Caseworkers and Data Entry

Caseworkers are commonly the originators of the bulk of an agency’s data in its Statewide Automated Child Welfare Information System (SACWIS) or other statewide system, and often this is where data quality begins. A study (Carrilio, 2008) on caseworkers’ use of computers and data systems found four variables relating to the accuracy of their data entry:

  1. Skills and experience with using computers (worker background and comfort level with use of computers)
  2. Perceived ease of use of the agency’s automated system (worker perception regarding user-friendliness of the system)
  3. Utility of the data (worker belief about usefulness and helpfulness of data being gathered)
  4. Attitude about the data (worker perception regarding importance of inputting and gathering the data)

Agencies already employ tools for checking the accuracy and completeness of data. States that continue to have significant data errors and inconsistencies should address any worker entry issues through training and coaching. A well-functioning help desk and other supports for direct delivery staff will also assist greatly in minimizing errors and ensuring a collective sense of responsibility for accurate data. In addition, States should examine how they define data elements to be captured, the clarity of instructions overall, and if data entry screens and systems are well-designed. Well-designed systems will have:

  • Clear screens
  • Well-spaced and uncluttered fields
  • Easy-to-read font sizes
  • Descriptive captions that are easy to understand
  • Information that flows in a logical order within the screen and from screen to screen
  • Ease of entry

Agency leaders should accept responsibility for the appropriate breadth, quality, and usefulness of an agency’s data, and should continually look for ways to improve data. When data are faulty or otherwise inadequate, management should ensure that effective processes are in place to identify, report, and address data errors, inconsistencies, and omissions at whatever juncture and level they may occur. Corrective mechanisms may involve instituting a vigorous data quality assurance (QA) process, training or re-training staff, re-examining skills of those analyzing the data, and/or creating partnerships with outside entities for training and technical assistance to ensure more effective data collection and analysis.