Editing techniques and benefits

1. EDITING

2. • Data editing is the application of checks to detect missing,
invalid or inconsistent entries or to point to data records that
are potentially in error.
• No matter what type of data you are working with, certain
edits are performed at different stages or phases of data
collection and processing.

3. TECHNIQUES OF DATA EDITING
• Validity and Completeness of Data
• Duplicate data entry.
• Outliers
• Logical inconsistencies

4. BENEFITS OF EDITING
• To ensure the accuracy of data
• To ensure the coherence of aggregated data
• To obtain the best possible data available
• To determine whether the data are complete

5. IMPORTANCE OF EDITING
• Cleaning up data is an imperative aspect of enhancing data
quality for statistical analysis purposes.
• The process of cleaning up data to ensure and verify its
accuracy is called data editing. Currently, data editing is an
under-described, albeit immensely important, component of
the data collection process.

6. STEPS IN EDITING
• When the researcher collects the data it is in raw form and it
needs to be edited, organized and analyzed. The raw data
needs to be transformed into a comprehensible form of data.

7. • The first steps in this process are to edit the data.
• The edited data is then coded and inferences are drawn

8. EDITING METHODS
• Editing is the process of selecting and preparing written,
photographic, visual, audible, or cinematic material used by a
person or an entity to convey a message or information

9. Interrelation of Editing and Coding
• A researcher should classify the raw data into some purposeful
and usable categories.
• Coding operation is usually done at this stage through which
the categories of data are transformed into symbols that may
be tabulated and counted
• . Editing is the procedure that improves the quality of the data
for coding.

10. Example of Editing in Reasearch
• Modifying your short story, cutting out some lines and adding
others, is one example of editing.

11. Purpose Of Editing
• Editing removes errors
• Improves your work flow
• Enhances your language and style

12. LIMITATIONS OF EDITING
• Data editing has its limitations with the capacity and resources
of any given study. These determinants can have a positive or
negative impact on the post-analysis of the data set. Below are
several determinants of data editing