MEd. in Learning and Instructional Design (MEd. LID)

Assuming no prior knowledge of statistics and coding and only minimal knowledge of math, the book teaches the fundamentals of survey research, predictive models, and causal inference while analyzing data from published studies with the statistical program R. It teaches not only how to perform the data analyses but also how to interpret the results and identify the analyses' strengths and limitations.

Provides researchers with an overview of rating scale analysis along with practical guidance on how to conduct such analyses with their own survey data. Author Stefanie A. Wind presents three categories of methods: Rasch models; non-Rasch Item Response Theory (IRT) models; and non-parametric models, together with practical examples. Tutorials, datasets, and software code (R and Facets) to accompany the book are available on the book's website.

This brand new edition offers examples and visualizations using real-life data, a revised discussion of statistical inference, and introductory examples in R and SPSS. The third edition has been extensively reorganized with shorter chapters and closer links between concepts and formulas, while retaining useful pedagogical features including key terms, practice exercises, a math refresher, and playful inserts on "the mathematical imagination." The Joy of Stats also places a strong emphasis on learning how to write and speak clearly about data results.

Workbook sections parallel chapters in the main text, giving students a chance to apply the lessons and techniques learned in each chapter in a statistical software setting. Detailed chapters teach students to reproduce results presented in the textbook, allowing them to become comfortable performing statistical analyses for evaluating causal claims through repeated practice. Step-by-step instructions for using R are provided, along with command lines and screenshots to demonstrate proper use of the software. Instructions for producing the figures and tables in the main text are integrated throughout the workbook. End-of-chapter exercises encourage students to formulate and evaluate their own hypotheses.

This brief text provides readers with a step-by-step procedure for incorporating secondary data into various mixed methods research designs, as well as identifying key characteristics of existing datasets that make them good candidates for mixed methods projects and giving ideas for new uses of secondary data. Introductory chapters help the reader understand the "what" and "why" of secondary data. Subsequent chapters address the use of secondary data in convergent, exploratory sequential, explanatory sequential, and other complex research designs. The final chapters delve into writing and reporting on projects before, during, and after the project.