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Introducing Quantitative Methods: A Practical Guide

Autor Daniela Aidley
en Limba Engleză Paperback – 14 oct 2018
This exciting new core textbook offers a clear and practical introduction to quantitative methods, taking a project-based approach. The author's extensive knowledge and straightforward writing style ensure that students are steered through the process step-by-step, from developing research questions and preparing data for analysis, to explaining how to present data in appropriate formats, avoid bias, and write up results and reports. Featuring a comprehensive pedagogical framework and companion website, readers are encouraged to follow practice analyses as they go, with examples given in both SPSS and Excel, and templates are provided for students' own research. In addition to covering the research project, chapters also cover the essential mathematical and statistical analyses that are a logical consequence of posing a quantitative research methods question.This is the perfect text for all social science students studying introductory modules on quantitative methods, research methods or statistics at undergraduate or postgraduate level. It also functions as an effective guide for undergraduate and postgraduate students faced with an independent research project.Accompanying online resources for this title can be found at bloomsburyonlineresources.com/introducing-quantitative-methods-a-practical-guide-by-daniela-aidley. These resources are designed to support teaching and learning when using this textbook and are available at no extra cost.
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Specificații

ISBN-13: 9781137487216
ISBN-10: 1137487216
Pagini: 388
Dimensiuni: 178 x 254 x 19 mm
Greutate: 0.68 kg
Ediția:1st ed. 2019
Editura: Bloomsbury Publishing
Colecția Red Globe Press
Locul publicării:London, United Kingdom

Caracteristici

Extensive pedagogical features are integrated throughout, including chapter objectives and summaries, 'Review and Reflect' questions, 'Look Out!' boxes to guide students through difficult sections, 'check your understanding' student exercises at the end of each chapter accompanied by solutions at the back of the book, and an excellent Excel functions glossary

Notă biografică

Daniela Aidley is a Professor of Business Psychology at the Applied University Westküste, Heide, Germany. Having first completed a Masters in Linguistics, Psychology and English, she worked at Harvey Nash HR Consulting and McKinsey, Inc. for five years before moving to the UK where she completed a PhD in Cognitive Psychology in 2012 at the University of Leicester. She then worked at Leicester University until 2017 when she moved back to Germany. Daniela is also a member of the Editorial Board at Sociological Research Online.

Cuprins

Introduction 1. The Research Question 2.Basic Concepts of Empirical Research 3. Data Collection 4. Basic Mathematics 5. Data Entry and Handling 6. Basic Concepts of Data Description and Analysis 7. Presentation and Illustration of Data 8. Choosing your Statistical Analyses 9. Statistical Analyses: Looking at Differences 10. Statistical Analyses: Looking at Effects 11. Statistical Analyses: Looking at Associations and Relationships 12. Writing up Research Appendix 1 - Standard Error of Skewness for Small Sample Sizes Appendix 2 - Critical Values for Wilcoxon Signed Rank Exercise Solutions General Glossary Excel Function Glossary References.

Recenzii

A much-needed text to help those new to quantitative research methods get started. The writing is clear, examples easy-to-follow, and the inclusion of both SPSS and Excel is a master stroke. Most importantly, Aidley puts the research question-not analytical techniques-at the heart of the book and guides her readers through the application of methods to gain meaningful insight.
A valuable resource for students and those of us who teach research methods at both undergraduate and postgraduate levels. Daniela Aidley guides students through the whole research process from identifying research questions suitable for quantitative inquiry through rigorous statistical analysis to writing up the project. It is this holistic approach that differentiates Daniela Aidley's book from other books on quantitative methods.
Introducing Quantitative Methods covers its subject very well: concepts are introduced and discussed without being belaboured; examples are numerous and diverse; and the writing style is clear and concise. Dr Aidley's knowledge and enthusiasm are evident on every page, and encourage the student to explore rather than fear the intimidating topic of conducting quantitative research. By including helpful reviews of various mathematical concepts as well, Dr. Aidley has created an ideal text for student and instructor alike. This is the text I wish I'd had as an undergraduate psychology major. It's the text I wish I'd have written as an instructor.
A very comprehensive yet incredibly accessible introduction to quantitative methods. Students of all levels who need to embark on quantitative research projects will find this book user-friendly, easy to read, and anticipatory of most questions they'd be asking. I will be adding this to my reading list.
Introducing Quantitative Methods has the potential to be a 'go to' resource at every stage of quantitative research, from pre-design right through to sharing research findings. It is particularly useful in providing choice between using Excel and SPSS for a wide range of statistical analyses, making the doing of quantitative research more achievable.
This textbook will make an excellent contribution to introducing research methods to undergraduates, postgraduates, and academics wanting to brush up on their statistical analysis. The text is logically ordered and accompanying online support provides further training to meet the needs of the user and help get their heads around the hard stuff! I found the clarification on associations and effects, particularly on casual language, an excellent and necessary section of the text. It is an important reminder for all readers both new to quantitative research and those refreshing their minds, that both their statistical models and writing up of findings must be aligned and well thought through before and after conducting advanced inferential statistical analysis.