Essential First Steps to Data Analysis

Carol S. Parke, Ph.D. is an Associate Professor in the Department of Educational Foundations and Leadership at Duquesne University, teaching statistics, research, and measurement courses in masters and doctoral programs in the School of Education. She has a Ph.D. in Research Methodology and an M.A. in Mathematics/Statistics from the University of Pittsburgh as well as a B.S. in Secondary Mathematics Education from Indiana University of Pennsylvania. Dr. Parke's 20 years of research ranges from the design and analysis of large-scale, long-term state and national assessment projects to working intimately with teachers in the classroom to show them how to collect and use data to make decisions and real-time, real-world improvement. Her work has appeared in research and practitioner journals in measurement, assessment, mathematics education, and statistics and includes "Using Assessment to Improve Middle-Grades Mathematics Teaching and Learning," a book for teachers on using performance assessment in mathematics classrooms.

Section 1. The Sample Module 1. Checking the Representativeness of a Sample Module 2. Splitting a File, Selecting Cases, Creating Standardized Values and Ranks Section 2. Nature and Distribution of Variables Module 3. Recoding, Counting, and Computing Variables Module 4. Determining the Scale of a Variable Module 5. Identifying and Addressing Outliers Section 3. Model Assumptions Module 6. Evaluating Model Assumptions for Testing Mean Differences Module 7. Evaluating Model Assumptions for Multiple Regression Analysis Section 4. Missing Data Module 8. Determining the Quantity and Nature of Missing Data Module 9. Quantifying Missing Data and Diagnosing its Patterns Section 5. Working with Multiple Data Files Module 10. Merging Files Module 11. Aggregating Data and Restructuring Files Module 12. Identifying a Cohort of Students

Having different research studies presented rather than using the same context throughout the text helps keep the material more interesting for the reader, as well as helping students generalize their learning across different research contexts. -- Julie Alonzo The 'real world' scenarios captivate the reader but also provide pertinent context to see just how it relates to the content area at hand in each module. -- Kyle M. Woosnam The strength of the text is in how the author identifies the goals of the analysis under discussion and then steps the reader through the tasks necessary to realize those goals. -- Claude Rubinson There are not many texts on data preparation in the field, so I believe this text would provide a unique contribution. ...I agree wholeheartedly with the preface, that the other guides are simple "how-to" books that do not effectively connect students with real examples that can serve as a guideline in their own analysis experiences. -- Carrie L. Cook A key strength of this text is that it focuses on the practical aspects of MANAGING research data rather than statistical programming or statistical analysis. -- Amy B. Jessop I like that the book is written with cases/stories-I think that especially for counseling/psych students, these stories are going to help them contextualize the ideas more so than they would without these stories. I also like that the book give the students example write-ups. I think that is a priceless addition to any textbook about conducting statistical tests. -- Karen H. Larwin