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E-raamat: Scientific Research and Methodology: An Introduction to Quantitative Research and Statistics [Taylor & Francis e-raamat]

(Associate Professor, Uni of Sushine Coast.)
  • Formaat: 534 pages, 136 Tables, black and white; 3 Line drawings, color; 258 Line drawings, black and white; 3 Illustrations, color; 258 Illustrations, black and white
  • Ilmumisaeg: 19-Aug-2025
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-13: 9781003394938
  • Taylor & Francis e-raamat
  • Hind: 133,87 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Tavahind: 191,24 €
  • Säästad 30%
  • Formaat: 534 pages, 136 Tables, black and white; 3 Line drawings, color; 258 Line drawings, black and white; 3 Illustrations, color; 258 Illustrations, black and white
  • Ilmumisaeg: 19-Aug-2025
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-13: 9781003394938

This textbook is designed for teaching quantitative research in the scientific, health and engineering disciplines at first-year undergraduate level, with an emphasis on statistics. Almost every dataset used is a real dataset, and is available online or in an associated R package SRMData.



This textbook is designed for teaching quantitative research in the scientific, health and engineering disciplines at first-year undergraduate level, with an emphasis on statistics. It covers the research process, including asking research questions, research design, data collection, summarising data, analysis and communication. Many real journal articles are used throughout the text as examples that demonstrate the use of the techniques.

Students are introduced to statistics as a method for answering questions. Descriptive research questions lead to analysis of single proportions and means. Repeated-measures research questions are answered using paired quantitative data. Relational research questions compare proportions, odds and means in different groups. Correlational research questions are studied using correlation and regression techniques.

Statistical topics include numerical summary methods (such as means, odds ratios and identification of outliers), graphing (such as histograms, case-profile plots and scatterplots), confidence intervals and hypothesis testing. Emphasis is placed on understanding and concepts; while calculations are shown in simple situations, they are deferred to software when the computations become tedious and disruptive to understanding.

Almost every dataset used is a real dataset, and is available online or in an associated R package SRMData. Software output is often used when calculations become onerous. The output is sufficiently generic that the book can be used in conjunction with any statistical software.

Preface
1. Research: an introduction Part I. Asking research questions
2. Research questions Part II. Research design
3. Overview of research design
4. Types of research studies
5. Ethics in research
6. External validity:
sampling
7. Internal validity
8. Research design limitations Part III.
Collecting data
9. Collecting data Part IV. Classifying and summarising data
10. Classifying data and variables
11. Summarising quantitative data
12.
Summarising qualitative data
13. Comparing quantitative data within
individuals
14. Comparing quantitative data between individuals
15. Comparing
qualitative data between individuals
16. Correlations between quantitative
variables
17. More details about tables and graphs Part V. Tools for
answering RQs
18. Probability
19. Sampling variation
20. Models and normal
distributions Part VI. Analysis
21. Introducing inference
22. Confidence
intervals: one proportion
23. Confidence intervals: one mean
24. More details
about CIs
25. Making decisions
26. Hypothesis tests: one proportion
27.
Hypothesis tests: one mean
28. More details about hypothesis testing
29. CIs
and tests: mean differences (paired data)
30. CIs and tests: comparing two
means
31. CIs and tests: comparing two odds or proportions
32. Finding sample
sizes for CIs
33. Correlation and regression
34. Selecting an analysis Part
VII. Reporting and reading research
35. Reporting and writing research
36.
Reading and critiquing research Appendix A. Datasets B. -score tables C.
Symbols, formulas, statistics and parameters Glossary Answers to odd-numbered
exercises Bibliography Index
Peter K. Dunn is Associate Professor of Biostatistics in the School of Science, Technology and Engineering at the University of the Sunshine Coast. He has published in the areas of generalized linear models, Tweedie distributions and statistical education, and has authored numerous R packages. He is an award-winning statistical educator who has been teaching statistics for over 30 years.