Interpreting Statistical Findings: A Guide for Health Professionals and Students
By: Jan Walker (author), Palo Almond (author)Paperback
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"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, UK 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 survey Explanations of basic statistical concepts Explanations 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!
Jan Walker is an experienced researcher, lecturer and supervisor. Her previous books include Psychology for Nurses and the Caring Professions 3/e (Open University Press, 2007). Palo Almond is a lecturer in public health and primary health care and an experienced research supervisor. She has over 30 years of experience in nursing, public health and midwifery practice, research and education.
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
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