"This book makes the task of interpreting statistical findings much more approachable and less daunting for those with little, or no, previous experience, and will provide a valuable reference for the more experienced researcher. I would recommend it to any student undertaking a Nursing Research module."
Conor Hamilton, Student Nurse, Queen’s University Belfast, UKNeed help interpreting other people's health research?This book offers guidance for students undertaking a critical review of quantitative research papers and will also help health professionals to understand and interpret statistical results within health-related research papers. The book requires little knowledge of statistics, includes worked examples and is broken into the following sections:
A worked example of a published RCT and a health surveyExplanations of basic statistical conceptsExplanations of common statistical tests A quick guide to statistical terms and concepts
Walker and Almond have helpfully cross-referenced throughout, so those requiring in-depth explanations or additional worked examples can locate these easily.Interpreting Statistical Research Findings is key reading for nursing and health care students and will help make this area of research much easier to tackle!
Part 1 Worked ExamplesThe randomised controlled trial
The Health survey
Part 2 Interpreting statistical concepts
Measuring variables: continuous, ordinal and categorical data
Describing continuous data: The normal distribution
Describing nonparametric data
Measuring concepts: Validity and reliability
Sampling data: Probability and non-probability samples
Sample size: criteria for judging adequacy
Testing hypotheses: what does p actually mean?
Part 3 Statistical tests
Introduction to inferential statistics
Comparing two independent (unrelated) groups: independent (unrelated) t test, Mann-Whitney U test, contingency analysis- Fisher's exact test and Chi-square test
Comparing three or more independent (unrelated) groups: One-way ANOVA, Kruskal Wallis test and Chi-square test
Comparing two sets of related data: Matched pairs or single-sample repeated measures- related (paired) t test, Wilcoxon signed rank test, sign test and McNemar's test
Complex group comparisons: ANOVA / ANCOVA, Friedman two-way ANOVA by ranks and Cochrane Q test
Simple tests of association: Correlation and linear regression
complex associations: Multiple and logistic regression
Part 4 Quick reference guide
I Framework for statistical review
II Glossary of terms
III Guide to statistical symbols
IV Overview of common statistical tests
V Guide to the assumptions that underpin statistical tests
VI Summary of statistical test selection and results
VII Extracts from statistical tables
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