A. Bibliography

• Alwin, L. (2002).  The will and the way of data use. School Administrator, 59(11), 11.
• Ananda, S. (June 28, 2002). Supporting high school students through assessment of academic and industry-valued skills: What have we learned?. San Francisco, CA: WestEd.

• Ardovino, J., Hollingsworth, J., & Ybarra, S. (2000). Multiple measures: Accurate ways to assess student achievement. Thousand Oaks, CA: Corwin Press.

• Armstrong, J., & Anthes, K. (2001). How data can help. American School Board Journal, 188(11), 38-41.

• Barnes, F. V., & Miller, M. (2001). Data analysis by walking around. School Administrator, 58(4), 20-22, 24-25.
• Barnett, H. (October 2001). Successful K-12 Technology Planning: Ten Essential Elements. ERIC Digest. U.S., New York: from EDRS Availability: Microfiche [$1.42 card(s)], Paper [$4.21]. ERIC Clearinghouse on Information & Technology, Syracuse University, 621 Skytop Rd., Suite 160, Syracuse, NY 13244-5290. Tel: 315-443-3640; Tel: 800-464-9107 (Toll Free); Fax: 315-443-5448; e-mail: eric@ericit.org; Web site: http://ericit.org/ithome.
• Bernhardt, V. L. (2003). No schools left behind. Educational Leadership, 60(5), 26-30.
• Bernhardt, V. L. (2000). Intersections. Journal of Staff Development, 21(1), 33-36.
• Bernhardt, V. L. (1998). Data Analysis for Comprehensive Schoolwide Improvement. Larchmont, NY: Eye on Education.
• Bernhardt, V. L. (2000). Designing and using databases for school improvement. Larchmont, NY: Eye On Education.

• Bernhardt, V. L. (2002). The school portfolio toolkit: A planning, implementation, and evaluation guide for continuous school improvement. Larchmont, NY: Eye On Education.

• Bernhardt, V. L. (1994). The school portfolio: A comprehensive framework for school improvement. Larchmont, NY: Eye on Education.
• Bernhardt, V. L. (2003). Using data to improve student learning in elementary schools. Larchmont, NY: Eye On Education.
• Bernhardt, V. L., von Blanckensee, L. L., Lauck, M. S., Rebello, F. F., Bonilla, G. L., & Tribbey, M. M. (2000). The example school portfolio—A companion to the school portfolio: A comprehensive framework for school improvement. Larchmont, NY: Eye On Education.
• Calhoun, E.F. (1991). A wide-angle lens: How to increase the variety, collection and use of data for school improvement. In Glickman, C.D. & Allen, L. (Eds.) Lessons from the field: Renewing schools through shared governance and action research. Athens, GA: Program for School Improvement, University of Georgia. 

• Carr, S. K. (2003). Data to count on. American School Board Journal, 190(1), 45-47.

• Carroll, S. R. & Carroll, D. J. Statistics made simple for school leaders: Data-driven decision making. Lanham, MD: Scarecrow Press, Inc.
• Chance, P. (2000). Technology tools for data-driven decision-making: Promising professional development for rural school leaders. Paper presented at the Annual Meeting of the National Rural Education Association (Charleston, SC, October 25-28, 2000).

• Cibulka, J., & Nakayama, M. (2000).  National partnership for excellence and accountability in teaching:  A practitioner’s guide to learning communities.  The Creation of High Performance Schools through Organizational and Individual Learning (RFP-97-0101, Project 4.4.1).  Deliverable Number 2530.

• Creighton, T. B. (2001). Schools and data: The educator's guide for using data to improve decision making. Thousand Oaks, CA: Corwin.

• Cromey, A., van der Ploeg, A., & Masini, B. (December 2000). The call for data-driven decision making in the midwest's schools: NCREL's response. U.S., Illinois:
• Daniels, R., & Johnson-Ferguson, V. (2001). Using data to help students achieve. Principal Leadership, 1(9), 58-60.
• Dubrovich, M. A. (2002). Student achievement data: Holding teachers accountable. Principal, 81(4), 30-32, 34.
• Education Commission of the States (ECS). Data-Driven Decision Making- issue brief that provides a good overview of the subject. 
• Fickes, M. (1998). Data-driven decision making. School Planning and Management, 37(4), 54, 56-57.
• Frey, S. (2000). One-stop shopping for data-driven educators. Leadership, 30(1), 28-30.
• Gaski, M., & Fawcett, G. (1999). Using the Ohio proficiency tests to make data-driven decisions. Ohio Reading Teacher, 33(2), 12-17.
• Gordon, E. W., & Bridglall, B. L. (Eds.). Toward a relational data management system for education
• Hoffman, E. S. (April 02, 2002). Can research improve technology planning policy?
• Holcomb, E. L. Getting Excited about Data: How to Combine People, Passion, and Proof
• Isaacs, M. L. (2003). Data-driven decision making: The engine of accountability. Professional School Counseling, 6(4), 288-295.
• Jerald, C. (2003). "Cooking with Data" to Reduce Achievement Gaps. ENC Focus 10(1) p.24-28.
• Johnson, J. H. (2000). Data-driven school improvement. Journal of School Improvement, 1(1), 16-19.
• Johnson, R. S. (2002). Using data to close the achievement gap: How to measure equity in our schools. Thousand Oaks,CA: Corwin Press.
• Kimball, C., & Cone, T. (2002). Performance assessment in real time. School Administrator, 4(59), 14-16,18-19.
• Kinder, A. (2000). D3M: Helping schools distill data. NCREL's Learning Point, 2(2), 4-7.
• Kroeger, M., Blaser, S., Raack, L., Cooper, C., & Kinder, A. (Eds.). (2000). How schools use data to help students learn. U.S., Illinois: from EDRS Availability: Microfiche [$1.42 card(s)], Paper [$4.21].
• Lachat, M. A. Data-driven high school reform: The breaking ranks model.
• LaFee, S. (2002). Data-driven districts. School Administrator, 59(11), 6-7,9-10,12,14-15.
• Lashway, L. (2002). Data analysis for school improvement. Research Roundup, 19(2)
• LeTendre, B. G. (2000). 6 steps to a solution. Journal of Staff Development, 21(1), 20-25.
• Levesque, K., Bradby, D., Rossi, K., & Teitelbaum, P. At your fingertips: Using everyday data to improve schools
• Levine, E. (2002). Building a data warehouse. American School Board Journal, 189(11), 48-50.
• Liddle, K. Data-driven success: How one elementary school mined assessment data to improve instruction.
• Ligon, G. Use of Testing/Evaluation information for school improvement. 
• Love, N. (2002). Using data/getting results: A practical guide for school improvement in mathematics and science. Norwood, MA: Christopher-Gordon.
• Love, N. (2003). Uses and Abuses of Data. ENC Focus 10(1) p.14-17.  
• Marzano, R. J. (2003). Using data: Two wrongs and a right. Educational Leadership, 60(5), 56-60.
• Mertler, C. A. (October 2002). Using Standardized Test Data to Guide Instruction and Intervention. ERIC Digest. U.S., Maryland: from EDRS Availability: Microfiche [$1.42 card(s)], Paper [$4.21]. ERIC Clearinghouse on Assessment and Evaluation, 1129 Shriver Laboratory, University of Maryland, College Park, MD 20742. Tel: 800-464-3742 (Toll Free).
• Michelson, A., & Rothenberg, J. (1993). Scholarly communication and information technology: Exploring the impact of changes in the research process on archives. rand reprints.
• Noyce, P., Perda, D., & Traver, R. (2000). Creating data-driven schools. Educational Leadership, 57(5), 52-56.
• Paben, S. (2002). What's in it for the busy leader?: Show administrators how technology works toward their vision. Journal of Staff Development, 23(1), 24-27.
• Pardini, P. (2000). Data, well done. Journal of Staff Development, 21(1), 12-18.
• Parsons, B. A. (2003). A tale of two schools' data. Educational Leadership, 60(5), 66-68.
• Protheroe, N. (2001). Improving teaching and learning with data-based decisions: Asking the right questions and acting on the answers. ERS Spectrum, 19(3), 4-9.
• Raack, L. (2000). Measuring student success. NCREL's Learning Point, 2(2), 12-14.
• Reeves, D. B. (2002). The Leader's Guide to Standards: A Blueprint for Educational Equity and Excellence. the Jossey-Bass Education Series. U.S., California: from EDRS Availability: None. Jossey-Bass, 989 Market Street, San Francisco, CA 94103-1741 ($27). Tel: 415-782-3177; Tel: 800-956-7739 (Toll Free); Fax: 415-951-8553; e-mail: agreer@jbp.com; Web site: http://www.josseybass.com.
• Reichardt, R. (December 2000). The state's role in supporting data-driven decision-making: A view of Wyoming. U.S., Colorado:
• Ross, R. (2002). Real data, real answers. American School Board Journal, 189(1), 46-47,51.
• Rudner, L. M., & Boston, C. (2003). Data warehousing: Beyond disaggregation. Educational Leadership, 60(5), 62-65.
• Rumery, K. Using data to inform instruction--stories from five states. "Maine," the way life should be.
• Schmoker, M. (2003). First things first: Demystifying data analysis. Educational Leadership, 60(5), 22-24.
• Schmoker, M. The results fieldbook: Practical strategies from dramatically improved schools
• Schwartz, W. (September 2002). Data-Driven Equity in Urban Schools. ERIC Digest. U.S., New York: from EDRS Availability: Microfiche [$1.42 card(s)], Paper [$4.21]. ERIC Clearinghouse on Urban Education, Institute for Urban and Minority Education, Box 40, Teachers College, Columbia University, New York, NY 10027. Tel: 800-601-4868 (Toll free); Tel: 212-678-3433; Fax: 212-678-4012; e-mail: eric-cue@columbia.edu.
•  Shorr, P.W. 10 Things You Always Wanted To Know About Data-Driven Decision Making, Scholastic Administrator
• Slotnik, W. J., & Gratz, D. B. (1999). Guiding improvement. Thrust for Educational Leadership, 28(3), 10-12.
• Sutherland, S. (2004). Creating a culture of data use for continuous improvement: A case study of an Edison project school. American Journal of Evaluation, 25, 3, 277-293.
• Todnem, G. R., & Warner, M. P. (1995). Demonstrating the benefits of staff development: Using technology to assess the school improvement process. Journal of Staff Development, 16(4), 62-63.
• Tropper, R. (1998). The Interpretation of data: An introduction to statistics for behavioral sciences. Pacific Grove, CA: Brooks/Cole Publishing Company.
• Wade, H. H. Data Inquiry and Analysis for Educational Reform. ERIC Digest
• Williams, F. (1992). Reasoning with statistics: How to read quantitative research, 4th edition. Orlando, FL: Harcourt Brace Jovanovich, Publishers.
• Wilson, B. L. Models for uses of data in school improvement: From fast-food to five-star restaurant
• Zuelke, D. C., & Nichols, T. M. Collaborative school climate action research for school improvement: Part II.

B. Tools

• Data-Driven Decision Making Tools- A collection of DDDM tools and templates sponsored by the California Department of Education at the Technology Information Center for Administrative Leadership (TICAL).
• D3M Buyer's Guide- A description and brief evaluation of fourteen K-12 decision-support tools for data collection, warehousing, analysis, and reporting written by Pamela Wheaton Shorr and sponsored by Scholastic Administrator.
• Education Commission of the States (ECS) NCLB Database Website- An excellent interactive website that provides current and detailed information on the 50 states' compliance progress with regard to the NCLB legislation. "The site’s goal is to provide state policymakers, staff and the public with information about the status of education policy in their state" (ECS NCLB Database Website).
• National Center for Education Statistics (NCES) Common Core of Data (CCD) - Public school locator, public school district locator, and build a table tools.
• North Central Regional Educational Laboratory (NCREL) Toolbelt - A collection of data-driven decision making tools.
• North Central Regional Educational Laboratory (NCREL) enGauge - Indicators of data-driven decision making.
• Tools for Data-Driven Decision Making - An excerpt from an article outlining three major types of products and services used to manage and use data written by Todd McIntire. See the complete article.
• Profiling Educational Technology Integration (PETI): Teacher Survey
• Profiling Educational Technology Integration (PETI): Building Survey
• Profiling Educational Technology Integration (PETI): District Survey
• Instrument Databank- A collection of 228 instruments specifically designed for educational technology research, evaluation, and data collection. This is a work in progress and is only intended to aid members in finding instruments. It was originally compiled by John Stroup, Mike Briscoe, Matt Dunleavy, and Walt Heinecke at the University of Virginia. It is not an endorsement of the validity and reliability of these instruments. SETDA members are encouraged to contribute to this database in order to make it as robust as possible. Additions and revisions can be sent to Matt Dunleavy at mdunleavy@setda.org .

 C. Websites

• D3M: Data-Driven Decision Making - Consortium for School Networking sponsored site.
• Data-Driven Decision Making - Eisenhower National Clearinghouse for Mathematics and Science Education (ENC) sponsored site with excellent resources and article
• Using Data to Improve Schools and Instruction - A large collection of websites, power points, and articles sponsored by the Business Roundtable for Education/Charter School Consortium. Many valuable resources can be found at this site.
• National School Boards Association (NSBA)- A collection of documents and websites addressing data-driven decision making.
• Using Data to Improve Schools: What's Working (2002).  American Association of School Administrators: Arlington, VA.

• Free Data Tutorials - [download] - www.schooldatatutorials.org
• Minnesota Surveys Free Tool - [download] - www.minnesotadatasurveys.org
• DDDDM White Paper - [download] - http://www.microsoft.com/education/ThoughtLeadersDDDM.mspx