The second edition of Statistics and Data Analysis for Nursing, uses a conversational style to teach students how to use statistical methods and procedures to analyze research findings. Students are guided through the complete analysis process from performing a statistical analysis to the rationale behind doing so. In addition, management of data, including how and why to recode variables for analysis, how to "clean" data, and how to work around missing data, is discussed.
- Research Examples - are used to illustrate key points in the text and to stimulate students’ thinking about research questions and analytic options.
- Clear, “user friendly” style - used in this book was designed to make the content digestible and nonintimidating. Concepts are introduced carefully and systematically, difficult ideas are presented clearly.
- Specific practical tips on performing analyses - every chapter includes several tips for applying the chapter’s lessons to real-life situations.
- Guidance on presenting statistical results - indicates what information to report in the text versus in tables and figures, and includes exemplary tables that can be used as templates for many statistical analyses.
- Exercises - Student exercises are included at the end of every chapter.
- Power Point slides - offer a more dynamic and colorful way to review textbook content, and also provide explicit guidance for undertaking SPSS analyses.
- Further aids to student learning - bolded terms when new concepts are introduced; succinct, bulleted summaries at the end of each chapter; and tables and figures that provide examples and graphic materials in support of the text discussion.
New to this Edition
New to This Edition
- UPDATED! - Research Examples utilizinging examples from an international mix of studies published by nurse researchers in 2006-2009
- Emphasis on Evidence-Based Practice
- Virtually every chapter in this edition offers content on evaluating the reliability of statistical results (as communicated through p values), the precision of statistical results (as communicated through confidence intervals), and the magnitude of results (as communicated through effect size indexes). Information on effect size is especially important, given its crucial role in meta-analyses.
- SPSS Version 16.0
- Computer output from statistical analyses is presented throughout the book, together with guidance on how to read the output. Three SPSS datasets are offered with the book on the book’s website.
- Missing Values
- The final chapter of the book covers strategies for detecting patterns of missing values, and approaches to dealing with ensuing problems. State-of-the-art imputation techniques are discussed.
- Scale Development
- In this edition, the chapter on factor analysis has been expanded, and a new section on evaluating internal consistency reliability has been added.
- Power Analysis
- Approaches for doing a power analysis to estimate sample size needs have been expanded and greatly simplified in this edition.
Table of Contents
Chapter Chapter Title
1 Introduction to Data Analysis in an Evidence-Based Practice Environment
2 Frequency Distribution: Tabulating and Displaying Data
3 Central Tendency, Variability, and Location
4 Correlation, Crosstabulation, and Risk Indexes: Describing Relationships:
5 Statistical Inference
6 t Tests
7 Analysis of Variance
8 Chi Square and Other Nonparametric Tests
9 Correlation and Simple Regression
10 Multiple Regression
11 Analysis of Covariance, MANOVA, and Other Related Multivariate Techniques
12 Using Logistic Regression
13 Factor Analysis and Internal Consistency Reliability Analysis
14 Missing Values
Appendix A: Theoretical Probability Tables
Appendix B: Power Analysis/Effect Size Tables
Appendix C: Tips on Handling Missing Data
Appendix D: Answers for Selected Exercises