Browse Books by Keyword: "Research Methods"
The Students' Guide to Learning Design and Research
Written by students for students, this book is a continually-evolving class project for students of educational technology, learning, and instructional design.
Introduction to Educational and Psychological Measurement Using R
This book provides an introduction to the theory and application of measurement in education and psychology. Topics include test development, item writing, item analysis, reliability, dimensionality, and item response theory. These topics come together in overviews of validity and, finally, test evaluation.
Validity and test evaluation are based on both qualitative and quantitative analysis of the properties of a measure. This book addresses the qualitative side using a simple argument-based approach. The quantitative side is addressed using descriptive and inferential statistical analyses, all of which are presented and visualized within the statistical environment R (R Core Team 2017).
The intended audience for this book includes advanced undergraduate and graduate students, practitioners, researchers, and educators. Knowledge of R is not a prerequisite to using this book. However, familiarity with data analysis and introductory statistics concepts, especially ones used in the social sciences, is recommended.
Qualitative Inquiry in Daily Life
This book is meant to teach researchers, evaluators, and practitioners such as educators how to use qualitative inquiry in daily life. Although this book may make the most sense if it is read in sequence, feel free to navigate to any chapter or appendix at any time.
R for Data Science
This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You’ll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You’ll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualising, and exploring data.
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