Data cleanup is a way of preparing and fixing the collected data for analysis by removing or modifying data that is incorrect, duplicated, incomplete, irrelevant, or improperly formatted. This data is usually not important or helpful when it comes to analyzing data because it may corrupt the process or provide inaccurate results. Most importantly, the goal of data cleaning is to create data sets that are standardized and uniform to allow analytical processes to be done.
Simple statistics
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Simple statistics provide descriptive information regarding the variables in the data set. It includes: Measures of Frequency (Count, Percent, Frequency), Measures of Central Tendency (Mean, Median, and Mode), Measures of Dispersion or Variation (Range, Variance, Standard Deviation), and Measures of Position (Percentile Ranks, Quartile Ranks).
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Data coding
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The voluminous data obtained through research undergoes a process of data coding which involves summarizing and converting the data collected to a set of consistent categories. This enables the data to be understood by computer software’s and conduct analysis.
Complex statistics
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Complex statistics enable us to draw inferences and make predictions from the sample data about the population. This includes: comparative analysis, correlation analysis, regression analysis (odds ratio), survival analysis (Cox regression & Kaplan Meier curve, Hazard ratio), reliability analysis, factor analysis, ROC curve analysis (sensitivity, specificity, accuracy, optimal cut off), and other statistical tests (including Chi square test, t – test , one way ANOVA, Post Hoc test , Non Parametric test, etc.).
