Data cleaning is the unglamorous foundation of reliable research. Garbage in, garbage out: no matter how sophisticated your statistical method, results from uncleaned data are misleading. Studies have been retracted not because of flawed analysis but because obvious data errors — impossible values, systematic miscoding, duplicated records — were not caught before the analysis ran.
A systematic data cleaning protocol starts with a data audit: checking variable ranges for implausible values (e.g., age = 200, blood pressure = 0), examining frequency distributions for categories that should not exist, identifying duplicate records, and counting missing values by variable and by participant.
Missing data handling is one of the most contentious decisions in data analysis. Listwise deletion (excluding any participant with any missing value) is simple but biases results when data are not missing completely at random. Multiple imputation is the current best practice for data missing at random, preserving sample size and producing unbiased estimates under the correct assumptions.
Outlier handling requires judgement, not rules. A data point 3 standard deviations from the mean might be an error or a genuine extreme observation. Examine each outlier in context — is it plausible given the study population? If it is a data entry error, correct or remove it and document the decision. If it is a genuine extreme value, run a sensitivity analysis with and without it and report both.
Our data cleaning and analysis service handles datasets in any format — SPSS .sav, Excel, CSV, SAS transport files, or Stata .dta. We document every cleaning decision in a cleaning log, so your methodology chapter can accurately describe the data preparation process. Analysis proceeds only after the cleaned dataset is reviewed and approved.
