The Importance Of Data Cleaning

Before interpretation and display of research results, it important to properly collect, code, clean and edit the data. Computers play a major role in different phases of research starting from conceptual, design and planning, data collection, data analysis and research publication phases. The main objective of data display is to summarize the characteristics of a data and to make the data more comprehensible and meaningful. Usually data is presented depending upon the type of data in different tables and graphs. This will enables not only understand the data behaviour, but also useful in choosing the different statistical tests to be applied.

Key words: Statistics, Types of data, Data editing, Display of data, Interpretation of Data

Data cleaning intends to identify and correct these errors or at least to minimize their impact on study results.[2]

Data cleaning deals with data problems once they have occurred. Error prevention strategies can reduce many problems but cannot eliminate them. In most clinical epidemiological studies, errors that need to be cleaned, at all costs, include missing sex, sex mis-specification, birth date or examination date errors, duplications or merging of records, and biologically impossible results. Data cleaning can be done three stage process, involving repeated cycles of screening, diagnosing, and editing of suspected data abnormalities. (Figure1)

1. Screening Phase: When screening data, it is convenientto distinguish four basic types of oddities: lack or excess of data; outliers, including inconsistencies;

(Figure 4)

2. Box plots: Box plot gives the characteristics of a single group of numbers, observations, or measurements, the centre and the spread about this central value. The extreme values referred to as outliers or study the distribution or pattern of the data values can easily viewed by Box plot. (Figure 5)

3.Polygons:As with histograms, when plotting polygons the phenomenon of interest is plotted along the horizontal axis while the vertical axis represents the number, proportion or percentage of observations per class interval, depending on whether or not the particular polygon is respectively, a frequency polygon, a relative frequency polygon or a percentage polygon. For example, the frequency polygon is a line graph connecting the midpoints of each class interval in a data set, plotted at a height corresponding to the frequency of the class. (Figure 6)

Advantages of polygons:

• The frequency polygon is simpler than its histogram counterpart.

• It sketches an outline of the data pattern more clearly.

• The polygon becomes increasingly smooth and curvelike as we increase the number of classes and the number of