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Scientific Research and Methodology: An Introduction to Quantitative Research and Statistics

Autor Peter K. Dunn
en Limba Engleză Hardback – 18 aug 2025
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.
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Specificații

ISBN-13: 9781032496726
ISBN-10: 103249672X
Pagini: 552
Ilustrații: 522
Dimensiuni: 178 x 254 x 34 mm
Greutate: 1.18 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC

Public țintă

Undergraduate Core

Cuprins

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

Notă biografică

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.

Descriere

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.