Normal view MARC view ISBD view

Conducting Quantitative Research in Education

By: Mat Roni, Saiyidi
Title By: Merga, Margaret Kristin | Morris, Julia Elizabeth
Material type: BookPublisher: Singapore : Springer, 2019.Description: viii, 206 p. 23 cm.ISBN: 9789811391323; 9811391327Subject(s): Management Education | Statistics for Social Sciences, Humanities, Law | Big Data/AnalyticsDDC classification: 370.72 MA CO Online resources: More online.
Summary:
This book provides a clear and straightforward guide for all those seeking to conduct quantitative research in the field of education, using primary research data samples. While positioned as less powerful and somehow inferior, non-parametric tests can be very useful where the research can only be designed to accommodate data structure which is ordinal or scale but violates a normality assumption, which is required for parametric tests. Non-parametric data are a staple of educational research, and as such, it is essential that educational researchers learn how to work with these data with confidence and rigour.
Tags from this library: No tags from this library for this title. Log in to add tags.
    average rating: 0.0 (0 votes)
Item type Home library Call number Status Notes Date due Barcode Item holds
REGULAR University of Wollongong in Dubai
Research Corner
370.72 MA CO (Browse shelf) Available Nov2019 T0063374
Total holds: 0

7.3.3 Output

Chapter1 Introduction --
Chapter2 Getting started: What, where, why --
Chapter3 Conducting research with children and students --
Chapter4 Data types and samples --
Chapter5 Data preparation --
Chapter6 Analysis: Difference between groups --
Chapter7 Analysis: Correlation --
Chapter8 Analysis: Regression --
Chapter9 Write up and research translation --
Chapter10 Conclusion and further reading.

This book provides a clear and straightforward guide for all those seeking to conduct quantitative research in the field of education, using primary research data samples. While positioned as less powerful and somehow inferior, non-parametric tests can be very useful where the research can only be designed to accommodate data structure which is ordinal or scale but violates a normality assumption, which is required for parametric tests. Non-parametric data are a staple of educational research, and as such, it is essential that educational researchers learn how to work with these data with confidence and rigour.

Powered by Koha