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 Carroll is 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.

In How to Become Data Literate, Susan Rovezzi Carroll and David J. Carroll, affiliated with Words & Numbers Research, provide succinct yet comprehensive support for administrators wading through applied basic quantitative statistics as a tool for data analysis. In fewer than 150 pages, the authors offer instruction for managing, manipulating, visualizing and interpreting the findings of data analysis projects. . . .[T]he book provides support for administrators seeking to understand and use common statistical techniques. I plan to apply this text in the education courses I teach and will strongly recommend it to my doctoral students. But the text also should be considered by educational leaders looking for an introduction to or a refresher of quantitative statistics. If your work responsibilities include making sense of data and analyzing results from a practical, programmatic perspective, or if you need support for quantitative statistics courses that you might be taking, I suggest you consider this work.

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

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 Carroll is 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.

In How to Become Data Literate, Susan Rovezzi Carroll and David J. Carroll, affiliated with Words & Numbers Research, provide succinct yet comprehensive support for administrators wading through applied basic quantitative statistics as a tool for data analysis. In fewer than 150 pages, the authors offer instruction for managing, manipulating, visualizing and interpreting the findings of data analysis projects. . . .[T]he book provides support for administrators seeking to understand and use common statistical techniques. I plan to apply this text in the education courses I teach and will strongly recommend it to my doctoral students. But the text also should be considered by educational leaders looking for an introduction to or a refresher of quantitative statistics. If your work responsibilities include making sense of data and analyzing results from a practical, programmatic perspective, or if you need support for quantitative statistics courses that you might be taking, I suggest you consider this work.

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

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 Carroll is 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.

In How to Become Data Literate, Susan Rovezzi Carroll and David J. Carroll, affiliated with Words & Numbers Research, provide succinct yet comprehensive support for administrators wading through applied basic quantitative statistics as a tool for data analysis. In fewer than 150 pages, the authors offer instruction for managing, manipulating, visualizing and interpreting the findings of data analysis projects. . . .[T]he book provides support for administrators seeking to understand and use common statistical techniques. I plan to apply this text in the education courses I teach and will strongly recommend it to my doctoral students. But the text also should be considered by educational leaders looking for an introduction to or a refresher of quantitative statistics. If your work responsibilities include making sense of data and analyzing results from a practical, programmatic perspective, or if you need support for quantitative statistics courses that you might be taking, I suggest you consider this work.

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