Statistics for Advanced Practice Nurses and Health Professionals / Edition 1

by Manfred Stommel PhD, Katherine J. Dontje PhD, FNP-BC

Overview

This comprehensive, graduate-level text for advanced practice nurses and other health care professionals provides state-of-the-art tools that facilitate the reading and interpretation of clinical research articles that use increasingly complex statistical techniques. It addresses clinically relevant topics in biostatistics beyond the usual introduction to linear models, such as survival analysis and evaluation of screening tests. The text emphasizes the importance of understanding the underlying logic of statistical inference and statistical models to support correct interpretation and effective translation into practice. It promotes appropriate statistical method selection for conducting translational research.

With a focus on disseminating information in easily understandable language, the text addresses basic statistical reasoning and four different classes of statistical models. The appendix provides refreshers on the algebraic underpinnings of statistics. More complex algebraic derivations are highlighted in boxes throughout the text chapters. The text explains how to work with the exponential expressions and logarithms necessary for the interpretation of logistic and hazard regression models and features clear explanations of more sophisticated statistical models, inference, and analyses. Chapters include examples from current research and multiple exercises designed to reinforce learning.

Key Features:

• End-of-chapter exercises include both problems of interpretation and numerical problems that can be solved via hand calculations.
• For instructors and students interested in practical data analysis, data sets and practice problems are available from Springer Publishing Company's website with instructions in SPSS, STATA, and SAS formats.
• At the end of each chapter is a "Literature Application" text box with interpretation questions about a recent research article that highlights the statistical model discussed in the chapter.
• Throughout the book, text boxes highlight the most important algebraic formulas useful in interpreting statistical methods.
• A chapter on data management practices and ethical issues of privacy maintenance is included.
• Nine appendices provide tables of major probability distributions, for example, normal, t- and F-distributions, and algebraic derivations of some of the most important results in statistics.

Product Details

ISBN-13:	9780826198242
Publisher:	Springer Publishing Company
Publication date:	06/09/2014
Edition description:	New Edition
Pages:	350
Product dimensions:	6.90(w) x 9.80(h) x 0.80(d)

About the Author

Manfred Stommel, PhD, is Professor, College of Nursing, Michigan State University.

Katherine Dontje, PhD, FNP-BC, is Director of the Graduate Clinical Programs and Assistant Professor/Nurse Practitioner, College of Nursing, Michigan State University.

Table of Contents

Preface

Acknowledgments

PART I: FOUNDATIONS FOR STATISTICAL THINKING

1. Introduction: The Role of Statistics in Research and Clinical Practice

2. Properties of Variables: Levels of Measurement

3. Descriptive Univariate Statistics

4. Probabilities

5. Logic of Statistical Inference: The Sampling Distribution and Significance Tests

6. Standard Errors, Confidence Intervals, and the Power of Statistical Tests

7. Research Designs and Statistical Analysis

PART II: MODELS FOR CONTINUOUS/INTERVAL-LEVEL OUTCOME MEASURES

8. t-Test

9. One-Way Analysis of Variance

10. Linear Regression and Pearson’s r Correlation

11. Factorial Analysis of Variance and Analysis of Covariance

12. Multiple Linear Regression

13. Repeated-Measures Analysis of Variance

14. Introduction to Mixed-Effects Regression Models

PART III: MODELS FOR CATEGORICAL OUTCOME MEASURES

15. Nonparametric/Ordinal Statistics

16. Frequency Cross-Tabulations: 2 ˚ 2 Tables

17. Logistic Regression With One Independent Variable

18. Logistic Regression Models With Multiple Predictors

PART IV: MODELS FOR TIME-TO-EVENT DATA/SURVIVAL ANALYSIS

19. Incidence Rates, Life Tables, and Survival Function

20. Comparing Survival Functions in Different Groups and Hazard Regression

PART V: MEASUREMENT MODELS

21. Reliability Coefficients and Medical Test Evaluation

22. Factor Analysis

PART VI: ISSUES IN DATA MANAGEMENT

23. Data Management and Privacy Concerns

APPENDICES

A. Estimating Population Variance From Sample Variance

B. One-Sided Probabilities for z-Scores of the Standard Normal Distribution

C. Table of Critical t-Values for Several Significance Levels of t-Distributions With Different Degrees of Freedom (df)

D. Normalizing a Nonnormal Distribution

E. Table of Critical f-Values at the Significance Level of a = 0.05 of f-Distributions With Different Degrees of Freedom (df)

F. Proof That Phi = Pearson’s r

G. Table of Critical Chi-Square Values for Several Significance Levels of Chi-Square Distributions With Different Degrees of Freedom (df)

H. Refresher on Exponential and Logarithmic Transformations

I. Standardization of Interval-Level Variables

J. Answers to Selected Exercises

Index

Editorial Reviews

Reviewer: Michalene A King, PhD, RN, CNE (Robert Morris University)
Description: This review of basic statistical tests and their application to healthcare research is designed to help nurses evaluate research studies to find the best evidence for clinical nursing practice.
Purpose: According to the authors, "the overall goal of this book is to provide the learner with a larger range of statistical models and concepts, which are used in clinical research and as a basis for evidence-based clinical practice," a worthy goal they meet.
Audience: The intended audience is graduate healthcare students, specifically students in doctor of nursing practice (DNP) programs and other advanced healthcare providers. Both authors are professors with extensive research experience, and one is the director of graduate clinical programs and a nurse practitioner.
Features: The first of the book's six sections discusses statistical concepts including design and statistical analysis. The next three describe models for various types of studies. Section five discusses measurement models and section six describes data management issues. The book includes statistical formulas with computation of statistics in the examples. One unique feature is access to online datasets for SPSS, SAS, STATA, and Excel that students can use. One problem area that I have seen with DNP students is selecting a design for their capstone projects. The chapter on design is brief and doesn't give enough information to assist in design choice.
Assessment: This is a unique book that focuses more on working statistical problems than discovering the appropriate statistics to use for a project/clinical problem. While it is useful to understand the computation of statistics, it is more useful for students to understand the mechanics of entering data into a computer program and applying the data to their project.

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