Illustrating Statistical Procedures: Finding Meaning in Quantitative Data

ISBN-13: 9789811525360 / Angielski / Twarda / 2020 / 737 str.

Ray W. Cooksey

Spis treści

Introduction.- Chapter 1: The logic and language of statistical analysis Reference.- Chapter 2: Measurement issues in quantitative research.- Chapter 3: Computer programs for analysing quantitative data.- Chapter 4: Example research context aand quantitative data set.- Chapter 5: Descriptive statistics for summarising data.- Chapter 6: Correlational statistics for characterising relationships.- Chapter 7: Inferential statistics for hypothesis testing relationships.- Chapter 8: Other commonly-used statistical procedures relationships.- Chapter 9: Specialised statistical procedures.- Appendices.- Index.

O autorze

Ray Cooksey is an Emeritus Professor at the UNE Business School, University of New England, Armidale, Australia. His research mainly focuses on the areas of decision making and cognition, chaos and complexity theory, organisational and behavioural systems theory, leadership, teacher judgment, pluralist approaches to research, climate change and adaptation, and multivariate statistics. 

Blurb

This book occupies a unique position in the field of statistical analysis in the behavioural and social sciences in that it targets learners who would benefit from learning more conceptually and less computationally about statistical procedures and the software packages that can be used to implement them.

This book provides a comprehensive overview of this important research skill domain with an emphasis on visual support for learning and better understanding. The primary focus is on fundamental concepts, procedures and interpretations of statistical analyses within a single broad illustrative research context.

The book covers a wide range of descriptive, correlational and inferential statistical procedures as well as more advanced procedures not typically covered in introductory and intermediate statistical texts.  It is an ideal reference for postgraduate students as well as for researchers seeking to broaden their conceptual exposure to what is possible in statistical analysis.