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Interpreting Statistical Findings: A Guide for Health Professionals and Students

Interpreting Statistical Findings: A Guide for Health Professionals and Students

a guide for health professionals and students

9780335235971
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Description
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, Queens University Belfast, UK

Need help interpreting other peoples 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 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!

Product Details
Open University Press
49601
9780335235971
9780335235971

Data sheet

Publication date
2010
Issue number
1
Cover
paperback
Pages count
232
Dimensions (mm)
155 x 231

  • 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- Fishers 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 McNemars 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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