How to Become Data Literate

In this follow up toStatistics Made Simple for School Leaders Carroll and Carroll have provided an updated, easy to comprehend, manual for practitioners. Now more than ever, educators are being held accountable by taxpayers, students, parents, government officials and the business community for supportable documentation of educational results. Data management has become everyones job and everyones concern. But the regression of data has exposed a raw nerve. The lack of comfort that many educators have in working with data poses a great challenge as school districts make the transition from a data rich to an information rich environment. How to Become Data Literate is the solution. Educators need the ability to formulate and answer questions using data as part of evidence-based thinking, selecting and using appropriate data tools, interpreting information from data, evaluating evidence-based differences, using data to solve real problems and communicating solutions. This book is intended to be a user-friendly, educators primer. It will leave the reader with the confident attitude that I can do this." In the long run, it is intended to underscore the magnificence of data. Decisions based on excellent data produce meaningful action strategies that benefit students, parents, staff, and the community at large.

Susan Rovezzi Carroll,PhD, is president of Words& Numbers Research, Inc. a research and evaluation firm that she founded in 1984. Projects have included quasi-experimental designs, original instrument development, and data analysis using SPSS and HLM for the U.S. Department of Education and the National Science Foundation.

David Carrollis vice president of Words& Numbers Research, Inc. and his expertise involves the development of indicators for assessing performance outcomes. He offers demographic data analysis, marketing strategy and execution, and organizational development to clients in education and other non-profit settings.

Introduction: The compelling case for data literacy
Chapter One: Speaking the language correctly
Chapter Two: Creating a snap shot of data with a picture
Chapter Three: Presenting a mountain of data with one number
Chapter Four: Understanding why range in your data is important
Chapter Five: Drawing a sample to represent a whole group
Chapter Six: Putting your assumptions to the test
Chapter Seven: T-tests: Examining differences between two groups
Chapter Eight: ANOVA: What if there are more than two groups?
Chapter Nine: Chi Square: Examining distributions for differences
Chapter Ten: Correlations: Detecting relationships
Chapter Eleven: Reporting your data clearly and strategically