Atjaunināt sīkdatņu piekrišanu

Practical Approach to Using Statistics in Health Research: From Planning to Reporting [Hardback]

(Liverpool John Moores University, Liverpool, UK), (Aston University in Birmingham, UK)
  • Formāts: Hardback, 240 pages, height x width x depth: 221x145x18 mm, weight: 476 g
  • Izdošanas datums: 08-Jun-2018
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 1119383579
  • ISBN-13: 9781119383574
Citas grāmatas par šo tēmu:
  • Hardback
  • Cena: 135,28 €
  • Grāmatu piegādes laiks ir 3-4 nedēļas, ja grāmata ir uz vietas izdevniecības noliktavā. Ja izdevējam nepieciešams publicēt jaunu tirāžu, grāmatas piegāde var aizkavēties.
  • Daudzums:
  • Ielikt grozā
  • Piegādes laiks - 4-6 nedēļas
  • Pievienot vēlmju sarakstam
  • Bibliotēkām
Practical Approach to Using Statistics in Health Research: From Planning to ReportingPalielināt
  • Formāts: Hardback, 240 pages, height x width x depth: 221x145x18 mm, weight: 476 g
  • Izdošanas datums: 08-Jun-2018
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 1119383579
  • ISBN-13: 9781119383574
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781119383574.html
Keywords:
"This book provides an outline with methodological steps of how to use statistics to analyze your research data. The book begins with a general introduction, which discusses what you should be trying to achieve with your statistical analysis. This involves describing the subjects you investigated and their outcomes, determining whether there is statistically significant evidence of differences in outcomes between groups of subjects, quantitatively describing effect sizes, and also determining whether anychanges are large enough to be of clinical significance. Next, the authors cover data types and choosing statistical tests. This includes identifying the factor and outcome, and also identifying the type of data used to record the outcome. Readers are then introduced to multiple testing, the Chi-square test, and independent samples and the two-sample t-test. The Man-Whitney test is discussed, as well as the One-way ANOVA. Readers are taught how to Carrying out the Kruskal-Wallis test and the McNemar's test. The Paired t-test is covered, as well as how to carry out the Wilcoxon paired samples test. Readers are shown how to carry out the repeated measures ANOVA and the Friedman test. This includes discussion of merits of change in median, change in proportions in categories, and changes in high/low categories. The book concludes with a discussion on correlation and regression methods, and a detailed analysis on Cronbach's alpha"--

A hands-on guide to using statistics in health research, from planning, through analysis, and on to reporting

A Practical Approach to Using Statistics in Health Research offers an easy to use, step-by-step guide for using statistics in health research. The authors use their experience of statistics and health research to explain how statistics fit in to all stages of the research process. They explain how to determine necessary sample sizes, interpret whether there are statistically significant difference in outcomes between groups, and use measured effect sizes to decide whether any changes are large enough to be relevant to professional practice.

The text walks you through how to identify the main outcome measure for your study and the factor which you think may influence that outcome and then determine what type of data will be used to record both of these.  It then describes how this information is used to select the most appropriate methods to report and analyze your data.  A step-by-step guide on how to use a range of common statistical procedures are then presented in separate chapters.  To help you make sure that you are using statistics robustly, the authors also explore topics such as multiple testing and how to check whether measured data follows a normal distribution.  Videos showing how to use computer packages to carry out all the various methods mentioned in the book are available on our companion web site. This book:

•    Covers statistical aspects of all the stages of health research from planning to final reporting

•    Explains how to report statistical planning, how analyses were performed, and the results and conclusion

•    Puts the spotlight on consideration of clinical significance and not just statistical significance

•    Explains the importance of reporting 95% confidence intervals for effect size

•    Includes a systematic guide for selection of statistical tests and uses example data sets and videos to help you understand exactly how to use statistics

Written as an introductory guide to statistics for healthcare professionals, students and lecturers in the fields of pharmacy, nursing, medicine, dentistry, physiotherapy, and occupational therapy, A Practical Approach to Using Statistics in Health Research:From Planning to Reporting is a handy reference that focuses on the application of statistical methods within the health research context. 

About the Companion Website xv
1 Introduction
1(6)
1.1 At Whom is This Book Aimed?
1(1)
1.2 At What Scale of Project is This Book Aimed?
2(1)
1.3 Why Might This Book be Useful for You?
2(1)
1.4 How to Use This Book
3(1)
1.5 Computer Based Statistics Packages
4(1)
1.6 Relevant Videos etc.
5(2)
2 Data Types
7(10)
2.1 What Types of Data are There and Why Does it Matter?
7(1)
2.2 Continuous Measured Data
7(6)
2.2.1 Continuous Measured Data -- Normal and Non-Normal Distribution
8(5)
2.2.2 Transforming Non-Normal Data
13(1)
2.3 Ordinal Data
13(1)
2.4 Categorical Data
14(1)
2.5 Ambiguous Cases
14(1)
2.5.1 A Continuously Varying Measure that has been Divided into a Small Number of Ranges
14(1)
2.5.2 Composite Scores with a Wide Range of Possible Values
15(1)
2.6 Relevant Videos etc.
15(2)
3 Presenting and Summarizing Data
17(10)
3.1 Continuous Measured Data
17(4)
3.1.1 Normally Distributed Data -- Using the Mean and Standard Deviation
18(1)
3.1.2 Data With Outliers, e.g. Skewed Data -- Using Quartiles and the Median
18(2)
3.1.3 Polymodal Data -- Using the Modes
20(1)
3.2 Ordinal Data
21(2)
3.2.1 Ordinal Scales With a Narrow Range of Possible Values
22(1)
3.2.2 Ordinal Scales With a Wide Range of Possible Values
22(1)
3.2.3 Dividing an Ordinal Scale Into a Small Number of Ranges (e.g. Satisfactory/Unsatisfactory or Poor/Acceptable/Good)
22(1)
3.2.4 Summary for Ordinal Data
23(1)
3.3 Categorical Data
23(1)
3.4 Relevant Videos etc.
24(3)
Appendix 1 An Example of the Insensitivity of the Median When Used to Describe Data from an Ordinal Scale With a Narrow Range of Possible Values
25(2)
4 Choosing a Statistical Test
27(12)
4.1 Identify the Factor and Outcome
27(2)
4.2 Identify the Type of Data Used to Record the Relevant Factor
29(1)
4.3 Statistical Methods Where the Factor is Categorical
30(4)
4.3.1 Identify the Type of Data Used to Record the Outcome
30(1)
4.3.2 Is Continuous Measured Outcome Data Normally Distributed or Can It Be Transformed to Normality?
30(1)
4.3.3 Identify Whether Your Sets of Outcome Data Are Related or Independent
31(1)
4.3.4 For the Factor, How Many Levels Are Being Studied?
32(1)
4.3.5 Determine the Appropriate Statistical Method for Studies with a Categorical Factor
32(2)
4.4 Correlation and Regression with a Measured Factor
34(4)
4.4.1 What Type of Data Was Used to Record Your Factor and Outcome?
34(1)
4.4.2 When Both the Factor and the Outcome Consist of Continuous Measured Values, Select Between Pearson and Spearman Correlation
34(4)
4.5 Relevant Additional Material
38(1)
5 Multiple Testing
39(4)
5.1 What Is Multiple Testing and Why Does It Matter?
39(1)
5.2 What Can We Do to Avoid an Excessive Risk of False Positives?
40(3)
5.2.1 Use of Omnibus Tests
40(1)
5.2.2 Distinguishing Between Primary and Secondary/Exploratory Analyses
40(1)
5.2.3 Bonferroni Correction
41(2)
6 Common Issues and Pitfalls
43(12)
6.1 Determining Equality of Standard Deviations
43(1)
6.2 How Do I Know, in Advance, How Large My SD Will Be?
43(1)
6.3 One-Sided Versus Two-Sided Testing
44(1)
6.4 Pitfalls That Make Data Look More Meaningful Than It Really Is
45(2)
6.4.1 Too Many Decimal Places
45(2)
6.4.2 Percentages with Small Sample Sizes
47(1)
6.5 Discussion of Statistically Significant Results
47(3)
6.6 Discussion of Non-Significant Results
50(1)
6.7 Describing Effect Sizes with Non-Parametric Tests
51(1)
6.8 Confusing Association with a Cause and Effect Relationship
52(3)
7 Contingency Chi-Square Test
55(18)
7.1 When Is the Test Appropriate?
55(1)
7.2 An Example
55(2)
7.3 Presenting the Data
57(2)
7.3.1 Contingency Tables
57(1)
7.3.2 Clustered or Stacked Bar Charts
57(2)
7.4 Data Requirements
59(1)
7.5 An Outline of the Test
59(1)
7.6 Planning Sample Sizes
59(1)
7.7 Carrying Out the Test
60(1)
7.8 Special Issues
61(1)
7.8.1 Yates Correction
61(1)
7.8.2 Low Expected Frequencies -- Fisher's Exact Test
61(1)
7.9 Describing the Effect Size
61(3)
7.9.1 Absolute Risk Difference (ARD)
62(1)
7.9.2 Number Needed to Treat (NNT)
63(1)
7.9.3 Risk Ratio (RR)
63(1)
7.9 A Odds Ratio (OR)
64(1)
7.9.5 Case: Control Studies
65(1)
7.10 How to Report the Analysis
65(2)
7.10.1 Methods
65(1)
7.10.2 Results
66(1)
7.10.3 Discussion
67(1)
7.11 Confounding and Logistic Regression
67(2)
7.11.1 Reporting the Detection of Confounding
68(1)
7.12 Larger Tables
69(1)
7.12.1 Collapsing Tables
69(1)
712.2 Reducing Tables
70(1)
7.13 Relevant Videos etc.
71(2)
8 Independent Samples (Two-Sample) T-Test
73(10)
8.1 When Is the Test Applied?
73(1)
8.2 An Example
73(2)
8.3 Presenting the Data
75(1)
8.3.1 Numerically
75(1)
8.3.2 Graphically
75(1)
8.4 Data Requirements
75(3)
8.4.1 Variables Required
75(1)
8.4.2 Normal Distribution of the Outcome Variable Within the Two Samples
75(3)
8.4.3 Equal Standard Deviations
78(1)
8.4.4 Equal Sample Sizes
78(1)
8.5 An Outline of the Test
78(1)
8.6 Planning Sample Sizes
79(1)
8.7 Carrying Out the Test
79(1)
8.8 Describing the Effect Size
79(1)
8.9 How to Describe the Test, the Statistical and Practical Significance of Your Findings in Your Report
80(1)
8.9.1 Methods Section
80(1)
8.9.2 Results Section
80(1)
8.9.3 Discussion Section
81(1)
8.10 Relevant Videos etc.
81(2)
9 Mann-Whitney Test
83(10)
9.1 When Is the Test Applied?
83(1)
9.2 An Example
83(2)
9.3 Presenting the Data
85(1)
9.3.1 Numerically
85(1)
9.3.2 Graphically
85(1)
9.3.3 Divide the Outcomes into Low and High Ranges
85(1)
9.4 Data Requirements
86(1)
9.4.1 Variables Required
86(1)
9.4.2 Normal Distributions and Equality of Standard Deviations
87(1)
9.4.3 Equal Sample Sizes
87(1)
9.5 An Outline of the Test
87(1)
9.6 Statistical Significance
87(1)
9.7 Planning Sample Sizes
87(1)
9.8 Carrying Out the Test
88(1)
9.9 Describing the Effect Size
88(1)
9.10 How to Report the Test
89(2)
9.10.1 Methods Section
89(1)
9.10.2 Results Section
89(1)
9.10.3 Discussion Section
90(1)
9.11 Relevant Videos etc.
91(2)
10 One-Way Analysis of Variance (ANOVA) -- Including Dunnett's and Tukey's Follow Up Tests
93(612)
10.1 When Is the Test Applied?
93(1)
10.2 An Example
93(1)
10.3 Presenting the Data
94(1)
10.3.1 Numerically
94(1)
10.3.2 Graphically
94(1)
10.4 Data Requirements
94(4)
10.4.1 Variables Required
94(1)
10.4.2 Normality of Distribution for the Outcome Variable Within the Three Samples
95(1)
10.4.3 Standard Deviations
96(2)
10.4.4 Sample Sizes
98(1)
10.5 An Outline of the Test
98(1)
10.6 Follow Up Tests
98(1)
10.7 Planning Sample Sizes
99(1)
10.8 Carrying Out the Test
100(1)
10.9 Describing the Effect Size
101(1)
10.10 How to Report the Test
101(2)
10.10.1 Methods
101(1)
10.10.2 Results Section
102(1)
10.10.3 Discussion Section
102(1)
10.11 Relevant Videos etc.
103(2)
11 Kruskal-Wallis
105(1)
11.1 When Is the Test Applied?
105(1)
11.2 An Example
105(1)
11.3 Presenting the Data
106(1)
11.3.1 Numerically
106(1)
11.3.2 Graphically
107(2)
11.4 Data Requirements
109(1)
11.4.1 Variables Required
109(1)
11.4.2 Normal Distributions and Standard Deviations
109(1)
11.4.3 Equal Sample Sizes
110(1)
11.5 An Outline of the Test
110(1)
11.6 Planning Sample Sizes
110(1)
11.7 Carrying Out the Test
110(1)
11.8 Describing the Effect Size
111(1)
11.9 Determining Which Group Differs from Which Other
111(1)
11.10 How to Report the Test
111(3)
11.10.1 Methods Section
111(1)
11.10.2 Results Section
112(1)
11.10.3 Discussion Section
113(1)
11.11 Relevant Videos etc.
114(1)
12 McNemar's Test
115(1)
12.1 When Is the Test Applied?
115(1)
12.2 An Example
115(1)
12.3 Presenting the Data
116(1)
12.4 Data Requirements
116(2)
12.5 An Outline of the Test
118(1)
12.6 Planning Sample Sizes
118(1)
12.7 Carrying Out the Test
119(1)
12.8 Describing the Effect Size
119(1)
12.9 How to Report the Test
119(1)
12.9.1 Methods Section
119(1)
12.9.2 Results Section
120(1)
12.9.3 Discussion Section
120(1)
12.10 Relevant Videos etc.
121(2)
13 Paired T-Test
123(10)
13.1 When Is the Test Applied?
123(2)
13.2 An Example
125(1)
13.3 Presenting the Data
125(1)
13.3.1 Numerically
125(1)
13.3.2 Graphically
125(1)
13.4 Data Requirements
126(1)
13.4.1 Variables Required
126(1)
13.4.2 Normal Distribution of the Outcome Data
126(2)
13.4.3 Equal Standard Deviations
128(1)
13.4.4 Equal Sample Sizes
128(1)
13.5 An Outline of the Test
128(1)
13.6 Planning Sample Sizes
129(1)
13.7 Carrying Out the Test
129(1)
13.8 Describing the Effect Size
129(1)
13.9 How to Report the Test
130(1)
13.9.1 Methods Section
130(1)
13.9.2 Results Section
130(1)
13.9.3 Discussion Section
131(1)
13.10 Relevant Videos etc.
131(2)
14 Wilcoxon Signed Rank Test
133(10)
14.1 When Is the Test Applied?
133(1)
14.2 An Example
134(1)
14.3 Presenting the Data
134(2)
14.3.1 Numerically
134(2)
14.3.2 Graphically
136(1)
14.4 Data Requirements
136(1)
14.4.1 Variables Required
136(1)
14.4.2 Normal Distributions and Equal Standard Deviations
137(1)
14.4.3 Equal Sample Sizes
137(1)
14.5 An Outline of the Test
137(1)
14.6 Planning Sample Sizes
138(1)
14.7 Carrying Out the Test
139(1)
14.8 Describing the Effect Size
139(1)
14.9 How to Report the Test
140(1)
14.9.1 Methods Section
140(1)
14.9.2 Results Section
140(1)
14.9.3 Discussion Section
141(1)
14.10 Relevant Videos etc.
141(2)
15 Repeated Measures Analysis of Variance
143(12)
15.1 When Is the Test Applied?
143(1)
15.2 An Example
144(1)
15.3 Presenting the Data
144(2)
15.3.1 Numerical Presentation of the Data
145(1)
15.3.2 Graphical Presentation of the Data
145(1)
15.4 Data Requirements
146(2)
15.4.1 Variables Required
146(2)
15.4.2 Normal Distribution of the Outcome Data
148(1)
15.4.3 Equal Standard Deviations
148(1)
15.4.4 Equal Sample Sizes
148(1)
15.5 An Outline of the Test
148(1)
15.6 Planning Sample Sizes
149(1)
15.7 Carrying Out the Test
150(1)
15.8 Describing the Effect Size
150(1)
15.9 How to Report the Test
151(2)
15.9.1 Methods Section
151(1)
15.9.2 Results Section
151(1)
15.9.3 Discussion Section
152(1)
15.10 Relevant Videos etc.
153(2)
16 Friedman Test
155(110)
16.1 When Is the Test Applied?
155(2)
16.2 An Example
157(1)
16.3 Presenting the Data
157(3)
16.3.1 Bar Charts of the Outcomes at Various Stages
157(1)
16.3.2 Summarizing the Data via Medians or Means
157(2)
16.3.3 Splitting the Data at Some Critical Point in the Scale
159(1)
16.4 Data Requirements
160(1)
16.4.1 Variables Required
160(1)
16.4.2 Normal Distribution and Standard Deviations in the Outcome Data
160(1)
16.4.3 Equal Sample Sizes
160(1)
16.5 An Outline of the Test
160(1)
16.6 Planning Sample Sizes
161(1)
16.7 Follow Up Tests
161(1)
16.8 Carrying Out the Tests
162(1)
16.9 Describing the Effect Size
162(1)
16.9.1 Median or Mean Values Among the Individual Changes
162(1)
16.9.2 Split the Scale
162(1)
16.10 How to Report the Test
162(2)
16.10.1 Methods Section
162(1)
16.10.2 Results Section
163(1)
16.10.3 Discussion Section
164(1)
16.11 Relevant Videos etc.
164(1)
17 Pearson Correlation
165(1)
17.1 Presenting the Data
165(1)
17.2 Correlation Coefficient and Statistical Significance
166(1)
17.3 Planning Sample Sizes
167(1)
17.4 Effect Size and Practical Relevance
167(2)
17.5 Regression
169(1)
17.6 How to Report the Analysis
170(102)
17.6.1 Methods
170(1)
17.6.2 Results
170(1)
17.6.3 Discussion
171(1)
17.7 Relevant Videos etc.
171(2)
18 Spearman Correlation
173(1)
18.1 Presenting the Data
173(1)
18.2 Testing for Evidence of Inappropriate Distributions
174(1)
18.3 Rho and Statistical Significance
174(1)
18.4 An Outline of the Significance Test
175(1)
18.5 Planning Sample Sizes
175(1)
18.6 Effect Size
176(1)
18.7 Where Both Measures Are Ordinal
176(1)
18.7.1 Educational Level and Willingness to Undertake Internet Research -- An Example Where Both Measures Are Ordinal
176(1)
18.7.2 Presenting the Data
177(1)
18.7.3 Rho and Statistical Significance
177(1)
18.7.4 Effect Size
178(1)
18.8 How to Report Spearman Correlation Analyses
178(2)
18.8.1 Methods
178(1)
18.8.2 Results
179(1)
18.8.3 Discussion
180(1)
18.9 Relevant Videos etc.
180(1)
19 Logistic Regression
181(8)
19.1 Use of Logistic Regression with Categorical Outcomes
181(1)
19.2 An Outline of the Significance Test
182(1)
19.3 Planning Sample Sizes
182(2)
19.4 Results of the Analysis
184(1)
19.5 Describing the Effect Size
184(1)
19.6 How to Report the Analysis
185(2)
19.6.1 Methods
185(1)
19.6.2 Results
186(1)
19.6.3 Discussion
186(1)
19.7 Relevant Videos etc.
187(2)
20 Cronbach's Alpha
189(8)
20.1 Appropriate Situations for the Use of Cronbach's Alpha
189(1)
20.2 Inappropriate Uses of Alpha
190(1)
20.3 Interpretation
190(1)
20.4 Reverse Scoring
191(1)
20.5 An Example
191(1)
20.6 Performing and Interpreting the Analysis
192(1)
20.7 How to Report Cronbach's Alpha Analyses
193(2)
20.7.1 Methods Section
193(1)
20.7.2 Results
194(1)
20.7.3 Discussion
194(1)
20.7 Relevant Videos etc.
195(2)
Glossary 197(12)
Videos 209(2)
Index 211
Adam Mackridge, Ph.D., is a Research Pharmacist at Betsi Cadwaladr University Health Board in North Wales. He has over 15 years of experience in planning, conducting and reporting health research. He received his PhD in Pharmacy Practice from Aston University in Birmingham, UK.

Philip Rowe, Ph.D., is a Visiting Research Fellow in the School of Pharmacy and Molecular Sciences at Liverpool John Moores University, Liverpool, UK. He is a Fellow of the Royal Statistical Society and has authored other statistically based books for Wiley.