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Data cleansing may also involve harmonization (or normalization) of data, which is the process of bringing together data of "varying file formats, naming conventions, and columns", [2] and transforming it into one cohesive data set; a simple example is the expansion of abbreviations ("st, rd, etc." to "street, road, etcetera").
Data often are missing in research in economics, sociology, and political science because governments or private entities choose not to, or fail to, report critical statistics, [1] or because the information is not available. Sometimes missing values are caused by the researcher—for example, when data collection is done improperly or mistakes ...
Data editing is defined as the process involving the review and adjustment of collected survey data. [1] Data editing helps define guidelines that will reduce potential bias and ensure consistent estimates leading to a clear analysis of the data set by correct inconsistent data using the methods later in this article. [2]
When substituting for a data point, it is known as "unit imputation"; when substituting for a component of a data point, it is known as "item imputation". There are three main problems that missing data causes: missing data can introduce a substantial amount of bias , make the handling and analysis of the data more arduous , and create ...
Problems with data quality don't only arise from incorrect data; inconsistent data is a problem as well. Eliminating data shadow systems and centralizing data in a warehouse is one of the initiatives a company can take to ensure data consistency.
In predictive analytics, data science, machine learning and related fields, concept drift or drift is an evolution of data that invalidates the data model.It happens when the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways.
Anomalies are instances or collections of data that occur very rarely in the data set and whose features differ significantly from most of the data. An outlier is an observation (or subset of observations) which appears to be inconsistent with the remainder of that set of data.
Mistakes in recording the data or coding it to standard classifications; Pseudo-opinions given by respondents when they have no opinion, but do not wish to say so; Other errors of collection, nonresponse, processing, or imputation of values for missing or inconsistent data. [3]