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Ohio Data Primer


Brief Annotated Bibliography of Resources for Using Data

Abelson, R. P. (1995). Statistics as principled argument. Hillsdale, NJ: Lawrence Erlbaum Associates.

Meaningful research tells a story with a point to it. Data analysis should not be pointlessly formal. It should make an interesting claim. It should tell a story, using intelligent interpretation of appropriate evidence from empirical measurement and observation.

Bailey, J. (1996). After thought: The computer challenge to human intelligence. New York: Basic Books.

Place, pace, pattern: these constitute the bulk of our efforts to understand the world via numbers.

Boudett, K., City E., & Murnane, R. (2005). Data wise: A step-by-step guide to using assessment results to improve teaching and learning. Cambridge, MA: Harvard University Press.

Examining test scores and other classroom data can become a catalyst for schoolwide conversations that enhance schools' ability to capture teachers' knowledge, foster collaboration, identify obstacles to change, and enhance school climate.

Brown, J. S., & Duguid, P. (2000). The social life of information. Cambridge, MA: Harvard Business School Press.

Data, information takes meaning socially. Information dissemination that ignores how humans interact fails to make its point.

Guskey, T. R., & Bailey, J. M.. (2001). Developing grading and reporting systems for student learning. Thousand Oaks, CA: Corwin Press.

Adds clarity to an underappreciated and murky topic.

Herman, J. & Winters, L. (1992). Tracking your school's success: A guide to sensible evaluation. Newbury Park, CA: Sage Publications.

A step-by-step guide to evaluating schools and programs, recording and measuring progress, and engaging in credible communication. Includes several example instruments.

Jerald, C. (2003). Cooking with data to reduce achievement gaps. ENC Focus 10(1), 24–28.

Uses a cooking analogy to make data analysis and interpretation more palatable.

Johnson, R. S. (2002). Using data to close the achievement gap: How to measure equity in our schools. Thousand Oaks, CA: Corwin Press.

Focuses on gathering, analyzing, and applying data to develop strategies that yield high achievement for all students.

Leithwood, K., Aitken, R. & Jantzl D. (2001). Making schools smarter: A system for monitoring school and district progress. (2nd ed.) Thousand Oaks, CA: Corwin Press.

A handbook for developing collaborative assessment and planning. Includes extensive array of survey forms for many audiences.

Levesque, K., Brady D., Rossi K., & Teitelbaum P. (1998). At your fingertips: Using everyday data to improve schools. Berkeley, CA: MPR Associates, Inc.

A workbook designed to help educators make better use of the data that typically exists in and around schools. Includes numerous examples and worksheets.

Love, N. (2002). Using data/getting results: A practical guide for school improvement in mathematics and science. Norwood, MA: Christopher-Gordon.

A sourcebook for framing collaborative inquiry around data. Features almost 100 pages of data tools, including collection templates, surveys, and monitoring forms.

Martin, P., & Bateson, P. (1993). Measuring behaviour: Ani ntroductory guide. (2nd ed.). London: Cambridge University Press.

An introductory text about the measurement of behavior. Raises key issues clearly and disposes of them briefly. Well worth the read, whether for quantitative or qualitative analysts.

McTighe, J. & Thomas, R.S. (2003). Backward design for forward action. Educational Leadership, 52–55.

It is more than about numbers: questions and concepts drive data-based learnings.

Pellegrino, J. W., Chudowsky, N., & Glaser R. (Eds.). (2001). Knowing what students know: The science and design of educational assessment. Washington, DC: National Academy Press.

Educational measurement constitutes a triad: cognition, observation, interpretation. All three are problematic.

Petersen, J. (2007). Learning facts: The brave new world of data-informed instruction. Education Next 2 (1, Winter) 36–42. (Available online at http://media.hoover.org/documents/ednext_20071_36.pdf)

Brief case studies of three schools, using annual and interim assessments to drive instructional choices and monitor progress.

Platt, J. R. (1964). Strong Inference. Science, 146 (3642, October 16), 347–353.

Certain systematic patterns of thought are more productive than others.

Popham, W. (1999). Classroom assessment: What teachers need to know. (2nd ed.) Boston: Allyn & Bacon.

Daily assessment issues for teachers and teachers-to-be, covered by a measurement expert with a sense of humor and good understanding of life in classrooms.

Porter, T. M. (1995). Trust in numbers: The pursuit of objectivity in science and public life. Princeton, NJ: Princeton University Press.

Traces the history of quantification in the modern world and dissects the cultural meaning of objectivity.

Supovitz, J.(2006). The case for district-based reform: Leading, building, and sustaining school improvement. Cambridge, MA: Harvard.

Chapter 5, "Districtwide Data Use", is a superlative summary of effective use of school performance data to drive instructional improvement and deepen faculty knowledge of content and practice.

Wagner, M., L. Fiester, Reisner E., et al. (1997). Making information work for you: A guide to collecting good information and using it to improve comprehensive strategies for children, families, and communities. Washington, D.C.: U.S. Department of Education.

Focuses on using data to improve children's lives. Offers principles, processes, and evaluation instruments for use in schools and communities.

Wurman, R. S. (2001). Information anxiety 2. Indianapolis, IN: Que.

Has there been an explosion of information or of noninformation? The originator of the term "information architecture" guides a tour of the information world.


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