Understanding Quantitative Data in Educational Research

Häftad, Engelska, 2020

AvNicoleta Gaciu

709 kr

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This book is designed to help you gain confidence in analysing and interpreting quantitative data and using appropriate statistical tests, by exploring, in plain language, a variety of data analysis methods.

Highly practical, each chapter includes step-by-step instructions on how to run specific statistical tests using R, practical tips on how to interpret results correctly and exercises to put into practice what you have learned.

It also includes guidance on how to use R and RStudio, how to visualise quantitative data, and the fundamentals of inferential statistics, estimations and hypothesis testing.

Produktinformation

Utgivningsdatum2020-12-02
Mått186 x 232 x 26 mm
Vikt650 g
FormatHäftad
SpråkEngelska
Antal sidor376
Upplaga1
FörlagSAGE Publications
ISBN9781473982154

Tillhör följande kategorier

Pedagogik inom Psykologi och pedagogik

Dr Nicoleta Gaciu is Senior Lecturer in Education at Oxford Brookes University, UK. Her academic and research specialisations in disciplines such as physics, statistics, computer sciences, research methods and business have given her the best opportunities to make connections across disciplines, to view real-life phenomena through different lenses and to take different perspectives, knowledge, logical and methodological approaches for interdisciplinary research.

Part 1: Understanding quantitative data and R1. Introduction to information, knowledge and quantitative data2. An introduction to R and RStudioPart 2: Data visualisation3. Graphical representation of dataPart 3: Providing information about data4. Descriptive statistics5. Measures of dispersion and distributions6. Normal distribution and standardised scoresPart 4: Making estimations and predictions from the data7. Fundamentals of inferential statistics8. Estimations and hypothesis testingPart 5: From sample to population9. One-sample tests10. Differences between the independent or dependent two samples11. Difference between more than two independent samples12. Difference between more than two dependent samplesPart 6: Relationships and predictions13. Relationship between variables14. Predictions for independent and dependent variables