-Q plots. Homoscedasticity and multicollinearity checks before analysis.
Delivered in your preferred format: SPSS .sav, Excel .xlsx, R .rds, Python .csv — with full documentation.
Most survey datasets cleaned and documented within 24–48 hours. Complex datasets accommodated promptly.
Every dataset is handled by a quantitative researcher who understands the methodological implications of every cleaning decision.
Your dataset is never shared with third parties. Strict NDA applies to every project.
Every decision documented in a cleaning log. Methodology justification included — paste straight into your chapter.
Share your raw dataset and tell us your research design, software preference, and any known data issues.
We inspect, treat, recode, and document every issue — with justification at each step.
Cleaned dataset plus full cleaning log and methodology notes — delivered in your preferred format.
Researchers with collected data that contains missing values, outliers, or coding errors
PhD and Master's students preparing datasets for SPSS, R, or Stata analysis
Academics submitting to journals that require documented data preparation procedures
Students who received feedback from supervisors about data quality concerns
Share your project details and we'll respond within hours with a plan, timeline, and quote — no commitment required.