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Essential First Steps to Data Analysis

Essential First Steps to Data Analysis
Scenario-Based Examples Using SPSS

February 2013 | 288 pages | SAGE Publications, Inc
A companion website is available for this text

The purpose of this book is to provide instruction and guidance on preparing quantitative data sets prior to answering a studyÆs research questions. Preparation may involve data management and manipulation tasks, data organization, structural changes to data files, or conducting preliminary analysis such as examining the scale of a variable, the validity of assumptions or the nature and extent of missing data. The ôresultsö from these essential first steps can also help guide a researcher in selecting the most appropriate statistical tests for his/her study. The book is intended to serve as a supplemental text in statistics or research courses offered in graduate programs in education, counseling, school psychology, behavioral sciences, and social sciences as well as undergraduate programs that contain a heavy emphasis on statistics. The content and issues covered are also beneficial for faculty and researchers who are knowledgeable about research design and able to use a statistical software package, but are unsure of the first steps to take with their data. Increasingly, faculty are forming partnerships with schools, clinics, and other institutions to help them analyze data in their extensive databases. This book can serve as a reference for helping them get existing data files in an appropriate form to run statistical analysis. This book is not a replacement for a statistics textbook. It assumes that readers have some knowledge of basic statistical concepts and use of statistical software, or that they will be learning these concepts and skills concurrently throughout the course. SPSS was chosen to illustrate the preparation, evaluation, and manipulation of data. However, students or researchers who do not use SPSS will benefit from the content since the overall structure and pedagogical approach of the book focuses heavily on the data issues and decisions to be made.

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
University of Oregon

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
Texas A&M University

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
University of Houston-Downtown

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
Georgia College & State University

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
The University of the Sciences in Phiadelphia

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
Youngstown State University

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