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Statistics and Data Analysis Literacy for Nurses [Minkštas viršelis]

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  • Formatas: Paperback / softback, 330 pages, aukštis x plotis: 254x178 mm, weight: 603 g
  • Išleidimo metai: 21-Nov-2022
  • Leidėjas: Springer Publishing Co Inc
  • ISBN-10: 0826165818
  • ISBN-13: 9780826165817
Kitos knygos pagal šią temą:
Statistics and Data Analysis Literacy for NursesDidesnis paveikslėlis
  • Formatas: Paperback / softback, 330 pages, aukštis x plotis: 254x178 mm, weight: 603 g
  • Išleidimo metai: 21-Nov-2022
  • Leidėjas: Springer Publishing Co Inc
  • ISBN-10: 0826165818
  • ISBN-13: 9780826165817
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9780826165817.html
Raktažodžiai:
Awarded third place in the 2023 AJN Book of the Year Awards in Nursing Research.



Takes the fear out of statistics through a combination of visual and practical learning!



"Statistical acumen and confidence have evaded many nurses for multiple reasons requiring educators to find alternate ways to bridge this knowledge gap. [ This book] fills a void and meets this need. Students and educators alike will benefit from its straightforward, pragmatic style in explaining applied statistical analysis. Drawing on the deep expertise of its authors, the book should become a go-to reference for anyone looking to gain knowledge in applied statistics."



Mary Ellen Smith Glasgow, PhD, RN, ACNS-BC, ANEF, FAAN



Dean and Professor



Duquesne University School of Nursing



The only biostatistics resource written specifically for MSN and DNP nursing students, this text provides a unique graphic representation of data to help students understand difficult analytic information. Modelled on an effective and proven method of learninggraphical illustrations first, technical details secondthe text helps students understand appropriate research design, measurement, and analysis of how results are calculated. Providing a gentle progression into the complexities of statistics, it builds upon concepts step-by-step, helping students to reinforce prior knowledge before moving on the new information.



Chapters initially focus on the practical application of content to illustrate how statistics are used, followed by working examplesaddressing both research and evidence-based content and epidemiology and population health contentto enhance understanding and underscore the importance of the methodology. The book integrates the latest changes from the ASA related to statistical significance tested and addresses recent deliberations about sample size. Abundant Instructor Resources include PowerPoints, a test bank, data sets, information on how to obtain online programming classes, and sample lesson plans and activities.



Key Features:









Uses a proven, step-by-step method including a graphic representation of data followed by analytic content

Follows graphical representation with examples of appropriate research design, measurement, and analysis of how results are calculated

Includes a basic review checklist

Provides chapter objectives, summaries, review questions, and key terms

Offers data sets for those instructors/students who wish to do analyses

Addresses SPSS and other software, including open source (JAMOVI, JASP, and R-Studio)

Includes a comprehensive Instructor Resource Package to ensure faculty have the tools they need to work with students

Recenzijos

Statistical acumen and confidence have evaded many nurses for multiple reasons requiring educators to find alternate ways to bridge this knowledge gap. [ This book] fills a void and meets this need. Students and educators alike will benefit from its straightforward, pragmatic style in explaining applied statistical analysis. Drawing on the deep expertise of its authors, the book should become a go-to reference for anyone looking to gain knowledge in applied statistics." Mary Ellen Smith Glasgow, PhD, RN, ACNS-BC, ANEF, FAAN

Dean and Professor

Duquesne University School of Nursing

Foreword xv
Mary Ellen Smith Glasgow
Preface xvii
Instructor Resources xix
Chapter 1 Statistical Understanding
1(20)
Introduction: The Need for Quantitative Literacy
2(1)
Basic Statistical Understanding
2(1)
Statistical Analysis Argumentation
2(1)
The Need to Focus on the Amount of Difference or Association
3(1)
p-Value Definition
3(1)
The Problem With p-Values
3(1)
p-Hacking the Easy Way
4(1)
Power Versus Accuracy
5(1)
Null-Hypothesis Statistical Test (NHST)
5(2)
Original NHST
7(1)
Current Problems With NHST
7(1)
Where the Focus Should Be and What to Do
7(1)
Am I Being Lied To?
8(1)
Fermi Estimation
8(1)
GRIM
8(2)
Right Censoring
10(1)
Causality
11(1)
Bayes Formula
12(1)
Scholar Before Researcher
13(1)
Summary
14(1)
End-of-Chapter Resources
15(3)
Appendix 1.1 Blood Pressure Null Hypothesis Example
18(3)
Chapter 2 Background Of Design, Measurement, And Analysis
21(24)
Introduction: Research and Quality Improvement Questions
21(1)
Important Quality Issues
22(1)
Traditional Hypotheses
22(1)
Research Questions
23(1)
Variables
23(1)
Independent and Dependent Variables
24(1)
Types of Data and Levels of Measurement
24(3)
Core Measurement Concepts (Believability)
27(1)
Classical Test Theory
27(1)
Item Response Theory
28(1)
Reliability and Stability
29(1)
KR-20/KR-21
29(1)
Cronbach Alpha Versus Guttman Lambda 4
30(1)
Test-Retest Reliability
31(1)
Inter-Rater and Intra-Rater Reliability
31(1)
Cohen's Kappa
31(2)
Validity
33(1)
Types of Validity
33(1)
Face and Content Validity
33(1)
Criterion Validity
34(1)
Construct Validity
34(1)
Consequential Validity
35(1)
Norm Groups
35(1)
Criterion
36(1)
Measurement, Categorization, and Metrics: The Good, Bad, and Ugly
36(2)
Metrics
38(1)
Summary
39(1)
End-of-Chapter Resources
40(5)
Chapter 3 Descriptive Analyses
45(20)
Introduction: Descriptive Analyses and Investigative Journalism
45(1)
Question Focus
45(1)
Analysis of Continuous Data
46(1)
Mean, Median, Mode, Standard Deviation, Variance, Skewness, Kurtosis
46(6)
Analysis of Categorical Data
52(1)
Cross-Tabulations
52(1)
Analysis of Mixed Continuous and Categorical Data
53(1)
Correlation
53(1)
Data Wrangling
54(1)
Missing Data
54(1)
Outliers and Nonnormal Distributions
55(1)
Transformations
56(1)
Examples of Transformations
57(1)
Multivariate Normality
58(1)
Scaling and Z-Scores
58(1)
Summary
59(1)
End-of-Chapter Resources
59(4)
Appendix 3.1
63(2)
Chapter 4 Visual Representations
65(24)
Introduction: The Importance of Visual Representation
65(1)
Figure of the Day
65(1)
Narratives
66(2)
Single Variable-Univariate
68(5)
Multivariate Visualization (MV)
73(1)
Analysis of Mixed Continuous and Categorical
73(4)
Two Continuous Variables
77(4)
Two Continuous With One Categorical Variable
81(1)
Data Wrangling-Visualization
82(1)
Nontraditional Representation
82(1)
Data of Daily Life
82(2)
Best of the Best Representation in History
84(1)
Summary
85(1)
End-of-Chapter Resources
86(3)
Chapter 5 Traditional Study Design
89(26)
Introduction: The Key to Good Design
89(1)
Variables: Independent and Dependent
90(1)
Experimental and Nonexperimental Designs
90(1)
Random Assignment/Selection
90(1)
Experiments
91(1)
Independent Groups
91(2)
Factorial Designs
93(1)
Repeated Measures/Dependency
94(1)
Paired Designs
94(1)
Repeated Designs
95(1)
Paired/Repeated With Independent/Experimental Groups
95(1)
Longitudinal
96(1)
Common Epidemiological Designs
96(1)
Randomized Controlled Trials
97(1)
Cohort Studies
98(1)
Case Control
99(1)
Nonexperimental
100(1)
Design Limitations
101(1)
Internal Validity
102(1)
Internal Social Effects
103(1)
External Validity
104(1)
Ecological Validity
105(1)
Statistical Validity
105(1)
Quality Improvement
106(2)
Sampling and Error
108(1)
Causality (and Back to Argumentation)
109(1)
Summary
109(1)
End-of-Chapter Resources
110(5)
Chapter 6 Variability Between And Within Groups
115(36)
Introduction: Understanding Variability
115(1)
How Much Focus
116(1)
Purpose and Question Focus
117(1)
Designs Associated With Two Group Analyses
117(1)
Graphical and Conceptual Representations
117(1)
Independent t-Test
118(1)
Paired f-Test
119(1)
One-Way ANOVA
119(1)
Technical Section
120(1)
The i-Value
121(2)
Compatibility Intervals: f-Test
123(1)
Effect Sizes
124(1)
Paired f-Tests
125(1)
Compatibility Intervals Paired f-Test and Effect Size
126(1)
One-Way ANOVA
127(1)
Source Table and Compatibility Intervals
127(1)
Effect Sizes
128(1)
Multiple Comparisons
129(2)
Bootstrapping
131(1)
Traditional Power and Alpha
132(1)
Core Results
132(1)
Independent f-Test
132(1)
Paired f-Test Core Results
133(1)
One-Way ANOVA: Core Results
134(1)
Full Independent f-Test Example
135(2)
Nonparametric Analyses
137(1)
Mann-Whitney
138(1)
Assumptions
138(1)
Research Questions, Hypotheses, Designs
138(1)
Mann-Whitney Results
139(1)
Wilcoxon Signed Rank Test
139(1)
Research Questions, Hypotheses, Designs
139(1)
Wilcoxon Results
140(1)
Kruskal-Wallis
140(1)
Research Questions
141(1)
Traditional Hypotheses
141(1)
Assumptions
141(1)
Kruskal-Wallis Results
142(1)
Most Defining Attributes
142(1)
Independent f-Test Attributes
143(1)
Paired (Dependent) t-Test
143(1)
ANOVA
144(1)
Nonparametric
144(1)
Summary
145(1)
End-of-Chapter Resources
145(5)
Appendix 6.1
150(1)
Chapter 7 Variability Between And Within Groups Expanded
151(28)
Introduction: Expanding Groups
151(1)
Question Focus and Purpose
152(1)
Factorial
152(1)
Repeated Measures
153(1)
Repeated Measures With Groups
154(1)
Ancova
154(1)
Hypotheses
155(1)
Graphical Representations
155(1)
Technical Section
155(4)
Factorial ANOVA 25S Source Table
159(1)
Compatibility Intervals and Effect Sizes
160(1)
Sample Size
160(1)
Repeated Measures
160(1)
Source Table and Sums of Squares--Repeated Measures
161(3)
Confidence Intervals and Effect Sizes
164(1)
Sample Size
165(1)
ANCOVA
165(1)
Assumptions
165(1)
Cautions to Start With 226 Analytic Steps
166(1)
Confidence Intervals and Effect Sizes
166(3)
Sample Size ANCOVA
168(1)
Core Results Sections
169(1)
Results Example: Factorial ANOVA
169(1)
ANCOVA
170(1)
Repeated Measures/Repeated Measures With Groups
170(1)
Nonparametric Analyses
171(1)
Most Defining Attributes
171(1)
Common Across Each
171(1)
Factorial ANOVA
172(1)
Repeated Measures/Repeated Measures With Groups
172(1)
ANCOVA
173(1)
Summary
174(1)
End-of-Chapter Resources
174(5)
Chapter 8 Correlation And Regression
179(28)
Introduction: Associations
179(1)
Purpose and Question Focus
180(1)
Correlation
180(1)
Regression
180(1)
Graphic Representation
181(1)
Linear Regression
182(1)
Graphic Representations
182(2)
Technical Section
184(1)
Correlations
184(1)
The Correct Correlation by Data
184(1)
Regression
185(1)
Assumptions
186(2)
Table Details
188(1)
ANOVA Table Adjusted R-Squared
189(1)
Beta Coefficients
189(1)
Data Types and Scales
190(1)
Variable Entry
191(1)
Partial and Part Correlations
192(2)
Sample Size
194(1)
Correcting for Unreliability
194(1)
Core Results
194(1)
Bivariate Regression: Core Results
194(1)
Tables
195(1)
Multivariate: Core Results
196(1)
Tables
197(1)
Effect Sizes
197(1)
Compatibility Intervals
198(1)
Nonparametric
198(1)
Most Defining Attributes--What Should I Look For?
199(1)
Causality, Spurious Correlations, and Regression
199(1)
Summary
200(1)
End-of-Chapter Resources
200(4)
Appendix 8.1
204(3)
Chapter 9 Logistic Regression
207(22)
Introduction: Outcomes Are Groups
207(1)
Purpose and Question Focus
207(1)
Graphic Representation
208(1)
Ogive Curve
209(1)
Technical Section
210(1)
Equations
211(1)
Fit Indices/Effect Size
211(3)
Estimation
214(1)
Compatibility Intervals
214(1)
Data Coding
214(1)
Continuous Variables
215(1)
Multiple Groups
215(1)
Technical Component of the Results Table
215(1)
Residuals
216(1)
Prediction Accuracy Tables
216(2)
ROC Curves
218(1)
Cut-Values, Input, Linearity Test
219(1)
Sample Size
220(1)
Core Results
220(1)
Bivariate
220(3)
Multivariate
223(2)
Most Defining Attributes--What Should I Look For?
225(1)
Summary
226(1)
End-of-Chapter Resources
226(3)
Chapter 10 Introduction To Bayesian Analysis
229(20)
Introduction
229(1)
Graphic Representation
229(3)
Technical Components
232(1)
Bayes Versus Fisher
233(2)
Why Use Bayes
235(1)
Change in Technology
235(1)
Key Topics
236(1)
Priors, What Are They?
237(1)
Likelihood
238(1)
Posterior
239(1)
Bayes Independent f-Test Example
240(1)
Traditional Independent f-Test
240(1)
Bayesian Analysis
241(2)
Change Prior
243(3)
What You Need to Know to Implement Bayes
246(1)
Summary
246(1)
Resource
246(1)
End-of-Chapter Resources
246(3)
Chapter 11 Quality Improvement
249(20)
Introduction: Improvement and Change
249(1)
Basic History
249(3)
Purpose and Question Focus
252(1)
Theory of Change
252(1)
PDCA
252(1)
PDSA
253(1)
PDSA Cycle Example
254(1)
Quantitative Goal
255(1)
Appreciate a System
256(1)
Profound Knowledge
256(2)
Original N-P
258(1)
Areas of Concern, Symptoms, Problems
258(1)
Lean
259(1)
Respect for the Customer
260(1)
Other QI Models
260(2)
Sampling
262(1)
Power Versus Accuracy Versus Time
262(1)
Unit of Analysis
262(1)
Risk
262(1)
Pecuniary Value /Cost
262(1)
PICO (T)
262(1)
Data Collection
263(1)
Analyses
263(1)
Healthcare QI Dimensions
263(1)
Summary
264(1)
End-of-Chapter Resources
264(5)
Chapter 12 Humility
269(12)
Introduction: Refocusing Your Thoughts
269(1)
Why Your Results Are Probably Wrong
269(1)
Stats and the Assumption on Randomness
270(2)
ATOM Model of Analysis From the American Statistical Association
272(1)
Accept Uncertainty
272(1)
Be Thoughtful
272(1)
Ziliak's Guinnessonometrics
273(1)
Openness
273(1)
Modesty
274(1)
What You Can Do
275(1)
Importance of Theory Regardless of Sample Size
275(1)
Probabilistic Causality
275(1)
Avoid Confirmation Bias
275(1)
Replicate the Work
276(1)
Interpret Within the Context of the Study
276(1)
Authors
276(1)
Detailed Measurement and Methods
277(1)
Summary
278(1)
End-of-Chapter Resources
278(3)
Appendix: Review Question Answers and Rationales 281(20)
Index 301
James B. Schreiber, PhD, is a professor of epidemiology and statistics in the School of Nursing at Duquesne University.

Melanie T. Turk, PhD, MSN, RN, is an Associate Professor at Duquesne University School of Nursing.