Quality Data is Key to Improving Education

The Data Quality Campaign (DQC) has been focused since 2005 on advocating for states to build robust state longitudinal data systems (SLDS). While states have made great progress in their data infrastructure, and should continue to emphasize this work, t data systems alone will not improve outcomes. It is time for both DQC and states to focus on building capacity to use the information that these systems are producing at every level – from classrooms to state houses. To impact system performance and student achievement, the ingrained culture must be replaced with one that focuses on data use for continuous improvement. The effective use of data to inform decisions, provide transparency, improve the measurement of outcomes, and fuel continuous improvement will not come to fruition unless there is a system wide focus on building capacity around the collection, analysis, dissemination, and use of this data, including through research

Workforce Data Quality Initiative

Texas’ Workforce Data Quality Initiative aimed to develop a comprehensive system for analysis of workforce and education participation and outcomes. In partnership with the Texas Workforce Commission, the Ray Marshall Center (RMC) is working to build, test, improve, and expand data linkages across linked individual-level, longitudinal education, and workforce records. Through this project, researchers would be able to identify and assess postsecondary pathways and transitions between education, employment, and other outcomes for students exiting the public school system as well as analyze the performance of the human capital development system in Texas, spanning secondary education through postsecondary education, and workforce training and employment.This report seeks to examine and analyze the postsecondary labor market outcomes of Texas high school graduates from the classes of 2008 and 2009. One advantage of looking at these two particular cohorts stems from differences in when they encountered the Great Recession: the class of 2008 graduated prior to the start of the recession in Texas and the class of 2009 graduated immediately after the start of the recession. It is likely that class of 2009 graduates factored in the regional changes in availability of employment as they weighed whether or not to apply for and enroll in college.Texas Workforce CommissionRay Marshall Center for the Study of Human Resource

Data sets and data quality in software engineering

OBJECTIVE - to assess the extent and types of techniques used to manage quality within software engineering data sets. We consider this a particularly interesting question in the context of initiatives to promote sharing and secondary analysis of data sets. METHOD - we perform a systematic review of available empirical software engineering studies. RESULTS - only 23 out of the many hundreds of studies assessed, explicitly considered data quality. CONCLUSIONS - first, the community needs to consider the quality and appropriateness of the data set being utilised; not all data sets are equal. Second, we need more research into means of identifying, and ideally repairing, noisy cases. Third, it should become routine to use sensitivity analysis to assess conclusion stability with respect to the assumptions that must be made concerning noise levels

Data quality predicts care quality: findings from a national clinical audit

Background: Missing clinical outcome data are a common occurrence in longitudinal studies. Data quality in clinical audit is a particular cause for concern. The relationship between departmental levels of missing clinical outcome data and care quality is not known. We hypothesise that completeness of key outcome data in a national audit predicts departmental performance. Methods: The National Clinical Audit for Rheumatoid and Early Inflammatory Arthritis (NCAREIA) collected data on care of patients with suspected rheumatoid arthritis (RA) from early 2014 to late 2015. This observational cohort study collected data on patient demographics, departmental variables, service quality measures including time to treatment, and the key RA clinical outcome measure, disease activity at baseline, and 3 months follow-up. A mixed effects model was conducted to identify departments with high/low proportions of missing baseline disease activity data with the results plotted on a caterpillar graph. A mixed effects model was conducted to assess if missing baseline disease activity predicted prompt treatment. Results: Six thousand two hundred five patients with complete treatment time data and a diagnosis of RA were recruited from 136 departments. 34.3% had missing disease activity at baseline. Mixed effects modelling identified 13 departments with high levels of missing disease activity, with a cluster observed in the Northwest of England. Missing baseline disease activity was associated with not commencing treatment promptly in an adjusted mix effects model, odds ratio 0.50 (95% CI 0.41 to 0.61, p < 0.0001). Conclusions: We have shown that poor engagement in a national audit program correlates with the quality of care provided. Our findings support the use of data completeness as an additional service quality indicator