Read more
Informationen zum Autor Jan Walker worked as a health visitor for eight years before taking a degree in psychology and then a PhD which focused pain in later life. Working as a university lecturer and reader, she taught psychology on a wide range of courses for nurses, allied health, medical and social care professionals, from introductory to masters level. She has held honorary contracts with several pain clinics, helping individuals to identify goals and resources for self management. She has conducted, supervised and published research on the lived experience of chronic pain and other chronic conditions. Jan is currently a Visiting Senior Research Fellow at the University of Southampton where she works closely with the Complementary Medicine Research Unit. Klappentext Need 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 conceptsWalker 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! Zusammenfassung This book is aimed at those studying and working in the field of health care! including nurses and the professions allied to medicine! who have little prior knowledge of statistics but for whom critical review of research is an essential skill. Inhaltsverzeichnis Part 1 Worked Examples The 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 guideI 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 ...
List of contents
Part 1 Worked Examples
The 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
