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Steps for Ensuring Data Quality - ESP Solutions Group

START2Is process well designed?1 Are requirements known?Compare policy, regulation, and procedures with instructions given to data providers, collection forms, and code in computer data providers and data processors in decisions to establish what is an established change-management with professional standards for data collection, analysis, and all personnel are knowledgeable, certified, and trained for their assigned for Ensuring data QualityData Quality is more than accuracy and reliability. High levels of data Quality are achieved when information is valid for the use to which it is applied and when decisionmakers have confidence in and rely upon the data .

S T A R T 2 Is process well designed? 1 Are requirements known? C ompa re policy, regul ation, and p rocedures with p instructions given to data providers,

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Transcription of Steps for Ensuring Data Quality - ESP Solutions Group

1 START2Is process well designed?1 Are requirements known?Compare policy, regulation, and procedures with instructions given to data providers, collection forms, and code in computer data providers and data processors in decisions to establish what is an established change-management with professional standards for data collection, analysis, and all personnel are knowledgeable, certified, and trained for their assigned for Ensuring data QualityData Quality is more than accuracy and reliability. High levels of data Quality are achieved when information is valid for the use to which it is applied and when decisionmakers have confidence in and rely upon the data .

2 Implement these Steps organization-wide to increase and maintain data to learn how to improve your data Quality ? ESP Solutions Group conducts comprehensive data Quality audits for local, state, and federal education agencies. We can assess your data processes, identify your strengths and weaknesses, and make practical recommendations on how to improve the data Quality across your entire organization. For more information on ESP s data Quality services, visit design by peers, agencies, and random checks during production.

3 Use most automated/validated level of data entry possible ( , codes in an automated application vs. paper forms).Preprint all available data . Limit times data are target dates are reasonable and entry verification at the earliest levels ( , upon key stroke vs. from printed audit report).Run maintenance before all production. Verify off-hour maintenance and staff problems are reported, documented,corrected, and communicated back to the source of the problem or checklists and signoffs for key sample data and on-site reviews during the Hierarchy of data QualityAs stages are completed, data Quality and usability increase.

4 How do your data measure up?Bad dataInconsistentforms of measurementData unavailableData collected by some at some timesData combined, aggregated, analyzed, summarizedDesignation of official data for decision makingPeriodicity established for collection and reportingAccuracy achieved for decision makingAccurate dataconsistent withdefinitionsReliable dataindependentof collectorInvalidData DefinedData-driven decisions made with confidenceAvailableValidOfficial-112436 Are data appropriately analyzed and reported?

5 Comparable data ; interpretablebeyond localcontextValid data consistent with construct beingdescribedQuality55 Are data verified and compared?3Is process well documented and communicated?Data4Is process well implemented?Provide training and certification for data providers and all new all requirements are available ( , computer hardware, software, network, etc.).Provide documentation for data providers and data immediate help for data a data dictionary and format audit reports for review by experts with knowledge of data to past runs, standards, or similar all conditions affecting interpretation of the data with those who have a stake in the conclusions fairly within a context for analysis techniques meet the requirements for proper technical reports or make available files with detailed data for data exchanges, crosswalks.

6 And translations for all calculations and conditional FERPA confidentiality student Group


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