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Improving Data Quality in Large-Scale, Performance-Based Program Evaluations

^* To whom correspondence should be addressed. E-mail: molly_aldridge{at}yahoo.com.

	Abstract

This article examines a systematic education and skill development strategy used for improving data quality in a large-scale, statewide program evaluation. This approach includes streamlining data collection procedures, operationalizing measures, developing and disseminating codebook information, and training those responsible for data entry. This data quality improvement strategy was examined in a process and outcome evaluation of North Carolina’s statewide comprehensive tobacco control program. We calculated error rates in data (i.e., data did not match code book definition or construct) submitted by program coordinators before and after implementation, and we observed a 62% decrease in the total number of errors after the intervention was implemented (p = .014; 95% confidence interval [CI]: 17.6, 144.5). This article demonstrates that the intervention was effective in improving data quality, so that data entered matched operational definitions, thereby improving confidence in and validity of evaluation results.

First published on June 29, 2009, doi:10.1177/1098214009338620
This version was published on September 17, 2009


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