Elite Research Reveals Ways to Effectively and Efficiently Increase Quality of Data

Oct. 22, 2012 - PRLog -- Elite Research reveals ways to effectively and efficiently increase quality of data.  Elite Research is a global provider of research design and statistical consulting.  Elite Research supports academic (http://eliteresearch.com/clients/academia), corporate (http://eliteresearch.com/clients/corporate), medical/health (http://eliteresearch.com/clients/medical), and nonprofit (http://eliteresearch.com/clients/nonprofit) researchers in designing, collecting, analysing, and reporting efficient and accurate results.

Many aspects of research have recently experienced changes that have affected the ways in which researchers interact with data.  Improvements in technology have made it easier for researchers to collect, manage, and analyze data.  These changes and improvements have undoubtedly increased the quality of data and of research in general, so researchers are now considering new ways to increase the quality of the data that they collect.  Researchers can increase the quality of the data that they collect by implementing specific procedures for each stage of data collection—production, management, and use—to insure that no stage of data collection contributes to data inaccuracies.

To improve data quality during the production stage of data collection, researchers must first identify whether their data will be collected by machines or by humans.  For data that will be collected by machines (e.g., data generated by medical instruments, such as MRI and CT machines), researchers should create documentation to record the calibration of the machines before, during, and after data collection to insure that the machines are operating correctly.  For data that will be collected by humans (e.g., data from one-on-one interviews, surveys, questionnaires), researchers should establish standardized methods for accurately recording data so that all humans participating in the process of data collection will follow the same procedures, which could eliminate some elements of human error that negatively affect data quality.  

         To improve data quality during the management and use stages of data collection, researchers must (a) screen data for accuracy before data are enter entered into databases, particularly data that have been collected by humans; (b) establish reliable systems for coding data, systems which could be easily understood and recreated years after the original research; and (c) establish systems to double-check correct entering and coding of data.  To screen data for accuracy before entering, researchers should determine if all important information has been included for the data (e.g., demographic information that is necessary to interpret the data, etc.).  To establish reliable systems for coding data, researchers should create codebooks in which they record important information that is necessary to interpreting data (e.g., names, descriptions, and formats of variables, methods of data collection, etc.).  Finally, to double-check that data have been entered and coded correctly, researchers should employ double-entry procedures.  

         Whatever future changes affect the ways in which researchers interact with data, the ultimate goal of all research will be to constantly increase the quality of data because increasing the quality of data will improve the relevance of research results.  Researchers can increase the quality of data by implementing specific procedures during each stage of data collection to insure that no stage of data collection contributes to data inaccuracies.  

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