Statistics for Evidence-Based Practice in Nursing 3rd Edition PDF

Preface
Acknowledgments
Reviewers
CHAPTER 1 Evidence-Based Practice in Nursing, or Why Do I Need
to Take Statistics?
Introduction
Statistics and Evidence-Based Practice
Summary
Critical Thinking Questions
Self-Quiz
References
CHAPTER 2 Statistical Essentials I
Statistics
Summary
Critical Thinking Questions
Self-Quiz
References
CHAPTER 3 Statistical Essentials II: Measurement
Variables and Data
Levels of Measurements
Reliability and Validity
Summary
Critical Thinking Questions
Self-Quiz
References
CHAPTER 4 Working with Microsoft Excel and IBM SPSS Statistics
Software (SPSS)®
Introduction to Spreadsheet and Statistical Software Packages
Basic Features of Microsoft Excel
Basic Features of SPSS
Summary
Critical Thinking Questions
Self-Quiz
References
CHAPTER 5 Organizing and Displaying Data
Introduction
Grouping Data
Graphs and Charts
Other Graphs and Charts
Presenting the Data in the Best Format
Summary
Critical Thinking Questions
Self-Quiz
References
CHAPTER 6 Descriptive Statistics
Introduction
Measures of Central Tendency
Measures of Variability
Normal Distribution
Confidence Interval
Summary
Critical Thinking Questions
Self-Quiz
References
CHAPTER 7 Hypothesis Testing
Introduction
General Steps In Hypothesis Testing
Hypotheses
Hypothesis Testing with an Example
Effect Size
Statistical Power
Summary
Critical Thinking Questions
Self-Quiz
References
CHAPTER 8 Getting Ready for the Analysis
Introduction
Missing Data
Outliers
Assumptions of Parametric Tests
Summary
Critical Thinking Questions
Self-Quiz
CHAPTER 9 Examining Relationships Between and Among
Variables
Introduction
Measuring Relationships
Summary
Critical Thinking Questions
Self-Quiz
Reference
CHAPTER 10 Modeling Relationships
Introduction
Doing and Interpreting Simple Linear Regression
Multiple Linear Regression
Logistic Regression
Summary
Critical Thinking Questions
Self-Quiz
References
CHAPTER 11 Tests for Comparing Group Means: Part I
Introduction
Choosing the Right Statistical Test
One-Sample t-Test
t-Test for Independent Groups
t-Test for Dependent Groups
One-Way Analysis of Variance
Multiple Comparisons
Reporting Planned Contrasts and/or Post Hoc Tests
Summary
Critical Thinking Questions
Self-Quiz
Reference
CHAPTER 12 Tests for Comparing Group Means: Part II
Introduction
Choosing the Right Statistical Test
ANCOVA
Factorial Analaysis of Variance (ANOVA)
Repeated Measures ANOVA
MANOVA
Summary
Critical Thinking Questions
Self-Quiz
References
CHAPTER 13 Nonparametric Tests
Introduction
Comparing Two Independent Groups: Wilcoxon Rank-Sum Test and/or Mann–Whitney Test
Comparing Two Dependent Groups: Wilcoxon Signed-Rank Test
Comparing Several Independent Groups: Kruskal–Wallis Test
Comparing Several Dependent Groups: Friedman’s ANOVA
Summary
Critical Thinking Questions
Self-Quiz
Reference
CHAPTER 14 Categorical Data Analysis: Nonparametric Tests of
Association
Introduction
Chi-Square (χ2) Test
Fisher’s Exact Test
Phi and Cramer’s V
Summary
Critical Thinking Questions
Self-Quiz
References
CHAPTER 15 Avoiding Common Statistical Mistakes
Introduction
Mistake 1: Overlooking the Importance of Checking Assumptions
Mistake 2: Arbitrarily Selecting the Sample Size
Mistake 3: Deleting Missing Data without Justification
Mistake 4: Mispresentation of the Data
Mistake 5: Implying Causation from Correlation
Mistake 6: Confusing Statistical Results with a Small P-Value with Practical or Clinical Importance
Mistake 7: Extrapolating a Regression Prediction Line
Mistake 8: Confusing Paired Data and Nonpaired Data
Mistake 9: Selecting Inappropriate Statistical Tests
Summary
Critical Thinking Questions
Self-Quiz
References
Appendix A: Random Number Table
Appendix B: How to Select the Correct Statistical Test
Appendix C: Critical Values
Glossary
Index
Statistics for Evidence-Based Practice in Nursing 3rd Edition PDF Ebook

Dr. Kim received his PhD in Applied Statistics and Research Methods at University of Northern Colorado. His research interests include statistics education, statistical process control, and statistical methods. His works have been published in Communications in Statistics – Simulation and Computation, Journal of Modern Applied Statistics Methods, Interstat: Statistics on the internet, Journal of Nursing Education, Pan-Asian Journal of Sport & Physical Education. Dr. Kim teaches graduate level statistics courses for both master’s and PhD students, and provides statistical supports for Mennonite College of Nursing faculty and students. Presently, Dr. Kim is assistant professor and college statistician for Mennonite College of Nursing.

Dr. Mallory received her PhD in Nursing Science at Indiana University in Indianapolis, and completed a postdoctoral fellowship at the School of Nursing at the University of North Carolina at Chapel Hill. Her research on prevention of HIV/AIDS among women has been funded by the National Institutes of Health and Illinois Department of Public Health and published in Health Care for Women International, Qualitative Health Research, The Journal of the Association of Nurses in AIDS Care, Applied Nursing Research, Research in Nursing & Health, and Journal of Advanced Nursing. Dr. Mallory teaches across nursing undergraduate and graduate curricula, including the PhD program. Presently, Dr. Mallory is professor, graduate program coordinator and Associate Dean for Research at Mennonite College of Nursing.