Using the State Instructional Resource Data

After getting a better understanding of the data in the four states and assessing the relative quality of those data, we wanted to know if the data could be used to answer policy questions so prevalent today. What should be spent on students to ensure they succeed? Who should teach children to help them achieve? How should these monetary and staffing resources be allocated to be effective?

Image of two people looking at a series of informational charts.

The state data are both available and accessible to answer basic, but important, questions about the adequacy and equity of state funding formulas and compensation for teachers and other staff. Searching the data to find out about teacher characteristics can be done relatively easy. This can be followed by an investigation into what impact these characteristics may have on student success. It’s possible to get a good grasp of what type of teachers might be allocated to different schools to impact student outcomes. That is, teachers with certain qualifications may be better placed at schools with high-need student populations or schools in particular locales.

About Spending

How instructional dollars are spent and how the spending varies across districts can be studied using state fiscal data. These data are available to learn about district spending in all four states and school spending in Texas. To see if funds allocated are in any way connected to student achievement, the fiscal data needs to be merged with performance data. Specific questions that can be answered with the state data include the following:

  • What are the differences in instructional spending across districts in the state?
  • Do districts that perform well allocate more instructional dollars to salaries and benefits?
  • How do districts of varying levels of performance allocate administrative versus instructional dollars?

More detailed questions about school and district spending on salaries can also be answered if individual staff data are merged with student performance data. For instance, determine how teacher pay can impact student performance with the merged data or find if salaries are distributed equitably between schools and districts. Also, using these data, see what effect salary has on the retention of qualified teachers. To answer this particular question, it would be necessary to have data on teacher mobility, which most of our states do not collect. However, it is not difficult to calculate mobility using existing state data on school and district teacher assignment over multiple years. Unfortunately, no current data in the state databases can tell us why teachers stay or leave.

Although answers to a number of important policy questions about instructional spending can be found with existing state data, additional data would increase our learning, especially in regard to teacher compensation. It would be helpful to have better measures for all funds paid to staff, such as individual data on the cost of benefits, bonuses, and incentives. With this more accurate depiction of total compensation, it’s feasible to ask about the influence of benefits and incentives on teacher recruitment or retention, particularly in shortage areas such as bilingual and special education.

States have little data on professional development, so it is impossible to know if their investments produce results.

Another instructional area where additional data would be helpful is professional development. States have little data on professional development, so it is impossible to know if their investments produce results. Each state collects data on the hours of professional development completed, but not data on actual or dollar-equivalent measures for teacher time, stipends, travel expenses, and costs for teacher substitutes. Information on the content would also be beneficial for addressing questions about the effectiveness of professional development, its relative costs, and the distribution of professional development resources across schools and districts.

About Teacher Quality

Teacher quality has always been an important policy issue, but NCLB has heightened our need to use good data to determine the quality of our teaching staff. We found only certain questions about teacher quality can be answered using the existing state data, i.e., data on teacher experience, education, and certification. Questions about other measures of quality, such as application of pedagogical techniques, teacher motivation, and classroom management skills require information not collected in state databases. Changes are being made, however. For example, New Mexico has begun to collect data on evaluations of teachers as one measure of teacher quality. Using all of the available teacher data and merging them with other databases, finding answers to questions about teacher quality, its relationship to teacher salary, and its impact on student performance are possible.

Merging data from state databases, you can understand whether allocating more teachers, administrators, or aides impacts student achievement.

Specific questions to ask using the data are as follows:

  • How does teacher experience, education, and/or certification relate to student achievement?
  • Do higher teacher salaries buy teachers with more experience, higher education levels, and advanced certification?
  • How are teachers who are educated at different teacher education institutions distributed across the state?
  • Do rural areas have a higher rate of uncertified teachers?
  • What is the connection between teacher retention and route to certification?

Although you can use current teacher quality data to find answers to many policy questions, improving these data are important to ensure accurate answers. For instance, we found it would be important to help districts better understand definitions for reporting experience, especially for teachers who transfer between districts or from other states. Also, if data on teacher degree major were collected, questions on how many infield teachers you have and how these teachers are distributed across the state could be answered. Combining these data with performance data, it is also possible to seek information on the impact of these teachers, especially in comparison to those not teaching in their field of study.

About Staffing Patterns

We found that using the existing state data, you can determine what staff resources comprise each school and how those resources differ across schools with varying levels of student performance. These staffing pattern profiles can be detailed for specific types of schools or teachers. For example, it’s easy to see how beginning teachers or administrators in small, medium, or large schools are distributed across the state. Or maybe find out about what teaching patterns exist for rural and urban schools, especially those that are low performing.

Another important education policy issue is class size, not only its relative cost but its connection to student performance. Merging data from state databases, you can understand whether allocating more teachers, administrators, or aides impacts student achievement. Be aware, however, that using a ratio of the number of students in a school to the number of teachers in that school is the least accurate measure of class size. It would be better to use data that directly links students to teachers and teachers to specific classes.

Next Page: Data System Reform: What Can Be Done

Published in Insights on Educational Policy, Practice, and Research Number 18, December 2005, Enhancing Data Use and Quality to Shape Education Policy